diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..4f65c1899ef0a63ef4484ee252f1f911a2649865
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,8 @@
+config
+notes
+dist
+.venv
+.vscode
+.idea
+.mypy_cache
+.pytest_cache
diff --git a/.gitignore b/.gitignore
index 117937a4db85aab110e1e26c82c46b37d3fb8175..2f859921bc84c29becef704932e4f951c3d01974 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,10 +4,11 @@ lib
config
tmp
+.venv
.vscode
.idea
*.code-workspace
__pycache__
-dist
\ No newline at end of file
+dist
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..7cf049731bbb2dbbcec88b5e2c2ac101f202a491
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,18 @@
+FROM jupyter/minimal-notebook:python-3.10
+# TODO: add venv instead of image with correct version
+# https://jupyter-docker-stacks.readthedocs.io/en/latest/using/recipes.html#add-a-custom-conda-environment-and-jupyter-kernel
+
+RUN rmdir work
+
+COPY . .
+RUN pip install poetry
+# RUN poetry config virtualenvs.in-project true
+RUN poetry config virtualenvs.create false
+RUN poetry install
+# RUN source .venv/bin/activate
+
+USER root
+RUN fix-permissions /home/jovyan
+
+# Switch back to jovyan to avoid accidental container runs as root
+USER ${NB_UID}
\ No newline at end of file
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..bfd1bb4c549db22d9d61b6f579b795badb03379a
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,11 @@
+
+
+docker-build:
+ docker build --tag fairnb .
+
+docker-run:
+ docker run -it -p 8888:8888 fairnb
+
+docker-save:
+ docker save -o dist/fairnb_image.tar fairnb
+ tar -zcvf dist/fairnb_image.tar.gz tmp/fairnb_image.tar
\ No newline at end of file
diff --git a/README.md b/README.md
index 67cf5d2585ec96bbaac7bfef027169c14a553cfe..904f6c65f666c8f34c57ff97efbe4fba407be315 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,6 @@
-# DBREPO-ISMIR
-
-## Description
-A Repository that shows the usage of the DBRepo and Invenio platforms to create a genre prediction SVM and make its results reproducible.
+# FAIRNB
+A Repository for FAIR data handling in Jupyter Notebooks.
+It shows the usage of the DBRepo and Invenio platforms to create a genre prediction SVM and make its results reproducible.
## Installation
Install poetry via pip or conda and run
@@ -10,25 +9,34 @@ Install poetry via pip or conda and run
Following packages are additionally needed: git-lfs, libsnfile1 (needed for librosa).
Install them with your package manager.
-To authenticate with the DBRepo and Invenio servers, rename example-config.yml located in the config folder to config.yml and enter you credentials in the placeholders.
+To authenticate with the DBRepo and Invenio servers, rename example-config_<platform>.yml located in the config folder
+to config_<platform>.yml and enter your credentials in the placeholders. Credentials are not needed for runs done fully locally.
## Usage
-The main notebook is called ismir_presentation.ipynb located in the notebooks folder.
+The main notebook is located in notebooks/main.ipynb. It executes the full workflow of the project.
+Alternatively, notebooks/standalone.ipynb also contains the full workflow.
-## Support
+### Docker
+A docker image is also provided for download on invenio, containing a running jupyter instance with all dependencies installed.
-## Roadmap
+Beware this docker image does not have a config file included and will not be able to authenticate with
+the DBRepo and Invenio servers.
+Therefore, if one wants to execute the notebooks locally, only_local must be set to true in the execute method.
+## Roadmap & Project Status
+The project is done in the context of a bachelor thesis, extensions are not planned for now.
## Contributing
-
+Fork the repository and open a pull request.
## Authors and acknowledgment
-
+Lukas Mahler
## License
-
-## Project status
-Currently in early development
+## References
+Docker Image on invenio: https://researchdata.tuwien.ac.at/
+DBRepo: https://dbrepo1.tuwien.ac.at/
+Invenio: https://researchdata.tuwien.ac.at/
+Thesis: https://repositum.tuwien.ac.at/
diff --git a/config/example-config.yml b/config/example-config.yml
deleted file mode 100644
index 9b21e8ac6d43afba2760db505876d4a68685dba6..0000000000000000000000000000000000000000
--- a/config/example-config.yml
+++ /dev/null
@@ -1,16 +0,0 @@
-# Credentials for Invenio
-invenio:
- host: https://test.researchdata.tuwien.ac.at
- credentials:
- token: <insert token from invenio>
-
-dbrepo:
- host: https://dbrepo2.ec.tuwien.ac.at
- credentials:
- username: <insert username from dbrepo>
- password: <insert password from dbrepo>
- container-id: 1
- database-id: 1
-
-
-
diff --git a/config/example-config_dbrepo.yml b/config/example-config_dbrepo.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a86a8f1a4fac1fb8fc7356225a8d89f4acdd87a4
--- /dev/null
+++ b/config/example-config_dbrepo.yml
@@ -0,0 +1,7 @@
+host: https://dbrepo1.ec.tuwien.ac.at
+container-id: <insert id>
+database-id: <insert id>
+credentials:
+ username: <insert username from dbrepo>
+ password: <insert password from dbrepo>
+ client_secret_key: <insert client secret key from dbrepo>
diff --git a/config/example-config_invenio.yml b/config/example-config_invenio.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ea9ba267f9dfd6a34bdab36bd9d23edef14ed86e
--- /dev/null
+++ b/config/example-config_invenio.yml
@@ -0,0 +1,3 @@
+host: https://researchdata.tuwien.ac.at
+credentials:
+ token: <insert token from invenio>
\ No newline at end of file
diff --git a/dbrepo_ismir/artefact.py b/dbrepo_ismir/artefact.py
deleted file mode 100644
index 3a77f308bb3b569c611ff088d9c57b599ed74302..0000000000000000000000000000000000000000
--- a/dbrepo_ismir/artefact.py
+++ /dev/null
@@ -1,401 +0,0 @@
-import copy
-from datetime import datetime
-from abc import ABC, abstractmethod
-from pathlib import Path
-from dataclasses import dataclass, field
-
-import pandas as pd
-import git
-
-from dbrepo_ismir.invenio import InvenioManager
-from definitions import BASE_PATH
-from dbrepo_ismir.dbrepo import DBRepoConnector
-
-METADATA_TABLE_NAME = "artefact_metadata"
-DEPENDENCY_TABLE_NAME = "artefact_dependencies"
-
-# TODO: Upload Datetime objects as Timestamps instead of str
-
-
-@dataclass
-class ArtefactMeta:
- id: str | None
- uri: str
- name: str
- description: str
- type: str
- commit: str
- branch: str
- repo_uri: str
- executed_file: str
- creation_time: datetime
- platform: str
-
- @classmethod
- def new(cls, name: str, description: str, executed_file: Path, type: str, uri: str, platform: str):
- repo = git.Repo(BASE_PATH)
-
- git_branch = repo.head.reference
- branch = git_branch.name
- commit = git_branch.repo.commit().hexsha
-
- # TODO: Better way to point to repo instead of ssh / https link
- # --> more general approach independent of authentication
- repo_uri = git_branch.repo.remote().url
-
- creation_time = datetime.now()
-
- executed_file_rel = executed_file.resolve().relative_to(BASE_PATH)
-
- return cls(
- id=None,
- name=name,
- description=description,
- uri=uri,
- commit=commit,
- repo_uri=repo_uri,
- creation_time=creation_time,
- branch=branch,
- executed_file=executed_file_rel.as_posix(),
- type=type,
- platform=platform
- )
-
- @classmethod
- def from_series(cls, df: pd.DataFrame):
- return cls(
- id=df["id"],
- name=df["name"],
- description=df["description"],
- uri=df["uri"],
- commit=df["commit"],
- repo_uri=df["repo_uri"],
- executed_file=df["executed_file"],
- creation_time=datetime.strptime(df["creation_time"], "%Y-%m-%d %H:%M:%S.%f"),
- branch=df["branch"],
- type=df["type"],
- platform=df["platform"]
- )
-
- def to_frame(self):
- return pd.DataFrame(
- {
- "id": pd.Series(self.id, dtype=str),
- "name": pd.Series(self.name, dtype=str),
- "description": pd.Series(self.description, dtype=str),
- "uri": pd.Series(self.uri, dtype=str),
- "commit": pd.Series(self.commit, dtype=str),
- "repo_uri": pd.Series(self.repo_uri, dtype=str),
- "executed_file": pd.Series(self.executed_file, dtype=str),
- "creation_time": pd.Series(self.creation_time, dtype=str),
- "branch": pd.Series(self.branch, dtype=str),
- "type": pd.Series(self.type, dtype=str),
- "platform": pd.Series(self.platform, dtype=str)
- }
- )
-
-
-@dataclass
-class Artefact(ABC):
- location: Path = field(init=True)
- dbrepo_connector: DBRepoConnector = field(init=True)
- name: str = field(init=True, default=None)
- description: str = field(init=True, default="")
- id: str | None = field(init=True, default=None)
- type: str = field(init=True, default=None)
- metadata_table_id: int | None = field(init=False, default=None)
- dependency_table_id: int | None = field(init=False, default=None)
- metadata: ArtefactMeta | None = field(init=False, default=None)
-
- @classmethod
- @abstractmethod
- def new(cls, *args, **kwargs):
- """ Create a new Artefact that exists at creation time only locally at 'location'. """
- raise NotImplementedError
-
- @classmethod
- @abstractmethod
- def existing(cls, *args, **kwargs):
- """ """
- raise NotImplementedError
-
- def __post_init__(self):
- # if record not yet created delay metadata loading etc. until upload
- if self.id is None:
- assert self.name
- assert self.description
- return
-
- # Download metadata if it exists
- self.metadata_table_id = self.dbrepo_connector.get_table(METADATA_TABLE_NAME)["id"]
- self.dependency_table_id = self.dbrepo_connector.get_table(DEPENDENCY_TABLE_NAME)["id"]
- self.download_metadata()
-
- @abstractmethod
- def download(self) -> ArtefactMeta:
- """ Download this Artefact and return the attached Metadata"""
- raise NotImplementedError
-
- @abstractmethod
- def upload(self, executed_file: Path):
- """ Upload this Artefact """
- raise NotImplementedError
-
- def exists_locally(self):
- """ Checks if a local File on 'location' exists. If so, True is returned, otherwise False """
- return self.location.exists() and self.location.is_file()
-
- def copy_to_location(self, new_location: Path):
- """ Copy local files to new location and return new deep copy of self with new location """
- # TODO: throw exception if no local files when copying?
- new = copy.deepcopy(self) # strip all references to self
- new.location = new_location
- new_location.parent.mkdir(parents=True, exist_ok=True)
- new_location.write_bytes(self.location.read_bytes()) # works as long file < RAM
- return new
-
- def download_metadata(self) -> ArtefactMeta:
- if self.metadata_table_id is None:
- self.metadata_table_id = self.dbrepo_connector.get_table(METADATA_TABLE_NAME)["id"]
- if self.dependency_table_id is None:
- self.dependency_table_id = self.dbrepo_connector.get_table(DEPENDENCY_TABLE_NAME)["id"]
-
- df = self.dbrepo_connector.download_table_as_df(str(self.metadata_table_id))
-
- row = df[df['id'] == self.id].iloc[0]
-
- self.metadata = ArtefactMeta.from_series(row)
- return self.metadata
-
- def upload_metadata(self, metadata: ArtefactMeta):
- metadata_table = self.create_metadata_table_if_not_exists(metadata)
- self.metadata_table_id = metadata_table["id"]
- dependency_table = self.create_dependency_table_if_not_exists()
- self.dependency_table_id = dependency_table["id"]
-
- self.dbrepo_connector.upload_data(
- metadata.to_frame().drop("id", axis=1),
- str(self.metadata_table_id)
- )
-
- df = self.dbrepo_connector.download_table_as_df(str(self.metadata_table_id))
-
- # FIXME: create robust version of id retrieval, if possible
- row = df.iloc[df['id'].idxmax()] # get newest row, as it should contain data
- meta = ArtefactMeta.from_series(row)
- assert meta.creation_time == metadata.creation_time
-
- self.id = meta.id
- self.metadata = meta
-
- def create_metadata_table_if_not_exists(self, metadata: ArtefactMeta):
- df = metadata.to_frame().drop('id', axis=1)
-
- return self.dbrepo_connector.create_table_if_not_exists(
- df,
- METADATA_TABLE_NAME,
- "Artefact metadata for all Artefacts of database"
- )
-
- def create_dependency_table_if_not_exists(self):
- # TODO: this table should be optional; is foreign_key supported in dbrepo?
-
- df = pd.DataFrame({
- 'artefact_id': pd.Series(dtype='int'),
- 'depends_on': pd.Series(dtype='int')
- })
-
- return self.dbrepo_connector.create_table_if_not_exists(
- df,
- DEPENDENCY_TABLE_NAME,
- "Artefact dependencies on other artefacts"
- )
-
-
-@dataclass
-class DbRepoArtefact(Artefact):
- table_name: str = field(init=True, default=None)
- table_description: str = field(init=True, default="")
- table_id: int = field(init=False, default=None)
-
- def __post_init__(self):
- super().__post_init__()
-
- if self.metadata is not None:
- self.table_id = int(self.metadata.uri.split('/')[-1])
- else:
- assert self.table_name is not None
-
-
- @classmethod
- def new(cls, name: str, description: str, location: Path, dbrepo_connector: DBRepoConnector,
- table_name: str, table_description: str, type: str):
- return cls(
- name=name,
- description=description,
- table_name=table_name,
- table_description=table_description,
- location=location,
- dbrepo_connector=dbrepo_connector,
- type=type
- )
-
- @classmethod
- def existing(cls, id: str, location: Path, dbrepo_connector: DBRepoConnector):
- return cls(
- id=id,
- location=location,
- dbrepo_connector=dbrepo_connector
- )
-
- def download(self):
- if not self.metadata:
- self.download_metadata()
- df = self.dbrepo_connector.download_table_as_df(str(self.table_id))
- df.to_csv(self.location)
-
- def upload(self, executed_file: Path):
- df = pd.read_csv(self.location)
-
- # add id column to df:
- df["artefact_id"] = -1 # temporary, needed for easy table schema retrieval
-
- # TODO: reference Artefact id via foreign keys in data tables and dependency table
-
- # create table if not exists
- # update uri etc
- # create table if it not already exists
- table = self.dbrepo_connector.create_table_if_not_exists(
- df,
- self.table_name,
- self.table_description
- )
- self.table_id = int(table["id"])
-
- metadata = ArtefactMeta.new(
- self.name,
- self.description,
- executed_file=executed_file,
- uri=f"{self.dbrepo_connector.host}/api/container/{self.dbrepo_connector.container_id}/database/"
- f"{self.dbrepo_connector.database_id}/table/{self.table_id}",
- type=self.type,
- platform="dbrepo"
- )
-
- self.upload_metadata(metadata)
- df["artefact_id"] = self.metadata.id # update the -1 form above with the correct value as it is now known
- self.upload_data(df)
-
- def upload_data(self, df: pd.DataFrame):
- assert self.id is not None
- assert self.table_id is not None
-
- df["id"] = self.id # add artefact id to df
-
- self.dbrepo_connector.upload_data(
- df,
- str(self.table_id)
- )
-
-
-@dataclass
-class InvenioArtefact(Artefact):
- invenio_manager: InvenioManager = field(init=True, default=None)
- record_metadata: dict = field(init=True, default=None)
- publish_record: bool = field(init=True, default=False)
-
- @classmethod
- def new(cls, invenio_manager: InvenioManager, record_metadata: dict,
- dbrepo_connector: DBRepoConnector, location: Path,
- name: str, description: str, type: str):
- return cls(
- invenio_manager=invenio_manager,
- record_metadata=record_metadata,
- dbrepo_connector=dbrepo_connector,
- location=location,
- name=name,
- description=description,
- type=type
- )
-
- @classmethod
- def existing(cls, id: str, location: Path, dbrepo_connector: DBRepoConnector, invenio_manager: InvenioManager):
- return cls(
- id=id,
- location=location,
- dbrepo_connector=dbrepo_connector,
- invenio_manager=invenio_manager
- )
-
-
- def __post_init__(self):
- super().__post_init__()
-
- assert self.invenio_manager is not None
-
- if not self.id:
- assert self.record_metadata is not None
-
- def upload(self, executed_file: Path):
- dir_path: Path
- regex: str
-
- if self.location.is_dir():
- dir_path = self.location
- regex = ".*"
- else:
- dir_path = self.location.parent
- regex = f".*/{self.location.name}"
-
- create_response = self.invenio_manager.create_record(self.record_metadata).json()
- record_id = create_response["id"]
- self.invenio_manager.upload_record(dir_path, regex)
-
- if self.publish_record:
- publish_response = self.invenio_manager.publish_record()
- uri = publish_response["links"]["self_doi"]
- else:
- uri = create_response["links"]["record"] # Invalid until published
-
- metadata = ArtefactMeta.new(
- name=self.name,
- description=self.description,
- executed_file=executed_file,
- uri=uri,
- type=self.type,
- platform="invenio"
- )
-
- self.upload_metadata(metadata)
-
- def download(self) -> ArtefactMeta:
- pass
-
-
-class RepositumArtefact(Artefact):
- # TODO: add implementation of RepositumArtefact
-
- @classmethod
- def new(cls):
- pass
-
- def upload(self, executed_file: Path):
- pass
-
- def upload_data(self):
- pass
-
- def download(self) -> ArtefactMeta:
- pass
-
-
-@dataclass
-class NbConfig:
- nb_location: Path
- artefacts: list[Artefact]
- dependencies: list[Artefact]
- nb_output_location: Path = field(init=True, default=None)
-
- def __post_init__(self):
- if not self.nb_output_location:
- self.nb_output_location = self.nb_location
diff --git a/dbrepo_ismir/authenticate.py b/dbrepo_ismir/authenticate.py
deleted file mode 100644
index 280efbbadd96bd0a1c49cbb2454c857e5ecad9e5..0000000000000000000000000000000000000000
--- a/dbrepo_ismir/authenticate.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-
-
-@dataclass
-class Credential(ABC):
- uri: str
-
- @abstractmethod
- def retrieve_token(self) -> str:
- raise NotImplementedError
-
-
-
-@dataclass
-class TokenCredential(Credential):
- token: str
-
- def retrieve_token(self):
- return self.token
-
-
-@dataclass
-class PasswordCredential(Credential, ABC):
- username: str
- password: str
-
-
-class DbRepoPasswordCredential(PasswordCredential):
-
- def retrieve_token(self) -> str:
- dbrepo = DBRepoConnector()
-
-
-@dataclass
-class AuthenticationConfig:
- credentials: list[Credential]
-
-
-class Authenticator:
- credentials: dict[str, TokenCredential] # List of credentials
-
- @staticmethod
- def strip_uri(uri: str):
- return uri
-
-
- @classmethod
- def set_up(cls, config: AuthenticationConfig):
- cls.credentials = {
- credential.uri: credential for credential in config.credentials
- }
-
- @classmethod
- def get_token(cls, uri: str):
- return cls.credentials[uri].token
diff --git a/dbrepo_ismir/reproducer.py b/dbrepo_ismir/reproducer.py
deleted file mode 100644
index 1dda9b220b2529b5106058f089e60597222b0d57..0000000000000000000000000000000000000000
--- a/dbrepo_ismir/reproducer.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import inspect
-import os
-from dataclasses import dataclass
-from pathlib import Path
-import IPython
-
-import jupyter_client
-import nbconvert.postprocessors
-import papermill
-from jupyter_client import KernelManager
-
-from dbrepo_ismir.artefact import Artefact, NbConfig, ArtefactMeta, DbRepoArtefact, InvenioArtefact
-import nbformat
-import nbclient
-from nbconvert.preprocessors import ExecutePreprocessor
-
-
-@dataclass
-class ReproducerConfig:
- pass
-
-
-class Reproducer:
- executeProcessor: ExecutePreprocessor = ExecutePreprocessor(timeout=600, kernel_name='python3')
-
- def __init__(self, config: ReproducerConfig):
- pass
-
- def _preprocess(self, nb_config: NbConfig, require_download: bool = False, only_local: bool = False):
- """ Set up the dependencies to allow for later execution """
- if not only_local:
- # load deps and save them to defined location
- [artefact.download() for artefact in nb_config.dependencies
- if (not artefact.exists_locally()) or require_download]
-
- def execute(self, nb_config: NbConfig, require_download: bool = False, only_local: bool = False, **kwargs):
- self._preprocess(nb_config, require_download, only_local)
-
- papermill.execute_notebook(
- nb_config.nb_location.resolve(),
- nb_config.nb_location.resolve(),
- parameters=dict(
- INPUT_PATHS={artefact.type: artefact.location.__str__() for artefact in nb_config.dependencies},
- OUTPUT_PATHS={artefact.type: artefact.location.__str__() for artefact in nb_config.artefacts}
- )
- )
-
- self._post_process(nb_config, only_local)
-
- def _post_process(self, nb_config: NbConfig, only_local: bool = False):
- if not only_local:
- # load generated artefact and upload it
- for artefact in nb_config.artefacts:
- # use inspect to get path of caller
- artefact.upload(nb_config.nb_location)
-
- def reproduce_artefact(self, artefact: Artefact):
- pass
-
-
- # class ExperimentReproducer:
- # def __init__(self):
- # pass
- # # self.config = self.configure()
- #
- # def artefact(self, creation_func, *args, input_artefacts: [str] = None, **kwargs):
- # """ Saves the created artefact which is returned by the creation_func as a DRO """
- #
- # if input_artefacts is not None:
- # # TODO: collect input artefacts
- # collected_artefacts = []
- # for artefact in input_artefacts:
- # collected_artefacts.append(self.collect_artefact(artefact))
- #
- # result = creation_func(args, input_artefacts, kwargs)
- #
- # return result
- #
- # def db_repo_artefact(self, function: Callable[..., pd.DataFrame], *args, **kwargs):
- # """ Saves the created dataframe to DBRepo while citing the inputs. """
- # df = function(args, kwargs)
- #
- # # TODO: upload code to DBRepo
- #
- #
- #
- #
- # def recreate_artefact(self, uri: str):
- # """ Checkout correct commit, download required artefacts and execute correct artefact code. """
- # pass
\ No newline at end of file
diff --git a/dbrepo_ismir/__init__.py b/fairnb/__init__.py
similarity index 100%
rename from dbrepo_ismir/__init__.py
rename to fairnb/__init__.py
diff --git a/fairnb/api/__init__.py b/fairnb/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/dbrepo_ismir/dbrepo.py b/fairnb/api/dbrepo.py
similarity index 52%
rename from dbrepo_ismir/dbrepo.py
rename to fairnb/api/dbrepo.py
index 64ae3bc62c26bc13a6a6ef0f31a805f45c26b63d..cdf3a9fba40083ad6eb1f42aa05498061eda4287 100644
--- a/dbrepo_ismir/dbrepo.py
+++ b/fairnb/api/dbrepo.py
@@ -1,19 +1,35 @@
import pathlib
import logging
-from typing import Any
+from tusclient import client
+from datetime import datetime
+from functools import wraps
+from typing import Any, Callable
import requests
import pandas as pd
import csv
from io import StringIO
+from keycloak import KeycloakOpenID
LOG = logging.getLogger(__name__)
+TIMEOUT = 600
+CHUNK_SIZE = 1024 * 1024 * 100
+def re_auth(func: Callable) -> Callable:
+ @wraps(func)
+ def inner(self, *args, **kwargs):
+ assert self.get_token_age()
+ age_seconds = (datetime.now() - self.get_token_age()).seconds
-# TODO: add method to automatically delete table content
+ if 60 * 10 < age_seconds < 60 * 25:
+ LOG.warning(f"Re-authenticating due to almost expired token")
+ self.refresh_token_keycloak()
+ if age_seconds >= 60*25:
+ LOG.warning(f"Re-login due to expired token")
+ self.authenticate_keycloak()
+ return func(self, *args, **kwargs)
+ return inner
-
-# and replace it with given df
# noinspection PyTypeChecker
class DBRepoConnector:
""" A class to make interaction with DBRepo via python code easier """
@@ -21,23 +37,42 @@ class DBRepoConnector:
def __init__(self,
username: str,
password: str,
+ client_secret_key: str,
host: str,
container_id: str,
database_id: str):
self.__token = None
+ self.__token_age: datetime = None
+ self.__refresh_token: str = None
self.headers = None
self.host = host
self.__username = username
self.__password = password
+ self.__client_secret_key = client_secret_key
self.container_id = container_id
self.database_id = database_id
- self.authenticate()
+ self.__keycloak_openid = KeycloakOpenID(
+ server_url=f"{host}/api/auth/",
+ client_id="dbrepo-client",
+ realm_name="dbrepo",
+ client_secret_key=client_secret_key,
+ )
+ self.authenticate_keycloak()
+ self.tusclient = client.TusClient(
+ f"{self.host}/api/upload/files/",
+ # headers=self.headers
+ headers={'Content-Type': 'application/offset+octet-stream'}
+ )
+
+ def get_token_age(self) -> datetime:
+ return self.__token_age
@classmethod
def from_config(cls, config: dict, credentials: dict):
return cls(
credentials["username"],
credentials["password"],
+ credentials["client_secret_key"],
config["host"],
config["container-id"],
config["database-id"]
@@ -49,18 +84,47 @@ class DBRepoConnector:
username = self.__username if username is None else username
password = self.__password if password is None else password
+ # /realms/dbrepo/protocol/openid-connect/token
response = requests.post(
f"{self.host}/api/auth",
json={"username": username, "password": password},
- timeout=60
+ timeout=TIMEOUT,
)
LOG.debug(response)
+
+ assert response.ok, "Authentication failed"
+
self.__token = response.json().get("token")
+ self.__token_age = datetime.now()
self.headers = {"Authorization": f"Bearer {self.__token}"}
return response
+ def authenticate_keycloak(self, username: str = None, password: str = None):
+
+ username = self.__username if username is None else username
+ password = self.__password if password is None else password
+
+ token = self.__keycloak_openid.token(username, password)
+
+ self.__token = token["access_token"]
+ self.__refresh_token = token["refresh_token"]
+ self.__token_age = datetime.now()
+ self.headers = {"Authorization": f"Bearer {self.__token}"}
+ return token
+
+ def refresh_token_keycloak(self, token: str = None):
+
+ token = self.__token if token is None else token
+
+ token = self.__keycloak_openid.refresh_token(self.__refresh_token)
+ self.__token = token["access_token"]
+ self.__refresh_token = token["refresh_token"]
+ self.headers = {"Authorization": f"Bearer {self.__token}"}
+ self.__token_age = datetime.now()
+ return token
+
@staticmethod
def _create_table_data(
dataframe: pd.DataFrame,
@@ -69,13 +133,13 @@ class DBRepoConnector:
mapped_data_types = dataframe.dtypes.astype(str).map(
{
- 'int64': 'number',
- 'int32': 'number',
- 'object': 'text',
- 'float32': 'decimal',
- 'float64': 'decimal',
- 'bool': 'boolean',
- 'datetime64[ns]': 'timestamp',
+ 'int64': {'type': 'int', 'size': 64},
+ 'int32': {'type': 'int', 'size': 64},
+ 'object': {'type': 'text'},
+ 'float32': {'type': 'float', 'size': 24},
+ 'float64': {'type': 'float', 'size': 24},
+ 'bool': {'type': 'bool'},
+ 'datetime64[ns]': {'type': 'timestamp', 'dfid': 1},
}
)
@@ -85,32 +149,34 @@ class DBRepoConnector:
"columns": [
{
"name": column_name,
- "type": mapped_data_types[column_name],
- # "dfid": 0,
- "unique": False,
+ "type": mapped_data_types[column_name].get('type'),
+ "dfid": mapped_data_types[column_name].get('dfid'),
+ "d": mapped_data_types.get("d"),
+ "enums": mapped_data_types[column_name].get("enums", []),
"primary_key": False,
"null_allowed": False,
- # "date_format": None if mapped_data_types[column_name] == 'timestamp' else None # TODO: add date_format
- # "check_expression": "string",
- # "foreign_key": "string",
- # "enum_values": ["string"]
+ "sets": [],
+ "size": mapped_data_types[column_name].get("size"),
} for column_name in dataframe.columns
- ]
+ ],
+ "constraints": {
+ "checks": [],
+ "foreign_keys": [],
+ "uniques": []
+ }
}
- # add date_format
-
LOG.debug(table)
-
return table
+ @re_auth
def get_tables(self) -> list[dict[str, Any]]:
""" Returns the metadata for all tables in the database """
response = requests.get(
- f"{self.host}/api/container/{self.container_id}/database/"
- f"{self.database_id}/table",
- timeout=60
+ f"{self.host}/api/database/"
+ f"{self.database_id}",
+ timeout=TIMEOUT,
)
LOG.debug(response)
@@ -118,12 +184,12 @@ class DBRepoConnector:
raise Exception("tables retrieval failed")
try:
- tables = response.json()
+ content = response.json()
except requests.exceptions.JSONDecodeError as e:
LOG.warning("get_tables response is not json serializable")
raise e
- return tables
+ return content["tables"]
def get_table(self, table_name: str) -> dict[str, Any] | None:
""" Returns table if it exists, returns None otherwise """
@@ -136,12 +202,14 @@ class DBRepoConnector:
return None
+ @re_auth
def create_table(self, dataframe: pd.DataFrame, table_name: str, table_descriptor: str):
""" Creates a new table """
+
data = self._create_table_data(dataframe, table_name, table_descriptor)
response = requests.post(
- f"{self.host}/api/container/{self.container_id}/database/{self.database_id}/table",
+ f"{self.host}/api/database/{self.database_id}/table",
json=data,
headers=self.headers
)
@@ -155,6 +223,7 @@ class DBRepoConnector:
return table
+ @re_auth
def create_table_if_not_exists(self,
dataframe: pd.DataFrame,
table_name: str,
@@ -165,39 +234,50 @@ class DBRepoConnector:
return table
+ @re_auth
def upload_data(self, dataframe: pd.DataFrame, table_id: str):
""" Uploads a Dataframe to specified table """
- # convert dataframe to a string buffer to make it uploadable
- dataframe.to_csv(string_io := StringIO(), index=False, quoting=csv.QUOTE_ALL)
+ dataframe.to_csv(
+ string_io := StringIO(),
+ index=False,
+ quoting=csv.QUOTE_ALL)
- # upload data to 'frontend' middleware
- response_upload = requests.post(
- f"{self.host}/server-middleware/upload",
- files={"file": string_io.getvalue()},
- headers=self.headers,
- timeout=60
+ uploader = self.tusclient.uploader(
+ file_stream=string_io,
+ chunk_size=CHUNK_SIZE,
)
- LOG.debug(response_upload)
-
- upload_location = response_upload.json().get("path")
- LOG.debug(f"Upload location: {upload_location}")
- # move data into table
- response_move = requests.post(
- f"{self.host}/api/container/{self.container_id}/database/{self.database_id}/table/{table_id}/data/import",
- json={"location": upload_location, "quote": '"', "separator": ",", "skip_lines": 1},
+ upload_url = uploader.create_url()
+ uploader.set_url(upload_url.replace('http', 'https')) # FIX: wrong location response
+ uploader.upload()
+
+ response_upload_import = requests.post(
+ f"{self.host}/api/database/{self.database_id}/table/{table_id}/data/import",
+ json={
+ "false_element": None,
+ "location": f"/tmp/{upload_url.split('/')[-1]}",
+ "null_element": None,
+ "quote": '"',
+ "separator": ",",
+ "skip_lines": 1,
+ "true_element": None
+ },
headers=self.headers
)
- LOG.debug(response_move)
+ LOG.debug(response_upload_import)
+
+ if not response_upload_import.ok:
+ LOG.warning(f"Move for table {table_id} failed: {response_upload_import}")
+ @re_auth
def delete_all_data(self, table_id: str):
# TODO: implement delete_all_data method and test it
response = requests.delete(
- f"{self.host}/api/container/{self.container_id}/database/"
+ f"{self.host}/api/database/" # cut after api: container/{self.container_id}/
f"{self.database_id}/table/{table_id}/data",
headers=self.headers,
- timeout=60
+ timeout=TIMEOUT
)
return response
@@ -205,28 +285,30 @@ class DBRepoConnector:
def download_page(self, table_id: str, page: int, size: int = 100):
""" Downloads a single page of content of a table """
response = requests.get(
- f"{self.host}/api/container/{self.container_id}/database/{self.database_id}/table/{table_id}"
+ f"{self.host}/api/database/{self.database_id}/table/{table_id}"
f"/data?page={page}&size={size}",
headers=self.headers,
- timeout=60
+ timeout=TIMEOUT
)
LOG.debug(response)
return response
+ @re_auth
def download_table(self, table_id: str):
""" Downloads a whole table and returns content of downloaded table """
response = requests.get(
- f"{self.host}/api/container/{self.container_id}/database/{self.database_id}/table/{table_id}/export",
+ f"{self.host}/api/database/{self.database_id}/table/{table_id}/export",
headers=self.headers,
- timeout=60
+ timeout=TIMEOUT
)
LOG.debug(response)
return response.text
+ @re_auth
def download_table_as_df(self, table_id: str):
""" Downloads a table and converts it into a pd.DataFrame """
@@ -235,6 +317,7 @@ class DBRepoConnector:
return df
+ @re_auth
def export_table(self, table_id: str, export_path: pathlib.Path):
""" Exports a whole table and writes the tables' content to export_path """
assert not export_path.is_dir(), "Export Path must be a file"
diff --git a/dbrepo_ismir/invenio.py b/fairnb/api/invenio.py
similarity index 64%
rename from dbrepo_ismir/invenio.py
rename to fairnb/api/invenio.py
index 9c01669be54138d64adaa5ce301c0666dc3823a6..663f408cc74e8cec0b07b53711b75f1653292674 100644
--- a/dbrepo_ismir/invenio.py
+++ b/fairnb/api/invenio.py
@@ -9,7 +9,6 @@ log = logging.getLogger(__name__)
class InvenioConnector:
-
def __init__(self, token: str, host: str | None = None):
self.host = host or "https://test.researchdata.tuwien.ac.at"
self.token = token
@@ -20,7 +19,7 @@ class InvenioConnector:
f"{self.host}/api/records",
data=json.dumps(data),
headers=self.headers,
- timeout=60
+ timeout=60,
)
log.debug(f"create draft record response: {response}\n{response.text}")
@@ -28,38 +27,34 @@ class InvenioConnector:
return response
def create_file_draft(self, record_id: str, upload_name: str):
- """ Create a file draft for a later upload of file data"""
+ """Create a file draft for a later upload of file data"""
response = rq.post(
f"{self.host}/api/records/{record_id}/draft/files",
- json=[
- {"key": upload_name}
- ],
+ json=[{"key": upload_name}],
headers=self.headers,
- timeout=60
+ timeout=60,
)
return response
- def upload_data(self, record_id: str,
- filepath: pathlib.Path, upload_name: str):
- """ Upload data for an already created draft file
- """
- with open(filepath, mode='rb') as f:
+ def upload_data(self, record_id: str, filepath: pathlib.Path, upload_name: str):
+ """Upload data for an already created draft file"""
+ with open(filepath, mode="rb") as f:
response = rq.put(
f"{self.host}/api/records/{record_id}/draft/files/{upload_name}/content",
data=f.read(),
- headers=self.headers
+ headers=self.headers,
)
return response
def commit_file(self, record_id: str, upload_name: str):
- """ Commits a file where all data was uploaded successfully"""
+ """Commits a file where all data was uploaded successfully"""
response = rq.post(
f"{self.host}/api/records/{record_id}/draft/files/{upload_name}/commit",
- headers=self.headers
+ headers=self.headers,
)
return response
@@ -80,13 +75,17 @@ class InvenioConnector:
log.info(f"Finished upload of {upload_name}")
log.debug(responses)
- def upload_files(self, record_id: str, filepaths: list[pathlib.Path], filenames: list[str]):
+ def upload_files(
+ self, record_id: str, filepaths: list[pathlib.Path], filenames: list[str]
+ ):
assert len(filepaths) == len(filenames)
log.info(f"Uploading {len(filepaths)} to {self.host}")
- args = [(record_id, filepath, "_".join(filepath.parts[-2:]))
- for filepath in filepaths]
+ args = [
+ (record_id, filepath, filename)
+ for filepath, filename in zip(filepaths, filenames)
+ ]
with ThreadPoolExecutor(8) as executor:
executor.map(lambda p: self.upload_file(*p), args)
@@ -94,50 +93,52 @@ class InvenioConnector:
def publish_draft(self, record_id: str):
response = rq.post(
f"{self.host}/api/records/{record_id}/draft/actions/publish",
- headers=self.headers
+ headers=self.headers,
).json()
return response
- def download_file(self, record_id: str, filename: str, out_path: pathlib.Path, expand: bool = True):
+ def download_file(
+ self, record_id: str, filename: str, out_path: pathlib.Path, expand: bool = True
+ ):
response = rq.get(
f"{self.host}/api/records/{record_id}/files/{filename}/content",
- headers=self.headers
+ headers=self.headers,
)
- file_path = out_path / \
- filename.replace("_", "/") if expand else out_path
+ file_path = out_path / filename.replace("_", "/") if expand else out_path
file_path.parent.mkdir(exist_ok=True, parents=True)
with open(file_path, "wb") as file:
file.write(response.content)
- def download_files(self, record_id: str, out_dir: pathlib.Path, expand: bool = True):
+ def download_files(
+ self, record_id: str, out_dir: pathlib.Path, expand: bool = True
+ ):
# get filenames for downloading
response = rq.get(
- f"{self.host}/api/records/{record_id}/files",
- headers=self.headers
+ f"{self.host}/api/records/{record_id}/files", headers=self.headers
)
# download content of files
- filenames = sorted([entry.get('key')
- for entry in response.json().get('entries')])
- args = [(record_id, filename, out_dir, expand)
- for filename in filenames]
+ filenames = sorted(
+ [entry.get("key") for entry in response.json().get("entries")]
+ )
+ args = [(record_id, filename, out_dir, expand) for filename in filenames]
with ThreadPoolExecutor(10) as executor:
executor.map(lambda p: self.download_file(*p), args)
class InvenioManager:
- """ A high level interface to up- and download files from invenio.
+ """A high level interface to up- and download files from invenio.
Utilizes state management:
1. record not assigned
2. record assigned
- 3. record published """
+ 3. record published"""
- def __init__(self, token: str, host: str, record_id: str | None = None):
- self.connector = InvenioConnector(token, host)
+ def __init__(self, invenio_connector: InvenioConnector, record_id: str | None = None):
+ self.connector = invenio_connector
self.record_id = record_id
self.doi = None
@@ -145,7 +146,9 @@ class InvenioManager:
assert not self.record_id, "Record must not already be assigned!"
def _validate_record_assigned(self):
- assert self.record_id, "Record must be assigned or created by calling create_record"
+ assert (
+ self.record_id
+ ), "Record must be assigned or created by calling create_record"
def _validate_not_published(self):
assert not self.doi, "Record must not have been published"
@@ -159,18 +162,34 @@ class InvenioManager:
return response
- def upload_record(self, directory: pathlib.Path, regex_pattern: str = '.*', globbing_pattern: str = '**/*.*'):
+ def upload_record(
+ self,
+ directory: pathlib.Path,
+ regex_pattern: str = ".*",
+ globbing_pattern: str = "**/*.*",
+ only_filename: bool = False,
+ ):
self._validate_record_assigned()
self._validate_not_published()
- filepaths = [path.absolute() for path in sorted(directory.glob(globbing_pattern))
- if re.match(regex_pattern, path.as_posix())]
+ filepaths = [
+ path.absolute()
+ for path in sorted(directory.glob(globbing_pattern))
+ if re.match(regex_pattern, path.as_posix())
+ ]
log.info(f"Picked up {len(filepaths)} files")
log.debug(f"Picked up files: {filepaths}")
- filenames = [path.relative_to(directory).as_posix().replace(
- '/', '_') for path in filepaths]
+ if only_filename:
+ filenames = [
+ path.name for path in filepaths
+ ]
+ else:
+ filenames = [
+ path.relative_to(directory).as_posix().replace("/", "_")
+ for path in filepaths
+ ]
self.connector.upload_files(str(self.record_id), filepaths, filenames)
diff --git a/fairnb/entity/__init__.py b/fairnb/entity/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/fairnb/entity/dbrepo_entity.py b/fairnb/entity/dbrepo_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..b54851f4cadd4b74424854af312de9eb99cef521
--- /dev/null
+++ b/fairnb/entity/dbrepo_entity.py
@@ -0,0 +1,95 @@
+import pandas as pd
+from dataclasses import dataclass, field
+from pathlib import Path
+from fairnb.entity.entity import Entity
+from fairnb.api.dbrepo import DBRepoConnector
+from fairnb.entity.entity_provenance import EntityProvenance
+
+
+@dataclass
+class DbRepoEntity(Entity):
+ table_name: str = field(init=True, default=None)
+ table_description: str = field(init=True, default="")
+ table_id: int = field(init=False, default=None)
+
+ def __post_init__(self):
+ super().__post_init__()
+
+ if self.metadata is not None: # equivalent to: self.id is not None
+ self.table_id = int(self.metadata.uri.split("/")[-1])
+ else:
+ assert self.table_name is not None # has to exist fot the ability to get table_id
+
+ @classmethod
+ def new(
+ cls,
+ name: str,
+ description: str,
+ location: Path,
+ dbrepo_connector: DBRepoConnector,
+ table_name: str,
+ table_description: str,
+ type: str,
+ ):
+ return cls(
+ name=name,
+ description=description,
+ table_name=table_name,
+ table_description=table_description,
+ location=location,
+ dbrepo_connector=dbrepo_connector,
+ type=type,
+ )
+
+ @classmethod
+ def existing(cls, id: str, location: Path, dbrepo_connector: DBRepoConnector):
+ return cls(id=id, location=location, dbrepo_connector=dbrepo_connector)
+
+ def download(self):
+ if not self.metadata:
+ self.download_provenance()
+ df = self.dbrepo_connector.download_table_as_df(str(self.table_id))
+
+ df = df[df['entity_id'] == self.id] # save only entity, not whole table
+ df.to_csv(self.location)
+
+ def upload(self, executed_file: Path, dependencies: list[Entity] = None):
+ df = pd.read_csv(self.location)
+
+ # add id column to df:
+ df["entity_id"] = -1 # temporary, needed for easy table schema retrieval
+
+ # create table if not exists
+ # update uri etc
+ # create table if it not already exists
+ table = self.dbrepo_connector.create_table_if_not_exists(
+ df, self.table_name, self.table_description
+ )
+ self.table_id = int(table["id"])
+
+ metadata = EntityProvenance.new(
+ self.name,
+ self.description,
+ executed_file=executed_file,
+ uri=f"{self.dbrepo_connector.host}/api/database/"
+ f"{self.dbrepo_connector.database_id}/table/{self.table_id}",
+ type=self.type,
+ platform="dbrepo",
+ )
+
+ self.upload_provenance(metadata)
+ df[
+ "entity_id"
+ ] = (
+ self.metadata.id
+ ) # update the -1 from above with the correct value as it is now known
+ self.upload_data(df)
+
+ self.upload_dependencies(dependencies)
+
+ def upload_data(self, df: pd.DataFrame):
+ assert self.id is not None
+ assert self.table_id is not None
+
+ df["id"] = self.id # add entity id to df
+ self.dbrepo_connector.upload_data(df, str(self.table_id))
diff --git a/fairnb/entity/entity.py b/fairnb/entity/entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..34675c632bc914bcc39a583a74aeb124ba145138
--- /dev/null
+++ b/fairnb/entity/entity.py
@@ -0,0 +1,177 @@
+import copy
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import pandas as pd
+
+from fairnb.api.dbrepo import DBRepoConnector
+from fairnb.entity.entity_provenance import EntityProvenance
+
+
+PROVENANCE_TABLE_NAME = "entity_provenance"
+DEPENDENCY_TABLE_NAME = "entity_dependencies"
+
+LOG = logging.getLogger(__name__)
+# TODO: Upload Datetime objects as Timestamps instead of str
+
+@dataclass
+class Entity(ABC):
+ """ A O-Prov Entity class used to represent an Entity created by a notebook.
+ Subtypes of this class implement the specific upload and download logic for the
+ platform the entity was persisted on.
+ """
+ location: Path = field(init=True)
+ dbrepo_connector: DBRepoConnector = field(init=True)
+ name: str = field(init=True, default=None)
+ description: str = field(init=True, default="")
+ id: str | None = field(init=True, default=None)
+ type: str = field(init=True, default=None)
+ metadata_table_id: int | None = field(init=False, default=None)
+ dependency_table_id: int | None = field(init=False, default=None)
+ metadata: EntityProvenance | None = field(init=False, default=None)
+
+ @classmethod
+ @abstractmethod
+ def new(cls, *args, **kwargs):
+ """Create a new entity which exists at creation time only locally at 'self.location'."""
+ raise NotImplementedError
+
+ @classmethod
+ @abstractmethod
+ def existing(cls, *args, **kwargs):
+ """Use an existing entity which is already uploaded to the database. """
+ raise NotImplementedError
+
+ def __post_init__(self):
+ assert self.location
+
+ # if record not yet created delay metadata loading etc. until upload
+ if self.id is None:
+ assert self.name
+ assert self.description
+ assert self.type
+ return
+
+ # Download metadata if it exists
+ self.metadata_table_id = self.dbrepo_connector.get_table(PROVENANCE_TABLE_NAME)[
+ "id"
+ ]
+ self.dependency_table_id = self.dbrepo_connector.get_table(
+ DEPENDENCY_TABLE_NAME
+ )["id"]
+ self.download_provenance()
+
+ @abstractmethod
+ def download(self) -> EntityProvenance:
+ """Download this Entity and return the attached EntityProvenance"""
+ raise NotImplementedError
+
+ @abstractmethod
+ def upload(self, executed_file: Path, dependencies=None):
+ """Upload this Entity"""
+ raise NotImplementedError
+
+ def exists_locally(self):
+ """Checks if local file or directory exists. If so, True is returned, otherwise False"""
+ return self.location.exists()
+
+ def copy_to_location(self, new_location: Path, overwrite: bool = True):
+ """Copy local files to new location and return new deep copy of self with new location"""
+
+ if new_location.exists() and not overwrite:
+ raise FileExistsError(f"File {new_location} already exists")
+
+ new = copy.deepcopy(self) # strip all references to self
+ new.location = new_location
+ new_location.parent.mkdir(parents=True, exist_ok=True)
+ new_location.write_bytes(self.location.read_bytes()) # works as long file < RAM
+ return new
+
+ def download_provenance(self) -> EntityProvenance:
+ """ Download provenance information by using self.id and assign it to self.metadata"""
+ assert self.id
+
+ if self.metadata_table_id is None:
+ self.metadata_table_id = self.dbrepo_connector.get_table(
+ PROVENANCE_TABLE_NAME
+ )["id"]
+ if self.dependency_table_id is None:
+ self.dependency_table_id = self.dbrepo_connector.get_table(
+ DEPENDENCY_TABLE_NAME
+ )["id"]
+
+ df = self.dbrepo_connector.download_table_as_df(str(self.metadata_table_id))
+
+ row = df[df["id"] == self.id].iloc[0]
+
+ self.metadata = EntityProvenance.from_series(row)
+ self.type = self.metadata.type
+ return self.metadata
+
+ def upload_provenance(self, provenance: EntityProvenance):
+ metadata_table = self.create_provenance_table_if_not_exists(provenance)
+ self.metadata_table_id = metadata_table["id"]
+ dependency_table = self.create_dependency_table_if_not_exists()
+ self.dependency_table_id = dependency_table["id"]
+
+ self.dbrepo_connector.upload_data(
+ provenance.to_frame().drop("id", axis=1), str(self.metadata_table_id)
+ )
+
+ df = self.dbrepo_connector.download_table_as_df(str(self.metadata_table_id))
+
+ # FIXME: create robust version of id retrieval, if possible
+ row = df.iloc[df["id"].idxmax()] # get the newest row, as it should contain the correct data
+ meta = EntityProvenance.from_series(row)
+ assert meta.creation_time == provenance.creation_time and meta.name == provenance.name
+
+ self.id = meta.id
+ self.metadata = meta
+
+ def upload_dependencies(self, dependencies):
+ """ Upload the dependency information for this Entity.
+ It lists all entities, which have an id, this entity depends on.
+ """
+
+ if dependencies is not None:
+ df = pd.DataFrame(
+ {
+ "entity_id": pd.Series(dtype="int"),
+ "depends_on": pd.Series(dtype="int"),
+ }
+ )
+
+ # TODO: how to handle missing id in dependency?
+
+ for dependency in dependencies:
+ if dependency.id is not None:
+ df = pd.concat([
+ df,
+ pd.DataFrame([{"entity_id": self.id,
+ "depends_on": dependency.id}])
+ ])
+ else:
+ LOG.warning("Dependency has no id, skipping dependency upload")
+
+ self.dbrepo_connector.upload_data(df, str(self.dependency_table_id))
+
+ def create_provenance_table_if_not_exists(self, provenance: EntityProvenance):
+ df = provenance.to_frame().drop("id", axis=1)
+
+ return self.dbrepo_connector.create_table_if_not_exists(
+ df, PROVENANCE_TABLE_NAME, "A table containing Provence information on all persisted Entities."
+ )
+
+ def create_dependency_table_if_not_exists(self):
+ df = pd.DataFrame(
+ {
+ "entity_id": pd.Series(dtype="int"),
+ "depends_on": pd.Series(dtype="int"),
+ }
+ )
+
+ return self.dbrepo_connector.create_table_if_not_exists(
+ df, DEPENDENCY_TABLE_NAME, "Entity dependencies on other entities"
+ )
diff --git a/fairnb/entity/entity_provenance.py b/fairnb/entity/entity_provenance.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcd551da7b0263c030ab9aad7583d63341aeb563
--- /dev/null
+++ b/fairnb/entity/entity_provenance.py
@@ -0,0 +1,102 @@
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+import re
+
+import git
+import pandas as pd
+
+from definitions import BASE_PATH
+
+
+@dataclass
+class EntityProvenance:
+ """ Data class providing Provenance Information about an Entity
+ """
+
+ id: str | None # id of entity, always unique
+ uri: str # unique resource identifier used to locate entity (can also be used to point to table containing entity)
+ name: str # name of specific entity describing the data it contains
+ description: str # more detailed description of the enitity
+ type: str # type of entity, if notebook is run with different data type stays the same
+ commit: str # the exact commit id of the repository at the time of execution
+ branch: str # the branch of the repository, makes manual search of commit easier
+ repo_uri: str # the uri of the repository, used to locate the repository
+ executed_file: str # path to notebook which was executed to create the entity
+ creation_time: datetime # timestamp of creation time of entity
+ platform: str # platform on which the entity is uploaded (e.g. dbrepo, invenio, ...)
+
+ @classmethod
+ def new(
+ cls,
+ name: str,
+ description: str,
+ executed_file: Path,
+ type: str,
+ uri: str,
+ platform: str,
+ ):
+ repo = git.Repo(BASE_PATH)
+
+ git_branch = repo.head.reference
+ branch = git_branch.name
+ commit = git_branch.repo.commit().hexsha
+
+ # TODO: Better way to point to repo instead of ssh / https link
+ # --> more general approach independent of authentication
+ repo_uri = git_branch.repo.remote().url
+ if repo_uri.startswith("ssh://"):
+ repo_uri = re.sub(":\d+/", "/", f"https://{repo_uri.split('@', 1)[1]}")
+
+ creation_time = datetime.now()
+
+ executed_file_rel = executed_file.resolve().relative_to(BASE_PATH)
+
+ return cls(
+ id=None,
+ name=name,
+ description=description,
+ uri=uri,
+ commit=commit,
+ repo_uri=repo_uri,
+ creation_time=creation_time,
+ branch=branch,
+ executed_file=executed_file_rel.as_posix(),
+ type=type,
+ platform=platform,
+ )
+
+ @classmethod
+ def from_series(cls, df: pd.DataFrame):
+ return cls(
+ id=df["id"],
+ name=df["name"],
+ description=df["description"],
+ uri=df["uri"],
+ commit=df["commit"],
+ repo_uri=df["repo_uri"],
+ executed_file=df["executed_file"],
+ creation_time=datetime.strptime(
+ df["creation_time"], "%Y-%m-%d %H:%M:%S.%f"
+ ), # TODO: replace with '%F %T'
+ branch=df["branch"],
+ type=df["type"],
+ platform=df["platform"],
+ )
+
+ def to_frame(self):
+ return pd.DataFrame(
+ {
+ "id": pd.Series(self.id, dtype=str),
+ "name": pd.Series(self.name, dtype=str),
+ "description": pd.Series(self.description, dtype=str),
+ "uri": pd.Series(self.uri, dtype=str),
+ "commit": pd.Series(self.commit, dtype=str),
+ "repo_uri": pd.Series(self.repo_uri, dtype=str),
+ "executed_file": pd.Series(self.executed_file, dtype=str),
+ "creation_time": pd.Series(self.creation_time, dtype=str),
+ "branch": pd.Series(self.branch, dtype=str),
+ "type": pd.Series(self.type, dtype=str),
+ "platform": pd.Series(self.platform, dtype=str),
+ }
+ )
diff --git a/fairnb/entity/invenio_entity.py b/fairnb/entity/invenio_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..f794105aa49c6ee1cf014d25134de2164d3cf6e1
--- /dev/null
+++ b/fairnb/entity/invenio_entity.py
@@ -0,0 +1,104 @@
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from fairnb.api.dbrepo import DBRepoConnector
+from fairnb.api.invenio import InvenioManager, InvenioConnector
+from fairnb.entity.entity import Entity
+from fairnb.entity.entity_provenance import EntityProvenance
+
+
+@dataclass
+class InvenioEntity(Entity):
+ invenio_manager: InvenioManager = field(init=True, default=None)
+ record_metadata: dict = field(init=True, default=None)
+ publish_record: bool = field(init=True, default=False)
+
+ @classmethod
+ def new(
+ cls,
+ record_metadata: dict,
+ location: Path,
+ name: str,
+ description: str,
+ type: str,
+ dbrepo_connector: DBRepoConnector,
+ invenio_connector: InvenioConnector,
+ publish_record: bool = False,
+ ):
+ return cls(
+ invenio_manager=InvenioManager(invenio_connector),
+ record_metadata=record_metadata,
+ dbrepo_connector=dbrepo_connector,
+ location=location,
+ name=name,
+ description=description,
+ type=type,
+ publish_record=publish_record,
+ )
+
+ @classmethod
+ def existing(
+ cls,
+ id: str,
+ location: Path,
+ dbrepo_connector: DBRepoConnector,
+ invenio_connector: InvenioConnector,
+ ):
+ return cls(
+ id=id,
+ location=location,
+ dbrepo_connector=dbrepo_connector,
+ invenio_manager=InvenioManager(invenio_connector)
+ )
+
+ def __post_init__(self):
+ super().__post_init__()
+
+ if not self.id:
+ assert self.record_metadata is not None
+ return
+
+ self.invenio_manager.record_id = self.metadata.uri.split('/')[-1]
+
+ def upload(self, executed_file: Path, dependencies: list[Entity] = None):
+ dir_path: Path
+ regex: str
+
+ if self.location.is_dir():
+ dir_path = self.location
+ regex = ".*"
+ else:
+ dir_path = self.location.parent
+ regex = f".*/{self.location.name}"
+
+ create_response = self.invenio_manager.create_record(
+ self.record_metadata
+ ).json()
+ record_id = create_response["id"]
+ self.invenio_manager.upload_record(dir_path,
+ regex,
+ only_filename=False if self.location.is_dir() else True)
+
+ if self.publish_record:
+ publish_response = self.invenio_manager.publish_record()
+ uri = publish_response["links"]["self_doi"]
+ else:
+ uri = create_response["links"]["record"] # Invalid until published
+
+ metadata = EntityProvenance.new(
+ name=self.name,
+ description=self.description,
+ executed_file=executed_file,
+ uri=uri,
+ type=self.type,
+ platform="invenio",
+ )
+
+ self.upload_provenance(metadata)
+ self.upload_dependencies(dependencies)
+
+ def download(self) -> EntityProvenance:
+ if not self.metadata:
+ self.download_provenance()
+
+ return self.invenio_manager.download_record(self.location)
diff --git a/fairnb/executor.py b/fairnb/executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c859b00aadfa99fd35145ca0ac08a14f26d0359
--- /dev/null
+++ b/fairnb/executor.py
@@ -0,0 +1,84 @@
+import papermill
+from nbconvert.preprocessors import ExecutePreprocessor
+
+from fairnb.entity.entity import Entity
+from fairnb.nb_config import NbConfig
+
+
+class Executor:
+ @staticmethod
+ def download_dependencies(nb_config: NbConfig, require_download: bool = False):
+ """ Set up the dependencies to allow for later execution """
+
+ # download dependencies if not already present
+ [entity.download() for entity in nb_config.dependencies
+ if (not entity.exists_locally()) or require_download]
+
+ @classmethod
+ def execute(cls, nb_config: NbConfig, require_download: bool = False, only_local: bool = False, **kwargs):
+ """ Execute the notebook specified in nb_config by providing nb_config.dependencies
+ and upload the generated Entities if only_local is False.
+ """
+
+ # TODO: allow parsing of additional paremeters (wanted?). use kwargs for that.
+
+ if not only_local:
+ cls.download_dependencies(nb_config, require_download)
+
+ cls.execute_notebook(nb_config)
+
+ if not only_local:
+ cls.upload_entities(nb_config)
+
+ @staticmethod
+ def execute_notebook(nb_config: NbConfig):
+ # papermill executes the notebook and inserts INPUT_PATHS and OUTPUT_PATHS into the notebook by either
+ # replacing the cell tagged with 'parameters' or by inserting a new cell at the top of the notebook
+ # with that tag.
+ papermill.execute_notebook(
+ nb_config.nb_location.resolve(),
+ nb_config.nb_location.resolve(),
+ parameters=dict(
+ INPUT_PATHS={entity.type: entity.location.__str__() for entity in nb_config.dependencies},
+ OUTPUT_PATHS={entity.type: entity.location.__str__() for entity in nb_config.entities}
+ )
+ )
+
+ @staticmethod
+ def upload_entities(nb_config: NbConfig):
+ # load generated entity and upload it
+ for entity in nb_config.entities:
+ # use inspect to get path of caller
+ entity.upload(nb_config.nb_location, nb_config.dependencies)
+
+ def reproduce_entity(self, entity: Entity):
+ pass
+
+ # TODO: additional functionality of executor class
+ # class ExperimentReproducer:
+ # def __init__(self):
+ # pass
+ # # self.config = self.configure()
+ #
+ # def entity(self, creation_func, *args, input_entity: [str] = None, **kwargs):
+ # """ Saves the created entity which is returned by the creation_func as a DRO """
+ #
+ # if input_entity is not None:
+ # # TODO: collect input entities
+ # collected_entities = []
+ # for entity in input_entities:
+ # collected_entities.append(self.collect_entity(entity))
+ #
+ # result = creation_func(args, input_entities, kwargs)
+ #
+ # return result
+ #
+ # def db_repo_entity(self, function: Callable[..., pd.DataFrame], *args, **kwargs):
+ # """ Saves the created dataframe to DBRepo while citing the inputs. """
+ # df = function(args, kwargs)
+ #
+ # # TODO: upload code to DBRepo
+ #
+ # def recreate_entity(self, uri: str):
+ # """ Checkout correct commit, download required artefacts and execute correct artefact code. """
+ # pass
diff --git a/dbrepo_ismir/ml.py b/fairnb/ml.py
similarity index 100%
rename from dbrepo_ismir/ml.py
rename to fairnb/ml.py
diff --git a/fairnb/nb_config.py b/fairnb/nb_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7f99518113e7ac4921848ed97b684e7ed530ce0
--- /dev/null
+++ b/fairnb/nb_config.py
@@ -0,0 +1,16 @@
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from fairnb.entity.entity import Entity
+
+
+@dataclass
+class NbConfig:
+ nb_location: Path
+ entities: list[Entity]
+ dependencies: list[Entity]
+ nb_output_location: Path = field(init=True, default=None)
+
+ def __post_init__(self):
+ if not self.nb_output_location:
+ self.nb_output_location = self.nb_location
diff --git a/dbrepo_ismir/util.py b/fairnb/util.py
similarity index 68%
rename from dbrepo_ismir/util.py
rename to fairnb/util.py
index cfde5914a4685cf373dc6e29b197a296c515c35e..41d67b8e784fa16a61aa35691a3c12302c2abda2 100644
--- a/dbrepo_ismir/util.py
+++ b/fairnb/util.py
@@ -4,8 +4,8 @@ import pathlib
import pandas as pd
import tarfile
-from dbrepo_ismir.dbrepo import DBRepoConnector
-from dbrepo_ismir.invenio import InvenioManager, InvenioConnector
+from fairnb.api.dbrepo import DBRepoConnector
+from fairnb.api.invenio import InvenioManager, InvenioConnector
from definitions import CONFIG_PATH
import yaml
@@ -27,39 +27,34 @@ class Util:
self.configs: dict[str, (dict, dict)] = {}
def get_config(self, path: pathlib.Path = None) -> (dict, dict):
- path = CONFIG_PATH if path is None else path
+ path = CONFIG_PATH / "config.yaml" if path is None else path
# use cached config if possible
if path.as_posix() not in self.configs:
- with open(path / "config.yml", "r") as file:
+ with open(path, "r") as file:
config: dict = yaml.safe_load(file)
- with open(path / "credentials.yml", "r") as file:
- credential: dict = yaml.safe_load(file)
-
- self.configs[path.as_posix()] = config, credential
+ self.configs[path.as_posix()] = config
return self.configs[path.as_posix()]
def get_dbrepo_connector(self, path: pathlib.Path = None):
- config, credentials = self.get_config(path=path)
+ config = self.get_config(path=path)
return DBRepoConnector.from_config(
- config["dbrepo"],
- credentials["dbrepo"]
+ config, config["credentials"]
)
def get_invenio_connector(self, path: pathlib.Path = None):
- config, credentials = self.get_config(path=path)
+ config = self.get_config(path=path)
return InvenioConnector(
- credentials["invenio"]["token"],
- config["invenio"]["host"]
+ token=config["credentials"]["token"],
+ host=config["host"]
)
def get_invenio_manager(self, path: pathlib.Path = None):
- config, credentials = self.get_config(path=path)
+ config = self.get_config(path=path)
return InvenioManager(
- credentials["invenio"]["token"],
- config["invenio"]["host"]
+ self.get_invenio_connector(path=path)
)
def load_input_table(self, path: pathlib.Path) -> pd.DataFrame:
diff --git a/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb
index 55ff5d3fe1a0928752d2bdd4525965ffec7b298e..24456088a68c3ede3dd0a13b0660d218120040b4 100644
--- a/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb
@@ -1,9 +1,38 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4389a8092677254e",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "source": [
+ "# Audio Files\n",
+ "\n",
+ "Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 2,
+ "id": "87ab37c6",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.015854,
+ "end_time": "2023-09-01T11:23:36.114552",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.098698",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"from definitions import BASE_PATH\n",
@@ -15,23 +44,47 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
+ "id": "1b4e6b01",
"metadata": {
+ "papermill": {
+ "duration": 0.01235,
+ "end_time": "2023-09-01T11:23:36.096700",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.084350",
+ "status": "completed"
+ },
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {}\n",
"OUTPUT_PATHS = {\n",
- " \"audio_tar\": (BASE_PATH / \"tmp\" / \"1_audio_files\" / \"output\" / \"emotifymusic.tar.gz\").__str__()\n",
+ " \"audio_tar\": str(BASE_PATH / \"tmp/1_audio_files/output/emotifymusic.tar.gz\")\n",
"}"
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {},
+ "execution_count": 4,
+ "id": "1e487573",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 2.541999,
+ "end_time": "2023-09-01T11:23:38.664303",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.122304",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# load provided files\n",
@@ -41,13 +94,27 @@
"dir_path.mkdir(parents=True, exist_ok=True)\n",
"# unzip to dir_path\n",
"with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
- " zfile.extractall(path=dir_path)\n"
+ " zfile.extractall(path=dir_path)"
]
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {},
+ "execution_count": 5,
+ "id": "c3193f35",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 1.066369,
+ "end_time": "2023-09-01T11:23:39.735691",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:38.669322",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"file_paths = list(dir_path.rglob('**/*.*'))\n",
@@ -55,22 +122,28 @@
"flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
"for path in file_paths:\n",
- " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n"
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
+ "execution_count": 6,
+ "id": "3272ea2b",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 15.267255,
+ "end_time": "2023-09-01T11:23:55.005410",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:39.738155",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
"source": [
"tar_path = Path(OUTPUT_PATHS[\"audio_tar\"])\n",
"tar_path.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -81,7 +154,6 @@
}
],
"metadata": {
- "celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@@ -97,9 +169,26 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 20.157944,
+ "end_time": "2023-09-01T11:23:55.227765",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {},
+ "OUTPUT_PATHS": {
+ "audio_tar": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/1_audio_files/output/emotifymusic.tar.gz"
+ }
+ },
+ "start_time": "2023-09-01T11:23:35.069821",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 5
}
diff --git a/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb
index 84a3f978993d3566cf13413af61126ca335e5238..2a1646457063b748a64d7fa7711c8f2ae9686943 100644
--- a/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb
@@ -2,7 +2,17 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {},
+ "id": "f48a4573",
+ "metadata": {
+ "papermill": {
+ "duration": 0.002709,
+ "end_time": "2023-09-01T11:35:09.037422",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.034713",
+ "status": "completed"
+ },
+ "tags": []
+ },
"source": [
"# Aggregate MFCC Features\n",
"\n",
@@ -12,19 +22,57 @@
{
"cell_type": "code",
"execution_count": 1,
+ "id": "389576b8",
"metadata": {
- "collapsed": true
+ "ExecuteTime": {
+ "end_time": "2023-08-14T15:32:41.535589478Z",
+ "start_time": "2023-08-14T15:32:40.986222405Z"
+ },
+ "collapsed": true,
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.044339Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.044011Z",
+ "iopub.status.idle": "2023-09-01T11:35:09.306707Z",
+ "shell.execute_reply": "2023-09-01T11:35:09.305772Z"
+ },
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "papermill": {
+ "duration": 0.268336,
+ "end_time": "2023-09-01T11:35:09.308546",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.040210",
+ "status": "completed"
+ },
+ "tags": []
},
"outputs": [],
"source": [
+ "from pathlib import Path\n",
+ "\n",
"import pandas as pd\n",
"from definitions import BASE_PATH"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "id": "26f640e0",
"metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.315577Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.314983Z",
+ "iopub.status.idle": "2023-09-01T11:35:09.320056Z",
+ "shell.execute_reply": "2023-09-01T11:35:09.318932Z"
+ },
+ "papermill": {
+ "duration": 0.010186,
+ "end_time": "2023-09-01T11:35:09.321555",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.311369",
+ "status": "completed"
+ },
"tags": [
"parameters"
]
@@ -45,20 +93,83 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
+ "execution_count": 3,
+ "id": "12fd5cf6",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.326762Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.326298Z",
+ "iopub.status.idle": "2023-09-01T11:35:09.329659Z",
+ "shell.execute_reply": "2023-09-01T11:35:09.329117Z"
+ },
+ "papermill": {
+ "duration": 0.007292,
+ "end_time": "2023-09-01T11:35:09.330862",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.323570",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {\n",
+ " \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/input/raw_features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv\"\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c5d9d980",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.335772Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.335118Z",
+ "iopub.status.idle": "2023-09-01T11:35:14.097619Z",
+ "shell.execute_reply": "2023-09-01T11:35:14.096620Z"
+ },
+ "papermill": {
+ "duration": 4.766846,
+ "end_time": "2023-09-01T11:35:14.099543",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.332697",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# inputs\n",
- "assert INPUT_PATH.exists() and INPUT_PATH.is_dir()\n",
- "\n",
- "raw_features = pd.read_csv(INPUT_PATH / \"raw_features.csv\", index_col=False)"
+ "raw_features = pd.read_csv(INPUT_PATHS[\"raw_features\"], index_col=False)"
]
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {},
+ "execution_count": 5,
+ "id": "99f75f47",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:14.106862Z",
+ "iopub.status.busy": "2023-09-01T11:35:14.106221Z",
+ "iopub.status.idle": "2023-09-01T11:35:18.117596Z",
+ "shell.execute_reply": "2023-09-01T11:35:18.116970Z"
+ },
+ "papermill": {
+ "duration": 4.018079,
+ "end_time": "2023-09-01T11:35:18.120249",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:14.102170",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -136,10 +247,10 @@
" <td>-562.85785</td>\n",
" <td>-96.164795</td>\n",
" <td>-219.259016</td>\n",
- " <td>53.561838</td>\n",
+ " <td>53.561839</td>\n",
" <td>-0.772320</td>\n",
" <td>0.029056</td>\n",
- " <td>259.63270</td>\n",
+ " <td>259.63272</td>\n",
" <td>215.094182</td>\n",
" <td>...</td>\n",
" <td>-27.458416</td>\n",
@@ -147,8 +258,8 @@
" <td>0.484271</td>\n",
" <td>8.660648</td>\n",
" <td>-0.479016</td>\n",
- " <td>-28.989983</td>\n",
- " <td>27.533710</td>\n",
+ " <td>-28.989979</td>\n",
+ " <td>27.533707</td>\n",
" <td>0.952658</td>\n",
" <td>10.477735</td>\n",
" <td>-0.185771</td>\n",
@@ -172,7 +283,7 @@
" <td>8.185075</td>\n",
" <td>0.208425</td>\n",
" <td>-38.095375</td>\n",
- " <td>31.397880</td>\n",
+ " <td>31.397882</td>\n",
" <td>-1.494916</td>\n",
" <td>10.917299</td>\n",
" <td>0.020985</td>\n",
@@ -195,8 +306,8 @@
" <td>-3.781627</td>\n",
" <td>9.191043</td>\n",
" <td>0.260886</td>\n",
- " <td>-22.667440</td>\n",
- " <td>50.992897</td>\n",
+ " <td>-22.667439</td>\n",
+ " <td>50.992905</td>\n",
" <td>1.600777</td>\n",
" <td>10.125545</td>\n",
" <td>0.595763</td>\n",
@@ -212,14 +323,14 @@
" <td>-0.366586</td>\n",
" <td>0.000000</td>\n",
" <td>194.26416</td>\n",
- " <td>148.226647</td>\n",
+ " <td>148.226648</td>\n",
" <td>...</td>\n",
- " <td>-44.843810</td>\n",
+ " <td>-44.843815</td>\n",
" <td>28.490644</td>\n",
" <td>-6.242015</td>\n",
" <td>10.546545</td>\n",
" <td>0.341848</td>\n",
- " <td>-25.040888</td>\n",
+ " <td>-25.040886</td>\n",
" <td>46.878204</td>\n",
" <td>1.844494</td>\n",
" <td>11.160392</td>\n",
@@ -270,7 +381,7 @@
" <td>-24.712723</td>\n",
" <td>23.410387</td>\n",
" <td>-4.502398</td>\n",
- " <td>6.687984</td>\n",
+ " <td>6.687983</td>\n",
" <td>0.238807</td>\n",
" </tr>\n",
" <tr>\n",
@@ -278,21 +389,21 @@
" <td>rock_96.mp3</td>\n",
" <td>rock</td>\n",
" <td>-541.23600</td>\n",
- " <td>27.163334</td>\n",
+ " <td>27.163332</td>\n",
" <td>-119.113996</td>\n",
" <td>58.420684</td>\n",
" <td>-0.957699</td>\n",
- " <td>-7.415961</td>\n",
+ " <td>-7.415959</td>\n",
" <td>210.49246</td>\n",
" <td>125.453699</td>\n",
" <td>...</td>\n",
" <td>-37.584858</td>\n",
- " <td>28.087936</td>\n",
+ " <td>28.087940</td>\n",
" <td>-9.704238</td>\n",
" <td>8.447620</td>\n",
" <td>0.112760</td>\n",
" <td>-38.147890</td>\n",
- " <td>21.814402</td>\n",
+ " <td>21.814400</td>\n",
" <td>-8.249507</td>\n",
" <td>7.807756</td>\n",
" <td>0.071968</td>\n",
@@ -316,7 +427,7 @@
" <td>7.727378</td>\n",
" <td>0.207489</td>\n",
" <td>-29.497524</td>\n",
- " <td>25.410654</td>\n",
+ " <td>25.410656</td>\n",
" <td>-3.356614</td>\n",
" <td>8.170526</td>\n",
" <td>0.160330</td>\n",
@@ -331,16 +442,16 @@
" <td>52.444200</td>\n",
" <td>-1.705641</td>\n",
" <td>0.000000</td>\n",
- " <td>187.04274</td>\n",
+ " <td>187.04272</td>\n",
" <td>96.440874</td>\n",
" <td>...</td>\n",
- " <td>-26.967848</td>\n",
- " <td>8.714737</td>\n",
+ " <td>-26.967852</td>\n",
+ " <td>8.714736</td>\n",
" <td>-9.511491</td>\n",
" <td>5.551820</td>\n",
" <td>-0.025604</td>\n",
- " <td>-23.020084</td>\n",
- " <td>13.948638</td>\n",
+ " <td>-23.020082</td>\n",
+ " <td>13.948639</td>\n",
" <td>-2.664985</td>\n",
" <td>5.051498</td>\n",
" <td>-0.258407</td>\n",
@@ -354,17 +465,17 @@
" <td>-49.380943</td>\n",
" <td>54.045627</td>\n",
" <td>-0.863093</td>\n",
- " <td>-32.930653</td>\n",
+ " <td>-32.930650</td>\n",
" <td>191.73538</td>\n",
" <td>93.971242</td>\n",
" <td>...</td>\n",
" <td>-21.929403</td>\n",
" <td>17.050608</td>\n",
" <td>-5.296691</td>\n",
- " <td>5.894963</td>\n",
+ " <td>5.894962</td>\n",
" <td>0.390705</td>\n",
" <td>-20.983192</td>\n",
- " <td>29.312023</td>\n",
+ " <td>29.312021</td>\n",
" <td>-0.321836</td>\n",
" <td>6.571660</td>\n",
" <td>0.384794</td>\n",
@@ -383,36 +494,36 @@
"4 classical_12.mp3 classical -562.67523 -148.133560 -270.975406 \n",
".. ... ... ... ... ... \n",
"395 rock_95.mp3 rock -553.11010 -5.218835 -193.506047 \n",
- "396 rock_96.mp3 rock -541.23600 27.163334 -119.113996 \n",
+ "396 rock_96.mp3 rock -541.23600 27.163332 -119.113996 \n",
"397 rock_97.mp3 rock -518.49500 58.526745 -66.267744 \n",
"398 rock_98.mp3 rock -518.64307 53.555115 -45.734517 \n",
"399 rock_99.mp3 rock -544.70310 75.612130 -49.380943 \n",
"\n",
" 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n",
"0 51.142183 -0.468374 0.000000 178.75162 111.332342 ... -44.098070 \n",
- "1 53.561838 -0.772320 0.029056 259.63270 215.094182 ... -27.458416 \n",
+ "1 53.561839 -0.772320 0.029056 259.63272 215.094182 ... -27.458416 \n",
"2 83.381622 -2.587179 0.000000 190.47589 112.471713 ... -27.335688 \n",
"3 76.246992 -2.402418 0.000000 159.42575 99.853645 ... -31.774948 \n",
- "4 52.191182 -0.366586 0.000000 194.26416 148.226647 ... -44.843810 \n",
+ "4 52.191182 -0.366586 0.000000 194.26416 148.226648 ... -44.843815 \n",
".. ... ... ... ... ... ... ... \n",
"395 76.869437 -0.201055 -89.948746 201.18045 111.724191 ... -27.043941 \n",
- "396 58.420684 -0.957699 -7.415961 210.49246 125.453699 ... -37.584858 \n",
+ "396 58.420684 -0.957699 -7.415959 210.49246 125.453699 ... -37.584858 \n",
"397 65.635619 -0.898026 -58.824410 175.20135 99.288265 ... -29.620445 \n",
- "398 52.444200 -1.705641 0.000000 187.04274 96.440874 ... -26.967848 \n",
- "399 54.045627 -0.863093 -32.930653 191.73538 93.971242 ... -21.929403 \n",
+ "398 52.444200 -1.705641 0.000000 187.04272 96.440874 ... -26.967852 \n",
+ "399 54.045627 -0.863093 -32.930650 191.73538 93.971242 ... -21.929403 \n",
"\n",
" 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n",
"0 47.308060 -3.713503 16.553984 0.230691 -46.794480 49.352516 -2.282116 \n",
- "1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n",
- "2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397880 -1.494916 \n",
- "3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n",
- "4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n",
+ "1 29.811110 0.484271 8.660648 -0.479016 -28.989979 27.533707 0.952658 \n",
+ "2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397882 -1.494916 \n",
+ "3 31.500881 -3.781627 9.191043 0.260886 -22.667439 50.992905 1.600777 \n",
+ "4 28.490644 -6.242015 10.546545 0.341848 -25.040886 46.878204 1.844494 \n",
".. ... ... ... ... ... ... ... \n",
"395 22.451445 -7.234634 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n",
- "396 28.087936 -9.704238 8.447620 0.112760 -38.147890 21.814402 -8.249507 \n",
- "397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410654 -3.356614 \n",
- "398 8.714737 -9.511491 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n",
- "399 17.050608 -5.296691 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n",
+ "396 28.087940 -9.704238 8.447620 0.112760 -38.147890 21.814400 -8.249507 \n",
+ "397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410656 -3.356614 \n",
+ "398 8.714736 -9.511491 5.551820 -0.025604 -23.020082 13.948639 -2.664985 \n",
+ "399 17.050608 -5.296691 5.894962 0.390705 -20.983192 29.312021 -0.321836 \n",
"\n",
" 39_std 39_skew \n",
"0 15.285639 0.171462 \n",
@@ -421,7 +532,7 @@
"3 10.125545 0.595763 \n",
"4 11.160392 0.503120 \n",
".. ... ... \n",
- "395 6.687984 0.238807 \n",
+ "395 6.687983 0.238807 \n",
"396 7.807756 0.071968 \n",
"397 8.170526 0.160330 \n",
"398 5.051498 -0.258407 \n",
@@ -430,7 +541,7 @@
"[400 rows x 202 columns]"
]
},
- "execution_count": 24,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -456,15 +567,32 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "metadata": {},
+ "execution_count": 6,
+ "id": "4ac5c765",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:18.127758Z",
+ "iopub.status.busy": "2023-09-01T11:35:18.127051Z",
+ "iopub.status.idle": "2023-09-01T11:35:18.220446Z",
+ "shell.execute_reply": "2023-09-01T11:35:18.219871Z"
+ },
+ "papermill": {
+ "duration": 0.100061,
+ "end_time": "2023-09-01T11:35:18.222876",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:18.122815",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# outputs\n",
- "OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
+ "aggregated_features_path = Path(OUTPUT_PATHS[\"aggregated_features\"]).resolve()\n",
+ "aggregated_features_path.parent.mkdir(parents=True, exist_ok=True)\n",
"\n",
"output = mfcc_merged\n",
- "output.to_csv(OUTPUT_PATH / \"features.csv\", index=False)"
+ "output.to_csv(aggregated_features_path, index=False)"
]
}
],
@@ -485,9 +613,28 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 10.352537,
+ "end_time": "2023-09-01T11:35:18.542818",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {
+ "raw_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/input/raw_features.csv"
+ },
+ "OUTPUT_PATHS": {
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv"
+ }
+ },
+ "start_time": "2023-09-01T11:35:08.190281",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 5
}
diff --git a/notebooks/.ipynb_checkpoints/4_split-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/4_split-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e4d2c5b8d040a4d02b422248156afd19de044258
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/4_split-checkpoint.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e92b4fe9",
+ "metadata": {
+ "papermill": {
+ "duration": 0.004009,
+ "end_time": "2023-09-01T11:35:21.835314",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:21.831305",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# Split the Features into Train and Test Set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5f1fae44",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:21.844307Z",
+ "iopub.status.busy": "2023-09-01T11:35:21.844022Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.144905Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.144381Z"
+ },
+ "papermill": {
+ "duration": 0.308442,
+ "end_time": "2023-09-01T11:35:22.147872",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:21.839430",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pathlib import Path\n",
+ "from definitions import BASE_PATH"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "01de1b27",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.156002Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.155641Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.160059Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.159355Z"
+ },
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.010206,
+ "end_time": "2023-09-01T11:35:22.161506",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.151300",
+ "status": "completed"
+ },
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Tagged with 'parameters'\n",
+ "from definitions import BASE_PATH\n",
+ "\n",
+ "INPUT_PATHS: dict[str, str] = {\n",
+ " \"features\": (BASE_PATH / \"tmp\" / \"4_split\" / \"input\" / \"features.csv\").__str__()\n",
+ "}\n",
+ "OUTPUT_PATHS: dict[str, str] = {\n",
+ " \"split\": (BASE_PATH / \"tmp\" / \"4_split\" / \"output\" / \"split.csv\").__str__()\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "205bb941",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:21.808793Z",
+ "iopub.status.busy": "2023-09-01T11:35:21.808502Z",
+ "iopub.status.idle": "2023-09-01T11:35:21.824152Z",
+ "shell.execute_reply": "2023-09-01T11:35:21.822789Z"
+ },
+ "papermill": {
+ "duration": 0.023269,
+ "end_time": "2023-09-01T11:35:21.827306",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:21.804037",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a4cc6800",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.190493Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.190038Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.217115Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.216124Z"
+ },
+ "papermill": {
+ "duration": 0.03203,
+ "end_time": "2023-09-01T11:35:22.218934",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.186904",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# INPUT\n",
+ "\n",
+ "for path in INPUT_PATHS.values():\n",
+ " assert Path(path).exists()\n",
+ "\n",
+ "features = pd.read_csv(INPUT_PATHS[\"features\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a186d0c4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.225158Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.224866Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.233993Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.232970Z"
+ },
+ "papermill": {
+ "duration": 0.014722,
+ "end_time": "2023-09-01T11:35:22.236276",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.221554",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "train = features.sample(frac=0.8).sort_index()\n",
+ "test = features.drop(train.index)\n",
+ "\n",
+ "split_true = pd.DataFrame({\n",
+ " \"filename\": train.filename,\n",
+ " \"train\": True\n",
+ "})\n",
+ "split_false = pd.DataFrame({\n",
+ " \"filename\": test.filename,\n",
+ " \"train\": False\n",
+ "})\n",
+ "\n",
+ "split_concat = pd.concat([split_true, split_false])\\\n",
+ " .sort_values(\"filename\")\\\n",
+ " .reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "091e0641",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.248578Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.248298Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.260910Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.260154Z"
+ },
+ "papermill": {
+ "duration": 0.022698,
+ "end_time": "2023-09-01T11:35:22.264468",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.241770",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>filename</th>\n",
+ " <th>train</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>classical_1.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>classical_10.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>classical_100.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>classical_11.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>classical_12.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>395</th>\n",
+ " <td>rock_95.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>396</th>\n",
+ " <td>rock_96.mp3</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>397</th>\n",
+ " <td>rock_97.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>398</th>\n",
+ " <td>rock_98.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>399</th>\n",
+ " <td>rock_99.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>400 rows × 2 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " filename train\n",
+ "0 classical_1.mp3 True\n",
+ "1 classical_10.mp3 True\n",
+ "2 classical_100.mp3 True\n",
+ "3 classical_11.mp3 True\n",
+ "4 classical_12.mp3 True\n",
+ ".. ... ...\n",
+ "395 rock_95.mp3 True\n",
+ "396 rock_96.mp3 False\n",
+ "397 rock_97.mp3 True\n",
+ "398 rock_98.mp3 True\n",
+ "399 rock_99.mp3 True\n",
+ "\n",
+ "[400 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "split_concat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "7b11b8bb",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.274622Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.274234Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.281519Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.280717Z"
+ },
+ "papermill": {
+ "duration": 0.01433,
+ "end_time": "2023-09-01T11:35:22.283192",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.268862",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# output\n",
+ "OUTPUT_PATH = Path(OUTPUT_PATHS[\"split\"])\n",
+ "OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "output = split_concat\n",
+ "output.to_csv(OUTPUT_PATH, index=False)"
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Tags",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 1.989508,
+ "end_time": "2023-09-01T11:35:22.603293",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv"
+ },
+ "OUTPUT_PATHS": {
+ "split": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv"
+ }
+ },
+ "start_time": "2023-09-01T11:35:20.613785",
+ "version": "2.4.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/5_ml_model-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/5_ml_model-checkpoint.ipynb
index 4153d2821277e123acc407ba2fc99879fc9aa18d..533a6917b6c112b831e8b49ca5bb682fdbea7d44 100644
--- a/notebooks/.ipynb_checkpoints/5_ml_model-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/5_ml_model-checkpoint.ipynb
@@ -27,9 +27,9 @@
"from sklearn.svm import SVC\n",
"\n",
"from definitions import BASE_PATH\n",
- "from dbrepo_ismir.util import Util\n",
+ "from fairnb.util import Util\n",
"\n",
- "from dbrepo_ismir.dbrepo import DBRepoConnector"
+ "from fairnb.dbrepo import DBRepoConnector"
]
},
{
diff --git a/notebooks/6_report.ipynb b/notebooks/.ipynb_checkpoints/6_report-checkpoint.ipynb
similarity index 100%
rename from notebooks/6_report.ipynb
rename to notebooks/.ipynb_checkpoints/6_report-checkpoint.ipynb
diff --git a/notebooks/.ipynb_checkpoints/standalone-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/standalone-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8052009cb2607b44e77034d4eea255218768766f
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/standalone-checkpoint.ipynb
@@ -0,0 +1,1051 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Standalone Notebook\n",
+ "\n",
+ "Notebook containing the same functionality as main.ipynb, but it includes all steps in one notebook and does not spin up separate Jupyter Kernels and uploads the entities directly."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "from concurrent.futures import ThreadPoolExecutor\n",
+ "from contextlib import contextmanager, redirect_stderr, redirect_stdout\n",
+ "\n",
+ "import librosa\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import yaml\n",
+ "from matplotlib import pyplot as plt\n",
+ "from pandas import DataFrame, Index\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "from fairnb.entity.dbrepo_entity import DbRepoEntity\n",
+ "from fairnb.entity.invenio_entity import InvenioEntity\n",
+ "from fairnb.executor import Executor\n",
+ "from fairnb.nb_config import NbConfig\n",
+ "from fairnb.util import Util\n",
+ "from definitions import BASE_PATH, RESOURCE_PATH\n",
+ "import tarfile\n",
+ "import zipfile\n",
+ "import os\n",
+ "from pathlib import Path\n",
+ "from definitions import CONFIG_PATH"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:23:50.320823230Z",
+ "start_time": "2023-09-06T16:23:48.109782272Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [],
+ "source": [
+ "# experiment executor setup\n",
+ "executor = Executor()\n",
+ "util = Util.get_instance() # util caches loaded credentials -> via Singleton\n",
+ "connector = util.get_dbrepo_connector(CONFIG_PATH / \"dbrepo_config.yml\")\n",
+ "# connector = None\n",
+ "invenio_connector = util.get_invenio_connector(CONFIG_PATH / \"invenio_config.yml\")\n",
+ "\n",
+ "NOTEBOOK_PATH = BASE_PATH / \"notebooks\"\n",
+ "LOCAL_PATH = BASE_PATH / \"tmp\" / \"standalone\"\n",
+ "NB_LOCATION = NOTEBOOK_PATH / \"standalone.ipynb\"\n",
+ "\n",
+ "ONLY_LOCAL = True"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:23:50.458322232Z",
+ "start_time": "2023-09-06T16:23:50.353619129Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1. Audio Files\n",
+ "\n",
+ "Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [],
+ "source": [
+ "tar_path = LOCAL_PATH / \"emotifymusic.tar.gz\"\n",
+ "\n",
+ "# load provided files\n",
+ "zip_path = BASE_PATH / \"resource\" / \"1_audio_files\" / \"emotifymusic.zip\"\n",
+ "dir_path = LOCAL_PATH / \"music\" / \"original\"\n",
+ "\n",
+ "dir_path.mkdir(parents=True, exist_ok=True)\n",
+ "# unzip to dir_path\n",
+ "with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
+ " zfile.extractall(path=dir_path)\n",
+ "\n",
+ "file_paths = list(dir_path.rglob('**/*.*'))\n",
+ "flattened_dir_path = LOCAL_PATH / \"music\" / \"flattened\"\n",
+ "flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "for path in file_paths:\n",
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n",
+ "\n",
+ "tar_path.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "with tarfile.open(tar_path, \"w:gz\") as file:\n",
+ " file.add(flattened_dir_path, arcname=os.path.sep)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:24:08.646424970Z",
+ "start_time": "2023-09-06T16:23:50.456742546Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "outputs": [],
+ "source": [
+ "if not ONLY_LOCAL:\n",
+ " metadata = yaml.safe_load(open(RESOURCE_PATH / \"1_audio_files\" / \"record_metadata.yml\", \"r\"))\n",
+ "\n",
+ " nb_config_audio_files = NbConfig(\n",
+ " nb_location=NB_LOCATION,\n",
+ " entities=[\n",
+ " audio_files_entity := InvenioEntity.new(\n",
+ " name = \"audio_tar\",\n",
+ " description = \"Raw music files\",\n",
+ " location = tar_path,\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"audio_tar\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[]\n",
+ " )\n",
+ "\n",
+ " executor.upload_entities(nb_config_audio_files)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:24:08.657491220Z",
+ "start_time": "2023-09-06T16:24:08.653629315Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2. Feature Extraction of Base audio Files from Invenio\n",
+ "\n",
+ "Load the audio files from the tar, and extract the MFCC features from them and store them in a dataframe."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "outputs": [],
+ "source": [
+ "DEFAULT_SAMPLING_RATE = 22050\n",
+ "\n",
+ "with tarfile.open(tar_path, \"r:gz\") as archive:\n",
+ " archive.extractall(path=(path_out := tar_path.with_suffix(\"\").with_suffix(\"\")))\n",
+ "\n",
+ "files = list(path_out.rglob(\"**/*.*\"))\n",
+ "\n",
+ "\n",
+ "@contextmanager\n",
+ "def suppress_stdout_stderr():\n",
+ " \"\"\"A context manager that redirects stdout and stderr to devnull\"\"\"\n",
+ " with open(os.devnull, 'w') as fnull:\n",
+ " with redirect_stderr(fnull) as err, redirect_stdout(fnull) as out:\n",
+ " yield err, out\n",
+ "\n",
+ "\n",
+ "def generate_mfcc_feature(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE, number_mfccs: int = 40):\n",
+ " x, _ = load_mp3(filepath, sr=sr)\n",
+ " assert sr == _\n",
+ " mfcc = librosa.feature.mfcc(x, sr=sr, n_mfcc=number_mfccs)\n",
+ "\n",
+ " # transpose to use mfcc bands as columns instead of rows\n",
+ " return pd.DataFrame(mfcc).transpose()\n",
+ "\n",
+ "\n",
+ "def load_mp3(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE):\n",
+ " x, sr = librosa.load(filepath, sr=sr) # extract wave (x) with sample rate (sr)\n",
+ " return x, sr\n",
+ "\n",
+ "\n",
+ "with suppress_stdout_stderr(), ThreadPoolExecutor(6) as executor:\n",
+ " dataframes = list(executor.map(\n",
+ " lambda args: generate_mfcc_feature(args), files)\n",
+ " )\n",
+ "for file, dataframe in zip(files, dataframes):\n",
+ " dataframe[\"sample\"] = dataframe.index.to_numpy(copy=True)\n",
+ " dataframe[\"filename\"] = file.name\n",
+ " dataframe[\"label\"] = file.name.split('_')[0] # extract genre from file name\n",
+ "\n",
+ "dataframe_concat = pd.concat(dataframes)\n",
+ "columns_old = list(dataframe_concat.columns)\n",
+ "columns = columns_old[-3:] + columns_old[:-3]\n",
+ "dataframe_concat = dataframe_concat[columns]\n",
+ "\n",
+ "raw_features = dataframe_concat"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:32:07.450652088Z",
+ "start_time": "2023-09-06T16:24:08.668477833Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[<matplotlib.lines.Line2D at 0x7efe441870a0>]"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "<Figure size 640x480 with 1 Axes>",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "example_mfcc = raw_features[raw_features.filename == \"rock_50.mp3\"].sort_values(\"sample\").iloc[:,:]\n",
+ "plt.plot(example_mfcc[15])\n",
+ "# plt.plot(example_mfcc[4])\n",
+ "\n",
+ "# librosa.display.waveshow(audio)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:32:08.167783042Z",
+ "start_time": "2023-09-06T16:32:07.535299813Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 3. Aggregate MFCC Features"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [],
+ "source": [
+ "# allow for direct entry if features were already created in earlier run\n",
+ "raw_features.to_csv(LOCAL_PATH / \"raw_features.csv\", index=False)\n",
+ "\n",
+ "if \"raw_features\" not in globals():\n",
+ " raw_features = pd.read_csv(LOCAL_PATH / \"raw_features.csv\")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:37.098344632Z",
+ "start_time": "2023-09-06T16:32:08.174101151Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " filename label 0_min 0_max 0_mean \\\n0 classical_1.mp3 classical -530.784363 -163.308350 -302.203156 \n1 classical_10.mp3 classical -562.857849 -96.164795 -219.259018 \n2 classical_100.mp3 classical -536.237366 -61.608826 -177.804108 \n3 classical_11.mp3 classical -536.457458 -120.429665 -222.126312 \n4 classical_12.mp3 classical -562.675232 -148.133560 -270.975403 \n.. ... ... ... ... ... \n395 rock_95.mp3 rock -553.110107 -5.218835 -193.506042 \n396 rock_96.mp3 rock -541.236023 27.163334 -119.113991 \n397 rock_97.mp3 rock -518.494995 58.526745 -66.267746 \n398 rock_98.mp3 rock -518.643066 53.555115 -45.734516 \n399 rock_99.mp3 rock -544.703125 75.612129 -49.380943 \n\n 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n0 51.142183 -0.468374 0.000000 178.751617 111.332344 ... -44.098068 \n1 53.561838 -0.772320 0.029056 259.632690 215.094193 ... -27.458416 \n2 83.381622 -2.587179 0.000000 190.475891 112.471710 ... -27.335688 \n3 76.246992 -2.402419 0.000000 159.425751 99.853645 ... -31.774948 \n4 52.191182 -0.366587 0.000000 194.264160 148.226654 ... -44.843811 \n.. ... ... ... ... ... ... ... \n395 76.869437 -0.201055 -89.948746 201.180450 111.724190 ... -27.043941 \n396 58.420684 -0.957699 -7.415961 210.492462 125.453690 ... -37.584858 \n397 65.635619 -0.898026 -58.824409 175.201355 99.288261 ... -29.620445 \n398 52.444200 -1.705641 0.000000 187.042740 96.440872 ... -26.967848 \n399 54.045627 -0.863093 -32.930653 191.735382 93.971237 ... -21.929403 \n\n 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n0 47.308060 -3.713503 16.553984 0.230691 -46.794479 49.352516 -2.282116 \n1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397881 -1.494916 \n3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n.. ... ... ... ... ... ... ... \n395 22.451445 -7.234633 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n396 28.087936 -9.704238 8.447620 0.112760 -38.147888 21.814402 -8.249507 \n397 26.325895 -5.722826 7.727378 0.207489 -29.497524 25.410654 -3.356615 \n398 8.714737 -9.511492 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n399 17.050608 -5.296690 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n\n 39_std 39_skew \n0 15.285639 0.171462 \n1 10.477735 -0.185771 \n2 10.917299 0.020984 \n3 10.125545 0.595763 \n4 11.160392 0.503120 \n.. ... ... \n395 6.687984 0.238807 \n396 7.807756 0.071968 \n397 8.170526 0.160330 \n398 5.051498 -0.258407 \n399 6.571660 0.384794 \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>...</td>\n <td>-44.843811</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "meta_columns = [\"sample\", \"filename\", \"label\"]\n",
+ "mfcc_aggregated = raw_features \\\n",
+ " .drop(meta_columns, axis=1, errors='ignore') \\\n",
+ " .groupby(raw_features.filename).agg(['min', 'max', 'mean', 'std', 'skew'])\n",
+ "\n",
+ "mfcc_meta = pd.DataFrame(raw_features['label'].groupby(raw_features.filename).last())\n",
+ "mfcc_meta.columns = pd.MultiIndex.from_arrays([['label'], ['']]) # needed for merge\n",
+ "mfcc_merged = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\n",
+ "\n",
+ "# reduce multi index to single index\n",
+ "one_level_cols = ['_'.join([str(el) for el in col]) for col in mfcc_merged.columns[1:]]\n",
+ "one_level_cols.insert(0, \"label\")\n",
+ "\n",
+ "mfcc_merged.columns = pd.Index(one_level_cols)\n",
+ "mfcc_merged = mfcc_merged.reset_index()\n",
+ "mfcc_merged"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:52.624562481Z",
+ "start_time": "2023-09-06T16:33:40.167149213Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 4. Split the Features into Train and Test Set"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " filename train\n0 classical_1.mp3 True\n1 classical_10.mp3 False\n2 classical_100.mp3 False\n3 classical_11.mp3 False\n4 classical_12.mp3 True\n.. ... ...\n395 rock_95.mp3 True\n396 rock_96.mp3 True\n397 rock_97.mp3 True\n398 rock_98.mp3 True\n399 rock_99.mp3 True\n\n[400 rows x 2 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>train</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 2 columns</p>\n</div>"
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "features = mfcc_merged\n",
+ "train = features.sample(frac=0.8).sort_index()\n",
+ "test = features.drop(train.index)\n",
+ "\n",
+ "split_true = pd.DataFrame({\n",
+ " \"filename\": train.filename,\n",
+ " \"train\": True\n",
+ "})\n",
+ "split_false = pd.DataFrame({\n",
+ " \"filename\": test.filename,\n",
+ " \"train\": False\n",
+ "})\n",
+ "\n",
+ "split = pd.concat([split_true, split_false]) \\\n",
+ " .sort_values(\"filename\") \\\n",
+ " .reset_index(drop=True)\n",
+ "split"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:52.625265280Z",
+ "start_time": "2023-09-06T16:33:52.600299846Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 5: Machine Learning model training and evaluation"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561838 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163334 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847563 \nclassical_10.mp3 -0.772320 0.029056 259.632690 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916949 \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 \n... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415961 210.492462 125.453690 31.908869 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \nrock_98.mp3 -1.705641 0.000000 187.042740 96.440872 24.137702 \nrock_99.mp3 -0.863093 -32.930653 191.735382 93.971237 33.410220 \n\n ... 38_max 38_mean 38_std 38_skew 39_min \\\nfilename ... \nclassical_1.mp3 ... 47.308060 -3.713503 16.553984 0.230691 -46.794479 \nclassical_10.mp3 ... 29.811110 0.484271 8.660648 -0.479016 -28.989983 \nclassical_100.mp3 ... 27.610388 -0.333233 8.185075 0.208425 -38.095375 \nclassical_11.mp3 ... 31.500881 -3.781627 9.191043 0.260886 -22.667440 \nclassical_12.mp3 ... 28.490644 -6.242015 10.546545 0.341848 -25.040888 \n... ... ... ... ... ... ... \nrock_95.mp3 ... 22.451445 -7.234633 8.471853 0.753855 -24.712723 \nrock_96.mp3 ... 28.087936 -9.704238 8.447620 0.112760 -38.147888 \nrock_97.mp3 ... 26.325895 -5.722826 7.727378 0.207489 -29.497524 \nrock_98.mp3 ... 8.714737 -9.511492 5.551820 -0.025604 -23.020084 \nrock_99.mp3 ... 17.050608 -5.296690 5.894963 0.390705 -20.983192 \n\n 39_max 39_mean 39_std 39_skew train \nfilename \nclassical_1.mp3 49.352516 -2.282116 15.285639 0.171462 True \nclassical_10.mp3 27.533710 0.952658 10.477735 -0.185771 False \nclassical_100.mp3 31.397881 -1.494916 10.917299 0.020984 False \nclassical_11.mp3 50.992897 1.600777 10.125545 0.595763 False \nclassical_12.mp3 46.878204 1.844494 11.160392 0.503120 True \n... ... ... ... ... ... \nrock_95.mp3 23.410387 -4.502398 6.687984 0.238807 True \nrock_96.mp3 21.814402 -8.249507 7.807756 0.071968 True \nrock_97.mp3 25.410654 -3.356615 8.170526 0.160330 True \nrock_98.mp3 13.948638 -2.664985 5.051498 -0.258407 True \nrock_99.mp3 29.312023 -0.321836 6.571660 0.384794 True \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n <th>train</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847563</td>\n <td>...</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916949</td>\n <td>...</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n <td>True</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908869</td>\n <td>...</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410220</td>\n <td>...</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\n",
+ "joined"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:52.968965270Z",
+ "start_time": "2023-09-06T16:33:52.652167547Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \nclassical_13.mp3 classical -637.720642 -177.713959 -361.834045 71.310080 \nclassical_14.mp3 classical -531.049438 -100.790543 -188.970749 58.287371 \nclassical_15.mp3 classical -555.129944 -96.139236 -209.245819 45.350121 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163334 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std ... \\\nfilename ... \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847563 ... \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 ... \nclassical_13.mp3 0.008326 0.000000 257.162842 211.556549 20.347035 ... \nclassical_14.mp3 -3.246618 0.000000 157.947922 86.563927 17.911136 ... \nclassical_15.mp3 -3.574710 0.000000 140.918640 109.309990 14.171102 ... \n... ... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 ... \nrock_96.mp3 -0.957699 -7.415961 210.492462 125.453690 31.908869 ... \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 ... \nrock_98.mp3 -1.705641 0.000000 187.042740 96.440872 24.137702 ... \nrock_99.mp3 -0.863093 -32.930653 191.735382 93.971237 33.410220 ... \n\n 38_min 38_max 38_mean 38_std 38_skew \\\nfilename \nclassical_1.mp3 -44.098068 47.308060 -3.713503 16.553984 0.230691 \nclassical_12.mp3 -44.843811 28.490644 -6.242015 10.546545 0.341848 \nclassical_13.mp3 -24.728806 18.424036 -0.275737 7.026148 -0.640964 \nclassical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \nclassical_15.mp3 -42.808113 24.146545 -7.260053 9.862490 0.097765 \n... ... ... ... ... ... \nrock_95.mp3 -27.043941 22.451445 -7.234633 8.471853 0.753855 \nrock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \nrock_97.mp3 -29.620445 26.325895 -5.722826 7.727378 0.207489 \nrock_98.mp3 -26.967848 8.714737 -9.511492 5.551820 -0.025604 \nrock_99.mp3 -21.929403 17.050608 -5.296690 5.894963 0.390705 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \nclassical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \nclassical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \nclassical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \nclassical_15.mp3 -31.394997 35.685539 -0.949139 11.141700 0.249278 \n... ... ... ... ... ... \nrock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \nrock_96.mp3 -38.147888 21.814402 -8.249507 7.807756 0.071968 \nrock_97.mp3 -29.497524 25.410654 -3.356615 8.170526 0.160330 \nrock_98.mp3 -23.020084 13.948638 -2.664985 5.051498 -0.258407 \nrock_99.mp3 -20.983192 29.312023 -0.321836 6.571660 0.384794 \n\n[320 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847563</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>-44.843811</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>classical_13.mp3</th>\n <td>classical</td>\n <td>-637.720642</td>\n <td>-177.713959</td>\n <td>-361.834045</td>\n <td>71.310080</td>\n <td>0.008326</td>\n <td>0.000000</td>\n <td>257.162842</td>\n <td>211.556549</td>\n <td>20.347035</td>\n <td>...</td>\n <td>-24.728806</td>\n <td>18.424036</td>\n <td>-0.275737</td>\n <td>7.026148</td>\n <td>-0.640964</td>\n <td>-24.319565</td>\n <td>18.439262</td>\n <td>-2.147022</td>\n <td>8.171929</td>\n <td>0.009566</td>\n </tr>\n <tr>\n <th>classical_14.mp3</th>\n <td>classical</td>\n <td>-531.049438</td>\n <td>-100.790543</td>\n <td>-188.970749</td>\n <td>58.287371</td>\n <td>-3.246618</td>\n <td>0.000000</td>\n <td>157.947922</td>\n <td>86.563927</td>\n <td>17.911136</td>\n <td>...</td>\n <td>-36.261154</td>\n <td>38.335831</td>\n <td>-5.770759</td>\n <td>12.254058</td>\n <td>0.805707</td>\n <td>-40.597336</td>\n <td>32.816467</td>\n <td>-0.543406</td>\n <td>11.467829</td>\n <td>-0.187037</td>\n </tr>\n <tr>\n <th>classical_15.mp3</th>\n <td>classical</td>\n <td>-555.129944</td>\n <td>-96.139236</td>\n <td>-209.245819</td>\n <td>45.350121</td>\n <td>-3.574710</td>\n <td>0.000000</td>\n <td>140.918640</td>\n <td>109.309990</td>\n <td>14.171102</td>\n <td>...</td>\n <td>-42.808113</td>\n <td>24.146545</td>\n <td>-7.260053</td>\n <td>9.862490</td>\n <td>0.097765</td>\n <td>-31.394997</td>\n <td>35.685539</td>\n <td>-0.949139</td>\n <td>11.141700</td>\n <td>0.249278</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908869</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410220</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>320 rows × 201 columns</p>\n</div>"
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\n",
+ "train"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.010226965Z",
+ "start_time": "2023-09-06T16:33:52.744721352Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561838 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_19.mp3 classical -543.642334 -106.038223 -216.909943 61.317534 \nclassical_20.mp3 classical -605.991516 -161.119308 -263.483093 49.157298 \n... ... ... ... ... ... \nrock_57.mp3 rock -543.735168 50.739136 -70.208893 83.040454 \nrock_66.mp3 rock -520.185791 21.333998 -79.359444 44.616105 \nrock_75.mp3 rock -519.826965 54.035805 -32.218468 33.789999 \nrock_81.mp3 rock -532.139099 52.119076 -117.146126 76.883343 \nrock_90.mp3 rock -501.955994 9.573563 -137.388382 46.025847 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_10.mp3 -0.772320 0.029056 259.632690 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916949 \nclassical_19.mp3 -3.473125 0.000000 151.947662 93.405411 22.029233 \nclassical_20.mp3 -0.856221 0.000000 191.926758 141.393814 17.754779 \n... ... ... ... ... ... \nrock_57.mp3 -2.913490 -51.877323 177.711395 89.957848 29.532071 \nrock_66.mp3 -2.708660 0.000000 162.490845 115.182426 18.106840 \nrock_75.mp3 -1.231267 1.666233 164.635895 93.935715 21.886208 \nrock_81.mp3 -0.656551 -44.119019 168.675858 101.038620 31.198018 \nrock_90.mp3 -0.004000 -7.494962 190.229202 112.531166 33.245804 \n\n ... 38_min 38_max 38_mean 38_std 38_skew \\\nfilename ... \nclassical_10.mp3 ... -27.458416 29.811110 0.484271 8.660648 -0.479016 \nclassical_100.mp3 ... -27.335688 27.610388 -0.333233 8.185075 0.208425 \nclassical_11.mp3 ... -31.774948 31.500881 -3.781627 9.191043 0.260886 \nclassical_19.mp3 ... -27.029385 30.682745 3.342259 8.420860 0.043171 \nclassical_20.mp3 ... -24.911243 38.551231 -2.274261 9.671005 0.719436 \n... ... ... ... ... ... ... \nrock_57.mp3 ... -30.258139 9.919489 -6.048107 5.045001 -0.187751 \nrock_66.mp3 ... -23.582970 16.230869 -4.445108 6.836216 -0.005944 \nrock_75.mp3 ... -29.449886 9.328630 -7.874899 6.538823 -0.428034 \nrock_81.mp3 ... -36.623711 23.897625 -3.552371 9.184054 -0.304160 \nrock_90.mp3 ... -23.657921 24.251360 -4.784957 6.215656 0.480860 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_10.mp3 -28.989983 27.533710 0.952658 10.477735 -0.185771 \nclassical_100.mp3 -38.095375 31.397881 -1.494916 10.917299 0.020984 \nclassical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \nclassical_19.mp3 -25.900257 36.766388 2.389575 10.099726 0.140336 \nclassical_20.mp3 -30.311798 29.272329 0.289613 9.590299 -0.244191 \n... ... ... ... ... ... \nrock_57.mp3 -19.538643 21.089222 -1.995280 5.352349 0.480205 \nrock_66.mp3 -16.087088 22.686642 2.065789 6.279558 0.069703 \nrock_75.mp3 -21.944729 18.833591 -2.557417 5.737269 -0.007298 \nrock_81.mp3 -34.576202 36.869560 -1.597456 10.409478 0.058469 \nrock_90.mp3 -21.904373 18.819710 -1.302765 5.389064 -0.064191 \n\n[80 rows x 201 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916949</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>classical_19.mp3</th>\n <td>classical</td>\n <td>-543.642334</td>\n <td>-106.038223</td>\n <td>-216.909943</td>\n <td>61.317534</td>\n <td>-3.473125</td>\n <td>0.000000</td>\n <td>151.947662</td>\n <td>93.405411</td>\n <td>22.029233</td>\n <td>...</td>\n <td>-27.029385</td>\n <td>30.682745</td>\n <td>3.342259</td>\n <td>8.420860</td>\n <td>0.043171</td>\n <td>-25.900257</td>\n <td>36.766388</td>\n <td>2.389575</td>\n <td>10.099726</td>\n <td>0.140336</td>\n </tr>\n <tr>\n <th>classical_20.mp3</th>\n <td>classical</td>\n <td>-605.991516</td>\n <td>-161.119308</td>\n <td>-263.483093</td>\n <td>49.157298</td>\n <td>-0.856221</td>\n <td>0.000000</td>\n <td>191.926758</td>\n <td>141.393814</td>\n <td>17.754779</td>\n <td>...</td>\n <td>-24.911243</td>\n <td>38.551231</td>\n <td>-2.274261</td>\n <td>9.671005</td>\n <td>0.719436</td>\n <td>-30.311798</td>\n <td>29.272329</td>\n <td>0.289613</td>\n <td>9.590299</td>\n <td>-0.244191</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_57.mp3</th>\n <td>rock</td>\n <td>-543.735168</td>\n <td>50.739136</td>\n <td>-70.208893</td>\n <td>83.040454</td>\n <td>-2.913490</td>\n <td>-51.877323</td>\n <td>177.711395</td>\n <td>89.957848</td>\n <td>29.532071</td>\n <td>...</td>\n <td>-30.258139</td>\n <td>9.919489</td>\n <td>-6.048107</td>\n <td>5.045001</td>\n <td>-0.187751</td>\n <td>-19.538643</td>\n <td>21.089222</td>\n <td>-1.995280</td>\n <td>5.352349</td>\n <td>0.480205</td>\n </tr>\n <tr>\n <th>rock_66.mp3</th>\n <td>rock</td>\n <td>-520.185791</td>\n <td>21.333998</td>\n <td>-79.359444</td>\n <td>44.616105</td>\n <td>-2.708660</td>\n <td>0.000000</td>\n <td>162.490845</td>\n <td>115.182426</td>\n <td>18.106840</td>\n <td>...</td>\n <td>-23.582970</td>\n <td>16.230869</td>\n <td>-4.445108</td>\n <td>6.836216</td>\n <td>-0.005944</td>\n <td>-16.087088</td>\n <td>22.686642</td>\n <td>2.065789</td>\n <td>6.279558</td>\n <td>0.069703</td>\n </tr>\n <tr>\n <th>rock_75.mp3</th>\n <td>rock</td>\n <td>-519.826965</td>\n <td>54.035805</td>\n <td>-32.218468</td>\n <td>33.789999</td>\n <td>-1.231267</td>\n <td>1.666233</td>\n <td>164.635895</td>\n <td>93.935715</td>\n <td>21.886208</td>\n <td>...</td>\n <td>-29.449886</td>\n <td>9.328630</td>\n <td>-7.874899</td>\n <td>6.538823</td>\n <td>-0.428034</td>\n <td>-21.944729</td>\n <td>18.833591</td>\n <td>-2.557417</td>\n <td>5.737269</td>\n <td>-0.007298</td>\n </tr>\n <tr>\n <th>rock_81.mp3</th>\n <td>rock</td>\n <td>-532.139099</td>\n <td>52.119076</td>\n <td>-117.146126</td>\n <td>76.883343</td>\n <td>-0.656551</td>\n <td>-44.119019</td>\n <td>168.675858</td>\n <td>101.038620</td>\n <td>31.198018</td>\n <td>...</td>\n <td>-36.623711</td>\n <td>23.897625</td>\n <td>-3.552371</td>\n <td>9.184054</td>\n <td>-0.304160</td>\n <td>-34.576202</td>\n <td>36.869560</td>\n <td>-1.597456</td>\n <td>10.409478</td>\n <td>0.058469</td>\n </tr>\n <tr>\n <th>rock_90.mp3</th>\n <td>rock</td>\n <td>-501.955994</td>\n <td>9.573563</td>\n <td>-137.388382</td>\n <td>46.025847</td>\n <td>-0.004000</td>\n <td>-7.494962</td>\n <td>190.229202</td>\n <td>112.531166</td>\n <td>33.245804</td>\n <td>...</td>\n <td>-23.657921</td>\n <td>24.251360</td>\n <td>-4.784957</td>\n <td>6.215656</td>\n <td>0.480860</td>\n <td>-21.904373</td>\n <td>18.819710</td>\n <td>-1.302765</td>\n <td>5.389064</td>\n <td>-0.064191</td>\n </tr>\n </tbody>\n</table>\n<p>80 rows × 201 columns</p>\n</div>"
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\n",
+ "test"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.064138270Z",
+ "start_time": "2023-09-06T16:33:52.930093810Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "( 0_min 0_max 0_mean 0_std 0_skew \\\n filename \n classical_1.mp3 -530.784363 -163.308350 -302.203156 51.142183 -0.468374 \n classical_12.mp3 -562.675232 -148.133560 -270.975403 52.191182 -0.366587 \n classical_13.mp3 -637.720642 -177.713959 -361.834045 71.310080 0.008326 \n classical_14.mp3 -531.049438 -100.790543 -188.970749 58.287371 -3.246618 \n classical_15.mp3 -555.129944 -96.139236 -209.245819 45.350121 -3.574710 \n ... ... ... ... ... ... \n rock_95.mp3 -553.110107 -5.218835 -193.506042 76.869437 -0.201055 \n rock_96.mp3 -541.236023 27.163334 -119.113991 58.420684 -0.957699 \n rock_97.mp3 -518.494995 58.526745 -66.267746 65.635619 -0.898026 \n rock_98.mp3 -518.643066 53.555115 -45.734516 52.444200 -1.705641 \n rock_99.mp3 -544.703125 75.612129 -49.380943 54.045627 -0.863093 \n \n 1_min 1_max 1_mean 1_std 1_skew ... \\\n filename ... \n classical_1.mp3 0.000000 178.751617 111.332344 24.847563 -0.402642 ... \n classical_12.mp3 0.000000 194.264160 148.226654 19.305008 -0.533256 ... \n classical_13.mp3 0.000000 257.162842 211.556549 20.347035 -1.050120 ... \n classical_14.mp3 0.000000 157.947922 86.563927 17.911136 0.244245 ... \n classical_15.mp3 0.000000 140.918640 109.309990 14.171102 -2.617227 ... \n ... ... ... ... ... ... ... \n rock_95.mp3 -89.948746 201.180450 111.724190 36.463584 -0.443224 ... \n rock_96.mp3 -7.415961 210.492462 125.453690 31.908869 -0.547468 ... \n rock_97.mp3 -58.824409 175.201355 99.288261 25.158417 -0.568056 ... \n rock_98.mp3 0.000000 187.042740 96.440872 24.137702 -0.145216 ... \n rock_99.mp3 -32.930653 191.735382 93.971237 33.410220 0.040112 ... \n \n 38_min 38_max 38_mean 38_std 38_skew \\\n filename \n classical_1.mp3 -44.098068 47.308060 -3.713503 16.553984 0.230691 \n classical_12.mp3 -44.843811 28.490644 -6.242015 10.546545 0.341848 \n classical_13.mp3 -24.728806 18.424036 -0.275737 7.026148 -0.640964 \n classical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \n classical_15.mp3 -42.808113 24.146545 -7.260053 9.862490 0.097765 \n ... ... ... ... ... ... \n rock_95.mp3 -27.043941 22.451445 -7.234633 8.471853 0.753855 \n rock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \n rock_97.mp3 -29.620445 26.325895 -5.722826 7.727378 0.207489 \n rock_98.mp3 -26.967848 8.714737 -9.511492 5.551820 -0.025604 \n rock_99.mp3 -21.929403 17.050608 -5.296690 5.894963 0.390705 \n \n 39_min 39_max 39_mean 39_std 39_skew \n filename \n classical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \n classical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \n classical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \n classical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \n classical_15.mp3 -31.394997 35.685539 -0.949139 11.141700 0.249278 \n ... ... ... ... ... ... \n rock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \n rock_96.mp3 -38.147888 21.814402 -8.249507 7.807756 0.071968 \n rock_97.mp3 -29.497524 25.410654 -3.356615 8.170526 0.160330 \n rock_98.mp3 -23.020084 13.948638 -2.664985 5.051498 -0.258407 \n rock_99.mp3 -20.983192 29.312023 -0.321836 6.571660 0.384794 \n \n [320 rows x 200 columns],\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]))"
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# remove labels\n",
+ "X = train.drop(['label'], axis=1, errors='ignore')\n",
+ "\n",
+ "columns: Index = X.columns\n",
+ "classnames = np.sort(np.unique(joined.label.values)) # -> [\"classical\", \"electronic\", \"pop\", \"rock\"]\n",
+ "\n",
+ "# map classname to an index and create dicts for easy lookup in O(1)\n",
+ "classname2index = {}\n",
+ "index2classname = {}\n",
+ "\n",
+ "for i, classname in enumerate(classnames):\n",
+ " classname2index[classname] = i\n",
+ " index2classname[i] = classname\n",
+ "\n",
+ "# map label to label index\n",
+ "y = np.array([classname2index[classname] for classname in train.label.values])\n",
+ "\n",
+ "(X, y)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.384132460Z",
+ "start_time": "2023-09-06T16:33:52.974140158Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(320, 200)\n",
+ "(80, 200)\n",
+ "0.25\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_test = test.drop(['label'], axis=1, errors='ignore')\n",
+ "\n",
+ "print(X.shape)\n",
+ "print(X_test.shape)\n",
+ "print(X_test.shape[0] / X.shape[0]) # fraction of test sample\n",
+ "\n",
+ "y_test = np.array([classname2index[classname] for classname in test.label.values])\n",
+ "y_test"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.385211474Z",
+ "start_time": "2023-09-06T16:33:53.017055988Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "array([[ 0.43337449, -1.7728706 , -1.32763933, ..., -0.68466574,\n 3.57466709, 0.10865617],\n [-0.42729588, -1.58097286, -0.98456168, ..., 0.50149939,\n 1.74587552, 1.04983892],\n [-2.45262044, -1.95504141, -1.98276254, ..., -0.64583406,\n 0.42103984, -0.35077496],\n ...,\n [ 0.76503978, 1.03241718, 1.2644193 , ..., -0.99352289,\n 0.42041778, 0.07706691],\n [ 0.76104364, 0.96954681, 1.49000365, ..., -0.7947189 ,\n -0.96229984, -1.11123088],\n [ 0.05773511, 1.24847596, 1.44994288, ..., -0.12119682,\n -0.2883865 , 0.71405216]])"
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Standardize for PCA\n",
+ "scaler = StandardScaler()\n",
+ "X_standardized = scaler.fit_transform(X.values)\n",
+ "X_test_standardized = scaler.transform(X_test.values)\n",
+ "\n",
+ "X_standardized"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.387124363Z",
+ "start_time": "2023-09-06T16:33:53.035337727Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.8569547942728654\n",
+ "(320, 50)\n",
+ "(80, 50)\n",
+ "(320,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Reduce Dimensions via PCA\n",
+ "pca = PCA(n_components=50).fit(X_standardized)\n",
+ "X_pca = pca.transform(X_standardized)\n",
+ "X_test_pca = pca.transform(X_test_standardized)\n",
+ "\n",
+ "print(sum(pca.explained_variance_ratio_))\n",
+ "print(X_pca.shape)\n",
+ "print(X_test_pca.shape)\n",
+ "print(y.shape)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.648047288Z",
+ "start_time": "2023-09-06T16:33:53.076945689Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.78125\n",
+ "[[ 2.54932913 -0.8297137 -2.64117401 ... 0.0628467 -1.08498817\n",
+ " -0.54600181]\n",
+ " [ 5.60625198 -1.68012408 -3.12748987 ... -0.79584617 -0.8702528\n",
+ " 0.32833321]\n",
+ " [-4.29091225 -0.05226949 -4.71139064 ... 0.01077759 0.51144486\n",
+ " -0.02390549]\n",
+ " ...\n",
+ " [-7.89826346 1.0391027 -5.76202999 ... -0.43377044 -1.18091018\n",
+ " -0.44692849]\n",
+ " [-8.30381697 -1.3222363 2.37698638 ... 0.11685826 -0.13683289\n",
+ " -1.10917816]\n",
+ " [-4.76845573 -7.78718752 3.16067256 ... -1.23649128 0.39003957\n",
+ " 0.95120336]]\n",
+ "[3 0 3 2 3 0 1 2 0 3 0 0 0 1 2 1 2 3 2 1 1 0 3 0 0 0 3 1 1 3 3 2 3 1 2 1 0\n",
+ " 1 0 1 3 0 0 0 0 3 3 3 0 3 3 3 1 2 2 0 1 2 1 2 3 2 1 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit SVM:\n",
+ "\n",
+ "X_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size = 0.2, random_state=4, shuffle = True)\n",
+ "\n",
+ "clf = SVC(kernel='rbf', probability=True)\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_val), y_val))\n",
+ "print(X_val)\n",
+ "print(y_val)\n"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.908081165Z",
+ "start_time": "2023-09-06T16:33:53.275197554Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.7068627450980391\n",
+ "{'C': 4, 'gamma': 0.01}\n",
+ "SVC(C=4, gamma=0.01)\n",
+ "0.78125\n"
+ ]
+ }
+ ],
+ "source": [
+ "# grid for C, gamma\n",
+ "C_grid = [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+ "gamma_grid = [0.001, 0.01, 0.1, 1, 10]\n",
+ "param_grid = {'C': C_grid, 'gamma': gamma_grid}\n",
+ "\n",
+ "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5, scoring=\"accuracy\")\n",
+ "grid.fit(X_train, y_train)\n",
+ "\n",
+ "# Find the best model\n",
+ "print(grid.best_score_)\n",
+ "print(grid.best_params_)\n",
+ "print(grid.best_estimator_)\n",
+ "print(accuracy_score(grid.predict(X_val), y_val))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.350344280Z",
+ "start_time": "2023-09-06T16:33:53.459579739Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.78125\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Optimal model\n",
+ "\n",
+ "clf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_val), y_val))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.440250619Z",
+ "start_time": "2023-09-06T16:34:01.354593939Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.7875\n",
+ "[[9.98186250e-01 7.07416061e-04 7.80359226e-04 3.25974952e-04]\n",
+ " [9.96457336e-01 8.61299907e-04 1.23630417e-03 1.44505986e-03]\n",
+ " [9.88168745e-01 3.07061855e-03 3.28921925e-03 5.47141688e-03]\n",
+ " [9.99725418e-01 2.34111856e-05 1.88388067e-04 6.27825592e-05]\n",
+ " [9.95553472e-01 1.24175862e-04 9.79386045e-04 3.34296563e-03]\n",
+ " [9.91956058e-01 5.30538055e-03 1.54189494e-03 1.19666681e-03]\n",
+ " [9.48805820e-01 4.51553061e-03 3.54571301e-02 1.12215196e-02]\n",
+ " [9.39011874e-01 1.25859524e-02 3.91467574e-02 9.25541627e-03]\n",
+ " [9.98779810e-01 3.54844256e-04 4.83538402e-04 3.81807584e-04]\n",
+ " [9.90943320e-01 6.68565763e-03 1.21029168e-03 1.16073074e-03]\n",
+ " [1.70650977e-01 4.12405105e-01 2.54547776e-01 1.62396142e-01]\n",
+ " [7.61283180e-01 5.85153569e-02 1.20185726e-01 6.00157375e-02]\n",
+ " [9.99812599e-01 5.80059091e-05 6.23304798e-05 6.70645603e-05]\n",
+ " [9.94666111e-01 3.87806861e-04 1.39823341e-03 3.54784853e-03]\n",
+ " [9.96732326e-01 2.09863248e-03 5.52203710e-04 6.16837643e-04]\n",
+ " [7.83508670e-01 2.25171519e-02 1.45607359e-01 4.83668199e-02]\n",
+ " [1.08947087e-01 1.43062693e-02 6.87834391e-01 1.88912253e-01]\n",
+ " [8.95644592e-01 1.19426746e-02 7.53917300e-02 1.70210036e-02]\n",
+ " [9.99765501e-01 4.27079401e-05 5.63234537e-05 1.35467724e-04]\n",
+ " [6.96168233e-01 2.32101342e-01 5.44296450e-02 1.73007797e-02]\n",
+ " [9.74327858e-01 6.29911950e-03 1.38415586e-02 5.53146410e-03]\n",
+ " [1.97208847e-03 9.39267826e-01 8.53321167e-03 5.02268735e-02]\n",
+ " [1.54495619e-03 9.67010528e-01 2.65155983e-02 4.92891741e-03]\n",
+ " [2.97549129e-03 7.41850329e-01 1.38927973e-01 1.16246207e-01]\n",
+ " [4.09082904e-03 4.48959962e-01 4.03601661e-01 1.43347548e-01]\n",
+ " [1.44331850e-03 8.96264314e-01 7.01120834e-02 3.21802844e-02]\n",
+ " [1.82609926e-02 6.93756720e-01 2.33339552e-01 5.46427349e-02]\n",
+ " [8.24945729e-02 6.49069944e-01 2.17144236e-01 5.12912463e-02]\n",
+ " [9.23976812e-02 7.76732703e-01 1.02546091e-01 2.83235249e-02]\n",
+ " [8.05320661e-01 1.41289007e-01 3.51733662e-02 1.82169657e-02]\n",
+ " [6.32741836e-04 8.05861715e-01 2.26476412e-02 1.70857902e-01]\n",
+ " [2.57706086e-02 4.08707961e-01 4.56859671e-01 1.08661759e-01]\n",
+ " [2.38704115e-03 8.17061213e-01 1.24634438e-01 5.59173084e-02]\n",
+ " [7.85891844e-04 3.23912838e-01 5.91400091e-02 6.16161261e-01]\n",
+ " [8.17398003e-03 5.92856683e-01 1.36843385e-01 2.62125952e-01]\n",
+ " [2.01727314e-03 9.18622935e-01 9.53308788e-03 6.98267044e-02]\n",
+ " [1.02655478e-01 4.15450834e-01 3.07215353e-01 1.74678335e-01]\n",
+ " [4.21319476e-04 3.08866780e-01 5.37141483e-01 1.53570417e-01]\n",
+ " [7.37060683e-04 6.26005330e-01 3.32306955e-01 4.09506550e-02]\n",
+ " [3.57947383e-03 3.38994309e-01 5.85661778e-01 7.17644389e-02]\n",
+ " [7.81396204e-04 9.05484100e-01 4.24574488e-02 5.12770549e-02]\n",
+ " [1.38673280e-02 8.44602740e-01 3.48480447e-02 1.06681887e-01]\n",
+ " [2.38678656e-02 3.16318564e-02 8.06543692e-01 1.37956586e-01]\n",
+ " [2.77447879e-02 1.76362240e-01 2.28042151e-01 5.67850821e-01]\n",
+ " [3.68498883e-03 1.93765262e-02 3.74744135e-01 6.02194350e-01]\n",
+ " [1.08250700e-01 1.31283644e-01 4.52307411e-01 3.08158244e-01]\n",
+ " [1.06117414e-03 4.57675502e-03 1.21562074e-01 8.72799997e-01]\n",
+ " [9.44284923e-03 6.01444265e-02 6.88640823e-01 2.41771902e-01]\n",
+ " [3.04315346e-03 4.91841391e-03 6.73229859e-01 3.18808574e-01]\n",
+ " [3.47407145e-03 3.38133523e-01 4.27311082e-01 2.31081323e-01]\n",
+ " [6.38991934e-02 2.34946580e-02 4.38116434e-01 4.74489715e-01]\n",
+ " [3.65337631e-02 1.47529551e-01 6.56699181e-01 1.59237505e-01]\n",
+ " [3.45369958e-02 4.86056971e-02 4.50222236e-01 4.66635071e-01]\n",
+ " [1.02874384e-01 1.14593225e-01 4.12828659e-01 3.69703732e-01]\n",
+ " [7.01929891e-04 4.72310828e-03 6.33659414e-01 3.60915547e-01]\n",
+ " [1.19280091e-03 2.91859540e-03 6.63499318e-01 3.32389286e-01]\n",
+ " [3.22247158e-04 1.86224604e-03 7.86294266e-01 2.11521241e-01]\n",
+ " [1.62556011e-02 1.10337495e-01 6.90066111e-01 1.83340793e-01]\n",
+ " [5.83937991e-03 8.91483148e-03 8.23557657e-01 1.61688132e-01]\n",
+ " [1.40771587e-03 1.99973215e-03 7.69667401e-01 2.26925151e-01]\n",
+ " [2.85627492e-03 4.09235838e-02 5.47033952e-01 4.09186189e-01]\n",
+ " [5.00914955e-02 5.66591605e-02 8.19154779e-01 7.40945653e-02]\n",
+ " [3.49728526e-04 1.03301315e-02 3.45356722e-01 6.43963418e-01]\n",
+ " [7.86355678e-03 5.17956142e-02 8.13005815e-01 1.27335014e-01]\n",
+ " [2.24811753e-04 1.50400751e-03 5.92965001e-01 4.05306180e-01]\n",
+ " [1.50292599e-03 1.18077604e-02 7.95371303e-01 1.91318010e-01]\n",
+ " [9.71722011e-04 1.93522506e-03 4.85836249e-01 5.11256804e-01]\n",
+ " [2.71508043e-04 8.47075628e-03 6.65359707e-02 9.24721765e-01]\n",
+ " [3.78695110e-04 7.86603624e-03 3.13831725e-01 6.77923544e-01]\n",
+ " [1.56018964e-03 5.04504439e-02 3.75946622e-01 5.72042744e-01]\n",
+ " [4.13000514e-03 4.44190116e-03 1.76929372e-01 8.14498722e-01]\n",
+ " [7.03938811e-04 5.20938587e-01 2.13900864e-01 2.64456610e-01]\n",
+ " [1.26489155e-03 3.03436964e-02 2.15768512e-01 7.52622900e-01]\n",
+ " [1.39383300e-04 2.39767850e-03 7.56550144e-01 2.40912794e-01]\n",
+ " [8.86305936e-04 4.62779469e-04 1.80744510e-01 8.17906405e-01]\n",
+ " [4.22976399e-04 5.51964764e-02 2.85832649e-01 6.58547898e-01]\n",
+ " [6.85422618e-04 1.63669725e-03 1.51783518e-01 8.45894362e-01]\n",
+ " [1.88958698e-03 1.04946968e-02 1.31240172e-01 8.56375545e-01]\n",
+ " [3.17948589e-03 2.18481451e-02 1.25011239e-01 8.49961130e-01]\n",
+ " [8.82291635e-04 1.06487654e-02 3.11812728e-01 6.76656214e-01]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit entire training sets\n",
+ "clf.fit(X_pca, y)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_test_pca), y_test))\n",
+ "print(clf.predict_proba(X_test_pca))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.583086439Z",
+ "start_time": "2023-09-06T16:34:01.473457338Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " label pred1 pred2 pred3 pred4\n",
+ "filename \n",
+ "classical_10.mp3 classical classical pop electronic rock\n",
+ "classical_100.mp3 classical classical pop rock electronic\n",
+ "classical_11.mp3 classical classical rock pop electronic\n",
+ "classical_19.mp3 classical classical pop rock electronic\n",
+ "classical_20.mp3 classical classical rock pop electronic\n",
+ "classical_21.mp3 classical classical electronic pop rock\n",
+ "classical_24.mp3 classical classical pop rock electronic\n",
+ "classical_27.mp3 classical classical pop electronic rock\n",
+ "classical_28.mp3 classical classical pop electronic rock\n",
+ "classical_29.mp3 classical classical electronic pop rock\n",
+ "classical_40.mp3 classical electronic pop rock classical\n",
+ "classical_49.mp3 classical classical pop electronic rock\n",
+ "classical_5.mp3 classical classical pop electronic rock\n",
+ "classical_51.mp3 classical classical rock pop electronic\n",
+ "classical_58.mp3 classical classical electronic pop rock\n",
+ "classical_69.mp3 classical classical pop rock electronic\n",
+ "classical_71.mp3 classical pop rock classical electronic\n",
+ "classical_8.mp3 classical classical pop rock electronic\n",
+ "classical_92.mp3 classical classical rock pop electronic\n",
+ "classical_97.mp3 classical classical electronic pop rock\n",
+ "classical_98.mp3 classical classical pop electronic rock\n",
+ "electronic_10.mp3 electronic electronic rock pop classical\n",
+ "electronic_100.mp3 electronic electronic pop rock classical\n",
+ "electronic_14.mp3 electronic electronic pop rock classical\n",
+ "electronic_2.mp3 electronic electronic pop rock classical\n",
+ "electronic_23.mp3 electronic electronic pop rock classical\n",
+ "electronic_27.mp3 electronic electronic pop rock classical\n",
+ "electronic_3.mp3 electronic electronic pop classical rock\n",
+ "electronic_32.mp3 electronic electronic pop classical rock\n",
+ "electronic_47.mp3 electronic classical electronic pop rock\n",
+ "electronic_48.mp3 electronic electronic rock pop classical\n",
+ "electronic_51.mp3 electronic pop electronic rock classical\n",
+ "electronic_52.mp3 electronic electronic pop rock classical\n",
+ "electronic_54.mp3 electronic rock electronic pop classical\n",
+ "electronic_55.mp3 electronic electronic rock pop classical\n",
+ "electronic_56.mp3 electronic electronic rock pop classical\n",
+ "electronic_57.mp3 electronic electronic pop rock classical\n",
+ "electronic_75.mp3 electronic pop electronic rock classical\n",
+ "electronic_77.mp3 electronic electronic pop rock classical\n",
+ "electronic_84.mp3 electronic pop electronic rock classical\n",
+ "electronic_86.mp3 electronic electronic rock pop classical\n",
+ "electronic_95.mp3 electronic electronic rock pop classical\n",
+ "pop_10.mp3 pop pop rock electronic classical\n",
+ "pop_100.mp3 pop rock pop electronic classical\n",
+ "pop_2.mp3 pop rock pop electronic classical\n",
+ "pop_34.mp3 pop pop rock electronic classical\n",
+ "pop_37.mp3 pop rock pop electronic classical\n",
+ "pop_41.mp3 pop pop rock electronic classical\n",
+ "pop_42.mp3 pop pop rock electronic classical\n",
+ "pop_47.mp3 pop pop electronic rock classical\n",
+ "pop_52.mp3 pop rock pop classical electronic\n",
+ "pop_57.mp3 pop pop rock electronic classical\n",
+ "pop_6.mp3 pop rock pop electronic classical\n",
+ "pop_61.mp3 pop pop rock electronic classical\n",
+ "pop_62.mp3 pop pop rock electronic classical\n",
+ "pop_64.mp3 pop pop rock electronic classical\n",
+ "pop_65.mp3 pop pop rock electronic classical\n",
+ "pop_7.mp3 pop pop rock electronic classical\n",
+ "pop_72.mp3 pop pop rock electronic classical\n",
+ "pop_73.mp3 pop pop rock electronic classical\n",
+ "pop_8.mp3 pop pop rock electronic classical\n",
+ "pop_86.mp3 pop pop rock electronic classical\n",
+ "pop_87.mp3 pop rock pop electronic classical\n",
+ "pop_92.mp3 pop pop rock electronic classical\n",
+ "pop_94.mp3 pop pop rock electronic classical\n",
+ "pop_97.mp3 pop pop rock electronic classical\n",
+ "rock_11.mp3 rock rock pop electronic classical\n",
+ "rock_22.mp3 rock rock pop electronic classical\n",
+ "rock_25.mp3 rock rock pop electronic classical\n",
+ "rock_26.mp3 rock rock pop electronic classical\n",
+ "rock_4.mp3 rock rock pop electronic classical\n",
+ "rock_41.mp3 rock electronic rock pop classical\n",
+ "rock_45.mp3 rock rock pop electronic classical\n",
+ "rock_48.mp3 rock pop rock electronic classical\n",
+ "rock_54.mp3 rock rock pop classical electronic\n",
+ "rock_57.mp3 rock rock pop electronic classical\n",
+ "rock_66.mp3 rock rock pop electronic classical\n",
+ "rock_75.mp3 rock rock pop electronic classical\n",
+ "rock_81.mp3 rock rock pop electronic classical\n",
+ "rock_90.mp3 rock rock pop electronic classical\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit the entire training sets\n",
+ "\n",
+ "def convert_to_labels(preds, i2c, k=3):\n",
+ " ans = []\n",
+ " ids = []\n",
+ " for p in preds:\n",
+ " idx = np.argsort(p)[::-1]\n",
+ " ids.append([i for i in idx[:k]])\n",
+ " ans.append([i2c[i] for i in idx[:k]])\n",
+ "\n",
+ " return ans, ids\n",
+ "\n",
+ "clf.fit(X_pca, y)\n",
+ "prediction_lists, percentage_lists = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\n",
+ "\n",
+ "# # Write to outputs\n",
+ "subm = pd.DataFrame(index=test.index)\n",
+ "subm['label'] = test.label.values\n",
+ "subm['pred1'] = [prediction_list[0] for prediction_list in prediction_lists]\n",
+ "subm['pred2'] = [prediction_list[1] for prediction_list in prediction_lists]\n",
+ "subm['pred3'] = [prediction_list[2] for prediction_list in prediction_lists]\n",
+ "subm['pred4'] = [prediction_list[3] for prediction_list in prediction_lists]\n",
+ "\n",
+ "pd.set_option('display.max_rows', None)\n",
+ "print(subm)\n",
+ "pd.reset_option('display.max_rows')"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.741253646Z",
+ "start_time": "2023-09-06T16:34:01.567005786Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "outputs": [],
+ "source": [
+ "with open(LOCAL_PATH / \"clf.pickle\", \"wb\") as file:\n",
+ " pickle.dump(clf, file)\n",
+ "subm.to_csv(LOCAL_PATH / \"submission.csv\", index=False)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.742181308Z",
+ "start_time": "2023-09-06T16:34:01.725149499Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "outputs": [],
+ "source": [
+ "if not ONLY_LOCAL:\n",
+ " with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
+ " metadata = yaml.safe_load(file)\n",
+ "\n",
+ " nb_config_ml = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " entities=[\n",
+ " ml_model_entity := InvenioEntity.new(\n",
+ " name=\"Standalone Machine Learning model\",\n",
+ " description=\"An ml model representing the trained clf\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"ml_model.pickle\",\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"clf\"\n",
+ " ),\n",
+ " test_result_entity := DbRepoEntity.new(\n",
+ " name=\"Standalone Test Result Entity\",\n",
+ " description=\"Result of tests on ml model\",\n",
+ " table_name=\"test_result\",\n",
+ " table_description=\"Test results of genre prediction on ml model\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"test_result.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " type=\"submission\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " audio_files_entity\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " executor.upload_entities(nb_config_ml)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.832515470Z",
+ "start_time": "2023-09-06T16:34:01.741700194Z"
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/1_audio_files.ipynb b/notebooks/1_audio_files.ipynb
index 504b059f5562150ecb54bf5431875ed279cf858d..14a85d7af5546b9d8dcdf619168915fcb5ad976b 100644
--- a/notebooks/1_audio_files.ipynb
+++ b/notebooks/1_audio_files.ipynb
@@ -1,8 +1,52 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4389a8092677254e",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.00365,
+ "end_time": "2023-10-10T20:19:34.354097",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:34.350447",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# Audio Files\n",
+ "\n",
+ "Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
+ "id": "87ab37c6",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:19:34.369926Z",
+ "iopub.status.busy": "2023-10-10T20:19:34.368623Z",
+ "iopub.status.idle": "2023-10-10T20:19:34.394688Z",
+ "shell.execute_reply": "2023-10-10T20:19:34.393354Z"
+ },
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.037909,
+ "end_time": "2023-10-10T20:19:34.398270",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:34.360361",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"from definitions import BASE_PATH\n",
@@ -10,27 +54,94 @@
"import zipfile\n",
"import os\n",
"from pathlib import Path"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "id": "1b4e6b01",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:19:34.412182Z",
+ "iopub.status.busy": "2023-10-10T20:19:34.410878Z",
+ "iopub.status.idle": "2023-10-10T20:19:34.418072Z",
+ "shell.execute_reply": "2023-10-10T20:19:34.416762Z"
+ },
+ "papermill": {
+ "duration": 0.0178,
+ "end_time": "2023-10-10T20:19:34.421245",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:34.403445",
+ "status": "completed"
+ },
+ "tags": [
+ "parameters"
+ ]
+ },
"outputs": [],
"source": [
- "OUTPUT_PATHS: dict[str, str] = {\n",
- " \"audio_tar\": (BASE_PATH / \"tmp\" / \"1_audio_files\" / \"output\" / \"emotifymusic.tar.gz\").__str__()\n",
+ "# Parameters\n",
+ "INPUT_PATHS = {}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"audio_tar\": str(BASE_PATH / \"tmp/1_audio_files/output/emotifymusic.tar.gz\")\n",
"}"
- ],
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a0c3731f",
"metadata": {
- "collapsed": false
- }
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:19:34.432077Z",
+ "iopub.status.busy": "2023-10-10T20:19:34.431120Z",
+ "iopub.status.idle": "2023-10-10T20:19:34.436917Z",
+ "shell.execute_reply": "2023-10-10T20:19:34.435800Z"
+ },
+ "papermill": {
+ "duration": 0.014193,
+ "end_time": "2023-10-10T20:19:34.439709",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:34.425516",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"audio_tar\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/1_audio_files/output/emotifymusic.tar.gz\"\n",
+ "}\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 4,
+ "id": "1e487573",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:19:34.446770Z",
+ "iopub.status.busy": "2023-10-10T20:19:34.446426Z",
+ "iopub.status.idle": "2023-10-10T20:19:36.570981Z",
+ "shell.execute_reply": "2023-10-10T20:19:36.570217Z"
+ },
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 2.131337,
+ "end_time": "2023-10-10T20:19:36.573889",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:34.442552",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# load provided files\n",
@@ -40,15 +151,33 @@
"dir_path.mkdir(parents=True, exist_ok=True)\n",
"# unzip to dir_path\n",
"with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
- " zfile.extractall(path=dir_path)\n"
- ],
- "metadata": {
- "collapsed": false
- }
+ " zfile.extractall(path=dir_path)"
+ ]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 5,
+ "id": "c3193f35",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:19:36.590650Z",
+ "iopub.status.busy": "2023-10-10T20:19:36.590408Z",
+ "iopub.status.idle": "2023-10-10T20:19:37.253257Z",
+ "shell.execute_reply": "2023-10-10T20:19:37.252729Z"
+ },
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.673083,
+ "end_time": "2023-10-10T20:19:37.254793",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:36.581710",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"file_paths = list(dir_path.rglob('**/*.*'))\n",
@@ -56,55 +185,79 @@
"flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
"for path in file_paths:\n",
- " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n"
- ],
- "metadata": {
- "collapsed": false
- }
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())"
+ ]
},
{
"cell_type": "code",
- "execution_count": 20,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
+ "execution_count": 6,
+ "id": "3272ea2b",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:19:37.268248Z",
+ "iopub.status.busy": "2023-10-10T20:19:37.267971Z",
+ "iopub.status.idle": "2023-10-10T20:19:50.606898Z",
+ "shell.execute_reply": "2023-10-10T20:19:50.606324Z"
+ },
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 13.347122,
+ "end_time": "2023-10-10T20:19:50.608576",
+ "exception": false,
+ "start_time": "2023-10-10T20:19:37.261454",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
"source": [
"tar_path = Path(OUTPUT_PATHS[\"audio_tar\"])\n",
"tar_path.parent.mkdir(parents=True, exist_ok=True)\n",
"\n",
"with tarfile.open(tar_path, \"w:gz\") as file:\n",
" file.add(flattened_dir_path, arcname=os.path.sep)"
- ],
- "metadata": {
- "collapsed": false
- }
+ ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 17.279795,
+ "end_time": "2023-10-10T20:19:50.829787",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {},
+ "OUTPUT_PATHS": {
+ "audio_tar": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/1_audio_files/output/emotifymusic.tar.gz"
+ }
+ },
+ "start_time": "2023-10-10T20:19:33.549992",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
-}
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/2_generate_features.ipynb b/notebooks/2_generate_features.ipynb
index acda19ea03d612005ae886e01f6444bdd5befc2b..97b795b2c61164bf2a0930d656ae4bac8fb487c6 100644
--- a/notebooks/2_generate_features.ipynb
+++ b/notebooks/2_generate_features.ipynb
@@ -2,7 +2,17 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {},
+ "id": "699a83ce",
+ "metadata": {
+ "papermill": {
+ "duration": 0.002815,
+ "end_time": "2023-10-10T20:22:20.918811",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:20.915996",
+ "status": "completed"
+ },
+ "tags": []
+ },
"source": [
"# Feature Extraction of Base audio files from Invenio"
]
@@ -10,8 +20,23 @@
{
"cell_type": "code",
"execution_count": 1,
+ "id": "6463a609",
"metadata": {
- "collapsed": true
+ "collapsed": true,
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:22:20.925526Z",
+ "iopub.status.busy": "2023-10-10T20:22:20.925242Z",
+ "iopub.status.idle": "2023-10-10T20:22:21.833418Z",
+ "shell.execute_reply": "2023-10-10T20:22:21.832909Z"
+ },
+ "papermill": {
+ "duration": 0.915204,
+ "end_time": "2023-10-10T20:22:21.836842",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:20.921638",
+ "status": "completed"
+ },
+ "tags": []
},
"outputs": [],
"source": [
@@ -27,8 +52,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "id": "f025335b",
"metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:22:21.844838Z",
+ "iopub.status.busy": "2023-10-10T20:22:21.844500Z",
+ "iopub.status.idle": "2023-10-10T20:22:21.850320Z",
+ "shell.execute_reply": "2023-10-10T20:22:21.849083Z"
+ },
+ "papermill": {
+ "duration": 0.013137,
+ "end_time": "2023-10-10T20:22:21.853195",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:21.840058",
+ "status": "completed"
+ },
"tags": [
"parameters"
]
@@ -49,7 +88,56 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "id": "704afac7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:22:21.865120Z",
+ "iopub.status.busy": "2023-10-10T20:22:21.864492Z",
+ "iopub.status.idle": "2023-10-10T20:22:21.868274Z",
+ "shell.execute_reply": "2023-10-10T20:22:21.867779Z"
+ },
+ "papermill": {
+ "duration": 0.012662,
+ "end_time": "2023-10-10T20:22:21.870977",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:21.858315",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {\n",
+ " \"audio_tar\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/2_generate_features/input/emotifymusic.tar.gz\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/2_generate_features/output/raw_features.csv\"\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "10f1b3cd",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:22:21.883213Z",
+ "iopub.status.busy": "2023-10-10T20:22:21.882982Z",
+ "iopub.status.idle": "2023-10-10T20:22:24.581248Z",
+ "shell.execute_reply": "2023-10-10T20:22:24.580333Z"
+ },
+ "papermill": {
+ "duration": 2.708308,
+ "end_time": "2023-10-10T20:22:24.584454",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:21.876146",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# inputs\n",
@@ -58,8 +146,7 @@
"\n",
"assert INPUT_PATH.exists() and INPUT_PATH.is_dir()\n",
"\n",
- "# files = load_input_audio_archive(gzip_audio_path := INPUT_PATH / \"audio_files.tar.gz\", gzip_audio_path.with_suffix(\"\"))\n",
- "with tarfile.open(audio_gz := INPUT_PATHS[\"audio_tar\"], \"r:gz\") as archive:\n",
+ "with tarfile.open(audio_gz := Path(INPUT_PATHS[\"audio_tar\"]).resolve(), \"r:gz\") as archive:\n",
" archive.extractall(path=(path_out := audio_gz.with_suffix(\"\").with_suffix(\"\")))\n",
"\n",
"files = list(path_out.rglob(\"**/*.*\"))"
@@ -67,8 +154,24 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
+ "execution_count": 5,
+ "id": "469af6f9",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:22:24.598204Z",
+ "iopub.status.busy": "2023-10-10T20:22:24.597956Z",
+ "iopub.status.idle": "2023-10-10T20:22:24.602484Z",
+ "shell.execute_reply": "2023-10-10T20:22:24.601691Z"
+ },
+ "papermill": {
+ "duration": 0.015478,
+ "end_time": "2023-10-10T20:22:24.606471",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:24.590993",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"@contextmanager\n",
@@ -81,8 +184,24 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
+ "execution_count": 6,
+ "id": "316f6c17",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:22:24.621126Z",
+ "iopub.status.busy": "2023-10-10T20:22:24.620845Z",
+ "iopub.status.idle": "2023-10-10T20:24:35.477364Z",
+ "shell.execute_reply": "2023-10-10T20:24:35.476322Z"
+ },
+ "papermill": {
+ "duration": 130.866953,
+ "end_time": "2023-10-10T20:24:35.479456",
+ "exception": false,
+ "start_time": "2023-10-10T20:22:24.612503",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"def generate_mfcc_feature(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE, number_mfccs: int = 40):\n",
@@ -105,8 +224,24 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {},
+ "execution_count": 7,
+ "id": "acc9bae8",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:24:35.488096Z",
+ "iopub.status.busy": "2023-10-10T20:24:35.487818Z",
+ "iopub.status.idle": "2023-10-10T20:24:36.254861Z",
+ "shell.execute_reply": "2023-10-10T20:24:36.254294Z"
+ },
+ "papermill": {
+ "duration": 0.772318,
+ "end_time": "2023-10-10T20:24:36.256138",
+ "exception": false,
+ "start_time": "2023-10-10T20:24:35.483820",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -156,122 +291,122 @@
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
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- " <td>0.740200</td>\n",
- " <td>-0.202103</td>\n",
- " <td>-0.685899</td>\n",
+ " <td>classical_8.mp3</td>\n",
+ " <td>classical</td>\n",
+ " <td>-513.835449</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
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+ " <td>0.000000</td>\n",
" <td>...</td>\n",
- " <td>-0.571889</td>\n",
- " <td>0.460704</td>\n",
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- " <td>-0.475038</td>\n",
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- " <td>-0.615929</td>\n",
- " <td>-0.313063</td>\n",
- " <td>0.622702</td>\n",
+ " <td>0.000000</td>\n",
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+ " <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
- " <td>rock_62.mp3</td>\n",
- " <td>rock</td>\n",
- " <td>-283.595123</td>\n",
- " <td>104.793701</td>\n",
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- " <td>-4.955809</td>\n",
- " <td>-11.049730</td>\n",
- " <td>-12.609512</td>\n",
+ " <td>classical_8.mp3</td>\n",
+ " <td>classical</td>\n",
+ " <td>-430.772858</td>\n",
+ " <td>99.951447</td>\n",
+ " <td>61.102493</td>\n",
+ " <td>28.070032</td>\n",
+ " <td>15.340330</td>\n",
+ " <td>15.008282</td>\n",
+ " <td>11.502503</td>\n",
" <td>...</td>\n",
- " <td>-8.575553</td>\n",
- " <td>7.450663</td>\n",
- " <td>3.012856</td>\n",
- " <td>-0.510788</td>\n",
- " <td>-4.468874</td>\n",
- " <td>-4.104988</td>\n",
- " <td>-0.391649</td>\n",
- " <td>3.345398</td>\n",
- " <td>2.204561</td>\n",
- " <td>-7.161574</td>\n",
+ " <td>-4.017534</td>\n",
+ " <td>-2.689229</td>\n",
+ " <td>-2.293572</td>\n",
+ " <td>-2.991963</td>\n",
+ " <td>-3.644343</td>\n",
+ " <td>-4.003089</td>\n",
+ " <td>-4.528318</td>\n",
+ " <td>-4.626081</td>\n",
+ " <td>-2.798346</td>\n",
+ " <td>0.923011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
- " <td>rock_62.mp3</td>\n",
- " <td>rock</td>\n",
- " <td>-84.322151</td>\n",
- " <td>101.870377</td>\n",
- " <td>-0.200612</td>\n",
- " <td>48.966591</td>\n",
- " <td>0.395384</td>\n",
- " <td>7.875898</td>\n",
- " <td>-8.082167</td>\n",
+ " <td>classical_8.mp3</td>\n",
+ " <td>classical</td>\n",
+ " <td>-312.093567</td>\n",
+ " <td>159.784668</td>\n",
+ " <td>31.906086</td>\n",
+ " <td>25.901234</td>\n",
+ " <td>6.815042</td>\n",
+ " <td>3.911939</td>\n",
+ " <td>21.410465</td>\n",
" <td>...</td>\n",
- " <td>-1.941671</td>\n",
- " <td>3.003232</td>\n",
- " <td>-9.348690</td>\n",
- " <td>1.886054</td>\n",
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- " <td>-2.186291</td>\n",
- " <td>1.340472</td>\n",
- " <td>-1.882954</td>\n",
+ " <td>3.267372</td>\n",
+ " <td>-2.944059</td>\n",
+ " <td>-7.677339</td>\n",
+ " <td>-3.628831</td>\n",
+ " <td>-4.110184</td>\n",
+ " <td>-14.840838</td>\n",
+ " <td>-3.495162</td>\n",
+ " <td>8.776964</td>\n",
+ " <td>-4.981813</td>\n",
+ " <td>-10.156776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
- " <td>rock_62.mp3</td>\n",
- " <td>rock</td>\n",
- " <td>-19.622435</td>\n",
- " <td>92.185410</td>\n",
- " <td>8.945698</td>\n",
- " <td>51.681049</td>\n",
- " <td>7.451919</td>\n",
- " <td>13.238035</td>\n",
- " <td>0.040309</td>\n",
+ " <td>classical_8.mp3</td>\n",
+ " <td>classical</td>\n",
+ " <td>-243.798019</td>\n",
+ " <td>168.200287</td>\n",
+ " <td>16.092997</td>\n",
+ " <td>34.248627</td>\n",
+ " <td>3.439126</td>\n",
+ " <td>4.217156</td>\n",
+ " <td>16.333824</td>\n",
" <td>...</td>\n",
- " <td>0.989069</td>\n",
- " <td>3.732528</td>\n",
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+ " <td>-7.971353</td>\n",
+ " <td>9.408128</td>\n",
+ " <td>-3.466177</td>\n",
+ " <td>-11.191519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
- " <td>rock_62.mp3</td>\n",
- " <td>rock</td>\n",
- " <td>-15.719015</td>\n",
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- " <td>46.291245</td>\n",
- " <td>11.866993</td>\n",
- " <td>14.065739</td>\n",
- " <td>3.912442</td>\n",
+ " <td>classical_8.mp3</td>\n",
+ " <td>classical</td>\n",
+ " <td>-250.946625</td>\n",
+ " <td>182.020203</td>\n",
+ " <td>12.093463</td>\n",
+ " <td>31.393484</td>\n",
+ " <td>10.792284</td>\n",
+ " <td>5.874646</td>\n",
+ " <td>15.635584</td>\n",
" <td>...</td>\n",
- " <td>2.546893</td>\n",
- " <td>4.681245</td>\n",
- " <td>-10.060188</td>\n",
- " <td>-0.001825</td>\n",
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- " <td>5.340800</td>\n",
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- " <td>1.635832</td>\n",
+ " <td>6.143005</td>\n",
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+ " <td>-7.456434</td>\n",
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+ " <td>4.395530</td>\n",
+ " <td>-5.535549</td>\n",
+ " <td>-9.764086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -300,122 +435,122 @@
" <tr>\n",
" <th>2581</th>\n",
" <td>2581</td>\n",
- " <td>pop_84.mp3</td>\n",
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- " <td>-0.537112</td>\n",
- " <td>47.537018</td>\n",
- " <td>-2.753691</td>\n",
- " <td>20.138699</td>\n",
- " <td>-12.179976</td>\n",
+ " <td>electronic_28.mp3</td>\n",
+ " <td>electronic</td>\n",
+ " <td>-4.531759</td>\n",
+ " <td>85.749336</td>\n",
+ " <td>3.175902</td>\n",
+ " <td>29.282883</td>\n",
+ " <td>10.520454</td>\n",
+ " <td>28.353235</td>\n",
+ " <td>7.040113</td>\n",
" <td>...</td>\n",
- " <td>-11.053669</td>\n",
- " <td>13.894144</td>\n",
- " <td>8.479092</td>\n",
- " <td>7.122847</td>\n",
- " <td>-14.929329</td>\n",
- " <td>-5.350380</td>\n",
- " <td>-13.549859</td>\n",
- " <td>-2.760207</td>\n",
- " <td>-6.507334</td>\n",
- " <td>-1.587169</td>\n",
+ " <td>-0.076582</td>\n",
+ " <td>10.373774</td>\n",
+ " <td>-3.842222</td>\n",
+ " <td>1.740638</td>\n",
+ " <td>-4.820115</td>\n",
+ " <td>5.424960</td>\n",
+ " <td>-0.350912</td>\n",
+ " <td>3.484543</td>\n",
+ " <td>4.927905</td>\n",
+ " <td>7.667750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2582</th>\n",
" <td>2582</td>\n",
- " <td>pop_84.mp3</td>\n",
- " <td>pop</td>\n",
- " <td>-24.393179</td>\n",
- " <td>76.078751</td>\n",
- " <td>-0.217696</td>\n",
- " <td>45.393887</td>\n",
- " <td>-3.839055</td>\n",
- " <td>18.908188</td>\n",
- " <td>-13.727843</td>\n",
+ " <td>electronic_28.mp3</td>\n",
+ " <td>electronic</td>\n",
+ " <td>-21.892481</td>\n",
+ " <td>64.973923</td>\n",
+ " <td>0.638062</td>\n",
+ " <td>30.259424</td>\n",
+ " <td>3.547897</td>\n",
+ " <td>25.982525</td>\n",
+ " <td>12.492319</td>\n",
" <td>...</td>\n",
- " <td>-4.695143</td>\n",
- " <td>15.707985</td>\n",
- " <td>8.134483</td>\n",
- " <td>-4.642361</td>\n",
- " <td>-16.337616</td>\n",
- " <td>1.411419</td>\n",
- " <td>-5.382087</td>\n",
- " <td>-2.032343</td>\n",
- " <td>-4.381462</td>\n",
- " <td>6.644224</td>\n",
+ " <td>-4.140548</td>\n",
+ " <td>8.154976</td>\n",
+ " <td>-8.581367</td>\n",
+ " <td>0.991196</td>\n",
+ " <td>-7.903484</td>\n",
+ " <td>5.064352</td>\n",
+ " <td>-7.015607</td>\n",
+ " <td>2.761323</td>\n",
+ " <td>2.499545</td>\n",
+ " <td>4.854020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2583</th>\n",
" <td>2583</td>\n",
- " <td>pop_84.mp3</td>\n",
- " <td>pop</td>\n",
- " <td>-32.004326</td>\n",
- " <td>73.496300</td>\n",
- " <td>7.648635</td>\n",
- " <td>48.266846</td>\n",
- " <td>-0.455808</td>\n",
- " <td>17.435381</td>\n",
- " <td>-14.363821</td>\n",
+ " <td>electronic_28.mp3</td>\n",
+ " <td>electronic</td>\n",
+ " <td>-26.937489</td>\n",
+ " <td>59.654442</td>\n",
+ " <td>3.198796</td>\n",
+ " <td>36.822197</td>\n",
+ " <td>-0.308186</td>\n",
+ " <td>17.223629</td>\n",
+ " <td>12.519827</td>\n",
" <td>...</td>\n",
- " <td>-2.071352</td>\n",
- " <td>17.130743</td>\n",
- " <td>0.093085</td>\n",
- " <td>-9.584680</td>\n",
- " <td>-14.788561</td>\n",
- " <td>3.801263</td>\n",
- " <td>-4.997859</td>\n",
- " <td>-0.796039</td>\n",
- " <td>-4.585654</td>\n",
- " <td>8.843502</td>\n",
+ " <td>-2.150106</td>\n",
+ " <td>6.751756</td>\n",
+ " <td>-8.335445</td>\n",
+ " <td>-3.181783</td>\n",
+ " <td>-11.748012</td>\n",
+ " <td>3.223699</td>\n",
+ " <td>-10.738268</td>\n",
+ " <td>-1.915628</td>\n",
+ " <td>-2.164130</td>\n",
+ " <td>-0.500030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2584</th>\n",
" <td>2584</td>\n",
- " <td>pop_84.mp3</td>\n",
- " <td>pop</td>\n",
- " <td>-24.098267</td>\n",
- " <td>74.569794</td>\n",
- " <td>1.918022</td>\n",
- " <td>42.742210</td>\n",
- " <td>-2.470837</td>\n",
- " <td>18.779152</td>\n",
- " <td>-18.865677</td>\n",
+ " <td>electronic_28.mp3</td>\n",
+ " <td>electronic</td>\n",
+ " <td>-37.675701</td>\n",
+ " <td>69.980713</td>\n",
+ " <td>6.486831</td>\n",
+ " <td>36.693054</td>\n",
+ " <td>-2.817516</td>\n",
+ " <td>14.450989</td>\n",
+ " <td>9.200117</td>\n",
" <td>...</td>\n",
- " <td>-8.722974</td>\n",
- " <td>14.073466</td>\n",
- " <td>0.714307</td>\n",
- " <td>-3.764169</td>\n",
- " <td>-16.944185</td>\n",
- " <td>-0.806753</td>\n",
- " <td>-6.225281</td>\n",
- " <td>2.324071</td>\n",
- " <td>-6.115126</td>\n",
- " <td>1.064405</td>\n",
+ " <td>0.592433</td>\n",
+ " <td>4.523458</td>\n",
+ " <td>-8.737437</td>\n",
+ " <td>-4.725236</td>\n",
+ " <td>-7.613096</td>\n",
+ " <td>1.976833</td>\n",
+ " <td>-9.998651</td>\n",
+ " <td>-1.651334</td>\n",
+ " <td>-1.831298</td>\n",
+ " <td>-1.857335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2585</th>\n",
" <td>2585</td>\n",
- " <td>pop_84.mp3</td>\n",
- " <td>pop</td>\n",
- " <td>-49.109211</td>\n",
- " <td>77.647949</td>\n",
- " <td>-3.673396</td>\n",
- " <td>42.419292</td>\n",
- " <td>-1.558257</td>\n",
- " <td>18.824373</td>\n",
- " <td>-23.748917</td>\n",
+ " <td>electronic_28.mp3</td>\n",
+ " <td>electronic</td>\n",
+ " <td>-69.959473</td>\n",
+ " <td>90.579102</td>\n",
+ " <td>12.684738</td>\n",
+ " <td>39.559166</td>\n",
+ " <td>-2.489999</td>\n",
+ " <td>13.447134</td>\n",
+ " <td>2.889965</td>\n",
" <td>...</td>\n",
- " <td>-10.903637</td>\n",
- " <td>10.563424</td>\n",
- " <td>3.245042</td>\n",
- " <td>3.819183</td>\n",
- " <td>-15.246656</td>\n",
- " <td>-0.539900</td>\n",
- " <td>-7.361567</td>\n",
- " <td>7.688834</td>\n",
- " <td>-3.895716</td>\n",
- " <td>-3.842279</td>\n",
+ " <td>2.153978</td>\n",
+ " <td>6.035127</td>\n",
+ " <td>-8.183851</td>\n",
+ " <td>-0.212283</td>\n",
+ " <td>-1.487655</td>\n",
+ " <td>-2.779953</td>\n",
+ " <td>-5.455588</td>\n",
+ " <td>0.809570</td>\n",
+ " <td>-1.209018</td>\n",
+ " <td>-1.631956</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -423,49 +558,62 @@
"</div>"
],
"text/plain": [
- " sample filename label 0 1 2 3 \\\n",
- "0 0 rock_62.mp3 rock -571.813416 -0.045195 -0.776617 0.130642 \n",
- "1 1 rock_62.mp3 rock -283.595123 104.793701 -28.864902 35.690403 \n",
- "2 2 rock_62.mp3 rock -84.322151 101.870377 -0.200612 48.966591 \n",
- "3 3 rock_62.mp3 rock -19.622435 92.185410 8.945698 51.681049 \n",
- "4 4 rock_62.mp3 rock -15.719015 84.805496 3.012265 46.291245 \n",
- "... ... ... ... ... ... ... ... \n",
- "2581 2581 pop_84.mp3 pop -20.295166 76.309967 -0.537112 47.537018 \n",
- "2582 2582 pop_84.mp3 pop -24.393179 76.078751 -0.217696 45.393887 \n",
- "2583 2583 pop_84.mp3 pop -32.004326 73.496300 7.648635 48.266846 \n",
- "2584 2584 pop_84.mp3 pop -24.098267 74.569794 1.918022 42.742210 \n",
- "2585 2585 pop_84.mp3 pop -49.109211 77.647949 -3.673396 42.419292 \n",
+ " sample filename label 0 1 \\\n",
+ "0 0 classical_8.mp3 classical -513.835449 0.000000 \n",
+ "1 1 classical_8.mp3 classical -430.772858 99.951447 \n",
+ "2 2 classical_8.mp3 classical -312.093567 159.784668 \n",
+ "3 3 classical_8.mp3 classical -243.798019 168.200287 \n",
+ "4 4 classical_8.mp3 classical -250.946625 182.020203 \n",
+ "... ... ... ... ... ... \n",
+ "2581 2581 electronic_28.mp3 electronic -4.531759 85.749336 \n",
+ "2582 2582 electronic_28.mp3 electronic -21.892481 64.973923 \n",
+ "2583 2583 electronic_28.mp3 electronic -26.937489 59.654442 \n",
+ "2584 2584 electronic_28.mp3 electronic -37.675701 69.980713 \n",
+ "2585 2585 electronic_28.mp3 electronic -69.959473 90.579102 \n",
+ "\n",
+ " 2 3 4 5 6 ... 30 \\\n",
+ "0 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
+ "1 61.102493 28.070032 15.340330 15.008282 11.502503 ... -4.017534 \n",
+ "2 31.906086 25.901234 6.815042 3.911939 21.410465 ... 3.267372 \n",
+ "3 16.092997 34.248627 3.439126 4.217156 16.333824 ... 8.645699 \n",
+ "4 12.093463 31.393484 10.792284 5.874646 15.635584 ... 6.143005 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "2581 3.175902 29.282883 10.520454 28.353235 7.040113 ... -0.076582 \n",
+ "2582 0.638062 30.259424 3.547897 25.982525 12.492319 ... -4.140548 \n",
+ "2583 3.198796 36.822197 -0.308186 17.223629 12.519827 ... -2.150106 \n",
+ "2584 6.486831 36.693054 -2.817516 14.450989 9.200117 ... 0.592433 \n",
+ "2585 12.684738 39.559166 -2.489999 13.447134 2.889965 ... 2.153978 \n",
"\n",
- " 4 5 6 ... 30 31 32 \\\n",
- "0 0.740200 -0.202103 -0.685899 ... -0.571889 0.460704 0.534769 \n",
- "1 -4.955809 -11.049730 -12.609512 ... -8.575553 7.450663 3.012856 \n",
- "2 0.395384 7.875898 -8.082167 ... -1.941671 3.003232 -9.348690 \n",
- "3 7.451919 13.238035 0.040309 ... 0.989069 3.732528 -10.594652 \n",
- "4 11.866993 14.065739 3.912442 ... 2.546893 4.681245 -10.060188 \n",
- "... ... ... ... ... ... ... ... \n",
- "2581 -2.753691 20.138699 -12.179976 ... -11.053669 13.894144 8.479092 \n",
- "2582 -3.839055 18.908188 -13.727843 ... -4.695143 15.707985 8.134483 \n",
- "2583 -0.455808 17.435381 -14.363821 ... -2.071352 17.130743 0.093085 \n",
- "2584 -2.470837 18.779152 -18.865677 ... -8.722974 14.073466 0.714307 \n",
- "2585 -1.558257 18.824373 -23.748917 ... -10.903637 10.563424 3.245042 \n",
+ " 31 32 33 34 35 36 \\\n",
+ "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "1 -2.689229 -2.293572 -2.991963 -3.644343 -4.003089 -4.528318 \n",
+ "2 -2.944059 -7.677339 -3.628831 -4.110184 -14.840838 -3.495162 \n",
+ "3 -5.766571 -5.486410 -3.288999 -3.853479 -19.015926 -7.971353 \n",
+ "4 -2.007963 -7.107271 -5.137182 -7.456434 -19.914568 -8.567856 \n",
+ "... ... ... ... ... ... ... \n",
+ "2581 10.373774 -3.842222 1.740638 -4.820115 5.424960 -0.350912 \n",
+ "2582 8.154976 -8.581367 0.991196 -7.903484 5.064352 -7.015607 \n",
+ "2583 6.751756 -8.335445 -3.181783 -11.748012 3.223699 -10.738268 \n",
+ "2584 4.523458 -8.737437 -4.725236 -7.613096 1.976833 -9.998651 \n",
+ "2585 6.035127 -8.183851 -0.212283 -1.487655 -2.779953 -5.455588 \n",
"\n",
- " 33 34 35 36 37 38 39 \n",
- "0 -0.529890 -0.475038 0.583885 0.398325 -0.615929 -0.313063 0.622702 \n",
- "1 -0.510788 -4.468874 -4.104988 -0.391649 3.345398 2.204561 -7.161574 \n",
- "2 1.886054 -5.747732 1.687546 0.704384 -2.186291 1.340472 -1.882954 \n",
- "3 2.572243 -1.231659 3.904081 -0.745448 1.233542 -2.789202 -0.164483 \n",
- "4 -0.001825 -0.918342 1.720158 -1.147007 5.340800 -6.961733 1.635832 \n",
- "... ... ... ... ... ... ... ... \n",
- "2581 7.122847 -14.929329 -5.350380 -13.549859 -2.760207 -6.507334 -1.587169 \n",
- "2582 -4.642361 -16.337616 1.411419 -5.382087 -2.032343 -4.381462 6.644224 \n",
- "2583 -9.584680 -14.788561 3.801263 -4.997859 -0.796039 -4.585654 8.843502 \n",
- "2584 -3.764169 -16.944185 -0.806753 -6.225281 2.324071 -6.115126 1.064405 \n",
- "2585 3.819183 -15.246656 -0.539900 -7.361567 7.688834 -3.895716 -3.842279 \n",
+ " 37 38 39 \n",
+ "0 0.000000 0.000000 0.000000 \n",
+ "1 -4.626081 -2.798346 0.923011 \n",
+ "2 8.776964 -4.981813 -10.156776 \n",
+ "3 9.408128 -3.466177 -11.191519 \n",
+ "4 4.395530 -5.535549 -9.764086 \n",
+ "... ... ... ... \n",
+ "2581 3.484543 4.927905 7.667750 \n",
+ "2582 2.761323 2.499545 4.854020 \n",
+ "2583 -1.915628 -2.164130 -0.500030 \n",
+ "2584 -1.651334 -1.831298 -1.857335 \n",
+ "2585 0.809570 -1.209018 -1.631956 \n",
"\n",
"[1029854 rows x 43 columns]"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -487,8 +635,24 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
+ "execution_count": 8,
+ "id": "0abf745b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:24:36.270995Z",
+ "iopub.status.busy": "2023-10-10T20:24:36.270774Z",
+ "iopub.status.idle": "2023-10-10T20:25:07.468362Z",
+ "shell.execute_reply": "2023-10-10T20:25:07.467787Z"
+ },
+ "papermill": {
+ "duration": 31.206394,
+ "end_time": "2023-10-10T20:25:07.470045",
+ "exception": false,
+ "start_time": "2023-10-10T20:24:36.263651",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# outputs\n",
@@ -514,9 +678,28 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 168.079285,
+ "end_time": "2023-10-10T20:25:08.092566",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/2_generate_features.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/2_generate_features.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {
+ "audio_tar": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/2_generate_features/input/emotifymusic.tar.gz"
+ },
+ "OUTPUT_PATHS": {
+ "raw_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/2_generate_features/output/raw_features.csv"
+ }
+ },
+ "start_time": "2023-10-10T20:22:20.013281",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 1
-}
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/3_aggregate_features.ipynb b/notebooks/3_aggregate_features.ipynb
index 84a3f978993d3566cf13413af61126ca335e5238..9dfa0965c4ef81176a7dd959dbff279d742479b7 100644
--- a/notebooks/3_aggregate_features.ipynb
+++ b/notebooks/3_aggregate_features.ipynb
@@ -2,7 +2,17 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {},
+ "id": "f48a4573",
+ "metadata": {
+ "papermill": {
+ "duration": 0.005214,
+ "end_time": "2023-10-10T20:29:39.798977",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:39.793763",
+ "status": "completed"
+ },
+ "tags": []
+ },
"source": [
"# Aggregate MFCC Features\n",
"\n",
@@ -12,19 +22,57 @@
{
"cell_type": "code",
"execution_count": 1,
+ "id": "389576b8",
"metadata": {
- "collapsed": true
+ "ExecuteTime": {
+ "end_time": "2023-08-14T15:32:41.535589478Z",
+ "start_time": "2023-08-14T15:32:40.986222405Z"
+ },
+ "collapsed": true,
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:29:39.811180Z",
+ "iopub.status.busy": "2023-10-10T20:29:39.810870Z",
+ "iopub.status.idle": "2023-10-10T20:29:40.112894Z",
+ "shell.execute_reply": "2023-10-10T20:29:40.112151Z"
+ },
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "papermill": {
+ "duration": 0.309491,
+ "end_time": "2023-10-10T20:29:40.114738",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:39.805247",
+ "status": "completed"
+ },
+ "tags": []
},
"outputs": [],
"source": [
+ "from pathlib import Path\n",
+ "\n",
"import pandas as pd\n",
"from definitions import BASE_PATH"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "id": "26f640e0",
"metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:29:40.122700Z",
+ "iopub.status.busy": "2023-10-10T20:29:40.122309Z",
+ "iopub.status.idle": "2023-10-10T20:29:40.127330Z",
+ "shell.execute_reply": "2023-10-10T20:29:40.126502Z"
+ },
+ "papermill": {
+ "duration": 0.010905,
+ "end_time": "2023-10-10T20:29:40.128867",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:40.117962",
+ "status": "completed"
+ },
"tags": [
"parameters"
]
@@ -45,20 +93,83 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
+ "execution_count": 3,
+ "id": "70fd8bf2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:29:40.134992Z",
+ "iopub.status.busy": "2023-10-10T20:29:40.134692Z",
+ "iopub.status.idle": "2023-10-10T20:29:40.138145Z",
+ "shell.execute_reply": "2023-10-10T20:29:40.137550Z"
+ },
+ "papermill": {
+ "duration": 0.008147,
+ "end_time": "2023-10-10T20:29:40.139591",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:40.131444",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {\n",
+ " \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/input/raw_features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv\"\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c5d9d980",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:29:40.145046Z",
+ "iopub.status.busy": "2023-10-10T20:29:40.144752Z",
+ "iopub.status.idle": "2023-10-10T20:29:44.318757Z",
+ "shell.execute_reply": "2023-10-10T20:29:44.318022Z"
+ },
+ "papermill": {
+ "duration": 4.179755,
+ "end_time": "2023-10-10T20:29:44.321578",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:40.141823",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# inputs\n",
- "assert INPUT_PATH.exists() and INPUT_PATH.is_dir()\n",
- "\n",
- "raw_features = pd.read_csv(INPUT_PATH / \"raw_features.csv\", index_col=False)"
+ "raw_features = pd.read_csv(INPUT_PATHS[\"raw_features\"], index_col=False)"
]
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {},
+ "execution_count": 5,
+ "id": "99f75f47",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:29:44.327809Z",
+ "iopub.status.busy": "2023-10-10T20:29:44.327547Z",
+ "iopub.status.idle": "2023-10-10T20:29:48.186600Z",
+ "shell.execute_reply": "2023-10-10T20:29:48.186091Z"
+ },
+ "papermill": {
+ "duration": 3.865747,
+ "end_time": "2023-10-10T20:29:48.189794",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:44.324047",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -136,10 +247,10 @@
" <td>-562.85785</td>\n",
" <td>-96.164795</td>\n",
" <td>-219.259016</td>\n",
- " <td>53.561838</td>\n",
+ " <td>53.561839</td>\n",
" <td>-0.772320</td>\n",
" <td>0.029056</td>\n",
- " <td>259.63270</td>\n",
+ " <td>259.63272</td>\n",
" <td>215.094182</td>\n",
" <td>...</td>\n",
" <td>-27.458416</td>\n",
@@ -147,8 +258,8 @@
" <td>0.484271</td>\n",
" <td>8.660648</td>\n",
" <td>-0.479016</td>\n",
- " <td>-28.989983</td>\n",
- " <td>27.533710</td>\n",
+ " <td>-28.989979</td>\n",
+ " <td>27.533707</td>\n",
" <td>0.952658</td>\n",
" <td>10.477735</td>\n",
" <td>-0.185771</td>\n",
@@ -172,7 +283,7 @@
" <td>8.185075</td>\n",
" <td>0.208425</td>\n",
" <td>-38.095375</td>\n",
- " <td>31.397880</td>\n",
+ " <td>31.397882</td>\n",
" <td>-1.494916</td>\n",
" <td>10.917299</td>\n",
" <td>0.020985</td>\n",
@@ -195,8 +306,8 @@
" <td>-3.781627</td>\n",
" <td>9.191043</td>\n",
" <td>0.260886</td>\n",
- " <td>-22.667440</td>\n",
- " <td>50.992897</td>\n",
+ " <td>-22.667439</td>\n",
+ " <td>50.992905</td>\n",
" <td>1.600777</td>\n",
" <td>10.125545</td>\n",
" <td>0.595763</td>\n",
@@ -212,14 +323,14 @@
" <td>-0.366586</td>\n",
" <td>0.000000</td>\n",
" <td>194.26416</td>\n",
- " <td>148.226647</td>\n",
+ " <td>148.226648</td>\n",
" <td>...</td>\n",
- " <td>-44.843810</td>\n",
+ " <td>-44.843815</td>\n",
" <td>28.490644</td>\n",
" <td>-6.242015</td>\n",
" <td>10.546545</td>\n",
" <td>0.341848</td>\n",
- " <td>-25.040888</td>\n",
+ " <td>-25.040886</td>\n",
" <td>46.878204</td>\n",
" <td>1.844494</td>\n",
" <td>11.160392</td>\n",
@@ -270,7 +381,7 @@
" <td>-24.712723</td>\n",
" <td>23.410387</td>\n",
" <td>-4.502398</td>\n",
- " <td>6.687984</td>\n",
+ " <td>6.687983</td>\n",
" <td>0.238807</td>\n",
" </tr>\n",
" <tr>\n",
@@ -278,21 +389,21 @@
" <td>rock_96.mp3</td>\n",
" <td>rock</td>\n",
" <td>-541.23600</td>\n",
- " <td>27.163334</td>\n",
+ " <td>27.163332</td>\n",
" <td>-119.113996</td>\n",
" <td>58.420684</td>\n",
" <td>-0.957699</td>\n",
- " <td>-7.415961</td>\n",
+ " <td>-7.415959</td>\n",
" <td>210.49246</td>\n",
" <td>125.453699</td>\n",
" <td>...</td>\n",
" <td>-37.584858</td>\n",
- " <td>28.087936</td>\n",
+ " <td>28.087940</td>\n",
" <td>-9.704238</td>\n",
" <td>8.447620</td>\n",
" <td>0.112760</td>\n",
" <td>-38.147890</td>\n",
- " <td>21.814402</td>\n",
+ " <td>21.814400</td>\n",
" <td>-8.249507</td>\n",
" <td>7.807756</td>\n",
" <td>0.071968</td>\n",
@@ -316,7 +427,7 @@
" <td>7.727378</td>\n",
" <td>0.207489</td>\n",
" <td>-29.497524</td>\n",
- " <td>25.410654</td>\n",
+ " <td>25.410656</td>\n",
" <td>-3.356614</td>\n",
" <td>8.170526</td>\n",
" <td>0.160330</td>\n",
@@ -331,16 +442,16 @@
" <td>52.444200</td>\n",
" <td>-1.705641</td>\n",
" <td>0.000000</td>\n",
- " <td>187.04274</td>\n",
+ " <td>187.04272</td>\n",
" <td>96.440874</td>\n",
" <td>...</td>\n",
- " <td>-26.967848</td>\n",
- " <td>8.714737</td>\n",
+ " <td>-26.967852</td>\n",
+ " <td>8.714736</td>\n",
" <td>-9.511491</td>\n",
" <td>5.551820</td>\n",
" <td>-0.025604</td>\n",
- " <td>-23.020084</td>\n",
- " <td>13.948638</td>\n",
+ " <td>-23.020082</td>\n",
+ " <td>13.948639</td>\n",
" <td>-2.664985</td>\n",
" <td>5.051498</td>\n",
" <td>-0.258407</td>\n",
@@ -354,17 +465,17 @@
" <td>-49.380943</td>\n",
" <td>54.045627</td>\n",
" <td>-0.863093</td>\n",
- " <td>-32.930653</td>\n",
+ " <td>-32.930650</td>\n",
" <td>191.73538</td>\n",
" <td>93.971242</td>\n",
" <td>...</td>\n",
" <td>-21.929403</td>\n",
" <td>17.050608</td>\n",
" <td>-5.296691</td>\n",
- " <td>5.894963</td>\n",
+ " <td>5.894962</td>\n",
" <td>0.390705</td>\n",
" <td>-20.983192</td>\n",
- " <td>29.312023</td>\n",
+ " <td>29.312021</td>\n",
" <td>-0.321836</td>\n",
" <td>6.571660</td>\n",
" <td>0.384794</td>\n",
@@ -383,36 +494,36 @@
"4 classical_12.mp3 classical -562.67523 -148.133560 -270.975406 \n",
".. ... ... ... ... ... \n",
"395 rock_95.mp3 rock -553.11010 -5.218835 -193.506047 \n",
- "396 rock_96.mp3 rock -541.23600 27.163334 -119.113996 \n",
+ "396 rock_96.mp3 rock -541.23600 27.163332 -119.113996 \n",
"397 rock_97.mp3 rock -518.49500 58.526745 -66.267744 \n",
"398 rock_98.mp3 rock -518.64307 53.555115 -45.734517 \n",
"399 rock_99.mp3 rock -544.70310 75.612130 -49.380943 \n",
"\n",
" 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n",
"0 51.142183 -0.468374 0.000000 178.75162 111.332342 ... -44.098070 \n",
- "1 53.561838 -0.772320 0.029056 259.63270 215.094182 ... -27.458416 \n",
+ "1 53.561839 -0.772320 0.029056 259.63272 215.094182 ... -27.458416 \n",
"2 83.381622 -2.587179 0.000000 190.47589 112.471713 ... -27.335688 \n",
"3 76.246992 -2.402418 0.000000 159.42575 99.853645 ... -31.774948 \n",
- "4 52.191182 -0.366586 0.000000 194.26416 148.226647 ... -44.843810 \n",
+ "4 52.191182 -0.366586 0.000000 194.26416 148.226648 ... -44.843815 \n",
".. ... ... ... ... ... ... ... \n",
"395 76.869437 -0.201055 -89.948746 201.18045 111.724191 ... -27.043941 \n",
- "396 58.420684 -0.957699 -7.415961 210.49246 125.453699 ... -37.584858 \n",
+ "396 58.420684 -0.957699 -7.415959 210.49246 125.453699 ... -37.584858 \n",
"397 65.635619 -0.898026 -58.824410 175.20135 99.288265 ... -29.620445 \n",
- "398 52.444200 -1.705641 0.000000 187.04274 96.440874 ... -26.967848 \n",
- "399 54.045627 -0.863093 -32.930653 191.73538 93.971242 ... -21.929403 \n",
+ "398 52.444200 -1.705641 0.000000 187.04272 96.440874 ... -26.967852 \n",
+ "399 54.045627 -0.863093 -32.930650 191.73538 93.971242 ... -21.929403 \n",
"\n",
" 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n",
"0 47.308060 -3.713503 16.553984 0.230691 -46.794480 49.352516 -2.282116 \n",
- "1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n",
- "2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397880 -1.494916 \n",
- "3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n",
- "4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n",
+ "1 29.811110 0.484271 8.660648 -0.479016 -28.989979 27.533707 0.952658 \n",
+ "2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397882 -1.494916 \n",
+ "3 31.500881 -3.781627 9.191043 0.260886 -22.667439 50.992905 1.600777 \n",
+ "4 28.490644 -6.242015 10.546545 0.341848 -25.040886 46.878204 1.844494 \n",
".. ... ... ... ... ... ... ... \n",
"395 22.451445 -7.234634 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n",
- "396 28.087936 -9.704238 8.447620 0.112760 -38.147890 21.814402 -8.249507 \n",
- "397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410654 -3.356614 \n",
- "398 8.714737 -9.511491 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n",
- "399 17.050608 -5.296691 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n",
+ "396 28.087940 -9.704238 8.447620 0.112760 -38.147890 21.814400 -8.249507 \n",
+ "397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410656 -3.356614 \n",
+ "398 8.714736 -9.511491 5.551820 -0.025604 -23.020082 13.948639 -2.664985 \n",
+ "399 17.050608 -5.296691 5.894962 0.390705 -20.983192 29.312021 -0.321836 \n",
"\n",
" 39_std 39_skew \n",
"0 15.285639 0.171462 \n",
@@ -421,7 +532,7 @@
"3 10.125545 0.595763 \n",
"4 11.160392 0.503120 \n",
".. ... ... \n",
- "395 6.687984 0.238807 \n",
+ "395 6.687983 0.238807 \n",
"396 7.807756 0.071968 \n",
"397 8.170526 0.160330 \n",
"398 5.051498 -0.258407 \n",
@@ -430,7 +541,7 @@
"[400 rows x 202 columns]"
]
},
- "execution_count": 24,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -456,15 +567,32 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "metadata": {},
+ "execution_count": 6,
+ "id": "4ac5c765",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-10-10T20:29:48.197061Z",
+ "iopub.status.busy": "2023-10-10T20:29:48.196826Z",
+ "iopub.status.idle": "2023-10-10T20:29:48.273847Z",
+ "shell.execute_reply": "2023-10-10T20:29:48.273103Z"
+ },
+ "papermill": {
+ "duration": 0.082818,
+ "end_time": "2023-10-10T20:29:48.275380",
+ "exception": false,
+ "start_time": "2023-10-10T20:29:48.192562",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# outputs\n",
- "OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
+ "aggregated_features_path = Path(OUTPUT_PATHS[\"aggregated_features\"]).resolve()\n",
+ "aggregated_features_path.parent.mkdir(parents=True, exist_ok=True)\n",
"\n",
"output = mfcc_merged\n",
- "output.to_csv(OUTPUT_PATH / \"features.csv\", index=False)"
+ "output.to_csv(aggregated_features_path, index=False)"
]
}
],
@@ -485,9 +613,28 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 9.80761,
+ "end_time": "2023-10-10T20:29:48.599137",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {
+ "raw_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/input/raw_features.csv"
+ },
+ "OUTPUT_PATHS": {
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv"
+ }
+ },
+ "start_time": "2023-10-10T20:29:38.791527",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 1
-}
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/notebooks/4_split.ipynb b/notebooks/4_split.ipynb
index c0e5c137f8921859ceccfc40d6bc53bb387d9c2f..371f87d8020e8c5e32f3fd71f1815f9d4274225e 100644
--- a/notebooks/4_split.ipynb
+++ b/notebooks/4_split.ipynb
@@ -1,67 +1,38 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "462c6dd4",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-02-22T22:06:53.776834Z",
- "iopub.status.busy": "2023-02-22T22:06:53.776344Z",
- "iopub.status.idle": "2023-02-22T22:06:53.784674Z",
- "shell.execute_reply": "2023-02-22T22:06:53.783920Z"
- },
- "papermill": {
- "duration": 0.01461,
- "end_time": "2023-02-22T22:06:53.786525",
- "exception": false,
- "start_time": "2023-02-22T22:06:53.771915",
- "status": "completed"
- },
- "tags": [
- "injected-parameters"
- ]
- },
- "outputs": [],
- "source": [
- "# Parameters\n",
- "INPUT_PATHS = {\"features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv\"}\n",
- "OUTPUT_PATHS = {\"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"}\n"
- ]
- },
{
"cell_type": "markdown",
"id": "e92b4fe9",
"metadata": {
"papermill": {
- "duration": 0.003942,
- "end_time": "2023-02-22T22:06:53.794630",
+ "duration": 0.005822,
+ "end_time": "2023-10-10T20:29:52.589509",
"exception": false,
- "start_time": "2023-02-22T22:06:53.790688",
+ "start_time": "2023-10-10T20:29:52.583687",
"status": "completed"
},
"tags": []
},
"source": [
- "# Split the given dataframe into a test and a train set"
+ "# Split the Features into Train and Test Set"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "5f1fae44",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:53.802861Z",
- "iopub.status.busy": "2023-02-22T22:06:53.802570Z",
- "iopub.status.idle": "2023-02-22T22:06:54.204257Z",
- "shell.execute_reply": "2023-02-22T22:06:54.203456Z"
+ "iopub.execute_input": "2023-10-10T20:29:52.604063Z",
+ "iopub.status.busy": "2023-10-10T20:29:52.602712Z",
+ "iopub.status.idle": "2023-10-10T20:29:52.903037Z",
+ "shell.execute_reply": "2023-10-10T20:29:52.902341Z"
},
"papermill": {
- "duration": 0.407755,
- "end_time": "2023-02-22T22:06:54.206303",
+ "duration": 0.310276,
+ "end_time": "2023-10-10T20:29:52.905670",
"exception": false,
- "start_time": "2023-02-22T22:06:53.798548",
+ "start_time": "2023-10-10T20:29:52.595394",
"status": "completed"
},
"tags": []
@@ -75,24 +46,26 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"id": "01de1b27",
"metadata": {
"collapsed": false,
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.218120Z",
- "iopub.status.busy": "2023-02-22T22:06:54.217222Z",
- "iopub.status.idle": "2023-02-22T22:06:54.225007Z",
- "shell.execute_reply": "2023-02-22T22:06:54.221894Z"
+ "iopub.execute_input": "2023-10-10T20:29:52.911502Z",
+ "iopub.status.busy": "2023-10-10T20:29:52.911091Z",
+ "iopub.status.idle": "2023-10-10T20:29:52.915967Z",
+ "shell.execute_reply": "2023-10-10T20:29:52.915019Z"
},
"papermill": {
- "duration": 0.016949,
- "end_time": "2023-02-22T22:06:54.227114",
+ "duration": 0.009356,
+ "end_time": "2023-10-10T20:29:52.917383",
"exception": false,
- "start_time": "2023-02-22T22:06:54.210165",
+ "start_time": "2023-10-10T20:29:52.908027",
"status": "completed"
},
- "tags": []
+ "tags": [
+ "parameters"
+ ]
},
"outputs": [],
"source": [
@@ -109,78 +82,53 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "85fd7edd",
+ "execution_count": 3,
+ "id": "d8169758",
"metadata": {
- "collapsed": false,
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.235495Z",
- "iopub.status.busy": "2023-02-22T22:06:54.235185Z",
- "iopub.status.idle": "2023-02-22T22:06:54.238945Z",
- "shell.execute_reply": "2023-02-22T22:06:54.238230Z"
+ "iopub.execute_input": "2023-10-10T20:29:52.922218Z",
+ "iopub.status.busy": "2023-10-10T20:29:52.921930Z",
+ "iopub.status.idle": "2023-10-10T20:29:52.925542Z",
+ "shell.execute_reply": "2023-10-10T20:29:52.924834Z"
},
"papermill": {
- "duration": 0.010662,
- "end_time": "2023-02-22T22:06:54.240943",
+ "duration": 0.007457,
+ "end_time": "2023-10-10T20:29:52.926785",
"exception": false,
- "start_time": "2023-02-22T22:06:54.230281",
+ "start_time": "2023-10-10T20:29:52.919328",
"status": "completed"
},
- "tags": []
+ "tags": [
+ "injected-parameters"
+ ]
},
"outputs": [],
"source": [
"# Parameters\n",
- "INPUT_PATHS = {\"features\": \"/home/lukas/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/tmp/4_split/input/features.csv\"}\n",
- "OUTPUT_PATHS = {\"split\": \"/home/lukas/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/tmp/4_split/output/split.csv\"}\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "95bb5d4a",
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.249999Z",
- "iopub.status.busy": "2023-02-22T22:06:54.249651Z",
- "iopub.status.idle": "2023-02-22T22:06:54.255623Z",
- "shell.execute_reply": "2023-02-22T22:06:54.254792Z"
- },
- "papermill": {
- "duration": 0.013695,
- "end_time": "2023-02-22T22:06:54.258076",
- "exception": false,
- "start_time": "2023-02-22T22:06:54.244381",
- "status": "completed"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "if not INPUT_PATHS[\"features\"]:\n",
- " INPUT_PATHS[\"features\"] = (BASE_PATH / \"tmp\" / \"4_split\" / \"input\" / \"features.csv\").__str__()\n",
- "\n",
- "if not OUTPUT_PATHS[\"split\"]:\n",
- " OUTPUT_PATHS[\"split\"] = (BASE_PATH / \"tmp\" / \"4_split\" / \"output\" / \"split.csv\").__str__()"
+ "INPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"\n",
+ "}\n"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"id": "a4cc6800",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.266338Z",
- "iopub.status.busy": "2023-02-22T22:06:54.266018Z",
- "iopub.status.idle": "2023-02-22T22:06:54.302050Z",
- "shell.execute_reply": "2023-02-22T22:06:54.301191Z"
+ "iopub.execute_input": "2023-10-10T20:29:52.931298Z",
+ "iopub.status.busy": "2023-10-10T20:29:52.931087Z",
+ "iopub.status.idle": "2023-10-10T20:29:52.953647Z",
+ "shell.execute_reply": "2023-10-10T20:29:52.952884Z"
},
"papermill": {
- "duration": 0.043378,
- "end_time": "2023-02-22T22:06:54.304654",
+ "duration": 0.026646,
+ "end_time": "2023-10-10T20:29:52.955241",
"exception": false,
- "start_time": "2023-02-22T22:06:54.261276",
+ "start_time": "2023-10-10T20:29:52.928595",
"status": "completed"
},
"tags": []
@@ -192,25 +140,25 @@
"for path in INPUT_PATHS.values():\n",
" assert Path(path).exists()\n",
"\n",
- "features = pd.read_csv(INPUT_PATHS[\"features\"])"
+ "features = pd.read_csv(INPUT_PATHS[\"aggregated_features\"])"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"id": "a186d0c4",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.314672Z",
- "iopub.status.busy": "2023-02-22T22:06:54.313277Z",
- "iopub.status.idle": "2023-02-22T22:06:54.325516Z",
- "shell.execute_reply": "2023-02-22T22:06:54.324791Z"
+ "iopub.execute_input": "2023-10-10T20:29:52.960691Z",
+ "iopub.status.busy": "2023-10-10T20:29:52.960246Z",
+ "iopub.status.idle": "2023-10-10T20:29:52.967937Z",
+ "shell.execute_reply": "2023-10-10T20:29:52.967204Z"
},
"papermill": {
- "duration": 0.019722,
- "end_time": "2023-02-22T22:06:54.327847",
+ "duration": 0.011851,
+ "end_time": "2023-10-10T20:29:52.969363",
"exception": false,
- "start_time": "2023-02-22T22:06:54.308125",
+ "start_time": "2023-10-10T20:29:52.957512",
"status": "completed"
},
"tags": []
@@ -236,20 +184,20 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"id": "091e0641",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.339778Z",
- "iopub.status.busy": "2023-02-22T22:06:54.337779Z",
- "iopub.status.idle": "2023-02-22T22:06:54.368008Z",
- "shell.execute_reply": "2023-02-22T22:06:54.365597Z"
+ "iopub.execute_input": "2023-10-10T20:29:52.976324Z",
+ "iopub.status.busy": "2023-10-10T20:29:52.975376Z",
+ "iopub.status.idle": "2023-10-10T20:29:52.997935Z",
+ "shell.execute_reply": "2023-10-10T20:29:52.996626Z"
},
"papermill": {
- "duration": 0.038635,
- "end_time": "2023-02-22T22:06:54.370257",
+ "duration": 0.028011,
+ "end_time": "2023-10-10T20:29:52.999430",
"exception": false,
- "start_time": "2023-02-22T22:06:54.331622",
+ "start_time": "2023-10-10T20:29:52.971419",
"status": "completed"
},
"tags": []
@@ -284,7 +232,7 @@
" <tr>\n",
" <th>0</th>\n",
" <td>classical_1.mp3</td>\n",
- " <td>True</td>\n",
+ " <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@@ -294,7 +242,7 @@
" <tr>\n",
" <th>2</th>\n",
" <td>classical_100.mp3</td>\n",
- " <td>True</td>\n",
+ " <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
@@ -343,9 +291,9 @@
],
"text/plain": [
" filename train\n",
- "0 classical_1.mp3 True\n",
+ "0 classical_1.mp3 False\n",
"1 classical_10.mp3 True\n",
- "2 classical_100.mp3 True\n",
+ "2 classical_100.mp3 False\n",
"3 classical_11.mp3 True\n",
"4 classical_12.mp3 True\n",
".. ... ...\n",
@@ -358,7 +306,7 @@
"[400 rows x 2 columns]"
]
},
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -369,20 +317,20 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"id": "7b11b8bb",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T22:06:54.382100Z",
- "iopub.status.busy": "2023-02-22T22:06:54.380345Z",
- "iopub.status.idle": "2023-02-22T22:06:54.390638Z",
- "shell.execute_reply": "2023-02-22T22:06:54.389791Z"
+ "iopub.execute_input": "2023-10-10T20:29:53.004736Z",
+ "iopub.status.busy": "2023-10-10T20:29:53.004384Z",
+ "iopub.status.idle": "2023-10-10T20:29:53.012049Z",
+ "shell.execute_reply": "2023-10-10T20:29:53.010855Z"
},
"papermill": {
- "duration": 0.018752,
- "end_time": "2023-02-22T22:06:54.392770",
+ "duration": 0.012188,
+ "end_time": "2023-10-10T20:29:53.013673",
"exception": false,
- "start_time": "2023-02-22T22:06:54.374018",
+ "start_time": "2023-10-10T20:29:53.001485",
"status": "completed"
},
"tags": []
@@ -415,28 +363,28 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
},
"papermill": {
"default_parameters": {},
- "duration": 2.055989,
- "end_time": "2023-02-22T22:06:54.814906",
+ "duration": 1.731275,
+ "end_time": "2023-10-10T20:29:53.335011",
"environment_variables": {},
"exception": null,
"input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
"output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
"parameters": {
"INPUT_PATHS": {
- "features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv"
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv"
},
"OUTPUT_PATHS": {
"split": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv"
}
},
- "start_time": "2023-02-22T22:06:52.758917",
+ "start_time": "2023-10-10T20:29:51.603736",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/notebooks/5_ml_model.ipynb b/notebooks/5_ml_model.ipynb
index 26febe0ae6c26e0487440112abe00deaafd36b5e..03622dbc7d6930b470f9773c2e47dc8d8c66509c 100644
--- a/notebooks/5_ml_model.ipynb
+++ b/notebooks/5_ml_model.ipynb
@@ -5,10 +5,10 @@
"id": "5de30442",
"metadata": {
"papermill": {
- "duration": 0.247241,
- "end_time": "2023-02-22T23:02:58.065597",
+ "duration": 0.004501,
+ "end_time": "2023-10-10T20:29:56.392851",
"exception": false,
- "start_time": "2023-02-22T23:02:57.818356",
+ "start_time": "2023-10-10T20:29:56.388350",
"status": "completed"
},
"tags": []
@@ -25,16 +25,16 @@
"id": "a2eb8998",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:02:58.316434Z",
- "iopub.status.busy": "2023-02-22T23:02:58.316102Z",
- "iopub.status.idle": "2023-02-22T23:02:59.398758Z",
- "shell.execute_reply": "2023-02-22T23:02:59.397746Z"
+ "iopub.execute_input": "2023-10-10T20:29:56.404718Z",
+ "iopub.status.busy": "2023-10-10T20:29:56.403976Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.046443Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.045488Z"
},
"papermill": {
- "duration": 1.216666,
- "end_time": "2023-02-22T23:02:59.404681",
+ "duration": 0.651867,
+ "end_time": "2023-10-10T20:29:57.049250",
"exception": false,
- "start_time": "2023-02-22T23:02:58.188015",
+ "start_time": "2023-10-10T20:29:56.397383",
"status": "completed"
},
"tags": []
@@ -61,16 +61,16 @@
"id": "8a8da20f",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:02:59.791455Z",
- "iopub.status.busy": "2023-02-22T23:02:59.791014Z",
- "iopub.status.idle": "2023-02-22T23:02:59.799995Z",
- "shell.execute_reply": "2023-02-22T23:02:59.798620Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.059270Z",
+ "iopub.status.busy": "2023-10-10T20:29:57.058896Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.064586Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.063604Z"
},
"papermill": {
- "duration": 0.16848,
- "end_time": "2023-02-22T23:02:59.805952",
+ "duration": 0.013884,
+ "end_time": "2023-10-10T20:29:57.067486",
"exception": false,
- "start_time": "2023-02-22T23:02:59.637472",
+ "start_time": "2023-10-10T20:29:57.053602",
"status": "completed"
},
"tags": [
@@ -96,19 +96,19 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "1b827561",
+ "id": "dca8296b",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:00.056022Z",
- "iopub.status.busy": "2023-02-22T23:03:00.055639Z",
- "iopub.status.idle": "2023-02-22T23:03:00.062609Z",
- "shell.execute_reply": "2023-02-22T23:03:00.061781Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.076447Z",
+ "iopub.status.busy": "2023-10-10T20:29:57.075691Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.080057Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.079206Z"
},
"papermill": {
- "duration": 0.121897,
- "end_time": "2023-02-22T23:03:00.065712",
+ "duration": 0.012321,
+ "end_time": "2023-10-10T20:29:57.083555",
"exception": false,
- "start_time": "2023-02-22T23:02:59.943815",
+ "start_time": "2023-10-10T20:29:57.071234",
"status": "completed"
},
"tags": [
@@ -118,8 +118,14 @@
"outputs": [],
"source": [
"# Parameters\n",
- "INPUT_PATHS = {\"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/split.csv\", \"features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/features.csv\"}\n",
- "OUTPUT_PATHS = {\"clf\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/ml_model.pickle\", \"submission\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/test_result.csv\"}\n"
+ "INPUT_PATHS = {\n",
+ " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/split.csv\",\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/features.csv\",\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"clf\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/output/ml_model.pickle\",\n",
+ " \"submission\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/output/test_result.csv\",\n",
+ "}\n"
]
},
{
@@ -128,16 +134,16 @@
"id": "6810272a",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:00.277109Z",
- "iopub.status.busy": "2023-02-22T23:03:00.276746Z",
- "iopub.status.idle": "2023-02-22T23:03:00.309845Z",
- "shell.execute_reply": "2023-02-22T23:03:00.309159Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.092871Z",
+ "iopub.status.busy": "2023-10-10T20:29:57.092295Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.118496Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.117541Z"
},
"papermill": {
- "duration": 0.137077,
- "end_time": "2023-02-22T23:03:00.312049",
+ "duration": 0.033571,
+ "end_time": "2023-10-10T20:29:57.120555",
"exception": false,
- "start_time": "2023-02-22T23:03:00.174972",
+ "start_time": "2023-10-10T20:29:57.086984",
"status": "completed"
},
"tags": []
@@ -146,7 +152,7 @@
"source": [
"# input\n",
"split: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"split\"])\n",
- "features: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"features\"])"
+ "features: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"aggregated_features\"])"
]
},
{
@@ -155,16 +161,16 @@
"id": "36f06fd6",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:00.535727Z",
- "iopub.status.busy": "2023-02-22T23:03:00.535434Z",
- "iopub.status.idle": "2023-02-22T23:03:00.566484Z",
- "shell.execute_reply": "2023-02-22T23:03:00.565659Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.129659Z",
+ "iopub.status.busy": "2023-10-10T20:29:57.129233Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.156435Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.155735Z"
},
"papermill": {
- "duration": 0.143691,
- "end_time": "2023-02-22T23:03:00.568732",
+ "duration": 0.033758,
+ "end_time": "2023-10-10T20:29:57.158194",
"exception": false,
- "start_time": "2023-02-22T23:03:00.425041",
+ "start_time": "2023-10-10T20:29:57.124436",
"status": "completed"
},
"tags": []
@@ -285,7 +291,7 @@
" <td>0.952658</td>\n",
" <td>10.477735</td>\n",
" <td>-0.185771</td>\n",
- " <td>False</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>classical_100.mp3</th>\n",
@@ -309,7 +315,7 @@
" <td>-1.494916</td>\n",
" <td>10.917299</td>\n",
" <td>0.020985</td>\n",
- " <td>True</td>\n",
+ " <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>classical_11.mp3</th>\n",
@@ -357,7 +363,7 @@
" <td>1.844494</td>\n",
" <td>11.160392</td>\n",
" <td>0.503120</td>\n",
- " <td>False</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -554,10 +560,10 @@
" 39_max 39_mean 39_std 39_skew train \n",
"filename \n",
"classical_1.mp3 49.352516 -2.282116 15.285639 0.171462 False \n",
- "classical_10.mp3 27.533707 0.952658 10.477735 -0.185771 False \n",
- "classical_100.mp3 31.397882 -1.494916 10.917299 0.020985 True \n",
+ "classical_10.mp3 27.533707 0.952658 10.477735 -0.185771 True \n",
+ "classical_100.mp3 31.397882 -1.494916 10.917299 0.020985 False \n",
"classical_11.mp3 50.992905 1.600777 10.125545 0.595763 True \n",
- "classical_12.mp3 46.878204 1.844494 11.160392 0.503120 False \n",
+ "classical_12.mp3 46.878204 1.844494 11.160392 0.503120 True \n",
"... ... ... ... ... ... \n",
"rock_95.mp3 23.410387 -4.502398 6.687983 0.238807 True \n",
"rock_96.mp3 21.814400 -8.249507 7.807756 0.071968 True \n",
@@ -584,16 +590,16 @@
"id": "265d042f",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:00.807555Z",
- "iopub.status.busy": "2023-02-22T23:03:00.807169Z",
- "iopub.status.idle": "2023-02-22T23:03:00.830354Z",
- "shell.execute_reply": "2023-02-22T23:03:00.829651Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.169022Z",
+ "iopub.status.busy": "2023-10-10T20:29:57.168524Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.190644Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.189670Z"
},
"papermill": {
- "duration": 0.136153,
- "end_time": "2023-02-22T23:03:00.831710",
+ "duration": 0.029559,
+ "end_time": "2023-10-10T20:29:57.191983",
"exception": false,
- "start_time": "2023-02-22T23:03:00.695557",
+ "start_time": "2023-10-10T20:29:57.162424",
"status": "completed"
},
"tags": []
@@ -669,28 +675,28 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>classical_100.mp3</th>\n",
+ " <th>classical_10.mp3</th>\n",
" <td>classical</td>\n",
- " <td>-536.23737</td>\n",
- " <td>-61.608826</td>\n",
- " <td>-177.804114</td>\n",
- " <td>83.381622</td>\n",
- " <td>-2.587179</td>\n",
- " <td>0.000000</td>\n",
- " <td>190.47589</td>\n",
- " <td>112.471713</td>\n",
- " <td>27.277553</td>\n",
+ " <td>-562.85785</td>\n",
+ " <td>-96.164795</td>\n",
+ " <td>-219.259016</td>\n",
+ " <td>53.561839</td>\n",
+ " <td>-0.772320</td>\n",
+ " <td>0.029056</td>\n",
+ " <td>259.63272</td>\n",
+ " <td>215.094182</td>\n",
+ " <td>18.388131</td>\n",
" <td>...</td>\n",
- " <td>-27.335688</td>\n",
- " <td>27.610388</td>\n",
- " <td>-0.333233</td>\n",
- " <td>8.185075</td>\n",
- " <td>0.208425</td>\n",
- " <td>-38.095375</td>\n",
- " <td>31.397882</td>\n",
- " <td>-1.494916</td>\n",
- " <td>10.917299</td>\n",
- " <td>0.020985</td>\n",
+ " <td>-27.458416</td>\n",
+ " <td>29.811110</td>\n",
+ " <td>0.484271</td>\n",
+ " <td>8.660648</td>\n",
+ " <td>-0.479016</td>\n",
+ " <td>-28.989979</td>\n",
+ " <td>27.533707</td>\n",
+ " <td>0.952658</td>\n",
+ " <td>10.477735</td>\n",
+ " <td>-0.185771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>classical_11.mp3</th>\n",
@@ -717,6 +723,30 @@
" <td>0.595763</td>\n",
" </tr>\n",
" <tr>\n",
+ " <th>classical_12.mp3</th>\n",
+ " <td>classical</td>\n",
+ " <td>-562.67523</td>\n",
+ " <td>-148.133560</td>\n",
+ " <td>-270.975406</td>\n",
+ " <td>52.191182</td>\n",
+ " <td>-0.366586</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>194.26416</td>\n",
+ " <td>148.226648</td>\n",
+ " <td>19.305008</td>\n",
+ " <td>...</td>\n",
+ " <td>-44.843815</td>\n",
+ " <td>28.490644</td>\n",
+ " <td>-6.242015</td>\n",
+ " <td>10.546545</td>\n",
+ " <td>0.341848</td>\n",
+ " <td>-25.040886</td>\n",
+ " <td>46.878204</td>\n",
+ " <td>1.844494</td>\n",
+ " <td>11.160392</td>\n",
+ " <td>0.503120</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <th>classical_13.mp3</th>\n",
" <td>classical</td>\n",
" <td>-637.72064</td>\n",
@@ -765,30 +795,6 @@
" <td>-0.187037</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>classical_15.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-555.12994</td>\n",
- " <td>-96.139240</td>\n",
- " <td>-209.245810</td>\n",
- " <td>45.350121</td>\n",
- " <td>-3.574710</td>\n",
- " <td>0.000000</td>\n",
- " <td>140.91864</td>\n",
- " <td>109.309985</td>\n",
- " <td>14.171102</td>\n",
- " <td>...</td>\n",
- " <td>-42.808117</td>\n",
- " <td>24.146545</td>\n",
- " <td>-7.260053</td>\n",
- " <td>9.862490</td>\n",
- " <td>0.097765</td>\n",
- " <td>-31.394999</td>\n",
- " <td>35.685540</td>\n",
- " <td>-0.949139</td>\n",
- " <td>11.141700</td>\n",
- " <td>0.249279</td>\n",
- " </tr>\n",
- " <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
@@ -938,61 +944,61 @@
"</div>"
],
"text/plain": [
- " label 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
- "classical_100.mp3 classical -536.23737 -61.608826 -177.804114 83.381622 \n",
- "classical_11.mp3 classical -536.45746 -120.429665 -222.126303 76.246992 \n",
- "classical_13.mp3 classical -637.72064 -177.713960 -361.834032 71.310080 \n",
- "classical_14.mp3 classical -531.04944 -100.790540 -188.970758 58.287371 \n",
- "classical_15.mp3 classical -555.12994 -96.139240 -209.245810 45.350121 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 rock -553.11010 -5.218835 -193.506047 76.869437 \n",
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- "rock_97.mp3 rock -518.49500 58.526745 -66.267744 65.635619 \n",
- "rock_98.mp3 rock -518.64307 53.555115 -45.734517 52.444200 \n",
- "rock_99.mp3 rock -544.70310 75.612130 -49.380943 54.045627 \n",
- "\n",
- " 0_skew 1_min 1_max 1_mean 1_std ... \\\n",
- "filename ... \n",
- "classical_100.mp3 -2.587179 0.000000 190.47589 112.471713 27.277553 ... \n",
- "classical_11.mp3 -2.402418 0.000000 159.42575 99.853645 21.916948 ... \n",
- "classical_13.mp3 0.008325 0.000000 257.16284 211.556558 20.347035 ... \n",
- "classical_14.mp3 -3.246618 0.000000 157.94792 86.563928 17.911136 ... \n",
- "classical_15.mp3 -3.574710 0.000000 140.91864 109.309985 14.171102 ... \n",
- "... ... ... ... ... ... ... \n",
- "rock_95.mp3 -0.201055 -89.948746 201.18045 111.724191 36.463584 ... \n",
- "rock_96.mp3 -0.957699 -7.415959 210.49246 125.453699 31.908870 ... \n",
- "rock_97.mp3 -0.898026 -58.824410 175.20135 99.288265 25.158417 ... \n",
- "rock_98.mp3 -1.705641 0.000000 187.04272 96.440874 24.137702 ... \n",
- "rock_99.mp3 -0.863093 -32.930650 191.73538 93.971242 33.410220 ... \n",
+ " label 0_min 0_max 0_mean 0_std \\\n",
+ "filename \n",
+ "classical_10.mp3 classical -562.85785 -96.164795 -219.259016 53.561839 \n",
+ "classical_11.mp3 classical -536.45746 -120.429665 -222.126303 76.246992 \n",
+ "classical_12.mp3 classical -562.67523 -148.133560 -270.975406 52.191182 \n",
+ "classical_13.mp3 classical -637.72064 -177.713960 -361.834032 71.310080 \n",
+ "classical_14.mp3 classical -531.04944 -100.790540 -188.970758 58.287371 \n",
+ "... ... ... ... ... ... \n",
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+ "rock_97.mp3 rock -518.49500 58.526745 -66.267744 65.635619 \n",
+ "rock_98.mp3 rock -518.64307 53.555115 -45.734517 52.444200 \n",
+ "rock_99.mp3 rock -544.70310 75.612130 -49.380943 54.045627 \n",
"\n",
- " 38_min 38_max 38_mean 38_std 38_skew \\\n",
- "filename \n",
- "classical_100.mp3 -27.335688 27.610388 -0.333233 8.185075 0.208425 \n",
- "classical_11.mp3 -31.774948 31.500881 -3.781627 9.191043 0.260886 \n",
- "classical_13.mp3 -24.728806 18.424034 -0.275736 7.026148 -0.640964 \n",
- "classical_14.mp3 -36.261150 38.335830 -5.770759 12.254058 0.805707 \n",
- "classical_15.mp3 -42.808117 24.146545 -7.260053 9.862490 0.097765 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -27.043941 22.451445 -7.234634 8.471853 0.753855 \n",
- "rock_96.mp3 -37.584858 28.087940 -9.704238 8.447620 0.112760 \n",
- "rock_97.mp3 -29.620445 26.325895 -5.722825 7.727378 0.207489 \n",
- "rock_98.mp3 -26.967852 8.714736 -9.511491 5.551820 -0.025604 \n",
- "rock_99.mp3 -21.929403 17.050608 -5.296691 5.894962 0.390705 \n",
+ " 0_skew 1_min 1_max 1_mean 1_std ... \\\n",
+ "filename ... \n",
+ "classical_10.mp3 -0.772320 0.029056 259.63272 215.094182 18.388131 ... \n",
+ "classical_11.mp3 -2.402418 0.000000 159.42575 99.853645 21.916948 ... \n",
+ "classical_12.mp3 -0.366586 0.000000 194.26416 148.226648 19.305008 ... \n",
+ "classical_13.mp3 0.008325 0.000000 257.16284 211.556558 20.347035 ... \n",
+ "classical_14.mp3 -3.246618 0.000000 157.94792 86.563928 17.911136 ... \n",
+ "... ... ... ... ... ... ... \n",
+ "rock_95.mp3 -0.201055 -89.948746 201.18045 111.724191 36.463584 ... \n",
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+ "rock_97.mp3 -0.898026 -58.824410 175.20135 99.288265 25.158417 ... \n",
+ "rock_98.mp3 -1.705641 0.000000 187.04272 96.440874 24.137702 ... \n",
+ "rock_99.mp3 -0.863093 -32.930650 191.73538 93.971242 33.410220 ... \n",
"\n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
+ " 38_min 38_max 38_mean 38_std 38_skew \\\n",
"filename \n",
- "classical_100.mp3 -38.095375 31.397882 -1.494916 10.917299 0.020985 \n",
- "classical_11.mp3 -22.667439 50.992905 1.600777 10.125545 0.595763 \n",
- "classical_13.mp3 -24.319565 18.439264 -2.147022 8.171929 0.009566 \n",
- "classical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \n",
- "classical_15.mp3 -31.394999 35.685540 -0.949139 11.141700 0.249279 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -24.712723 23.410387 -4.502398 6.687983 0.238807 \n",
- "rock_96.mp3 -38.147890 21.814400 -8.249507 7.807756 0.071968 \n",
- "rock_97.mp3 -29.497524 25.410656 -3.356614 8.170526 0.160330 \n",
- "rock_98.mp3 -23.020082 13.948639 -2.664985 5.051498 -0.258407 \n",
- "rock_99.mp3 -20.983192 29.312021 -0.321836 6.571660 0.384794 \n",
+ "classical_10.mp3 -27.458416 29.811110 0.484271 8.660648 -0.479016 \n",
+ "classical_11.mp3 -31.774948 31.500881 -3.781627 9.191043 0.260886 \n",
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+ "classical_13.mp3 -24.728806 18.424034 -0.275736 7.026148 -0.640964 \n",
+ "classical_14.mp3 -36.261150 38.335830 -5.770759 12.254058 0.805707 \n",
+ "... ... ... ... ... ... \n",
+ "rock_95.mp3 -27.043941 22.451445 -7.234634 8.471853 0.753855 \n",
+ "rock_96.mp3 -37.584858 28.087940 -9.704238 8.447620 0.112760 \n",
+ "rock_97.mp3 -29.620445 26.325895 -5.722825 7.727378 0.207489 \n",
+ "rock_98.mp3 -26.967852 8.714736 -9.511491 5.551820 -0.025604 \n",
+ "rock_99.mp3 -21.929403 17.050608 -5.296691 5.894962 0.390705 \n",
+ "\n",
+ " 39_min 39_max 39_mean 39_std 39_skew \n",
+ "filename \n",
+ "classical_10.mp3 -28.989979 27.533707 0.952658 10.477735 -0.185771 \n",
+ "classical_11.mp3 -22.667439 50.992905 1.600777 10.125545 0.595763 \n",
+ "classical_12.mp3 -25.040886 46.878204 1.844494 11.160392 0.503120 \n",
+ "classical_13.mp3 -24.319565 18.439264 -2.147022 8.171929 0.009566 \n",
+ "classical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \n",
+ "... ... ... ... ... ... \n",
+ "rock_95.mp3 -24.712723 23.410387 -4.502398 6.687983 0.238807 \n",
+ "rock_96.mp3 -38.147890 21.814400 -8.249507 7.807756 0.071968 \n",
+ "rock_97.mp3 -29.497524 25.410656 -3.356614 8.170526 0.160330 \n",
+ "rock_98.mp3 -23.020082 13.948639 -2.664985 5.051498 -0.258407 \n",
+ "rock_99.mp3 -20.983192 29.312021 -0.321836 6.571660 0.384794 \n",
"\n",
"[320 rows x 201 columns]"
]
@@ -1013,16 +1019,16 @@
"id": "1649ce52",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:01.090945Z",
- "iopub.status.busy": "2023-02-22T23:03:01.090447Z",
- "iopub.status.idle": "2023-02-22T23:03:01.125338Z",
- "shell.execute_reply": "2023-02-22T23:03:01.124530Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.201997Z",
+ "iopub.status.busy": "2023-10-10T20:29:57.201308Z",
+ "iopub.status.idle": "2023-10-10T20:29:57.222303Z",
+ "shell.execute_reply": "2023-10-10T20:29:57.221426Z"
},
"papermill": {
- "duration": 0.167995,
- "end_time": "2023-02-22T23:03:01.126884",
+ "duration": 0.027584,
+ "end_time": "2023-10-10T20:29:57.223599",
"exception": false,
- "start_time": "2023-02-22T23:03:00.958889",
+ "start_time": "2023-10-10T20:29:57.196015",
"status": "completed"
},
"tags": []
@@ -1122,52 +1128,28 @@
" <td>0.171462</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>classical_10.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-562.85785</td>\n",
- " <td>-96.164795</td>\n",
- " <td>-219.259016</td>\n",
- " <td>53.561839</td>\n",
- " <td>-0.772320</td>\n",
- " <td>0.029056</td>\n",
- " <td>259.63272</td>\n",
- " <td>215.094182</td>\n",
- " <td>18.388131</td>\n",
- " <td>...</td>\n",
- " <td>-27.458416</td>\n",
- " <td>29.811110</td>\n",
- " <td>0.484271</td>\n",
- " <td>8.660648</td>\n",
- " <td>-0.479016</td>\n",
- " <td>-28.989979</td>\n",
- " <td>27.533707</td>\n",
- " <td>0.952658</td>\n",
- " <td>10.477735</td>\n",
- " <td>-0.185771</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_12.mp3</th>\n",
+ " <th>classical_100.mp3</th>\n",
" <td>classical</td>\n",
- " <td>-562.67523</td>\n",
- " <td>-148.133560</td>\n",
- " <td>-270.975406</td>\n",
- " <td>52.191182</td>\n",
- " <td>-0.366586</td>\n",
+ " <td>-536.23737</td>\n",
+ " <td>-61.608826</td>\n",
+ " <td>-177.804114</td>\n",
+ " <td>83.381622</td>\n",
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" <td>0.000000</td>\n",
- " <td>194.26416</td>\n",
- " <td>148.226648</td>\n",
- " <td>19.305008</td>\n",
+ " <td>190.47589</td>\n",
+ " <td>112.471713</td>\n",
+ " <td>27.277553</td>\n",
" <td>...</td>\n",
- " <td>-44.843815</td>\n",
- " <td>28.490644</td>\n",
- " <td>-6.242015</td>\n",
- " <td>10.546545</td>\n",
- " <td>0.341848</td>\n",
- " <td>-25.040886</td>\n",
- " <td>46.878204</td>\n",
- " <td>1.844494</td>\n",
- " <td>11.160392</td>\n",
- " <td>0.503120</td>\n",
+ " <td>-27.335688</td>\n",
+ " <td>27.610388</td>\n",
+ " <td>-0.333233</td>\n",
+ " <td>8.185075</td>\n",
+ " <td>0.208425</td>\n",
+ " <td>-38.095375</td>\n",
+ " <td>31.397882</td>\n",
+ " <td>-1.494916</td>\n",
+ " <td>10.917299</td>\n",
+ " <td>0.020985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>classical_16.mp3</th>\n",
@@ -1194,28 +1176,52 @@
" <td>0.088775</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>classical_19.mp3</th>\n",
+ " <th>classical_2.mp3</th>\n",
+ " <td>classical</td>\n",
+ " <td>-549.40650</td>\n",
+ " <td>-192.532060</td>\n",
+ " <td>-293.008970</td>\n",
+ " <td>27.207027</td>\n",
+ " <td>-0.426848</td>\n",
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+ " <td>231.03737</td>\n",
+ " <td>198.662515</td>\n",
+ " <td>14.957660</td>\n",
+ " <td>...</td>\n",
+ " <td>-25.912935</td>\n",
+ " <td>24.293318</td>\n",
+ " <td>0.746096</td>\n",
+ " <td>8.240027</td>\n",
+ " <td>-0.022513</td>\n",
+ " <td>-18.561390</td>\n",
+ " <td>23.484133</td>\n",
+ " <td>3.115819</td>\n",
+ " <td>7.220346</td>\n",
+ " <td>0.242364</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>classical_20.mp3</th>\n",
" <td>classical</td>\n",
- " <td>-543.64233</td>\n",
- " <td>-106.038220</td>\n",
- " <td>-216.909956</td>\n",
- " <td>61.317534</td>\n",
- " <td>-3.473125</td>\n",
+ " <td>-605.99150</td>\n",
+ " <td>-161.119310</td>\n",
+ " <td>-263.483084</td>\n",
+ " <td>49.157298</td>\n",
+ " <td>-0.856221</td>\n",
" <td>0.000000</td>\n",
- " <td>151.94766</td>\n",
- " <td>93.405407</td>\n",
- " <td>22.029233</td>\n",
- " <td>...</td>\n",
- " <td>-27.029383</td>\n",
- " <td>30.682745</td>\n",
- " <td>3.342260</td>\n",
- " <td>8.420860</td>\n",
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- " <td>-25.900253</td>\n",
- " <td>36.766388</td>\n",
- " <td>2.389575</td>\n",
- " <td>10.099726</td>\n",
- " <td>0.140336</td>\n",
+ " <td>191.92674</td>\n",
+ " <td>141.393817</td>\n",
+ " <td>17.754779</td>\n",
+ " <td>...</td>\n",
+ " <td>-24.911243</td>\n",
+ " <td>38.551230</td>\n",
+ " <td>-2.274261</td>\n",
+ " <td>9.671005</td>\n",
+ " <td>0.719436</td>\n",
+ " <td>-30.311796</td>\n",
+ " <td>29.272333</td>\n",
+ " <td>0.289613</td>\n",
+ " <td>9.590299</td>\n",
+ " <td>-0.244191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -1242,76 +1248,52 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>rock_64.mp3</th>\n",
+ " <th>rock_61.mp3</th>\n",
" <td>rock</td>\n",
- " <td>-522.86610</td>\n",
- " <td>-2.023562</td>\n",
- " <td>-138.488102</td>\n",
- " <td>73.637453</td>\n",
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- " <td>-9.035665</td>\n",
- " <td>194.30304</td>\n",
- " <td>117.368186</td>\n",
- " <td>30.024294</td>\n",
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- " <td>-3.909715</td>\n",
- " <td>7.212336</td>\n",
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- " <td>-29.665760</td>\n",
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- " <td>0.261991</td>\n",
- " <td>7.573523</td>\n",
- " <td>-0.092932</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_68.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-561.14280</td>\n",
- " <td>12.340981</td>\n",
- " <td>-96.624711</td>\n",
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- " <td>167.53100</td>\n",
- " <td>88.403006</td>\n",
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- " <td>...</td>\n",
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- " <td>27.931776</td>\n",
- " <td>-2.982000</td>\n",
- " <td>9.721937</td>\n",
- " <td>0.089433</td>\n",
- " <td>-23.004597</td>\n",
- " <td>24.202961</td>\n",
- " <td>3.406390</td>\n",
- " <td>7.585782</td>\n",
- " <td>-0.129014</td>\n",
+ " <td>-581.70320</td>\n",
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+ " <td>0.000000</td>\n",
+ " <td>277.18048</td>\n",
+ " <td>218.884291</td>\n",
+ " <td>32.386435</td>\n",
+ " <td>...</td>\n",
+ " <td>-16.274982</td>\n",
+ " <td>10.830383</td>\n",
+ " <td>-1.030385</td>\n",
+ " <td>3.743717</td>\n",
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+ " <td>-1.491518</td>\n",
+ " <td>3.449319</td>\n",
+ " <td>-0.126638</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>rock_71.mp3</th>\n",
+ " <th>rock_70.mp3</th>\n",
" <td>rock</td>\n",
- " <td>-513.19495</td>\n",
- " <td>8.192442</td>\n",
- " <td>-128.668100</td>\n",
- " <td>98.814925</td>\n",
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- " <td>14.180359</td>\n",
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- " <td>4.317564</td>\n",
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- " <td>0.126587</td>\n",
- " <td>4.020606</td>\n",
- " <td>-0.219710</td>\n",
+ " <td>-560.31934</td>\n",
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+ " <td>185.52118</td>\n",
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+ " <td>28.852134</td>\n",
+ " <td>...</td>\n",
+ " <td>-26.226246</td>\n",
+ " <td>27.198473</td>\n",
+ " <td>-3.917270</td>\n",
+ " <td>8.094776</td>\n",
+ " <td>0.075448</td>\n",
+ " <td>-26.580010</td>\n",
+ " <td>26.222483</td>\n",
+ " <td>-5.732453</td>\n",
+ " <td>7.377854</td>\n",
+ " <td>0.551676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rock_72.mp3</th>\n",
@@ -1338,28 +1320,52 @@
" <td>0.051207</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>rock_94.mp3</th>\n",
+ " <th>rock_83.mp3</th>\n",
" <td>rock</td>\n",
- " <td>-531.79425</td>\n",
- " <td>39.474583</td>\n",
- " <td>-78.520463</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>rock_88.mp3</th>\n",
+ " <td>rock</td>\n",
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" </tr>\n",
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@@ -1367,61 +1373,61 @@
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- " label 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
- "classical_1.mp3 classical -530.78436 -163.308350 -302.203167 51.142183 \n",
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- "... ... ... ... ... ... \n",
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- "rock_94.mp3 rock -531.79425 39.474583 -78.520463 51.256666 \n",
+ " label 0_min 0_max 0_mean 0_std \\\n",
+ "filename \n",
+ "classical_1.mp3 classical -530.78436 -163.308350 -302.203167 51.142183 \n",
+ "classical_100.mp3 classical -536.23737 -61.608826 -177.804114 83.381622 \n",
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+ "classical_20.mp3 classical -605.99150 -161.119310 -263.483084 49.157298 \n",
+ "... ... ... ... ... ... \n",
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+ "rock_83.mp3 rock -525.08470 53.723972 -179.776997 88.419631 \n",
+ "rock_88.mp3 rock -539.97880 44.375150 -126.955020 88.140999 \n",
"\n",
- " 0_skew 1_min 1_max 1_mean 1_std ... \\\n",
- "filename ... \n",
- "classical_1.mp3 -0.468374 0.000000 178.75162 111.332342 24.847562 ... \n",
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- "classical_12.mp3 -0.366586 0.000000 194.26416 148.226648 19.305008 ... \n",
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- "... ... ... ... ... ... ... \n",
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- "rock_71.mp3 -1.667229 -34.499084 142.19391 88.050960 28.383479 ... \n",
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- "rock_94.mp3 -0.846796 -15.139265 177.08032 79.627048 33.557076 ... \n",
+ " 0_skew 1_min 1_max 1_mean 1_std ... \\\n",
+ "filename ... \n",
+ "classical_1.mp3 -0.468374 0.000000 178.75162 111.332342 24.847562 ... \n",
+ "classical_100.mp3 -2.587179 0.000000 190.47589 112.471713 27.277553 ... \n",
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"\n",
- " 38_min 38_max 38_mean 38_std 38_skew \\\n",
- "filename \n",
- "classical_1.mp3 -44.098070 47.308060 -3.713503 16.553984 0.230691 \n",
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- "classical_19.mp3 -27.029383 30.682745 3.342260 8.420860 0.043171 \n",
- "... ... ... ... ... ... \n",
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- "rock_94.mp3 -34.662370 26.375679 -4.778466 6.754501 0.157858 \n",
+ " 38_min 38_max 38_mean 38_std 38_skew \\\n",
+ "filename \n",
+ "classical_1.mp3 -44.098070 47.308060 -3.713503 16.553984 0.230691 \n",
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+ "rock_83.mp3 -30.588310 33.064934 3.921451 7.783732 0.054672 \n",
+ "rock_88.mp3 -34.726500 26.706833 -5.827121 8.260717 0.275225 \n",
"\n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
- "filename \n",
- "classical_1.mp3 -46.794480 49.352516 -2.282116 15.285639 0.171462 \n",
- "classical_10.mp3 -28.989979 27.533707 0.952658 10.477735 -0.185771 \n",
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- "rock_94.mp3 -22.063726 29.165360 1.443975 6.737420 -0.092049 \n",
+ " 39_min 39_max 39_mean 39_std 39_skew \n",
+ "filename \n",
+ "classical_1.mp3 -46.794480 49.352516 -2.282116 15.285639 0.171462 \n",
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+ "classical_20.mp3 -30.311796 29.272333 0.289613 9.590299 -0.244191 \n",
+ "... ... ... ... ... ... \n",
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+ "rock_83.mp3 -25.112260 29.217503 5.763236 7.981292 -0.062040 \n",
+ "rock_88.mp3 -31.036520 27.423218 -4.715363 6.544117 0.184718 \n",
"\n",
"[80 rows x 201 columns]"
]
@@ -1442,16 +1448,16 @@
"id": "1e904bf3",
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"execution": {
- "iopub.execute_input": "2023-02-22T23:03:01.479407Z",
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- "iopub.status.idle": "2023-02-22T23:03:01.501631Z",
- "shell.execute_reply": "2023-02-22T23:03:01.500775Z"
+ "iopub.execute_input": "2023-10-10T20:29:57.234538Z",
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+ "shell.execute_reply": "2023-10-10T20:29:57.253465Z"
},
"papermill": {
- "duration": 0.213843,
- "end_time": "2023-02-22T23:03:01.503226",
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"exception": false,
- "start_time": "2023-02-22T23:03:01.289383",
+ "start_time": "2023-10-10T20:29:57.228319",
"status": "completed"
},
"tags": []
@@ -1460,67 +1466,67 @@
{
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" \n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
+ " 38_min 38_max 38_mean 38_std 38_skew \\\n",
" filename \n",
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]
}
],
@@ -1978,122 +1984,32 @@
"id": "bbd99cb8",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:06.627188Z",
- "iopub.status.busy": "2023-02-22T23:03:06.626970Z",
- "iopub.status.idle": "2023-02-22T23:03:06.637783Z",
- "shell.execute_reply": "2023-02-22T23:03:06.636973Z"
+ "iopub.execute_input": "2023-10-10T20:29:59.284845Z",
+ "iopub.status.busy": "2023-10-10T20:29:59.284598Z",
+ "iopub.status.idle": "2023-10-10T20:29:59.288730Z",
+ "shell.execute_reply": "2023-10-10T20:29:59.287939Z"
},
"papermill": {
- "duration": 0.191268,
- "end_time": "2023-02-22T23:03:06.639436",
+ "duration": 0.013431,
+ "end_time": "2023-10-10T20:29:59.291504",
"exception": false,
- "start_time": "2023-02-22T23:03:06.448168",
+ "start_time": "2023-10-10T20:29:59.278073",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
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- " [9.66280944e-04, 1.03781554e-02, 6.97353752e-01, 2.91301811e-01],\n",
- " [3.60192689e-03, 3.36837271e-02, 2.02638004e-01, 7.60076342e-01],\n",
- " [2.81093594e-03, 4.40217511e-03, 7.78084141e-02, 9.14978475e-01],\n",
- " [1.89182574e-02, 1.11047296e-02, 2.33951599e-01, 7.36025414e-01],\n",
- " [9.48157678e-03, 3.86792466e-01, 2.06744525e-01, 3.96981432e-01],\n",
- " [1.01985223e-03, 6.78334362e-03, 1.95467521e-02, 9.72650052e-01],\n",
- " [9.86246658e-04, 2.39032615e-03, 3.10603803e-01, 6.86019624e-01]])"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "svc_path = BASE_PATH / \"out\" / \"SVC\"/ \"clf.pickle\"\n",
- "svc_path.parent.mkdir(parents=True, exist_ok=True)\n",
- "\n",
- "with open(svc_path, \"wb\") as file:\n",
- " pickle.dump(clf, file)\n",
- "\n",
- "with open(svc_path, \"rb\") as file:\n",
- " loaded = pickle.load(file)\n",
+ "# svc_path = BASE_PATH / \"out\" / \"SVC\"/ \"clf.pickle\"\n",
+ "# svc_path.parent.mkdir(parents=True, exist_ok=True)\n",
+ "# \n",
+ "# with open(svc_path, \"wb\") as file:\n",
+ "# pickle.dump(clf, file)\n",
+ "# \n",
+ "# with open(svc_path, \"rb\") as file:\n",
+ "# loaded = pickle.load(file)\n",
"\n",
- "loaded.predict_proba(X_test_pca)"
+ "# loaded.predict_proba(X_test_pca)"
]
},
{
@@ -2102,16 +2018,16 @@
"id": "af3c36d2",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:06.999779Z",
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- "iopub.status.idle": "2023-02-22T23:03:07.102856Z",
- "shell.execute_reply": "2023-02-22T23:03:07.102108Z"
+ "iopub.execute_input": "2023-10-10T20:29:59.308349Z",
+ "iopub.status.busy": "2023-10-10T20:29:59.308060Z",
+ "iopub.status.idle": "2023-10-10T20:29:59.373037Z",
+ "shell.execute_reply": "2023-10-10T20:29:59.372087Z"
},
"papermill": {
- "duration": 0.29437,
- "end_time": "2023-02-22T23:03:07.104411",
+ "duration": 0.077343,
+ "end_time": "2023-10-10T20:29:59.375697",
"exception": false,
- "start_time": "2023-02-22T23:03:06.810041",
+ "start_time": "2023-10-10T20:29:59.298354",
"status": "completed"
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"tags": []
@@ -2121,88 +2037,88 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " label pred1 pred2 pred3 pred4\n",
- "filename \n",
- "classical_1.mp3 classical classical electronic pop rock\n",
- "classical_10.mp3 classical classical electronic pop rock\n",
- "classical_12.mp3 classical classical electronic pop rock\n",
- "classical_16.mp3 classical classical electronic rock pop\n",
- "classical_19.mp3 classical classical pop electronic rock\n",
- "classical_2.mp3 classical classical electronic rock pop\n",
- "classical_20.mp3 classical classical rock pop electronic\n",
- "classical_24.mp3 classical classical pop rock electronic\n",
- "classical_26.mp3 classical classical pop rock electronic\n",
- "classical_45.mp3 classical classical electronic pop rock\n",
- "classical_53.mp3 classical classical pop rock electronic\n",
- "classical_60.mp3 classical classical electronic pop rock\n",
- "classical_69.mp3 classical classical pop rock electronic\n",
- "classical_81.mp3 classical classical electronic pop rock\n",
- "classical_83.mp3 classical classical electronic pop rock\n",
- "classical_87.mp3 classical classical pop electronic rock\n",
- "classical_89.mp3 classical classical electronic rock pop\n",
- "classical_93.mp3 classical classical electronic pop rock\n",
- "electronic_12.mp3 electronic classical electronic pop rock\n",
- "electronic_20.mp3 electronic electronic pop rock classical\n",
- "electronic_24.mp3 electronic classical pop electronic rock\n",
- "electronic_25.mp3 electronic electronic rock pop classical\n",
- "electronic_27.mp3 electronic electronic pop rock classical\n",
- "electronic_28.mp3 electronic electronic pop rock classical\n",
- "electronic_29.mp3 electronic electronic pop classical rock\n",
- "electronic_30.mp3 electronic electronic pop rock classical\n",
- "electronic_43.mp3 electronic electronic rock pop classical\n",
- "electronic_46.mp3 electronic electronic pop classical rock\n",
- "electronic_47.mp3 electronic classical electronic pop rock\n",
- "electronic_50.mp3 electronic electronic pop rock classical\n",
- "electronic_55.mp3 electronic electronic rock pop classical\n",
- "electronic_59.mp3 electronic electronic rock pop classical\n",
- "electronic_60.mp3 electronic rock pop electronic classical\n",
- "electronic_62.mp3 electronic electronic pop classical rock\n",
- "electronic_65.mp3 electronic pop classical rock electronic\n",
- "electronic_69.mp3 electronic electronic pop rock classical\n",
- "electronic_70.mp3 electronic electronic classical pop rock\n",
- "electronic_79.mp3 electronic electronic rock pop classical\n",
- "electronic_86.mp3 electronic electronic pop rock classical\n",
- "electronic_87.mp3 electronic electronic pop rock classical\n",
- "electronic_89.mp3 electronic electronic classical pop rock\n",
- "electronic_97.mp3 electronic electronic pop rock classical\n",
- "electronic_98.mp3 electronic electronic pop rock classical\n",
- "pop_12.mp3 pop rock pop electronic classical\n",
- "pop_20.mp3 pop pop electronic rock classical\n",
- "pop_22.mp3 pop pop electronic rock classical\n",
- "pop_28.mp3 pop electronic pop rock classical\n",
- "pop_35.mp3 pop rock pop electronic classical\n",
- "pop_36.mp3 pop rock pop electronic classical\n",
- "pop_38.mp3 pop rock pop electronic classical\n",
- "pop_41.mp3 pop pop rock electronic classical\n",
- "pop_45.mp3 pop electronic rock pop classical\n",
- "pop_46.mp3 pop rock pop electronic classical\n",
- "pop_53.mp3 pop pop rock electronic classical\n",
- "pop_55.mp3 pop pop rock electronic classical\n",
- "pop_56.mp3 pop pop rock classical electronic\n",
- "pop_63.mp3 pop pop rock electronic classical\n",
- "pop_72.mp3 pop pop rock classical electronic\n",
- "pop_73.mp3 pop pop rock electronic classical\n",
- "pop_74.mp3 pop pop rock classical electronic\n",
- "pop_80.mp3 pop pop rock electronic classical\n",
- "pop_85.mp3 pop pop rock electronic classical\n",
- "pop_92.mp3 pop pop rock electronic classical\n",
- "pop_97.mp3 pop pop rock electronic classical\n",
- "rock_10.mp3 rock pop electronic rock classical\n",
- "rock_11.mp3 rock rock pop classical electronic\n",
- "rock_14.mp3 rock pop rock electronic classical\n",
- "rock_23.mp3 rock rock pop electronic classical\n",
- "rock_24.mp3 rock rock pop electronic classical\n",
- "rock_33.mp3 rock pop rock classical electronic\n",
- "rock_36.mp3 rock pop rock electronic classical\n",
- "rock_39.mp3 rock rock pop electronic classical\n",
- "rock_43.mp3 rock rock electronic pop classical\n",
- "rock_48.mp3 rock pop rock electronic classical\n",
- "rock_52.mp3 rock rock pop electronic classical\n",
- "rock_64.mp3 rock rock pop electronic classical\n",
- "rock_68.mp3 rock rock pop classical electronic\n",
- "rock_71.mp3 rock rock electronic pop classical\n",
- "rock_72.mp3 rock rock pop electronic classical\n",
- "rock_94.mp3 rock rock pop electronic classical\n"
+ " label pred1 pred2 pred3 pred4\n",
+ "filename \n",
+ "classical_1.mp3 classical electronic classical pop rock\n",
+ "classical_100.mp3 classical classical electronic rock pop\n",
+ "classical_16.mp3 classical classical electronic pop rock\n",
+ "classical_2.mp3 classical classical electronic rock pop\n",
+ "classical_20.mp3 classical classical rock pop electronic\n",
+ "classical_24.mp3 classical classical pop rock electronic\n",
+ "classical_33.mp3 classical classical electronic pop rock\n",
+ "classical_35.mp3 classical classical electronic pop rock\n",
+ "classical_38.mp3 classical classical pop rock electronic\n",
+ "classical_42.mp3 classical classical electronic pop rock\n",
+ "classical_46.mp3 classical classical electronic pop rock\n",
+ "classical_48.mp3 classical classical pop electronic rock\n",
+ "classical_51.mp3 classical classical rock pop electronic\n",
+ "classical_59.mp3 classical classical electronic rock pop\n",
+ "classical_64.mp3 classical classical electronic rock pop\n",
+ "classical_68.mp3 classical classical electronic pop rock\n",
+ "classical_7.mp3 classical classical electronic pop rock\n",
+ "classical_80.mp3 classical classical electronic pop rock\n",
+ "classical_87.mp3 classical classical pop electronic rock\n",
+ "classical_93.mp3 classical classical electronic rock pop\n",
+ "classical_96.mp3 classical classical pop electronic rock\n",
+ "classical_99.mp3 classical classical electronic pop rock\n",
+ "electronic_100.mp3 electronic electronic pop rock classical\n",
+ "electronic_14.mp3 electronic electronic pop rock classical\n",
+ "electronic_19.mp3 electronic classical electronic pop rock\n",
+ "electronic_24.mp3 electronic classical electronic pop rock\n",
+ "electronic_32.mp3 electronic electronic classical pop rock\n",
+ "electronic_41.mp3 electronic pop classical rock electronic\n",
+ "electronic_43.mp3 electronic electronic rock pop classical\n",
+ "electronic_47.mp3 electronic classical electronic pop rock\n",
+ "electronic_52.mp3 electronic electronic pop rock classical\n",
+ "electronic_54.mp3 electronic electronic rock pop classical\n",
+ "electronic_59.mp3 electronic electronic rock pop classical\n",
+ "electronic_6.mp3 electronic electronic pop rock classical\n",
+ "electronic_61.mp3 electronic electronic pop rock classical\n",
+ "electronic_66.mp3 electronic classical pop rock electronic\n",
+ "electronic_73.mp3 electronic electronic pop rock classical\n",
+ "electronic_8.mp3 electronic electronic rock pop classical\n",
+ "electronic_81.mp3 electronic electronic rock pop classical\n",
+ "electronic_90.mp3 electronic electronic pop classical rock\n",
+ "electronic_92.mp3 electronic electronic pop rock classical\n",
+ "electronic_93.mp3 electronic electronic pop rock classical\n",
+ "electronic_98.mp3 electronic electronic pop rock classical\n",
+ "pop_100.mp3 pop rock pop electronic classical\n",
+ "pop_14.mp3 pop pop rock electronic classical\n",
+ "pop_17.mp3 pop pop rock electronic classical\n",
+ "pop_19.mp3 pop electronic classical pop rock\n",
+ "pop_28.mp3 pop electronic pop rock classical\n",
+ "pop_31.mp3 pop rock pop electronic classical\n",
+ "pop_50.mp3 pop pop rock electronic classical\n",
+ "pop_53.mp3 pop pop rock electronic classical\n",
+ "pop_54.mp3 pop pop electronic rock classical\n",
+ "pop_61.mp3 pop pop rock electronic classical\n",
+ "pop_66.mp3 pop pop rock electronic classical\n",
+ "pop_68.mp3 pop rock pop electronic classical\n",
+ "pop_71.mp3 pop electronic pop rock classical\n",
+ "pop_72.mp3 pop pop rock classical electronic\n",
+ "pop_8.mp3 pop pop rock electronic classical\n",
+ "pop_80.mp3 pop pop rock electronic classical\n",
+ "pop_83.mp3 pop pop rock electronic classical\n",
+ "pop_87.mp3 pop rock pop electronic classical\n",
+ "pop_93.mp3 pop rock pop electronic classical\n",
+ "pop_96.mp3 pop pop rock electronic classical\n",
+ "pop_99.mp3 pop pop rock electronic classical\n",
+ "rock_17.mp3 rock pop rock electronic classical\n",
+ "rock_20.mp3 rock rock pop electronic classical\n",
+ "rock_22.mp3 rock rock pop electronic classical\n",
+ "rock_26.mp3 rock rock pop electronic classical\n",
+ "rock_29.mp3 rock electronic rock pop classical\n",
+ "rock_34.mp3 rock rock pop electronic classical\n",
+ "rock_37.mp3 rock rock pop electronic classical\n",
+ "rock_41.mp3 rock electronic pop rock classical\n",
+ "rock_48.mp3 rock pop rock electronic classical\n",
+ "rock_5.mp3 rock rock pop classical electronic\n",
+ "rock_58.mp3 rock rock electronic pop classical\n",
+ "rock_61.mp3 rock electronic pop rock classical\n",
+ "rock_70.mp3 rock rock pop electronic classical\n",
+ "rock_72.mp3 rock rock pop electronic classical\n",
+ "rock_83.mp3 rock rock pop electronic classical\n",
+ "rock_88.mp3 rock rock pop electronic classical\n"
]
}
],
@@ -2241,16 +2157,16 @@
"id": "4a32007a",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:07.475035Z",
- "iopub.status.busy": "2023-02-22T23:03:07.474479Z",
- "iopub.status.idle": "2023-02-22T23:03:07.481194Z",
- "shell.execute_reply": "2023-02-22T23:03:07.480580Z"
+ "iopub.execute_input": "2023-10-10T20:29:59.404777Z",
+ "iopub.status.busy": "2023-10-10T20:29:59.404554Z",
+ "iopub.status.idle": "2023-10-10T20:29:59.409685Z",
+ "shell.execute_reply": "2023-10-10T20:29:59.409125Z"
},
"papermill": {
- "duration": 0.192373,
- "end_time": "2023-02-22T23:03:07.482795",
+ "duration": 0.022785,
+ "end_time": "2023-10-10T20:29:59.411911",
"exception": false,
- "start_time": "2023-02-22T23:03:07.290422",
+ "start_time": "2023-10-10T20:29:59.389126",
"status": "completed"
},
"tags": []
@@ -2271,16 +2187,16 @@
"id": "99782035",
"metadata": {
"execution": {
- "iopub.execute_input": "2023-02-22T23:03:07.879278Z",
- "iopub.status.busy": "2023-02-22T23:03:07.878970Z",
- "iopub.status.idle": "2023-02-22T23:03:07.882063Z",
- "shell.execute_reply": "2023-02-22T23:03:07.881460Z"
+ "iopub.execute_input": "2023-10-10T20:29:59.438539Z",
+ "iopub.status.busy": "2023-10-10T20:29:59.438119Z",
+ "iopub.status.idle": "2023-10-10T20:29:59.440743Z",
+ "shell.execute_reply": "2023-10-10T20:29:59.440281Z"
},
"papermill": {
- "duration": 0.213895,
- "end_time": "2023-02-22T23:03:07.883621",
+ "duration": 0.018895,
+ "end_time": "2023-10-10T20:29:59.443247",
"exception": false,
- "start_time": "2023-02-22T23:03:07.669726",
+ "start_time": "2023-10-10T20:29:59.424352",
"status": "completed"
},
"tags": []
@@ -2310,30 +2226,30 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
},
"papermill": {
"default_parameters": {},
- "duration": 11.734686,
- "end_time": "2023-02-22T23:03:08.479957",
+ "duration": 4.35191,
+ "end_time": "2023-10-10T20:29:59.873795",
"environment_variables": {},
"exception": null,
"input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/5_ml_model.ipynb",
"output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/5_ml_model.ipynb",
"parameters": {
"INPUT_PATHS": {
- "features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/features.csv",
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/features.csv",
"split": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/input/split.csv"
},
"OUTPUT_PATHS": {
- "clf": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/ml_model.pickle",
- "submission": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/test_result.csv"
+ "clf": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/output/ml_model.pickle",
+ "submission": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/output/test_result.csv"
}
},
- "start_time": "2023-02-22T23:02:56.745271",
+ "start_time": "2023-10-10T20:29:55.521885",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/notebooks/ismir_presentation.ipynb b/notebooks/ismir_presentation.ipynb
deleted file mode 100644
index 62ff314a62b0d7920d2467e81211d983e6f0b9dc..0000000000000000000000000000000000000000
--- a/notebooks/ismir_presentation.ipynb
+++ /dev/null
@@ -1,3508 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Musicology data Usecase presentation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "outputs": [],
- "source": [
- "# TODO: add metadata tables and upload them\n",
- "# TODO: add experiment management"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import requests as rq\n",
- "import yaml\n",
- "import logging\n",
- "import concurrent.futures\n",
- "import zipfile"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# local package imports\n",
- "from dbrepo_ismir.dbrepo import DBRepoConnector\n",
- "from dbrepo_ismir.invenio import InvenioManager\n",
- "from dbrepo_ismir.invenio import InvenioConnector\n",
- "from dbrepo_ismir.ml import AudioMachineLearningUtils\n",
- "from dbrepo_ismir.ml import AudioFile\n",
- "from definitions import BASE_PATH"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "LOG = logging.getLogger(__name__)\n",
- "logging.basicConfig(level=logging.INFO)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "# create directories\n",
- "RESPONSE_PATH = BASE_PATH / \"out\" / \"responses\"\n",
- "RESPONSE_PATH.mkdir(exist_ok=True, parents=True)\n",
- "(BASE_PATH / \"out\" / \"music\").mkdir(exist_ok=True, parents=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "# load configuration\n",
- "\n",
- "with open(BASE_PATH / \"config\" / \"config.yml\", \"r\") as file:\n",
- " config: dict = yaml.safe_load(file)\n",
- "\n",
- "with open(BASE_PATH / \"config\" / \"credentials.yml\", \"r\") as file:\n",
- " credentials: dict = yaml.safe_load(file)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "dbrepo_connector = DBRepoConnector(\n",
- " credentials[\"dbrepo\"][\"username\"],\n",
- " credentials[\"dbrepo\"][\"password\"],\n",
- " config[\"dbrepo\"][\"host\"],\n",
- " config[\"dbrepo\"][\"container-id\"],\n",
- " config[\"dbrepo\"][\"database-id\"]\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "invenio_connector = InvenioConnector(\n",
- " credentials[\"invenio\"][\"token\"],\n",
- " config[\"invenio\"][\"host\"]\n",
- ")\n",
- "\n",
- "invenio_manager = InvenioManager(\n",
- " credentials[\"invenio\"][\"token\"]\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "outputs": [],
- "source": [
- "machine_learning_utils = AudioMachineLearningUtils()"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "# unzip music files to data dir\n",
- "(BASE_PATH / \"data\" / \"mp3\").mkdir(parents=True, exist_ok=True)\n",
- "\n",
- "with zipfile.ZipFile(BASE_PATH / \"resource\" / \"emotifymusic.zip\", \"r\") as zfile:\n",
- " zfile.extractall(path=BASE_PATH / \"data\" / \"mp3\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Upload / Download from invenio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": "<Response [201]>"
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "with open(BASE_PATH / \"resource\" / \"record_metadata.yml\", \"r\") as file:\n",
- " record_metadata = yaml.safe_load(file)\n",
- "\n",
- "invenio_manager.create_record(record_metadata)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:dbrepo_ismir.invenio:Picked up 400 files\n",
- "INFO:dbrepo_ismir.invenio:Uploading 400 to https://test.researchdata.tuwien.ac.at\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/1.mp3 as classical_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/10.mp3 as classical_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/100.mp3 as classical_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/11.mp3 as classical_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/12.mp3 as classical_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/13.mp3 as classical_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/14.mp3 as classical_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/15.mp3 as classical_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/16.mp3 as classical_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/17.mp3 as classical_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/18.mp3 as classical_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/19.mp3 as classical_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/2.mp3 as classical_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/20.mp3 as classical_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/21.mp3 as classical_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/22.mp3 as classical_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/23.mp3 as classical_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/24.mp3 as classical_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/25.mp3 as classical_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/26.mp3 as classical_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/27.mp3 as classical_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/28.mp3 as classical_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/29.mp3 as classical_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/3.mp3 as classical_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/30.mp3 as classical_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/31.mp3 as classical_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/32.mp3 as classical_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/33.mp3 as classical_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/34.mp3 as classical_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/35.mp3 as classical_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/36.mp3 as classical_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/37.mp3 as classical_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/38.mp3 as classical_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/39.mp3 as classical_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/4.mp3 as classical_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/40.mp3 as classical_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/41.mp3 as classical_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/42.mp3 as classical_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/43.mp3 as classical_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/44.mp3 as classical_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/45.mp3 as classical_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/46.mp3 as classical_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/47.mp3 as classical_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/48.mp3 as classical_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/49.mp3 as classical_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/5.mp3 as classical_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/50.mp3 as classical_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/51.mp3 as classical_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/52.mp3 as classical_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/53.mp3 as classical_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/54.mp3 as classical_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/55.mp3 as classical_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/56.mp3 as classical_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/57.mp3 as classical_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/58.mp3 as classical_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/59.mp3 as classical_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/6.mp3 as classical_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/60.mp3 as classical_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/61.mp3 as classical_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/62.mp3 as classical_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/63.mp3 as classical_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/64.mp3 as classical_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/65.mp3 as classical_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/66.mp3 as classical_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/67.mp3 as classical_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/68.mp3 as classical_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/69.mp3 as classical_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/7.mp3 as classical_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/70.mp3 as classical_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/71.mp3 as classical_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/72.mp3 as classical_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/73.mp3 as classical_73.mp3\n",
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- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/74.mp3 as classical_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/75.mp3 as classical_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/76.mp3 as classical_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/77.mp3 as classical_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/78.mp3 as classical_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/79.mp3 as classical_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/8.mp3 as classical_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/80.mp3 as classical_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/81.mp3 as classical_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/82.mp3 as classical_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/83.mp3 as classical_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/84.mp3 as classical_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/85.mp3 as classical_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/86.mp3 as classical_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/87.mp3 as classical_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/88.mp3 as classical_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/89.mp3 as classical_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/9.mp3 as classical_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/90.mp3 as classical_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/91.mp3 as classical_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/92.mp3 as classical_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/93.mp3 as classical_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/94.mp3 as classical_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/95.mp3 as classical_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/96.mp3 as classical_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/97.mp3 as classical_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/98.mp3 as classical_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/99.mp3 as classical_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/1.mp3 as electronic_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/10.mp3 as electronic_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/100.mp3 as electronic_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/11.mp3 as electronic_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/12.mp3 as electronic_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/13.mp3 as electronic_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/14.mp3 as electronic_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/15.mp3 as electronic_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/16.mp3 as electronic_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/17.mp3 as electronic_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of classical_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/18.mp3 as electronic_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/19.mp3 as electronic_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/2.mp3 as electronic_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/20.mp3 as electronic_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/21.mp3 as electronic_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/22.mp3 as electronic_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/23.mp3 as electronic_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/24.mp3 as electronic_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/25.mp3 as electronic_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/26.mp3 as electronic_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/27.mp3 as electronic_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/28.mp3 as electronic_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/29.mp3 as electronic_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/3.mp3 as electronic_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/30.mp3 as electronic_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/31.mp3 as electronic_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/32.mp3 as electronic_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/33.mp3 as electronic_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/34.mp3 as electronic_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/35.mp3 as electronic_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/36.mp3 as electronic_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/37.mp3 as electronic_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/38.mp3 as electronic_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/39.mp3 as electronic_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/4.mp3 as electronic_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/40.mp3 as electronic_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/41.mp3 as electronic_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/42.mp3 as electronic_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/43.mp3 as electronic_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/44.mp3 as electronic_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/45.mp3 as electronic_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/46.mp3 as electronic_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/47.mp3 as electronic_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/48.mp3 as electronic_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/49.mp3 as electronic_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/5.mp3 as electronic_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/50.mp3 as electronic_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/51.mp3 as electronic_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/52.mp3 as electronic_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/53.mp3 as electronic_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/54.mp3 as electronic_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/55.mp3 as electronic_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/56.mp3 as electronic_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/57.mp3 as electronic_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/58.mp3 as electronic_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/59.mp3 as electronic_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/6.mp3 as electronic_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/60.mp3 as electronic_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/61.mp3 as electronic_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/62.mp3 as electronic_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/63.mp3 as electronic_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/64.mp3 as electronic_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/65.mp3 as electronic_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/66.mp3 as electronic_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/67.mp3 as electronic_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/68.mp3 as electronic_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/69.mp3 as electronic_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/7.mp3 as electronic_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/70.mp3 as electronic_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/71.mp3 as electronic_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/72.mp3 as electronic_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/73.mp3 as electronic_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/74.mp3 as electronic_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/75.mp3 as electronic_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/76.mp3 as electronic_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/77.mp3 as electronic_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/78.mp3 as electronic_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/79.mp3 as electronic_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/8.mp3 as electronic_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/80.mp3 as electronic_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/81.mp3 as electronic_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/82.mp3 as electronic_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/83.mp3 as electronic_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/84.mp3 as electronic_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/85.mp3 as electronic_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/86.mp3 as electronic_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/87.mp3 as electronic_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/88.mp3 as electronic_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/89.mp3 as electronic_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/9.mp3 as electronic_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/90.mp3 as electronic_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/91.mp3 as electronic_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/92.mp3 as electronic_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/93.mp3 as electronic_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/94.mp3 as electronic_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/95.mp3 as electronic_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/96.mp3 as electronic_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/97.mp3 as electronic_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/98.mp3 as electronic_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/electronic/99.mp3 as electronic_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/1.mp3 as pop_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/10.mp3 as pop_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/100.mp3 as pop_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/11.mp3 as pop_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/12.mp3 as pop_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/13.mp3 as pop_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/14.mp3 as pop_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/15.mp3 as pop_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/16.mp3 as pop_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/17.mp3 as pop_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/18.mp3 as pop_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/19.mp3 as pop_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/2.mp3 as pop_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/20.mp3 as pop_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/21.mp3 as pop_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/22.mp3 as pop_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/23.mp3 as pop_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/24.mp3 as pop_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/25.mp3 as pop_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/26.mp3 as pop_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/27.mp3 as pop_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/28.mp3 as pop_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/29.mp3 as pop_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/3.mp3 as pop_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/30.mp3 as pop_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/31.mp3 as pop_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/32.mp3 as pop_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/33.mp3 as pop_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/34.mp3 as pop_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/35.mp3 as pop_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/36.mp3 as pop_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/37.mp3 as pop_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/38.mp3 as pop_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/39.mp3 as pop_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/4.mp3 as pop_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of electronic_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/40.mp3 as pop_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/41.mp3 as pop_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/42.mp3 as pop_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/43.mp3 as pop_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/44.mp3 as pop_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/45.mp3 as pop_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/46.mp3 as pop_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/47.mp3 as pop_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/48.mp3 as pop_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/49.mp3 as pop_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/5.mp3 as pop_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/50.mp3 as pop_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/51.mp3 as pop_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/52.mp3 as pop_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/53.mp3 as pop_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/54.mp3 as pop_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/55.mp3 as pop_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/56.mp3 as pop_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/57.mp3 as pop_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/58.mp3 as pop_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/59.mp3 as pop_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/6.mp3 as pop_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/60.mp3 as pop_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/61.mp3 as pop_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/62.mp3 as pop_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/63.mp3 as pop_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/64.mp3 as pop_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/65.mp3 as pop_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/66.mp3 as pop_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/67.mp3 as pop_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/68.mp3 as pop_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/69.mp3 as pop_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/7.mp3 as pop_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/70.mp3 as pop_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/71.mp3 as pop_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/72.mp3 as pop_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/73.mp3 as pop_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/74.mp3 as pop_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/75.mp3 as pop_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/76.mp3 as pop_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/77.mp3 as pop_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/78.mp3 as pop_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/79.mp3 as pop_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/8.mp3 as pop_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/80.mp3 as pop_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/81.mp3 as pop_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/82.mp3 as pop_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/83.mp3 as pop_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/84.mp3 as pop_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/85.mp3 as pop_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/86.mp3 as pop_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/87.mp3 as pop_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/88.mp3 as pop_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/89.mp3 as pop_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/9.mp3 as pop_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/90.mp3 as pop_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/91.mp3 as pop_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/92.mp3 as pop_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/93.mp3 as pop_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/94.mp3 as pop_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/95.mp3 as pop_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/96.mp3 as pop_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/97.mp3 as pop_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/98.mp3 as pop_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/pop/99.mp3 as pop_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/1.mp3 as rock_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/10.mp3 as rock_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/100.mp3 as rock_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/11.mp3 as rock_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/12.mp3 as rock_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/13.mp3 as rock_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/14.mp3 as rock_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of pop_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/15.mp3 as rock_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_1.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/16.mp3 as rock_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_10.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/17.mp3 as rock_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_11.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/18.mp3 as rock_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_13.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/19.mp3 as rock_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_12.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/2.mp3 as rock_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_100.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/20.mp3 as rock_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_18.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/21.mp3 as rock_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_17.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/22.mp3 as rock_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_20.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/23.mp3 as rock_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_14.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/24.mp3 as rock_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_15.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_16.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/25.mp3 as rock_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/26.mp3 as rock_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_19.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/27.mp3 as rock_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_2.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_22.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/28.mp3 as rock_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/29.mp3 as rock_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_24.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/3.mp3 as rock_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_21.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/30.mp3 as rock_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_23.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/31.mp3 as rock_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_26.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/32.mp3 as rock_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_25.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/33.mp3 as rock_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_27.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/34.mp3 as rock_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_29.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/35.mp3 as rock_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_31.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/36.mp3 as rock_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_30.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/37.mp3 as rock_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_35.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/38.mp3 as rock_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_34.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/39.mp3 as rock_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_28.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/4.mp3 as rock_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_32.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/40.mp3 as rock_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_36.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/41.mp3 as rock_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_3.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/42.mp3 as rock_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_37.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/43.mp3 as rock_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_33.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/44.mp3 as rock_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_38.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/45.mp3 as rock_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_40.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/46.mp3 as rock_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_4.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/47.mp3 as rock_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_44.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/48.mp3 as rock_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_45.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/49.mp3 as rock_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_41.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/5.mp3 as rock_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_42.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/50.mp3 as rock_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_39.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/51.mp3 as rock_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_43.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/52.mp3 as rock_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_47.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/53.mp3 as rock_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_46.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/54.mp3 as rock_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_48.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/55.mp3 as rock_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_5.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/56.mp3 as rock_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_50.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/57.mp3 as rock_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_54.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/58.mp3 as rock_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_52.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/59.mp3 as rock_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_56.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/6.mp3 as rock_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_51.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/60.mp3 as rock_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_49.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/61.mp3 as rock_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_53.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/62.mp3 as rock_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_6.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/63.mp3 as rock_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_59.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/64.mp3 as rock_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_57.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/65.mp3 as rock_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_60.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/66.mp3 as rock_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_58.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/67.mp3 as rock_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_61.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/68.mp3 as rock_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_55.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/69.mp3 as rock_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_62.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/7.mp3 as rock_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_66.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/70.mp3 as rock_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_63.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/71.mp3 as rock_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_68.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/72.mp3 as rock_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_7.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/73.mp3 as rock_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_64.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/74.mp3 as rock_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_65.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/75.mp3 as rock_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_70.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/76.mp3 as rock_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_67.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/77.mp3 as rock_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_69.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/78.mp3 as rock_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_71.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/79.mp3 as rock_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_73.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/8.mp3 as rock_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_72.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/80.mp3 as rock_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_74.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/81.mp3 as rock_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_79.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/82.mp3 as rock_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_78.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/83.mp3 as rock_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_75.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/84.mp3 as rock_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_80.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/85.mp3 as rock_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_8.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/86.mp3 as rock_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_77.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/87.mp3 as rock_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_76.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/88.mp3 as rock_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_81.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/89.mp3 as rock_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_86.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/9.mp3 as rock_9.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_82.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/90.mp3 as rock_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_84.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/91.mp3 as rock_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_85.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/92.mp3 as rock_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_92.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/93.mp3 as rock_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_91.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/94.mp3 as rock_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_93.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/95.mp3 as rock_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_94.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/96.mp3 as rock_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_95.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/97.mp3 as rock_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_96.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/98.mp3 as rock_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_97.mp3\n",
- "INFO:dbrepo_ismir.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/99.mp3 as rock_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_98.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_99.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_87.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_83.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_88.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_89.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_90.mp3\n",
- "INFO:dbrepo_ismir.invenio:Finished upload of rock_9.mp3\n"
- ]
- }
- ],
- "source": [
- "invenio_manager.upload_record(\n",
- " BASE_PATH / \"data\" / \"mp3\",\n",
- " \".*\",\n",
- " \"**/*.*\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# !!! Beware publishing of record is not reversible !!!\n",
- "\n",
- "publish_response = invenio_manager.publish_record()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": "'https://test.researchdata.tuwien.ac.at/records/h6d02-vmm08'"
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "try:\n",
- " dro_url = publish_response.json()[\"links\"][\"self_doi\"]\n",
- "except NameError as e:\n",
- " dro_url = \"https://test.researchdata.tuwien.ac.at/records/h6d02-vmm08\"\n",
- " \n",
- "record_id = dro_url.split('/')[-1]\n",
- "\n",
- "# Could continue with invenio_manager instead of invenio_manager_published\n",
- "# given the newly created record was also published with above cell\n",
- "invenio_manager_published = InvenioManager(\n",
- " invenio_connector.token, \n",
- " record_id=record_id\n",
- ")\n",
- "\n",
- "dro_url"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [],
- "source": [
- "filename = \"electronic_1.mp3\"\n",
- "\n",
- "# get record description\n",
- "response = rq.get(\n",
- " f\"{invenio_connector.host}/api/records/{record_id}\",\n",
- " headers=invenio_connector.headers,\n",
- ")\n",
- "\n",
- "# get list of files \n",
- "response = rq.get(\n",
- " f\"{invenio_connector.host}/api/records/{record_id}/files\",\n",
- " headers=invenio_connector.headers,\n",
- ")\n",
- "\n",
- "# get metadata of file\n",
- "response = rq.get(\n",
- " f\"{invenio_connector.host}/api/records/{record_id}/files/{filename}\",\n",
- " headers=invenio_connector.headers\n",
- ")\n",
- "\n",
- "# download a file\n",
- "response = rq.get(\n",
- " f\"{invenio_connector.host}/api/records/{record_id}/files/{filename}/content\",\n",
- " headers=invenio_connector.headers\n",
- ")\n",
- "\n",
- "with open(BASE_PATH / \"out\" / filename, \"wb\") as file:\n",
- " file.write(response.content)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download all Files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [],
- "source": [
- "invenio_manager_published.download_record(BASE_PATH / \"out\" / \"music\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Feature Extraction of Downloaded files from Invenio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": "[PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/1.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/10.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/100.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/11.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/12.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/13.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/14.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/15.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/16.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/17.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/18.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/19.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/2.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/20.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/21.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/22.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/23.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/24.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/25.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/classical/26.mp3'),\n 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PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/81.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/82.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/83.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/84.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/85.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/86.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/87.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/88.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/89.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/9.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/90.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/91.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/92.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/93.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/94.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/95.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/96.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/97.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/98.mp3'),\n PosixPath('/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/data/mp3/rock/99.mp3')]"
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pathlib\n",
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "from contextlib import contextmanager,redirect_stderr,redirect_stdout\n",
- "from os import devnull\n",
- "from typing import Any\n",
- "from root import BASE_PATH\n",
- "import os\n",
- "\n",
- "MP3_PATH = BASE_PATH / \"data\" / \"mp3\"\n",
- "WAV_PATH = BASE_PATH / \"data\" / \"wav\"\n",
- "MFCC_PATH = BASE_PATH / \"out\" / \"mfcc\"\n",
- "MFCC_PATH.mkdir(parents=True, exist_ok=True)\n",
- "mp3_filepaths = [path for path in sorted(MP3_PATH.glob('**/*.mp3'))]\n",
- "\n",
- "mp3_filepaths"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [],
- "source": [
- "import librosa\n",
- "import librosa.display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'os' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[33], line 12\u001B[0m\n\u001B[1;32m 10\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m input_path \u001B[38;5;129;01min\u001B[39;00m mp3_filepaths:\n\u001B[1;32m 11\u001B[0m output_path \u001B[38;5;241m=\u001B[39m (WAV_PATH \u001B[38;5;241m/\u001B[39m input_path\u001B[38;5;241m.\u001B[39mrelative_to(MP3_PATH))\u001B[38;5;241m.\u001B[39mwith_suffix(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.wav\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m---> 12\u001B[0m \u001B[43mconvert_file\u001B[49m\u001B[43m(\u001B[49m\u001B[43minput_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moutput_path\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 15\u001B[0m \u001B[38;5;66;03m# Pick up all files in data dir with .wav suffix\u001B[39;00m\n\u001B[1;32m 16\u001B[0m wav_filepaths \u001B[38;5;241m=\u001B[39m [path \u001B[38;5;28;01mfor\u001B[39;00m path \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28msorted\u001B[39m((BASE_PATH \u001B[38;5;241m/\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mglob(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m**/*.wav\u001B[39m\u001B[38;5;124m'\u001B[39m)) ]\n",
- "Cell \u001B[0;32mIn[33], line 8\u001B[0m, in \u001B[0;36mconvert_file\u001B[0;34m(input_path, output_path)\u001B[0m\n\u001B[1;32m 6\u001B[0m output_path\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(exist_ok\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m 7\u001B[0m \u001B[38;5;66;03m# convert using mpg123\u001B[39;00m\n\u001B[0;32m----> 8\u001B[0m \u001B[43mos\u001B[49m\u001B[38;5;241m.\u001B[39msystem(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmpg123 -q -w \u001B[39m\u001B[38;5;132;01m{\u001B[39;00moutput_path\u001B[38;5;241m.\u001B[39mas_posix()\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m{\u001B[39;00minput_path\u001B[38;5;241m.\u001B[39mas_posix()\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
- "\u001B[0;31mNameError\u001B[0m: name 'os' is not defined"
- ]
- }
- ],
- "source": [
- "# convert mp3 files to wav\n",
- "# mpg123 required\n",
- "\n",
- "def convert_file(input_path: pathlib.Path, output_path: pathlib.Path) -> None:\n",
- " # Make sure subdir exists\n",
- " output_path.parent.mkdir(exist_ok=True, parents=True)\n",
- " # convert using mpg123\n",
- " os.system(f\"mpg123 -q -w {output_path.as_posix()} {input_path.as_posix()}\")\n",
- "\n",
- "for input_path in mp3_filepaths:\n",
- " output_path = (WAV_PATH / input_path.relative_to(MP3_PATH)).with_suffix(\".wav\")\n",
- " convert_file(input_path, output_path)\n",
- "\n",
- "\n",
- "# Pick up all files in data dir with .wav suffix\n",
- "wav_filepaths = [path for path in sorted((BASE_PATH / \"data\").glob('**/*.wav')) ]\n",
- "\n",
- "len(mp3_filepaths)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "# new mfcc df creation:\n",
- "\n",
- "audiofiles: list[AudioFile] = [\n",
- " AudioFile(\n",
- " MP3_PATH,\n",
- " filepath,\n",
- " filepath.parent.name\n",
- " ) for filepath in MP3_PATH.glob(\"**/*\")\n",
- "]\n",
- "\n",
- "mfcc_df = machine_learning_utils.generate_multiple_mfcc_features(\n",
- " audiofiles\n",
- ")\n",
- "\n"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
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<td>9.361112</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_12.mp3</td>\n <td>3</td>\n <td>classical</td>\n <td>12</td>\n <td>-240.433258</td>\n <td>145.206360</td>\n <td>-4.461696</td>\n <td>11.758677</td>\n <td>-10.757431</td>\n <td>1.254790</td>\n <td>...</td>\n <td>-6.263988</td>\n <td>11.113757</td>\n <td>20.267780</td>\n <td>-2.154666</td>\n <td>-5.127759</td>\n <td>2.469068</td>\n <td>-12.121831</td>\n <td>-21.083326</td>\n <td>-7.469269</td>\n <td>10.618135</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>4</td>\n <td>classical</td>\n <td>12</td>\n <td>-265.707764</td>\n <td>132.303268</td>\n <td>-7.728113</td>\n <td>9.628896</td>\n <td>-20.384869</td>\n <td>-1.778922</td>\n <td>...</td>\n <td>-21.676682</td>\n <td>5.254867</td>\n <td>18.119230</td>\n <td>-5.845078</td>\n <td>-6.094512</td>\n <td>-2.550637</td>\n <td>-7.051765</td>\n <td>-23.662018</td>\n <td>-17.173141</td>\n <td>5.203885</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2581</th>\n <td>rock_99.mp3</td>\n <td>2581</td>\n <td>rock</td>\n <td>99</td>\n <td>21.787605</td>\n <td>85.580826</td>\n <td>-46.306671</td>\n <td>18.197096</td>\n <td>-2.197520</td>\n <td>15.667412</td>\n <td>...</td>\n <td>-16.796026</td>\n <td>-4.496211</td>\n <td>-5.669511</td>\n <td>1.081637</td>\n <td>-2.461577</td>\n <td>2.990703</td>\n <td>4.621572</td>\n <td>-0.688445</td>\n <td>-12.773129</td>\n <td>-2.747378</td>\n </tr>\n <tr>\n <th>2582</th>\n <td>rock_99.mp3</td>\n <td>2582</td>\n <td>rock</td>\n <td>99</td>\n <td>15.572616</td>\n <td>77.452782</td>\n <td>-34.896511</td>\n <td>23.128643</td>\n <td>-2.260536</td>\n <td>10.366127</td>\n <td>...</td>\n <td>-21.933458</td>\n <td>-12.466309</td>\n <td>-6.501689</td>\n <td>8.830140</td>\n <td>-1.279472</td>\n <td>0.076493</td>\n <td>-0.084034</td>\n <td>2.975086</td>\n <td>-14.551804</td>\n <td>-13.077312</td>\n </tr>\n <tr>\n <th>2583</th>\n <td>rock_99.mp3</td>\n <td>2583</td>\n <td>rock</td>\n <td>99</td>\n <td>13.339417</td>\n <td>82.475601</td>\n <td>-30.214211</td>\n <td>25.814865</td>\n <td>-4.943372</td>\n <td>10.197868</td>\n <td>...</td>\n <td>-16.537088</td>\n <td>-9.494637</td>\n <td>-5.872965</td>\n <td>6.031368</td>\n <td>0.235092</td>\n <td>-0.736026</td>\n <td>-3.343213</td>\n <td>2.876546</td>\n <td>-16.325350</td>\n <td>-10.109643</td>\n </tr>\n <tr>\n <th>2584</th>\n <td>rock_99.mp3</td>\n <td>2584</td>\n <td>rock</td>\n <td>99</td>\n <td>13.213579</td>\n <td>92.493988</td>\n <td>-31.854889</td>\n <td>29.340508</td>\n <td>-3.183558</td>\n <td>8.847383</td>\n <td>...</td>\n <td>-13.900535</td>\n <td>-6.496171</td>\n <td>-5.673679</td>\n <td>0.987822</td>\n <td>-2.714684</td>\n <td>1.725184</td>\n <td>-1.239075</td>\n <td>2.953235</td>\n <td>-19.870598</td>\n <td>-3.944447</td>\n </tr>\n <tr>\n <th>2585</th>\n <td>rock_99.mp3</td>\n <td>2585</td>\n <td>rock</td>\n <td>99</td>\n <td>-10.291037</td>\n <td>103.562256</td>\n <td>-25.215910</td>\n <td>27.055992</td>\n <td>-3.575981</td>\n <td>8.064964</td>\n <td>...</td>\n <td>-15.524373</td>\n <td>-4.153163</td>\n <td>-2.979839</td>\n <td>3.092370</td>\n <td>-1.781236</td>\n <td>0.628403</td>\n <td>2.387391</td>\n <td>1.865388</td>\n <td>-19.182354</td>\n <td>3.445506</td>\n </tr>\n </tbody>\n</table>\n<p>1027268 rows × 44 columns</p>\n</div>"
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mfccs: list[pd.DataFrame] = []\n",
- "audios: dict[str, Any] = {}\n",
- "\n",
- "@contextmanager\n",
- "def suppress_stdout_stderr():\n",
- " \"\"\"A context manager that redirects stdout and stderr to devnull\"\"\"\n",
- " with open(devnull, 'w') as fnull:\n",
- " with redirect_stderr(fnull) as err, redirect_stdout(fnull) as out:\n",
- " yield err, out\n",
- "\n",
- "\n",
- "def write_mfcc(filepath: pathlib.Path, mfccs: list[pd.DataFrame]):\n",
- " # Load mp3 -> Signal x, Sampling Rate sr\n",
- " x, sr = librosa.load(filepath, sr=22050)\n",
- " mfcc = librosa.feature.mfcc(x, sr = sr, n_mfcc=40)\n",
- " \n",
- " df = pd.DataFrame(mfcc).transpose()\n",
- " df['filename'] = f\"{filepath.parent.name}_{filepath.name}\"\n",
- " df['sample'] = range(len(mfcc[0]))\n",
- " df['genre'] = filepath.parent.name\n",
- " df['filenumber'] = int(filepath.name.split('_')[0].split('.')[0])\n",
- " mfccs.append(df)\n",
- " \n",
- " audios[f\"{filepath.parent.name}_{filepath.name}\"] = x\n",
- " \n",
- " \n",
- "args = [(filepath, mfccs) for filepath in mp3_filepaths]\n",
- "\n",
- "with suppress_stdout_stderr():\n",
- " with concurrent.futures.ThreadPoolExecutor(6) as executor:\n",
- " executor.map(lambda p: write_mfcc(*p), args)\n",
- "\n",
- "mfcc_df = pd.concat(mfccs)\n",
- "\n",
- "columns = list(mfcc_df.columns)\n",
- "columns_new = columns[-4:] + columns[:-4]\n",
- "mfcc_df = mfcc_df[columns_new]\n",
- "\n",
- "\n",
- "mfcc_df.to_csv(MFCC_PATH / \"mfcc.csv\")\n",
- "mfcc_df.to_pickle(MFCC_PATH / \"mfcc.pickle\")\n",
- "\n",
- "\n",
- "mfcc_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": " filename sample genre filenumber 0 1 \\\n0 classical_12.mp3 0 classical 12 -562.675171 0.000000 \n1 classical_12.mp3 1 classical 12 -377.158630 140.093323 \n2 classical_12.mp3 2 classical 12 -258.835602 152.421295 \n3 classical_12.mp3 3 classical 12 -240.433258 145.206360 \n4 classical_12.mp3 4 classical 12 -265.707764 132.303268 \n... ... ... ... ... ... ... \n2581 rock_99.mp3 2581 rock 99 21.787605 85.580826 \n2582 rock_99.mp3 2582 rock 99 15.572616 77.452782 \n2583 rock_99.mp3 2583 rock 99 13.339417 82.475601 \n2584 rock_99.mp3 2584 rock 99 13.213579 92.493988 \n2585 rock_99.mp3 2585 rock 99 -10.291037 103.562256 \n\n 2 3 4 5 ... 30 31 \\\n0 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 \n1 11.552977 6.869974 2.597836 -12.081018 ... 3.403205 10.121262 \n2 -1.228345 13.090727 -5.023252 -4.367101 ... 0.327813 11.534899 \n3 -4.461696 11.758677 -10.757431 1.254790 ... -6.263988 11.113757 \n4 -7.728113 9.628896 -20.384869 -1.778922 ... -21.676682 5.254867 \n... ... ... ... ... ... ... ... \n2581 -46.306671 18.197096 -2.197520 15.667412 ... -16.796026 -4.496211 \n2582 -34.896511 23.128643 -2.260536 10.366127 ... -21.933458 -12.466309 \n2583 -30.214211 25.814865 -4.943372 10.197868 ... -16.537088 -9.494637 \n2584 -31.854889 29.340508 -3.183558 8.847383 ... -13.900535 -6.496171 \n2585 -25.215910 27.055992 -3.575981 8.064964 ... -15.524373 -4.153163 \n\n 32 33 34 35 36 37 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 11.495217 -1.086922 -7.269203 1.892457 -4.588193 -5.652980 \n2 19.050909 -0.327993 -7.341021 3.830878 -10.513655 -14.950528 \n3 20.267780 -2.154666 -5.127759 2.469068 -12.121831 -21.083326 \n4 18.119230 -5.845078 -6.094512 -2.550637 -7.051765 -23.662018 \n... ... ... ... ... ... ... \n2581 -5.669511 1.081637 -2.461577 2.990703 4.621572 -0.688445 \n2582 -6.501689 8.830140 -1.279472 0.076493 -0.084034 2.975086 \n2583 -5.872965 6.031368 0.235092 -0.736026 -3.343213 2.876546 \n2584 -5.673679 0.987822 -2.714684 1.725184 -1.239075 2.953235 \n2585 -2.979839 3.092370 -1.781236 0.628403 2.387391 1.865388 \n\n 38 39 \n0 0.000000 0.000000 \n1 -2.624125 8.233236 \n2 -1.946501 9.361112 \n3 -7.469269 10.618135 \n4 -17.173141 5.203885 \n... ... ... \n2581 -12.773129 -2.747378 \n2582 -14.551804 -13.077312 \n2583 -16.325350 -10.109643 \n2584 -19.870598 -3.944447 \n2585 -19.182354 3.445506 \n\n[1027268 rows x 44 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>sample</th>\n <th>genre</th>\n <th>filenumber</th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>...</th>\n <th>30</th>\n <th>31</th>\n <th>32</th>\n <th>33</th>\n <th>34</th>\n <th>35</th>\n <th>36</th>\n <th>37</th>\n <th>38</th>\n <th>39</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_12.mp3</td>\n <td>0</td>\n <td>classical</td>\n <td>12</td>\n <td>-562.675171</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_12.mp3</td>\n <td>1</td>\n <td>classical</td>\n <td>12</td>\n <td>-377.158630</td>\n <td>140.093323</td>\n <td>11.552977</td>\n <td>6.869974</td>\n <td>2.597836</td>\n <td>-12.081018</td>\n <td>...</td>\n <td>3.403205</td>\n <td>10.121262</td>\n <td>11.495217</td>\n <td>-1.086922</td>\n <td>-7.269203</td>\n <td>1.892457</td>\n <td>-4.588193</td>\n <td>-5.652980</td>\n <td>-2.624125</td>\n <td>8.233236</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_12.mp3</td>\n <td>2</td>\n <td>classical</td>\n <td>12</td>\n <td>-258.835602</td>\n <td>152.421295</td>\n <td>-1.228345</td>\n <td>13.090727</td>\n <td>-5.023252</td>\n <td>-4.367101</td>\n <td>...</td>\n <td>0.327813</td>\n <td>11.534899</td>\n <td>19.050909</td>\n <td>-0.327993</td>\n <td>-7.341021</td>\n <td>3.830878</td>\n <td>-10.513655</td>\n <td>-14.950528</td>\n <td>-1.946501</td>\n <td>9.361112</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_12.mp3</td>\n <td>3</td>\n <td>classical</td>\n <td>12</td>\n <td>-240.433258</td>\n <td>145.206360</td>\n <td>-4.461696</td>\n <td>11.758677</td>\n <td>-10.757431</td>\n <td>1.254790</td>\n <td>...</td>\n <td>-6.263988</td>\n <td>11.113757</td>\n <td>20.267780</td>\n <td>-2.154666</td>\n <td>-5.127759</td>\n <td>2.469068</td>\n <td>-12.121831</td>\n <td>-21.083326</td>\n <td>-7.469269</td>\n <td>10.618135</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>4</td>\n <td>classical</td>\n <td>12</td>\n <td>-265.707764</td>\n <td>132.303268</td>\n <td>-7.728113</td>\n <td>9.628896</td>\n <td>-20.384869</td>\n <td>-1.778922</td>\n <td>...</td>\n <td>-21.676682</td>\n <td>5.254867</td>\n <td>18.119230</td>\n <td>-5.845078</td>\n <td>-6.094512</td>\n <td>-2.550637</td>\n <td>-7.051765</td>\n <td>-23.662018</td>\n <td>-17.173141</td>\n <td>5.203885</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2581</th>\n <td>rock_99.mp3</td>\n <td>2581</td>\n <td>rock</td>\n <td>99</td>\n <td>21.787605</td>\n <td>85.580826</td>\n <td>-46.306671</td>\n <td>18.197096</td>\n <td>-2.197520</td>\n <td>15.667412</td>\n <td>...</td>\n <td>-16.796026</td>\n <td>-4.496211</td>\n <td>-5.669511</td>\n <td>1.081637</td>\n <td>-2.461577</td>\n <td>2.990703</td>\n <td>4.621572</td>\n <td>-0.688445</td>\n <td>-12.773129</td>\n <td>-2.747378</td>\n </tr>\n <tr>\n <th>2582</th>\n <td>rock_99.mp3</td>\n <td>2582</td>\n <td>rock</td>\n <td>99</td>\n <td>15.572616</td>\n <td>77.452782</td>\n <td>-34.896511</td>\n <td>23.128643</td>\n <td>-2.260536</td>\n <td>10.366127</td>\n <td>...</td>\n <td>-21.933458</td>\n <td>-12.466309</td>\n <td>-6.501689</td>\n <td>8.830140</td>\n <td>-1.279472</td>\n <td>0.076493</td>\n <td>-0.084034</td>\n <td>2.975086</td>\n <td>-14.551804</td>\n <td>-13.077312</td>\n </tr>\n <tr>\n <th>2583</th>\n <td>rock_99.mp3</td>\n <td>2583</td>\n <td>rock</td>\n <td>99</td>\n <td>13.339417</td>\n <td>82.475601</td>\n <td>-30.214211</td>\n <td>25.814865</td>\n <td>-4.943372</td>\n <td>10.197868</td>\n <td>...</td>\n <td>-16.537088</td>\n <td>-9.494637</td>\n <td>-5.872965</td>\n <td>6.031368</td>\n <td>0.235092</td>\n <td>-0.736026</td>\n <td>-3.343213</td>\n <td>2.876546</td>\n <td>-16.325350</td>\n <td>-10.109643</td>\n </tr>\n <tr>\n <th>2584</th>\n <td>rock_99.mp3</td>\n <td>2584</td>\n <td>rock</td>\n <td>99</td>\n <td>13.213579</td>\n <td>92.493988</td>\n <td>-31.854889</td>\n <td>29.340508</td>\n <td>-3.183558</td>\n <td>8.847383</td>\n <td>...</td>\n <td>-13.900535</td>\n <td>-6.496171</td>\n <td>-5.673679</td>\n <td>0.987822</td>\n <td>-2.714684</td>\n <td>1.725184</td>\n <td>-1.239075</td>\n <td>2.953235</td>\n <td>-19.870598</td>\n <td>-3.944447</td>\n </tr>\n <tr>\n <th>2585</th>\n <td>rock_99.mp3</td>\n <td>2585</td>\n <td>rock</td>\n <td>99</td>\n <td>-10.291037</td>\n <td>103.562256</td>\n <td>-25.215910</td>\n <td>27.055992</td>\n <td>-3.575981</td>\n <td>8.064964</td>\n <td>...</td>\n <td>-15.524373</td>\n <td>-4.153163</td>\n <td>-2.979839</td>\n <td>3.092370</td>\n <td>-1.781236</td>\n <td>0.628403</td>\n <td>2.387391</td>\n <td>1.865388</td>\n <td>-19.182354</td>\n <td>3.445506</td>\n </tr>\n </tbody>\n</table>\n<p>1027268 rows × 44 columns</p>\n</div>"
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mfcc_df = pd.read_pickle(MFCC_PATH / \"mfcc.pickle\")\n",
- "\n",
- "mfcc_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "outputs": [],
- "source": [
- "import git\n",
- "import datetime\n",
- "\n",
- "repo = git.Repo(BASE_PATH)\n",
- "branch = repo.head.reference\n",
- "branch_name = branch.name\n",
- "commit_sha = branch.repo.commit().hexsha\n"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "raw_mfcc_table = dbrepo_connector.create_table_if_not_exists(\n",
- " mfcc_df,\n",
- " \"raw_mfccs\",\n",
- " f\"Contains the raw mfcc features created by the repo {repo_id} on the commit {commit_sha} executed on {datetime.datetime.now()}. \"\n",
- " f\"The files used for Mfcc generation are located in {dro_url}.\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dbrepo_connector.upload_data(mfcc_df, raw_mfcc_table[\"id\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": " filename sample genre filenumber 0 1 \\\n0 rock_99.mp3 0 rock 99 -544.703064 0.000000 \n1 rock_99.mp3 1 rock 99 -314.351959 79.454163 \n2 rock_99.mp3 2 rock 99 -95.725487 62.870975 \n3 rock_99.mp3 3 rock 99 -32.967800 64.183823 \n4 rock_99.mp3 4 rock 99 -57.435410 74.732780 \n... ... ... ... ... ... ... \n2581 rock_99.mp3 2581 rock 99 21.787605 85.580826 \n2582 rock_99.mp3 2582 rock 99 15.572616 77.452782 \n2583 rock_99.mp3 2583 rock 99 13.339417 82.475601 \n2584 rock_99.mp3 2584 rock 99 13.213579 92.493988 \n2585 rock_99.mp3 2585 rock 99 -10.291037 103.562256 \n\n 2 3 4 5 ... 30 31 \\\n0 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 \n1 40.786179 42.943565 17.910450 36.948647 ... -6.425640 -5.075531 \n2 22.900814 37.672192 25.649731 37.942169 ... -4.761521 -2.742242 \n3 14.466274 40.246658 28.057013 37.012337 ... -0.716160 2.391909 \n4 7.515693 51.356144 28.807529 34.490242 ... -0.548289 6.551604 \n... ... ... ... ... ... ... ... \n2581 -46.306671 18.197096 -2.197520 15.667412 ... -16.796026 -4.496211 \n2582 -34.896511 23.128643 -2.260536 10.366127 ... -21.933458 -12.466309 \n2583 -30.214211 25.814865 -4.943372 10.197868 ... -16.537088 -9.494637 \n2584 -31.854889 29.340508 -3.183558 8.847383 ... -13.900535 -6.496171 \n2585 -25.215910 27.055992 -3.575981 8.064964 ... -15.524373 -4.153163 \n\n 32 33 34 35 36 37 38 \\\n0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n1 -1.146745 0.952471 -3.022435 3.063828 -2.753643 0.315048 0.078572 \n2 -3.275497 3.458089 -15.416247 1.444466 0.090877 6.199932 -4.442922 \n3 -2.070962 3.593476 -15.645985 1.241058 2.785069 7.497619 -5.391083 \n4 -0.266161 4.137842 -12.027081 0.342362 -0.084035 4.941658 -7.381403 \n... ... ... ... ... ... ... ... \n2581 -5.669511 1.081637 -2.461577 2.990703 4.621572 -0.688445 -12.773129 \n2582 -6.501689 8.830140 -1.279472 0.076493 -0.084034 2.975086 -14.551804 \n2583 -5.872965 6.031368 0.235092 -0.736026 -3.343213 2.876546 -16.325350 \n2584 -5.673679 0.987822 -2.714684 1.725184 -1.239075 2.953235 -19.870598 \n2585 -2.979839 3.092370 -1.781236 0.628403 2.387391 1.865388 -19.182354 \n\n 39 \n0 0.000000 \n1 -5.648582 \n2 -3.913424 \n3 -3.487692 \n4 -4.312478 \n... ... \n2581 -2.747378 \n2582 -13.077312 \n2583 -10.109643 \n2584 -3.944447 \n2585 3.445506 \n\n[2586 rows x 44 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>sample</th>\n <th>genre</th>\n <th>filenumber</th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>...</th>\n <th>30</th>\n <th>31</th>\n <th>32</th>\n <th>33</th>\n <th>34</th>\n <th>35</th>\n <th>36</th>\n <th>37</th>\n <th>38</th>\n <th>39</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>rock_99.mp3</td>\n <td>0</td>\n <td>rock</td>\n <td>99</td>\n <td>-544.703064</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>rock_99.mp3</td>\n <td>1</td>\n <td>rock</td>\n <td>99</td>\n <td>-314.351959</td>\n <td>79.454163</td>\n <td>40.786179</td>\n <td>42.943565</td>\n <td>17.910450</td>\n <td>36.948647</td>\n <td>...</td>\n <td>-6.425640</td>\n <td>-5.075531</td>\n <td>-1.146745</td>\n <td>0.952471</td>\n <td>-3.022435</td>\n <td>3.063828</td>\n <td>-2.753643</td>\n <td>0.315048</td>\n <td>0.078572</td>\n <td>-5.648582</td>\n </tr>\n <tr>\n <th>2</th>\n <td>rock_99.mp3</td>\n <td>2</td>\n <td>rock</td>\n <td>99</td>\n <td>-95.725487</td>\n <td>62.870975</td>\n <td>22.900814</td>\n <td>37.672192</td>\n <td>25.649731</td>\n <td>37.942169</td>\n <td>...</td>\n <td>-4.761521</td>\n <td>-2.742242</td>\n <td>-3.275497</td>\n <td>3.458089</td>\n <td>-15.416247</td>\n <td>1.444466</td>\n <td>0.090877</td>\n <td>6.199932</td>\n <td>-4.442922</td>\n <td>-3.913424</td>\n </tr>\n <tr>\n <th>3</th>\n <td>rock_99.mp3</td>\n <td>3</td>\n <td>rock</td>\n <td>99</td>\n <td>-32.967800</td>\n <td>64.183823</td>\n <td>14.466274</td>\n <td>40.246658</td>\n <td>28.057013</td>\n <td>37.012337</td>\n <td>...</td>\n <td>-0.716160</td>\n <td>2.391909</td>\n <td>-2.070962</td>\n <td>3.593476</td>\n <td>-15.645985</td>\n <td>1.241058</td>\n <td>2.785069</td>\n <td>7.497619</td>\n <td>-5.391083</td>\n <td>-3.487692</td>\n </tr>\n <tr>\n <th>4</th>\n <td>rock_99.mp3</td>\n <td>4</td>\n <td>rock</td>\n <td>99</td>\n <td>-57.435410</td>\n <td>74.732780</td>\n <td>7.515693</td>\n <td>51.356144</td>\n <td>28.807529</td>\n <td>34.490242</td>\n <td>...</td>\n <td>-0.548289</td>\n <td>6.551604</td>\n <td>-0.266161</td>\n <td>4.137842</td>\n <td>-12.027081</td>\n <td>0.342362</td>\n <td>-0.084035</td>\n <td>4.941658</td>\n <td>-7.381403</td>\n <td>-4.312478</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>2581</th>\n <td>rock_99.mp3</td>\n <td>2581</td>\n <td>rock</td>\n <td>99</td>\n <td>21.787605</td>\n <td>85.580826</td>\n <td>-46.306671</td>\n <td>18.197096</td>\n <td>-2.197520</td>\n <td>15.667412</td>\n <td>...</td>\n <td>-16.796026</td>\n <td>-4.496211</td>\n <td>-5.669511</td>\n <td>1.081637</td>\n <td>-2.461577</td>\n <td>2.990703</td>\n <td>4.621572</td>\n <td>-0.688445</td>\n <td>-12.773129</td>\n <td>-2.747378</td>\n </tr>\n <tr>\n <th>2582</th>\n <td>rock_99.mp3</td>\n <td>2582</td>\n <td>rock</td>\n <td>99</td>\n <td>15.572616</td>\n <td>77.452782</td>\n <td>-34.896511</td>\n <td>23.128643</td>\n <td>-2.260536</td>\n <td>10.366127</td>\n <td>...</td>\n <td>-21.933458</td>\n <td>-12.466309</td>\n <td>-6.501689</td>\n <td>8.830140</td>\n <td>-1.279472</td>\n <td>0.076493</td>\n <td>-0.084034</td>\n <td>2.975086</td>\n <td>-14.551804</td>\n <td>-13.077312</td>\n </tr>\n <tr>\n <th>2583</th>\n <td>rock_99.mp3</td>\n <td>2583</td>\n <td>rock</td>\n <td>99</td>\n <td>13.339417</td>\n <td>82.475601</td>\n <td>-30.214211</td>\n <td>25.814865</td>\n <td>-4.943372</td>\n <td>10.197868</td>\n <td>...</td>\n <td>-16.537088</td>\n <td>-9.494637</td>\n <td>-5.872965</td>\n <td>6.031368</td>\n <td>0.235092</td>\n <td>-0.736026</td>\n <td>-3.343213</td>\n <td>2.876546</td>\n <td>-16.325350</td>\n <td>-10.109643</td>\n </tr>\n <tr>\n <th>2584</th>\n <td>rock_99.mp3</td>\n <td>2584</td>\n <td>rock</td>\n <td>99</td>\n <td>13.213579</td>\n <td>92.493988</td>\n <td>-31.854889</td>\n <td>29.340508</td>\n <td>-3.183558</td>\n <td>8.847383</td>\n <td>...</td>\n <td>-13.900535</td>\n <td>-6.496171</td>\n <td>-5.673679</td>\n <td>0.987822</td>\n <td>-2.714684</td>\n <td>1.725184</td>\n <td>-1.239075</td>\n <td>2.953235</td>\n <td>-19.870598</td>\n <td>-3.944447</td>\n </tr>\n <tr>\n <th>2585</th>\n <td>rock_99.mp3</td>\n <td>2585</td>\n <td>rock</td>\n <td>99</td>\n <td>-10.291037</td>\n <td>103.562256</td>\n <td>-25.215910</td>\n <td>27.055992</td>\n <td>-3.575981</td>\n <td>8.064964</td>\n <td>...</td>\n <td>-15.524373</td>\n <td>-4.153163</td>\n <td>-2.979839</td>\n <td>3.092370</td>\n <td>-1.781236</td>\n <td>0.628403</td>\n <td>2.387391</td>\n <td>1.865388</td>\n <td>-19.182354</td>\n <td>3.445506</td>\n </tr>\n </tbody>\n</table>\n<p>2586 rows × 44 columns</p>\n</div>"
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "example_mfcc = mfcc_df[mfcc_df[\"filename\"] == \"rock_99.mp3\"].sort_values(\"sample\").iloc[:, :]\n",
- "# librosa.display.specshow(mfccs, sr=sr, x_axis='time')\n",
- "\n",
- "example_mfcc"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": "[<matplotlib.lines.Line2D at 0x7fcda2fad150>]"
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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\n"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(example_mfcc[39])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": "<librosa.display.AdaptiveWaveplot at 0x7fcda6af3d00>"
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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\n"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "audio = audios[\"rock_50.mp3\"]\n",
- "\n",
- "librosa.display.waveshow(audio)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Machine Learning analysis of MFCC Features"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Base: https://www.kaggle.com/code/anmour/svm-using-mfcc-features/notebook\n",
- "\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import os\n",
- "import pickle\n",
- "\n",
- "from sklearn.model_selection import GridSearchCV\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.metrics import accuracy_score\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.decomposition import PCA\n",
- "\n",
- "from sklearn.svm import SVC\n",
- "from root import BASE_PATH\n",
- "\n",
- "meta_columns = [\"filename\", \"sample\", \"genre\", \"filenumber\"]\n",
- "# 22 / 73"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>genre</th>\n",
- " <th>0_min</th>\n",
- " <th>0_max</th>\n",
- " <th>0_mean</th>\n",
- " <th>0_std</th>\n",
- " <th>0_skew</th>\n",
- " <th>1_min</th>\n",
- " <th>1_max</th>\n",
- " <th>1_mean</th>\n",
- " <th>1_std</th>\n",
- " <th>...</th>\n",
- " <th>38_min</th>\n",
- " <th>38_max</th>\n",
- " <th>38_mean</th>\n",
- " <th>38_std</th>\n",
- " <th>38_skew</th>\n",
- " <th>39_min</th>\n",
- " <th>39_max</th>\n",
- " <th>39_mean</th>\n",
- " <th>39_std</th>\n",
- " <th>39_skew</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>filename</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>classical_1.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-530.784302</td>\n",
- " <td>-163.308640</td>\n",
- " <td>-302.203217</td>\n",
- " <td>51.141998</td>\n",
- " <td>-0.468354</td>\n",
- " <td>0.000000</td>\n",
- " <td>178.750244</td>\n",
- " <td>111.332512</td>\n",
- " <td>24.847670</td>\n",
- " <td>...</td>\n",
- " <td>-44.103783</td>\n",
- " <td>47.302727</td>\n",
- " <td>-3.713539</td>\n",
- " <td>16.553630</td>\n",
- " <td>0.230696</td>\n",
- " <td>-46.790031</td>\n",
- " <td>49.353710</td>\n",
- " <td>-2.282033</td>\n",
- " <td>15.285611</td>\n",
- " <td>0.171425</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_10.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-562.857056</td>\n",
- " <td>-96.162895</td>\n",
- " <td>-219.258865</td>\n",
- " <td>53.561945</td>\n",
- " <td>-0.772325</td>\n",
- " <td>0.029843</td>\n",
- " <td>259.636963</td>\n",
- " <td>215.093994</td>\n",
- " <td>18.388213</td>\n",
- " <td>...</td>\n",
- " <td>-27.458200</td>\n",
- " <td>29.808735</td>\n",
- " <td>0.484320</td>\n",
- " <td>8.660671</td>\n",
- " <td>-0.479001</td>\n",
- " <td>-28.984264</td>\n",
- " <td>27.534916</td>\n",
- " <td>0.952575</td>\n",
- " <td>10.477753</td>\n",
- " <td>-0.185726</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_100.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-536.237244</td>\n",
- " <td>-61.609112</td>\n",
- " <td>-177.803986</td>\n",
- " <td>83.381454</td>\n",
- " <td>-2.587178</td>\n",
- " <td>0.000000</td>\n",
- " <td>190.474884</td>\n",
- " <td>112.471695</td>\n",
- " <td>27.277388</td>\n",
- " <td>...</td>\n",
- " <td>-27.338915</td>\n",
- " <td>27.608841</td>\n",
- " <td>-0.333135</td>\n",
- " <td>8.185168</td>\n",
- " <td>0.208413</td>\n",
- " <td>-38.094074</td>\n",
- " <td>31.399227</td>\n",
- " <td>-1.494779</td>\n",
- " <td>10.917259</td>\n",
- " <td>0.020986</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_11.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-536.457520</td>\n",
- " <td>-120.430145</td>\n",
- " <td>-222.126129</td>\n",
- " <td>76.246935</td>\n",
- " <td>-2.402427</td>\n",
- " <td>0.000000</td>\n",
- " <td>159.434616</td>\n",
- " <td>99.853493</td>\n",
- " <td>21.916783</td>\n",
- " <td>...</td>\n",
- " <td>-31.773474</td>\n",
- " <td>31.498913</td>\n",
- " <td>-3.781701</td>\n",
- " <td>9.191031</td>\n",
- " <td>0.260900</td>\n",
- " <td>-22.666813</td>\n",
- " <td>50.993847</td>\n",
- " <td>1.600794</td>\n",
- " <td>10.125588</td>\n",
- " <td>0.595789</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_12.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-562.675171</td>\n",
- " <td>-148.132309</td>\n",
- " <td>-270.974976</td>\n",
- " <td>52.190831</td>\n",
- " <td>-0.366585</td>\n",
- " <td>0.000000</td>\n",
- " <td>194.266327</td>\n",
- " <td>148.226334</td>\n",
- " <td>19.304869</td>\n",
- " <td>...</td>\n",
- " <td>-44.846069</td>\n",
- " <td>28.488243</td>\n",
- " <td>-6.241872</td>\n",
- " <td>10.546620</td>\n",
- " <td>0.341796</td>\n",
- " <td>-25.048683</td>\n",
- " <td>46.877155</td>\n",
- " <td>1.844356</td>\n",
- " <td>11.160211</td>\n",
- " <td>0.503148</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_95.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-553.109985</td>\n",
- " <td>-5.219127</td>\n",
- " <td>-193.506073</td>\n",
- " <td>76.869574</td>\n",
- " <td>-0.201062</td>\n",
- " <td>-89.950394</td>\n",
- " <td>201.183289</td>\n",
- " <td>111.724190</td>\n",
- " <td>36.463682</td>\n",
- " <td>...</td>\n",
- " <td>-27.043680</td>\n",
- " <td>22.443615</td>\n",
- " <td>-7.234814</td>\n",
- " <td>8.471876</td>\n",
- " <td>0.753810</td>\n",
- " <td>-24.716900</td>\n",
- " <td>23.410700</td>\n",
- " <td>-4.502257</td>\n",
- " <td>6.688022</td>\n",
- " <td>0.238779</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_96.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-541.236084</td>\n",
- " <td>27.163366</td>\n",
- " <td>-119.113907</td>\n",
- " <td>58.420538</td>\n",
- " <td>-0.957677</td>\n",
- " <td>-7.415216</td>\n",
- " <td>210.489319</td>\n",
- " <td>125.453644</td>\n",
- " <td>31.908835</td>\n",
- " <td>...</td>\n",
- " <td>-37.584599</td>\n",
- " <td>28.085239</td>\n",
- " <td>-9.704237</td>\n",
- " <td>8.447570</td>\n",
- " <td>0.112745</td>\n",
- " <td>-38.150497</td>\n",
- " <td>21.813221</td>\n",
- " <td>-8.249475</td>\n",
- " <td>7.807696</td>\n",
- " <td>0.071979</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_97.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-518.497375</td>\n",
- " <td>58.526649</td>\n",
- " <td>-66.267700</td>\n",
- " <td>65.635605</td>\n",
- " <td>-0.898028</td>\n",
- " <td>-58.824471</td>\n",
- " <td>175.198486</td>\n",
- " <td>99.288246</td>\n",
- " <td>25.158395</td>\n",
- " <td>...</td>\n",
- " <td>-29.620762</td>\n",
- " <td>26.325855</td>\n",
- " <td>-5.722816</td>\n",
- " <td>7.727353</td>\n",
- " <td>0.207499</td>\n",
- " <td>-29.497421</td>\n",
- " <td>25.410231</td>\n",
- " <td>-3.356631</td>\n",
- " <td>8.170547</td>\n",
- " <td>0.160343</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_98.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-518.643066</td>\n",
- " <td>53.555084</td>\n",
- " <td>-45.734737</td>\n",
- " <td>52.443934</td>\n",
- " <td>-1.705686</td>\n",
- " <td>0.000000</td>\n",
- " <td>187.040344</td>\n",
- " <td>96.440956</td>\n",
- " <td>24.137639</td>\n",
- " <td>...</td>\n",
- " <td>-26.963133</td>\n",
- " <td>8.714615</td>\n",
- " <td>-9.511550</td>\n",
- " <td>5.551747</td>\n",
- " <td>-0.025625</td>\n",
- " <td>-23.019743</td>\n",
- " <td>13.949155</td>\n",
- " <td>-2.664933</td>\n",
- " <td>5.051528</td>\n",
- " <td>-0.258403</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_99.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-544.703064</td>\n",
- " <td>75.608696</td>\n",
- " <td>-49.380920</td>\n",
- " <td>54.045598</td>\n",
- " <td>-0.863099</td>\n",
- " <td>-32.928108</td>\n",
- " <td>191.734619</td>\n",
- " <td>93.971260</td>\n",
- " <td>33.410192</td>\n",
- " <td>...</td>\n",
- " <td>-21.929461</td>\n",
- " <td>17.050976</td>\n",
- " <td>-5.296696</td>\n",
- " <td>5.894940</td>\n",
- " <td>0.390704</td>\n",
- " <td>-20.984795</td>\n",
- " <td>29.312206</td>\n",
- " <td>-0.321817</td>\n",
- " <td>6.571661</td>\n",
- " <td>0.384814</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>400 rows × 201 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " genre 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
- "classical_1.mp3 classical -530.784302 -163.308640 -302.203217 51.141998 \n",
- "classical_10.mp3 classical -562.857056 -96.162895 -219.258865 53.561945 \n",
- "classical_100.mp3 classical -536.237244 -61.609112 -177.803986 83.381454 \n",
- "classical_11.mp3 classical -536.457520 -120.430145 -222.126129 76.246935 \n",
- "classical_12.mp3 classical -562.675171 -148.132309 -270.974976 52.190831 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 rock -553.109985 -5.219127 -193.506073 76.869574 \n",
- "rock_96.mp3 rock -541.236084 27.163366 -119.113907 58.420538 \n",
- "rock_97.mp3 rock -518.497375 58.526649 -66.267700 65.635605 \n",
- "rock_98.mp3 rock -518.643066 53.555084 -45.734737 52.443934 \n",
- "rock_99.mp3 rock -544.703064 75.608696 -49.380920 54.045598 \n",
- "\n",
- " 0_skew 1_min 1_max 1_mean 1_std \\\n",
- "filename \n",
- "classical_1.mp3 -0.468354 0.000000 178.750244 111.332512 24.847670 \n",
- "classical_10.mp3 -0.772325 0.029843 259.636963 215.093994 18.388213 \n",
- "classical_100.mp3 -2.587178 0.000000 190.474884 112.471695 27.277388 \n",
- "classical_11.mp3 -2.402427 0.000000 159.434616 99.853493 21.916783 \n",
- "classical_12.mp3 -0.366585 0.000000 194.266327 148.226334 19.304869 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -0.201062 -89.950394 201.183289 111.724190 36.463682 \n",
- "rock_96.mp3 -0.957677 -7.415216 210.489319 125.453644 31.908835 \n",
- "rock_97.mp3 -0.898028 -58.824471 175.198486 99.288246 25.158395 \n",
- "rock_98.mp3 -1.705686 0.000000 187.040344 96.440956 24.137639 \n",
- "rock_99.mp3 -0.863099 -32.928108 191.734619 93.971260 33.410192 \n",
- "\n",
- " ... 38_min 38_max 38_mean 38_std 38_skew \\\n",
- "filename ... \n",
- "classical_1.mp3 ... -44.103783 47.302727 -3.713539 16.553630 0.230696 \n",
- "classical_10.mp3 ... -27.458200 29.808735 0.484320 8.660671 -0.479001 \n",
- "classical_100.mp3 ... -27.338915 27.608841 -0.333135 8.185168 0.208413 \n",
- "classical_11.mp3 ... -31.773474 31.498913 -3.781701 9.191031 0.260900 \n",
- "classical_12.mp3 ... -44.846069 28.488243 -6.241872 10.546620 0.341796 \n",
- "... ... ... ... ... ... ... \n",
- "rock_95.mp3 ... -27.043680 22.443615 -7.234814 8.471876 0.753810 \n",
- "rock_96.mp3 ... -37.584599 28.085239 -9.704237 8.447570 0.112745 \n",
- "rock_97.mp3 ... -29.620762 26.325855 -5.722816 7.727353 0.207499 \n",
- "rock_98.mp3 ... -26.963133 8.714615 -9.511550 5.551747 -0.025625 \n",
- "rock_99.mp3 ... -21.929461 17.050976 -5.296696 5.894940 0.390704 \n",
- "\n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
- "filename \n",
- "classical_1.mp3 -46.790031 49.353710 -2.282033 15.285611 0.171425 \n",
- "classical_10.mp3 -28.984264 27.534916 0.952575 10.477753 -0.185726 \n",
- "classical_100.mp3 -38.094074 31.399227 -1.494779 10.917259 0.020986 \n",
- "classical_11.mp3 -22.666813 50.993847 1.600794 10.125588 0.595789 \n",
- "classical_12.mp3 -25.048683 46.877155 1.844356 11.160211 0.503148 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -24.716900 23.410700 -4.502257 6.688022 0.238779 \n",
- "rock_96.mp3 -38.150497 21.813221 -8.249475 7.807696 0.071979 \n",
- "rock_97.mp3 -29.497421 25.410231 -3.356631 8.170547 0.160343 \n",
- "rock_98.mp3 -23.019743 13.949155 -2.664933 5.051528 -0.258403 \n",
- "rock_99.mp3 -20.984795 29.312206 -0.321817 6.571661 0.384814 \n",
- "\n",
- "[400 rows x 201 columns]"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Aggregate mfccs\n",
- "\n",
- "mfcc_raw: pd.DataFrame = pd.read_pickle(BASE_PATH / \"out\" / \"mfcc\" / \"mfcc.pickle\").set_index('filename')\n",
- "\n",
- "\n",
- "# Aggregate from n rows per file to 1 (calculate min, max, etc. for each feature)\n",
- "\n",
- "mfcc_aggregated = mfcc_raw.drop(meta_columns, axis=1, errors='ignore').groupby(mfcc_raw.index).agg(['min', 'max', 'mean', 'std', 'skew'])\n",
- "mfcc_meta = pd.DataFrame(mfcc_raw['genre'].groupby(mfcc_raw.index).last())\n",
- "mfcc_meta.columns = pd.MultiIndex.from_arrays([['genre'], ['']])\n",
- "mfcc_merged: pd.DataFrame = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\n",
- "\n",
- "\n",
- "one_level_cols = ['_'.join([str(el) for el in col]) for col in mfcc_merged.columns[1:]]\n",
- "one_level_cols.insert(0, \"genre\")\n",
- "\n",
- "mfcc_merged.columns = pd.Index(one_level_cols)\n",
- "mfcc_merged"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"GET /api/container/1/database/1/table HTTP/1.1\" 200 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [200]>\n"
- ]
- }
- ],
- "source": [
- "# upload aggregated dataframe to dbrepo\n",
- "table_name = dbrepo_config[\"tables\"][\"aggregated_mfcc\"][\"name\"]\n",
- "\n",
- "# Retrieve table or create new one \n",
- "table = dbrepo_connector.create_table_if_not_exists(\n",
- " mfcc_merged.reset_index(), \n",
- " dbrepo_config[\"tables\"][\"aggregated_mfcc\"][\"name\"], \n",
- " dbrepo_config[\"tables\"][\"aggregated_mfcc\"][\"description\"]\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"POST /server-middleware/upload HTTP/1.1\" 201 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [201]>\n",
- "DEBUG:dbrepo_ismir.dbrepo:Upload location: /tmp/028df20f43b26e551a4b68e4a1cfa305\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"POST /api/container/1/database/1/table/11/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [202]>\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "11"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dbrepo_connector.upload_data(mfcc_merged.reset_index(), table[\"id\"])\n",
- "\n",
- "table[\"id\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"GET /api/container/1/database/1/table HTTP/1.1\" 200 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [200]>\n",
- "DEBUG:dbrepo_ismir.dbrepo:{'name': 'Train_set', 'description': 'descr', 'columns': [{'name': 'filename', 'type': 'text', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': 'genre', 'type': 'text', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '2_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '2_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '2_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '2_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '2_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '3_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '3_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '3_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '3_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '3_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '4_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '4_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '4_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '4_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '4_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '5_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '5_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '5_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '5_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '5_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '6_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '6_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '6_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '6_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '6_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '7_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '7_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '7_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '7_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '7_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '8_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '8_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '8_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '8_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '8_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '9_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '9_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '9_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '9_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '9_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '10_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '10_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '10_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '10_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '10_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '11_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '11_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '11_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '11_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '11_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '12_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '12_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '12_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '12_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '12_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '13_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '13_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '13_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '13_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '13_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '14_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '14_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '14_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '14_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '14_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '15_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '15_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '15_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '15_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '15_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '16_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '16_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '16_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '16_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '16_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '17_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '17_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '17_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '17_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '17_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '18_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '18_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '18_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '18_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '18_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '19_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '19_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '19_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '19_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '19_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '20_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '20_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '20_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '20_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '20_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '21_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '21_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '21_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '21_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '21_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '22_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '22_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '22_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '22_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '22_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '23_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '23_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '23_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '23_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '23_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '24_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '24_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '24_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '24_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '24_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '25_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '25_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '25_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '25_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '25_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '26_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '26_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '26_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '26_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '26_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '27_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '27_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '27_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '27_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '27_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '28_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '28_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '28_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '28_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '28_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '29_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '29_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '29_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '29_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '29_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '30_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '30_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '30_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '30_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '30_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '31_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '31_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '31_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '31_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '31_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '32_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '32_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '32_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '32_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '32_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '33_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '33_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '33_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '33_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '33_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '34_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '34_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '34_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '34_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '34_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '35_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '35_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '35_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '35_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '35_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '36_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '36_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '36_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '36_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '36_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '37_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '37_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '37_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '37_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '37_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '38_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '38_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '38_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '38_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '38_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '39_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '39_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '39_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '39_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '39_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}]}\n",
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- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"POST /api/container/1/database/1/table HTTP/1.1\" 201 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [201]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"GET /api/container/1/database/1/table HTTP/1.1\" 200 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"POST /server-middleware/upload HTTP/1.1\" 201 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [201]>\n",
- "DEBUG:dbrepo_ismir.dbrepo:Upload location: /tmp/e961f22c4cd2d2dfd66f9441cf473d07\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"POST /api/container/1/database/1/table/12/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [202]>\n"
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- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
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- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>genre</th>\n",
- " <th>0_min</th>\n",
- " <th>0_max</th>\n",
- " <th>0_mean</th>\n",
- " <th>0_std</th>\n",
- " <th>0_skew</th>\n",
- " <th>1_min</th>\n",
- " <th>1_max</th>\n",
- " <th>1_mean</th>\n",
- " <th>1_std</th>\n",
- " <th>...</th>\n",
- " <th>38_min</th>\n",
- " <th>38_max</th>\n",
- " <th>38_mean</th>\n",
- " <th>38_std</th>\n",
- " <th>38_skew</th>\n",
- " <th>39_min</th>\n",
- " <th>39_max</th>\n",
- " <th>39_mean</th>\n",
- " <th>39_std</th>\n",
- " <th>39_skew</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>filename</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>classical_1.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-530.784302</td>\n",
- " <td>-163.308640</td>\n",
- " <td>-302.203217</td>\n",
- " <td>51.141998</td>\n",
- " <td>-0.468354</td>\n",
- " <td>0.000000</td>\n",
- " <td>178.750244</td>\n",
- " <td>111.332512</td>\n",
- " <td>24.847670</td>\n",
- " <td>...</td>\n",
- " <td>-44.103783</td>\n",
- " <td>47.302727</td>\n",
- " <td>-3.713539</td>\n",
- " <td>16.553630</td>\n",
- " <td>0.230696</td>\n",
- " <td>-46.790031</td>\n",
- " <td>49.353710</td>\n",
- " <td>-2.282033</td>\n",
- " <td>15.285611</td>\n",
- " <td>0.171425</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_10.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-562.857056</td>\n",
- " <td>-96.162895</td>\n",
- " <td>-219.258865</td>\n",
- " <td>53.561945</td>\n",
- " <td>-0.772325</td>\n",
- " <td>0.029843</td>\n",
- " <td>259.636963</td>\n",
- " <td>215.093994</td>\n",
- " <td>18.388213</td>\n",
- " <td>...</td>\n",
- " <td>-27.458200</td>\n",
- " <td>29.808735</td>\n",
- " <td>0.484320</td>\n",
- " <td>8.660671</td>\n",
- " <td>-0.479001</td>\n",
- " <td>-28.984264</td>\n",
- " <td>27.534916</td>\n",
- " <td>0.952575</td>\n",
- " <td>10.477753</td>\n",
- " <td>-0.185726</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_100.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-536.237244</td>\n",
- " <td>-61.609112</td>\n",
- " <td>-177.803986</td>\n",
- " <td>83.381454</td>\n",
- " <td>-2.587178</td>\n",
- " <td>0.000000</td>\n",
- " <td>190.474884</td>\n",
- " <td>112.471695</td>\n",
- " <td>27.277388</td>\n",
- " <td>...</td>\n",
- " <td>-27.338915</td>\n",
- " <td>27.608841</td>\n",
- " <td>-0.333135</td>\n",
- " <td>8.185168</td>\n",
- " <td>0.208413</td>\n",
- " <td>-38.094074</td>\n",
- " <td>31.399227</td>\n",
- " <td>-1.494779</td>\n",
- " <td>10.917259</td>\n",
- " <td>0.020986</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_11.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-536.457520</td>\n",
- " <td>-120.430145</td>\n",
- " <td>-222.126129</td>\n",
- " <td>76.246935</td>\n",
- " <td>-2.402427</td>\n",
- " <td>0.000000</td>\n",
- " <td>159.434616</td>\n",
- " <td>99.853493</td>\n",
- " <td>21.916783</td>\n",
- " <td>...</td>\n",
- " <td>-31.773474</td>\n",
- " <td>31.498913</td>\n",
- " <td>-3.781701</td>\n",
- " <td>9.191031</td>\n",
- " <td>0.260900</td>\n",
- " <td>-22.666813</td>\n",
- " <td>50.993847</td>\n",
- " <td>1.600794</td>\n",
- " <td>10.125588</td>\n",
- " <td>0.595789</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_12.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-562.675171</td>\n",
- " <td>-148.132309</td>\n",
- " <td>-270.974976</td>\n",
- " <td>52.190831</td>\n",
- " <td>-0.366585</td>\n",
- " <td>0.000000</td>\n",
- " <td>194.266327</td>\n",
- " <td>148.226334</td>\n",
- " <td>19.304869</td>\n",
- " <td>...</td>\n",
- " <td>-44.846069</td>\n",
- " <td>28.488243</td>\n",
- " <td>-6.241872</td>\n",
- " <td>10.546620</td>\n",
- " <td>0.341796</td>\n",
- " <td>-25.048683</td>\n",
- " <td>46.877155</td>\n",
- " <td>1.844356</td>\n",
- " <td>11.160211</td>\n",
- " <td>0.503148</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_95.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-553.109985</td>\n",
- " <td>-5.219127</td>\n",
- " <td>-193.506073</td>\n",
- " <td>76.869574</td>\n",
- " <td>-0.201062</td>\n",
- " <td>-89.950394</td>\n",
- " <td>201.183289</td>\n",
- " <td>111.724190</td>\n",
- " <td>36.463682</td>\n",
- " <td>...</td>\n",
- " <td>-27.043680</td>\n",
- " <td>22.443615</td>\n",
- " <td>-7.234814</td>\n",
- " <td>8.471876</td>\n",
- " <td>0.753810</td>\n",
- " <td>-24.716900</td>\n",
- " <td>23.410700</td>\n",
- " <td>-4.502257</td>\n",
- " <td>6.688022</td>\n",
- " <td>0.238779</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_96.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-541.236084</td>\n",
- " <td>27.163366</td>\n",
- " <td>-119.113907</td>\n",
- " <td>58.420538</td>\n",
- " <td>-0.957677</td>\n",
- " <td>-7.415216</td>\n",
- " <td>210.489319</td>\n",
- " <td>125.453644</td>\n",
- " <td>31.908835</td>\n",
- " <td>...</td>\n",
- " <td>-37.584599</td>\n",
- " <td>28.085239</td>\n",
- " <td>-9.704237</td>\n",
- " <td>8.447570</td>\n",
- " <td>0.112745</td>\n",
- " <td>-38.150497</td>\n",
- " <td>21.813221</td>\n",
- " <td>-8.249475</td>\n",
- " <td>7.807696</td>\n",
- " <td>0.071979</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_97.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-518.497375</td>\n",
- " <td>58.526649</td>\n",
- " <td>-66.267700</td>\n",
- " <td>65.635605</td>\n",
- " <td>-0.898028</td>\n",
- " <td>-58.824471</td>\n",
- " <td>175.198486</td>\n",
- " <td>99.288246</td>\n",
- " <td>25.158395</td>\n",
- " <td>...</td>\n",
- " <td>-29.620762</td>\n",
- " <td>26.325855</td>\n",
- " <td>-5.722816</td>\n",
- " <td>7.727353</td>\n",
- " <td>0.207499</td>\n",
- " <td>-29.497421</td>\n",
- " <td>25.410231</td>\n",
- " <td>-3.356631</td>\n",
- " <td>8.170547</td>\n",
- " <td>0.160343</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_98.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-518.643066</td>\n",
- " <td>53.555084</td>\n",
- " <td>-45.734737</td>\n",
- " <td>52.443934</td>\n",
- " <td>-1.705686</td>\n",
- " <td>0.000000</td>\n",
- " <td>187.040344</td>\n",
- " <td>96.440956</td>\n",
- " <td>24.137639</td>\n",
- " <td>...</td>\n",
- " <td>-26.963133</td>\n",
- " <td>8.714615</td>\n",
- " <td>-9.511550</td>\n",
- " <td>5.551747</td>\n",
- " <td>-0.025625</td>\n",
- " <td>-23.019743</td>\n",
- " <td>13.949155</td>\n",
- " <td>-2.664933</td>\n",
- " <td>5.051528</td>\n",
- " <td>-0.258403</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_99.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-544.703064</td>\n",
- " <td>75.608696</td>\n",
- " <td>-49.380920</td>\n",
- " <td>54.045598</td>\n",
- " <td>-0.863099</td>\n",
- " <td>-32.928108</td>\n",
- " <td>191.734619</td>\n",
- " <td>93.971260</td>\n",
- " <td>33.410192</td>\n",
- " <td>...</td>\n",
- " <td>-21.929461</td>\n",
- " <td>17.050976</td>\n",
- " <td>-5.296696</td>\n",
- " <td>5.894940</td>\n",
- " <td>0.390704</td>\n",
- " <td>-20.984795</td>\n",
- " <td>29.312206</td>\n",
- " <td>-0.321817</td>\n",
- " <td>6.571661</td>\n",
- " <td>0.384814</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>320 rows × 201 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " genre 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
- "classical_1.mp3 classical -530.784302 -163.308640 -302.203217 51.141998 \n",
- "classical_10.mp3 classical -562.857056 -96.162895 -219.258865 53.561945 \n",
- "classical_100.mp3 classical -536.237244 -61.609112 -177.803986 83.381454 \n",
- "classical_11.mp3 classical -536.457520 -120.430145 -222.126129 76.246935 \n",
- "classical_12.mp3 classical -562.675171 -148.132309 -270.974976 52.190831 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 rock -553.109985 -5.219127 -193.506073 76.869574 \n",
- "rock_96.mp3 rock -541.236084 27.163366 -119.113907 58.420538 \n",
- "rock_97.mp3 rock -518.497375 58.526649 -66.267700 65.635605 \n",
- "rock_98.mp3 rock -518.643066 53.555084 -45.734737 52.443934 \n",
- "rock_99.mp3 rock -544.703064 75.608696 -49.380920 54.045598 \n",
- "\n",
- " 0_skew 1_min 1_max 1_mean 1_std \\\n",
- "filename \n",
- "classical_1.mp3 -0.468354 0.000000 178.750244 111.332512 24.847670 \n",
- "classical_10.mp3 -0.772325 0.029843 259.636963 215.093994 18.388213 \n",
- "classical_100.mp3 -2.587178 0.000000 190.474884 112.471695 27.277388 \n",
- "classical_11.mp3 -2.402427 0.000000 159.434616 99.853493 21.916783 \n",
- "classical_12.mp3 -0.366585 0.000000 194.266327 148.226334 19.304869 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -0.201062 -89.950394 201.183289 111.724190 36.463682 \n",
- "rock_96.mp3 -0.957677 -7.415216 210.489319 125.453644 31.908835 \n",
- "rock_97.mp3 -0.898028 -58.824471 175.198486 99.288246 25.158395 \n",
- "rock_98.mp3 -1.705686 0.000000 187.040344 96.440956 24.137639 \n",
- "rock_99.mp3 -0.863099 -32.928108 191.734619 93.971260 33.410192 \n",
- "\n",
- " ... 38_min 38_max 38_mean 38_std 38_skew \\\n",
- "filename ... \n",
- "classical_1.mp3 ... -44.103783 47.302727 -3.713539 16.553630 0.230696 \n",
- "classical_10.mp3 ... -27.458200 29.808735 0.484320 8.660671 -0.479001 \n",
- "classical_100.mp3 ... -27.338915 27.608841 -0.333135 8.185168 0.208413 \n",
- "classical_11.mp3 ... -31.773474 31.498913 -3.781701 9.191031 0.260900 \n",
- "classical_12.mp3 ... -44.846069 28.488243 -6.241872 10.546620 0.341796 \n",
- "... ... ... ... ... ... ... \n",
- "rock_95.mp3 ... -27.043680 22.443615 -7.234814 8.471876 0.753810 \n",
- "rock_96.mp3 ... -37.584599 28.085239 -9.704237 8.447570 0.112745 \n",
- "rock_97.mp3 ... -29.620762 26.325855 -5.722816 7.727353 0.207499 \n",
- "rock_98.mp3 ... -26.963133 8.714615 -9.511550 5.551747 -0.025625 \n",
- "rock_99.mp3 ... -21.929461 17.050976 -5.296696 5.894940 0.390704 \n",
- "\n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
- "filename \n",
- "classical_1.mp3 -46.790031 49.353710 -2.282033 15.285611 0.171425 \n",
- "classical_10.mp3 -28.984264 27.534916 0.952575 10.477753 -0.185726 \n",
- "classical_100.mp3 -38.094074 31.399227 -1.494779 10.917259 0.020986 \n",
- "classical_11.mp3 -22.666813 50.993847 1.600794 10.125588 0.595789 \n",
- "classical_12.mp3 -25.048683 46.877155 1.844356 11.160211 0.503148 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -24.716900 23.410700 -4.502257 6.688022 0.238779 \n",
- "rock_96.mp3 -38.150497 21.813221 -8.249475 7.807696 0.071979 \n",
- "rock_97.mp3 -29.497421 25.410231 -3.356631 8.170547 0.160343 \n",
- "rock_98.mp3 -23.019743 13.949155 -2.664933 5.051528 -0.258403 \n",
- "rock_99.mp3 -20.984795 29.312206 -0.321817 6.571661 0.384814 \n",
- "\n",
- "[320 rows x 201 columns]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train: pd.DataFrame = mfcc_merged.sample(frac = 0.8).sort_index()\n",
- "\n",
- "table = dbrepo_connector.create_table_if_not_exists(\n",
- " train.reset_index(),\n",
- " dbrepo_config[\"tables\"][\"train_set\"][\"name\"], \n",
- " dbrepo_config[\"tables\"][\"train_set\"][\"description\"]\n",
- ")\n",
- "dbrepo_connector.upload_data(train.reset_index(), table[\"id\"])\n",
- "\n",
- "train"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo2.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo2.ec.tuwien.ac.at:443 \"GET /api/container/1/database/1/table HTTP/1.1\" 200 None\n",
- "DEBUG:dbrepo_ismir.dbrepo:<Response [200]>\n",
- "DEBUG:dbrepo_ismir.dbrepo:{'name': 'Test_set', 'description': 'descr', 'columns': [{'name': 'filename', 'type': 'text', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': 'genre', 'type': 'text', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_mean', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_std', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '0_skew', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_min', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': '1_max', 'type': 'decimal', 'unique': False, 'primary_key': False, 'null_allowed': False}, {'name': 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- " <td>27.699514</td>\n",
- " <td>...</td>\n",
- " <td>-37.205727</td>\n",
- " <td>28.788366</td>\n",
- " <td>-1.942927</td>\n",
- " <td>8.390800</td>\n",
- " <td>-0.355343</td>\n",
- " <td>-24.297161</td>\n",
- " <td>29.054634</td>\n",
- " <td>-1.324975</td>\n",
- " <td>8.486928</td>\n",
- " <td>0.304785</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_26.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-569.712158</td>\n",
- " <td>-0.070531</td>\n",
- " <td>-143.714355</td>\n",
- " <td>98.262949</td>\n",
- " <td>-2.543314</td>\n",
- " <td>0.000000</td>\n",
- " <td>128.069717</td>\n",
- " <td>76.152962</td>\n",
- " <td>13.718037</td>\n",
- " <td>...</td>\n",
- " <td>-24.739586</td>\n",
- " <td>17.435701</td>\n",
- " <td>-7.886369</td>\n",
- " <td>5.901740</td>\n",
- " <td>0.369380</td>\n",
- " <td>-19.358587</td>\n",
- " <td>23.100426</td>\n",
- " <td>-0.108562</td>\n",
- " <td>6.584214</td>\n",
- " <td>0.187470</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_34.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-539.208862</td>\n",
- " <td>-125.778076</td>\n",
- " <td>-264.174103</td>\n",
- " <td>49.029822</td>\n",
- " <td>0.024207</td>\n",
- " <td>0.000000</td>\n",
- " <td>200.549286</td>\n",
- " <td>151.797653</td>\n",
- " <td>20.997639</td>\n",
- " <td>...</td>\n",
- " <td>-18.701038</td>\n",
- " <td>28.490271</td>\n",
- " <td>1.823272</td>\n",
- " <td>7.751112</td>\n",
- " <td>0.125583</td>\n",
- " <td>-27.106770</td>\n",
- " <td>26.210209</td>\n",
- " <td>-3.567762</td>\n",
- " <td>8.418179</td>\n",
- " <td>0.507118</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_37.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-537.953369</td>\n",
- " <td>-107.239861</td>\n",
- " <td>-245.100616</td>\n",
- " <td>55.113451</td>\n",
- " <td>-0.390227</td>\n",
- " <td>0.000000</td>\n",
- " <td>193.515350</td>\n",
- " <td>137.776245</td>\n",
- " <td>30.082837</td>\n",
- " <td>...</td>\n",
- " <td>-23.498358</td>\n",
- " <td>32.250309</td>\n",
- " <td>-2.397477</td>\n",
- " <td>6.976093</td>\n",
- " <td>0.604056</td>\n",
- " <td>-21.997433</td>\n",
- " <td>16.448553</td>\n",
- " <td>-2.813462</td>\n",
- " <td>5.559475</td>\n",
- " <td>0.041881</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_39.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-578.847290</td>\n",
- " <td>-55.479786</td>\n",
- " <td>-183.752762</td>\n",
- " <td>69.140362</td>\n",
- " <td>-0.577054</td>\n",
- " <td>0.000000</td>\n",
- " <td>193.846619</td>\n",
- " <td>127.058319</td>\n",
- " <td>29.295309</td>\n",
- " <td>...</td>\n",
- " <td>-48.676907</td>\n",
- " <td>24.563923</td>\n",
- " <td>-7.810217</td>\n",
- " <td>11.568093</td>\n",
- " <td>-0.106692</td>\n",
- " <td>-24.327251</td>\n",
- " <td>40.172852</td>\n",
- " <td>-0.077929</td>\n",
- " <td>10.646977</td>\n",
- " <td>0.492465</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_65.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-533.449341</td>\n",
- " <td>-25.973362</td>\n",
- " <td>-192.106262</td>\n",
- " <td>77.238210</td>\n",
- " <td>-0.819074</td>\n",
- " <td>-29.258015</td>\n",
- " <td>208.906296</td>\n",
- " <td>125.367722</td>\n",
- " <td>32.599015</td>\n",
- " <td>...</td>\n",
- " <td>-30.041363</td>\n",
- " <td>28.924728</td>\n",
- " <td>-7.925346</td>\n",
- " <td>6.096354</td>\n",
- " <td>0.581601</td>\n",
- " <td>-23.952833</td>\n",
- " <td>24.913488</td>\n",
- " <td>-1.928245</td>\n",
- " <td>6.399965</td>\n",
- " <td>0.176792</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_68.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-561.144775</td>\n",
- " <td>12.341146</td>\n",
- " <td>-96.624611</td>\n",
- " <td>47.400105</td>\n",
- " <td>-0.653529</td>\n",
- " <td>-23.055605</td>\n",
- " <td>167.529495</td>\n",
- " <td>88.402924</td>\n",
- " <td>29.370019</td>\n",
- " <td>...</td>\n",
- " <td>-32.008865</td>\n",
- " <td>27.933788</td>\n",
- " <td>-2.982027</td>\n",
- " <td>9.721906</td>\n",
- " <td>0.089447</td>\n",
- " <td>-23.004169</td>\n",
- " <td>24.203032</td>\n",
- " <td>3.406437</td>\n",
- " <td>7.585782</td>\n",
- " <td>-0.129008</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_73.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-592.886169</td>\n",
- " <td>41.701981</td>\n",
- " <td>-153.154663</td>\n",
- " <td>106.421390</td>\n",
- " <td>-0.994741</td>\n",
- " <td>0.000000</td>\n",
- " <td>215.729462</td>\n",
- " <td>115.183716</td>\n",
- " <td>33.206506</td>\n",
- " <td>...</td>\n",
- " <td>-24.936481</td>\n",
- " <td>24.261497</td>\n",
- " <td>-2.783076</td>\n",
- " <td>6.734214</td>\n",
- " <td>0.418152</td>\n",
- " <td>-13.621414</td>\n",
- " <td>26.187157</td>\n",
- " <td>3.596104</td>\n",
- " <td>5.598480</td>\n",
- " <td>0.126061</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_76.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-540.481323</td>\n",
- " <td>-49.008457</td>\n",
- " <td>-183.800781</td>\n",
- " <td>62.901296</td>\n",
- " <td>-0.607871</td>\n",
- " <td>-30.659517</td>\n",
- " <td>182.062378</td>\n",
- " <td>116.824356</td>\n",
- " <td>28.140195</td>\n",
- " <td>...</td>\n",
- " <td>-29.996357</td>\n",
- " <td>27.309448</td>\n",
- " <td>-4.922373</td>\n",
- " <td>8.220957</td>\n",
- " <td>0.080346</td>\n",
- " <td>-29.446217</td>\n",
- " <td>26.686302</td>\n",
- " <td>-6.446850</td>\n",
- " <td>7.489243</td>\n",
- " <td>0.572411</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_89.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-547.486755</td>\n",
- " <td>57.036377</td>\n",
- " <td>-138.844193</td>\n",
- " <td>83.231726</td>\n",
- " <td>-0.429822</td>\n",
- " <td>-7.842898</td>\n",
- " <td>210.354797</td>\n",
- " <td>100.083328</td>\n",
- " <td>32.894963</td>\n",
- " <td>...</td>\n",
- " <td>-35.628502</td>\n",
- " <td>24.877861</td>\n",
- " <td>-3.417018</td>\n",
- " <td>8.141689</td>\n",
- " <td>-0.068637</td>\n",
- " <td>-29.531443</td>\n",
- " <td>33.873131</td>\n",
- " <td>-1.116988</td>\n",
- " <td>8.490578</td>\n",
- " <td>0.084522</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>80 rows × 201 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " genre 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
- "classical_24.mp3 classical -587.785461 -11.066548 -147.241623 63.913766 \n",
- "classical_26.mp3 classical -569.712158 -0.070531 -143.714355 98.262949 \n",
- "classical_34.mp3 classical -539.208862 -125.778076 -264.174103 49.029822 \n",
- "classical_37.mp3 classical -537.953369 -107.239861 -245.100616 55.113451 \n",
- "classical_39.mp3 classical -578.847290 -55.479786 -183.752762 69.140362 \n",
- "... ... ... ... ... ... \n",
- "rock_65.mp3 rock -533.449341 -25.973362 -192.106262 77.238210 \n",
- "rock_68.mp3 rock -561.144775 12.341146 -96.624611 47.400105 \n",
- "rock_73.mp3 rock -592.886169 41.701981 -153.154663 106.421390 \n",
- "rock_76.mp3 rock -540.481323 -49.008457 -183.800781 62.901296 \n",
- "rock_89.mp3 rock -547.486755 57.036377 -138.844193 83.231726 \n",
- "\n",
- " 0_skew 1_min 1_max 1_mean 1_std ... \\\n",
- "filename ... \n",
- "classical_24.mp3 -1.441499 0.000000 214.555634 136.879730 27.699514 ... \n",
- "classical_26.mp3 -2.543314 0.000000 128.069717 76.152962 13.718037 ... \n",
- "classical_34.mp3 0.024207 0.000000 200.549286 151.797653 20.997639 ... \n",
- "classical_37.mp3 -0.390227 0.000000 193.515350 137.776245 30.082837 ... \n",
- "classical_39.mp3 -0.577054 0.000000 193.846619 127.058319 29.295309 ... \n",
- "... ... ... ... ... ... ... \n",
- "rock_65.mp3 -0.819074 -29.258015 208.906296 125.367722 32.599015 ... \n",
- "rock_68.mp3 -0.653529 -23.055605 167.529495 88.402924 29.370019 ... \n",
- "rock_73.mp3 -0.994741 0.000000 215.729462 115.183716 33.206506 ... \n",
- "rock_76.mp3 -0.607871 -30.659517 182.062378 116.824356 28.140195 ... \n",
- "rock_89.mp3 -0.429822 -7.842898 210.354797 100.083328 32.894963 ... \n",
- "\n",
- " 38_min 38_max 38_mean 38_std 38_skew \\\n",
- "filename \n",
- "classical_24.mp3 -37.205727 28.788366 -1.942927 8.390800 -0.355343 \n",
- "classical_26.mp3 -24.739586 17.435701 -7.886369 5.901740 0.369380 \n",
- "classical_34.mp3 -18.701038 28.490271 1.823272 7.751112 0.125583 \n",
- "classical_37.mp3 -23.498358 32.250309 -2.397477 6.976093 0.604056 \n",
- "classical_39.mp3 -48.676907 24.563923 -7.810217 11.568093 -0.106692 \n",
- "... ... ... ... ... ... \n",
- "rock_65.mp3 -30.041363 28.924728 -7.925346 6.096354 0.581601 \n",
- "rock_68.mp3 -32.008865 27.933788 -2.982027 9.721906 0.089447 \n",
- "rock_73.mp3 -24.936481 24.261497 -2.783076 6.734214 0.418152 \n",
- "rock_76.mp3 -29.996357 27.309448 -4.922373 8.220957 0.080346 \n",
- "rock_89.mp3 -35.628502 24.877861 -3.417018 8.141689 -0.068637 \n",
- "\n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
- "filename \n",
- "classical_24.mp3 -24.297161 29.054634 -1.324975 8.486928 0.304785 \n",
- "classical_26.mp3 -19.358587 23.100426 -0.108562 6.584214 0.187470 \n",
- "classical_34.mp3 -27.106770 26.210209 -3.567762 8.418179 0.507118 \n",
- "classical_37.mp3 -21.997433 16.448553 -2.813462 5.559475 0.041881 \n",
- "classical_39.mp3 -24.327251 40.172852 -0.077929 10.646977 0.492465 \n",
- "... ... ... ... ... ... \n",
- "rock_65.mp3 -23.952833 24.913488 -1.928245 6.399965 0.176792 \n",
- "rock_68.mp3 -23.004169 24.203032 3.406437 7.585782 -0.129008 \n",
- "rock_73.mp3 -13.621414 26.187157 3.596104 5.598480 0.126061 \n",
- "rock_76.mp3 -29.446217 26.686302 -6.446850 7.489243 0.572411 \n",
- "rock_89.mp3 -29.531443 33.873131 -1.116988 8.490578 0.084522 \n",
- "\n",
- "[80 rows x 201 columns]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test: pd.DataFrame = mfcc_merged.drop(train.index)\n",
- "\n",
- "table = dbrepo_connector.create_table_if_not_exists(\n",
- " test.reset_index(),\n",
- " dbrepo_config[\"tables\"][\"test_set\"][\"name\"], \n",
- " dbrepo_config[\"tables\"][\"test_set\"][\"description\"]\n",
- ")\n",
- "dbrepo_connector.upload_data(test.reset_index(), table[\"id\"])\n",
- "\n",
- "test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "( 0_min 0_max 0_mean 0_std 0_skew \\\n",
- " filename \n",
- " classical_1.mp3 -530.784302 -163.308640 -302.203217 51.141998 -0.468354 \n",
- " classical_10.mp3 -562.857056 -96.162895 -219.258865 53.561945 -0.772325 \n",
- " classical_100.mp3 -536.237244 -61.609112 -177.803986 83.381454 -2.587178 \n",
- " classical_11.mp3 -536.457520 -120.430145 -222.126129 76.246935 -2.402427 \n",
- " classical_12.mp3 -562.675171 -148.132309 -270.974976 52.190831 -0.366585 \n",
- " ... ... ... ... ... ... \n",
- " rock_95.mp3 -553.109985 -5.219127 -193.506073 76.869574 -0.201062 \n",
- " rock_96.mp3 -541.236084 27.163366 -119.113907 58.420538 -0.957677 \n",
- " rock_97.mp3 -518.497375 58.526649 -66.267700 65.635605 -0.898028 \n",
- " rock_98.mp3 -518.643066 53.555084 -45.734737 52.443934 -1.705686 \n",
- " rock_99.mp3 -544.703064 75.608696 -49.380920 54.045598 -0.863099 \n",
- " \n",
- " 1_min 1_max 1_mean 1_std 1_skew \\\n",
- " filename \n",
- " classical_1.mp3 0.000000 178.750244 111.332512 24.847670 -0.402631 \n",
- " classical_10.mp3 0.029843 259.636963 215.093994 18.388213 -1.528703 \n",
- " classical_100.mp3 0.000000 190.474884 112.471695 27.277388 -1.318526 \n",
- " classical_11.mp3 0.000000 159.434616 99.853493 21.916783 -1.176901 \n",
- " classical_12.mp3 0.000000 194.266327 148.226334 19.304869 -0.533313 \n",
- " ... ... ... ... ... ... \n",
- " rock_95.mp3 -89.950394 201.183289 111.724190 36.463682 -0.443205 \n",
- " rock_96.mp3 -7.415216 210.489319 125.453644 31.908835 -0.547472 \n",
- " rock_97.mp3 -58.824471 175.198486 99.288246 25.158395 -0.568067 \n",
- " rock_98.mp3 0.000000 187.040344 96.440956 24.137639 -0.145235 \n",
- " rock_99.mp3 -32.928108 191.734619 93.971260 33.410192 0.040113 \n",
- " \n",
- " ... 38_min 38_max 38_mean 38_std 38_skew \\\n",
- " filename ... \n",
- " classical_1.mp3 ... -44.103783 47.302727 -3.713539 16.553630 0.230696 \n",
- " classical_10.mp3 ... -27.458200 29.808735 0.484320 8.660671 -0.479001 \n",
- " classical_100.mp3 ... -27.338915 27.608841 -0.333135 8.185168 0.208413 \n",
- " classical_11.mp3 ... -31.773474 31.498913 -3.781701 9.191031 0.260900 \n",
- " classical_12.mp3 ... -44.846069 28.488243 -6.241872 10.546620 0.341796 \n",
- " ... ... ... ... ... ... ... \n",
- " rock_95.mp3 ... -27.043680 22.443615 -7.234814 8.471876 0.753810 \n",
- " rock_96.mp3 ... -37.584599 28.085239 -9.704237 8.447570 0.112745 \n",
- " rock_97.mp3 ... -29.620762 26.325855 -5.722816 7.727353 0.207499 \n",
- " rock_98.mp3 ... -26.963133 8.714615 -9.511550 5.551747 -0.025625 \n",
- " rock_99.mp3 ... -21.929461 17.050976 -5.296696 5.894940 0.390704 \n",
- " \n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
- " filename \n",
- " classical_1.mp3 -46.790031 49.353710 -2.282033 15.285611 0.171425 \n",
- " classical_10.mp3 -28.984264 27.534916 0.952575 10.477753 -0.185726 \n",
- " classical_100.mp3 -38.094074 31.399227 -1.494779 10.917259 0.020986 \n",
- " classical_11.mp3 -22.666813 50.993847 1.600794 10.125588 0.595789 \n",
- " classical_12.mp3 -25.048683 46.877155 1.844356 11.160211 0.503148 \n",
- " ... ... ... ... ... ... \n",
- " rock_95.mp3 -24.716900 23.410700 -4.502257 6.688022 0.238779 \n",
- " rock_96.mp3 -38.150497 21.813221 -8.249475 7.807696 0.071979 \n",
- " rock_97.mp3 -29.497421 25.410231 -3.356631 8.170547 0.160343 \n",
- " rock_98.mp3 -23.019743 13.949155 -2.664933 5.051528 -0.258403 \n",
- " rock_99.mp3 -20.984795 29.312206 -0.321817 6.571661 0.384814 \n",
- " \n",
- " [320 rows x 200 columns],\n",
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- " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
- " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
- " 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
- " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
- " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
- " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]))"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# remove labels\n",
- "X = train.drop(meta_columns, axis=1, errors='ignore')\n",
- "\n",
- "columns: pd.Index = X.columns\n",
- "classnames = np.sort(np.unique(mfcc_merged.genre.values)) # => [\"classical\", \"electric\", \"pop\", \"rock\"]\n",
- "\n",
- "# map classname to an index and create dicts for easy lookup in O(1) \n",
- "classname2index = {}\n",
- "index2classname = {}\n",
- "for i, classname in enumerate(classnames):\n",
- " classname2index[classname] = i\n",
- " index2classname[i] = classname\n",
- "\n",
- "# map genre to genre index \n",
- "y = np.array([classname2index[classname] for classname in train.genre.values])\n",
- "\n",
- "(X, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(320, 200)\n",
- "(80, 200)\n",
- "0.25\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
- " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,\n",
- " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "X_test = test.drop(meta_columns, axis=1, errors='ignore')\n",
- "\n",
- "print(X.shape)\n",
- "print(X_test.shape)\n",
- "print(X_test.shape[0] / X.shape[0])\n",
- "print(X_test)\n",
- "\n",
- "y_test = np.array([classname2index[classname] for classname in test.genre.values])\n",
- "\n",
- "y_test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0.38358037, -1.78176967, -1.36991948, ..., -0.7364226 ,\n",
- " 3.30655734, 0.08175137],\n",
- " [-0.44416696, -0.93370955, -0.44485224, ..., 0.22201585,\n",
- " 1.30012279, -0.92935696],\n",
- " [ 0.24284851, -0.49729044, 0.01748849, ..., -0.50315331,\n",
- " 1.4835392 , -0.34414774],\n",
- " ...,\n",
- " [ 0.7006866 , 1.02004099, 1.26143774, ..., -1.05483408,\n",
- " 0.33726991, 0.05037821],\n",
- " [ 0.69692655, 0.95724943, 1.49043913, ..., -0.84987893,\n",
- " -0.96437185, -1.13511025],\n",
- " [ 0.02435901, 1.23578963, 1.44977373, ..., -0.15559615,\n",
- " -0.32998362, 0.68586277]])"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Standardize for PCA\n",
- "\n",
- "scaler = StandardScaler()\n",
- "X_standardized = scaler.fit_transform(X.values)\n",
- "X_test_standardized = scaler.transform(X_test.values)\n",
- "\n",
- "X_standardized"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8584729958125247\n",
- "(320, 50)\n",
- "(80, 50)\n",
- "(320,)\n"
- ]
- }
- ],
- "source": [
- "# Reduce Dimensions via PCA\n",
- "pca = PCA(n_components=50).fit(X_standardized)\n",
- "X_pca = pca.transform(X_standardized)\n",
- "X_test_pca = pca.transform(X_test_standardized)\n",
- "\n",
- "print(sum(pca.explained_variance_ratio_))\n",
- "print(X_pca.shape)\n",
- "print(X_test_pca.shape)\n",
- "print(y.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.78125\n",
- "[[-9.06660619e+00 -4.95915611e-01 -2.41534097e+00 ... 2.40411364e-01\n",
- " 1.72789259e+00 -4.91311052e-01]\n",
- " [ 5.16773703e+00 -3.83363657e+00 3.92975061e+00 ... -5.86681505e-01\n",
- " 2.09582876e+00 -2.22137851e-01]\n",
- " [ 7.22948970e+00 4.63699554e+00 -5.58224821e+00 ... 1.56743403e+00\n",
- " 4.58491534e-03 -1.49357831e+00]\n",
- " ...\n",
- " [-1.01961591e+01 -3.29396783e+00 7.97884202e+00 ... -5.79046872e-01\n",
- " -3.02055536e-01 6.86944177e-01]\n",
- " [-9.99506754e+00 2.99935698e+00 5.78776848e-01 ... 1.34509710e+00\n",
- " -5.60670240e-01 7.77889374e-01]\n",
- " [-3.40625023e+00 -6.51370931e+00 1.73600129e+00 ... 1.38403616e+00\n",
- " 1.20548003e+00 -4.88529043e-01]]\n",
- "[3 0 3 2 3 0 1 2 0 3 0 0 0 1 2 1 2 3 2 1 1 0 3 0 0 0 3 1 1 3 3 2 3 1 2 1 0\n",
- " 1 0 1 3 0 0 0 0 3 3 3 0 3 3 3 1 2 2 0 1 2 1 2 3 2 1 0]\n"
- ]
- }
- ],
- "source": [
- "# Fit SVM:\n",
- "\n",
- "X_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size = 0.2, random_state=4, shuffle = True)\n",
- "\n",
- "clf = SVC(kernel='rbf', probability=True)\n",
- "clf.fit(X_train, y_train)\n",
- "\n",
- "print(accuracy_score(clf.predict(X_val), y_val))\n",
- "print(X_val)\n",
- "print(y_val)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.7380090497737557\n",
- "{'C': 3, 'gamma': 0.01}\n",
- "SVC(C=3, gamma=0.01)\n",
- "0.8125\n"
- ]
- }
- ],
- "source": [
- "# grid for C, gamma\n",
- "C_grid = [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
- "gamma_grid = [0.001, 0.01, 0.1, 1, 10]\n",
- "param_grid = {'C': C_grid, 'gamma': gamma_grid}\n",
- "\n",
- "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5, scoring=\"accuracy\")\n",
- "grid.fit(X_train, y_train)\n",
- "\n",
- "# Find the best model\n",
- "print(grid.best_score_)\n",
- "print(grid.best_params_)\n",
- "print(grid.best_estimator_)\n",
- "print(accuracy_score(grid.predict(X_val), y_val))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8125\n"
- ]
- }
- ],
- "source": [
- "# Optimal model\n",
- "\n",
- "clf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\n",
- "clf.fit(X_train, y_train)\n",
- "\n",
- "print(accuracy_score(clf.predict(X_val), y_val))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.7375\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "array([[9.91845216e-01, 9.24897482e-04, 4.93176202e-03, 2.29812446e-03],\n",
- " [6.01723317e-01, 8.92131018e-02, 1.83536877e-01, 1.25526704e-01],\n",
- " [9.92980326e-01, 4.06235741e-03, 2.13046542e-03, 8.26851594e-04],\n",
- " [3.36749364e-01, 1.30192826e-01, 3.15545229e-01, 2.17512580e-01],\n",
- " [9.89931681e-01, 6.12638061e-03, 1.50677120e-03, 2.43516759e-03],\n",
- " [1.39230069e-01, 4.47802074e-01, 2.20919925e-01, 1.92047932e-01],\n",
- " [9.98585140e-01, 9.17569360e-04, 1.75937182e-04, 3.21353035e-04],\n",
- " [8.72771142e-01, 2.36396678e-02, 7.07467544e-02, 3.28424360e-02],\n",
- " [9.97024364e-01, 2.13307247e-03, 5.99407714e-04, 2.43156147e-04],\n",
- " [9.57312269e-01, 3.64489882e-02, 1.52869112e-03, 4.71005179e-03],\n",
- " [9.63926751e-01, 2.23904373e-02, 5.25083368e-03, 8.43197798e-03],\n",
- " [9.76882097e-01, 1.80467754e-02, 2.91550876e-03, 2.15561864e-03],\n",
- " [9.86101027e-01, 5.93593359e-03, 5.77710502e-03, 2.18593488e-03],\n",
- " [3.81785673e-01, 2.11287208e-01, 3.47139524e-01, 5.97875941e-02],\n",
- " [9.99770590e-01, 9.72645628e-05, 7.14734949e-05, 6.06717729e-05],\n",
- " [9.99718208e-01, 9.59327529e-05, 4.68831010e-05, 1.38975725e-04],\n",
- " [9.98664350e-01, 8.23590644e-04, 2.19002749e-04, 2.93056633e-04],\n",
- " [9.95653630e-01, 2.95465890e-03, 1.04334232e-03, 3.48368483e-04],\n",
- " [9.70777165e-01, 1.19497560e-02, 1.38728808e-02, 3.40019859e-03],\n",
- " [9.95049690e-01, 3.12969093e-03, 8.45368621e-04, 9.75250113e-04],\n",
- " [9.74303099e-01, 1.32680969e-02, 8.50008213e-03, 3.92872185e-03],\n",
- " [8.42731463e-04, 9.90462808e-01, 6.33355769e-03, 2.36090249e-03],\n",
- " [4.79459926e-01, 4.52791677e-01, 4.74690463e-02, 2.02793511e-02],\n",
- " [2.91647861e-03, 4.64246852e-01, 3.83087536e-01, 1.49749134e-01],\n",
- " [8.16342174e-04, 9.51012328e-01, 4.22650314e-02, 5.90629837e-03],\n",
- " [5.96774800e-03, 9.60497995e-01, 2.10855410e-02, 1.24487156e-02],\n",
- " [7.99161377e-01, 1.79424971e-01, 1.43393369e-02, 7.07431535e-03],\n",
- " [1.04674077e-02, 4.40818554e-01, 3.00938632e-01, 2.47775407e-01],\n",
- " [3.25356778e-02, 9.10803927e-01, 1.83357856e-02, 3.83246099e-02],\n",
- " [1.64280091e-02, 8.00394778e-01, 1.47340994e-01, 3.58362194e-02],\n",
- " [1.55786197e-02, 3.99290574e-02, 8.58796140e-01, 8.56961829e-02],\n",
- " [3.95230322e-02, 7.43255541e-01, 1.68078144e-01, 4.91432823e-02],\n",
- " [1.31781934e-01, 5.34164147e-01, 2.51290486e-01, 8.27634332e-02],\n",
- " [3.81509290e-03, 9.16339760e-01, 3.83186127e-02, 4.15265341e-02],\n",
- " [1.61681433e-02, 9.31321871e-01, 2.21475379e-02, 3.03624483e-02],\n",
- " [1.44161658e-01, 5.92306368e-01, 1.34989241e-01, 1.28542733e-01],\n",
- " [7.82461250e-01, 1.58250377e-01, 3.69600606e-02, 2.23283128e-02],\n",
- " [5.60707146e-04, 7.02126528e-01, 1.78411209e-02, 2.79471644e-01],\n",
- " [8.31109051e-04, 9.92480069e-01, 2.32421286e-03, 4.36460947e-03],\n",
- " [1.56240559e-03, 8.36462907e-01, 2.31242202e-02, 1.38850468e-01],\n",
- " [1.32580055e-03, 9.38320075e-01, 4.96939046e-02, 1.06602203e-02],\n",
- " [1.38794668e-01, 1.54264304e-01, 5.59874444e-01, 1.47066585e-01],\n",
- " [7.50775780e-04, 8.21963737e-03, 2.92388248e-01, 6.98641339e-01],\n",
- " [8.02987949e-01, 1.22644045e-01, 5.59497407e-02, 1.84182649e-02],\n",
- " [2.41891759e-02, 1.47958054e-02, 7.67942672e-01, 1.93072347e-01],\n",
- " [1.62547897e-02, 2.22215753e-02, 7.78110676e-01, 1.83412959e-01],\n",
- " [5.71715525e-03, 6.20659434e-03, 6.24328654e-01, 3.63747596e-01],\n",
- " [9.45274448e-04, 5.58115112e-03, 8.61143225e-02, 9.07359252e-01],\n",
- " [1.62382684e-04, 8.57613719e-03, 6.63092127e-01, 3.28169354e-01],\n",
- " [6.07479713e-03, 5.65986706e-01, 9.75020184e-02, 3.30436478e-01],\n",
- " [1.71555533e-02, 4.30539952e-03, 6.56048108e-01, 3.22490940e-01],\n",
- " [4.16744375e-02, 4.57777442e-02, 3.67444591e-01, 5.45103227e-01],\n",
- " [1.89877253e-03, 2.98876223e-03, 6.94207088e-01, 3.00905377e-01],\n",
- " [3.75162390e-04, 8.48227425e-01, 1.02731287e-01, 4.86661259e-02],\n",
- " [7.84318955e-03, 3.57443159e-03, 7.85589067e-01, 2.02993312e-01],\n",
- " [1.01530506e-03, 8.68132661e-03, 4.96617768e-01, 4.93685600e-01],\n",
- " [5.34355379e-02, 4.57249806e-02, 8.52273837e-01, 4.85656449e-02],\n",
- " [1.12516521e-03, 1.02994876e-03, 6.86417759e-01, 3.11427127e-01],\n",
- " [1.58213796e-02, 1.00491368e-02, 7.33944360e-01, 2.40185123e-01],\n",
- " [7.62333824e-04, 6.80082275e-03, 5.48146346e-01, 4.44290497e-01],\n",
- " [4.36889729e-04, 6.33505542e-04, 4.40680010e-01, 5.58249595e-01],\n",
- " [1.18966250e-02, 1.35438849e-02, 8.29531765e-01, 1.45027725e-01],\n",
- " [8.07653263e-04, 2.67535604e-03, 3.94127389e-01, 6.02389601e-01],\n",
- " [5.05221228e-03, 1.02922343e-01, 6.51973559e-01, 2.40051886e-01],\n",
- " [4.78427472e-04, 1.06938816e-02, 1.93545465e-02, 9.69473144e-01],\n",
- " [3.34173601e-04, 4.43903929e-03, 4.61179231e-02, 9.49108864e-01],\n",
- " [2.37711828e-03, 1.70155915e-02, 4.96167715e-01, 4.84439576e-01],\n",
- " [2.55589009e-03, 6.80031325e-01, 7.81276301e-02, 2.39285155e-01],\n",
- " [1.32954460e-01, 3.26210712e-02, 6.26442374e-01, 2.07982095e-01],\n",
- " [1.88338641e-03, 5.54541569e-02, 2.11175008e-01, 7.31487449e-01],\n",
- " [6.17016230e-03, 4.84252773e-02, 3.99923817e-01, 5.45480743e-01],\n",
- " [9.80998496e-05, 8.05418966e-04, 8.17967510e-01, 1.81128972e-01],\n",
- " [3.05929977e-03, 8.53493126e-01, 9.29741568e-03, 1.34150158e-01],\n",
- " [2.01772300e-03, 3.98632319e-04, 2.43445335e-01, 7.54138310e-01],\n",
- " [5.38091290e-04, 7.81047201e-04, 5.55830003e-02, 9.43097861e-01],\n",
- " [2.05641079e-03, 1.17671840e-02, 3.50446042e-01, 6.35730363e-01],\n",
- " [1.30243287e-02, 1.17606642e-02, 2.68970213e-01, 7.06244794e-01],\n",
- " [1.23925453e-02, 2.28662674e-01, 5.87280033e-01, 1.71664748e-01],\n",
- " [1.28705627e-03, 1.99026369e-03, 9.06177034e-02, 9.06104977e-01],\n",
- " [6.18237178e-04, 3.63374936e-03, 2.45263457e-01, 7.50484556e-01]])"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Fit entire training sets\n",
- "clf.fit(X_pca, y)\n",
- "\n",
- "print(accuracy_score(clf.predict(X_test_pca), y_test))\n",
- "print(clf.predict_proba(X_test_pca))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[9.91845216e-01, 9.24897482e-04, 4.93176202e-03, 2.29812446e-03],\n",
- " [6.01723317e-01, 8.92131018e-02, 1.83536877e-01, 1.25526704e-01],\n",
- " [9.92980326e-01, 4.06235741e-03, 2.13046542e-03, 8.26851594e-04],\n",
- " [3.36749364e-01, 1.30192826e-01, 3.15545229e-01, 2.17512580e-01],\n",
- " [9.89931681e-01, 6.12638061e-03, 1.50677120e-03, 2.43516759e-03],\n",
- " [1.39230069e-01, 4.47802074e-01, 2.20919925e-01, 1.92047932e-01],\n",
- " [9.98585140e-01, 9.17569360e-04, 1.75937182e-04, 3.21353035e-04],\n",
- " [8.72771142e-01, 2.36396678e-02, 7.07467544e-02, 3.28424360e-02],\n",
- " [9.97024364e-01, 2.13307247e-03, 5.99407714e-04, 2.43156147e-04],\n",
- " [9.57312269e-01, 3.64489882e-02, 1.52869112e-03, 4.71005179e-03],\n",
- " [9.63926751e-01, 2.23904373e-02, 5.25083368e-03, 8.43197798e-03],\n",
- " [9.76882097e-01, 1.80467754e-02, 2.91550876e-03, 2.15561864e-03],\n",
- " [9.86101027e-01, 5.93593359e-03, 5.77710502e-03, 2.18593488e-03],\n",
- " [3.81785673e-01, 2.11287208e-01, 3.47139524e-01, 5.97875941e-02],\n",
- " [9.99770590e-01, 9.72645628e-05, 7.14734949e-05, 6.06717729e-05],\n",
- " [9.99718208e-01, 9.59327529e-05, 4.68831010e-05, 1.38975725e-04],\n",
- " [9.98664350e-01, 8.23590644e-04, 2.19002749e-04, 2.93056633e-04],\n",
- " [9.95653630e-01, 2.95465890e-03, 1.04334232e-03, 3.48368483e-04],\n",
- " [9.70777165e-01, 1.19497560e-02, 1.38728808e-02, 3.40019859e-03],\n",
- " [9.95049690e-01, 3.12969093e-03, 8.45368621e-04, 9.75250113e-04],\n",
- " [9.74303099e-01, 1.32680969e-02, 8.50008213e-03, 3.92872185e-03],\n",
- " [8.42731463e-04, 9.90462808e-01, 6.33355769e-03, 2.36090249e-03],\n",
- " [4.79459926e-01, 4.52791677e-01, 4.74690463e-02, 2.02793511e-02],\n",
- " [2.91647861e-03, 4.64246852e-01, 3.83087536e-01, 1.49749134e-01],\n",
- " [8.16342174e-04, 9.51012328e-01, 4.22650314e-02, 5.90629837e-03],\n",
- " [5.96774800e-03, 9.60497995e-01, 2.10855410e-02, 1.24487156e-02],\n",
- " [7.99161377e-01, 1.79424971e-01, 1.43393369e-02, 7.07431535e-03],\n",
- " [1.04674077e-02, 4.40818554e-01, 3.00938632e-01, 2.47775407e-01],\n",
- " [3.25356778e-02, 9.10803927e-01, 1.83357856e-02, 3.83246099e-02],\n",
- " [1.64280091e-02, 8.00394778e-01, 1.47340994e-01, 3.58362194e-02],\n",
- " [1.55786197e-02, 3.99290574e-02, 8.58796140e-01, 8.56961829e-02],\n",
- " [3.95230322e-02, 7.43255541e-01, 1.68078144e-01, 4.91432823e-02],\n",
- " [1.31781934e-01, 5.34164147e-01, 2.51290486e-01, 8.27634332e-02],\n",
- " [3.81509290e-03, 9.16339760e-01, 3.83186127e-02, 4.15265341e-02],\n",
- " [1.61681433e-02, 9.31321871e-01, 2.21475379e-02, 3.03624483e-02],\n",
- " [1.44161658e-01, 5.92306368e-01, 1.34989241e-01, 1.28542733e-01],\n",
- " [7.82461250e-01, 1.58250377e-01, 3.69600606e-02, 2.23283128e-02],\n",
- " [5.60707146e-04, 7.02126528e-01, 1.78411209e-02, 2.79471644e-01],\n",
- " [8.31109051e-04, 9.92480069e-01, 2.32421286e-03, 4.36460947e-03],\n",
- " [1.56240559e-03, 8.36462907e-01, 2.31242202e-02, 1.38850468e-01],\n",
- " [1.32580055e-03, 9.38320075e-01, 4.96939046e-02, 1.06602203e-02],\n",
- " [1.38794668e-01, 1.54264304e-01, 5.59874444e-01, 1.47066585e-01],\n",
- " [7.50775780e-04, 8.21963737e-03, 2.92388248e-01, 6.98641339e-01],\n",
- " [8.02987949e-01, 1.22644045e-01, 5.59497407e-02, 1.84182649e-02],\n",
- " [2.41891759e-02, 1.47958054e-02, 7.67942672e-01, 1.93072347e-01],\n",
- " [1.62547897e-02, 2.22215753e-02, 7.78110676e-01, 1.83412959e-01],\n",
- " [5.71715525e-03, 6.20659434e-03, 6.24328654e-01, 3.63747596e-01],\n",
- " [9.45274448e-04, 5.58115112e-03, 8.61143225e-02, 9.07359252e-01],\n",
- " [1.62382684e-04, 8.57613719e-03, 6.63092127e-01, 3.28169354e-01],\n",
- " [6.07479713e-03, 5.65986706e-01, 9.75020184e-02, 3.30436478e-01],\n",
- " [1.71555533e-02, 4.30539952e-03, 6.56048108e-01, 3.22490940e-01],\n",
- " [4.16744375e-02, 4.57777442e-02, 3.67444591e-01, 5.45103227e-01],\n",
- " [1.89877253e-03, 2.98876223e-03, 6.94207088e-01, 3.00905377e-01],\n",
- " [3.75162390e-04, 8.48227425e-01, 1.02731287e-01, 4.86661259e-02],\n",
- " [7.84318955e-03, 3.57443159e-03, 7.85589067e-01, 2.02993312e-01],\n",
- " [1.01530506e-03, 8.68132661e-03, 4.96617768e-01, 4.93685600e-01],\n",
- " [5.34355379e-02, 4.57249806e-02, 8.52273837e-01, 4.85656449e-02],\n",
- " [1.12516521e-03, 1.02994876e-03, 6.86417759e-01, 3.11427127e-01],\n",
- " [1.58213796e-02, 1.00491368e-02, 7.33944360e-01, 2.40185123e-01],\n",
- " [7.62333824e-04, 6.80082275e-03, 5.48146346e-01, 4.44290497e-01],\n",
- " [4.36889729e-04, 6.33505542e-04, 4.40680010e-01, 5.58249595e-01],\n",
- " [1.18966250e-02, 1.35438849e-02, 8.29531765e-01, 1.45027725e-01],\n",
- " [8.07653263e-04, 2.67535604e-03, 3.94127389e-01, 6.02389601e-01],\n",
- " [5.05221228e-03, 1.02922343e-01, 6.51973559e-01, 2.40051886e-01],\n",
- " [4.78427472e-04, 1.06938816e-02, 1.93545465e-02, 9.69473144e-01],\n",
- " [3.34173601e-04, 4.43903929e-03, 4.61179231e-02, 9.49108864e-01],\n",
- " [2.37711828e-03, 1.70155915e-02, 4.96167715e-01, 4.84439576e-01],\n",
- " [2.55589009e-03, 6.80031325e-01, 7.81276301e-02, 2.39285155e-01],\n",
- " [1.32954460e-01, 3.26210712e-02, 6.26442374e-01, 2.07982095e-01],\n",
- " [1.88338641e-03, 5.54541569e-02, 2.11175008e-01, 7.31487449e-01],\n",
- " [6.17016230e-03, 4.84252773e-02, 3.99923817e-01, 5.45480743e-01],\n",
- " [9.80998496e-05, 8.05418966e-04, 8.17967510e-01, 1.81128972e-01],\n",
- " [3.05929977e-03, 8.53493126e-01, 9.29741568e-03, 1.34150158e-01],\n",
- " [2.01772300e-03, 3.98632319e-04, 2.43445335e-01, 7.54138310e-01],\n",
- " [5.38091290e-04, 7.81047201e-04, 5.55830003e-02, 9.43097861e-01],\n",
- " [2.05641079e-03, 1.17671840e-02, 3.50446042e-01, 6.35730363e-01],\n",
- " [1.30243287e-02, 1.17606642e-02, 2.68970213e-01, 7.06244794e-01],\n",
- " [1.23925453e-02, 2.28662674e-01, 5.87280033e-01, 1.71664748e-01],\n",
- " [1.28705627e-03, 1.99026369e-03, 9.06177034e-02, 9.06104977e-01],\n",
- " [6.18237178e-04, 3.63374936e-03, 2.45263457e-01, 7.50484556e-01]])"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "svc_path = BASE_PATH / \"out\" / \"SVC\"/ \"clf.pickle\"\n",
- "svc_path.parent.mkdir(parents=True, exist_ok=True)\n",
- "\n",
- "with open(svc_path, \"wb\") as file:\n",
- " pickle.dump(clf, file)\n",
- " \n",
- "with open(svc_path, \"rb\") as file:\n",
- " loaded = pickle.load(file)\n",
- " \n",
- "loaded.predict_proba(X_test_pca)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " genre pred1 pred2 pred3 pred4\n",
- "filename \n",
- "classical_24.mp3 classical classical pop rock electronic\n",
- "classical_26.mp3 classical classical pop rock electronic\n",
- "classical_34.mp3 classical classical electronic pop rock\n",
- "classical_37.mp3 classical pop classical rock electronic\n",
- "classical_39.mp3 classical classical electronic rock pop\n",
- "classical_40.mp3 classical electronic pop rock classical\n",
- "classical_44.mp3 classical classical electronic rock pop\n",
- "classical_49.mp3 classical classical pop rock electronic\n",
- "classical_54.mp3 classical classical electronic pop rock\n",
- "classical_55.mp3 classical classical electronic rock pop\n",
- "classical_59.mp3 classical classical electronic rock pop\n",
- "classical_60.mp3 classical classical electronic pop rock\n",
- "classical_65.mp3 classical classical pop electronic rock\n",
- "classical_68.mp3 classical pop classical electronic rock\n",
- "classical_7.mp3 classical classical pop electronic rock\n",
- "classical_75.mp3 classical classical rock electronic pop\n",
- "classical_79.mp3 classical classical electronic rock pop\n",
- "classical_80.mp3 classical classical electronic pop rock\n",
- "classical_87.mp3 classical classical pop electronic rock\n",
- "classical_88.mp3 classical classical electronic pop rock\n",
- "classical_91.mp3 classical classical electronic pop rock\n",
- "electronic_1.mp3 electronic electronic pop rock classical\n",
- "electronic_12.mp3 electronic electronic classical pop rock\n",
- "electronic_13.mp3 electronic electronic pop rock classical\n",
- "electronic_15.mp3 electronic electronic pop rock classical\n",
- "electronic_16.mp3 electronic electronic pop rock classical\n",
- "electronic_17.mp3 electronic classical electronic pop rock\n",
- "electronic_21.mp3 electronic electronic pop rock classical\n",
- "electronic_25.mp3 electronic electronic rock classical pop\n",
- "electronic_27.mp3 electronic electronic pop rock classical\n",
- "electronic_36.mp3 electronic pop rock electronic classical\n",
- "electronic_37.mp3 electronic electronic pop rock classical\n",
- "electronic_38.mp3 electronic electronic pop classical rock\n",
- "electronic_4.mp3 electronic electronic pop rock classical\n",
- "electronic_44.mp3 electronic electronic pop rock classical\n",
- "electronic_45.mp3 electronic electronic pop classical rock\n",
- "electronic_47.mp3 electronic classical electronic pop rock\n",
- "electronic_54.mp3 electronic electronic rock pop classical\n",
- "electronic_79.mp3 electronic electronic pop rock classical\n",
- "electronic_80.mp3 electronic electronic rock pop classical\n",
- "electronic_88.mp3 electronic electronic pop rock classical\n",
- "pop_1.mp3 pop pop electronic rock classical\n",
- "pop_21.mp3 pop rock pop electronic classical\n",
- "pop_24.mp3 pop classical electronic pop rock\n",
- "pop_27.mp3 pop pop rock classical electronic\n",
- "pop_32.mp3 pop pop rock electronic classical\n",
- "pop_36.mp3 pop pop rock electronic classical\n",
- "pop_37.mp3 pop rock pop electronic classical\n",
- "pop_44.mp3 pop pop rock electronic classical\n",
- "pop_45.mp3 pop electronic rock pop classical\n",
- "pop_56.mp3 pop pop rock classical electronic\n",
- "pop_6.mp3 pop rock pop electronic classical\n",
- "pop_64.mp3 pop pop rock electronic classical\n",
- "pop_71.mp3 pop electronic pop rock classical\n",
- "pop_74.mp3 pop pop rock classical electronic\n",
- "pop_75.mp3 pop rock pop electronic classical\n",
- "pop_86.mp3 pop pop rock electronic classical\n",
- "pop_89.mp3 pop pop rock classical electronic\n",
- "pop_90.mp3 pop pop rock classical electronic\n",
- "pop_91.mp3 pop pop rock electronic classical\n",
- "pop_94.mp3 pop rock pop electronic classical\n",
- "pop_99.mp3 pop pop rock electronic classical\n",
- "rock_14.mp3 rock rock pop electronic classical\n",
- "rock_19.mp3 rock pop rock electronic classical\n",
- "rock_22.mp3 rock rock pop electronic classical\n",
- "rock_23.mp3 rock rock pop electronic classical\n",
- "rock_27.mp3 rock rock pop electronic classical\n",
- "rock_30.mp3 rock electronic rock pop classical\n",
- "rock_33.mp3 rock pop rock classical electronic\n",
- "rock_35.mp3 rock rock pop electronic classical\n",
- "rock_45.mp3 rock rock pop electronic classical\n",
- "rock_48.mp3 rock pop rock electronic classical\n",
- "rock_50.mp3 rock electronic rock pop classical\n",
- "rock_54.mp3 rock rock pop classical electronic\n",
- "rock_63.mp3 rock rock pop electronic classical\n",
- "rock_65.mp3 rock rock pop electronic classical\n",
- "rock_68.mp3 rock rock pop classical electronic\n",
- "rock_73.mp3 rock pop electronic rock classical\n",
- "rock_76.mp3 rock rock pop electronic classical\n",
- "rock_89.mp3 rock rock pop electronic classical\n"
- ]
- }
- ],
- "source": [
- "# Fit the entire training sets\n",
- "\n",
- "def convert_to_labels(preds, i2c, k=3):\n",
- " ans = []\n",
- " ids = []\n",
- " for p in preds:\n",
- " idx = np.argsort(p)[::-1]\n",
- " ids.append([i for i in idx[:k]])\n",
- " ans.append([i2c[i] for i in idx[:k]])\n",
- "\n",
- " return ans, ids\n",
- "\n",
- "clf.fit(X_pca, y)\n",
- "prediction_lists, percentage_lists = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\n",
- "\n",
- "# # Write to outputs\n",
- "subm = pd.DataFrame(index=test.index)\n",
- "subm['genre'] = test.genre.values\n",
- "subm['pred1'] = [prediction_list[0] for prediction_list in prediction_lists]\n",
- "subm['pred2'] = [prediction_list[1] for prediction_list in prediction_lists]\n",
- "subm['pred3'] = [prediction_list[2] for prediction_list in prediction_lists]\n",
- "subm['pred4'] = [prediction_list[3] for prediction_list in prediction_lists]\n",
- "# subm.to_csv('submission.csv', index=False)\n",
- "\n",
- "pd.set_option('display.max_rows', None)\n",
- "print(subm)\n",
- "pd.reset_option('display.max_rows')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "e67b2b89f4e741058e1552d7f9f0ffaaf2528c880404ac6e8c3c1b8dbbdff8e0"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/main.ipynb b/notebooks/main.ipynb
index 2a20716d1f73bfd468332dab4876eaca1df58e04..d20532b244283f4ac5746627351e2692260b93f4 100644
--- a/notebooks/main.ipynb
+++ b/notebooks/main.ipynb
@@ -3,8 +3,10 @@
{
"cell_type": "markdown",
"source": [
- "#\n",
- "\n"
+ "# Main Notebook\n",
+ "Executes all research notebooks one by one and initializes needed connectors and nbconfig.\n",
+ "\n",
+ "The entities created by one notebook are passed to the next notebook as dependencies, while moving the entites location. This way the output entities are separated from the input entities."
],
"metadata": {
"collapsed": false
@@ -13,89 +15,437 @@
{
"cell_type": "code",
"execution_count": 1,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "test\n"
- ]
+ "metadata": {
+ "collapsed": true,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:19:32.437151147Z",
+ "start_time": "2023-10-10T20:19:32.416560426Z"
}
- ],
+ },
+ "outputs": [],
"source": [
- "%run test.ipynb\n",
- "# from ipynb.fs.full.test import testing\n",
- "# testing()"
- ],
- "metadata": {
- "collapsed": false
- }
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:19:33.208604535Z",
+ "start_time": "2023-10-10T20:19:32.416869981Z"
+ }
+ },
"outputs": [],
"source": [
- "%run 1_audio_files.ipynb"
- ],
- "metadata": {
- "collapsed": false
- }
+ "import yaml\n",
+ "\n",
+ "import logging\n",
+ "\n",
+ "from definitions import CONFIG_PATH, BASE_PATH, RESOURCE_PATH\n",
+ "from fairnb.entity.dbrepo_entity import DbRepoEntity\n",
+ "from fairnb.entity.invenio_entity import InvenioEntity\n",
+ "from fairnb.nb_config import NbConfig\n",
+ "from fairnb.executor import Executor\n",
+ "from fairnb.util import Util"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false,
+ "lines_to_next_cell": 2,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:19:33.338327558Z",
+ "start_time": "2023-10-10T20:19:33.211495550Z"
+ }
+ },
"outputs": [],
"source": [
- "%run 2_generate_features.ipynb"
- ],
- "metadata": {
- "collapsed": false
- }
+ "logging.basicConfig(\n",
+ " level=logging.INFO\n",
+ ")\n",
+ "\n",
+ "ONLY_LOCAL = False\n",
+ "\n",
+ "# experiment executor setup\n",
+ "executor = Executor()\n",
+ "util = Util.get_instance() # util caches loaded credentials -> via Singleton\n",
+ "connector = util.get_dbrepo_connector(CONFIG_PATH / \"dbrepo_config.yml\") if not ONLY_LOCAL else None\n",
+ "invenio_connector = util.get_invenio_connector(CONFIG_PATH / \"invenio_config.yml\") if not ONLY_LOCAL else None\n",
+ "\n",
+ "NOTEBOOK_PATH = BASE_PATH / \"notebooks\"\n",
+ "LOCAL_PATH = BASE_PATH / \"tmp\""
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "%run 3_aggregate_features.ipynb"
- ],
+ "execution_count": 5,
"metadata": {
- "collapsed": false
- }
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:22:19.285948692Z",
+ "start_time": "2023-10-10T20:19:33.368231905Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb\n",
+ "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "Executing: 0%| | 0/7 [00:00<?, ?cell/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "51fe3c161031485d92d9cd7d23edc4d3"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Executing notebook with kernel: python3\n",
+ "INFO:fairnb.api.invenio:Picked up 1 files\n",
+ "INFO:fairnb.api.invenio:Uploading 1 to https://test.researchdata.tuwien.ac.at\n",
+ "INFO:fairnb.api.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/1_audio_files/output/emotifymusic.tar.gz as emotifymusic.tar.gz\n",
+ "INFO:fairnb.api.invenio:Finished upload of emotifymusic.tar.gz\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ------------- Convert Audio Files for Invenio ----\n",
+ "\n",
+ "metadata = yaml.safe_load(open(RESOURCE_PATH / \"1_audio_files\" / \"record_metadata.yml\", \"r\"))\n",
+ "\n",
+ "nb_config_audio_files = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"1_audio_files.ipynb\",\n",
+ " entities=[\n",
+ " audio_files_entity := InvenioEntity.new(\n",
+ " name = \"audio_tar\",\n",
+ " description = \"Raw music files\",\n",
+ " location=LOCAL_PATH / \"1_audio_files\" / \"output\" / \"emotifymusic.tar.gz\",\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"audio_tar\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[]\n",
+ ")\n",
+ "\n",
+ "executor.execute(nb_config_audio_files, only_local=ONLY_LOCAL)"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "%run 4_split.ipynb"
- ],
+ "execution_count": 6,
"metadata": {
- "collapsed": false
- }
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:29:37.869662601Z",
+ "start_time": "2023-10-10T20:22:19.292012183Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/2_generate_features.ipynb\n",
+ "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/2_generate_features.ipynb\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "Executing: 0%| | 0/9 [00:00<?, ?cell/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1542f4273cb540f391061731ae294f62"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Executing notebook with kernel: python3\n",
+ "WARNING:fairnb.api.dbrepo:Re-authenticating due to almost expired token\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ------------- Raw feature generation -------------\n",
+ "nb_config_generate_features = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"2_generate_features.ipynb\",\n",
+ " entities=[\n",
+ " raw_features_entity := DbRepoEntity.new(\n",
+ " name=\"raw_features\",\n",
+ " description=\"Raw features of audio files.\",\n",
+ " location=LOCAL_PATH / \"2_generate_features\" / \"output\" / \"raw_features.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " table_name=\"raw_features\",\n",
+ " table_description=\"desc\",\n",
+ " type=\"raw_features\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " audio_files_entity.copy_to_location(LOCAL_PATH / \"2_generate_features\" / \"input\" / \"emotifymusic.tar.gz\")\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "executor.execute(nb_config_generate_features, only_local=ONLY_LOCAL)\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "%run 5_ml_model.ipynb"
- ],
+ "execution_count": 7,
"metadata": {
- "collapsed": false
- }
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:29:51.551709980Z",
+ "start_time": "2023-10-10T20:29:37.875148186Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb\n",
+ "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "Executing: 0%| | 0/7 [00:00<?, ?cell/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3a3d98af87954445acbde5eb85faaba0"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Executing notebook with kernel: python3\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ------------- Feature Aggregation ----------------\n",
+ "if \"raw_features_entity\" not in globals():\n",
+ " raw_features_entity = DbRepoEntity.new(\n",
+ " location=LOCAL_PATH / \"2_generate_features\" / \"output\" / \"raw_features.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " name=\"raw_features\",\n",
+ " description=\"Raw features of audio files.\",\n",
+ " table_name=\"raw_features\",\n",
+ " table_description=\"desc\",\n",
+ " type=\"raw_features\"\n",
+ " )\n",
+ "\n",
+ "nb_config_aggregate_features = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"3_aggregate_features.ipynb\",\n",
+ " entities=[\n",
+ " features_entity := DbRepoEntity.new(\n",
+ " name=\"aggregated_features\",\n",
+ " description=\"Aggregated features of audio files.\",\n",
+ " location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " table_name=\"aggregated_features\",\n",
+ " table_description=\"Aggregated features of audio files\",\n",
+ " type=\"aggregated_features\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " raw_features_entity.copy_to_location(LOCAL_PATH / \"3_aggregate_features\" / \"input\" / \"raw_features.csv\")\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "executor.execute(nb_config_aggregate_features, only_local=ONLY_LOCAL)"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "%run 6_report.ipynb"
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:29:55.469694709Z",
+ "start_time": "2023-10-10T20:29:51.551964304Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb\n",
+ "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "Executing: 0%| | 0/8 [00:00<?, ?cell/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "340a4bbf08d144aba5521599be170809"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Executing notebook with kernel: python3\n"
+ ]
+ }
],
+ "source": [
+ "# Load features from disk if not already in memory\n",
+ "if \"features_entity\" not in globals():\n",
+ " features_entity = DbRepoEntity.new(\n",
+ " location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " name=\"features\",\n",
+ " description=\"Aggregated features of audio files.\",\n",
+ " table_name=\"aggregated_features\",\n",
+ " table_description=\"Aggregated features of audio files\",\n",
+ " type=\"aggregated_features\"\n",
+ " )\n",
+ "\n",
+ " # or load directly from dbrepo:\n",
+ " # features_entity = DbRepoEntity.existing(\n",
+ " # id=1,\n",
+ " # dbrepo_connector=connector,\n",
+ " # location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\"\n",
+ " # )\n",
+ "\n",
+ "# -------------- SPLITTING -------------------------\n",
+ "nb_config_splits = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"4_split.ipynb\",\n",
+ " entities=[\n",
+ " split_entity := DbRepoEntity.new(\n",
+ " name=\"splits\",\n",
+ " description=\"Splits of aggregated data into testing and training subbsets.\",\n",
+ " table_name=\"splits_table\",\n",
+ " table_description=\"Splits of aggregated data into testing and training subbsets.\",\n",
+ " location=LOCAL_PATH / \"4_split\" / \"output\" / \"split.csv\", # location where script saves generated entity\n",
+ " dbrepo_connector=connector,\n",
+ " type=\"split\",\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " features_entity.copy_to_location(LOCAL_PATH / \"4_split\" / \"input\" / \"features.csv\")\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "# generate splits\n",
+ "executor.execute(nb_config_splits, only_local=ONLY_LOCAL)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
"metadata": {
- "collapsed": false
- }
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:30:03.919914535Z",
+ "start_time": "2023-10-10T20:29:55.479897609Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/5_ml_model.ipynb\n",
+ "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/5_ml_model.ipynb\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "Executing: 0%| | 0/20 [00:00<?, ?cell/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d53f45cbcb33427eafa4c7c174b9dd65"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:papermill:Executing notebook with kernel: python3\n",
+ "INFO:fairnb.api.invenio:Picked up 1 files\n",
+ "INFO:fairnb.api.invenio:Uploading 1 to https://test.researchdata.tuwien.ac.at\n",
+ "INFO:fairnb.api.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/5_ml_model/output/ml_model.pickle as ml_model.pickle\n",
+ "INFO:fairnb.api.invenio:Finished upload of ml_model.pickle\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -------------- ML ------------------------------\n",
+ "with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
+ " metadata = yaml.safe_load(file)\n",
+ "\n",
+ "\n",
+ "nb_config_ml = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " entities=[\n",
+ " ml_model_entity := InvenioEntity.new(\n",
+ " name=\"ml_model\",\n",
+ " description=\"An ml model representing the trained clf\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"ml_model.pickle\",\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"clf\"\n",
+ " ),\n",
+ " test_result_entity := DbRepoEntity.new(\n",
+ " name=\"test_result_entity\",\n",
+ " description=\"Result of tests on ml model\",\n",
+ " table_name=\"test_result\",\n",
+ " table_description=\"Test results of genre prediction on ml model\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"test_result.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " type=\"submission\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " split_entity.copy_to_location(LOCAL_PATH / \"5_ml_model\" / \"input\" / \"split.csv\"), # pass in old entity as new dependency\n",
+ " features_entity.copy_to_location(LOCAL_PATH / \"5_ml_model\" / \"input\" / \"features.csv\")\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "# run ml\n",
+ "executor.execute(nb_config_ml, only_local=ONLY_LOCAL)"
+ ]
}
],
"metadata": {
diff --git a/notebooks/mir.ipynb b/notebooks/mir.ipynb
deleted file mode 100644
index 4c7ef7b942b3a275e45f088014bed3ced3a7df8e..0000000000000000000000000000000000000000
--- a/notebooks/mir.ipynb
+++ /dev/null
@@ -1,362 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import re\n",
- "import requests as rq\n",
- "import yaml\n",
- "import os\n",
- "import pathlib\n",
- "import json\n",
- "import concurrent.futures\n",
- "from root import BASE_PATH\n",
- "\n",
- "\n",
- "RESPONSE_PATH = BASE_PATH / \"out\" / \"responses\"\n",
- "RESPONSE_PATH.mkdir(exist_ok=True, parents=True)\n",
- "(BASE_PATH / \"out\" / \"music\").mkdir(exist_ok=True, parents=True)\n",
- "\n",
- "with open(BASE_PATH / \"config\" / \"config.yml\", \"r\") as file:\n",
- " config = yaml.safe_load(file)\n",
- "\n",
- "\n",
- "host = config.get(\"host\") or \"https://test.researchdata.tuwien.ac.at\"\n",
- "\n",
- "# Require token from config file\n",
- "token = config.get(\"credentials\").get(\"token\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Find all files located in the data directory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "pattern = \"(.*/[a-zA-z0-9_-]+\\\\.mp3|.wav)\"\n",
- "\n",
- "filepaths = [path.absolute() for path in sorted(BASE_PATH.glob('data/**/*.*')) \n",
- " if re.match(pattern, path.as_posix())]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Setup Request header to use authentication token"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "headers= {\n",
- " \"Authorization\": f\"Bearer {token}\"\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load provided Metadata"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "metadata: dict[str, dict]\n",
- "\n",
- "with open(BASE_PATH / \"resource\" / \"record_metadata.yml\", \"r\") as file:\n",
- " metadata = yaml.safe_load(file)\n",
- " \n",
- " \n",
- "metadata.get(\"metadata\")\n",
- "\n",
- "print(metadata)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create a record draft with provided metadata"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "response = rq.post(\n",
- " f\"{host}/api/records\", \n",
- " data = json.dumps(metadata), \n",
- " headers=headers,\n",
- " \n",
- " )\n",
- "print(response)\n",
- "\n",
- "\n",
- "with open(RESPONSE_PATH / \"draft_response.yml\", \"w\") as f:\n",
- " yaml.dump(response.json(), f) \n",
- "\n",
- "print(response.json())\n",
- "record_id = response.json().get(\"id\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_file_draft(upload_name: str):\n",
- " \"\"\" Create a file draft for a later upload of file data\n",
- " \"\"\"\n",
- " return rq.post(\n",
- " f\"{host}/api/records/{record_id}/draft/files\",\n",
- " json=[\n",
- " {\"key\": upload_name}\n",
- " ],\n",
- " headers=headers\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def upload_data(filepath: pathlib.Path, upload_name: str):\n",
- " \"\"\" Upload data for an already created draft file\n",
- " \"\"\"\n",
- " with open(filepath, mode='rb') as f:\n",
- " return rq.put(\n",
- " f\"{host}/api/records/{record_id}/draft/files/{upload_name}/content\", \n",
- " data=f.read(),\n",
- " headers=headers\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def commit_file(upload_name: str):\n",
- " \"\"\" Commits a file where all data was uploaded successfully\n",
- " \"\"\"\n",
- " return rq.post(\n",
- " f\"{host}/api/records/{record_id}/draft/files/{upload_name}/commit\",\n",
- " headers=headers\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def upload_file(filepath: pathlib.Path, upload_name: str):\n",
- " \"\"\" Creates a draft file, uploads the data and commits that file\n",
- " \"\"\"\n",
- " response_draft = create_file_draft(upload_name)\n",
- " response_data = upload_data(filepath, upload_name)\n",
- " response_commit = commit_file(upload_name)\n",
- " \n",
- " responses = {\n",
- " \"draft\": response_draft.json(),\n",
- " \"data\": response_data.json(),\n",
- " \"commit\": response_commit.json(),\n",
- " }\n",
- " \n",
- " with open(BASE_PATH / \"out\" / \"responses\" / f\"upload_{upload_name}.yml\", \"w\") as file:\n",
- " yaml.safe_dump(responses, file)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Upload files in a multithreaded manner to speedup upload\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "args = [(filepath, \"_\".join(filepath.parts[-2:])) \n",
- " for filepath in filepaths]\n",
- "\n",
- "with concurrent.futures.ThreadPoolExecutor(10) as executor:\n",
- " executor.map(lambda p: upload_file(*p), args)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Publish\n",
- "\n",
- "response = rq.post(\n",
- " f\"{host}/api/records/{record_id}/draft/actions/publish\",\n",
- " headers=headers\n",
- " ).json()\n",
- "print(response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(dro_url := response[\"links\"][\"self_doi\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dro_url = \"https://test.researchdata.tuwien.ac.at/records/h6d02-vmm08\"\n",
- "record_number = dro_url.split('/')[-1]\n",
- "filename = \"electronic_1.mp3\"\n",
- "\n",
- "# get record description\n",
- "response = rq.get(\n",
- " f\"{host}/api/records/{record_number}\",\n",
- " headers=headers,\n",
- ")\n",
- "\n",
- "# get list of files \n",
- "response = rq.get(\n",
- " f\"{host}/api/records/{record_number}/files\",\n",
- " headers=headers,\n",
- ")\n",
- "\n",
- "# get metadata of file\n",
- "response = rq.get(\n",
- " f\"{host}/api/records/{record_number}/files/{filename}\",\n",
- " headers=headers\n",
- ")\n",
- "\n",
- "# download a file\n",
- "response = rq.get(\n",
- " f\"{host}/api/records/{record_number}/files/{filename}/content\",\n",
- " headers=headers\n",
- ")\n",
- "\n",
- "\n",
- "with open(BASE_PATH / \"out\" / \"music\" / filename, \"wb\") as file:\n",
- " file.write(response.content)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download all Files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def download_file(record_id: str, filename: str, out_path: pathlib.Path):\n",
- " response = rq.get(\n",
- " f\"{host}/api/records/{record_id}/files/{filename}/content\",\n",
- " headers=headers\n",
- " )\n",
- " \n",
- " out_path.parent.mkdir(exist_ok=True, parents=True)\n",
- " \n",
- " with open(out_path, \"wb\") as file:\n",
- " file.write(response.content)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# get filenames\n",
- "response = rq.get(\n",
- " f\"{host}/api/records/{record_number}/files\",\n",
- " headers=headers,\n",
- ")\n",
- "\n",
- "# download files\n",
- "filenames = sorted([entry.get('key') for entry in response.json().get('entries')])\n",
- "args = [(record_number, filename, BASE_PATH / 'out' / 'music' / filename.split(\"_\")[0] / filename.split(\"_\")[1])\n",
- " for filename in filenames]\n",
- "\n",
- "with concurrent.futures.ThreadPoolExecutor(10) as executor:\n",
- " executor.map(lambda p: download_file(*p), args)\n",
- " "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8 (main, Nov 22 2022, 16:07:51) [GCC 12.2.0]"
- },
- "vscode": {
- "interpreter": {
- "hash": "bbdee169b059bb18a50f2776b13bd9f52e966fe858de13011c8689bad2819b82"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/misc.ipynb b/notebooks/misc.ipynb
deleted file mode 100644
index 637a414c6b7e6abe6737507c2b8ed33218d66a57..0000000000000000000000000000000000000000
--- a/notebooks/misc.ipynb
+++ /dev/null
@@ -1,544 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "\n",
- "import yaml\n",
- "# poc for reproducibility as wrapper\n",
- "%load_ext autoreload\n",
- "%autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "outputs": [],
- "source": [
- "from definitions import CONFIG_PATH, BASE_PATH, RESOURCE_PATH\n",
- "from dbrepo_ismir.artefact import ArtefactMeta, RepositumArtefact, NbConfig, InvenioArtefact, DbRepoArtefact\n",
- "from dbrepo_ismir.reproducer import Reproducer\n",
- "from dbrepo_ismir.util import Util"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "outputs": [],
- "source": [
- "\n",
- "# experiment executor setup\n",
- "rep_config = None\n",
- "reproducer = Reproducer(rep_config)\n",
- "util = Util.get_instance() # util caches loaded credentials -> via Singleton\n",
- "connector = util.get_dbrepo_connector(CONFIG_PATH)\n",
- "\n",
- "\n",
- "NOTEBOOK_PATH = BASE_PATH / \"notebooks\"\n",
- "LOCAL_PATH = BASE_PATH / \"tmp\"\n"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "'Reproducer' object has no attribute 'create_dependency_table'",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[4], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mreproducer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_dependency_table\u001B[49m(connector)\n\u001B[1;32m 2\u001B[0m reproducer\u001B[38;5;241m.\u001B[39mcreate_metadata_table(connector)\n",
- "\u001B[0;31mAttributeError\u001B[0m: 'Reproducer' object has no attribute 'create_dependency_table'"
- ]
- }
- ],
- "source": [
- "# reproducer.create_dependency_table(connector) # optional\n",
- "# reproducer.create_metadata_table(connector) # optional"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "outputs": [],
- "source": [
- "# ------------- Convert Audio Files for Invenio ----\n",
- "nb_config_audio_files = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"1_audio_files.ipynb\",\n",
- " artefacts=[\n",
- " audio_files_artefact := InvenioArtefact(\n",
- " id=None,\n",
- " location=LOCAL_PATH / \"1_audio_files\" / \"output\" / \"emotifymusic.tar.gz\",\n",
- " dbrepo_connector=connector\n",
- " )\n",
- " ],\n",
- " dependencies=[]\n",
- ")\n",
- "\n",
- "reproducer.execute(nb_config_audio_files, only_local=True)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "outputs": [],
- "source": [
- "# ------------- Raw feature generation -------------\n",
- "nb_config_generate_features = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"2_generate_features.ipynb\",\n",
- " artefacts=[\n",
- " raw_features_artefact := DbRepoArtefact(\n",
- " id=None,\n",
- " location=LOCAL_PATH / \"2_generate_features\" / \"output\" / \"raw_features.csv\",\n",
- " dbrepo_connector=connector,\n",
- " table_name=\"raw_features\",\n",
- " table_description=\"desc\",\n",
- " )\n",
- " ],\n",
- " dependencies=[\n",
- " audio_files_artefact.copy_to_location(LOCAL_PATH / \"2_generate_features\" / \"input\" / \"emotifymusic.tar.gz\")\n",
- " ]\n",
- ")\n",
- "\n",
- "reproducer.execute(nb_config_generate_features, only_local=True)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "outputs": [],
- "source": [
- "# ------------- Feature Aggregation ----------------\n",
- "nb_config_aggregate_features = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"3_aggregate_features.ipynb\",\n",
- " artefacts=[\n",
- " features_artefact := DbRepoArtefact(\n",
- " id=None,\n",
- " location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\",\n",
- " dbrepo_connector=connector\n",
- " )\n",
- " ],\n",
- " dependencies=[\n",
- " raw_features_artefact.copy_to_location(LOCAL_PATH / \"3_aggregate_features\" / \"input\" / \"raw_features.csv\")\n",
- " ]\n",
- ")\n",
- "\n",
- "reproducer.execute(nb_config_aggregate_features, only_local=True)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Black is not installed, parameters wont be formatted\n"
- ]
- },
- {
- "data": {
- "text/plain": "Executing: 0%| | 0/10 [00:00<?, ?cell/s]",
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "80c0e5b6706249b9ae527b0079bc1889"
- }
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "if not \"features_artefact\" in globals():\n",
- " features_artefact = DbRepoArtefact(\n",
- " id=None,\n",
- " location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\",\n",
- " dbrepo_connector=connector,\n",
- " name=\"features1\",\n",
- " description=\"Aggregated features2\",\n",
- " table_name=\"features_table\",\n",
- " table_description=\"description of features table\",\n",
- " type=\"features\"\n",
- " )\n",
- "\n",
- "# -------------- SPLITTING -------------------------\n",
- "nb_config_splits = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"4_split.ipynb\",\n",
- " artefacts=[\n",
- " split_artefact := DbRepoArtefact(\n",
- " id=None,\n",
- " name=\"splits\",\n",
- " description=\"Splits of aggregated data into testing and training subbsets.\",\n",
- " table_name=\"splits_table5\",\n",
- " table_description=\"splits_table_description\",\n",
- " location=LOCAL_PATH / \"4_split\" / \"output\" / \"split.csv\", # location where script saves generated artefact\n",
- " dbrepo_connector=connector,\n",
- " type=\"split\",\n",
- " )\n",
- " ],\n",
- " dependencies=[\n",
- " features_artefact.copy_to_location(LOCAL_PATH / \"4_split\" / \"input\" / \"features.csv\")\n",
- " # DbRepoArtefact(\n",
- " # id=None,\n",
- " # # uri=\"dbrepo1.ec.tuwien.ac.at/container/15/database/15/table/25\",\n",
- " # location=LOCAL_PATH / \"4_split\" / \"input\" / \"features.csv\",\n",
- " # dbrepo_connector=connector\n",
- " # )\n",
- " ]\n",
- ")\n",
- "\n",
- "# generate splits\n",
- "reproducer.execute(nb_config_splits, only_local=False)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "outputs": [
- {
- "data": {
- "text/plain": "ArtefactMeta(id=24, uri='https://dbrepo2.ec.tuwien.ac.at/api/container/9/database/9/table/20', name='splits', description='Splits of aggregated data into testing and training subbsets.', commit='050d19049fb6456f8b21935308dba3b36aa2e857', branch='dev', repo_uri='origin', executed_file='notebooks/4_split.ipynb', creation_time=datetime.datetime(2023, 2, 16, 14, 53, 19))"
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "split_artefact.metadata"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Black is not installed, parameters wont be formatted\n"
- ]
- },
- {
- "data": {
- "text/plain": "Executing: 0%| | 0/20 [00:00<?, ?cell/s]",
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "3775e654b17c4ed0a9b6ec62b7fb7182"
- }
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "ename": "Exception",
- "evalue": "Table creation failed for table test_result_artefact",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mException\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[7], line 36\u001B[0m\n\u001B[1;32m 7\u001B[0m nb_config_ml \u001B[38;5;241m=\u001B[39m NbConfig(\n\u001B[1;32m 8\u001B[0m nb_location\u001B[38;5;241m=\u001B[39mNOTEBOOK_PATH \u001B[38;5;241m/\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m5_ml_model.ipynb\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 9\u001B[0m artefacts\u001B[38;5;241m=\u001B[39m[\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 32\u001B[0m ]\n\u001B[1;32m 33\u001B[0m )\n\u001B[1;32m 35\u001B[0m \u001B[38;5;66;03m# run ml\u001B[39;00m\n\u001B[0;32m---> 36\u001B[0m \u001B[43mreproducer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config_ml\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43monly_local\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/dbrepo_ismir/reproducer.py:48\u001B[0m, in \u001B[0;36mReproducer.execute\u001B[0;34m(self, nb_config, require_download, only_local, **kwargs)\u001B[0m\n\u001B[1;32m 37\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_preprocess(nb_config, require_download, only_local)\n\u001B[1;32m 39\u001B[0m papermill\u001B[38;5;241m.\u001B[39mexecute_notebook(\n\u001B[1;32m 40\u001B[0m nb_config\u001B[38;5;241m.\u001B[39mnb_location\u001B[38;5;241m.\u001B[39mresolve(),\n\u001B[1;32m 41\u001B[0m nb_config\u001B[38;5;241m.\u001B[39mnb_location\u001B[38;5;241m.\u001B[39mresolve(),\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 45\u001B[0m )\n\u001B[1;32m 46\u001B[0m )\n\u001B[0;32m---> 48\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_post_process\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43monly_local\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/dbrepo_ismir/reproducer.py:55\u001B[0m, in \u001B[0;36mReproducer._post_process\u001B[0;34m(self, nb_config, only_local)\u001B[0m\n\u001B[1;32m 51\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m only_local:\n\u001B[1;32m 52\u001B[0m \u001B[38;5;66;03m# load generated artefact and upload it\u001B[39;00m\n\u001B[1;32m 53\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m artefact \u001B[38;5;129;01min\u001B[39;00m nb_config\u001B[38;5;241m.\u001B[39martefacts:\n\u001B[1;32m 54\u001B[0m \u001B[38;5;66;03m# use inspect to get path of caller\u001B[39;00m\n\u001B[0;32m---> 55\u001B[0m \u001B[43martefact\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnb_location\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/dbrepo_ismir/artefact.py:259\u001B[0m, in \u001B[0;36mDbRepoArtefact.upload\u001B[0;34m(self, executed_file)\u001B[0m\n\u001B[1;32m 252\u001B[0m df[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124martefact_id\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m \u001B[38;5;66;03m# temporary, needed for easy table schema retrieval\u001B[39;00m\n\u001B[1;32m 254\u001B[0m \u001B[38;5;66;03m# TODO: reference Artefact id via foreign keys in data tables and dependency table\u001B[39;00m\n\u001B[1;32m 255\u001B[0m \n\u001B[1;32m 256\u001B[0m \u001B[38;5;66;03m# create table if not exists\u001B[39;00m\n\u001B[1;32m 257\u001B[0m \u001B[38;5;66;03m# update uri etc\u001B[39;00m\n\u001B[1;32m 258\u001B[0m \u001B[38;5;66;03m# create table if it not already exists\u001B[39;00m\n\u001B[0;32m--> 259\u001B[0m table \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdbrepo_connector\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_table_if_not_exists\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 260\u001B[0m \u001B[43m \u001B[49m\u001B[43mdf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 261\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtable_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 262\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtable_description\u001B[49m\n\u001B[1;32m 263\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 264\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtable_id \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(table[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mid\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[1;32m 266\u001B[0m metadata \u001B[38;5;241m=\u001B[39m ArtefactMeta\u001B[38;5;241m.\u001B[39mnew(\n\u001B[1;32m 267\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname,\n\u001B[1;32m 268\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdescription,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 272\u001B[0m \u001B[38;5;28mtype\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtype\n\u001B[1;32m 273\u001B[0m )\n",
- "File \u001B[0;32m~/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/dbrepo_ismir/dbrepo.py:164\u001B[0m, in \u001B[0;36mDBRepoConnector.create_table_if_not_exists\u001B[0;34m(self, dataframe, table_name, table_descriptor)\u001B[0m\n\u001B[1;32m 158\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcreate_table_if_not_exists\u001B[39m(\u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 159\u001B[0m dataframe: pd\u001B[38;5;241m.\u001B[39mDataFrame,\n\u001B[1;32m 160\u001B[0m table_name: \u001B[38;5;28mstr\u001B[39m,\n\u001B[1;32m 161\u001B[0m table_descriptor: \u001B[38;5;28mstr\u001B[39m\n\u001B[1;32m 162\u001B[0m ):\n\u001B[1;32m 163\u001B[0m table \u001B[38;5;241m=\u001B[39m table \u001B[38;5;28;01mif\u001B[39;00m (table \u001B[38;5;241m:=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_table(table_name)) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \\\n\u001B[0;32m--> 164\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_table\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataframe\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtable_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtable_descriptor\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 166\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m table\n",
- "File \u001B[0;32m~/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/dbrepo_ismir/dbrepo.py:154\u001B[0m, in \u001B[0;36mDBRepoConnector.create_table\u001B[0;34m(self, dataframe, table_name, table_descriptor)\u001B[0m\n\u001B[1;32m 151\u001B[0m table \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_table(table_name)\n\u001B[1;32m 153\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m table:\n\u001B[0;32m--> 154\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTable creation failed for table \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtable_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 156\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m table\n",
- "\u001B[0;31mException\u001B[0m: Table creation failed for table test_result_artefact"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Black is not installed, parameters wont be formatted\n"
- ]
- },
- {
- "data": {
- "text/plain": "Executing: 0%| | 0/20 [00:00<?, ?cell/s]",
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "5db03675ff2c4680addb862e32256860"
- }
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# -------------- ML ------------------------------\n",
- "with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_artefact_metadata.yml\", \"r\") as file:\n",
- " metadata = yaml.safe_load(file)\n",
- "\n",
- "manager = util.get_invenio_manager(CONFIG_PATH)\n",
- "\n",
- "nb_config_ml = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
- " artefacts=[\n",
- " ml_model_artefact := InvenioArtefact.new(\n",
- " name=\"ml_model\",\n",
- " description=\"An ml model representing the trained clf\",\n",
- " location=LOCAL_PATH / \"5_ml_model\" / \"ml_model.pickle\",\n",
- " dbrepo_connector=connector,\n",
- " invenio_manager=manager,\n",
- " record_metadata=metadata,\n",
- " type=\"clf\"\n",
- " ),\n",
- " test_result_artefact := DbRepoArtefact.new(\n",
- " name=\"test_result_artefact\",\n",
- " description=\"Result of tests on ml model\",\n",
- " table_name=\"test_result_artefact\",\n",
- " table_description=\"description\",\n",
- " location=LOCAL_PATH / \"5_ml_model\" / \"test_result.csv\",\n",
- " dbrepo_connector=connector,\n",
- " type=\"submission\"\n",
- " )\n",
- " ],\n",
- " dependencies=[\n",
- " split_artefact.copy_to_location(LOCAL_PATH / \"5_ml_model\" / \"input\" / \"split.csv\"), # pass in old artefact as new dependency\n",
- " features_artefact.copy_to_location(LOCAL_PATH / \"5_ml_model\" / \"input\" / \"features.csv\")\n",
- " ]\n",
- ")\n",
- "\n",
- "# run ml\n",
- "reproducer.execute(nb_config_ml, only_local=False)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "# -------------- REPORT -----------------------\n",
- "nb_config_report = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"6_report.ipynb\",\n",
- " artefacts=[\n",
- " report_artefact := RepositumArtefact(\n",
- " id=None,\n",
- " location=LOCAL_PATH / \"6_report\" / \"report.pdf\",\n",
- " dbrepo_connector=connector\n",
- " )\n",
- " ],\n",
- " dependencies=[\n",
- " ml_model_artefact.copy_to_location(LOCAL_PATH / \"6_report\" / \"ml_model.pickle\"),\n",
- " test_result_artefact.copy_to_location(LOCAL_PATH / \"6_report\" / \"test_result.csv\")\n",
- " ]\n",
- ")\n",
- "\n",
- "# upload report\n",
- "reproducer.execute(nb_config_report)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[Parameter('a', int, value=10), Parameter('test', str, value='asdfjklasdf')]\n",
- "[Parameter('a', int, value=10), Parameter('test', str, value='asdf')]\n",
- "[Parameter('a', int, value=10), Parameter('test', str, value='asdf')]\n",
- "[Parameter('a', int, value=10), Parameter('test', str, value='asdf')]\n"
- ]
- }
- ],
- "source": [
- "import nbparameterise\n",
- "import nbclient\n",
- "import nbformat\n",
- "\n",
- "with open(\"testing.ipynb\") as file:\n",
- " nb = nbformat.read(file, nbformat.NO_CONVERT)\n",
- "\n",
- "orig_parameters = nbparameterise.extract_parameters(nb)\n",
- "\n",
- "print(orig_parameters)\n",
- "\n",
- "params = nbparameterise.parameter_values(orig_parameters, test=\"asdf\")\n",
- "\n",
- "new_nb = nbparameterise.replace_definitions(nb, params)\n",
- "executed_nb = nbclient.execute(new_nb)\n",
- "\n",
- "with open(\"testing.ipynb\", \"w\") as file:\n",
- " nbformat.write(executed_nb, file)"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "outputs": [],
- "source": [
- "import git\n",
- "import datetime\n",
- "from definitions import BASE_PATH\n",
- "\n",
- "repo = git.Repo(BASE_PATH)\n",
- "branch = repo.head.reference\n",
- "branch_name = branch.name\n",
- "commit_sha = branch.repo.commit().hexsha"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "outputs": [
- {
- "data": {
- "text/plain": "'dev'"
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "branch_name"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "outputs": [
- {
- "data": {
- "text/plain": "'ssh://git@gitlab.tuwien.ac.at:822/martin.weise/dbrepo-ismir.git'"
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "branch.repo.remote()"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "outputs": [
- {
- "data": {
- "text/plain": "'17add59eb663db83d6865c3f7733ab11e89b689b'"
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "branch.repo.commit().hexsha"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "testobj = pd.DataFrame()\n",
- "\n"
- ],
- "metadata": {
- "collapsed": false
- }
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/ml_analysis.ipynb b/notebooks/ml_analysis.ipynb
deleted file mode 100644
index 368e2407458c600342e47a71cf028a7972b45e4a..0000000000000000000000000000000000000000
--- a/notebooks/ml_analysis.ipynb
+++ /dev/null
@@ -1,309 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Base: https://www.kaggle.com/code/anmour/svm-using-mfcc-features/notebook\n",
- "\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import os\n",
- "import librosa\n",
- "import scipy\n",
- "import pickle\n",
- "from scipy.stats import skew\n",
- "# from tqdm import tqdm, tqdm_pandas\n",
- "\n",
- "\n",
- "# tqdm.pandas()\n",
- "\n",
- "from sklearn.model_selection import GridSearchCV\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.metrics import accuracy_score\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.decomposition import PCA\n",
- "\n",
- "from sklearn.svm import SVC\n",
- "\n",
- "from pathlib import Path\n",
- "\n",
- "BASE_PATH = Path(\"\").absolute().parent.parent\n",
- "\n",
- "meta_columns = [\"filename\", \"sample\", \"genre\", \"filenumber\"]\n",
- "# 22 / 73"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mfcc_raw: pd.DataFrame = pd.read_pickle(BASE_PATH / \"out\" / \"mfcc\" / \"mfcc.pickle\").set_index('filename')\n",
- "\n",
- "\n",
- "# Aggregate from n rows per file to 1 (calculate min, max, etc. for each feature)\n",
- "\n",
- "mfcc_aggregated = mfcc_raw.drop(meta_columns, axis=1, errors='ignore').groupby(mfcc_raw.index).agg(['min', 'max', 'mean', 'std', 'skew'])\n",
- "mfcc_meta = pd.DataFrame(mfcc_raw['genre'].groupby(mfcc_raw.index).last())\n",
- "mfcc_meta.columns = pd.MultiIndex.from_arrays([['genre'], ['']])\n",
- "mfcc_merged = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\n",
- "\n",
- "\n",
- "# print(mfcc_aggregated)\n",
- "# print(mfcc_aggregated_meta)\n",
- "mfcc_merged"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "train: pd.DataFrame = mfcc_merged.sample(frac = 0.8).sort_index()\n",
- "\n",
- "train"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "test: pd.DataFrame = mfcc_merged.drop(train.index)\n",
- "\n",
- "test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# remove labels\n",
- "X = train.drop(meta_columns, level=0, axis=1, errors='ignore')\n",
- "\n",
- "columns: pd.Index = X.columns\n",
- "classnames = np.sort(np.unique(mfcc_merged.genre.values)) # => [\"classical\", \"electric\", \"pop\", \"rock\"]\n",
- "\n",
- "# map classname to an index and create dicts for easy lookup in O(1) \n",
- "classname2index = {}\n",
- "index2classname = {}\n",
- "for i, classname in enumerate(classnames):\n",
- " classname2index[classname] = i\n",
- " index2classname[i] = classname\n",
- "\n",
- "# map genre to genre index \n",
- "y = np.array([classname2index[classname] for classname in train.genre.values])\n",
- "\n",
- "(X, y)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "X_test = test.drop(meta_columns, level=0, axis=1, errors='ignore')\n",
- "\n",
- "print(X.shape)\n",
- "print(X_test.shape)\n",
- "print(X_test.shape[0] / X.shape[0])\n",
- "X_test\n",
- "\n",
- "y_test = np.array([classname2index[classname] for classname in test.genre.values])\n",
- "\n",
- "y_test"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Standardize for PCA\n",
- "\n",
- "scaler = StandardScaler()\n",
- "X_standardized = scaler.fit_transform(X.values)\n",
- "X_test_standardized = scaler.transform(X_test.values)\n",
- "\n",
- "X_standardized"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Reduce Dimensions via PCA\n",
- "pca = PCA(n_components=50).fit(X_standardized)\n",
- "X_pca = pca.transform(X_standardized)\n",
- "X_test_pca = pca.transform(X_test_standardized)\n",
- "\n",
- "print(sum(pca.explained_variance_ratio_))\n",
- "\n",
- "print(X_pca.shape)\n",
- "print(X_test_pca.shape)\n",
- "print(y.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Fit SVM:\n",
- "\n",
- "X_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size = 0.2, random_state=4, shuffle = True)\n",
- "\n",
- "clf = SVC(kernel='rbf', probability=True)\n",
- "clf.fit(X_train, y_train)\n",
- "\n",
- "print(accuracy_score(clf.predict(X_val), y_val))\n",
- "\n",
- "print(X_val)\n",
- "print(y_val)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# grid for C, gamma\n",
- "C_grid = [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
- "gamma_grid = [0.001, 0.01, 0.1, 1, 10]\n",
- "param_grid = {'C': C_grid, 'gamma': gamma_grid}\n",
- "\n",
- "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5, scoring=\"accuracy\")\n",
- "grid.fit(X_train, y_train)\n",
- "\n",
- "# Find the best model\n",
- "print(grid.best_score_)\n",
- "print(grid.best_params_)\n",
- "print(grid.best_estimator_)\n",
- "print(accuracy_score(grid.predict(X_val), y_val))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Optimal model\n",
- "\n",
- "clf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\n",
- "clf.fit(X_train, y_train)\n",
- "\n",
- "print(accuracy_score(clf.predict(X_val), y_val))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Fit entire training sets\n",
- "clf.fit(X_pca, y)\n",
- "\n",
- "print(accuracy_score(clf.predict(X_test_pca), y_test))\n",
- "\n",
- "clf.predict_proba(X_test_pca)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "svc_path = BASE_PATH / \"out\" / \"SVC\"/ \"clf.pickle\"\n",
- "svc_path.parent.mkdir(parents=True, exist_ok=True)\n",
- "\n",
- "with open(svc_path, \"wb\") as file:\n",
- " pickle.dump(clf, file)\n",
- " \n",
- "with open(svc_path, \"rb\") as file:\n",
- " loaded = pickle.load(file)\n",
- " \n",
- "loaded.predict_proba(X_test_pca)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Fit the entire training sets\n",
- "\n",
- "def convert_to_labels(preds, i2c, k=3):\n",
- " ans = []\n",
- " ids = []\n",
- " for p in preds:\n",
- " idx = np.argsort(p)[::-1]\n",
- " ids.append([i for i in idx[:k]])\n",
- " ans.append([i2c[i] for i in idx[:k]])\n",
- "\n",
- " return ans, ids\n",
- "\n",
- "clf.fit(X_pca, y)\n",
- "prediction_lists, _ = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\n",
- "\n",
- "# # Write to outputs\n",
- "subm = pd.DataFrame(index=test.index)\n",
- "subm['genre'] = test.genre.values\n",
- "subm['pred1'] = [prediction_list[0] for prediction_list in prediction_lists]\n",
- "subm['pred2'] = [prediction_list[1] for prediction_list in prediction_lists]\n",
- "subm['pred3'] = [prediction_list[2] for prediction_list in prediction_lists]\n",
- "subm['pred4'] = [prediction_list[3] for prediction_list in prediction_lists]\n",
- "# subm.to_csv('submission.csv', index=False)\n",
- "\n",
- "pd.set_option('display.max_rows', None)\n",
- "print(subm)\n",
- "pd.reset_option('display.max_rows')\n",
- "\n",
- "# print([prediction_list[0] for prediction_list in prediction_lists])"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.10.8 64-bit ('3.10.8')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8 (main, Nov 22 2022, 16:07:51) [GCC 12.2.0]"
- },
- "vscode": {
- "interpreter": {
- "hash": "bbdee169b059bb18a50f2776b13bd9f52e966fe858de13011c8689bad2819b82"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/standalone.ipynb b/notebooks/standalone.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c1b8f1655daef5ce8ce54ecae40ef75a74e6ffab
--- /dev/null
+++ b/notebooks/standalone.ipynb
@@ -0,0 +1,1075 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Standalone Notebook\n",
+ "\n",
+ "Notebook containing the same functionality as main.ipynb, but it includes all steps in one notebook and does not spin up separate Jupyter Kernels and uploads the entities directly."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "from concurrent.futures import ThreadPoolExecutor\n",
+ "from contextlib import contextmanager, redirect_stderr, redirect_stdout\n",
+ "\n",
+ "import logging\n",
+ "import librosa\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import yaml\n",
+ "from matplotlib import pyplot as plt\n",
+ "from pandas import DataFrame, Index\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "from fairnb.entity.dbrepo_entity import DbRepoEntity\n",
+ "from fairnb.entity.invenio_entity import InvenioEntity\n",
+ "from fairnb.executor import Executor\n",
+ "from fairnb.nb_config import NbConfig\n",
+ "from fairnb.util import Util\n",
+ "from definitions import BASE_PATH, RESOURCE_PATH\n",
+ "import tarfile\n",
+ "import zipfile\n",
+ "import os\n",
+ "from pathlib import Path\n",
+ "from definitions import CONFIG_PATH"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:51:04.540021095Z",
+ "start_time": "2023-10-10T20:51:04.517646578Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "outputs": [],
+ "source": [
+ "logging.basicConfig(\n",
+ " level=logging.INFO\n",
+ ")\n",
+ "\n",
+ "ONLY_LOCAL = True\n",
+ "\n",
+ "# experiment executor setup\n",
+ "executor = Executor()\n",
+ "util = Util.get_instance() # util caches loaded credentials -> via Singleton\n",
+ "connector = util.get_dbrepo_connector(CONFIG_PATH / \"dbrepo_config.yml\") if not ONLY_LOCAL else None\n",
+ "invenio_connector = util.get_invenio_connector(CONFIG_PATH / \"invenio_config.yml\") if not ONLY_LOCAL else None\n",
+ "\n",
+ "NOTEBOOK_PATH = BASE_PATH / \"notebooks\"\n",
+ "LOCAL_PATH = BASE_PATH / \"tmp\" / \"standalone\"\n",
+ "NB_LOCATION = NOTEBOOK_PATH / \"standalone.ipynb\""
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:51:06.316096074Z",
+ "start_time": "2023-10-10T20:51:06.194316265Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1. Audio Files\n",
+ "\n",
+ "Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [],
+ "source": [
+ "tar_path = LOCAL_PATH / \"emotifymusic.tar.gz\"\n",
+ "\n",
+ "# load provided files\n",
+ "zip_path = BASE_PATH / \"resource\" / \"1_audio_files\" / \"emotifymusic.zip\"\n",
+ "dir_path = LOCAL_PATH / \"music\" / \"original\"\n",
+ "\n",
+ "dir_path.mkdir(parents=True, exist_ok=True)\n",
+ "# unzip to dir_path\n",
+ "with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
+ " zfile.extractall(path=dir_path)\n",
+ "\n",
+ "file_paths = list(dir_path.rglob('**/*.*'))\n",
+ "flattened_dir_path = LOCAL_PATH / \"music\" / \"flattened\"\n",
+ "flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "for path in file_paths:\n",
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n",
+ "\n",
+ "tar_path.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "with tarfile.open(tar_path, \"w:gz\") as file:\n",
+ " file.add(flattened_dir_path, arcname=os.path.sep)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:38:05.628405202Z",
+ "start_time": "2023-10-10T20:37:48.721045776Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "outputs": [],
+ "source": [
+ "if not ONLY_LOCAL:\n",
+ " metadata = yaml.safe_load(open(RESOURCE_PATH / \"1_audio_files\" / \"record_metadata.yml\", \"r\"))\n",
+ "\n",
+ " nb_config_audio_files = NbConfig(\n",
+ " nb_location=NB_LOCATION,\n",
+ " entities=[\n",
+ " audio_files_entity := InvenioEntity.new(\n",
+ " name = \"standalone audio_tar\",\n",
+ " description = \"Raw music files\",\n",
+ " location = tar_path,\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"audio_tar\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[]\n",
+ " )\n",
+ "\n",
+ " executor.upload_entities(nb_config_audio_files)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:40:33.684566122Z",
+ "start_time": "2023-10-10T20:38:05.629351864Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2. Feature Extraction of Base audio Files from Invenio\n",
+ "\n",
+ "Load the audio files from the tar, and extract the MFCC features from them and store them in a dataframe."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "outputs": [],
+ "source": [
+ "DEFAULT_SAMPLING_RATE = 22050\n",
+ "\n",
+ "with tarfile.open(tar_path, \"r:gz\") as archive:\n",
+ " archive.extractall(path=(path_out := tar_path.with_suffix(\"\").with_suffix(\"\")))\n",
+ "\n",
+ "files = list(path_out.rglob(\"**/*.*\"))\n",
+ "\n",
+ "\n",
+ "@contextmanager\n",
+ "def suppress_stdout_stderr():\n",
+ " \"\"\"A context manager that redirects stdout and stderr to devnull\"\"\"\n",
+ " with open(os.devnull, 'w') as fnull:\n",
+ " with redirect_stderr(fnull) as err, redirect_stdout(fnull) as out:\n",
+ " yield err, out\n",
+ "\n",
+ "\n",
+ "def generate_mfcc_feature(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE, number_mfccs: int = 40):\n",
+ " x, _ = load_mp3(filepath, sr=sr)\n",
+ " assert sr == _\n",
+ " mfcc = librosa.feature.mfcc(x, sr=sr, n_mfcc=number_mfccs)\n",
+ "\n",
+ " # transpose to use mfcc bands as columns instead of rows\n",
+ " return pd.DataFrame(mfcc).transpose()\n",
+ "\n",
+ "\n",
+ "def load_mp3(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE):\n",
+ " x, sr = librosa.load(filepath, sr=sr) # extract wave (x) with sample rate (sr)\n",
+ " return x, sr\n",
+ "\n",
+ "\n",
+ "with suppress_stdout_stderr(), ThreadPoolExecutor(6) as pool_executor:\n",
+ " dataframes = list(pool_executor.map(\n",
+ " lambda args: generate_mfcc_feature(args), files)\n",
+ " )\n",
+ "for file, dataframe in zip(files, dataframes):\n",
+ " dataframe[\"sample\"] = dataframe.index.to_numpy(copy=True)\n",
+ " dataframe[\"filename\"] = file.name\n",
+ " dataframe[\"label\"] = file.name.split('_')[0] # extract genre from file name\n",
+ "\n",
+ "dataframe_concat = pd.concat(dataframes)\n",
+ "columns_old = list(dataframe_concat.columns)\n",
+ "columns = columns_old[-3:] + columns_old[:-3]\n",
+ "dataframe_concat = dataframe_concat[columns]\n",
+ "\n",
+ "raw_features = dataframe_concat"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:01.473344364Z",
+ "start_time": "2023-10-10T20:40:33.726421123Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[<matplotlib.lines.Line2D at 0x7fabbd4d9480>]"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "<Figure size 640x480 with 1 Axes>",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "example_mfcc = raw_features[raw_features.filename == \"rock_50.mp3\"].sort_values(\"sample\").iloc[:,:]\n",
+ "plt.plot(example_mfcc[15])\n",
+ "# plt.plot(example_mfcc[4])\n",
+ "\n",
+ "# librosa.display.waveshow(audio)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:02.011511025Z",
+ "start_time": "2023-10-10T20:43:01.527557472Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 3. Aggregate MFCC Features"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [],
+ "source": [
+ "# allow for direct entry if features were already created in earlier run\n",
+ "raw_features.to_csv(LOCAL_PATH / \"raw_features.csv\", index=False)\n",
+ "\n",
+ "if \"raw_features\" not in globals():\n",
+ " raw_features = pd.read_csv(LOCAL_PATH / \"raw_features.csv\")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:33.374674832Z",
+ "start_time": "2023-10-10T20:43:02.009164981Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " filename label 0_min 0_max 0_mean \\\n0 classical_1.mp3 classical -530.784363 -163.308350 -302.203156 \n1 classical_10.mp3 classical -562.857849 -96.164795 -219.259018 \n2 classical_100.mp3 classical -536.237366 -61.608826 -177.804108 \n3 classical_11.mp3 classical -536.457458 -120.429665 -222.126312 \n4 classical_12.mp3 classical -562.675232 -148.133560 -270.975403 \n.. ... ... ... ... ... \n395 rock_95.mp3 rock -553.110107 -5.218835 -193.506042 \n396 rock_96.mp3 rock -541.236023 27.163332 -119.113991 \n397 rock_97.mp3 rock -518.494995 58.526745 -66.267746 \n398 rock_98.mp3 rock -518.643066 53.555115 -45.734516 \n399 rock_99.mp3 rock -544.703125 75.612129 -49.380943 \n\n 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n0 51.142183 -0.468374 0.000000 178.751617 111.332344 ... -44.098068 \n1 53.561839 -0.772320 0.029056 259.632721 215.094193 ... -27.458416 \n2 83.381622 -2.587179 0.000000 190.475891 112.471710 ... -27.335688 \n3 76.246992 -2.402419 0.000000 159.425751 99.853645 ... -31.774948 \n4 52.191182 -0.366587 0.000000 194.264160 148.226654 ... -44.843815 \n.. ... ... ... ... ... ... ... \n395 76.869437 -0.201055 -89.948746 201.180450 111.724190 ... -27.043941 \n396 58.420684 -0.957699 -7.415959 210.492462 125.453690 ... -37.584858 \n397 65.635619 -0.898026 -58.824409 175.201355 99.288261 ... -29.620445 \n398 52.444200 -1.705641 0.000000 187.042725 96.440872 ... -26.967852 \n399 54.045627 -0.863093 -32.930649 191.735382 93.971237 ... -21.929403 \n\n 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n0 47.308060 -3.713503 16.553984 0.230691 -46.794479 49.352516 -2.282116 \n1 29.811110 0.484271 8.660648 -0.479016 -28.989979 27.533707 0.952658 \n2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397882 -1.494916 \n3 31.500881 -3.781627 9.191043 0.260886 -22.667439 50.992905 1.600777 \n4 28.490644 -6.242015 10.546545 0.341848 -25.040886 46.878204 1.844494 \n.. ... ... ... ... ... ... ... \n395 22.451445 -7.234633 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n396 28.087940 -9.704238 8.447620 0.112760 -38.147888 21.814400 -8.249507 \n397 26.325895 -5.722826 7.727378 0.207489 -29.497524 25.410656 -3.356615 \n398 8.714736 -9.511492 5.551820 -0.025604 -23.020082 13.948639 -2.664985 \n399 17.050608 -5.296690 5.894962 0.390705 -20.983192 29.312021 -0.321836 \n\n 39_std 39_skew \n0 15.285639 0.171462 \n1 10.477735 -0.185771 \n2 10.917299 0.020984 \n3 10.125545 0.595763 \n4 11.160392 0.503120 \n.. ... ... \n395 6.687983 0.238807 \n396 7.807756 0.071968 \n397 8.170526 0.160330 \n398 5.051498 -0.258407 \n399 6.571660 0.384794 \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561839</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632721</td>\n <td>215.094193</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989979</td>\n <td>27.533707</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397882</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667439</td>\n <td>50.992905</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>...</td>\n <td>-44.843815</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040886</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687983</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163332</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415959</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087940</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814400</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410656</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042725</td>\n <td>96.440872</td>\n <td>...</td>\n <td>-26.967852</td>\n <td>8.714736</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020082</td>\n <td>13.948639</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930649</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894962</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312021</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "meta_columns = [\"sample\", \"filename\", \"label\"]\n",
+ "mfcc_aggregated = raw_features \\\n",
+ " .drop(meta_columns, axis=1, errors='ignore') \\\n",
+ " .groupby(raw_features.filename).agg(['min', 'max', 'mean', 'std', 'skew'])\n",
+ "\n",
+ "mfcc_meta = pd.DataFrame(raw_features['label'].groupby(raw_features.filename).last())\n",
+ "mfcc_meta.columns = pd.MultiIndex.from_arrays([['label'], ['']]) # needed for merge\n",
+ "mfcc_merged = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\n",
+ "\n",
+ "# reduce multi index to single index\n",
+ "one_level_cols = ['_'.join([str(el) for el in col]) for col in mfcc_merged.columns[1:]]\n",
+ "one_level_cols.insert(0, \"label\")\n",
+ "\n",
+ "mfcc_merged.columns = pd.Index(one_level_cols)\n",
+ "mfcc_merged = mfcc_merged.reset_index()\n",
+ "mfcc_merged"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.339271068Z",
+ "start_time": "2023-10-10T20:43:33.447974674Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 4. Split the Features into Train and Test Set"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " filename train\n0 classical_1.mp3 True\n1 classical_10.mp3 True\n2 classical_100.mp3 True\n3 classical_11.mp3 True\n4 classical_12.mp3 False\n.. ... ...\n395 rock_95.mp3 True\n396 rock_96.mp3 True\n397 rock_97.mp3 True\n398 rock_98.mp3 False\n399 rock_99.mp3 False\n\n[400 rows x 2 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>train</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 2 columns</p>\n</div>"
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "features = mfcc_merged\n",
+ "train = features.sample(frac=0.8).sort_index()\n",
+ "test = features.drop(train.index)\n",
+ "\n",
+ "split_true = pd.DataFrame({\n",
+ " \"filename\": train.filename,\n",
+ " \"train\": True\n",
+ "})\n",
+ "split_false = pd.DataFrame({\n",
+ " \"filename\": test.filename,\n",
+ " \"train\": False\n",
+ "})\n",
+ "\n",
+ "split = pd.concat([split_true, split_false]) \\\n",
+ " .sort_values(\"filename\") \\\n",
+ " .reset_index(drop=True)\n",
+ "split"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.483050623Z",
+ "start_time": "2023-10-10T20:43:37.341442870Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 5: Machine Learning model training and evaluation"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561839 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163332 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847562 \nclassical_10.mp3 -0.772320 0.029056 259.632721 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916948 \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 \n... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415959 210.492462 125.453690 31.908870 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \nrock_98.mp3 -1.705641 0.000000 187.042725 96.440872 24.137702 \nrock_99.mp3 -0.863093 -32.930649 191.735382 93.971237 33.410221 \n\n ... 38_max 38_mean 38_std 38_skew 39_min \\\nfilename ... \nclassical_1.mp3 ... 47.308060 -3.713503 16.553984 0.230691 -46.794479 \nclassical_10.mp3 ... 29.811110 0.484271 8.660648 -0.479016 -28.989979 \nclassical_100.mp3 ... 27.610388 -0.333233 8.185075 0.208425 -38.095375 \nclassical_11.mp3 ... 31.500881 -3.781627 9.191043 0.260886 -22.667439 \nclassical_12.mp3 ... 28.490644 -6.242015 10.546545 0.341848 -25.040886 \n... ... ... ... ... ... ... \nrock_95.mp3 ... 22.451445 -7.234633 8.471853 0.753855 -24.712723 \nrock_96.mp3 ... 28.087940 -9.704238 8.447620 0.112760 -38.147888 \nrock_97.mp3 ... 26.325895 -5.722826 7.727378 0.207489 -29.497524 \nrock_98.mp3 ... 8.714736 -9.511492 5.551820 -0.025604 -23.020082 \nrock_99.mp3 ... 17.050608 -5.296690 5.894962 0.390705 -20.983192 \n\n 39_max 39_mean 39_std 39_skew train \nfilename \nclassical_1.mp3 49.352516 -2.282116 15.285639 0.171462 True \nclassical_10.mp3 27.533707 0.952658 10.477735 -0.185771 True \nclassical_100.mp3 31.397882 -1.494916 10.917299 0.020984 True \nclassical_11.mp3 50.992905 1.600777 10.125545 0.595763 True \nclassical_12.mp3 46.878204 1.844494 11.160392 0.503120 False \n... ... ... ... ... ... \nrock_95.mp3 23.410387 -4.502398 6.687983 0.238807 True \nrock_96.mp3 21.814400 -8.249507 7.807756 0.071968 True \nrock_97.mp3 25.410656 -3.356615 8.170526 0.160330 True \nrock_98.mp3 13.948639 -2.664985 5.051498 -0.258407 False \nrock_99.mp3 29.312021 -0.321836 6.571660 0.384794 False \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n <th>train</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847562</td>\n <td>...</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561839</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632721</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989979</td>\n <td>27.533707</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397882</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916948</td>\n <td>...</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667439</td>\n <td>50.992905</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040886</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n <td>False</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687983</td>\n <td>0.238807</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163332</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415959</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908870</td>\n <td>...</td>\n <td>28.087940</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814400</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410656</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042725</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>8.714736</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020082</td>\n <td>13.948639</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n <td>False</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930649</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410221</td>\n <td>...</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894962</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312021</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\n",
+ "joined"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.521919073Z",
+ "start_time": "2023-10-10T20:43:37.383043167Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561839 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_13.mp3 classical -637.720642 -177.713959 -361.834045 71.310080 \n... ... ... ... ... ... \nrock_93.mp3 rock -570.466492 -26.067888 -302.483093 96.569376 \nrock_94.mp3 rock -531.794250 39.474583 -78.520462 51.256666 \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163332 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847562 \nclassical_10.mp3 -0.772320 0.029056 259.632721 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916948 \nclassical_13.mp3 0.008326 0.000000 257.162842 211.556549 20.347035 \n... ... ... ... ... ... \nrock_93.mp3 0.159026 -89.999680 211.889099 103.686363 40.373591 \nrock_94.mp3 -0.846796 -15.139265 177.080322 79.627045 33.557076 \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415959 210.492462 125.453690 31.908870 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \n\n ... 38_min 38_max 38_mean 38_std 38_skew \\\nfilename ... \nclassical_1.mp3 ... -44.098068 47.308060 -3.713503 16.553984 0.230691 \nclassical_10.mp3 ... -27.458416 29.811110 0.484271 8.660648 -0.479016 \nclassical_100.mp3 ... -27.335688 27.610388 -0.333233 8.185075 0.208425 \nclassical_11.mp3 ... -31.774948 31.500881 -3.781627 9.191043 0.260886 \nclassical_13.mp3 ... -24.728806 18.424034 -0.275736 7.026148 -0.640964 \n... ... ... ... ... ... ... \nrock_93.mp3 ... -28.903784 35.712753 2.073339 10.995769 0.249798 \nrock_94.mp3 ... -34.662369 26.375679 -4.778466 6.754501 0.157858 \nrock_95.mp3 ... -27.043941 22.451445 -7.234633 8.471853 0.753855 \nrock_96.mp3 ... -37.584858 28.087940 -9.704238 8.447620 0.112760 \nrock_97.mp3 ... -29.620445 26.325895 -5.722826 7.727378 0.207489 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \nclassical_10.mp3 -28.989979 27.533707 0.952658 10.477735 -0.185771 \nclassical_100.mp3 -38.095375 31.397882 -1.494916 10.917299 0.020984 \nclassical_11.mp3 -22.667439 50.992905 1.600777 10.125545 0.595763 \nclassical_13.mp3 -24.319565 18.439264 -2.147022 8.171929 0.009566 \n... ... ... ... ... ... \nrock_93.mp3 -30.178169 30.612564 -4.677735 8.877041 0.149639 \nrock_94.mp3 -22.063726 29.165359 1.443975 6.737420 -0.092049 \nrock_95.mp3 -24.712723 23.410387 -4.502398 6.687983 0.238807 \nrock_96.mp3 -38.147888 21.814400 -8.249507 7.807756 0.071968 \nrock_97.mp3 -29.497524 25.410656 -3.356615 8.170526 0.160330 \n\n[320 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847562</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561839</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632721</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989979</td>\n <td>27.533707</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397882</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916948</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667439</td>\n <td>50.992905</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>classical_13.mp3</th>\n <td>classical</td>\n <td>-637.720642</td>\n <td>-177.713959</td>\n <td>-361.834045</td>\n <td>71.310080</td>\n <td>0.008326</td>\n <td>0.000000</td>\n <td>257.162842</td>\n <td>211.556549</td>\n <td>20.347035</td>\n <td>...</td>\n <td>-24.728806</td>\n <td>18.424034</td>\n <td>-0.275736</td>\n <td>7.026148</td>\n <td>-0.640964</td>\n <td>-24.319565</td>\n <td>18.439264</td>\n <td>-2.147022</td>\n <td>8.171929</td>\n <td>0.009566</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_93.mp3</th>\n <td>rock</td>\n <td>-570.466492</td>\n <td>-26.067888</td>\n <td>-302.483093</td>\n <td>96.569376</td>\n <td>0.159026</td>\n <td>-89.999680</td>\n <td>211.889099</td>\n <td>103.686363</td>\n <td>40.373591</td>\n <td>...</td>\n <td>-28.903784</td>\n <td>35.712753</td>\n <td>2.073339</td>\n <td>10.995769</td>\n <td>0.249798</td>\n <td>-30.178169</td>\n <td>30.612564</td>\n <td>-4.677735</td>\n <td>8.877041</td>\n <td>0.149639</td>\n </tr>\n <tr>\n <th>rock_94.mp3</th>\n <td>rock</td>\n <td>-531.794250</td>\n <td>39.474583</td>\n <td>-78.520462</td>\n <td>51.256666</td>\n <td>-0.846796</td>\n <td>-15.139265</td>\n <td>177.080322</td>\n <td>79.627045</td>\n <td>33.557076</td>\n <td>...</td>\n <td>-34.662369</td>\n <td>26.375679</td>\n <td>-4.778466</td>\n <td>6.754501</td>\n <td>0.157858</td>\n <td>-22.063726</td>\n <td>29.165359</td>\n <td>1.443975</td>\n <td>6.737420</td>\n <td>-0.092049</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687983</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163332</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415959</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908870</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087940</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814400</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410656</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n </tbody>\n</table>\n<p>320 rows × 201 columns</p>\n</div>"
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\n",
+ "train"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.523494609Z",
+ "start_time": "2023-10-10T20:43:37.426426832Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \nclassical_18.mp3 classical -570.243713 -188.959915 -287.521362 35.322711 \nclassical_19.mp3 classical -543.642334 -106.038223 -216.909943 61.317534 \nclassical_2.mp3 classical -549.406494 -192.532059 -293.008972 27.207027 \nclassical_22.mp3 classical -541.936157 -226.866425 -335.226593 50.647623 \n... ... ... ... ... ... \nrock_82.mp3 rock -544.772827 -57.477421 -285.930237 81.922620 \nrock_84.mp3 rock -553.272583 33.457363 -112.009064 65.035953 \nrock_9.mp3 rock -551.421570 43.786930 -97.513893 78.041649 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std ... \\\nfilename ... \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 ... \nclassical_18.mp3 -0.559489 0.714611 243.072388 207.617828 17.594885 ... \nclassical_19.mp3 -3.473125 0.000000 151.947662 93.405411 22.029233 ... \nclassical_2.mp3 -0.426848 0.000000 231.037369 198.662506 14.957660 ... \nclassical_22.mp3 -0.545184 0.000000 176.146393 133.592239 17.983436 ... \n... ... ... ... ... ... ... \nrock_82.mp3 0.506014 -83.866180 177.183060 105.448738 35.660641 ... \nrock_84.mp3 -0.535031 -6.800635 195.284622 105.075165 32.223748 ... \nrock_9.mp3 -0.795955 -39.516315 157.909393 76.734253 26.308925 ... \nrock_98.mp3 -1.705641 0.000000 187.042725 96.440872 24.137702 ... \nrock_99.mp3 -0.863093 -32.930649 191.735382 93.971237 33.410221 ... \n\n 38_min 38_max 38_mean 38_std 38_skew \\\nfilename \nclassical_12.mp3 -44.843815 28.490644 -6.242015 10.546545 0.341848 \nclassical_18.mp3 -21.249855 22.553038 -1.832725 6.320877 0.191670 \nclassical_19.mp3 -27.029383 30.682745 3.342259 8.420860 0.043171 \nclassical_2.mp3 -25.912935 24.293318 0.746096 8.240027 -0.022513 \nclassical_22.mp3 -29.110729 27.870188 -0.569063 8.987627 0.238096 \n... ... ... ... ... ... \nrock_82.mp3 -31.321337 29.233349 -2.918372 8.329695 0.080753 \nrock_84.mp3 -28.911598 27.619001 -5.295718 6.987569 0.206062 \nrock_9.mp3 -38.184456 27.128735 -2.393547 7.633860 0.357629 \nrock_98.mp3 -26.967852 8.714736 -9.511492 5.551820 -0.025604 \nrock_99.mp3 -21.929403 17.050608 -5.296690 5.894962 0.390705 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_12.mp3 -25.040886 46.878204 1.844494 11.160392 0.503120 \nclassical_18.mp3 -17.271332 23.015621 -0.735780 6.175781 0.406759 \nclassical_19.mp3 -25.900253 36.766388 2.389575 10.099726 0.140336 \nclassical_2.mp3 -18.561390 23.484133 3.115819 7.220346 0.242364 \nclassical_22.mp3 -18.535694 41.965923 3.331284 9.619688 0.652851 \n... ... ... ... ... ... \nrock_82.mp3 -30.654737 29.915272 -2.396760 7.691486 0.180867 \nrock_84.mp3 -21.169910 31.117376 -0.642526 6.866395 0.398194 \nrock_9.mp3 -27.389053 24.929546 -1.376936 5.924625 -0.053863 \nrock_98.mp3 -23.020082 13.948639 -2.664985 5.051498 -0.258407 \nrock_99.mp3 -20.983192 29.312021 -0.321836 6.571660 0.384794 \n\n[80 rows x 201 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>-44.843815</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040886</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>classical_18.mp3</th>\n <td>classical</td>\n <td>-570.243713</td>\n <td>-188.959915</td>\n <td>-287.521362</td>\n <td>35.322711</td>\n <td>-0.559489</td>\n <td>0.714611</td>\n <td>243.072388</td>\n <td>207.617828</td>\n <td>17.594885</td>\n <td>...</td>\n <td>-21.249855</td>\n <td>22.553038</td>\n <td>-1.832725</td>\n <td>6.320877</td>\n <td>0.191670</td>\n <td>-17.271332</td>\n <td>23.015621</td>\n <td>-0.735780</td>\n <td>6.175781</td>\n <td>0.406759</td>\n </tr>\n <tr>\n <th>classical_19.mp3</th>\n <td>classical</td>\n <td>-543.642334</td>\n <td>-106.038223</td>\n <td>-216.909943</td>\n <td>61.317534</td>\n <td>-3.473125</td>\n <td>0.000000</td>\n <td>151.947662</td>\n <td>93.405411</td>\n <td>22.029233</td>\n <td>...</td>\n <td>-27.029383</td>\n <td>30.682745</td>\n <td>3.342259</td>\n <td>8.420860</td>\n <td>0.043171</td>\n <td>-25.900253</td>\n <td>36.766388</td>\n <td>2.389575</td>\n <td>10.099726</td>\n <td>0.140336</td>\n </tr>\n <tr>\n <th>classical_2.mp3</th>\n <td>classical</td>\n <td>-549.406494</td>\n <td>-192.532059</td>\n <td>-293.008972</td>\n <td>27.207027</td>\n <td>-0.426848</td>\n <td>0.000000</td>\n <td>231.037369</td>\n <td>198.662506</td>\n <td>14.957660</td>\n <td>...</td>\n <td>-25.912935</td>\n <td>24.293318</td>\n <td>0.746096</td>\n <td>8.240027</td>\n <td>-0.022513</td>\n <td>-18.561390</td>\n <td>23.484133</td>\n <td>3.115819</td>\n <td>7.220346</td>\n <td>0.242364</td>\n </tr>\n <tr>\n <th>classical_22.mp3</th>\n <td>classical</td>\n <td>-541.936157</td>\n <td>-226.866425</td>\n <td>-335.226593</td>\n <td>50.647623</td>\n <td>-0.545184</td>\n <td>0.000000</td>\n <td>176.146393</td>\n <td>133.592239</td>\n <td>17.983436</td>\n <td>...</td>\n <td>-29.110729</td>\n <td>27.870188</td>\n <td>-0.569063</td>\n <td>8.987627</td>\n <td>0.238096</td>\n <td>-18.535694</td>\n <td>41.965923</td>\n <td>3.331284</td>\n <td>9.619688</td>\n <td>0.652851</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_82.mp3</th>\n <td>rock</td>\n <td>-544.772827</td>\n <td>-57.477421</td>\n <td>-285.930237</td>\n <td>81.922620</td>\n <td>0.506014</td>\n <td>-83.866180</td>\n <td>177.183060</td>\n <td>105.448738</td>\n <td>35.660641</td>\n <td>...</td>\n <td>-31.321337</td>\n <td>29.233349</td>\n <td>-2.918372</td>\n <td>8.329695</td>\n <td>0.080753</td>\n <td>-30.654737</td>\n <td>29.915272</td>\n <td>-2.396760</td>\n <td>7.691486</td>\n <td>0.180867</td>\n </tr>\n <tr>\n <th>rock_84.mp3</th>\n <td>rock</td>\n <td>-553.272583</td>\n <td>33.457363</td>\n <td>-112.009064</td>\n <td>65.035953</td>\n <td>-0.535031</td>\n <td>-6.800635</td>\n <td>195.284622</td>\n <td>105.075165</td>\n <td>32.223748</td>\n <td>...</td>\n <td>-28.911598</td>\n <td>27.619001</td>\n <td>-5.295718</td>\n <td>6.987569</td>\n <td>0.206062</td>\n <td>-21.169910</td>\n <td>31.117376</td>\n <td>-0.642526</td>\n <td>6.866395</td>\n <td>0.398194</td>\n </tr>\n <tr>\n <th>rock_9.mp3</th>\n <td>rock</td>\n <td>-551.421570</td>\n <td>43.786930</td>\n <td>-97.513893</td>\n <td>78.041649</td>\n <td>-0.795955</td>\n <td>-39.516315</td>\n <td>157.909393</td>\n <td>76.734253</td>\n <td>26.308925</td>\n <td>...</td>\n <td>-38.184456</td>\n <td>27.128735</td>\n <td>-2.393547</td>\n <td>7.633860</td>\n <td>0.357629</td>\n <td>-27.389053</td>\n <td>24.929546</td>\n <td>-1.376936</td>\n <td>5.924625</td>\n <td>-0.053863</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042725</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>-26.967852</td>\n <td>8.714736</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020082</td>\n <td>13.948639</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930649</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410221</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894962</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312021</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>80 rows × 201 columns</p>\n</div>"
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\n",
+ "test"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.532989116Z",
+ "start_time": "2023-10-10T20:43:37.469652946Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "( 0_min 0_max 0_mean 0_std 0_skew \\\n filename \n classical_1.mp3 -530.784363 -163.308350 -302.203156 51.142183 -0.468374 \n classical_10.mp3 -562.857849 -96.164795 -219.259018 53.561839 -0.772320 \n classical_100.mp3 -536.237366 -61.608826 -177.804108 83.381622 -2.587179 \n classical_11.mp3 -536.457458 -120.429665 -222.126312 76.246992 -2.402419 \n classical_13.mp3 -637.720642 -177.713959 -361.834045 71.310080 0.008326 \n ... ... ... ... ... ... \n rock_93.mp3 -570.466492 -26.067888 -302.483093 96.569376 0.159026 \n rock_94.mp3 -531.794250 39.474583 -78.520462 51.256666 -0.846796 \n rock_95.mp3 -553.110107 -5.218835 -193.506042 76.869437 -0.201055 \n rock_96.mp3 -541.236023 27.163332 -119.113991 58.420684 -0.957699 \n rock_97.mp3 -518.494995 58.526745 -66.267746 65.635619 -0.898026 \n \n 1_min 1_max 1_mean 1_std 1_skew \\\n filename \n classical_1.mp3 0.000000 178.751617 111.332344 24.847562 -0.402642 \n classical_10.mp3 0.029056 259.632721 215.094193 18.388131 -1.528750 \n classical_100.mp3 0.000000 190.475891 112.471710 27.277553 -1.318523 \n classical_11.mp3 0.000000 159.425751 99.853645 21.916948 -1.176922 \n classical_13.mp3 0.000000 257.162842 211.556549 20.347035 -1.050120 \n ... ... ... ... ... ... \n rock_93.mp3 -89.999680 211.889099 103.686363 40.373591 -1.760946 \n rock_94.mp3 -15.139265 177.080322 79.627045 33.557076 0.103628 \n rock_95.mp3 -89.948746 201.180450 111.724190 36.463584 -0.443224 \n rock_96.mp3 -7.415959 210.492462 125.453690 31.908870 -0.547468 \n rock_97.mp3 -58.824409 175.201355 99.288261 25.158417 -0.568056 \n \n ... 38_min 38_max 38_mean 38_std 38_skew \\\n filename ... \n classical_1.mp3 ... -44.098068 47.308060 -3.713503 16.553984 0.230691 \n classical_10.mp3 ... -27.458416 29.811110 0.484271 8.660648 -0.479016 \n classical_100.mp3 ... -27.335688 27.610388 -0.333233 8.185075 0.208425 \n classical_11.mp3 ... -31.774948 31.500881 -3.781627 9.191043 0.260886 \n classical_13.mp3 ... -24.728806 18.424034 -0.275736 7.026148 -0.640964 \n ... ... ... ... ... ... ... \n rock_93.mp3 ... -28.903784 35.712753 2.073339 10.995769 0.249798 \n rock_94.mp3 ... -34.662369 26.375679 -4.778466 6.754501 0.157858 \n rock_95.mp3 ... -27.043941 22.451445 -7.234633 8.471853 0.753855 \n rock_96.mp3 ... -37.584858 28.087940 -9.704238 8.447620 0.112760 \n rock_97.mp3 ... -29.620445 26.325895 -5.722826 7.727378 0.207489 \n \n 39_min 39_max 39_mean 39_std 39_skew \n filename \n classical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \n classical_10.mp3 -28.989979 27.533707 0.952658 10.477735 -0.185771 \n classical_100.mp3 -38.095375 31.397882 -1.494916 10.917299 0.020984 \n classical_11.mp3 -22.667439 50.992905 1.600777 10.125545 0.595763 \n classical_13.mp3 -24.319565 18.439264 -2.147022 8.171929 0.009566 \n ... ... ... ... ... ... \n rock_93.mp3 -30.178169 30.612564 -4.677735 8.877041 0.149639 \n rock_94.mp3 -22.063726 29.165359 1.443975 6.737420 -0.092049 \n rock_95.mp3 -24.712723 23.410387 -4.502398 6.687983 0.238807 \n rock_96.mp3 -38.147888 21.814400 -8.249507 7.807756 0.071968 \n rock_97.mp3 -29.497524 25.410656 -3.356615 8.170526 0.160330 \n \n [320 rows x 200 columns],\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]))"
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# remove labels\n",
+ "X = train.drop(['label'], axis=1, errors='ignore')\n",
+ "\n",
+ "columns: Index = X.columns\n",
+ "classnames = np.sort(np.unique(joined.label.values)) # -> [\"classical\", \"electronic\", \"pop\", \"rock\"]\n",
+ "\n",
+ "# map classname to an index and create dicts for easy lookup in O(1)\n",
+ "classname2index = {}\n",
+ "index2classname = {}\n",
+ "\n",
+ "for i, classname in enumerate(classnames):\n",
+ " classname2index[classname] = i\n",
+ " index2classname[i] = classname\n",
+ "\n",
+ "# map label to label index\n",
+ "y = np.array([classname2index[classname] for classname in train.label.values])\n",
+ "\n",
+ "(X, y)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.537246329Z",
+ "start_time": "2023-10-10T20:43:37.469936549Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(320, 200)\n",
+ "(80, 200)\n",
+ "0.25\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_test = test.drop(['label'], axis=1, errors='ignore')\n",
+ "\n",
+ "print(X.shape)\n",
+ "print(X_test.shape)\n",
+ "print(X_test.shape[0] / X.shape[0]) # fraction of test sample\n",
+ "\n",
+ "y_test = np.array([classname2index[classname] for classname in test.label.values])\n",
+ "y_test"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.717205547Z",
+ "start_time": "2023-10-10T20:43:37.487249746Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "array([[ 0.42613404, -1.82530004, -1.35373394, ..., -0.72391099,\n 3.57791914, 0.11656626],\n [-0.40732633, -0.95390951, -0.43778646, ..., 0.24817291,\n 1.42713353, -0.91164704],\n [ 0.28443252, -0.50544138, 0.0199978 , ..., -0.48734903,\n 1.62376978, -0.31654845],\n ...,\n [-0.15402189, 0.22638917, -0.15339779, ..., -1.39112939,\n -0.26818789, 0.31040331],\n [ 0.15453761, 0.64664565, 0.66810948, ..., -2.51717512,\n 0.23273515, -0.16980445],\n [ 0.74548507, 1.05368071, 1.25168761, ..., -1.0468092 ,\n 0.39501813, 0.08452671]])"
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Standardize for PCA\n",
+ "scaler = StandardScaler()\n",
+ "X_standardized = scaler.fit_transform(X.values)\n",
+ "X_test_standardized = scaler.transform(X_test.values)\n",
+ "\n",
+ "X_standardized"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.742380165Z",
+ "start_time": "2023-10-10T20:43:37.512601397Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.8567747879571861\n",
+ "(320, 50)\n",
+ "(80, 50)\n",
+ "(320,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Reduce Dimensions via PCA\n",
+ "pca = PCA(n_components=50).fit(X_standardized)\n",
+ "X_pca = pca.transform(X_standardized)\n",
+ "X_test_pca = pca.transform(X_test_standardized)\n",
+ "\n",
+ "print(sum(pca.explained_variance_ratio_))\n",
+ "print(X_pca.shape)\n",
+ "print(X_test_pca.shape)\n",
+ "print(y.shape)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.742719199Z",
+ "start_time": "2023-10-10T20:43:37.556397316Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.765625\n",
+ "[[-7.47135617 -4.0735689 -1.75958109 ... 0.03567986 0.25908476\n",
+ " -1.02348857]\n",
+ " [ 7.98657438 -0.32835412 1.61837082 ... -0.30170778 -1.12335641\n",
+ " 1.1826182 ]\n",
+ " [ 6.29134947 -2.71877979 -6.46515986 ... -0.69992826 -0.19818608\n",
+ " 0.11547002]\n",
+ " ...\n",
+ " [ 4.54103139 -1.40004059 3.28019036 ... -0.04582758 -1.81567251\n",
+ " 0.70422807]\n",
+ " [ 6.58645856 -0.91251805 -0.24337363 ... 1.56437846 1.11739736\n",
+ " 2.74055111]\n",
+ " [ 2.48429716 -4.80891624 4.67724279 ... -0.80355261 0.54566587\n",
+ " -2.43086802]]\n",
+ "[3 0 3 2 3 0 1 2 0 3 0 0 0 1 2 1 2 3 2 1 1 0 3 0 0 0 3 1 1 3 3 2 3 1 2 2 0\n",
+ " 1 0 1 3 0 0 0 0 3 3 3 0 3 3 3 1 2 2 0 1 2 1 2 3 2 1 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit SVM:\n",
+ "\n",
+ "X_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size = 0.2, random_state=4, shuffle = True)\n",
+ "\n",
+ "clf = SVC(kernel='rbf', probability=True)\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_val), y_val))\n",
+ "print(X_val)\n",
+ "print(y_val)\n"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:37.833888094Z",
+ "start_time": "2023-10-10T20:43:37.613471972Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.7425339366515837\n",
+ "{'C': 1, 'gamma': 0.01}\n",
+ "SVC(C=1, gamma=0.01)\n",
+ "0.8125\n"
+ ]
+ }
+ ],
+ "source": [
+ "# grid for C, gamma\n",
+ "C_grid = [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+ "gamma_grid = [0.001, 0.01, 0.1, 1, 10]\n",
+ "param_grid = {'C': C_grid, 'gamma': gamma_grid}\n",
+ "\n",
+ "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5, scoring=\"accuracy\")\n",
+ "grid.fit(X_train, y_train)\n",
+ "\n",
+ "# Find the best model\n",
+ "print(grid.best_score_)\n",
+ "print(grid.best_params_)\n",
+ "print(grid.best_estimator_)\n",
+ "print(accuracy_score(grid.predict(X_val), y_val))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:40.405459626Z",
+ "start_time": "2023-10-10T20:43:37.685840465Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.796875\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Optimal model\n",
+ "\n",
+ "clf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_val), y_val))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:40.457934105Z",
+ "start_time": "2023-10-10T20:43:40.393926867Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.775\n",
+ "[[9.42040797e-01 2.70174155e-02 2.24723009e-02 8.46948646e-03]\n",
+ " [9.96740378e-01 1.69566371e-03 5.62068675e-04 1.00188957e-03]\n",
+ " [9.99641103e-01 2.61229482e-05 2.29803429e-04 1.02970864e-04]\n",
+ " [9.92994584e-01 3.84209358e-03 1.55640848e-03 1.60691385e-03]\n",
+ " [9.76972726e-01 4.65350915e-03 1.43338762e-02 4.03988862e-03]\n",
+ " [7.95407527e-01 4.84864390e-02 8.52062441e-02 7.08997901e-02]\n",
+ " [9.98557419e-01 2.09205917e-04 7.14230514e-04 5.19144171e-04]\n",
+ " [9.40476603e-01 3.49122929e-02 1.69428596e-02 7.66824460e-03]\n",
+ " [8.39761011e-01 4.38310450e-03 9.41041205e-02 6.17517637e-02]\n",
+ " [2.50318465e-01 1.37819918e-01 4.59288899e-01 1.52572718e-01]\n",
+ " [9.92696391e-01 3.20809762e-03 2.05803543e-03 2.03747550e-03]\n",
+ " [9.98690083e-01 1.69577396e-04 6.76374089e-04 4.63965809e-04]\n",
+ " [1.62411839e-01 3.13404410e-01 3.82366473e-01 1.41817279e-01]\n",
+ " [9.26101583e-01 4.83188150e-02 1.73274245e-02 8.25217729e-03]\n",
+ " [8.76874571e-01 6.46209287e-02 2.33451795e-02 3.51593204e-02]\n",
+ " [9.98893757e-01 4.70821073e-04 3.88879673e-04 2.46542605e-04]\n",
+ " [9.98165353e-01 2.48150136e-04 5.93550303e-04 9.92946849e-04]\n",
+ " [9.97924494e-01 1.53141224e-03 2.49974948e-04 2.94118560e-04]\n",
+ " [2.20837801e-01 9.23419164e-03 4.79730588e-01 2.90197419e-01]\n",
+ " [9.93523987e-01 5.01163077e-03 6.82304736e-04 7.82077822e-04]\n",
+ " [9.99353412e-01 1.91613208e-05 1.31625123e-04 4.95801957e-04]\n",
+ " [1.34051158e-03 9.81984508e-01 9.71471844e-03 6.96026153e-03]\n",
+ " [4.23594840e-03 4.00700590e-01 3.36815417e-01 2.58248046e-01]\n",
+ " [1.39718596e-03 9.55091999e-01 7.89301953e-03 3.56177954e-02]\n",
+ " [9.05305283e-01 2.44194916e-02 5.27647495e-02 1.75104758e-02]\n",
+ " [6.54443813e-04 9.08179938e-01 1.02579366e-02 8.09076815e-02]\n",
+ " [1.04887860e-01 4.84429978e-01 3.18205740e-01 9.24764216e-02]\n",
+ " [7.21557359e-01 1.83615195e-01 6.18721579e-02 3.29552881e-02]\n",
+ " [1.12591815e-02 7.02854455e-01 1.74324699e-01 1.11561664e-01]\n",
+ " [3.40834219e-02 2.35798041e-01 5.98814568e-01 1.31303970e-01]\n",
+ " [1.00316726e-02 8.48188499e-01 5.27064479e-02 8.90733801e-02]\n",
+ " [2.64847321e-03 8.84959697e-01 7.24790029e-03 1.05143929e-01]\n",
+ " [1.14737746e-01 3.54443868e-01 4.09198161e-01 1.21620225e-01]\n",
+ " [4.10243526e-03 8.75972699e-01 9.35401686e-02 2.63846974e-02]\n",
+ " [1.64710505e-03 8.54733046e-01 1.12462393e-02 1.32373609e-01]\n",
+ " [1.68779748e-03 7.83834395e-01 1.66376175e-01 4.81016319e-02]\n",
+ " [2.73140922e-03 9.58337246e-01 7.75738177e-03 3.11739630e-02]\n",
+ " [1.09130510e-03 3.01394038e-01 5.27663335e-01 1.69851323e-01]\n",
+ " [2.45514360e-04 6.03104425e-01 8.67940558e-03 3.87970655e-01]\n",
+ " [5.05012887e-02 8.47586989e-01 7.06306173e-02 3.12811050e-02]\n",
+ " [3.71938549e-04 9.29722757e-01 2.28770191e-02 4.70282851e-02]\n",
+ " [7.10002677e-02 7.66495556e-01 1.19039172e-01 4.34650037e-02]\n",
+ " [1.02766752e-02 5.82721296e-01 2.72757462e-01 1.34244567e-01]\n",
+ " [1.14582797e-03 9.85977484e-01 6.38762444e-03 6.48906409e-03]\n",
+ " [4.53745403e-02 2.48837938e-01 5.29633193e-01 1.76154329e-01]\n",
+ " [4.32936989e-02 8.45542568e-01 6.80961904e-02 4.30675429e-02]\n",
+ " [4.65359070e-02 6.35862295e-01 2.30158882e-01 8.74429162e-02]\n",
+ " [7.59325408e-02 8.40362013e-01 2.18715264e-02 6.18339202e-02]\n",
+ " [7.74988396e-03 3.63228281e-01 5.42668687e-01 8.63531481e-02]\n",
+ " [4.20571606e-02 1.67928744e-01 6.58766816e-01 1.31247279e-01]\n",
+ " [7.29951430e-04 2.07820843e-02 3.67399758e-01 6.11088207e-01]\n",
+ " [8.68530750e-03 3.21982370e-03 6.58963456e-01 3.29131412e-01]\n",
+ " [7.38814319e-02 1.53334483e-01 4.19653490e-01 3.53130595e-01]\n",
+ " [1.07906612e-03 7.04123005e-03 1.23280108e-01 8.68599596e-01]\n",
+ " [5.84123332e-03 7.91638288e-02 5.95835326e-01 3.19159612e-01]\n",
+ " [2.69625390e-03 5.06496587e-03 5.30645726e-01 4.61593054e-01]\n",
+ " [7.79797043e-03 1.92759686e-02 6.97763605e-01 2.75162456e-01]\n",
+ " [5.41446813e-04 2.94305688e-03 9.44287476e-01 5.22280206e-02]\n",
+ " [6.26051598e-04 7.42141945e-01 1.98916038e-01 5.83159655e-02]\n",
+ " [4.07263950e-04 1.61189530e-02 7.18765870e-01 2.64707913e-01]\n",
+ " [1.34365605e-02 1.10543156e-01 5.26627982e-01 3.49392301e-01]\n",
+ " [5.71001422e-04 1.91240815e-03 6.06491277e-01 3.91025314e-01]\n",
+ " [2.81713063e-03 7.97382406e-03 8.40331020e-01 1.48878026e-01]\n",
+ " [1.17135558e-03 2.12319620e-02 2.47315117e-01 7.30281566e-01]\n",
+ " [5.42053546e-02 4.77429988e-01 3.58152424e-01 1.10212234e-01]\n",
+ " [6.02720306e-04 1.06889031e-02 9.19066116e-02 8.96801765e-01]\n",
+ " [9.36504287e-04 1.48006780e-02 5.73367912e-02 9.26926027e-01]\n",
+ " [4.89026733e-03 8.74350191e-02 3.48862678e-01 5.58812036e-01]\n",
+ " [2.24637260e-03 3.92870613e-02 3.80617764e-01 5.77848802e-01]\n",
+ " [9.03373107e-04 5.48306894e-03 7.39968576e-01 2.53644982e-01]\n",
+ " [8.62452915e-03 9.29444439e-03 3.40040565e-01 6.42040462e-01]\n",
+ " [1.08525799e-03 6.93149715e-02 3.33566650e-01 5.96033120e-01]\n",
+ " [4.52558455e-03 5.03194459e-01 7.19542524e-02 4.20325704e-01]\n",
+ " [2.49289567e-04 6.64428036e-04 4.53650391e-02 9.53721243e-01]\n",
+ " [4.34121870e-02 1.45051484e-01 5.31879800e-01 2.79656529e-01]\n",
+ " [4.63708910e-02 2.55131108e-01 2.77371495e-01 4.21126506e-01]\n",
+ " [8.46465104e-05 1.08384791e-03 4.85811151e-02 9.50250390e-01]\n",
+ " [1.69882429e-04 4.50546759e-03 9.13036112e-02 9.04021039e-01]\n",
+ " [1.53879841e-03 9.81121479e-03 1.96134562e-01 7.92515425e-01]\n",
+ " [2.17530214e-04 4.54280426e-04 2.60607964e-01 7.38720225e-01]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit entire training sets\n",
+ "clf.fit(X_pca, y)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_test_pca), y_test))\n",
+ "print(clf.predict_proba(X_test_pca))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:40.511453725Z",
+ "start_time": "2023-10-10T20:43:40.431195930Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " label pred1 pred2 pred3 pred4\n",
+ "filename \n",
+ "classical_12.mp3 classical classical electronic pop rock\n",
+ "classical_18.mp3 classical classical electronic rock pop\n",
+ "classical_19.mp3 classical classical pop rock electronic\n",
+ "classical_2.mp3 classical classical electronic rock pop\n",
+ "classical_22.mp3 classical classical pop electronic rock\n",
+ "classical_26.mp3 classical classical pop rock electronic\n",
+ "classical_28.mp3 classical classical pop rock electronic\n",
+ "classical_33.mp3 classical classical electronic pop rock\n",
+ "classical_38.mp3 classical classical pop rock electronic\n",
+ "classical_43.mp3 classical pop classical rock electronic\n",
+ "classical_45.mp3 classical classical electronic rock pop\n",
+ "classical_48.mp3 classical classical pop rock electronic\n",
+ "classical_52.mp3 classical pop electronic classical rock\n",
+ "classical_57.mp3 classical classical electronic pop rock\n",
+ "classical_59.mp3 classical classical electronic rock pop\n",
+ "classical_6.mp3 classical classical electronic pop rock\n",
+ "classical_62.mp3 classical classical rock pop electronic\n",
+ "classical_67.mp3 classical classical electronic rock pop\n",
+ "classical_71.mp3 classical pop rock classical electronic\n",
+ "classical_85.mp3 classical classical electronic rock pop\n",
+ "classical_86.mp3 classical classical rock pop electronic\n",
+ "electronic_1.mp3 electronic electronic pop rock classical\n",
+ "electronic_13.mp3 electronic electronic pop rock classical\n",
+ "electronic_18.mp3 electronic electronic rock pop classical\n",
+ "electronic_26.mp3 electronic classical pop electronic rock\n",
+ "electronic_33.mp3 electronic electronic rock pop classical\n",
+ "electronic_38.mp3 electronic electronic pop classical rock\n",
+ "electronic_47.mp3 electronic classical electronic pop rock\n",
+ "electronic_50.mp3 electronic electronic pop rock classical\n",
+ "electronic_51.mp3 electronic pop electronic rock classical\n",
+ "electronic_55.mp3 electronic electronic rock pop classical\n",
+ "electronic_56.mp3 electronic electronic rock pop classical\n",
+ "electronic_57.mp3 electronic pop electronic rock classical\n",
+ "electronic_58.mp3 electronic electronic pop rock classical\n",
+ "electronic_59.mp3 electronic electronic rock pop classical\n",
+ "electronic_7.mp3 electronic electronic pop rock classical\n",
+ "electronic_73.mp3 electronic electronic rock pop classical\n",
+ "electronic_75.mp3 electronic pop electronic rock classical\n",
+ "electronic_81.mp3 electronic electronic rock pop classical\n",
+ "electronic_85.mp3 electronic electronic pop classical rock\n",
+ "electronic_86.mp3 electronic electronic rock pop classical\n",
+ "electronic_91.mp3 electronic electronic pop classical rock\n",
+ "electronic_92.mp3 electronic electronic pop rock classical\n",
+ "electronic_93.mp3 electronic electronic pop rock classical\n",
+ "electronic_94.mp3 electronic pop electronic rock classical\n",
+ "electronic_97.mp3 electronic electronic pop classical rock\n",
+ "pop_18.mp3 pop electronic pop rock classical\n",
+ "pop_19.mp3 pop electronic classical rock pop\n",
+ "pop_28.mp3 pop pop electronic rock classical\n",
+ "pop_3.mp3 pop pop electronic rock classical\n",
+ "pop_31.mp3 pop rock pop electronic classical\n",
+ "pop_33.mp3 pop pop rock classical electronic\n",
+ "pop_34.mp3 pop pop rock electronic classical\n",
+ "pop_37.mp3 pop rock pop electronic classical\n",
+ "pop_4.mp3 pop pop rock electronic classical\n",
+ "pop_42.mp3 pop pop rock electronic classical\n",
+ "pop_53.mp3 pop pop rock electronic classical\n",
+ "pop_66.mp3 pop pop rock electronic classical\n",
+ "pop_71.mp3 pop electronic pop rock classical\n",
+ "pop_75.mp3 pop pop rock electronic classical\n",
+ "pop_77.mp3 pop pop rock electronic classical\n",
+ "pop_82.mp3 pop pop rock electronic classical\n",
+ "pop_99.mp3 pop pop rock electronic classical\n",
+ "rock_1.mp3 rock rock pop electronic classical\n",
+ "rock_21.mp3 rock electronic pop rock classical\n",
+ "rock_23.mp3 rock rock pop electronic classical\n",
+ "rock_24.mp3 rock rock pop electronic classical\n",
+ "rock_26.mp3 rock rock pop electronic classical\n",
+ "rock_27.mp3 rock rock pop electronic classical\n",
+ "rock_49.mp3 rock pop rock electronic classical\n",
+ "rock_56.mp3 rock rock pop classical electronic\n",
+ "rock_57.mp3 rock rock pop electronic classical\n",
+ "rock_58.mp3 rock electronic rock pop classical\n",
+ "rock_78.mp3 rock rock pop electronic classical\n",
+ "rock_79.mp3 rock pop rock electronic classical\n",
+ "rock_82.mp3 rock rock pop electronic classical\n",
+ "rock_84.mp3 rock rock pop electronic classical\n",
+ "rock_9.mp3 rock rock pop electronic classical\n",
+ "rock_98.mp3 rock rock pop electronic classical\n",
+ "rock_99.mp3 rock rock pop electronic classical\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit the entire training sets\n",
+ "\n",
+ "def convert_to_labels(preds, i2c, k=3):\n",
+ " ans = []\n",
+ " ids = []\n",
+ " for p in preds:\n",
+ " idx = np.argsort(p)[::-1]\n",
+ " ids.append([i for i in idx[:k]])\n",
+ " ans.append([i2c[i] for i in idx[:k]])\n",
+ "\n",
+ " return ans, ids\n",
+ "\n",
+ "clf.fit(X_pca, y)\n",
+ "prediction_lists, percentage_lists = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\n",
+ "\n",
+ "# # Write to outputs\n",
+ "subm = pd.DataFrame(index=test.index)\n",
+ "subm['label'] = test.label.values\n",
+ "subm['pred1'] = [prediction_list[0] for prediction_list in prediction_lists]\n",
+ "subm['pred2'] = [prediction_list[1] for prediction_list in prediction_lists]\n",
+ "subm['pred3'] = [prediction_list[2] for prediction_list in prediction_lists]\n",
+ "subm['pred4'] = [prediction_list[3] for prediction_list in prediction_lists]\n",
+ "\n",
+ "pd.set_option('display.max_rows', None)\n",
+ "print(subm)\n",
+ "pd.reset_option('display.max_rows')"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:40.571797545Z",
+ "start_time": "2023-10-10T20:43:40.484151084Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "outputs": [],
+ "source": [
+ "with open(LOCAL_PATH / \"clf.pickle\", \"wb\") as file:\n",
+ " pickle.dump(clf, file)\n",
+ "subm.to_csv(LOCAL_PATH / \"submission.csv\", index=False)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:43:40.572096508Z",
+ "start_time": "2023-10-10T20:43:40.552658198Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:fairnb.api.invenio:Picked up 1 files\n",
+ "INFO:fairnb.api.invenio:Uploading 1 to https://test.researchdata.tuwien.ac.at\n",
+ "INFO:fairnb.api.invenio:Uploading /home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/standalone/clf.pickle as clf.pickle\n",
+ "INFO:fairnb.api.invenio:Finished upload of clf.pickle\n"
+ ]
+ }
+ ],
+ "source": [
+ "if not ONLY_LOCAL:\n",
+ " with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
+ " metadata = yaml.safe_load(file)\n",
+ "\n",
+ " nb_config_ml = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " entities=[\n",
+ " ml_model_entity := InvenioEntity.new(\n",
+ " name=\"Standalone Machine Learning model\",\n",
+ " description=\"An ml model representing the trained clf\",\n",
+ " location=LOCAL_PATH / \"clf.pickle\",\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"clf\"\n",
+ " ),\n",
+ " test_result_entity := DbRepoEntity.new(\n",
+ " name=\"Standalone Test Result Entity\",\n",
+ " description=\"Result of tests on ml model\",\n",
+ " table_name=\"test_result\",\n",
+ " table_description=\"Test results of genre prediction on ml model\",\n",
+ " location=LOCAL_PATH / \"submission.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " type=\"submission\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " audio_files_entity\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " executor.upload_entities(nb_config_ml)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-10T20:53:51.300279538Z",
+ "start_time": "2023-10-10T20:53:47.923032557Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
deleted file mode 100644
index f118313d6389b78ae22ce08add17ce04a68ef30e..0000000000000000000000000000000000000000
--- a/notebooks/testing.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true,
- "execution": {
- "iopub.execute_input": "2023-02-10T17:30:24.890382Z",
- "iopub.status.busy": "2023-02-10T17:30:24.889669Z",
- "iopub.status.idle": "2023-02-10T17:30:24.898430Z",
- "shell.execute_reply": "2023-02-10T17:30:24.897675Z"
- }
- },
- "outputs": [],
- "source": [
- "a = 10\n",
- "test = 'asdf'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-02-10T17:30:24.902004Z",
- "iopub.status.busy": "2023-02-10T17:30:24.901559Z",
- "iopub.status.idle": "2023-02-10T17:30:24.905188Z",
- "shell.execute_reply": "2023-02-10T17:30:24.904378Z"
- }
- },
- "outputs": [],
- "source": [
- "b= 20"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-02-10T17:30:24.908647Z",
- "iopub.status.busy": "2023-02-10T17:30:24.908103Z",
- "iopub.status.idle": "2023-02-10T17:30:24.927086Z",
- "shell.execute_reply": "2023-02-10T17:30:24.926259Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'asdf'"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/poetry.lock b/poetry.lock
index 49f5338f59cce565eac08662fa32e7c1d7efca6d..89579b7508b20613f50cd27b8936489c3949a46c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,140 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
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+category = "main"
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category = "main"
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name = "argon2-cffi-bindings"
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test = ["astroid", "pytest"]
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version = "0.2.0"
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description = "Screen-scraping library"
category = "main"
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html5lib = ["html5lib"]
lxml = ["lxml"]
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-description = "GitPython is a python library used to interact with Git repositories"
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category = "main"
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python-versions = ">=3.7"
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ipython = ">=7.23.1"
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matplotlib-inline = ">=0.1"
nest-asyncio = "*"
packaging = "*"
psutil = "*"
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tornado = ">=6.1"
traitlets = ">=5.4.0"
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cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
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description = "IPython: Productive Interactive Computing"
category = "main"
optional = false
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pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
pickleshare = "*"
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stack-data = "*"
traitlets = ">=5"
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name = "isoduration"
@@ -831,22 +1280,22 @@ arrow = ">=0.15.0"
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name = "jedi"
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python-versions = ">=3.6"
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@@ -869,49 +1318,66 @@ i18n = ["Babel (>=2.7)"]
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@@ -942,140 +1423,161 @@ qtconsole = "*"
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cycler = ">=0.10"
fonttools = ">=4.22.0"
kiwisolver = ">=1.0.1"
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python-dateutil = ">=2.7"
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
[metadata]
lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "7bf1a00837513734f37c12c47369c7c9c1379a1f98af1f34d76331a94875bd9c"
+python-versions = "3.10.*"
+content-hash = "0821c482ad42cd1b32b3a6dc4f836986f50ece1207603ac165337971c32ff626"
diff --git a/pyproject.toml b/pyproject.toml
index b517850c5cab3063044714296216af1413547052..a38b16056a8863c00f6c5f468c0a7579ff5188a4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,10 +4,10 @@ version = "0.1.0"
description = ""
authors = ["Lukas Mahler <e11908553@student.tuwien.ac.at>"]
readme = "README.md"
-packages = [{include = "dbrepo_ismir"}]
+packages = [{include = "fairnb"}]
[tool.poetry.dependencies]
-python = "^3.10"
+python = "3.10.*"
ipykernel = "^6.19.4"
requests = "^2.28.1"
pandas = "^1.5.2"
@@ -20,10 +20,15 @@ jupyter = "^1.0.0"
git-python = "^1.0.3"
ipynb = "^0.5.1"
papermill = "^2.4.0"
+black = "^23.7.0"
+oic = "^1.6.1"
+python-keycloak = "^3.3.0"
+tuspy = "^1.0.1"
[tool.poetry.group.dev.dependencies]
pytest = "^7.2.0"
+pip = "*"
[build-system]
requires = ["poetry-core"]
diff --git a/resource/5_ml_model/ml_model_artefact_metadata.yml b/resource/5_ml_model/ml_model_entity_metadata.yml
similarity index 100%
rename from resource/5_ml_model/ml_model_artefact_metadata.yml
rename to resource/5_ml_model/ml_model_entity_metadata.yml
diff --git a/test/integration/__init__.py b/test/integration/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/test/integration/test_download_entities.py b/test/integration/test_download_entities.py
new file mode 100644
index 0000000000000000000000000000000000000000..55d1c4bc87ea33ede59ca378fdc600150d523ebd
--- /dev/null
+++ b/test/integration/test_download_entities.py
@@ -0,0 +1,31 @@
+import yaml
+from pytest import fixture
+
+from fairnb.api.dbrepo import DBRepoConnector
+from fairnb.entity.dbrepo_entity import DbRepoEntity
+from definitions import BASE_PATH
+
+
+@fixture
+def connector():
+ with open(BASE_PATH / "config" / "test-config.yml", "r") as file:
+ config = yaml.safe_load(file)["dbrepo"]
+
+ return DBRepoConnector(
+ config["credentials"]["username"],
+ config["credentials"]["password"],
+ config["credentials"]["client_secret_key"],
+ config["host"],
+ config["container-id"],
+ config["database-id"]
+ )
+
+
+def test_download_dbrepo_existing_works(connector: DBRepoConnector):
+ entity = DbRepoEntity.existing(
+ id="1",
+ location=BASE_PATH / "tmp" / "test" / "dbrepo_entity.csv",
+ dbrepo_connector=connector,
+ )
+
+ entity.download()
diff --git a/test/test.ipynb b/test/test.ipynb
index e46d973bb3ef6b585077ad715ba2417b2d372b26..018a660533c8cf47a3c029e10a00773569026130 100644
--- a/test/test.ipynb
+++ b/test/test.ipynb
@@ -1,4 +1,4 @@
-{
+
"cells": [
{
"cell_type": "code",
@@ -51,13 +51,12 @@
"## DBREPO"
]
},
- {
- "cell_type": "code",
+ { "cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "from dbrepo_ismir.dbrepo import DBRepoConnector"
+ "from fairnb.dbrepo import DBRepoConnector"
]
},
{
diff --git a/test/test_dbrepo.py b/test/test_dbrepo.py
index 2afa0acaa3a8cf625e8395c7f7566562cfd2736e..91c66d7414a6aa5d3568845baca53f06a440392a 100644
--- a/test/test_dbrepo.py
+++ b/test/test_dbrepo.py
@@ -2,7 +2,7 @@ import unittest
import pandas as pd
from unittest import mock
import yaml
-from dbrepo_ismir.dbrepo import DBRepoConnector
+from fairnb.api.dbrepo import DBRepoConnector
from definitions import BASE_PATH
@@ -59,7 +59,7 @@ class DBRepoConnectorTester(unittest.TestCase):
assert self.connector_wrong_pw.__token is None
@mock.patch(
- 'dbrepo_ismir.dbrepo.DBRepoConnector.download_table',
+ 'fairnb.dbrepo.DBRepoConnector.download_table',
mocked_download_table
)
def test_download_table_as_df(self):