diff --git a/config/example-config_dbrepo.yml b/config/example-config_dbrepo.yml
index 21013bc6d4dfa7d254c3cdcb727b261ecaab8a3b..f954ceaf4a1bdfaf435edefe586cb913c2439a3e 100644
--- a/config/example-config_dbrepo.yml
+++ b/config/example-config_dbrepo.yml
@@ -1,5 +1,5 @@
host: https://dbrepo1.ec.tuwien.ac.at
-database-id: <insert database id>
+database-id: 23
credentials:
username: <insert username from dbrepo>
password: <insert password from dbrepo>
diff --git a/config/example-config_invenio.yml b/config/example-config_invenio.yml
index ea9ba267f9dfd6a34bdab36bd9d23edef14ed86e..f86c028190534773abbf8f37da5dfd7c5136bf70 100644
--- a/config/example-config_invenio.yml
+++ b/config/example-config_invenio.yml
@@ -1,3 +1,3 @@
host: https://researchdata.tuwien.ac.at
credentials:
- token: <insert token from invenio>
\ No newline at end of file
+ token: <insert token of InvenioRDM API>
\ No newline at end of file
diff --git a/fairnb/api/dbrepo.py b/fairnb/api/dbrepo.py
index 8c2145d58b267009c9214f6b690fd424732f516a..e6e76818434b73bf216e8645c691c918ef5d1741 100644
--- a/fairnb/api/dbrepo.py
+++ b/fairnb/api/dbrepo.py
@@ -12,7 +12,7 @@ from keycloak import KeycloakOpenID
LOG = logging.getLogger(__name__)
TIMEOUT = 600
-CHUNK_SIZE = 1024 * 1024 * 100
+CHUNK_SIZE = 1024 * 1024 * 20
def re_auth(func: Callable) -> Callable:
@wraps(func)
@@ -210,10 +210,10 @@ class DBRepoConnector:
return None
@re_auth
- def create_table(self, dataframe: pd.DataFrame, table_name: str, table_descriptor: str):
+ def create_table(self, dataframe: pd.DataFrame, table_name: str, table_description: str):
""" Creates a new table """
- data = self._create_table_data(dataframe, table_name, table_descriptor)
+ data = self._create_table_data(dataframe, table_name, table_description)
response = requests.post(
f"{self.host}/api/database/{self.database_id}/table",
@@ -234,10 +234,10 @@ class DBRepoConnector:
def create_table_if_not_exists(self,
dataframe: pd.DataFrame,
table_name: str,
- table_descriptor: str
+ table_description: str
):
table = table if (table := self.get_table(table_name)) is not None else \
- self.create_table(dataframe, table_name, table_descriptor)
+ self.create_table(dataframe, table_name, table_description)
return table
@@ -269,6 +269,7 @@ class DBRepoConnector:
"quote": '"',
"separator": ",",
"skip_lines": 1,
+ "line_termination": "\n",
"true_element": "True"
},
headers=self.headers
diff --git a/fairnb/api/invenio.py b/fairnb/api/invenio.py
index 663f408cc74e8cec0b07b53711b75f1653292674..f5f513bd607f34b6472094f9c5989e0adcde9c12 100644
--- a/fairnb/api/invenio.py
+++ b/fairnb/api/invenio.py
@@ -8,7 +8,7 @@ import requests as rq
log = logging.getLogger(__name__)
-class InvenioConnector:
+class InvenioRDMConnector:
def __init__(self, token: str, host: str | None = None):
self.host = host or "https://test.researchdata.tuwien.ac.at"
self.token = token
@@ -130,14 +130,14 @@ class InvenioConnector:
executor.map(lambda p: self.download_file(*p), args)
-class InvenioManager:
+class InvenioRDMManager:
"""A high level interface to up- and download files from invenio.
Utilizes state management:
1. record not assigned
2. record assigned
3. record published"""
- def __init__(self, invenio_connector: InvenioConnector, record_id: str | None = None):
+ def __init__(self, invenio_connector: InvenioRDMConnector, record_id: str | None = None):
self.connector = invenio_connector
self.record_id = record_id
self.doi = None
diff --git a/fairnb/entity/dbrepo_entity.py b/fairnb/entity/dbrepo_entity.py
index e1ab588176d724372a4d0593eef3f44b3c96bece..f4b18df1565781fd7221e90cc01d5baf0723e844 100644
--- a/fairnb/entity/dbrepo_entity.py
+++ b/fairnb/entity/dbrepo_entity.py
@@ -9,7 +9,7 @@ from fairnb.entity.entity_provenance import EntityProvenance
@dataclass
-class DbRepoEntity(Entity):
+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)
@@ -19,7 +19,7 @@ class DbRepoEntity(Entity):
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])
+ self.table_id = int(self.metadata.pi.split("/")[-1])
else:
assert self.table_name is not None # has to exist fot the ability to get table_id
@@ -60,9 +60,10 @@ class DbRepoEntity(Entity):
self.location.resolve().parent.mkdir(parents=True, exist_ok=True)
df.to_csv(self.location, index=False)
- def upload(self, executed_file: Path, dependencies: list[Entity] = None,
+ def upload(self, executed_file: Path, main_file: Path,
+ dependencies: list[Entity] = None,
start_time: datetime = datetime.now(),
- end_time: datetime = datetime.now()):
+ end_time: datetime = datetime.now()) -> EntityProvenance:
df = pd.read_csv(self.location)
# add id column to df:
@@ -80,10 +81,11 @@ class DbRepoEntity(Entity):
self.name,
self.description,
executed_file=executed_file,
- uri=f"{self.dbrepo_connector.host}/database/"
+ main_file=main_file,
+ pi=f"{self.dbrepo_connector.host}/database/"
f"{self.dbrepo_connector.database_id}/table/{self.table_id}",
type=self.type,
- platform=self.repository,
+ repository=self.repository,
started_at=start_time,
ended_at=end_time
)
@@ -98,6 +100,8 @@ class DbRepoEntity(Entity):
self.upload_dependencies(dependencies)
+ return self.metadata
+
def upload_data(self, df: pd.DataFrame):
assert self.id is not None
assert self.table_id is not None
diff --git a/fairnb/entity/entity.py b/fairnb/entity/entity.py
index 0875712014842934ad8c281a40ad8df2133060ee..2028c1eeb698ad0f5a1520353d056aff07d05d86 100644
--- a/fairnb/entity/entity.py
+++ b/fairnb/entity/entity.py
@@ -11,12 +11,13 @@ from fairnb.api.dbrepo import DBRepoConnector
from fairnb.entity.entity_provenance import EntityProvenance
-PROVENANCE_TABLE_NAME = "entity_provenance_test3"
-DEPENDENCY_TABLE_NAME = "entity_dependencies_test3"
+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.
@@ -65,12 +66,12 @@ class Entity(ABC):
self.download_provenance()
@abstractmethod
- def download(self) -> EntityProvenance:
+ def download(self):
"""Download this Entity and return the attached EntityProvenance"""
raise NotImplementedError
@abstractmethod
- def upload(self, executed_file: Path, dependencies: list, started_at=datetime.now(), ended_at=datetime.now()):
+ def upload(self, executed_file: Path, main_file: Path, dependencies: list, started_at=datetime.now(), ended_at=datetime.now()):
"""Upload this Entity"""
raise NotImplementedError
@@ -131,6 +132,8 @@ class Entity(ABC):
self.id = meta.id
self.metadata = meta
+ LOG.info(f"Uploaded provenance information for {self.name} with id {self.id}: {self.metadata}")
+
def upload_dependencies(self, dependencies):
""" Upload the dependency information for this Entity.
It lists all entities, which have an id, this entity depends on.
diff --git a/fairnb/entity/entity_provenance.py b/fairnb/entity/entity_provenance.py
index 9fd0556f42b7469cfdda7b402e6ac945c3c75cf3..910df8a512b87bffe33ab561590e191f78f7de6d 100644
--- a/fairnb/entity/entity_provenance.py
+++ b/fairnb/entity/entity_provenance.py
@@ -15,7 +15,7 @@ class EntityProvenance:
"""
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)
+ pi: str # persistent 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
@@ -23,9 +23,10 @@ class EntityProvenance:
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
- started_at: datetime # start time of execution where entity was created
- ended_at: datetime # end time of execution where entity was created
- platform: str # platform on which the entity is uploaded (e.g. dbrepo, invenio, ...)
+ main_file: str # path to the main file executing the notebook
+ started_at: datetime # start time of execution where entity was created
+ ended_at: datetime # end time of execution where entity was created
+ repository: str # platform on which the entity is uploaded (e.g. dbrepo, invenio, ...)
@classmethod
def new(
@@ -33,9 +34,10 @@ class EntityProvenance:
name: str,
description: str,
executed_file: Path,
+ main_file: Path,
type: str,
- uri: str,
- platform: str,
+ pi: str,
+ repository: str,
started_at: datetime,
ended_at: datetime
):
@@ -50,20 +52,22 @@ class EntityProvenance:
repo_uri = re.sub(":\d+/", "/", f"https://{repo_uri.split('@', 1)[1]}")
executed_file_rel = executed_file.resolve().relative_to(BASE_PATH)
+ main_file_rel = main_file.resolve().relative_to(BASE_PATH)
return cls(
id=None,
name=name,
description=description,
- uri=uri,
+ pi=pi,
commit=commit,
repo_uri=repo_uri,
started_at=started_at,
ended_at=ended_at,
branch=branch,
executed_file=executed_file_rel.as_posix(),
+ main_file=main_file_rel.as_posix(),
type=type,
- platform=platform,
+ repository=repository,
)
@classmethod
@@ -72,10 +76,11 @@ class EntityProvenance:
id=df["id"],
name=df["name"],
description=df["description"],
- uri=df["uri"],
+ pi=df["pi"],
commit=df["commit"],
repo_uri=df["git_uri"],
executed_file=df["executed_file"],
+ main_file=df["main_file"],
started_at=datetime.strptime(
df["started_at"], "%Y-%m-%d %H:%M:%S.%f"
), # TODO: replace with '%F %T'
@@ -84,7 +89,7 @@ class EntityProvenance:
),
branch=df["branch"],
type=df["type"],
- platform=df["repository"],
+ repository=df["repository"],
)
def to_frame(self):
@@ -93,14 +98,15 @@ class EntityProvenance:
"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),
+ "pi": pd.Series(self.pi, dtype=str),
"commit": pd.Series(self.commit, dtype=str),
"git_uri": pd.Series(self.repo_uri, dtype=str),
"executed_file": pd.Series(self.executed_file, dtype=str),
+ "main_file": pd.Series(self.main_file, dtype=str),
"started_at": pd.Series(self.started_at, dtype=str),
"ended_at": pd.Series(self.ended_at, dtype=str),
"branch": pd.Series(self.branch, dtype=str),
"type": pd.Series(self.type, dtype=str),
- "repository": pd.Series(self.platform, dtype=str),
+ "repository": pd.Series(self.repository, dtype=str),
}
)
diff --git a/fairnb/entity/invenio_entity.py b/fairnb/entity/invenio_entity.py
index 64a680af93d0d3a1f40369de7c44dfc8e69a6df0..8a1a62f67dbe747a34066ecd7e1933de8cf505ae 100644
--- a/fairnb/entity/invenio_entity.py
+++ b/fairnb/entity/invenio_entity.py
@@ -3,14 +3,14 @@ from datetime import datetime
from pathlib import Path
from fairnb.api.dbrepo import DBRepoConnector
-from fairnb.api.invenio import InvenioManager, InvenioConnector
+from fairnb.api.invenio import InvenioRDMManager, InvenioRDMConnector
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)
+class InvenioRDMEntity(Entity):
+ invenio_manager: InvenioRDMManager = field(init=True, default=None)
record_metadata: dict = field(init=True, default=None)
publish_record: bool = field(init=True, default=False)
platform: str = field(init=False, default="https://doi.org/10.17616/R31NJMYD")
@@ -24,11 +24,11 @@ class InvenioEntity(Entity):
description: str,
type: str,
dbrepo_connector: DBRepoConnector,
- invenio_connector: InvenioConnector,
+ invenio_connector: InvenioRDMConnector,
publish_record: bool = False,
):
return cls(
- invenio_manager=InvenioManager(invenio_connector),
+ invenio_manager=InvenioRDMManager(invenio_connector),
record_metadata=record_metadata,
dbrepo_connector=dbrepo_connector,
location=location,
@@ -44,13 +44,13 @@ class InvenioEntity(Entity):
id: str,
location: Path,
dbrepo_connector: DBRepoConnector,
- invenio_connector: InvenioConnector,
+ invenio_connector: InvenioRDMConnector,
):
return cls(
id=id,
location=location,
dbrepo_connector=dbrepo_connector,
- invenio_manager=InvenioManager(invenio_connector)
+ invenio_manager=InvenioRDMManager(invenio_connector)
)
def __post_init__(self):
@@ -60,9 +60,10 @@ class InvenioEntity(Entity):
assert self.record_metadata is not None
return
- self.invenio_manager.record_id = self.metadata.uri.split('/')[-1]
+ self.invenio_manager.record_id = self.metadata.pi.split('/')[-1]
- def upload(self, executed_file: Path, dependencies: list[Entity] = None, started_at=datetime.now(), ended_at=datetime.now()):
+ def upload(self, executed_file: Path, main_file: Path,
+ dependencies: list[Entity] = None, started_at=datetime.now(), ended_at=datetime.now()):
dir_path: Path
regex: str
@@ -91,9 +92,10 @@ class InvenioEntity(Entity):
name=self.name,
description=self.description,
executed_file=executed_file,
- uri=uri.replace('/api', ''),
+ main_file=main_file,
+ pi=uri.replace('/api', ''),
type=self.type,
- platform=self.platform,
+ repository=self.platform,
started_at=started_at,
ended_at=ended_at,
)
diff --git a/fairnb/executor.py b/fairnb/executor.py
index 4b91711480ce2fb840968a19414eeb59fab3d4a4..4a3ec7e05717367e6fb919536ac71ee6788b45b6 100644
--- a/fairnb/executor.py
+++ b/fairnb/executor.py
@@ -73,6 +73,7 @@ class Executor:
# use inspect to get path of caller
entity.upload(
nb_config.nb_location,
+ nb_config.main_location,
nb_config.dependencies,
nb_config.started_at,
nb_config.ended_at
diff --git a/fairnb/nb_config.py b/fairnb/nb_config.py
index 71d8a21ef786241cc8f2276a4a4dff395647e05f..0d581992a72ea04a1e43ad7fc0f263b751ca261a 100644
--- a/fairnb/nb_config.py
+++ b/fairnb/nb_config.py
@@ -8,6 +8,7 @@ from fairnb.entity.entity import Entity
@dataclass
class NbConfig:
nb_location: Path
+ main_location: Path
entities: list[Entity]
dependencies: list[Entity]
nb_output_location: Path = field(init=True, default=None)
diff --git a/fairnb/util.py b/fairnb/util.py
index 41d67b8e784fa16a61aa35691a3c12302c2abda2..4f412b6772938569fa1ed1109ecf36c1b0a789ad 100644
--- a/fairnb/util.py
+++ b/fairnb/util.py
@@ -5,7 +5,7 @@ import pandas as pd
import tarfile
from fairnb.api.dbrepo import DBRepoConnector
-from fairnb.api.invenio import InvenioManager, InvenioConnector
+from fairnb.api.invenio import InvenioRDMManager, InvenioRDMConnector
from definitions import CONFIG_PATH
import yaml
@@ -46,14 +46,14 @@ class Util:
def get_invenio_connector(self, path: pathlib.Path = None):
config = self.get_config(path=path)
- return InvenioConnector(
+ return InvenioRDMConnector(
token=config["credentials"]["token"],
host=config["host"]
)
def get_invenio_manager(self, path: pathlib.Path = None):
config = self.get_config(path=path)
- return InvenioManager(
+ return InvenioRDMManager(
self.get_invenio_connector(path=path)
)
diff --git a/notebooks/1_audio_files.ipynb b/notebooks/1_audio_files.ipynb
index 8e1c4d52b4c83ea49c3f70d23499e8662a68f476..f358a43002abfa65a8ff1a07084c7ebd8a9874a9 100644
--- a/notebooks/1_audio_files.ipynb
+++ b/notebooks/1_audio_files.ipynb
@@ -5,14 +5,11 @@
"id": "4389a8092677254e",
"metadata": {
"collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- },
"papermill": {
- "duration": 0.011665,
- "end_time": "2024-02-14T15:11:50.335199",
+ "duration": 0.002827,
+ "end_time": "2024-02-19T14:22:35.188097",
"exception": false,
- "start_time": "2024-02-14T15:11:50.323534",
+ "start_time": "2024-02-19T14:22:35.185270",
"status": "completed"
},
"tags": []
@@ -25,24 +22,21 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "87ab37c6",
"metadata": {
"collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-14T15:11:50.362588Z",
- "iopub.status.busy": "2024-02-14T15:11:50.361890Z",
- "iopub.status.idle": "2024-02-14T15:11:50.376714Z",
- "shell.execute_reply": "2024-02-14T15:11:50.375427Z"
- },
- "jupyter": {
- "outputs_hidden": false
+ "iopub.execute_input": "2024-02-19T14:22:35.197216Z",
+ "iopub.status.busy": "2024-02-19T14:22:35.196436Z",
+ "iopub.status.idle": "2024-02-19T14:22:35.210335Z",
+ "shell.execute_reply": "2024-02-19T14:22:35.209728Z"
},
"papermill": {
- "duration": 0.03707,
- "end_time": "2024-02-14T15:11:50.383410",
+ "duration": 0.021476,
+ "end_time": "2024-02-19T14:22:35.213177",
"exception": false,
- "start_time": "2024-02-14T15:11:50.346340",
+ "start_time": "2024-02-19T14:22:35.191701",
"status": "completed"
},
"tags": []
@@ -58,20 +52,20 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "1b4e6b01",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-14T15:11:50.393351Z",
- "iopub.status.busy": "2024-02-14T15:11:50.392977Z",
- "iopub.status.idle": "2024-02-14T15:11:50.398211Z",
- "shell.execute_reply": "2024-02-14T15:11:50.396892Z"
+ "iopub.execute_input": "2024-02-19T14:22:35.226807Z",
+ "iopub.status.busy": "2024-02-19T14:22:35.226532Z",
+ "iopub.status.idle": "2024-02-19T14:22:35.230103Z",
+ "shell.execute_reply": "2024-02-19T14:22:35.229553Z"
},
"papermill": {
- "duration": 0.016942,
- "end_time": "2024-02-14T15:11:50.404602",
+ "duration": 0.015406,
+ "end_time": "2024-02-19T14:22:35.234431",
"exception": false,
- "start_time": "2024-02-14T15:11:50.387660",
+ "start_time": "2024-02-19T14:22:35.219025",
"status": "completed"
},
"tags": [
@@ -89,20 +83,20 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "15dea136",
+ "execution_count": null,
+ "id": "1a6df3b0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-14T15:11:50.419109Z",
- "iopub.status.busy": "2024-02-14T15:11:50.418653Z",
- "iopub.status.idle": "2024-02-14T15:11:50.424595Z",
- "shell.execute_reply": "2024-02-14T15:11:50.422937Z"
+ "iopub.execute_input": "2024-02-19T14:22:35.246368Z",
+ "iopub.status.busy": "2024-02-19T14:22:35.246128Z",
+ "iopub.status.idle": "2024-02-19T14:22:35.249816Z",
+ "shell.execute_reply": "2024-02-19T14:22:35.249076Z"
},
"papermill": {
- "duration": 0.0277,
- "end_time": "2024-02-14T15:11:50.438068",
+ "duration": 0.014063,
+ "end_time": "2024-02-19T14:22:35.253487",
"exception": false,
- "start_time": "2024-02-14T15:11:50.410368",
+ "start_time": "2024-02-19T14:22:35.239424",
"status": "completed"
},
"tags": [
@@ -114,30 +108,27 @@
"# Parameters\n",
"INPUT_PATHS = {}\n",
"OUTPUT_PATHS = {\n",
- " \"audio_tar\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/1_audio_files/output/emotifymusic.tar.gz\"\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": 4,
+ "execution_count": null,
"id": "1e487573",
"metadata": {
"collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-14T15:11:50.448192Z",
- "iopub.status.busy": "2024-02-14T15:11:50.447685Z",
- "iopub.status.idle": "2024-02-14T15:11:53.138587Z",
- "shell.execute_reply": "2024-02-14T15:11:53.137552Z"
- },
- "jupyter": {
- "outputs_hidden": false
+ "iopub.execute_input": "2024-02-19T14:22:35.268441Z",
+ "iopub.status.busy": "2024-02-19T14:22:35.268216Z",
+ "iopub.status.idle": "2024-02-19T14:22:37.658264Z",
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diff --git a/notebooks/2_generate_features.ipynb b/notebooks/2_generate_features.ipynb
index 97b795b2c61164bf2a0930d656ae4bac8fb487c6..2911ef2226f559617d26c709001e32e036e341f1 100644
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- " <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>electronic_28.mp3</td>\n",
- " <td>electronic</td>\n",
- " <td>-21.892481</td>\n",
- " <td>64.973923</td>\n",
- " <td>0.638062</td>\n",
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- " <td>-7.903484</td>\n",
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- " <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>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",
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- " <td>-2.164130</td>\n",
- " <td>-0.500030</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2584</th>\n",
- " <td>2584</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",
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- " <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>electronic_28.mp3</td>\n",
- " <td>electronic</td>\n",
- " <td>-69.959473</td>\n",
- " <td>90.579102</td>\n",
- " <td>12.684738</td>\n",
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- " <td>-2.489999</td>\n",
- " <td>13.447134</td>\n",
- " <td>2.889965</td>\n",
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- " <td>0.809570</td>\n",
- " <td>-1.209018</td>\n",
- " <td>-1.631956</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>1029854 rows × 43 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " 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",
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- "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",
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- "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",
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- "2585 6.035127 -8.183851 -0.212283 -1.487655 -2.779953 -5.455588 \n",
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- "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",
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- "2584 -1.651334 -1.831298 -1.857335 \n",
- "2585 0.809570 -1.209018 -1.631956 \n",
- "\n",
- "[1029854 rows x 43 columns]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"for file, dataframe in zip(files, dataframes):\n",
" dataframe[\"sample\"] = dataframe.index.to_numpy(copy=True)\n",
@@ -635,20 +260,20 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"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"
+ "iopub.execute_input": "2024-02-19T14:37:44.992409Z",
+ "iopub.status.busy": "2024-02-19T14:37:44.991617Z",
+ "iopub.status.idle": "2024-02-19T14:38:17.017952Z",
+ "shell.execute_reply": "2024-02-19T14:38:17.017278Z"
},
"papermill": {
- "duration": 31.206394,
- "end_time": "2023-10-10T20:25:07.470045",
+ "duration": 32.032086,
+ "end_time": "2024-02-19T14:38:17.019559",
"exception": false,
- "start_time": "2023-10-10T20:24:36.263651",
+ "start_time": "2024-02-19T14:37:44.987473",
"status": "completed"
},
"tags": []
@@ -682,8 +307,8 @@
},
"papermill": {
"default_parameters": {},
- "duration": 168.079285,
- "end_time": "2023-10-10T20:25:08.092566",
+ "duration": 186.073807,
+ "end_time": "2024-02-19T14:38:17.641976",
"environment_variables": {},
"exception": null,
"input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/2_generate_features.ipynb",
@@ -696,10 +321,10 @@
"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",
+ "start_time": "2024-02-19T14:35:11.568169",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/3_aggregate_features.ipynb b/notebooks/3_aggregate_features.ipynb
index d053984db13890b6e523de2221e2aaac3c3f9486..c137e5d737340858068064b2ae968667ad0fd2a3 100644
--- a/notebooks/3_aggregate_features.ipynb
+++ b/notebooks/3_aggregate_features.ipynb
@@ -5,10 +5,10 @@
"id": "f48a4573",
"metadata": {
"papermill": {
- "duration": 0.007574,
- "end_time": "2024-02-15T15:10:25.602842",
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"exception": false,
- "start_time": "2024-02-15T15:10:25.595268",
+ "start_time": "2024-02-19T14:43:18.922990",
"status": "completed"
},
"tags": []
@@ -21,28 +21,24 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "389576b8",
"metadata": {
- "ExecuteTime": {
- "end_time": "2023-08-14T15:32:41.535589478Z",
- "start_time": "2023-08-14T15:32:40.986222405Z"
- },
"collapsed": true,
"execution": {
- "iopub.execute_input": "2024-02-15T15:10:25.622644Z",
- "iopub.status.busy": "2024-02-15T15:10:25.621412Z",
- "iopub.status.idle": "2024-02-15T15:10:26.300635Z",
- "shell.execute_reply": "2024-02-15T15:10:26.298854Z"
+ "iopub.execute_input": "2024-02-19T14:43:18.941968Z",
+ "iopub.status.busy": "2024-02-19T14:43:18.940586Z",
+ "iopub.status.idle": "2024-02-19T14:43:19.225227Z",
+ "shell.execute_reply": "2024-02-19T14:43:19.224264Z"
},
"jupyter": {
"outputs_hidden": true
},
"papermill": {
- "duration": 0.697649,
- "end_time": "2024-02-15T15:10:26.308493",
+ "duration": 0.295054,
+ "end_time": "2024-02-19T14:43:19.228421",
"exception": false,
- "start_time": "2024-02-15T15:10:25.610844",
+ "start_time": "2024-02-19T14:43:18.933367",
"status": "completed"
},
"tags": []
@@ -57,20 +53,20 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "26f640e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T15:10:26.329340Z",
- "iopub.status.busy": "2024-02-15T15:10:26.327934Z",
- "iopub.status.idle": "2024-02-15T15:10:26.348148Z",
- "shell.execute_reply": "2024-02-15T15:10:26.345286Z"
+ "iopub.execute_input": "2024-02-19T14:43:19.235696Z",
+ "iopub.status.busy": "2024-02-19T14:43:19.235399Z",
+ "iopub.status.idle": "2024-02-19T14:43:19.240990Z",
+ "shell.execute_reply": "2024-02-19T14:43:19.240022Z"
},
"papermill": {
- "duration": 0.050433,
- "end_time": "2024-02-15T15:10:26.366702",
+ "duration": 0.012583,
+ "end_time": "2024-02-19T14:43:19.243948",
"exception": false,
- "start_time": "2024-02-15T15:10:26.316269",
+ "start_time": "2024-02-19T14:43:19.231365",
"status": "completed"
},
"tags": [
@@ -93,20 +89,20 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "88ecee07",
+ "execution_count": null,
+ "id": "40dbf7fa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T15:10:26.382035Z",
- "iopub.status.busy": "2024-02-15T15:10:26.381041Z",
- "iopub.status.idle": "2024-02-15T15:10:26.389326Z",
- "shell.execute_reply": "2024-02-15T15:10:26.387547Z"
+ "iopub.execute_input": "2024-02-19T14:43:19.248798Z",
+ "iopub.status.busy": "2024-02-19T14:43:19.248350Z",
+ "iopub.status.idle": "2024-02-19T14:43:19.251965Z",
+ "shell.execute_reply": "2024-02-19T14:43:19.251370Z"
},
"papermill": {
- "duration": 0.034885,
- "end_time": "2024-02-15T15:10:26.405941",
+ "duration": 0.007812,
+ "end_time": "2024-02-19T14:43:19.253560",
"exception": false,
- "start_time": "2024-02-15T15:10:26.371056",
+ "start_time": "2024-02-19T14:43:19.245748",
"status": "completed"
},
"tags": [
@@ -117,29 +113,29 @@
"source": [
"# Parameters\n",
"INPUT_PATHS = {\n",
- " \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/input/raw_features.csv\"\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/fairnb/tmp/3_aggregate_features/output/features.csv\"\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,
+ "execution_count": null,
"id": "c5d9d980",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T15:10:26.423067Z",
- "iopub.status.busy": "2024-02-15T15:10:26.421685Z",
- "iopub.status.idle": "2024-02-15T15:10:39.968586Z",
- "shell.execute_reply": "2024-02-15T15:10:39.967418Z"
+ "iopub.execute_input": "2024-02-19T14:43:19.263504Z",
+ "iopub.status.busy": "2024-02-19T14:43:19.263172Z",
+ "iopub.status.idle": "2024-02-19T14:43:23.707599Z",
+ "shell.execute_reply": "2024-02-19T14:43:23.706545Z"
},
"papermill": {
- "duration": 13.561331,
- "end_time": "2024-02-15T15:10:39.974046",
+ "duration": 4.452062,
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"exception": false,
- "start_time": "2024-02-15T15:10:26.412715",
+ "start_time": "2024-02-19T14:43:19.257537",
"status": "completed"
},
"tags": []
@@ -152,400 +148,25 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "99f75f47",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T15:10:39.992721Z",
- "iopub.status.busy": "2024-02-15T15:10:39.992127Z",
- "iopub.status.idle": "2024-02-15T15:10:47.425790Z",
- "shell.execute_reply": "2024-02-15T15:10:47.423657Z"
+ "iopub.execute_input": "2024-02-19T14:43:23.721962Z",
+ "iopub.status.busy": "2024-02-19T14:43:23.721698Z",
+ "iopub.status.idle": "2024-02-19T14:43:27.486305Z",
+ "shell.execute_reply": "2024-02-19T14:43:27.485675Z"
},
"papermill": {
- "duration": 7.455977,
- "end_time": "2024-02-15T15:10:47.436642",
+ "duration": 3.772661,
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"exception": false,
- "start_time": "2024-02-15T15:10:39.980665",
+ "start_time": "2024-02-19T14:43:23.715361",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "data": {
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- " <tbody>\n",
- " <tr>\n",
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- " <td>0.171462</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>classical_10.mp3</td>\n",
- " <td>classical</td>\n",
- " <td>-562.85785</td>\n",
- " <td>-96.164795</td>\n",
- " <td>-219.259016</td>\n",
- " <td>53.561838</td>\n",
- " <td>-0.772320</td>\n",
- " <td>0.029056</td>\n",
- " <td>259.63270</td>\n",
- " <td>215.094182</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>-536.23737</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>...</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>31.397880</td>\n",
- " <td>-1.494916</td>\n",
- " <td>10.917299</td>\n",
- " <td>0.020985</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>classical_11.mp3</td>\n",
- " <td>classical</td>\n",
- " <td>-536.45746</td>\n",
- " <td>-120.429665</td>\n",
- " <td>-222.126303</td>\n",
- " <td>76.246992</td>\n",
- " <td>-2.402418</td>\n",
- " <td>0.000000</td>\n",
- " <td>159.42575</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>-562.67523</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>148.226647</td>\n",
- " <td>...</td>\n",
- " <td>-44.843810</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>-553.11010</td>\n",
- " <td>-5.218835</td>\n",
- " <td>-193.506047</td>\n",
- " <td>76.869437</td>\n",
- " <td>-0.201055</td>\n",
- " <td>-89.948746</td>\n",
- " <td>201.18045</td>\n",
- " <td>111.724191</td>\n",
- " <td>...</td>\n",
- " <td>-27.043941</td>\n",
- " <td>22.451445</td>\n",
- " <td>-7.234634</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>-541.23600</td>\n",
- " <td>27.163334</td>\n",
- " <td>-119.113996</td>\n",
- " <td>58.420684</td>\n",
- " <td>-0.957699</td>\n",
- " <td>-7.415961</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>-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>-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>-518.49500</td>\n",
- " <td>58.526745</td>\n",
- " <td>-66.267744</td>\n",
- " <td>65.635619</td>\n",
- " <td>-0.898026</td>\n",
- " <td>-58.824410</td>\n",
- " <td>175.20135</td>\n",
- " <td>99.288265</td>\n",
- " <td>...</td>\n",
- " <td>-29.620445</td>\n",
- " <td>26.325895</td>\n",
- " <td>-5.722825</td>\n",
- " <td>7.727378</td>\n",
- " <td>0.207489</td>\n",
- " <td>-29.497524</td>\n",
- " <td>25.410654</td>\n",
- " <td>-3.356614</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>-518.64307</td>\n",
- " <td>53.555115</td>\n",
- " <td>-45.734517</td>\n",
- " <td>52.444200</td>\n",
- " <td>-1.705641</td>\n",
- " <td>0.000000</td>\n",
- " <td>187.04274</td>\n",
- " <td>96.440874</td>\n",
- " <td>...</td>\n",
- " <td>-26.967848</td>\n",
- " <td>8.714737</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>-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>-544.70310</td>\n",
- " <td>75.612130</td>\n",
- " <td>-49.380943</td>\n",
- " <td>54.045627</td>\n",
- " <td>-0.863093</td>\n",
- " <td>-32.930653</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>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>"
- ],
- "text/plain": [
- " filename label 0_min 0_max 0_mean \\\n",
- "0 classical_1.mp3 classical -530.78436 -163.308350 -302.203167 \n",
- "1 classical_10.mp3 classical -562.85785 -96.164795 -219.259016 \n",
- "2 classical_100.mp3 classical -536.23737 -61.608826 -177.804114 \n",
- "3 classical_11.mp3 classical -536.45746 -120.429665 -222.126303 \n",
- "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",
- "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",
- "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",
- ".. ... ... ... ... ... ... ... \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",
- "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",
- "\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",
- ".. ... ... ... ... ... ... ... \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",
- "\n",
- " 39_std 39_skew \n",
- "0 15.285639 0.171462 \n",
- "1 10.477735 -0.185771 \n",
- "2 10.917299 0.020985 \n",
- "3 10.125545 0.595763 \n",
- "4 11.160392 0.503120 \n",
- ".. ... ... \n",
- "395 6.687984 0.238807 \n",
- "396 7.807756 0.071968 \n",
- "397 8.170526 0.160330 \n",
- "398 5.051498 -0.258407 \n",
- "399 6.571660 0.384794 \n",
- "\n",
- "[400 rows x 202 columns]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"meta_columns = [\"sample\", \"filename\", \"label\"]\n",
"mfcc_aggregated = raw_features\\\n",
@@ -567,20 +188,20 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "4ac5c765",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T15:10:47.454568Z",
- "iopub.status.busy": "2024-02-15T15:10:47.452996Z",
- "iopub.status.idle": "2024-02-15T15:10:47.646600Z",
- "shell.execute_reply": "2024-02-15T15:10:47.644995Z"
+ "iopub.execute_input": "2024-02-19T14:43:27.495015Z",
+ "iopub.status.busy": "2024-02-19T14:43:27.494787Z",
+ "iopub.status.idle": "2024-02-19T14:43:27.574541Z",
+ "shell.execute_reply": "2024-02-19T14:43:27.573938Z"
},
"papermill": {
- "duration": 0.209091,
- "end_time": "2024-02-15T15:10:47.653114",
+ "duration": 0.084978,
+ "end_time": "2024-02-19T14:43:27.576110",
"exception": false,
- "start_time": "2024-02-15T15:10:47.444023",
+ "start_time": "2024-02-19T14:43:27.491132",
"status": "completed"
},
"tags": []
@@ -617,24 +238,24 @@
},
"papermill": {
"default_parameters": {},
- "duration": 24.653494,
- "end_time": "2024-02-15T15:10:48.496631",
+ "duration": 9.950754,
+ "end_time": "2024-02-19T14:43:27.897395",
"environment_variables": {},
"exception": null,
- "input_path": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/3_aggregate_features.ipynb",
- "output_path": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/3_aggregate_features.ipynb",
+ "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/fairnb/tmp/3_aggregate_features/input/raw_features.csv"
+ "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/fairnb/tmp/3_aggregate_features/output/features.csv"
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv"
}
},
- "start_time": "2024-02-15T15:10:23.843137",
+ "start_time": "2024-02-19T14:43:17.946641",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/4_split.ipynb b/notebooks/4_split.ipynb
index 1f1b73f21b5eeb7d2191514602fc550b2a8c6d6e..bd832bc217bbd68aacec9a0ff1f80bb67d45bf64 100644
--- a/notebooks/4_split.ipynb
+++ b/notebooks/4_split.ipynb
@@ -5,10 +5,10 @@
"id": "e92b4fe9",
"metadata": {
"papermill": {
- "duration": 0.049834,
- "end_time": "2024-02-15T10:33:02.348237",
+ "duration": 0.002429,
+ "end_time": "2024-02-19T14:43:32.098156",
"exception": false,
- "start_time": "2024-02-15T10:33:02.298403",
+ "start_time": "2024-02-19T14:43:32.095727",
"status": "completed"
},
"tags": []
@@ -19,20 +19,20 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "5f1fae44",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T10:33:02.466247Z",
- "iopub.status.busy": "2024-02-15T10:33:02.465228Z",
- "iopub.status.idle": "2024-02-15T10:33:02.788415Z",
- "shell.execute_reply": "2024-02-15T10:33:02.786729Z"
+ "iopub.execute_input": "2024-02-19T14:43:32.107309Z",
+ "iopub.status.busy": "2024-02-19T14:43:32.106710Z",
+ "iopub.status.idle": "2024-02-19T14:43:32.385910Z",
+ "shell.execute_reply": "2024-02-19T14:43:32.385222Z"
},
"papermill": {
- "duration": 0.375883,
- "end_time": "2024-02-15T10:33:02.792965",
+ "duration": 0.287217,
+ "end_time": "2024-02-19T14:43:32.388761",
"exception": false,
- "start_time": "2024-02-15T10:33:02.417082",
+ "start_time": "2024-02-19T14:43:32.101544",
"status": "completed"
},
"tags": []
@@ -46,21 +46,21 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "01de1b27",
"metadata": {
"collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:33:02.889367Z",
- "iopub.status.busy": "2024-02-15T10:33:02.888720Z",
- "iopub.status.idle": "2024-02-15T10:33:02.896405Z",
- "shell.execute_reply": "2024-02-15T10:33:02.894335Z"
+ "iopub.execute_input": "2024-02-19T14:43:32.397904Z",
+ "iopub.status.busy": "2024-02-19T14:43:32.397608Z",
+ "iopub.status.idle": "2024-02-19T14:43:32.402480Z",
+ "shell.execute_reply": "2024-02-19T14:43:32.401236Z"
},
"papermill": {
- "duration": 0.064899,
- "end_time": "2024-02-15T10:33:02.906693",
+ "duration": 0.010551,
+ "end_time": "2024-02-19T14:43:32.404108",
"exception": false,
- "start_time": "2024-02-15T10:33:02.841794",
+ "start_time": "2024-02-19T14:43:32.393557",
"status": "completed"
},
"tags": [
@@ -82,20 +82,20 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "e99ca0ba",
+ "execution_count": null,
+ "id": "fdc0a0a6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T10:33:03.003765Z",
- "iopub.status.busy": "2024-02-15T10:33:03.002863Z",
- "iopub.status.idle": "2024-02-15T10:33:03.010409Z",
- "shell.execute_reply": "2024-02-15T10:33:03.008249Z"
+ "iopub.execute_input": "2024-02-19T14:43:32.409447Z",
+ "iopub.status.busy": "2024-02-19T14:43:32.409208Z",
+ "iopub.status.idle": "2024-02-19T14:43:32.412502Z",
+ "shell.execute_reply": "2024-02-19T14:43:32.411939Z"
},
"papermill": {
- "duration": 0.069972,
- "end_time": "2024-02-15T10:33:03.021426",
+ "duration": 0.007467,
+ "end_time": "2024-02-19T14:43:32.413665",
"exception": false,
- "start_time": "2024-02-15T10:33:02.951454",
+ "start_time": "2024-02-19T14:43:32.406198",
"status": "completed"
},
"tags": [
@@ -106,29 +106,29 @@
"source": [
"# Parameters\n",
"INPUT_PATHS = {\n",
- " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/input/features.csv\"\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/fairnb/tmp/4_split/output/split.csv\"\n",
+ " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"\n",
"}\n"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "a4cc6800",
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- "iopub.status.busy": "2024-02-15T10:33:03.120049Z",
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@@ -145,27 +145,27 @@
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{
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"id": "a186d0c4",
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+ "start_time": "2024-02-19T14:43:32.461339",
"status": "completed"
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"tags": []
},
"outputs": [],
"source": [
- "train = features.sample(frac=0.8).sort_index()\n",
+ "train = features.sample(frac=0.8, random_state=11908553).sort_index()\n",
"test = features.drop(train.index)\n",
"\n",
"split_true = pd.DataFrame({\n",
@@ -184,153 +184,45 @@
},
{
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- "execution_count": 6,
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"id": "091e0641",
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- "\n",
- "[400 rows x 2 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
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+ "outputs": [],
"source": [
"split_concat"
]
},
{
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@@ -367,24 +259,24 @@
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+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
"parameters": {
"INPUT_PATHS": {
- "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/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/fairnb/tmp/4_split/output/split.csv"
+ "split": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv"
}
},
- "start_time": "2024-02-15T10:33:01.356084",
+ "start_time": "2024-02-19T14:43:31.186700",
"version": "2.4.0"
}
},
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"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/5_ml_model.ipynb b/notebooks/5_ml_model.ipynb
index 8629a3c6dc7e03d57f31b1ddcefe32307bb70749..3b1bfccc941babf5c40614ab7c5ca3a0b611073c 100644
--- a/notebooks/5_ml_model.ipynb
+++ b/notebooks/5_ml_model.ipynb
@@ -5,10 +5,10 @@
"id": "5de30442",
"metadata": {
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"status": "completed"
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"tags": []
@@ -21,20 +21,20 @@
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{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "a2eb8998",
"metadata": {
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- "iopub.status.idle": "2024-02-15T10:06:44.122908Z",
- "shell.execute_reply": "2024-02-15T10:06:44.121677Z"
+ "iopub.execute_input": "2024-02-19T14:43:37.348184Z",
+ "iopub.status.busy": "2024-02-19T14:43:37.347036Z",
+ "iopub.status.idle": "2024-02-19T14:43:38.479176Z",
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},
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"exception": false,
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+ "start_time": "2024-02-19T14:43:37.333390",
"status": "completed"
},
"tags": []
@@ -42,35 +42,38 @@
"outputs": [],
"source": [
"import pickle\n",
+ "from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import DataFrame, Index\n",
"from sklearn.decomposition import PCA\n",
- "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.metrics import accuracy_score, confusion_matrix\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.svm import SVC\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
"\n",
"from definitions import BASE_PATH"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "8a8da20f",
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-02-15T10:06:44.147761Z",
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+ "iopub.execute_input": "2024-02-19T14:43:38.503653Z",
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"tags": [
@@ -95,20 +98,20 @@
},
{
"cell_type": "code",
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- "id": "08b56684",
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"status": "completed"
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"tags": [
@@ -119,31 +122,31 @@
"source": [
"# Parameters\n",
"INPUT_PATHS = {\n",
- " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/split.csv\",\n",
- " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/features.csv\",\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/fairnb/tmp/5_ml_model/output/ml_model.pickle\",\n",
- " \"submission\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/test_result.csv\",\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"
]
},
{
"cell_type": "code",
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"tags": []
@@ -157,428 +160,25 @@
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- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>classical_1.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-530.78436</td>\n",
- " <td>-163.308350</td>\n",
- " <td>-302.203167</td>\n",
- " <td>51.142183</td>\n",
- " <td>-0.468374</td>\n",
- " <td>0.000000</td>\n",
- " <td>178.75162</td>\n",
- " <td>111.332342</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>-46.794480</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>-562.85785</td>\n",
- " <td>-96.164795</td>\n",
- " <td>-219.259016</td>\n",
- " <td>53.561838</td>\n",
- " <td>-0.772320</td>\n",
- " <td>0.029056</td>\n",
- " <td>259.63270</td>\n",
- " <td>215.094182</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>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_100.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>...</td>\n",
- " <td>27.610388</td>\n",
- " <td>-0.333233</td>\n",
- " <td>8.185075</td>\n",
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- " <td>-38.095375</td>\n",
- " <td>31.397880</td>\n",
- " <td>-1.494916</td>\n",
- " <td>10.917299</td>\n",
- " <td>0.020985</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_11.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-536.45746</td>\n",
- " <td>-120.429665</td>\n",
- " <td>-222.126303</td>\n",
- " <td>76.246992</td>\n",
- " <td>-2.402418</td>\n",
- " <td>0.000000</td>\n",
- " <td>159.42575</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>True</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>148.226647</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>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
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- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_95.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-553.11010</td>\n",
- " <td>-5.218835</td>\n",
- " <td>-193.506047</td>\n",
- " <td>76.869437</td>\n",
- " <td>-0.201055</td>\n",
- " <td>-89.948746</td>\n",
- " <td>201.18045</td>\n",
- " <td>111.724191</td>\n",
- " <td>36.463584</td>\n",
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- " <td>22.451445</td>\n",
- " <td>-7.234634</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>-541.23600</td>\n",
- " <td>27.163334</td>\n",
- " <td>-119.113996</td>\n",
- " <td>58.420684</td>\n",
- " <td>-0.957699</td>\n",
- " <td>-7.415961</td>\n",
- " <td>210.49246</td>\n",
- " <td>125.453699</td>\n",
- " <td>31.908869</td>\n",
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- " <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>-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>-518.49500</td>\n",
- " <td>58.526745</td>\n",
- " <td>-66.267744</td>\n",
- " <td>65.635619</td>\n",
- " <td>-0.898026</td>\n",
- " <td>-58.824410</td>\n",
- " <td>175.20135</td>\n",
- " <td>99.288265</td>\n",
- " <td>25.158416</td>\n",
- " <td>...</td>\n",
- " <td>26.325895</td>\n",
- " <td>-5.722825</td>\n",
- " <td>7.727378</td>\n",
- " <td>0.207489</td>\n",
- " <td>-29.497524</td>\n",
- " <td>25.410654</td>\n",
- " <td>-3.356614</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>-518.64307</td>\n",
- " <td>53.555115</td>\n",
- " <td>-45.734517</td>\n",
- " <td>52.444200</td>\n",
- " <td>-1.705641</td>\n",
- " <td>0.000000</td>\n",
- " <td>187.04274</td>\n",
- " <td>96.440874</td>\n",
- " <td>24.137702</td>\n",
- " <td>...</td>\n",
- " <td>8.714737</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>-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>-544.70310</td>\n",
- " <td>75.612130</td>\n",
- " <td>-49.380943</td>\n",
- " <td>54.045627</td>\n",
- " <td>-0.863093</td>\n",
- " <td>-32.930653</td>\n",
- " <td>191.73538</td>\n",
- " <td>93.971242</td>\n",
- " <td>33.410220</td>\n",
- " <td>...</td>\n",
- " <td>17.050608</td>\n",
- " <td>-5.296691</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>"
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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",
- "classical_10.mp3 classical -562.85785 -96.164795 -219.259016 53.561838 \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_12.mp3 classical -562.67523 -148.133560 -270.975406 52.191182 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 rock -553.11010 -5.218835 -193.506047 76.869437 \n",
- "rock_96.mp3 rock -541.23600 27.163334 -119.113996 58.420684 \n",
- "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_1.mp3 -0.468374 0.000000 178.75162 111.332342 24.847563 ... \n",
- "classical_10.mp3 -0.772320 0.029056 259.63270 215.094182 18.388131 ... \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 21.916949 ... \n",
- "classical_12.mp3 -0.366586 0.000000 194.26416 148.226647 19.305008 ... \n",
- "... ... ... ... ... ... ... \n",
- "rock_95.mp3 -0.201055 -89.948746 201.18045 111.724191 36.463584 ... \n",
- "rock_96.mp3 -0.957699 -7.415961 210.49246 125.453699 31.908869 ... \n",
- "rock_97.mp3 -0.898026 -58.824410 175.20135 99.288265 25.158416 ... \n",
- "rock_98.mp3 -1.705641 0.000000 187.04274 96.440874 24.137702 ... \n",
- "rock_99.mp3 -0.863093 -32.930653 191.73538 93.971242 33.410220 ... \n",
- "\n",
- " 38_max 38_mean 38_std 38_skew 39_min \\\n",
- "filename \n",
- "classical_1.mp3 47.308060 -3.713503 16.553984 0.230691 -46.794480 \n",
- "classical_10.mp3 29.811110 0.484271 8.660648 -0.479016 -28.989983 \n",
- "classical_100.mp3 27.610388 -0.333233 8.185075 0.208425 -38.095375 \n",
- "classical_11.mp3 31.500881 -3.781627 9.191043 0.260886 -22.667440 \n",
- "classical_12.mp3 28.490644 -6.242015 10.546545 0.341848 -25.040888 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 22.451445 -7.234634 8.471853 0.753855 -24.712723 \n",
- "rock_96.mp3 28.087936 -9.704238 8.447620 0.112760 -38.147890 \n",
- "rock_97.mp3 26.325895 -5.722825 7.727378 0.207489 -29.497524 \n",
- "rock_98.mp3 8.714737 -9.511491 5.551820 -0.025604 -23.020084 \n",
- "rock_99.mp3 17.050608 -5.296691 5.894963 0.390705 -20.983192 \n",
- "\n",
- " 39_max 39_mean 39_std 39_skew train \n",
- "filename \n",
- "classical_1.mp3 49.352516 -2.282116 15.285639 0.171462 True \n",
- "classical_10.mp3 27.533710 0.952658 10.477735 -0.185771 True \n",
- "classical_100.mp3 31.397880 -1.494916 10.917299 0.020985 True \n",
- "classical_11.mp3 50.992897 1.600777 10.125545 0.595763 True \n",
- "classical_12.mp3 46.878204 1.844494 11.160392 0.503120 False \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 23.410387 -4.502398 6.687984 0.238807 True \n",
- "rock_96.mp3 21.814402 -8.249507 7.807756 0.071968 True \n",
- "rock_97.mp3 25.410654 -3.356614 8.170526 0.160330 True \n",
- "rock_98.mp3 13.948638 -2.664985 5.051498 -0.258407 True \n",
- "rock_99.mp3 29.312023 -0.321836 6.571660 0.384794 True \n",
- "\n",
- "[400 rows x 202 columns]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\n",
"joined"
@@ -586,428 +186,25 @@
},
{
"cell_type": "code",
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+ "execution_count": null,
"id": "265d042f",
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+ "iopub.status.idle": "2024-02-19T14:43:38.696894Z",
+ "shell.execute_reply": "2024-02-19T14:43:38.696151Z"
},
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- "duration": 0.076899,
- "end_time": "2024-02-15T10:06:44.403881",
+ "duration": 0.032355,
+ "end_time": "2024-02-19T14:43:38.698270",
"exception": false,
- "start_time": "2024-02-15T10:06:44.326982",
+ "start_time": "2024-02-19T14:43:38.665915",
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"tags": []
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- " </tr>\n",
- " <tr>\n",
- " <th>classical_11.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-536.45746</td>\n",
- " <td>-120.429665</td>\n",
- " <td>-222.126303</td>\n",
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- " <td>1.600777</td>\n",
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- " <td>0.595763</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>classical_13.mp3</th>\n",
- " <td>classical</td>\n",
- " <td>-637.72064</td>\n",
- " <td>-177.713960</td>\n",
- " <td>-361.834032</td>\n",
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- " <tr>\n",
- " <th>rock_95.mp3</th>\n",
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- " <tr>\n",
- " <th>rock_96.mp3</th>\n",
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- " <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>-518.49500</td>\n",
- " <td>58.526745</td>\n",
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- " <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>-518.64307</td>\n",
- " <td>53.555115</td>\n",
- " <td>-45.734517</td>\n",
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- " <td>5.051498</td>\n",
- " <td>-0.258407</td>\n",
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- " <th>rock_99.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-544.70310</td>\n",
- " <td>75.612130</td>\n",
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- " <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",
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- " label 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
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- "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",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 rock -553.11010 -5.218835 -193.506047 76.869437 \n",
- "rock_96.mp3 rock -541.23600 27.163334 -119.113996 58.420684 \n",
- "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_1.mp3 -0.468374 0.000000 178.75162 111.332342 24.847563 ... \n",
- "classical_10.mp3 -0.772320 0.029056 259.63270 215.094182 18.388131 ... \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 21.916949 ... \n",
- "classical_13.mp3 0.008325 0.000000 257.16284 211.556558 20.347034 ... \n",
- "... ... ... ... ... ... ... \n",
- "rock_95.mp3 -0.201055 -89.948746 201.18045 111.724191 36.463584 ... \n",
- "rock_96.mp3 -0.957699 -7.415961 210.49246 125.453699 31.908869 ... \n",
- "rock_97.mp3 -0.898026 -58.824410 175.20135 99.288265 25.158416 ... \n",
- "rock_98.mp3 -1.705641 0.000000 187.04274 96.440874 24.137702 ... \n",
- "rock_99.mp3 -0.863093 -32.930653 191.73538 93.971242 33.410220 ... \n",
- "\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",
- "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 18.424036 -0.275736 7.026148 -0.640964 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -27.043941 22.451445 -7.234634 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 -5.722825 7.727378 0.207489 \n",
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- "rock_99.mp3 -21.929403 17.050608 -5.296691 5.894963 0.390705 \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 -28.989983 27.533710 0.952658 10.477735 -0.185771 \n",
- "classical_100.mp3 -38.095375 31.397880 -1.494916 10.917299 0.020985 \n",
- "classical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \n",
- "classical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \n",
- "... ... ... ... ... ... \n",
- "rock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \n",
- "rock_96.mp3 -38.147890 21.814402 -8.249507 7.807756 0.071968 \n",
- "rock_97.mp3 -29.497524 25.410654 -3.356614 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 201 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
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+ "outputs": [],
"source": [
"train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\n",
"train"
@@ -1015,428 +212,25 @@
},
{
"cell_type": "code",
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+ "execution_count": null,
"id": "1649ce52",
"metadata": {
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- "shell.execute_reply": "2024-02-15T10:06:44.459420Z"
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+ "iopub.status.busy": "2024-02-19T14:43:38.714388Z",
+ "iopub.status.idle": "2024-02-19T14:43:38.737616Z",
+ "shell.execute_reply": "2024-02-19T14:43:38.737067Z"
},
"papermill": {
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- "end_time": "2024-02-15T10:06:44.469300",
+ "duration": 0.032666,
+ "end_time": "2024-02-19T14:43:38.739010",
"exception": false,
- "start_time": "2024-02-15T10:06:44.412675",
+ "start_time": "2024-02-19T14:43:38.706344",
"status": "completed"
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"tags": []
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- " <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_85.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-556.08203</td>\n",
- " <td>44.890602</td>\n",
- " <td>-72.618399</td>\n",
- " <td>80.272023</td>\n",
- " <td>-2.269420</td>\n",
- " <td>-13.219891</td>\n",
- " <td>205.14955</td>\n",
- " <td>96.863927</td>\n",
- " <td>38.352424</td>\n",
- " <td>...</td>\n",
- " <td>-22.633102</td>\n",
- " <td>13.513550</td>\n",
- " <td>-3.126545</td>\n",
- " <td>5.035097</td>\n",
- " <td>-0.035805</td>\n",
- " <td>-19.814285</td>\n",
- " <td>18.576450</td>\n",
- " <td>-1.172361</td>\n",
- " <td>6.078238</td>\n",
- " <td>-0.048851</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_86.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-534.40650</td>\n",
- " <td>42.919650</td>\n",
- " <td>-93.601685</td>\n",
- " <td>62.192619</td>\n",
- " <td>-0.869415</td>\n",
- " <td>0.000000</td>\n",
- " <td>206.32501</td>\n",
- " <td>128.047509</td>\n",
- " <td>30.374850</td>\n",
- " <td>...</td>\n",
- " <td>-30.471783</td>\n",
- " <td>20.564953</td>\n",
- " <td>-3.383356</td>\n",
- " <td>6.405211</td>\n",
- " <td>-0.185147</td>\n",
- " <td>-28.917618</td>\n",
- " <td>26.702751</td>\n",
- " <td>-1.950565</td>\n",
- " <td>6.725107</td>\n",
- " <td>-0.253487</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_88.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-539.97880</td>\n",
- " <td>44.375150</td>\n",
- " <td>-126.955020</td>\n",
- " <td>88.140999</td>\n",
- " <td>-1.700578</td>\n",
- " <td>-19.007393</td>\n",
- " <td>201.99960</td>\n",
- " <td>99.760978</td>\n",
- " <td>32.572320</td>\n",
- " <td>...</td>\n",
- " <td>-34.726500</td>\n",
- " <td>26.706833</td>\n",
- " <td>-5.827121</td>\n",
- " <td>8.260717</td>\n",
- " <td>0.275225</td>\n",
- " <td>-31.036520</td>\n",
- " <td>27.423218</td>\n",
- " <td>-4.715363</td>\n",
- " <td>6.544117</td>\n",
- " <td>0.184718</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_92.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-532.89110</td>\n",
- " <td>13.948147</td>\n",
- " <td>-206.891688</td>\n",
- " <td>80.812274</td>\n",
- " <td>0.090286</td>\n",
- " <td>-47.724570</td>\n",
- " <td>179.76506</td>\n",
- " <td>109.954998</td>\n",
- " <td>37.880477</td>\n",
- " <td>...</td>\n",
- " <td>-37.614220</td>\n",
- " <td>21.420666</td>\n",
- " <td>-8.287362</td>\n",
- " <td>7.851784</td>\n",
- " <td>-0.080285</td>\n",
- " <td>-41.547260</td>\n",
- " <td>25.628895</td>\n",
- " <td>-9.046777</td>\n",
- " <td>8.779821</td>\n",
- " <td>0.071449</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>rock_93.mp3</th>\n",
- " <td>rock</td>\n",
- " <td>-570.46650</td>\n",
- " <td>-26.067888</td>\n",
- " <td>-302.483118</td>\n",
- " <td>96.569376</td>\n",
- " <td>0.159026</td>\n",
- " <td>-89.999680</td>\n",
- " <td>211.88910</td>\n",
- " <td>103.686365</td>\n",
- " <td>40.373592</td>\n",
- " <td>...</td>\n",
- " <td>-28.903786</td>\n",
- " <td>35.712753</td>\n",
- " <td>2.073339</td>\n",
- " <td>10.995769</td>\n",
- " <td>0.249798</td>\n",
- " <td>-30.178170</td>\n",
- " <td>30.612560</td>\n",
- " <td>-4.677735</td>\n",
- " <td>8.877041</td>\n",
- " <td>0.149639</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>80 rows × 201 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " label 0_min 0_max 0_mean 0_std \\\n",
- "filename \n",
- "classical_12.mp3 classical -562.67523 -148.133560 -270.975406 52.191182 \n",
- "classical_2.mp3 classical -549.40650 -192.532060 -293.008969 27.207028 \n",
- "classical_20.mp3 classical -605.99150 -161.119310 -263.483084 49.157298 \n",
- "classical_27.mp3 classical -595.41895 -78.118810 -265.344461 104.892303 \n",
- "classical_39.mp3 classical -578.84720 -55.479320 -183.753039 69.140628 \n",
- "... ... ... ... ... ... \n",
- "rock_85.mp3 rock -556.08203 44.890602 -72.618399 80.272023 \n",
- "rock_86.mp3 rock -534.40650 42.919650 -93.601685 62.192619 \n",
- "rock_88.mp3 rock -539.97880 44.375150 -126.955020 88.140999 \n",
- "rock_92.mp3 rock -532.89110 13.948147 -206.891688 80.812274 \n",
- "rock_93.mp3 rock -570.46650 -26.067888 -302.483118 96.569376 \n",
- "\n",
- " 0_skew 1_min 1_max 1_mean 1_std ... \\\n",
- "filename ... \n",
- "classical_12.mp3 -0.366586 0.000000 194.26416 148.226647 19.305008 ... \n",
- "classical_2.mp3 -0.426848 0.000000 231.03738 198.662514 14.957660 ... \n",
- "classical_20.mp3 -0.856221 0.000000 191.92676 141.393817 17.754779 ... \n",
- "classical_27.mp3 -0.526604 0.000000 200.61633 144.208488 25.198761 ... \n",
- "classical_39.mp3 -0.577055 0.000000 193.84949 127.058496 29.295691 ... \n",
- "... ... ... ... ... ... ... \n",
- "rock_85.mp3 -2.269420 -13.219891 205.14955 96.863927 38.352424 ... \n",
- "rock_86.mp3 -0.869415 0.000000 206.32501 128.047509 30.374850 ... \n",
- "rock_88.mp3 -1.700578 -19.007393 201.99960 99.760978 32.572320 ... \n",
- "rock_92.mp3 0.090286 -47.724570 179.76506 109.954998 37.880477 ... \n",
- "rock_93.mp3 0.159026 -89.999680 211.88910 103.686365 40.373592 ... \n",
- "\n",
- " 38_min 38_max 38_mean 38_std 38_skew \\\n",
- "filename \n",
- "classical_12.mp3 -44.843810 28.490644 -6.242015 10.546545 0.341848 \n",
- "classical_2.mp3 -25.912933 24.293318 0.746096 8.240027 -0.022513 \n",
- "classical_20.mp3 -24.911243 38.551230 -2.274261 9.671005 0.719436 \n",
- "classical_27.mp3 -28.797087 20.897750 -5.761607 7.108055 0.360305 \n",
- "classical_39.mp3 -48.678460 24.566566 -7.810246 11.568188 -0.106704 \n",
- "... ... ... ... ... ... \n",
- "rock_85.mp3 -22.633102 13.513550 -3.126545 5.035097 -0.035805 \n",
- "rock_86.mp3 -30.471783 20.564953 -3.383356 6.405211 -0.185147 \n",
- "rock_88.mp3 -34.726500 26.706833 -5.827121 8.260717 0.275225 \n",
- "rock_92.mp3 -37.614220 21.420666 -8.287362 7.851784 -0.080285 \n",
- "rock_93.mp3 -28.903786 35.712753 2.073339 10.995769 0.249798 \n",
- "\n",
- " 39_min 39_max 39_mean 39_std 39_skew \n",
- "filename \n",
- "classical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \n",
- "classical_2.mp3 -18.561390 23.484133 3.115819 7.220346 0.242364 \n",
- "classical_20.mp3 -30.311798 29.272330 0.289613 9.590299 -0.244191 \n",
- "classical_27.mp3 -39.705540 25.803795 -2.736776 10.101577 -0.463730 \n",
- "classical_39.mp3 -24.328775 40.172250 -0.078006 10.646963 0.492488 \n",
- "... ... ... ... ... ... \n",
- "rock_85.mp3 -19.814285 18.576450 -1.172361 6.078238 -0.048851 \n",
- "rock_86.mp3 -28.917618 26.702751 -1.950565 6.725107 -0.253487 \n",
- "rock_88.mp3 -31.036520 27.423218 -4.715363 6.544117 0.184718 \n",
- "rock_92.mp3 -41.547260 25.628895 -9.046777 8.779821 0.071449 \n",
- "rock_93.mp3 -30.178170 30.612560 -4.677735 8.877041 0.149639 \n",
- "\n",
- "[80 rows x 201 columns]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\n",
"test"
@@ -1444,107 +238,26 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "1e904bf3",
+ "execution_count": null,
+ "id": "1c01673464cb048e",
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},
"papermill": {
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- "end_time": "2024-02-15T10:06:44.527636",
+ "duration": 0.025115,
+ "end_time": "2024-02-19T14:43:38.772716",
"exception": false,
- "start_time": "2024-02-15T10:06:44.478980",
+ "start_time": "2024-02-19T14:43:38.747601",
"status": "completed"
},
"tags": []
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- "outputs": [
- {
- "data": {
- "text/plain": [
- "( 0_min 0_max 0_mean 0_std 0_skew \\\n",
- " filename \n",
- " classical_1.mp3 -530.78436 -163.308350 -302.203167 51.142183 -0.468374 \n",
- " classical_10.mp3 -562.85785 -96.164795 -219.259016 53.561838 -0.772320 \n",
- " classical_100.mp3 -536.23737 -61.608826 -177.804114 83.381622 -2.587179 \n",
- " classical_11.mp3 -536.45746 -120.429665 -222.126303 76.246992 -2.402418 \n",
- " classical_13.mp3 -637.72064 -177.713960 -361.834032 71.310080 0.008325 \n",
- " ... ... ... ... ... ... \n",
- " rock_95.mp3 -553.11010 -5.218835 -193.506047 76.869437 -0.201055 \n",
- " rock_96.mp3 -541.23600 27.163334 -119.113996 58.420684 -0.957699 \n",
- " rock_97.mp3 -518.49500 58.526745 -66.267744 65.635619 -0.898026 \n",
- " rock_98.mp3 -518.64307 53.555115 -45.734517 52.444200 -1.705641 \n",
- " rock_99.mp3 -544.70310 75.612130 -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 178.75162 111.332342 24.847563 -0.402642 ... \n",
- " classical_10.mp3 0.029056 259.63270 215.094182 18.388131 -1.528751 ... \n",
- " classical_100.mp3 0.000000 190.47589 112.471713 27.277553 -1.318523 ... \n",
- " classical_11.mp3 0.000000 159.42575 99.853645 21.916949 -1.176922 ... \n",
- " classical_13.mp3 0.000000 257.16284 211.556558 20.347034 -1.050119 ... \n",
- " ... ... ... ... ... ... ... \n",
- " rock_95.mp3 -89.948746 201.18045 111.724191 36.463584 -0.443224 ... \n",
- " rock_96.mp3 -7.415961 210.49246 125.453699 31.908869 -0.547469 ... \n",
- " rock_97.mp3 -58.824410 175.20135 99.288265 25.158416 -0.568057 ... \n",
- " rock_98.mp3 0.000000 187.04274 96.440874 24.137702 -0.145217 ... \n",
- " rock_99.mp3 -32.930653 191.73538 93.971242 33.410220 0.040113 ... \n",
- " \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",
- " 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 18.424036 -0.275736 7.026148 -0.640964 \n",
- " ... ... ... ... ... ... \n",
- " rock_95.mp3 -27.043941 22.451445 -7.234634 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 -5.722825 7.727378 0.207489 \n",
- " rock_98.mp3 -26.967848 8.714737 -9.511491 5.551820 -0.025604 \n",
- " rock_99.mp3 -21.929403 17.050608 -5.296691 5.894963 0.390705 \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 -28.989983 27.533710 0.952658 10.477735 -0.185771 \n",
- " classical_100.mp3 -38.095375 31.397880 -1.494916 10.917299 0.020985 \n",
- " classical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \n",
- " classical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \n",
- " ... ... ... ... ... ... \n",
- " rock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \n",
- " rock_96.mp3 -38.147890 21.814402 -8.249507 7.807756 0.071968 \n",
- " rock_97.mp3 -29.497524 25.410654 -3.356614 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, 1, 1, 1, 1, 1, 1, 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, 2, 2, 2, 2,\n",
- " 2, 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": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# remove labels\n",
"X = train.drop(['label'], axis=1, errors='ignore')\n",
@@ -1568,48 +281,26 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "32e5e889",
+ "execution_count": null,
+ "id": "41ce60fbed0a23bc",
"metadata": {
+ "collapsed": false,
"execution": {
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- "shell.execute_reply": "2024-02-15T10:06:44.564546Z"
+ "iopub.execute_input": "2024-02-19T14:43:38.786997Z",
+ "iopub.status.busy": "2024-02-19T14:43:38.785821Z",
+ "iopub.status.idle": "2024-02-19T14:43:38.798214Z",
+ "shell.execute_reply": "2024-02-19T14:43:38.797313Z"
},
"papermill": {
- "duration": 0.042899,
- "end_time": "2024-02-15T10:06:44.582227",
+ "duration": 0.023428,
+ "end_time": "2024-02-19T14:43:38.802230",
"exception": false,
- "start_time": "2024-02-15T10:06:44.539328",
+ "start_time": "2024-02-19T14:43:38.778802",
"status": "completed"
},
"tags": []
},
- "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, 2, 2, 2, 2, 2, 2, 2, 2,\n",
- " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
- " 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"X_test = test.drop(['label'], axis=1, errors='ignore')\n",
"\n",
@@ -1623,48 +314,26 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "id": "e165922f",
+ "execution_count": null,
+ "id": "99dc29024df3d251",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:44.600958Z",
- "iopub.status.busy": "2024-02-15T10:06:44.600601Z",
- "iopub.status.idle": "2024-02-15T10:06:44.616543Z",
- "shell.execute_reply": "2024-02-15T10:06:44.614861Z"
+ "iopub.execute_input": "2024-02-19T14:43:38.818158Z",
+ "iopub.status.busy": "2024-02-19T14:43:38.817887Z",
+ "iopub.status.idle": "2024-02-19T14:43:38.826644Z",
+ "shell.execute_reply": "2024-02-19T14:43:38.825871Z"
},
"papermill": {
- "duration": 0.030524,
- "end_time": "2024-02-15T10:06:44.621802",
+ "duration": 0.019206,
+ "end_time": "2024-02-19T14:43:38.828092",
"exception": false,
- "start_time": "2024-02-15T10:06:44.591278",
+ "start_time": "2024-02-19T14:43:38.808886",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[ 0.38209988, -1.79901606, -1.34294124, ..., -0.7312519 ,\n",
- " 3.4358529 , 0.11530124],\n",
- " [-0.42728837, -0.93236007, -0.41652953, ..., 0.22563011,\n",
- " 1.37555438, -0.86835549],\n",
- " [ 0.24449084, -0.48632861, 0.04648451, ..., -0.49838941,\n",
- " 1.56391778, -0.29904453],\n",
- " ...,\n",
- " [ 0.69222714, 1.06432227, 1.29224565, ..., -1.0491004 ,\n",
- " 0.38686173, 0.08464998],\n",
- " [ 0.68849053, 1.00015092, 1.52158336, ..., -0.84450893,\n",
- " -0.94971424, -1.06836048],\n",
- " [ 0.03085452, 1.28485202, 1.48085606, ..., -0.15137928,\n",
- " -0.29828957, 0.70271937]])"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Standardize for PCA\n",
"scaler = StandardScaler()\n",
@@ -1676,39 +345,29 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "d389fd70",
+ "execution_count": null,
+ "id": "3f30e11dc4688246",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:44.641936Z",
- "iopub.status.busy": "2024-02-15T10:06:44.641290Z",
- "iopub.status.idle": "2024-02-15T10:06:44.692096Z",
- "shell.execute_reply": "2024-02-15T10:06:44.690837Z"
+ "iopub.execute_input": "2024-02-19T14:43:38.862268Z",
+ "iopub.status.busy": "2024-02-19T14:43:38.861826Z",
+ "iopub.status.idle": "2024-02-19T14:43:38.905156Z",
+ "shell.execute_reply": "2024-02-19T14:43:38.904157Z"
},
"papermill": {
- "duration": 0.070334,
- "end_time": "2024-02-15T10:06:44.700812",
+ "duration": 0.065427,
+ "end_time": "2024-02-19T14:43:38.909651",
"exception": false,
- "start_time": "2024-02-15T10:06:44.630478",
+ "start_time": "2024-02-19T14:43:38.844224",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8557392011152061\n",
- "(320, 50)\n",
- "(80, 50)\n",
- "(320,)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Reduce Dimensions via PCA\n",
- "pca = PCA(n_components=50).fit(X_standardized)\n",
+ "pca = PCA(n_components=30).fit(X_standardized)\n",
"X_pca = pca.transform(X_standardized)\n",
"X_test_pca = pca.transform(X_test_standardized)\n",
"\n",
@@ -1720,48 +379,26 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "aa1d9036",
+ "execution_count": null,
+ "id": "21bf974f979ae1f4",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:44.764588Z",
- "iopub.status.busy": "2024-02-15T10:06:44.764242Z",
- "iopub.status.idle": "2024-02-15T10:06:44.838160Z",
- "shell.execute_reply": "2024-02-15T10:06:44.837012Z"
+ "iopub.execute_input": "2024-02-19T14:43:38.967756Z",
+ "iopub.status.busy": "2024-02-19T14:43:38.967401Z",
+ "iopub.status.idle": "2024-02-19T14:43:39.005473Z",
+ "shell.execute_reply": "2024-02-19T14:43:39.004883Z"
},
"papermill": {
- "duration": 0.115738,
- "end_time": "2024-02-15T10:06:44.842730",
+ "duration": 0.068904,
+ "end_time": "2024-02-19T14:43:39.006776",
"exception": false,
- "start_time": "2024-02-15T10:06:44.726992",
+ "start_time": "2024-02-19T14:43:38.937872",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.6875\n",
- "[[-4.64558613 3.08838305 -1.47175688 ... -1.24828691 -0.70095473\n",
- " 0.01689286]\n",
- " [ 5.85968202 -2.1047151 -3.35419664 ... -1.48822402 1.00205068\n",
- " -0.98882563]\n",
- " [ 6.52471238 -2.88386219 -5.91379963 ... 0.08618421 0.03366275\n",
- " -0.55189302]\n",
- " ...\n",
- " [ 5.3496866 3.90245458 -4.07128854 ... -0.82356091 -0.7968544\n",
- " 0.26045289]\n",
- " [ 6.68981697 -1.18340439 -0.12267599 ... 1.33593613 -2.8015435\n",
- " 0.5028293 ]\n",
- " [-4.78063681 -7.16377441 4.09506551 ... -1.0308011 0.83671387\n",
- " -0.07027211]]\n",
- "[3 0 3 2 3 0 1 2 0 3 0 0 0 1 2 1 2 3 1 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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Fit SVM:\n",
"\n",
@@ -1770,442 +407,313 @@
"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)"
+ "print(accuracy_score(clf.predict(X_val), y_val))"
]
},
{
"cell_type": "code",
- "execution_count": 13,
- "id": "fc48c86e",
+ "execution_count": null,
+ "id": "6099c8ae2b4be921",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:44.862363Z",
- "iopub.status.busy": "2024-02-15T10:06:44.861486Z",
- "iopub.status.idle": "2024-02-15T10:06:46.923607Z",
- "shell.execute_reply": "2024-02-15T10:06:46.922543Z"
+ "iopub.execute_input": "2024-02-19T14:43:39.022652Z",
+ "iopub.status.busy": "2024-02-19T14:43:39.022268Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.006558Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.006078Z"
},
"papermill": {
- "duration": 2.081139,
- "end_time": "2024-02-15T10:06:46.932532",
+ "duration": 2.994462,
+ "end_time": "2024-02-19T14:43:42.007899",
"exception": false,
- "start_time": "2024-02-15T10:06:44.851393",
+ "start_time": "2024-02-19T14:43:39.013437",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.7343891402714932\n",
- "{'C': 3, 'gamma': 0.01}\n",
- "SVC(C=3, gamma=0.01)\n",
- "0.78125\n"
- ]
- }
- ],
+ "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",
+ "C_grid = [0.0001, 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",
+ "grid.fit(X_pca, y)\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))"
+ "print(grid.best_estimator_)"
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "7cf87469",
+ "execution_count": null,
+ "id": "43a8791efe8809f4",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:46.952751Z",
- "iopub.status.busy": "2024-02-15T10:06:46.952327Z",
- "iopub.status.idle": "2024-02-15T10:06:46.997919Z",
- "shell.execute_reply": "2024-02-15T10:06:46.996989Z"
+ "iopub.execute_input": "2024-02-19T14:43:42.043179Z",
+ "iopub.status.busy": "2024-02-19T14:43:42.042950Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.088754Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.088214Z"
},
"papermill": {
- "duration": 0.064398,
- "end_time": "2024-02-15T10:06:47.003632",
+ "duration": 0.064209,
+ "end_time": "2024-02-19T14:43:42.090026",
"exception": false,
- "start_time": "2024-02-15T10:06:46.939234",
+ "start_time": "2024-02-19T14:43:42.025817",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.78125\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# Optimal model\n",
+ "# Fit entire training sets with optimal model\n",
"\n",
"clf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\n",
- "clf.fit(X_train, y_train)\n",
+ "clf.fit(X_pca, y)\n",
+ "proba = clf.predict_proba(X_test_pca)\n",
"\n",
- "print(accuracy_score(clf.predict(X_val), y_val))"
+ "print(f\"Accuracy score: {accuracy_score(clf.predict(X_test_pca), y_test)}\")"
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "5a754cd1",
+ "execution_count": null,
+ "id": "28c779539faeb27c",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:47.039860Z",
- "iopub.status.busy": "2024-02-15T10:06:47.038736Z",
- "iopub.status.idle": "2024-02-15T10:06:47.137886Z",
- "shell.execute_reply": "2024-02-15T10:06:47.137031Z"
+ "iopub.execute_input": "2024-02-19T14:43:42.127450Z",
+ "iopub.status.busy": "2024-02-19T14:43:42.127111Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.160469Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.159848Z"
},
"papermill": {
- "duration": 0.123554,
- "end_time": "2024-02-15T10:06:47.144511",
+ "duration": 0.053263,
+ "end_time": "2024-02-19T14:43:42.162096",
"exception": false,
- "start_time": "2024-02-15T10:06:47.020957",
+ "start_time": "2024-02-19T14:43:42.108833",
"status": "completed"
},
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8\n",
- "[[9.60125451e-01 2.54410379e-02 1.00183548e-02 4.41515609e-03]\n",
- " [9.93544791e-01 4.04634019e-03 1.20649558e-03 1.20237342e-03]\n",
- " [9.97430192e-01 1.76800719e-04 5.38565546e-04 1.85444214e-03]\n",
- " [9.79967977e-01 6.86113735e-03 9.68497114e-03 3.48591496e-03]\n",
- " [9.91884967e-01 5.17290348e-03 1.26266158e-03 1.67946793e-03]\n",
- " [9.85578464e-01 9.44992493e-03 3.75086068e-03 1.22075036e-03]\n",
- " [2.04862989e-01 4.53621014e-01 1.34373358e-01 2.07142639e-01]\n",
- " [9.99181855e-01 4.86945868e-04 2.22608725e-04 1.08590413e-04]\n",
- " [9.92658119e-01 3.47218548e-03 2.74696376e-03 1.12273207e-03]\n",
- " [9.99656357e-01 1.12727916e-04 1.43400994e-04 8.75138776e-05]\n",
- " [8.47319131e-01 4.69014094e-02 7.09411516e-02 3.48383077e-02]\n",
- " [1.28380278e-01 3.67332428e-01 3.59429595e-01 1.44857699e-01]\n",
- " [9.96413445e-01 2.75890076e-03 4.65504357e-04 3.62150045e-04]\n",
- " [9.98826125e-01 7.62447290e-04 3.01490088e-04 1.09937383e-04]\n",
- " [9.99401836e-01 8.67850526e-05 3.74373911e-04 1.37005308e-04]\n",
- " [9.97955498e-01 1.69931669e-03 1.73626292e-04 1.71558652e-04]\n",
- " [8.45643860e-01 1.33426916e-02 9.97412359e-02 4.12722121e-02]\n",
- " [9.82092462e-01 1.15346135e-02 3.19973757e-03 3.17318740e-03]\n",
- " [9.83213850e-01 1.24420959e-02 3.26304918e-03 1.08100527e-03]\n",
- " [9.99642856e-01 7.19184901e-05 1.55316717e-04 1.29908898e-04]\n",
- " [9.97979494e-01 1.76870557e-03 1.31807873e-04 1.19992584e-04]\n",
- " [4.92333515e-04 9.38096306e-01 2.10469538e-02 4.03644064e-02]\n",
- " [9.45551189e-03 4.32699483e-01 4.16341606e-01 1.41503399e-01]\n",
- " [9.13893710e-03 4.44229440e-01 3.15860710e-01 2.30770912e-01]\n",
- " [6.79828415e-02 6.71681498e-01 2.09457159e-01 5.08785014e-02]\n",
- " [1.68076034e-04 9.71769830e-01 2.24441690e-03 2.58176775e-02]\n",
- " [5.73737808e-02 8.61494512e-02 5.86365884e-01 2.70110884e-01]\n",
- " [1.18603200e-01 5.68582627e-01 2.33418558e-01 7.93956149e-02]\n",
- " [1.11117289e-02 9.36048570e-01 2.07419839e-02 3.20977167e-02]\n",
- " [4.27128683e-03 2.53015466e-01 4.52073691e-01 2.90639556e-01]\n",
- " [8.49595708e-03 6.37021927e-01 1.52099758e-01 2.02382358e-01]\n",
- " [9.29855946e-04 8.43628458e-01 1.67412440e-02 1.38700442e-01]\n",
- " [5.75440080e-02 6.65893968e-01 1.18869183e-01 1.57692841e-01]\n",
- " [7.28891949e-02 6.97755501e-01 1.23916666e-01 1.05438637e-01]\n",
- " [1.00364172e-01 3.05951082e-01 4.02534596e-01 1.91150150e-01]\n",
- " [2.71956862e-04 5.43067021e-01 1.43066793e-02 4.42354343e-01]\n",
- " [8.60586155e-02 8.06134589e-02 6.12157762e-01 2.21170163e-01]\n",
- " [4.54205646e-02 3.77922605e-02 7.46222645e-01 1.70564530e-01]\n",
- " [2.60732219e-02 1.78887893e-01 3.03253706e-01 4.91785179e-01]\n",
- " [1.76685545e-01 1.49702306e-01 5.30947449e-01 1.42664700e-01]\n",
- " [2.10423538e-02 3.16261307e-02 6.86655601e-01 2.60675914e-01]\n",
- " [5.10365555e-03 9.06077798e-03 3.10609892e-01 6.75225674e-01]\n",
- " [1.85590659e-04 4.20187052e-01 2.54067881e-01 3.25559476e-01]\n",
- " [1.84121015e-03 1.49368051e-03 5.94696830e-01 4.01968279e-01]\n",
- " [9.94756099e-03 1.98337895e-02 6.10189918e-01 3.60028732e-01]\n",
- " [1.06218859e-02 5.83443846e-02 4.09385718e-01 5.21648011e-01]\n",
- " [2.51610276e-01 1.06475171e-01 4.02323327e-01 2.39591226e-01]\n",
- " [1.05739190e-03 4.80039248e-03 7.84298209e-01 2.09844007e-01]\n",
- " [1.20304373e-03 2.49929289e-03 4.25498367e-01 5.70799297e-01]\n",
- " [5.17165422e-04 2.44187897e-03 7.70942808e-01 2.26098148e-01]\n",
- " [1.48279902e-01 4.34212254e-01 3.33486768e-01 8.40210765e-02]\n",
- " [6.49493657e-03 2.03203941e-03 6.76591245e-01 3.14881779e-01]\n",
- " [1.42643647e-03 3.00507802e-02 7.66466942e-01 2.02055842e-01]\n",
- " [2.71205953e-04 1.64674206e-03 5.18908081e-01 4.79173971e-01]\n",
- " [6.18460044e-04 8.65733199e-03 7.31160871e-01 2.59563337e-01]\n",
- " [5.99851686e-04 9.88068783e-03 3.18075020e-01 6.71444441e-01]\n",
- " [8.92857719e-05 2.49912334e-03 8.22928402e-01 1.74483188e-01]\n",
- " [4.08821963e-03 4.01685411e-03 2.22308630e-01 7.69586296e-01]\n",
- " [3.85280110e-04 4.28844983e-03 4.38873417e-01 5.56452853e-01]\n",
- " [7.77946831e-04 9.39309422e-03 1.89573855e-01 8.00255104e-01]\n",
- " [1.07826925e-03 4.48667610e-03 1.68966113e-01 8.25468942e-01]\n",
- " [4.32984844e-03 3.71263242e-02 1.74061879e-01 7.84481948e-01]\n",
- " [8.91964233e-04 4.60229508e-03 2.56203571e-01 7.38302169e-01]\n",
- " [1.53170345e-04 2.66905629e-03 8.05893086e-01 1.91284687e-01]\n",
- " [3.76678169e-04 2.66687172e-02 1.35691366e-01 8.37263238e-01]\n",
- " [1.87189571e-03 2.95477730e-02 1.83614398e-01 7.84965933e-01]\n",
- " [3.65699757e-04 4.65723230e-02 1.96467002e-01 7.56594975e-01]\n",
- " [3.91020418e-03 2.21215837e-02 3.46096170e-01 6.27872042e-01]\n",
- " [3.53128321e-04 1.26062549e-03 4.04030924e-01 5.94355323e-01]\n",
- " [3.85531972e-04 1.67060179e-03 5.14520249e-01 4.83423617e-01]\n",
- " [4.01176053e-04 1.39364758e-03 5.62411421e-01 4.35793755e-01]\n",
- " [2.19890976e-02 4.13933530e-01 3.17505597e-01 2.46571775e-01]\n",
- " [2.63540892e-03 1.60423321e-02 1.69895446e-01 8.11426813e-01]\n",
- " [5.95478507e-04 7.12069104e-04 9.01272706e-02 9.08565182e-01]\n",
- " [2.56904495e-04 3.92709426e-03 3.41668674e-01 6.54147328e-01]\n",
- " [3.34122792e-04 5.02991556e-03 3.01652248e-01 6.92983714e-01]\n",
- " [1.74105457e-03 1.54657507e-02 2.27888902e-01 7.54904293e-01]\n",
- " [3.34518377e-02 5.51052761e-02 3.32962366e-01 5.78480520e-01]\n",
- " [1.16808056e-03 1.31231889e-03 1.63219289e-01 8.34300311e-01]\n",
- " [8.88813523e-02 1.55465620e-01 3.86988580e-01 3.68664447e-01]]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# Fit entire training sets\n",
- "clf.fit(X_pca, y)\n",
+ "# Fit the entire training sets\n",
"\n",
- "print(accuracy_score(clf.predict(X_test_pca), y_test))\n",
- "print(clf.predict_proba(X_test_pca))"
+ "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",
+ "prediction_lists, percentage_lists = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\n",
+ "\n",
+ "genres = [\"classical\", \"electronic\", \"pop\", \"rock\"]\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",
+ "\n",
+ "proba_df = pd.DataFrame(index=test.index)\n",
+ "proba_df['label'] = test.label.values\n",
+ "proba_df[genres[0]] = proba[:,0:1]\n",
+ "proba_df[genres[1]] = proba[:,1:2]\n",
+ "proba_df[genres[2]] = proba[:,2:3]\n",
+ "proba_df[genres[3]] = proba[:,3:4]\n",
+ "pd.set_option('display.max_rows', None)\n",
+ "# print(subm)\n",
+ "display(subm)\n",
+ "display(proba_df)\n",
+ "pd.reset_option('display.max_rows')"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "bbd99cb8",
+ "execution_count": null,
+ "id": "a816521f533c6539",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:47.183458Z",
- "iopub.status.busy": "2024-02-15T10:06:47.182273Z",
- "iopub.status.idle": "2024-02-15T10:06:47.189844Z",
- "shell.execute_reply": "2024-02-15T10:06:47.188149Z"
+ "iopub.execute_input": "2024-02-19T14:43:42.177686Z",
+ "iopub.status.busy": "2024-02-19T14:43:42.177194Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.443722Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.443110Z"
},
"papermill": {
- "duration": 0.030742,
- "end_time": "2024-02-15T10:06:47.201582",
+ "duration": 0.275911,
+ "end_time": "2024-02-19T14:43:42.445154",
"exception": false,
- "start_time": "2024-02-15T10:06:47.170840",
+ "start_time": "2024-02-19T14:43:42.169243",
"status": "completed"
},
"tags": []
},
"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",
+ "conf_matrix = pd.DataFrame(confusion_matrix(subm['label'], subm['pred1']), columns=genres, index=genres)\n",
"\n",
- "# loaded.predict_proba(X_test_pca)"
+ "plt.figure(dpi=200)\n",
+ "display(sns.heatmap(conf_matrix, annot=True).set( xlabel=\"Prediction\", ylabel=\"Actual\"))\n"
]
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "af3c36d2",
+ "execution_count": null,
+ "id": "d2d7e5ef892ec807",
"metadata": {
+ "collapsed": false,
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:47.226325Z",
- "iopub.status.busy": "2024-02-15T10:06:47.225952Z",
- "iopub.status.idle": "2024-02-15T10:06:47.310314Z",
- "shell.execute_reply": "2024-02-15T10:06:47.309263Z"
+ "iopub.execute_input": "2024-02-19T14:43:42.467717Z",
+ "iopub.status.busy": "2024-02-19T14:43:42.467306Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.680469Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.679820Z"
},
"papermill": {
- "duration": 0.101143,
- "end_time": "2024-02-15T10:06:47.316068",
+ "duration": 0.225727,
+ "end_time": "2024-02-19T14:43:42.681847",
"exception": false,
- "start_time": "2024-02-15T10:06:47.214925",
+ "start_time": "2024-02-19T14:43:42.456120",
"status": "completed"
},
"tags": []
},
- "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_2.mp3 classical classical electronic pop rock\n",
- "classical_20.mp3 classical classical rock pop electronic\n",
- "classical_27.mp3 classical classical pop electronic rock\n",
- "classical_39.mp3 classical classical electronic rock pop\n",
- "classical_4.mp3 classical classical electronic pop rock\n",
- "classical_40.mp3 classical electronic rock classical pop\n",
- "classical_46.mp3 classical classical electronic pop rock\n",
- "classical_47.mp3 classical classical electronic pop rock\n",
- "classical_48.mp3 classical classical pop electronic rock\n",
- "classical_49.mp3 classical classical pop electronic rock\n",
- "classical_52.mp3 classical electronic pop rock classical\n",
- "classical_54.mp3 classical classical electronic pop rock\n",
- "classical_6.mp3 classical classical electronic pop rock\n",
- "classical_62.mp3 classical classical pop rock electronic\n",
- "classical_67.mp3 classical classical electronic pop rock\n",
- "classical_69.mp3 classical classical pop rock electronic\n",
- "classical_82.mp3 classical classical electronic pop rock\n",
- "classical_9.mp3 classical classical electronic pop rock\n",
- "classical_92.mp3 classical classical pop rock electronic\n",
- "classical_94.mp3 classical classical electronic pop rock\n",
- "electronic_11.mp3 electronic electronic rock pop classical\n",
- "electronic_20.mp3 electronic electronic pop rock classical\n",
- "electronic_21.mp3 electronic electronic pop rock classical\n",
- "electronic_3.mp3 electronic electronic pop classical rock\n",
- "electronic_35.mp3 electronic electronic rock pop classical\n",
- "electronic_36.mp3 electronic pop rock electronic classical\n",
- "electronic_38.mp3 electronic electronic pop classical rock\n",
- "electronic_44.mp3 electronic electronic rock pop classical\n",
- "electronic_49.mp3 electronic pop rock electronic classical\n",
- "electronic_55.mp3 electronic electronic rock pop classical\n",
- "electronic_59.mp3 electronic electronic rock pop classical\n",
- "electronic_61.mp3 electronic electronic rock pop classical\n",
- "electronic_62.mp3 electronic electronic pop rock classical\n",
- "electronic_63.mp3 electronic pop electronic rock classical\n",
- "electronic_81.mp3 electronic electronic rock pop classical\n",
- "pop_1.mp3 pop pop rock electronic classical\n",
- "pop_10.mp3 pop pop rock classical electronic\n",
- "pop_100.mp3 pop rock pop electronic classical\n",
- "pop_25.mp3 pop pop classical electronic rock\n",
- "pop_32.mp3 pop pop rock electronic classical\n",
- "pop_38.mp3 pop rock pop electronic classical\n",
- "pop_39.mp3 pop electronic rock pop classical\n",
- "pop_50.mp3 pop pop rock classical electronic\n",
- "pop_53.mp3 pop pop rock electronic classical\n",
- "pop_58.mp3 pop rock pop electronic classical\n",
- "pop_61.mp3 pop pop rock classical electronic\n",
- "pop_62.mp3 pop pop rock electronic classical\n",
- "pop_64.mp3 pop rock pop electronic classical\n",
- "pop_65.mp3 pop pop rock electronic classical\n",
- "pop_70.mp3 pop electronic pop classical rock\n",
- "pop_79.mp3 pop pop rock classical electronic\n",
- "pop_80.mp3 pop pop rock electronic classical\n",
- "pop_82.mp3 pop pop rock electronic classical\n",
- "pop_85.mp3 pop pop rock electronic classical\n",
- "pop_91.mp3 pop rock pop electronic classical\n",
- "pop_98.mp3 pop pop rock electronic classical\n",
- "rock_18.mp3 rock rock pop electronic classical\n",
- "rock_2.mp3 rock rock pop electronic classical\n",
- "rock_23.mp3 rock rock pop electronic classical\n",
- "rock_32.mp3 rock rock pop electronic classical\n",
- "rock_45.mp3 rock rock pop electronic classical\n",
- "rock_46.mp3 rock rock pop electronic classical\n",
- "rock_48.mp3 rock pop rock electronic classical\n",
- "rock_51.mp3 rock rock pop electronic classical\n",
- "rock_52.mp3 rock rock pop electronic classical\n",
- "rock_57.mp3 rock rock pop electronic classical\n",
- "rock_6.mp3 rock rock pop electronic classical\n",
- "rock_62.mp3 rock rock pop electronic classical\n",
- "rock_63.mp3 rock pop rock electronic classical\n",
- "rock_66.mp3 rock pop rock electronic classical\n",
- "rock_73.mp3 rock electronic pop rock classical\n",
- "rock_75.mp3 rock rock pop electronic classical\n",
- "rock_78.mp3 rock rock pop electronic classical\n",
- "rock_80.mp3 rock rock pop electronic classical\n",
- "rock_85.mp3 rock rock pop electronic classical\n",
- "rock_86.mp3 rock rock pop electronic classical\n",
- "rock_88.mp3 rock rock pop electronic classical\n",
- "rock_92.mp3 rock rock pop electronic classical\n",
- "rock_93.mp3 rock pop rock electronic classical\n"
- ]
- }
- ],
+ "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",
+ "subm_top_2 = subm.copy()\n",
+ "subm_top_2[\"top_2\"] = subm.apply(lambda row: row.get(\"pred2\") if row.get(\"label\") == row.get(\"pred2\") else row.get(\"pred1\"), axis=1)\n",
"\n",
- " return ans, ids\n",
+ "conf_matrix_top_2 = pd.DataFrame(confusion_matrix(subm['label'], subm_top_2[\"top_2\"]), columns=genres, index=genres)\n",
+ "accuracy_score_top_2 = sum(sum(conf_matrix_top_2.values * np.identity(4))) / sum(sum(conf_matrix_top_2.values))\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",
+ "print(f\"Accuracy for top 2 predictions: {accuracy_score_top_2}\")\n",
+ "display(sns.heatmap(conf_matrix_top_2, annot=True).set( xlabel=\"Prediction\", ylabel=\"Actual\"))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4433589d09bda6e5",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2024-02-19T14:43:42.706159Z",
+ "iopub.status.busy": "2024-02-19T14:43:42.705690Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.935380Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.934852Z"
+ },
+ "papermill": {
+ "duration": 0.242337,
+ "end_time": "2024-02-19T14:43:42.936712",
+ "exception": false,
+ "start_time": "2024-02-19T14:43:42.694375",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "display(sns.heatmap(proba_df.corr(numeric_only=True), vmin=-1, vmax=1, annot=True).set(title=\"Correlation heatmap of prediction probabilities\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "209e3007ae290ede",
+ "metadata": {
+ "collapsed": false,
+ "papermill": {
+ "duration": 0.010969,
+ "end_time": "2024-02-19T14:43:42.957610",
+ "exception": false,
+ "start_time": "2024-02-19T14:43:42.946641",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "### Interpretation of results:\n",
"\n",
- "pd.set_option('display.max_rows', None)\n",
- "print(subm)\n",
- "pd.reset_option('display.max_rows')"
+ "The confusion matrix shows the true labels on the y-axis, the predicted values on the x-axis.\n",
+ "Classical music was predicted well, with 1 wrong classification for electronic. \n",
+ "The most misclassifications has pop, with a true positive rate of 44.44%, due to wrong classifications towards electronic (4) and rock (6).\n",
+ "A high correlation between rock and pop can also be seen in the correlation plot between prediction probabilities.\n"
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "4a32007a",
+ "execution_count": null,
+ "id": "bbd99cb8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:47.339543Z",
- "iopub.status.busy": "2024-02-15T10:06:47.339176Z",
- "iopub.status.idle": "2024-02-15T10:06:47.348577Z",
- "shell.execute_reply": "2024-02-15T10:06:47.347603Z"
+ "iopub.execute_input": "2024-02-19T14:43:42.978152Z",
+ "iopub.status.busy": "2024-02-19T14:43:42.977857Z",
+ "iopub.status.idle": "2024-02-19T14:43:42.982716Z",
+ "shell.execute_reply": "2024-02-19T14:43:42.981209Z"
},
"papermill": {
- "duration": 0.026838,
- "end_time": "2024-02-15T10:06:47.354585",
+ "duration": 0.017398,
+ "end_time": "2024-02-19T14:43:42.984269",
"exception": false,
- "start_time": "2024-02-15T10:06:47.327747",
+ "start_time": "2024-02-19T14:43:42.966871",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
- "# output\n",
- "OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
+ "# test pickle saving & loading\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",
- "with open(OUTPUT_PATHS[\"clf\"], \"wb\") as file:\n",
- " pickle.dump(clf, file)\n",
- "subm.to_csv(OUTPUT_PATHS[\"submission\"], index=False)"
+ "# loaded.predict_proba(X_test_pca)"
]
},
{
"cell_type": "code",
- "execution_count": 19,
- "id": "99782035",
+ "execution_count": null,
+ "id": "4a32007a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-15T10:06:47.379460Z",
- "iopub.status.busy": "2024-02-15T10:06:47.378958Z",
- "iopub.status.idle": "2024-02-15T10:06:47.383963Z",
- "shell.execute_reply": "2024-02-15T10:06:47.382753Z"
+ "iopub.execute_input": "2024-02-19T14:43:43.005520Z",
+ "iopub.status.busy": "2024-02-19T14:43:43.004468Z",
+ "iopub.status.idle": "2024-02-19T14:43:43.016613Z",
+ "shell.execute_reply": "2024-02-19T14:43:43.015593Z"
},
"papermill": {
- "duration": 0.026084,
- "end_time": "2024-02-15T10:06:47.392052",
+ "duration": 0.025414,
+ "end_time": "2024-02-19T14:43:43.018135",
"exception": false,
- "start_time": "2024-02-15T10:06:47.365968",
+ "start_time": "2024-02-19T14:43:42.992721",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
- "# def get_result() -> pd.DataFrame:\n",
- "# \"\"\" Return the produced artefact of this notebook \"\"\"\n",
- "# return result"
+ "# output\n",
+ "Path(OUTPUT_PATHS[\"clf\"]).resolve().parent.mkdir(parents=True, exist_ok=True)\n",
+ "Path(OUTPUT_PATHS[\"submission\"]).resolve().parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "with open(OUTPUT_PATHS[\"clf\"], \"wb\") as file:\n",
+ " pickle.dump(clf, file)\n",
+ "subm.to_csv(OUTPUT_PATHS[\"submission\"], index=False)"
]
}
],
@@ -2230,26 +738,26 @@
},
"papermill": {
"default_parameters": {},
- "duration": 5.631111,
- "end_time": "2024-02-15T10:06:47.825204",
+ "duration": 7.136567,
+ "end_time": "2024-02-19T14:43:43.546117",
"environment_variables": {},
"exception": null,
- "input_path": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/5_ml_model.ipynb",
- "output_path": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/5_ml_model.ipynb",
+ "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": {
- "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/features.csv",
- "split": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/split.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/fairnb/tmp/5_ml_model/output/ml_model.pickle",
- "submission": "/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/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": "2024-02-15T10:06:42.194093",
+ "start_time": "2024-02-19T14:43:36.409550",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/main.ipynb b/notebooks/main.ipynb
index f7fe9621861f5114e28432206ae11c518cf8e655..b281b765f22792253f7394a3eb8cf945e07b2665 100644
--- a/notebooks/main.ipynb
+++ b/notebooks/main.ipynb
@@ -14,13 +14,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
- "collapsed": true,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:09:59.884216173Z",
- "start_time": "2024-02-15T15:09:59.870149504Z"
- }
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -30,13 +26,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:10:02.620385526Z",
- "start_time": "2024-02-15T15:09:59.895849547Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -45,8 +37,8 @@
"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.entity.dbrepo_entity import DBRepoEntity\n",
+ "from fairnb.entity.invenio_entity import InvenioRDMEntity\n",
"from fairnb.nb_config import NbConfig\n",
"from fairnb.executor import Executor\n",
"from fairnb.util import Util"
@@ -54,30 +46,15 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"collapsed": false,
- "lines_to_next_cell": 2,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:10:02.885011109Z",
- "start_time": "2024-02-15T15:10:02.632734781Z"
- }
+ "lines_to_next_cell": 2
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:urllib3.util.retry:Converted retries value: 1 -> Retry(total=1, connect=None, read=None, redirect=None, status=None)\n",
- "DEBUG:urllib3.util.retry:Converted retries value: 1 -> Retry(total=1, connect=None, read=None, redirect=None, status=None)\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/auth/realms/dbrepo/protocol/openid-connect/token HTTP/1.1\" 200 4267\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"logging.basicConfig(\n",
- " level=logging.DEBUG\n",
+ " level=logging.INFO\n",
")\n",
"\n",
"ONLY_LOCAL = False\n",
@@ -89,28 +66,27 @@
"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\""
+ "LOCAL_PATH = BASE_PATH / \"tmp\"\n",
+ "MAIN_PATH = BASE_PATH / \"notebooks\" / \"main.ipynb\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "start_time": "2024-02-15T14:48:07.550571919Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
- "# ------------- Convert Audio Files for Invenio ----\n",
+ "# ------------- Convert Audio Files for TUWRD ----\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",
+ " main_location=MAIN_PATH,\n",
" entities=[\n",
- " audio_files_entity := InvenioEntity.new(\n",
+ " audio_files_entity := InvenioRDMEntity.new(\n",
" name = \"audio_tar\",\n",
" description = \"Raw music files\",\n",
" location=LOCAL_PATH / \"1_audio_files\" / \"output\" / \"emotifymusic.tar.gz\",\n",
@@ -130,9 +106,6 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "ExecuteTime": {
- "start_time": "2023-10-12T15:05:43.645571236Z"
- },
"collapsed": false
},
"outputs": [],
@@ -140,14 +113,15 @@
"# ------------- Raw feature generation -------------\n",
"nb_config_generate_features = NbConfig(\n",
" nb_location=NOTEBOOK_PATH / \"2_generate_features.ipynb\",\n",
+ " main_location=MAIN_PATH,\n",
" entities=[\n",
- " raw_features_entity := DbRepoEntity.new(\n",
+ " raw_features_entity := DBRepoEntity.new(\n",
" name=\"raw_features\",\n",
- " description=\"Raw features of audio files.\",\n",
+ " description=\"Raw MFCC 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",
+ " table_description=\"Raw MFCC features of audio files for genre prediction.\",\n",
" type=\"raw_features\"\n",
" )\n",
" ],\n",
@@ -161,233 +135,42 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:10:53.212107678Z",
- "start_time": "2024-02-15T15:10:18.507162283Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/3_aggregate_features.ipynb\n",
- "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/3_aggregate_features.ipynb\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'INPUT_PATHS' (prefix='# Parameters\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"raw_features\"' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/input/raw_features.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'OUTPUT_PATHS' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"aggregated_features\"' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/output/features.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:ENDMARKER '' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:Stop.\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'INPUT_PATHS' (prefix='# Parameters\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"raw_features\"' (prefix='\\n ')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/input/raw_features.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'OUTPUT_PATHS' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"aggregated_features\"' (prefix='\\n ')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/output/features.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:ENDMARKER '' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:Stop.\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": "4f236898f16c4a99aafd3e6874501df0"
- }
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:asyncio:Using selector: EpollSelector\n",
- "DEBUG:asyncio:Using selector: EpollSelector\n",
- "INFO:papermill:Executing notebook with kernel: python3\n",
- "DEBUG:papermill:Skipping non-executing cell 0\n",
- "DEBUG:papermill:Executing cell:\n",
- "from pathlib import Path\n",
- "\n",
- "import pandas as pd\n",
- "from definitions import BASE_PATH\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'from pathlib import Path\\n\\nimport pandas as pd\\nfrom definitions import BASE_PATH', 'execution_count': 1}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "INPUT_PATH = BASE_PATH / \"tmp\" / \"3_aggregate_features\" / \"input\"\n",
- "OUTPUT_PATH = BASE_PATH / \"tmp\" / \"3_aggregate_features\" / \"output\"\n",
- "\n",
- "INPUT_PATHS: dict[str, str] = {\n",
- " \"raw_features\": (INPUT_PATH / \"raw_features.csv\").__str__()\n",
- "}\n",
- "\n",
- "OUTPUT_PATHS: dict[str, str] = {\n",
- " \"features\": (OUTPUT_PATH / \"features.csv\").__str__()\n",
- "}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'INPUT_PATH = BASE_PATH / \"tmp\" / \"3_aggregate_features\" / \"input\"\\nOUTPUT_PATH = BASE_PATH / \"tmp\" / \"3_aggregate_features\" / \"output\"\\n\\nINPUT_PATHS: dict[str, str] = {\\n \"raw_features\": (INPUT_PATH / \"raw_features.csv\").__str__()\\n}\\n\\nOUTPUT_PATHS: dict[str, str] = {\\n \"features\": (OUTPUT_PATH / \"features.csv\").__str__()\\n}', 'execution_count': 2}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# Parameters\n",
- "INPUT_PATHS = {\n",
- " \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/input/raw_features.csv\"\n",
- "}\n",
- "OUTPUT_PATHS = {\n",
- " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/output/features.csv\"\n",
- "}\n",
- "\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Parameters\\nINPUT_PATHS = {\\n \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/input/raw_features.csv\"\\n}\\nOUTPUT_PATHS = {\\n \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/3_aggregate_features/output/features.csv\"\\n}\\n', 'execution_count': 3}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# inputs\n",
- "raw_features = pd.read_csv(INPUT_PATHS[\"raw_features\"], index_col=False)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# inputs\\nraw_features = pd.read_csv(INPUT_PATHS[\"raw_features\"], index_col=False)', 'execution_count': 4}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "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\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'meta_columns = [\"sample\", \"filename\", \"label\"]\\nmfcc_aggregated = raw_features\\\\\\n .drop(meta_columns, axis=1, errors=\\'ignore\\')\\\\\\n .groupby(raw_features.filename).agg([\\'min\\', \\'max\\', \\'mean\\', \\'std\\', \\'skew\\'])\\n\\nmfcc_meta = pd.DataFrame(raw_features[\\'label\\'].groupby(raw_features.filename).last())\\nmfcc_meta.columns = pd.MultiIndex.from_arrays([[\\'label\\'], [\\'\\']]) # needed for merge\\nmfcc_merged = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\\n\\n# reduce multi index to single index\\none_level_cols = [\\'_\\'.join([str(el) for el in col]) for col in mfcc_merged.columns[1:]]\\none_level_cols.insert(0, \"label\")\\n\\nmfcc_merged.columns = pd.Index(one_level_cols)\\nmfcc_merged = mfcc_merged.reset_index()\\nmfcc_merged', 'execution_count': 5}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': ' filename label 0_min 0_max 0_mean \\\\\\n0 classical_1.mp3 classical -530.78436 -163.308350 -302.203167 \\n1 classical_10.mp3 classical -562.85785 -96.164795 -219.259016 \\n2 classical_100.mp3 classical -536.23737 -61.608826 -177.804114 \\n3 classical_11.mp3 classical -536.45746 -120.429665 -222.126303 \\n4 classical_12.mp3 classical -562.67523 -148.133560 -270.975406 \\n.. ... ... ... ... ... \\n395 rock_95.mp3 rock -553.11010 -5.218835 -193.506047 \\n396 rock_96.mp3 rock -541.23600 27.163334 -119.113996 \\n397 rock_97.mp3 rock -518.49500 58.526745 -66.267744 \\n398 rock_98.mp3 rock -518.64307 53.555115 -45.734517 \\n399 rock_99.mp3 rock -544.70310 75.612130 -49.380943 \\n\\n 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\\\\n0 51.142183 -0.468374 0.000000 178.75162 111.332342 ... -44.098070 \\n1 53.561838 -0.772320 0.029056 259.63270 215.094182 ... -27.458416 \\n2 83.381622 -2.587179 0.000000 190.47589 112.471713 ... -27.335688 \\n3 76.246992 -2.402418 0.000000 159.42575 99.853645 ... -31.774948 \\n4 52.191182 -0.366586 0.000000 194.26416 148.226647 ... -44.843810 \\n.. ... ... ... ... ... ... ... \\n395 76.869437 -0.201055 -89.948746 201.18045 111.724191 ... -27.043941 \\n396 58.420684 -0.957699 -7.415961 210.49246 125.453699 ... -37.584858 \\n397 65.635619 -0.898026 -58.824410 175.20135 99.288265 ... -29.620445 \\n398 52.444200 -1.705641 0.000000 187.04274 96.440874 ... -26.967848 \\n399 54.045627 -0.863093 -32.930653 191.73538 93.971242 ... -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 -46.794480 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 31.397880 -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 -7.234634 8.471853 0.753855 -24.712723 23.410387 -4.502398 \\n396 28.087936 -9.704238 8.447620 0.112760 -38.147890 21.814402 -8.249507 \\n397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410654 -3.356614 \\n398 8.714737 -9.511491 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \\n399 17.050608 -5.296691 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 0.020985 \\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>-530.78436</td>\\n <td>-163.308350</td>\\n <td>-302.203167</td>\\n <td>51.142183</td>\\n <td>-0.468374</td>\\n <td>0.000000</td>\\n <td>178.75162</td>\\n <td>111.332342</td>\\n <td>...</td>\\n <td>-44.098070</td>\\n <td>47.308060</td>\\n <td>-3.713503</td>\\n <td>16.553984</td>\\n <td>0.230691</td>\\n <td>-46.794480</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>-562.85785</td>\\n <td>-96.164795</td>\\n <td>-219.259016</td>\\n <td>53.561838</td>\\n <td>-0.772320</td>\\n <td>0.029056</td>\\n <td>259.63270</td>\\n <td>215.094182</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>-536.23737</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>...</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>31.397880</td>\\n <td>-1.494916</td>\\n <td>10.917299</td>\\n <td>0.020985</td>\\n </tr>\\n <tr>\\n <th>3</th>\\n <td>classical_11.mp3</td>\\n <td>classical</td>\\n <td>-536.45746</td>\\n <td>-120.429665</td>\\n <td>-222.126303</td>\\n <td>76.246992</td>\\n <td>-2.402418</td>\\n <td>0.000000</td>\\n <td>159.42575</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>-562.67523</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>148.226647</td>\\n <td>...</td>\\n <td>-44.843810</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>-553.11010</td>\\n <td>-5.218835</td>\\n <td>-193.506047</td>\\n <td>76.869437</td>\\n <td>-0.201055</td>\\n <td>-89.948746</td>\\n <td>201.18045</td>\\n <td>111.724191</td>\\n <td>...</td>\\n <td>-27.043941</td>\\n <td>22.451445</td>\\n <td>-7.234634</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>-541.23600</td>\\n <td>27.163334</td>\\n <td>-119.113996</td>\\n <td>58.420684</td>\\n <td>-0.957699</td>\\n <td>-7.415961</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>-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>-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>-518.49500</td>\\n <td>58.526745</td>\\n <td>-66.267744</td>\\n <td>65.635619</td>\\n <td>-0.898026</td>\\n <td>-58.824410</td>\\n <td>175.20135</td>\\n <td>99.288265</td>\\n <td>...</td>\\n <td>-29.620445</td>\\n <td>26.325895</td>\\n <td>-5.722825</td>\\n <td>7.727378</td>\\n <td>0.207489</td>\\n <td>-29.497524</td>\\n <td>25.410654</td>\\n <td>-3.356614</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>-518.64307</td>\\n <td>53.555115</td>\\n <td>-45.734517</td>\\n <td>52.444200</td>\\n <td>-1.705641</td>\\n <td>0.000000</td>\\n <td>187.04274</td>\\n <td>96.440874</td>\\n <td>...</td>\\n <td>-26.967848</td>\\n <td>8.714737</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>-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>-544.70310</td>\\n <td>75.612130</td>\\n <td>-49.380943</td>\\n <td>54.045627</td>\\n <td>-0.863093</td>\\n <td>-32.930653</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>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>'}, 'metadata': {}, 'execution_count': 5}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# outputs\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(aggregated_features_path, index=False)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# outputs\\naggregated_features_path = Path(OUTPUT_PATHS[\"aggregated_features\"]).resolve()\\naggregated_features_path.parent.mkdir(parents=True, exist_ok=True)\\n\\noutput = mfcc_merged\\noutput.to_csv(aggregated_features_path, index=False)', 'execution_count': 6}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/home/lukas/Programming/uni/bachelorarbeit/fairnb, universal_newlines=False, shell=None, istream=<valid stream>)\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/upload/files/ HTTP/1.1\" 201 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"PATCH /api/upload/files/c001319e0089589bcca621cab5904826+3d835979cfc4cb0848002ebccf87cb1339c512c9_dba749eee6a3455895b380400b84d1ea HTTP/1.1\" 204 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/database/19/table/103/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:fairnb.api.dbrepo:Uploaded dataframe using tui: <Response [202]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19/table/103/export HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n"
- ]
- },
- {
- "ename": "TypeError",
- "evalue": "reduction operation 'argmax' not allowed for this dtype",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[4], line 31\u001B[0m\n\u001B[1;32m 3\u001B[0m raw_features_entity \u001B[38;5;241m=\u001B[39m DbRepoEntity\u001B[38;5;241m.\u001B[39mnew(\n\u001B[1;32m 4\u001B[0m location\u001B[38;5;241m=\u001B[39mLOCAL_PATH \u001B[38;5;241m/\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m2_generate_features\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m/\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m/\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw_features.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 5\u001B[0m dbrepo_connector\u001B[38;5;241m=\u001B[39mconnector,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 10\u001B[0m \u001B[38;5;28mtype\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw_features\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 11\u001B[0m )\n\u001B[1;32m 13\u001B[0m nb_config_aggregate_features \u001B[38;5;241m=\u001B[39m NbConfig(\n\u001B[1;32m 14\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;124m3_aggregate_features.ipynb\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 15\u001B[0m entities\u001B[38;5;241m=\u001B[39m[\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 28\u001B[0m ]\n\u001B[1;32m 29\u001B[0m )\n\u001B[0;32m---> 31\u001B[0m \u001B[43mexecutor\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config_aggregate_features\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43monly_local\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mONLY_LOCAL\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/executor.py:47\u001B[0m, in \u001B[0;36mExecutor.execute\u001B[0;34m(cls, nb_config, require_download, only_local, **kwargs)\u001B[0m\n\u001B[1;32m 44\u001B[0m nb_config\u001B[38;5;241m.\u001B[39mended_at \u001B[38;5;241m=\u001B[39m ended_at\n\u001B[1;32m 46\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m only_local:\n\u001B[0;32m---> 47\u001B[0m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload_entities\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/executor.py:74\u001B[0m, in \u001B[0;36mExecutor.upload_entities\u001B[0;34m(nb_config)\u001B[0m\n\u001B[1;32m 69\u001B[0m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[1;32m 70\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mupload_entities\u001B[39m(nb_config: NbConfig):\n\u001B[1;32m 71\u001B[0m \u001B[38;5;66;03m# load generated entity and upload it\u001B[39;00m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m entity \u001B[38;5;129;01min\u001B[39;00m nb_config\u001B[38;5;241m.\u001B[39mentities:\n\u001B[1;32m 73\u001B[0m \u001B[38;5;66;03m# use inspect to get path of caller\u001B[39;00m\n\u001B[0;32m---> 74\u001B[0m \u001B[43mentity\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 75\u001B[0m \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\u001B[1;32m 76\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdependencies\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 77\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstarted_at\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 78\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mended_at\u001B[49m\n\u001B[1;32m 79\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/entity/dbrepo_entity.py:91\u001B[0m, in \u001B[0;36mDbRepoEntity.upload\u001B[0;34m(self, executed_file, dependencies, start_time, end_time)\u001B[0m\n\u001B[1;32m 77\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 79\u001B[0m metadata \u001B[38;5;241m=\u001B[39m EntityProvenance\u001B[38;5;241m.\u001B[39mnew(\n\u001B[1;32m 80\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname,\n\u001B[1;32m 81\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdescription,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 88\u001B[0m ended_at\u001B[38;5;241m=\u001B[39mend_time\n\u001B[1;32m 89\u001B[0m )\n\u001B[0;32m---> 91\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload_provenance\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmetadata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 92\u001B[0m df[\n\u001B[1;32m 93\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mentity_id\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 94\u001B[0m ] \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 95\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata\u001B[38;5;241m.\u001B[39mid\n\u001B[1;32m 96\u001B[0m ) \u001B[38;5;66;03m# update the -1 from above with the correct value as it is now known\u001B[39;00m\n\u001B[1;32m 97\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mupload_data(df)\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/entity/entity.py:127\u001B[0m, in \u001B[0;36mEntity.upload_provenance\u001B[0;34m(self, provenance)\u001B[0m\n\u001B[1;32m 124\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdbrepo_connector\u001B[38;5;241m.\u001B[39mdownload_table_as_df(\u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata_table_id))\n\u001B[1;32m 126\u001B[0m \u001B[38;5;66;03m# FIXME: create robust version of id retrieval, if possible\u001B[39;00m\n\u001B[0;32m--> 127\u001B[0m row \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39miloc[\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mid\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43midxmax\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m] \u001B[38;5;66;03m# get the newest row, as it should contain the correct data\u001B[39;00m\n\u001B[1;32m 128\u001B[0m meta \u001B[38;5;241m=\u001B[39m EntityProvenance\u001B[38;5;241m.\u001B[39mfrom_series(row)\n\u001B[1;32m 129\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m meta\u001B[38;5;241m.\u001B[39mstarted_at \u001B[38;5;241m==\u001B[39m provenance\u001B[38;5;241m.\u001B[39mstarted_at \u001B[38;5;129;01mand\u001B[39;00m meta\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m==\u001B[39m provenance\u001B[38;5;241m.\u001B[39mname\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/pandas/core/series.py:2564\u001B[0m, in \u001B[0;36mSeries.idxmax\u001B[0;34m(self, axis, skipna, *args, **kwargs)\u001B[0m\n\u001B[1;32m 2500\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21midxmax\u001B[39m(\u001B[38;5;28mself\u001B[39m, axis: Axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m, skipna: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Hashable:\n\u001B[1;32m 2501\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 2502\u001B[0m \u001B[38;5;124;03m Return the row label of the maximum value.\u001B[39;00m\n\u001B[1;32m 2503\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 2562\u001B[0m \u001B[38;5;124;03m nan\u001B[39;00m\n\u001B[1;32m 2563\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m-> 2564\u001B[0m i \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margmax\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mskipna\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 2565\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m i \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m 2566\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m np\u001B[38;5;241m.\u001B[39mnan\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/pandas/core/base.py:655\u001B[0m, in \u001B[0;36mIndexOpsMixin.argmax\u001B[0;34m(self, axis, skipna, *args, **kwargs)\u001B[0m\n\u001B[1;32m 651\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m delegate\u001B[38;5;241m.\u001B[39margmax()\n\u001B[1;32m 652\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 653\u001B[0m \u001B[38;5;66;03m# error: Incompatible return value type (got \"Union[int, ndarray]\", expected\u001B[39;00m\n\u001B[1;32m 654\u001B[0m \u001B[38;5;66;03m# \"int\")\u001B[39;00m\n\u001B[0;32m--> 655\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mnanops\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnanargmax\u001B[49m\u001B[43m(\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m# type: ignore[return-value]\u001B[39;49;00m\n\u001B[1;32m 656\u001B[0m \u001B[43m \u001B[49m\u001B[43mdelegate\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mskipna\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mskipna\u001B[49m\n\u001B[1;32m 657\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/pandas/core/nanops.py:88\u001B[0m, in \u001B[0;36mdisallow.__call__.<locals>._f\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 86\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcheck(obj) \u001B[38;5;28;01mfor\u001B[39;00m obj \u001B[38;5;129;01min\u001B[39;00m obj_iter):\n\u001B[1;32m 87\u001B[0m f_name \u001B[38;5;241m=\u001B[39m f\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;241m.\u001B[39mreplace(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnan\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m---> 88\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[1;32m 89\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mreduction operation \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mf_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m not allowed for this dtype\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 90\u001B[0m )\n\u001B[1;32m 91\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 92\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m np\u001B[38;5;241m.\u001B[39merrstate(invalid\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n",
- "\u001B[0;31mTypeError\u001B[0m: reduction operation 'argmax' not allowed for this dtype"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ------------- Feature Aggregation ----------------\n",
- "if \"raw_features_entity\" not in globals():\n",
- " raw_features_entity = DbRepoEntity.new(\n",
+ "if \"raw_features_entity\" not in globals(): # load saved entity if not already in memory\n",
+ " raw_features_entity = DBRepoEntity.existing(\n",
+ " id=\"3\",\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",
+ " # use new for direct entry in ONLY_LOCAL if raw features already created (or downloaded)\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",
+ " main_location=MAIN_PATH,\n",
" entities=[\n",
- " features_entity := DbRepoEntity.new(\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_tst3\",\n",
- " table_description=\"Aggregated features of audio files\",\n",
+ " table_name=\"aggregated_features\",\n",
+ " table_description=\"Aggregated MFCC features of audio files\",\n",
" type=\"aggregated_features\"\n",
" )\n",
" ],\n",
@@ -401,243 +184,15 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T10:39:10.769360407Z",
- "start_time": "2024-02-15T10:33:01.190550938Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/4_split.ipynb\n",
- "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/4_split.ipynb\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'INPUT_PATHS' (prefix='# Parameters\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"aggregated_features\"' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/input/features.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'OUTPUT_PATHS' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"split\"' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/output/split.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:ENDMARKER '' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:Stop.\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'INPUT_PATHS' (prefix='# Parameters\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"aggregated_features\"' (prefix='\\n ')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/input/features.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'OUTPUT_PATHS' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"split\"' (prefix='\\n ')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/output/split.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:ENDMARKER '' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:Stop.\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": "d777fdec2b24474cbb0c8ff40550b597"
- }
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:asyncio:Using selector: EpollSelector\n",
- "INFO:papermill:Executing notebook with kernel: python3\n",
- "DEBUG:papermill:Skipping non-executing cell 0\n",
- "DEBUG:papermill:Executing cell:\n",
- "import pandas as pd\n",
- "from pathlib import Path\n",
- "from definitions import BASE_PATH\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'import pandas as pd\\nfrom pathlib import Path\\nfrom definitions import BASE_PATH', 'execution_count': 1}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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",
- "}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Tagged with \\'parameters\\'\\nfrom definitions import BASE_PATH\\n\\nINPUT_PATHS: dict[str, str] = {\\n \"features\": (BASE_PATH / \"tmp\" / \"4_split\" / \"input\" / \"features.csv\").__str__()\\n}\\nOUTPUT_PATHS: dict[str, str] = {\\n \"split\": (BASE_PATH / \"tmp\" / \"4_split\" / \"output\" / \"split.csv\").__str__()\\n}', 'execution_count': 2}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# Parameters\n",
- "INPUT_PATHS = {\n",
- " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/input/features.csv\"\n",
- "}\n",
- "OUTPUT_PATHS = {\n",
- " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/output/split.csv\"\n",
- "}\n",
- "\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Parameters\\nINPUT_PATHS = {\\n \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/input/features.csv\"\\n}\\nOUTPUT_PATHS = {\\n \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/4_split/output/split.csv\"\\n}\\n', 'execution_count': 3}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# INPUT\n",
- "\n",
- "for path in INPUT_PATHS.values():\n",
- " assert Path(path).exists()\n",
- "\n",
- "features = pd.read_csv(INPUT_PATHS[\"aggregated_features\"])\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# INPUT\\n\\nfor path in INPUT_PATHS.values():\\n assert Path(path).exists()\\n\\nfeatures = pd.read_csv(INPUT_PATHS[\"aggregated_features\"])', 'execution_count': 4}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\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_concat = pd.concat([split_true, split_false])\\\n",
- " .sort_values(\"filename\")\\\n",
- " .reset_index(drop=True)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'train = features.sample(frac=0.8).sort_index()\\ntest = features.drop(train.index)\\n\\nsplit_true = pd.DataFrame({\\n \"filename\": train.filename,\\n \"train\": True\\n})\\nsplit_false = pd.DataFrame({\\n \"filename\": test.filename,\\n \"train\": False\\n})\\n\\nsplit_concat = pd.concat([split_true, split_false])\\\\\\n .sort_values(\"filename\")\\\\\\n .reset_index(drop=True)', 'execution_count': 5}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "split_concat\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'split_concat', 'execution_count': 6}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': ' filename train\\n0 classical_1.mp3 False\\n1 classical_10.mp3 True\\n2 classical_100.mp3 False\\n3 classical_11.mp3 True\\n4 classical_12.mp3 True\\n.. ... ...\\n395 rock_95.mp3 False\\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>False</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>False</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>False</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>'}, 'metadata': {}, 'execution_count': 6}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# output\\nOUTPUT_PATH = Path(OUTPUT_PATHS[\"split\"])\\nOUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)\\n\\noutput = split_concat\\noutput.to_csv(OUTPUT_PATH, index=False)', 'execution_count': 7}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "WARNING:fairnb.api.dbrepo:Re-authenticating due to (almost) expired token\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (3): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/auth/realms/dbrepo/protocol/openid-connect/token HTTP/1.1\" 200 4267\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/home/lukas/Programming/uni/bachelorarbeit/fairnb, universal_newlines=False, shell=None, istream=<valid stream>)\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/upload/files/ HTTP/1.1\" 201 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"PATCH /api/upload/files/280891ee3d393b5cb0faf185818055bd+59b9f536a0a43bba99d019a829fb2d3c7dbb77ea_1c52495d4ec6432db7a9049235fa1f1d HTTP/1.1\" 204 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/database/19/table/91/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:fairnb.api.dbrepo:<Response [202]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19/table/91/export HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/upload/files/ HTTP/1.1\" 201 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"PATCH /api/upload/files/4e52b6bffd74c0a1af2b2618ae5085b7+dc66a3a3ad997e42d951ce25807aaf2b51d89aea_5482523df590418782631e57128368cb HTTP/1.1\" 204 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/database/19/table/94/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:fairnb.api.dbrepo:<Response [202]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/upload/files/ HTTP/1.1\" 201 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"PATCH /api/upload/files/627aa5a411b5079c326a700a173a4caf+eefae4f60517fd6af1f108a9a3ff7e1d831e4de5_1673e7a80ea4408d88d98fe6abc47ef4 HTTP/1.1\" 204 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/database/19/table/92/data/import HTTP/1.1\" 401 0\n",
- "DEBUG:charset_normalizer:Encoding detection on empty bytes, assuming utf_8 intention.\n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[20], line 40\u001B[0m\n\u001B[1;32m 21\u001B[0m nb_config_splits \u001B[38;5;241m=\u001B[39m NbConfig(\n\u001B[1;32m 22\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;124m4_split.ipynb\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 23\u001B[0m entities\u001B[38;5;241m=\u001B[39m[\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 36\u001B[0m ]\n\u001B[1;32m 37\u001B[0m )\n\u001B[1;32m 39\u001B[0m \u001B[38;5;66;03m# generate splits\u001B[39;00m\n\u001B[0;32m---> 40\u001B[0m \u001B[43mexecutor\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config_splits\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43monly_local\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mONLY_LOCAL\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/executor.py:47\u001B[0m, in \u001B[0;36mExecutor.execute\u001B[0;34m(cls, nb_config, require_download, only_local, **kwargs)\u001B[0m\n\u001B[1;32m 44\u001B[0m nb_config\u001B[38;5;241m.\u001B[39mended_at \u001B[38;5;241m=\u001B[39m ended_at\n\u001B[1;32m 46\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m only_local:\n\u001B[0;32m---> 47\u001B[0m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload_entities\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/executor.py:74\u001B[0m, in \u001B[0;36mExecutor.upload_entities\u001B[0;34m(nb_config)\u001B[0m\n\u001B[1;32m 69\u001B[0m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[1;32m 70\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mupload_entities\u001B[39m(nb_config: NbConfig):\n\u001B[1;32m 71\u001B[0m \u001B[38;5;66;03m# load generated entity and upload it\u001B[39;00m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m entity \u001B[38;5;129;01min\u001B[39;00m nb_config\u001B[38;5;241m.\u001B[39mentities:\n\u001B[1;32m 73\u001B[0m \u001B[38;5;66;03m# use inspect to get path of caller\u001B[39;00m\n\u001B[0;32m---> 74\u001B[0m \u001B[43mentity\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 75\u001B[0m \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\u001B[1;32m 76\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdependencies\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 77\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstarted_at\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 78\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mended_at\u001B[49m\n\u001B[1;32m 79\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/entity/dbrepo_entity.py:98\u001B[0m, in \u001B[0;36mDbRepoEntity.upload\u001B[0;34m(self, executed_file, dependencies, start_time, end_time)\u001B[0m\n\u001B[1;32m 91\u001B[0m df[\n\u001B[1;32m 92\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mentity_id\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 93\u001B[0m ] \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 94\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata\u001B[38;5;241m.\u001B[39mid\n\u001B[1;32m 95\u001B[0m ) \u001B[38;5;66;03m# update the -1 from above with the correct value as it is now known\u001B[39;00m\n\u001B[1;32m 96\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mupload_data(df)\n\u001B[0;32m---> 98\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload_dependencies\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdependencies\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/entity/entity.py:159\u001B[0m, in \u001B[0;36mEntity.upload_dependencies\u001B[0;34m(self, dependencies)\u001B[0m\n\u001B[1;32m 156\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 157\u001B[0m LOG\u001B[38;5;241m.\u001B[39mwarning(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDependency has no id, skipping dependency upload\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 159\u001B[0m \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[43mupload_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdependency_table_id\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/api/dbrepo.py:30\u001B[0m, in \u001B[0;36mre_auth.<locals>.inner\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 28\u001B[0m LOG\u001B[38;5;241m.\u001B[39mwarning(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRe-authenticating due to (almost) expired refresh token\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 29\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mauthenticate_keycloak()\n\u001B[0;32m---> 30\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/api/dbrepo.py:276\u001B[0m, in \u001B[0;36mDBRepoConnector.upload_data\u001B[0;34m(self, dataframe, table_id)\u001B[0m\n\u001B[1;32m 261\u001B[0m uploader\u001B[38;5;241m.\u001B[39mupload()\n\u001B[1;32m 263\u001B[0m response_upload_import \u001B[38;5;241m=\u001B[39m requests\u001B[38;5;241m.\u001B[39mpost(\n\u001B[1;32m 264\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhost\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/api/database/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdatabase_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/table/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtable_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/data/import\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 265\u001B[0m json\u001B[38;5;241m=\u001B[39m{\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 274\u001B[0m headers\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mheaders\n\u001B[1;32m 275\u001B[0m )\n\u001B[0;32m--> 276\u001B[0m \u001B[43mLOG\u001B[49m\u001B[38;5;241m.\u001B[39mdebug(response_upload_import)\n\u001B[1;32m 278\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m response_upload_import\u001B[38;5;241m.\u001B[39mok:\n\u001B[1;32m 279\u001B[0m LOG\u001B[38;5;241m.\u001B[39mwarning(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMove for table \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtable_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m failed: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresponse_upload_import\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/api/dbrepo.py:276\u001B[0m, in \u001B[0;36mDBRepoConnector.upload_data\u001B[0;34m(self, dataframe, table_id)\u001B[0m\n\u001B[1;32m 261\u001B[0m uploader\u001B[38;5;241m.\u001B[39mupload()\n\u001B[1;32m 263\u001B[0m response_upload_import \u001B[38;5;241m=\u001B[39m requests\u001B[38;5;241m.\u001B[39mpost(\n\u001B[1;32m 264\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhost\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/api/database/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdatabase_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/table/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtable_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/data/import\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 265\u001B[0m json\u001B[38;5;241m=\u001B[39m{\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 274\u001B[0m headers\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mheaders\n\u001B[1;32m 275\u001B[0m )\n\u001B[0;32m--> 276\u001B[0m \u001B[43mLOG\u001B[49m\u001B[38;5;241m.\u001B[39mdebug(response_upload_import)\n\u001B[1;32m 278\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m response_upload_import\u001B[38;5;241m.\u001B[39mok:\n\u001B[1;32m 279\u001B[0m LOG\u001B[38;5;241m.\u001B[39mwarning(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMove for table \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtable_id\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m failed: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresponse_upload_import\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
- "File \u001B[0;32m~/.local/share/JetBrains/Toolbox/apps/pycharm-professional/plugins/python/helpers/pydev/_pydevd_bundle/pydevd_frame.py:755\u001B[0m, in \u001B[0;36mPyDBFrame.trace_dispatch\u001B[0;34m(self, frame, event, arg)\u001B[0m\n\u001B[1;32m 753\u001B[0m \u001B[38;5;66;03m# if thread has a suspend flag, we suspend with a busy wait\u001B[39;00m\n\u001B[1;32m 754\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m info\u001B[38;5;241m.\u001B[39mpydev_state \u001B[38;5;241m==\u001B[39m STATE_SUSPEND:\n\u001B[0;32m--> 755\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdo_wait_suspend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mthread\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mframe\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mevent\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43marg\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 756\u001B[0m \u001B[38;5;66;03m# No need to reset frame.f_trace to keep the same trace function.\u001B[39;00m\n\u001B[1;32m 757\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtrace_dispatch\n",
- "File \u001B[0;32m~/.local/share/JetBrains/Toolbox/apps/pycharm-professional/plugins/python/helpers/pydev/_pydevd_bundle/pydevd_frame.py:412\u001B[0m, in \u001B[0;36mPyDBFrame.do_wait_suspend\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 411\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mdo_wait_suspend\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m--> 412\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_args\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdo_wait_suspend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/.local/share/JetBrains/Toolbox/apps/pycharm-professional/plugins/python/helpers/pydev/pydevd.py:1184\u001B[0m, in \u001B[0;36mPyDB.do_wait_suspend\u001B[0;34m(self, thread, frame, event, arg, send_suspend_message, is_unhandled_exception)\u001B[0m\n\u001B[1;32m 1181\u001B[0m from_this_thread\u001B[38;5;241m.\u001B[39mappend(frame_id)\n\u001B[1;32m 1183\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_threads_suspended_single_notification\u001B[38;5;241m.\u001B[39mnotify_thread_suspended(thread_id, stop_reason):\n\u001B[0;32m-> 1184\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_do_wait_suspend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mthread\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mframe\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mevent\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43marg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msuspend_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfrom_this_thread\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/.local/share/JetBrains/Toolbox/apps/pycharm-professional/plugins/python/helpers/pydev/pydevd.py:1199\u001B[0m, in \u001B[0;36mPyDB._do_wait_suspend\u001B[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread)\u001B[0m\n\u001B[1;32m 1196\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_mpl_hook()\n\u001B[1;32m 1198\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprocess_internal_commands()\n\u001B[0;32m-> 1199\u001B[0m \u001B[43mtime\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msleep\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m0.01\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1201\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcancel_async_evaluation(get_current_thread_id(thread), \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mid\u001B[39m(frame)))\n\u001B[1;32m 1203\u001B[0m \u001B[38;5;66;03m# process any stepping instructions\u001B[39;00m\n",
- "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Load features from disk if not already in memory\n",
"if \"features_entity\" not in globals():\n",
- " features_entity = DbRepoEntity.new(\n",
+ " features_entity = DBRepoEntity.new(\n",
" location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\",\n",
" dbrepo_connector=connector,\n",
" name=\"features\",\n",
@@ -648,7 +203,7 @@
" )\n",
"\n",
" # or load directly from dbrepo:\n",
- " # features_entity = DbRepoEntity.existing(\n",
+ " # features_entity = DBRepoEntity.existing(\n",
" # id=1,\n",
" # dbrepo_connector=connector,\n",
" # location=LOCAL_PATH / \"3_aggregate_features\" / \"output\" / \"features.csv\"\n",
@@ -657,12 +212,13 @@
"# -------------- SPLITTING -------------------------\n",
"nb_config_splits = NbConfig(\n",
" nb_location=NOTEBOOK_PATH / \"4_split.ipynb\",\n",
+ " main_location=MAIN_PATH,\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_tst1\",\n",
- " table_description=\"Splits of aggregated data into testing and training subbsets.\",\n",
+ " split_entity := DBRepoEntity.new(\n",
+ " name=\"test/train split\",\n",
+ " description=\"Split of aggregated data into testing and training subsets using 11908553 as seed.\",\n",
+ " table_name=\"splits_table\",\n",
+ " table_description=\"Splits of aggregated data into testing and training subsets.\",\n",
" location=LOCAL_PATH / \"4_split\" / \"output\" / \"split.csv\", # location where script saves generated entity\n",
" dbrepo_connector=connector,\n",
" type=\"split\",\n",
@@ -679,528 +235,11 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T10:10:06.010467579Z",
- "start_time": "2024-02-15T10:06:42.042644190Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:papermill:Input Notebook: /home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/5_ml_model.ipynb\n",
- "INFO:papermill:Output Notebook: /home/lukas/Programming/uni/bachelorarbeit/fairnb/notebooks/5_ml_model.ipynb\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'INPUT_PATHS' (prefix='# Parameters\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"split\"' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/split.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:COMMA ',' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"aggregated_features\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
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- "DEBUG:blib2to3.pgen2.driver:Stop.\n",
- "DEBUG:blib2to3.pgen2.driver:NAME 'INPUT_PATHS' (prefix='# Parameters\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:EQUAL '=' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:LBRACE '{' (prefix=' ')\n",
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- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/split.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:COMMA ',' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"aggregated_features\"' (prefix='\\n ')\n",
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- "DEBUG:blib2to3.pgen2.driver:COMMA ',' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"submission\"' (prefix='\\n ')\n",
- "DEBUG:blib2to3.pgen2.driver:COLON ':' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:STRING '\"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/test_result.csv\"' (prefix=' ')\n",
- "DEBUG:blib2to3.pgen2.driver:COMMA ',' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:RBRACE '}' (prefix='\\n')\n",
- "DEBUG:blib2to3.pgen2.driver:NEWLINE '\\n' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:ENDMARKER '' (prefix='')\n",
- "DEBUG:blib2to3.pgen2.driver:Stop.\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": "6d79d7d1a6354fd18ed65812ef6824fc"
- }
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "DEBUG:asyncio:Using selector: EpollSelector\n",
- "INFO:papermill:Executing notebook with kernel: python3\n",
- "DEBUG:papermill:Skipping non-executing cell 0\n",
- "DEBUG:papermill:Executing cell:\n",
- "import pickle\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd\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 definitions import BASE_PATH\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'import pickle\\n\\nimport numpy as np\\nimport pandas as pd\\nfrom pandas import DataFrame, Index\\nfrom sklearn.decomposition import PCA\\nfrom sklearn.metrics import accuracy_score\\nfrom sklearn.model_selection import train_test_split, GridSearchCV\\nfrom sklearn.preprocessing import StandardScaler\\nfrom sklearn.svm import SVC\\n\\nfrom definitions import BASE_PATH', 'execution_count': 1}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# Tagged with 'parameters'\n",
- "INPUT_PATH = BASE_PATH / \"tmp\" / \"5_ml_model\" / \"input\"\n",
- "OUTPUT_PATH = BASE_PATH / \"tmp\" / \"5_ml_model\" / \"output\"\n",
- "\n",
- "INPUT_PATHS: dict[str, str] = {\n",
- " \"split\": (INPUT_PATH / \"split.csv\").__str__(),\n",
- " \"features\": (INPUT_PATH / \"features.csv\").__str__()\n",
- "}\n",
- "OUTPUT_PATHS: dict[str, str] = {\n",
- " \"submission\": (OUTPUT_PATH / \"submission.csv\").__str__(),\n",
- " \"clf\": (OUTPUT_PATH / \"clf.pickle\").__str__()\n",
- "}\n",
- "\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Tagged with \\'parameters\\'\\nINPUT_PATH = BASE_PATH / \"tmp\" / \"5_ml_model\" / \"input\"\\nOUTPUT_PATH = BASE_PATH / \"tmp\" / \"5_ml_model\" / \"output\"\\n\\nINPUT_PATHS: dict[str, str] = {\\n \"split\": (INPUT_PATH / \"split.csv\").__str__(),\\n \"features\": (INPUT_PATH / \"features.csv\").__str__()\\n}\\nOUTPUT_PATHS: dict[str, str] = {\\n \"submission\": (OUTPUT_PATH / \"submission.csv\").__str__(),\\n \"clf\": (OUTPUT_PATH / \"clf.pickle\").__str__()\\n}\\n', 'execution_count': 2}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# Parameters\n",
- "INPUT_PATHS = {\n",
- " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/split.csv\",\n",
- " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/features.csv\",\n",
- "}\n",
- "OUTPUT_PATHS = {\n",
- " \"clf\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/ml_model.pickle\",\n",
- " \"submission\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/test_result.csv\",\n",
- "}\n",
- "\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Parameters\\nINPUT_PATHS = {\\n \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/split.csv\",\\n \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/input/features.csv\",\\n}\\nOUTPUT_PATHS = {\\n \"clf\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/ml_model.pickle\",\\n \"submission\": \"/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/test_result.csv\",\\n}\\n', 'execution_count': 3}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# input\n",
- "split: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"split\"])\n",
- "features: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"aggregated_features\"])\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# input\\nsplit: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"split\"])\\nfeatures: pd.DataFrame = pd.read_csv(INPUT_PATHS[\"aggregated_features\"])', 'execution_count': 4}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\n",
- "joined\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\\njoined', 'execution_count': 5}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': ' label 0_min 0_max 0_mean 0_std \\\\\\nfilename \\nclassical_1.mp3 classical -530.78436 -163.308350 -302.203167 51.142183 \\nclassical_10.mp3 classical -562.85785 -96.164795 -219.259016 53.561838 \\nclassical_100.mp3 classical -536.23737 -61.608826 -177.804114 83.381622 \\nclassical_11.mp3 classical -536.45746 -120.429665 -222.126303 76.246992 \\nclassical_12.mp3 classical -562.67523 -148.133560 -270.975406 52.191182 \\n... ... ... ... ... ... \\nrock_95.mp3 rock -553.11010 -5.218835 -193.506047 76.869437 \\nrock_96.mp3 rock -541.23600 27.163334 -119.113996 58.420684 \\nrock_97.mp3 rock -518.49500 58.526745 -66.267744 65.635619 \\nrock_98.mp3 rock -518.64307 53.555115 -45.734517 52.444200 \\nrock_99.mp3 rock -544.70310 75.612130 -49.380943 54.045627 \\n\\n 0_skew 1_min 1_max 1_mean 1_std ... \\\\\\nfilename ... \\nclassical_1.mp3 -0.468374 0.000000 178.75162 111.332342 24.847563 ... \\nclassical_10.mp3 -0.772320 0.029056 259.63270 215.094182 18.388131 ... \\nclassical_100.mp3 -2.587179 0.000000 190.47589 112.471713 27.277553 ... \\nclassical_11.mp3 -2.402418 0.000000 159.42575 99.853645 21.916949 ... \\nclassical_12.mp3 -0.366586 0.000000 194.26416 148.226647 19.305008 ... \\n... ... ... ... ... ... ... \\nrock_95.mp3 -0.201055 -89.948746 201.18045 111.724191 36.463584 ... \\nrock_96.mp3 -0.957699 -7.415961 210.49246 125.453699 31.908869 ... \\nrock_97.mp3 -0.898026 -58.824410 175.20135 99.288265 25.158416 ... \\nrock_98.mp3 -1.705641 0.000000 187.04274 96.440874 24.137702 ... \\nrock_99.mp3 -0.863093 -32.930653 191.73538 93.971242 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 -46.794480 \\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 -7.234634 8.471853 0.753855 -24.712723 \\nrock_96.mp3 28.087936 -9.704238 8.447620 0.112760 -38.147890 \\nrock_97.mp3 26.325895 -5.722825 7.727378 0.207489 -29.497524 \\nrock_98.mp3 8.714737 -9.511491 5.551820 -0.025604 -23.020084 \\nrock_99.mp3 17.050608 -5.296691 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 True \\nclassical_100.mp3 31.397880 -1.494916 10.917299 0.020985 True \\nclassical_11.mp3 50.992897 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 6.687984 0.238807 True \\nrock_96.mp3 21.814402 -8.249507 7.807756 0.071968 True \\nrock_97.mp3 25.410654 -3.356614 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>-530.78436</td>\\n <td>-163.308350</td>\\n <td>-302.203167</td>\\n <td>51.142183</td>\\n <td>-0.468374</td>\\n <td>0.000000</td>\\n <td>178.75162</td>\\n <td>111.332342</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>-46.794480</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>-562.85785</td>\\n <td>-96.164795</td>\\n <td>-219.259016</td>\\n <td>53.561838</td>\\n <td>-0.772320</td>\\n <td>0.029056</td>\\n <td>259.63270</td>\\n <td>215.094182</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>True</td>\\n </tr>\\n <tr>\\n <th>classical_100.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>...</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>31.397880</td>\\n <td>-1.494916</td>\\n <td>10.917299</td>\\n <td>0.020985</td>\\n <td>True</td>\\n </tr>\\n <tr>\\n <th>classical_11.mp3</th>\\n <td>classical</td>\\n <td>-536.45746</td>\\n <td>-120.429665</td>\\n <td>-222.126303</td>\\n <td>76.246992</td>\\n <td>-2.402418</td>\\n <td>0.000000</td>\\n <td>159.42575</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>True</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>148.226647</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>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>-553.11010</td>\\n <td>-5.218835</td>\\n <td>-193.506047</td>\\n <td>76.869437</td>\\n <td>-0.201055</td>\\n <td>-89.948746</td>\\n <td>201.18045</td>\\n <td>111.724191</td>\\n <td>36.463584</td>\\n <td>...</td>\\n <td>22.451445</td>\\n <td>-7.234634</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>-541.23600</td>\\n <td>27.163334</td>\\n <td>-119.113996</td>\\n <td>58.420684</td>\\n <td>-0.957699</td>\\n <td>-7.415961</td>\\n <td>210.49246</td>\\n <td>125.453699</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>-38.147890</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>-518.49500</td>\\n <td>58.526745</td>\\n <td>-66.267744</td>\\n <td>65.635619</td>\\n <td>-0.898026</td>\\n <td>-58.824410</td>\\n <td>175.20135</td>\\n <td>99.288265</td>\\n <td>25.158416</td>\\n <td>...</td>\\n <td>26.325895</td>\\n <td>-5.722825</td>\\n <td>7.727378</td>\\n <td>0.207489</td>\\n <td>-29.497524</td>\\n <td>25.410654</td>\\n <td>-3.356614</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>-518.64307</td>\\n <td>53.555115</td>\\n <td>-45.734517</td>\\n <td>52.444200</td>\\n <td>-1.705641</td>\\n <td>0.000000</td>\\n <td>187.04274</td>\\n <td>96.440874</td>\\n <td>24.137702</td>\\n <td>...</td>\\n <td>8.714737</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>-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>-544.70310</td>\\n <td>75.612130</td>\\n <td>-49.380943</td>\\n <td>54.045627</td>\\n <td>-0.863093</td>\\n <td>-32.930653</td>\\n <td>191.73538</td>\\n <td>93.971242</td>\\n <td>33.410220</td>\\n <td>...</td>\\n <td>17.050608</td>\\n <td>-5.296691</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>'}, 'metadata': {}, 'execution_count': 5}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\n",
- "train\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\\ntrain', 'execution_count': 6}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': ' label 0_min 0_max 0_mean 0_std \\\\\\nfilename \\nclassical_1.mp3 classical -530.78436 -163.308350 -302.203167 51.142183 \\nclassical_10.mp3 classical -562.85785 -96.164795 -219.259016 53.561838 \\nclassical_100.mp3 classical -536.23737 -61.608826 -177.804114 83.381622 \\nclassical_11.mp3 classical -536.45746 -120.429665 -222.126303 76.246992 \\nclassical_13.mp3 classical -637.72064 -177.713960 -361.834032 71.310080 \\n... ... ... ... ... ... \\nrock_95.mp3 rock -553.11010 -5.218835 -193.506047 76.869437 \\nrock_96.mp3 rock -541.23600 27.163334 -119.113996 58.420684 \\nrock_97.mp3 rock -518.49500 58.526745 -66.267744 65.635619 \\nrock_98.mp3 rock -518.64307 53.555115 -45.734517 52.444200 \\nrock_99.mp3 rock -544.70310 75.612130 -49.380943 54.045627 \\n\\n 0_skew 1_min 1_max 1_mean 1_std ... \\\\\\nfilename ... \\nclassical_1.mp3 -0.468374 0.000000 178.75162 111.332342 24.847563 ... \\nclassical_10.mp3 -0.772320 0.029056 259.63270 215.094182 18.388131 ... \\nclassical_100.mp3 -2.587179 0.000000 190.47589 112.471713 27.277553 ... \\nclassical_11.mp3 -2.402418 0.000000 159.42575 99.853645 21.916949 ... \\nclassical_13.mp3 0.008325 0.000000 257.16284 211.556558 20.347034 ... \\n... ... ... ... ... ... ... \\nrock_95.mp3 -0.201055 -89.948746 201.18045 111.724191 36.463584 ... \\nrock_96.mp3 -0.957699 -7.415961 210.49246 125.453699 31.908869 ... \\nrock_97.mp3 -0.898026 -58.824410 175.20135 99.288265 25.158416 ... \\nrock_98.mp3 -1.705641 0.000000 187.04274 96.440874 24.137702 ... \\nrock_99.mp3 -0.863093 -32.930653 191.73538 93.971242 33.410220 ... \\n\\n 38_min 38_max 38_mean 38_std 38_skew \\\\\\nfilename \\nclassical_1.mp3 -44.098070 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 18.424036 -0.275736 7.026148 -0.640964 \\n... ... ... ... ... ... \\nrock_95.mp3 -27.043941 22.451445 -7.234634 8.471853 0.753855 \\nrock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \\nrock_97.mp3 -29.620445 26.325895 -5.722825 7.727378 0.207489 \\nrock_98.mp3 -26.967848 8.714737 -9.511491 5.551820 -0.025604 \\nrock_99.mp3 -21.929403 17.050608 -5.296691 5.894963 0.390705 \\n\\n 39_min 39_max 39_mean 39_std 39_skew \\nfilename \\nclassical_1.mp3 -46.794480 49.352516 -2.282116 15.285639 0.171462 \\nclassical_10.mp3 -28.989983 27.533710 0.952658 10.477735 -0.185771 \\nclassical_100.mp3 -38.095375 31.397880 -1.494916 10.917299 0.020985 \\nclassical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \\nclassical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \\n... ... ... ... ... ... \\nrock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \\nrock_96.mp3 -38.147890 21.814402 -8.249507 7.807756 0.071968 \\nrock_97.mp3 -29.497524 25.410654 -3.356614 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]', '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_1.mp3</th>\\n <td>classical</td>\\n <td>-530.78436</td>\\n <td>-163.308350</td>\\n <td>-302.203167</td>\\n <td>51.142183</td>\\n <td>-0.468374</td>\\n <td>0.000000</td>\\n <td>178.75162</td>\\n <td>111.332342</td>\\n <td>24.847563</td>\\n <td>...</td>\\n <td>-44.098070</td>\\n <td>47.308060</td>\\n <td>-3.713503</td>\\n <td>16.553984</td>\\n <td>0.230691</td>\\n <td>-46.794480</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>-562.85785</td>\\n <td>-96.164795</td>\\n <td>-219.259016</td>\\n <td>53.561838</td>\\n <td>-0.772320</td>\\n <td>0.029056</td>\\n <td>259.63270</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>-28.989983</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>-536.23737</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>...</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>31.397880</td>\\n <td>-1.494916</td>\\n <td>10.917299</td>\\n <td>0.020985</td>\\n </tr>\\n <tr>\\n <th>classical_11.mp3</th>\\n <td>classical</td>\\n <td>-536.45746</td>\\n <td>-120.429665</td>\\n <td>-222.126303</td>\\n <td>76.246992</td>\\n <td>-2.402418</td>\\n <td>0.000000</td>\\n <td>159.42575</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_13.mp3</th>\\n <td>classical</td>\\n <td>-637.72064</td>\\n <td>-177.713960</td>\\n <td>-361.834032</td>\\n <td>71.310080</td>\\n <td>0.008325</td>\\n <td>0.000000</td>\\n <td>257.16284</td>\\n <td>211.556558</td>\\n <td>20.347034</td>\\n <td>...</td>\\n <td>-24.728806</td>\\n <td>18.424036</td>\\n <td>-0.275736</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>...</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>-553.11010</td>\\n <td>-5.218835</td>\\n <td>-193.506047</td>\\n <td>76.869437</td>\\n <td>-0.201055</td>\\n <td>-89.948746</td>\\n <td>201.18045</td>\\n <td>111.724191</td>\\n <td>36.463584</td>\\n <td>...</td>\\n <td>-27.043941</td>\\n <td>22.451445</td>\\n <td>-7.234634</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>-541.23600</td>\\n <td>27.163334</td>\\n <td>-119.113996</td>\\n <td>58.420684</td>\\n <td>-0.957699</td>\\n <td>-7.415961</td>\\n <td>210.49246</td>\\n <td>125.453699</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>-38.147890</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>-518.49500</td>\\n <td>58.526745</td>\\n <td>-66.267744</td>\\n <td>65.635619</td>\\n <td>-0.898026</td>\\n <td>-58.824410</td>\\n <td>175.20135</td>\\n <td>99.288265</td>\\n <td>25.158416</td>\\n <td>...</td>\\n <td>-29.620445</td>\\n <td>26.325895</td>\\n <td>-5.722825</td>\\n <td>7.727378</td>\\n <td>0.207489</td>\\n <td>-29.497524</td>\\n <td>25.410654</td>\\n <td>-3.356614</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>-518.64307</td>\\n <td>53.555115</td>\\n <td>-45.734517</td>\\n <td>52.444200</td>\\n <td>-1.705641</td>\\n <td>0.000000</td>\\n <td>187.04274</td>\\n <td>96.440874</td>\\n <td>24.137702</td>\\n <td>...</td>\\n <td>-26.967848</td>\\n <td>8.714737</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>-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>-544.70310</td>\\n <td>75.612130</td>\\n <td>-49.380943</td>\\n <td>54.045627</td>\\n <td>-0.863093</td>\\n <td>-32.930653</td>\\n <td>191.73538</td>\\n <td>93.971242</td>\\n <td>33.410220</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>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>'}, 'metadata': {}, 'execution_count': 6}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\n",
- "test\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': 'test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\\ntest', 'execution_count': 7}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': ' label 0_min 0_max 0_mean 0_std \\\\\\nfilename \\nclassical_12.mp3 classical -562.67523 -148.133560 -270.975406 52.191182 \\nclassical_2.mp3 classical -549.40650 -192.532060 -293.008969 27.207028 \\nclassical_20.mp3 classical -605.99150 -161.119310 -263.483084 49.157298 \\nclassical_27.mp3 classical -595.41895 -78.118810 -265.344461 104.892303 \\nclassical_39.mp3 classical -578.84720 -55.479320 -183.753039 69.140628 \\n... ... ... ... ... ... \\nrock_85.mp3 rock -556.08203 44.890602 -72.618399 80.272023 \\nrock_86.mp3 rock -534.40650 42.919650 -93.601685 62.192619 \\nrock_88.mp3 rock -539.97880 44.375150 -126.955020 88.140999 \\nrock_92.mp3 rock -532.89110 13.948147 -206.891688 80.812274 \\nrock_93.mp3 rock -570.46650 -26.067888 -302.483118 96.569376 \\n\\n 0_skew 1_min 1_max 1_mean 1_std ... \\\\\\nfilename ... \\nclassical_12.mp3 -0.366586 0.000000 194.26416 148.226647 19.305008 ... \\nclassical_2.mp3 -0.426848 0.000000 231.03738 198.662514 14.957660 ... \\nclassical_20.mp3 -0.856221 0.000000 191.92676 141.393817 17.754779 ... \\nclassical_27.mp3 -0.526604 0.000000 200.61633 144.208488 25.198761 ... \\nclassical_39.mp3 -0.577055 0.000000 193.84949 127.058496 29.295691 ... \\n... ... ... ... ... ... ... \\nrock_85.mp3 -2.269420 -13.219891 205.14955 96.863927 38.352424 ... \\nrock_86.mp3 -0.869415 0.000000 206.32501 128.047509 30.374850 ... \\nrock_88.mp3 -1.700578 -19.007393 201.99960 99.760978 32.572320 ... \\nrock_92.mp3 0.090286 -47.724570 179.76506 109.954998 37.880477 ... \\nrock_93.mp3 0.159026 -89.999680 211.88910 103.686365 40.373592 ... \\n\\n 38_min 38_max 38_mean 38_std 38_skew \\\\\\nfilename \\nclassical_12.mp3 -44.843810 28.490644 -6.242015 10.546545 0.341848 \\nclassical_2.mp3 -25.912933 24.293318 0.746096 8.240027 -0.022513 \\nclassical_20.mp3 -24.911243 38.551230 -2.274261 9.671005 0.719436 \\nclassical_27.mp3 -28.797087 20.897750 -5.761607 7.108055 0.360305 \\nclassical_39.mp3 -48.678460 24.566566 -7.810246 11.568188 -0.106704 \\n... ... ... ... ... ... \\nrock_85.mp3 -22.633102 13.513550 -3.126545 5.035097 -0.035805 \\nrock_86.mp3 -30.471783 20.564953 -3.383356 6.405211 -0.185147 \\nrock_88.mp3 -34.726500 26.706833 -5.827121 8.260717 0.275225 \\nrock_92.mp3 -37.614220 21.420666 -8.287362 7.851784 -0.080285 \\nrock_93.mp3 -28.903786 35.712753 2.073339 10.995769 0.249798 \\n\\n 39_min 39_max 39_mean 39_std 39_skew \\nfilename \\nclassical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \\nclassical_2.mp3 -18.561390 23.484133 3.115819 7.220346 0.242364 \\nclassical_20.mp3 -30.311798 29.272330 0.289613 9.590299 -0.244191 \\nclassical_27.mp3 -39.705540 25.803795 -2.736776 10.101577 -0.463730 \\nclassical_39.mp3 -24.328775 40.172250 -0.078006 10.646963 0.492488 \\n... ... ... ... ... ... \\nrock_85.mp3 -19.814285 18.576450 -1.172361 6.078238 -0.048851 \\nrock_86.mp3 -28.917618 26.702751 -1.950565 6.725107 -0.253487 \\nrock_88.mp3 -31.036520 27.423218 -4.715363 6.544117 0.184718 \\nrock_92.mp3 -41.547260 25.628895 -9.046777 8.779821 0.071449 \\nrock_93.mp3 -30.178170 30.612560 -4.677735 8.877041 0.149639 \\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>-562.67523</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>148.226647</td>\\n <td>19.305008</td>\\n <td>...</td>\\n <td>-44.843810</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_2.mp3</th>\\n <td>classical</td>\\n <td>-549.40650</td>\\n <td>-192.532060</td>\\n <td>-293.008969</td>\\n <td>27.207028</td>\\n <td>-0.426848</td>\\n <td>0.000000</td>\\n <td>231.03738</td>\\n <td>198.662514</td>\\n <td>14.957660</td>\\n <td>...</td>\\n <td>-25.912933</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>-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>191.92676</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>-30.311798</td>\\n <td>29.272330</td>\\n <td>0.289613</td>\\n <td>9.590299</td>\\n <td>-0.244191</td>\\n </tr>\\n <tr>\\n <th>classical_27.mp3</th>\\n <td>classical</td>\\n <td>-595.41895</td>\\n <td>-78.118810</td>\\n <td>-265.344461</td>\\n <td>104.892303</td>\\n <td>-0.526604</td>\\n <td>0.000000</td>\\n <td>200.61633</td>\\n <td>144.208488</td>\\n <td>25.198761</td>\\n <td>...</td>\\n <td>-28.797087</td>\\n <td>20.897750</td>\\n <td>-5.761607</td>\\n <td>7.108055</td>\\n <td>0.360305</td>\\n <td>-39.705540</td>\\n <td>25.803795</td>\\n <td>-2.736776</td>\\n <td>10.101577</td>\\n <td>-0.463730</td>\\n </tr>\\n <tr>\\n <th>classical_39.mp3</th>\\n <td>classical</td>\\n <td>-578.84720</td>\\n <td>-55.479320</td>\\n <td>-183.753039</td>\\n <td>69.140628</td>\\n <td>-0.577055</td>\\n <td>0.000000</td>\\n <td>193.84949</td>\\n <td>127.058496</td>\\n <td>29.295691</td>\\n <td>...</td>\\n <td>-48.678460</td>\\n <td>24.566566</td>\\n <td>-7.810246</td>\\n <td>11.568188</td>\\n <td>-0.106704</td>\\n <td>-24.328775</td>\\n <td>40.172250</td>\\n <td>-0.078006</td>\\n <td>10.646963</td>\\n <td>0.492488</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_85.mp3</th>\\n <td>rock</td>\\n <td>-556.08203</td>\\n <td>44.890602</td>\\n <td>-72.618399</td>\\n <td>80.272023</td>\\n <td>-2.269420</td>\\n <td>-13.219891</td>\\n <td>205.14955</td>\\n <td>96.863927</td>\\n <td>38.352424</td>\\n <td>...</td>\\n <td>-22.633102</td>\\n <td>13.513550</td>\\n <td>-3.126545</td>\\n <td>5.035097</td>\\n <td>-0.035805</td>\\n <td>-19.814285</td>\\n <td>18.576450</td>\\n <td>-1.172361</td>\\n <td>6.078238</td>\\n <td>-0.048851</td>\\n </tr>\\n <tr>\\n <th>rock_86.mp3</th>\\n <td>rock</td>\\n <td>-534.40650</td>\\n <td>42.919650</td>\\n <td>-93.601685</td>\\n <td>62.192619</td>\\n <td>-0.869415</td>\\n <td>0.000000</td>\\n <td>206.32501</td>\\n <td>128.047509</td>\\n <td>30.374850</td>\\n <td>...</td>\\n <td>-30.471783</td>\\n <td>20.564953</td>\\n <td>-3.383356</td>\\n <td>6.405211</td>\\n <td>-0.185147</td>\\n <td>-28.917618</td>\\n <td>26.702751</td>\\n <td>-1.950565</td>\\n <td>6.725107</td>\\n <td>-0.253487</td>\\n </tr>\\n <tr>\\n <th>rock_88.mp3</th>\\n <td>rock</td>\\n <td>-539.97880</td>\\n <td>44.375150</td>\\n <td>-126.955020</td>\\n <td>88.140999</td>\\n <td>-1.700578</td>\\n <td>-19.007393</td>\\n <td>201.99960</td>\\n <td>99.760978</td>\\n <td>32.572320</td>\\n <td>...</td>\\n <td>-34.726500</td>\\n <td>26.706833</td>\\n <td>-5.827121</td>\\n <td>8.260717</td>\\n <td>0.275225</td>\\n <td>-31.036520</td>\\n <td>27.423218</td>\\n <td>-4.715363</td>\\n <td>6.544117</td>\\n <td>0.184718</td>\\n </tr>\\n <tr>\\n <th>rock_92.mp3</th>\\n <td>rock</td>\\n <td>-532.89110</td>\\n <td>13.948147</td>\\n <td>-206.891688</td>\\n <td>80.812274</td>\\n <td>0.090286</td>\\n <td>-47.724570</td>\\n <td>179.76506</td>\\n <td>109.954998</td>\\n <td>37.880477</td>\\n <td>...</td>\\n <td>-37.614220</td>\\n <td>21.420666</td>\\n <td>-8.287362</td>\\n <td>7.851784</td>\\n <td>-0.080285</td>\\n <td>-41.547260</td>\\n <td>25.628895</td>\\n <td>-9.046777</td>\\n <td>8.779821</td>\\n <td>0.071449</td>\\n </tr>\\n <tr>\\n <th>rock_93.mp3</th>\\n <td>rock</td>\\n <td>-570.46650</td>\\n <td>-26.067888</td>\\n <td>-302.483118</td>\\n <td>96.569376</td>\\n <td>0.159026</td>\\n <td>-89.999680</td>\\n <td>211.88910</td>\\n <td>103.686365</td>\\n <td>40.373592</td>\\n <td>...</td>\\n <td>-28.903786</td>\\n <td>35.712753</td>\\n <td>2.073339</td>\\n <td>10.995769</td>\\n <td>0.249798</td>\\n <td>-30.178170</td>\\n <td>30.612560</td>\\n <td>-4.677735</td>\\n <td>8.877041</td>\\n <td>0.149639</td>\\n </tr>\\n </tbody>\\n</table>\\n<p>80 rows × 201 columns</p>\\n</div>'}, 'metadata': {}, 'execution_count': 7}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# remove labels\\nX = train.drop([\\'label\\'], axis=1, errors=\\'ignore\\')\\n\\ncolumns: Index = X.columns\\nclassnames = 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)\\nclassname2index = {}\\nindex2classname = {}\\n\\nfor i, classname in enumerate(classnames):\\n classname2index[classname] = i\\n index2classname[i] = classname\\n\\n# map label to label index\\ny = np.array([classname2index[classname] for classname in train.label.values])\\n\\n(X, y)', 'execution_count': 8}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': '( 0_min 0_max 0_mean 0_std 0_skew \\\\\\n filename \\n classical_1.mp3 -530.78436 -163.308350 -302.203167 51.142183 -0.468374 \\n classical_10.mp3 -562.85785 -96.164795 -219.259016 53.561838 -0.772320 \\n classical_100.mp3 -536.23737 -61.608826 -177.804114 83.381622 -2.587179 \\n classical_11.mp3 -536.45746 -120.429665 -222.126303 76.246992 -2.402418 \\n classical_13.mp3 -637.72064 -177.713960 -361.834032 71.310080 0.008325 \\n ... ... ... ... ... ... \\n rock_95.mp3 -553.11010 -5.218835 -193.506047 76.869437 -0.201055 \\n rock_96.mp3 -541.23600 27.163334 -119.113996 58.420684 -0.957699 \\n rock_97.mp3 -518.49500 58.526745 -66.267744 65.635619 -0.898026 \\n rock_98.mp3 -518.64307 53.555115 -45.734517 52.444200 -1.705641 \\n rock_99.mp3 -544.70310 75.612130 -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 178.75162 111.332342 24.847563 -0.402642 ... \\n classical_10.mp3 0.029056 259.63270 215.094182 18.388131 -1.528751 ... \\n classical_100.mp3 0.000000 190.47589 112.471713 27.277553 -1.318523 ... \\n classical_11.mp3 0.000000 159.42575 99.853645 21.916949 -1.176922 ... \\n classical_13.mp3 0.000000 257.16284 211.556558 20.347034 -1.050119 ... \\n ... ... ... ... ... ... ... \\n rock_95.mp3 -89.948746 201.18045 111.724191 36.463584 -0.443224 ... \\n rock_96.mp3 -7.415961 210.49246 125.453699 31.908869 -0.547469 ... \\n rock_97.mp3 -58.824410 175.20135 99.288265 25.158416 -0.568057 ... \\n rock_98.mp3 0.000000 187.04274 96.440874 24.137702 -0.145217 ... \\n rock_99.mp3 -32.930653 191.73538 93.971242 33.410220 0.040113 ... \\n \\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 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 18.424036 -0.275736 7.026148 -0.640964 \\n ... ... ... ... ... ... \\n rock_95.mp3 -27.043941 22.451445 -7.234634 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 -5.722825 7.727378 0.207489 \\n rock_98.mp3 -26.967848 8.714737 -9.511491 5.551820 -0.025604 \\n rock_99.mp3 -21.929403 17.050608 -5.296691 5.894963 0.390705 \\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 -28.989983 27.533710 0.952658 10.477735 -0.185771 \\n classical_100.mp3 -38.095375 31.397880 -1.494916 10.917299 0.020985 \\n classical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \\n classical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \\n ... ... ... ... ... ... \\n rock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \\n rock_96.mp3 -38.147890 21.814402 -8.249507 7.807756 0.071968 \\n rock_97.mp3 -29.497524 25.410654 -3.356614 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, 1, 1, 1, 1, 1, 1, 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, 2, 2, 2, 2,\\n 2, 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]))'}, 'metadata': {}, 'execution_count': 8}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "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\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': \"X_test = test.drop(['label'], axis=1, errors='ignore')\\n\\nprint(X.shape)\\nprint(X_test.shape)\\nprint(X_test.shape[0] / X.shape[0]) # fraction of test sample\\n\\ny_test = np.array([classname2index[classname] for classname in test.label.values])\\ny_test\", 'execution_count': 9}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': '(320, 200)\\n(80, 200)\\n0.25\\n'}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'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, 2, 2, 2, 2, 2, 2, 2, 2,\\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])'}, 'metadata': {}, 'execution_count': 9}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Standardize for PCA\\nscaler = StandardScaler()\\nX_standardized = scaler.fit_transform(X.values)\\nX_test_standardized = scaler.transform(X_test.values)\\n\\nX_standardized', 'execution_count': 10}\n",
- "DEBUG:papermill:msg_type: execute_result\n",
- "DEBUG:papermill:content: {'data': {'text/plain': 'array([[ 0.38209988, -1.79901606, -1.34294124, ..., -0.7312519 ,\\n 3.4358529 , 0.11530124],\\n [-0.42728837, -0.93236007, -0.41652953, ..., 0.22563011,\\n 1.37555438, -0.86835549],\\n [ 0.24449084, -0.48632861, 0.04648451, ..., -0.49838941,\\n 1.56391778, -0.29904453],\\n ...,\\n [ 0.69222714, 1.06432227, 1.29224565, ..., -1.0491004 ,\\n 0.38686173, 0.08464998],\\n [ 0.68849053, 1.00015092, 1.52158336, ..., -0.84450893,\\n -0.94971424, -1.06836048],\\n [ 0.03085452, 1.28485202, 1.48085606, ..., -0.15137928,\\n -0.29828957, 0.70271937]])'}, 'metadata': {}, 'execution_count': 10}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Reduce Dimensions via PCA\\npca = PCA(n_components=50).fit(X_standardized)\\nX_pca = pca.transform(X_standardized)\\nX_test_pca = pca.transform(X_test_standardized)\\n\\nprint(sum(pca.explained_variance_ratio_))\\nprint(X_pca.shape)\\nprint(X_test_pca.shape)\\nprint(y.shape)', 'execution_count': 11}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': '0.8557392011152061\\n(320, 50)\\n(80, 50)\\n(320,)\\n'}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': \"# Fit SVM:\\n\\nX_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size = 0.2, random_state=4, shuffle = True)\\n\\nclf = SVC(kernel='rbf', probability=True)\\nclf.fit(X_train, y_train)\\n\\nprint(accuracy_score(clf.predict(X_val), y_val))\\nprint(X_val)\\nprint(y_val)\", 'execution_count': 12}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': '0.6875\\n[[-4.64558613 3.08838305 -1.47175688 ... -1.24828691 -0.70095473\\n 0.01689286]\\n [ 5.85968202 -2.1047151 -3.35419664 ... -1.48822402 1.00205068\\n -0.98882563]\\n [ 6.52471238 -2.88386219 -5.91379963 ... 0.08618421 0.03366275\\n -0.55189302]\\n ...\\n [ 5.3496866 3.90245458 -4.07128854 ... -0.82356091 -0.7968544\\n 0.26045289]\\n [ 6.68981697 -1.18340439 -0.12267599 ... 1.33593613 -2.8015435\\n 0.5028293 ]\\n [-4.78063681 -7.16377441 4.09506551 ... -1.0308011 0.83671387\\n -0.07027211]]\\n[3 0 3 2 3 0 1 2 0 3 0 0 0 1 2 1 2 3 1 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'}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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))\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# grid for C, gamma\\nC_grid = [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\\ngamma_grid = [0.001, 0.01, 0.1, 1, 10]\\nparam_grid = {\\'C\\': C_grid, \\'gamma\\': gamma_grid}\\n\\ngrid = GridSearchCV(SVC(kernel=\\'rbf\\'), param_grid, cv=5, scoring=\"accuracy\")\\ngrid.fit(X_train, y_train)\\n\\n# Find the best model\\nprint(grid.best_score_)\\nprint(grid.best_params_)\\nprint(grid.best_estimator_)\\nprint(accuracy_score(grid.predict(X_val), y_val))', 'execution_count': 13}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': \"0.7343891402714932\\n{'C': 3, 'gamma': 0.01}\\nSVC(C=3, gamma=0.01)\\n0.78125\\n\"}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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))\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': \"# Optimal model\\n\\nclf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\\nclf.fit(X_train, y_train)\\n\\nprint(accuracy_score(clf.predict(X_val), y_val))\", 'execution_count': 14}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': '0.78125\\n'}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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))\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# Fit entire training sets\\nclf.fit(X_pca, y)\\n\\nprint(accuracy_score(clf.predict(X_test_pca), y_test))\\nprint(clf.predict_proba(X_test_pca))', 'execution_count': 15}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': '0.8\\n[[9.60125451e-01 2.54410379e-02 1.00183548e-02 4.41515609e-03]\\n [9.93544791e-01 4.04634019e-03 1.20649558e-03 1.20237342e-03]\\n [9.97430192e-01 1.76800719e-04 5.38565546e-04 1.85444214e-03]\\n [9.79967977e-01 6.86113735e-03 9.68497114e-03 3.48591496e-03]\\n [9.91884967e-01 5.17290348e-03 1.26266158e-03 1.67946793e-03]\\n [9.85578464e-01 9.44992493e-03 3.75086068e-03 1.22075036e-03]\\n [2.04862989e-01 4.53621014e-01 1.34373358e-01 2.07142639e-01]\\n [9.99181855e-01 4.86945868e-04 2.22608725e-04 1.08590413e-04]\\n [9.92658119e-01 3.47218548e-03 2.74696376e-03 1.12273207e-03]\\n [9.99656357e-01 1.12727916e-04 1.43400994e-04 8.75138776e-05]\\n [8.47319131e-01 4.69014094e-02 7.09411516e-02 3.48383077e-02]\\n [1.28380278e-01 3.67332428e-01 3.59429595e-01 1.44857699e-01]\\n [9.96413445e-01 2.75890076e-03 4.65504357e-04 3.62150045e-04]\\n [9.98826125e-01 7.62447290e-04 3.01490088e-04 1.09937383e-04]\\n [9.99401836e-01 8.67850526e-05 3.74373911e-04 1.37005308e-04]\\n [9.97955498e-01 1.69931669e-03 1.73626292e-04 1.71558652e-04]\\n [8.45643860e-01 1.33426916e-02 9.97412359e-02 4.12722121e-02]\\n [9.82092462e-01 1.15346135e-02 3.19973757e-03 3.17318740e-03]\\n [9.83213850e-01 1.24420959e-02 3.26304918e-03 1.08100527e-03]\\n [9.99642856e-01 7.19184901e-05 1.55316717e-04 1.29908898e-04]\\n [9.97979494e-01 1.76870557e-03 1.31807873e-04 1.19992584e-04]\\n [4.92333515e-04 9.38096306e-01 2.10469538e-02 4.03644064e-02]\\n [9.45551189e-03 4.32699483e-01 4.16341606e-01 1.41503399e-01]\\n [9.13893710e-03 4.44229440e-01 3.15860710e-01 2.30770912e-01]\\n [6.79828415e-02 6.71681498e-01 2.09457159e-01 5.08785014e-02]\\n [1.68076034e-04 9.71769830e-01 2.24441690e-03 2.58176775e-02]\\n [5.73737808e-02 8.61494512e-02 5.86365884e-01 2.70110884e-01]\\n [1.18603200e-01 5.68582627e-01 2.33418558e-01 7.93956149e-02]\\n [1.11117289e-02 9.36048570e-01 2.07419839e-02 3.20977167e-02]\\n [4.27128683e-03 2.53015466e-01 4.52073691e-01 2.90639556e-01]\\n [8.49595708e-03 6.37021927e-01 1.52099758e-01 2.02382358e-01]\\n [9.29855946e-04 8.43628458e-01 1.67412440e-02 1.38700442e-01]\\n [5.75440080e-02 6.65893968e-01 1.18869183e-01 1.57692841e-01]\\n [7.28891949e-02 6.97755501e-01 1.23916666e-01 1.05438637e-01]\\n [1.00364172e-01 3.05951082e-01 4.02534596e-01 1.91150150e-01]\\n [2.71956862e-04 5.43067021e-01 1.43066793e-02 4.42354343e-01]\\n [8.60586155e-02 8.06134589e-02 6.12157762e-01 2.21170163e-01]\\n [4.54205646e-02 3.77922605e-02 7.46222645e-01 1.70564530e-01]\\n [2.60732219e-02 1.78887893e-01 3.03253706e-01 4.91785179e-01]\\n [1.76685545e-01 1.49702306e-01 5.30947449e-01 1.42664700e-01]\\n [2.10423538e-02 3.16261307e-02 6.86655601e-01 2.60675914e-01]\\n [5.10365555e-03 9.06077798e-03 3.10609892e-01 6.75225674e-01]\\n [1.85590659e-04 4.20187052e-01 2.54067881e-01 3.25559476e-01]\\n [1.84121015e-03 1.49368051e-03 5.94696830e-01 4.01968279e-01]\\n [9.94756099e-03 1.98337895e-02 6.10189918e-01 3.60028732e-01]\\n [1.06218859e-02 5.83443846e-02 4.09385718e-01 5.21648011e-01]\\n [2.51610276e-01 1.06475171e-01 4.02323327e-01 2.39591226e-01]\\n [1.05739190e-03 4.80039248e-03 7.84298209e-01 2.09844007e-01]\\n [1.20304373e-03 2.49929289e-03 4.25498367e-01 5.70799297e-01]\\n [5.17165422e-04 2.44187897e-03 7.70942808e-01 2.26098148e-01]\\n [1.48279902e-01 4.34212254e-01 3.33486768e-01 8.40210765e-02]\\n [6.49493657e-03 2.03203941e-03 6.76591245e-01 3.14881779e-01]\\n [1.42643647e-03 3.00507802e-02 7.66466942e-01 2.02055842e-01]\\n [2.71205953e-04 1.64674206e-03 5.18908081e-01 4.79173971e-01]\\n [6.18460044e-04 8.65733199e-03 7.31160871e-01 2.59563337e-01]\\n [5.99851686e-04 9.88068783e-03 3.18075020e-01 6.71444441e-01]\\n [8.92857719e-05 2.49912334e-03 8.22928402e-01 1.74483188e-01]\\n [4.08821963e-03 4.01685411e-03 2.22308630e-01 7.69586296e-01]\\n [3.85280110e-04 4.28844983e-03 4.38873417e-01 5.56452853e-01]\\n [7.77946831e-04 9.39309422e-03 1.89573855e-01 8.00255104e-01]\\n [1.07826925e-03 4.48667610e-03 1.68966113e-01 8.25468942e-01]\\n [4.32984844e-03 3.71263242e-02 1.74061879e-01 7.84481948e-01]\\n [8.91964233e-04 4.60229508e-03 2.56203571e-01 7.38302169e-01]\\n [1.53170345e-04 2.66905629e-03 8.05893086e-01 1.91284687e-01]\\n [3.76678169e-04 2.66687172e-02 1.35691366e-01 8.37263238e-01]\\n [1.87189571e-03 2.95477730e-02 1.83614398e-01 7.84965933e-01]\\n [3.65699757e-04 4.65723230e-02 1.96467002e-01 7.56594975e-01]\\n [3.91020418e-03 2.21215837e-02 3.46096170e-01 6.27872042e-01]\\n [3.53128321e-04 1.26062549e-03 4.04030924e-01 5.94355323e-01]\\n [3.85531972e-04 1.67060179e-03 5.14520249e-01 4.83423617e-01]\\n [4.01176053e-04 1.39364758e-03 5.62411421e-01 4.35793755e-01]\\n [2.19890976e-02 4.13933530e-01 3.17505597e-01 2.46571775e-01]\\n [2.63540892e-03 1.60423321e-02 1.69895446e-01 8.11426813e-01]\\n [5.95478507e-04 7.12069104e-04 9.01272706e-02 9.08565182e-01]\\n [2.56904495e-04 3.92709426e-03 3.41668674e-01 6.54147328e-01]\\n [3.34122792e-04 5.02991556e-03 3.01652248e-01 6.92983714e-01]\\n [1.74105457e-03 1.54657507e-02 2.27888902e-01 7.54904293e-01]\\n [3.34518377e-02 5.51052761e-02 3.32962366e-01 5.78480520e-01]\\n [1.16808056e-03 1.31231889e-03 1.63219289e-01 8.34300311e-01]\\n [8.88813523e-02 1.55465620e-01 3.86988580e-01 3.68664447e-01]]\\n'}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\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)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# 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)', 'execution_count': 16}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# 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')\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': \"# Fit the entire training sets\\n\\ndef 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\\nclf.fit(X_pca, y)\\nprediction_lists, percentage_lists = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\\n\\n# # Write to outputs\\nsubm = pd.DataFrame(index=test.index)\\nsubm['label'] = test.label.values\\nsubm['pred1'] = [prediction_list[0] for prediction_list in prediction_lists]\\nsubm['pred2'] = [prediction_list[1] for prediction_list in prediction_lists]\\nsubm['pred3'] = [prediction_list[2] for prediction_list in prediction_lists]\\nsubm['pred4'] = [prediction_list[3] for prediction_list in prediction_lists]\\n\\npd.set_option('display.max_rows', None)\\nprint(subm)\\npd.reset_option('display.max_rows')\", 'execution_count': 17}\n",
- "DEBUG:papermill:msg_type: stream\n",
- "DEBUG:papermill:content: {'name': 'stdout', 'text': ' label pred1 pred2 pred3 pred4\\nfilename \\nclassical_12.mp3 classical classical electronic pop rock\\nclassical_2.mp3 classical classical electronic pop rock\\nclassical_20.mp3 classical classical rock pop electronic\\nclassical_27.mp3 classical classical pop electronic rock\\nclassical_39.mp3 classical classical electronic rock pop\\nclassical_4.mp3 classical classical electronic pop rock\\nclassical_40.mp3 classical electronic rock classical pop\\nclassical_46.mp3 classical classical electronic pop rock\\nclassical_47.mp3 classical classical electronic pop rock\\nclassical_48.mp3 classical classical pop electronic rock\\nclassical_49.mp3 classical classical pop electronic rock\\nclassical_52.mp3 classical electronic pop rock classical\\nclassical_54.mp3 classical classical electronic pop rock\\nclassical_6.mp3 classical classical electronic pop rock\\nclassical_62.mp3 classical classical pop rock electronic\\nclassical_67.mp3 classical classical electronic pop rock\\nclassical_69.mp3 classical classical pop rock electronic\\nclassical_82.mp3 classical classical electronic pop rock\\nclassical_9.mp3 classical classical electronic pop rock\\nclassical_92.mp3 classical classical pop rock electronic\\nclassical_94.mp3 classical classical electronic pop rock\\nelectronic_11.mp3 electronic electronic rock pop classical\\nelectronic_20.mp3 electronic electronic pop rock classical\\nelectronic_21.mp3 electronic electronic pop rock classical\\nelectronic_3.mp3 electronic electronic pop classical rock\\nelectronic_35.mp3 electronic electronic rock pop classical\\nelectronic_36.mp3 electronic pop rock electronic classical\\nelectronic_38.mp3 electronic electronic pop classical rock\\nelectronic_44.mp3 electronic electronic rock pop classical\\nelectronic_49.mp3 electronic pop rock electronic classical\\nelectronic_55.mp3 electronic electronic rock pop classical\\nelectronic_59.mp3 electronic electronic rock pop classical\\nelectronic_61.mp3 electronic electronic rock pop classical\\nelectronic_62.mp3 electronic electronic pop rock classical\\nelectronic_63.mp3 electronic pop electronic rock classical\\nelectronic_81.mp3 electronic electronic rock pop classical\\npop_1.mp3 pop pop rock electronic classical\\npop_10.mp3 pop pop rock classical electronic\\npop_100.mp3 pop rock pop electronic classical\\npop_25.mp3 pop pop classical electronic rock\\npop_32.mp3 pop pop rock electronic classical\\npop_38.mp3 pop rock pop electronic classical\\npop_39.mp3 pop electronic rock pop classical\\npop_50.mp3 pop pop rock classical electronic\\npop_53.mp3 pop pop rock electronic classical\\npop_58.mp3 pop rock pop electronic classical\\npop_61.mp3 pop pop rock classical electronic\\npop_62.mp3 pop pop rock electronic classical\\npop_64.mp3 pop rock pop electronic classical\\npop_65.mp3 pop pop rock electronic classical\\npop_70.mp3 pop electronic pop classical rock\\npop_79.mp3 pop pop rock classical electronic\\npop_80.mp3 pop pop rock electronic classical\\npop_82.mp3 pop pop rock electronic classical\\npop_85.mp3 pop pop rock electronic classical\\npop_91.mp3 pop rock pop electronic classical\\npop_98.mp3 pop pop rock electronic classical\\nrock_18.mp3 rock rock pop electronic classical\\nrock_2.mp3 rock rock pop electronic classical\\nrock_23.mp3 rock rock pop electronic classical\\nrock_32.mp3 rock rock pop electronic classical\\nrock_45.mp3 rock rock pop electronic classical\\nrock_46.mp3 rock rock pop electronic classical\\nrock_48.mp3 rock pop rock electronic classical\\nrock_51.mp3 rock rock pop electronic classical\\nrock_52.mp3 rock rock pop electronic classical\\nrock_57.mp3 rock rock pop electronic classical\\nrock_6.mp3 rock rock pop electronic classical\\nrock_62.mp3 rock rock pop electronic classical\\nrock_63.mp3 rock pop rock electronic classical\\nrock_66.mp3 rock pop rock electronic classical\\nrock_73.mp3 rock electronic pop rock classical\\nrock_75.mp3 rock rock pop electronic classical\\nrock_78.mp3 rock rock pop electronic classical\\nrock_80.mp3 rock rock pop electronic classical\\nrock_85.mp3 rock rock pop electronic classical\\nrock_86.mp3 rock rock pop electronic classical\\nrock_88.mp3 rock rock pop electronic classical\\nrock_92.mp3 rock rock pop electronic classical\\nrock_93.mp3 rock pop rock electronic classical\\n'}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# output\n",
- "OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
- "\n",
- "with open(OUTPUT_PATHS[\"clf\"], \"wb\") as file:\n",
- " pickle.dump(clf, file)\n",
- "subm.to_csv(OUTPUT_PATHS[\"submission\"], index=False)\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# output\\nOUTPUT_PATH.mkdir(parents=True, exist_ok=True)\\n\\nwith open(OUTPUT_PATHS[\"clf\"], \"wb\") as file:\\n pickle.dump(clf, file)\\nsubm.to_csv(OUTPUT_PATHS[\"submission\"], index=False)', 'execution_count': 18}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:papermill:Executing cell:\n",
- "# def get_result() -> pd.DataFrame:\n",
- "# \"\"\" Return the produced artefact of this notebook \"\"\"\n",
- "# return result\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'busy'}\n",
- "DEBUG:papermill:msg_type: execute_input\n",
- "DEBUG:papermill:content: {'code': '# def get_result() -> pd.DataFrame:\\n# \"\"\" Return the produced artefact of this notebook \"\"\"\\n# return result', 'execution_count': 19}\n",
- "DEBUG:papermill:msg_type: status\n",
- "DEBUG:papermill:content: {'execution_state': 'idle'}\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): test.researchdata.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://test.researchdata.tuwien.ac.at:443 \"POST /api/records HTTP/1.1\" 201 2243\n",
- "DEBUG:fairnb.api.invenio:create draft record response: <Response [201]>\n",
- "{\"id\": \"fx913-1k105\", \"created\": \"2024-02-15T10:06:48.033919+00:00\", \"updated\": \"2024-02-15T10:06:48.058804+00:00\", \"links\": {\"self\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft\", \"self_html\": \"https://test.researchdata.tuwien.ac.at/uploads/fx913-1k105\", \"self_iiif_manifest\": \"https://test.researchdata.tuwien.ac.at/api/iiif/draft:fx913-1k105/manifest\", \"self_iiif_sequence\": \"https://test.researchdata.tuwien.ac.at/api/iiif/draft:fx913-1k105/sequence/default\", \"files\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files\", \"record\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105\", \"record_html\": \"https://test.researchdata.tuwien.ac.at/records/fx913-1k105\", \"publish\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/actions/publish\", \"review\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/review\", \"versions\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/versions\", \"access_links\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/access/links\", \"reserve_doi\": \"https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/pids/doi\"}, \"revision_id\": 4, \"parent\": {\"id\": \"zybg2-g7690\", \"access\": {\"owned_by\": [{\"user\": 102}], \"links\": []}, \"communities\": {}}, \"versions\": {\"is_latest\": false, \"is_latest_draft\": true, \"index\": 1}, \"is_published\": false, \"is_draft\": true, \"expires_at\": \"2024-02-15 10:06:48.033946\", \"pids\": {}, \"metadata\": {\"resource_type\": {\"id\": \"sound\", \"title\": {\"de\": \"Audio\", \"en\": \"Audio\"}}, \"creators\": [{\"person_or_org\": {\"type\": \"personal\", \"name\": \"Mahler, Lukas\", \"given_name\": \"Lukas\", \"family_name\": \"Mahler\", \"identifiers\": [{\"identifier\": \"0000-0002-8985-8139\", \"scheme\": \"orcid\"}]}, \"affiliations\": [{\"name\": \"Technical University of Vienna\"}]}], \"title\": \"DBREPO ISMIR test result artefact\", \"publication_date\": \"2022-01-01\"}, \"custom_fields\": {}, \"access\": {\"record\": \"public\", \"files\": \"public\", \"embargo\": {\"active\": false, \"reason\": null}, \"status\": \"metadata-only\"}, \"files\": {\"enabled\": true, \"order\": []}, \"status\": \"draft\", \"errors\": [{\"field\": \"metadata.publisher\", \"messages\": [\"Missing publisher field required for DOI registration.\"]}]}\n",
- "INFO:fairnb.api.invenio:Picked up 1 files\n",
- "DEBUG:fairnb.api.invenio:Picked up files: [PosixPath('/home/lukas/Programming/uni/bachelorarbeit/fairnb/tmp/5_ml_model/output/ml_model.pickle')]\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/fairnb/tmp/5_ml_model/output/ml_model.pickle as ml_model.pickle\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): test.researchdata.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://test.researchdata.tuwien.ac.at:443 \"POST /api/records/fx913-1k105/draft/files HTTP/1.1\" 201 663\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): test.researchdata.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://test.researchdata.tuwien.ac.at:443 \"PUT /api/records/fx913-1k105/draft/files/ml_model.pickle/content HTTP/1.1\" 200 496\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): test.researchdata.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://test.researchdata.tuwien.ac.at:443 \"POST /api/records/fx913-1k105/draft/files/ml_model.pickle/commit HTTP/1.1\" 200 None\n",
- "INFO:fairnb.api.invenio:Finished upload of ml_model.pickle\n",
- "DEBUG:fairnb.api.invenio:{'draft': {'enabled': True, 'links': {'self': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files'}, 'entries': [{'key': 'ml_model.pickle', 'created': '2024-02-15T10:06:48.416966+00:00', 'updated': '2024-02-15T10:06:48.420681+00:00', 'status': 'pending', 'metadata': None, 'links': {'self': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle', 'content': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle/content', 'commit': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle/commit'}}], 'default_preview': None, 'order': []}, 'data': {'key': 'ml_model.pickle', 'created': '2024-02-15T10:06:48.416966+00:00', 'updated': '2024-02-15T10:06:48.420681+00:00', 'status': 'pending', 'metadata': None, 'links': {'self': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle', 'content': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle/content', 'commit': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle/commit'}}, 'commit': {'key': 'ml_model.pickle', 'storage_class': 'L', 'checksum': 'md5:9f9c4d76ffce8fee1a849ca26974f73e', 'size': 129388, 'created': '2024-02-15T10:06:48.416966+00:00', 'updated': '2024-02-15T10:06:49.031790+00:00', 'status': 'completed', 'metadata': None, 'mimetype': 'application/octet-stream', 'version_id': '8e1152c0-d294-45da-85c6-c95447f4bd36', 'file_id': '5178fe82-b1a8-4aea-be49-c2159bcbc066', 'bucket_id': '412d68ff-5cb6-44ac-a08b-9a4f201e9578', 'links': {'self': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle', 'content': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle/content', 'commit': 'https://test.researchdata.tuwien.ac.at/api/records/fx913-1k105/draft/files/ml_model.pickle/commit'}}}\n",
- "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/home/lukas/Programming/uni/bachelorarbeit/fairnb, universal_newlines=False, shell=None, istream=<valid stream>)\n",
- "WARNING:fairnb.api.dbrepo:Re-authenticating due to (almost) expired token\n",
- "DEBUG:urllib3.connectionpool:Resetting dropped connection: dbrepo1.ec.tuwien.ac.at\n",
- "DEBUG:urllib3.util.retry:Incremented Retry for (url='/api/auth/realms/dbrepo/protocol/openid-connect/token'): Retry(total=0, connect=None, read=None, redirect=None, status=None)\n",
- "WARNING:urllib3.connectionpool:Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x6ffd32b9f640>, 'Connection to dbrepo1.ec.tuwien.ac.at timed out. (connect timeout=60)')': /api/auth/realms/dbrepo/protocol/openid-connect/token\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (2): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/auth/realms/dbrepo/protocol/openid-connect/token HTTP/1.1\" 200 4267\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/upload/files/ HTTP/1.1\" 201 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"PATCH /api/upload/files/dac2a248f4bb95d8da4e8d45a0cedf3b+6107180fc3e2f9f637bad5cd9300d53fc6102bb4_7fc52b7c8b124bba999f205c8c4169ca HTTP/1.1\" 204 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/database/19/table/91/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:fairnb.api.dbrepo:<Response [202]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19/table/91/export HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/upload/files/ HTTP/1.1\" 201 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"PATCH /api/upload/files/1e31f6b86a640f4b9f6c760743132617+a4b74620c1c5d4c3c3eb16aba78e6de9c99c733c_b6d8ce23fa524eab8ed16ff219a30adf HTTP/1.1\" 204 0\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"POST /api/database/19/table/92/data/import HTTP/1.1\" 202 0\n",
- "DEBUG:fairnb.api.dbrepo:<Response [202]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:git.cmd:Popen(['git', 'cat-file', '--batch-check'], cwd=/home/lukas/Programming/uni/bachelorarbeit/fairnb, universal_newlines=False, shell=None, istream=<valid stream>)\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n",
- "DEBUG:urllib3.connectionpool:https://dbrepo1.ec.tuwien.ac.at:443 \"GET /api/database/19 HTTP/1.1\" 200 None\n",
- "DEBUG:fairnb.api.dbrepo:<Response [200]>\n",
- "DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): dbrepo1.ec.tuwien.ac.at:443\n"
- ]
- },
- {
- "ename": "TusCommunicationError",
- "evalue": "HTTPSConnectionPool(host='dbrepo1.ec.tuwien.ac.at', port=443): Max retries exceeded with url: /api/upload/files/ (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x6ffd20907790>, 'Connection to dbrepo1.ec.tuwien.ac.at timed out. (connect timeout=None)'))",
- "output_type": "error",
- "traceback": [
- "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[0;31mTimeoutError\u001B[0m Traceback (most recent call last)",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connection.py:203\u001B[0m, in \u001B[0;36mHTTPConnection._new_conn\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 202\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 203\u001B[0m sock \u001B[38;5;241m=\u001B[39m \u001B[43mconnection\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_connection\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 204\u001B[0m \u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_dns_host\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mport\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 205\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 206\u001B[0m \u001B[43m \u001B[49m\u001B[43msource_address\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msource_address\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 207\u001B[0m \u001B[43m \u001B[49m\u001B[43msocket_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msocket_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 208\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 209\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m socket\u001B[38;5;241m.\u001B[39mgaierror \u001B[38;5;28;01mas\u001B[39;00m e:\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/util/connection.py:85\u001B[0m, in \u001B[0;36mcreate_connection\u001B[0;34m(address, timeout, source_address, socket_options)\u001B[0m\n\u001B[1;32m 84\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 85\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m err\n\u001B[1;32m 86\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m 87\u001B[0m \u001B[38;5;66;03m# Break explicitly a reference cycle\u001B[39;00m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/util/connection.py:73\u001B[0m, in \u001B[0;36mcreate_connection\u001B[0;34m(address, timeout, source_address, socket_options)\u001B[0m\n\u001B[1;32m 72\u001B[0m sock\u001B[38;5;241m.\u001B[39mbind(source_address)\n\u001B[0;32m---> 73\u001B[0m \u001B[43msock\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconnect\u001B[49m\u001B[43m(\u001B[49m\u001B[43msa\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 74\u001B[0m \u001B[38;5;66;03m# Break explicitly a reference cycle\u001B[39;00m\n",
- "\u001B[0;31mTimeoutError\u001B[0m: [Errno 110] Connection timed out",
- "\nThe above exception was the direct cause of the following exception:\n",
- "\u001B[0;31mConnectTimeoutError\u001B[0m Traceback (most recent call last)",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:790\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[0m\n\u001B[1;32m 789\u001B[0m \u001B[38;5;66;03m# Make the request on the HTTPConnection object\u001B[39;00m\n\u001B[0;32m--> 790\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_make_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 791\u001B[0m \u001B[43m \u001B[49m\u001B[43mconn\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 792\u001B[0m \u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 793\u001B[0m \u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 794\u001B[0m \u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout_obj\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 795\u001B[0m \u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 796\u001B[0m \u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 797\u001B[0m \u001B[43m \u001B[49m\u001B[43mchunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 798\u001B[0m \u001B[43m \u001B[49m\u001B[43mretries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mretries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 799\u001B[0m \u001B[43m \u001B[49m\u001B[43mresponse_conn\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mresponse_conn\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 800\u001B[0m \u001B[43m \u001B[49m\u001B[43mpreload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpreload_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 801\u001B[0m \u001B[43m \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdecode_content\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 802\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mresponse_kw\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 803\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 805\u001B[0m \u001B[38;5;66;03m# Everything went great!\u001B[39;00m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:491\u001B[0m, in \u001B[0;36mHTTPConnectionPool._make_request\u001B[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001B[0m\n\u001B[1;32m 490\u001B[0m new_e \u001B[38;5;241m=\u001B[39m _wrap_proxy_error(new_e, conn\u001B[38;5;241m.\u001B[39mproxy\u001B[38;5;241m.\u001B[39mscheme)\n\u001B[0;32m--> 491\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m new_e\n\u001B[1;32m 493\u001B[0m \u001B[38;5;66;03m# conn.request() calls http.client.*.request, not the method in\u001B[39;00m\n\u001B[1;32m 494\u001B[0m \u001B[38;5;66;03m# urllib3.request. It also calls makefile (recv) on the socket.\u001B[39;00m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:467\u001B[0m, in \u001B[0;36mHTTPConnectionPool._make_request\u001B[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001B[0m\n\u001B[1;32m 466\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 467\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_conn\u001B[49m\u001B[43m(\u001B[49m\u001B[43mconn\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 468\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (SocketTimeout, BaseSSLError) \u001B[38;5;28;01mas\u001B[39;00m e:\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:1092\u001B[0m, in \u001B[0;36mHTTPSConnectionPool._validate_conn\u001B[0;34m(self, conn)\u001B[0m\n\u001B[1;32m 1091\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m conn\u001B[38;5;241m.\u001B[39mis_closed:\n\u001B[0;32m-> 1092\u001B[0m \u001B[43mconn\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconnect\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1094\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m conn\u001B[38;5;241m.\u001B[39mis_verified:\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connection.py:611\u001B[0m, in \u001B[0;36mHTTPSConnection.connect\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 610\u001B[0m sock: socket\u001B[38;5;241m.\u001B[39msocket \u001B[38;5;241m|\u001B[39m ssl\u001B[38;5;241m.\u001B[39mSSLSocket\n\u001B[0;32m--> 611\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msock \u001B[38;5;241m=\u001B[39m sock \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_new_conn\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 612\u001B[0m server_hostname: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhost\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connection.py:212\u001B[0m, in \u001B[0;36mHTTPConnection._new_conn\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 211\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m SocketTimeout \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m--> 212\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ConnectTimeoutError(\n\u001B[1;32m 213\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 214\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConnection to \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhost\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m timed out. (connect timeout=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtimeout\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m)\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 215\u001B[0m ) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01me\u001B[39;00m\n\u001B[1;32m 217\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mOSError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n",
- "\u001B[0;31mConnectTimeoutError\u001B[0m: (<urllib3.connection.HTTPSConnection object at 0x6ffd20907790>, 'Connection to dbrepo1.ec.tuwien.ac.at timed out. (connect timeout=None)')",
- "\nThe above exception was the direct cause of the following exception:\n",
- "\u001B[0;31mMaxRetryError\u001B[0m Traceback (most recent call last)",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/requests/adapters.py:486\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[1;32m 485\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 486\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[43mconn\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murlopen\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 487\u001B[0m \u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 488\u001B[0m \u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 489\u001B[0m \u001B[43m \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 490\u001B[0m \u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 491\u001B[0m \u001B[43m \u001B[49m\u001B[43mredirect\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\u001B[1;32m 492\u001B[0m \u001B[43m \u001B[49m\u001B[43massert_same_host\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\u001B[1;32m 493\u001B[0m \u001B[43m \u001B[49m\u001B[43mpreload_content\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\u001B[1;32m 494\u001B[0m \u001B[43m \u001B[49m\u001B[43mdecode_content\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\u001B[1;32m 495\u001B[0m \u001B[43m \u001B[49m\u001B[43mretries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmax_retries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 496\u001B[0m \u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 497\u001B[0m \u001B[43m \u001B[49m\u001B[43mchunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 498\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 500\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (ProtocolError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:844\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[0m\n\u001B[1;32m 842\u001B[0m new_e \u001B[38;5;241m=\u001B[39m ProtocolError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConnection aborted.\u001B[39m\u001B[38;5;124m\"\u001B[39m, new_e)\n\u001B[0;32m--> 844\u001B[0m retries \u001B[38;5;241m=\u001B[39m \u001B[43mretries\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mincrement\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 845\u001B[0m \u001B[43m \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merror\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnew_e\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_pool\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_stacktrace\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msys\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexc_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[1;32m 846\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 847\u001B[0m retries\u001B[38;5;241m.\u001B[39msleep()\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/urllib3/util/retry.py:515\u001B[0m, in \u001B[0;36mRetry.increment\u001B[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001B[0m\n\u001B[1;32m 514\u001B[0m reason \u001B[38;5;241m=\u001B[39m error \u001B[38;5;129;01mor\u001B[39;00m ResponseError(cause)\n\u001B[0;32m--> 515\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m MaxRetryError(_pool, url, reason) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mreason\u001B[39;00m \u001B[38;5;66;03m# type: ignore[arg-type]\u001B[39;00m\n\u001B[1;32m 517\u001B[0m log\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIncremented Retry for (url=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m): \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, url, new_retry)\n",
- "\u001B[0;31mMaxRetryError\u001B[0m: HTTPSConnectionPool(host='dbrepo1.ec.tuwien.ac.at', port=443): Max retries exceeded with url: /api/upload/files/ (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x6ffd20907790>, 'Connection to dbrepo1.ec.tuwien.ac.at timed out. (connect timeout=None)'))",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001B[0;31mConnectTimeout\u001B[0m Traceback (most recent call last)",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/tusclient/request.py:18\u001B[0m, in \u001B[0;36mcatch_requests_error.<locals>._wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 17\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 18\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 19\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m requests\u001B[38;5;241m.\u001B[39mexceptions\u001B[38;5;241m.\u001B[39mRequestException \u001B[38;5;28;01mas\u001B[39;00m error:\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/tusclient/uploader/uploader.py:64\u001B[0m, in \u001B[0;36mUploader.create_url\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 59\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 60\u001B[0m \u001B[38;5;124;03mReturn upload url.\u001B[39;00m\n\u001B[1;32m 61\u001B[0m \n\u001B[1;32m 62\u001B[0m \u001B[38;5;124;03mMakes request to tus server to create a new upload url for the required file upload.\u001B[39;00m\n\u001B[1;32m 63\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m---> 64\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[43mrequests\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 65\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_url_creation_headers\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 66\u001B[0m \u001B[43m \u001B[49m\u001B[43mverify\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mverify_tls_cert\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 67\u001B[0m url \u001B[38;5;241m=\u001B[39m resp\u001B[38;5;241m.\u001B[39mheaders\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlocation\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/requests/api.py:115\u001B[0m, in \u001B[0;36mpost\u001B[0;34m(url, data, json, **kwargs)\u001B[0m\n\u001B[1;32m 104\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Sends a POST request.\u001B[39;00m\n\u001B[1;32m 105\u001B[0m \n\u001B[1;32m 106\u001B[0m \u001B[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 112\u001B[0m \u001B[38;5;124;03m:rtype: requests.Response\u001B[39;00m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m--> 115\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mpost\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mjson\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mjson\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/requests/api.py:59\u001B[0m, in \u001B[0;36mrequest\u001B[0;34m(method, url, **kwargs)\u001B[0m\n\u001B[1;32m 58\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m sessions\u001B[38;5;241m.\u001B[39mSession() \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[0;32m---> 59\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43msession\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/requests/sessions.py:589\u001B[0m, in \u001B[0;36mSession.request\u001B[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001B[0m\n\u001B[1;32m 588\u001B[0m send_kwargs\u001B[38;5;241m.\u001B[39mupdate(settings)\n\u001B[0;32m--> 589\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mprep\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43msend_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 591\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m resp\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/requests/sessions.py:703\u001B[0m, in \u001B[0;36mSession.send\u001B[0;34m(self, request, **kwargs)\u001B[0m\n\u001B[1;32m 702\u001B[0m \u001B[38;5;66;03m# Send the request\u001B[39;00m\n\u001B[0;32m--> 703\u001B[0m r \u001B[38;5;241m=\u001B[39m \u001B[43madapter\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 705\u001B[0m \u001B[38;5;66;03m# Total elapsed time of the request (approximately)\u001B[39;00m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/requests/adapters.py:507\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[1;32m 506\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(e\u001B[38;5;241m.\u001B[39mreason, NewConnectionError):\n\u001B[0;32m--> 507\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ConnectTimeout(e, request\u001B[38;5;241m=\u001B[39mrequest)\n\u001B[1;32m 509\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(e\u001B[38;5;241m.\u001B[39mreason, ResponseError):\n",
- "\u001B[0;31mConnectTimeout\u001B[0m: HTTPSConnectionPool(host='dbrepo1.ec.tuwien.ac.at', port=443): Max retries exceeded with url: /api/upload/files/ (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x6ffd20907790>, 'Connection to dbrepo1.ec.tuwien.ac.at timed out. (connect timeout=None)'))",
- "\nDuring handling of the above exception, another exception occurred:\n",
- "\u001B[0;31mTusCommunicationError\u001B[0m Traceback (most recent call last)",
- "Cell \u001B[0;32mIn[14], line 35\u001B[0m\n\u001B[1;32m 6\u001B[0m nb_config_ml \u001B[38;5;241m=\u001B[39m NbConfig(\n\u001B[1;32m 7\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 8\u001B[0m entities\u001B[38;5;241m=\u001B[39m[\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 31\u001B[0m ]\n\u001B[1;32m 32\u001B[0m )\n\u001B[1;32m 34\u001B[0m \u001B[38;5;66;03m# run ml\u001B[39;00m\n\u001B[0;32m---> 35\u001B[0m \u001B[43mexecutor\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[43mONLY_LOCAL\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/executor.py:47\u001B[0m, in \u001B[0;36mExecutor.execute\u001B[0;34m(cls, nb_config, require_download, only_local, **kwargs)\u001B[0m\n\u001B[1;32m 44\u001B[0m nb_config\u001B[38;5;241m.\u001B[39mended_at \u001B[38;5;241m=\u001B[39m ended_at\n\u001B[1;32m 46\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m only_local:\n\u001B[0;32m---> 47\u001B[0m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload_entities\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnb_config\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/executor.py:74\u001B[0m, in \u001B[0;36mExecutor.upload_entities\u001B[0;34m(nb_config)\u001B[0m\n\u001B[1;32m 69\u001B[0m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[1;32m 70\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mupload_entities\u001B[39m(nb_config: NbConfig):\n\u001B[1;32m 71\u001B[0m \u001B[38;5;66;03m# load generated entity and upload it\u001B[39;00m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m entity \u001B[38;5;129;01min\u001B[39;00m nb_config\u001B[38;5;241m.\u001B[39mentities:\n\u001B[1;32m 73\u001B[0m \u001B[38;5;66;03m# use inspect to get path of caller\u001B[39;00m\n\u001B[0;32m---> 74\u001B[0m \u001B[43mentity\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 75\u001B[0m \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\u001B[1;32m 76\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdependencies\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 77\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstarted_at\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 78\u001B[0m \u001B[43m \u001B[49m\u001B[43mnb_config\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mended_at\u001B[49m\n\u001B[1;32m 79\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/entity/dbrepo_entity.py:90\u001B[0m, in \u001B[0;36mDbRepoEntity.upload\u001B[0;34m(self, executed_file, dependencies, start_time, end_time)\u001B[0m\n\u001B[1;32m 76\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 78\u001B[0m metadata \u001B[38;5;241m=\u001B[39m EntityProvenance\u001B[38;5;241m.\u001B[39mnew(\n\u001B[1;32m 79\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname,\n\u001B[1;32m 80\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdescription,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 87\u001B[0m ended_at\u001B[38;5;241m=\u001B[39mend_time\n\u001B[1;32m 88\u001B[0m )\n\u001B[0;32m---> 90\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupload_provenance\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmetadata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 91\u001B[0m df[\n\u001B[1;32m 92\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mentity_id\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 93\u001B[0m ] \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 94\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata\u001B[38;5;241m.\u001B[39mid\n\u001B[1;32m 95\u001B[0m ) \u001B[38;5;66;03m# update the -1 from above with the correct value as it is now known\u001B[39;00m\n\u001B[1;32m 96\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mupload_data(df)\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/entity/entity.py:120\u001B[0m, in \u001B[0;36mEntity.upload_provenance\u001B[0;34m(self, provenance)\u001B[0m\n\u001B[1;32m 117\u001B[0m dependency_table \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcreate_dependency_table_if_not_exists()\n\u001B[1;32m 118\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdependency_table_id \u001B[38;5;241m=\u001B[39m dependency_table[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mid\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m--> 120\u001B[0m \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[43mupload_data\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 121\u001B[0m \u001B[43m \u001B[49m\u001B[43mprovenance\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_frame\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdrop\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mid\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmetadata_table_id\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 122\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 124\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdbrepo_connector\u001B[38;5;241m.\u001B[39mdownload_table_as_df(\u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmetadata_table_id))\n\u001B[1;32m 126\u001B[0m \u001B[38;5;66;03m# FIXME: create robust version of id retrieval, if possible\u001B[39;00m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/api/dbrepo.py:30\u001B[0m, in \u001B[0;36mre_auth.<locals>.inner\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 28\u001B[0m LOG\u001B[38;5;241m.\u001B[39mwarning(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRe-authenticating due to (almost) expired refresh token\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 29\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mauthenticate_keycloak()\n\u001B[0;32m---> 30\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/fairnb/api/dbrepo.py:258\u001B[0m, in \u001B[0;36mDBRepoConnector.upload_data\u001B[0;34m(self, dataframe, table_id)\u001B[0m\n\u001B[1;32m 248\u001B[0m dataframe\u001B[38;5;241m.\u001B[39mto_csv(\n\u001B[1;32m 249\u001B[0m string_io \u001B[38;5;241m:=\u001B[39m StringIO(),\n\u001B[1;32m 250\u001B[0m index\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[1;32m 251\u001B[0m quoting\u001B[38;5;241m=\u001B[39mcsv\u001B[38;5;241m.\u001B[39mQUOTE_ALL)\n\u001B[1;32m 253\u001B[0m uploader \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtusclient\u001B[38;5;241m.\u001B[39muploader(\n\u001B[1;32m 254\u001B[0m file_stream\u001B[38;5;241m=\u001B[39mstring_io,\n\u001B[1;32m 255\u001B[0m chunk_size\u001B[38;5;241m=\u001B[39mCHUNK_SIZE,\n\u001B[1;32m 256\u001B[0m )\n\u001B[0;32m--> 258\u001B[0m upload_url \u001B[38;5;241m=\u001B[39m \u001B[43muploader\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_url\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 259\u001B[0m upload_url \u001B[38;5;241m=\u001B[39m upload_url\u001B[38;5;241m.\u001B[39mreplace(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhttp\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhttps\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m 260\u001B[0m uploader\u001B[38;5;241m.\u001B[39mset_url(upload_url) \u001B[38;5;66;03m# FIX: wrong location response\u001B[39;00m\n",
- "File \u001B[0;32m~/Programming/uni/bachelorarbeit/fairnb/.venv/lib/python3.10/site-packages/tusclient/request.py:20\u001B[0m, in \u001B[0;36mcatch_requests_error.<locals>._wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 18\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 19\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m requests\u001B[38;5;241m.\u001B[39mexceptions\u001B[38;5;241m.\u001B[39mRequestException \u001B[38;5;28;01mas\u001B[39;00m error:\n\u001B[0;32m---> 20\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m TusCommunicationError(error)\n",
- "\u001B[0;31mTusCommunicationError\u001B[0m: HTTPSConnectionPool(host='dbrepo1.ec.tuwien.ac.at', port=443): Max retries exceeded with url: /api/upload/files/ (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x6ffd20907790>, 'Connection to dbrepo1.ec.tuwien.ac.at timed out. (connect timeout=None)'))"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# -------------- ML ------------------------------\n",
"with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
@@ -1209,8 +248,9 @@
"\n",
"nb_config_ml = NbConfig(\n",
" nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " main_location=MAIN_PATH,\n",
" entities=[\n",
- " ml_model_entity := InvenioEntity.new(\n",
+ " ml_model_entity := InvenioRDMEntity.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",
@@ -1219,14 +259,14 @@
" 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",
+ " test_result_entity := DBRepoEntity.new(\n",
+ " name=\"prediction_results\",\n",
+ " description=\"Result of predictions for ml model\",\n",
+ " table_name=\"prediction_result\",\n",
+ " table_description=\"Prediction 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",
+ " type=\"prediction_result\"\n",
" )\n",
" ],\n",
" dependencies=[\n",
diff --git a/notebooks/standalone.ipynb b/notebooks/standalone.ipynb
index f9116a861183853662cf237f0abff91e67482ff1..8f3c33ac71f01352d16045948e17622ff2c7d4b1 100644
--- a/notebooks/standalone.ipynb
+++ b/notebooks/standalone.ipynb
@@ -13,13 +13,9 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T17:23:27.929106758Z",
- "start_time": "2024-02-15T17:23:27.398716975Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -44,8 +40,8 @@
"from sklearn.svm import SVC\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
- "from fairnb.entity.dbrepo_entity import DbRepoEntity\n",
- "from fairnb.entity.invenio_entity import InvenioEntity\n",
+ "from fairnb.entity.dbrepo_entity import DBRepoEntity\n",
+ "from fairnb.entity.invenio_entity import InvenioRDMEntity\n",
"from fairnb.executor import Executor\n",
"from fairnb.nb_config import NbConfig\n",
"from fairnb.util import Util\n",
@@ -59,13 +55,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:15:16.261447298Z",
- "start_time": "2024-02-15T15:15:16.189201715Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -101,13 +93,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:15:53.366720580Z",
- "start_time": "2024-02-15T15:15:17.912905124Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -137,13 +125,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:15:53.422135188Z",
- "start_time": "2024-02-15T15:15:53.410867059Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -152,8 +136,9 @@
"\n",
" nb_config_audio_files = NbConfig(\n",
" nb_location=NB_LOCATION,\n",
+ " main_location=NB_LOCATION,\n",
" entities=[\n",
- " audio_files_entity := InvenioEntity.new(\n",
+ " audio_files_entity := InvenioRDMEntity.new(\n",
" name = \"standalone audio_tar\",\n",
" description = \"Raw music files\",\n",
" location = tar_path,\n",
@@ -183,13 +168,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:22:56.491700713Z",
- "start_time": "2024-02-15T15:15:53.412008839Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -242,32 +223,11 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T15:42:52.379472642Z",
- "start_time": "2024-02-15T15:42:51.690347535Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "data": {
- "text/plain": "[<matplotlib.lines.Line2D at 0x76f510f201c0>]"
- },
- "execution_count": 10,
- "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"
- }
- ],
+ "outputs": [],
"source": [
"example_mfcc = raw_features[raw_features.filename == \"rock_50.mp3\"].sort_values(\"sample\").iloc[:,:]\n",
"plt.plot(example_mfcc[15])\n",
@@ -287,13 +247,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T16:56:51.619436944Z",
- "start_time": "2024-02-15T16:56:14.821458276Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -306,25 +262,11 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T16:57:23.139681652Z",
- "start_time": "2024-02-15T16:57:17.049446073Z"
- }
+ "collapsed": false
},
- "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": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"meta_columns = [\"sample\", \"filename\", \"label\"]\n",
"mfcc_aggregated = raw_features \\\n",
@@ -346,24 +288,7 @@
},
{
"cell_type": "code",
- "outputs": [
- {
- "data": {
- "text/plain": "<Axes: >"
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 2 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from math import log\n",
"# mfcc_merged.corr()\n",
@@ -371,13 +296,9 @@
"# (mfcc_merged[mfcc_merged[\"label\"] == \"rock\"].iloc[:,range(4, len(mfcc_merged.columns), 5)].plot)"
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T17:31:28.463293796Z",
- "start_time": "2024-02-15T17:31:27.732351621Z"
- }
+ "collapsed": false
},
- "execution_count": 79
+ "execution_count": null
},
{
"cell_type": "markdown",
@@ -390,25 +311,11 @@
},
{
"cell_type": "code",
- "execution_count": 82,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T18:18:21.329072619Z",
- "start_time": "2024-02-15T18:18:21.282917798Z"
- }
+ "collapsed": false
},
- "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 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>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>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": 82,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"features = mfcc_merged\n",
"train = features.sample(frac=0.8, random_state=11908553).sort_index()\n",
@@ -440,25 +347,11 @@
},
{
"cell_type": "code",
- "execution_count": 83,
+ "execution_count": null,
"metadata": {
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- "ExecuteTime": {
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- "start_time": "2024-02-15T18:18:54.375167891Z"
- }
+ "collapsed": false
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- {
- "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 True \nclassical_100.mp3 31.397881 -1.494916 10.917299 0.020984 True \nclassical_11.mp3 50.992897 1.600777 10.125545 0.595763 True \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]",
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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>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>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>31.397881</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>21.916949</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>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>-25.040888</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": 83,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\n",
"joined"
@@ -466,25 +359,11 @@
},
{
"cell_type": "code",
- "execution_count": 84,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T18:19:00.591215953Z",
- "start_time": "2024-02-15T18:19:00.540580460Z"
- }
+ "collapsed": false
},
- "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_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_12.mp3 ... -44.843811 28.490644 -6.242015 10.546545 0.341848 \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_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_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \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_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_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>...</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": 84,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\n",
"train"
@@ -492,25 +371,11 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T18:19:05.778658012Z",
- "start_time": "2024-02-15T18:19:05.712928730Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "data": {
- "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_14.mp3 classical -531.049438 -100.790543 -188.970749 58.287371 \nclassical_19.mp3 classical -543.642334 -106.038223 -216.909943 61.317534 \nclassical_22.mp3 classical -541.936157 -226.866425 -335.226593 50.647623 \nclassical_27.mp3 classical -595.418945 -78.118813 -265.344452 104.892303 \nclassical_28.mp3 classical -586.019897 -129.735809 -258.647858 62.885900 \n... ... ... ... ... ... \nrock_73.mp3 rock -592.886169 41.701897 -153.154892 106.421560 \nrock_77.mp3 rock -539.358521 35.718674 -179.586624 84.650255 \nrock_79.mp3 rock -546.266846 40.547977 -92.666176 70.381178 \nrock_8.mp3 rock -497.713226 23.375931 -88.147797 81.523614 \nrock_91.mp3 rock -533.061218 25.355713 -158.489578 74.151701 \n\n 0_skew 1_min 1_max 1_mean 1_std ... \\\nfilename ... \nclassical_14.mp3 -3.246618 0.000000 157.947922 86.563927 17.911136 ... \nclassical_19.mp3 -3.473125 0.000000 151.947662 93.405411 22.029233 ... \nclassical_22.mp3 -0.545184 0.000000 176.146393 133.592239 17.983436 ... \nclassical_27.mp3 -0.526604 0.000000 200.616333 144.208496 25.198761 ... \nclassical_28.mp3 -1.322063 0.000000 202.235626 150.812439 24.929648 ... \n... ... ... ... ... ... ... \nrock_73.mp3 -0.994740 0.000000 215.729919 115.183861 33.206780 ... \nrock_77.mp3 -0.219876 -38.462662 223.537796 127.873802 40.245428 ... \nrock_79.mp3 -1.007915 -28.949915 209.030945 103.412766 35.947907 ... \nrock_8.mp3 -1.833271 0.000000 160.661163 107.283173 22.091759 ... \nrock_91.mp3 -0.529297 -29.862530 204.165237 107.615341 39.961011 ... \n\n 38_min 38_max 38_mean 38_std 38_skew \\\nfilename \nclassical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \nclassical_19.mp3 -27.029385 30.682745 3.342259 8.420860 0.043171 \nclassical_22.mp3 -29.110729 27.870190 -0.569063 8.987627 0.238096 \nclassical_27.mp3 -28.797087 20.897751 -5.761607 7.108055 0.360305 \nclassical_28.mp3 -29.485439 37.300678 1.431255 10.245150 0.195289 \n... ... ... ... ... ... \nrock_73.mp3 -24.936195 24.260921 -2.783082 6.734193 0.418109 \nrock_77.mp3 -43.137642 22.787941 -4.591152 8.628223 -0.248479 \nrock_79.mp3 -28.984898 23.744232 -4.107946 6.492144 0.329881 \nrock_8.mp3 -19.778898 7.288054 -6.099163 4.362437 -0.103457 \nrock_91.mp3 -25.712143 15.506596 -7.065026 6.016990 0.236868 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \nclassical_19.mp3 -25.900257 36.766388 2.389575 10.099726 0.140336 \nclassical_22.mp3 -18.535694 41.965927 3.331284 9.619688 0.652851 \nclassical_27.mp3 -39.705540 25.803795 -2.736776 10.101577 -0.463730 \nclassical_28.mp3 -47.261536 52.326958 -1.204363 14.523197 0.225080 \n... ... ... ... ... ... \nrock_73.mp3 -13.622139 26.186539 3.595927 5.598527 0.126129 \nrock_77.mp3 -32.774506 29.059296 -3.888315 8.583189 0.047952 \nrock_79.mp3 -47.077301 24.408516 -4.148662 9.912590 -1.244573 \nrock_8.mp3 -24.742708 15.181401 -2.608342 5.046914 -0.336846 \nrock_91.mp3 -28.482529 20.222202 -1.086115 6.034919 0.097198 \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_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_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_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>27.870190</td>\n <td>-0.569063</td>\n <td>8.987627</td>\n <td>0.238096</td>\n <td>-18.535694</td>\n <td>41.965927</td>\n <td>3.331284</td>\n <td>9.619688</td>\n <td>0.652851</td>\n </tr>\n <tr>\n <th>classical_27.mp3</th>\n <td>classical</td>\n <td>-595.418945</td>\n <td>-78.118813</td>\n <td>-265.344452</td>\n <td>104.892303</td>\n <td>-0.526604</td>\n <td>0.000000</td>\n <td>200.616333</td>\n <td>144.208496</td>\n <td>25.198761</td>\n <td>...</td>\n <td>-28.797087</td>\n <td>20.897751</td>\n <td>-5.761607</td>\n <td>7.108055</td>\n <td>0.360305</td>\n <td>-39.705540</td>\n <td>25.803795</td>\n <td>-2.736776</td>\n <td>10.101577</td>\n <td>-0.463730</td>\n </tr>\n <tr>\n <th>classical_28.mp3</th>\n <td>classical</td>\n <td>-586.019897</td>\n <td>-129.735809</td>\n <td>-258.647858</td>\n <td>62.885900</td>\n <td>-1.322063</td>\n <td>0.000000</td>\n <td>202.235626</td>\n <td>150.812439</td>\n <td>24.929648</td>\n <td>...</td>\n <td>-29.485439</td>\n <td>37.300678</td>\n <td>1.431255</td>\n <td>10.245150</td>\n <td>0.195289</td>\n <td>-47.261536</td>\n <td>52.326958</td>\n <td>-1.204363</td>\n <td>14.523197</td>\n <td>0.225080</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_73.mp3</th>\n <td>rock</td>\n <td>-592.886169</td>\n <td>41.701897</td>\n <td>-153.154892</td>\n <td>106.421560</td>\n <td>-0.994740</td>\n <td>0.000000</td>\n <td>215.729919</td>\n <td>115.183861</td>\n <td>33.206780</td>\n <td>...</td>\n <td>-24.936195</td>\n <td>24.260921</td>\n <td>-2.783082</td>\n <td>6.734193</td>\n <td>0.418109</td>\n <td>-13.622139</td>\n <td>26.186539</td>\n <td>3.595927</td>\n <td>5.598527</td>\n <td>0.126129</td>\n </tr>\n <tr>\n <th>rock_77.mp3</th>\n <td>rock</td>\n <td>-539.358521</td>\n <td>35.718674</td>\n <td>-179.586624</td>\n <td>84.650255</td>\n <td>-0.219876</td>\n <td>-38.462662</td>\n <td>223.537796</td>\n <td>127.873802</td>\n <td>40.245428</td>\n <td>...</td>\n <td>-43.137642</td>\n <td>22.787941</td>\n <td>-4.591152</td>\n <td>8.628223</td>\n <td>-0.248479</td>\n <td>-32.774506</td>\n <td>29.059296</td>\n <td>-3.888315</td>\n <td>8.583189</td>\n <td>0.047952</td>\n </tr>\n <tr>\n <th>rock_79.mp3</th>\n <td>rock</td>\n <td>-546.266846</td>\n <td>40.547977</td>\n <td>-92.666176</td>\n <td>70.381178</td>\n <td>-1.007915</td>\n <td>-28.949915</td>\n <td>209.030945</td>\n <td>103.412766</td>\n <td>35.947907</td>\n <td>...</td>\n <td>-28.984898</td>\n <td>23.744232</td>\n <td>-4.107946</td>\n <td>6.492144</td>\n <td>0.329881</td>\n <td>-47.077301</td>\n <td>24.408516</td>\n <td>-4.148662</td>\n <td>9.912590</td>\n <td>-1.244573</td>\n </tr>\n <tr>\n <th>rock_8.mp3</th>\n <td>rock</td>\n <td>-497.713226</td>\n <td>23.375931</td>\n <td>-88.147797</td>\n <td>81.523614</td>\n <td>-1.833271</td>\n <td>0.000000</td>\n <td>160.661163</td>\n <td>107.283173</td>\n <td>22.091759</td>\n <td>...</td>\n <td>-19.778898</td>\n <td>7.288054</td>\n <td>-6.099163</td>\n <td>4.362437</td>\n <td>-0.103457</td>\n <td>-24.742708</td>\n <td>15.181401</td>\n <td>-2.608342</td>\n <td>5.046914</td>\n <td>-0.336846</td>\n </tr>\n <tr>\n <th>rock_91.mp3</th>\n <td>rock</td>\n <td>-533.061218</td>\n <td>25.355713</td>\n <td>-158.489578</td>\n <td>74.151701</td>\n <td>-0.529297</td>\n <td>-29.862530</td>\n <td>204.165237</td>\n <td>107.615341</td>\n <td>39.961011</td>\n <td>...</td>\n <td>-25.712143</td>\n <td>15.506596</td>\n <td>-7.065026</td>\n <td>6.016990</td>\n <td>0.236868</td>\n <td>-28.482529</td>\n <td>20.222202</td>\n <td>-1.086115</td>\n <td>6.034919</td>\n <td>0.097198</td>\n </tr>\n </tbody>\n</table>\n<p>80 rows × 201 columns</p>\n</div>"
- },
- "execution_count": 85,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\n",
"test"
@@ -518,24 +383,11 @@
},
{
"cell_type": "code",
- "execution_count": 87,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T18:19:26.464047065Z",
- "start_time": "2024-02-15T18:19:26.395437410Z"
- }
+ "collapsed": false
},
- "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 53.561838 -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_12.mp3 -562.675232 -148.133560 -270.975403 52.191182 -0.366587 \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_10.mp3 0.029056 259.632690 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 21.916949 -1.176922 \n classical_12.mp3 0.000000 194.264160 148.226654 19.305008 -0.533256 \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_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_12.mp3 ... -44.843811 28.490644 -6.242015 10.546545 0.341848 \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_10.mp3 -28.989983 27.533710 0.952658 10.477735 -0.185771 \n classical_100.mp3 -38.095375 31.397881 -1.494916 10.917299 0.020984 \n classical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \n classical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \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, 0, 0, 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, 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, 2, 2, 2, 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": 87,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# remove labels\n",
"X = train.drop(['label'], axis=1, errors='ignore')\n",
@@ -559,33 +411,11 @@
},
{
"cell_type": "code",
- "execution_count": 88,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T18:19:31.118105061Z",
- "start_time": "2024-02-15T18:19:31.077256730Z"
- }
+ "collapsed": false
},
- "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, 1, 1, 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, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
- },
- "execution_count": 88,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"X_test = test.drop(['label'], axis=1, errors='ignore')\n",
"\n",
@@ -599,24 +429,11 @@
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T18:19:48.396534314Z",
- "start_time": "2024-02-15T18:19:48.343232378Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "data": {
- "text/plain": "array([[ 0.41312229, -1.8814851 , -1.37421169, ..., -0.68911516,\n 3.50451929, 0.07520041],\n [-0.42816033, -0.99060625, -0.4404758 , ..., 0.28654076,\n 1.37798859, -0.95198386],\n [ 0.27009086, -0.5321083 , 0.02619898, ..., -0.45168397,\n 1.5724073 , -0.35748084],\n ...,\n [ 0.73547052, 1.06188296, 1.28180917, ..., -1.01319989,\n 0.35751256, 0.04319288],\n [ 0.73158663, 0.99591802, 1.51296008, ..., -0.80459427,\n -1.02203015, -1.16083939],\n [ 0.0480353 , 1.28857646, 1.47191077, ..., -0.09786553,\n -0.34966421, 0.68861256]])"
- },
- "execution_count": 89,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Standardize for PCA\n",
"scaler = StandardScaler()\n",
@@ -628,26 +445,11 @@
},
{
"cell_type": "code",
- "execution_count": 219,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:02:13.236651909Z",
- "start_time": "2024-02-15T21:02:13.017853296Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.748594193433371\n",
- "(320, 30)\n",
- "(80, 30)\n",
- "(320,)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Reduce Dimensions via PCA\n",
"pca = PCA(n_components=30).fit(X_standardized)\n",
@@ -662,23 +464,11 @@
},
{
"cell_type": "code",
- "execution_count": 220,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:02:15.245844534Z",
- "start_time": "2024-02-15T21:02:15.168644880Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.8125\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Fit SVM:\n",
"\n",
@@ -692,25 +482,11 @@
},
{
"cell_type": "code",
- "execution_count": 221,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:02:24.826839877Z",
- "start_time": "2024-02-15T21:02:17.112920147Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.765625\n",
- "{'C': 4, 'gamma': 0.01}\n",
- "SVC(C=4, gamma=0.01)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# grid for C, gamma\n",
"C_grid = [0.0001, 0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
@@ -728,23 +504,11 @@
},
{
"cell_type": "code",
- "execution_count": 223,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:03:27.825939392Z",
- "start_time": "2024-02-15T21:03:27.720198770Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy score: 0.7625\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Fit entire training sets with optimal model\n",
"\n",
@@ -757,32 +521,11 @@
},
{
"cell_type": "code",
- "execution_count": 224,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:03:30.177134123Z",
- "start_time": "2024-02-15T21:03:30.061280071Z"
- }
+ "collapsed": false
},
- "outputs": [
- {
- "data": {
- "text/plain": " label pred1 pred2 pred3 pred4\nfilename \nclassical_14.mp3 classical classical electronic pop rock\nclassical_19.mp3 classical classical electronic pop rock\nclassical_22.mp3 classical classical pop electronic rock\nclassical_27.mp3 classical classical electronic pop rock\nclassical_28.mp3 classical classical electronic pop rock\nclassical_29.mp3 classical classical electronic pop rock\nclassical_30.mp3 classical classical electronic pop rock\nclassical_35.mp3 classical classical electronic pop rock\nclassical_46.mp3 classical classical electronic pop rock\nclassical_60.mp3 classical classical electronic pop rock\nclassical_66.mp3 classical classical rock electronic pop\nclassical_69.mp3 classical classical pop rock electronic\nclassical_79.mp3 classical classical electronic pop rock\nclassical_83.mp3 classical electronic classical pop rock\nclassical_87.mp3 classical classical pop electronic rock\nclassical_89.mp3 classical classical electronic rock pop\nclassical_9.mp3 classical classical electronic pop rock\nclassical_91.mp3 classical classical electronic pop rock\nclassical_99.mp3 classical classical electronic pop rock\nelectronic_100.mp3 electronic electronic pop classical rock\nelectronic_13.mp3 electronic electronic pop rock classical\nelectronic_18.mp3 electronic electronic pop rock classical\nelectronic_25.mp3 electronic electronic classical pop rock\nelectronic_31.mp3 electronic electronic pop rock classical\nelectronic_32.mp3 electronic electronic pop classical rock\nelectronic_39.mp3 electronic electronic pop rock classical\nelectronic_49.mp3 electronic pop rock electronic classical\nelectronic_50.mp3 electronic electronic pop rock classical\nelectronic_58.mp3 electronic electronic pop rock classical\nelectronic_61.mp3 electronic electronic pop rock classical\nelectronic_65.mp3 electronic pop classical rock electronic\nelectronic_69.mp3 electronic electronic pop classical rock\nelectronic_7.mp3 electronic electronic pop rock classical\nelectronic_70.mp3 electronic classical electronic pop rock\nelectronic_71.mp3 electronic electronic pop rock classical\nelectronic_72.mp3 electronic electronic rock pop classical\nelectronic_74.mp3 electronic electronic pop rock classical\nelectronic_77.mp3 electronic electronic pop rock classical\nelectronic_80.mp3 electronic electronic rock pop classical\nelectronic_88.mp3 electronic electronic pop rock classical\nelectronic_90.mp3 electronic electronic pop rock classical\npop_12.mp3 pop rock pop electronic classical\npop_15.mp3 pop rock pop electronic classical\npop_16.mp3 pop electronic pop rock classical\npop_19.mp3 pop electronic classical rock pop\npop_23.mp3 pop pop electronic rock classical\npop_35.mp3 pop rock pop electronic classical\npop_37.mp3 pop rock pop electronic classical\npop_39.mp3 pop electronic pop rock classical\npop_4.mp3 pop pop rock electronic classical\npop_47.mp3 pop electronic pop rock classical\npop_50.mp3 pop pop rock electronic classical\npop_59.mp3 pop pop rock electronic classical\npop_68.mp3 pop rock pop electronic classical\npop_73.mp3 pop pop rock electronic classical\npop_76.mp3 pop pop rock electronic classical\npop_77.mp3 pop pop rock electronic classical\npop_80.mp3 pop pop rock electronic classical\npop_87.mp3 pop rock pop electronic classical\nrock_1.mp3 rock rock pop electronic classical\nrock_13.mp3 rock pop classical rock electronic\nrock_22.mp3 rock rock pop electronic classical\nrock_24.mp3 rock rock pop electronic classical\nrock_27.mp3 rock pop rock electronic classical\nrock_35.mp3 rock rock pop electronic classical\nrock_36.mp3 rock rock pop electronic classical\nrock_41.mp3 rock electronic pop rock classical\nrock_51.mp3 rock rock pop electronic classical\nrock_60.mp3 rock electronic pop rock classical\nrock_61.mp3 rock electronic pop rock classical\nrock_62.mp3 rock rock pop electronic classical\nrock_66.mp3 rock rock pop electronic classical\nrock_69.mp3 rock rock pop electronic classical\nrock_7.mp3 rock rock pop electronic classical\nrock_72.mp3 rock rock pop electronic classical\nrock_73.mp3 rock pop rock electronic classical\nrock_77.mp3 rock rock pop electronic classical\nrock_79.mp3 rock rock pop electronic classical\nrock_8.mp3 rock rock pop electronic classical\nrock_91.mp3 rock rock pop electronic classical",
- "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>pred1</th>\n <th>pred2</th>\n <th>pred3</th>\n <th>pred4</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_14.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_19.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_22.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>pop</td>\n <td>electronic</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_27.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_28.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_29.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_30.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_35.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_46.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_60.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_66.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>rock</td>\n <td>electronic</td>\n <td>pop</td>\n </tr>\n <tr>\n <th>classical_69.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n </tr>\n <tr>\n <th>classical_79.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_83.mp3</th>\n <td>classical</td>\n <td>electronic</td>\n <td>classical</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_87.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>pop</td>\n <td>electronic</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_89.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>rock</td>\n <td>pop</td>\n </tr>\n <tr>\n <th>classical_9.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_91.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>classical_99.mp3</th>\n <td>classical</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>electronic_100.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>classical</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>electronic_13.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_18.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_25.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>classical</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>electronic_31.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_32.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>classical</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>electronic_39.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_49.mp3</th>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_50.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_58.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_61.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_65.mp3</th>\n <td>electronic</td>\n <td>pop</td>\n <td>classical</td>\n <td>rock</td>\n <td>electronic</td>\n </tr>\n <tr>\n <th>electronic_69.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>classical</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>electronic_7.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_70.mp3</th>\n <td>electronic</td>\n <td>classical</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n </tr>\n <tr>\n <th>electronic_71.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_72.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>rock</td>\n <td>pop</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_74.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_77.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_80.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>rock</td>\n <td>pop</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_88.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>electronic_90.mp3</th>\n <td>electronic</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_12.mp3</th>\n <td>pop</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_15.mp3</th>\n <td>pop</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_16.mp3</th>\n <td>pop</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_19.mp3</th>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n <td>rock</td>\n <td>pop</td>\n </tr>\n <tr>\n <th>pop_23.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>electronic</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_35.mp3</th>\n <td>pop</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_37.mp3</th>\n <td>pop</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_39.mp3</th>\n <td>pop</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_4.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_47.mp3</th>\n <td>pop</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_50.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_59.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_68.mp3</th>\n <td>pop</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_73.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_76.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_77.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_80.mp3</th>\n <td>pop</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>pop_87.mp3</th>\n <td>pop</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_1.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_13.mp3</th>\n <td>rock</td>\n <td>pop</td>\n <td>classical</td>\n <td>rock</td>\n <td>electronic</td>\n </tr>\n <tr>\n <th>rock_22.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_24.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_27.mp3</th>\n <td>rock</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_35.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_36.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_41.mp3</th>\n <td>rock</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_51.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_60.mp3</th>\n <td>rock</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_61.mp3</th>\n <td>rock</td>\n <td>electronic</td>\n <td>pop</td>\n <td>rock</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_62.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_66.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_69.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_7.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_72.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_73.mp3</th>\n <td>rock</td>\n <td>pop</td>\n <td>rock</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_77.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_79.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_8.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n <tr>\n <th>rock_91.mp3</th>\n <td>rock</td>\n <td>rock</td>\n <td>pop</td>\n <td>electronic</td>\n <td>classical</td>\n </tr>\n </tbody>\n</table>\n</div>"
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- "text/plain": " label classical electronic pop rock\nfilename \nclassical_14.mp3 classical 0.925965 0.065252 0.006648 0.002135\nclassical_19.mp3 classical 0.999422 0.000337 0.000159 0.000081\nclassical_22.mp3 classical 0.937748 0.020221 0.037593 0.004438\nclassical_27.mp3 classical 0.987477 0.005166 0.004478 0.002879\nclassical_28.mp3 classical 0.991594 0.004317 0.003297 0.000792\nclassical_29.mp3 classical 0.934995 0.056594 0.005521 0.002890\nclassical_30.mp3 classical 0.941419 0.043659 0.010996 0.003926\nclassical_35.mp3 classical 0.990555 0.007352 0.001354 0.000740\nclassical_46.mp3 classical 0.994143 0.004008 0.001441 0.000408\nclassical_60.mp3 classical 0.985542 0.011265 0.002424 0.000769\nclassical_66.mp3 classical 0.966900 0.010993 0.008897 0.013210\nclassical_69.mp3 classical 0.846153 0.026670 0.096475 0.030703\nclassical_79.mp3 classical 0.995726 0.003362 0.000490 0.000422\nclassical_83.mp3 classical 0.339155 0.396894 0.241362 0.022589\nclassical_87.mp3 classical 0.979284 0.005159 0.013257 0.002300\nclassical_89.mp3 classical 0.987688 0.009909 0.001083 0.001320\nclassical_9.mp3 classical 0.987905 0.008923 0.002648 0.000524\nclassical_91.mp3 classical 0.955672 0.032070 0.006733 0.005524\nclassical_99.mp3 classical 0.872370 0.099102 0.020143 0.008385\nelectronic_100.mp3 electronic 0.002803 0.984504 0.010454 0.002239\nelectronic_13.mp3 electronic 0.009089 0.515113 0.358117 0.117681\nelectronic_18.mp3 electronic 0.002440 0.984207 0.009388 0.003965\nelectronic_25.mp3 electronic 0.023760 0.961638 0.008666 0.005936\nelectronic_31.mp3 electronic 0.002302 0.947022 0.031520 0.019156\nelectronic_32.mp3 electronic 0.094542 0.766320 0.122180 0.016958\nelectronic_39.mp3 electronic 0.017045 0.759518 0.190532 0.032906\nelectronic_49.mp3 electronic 0.005468 0.299359 0.354465 0.340708\nelectronic_50.mp3 electronic 0.016311 0.766572 0.174612 0.042505\nelectronic_58.mp3 electronic 0.003758 0.831129 0.138715 0.026397\nelectronic_61.mp3 electronic 0.027519 0.902310 0.038091 0.032081\nelectronic_65.mp3 electronic 0.178326 0.080514 0.632677 0.108484\nelectronic_69.mp3 electronic 0.006539 0.969710 0.017215 0.006536\nelectronic_7.mp3 electronic 0.001669 0.844991 0.135112 0.018228\nelectronic_70.mp3 electronic 0.488814 0.430486 0.043033 0.037666\nelectronic_71.mp3 electronic 0.001112 0.981605 0.009047 0.008236\nelectronic_72.mp3 electronic 0.001482 0.772798 0.068921 0.156799\nelectronic_74.mp3 electronic 0.053454 0.651853 0.224057 0.070636\nelectronic_77.mp3 electronic 0.004201 0.959526 0.030937 0.005336\nelectronic_80.mp3 electronic 0.004195 0.787130 0.031932 0.176743\nelectronic_88.mp3 electronic 0.003710 0.910954 0.078526 0.006810\nelectronic_90.mp3 electronic 0.073273 0.719169 0.127858 0.079700\npop_12.mp3 pop 0.000399 0.002274 0.198433 0.798894\npop_15.mp3 pop 0.000688 0.002268 0.322026 0.675018\npop_16.mp3 pop 0.059263 0.503587 0.296528 0.140622\npop_19.mp3 pop 0.085615 0.859067 0.025328 0.029990\npop_23.mp3 pop 0.002328 0.331736 0.554105 0.111831\npop_35.mp3 pop 0.001280 0.003146 0.209664 0.785910\npop_37.mp3 pop 0.000415 0.001971 0.196326 0.801288\npop_39.mp3 pop 0.001335 0.711550 0.200485 0.086630\npop_4.mp3 pop 0.011750 0.147964 0.540080 0.300206\npop_47.mp3 pop 0.004379 0.495875 0.399103 0.100643\npop_50.mp3 pop 0.000827 0.002277 0.723224 0.273672\npop_59.mp3 pop 0.001025 0.005752 0.788940 0.204283\npop_68.mp3 pop 0.000767 0.002830 0.283495 0.712908\npop_73.mp3 pop 0.001074 0.002167 0.753129 0.243629\npop_76.mp3 pop 0.001763 0.021351 0.526564 0.450322\npop_77.mp3 pop 0.014080 0.108893 0.519505 0.357522\npop_80.mp3 pop 0.005269 0.148692 0.586638 0.259400\npop_87.mp3 pop 0.000806 0.008741 0.227895 0.762557\nrock_1.mp3 rock 0.000498 0.007474 0.273741 0.718287\nrock_13.mp3 rock 0.240288 0.033601 0.657906 0.068205\nrock_22.mp3 rock 0.000315 0.004363 0.088815 0.906506\nrock_24.mp3 rock 0.000537 0.004526 0.182356 0.812582\nrock_27.mp3 rock 0.001235 0.030715 0.568293 0.399758\nrock_35.mp3 rock 0.011880 0.112458 0.336041 0.539620\nrock_36.mp3 rock 0.001290 0.102655 0.428641 0.467414\nrock_41.mp3 rock 0.002773 0.609287 0.264932 0.123007\nrock_51.mp3 rock 0.000489 0.022371 0.239999 0.737141\nrock_60.mp3 rock 0.001606 0.959144 0.029477 0.009774\nrock_61.mp3 rock 0.010072 0.924942 0.037895 0.027092\nrock_62.mp3 rock 0.000158 0.002162 0.326680 0.671000\nrock_66.mp3 rock 0.001493 0.004598 0.281867 0.712041\nrock_69.mp3 rock 0.007581 0.062220 0.274924 0.655275\nrock_7.mp3 rock 0.000154 0.001654 0.289709 0.708482\nrock_72.mp3 rock 0.003920 0.006517 0.169623 0.819940\nrock_73.mp3 rock 0.011498 0.160793 0.455101 0.372607\nrock_77.mp3 rock 0.005786 0.027944 0.383715 0.582555\nrock_79.mp3 rock 0.020186 0.179854 0.312571 0.487390\nrock_8.mp3 rock 0.030377 0.050505 0.140137 0.778982\nrock_91.mp3 rock 0.000543 0.002976 0.185036 0.811445",
- "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>classical</th>\n <th>electronic</th>\n <th>pop</th>\n <th>rock</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 </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_14.mp3</th>\n <td>classical</td>\n <td>0.925965</td>\n <td>0.065252</td>\n <td>0.006648</td>\n <td>0.002135</td>\n </tr>\n <tr>\n <th>classical_19.mp3</th>\n <td>classical</td>\n <td>0.999422</td>\n <td>0.000337</td>\n <td>0.000159</td>\n <td>0.000081</td>\n </tr>\n <tr>\n <th>classical_22.mp3</th>\n <td>classical</td>\n <td>0.937748</td>\n <td>0.020221</td>\n <td>0.037593</td>\n <td>0.004438</td>\n </tr>\n <tr>\n <th>classical_27.mp3</th>\n <td>classical</td>\n <td>0.987477</td>\n <td>0.005166</td>\n <td>0.004478</td>\n <td>0.002879</td>\n </tr>\n <tr>\n <th>classical_28.mp3</th>\n <td>classical</td>\n <td>0.991594</td>\n <td>0.004317</td>\n <td>0.003297</td>\n <td>0.000792</td>\n </tr>\n <tr>\n <th>classical_29.mp3</th>\n <td>classical</td>\n <td>0.934995</td>\n <td>0.056594</td>\n <td>0.005521</td>\n <td>0.002890</td>\n </tr>\n <tr>\n <th>classical_30.mp3</th>\n <td>classical</td>\n <td>0.941419</td>\n <td>0.043659</td>\n <td>0.010996</td>\n <td>0.003926</td>\n </tr>\n <tr>\n <th>classical_35.mp3</th>\n <td>classical</td>\n <td>0.990555</td>\n <td>0.007352</td>\n <td>0.001354</td>\n <td>0.000740</td>\n </tr>\n <tr>\n <th>classical_46.mp3</th>\n <td>classical</td>\n <td>0.994143</td>\n <td>0.004008</td>\n <td>0.001441</td>\n <td>0.000408</td>\n </tr>\n <tr>\n <th>classical_60.mp3</th>\n <td>classical</td>\n <td>0.985542</td>\n <td>0.011265</td>\n <td>0.002424</td>\n <td>0.000769</td>\n </tr>\n <tr>\n <th>classical_66.mp3</th>\n <td>classical</td>\n <td>0.966900</td>\n <td>0.010993</td>\n <td>0.008897</td>\n <td>0.013210</td>\n </tr>\n <tr>\n <th>classical_69.mp3</th>\n <td>classical</td>\n <td>0.846153</td>\n <td>0.026670</td>\n <td>0.096475</td>\n <td>0.030703</td>\n </tr>\n <tr>\n <th>classical_79.mp3</th>\n <td>classical</td>\n <td>0.995726</td>\n <td>0.003362</td>\n <td>0.000490</td>\n <td>0.000422</td>\n </tr>\n <tr>\n <th>classical_83.mp3</th>\n <td>classical</td>\n <td>0.339155</td>\n <td>0.396894</td>\n <td>0.241362</td>\n <td>0.022589</td>\n </tr>\n <tr>\n <th>classical_87.mp3</th>\n <td>classical</td>\n <td>0.979284</td>\n <td>0.005159</td>\n <td>0.013257</td>\n <td>0.002300</td>\n </tr>\n <tr>\n <th>classical_89.mp3</th>\n <td>classical</td>\n <td>0.987688</td>\n <td>0.009909</td>\n <td>0.001083</td>\n <td>0.001320</td>\n </tr>\n <tr>\n <th>classical_9.mp3</th>\n <td>classical</td>\n <td>0.987905</td>\n <td>0.008923</td>\n <td>0.002648</td>\n <td>0.000524</td>\n </tr>\n <tr>\n <th>classical_91.mp3</th>\n <td>classical</td>\n <td>0.955672</td>\n <td>0.032070</td>\n <td>0.006733</td>\n <td>0.005524</td>\n </tr>\n <tr>\n <th>classical_99.mp3</th>\n <td>classical</td>\n <td>0.872370</td>\n <td>0.099102</td>\n <td>0.020143</td>\n <td>0.008385</td>\n </tr>\n <tr>\n <th>electronic_100.mp3</th>\n <td>electronic</td>\n <td>0.002803</td>\n <td>0.984504</td>\n <td>0.010454</td>\n <td>0.002239</td>\n </tr>\n <tr>\n <th>electronic_13.mp3</th>\n <td>electronic</td>\n <td>0.009089</td>\n <td>0.515113</td>\n <td>0.358117</td>\n <td>0.117681</td>\n </tr>\n <tr>\n <th>electronic_18.mp3</th>\n <td>electronic</td>\n <td>0.002440</td>\n <td>0.984207</td>\n <td>0.009388</td>\n <td>0.003965</td>\n </tr>\n <tr>\n <th>electronic_25.mp3</th>\n <td>electronic</td>\n <td>0.023760</td>\n <td>0.961638</td>\n <td>0.008666</td>\n <td>0.005936</td>\n </tr>\n <tr>\n <th>electronic_31.mp3</th>\n <td>electronic</td>\n <td>0.002302</td>\n <td>0.947022</td>\n <td>0.031520</td>\n <td>0.019156</td>\n </tr>\n <tr>\n <th>electronic_32.mp3</th>\n <td>electronic</td>\n <td>0.094542</td>\n <td>0.766320</td>\n <td>0.122180</td>\n <td>0.016958</td>\n </tr>\n <tr>\n <th>electronic_39.mp3</th>\n <td>electronic</td>\n <td>0.017045</td>\n <td>0.759518</td>\n <td>0.190532</td>\n <td>0.032906</td>\n </tr>\n <tr>\n <th>electronic_49.mp3</th>\n <td>electronic</td>\n <td>0.005468</td>\n <td>0.299359</td>\n <td>0.354465</td>\n <td>0.340708</td>\n </tr>\n <tr>\n <th>electronic_50.mp3</th>\n <td>electronic</td>\n <td>0.016311</td>\n <td>0.766572</td>\n <td>0.174612</td>\n <td>0.042505</td>\n </tr>\n <tr>\n <th>electronic_58.mp3</th>\n <td>electronic</td>\n <td>0.003758</td>\n <td>0.831129</td>\n <td>0.138715</td>\n <td>0.026397</td>\n </tr>\n <tr>\n <th>electronic_61.mp3</th>\n <td>electronic</td>\n <td>0.027519</td>\n <td>0.902310</td>\n <td>0.038091</td>\n <td>0.032081</td>\n </tr>\n <tr>\n <th>electronic_65.mp3</th>\n <td>electronic</td>\n <td>0.178326</td>\n <td>0.080514</td>\n <td>0.632677</td>\n <td>0.108484</td>\n </tr>\n <tr>\n <th>electronic_69.mp3</th>\n <td>electronic</td>\n <td>0.006539</td>\n <td>0.969710</td>\n <td>0.017215</td>\n <td>0.006536</td>\n </tr>\n <tr>\n <th>electronic_7.mp3</th>\n <td>electronic</td>\n <td>0.001669</td>\n <td>0.844991</td>\n <td>0.135112</td>\n <td>0.018228</td>\n </tr>\n <tr>\n <th>electronic_70.mp3</th>\n <td>electronic</td>\n <td>0.488814</td>\n <td>0.430486</td>\n <td>0.043033</td>\n <td>0.037666</td>\n </tr>\n <tr>\n <th>electronic_71.mp3</th>\n <td>electronic</td>\n <td>0.001112</td>\n <td>0.981605</td>\n <td>0.009047</td>\n <td>0.008236</td>\n </tr>\n <tr>\n <th>electronic_72.mp3</th>\n <td>electronic</td>\n <td>0.001482</td>\n <td>0.772798</td>\n <td>0.068921</td>\n <td>0.156799</td>\n </tr>\n <tr>\n <th>electronic_74.mp3</th>\n <td>electronic</td>\n <td>0.053454</td>\n <td>0.651853</td>\n <td>0.224057</td>\n <td>0.070636</td>\n </tr>\n <tr>\n <th>electronic_77.mp3</th>\n <td>electronic</td>\n <td>0.004201</td>\n <td>0.959526</td>\n <td>0.030937</td>\n <td>0.005336</td>\n </tr>\n <tr>\n <th>electronic_80.mp3</th>\n <td>electronic</td>\n <td>0.004195</td>\n <td>0.787130</td>\n <td>0.031932</td>\n <td>0.176743</td>\n </tr>\n <tr>\n <th>electronic_88.mp3</th>\n <td>electronic</td>\n <td>0.003710</td>\n <td>0.910954</td>\n <td>0.078526</td>\n <td>0.006810</td>\n </tr>\n <tr>\n <th>electronic_90.mp3</th>\n <td>electronic</td>\n <td>0.073273</td>\n <td>0.719169</td>\n <td>0.127858</td>\n <td>0.079700</td>\n </tr>\n <tr>\n <th>pop_12.mp3</th>\n <td>pop</td>\n <td>0.000399</td>\n <td>0.002274</td>\n <td>0.198433</td>\n <td>0.798894</td>\n </tr>\n <tr>\n <th>pop_15.mp3</th>\n <td>pop</td>\n <td>0.000688</td>\n <td>0.002268</td>\n <td>0.322026</td>\n <td>0.675018</td>\n </tr>\n <tr>\n <th>pop_16.mp3</th>\n <td>pop</td>\n <td>0.059263</td>\n <td>0.503587</td>\n <td>0.296528</td>\n <td>0.140622</td>\n </tr>\n <tr>\n <th>pop_19.mp3</th>\n <td>pop</td>\n <td>0.085615</td>\n <td>0.859067</td>\n <td>0.025328</td>\n <td>0.029990</td>\n </tr>\n <tr>\n <th>pop_23.mp3</th>\n <td>pop</td>\n <td>0.002328</td>\n <td>0.331736</td>\n <td>0.554105</td>\n <td>0.111831</td>\n </tr>\n <tr>\n <th>pop_35.mp3</th>\n <td>pop</td>\n <td>0.001280</td>\n <td>0.003146</td>\n <td>0.209664</td>\n <td>0.785910</td>\n </tr>\n <tr>\n <th>pop_37.mp3</th>\n <td>pop</td>\n <td>0.000415</td>\n <td>0.001971</td>\n <td>0.196326</td>\n <td>0.801288</td>\n </tr>\n <tr>\n <th>pop_39.mp3</th>\n <td>pop</td>\n <td>0.001335</td>\n <td>0.711550</td>\n <td>0.200485</td>\n <td>0.086630</td>\n </tr>\n <tr>\n <th>pop_4.mp3</th>\n <td>pop</td>\n <td>0.011750</td>\n <td>0.147964</td>\n <td>0.540080</td>\n <td>0.300206</td>\n </tr>\n <tr>\n <th>pop_47.mp3</th>\n <td>pop</td>\n <td>0.004379</td>\n <td>0.495875</td>\n <td>0.399103</td>\n <td>0.100643</td>\n </tr>\n <tr>\n <th>pop_50.mp3</th>\n <td>pop</td>\n <td>0.000827</td>\n <td>0.002277</td>\n <td>0.723224</td>\n <td>0.273672</td>\n </tr>\n <tr>\n <th>pop_59.mp3</th>\n <td>pop</td>\n <td>0.001025</td>\n <td>0.005752</td>\n <td>0.788940</td>\n <td>0.204283</td>\n </tr>\n <tr>\n <th>pop_68.mp3</th>\n <td>pop</td>\n <td>0.000767</td>\n <td>0.002830</td>\n <td>0.283495</td>\n <td>0.712908</td>\n </tr>\n <tr>\n <th>pop_73.mp3</th>\n <td>pop</td>\n <td>0.001074</td>\n <td>0.002167</td>\n <td>0.753129</td>\n <td>0.243629</td>\n </tr>\n <tr>\n <th>pop_76.mp3</th>\n <td>pop</td>\n <td>0.001763</td>\n <td>0.021351</td>\n <td>0.526564</td>\n <td>0.450322</td>\n </tr>\n <tr>\n <th>pop_77.mp3</th>\n <td>pop</td>\n <td>0.014080</td>\n <td>0.108893</td>\n <td>0.519505</td>\n <td>0.357522</td>\n </tr>\n <tr>\n <th>pop_80.mp3</th>\n <td>pop</td>\n <td>0.005269</td>\n <td>0.148692</td>\n <td>0.586638</td>\n <td>0.259400</td>\n </tr>\n <tr>\n <th>pop_87.mp3</th>\n <td>pop</td>\n <td>0.000806</td>\n <td>0.008741</td>\n <td>0.227895</td>\n <td>0.762557</td>\n </tr>\n <tr>\n <th>rock_1.mp3</th>\n <td>rock</td>\n <td>0.000498</td>\n <td>0.007474</td>\n <td>0.273741</td>\n <td>0.718287</td>\n </tr>\n <tr>\n <th>rock_13.mp3</th>\n <td>rock</td>\n <td>0.240288</td>\n <td>0.033601</td>\n <td>0.657906</td>\n <td>0.068205</td>\n </tr>\n <tr>\n <th>rock_22.mp3</th>\n <td>rock</td>\n <td>0.000315</td>\n <td>0.004363</td>\n <td>0.088815</td>\n <td>0.906506</td>\n </tr>\n <tr>\n <th>rock_24.mp3</th>\n <td>rock</td>\n <td>0.000537</td>\n <td>0.004526</td>\n <td>0.182356</td>\n <td>0.812582</td>\n </tr>\n <tr>\n <th>rock_27.mp3</th>\n <td>rock</td>\n <td>0.001235</td>\n <td>0.030715</td>\n <td>0.568293</td>\n <td>0.399758</td>\n </tr>\n <tr>\n <th>rock_35.mp3</th>\n <td>rock</td>\n <td>0.011880</td>\n <td>0.112458</td>\n <td>0.336041</td>\n <td>0.539620</td>\n </tr>\n <tr>\n <th>rock_36.mp3</th>\n <td>rock</td>\n <td>0.001290</td>\n <td>0.102655</td>\n <td>0.428641</td>\n <td>0.467414</td>\n </tr>\n <tr>\n <th>rock_41.mp3</th>\n <td>rock</td>\n <td>0.002773</td>\n <td>0.609287</td>\n <td>0.264932</td>\n <td>0.123007</td>\n </tr>\n <tr>\n <th>rock_51.mp3</th>\n <td>rock</td>\n <td>0.000489</td>\n <td>0.022371</td>\n <td>0.239999</td>\n <td>0.737141</td>\n </tr>\n <tr>\n <th>rock_60.mp3</th>\n <td>rock</td>\n <td>0.001606</td>\n <td>0.959144</td>\n <td>0.029477</td>\n <td>0.009774</td>\n </tr>\n <tr>\n <th>rock_61.mp3</th>\n <td>rock</td>\n <td>0.010072</td>\n <td>0.924942</td>\n <td>0.037895</td>\n <td>0.027092</td>\n </tr>\n <tr>\n <th>rock_62.mp3</th>\n <td>rock</td>\n <td>0.000158</td>\n <td>0.002162</td>\n <td>0.326680</td>\n <td>0.671000</td>\n </tr>\n <tr>\n <th>rock_66.mp3</th>\n <td>rock</td>\n <td>0.001493</td>\n <td>0.004598</td>\n <td>0.281867</td>\n <td>0.712041</td>\n </tr>\n <tr>\n <th>rock_69.mp3</th>\n <td>rock</td>\n <td>0.007581</td>\n <td>0.062220</td>\n <td>0.274924</td>\n <td>0.655275</td>\n </tr>\n <tr>\n <th>rock_7.mp3</th>\n <td>rock</td>\n <td>0.000154</td>\n <td>0.001654</td>\n <td>0.289709</td>\n <td>0.708482</td>\n </tr>\n <tr>\n <th>rock_72.mp3</th>\n <td>rock</td>\n <td>0.003920</td>\n <td>0.006517</td>\n <td>0.169623</td>\n <td>0.819940</td>\n </tr>\n <tr>\n <th>rock_73.mp3</th>\n <td>rock</td>\n <td>0.011498</td>\n <td>0.160793</td>\n <td>0.455101</td>\n <td>0.372607</td>\n </tr>\n <tr>\n <th>rock_77.mp3</th>\n <td>rock</td>\n <td>0.005786</td>\n <td>0.027944</td>\n <td>0.383715</td>\n <td>0.582555</td>\n </tr>\n <tr>\n <th>rock_79.mp3</th>\n <td>rock</td>\n <td>0.020186</td>\n <td>0.179854</td>\n <td>0.312571</td>\n <td>0.487390</td>\n </tr>\n <tr>\n <th>rock_8.mp3</th>\n <td>rock</td>\n <td>0.030377</td>\n <td>0.050505</td>\n <td>0.140137</td>\n <td>0.778982</td>\n </tr>\n <tr>\n <th>rock_91.mp3</th>\n <td>rock</td>\n <td>0.000543</td>\n <td>0.002976</td>\n <td>0.185036</td>\n <td>0.811445</td>\n </tr>\n </tbody>\n</table>\n</div>"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Fit the entire training sets\n",
"\n",
@@ -823,64 +566,21 @@
},
{
"cell_type": "code",
- "outputs": [
- {
- "data": {
- "text/plain": "[Text(0.5, 144.1333333333333, 'Prediction'),\n Text(307.3333333333333, 0.5, 'Actual')]"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "<Figure size 3840x2880 with 2 Axes>",
- "image/png": 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y1s233BmzZs2ucz0/Pz++c96ZjXIWAECuat95qzjjirNrXfvs4/nx7xvuy3AiAIDs6LDrdinXl82cFwtfm9GoZy546b1Y/uFnKfd0GNSnUc8EAEinvPwWcfQVp6fc8/Dlt8dn07yxCgBERFR7NNkHDVdSUhLV1XX/LiYSiRg1alQGE20ZlixZEs8++2zKPbvvvnuG0pBNdf90JmzhLrjggmxHACBH5eXlxUknHZdyzx//eGvK/9DZFKtXr44//fn2+MPvr6pzz8knj4xLf/IrxUUAoNn61pXnRNut2ta69tfLb46K8ooMJwIAyI7i7bqlXP/8mXfScu7nz7wdbftsXed6fbkAAHLJnicdEl2271Hn+rQnJ8XbD6d+924AAJqvZDIZJSUlKffst99+0bNnzwwl2nLcddddsWrVqpR7Bg8enKE0ZJOiC9RB0QWAunz10AOjR4+6/1J/1apVcc+9Yxv1zLv++UBcd+3lUVRUVOv6Ntt0j0MOPiD++8yLjXouAEAu2Gvo3jF4+JBa155/+Ll487k3MpwIACB7Ctq3Sbm+NE3vOr6knvsWday9lAwAkGsSiUQc+M1hda6vrlodT1xnejAAwJbslVdeiblz56bcM3r06Ayl2XLMmTMn/v73v6fcU1hYGIcddliGEpFNedkOAADQ1BxzzOEp1x997OkoLV3RqGcuXbosHn/ivyn3HFtPLgCApqhlm1Zx7tXn17pWurQ0br/y1gwnAgDIrhaFqd/HrnzR8rScW74w9X1btCxIy7kAAI2t/+F7Ruc+3etcf+/x1+LLWfMymAgAgFwzZsyYlOvt27ePww/3s1qNqby8PC6++OIoKytLue+YY46J4uLiDKUim0x0AQDYRIcdVvu7idd49NGJaTn30UefiuNGHFXn+mFDU+cCAGiKvnHpGdG5R5da1+7+9Z2xZMGSzAYCAMiyyuWrUq5XrSxPy7mr67lvZWnqXAAAuWKPrx2Ucv21e57KUBIAAHLRsmXL4sknn0y5Z/jw4VFUVJShRHXr169fyvXjjz8+rrvuugbff9KkSbHXXns1+Pkbq7y8PC688MJ49913U+5LJBLx7W9/O+15yA0mugAAbIKtt+4aO++0Y8o9Tz39QlrOnvjUcynXdxnQP7p1q/2HQAEAmqJ+e/aPI79+dK1r0ydNiyfufjzDiQAAsq98cWnK9aIO6Xk3w8J67luxKHUuAIBc0LJd69jhoEF1ri/7fHF89PJ7GUwEAECuGT9+fJSXp37Tl9GjR2coTXadd955ccYZZ8TLL7+ctjNmz54dJ554Yjz77LP17j3xxBNj++23T1sWcouiCwDAJth7791Srn/yydz49NP0jDL/+ONPY968z1Lu2Xuv3dJyNgBApuUX5Md3r78w8vI2/PZVVWVV3HzZn7OQCgAg+5bN+DTlesuuW6Xl3JZd26dcL/3ki7ScCwDQmHY+cu/ILyqoc/39p97IYBoAAHLR2LFjU64PGDAgdtpppwylyb5XXnklzjzzzDjuuOPinnvuiSVLljTKfcvKyuKf//xnjBo1KqZPn17v/q5du8YPfvCDRjmbpkHRBQBgE+yx+64p19948520nj95ytsp13fbbUBazwcAyJTRF54YPXf4Sq1rD9/2YHzy/scZTgQAkBu+fPX9lOud9+2XlnO77Nc/5fqXr81Iy7kAAI1p+8G7pFz/8OWpGUoCAEAumj59ekydmvprwhNOOCFDaXLL9OnT46qrrorBgwfHGWecEf/4xz/i7bffjsrKyo2+R3V1dUyfPj1uuummOOSQQ+Lqq6+OFStW1Pu8goKCuPHGG6N9+9RvxkPzkp/tAAAATcmgQamLJO+8My2t57/zzrQYfuwRda7vtlvqb84DADQF2+7QM0adX/u4788/+Tzu/8N9GU4EAJA7vnhhaqxeVREtWhXWut71wAGRV5gf1RVVjXZmXsuC6Hpg3d8XS66ujgUvvtdo5wEApEvv/XZOuf7pGzMzlAQAmpbqbAeADBkzZkzK9aKiohg+fHiG0uSmysrKeOWVV+KVV16JiIjCwsLYbrvtonfv3tGtW7fo3LlztG7dOgoLC6OsrCyWLl0aS5cujblz58bkyZNj2bJlm3ReixYt4te//nXsvvvu6Xg55DBFFwCATbDDDn1Srn8w86O0nj9z5uyU63379k7r+QAA6ZZIJOK7v74wCooKal3/2xU3R0VZeYZTAQDkjsqlK+Pjkhejz2mH1rpeuFWb2P6MofHBrY832pk7nH1kFLRrXef6vCenxKr5ixrtPACAdOjYq1u069ahzvVVS1fE4k8X1HufvBZ50Wm7raNDz67Rsl2raFFYEJWryqN8RVksnbcwlny6ICpW+v4VAEBTU1FREePHj0+554gjjoh27dplKFHTUFFRETNmzIgZMxp/4nNRUVFcd911MWzYsEa/N7lP0QUAYBNs12vblOuz6imibK5Zs1IXabbr1TOt5wMApNvRZxwT/ffaqda1F8Y/H1OemZzhRAAAuWfGLROi1+jB0aKOcnD/742IOeNfjbLPFm/2Wa237Rz9L0j9LpUz/vrYZp8DAJBu3XfulXJ94ezP6lxr3aFtDBp5YPQ/bI/otXe/yK/j67CIiOrq6lgwc158Mun9mPafyTHrxXdideXqBucGACAzJk6cGEuWLEm5Z/To0ZkJQ/To0SNuvPHG2HXXXbMdhSxRdAEA2EjdunWJVq1apdwzb37d3wBvDPPmf55yvbi4TXTp0ikWLFiY1hwAAOnQqXvnOO3/fb3WtRVLS+P2K2/NcCIAgNy0fOb8eO8P42LXS0+sdb1l5/Yx+K4fxjPH/zKqVpQ1+JyCrdrEkHt+FIUdiuvc89G/nokvX5ne4DMAADKlW7/Ubxi36JMvNrjWplO7+Or3R8fuJwyJwlZFG3VOXl5edNtx2+i247ax96mHRemXS+PVfz4ZL//jiShbtqJB2QEASL8xY8akXO/Zs2fsu+++GUqz5WrRokWceuqpcfHFF0dxcd3fl6T5y8t2AACApqJH92717vnss/rHmW+Ozz7b8Bvs6+vRY+u0ZgAASJdzf3V+tG7buta1u6//Zyz+YvPfkRwAoLmYftPD8dkzb9e53mHX7eKwx38Z7Xf+SoPu33H37WPo41dHux23qXNP6UefxZs/v7tB9wcAyLQufev+uiYiovTLpet8vueJh8TFT/8u9j196EaXXGpT3Ll9HPb90fH9//4u9jr50AbfBwCA9Jk3b168/PLLKfeccMIJkUgkMpQoNxxwwAEZK5sUFBTEyJEjY8KECXHFFVcouWCiC6RTMpmMBQsWxBdffBGLFi2K8vLyKC8vj4iIoqKiKCoqik6dOkWXLl2iS5cuW9wfgABNTadOHVKuL126LCoqKtKaYdWqsli+vDTatq37C/lOHVPnBADIRYOHD4m9h+5T69r7k6fHE3c/luFEAAA5rjoZL571+xhyz4+i6wE717qlXd8ecdijV8UnJS/GB7c9EUvf+6Te23YY1Cd2OOeo6Dl838grqPuvElfOXRjPnnhtVC1f1eCXAACQSe17dEq5vnLhsoiIyMtvEcf96uzY86TGLaW06dQuRl737djh4EFR8v/+GuWlvo4CAMgVJSUlUV1dXed6Xl5eHH/88RlMlBtuvPHGWL16dUydOjVeeeWVmDJlSrz33nvx+eefN8r9CwoKYvfdd48jjjgijjnmmOjYsWOj3JfmQdEFGtFnn30WL774YkyaNCnef//9mDVr1kb/wHNhYWFsv/320a9fv9hnn33igAMOiG7d6p8cAEDmdKinQLJsWWlGcixbtjxl0aVDx60ykgMAoLEUty+Ob/7i27WuVVVWxc2X/TmSyWSGUwEA5L7qssp4/tTrY9DPT4u+Zx1e654WRQXR+5RDovcph8Sq+Yviy9dnROlHn0XFkhVRtbI88tu0jMKt2kTb7btH5713jJZdt6r33MVvfxQvf/uPsfLTLxv5FQEApE/ber7OKStdFXkt8uLEGy+IXYbtm7YcA47eJzr07BJ3fOO6WLloedrOAQBg411wwQVxwQUXZDvGJnv//ffTfkaLFi1i4MCBMXDgwDXXFi9eHNOmTYuZM2fG/PnzY/78+fHZZ5/FwoULY9WqVbFq1aooKyuLZDIZhYWFUVRUFB06dIiuXbvGNttsEzvssEP0798/dtttt2jdunXaXwNNk6ILbKZly5bFQw89FOPGjYtp06atub6pP4BTXl4e7733XkybNi0efPDBiIgYMGBAHH/88TFixIho27ZtY8YGoAE6bNU+5fry0swUXZaXrki53rHDVhnJAQDQWM762bdiqy61l4rH//2h+Hj67MwGAgBoQqrLK+ONn9wR8ye+EbtefnJstfNX6tzbqnvH6Dlivwaftbq8Mmb+/Yl457p/R7JydYPvAwCQDcVdUv9d3+rKqhj+y7PTWnKp0WOX3nH2vZfHraOvNNkFAIAmp0OHDnHAAQfEAQcckO0oNGOKLtBAS5Ysidtuuy3+9a9/xcqVK2sttiQSiU26ZzKZXOc+7777bkydOjX+8Ic/xGmnnRbf/OY3o127dpudHYCGadmyKOX6ihUrM5KjtJ6iS305AQByycDBg+KrXzus1rUv5nwe9/3+XxlOBADQNH329Fvx2dNvRY+j94reJx8c3YbsEi1aFTbKvSuXrYxPxr0U0258KFbNW9Qo9wQAyKT8ooIoKEr9tdGux+wXfQ4YUOd6xary+PDFqTHtyUkx793ZUfrl0li5aFkUtW0dbbtsFZ37dI/+Q/eIHQ/dPdp0rP/NTLfu/5U48aYL459nXb/JrwcAAKC5U3SBBigpKYnf/OY3sWTJknWKKbUVWzZ2sksikdjg+TXFl9LS0vjb3/4WDzzwQPz4xz+O4447bvNeAAANUlhYkHJ9dVVm3sWyvnPqywkAkCsKWxbF+dd+t871v/30lqgoK89gIgCApm/eY5Ni+Qdz4yujDox+5x2zWWWX6oqqmP6XR2LaHx+M6rLKRkwJAJBZ+fWUXCKizpJLdXV1vDXuhXjiun9F6YKlG6yvXLQ8Vi5aHp+/PyemPvZa5BcVxEHnj4jB5x4bha1Sv0Fdv0N3i/3OPDJeueOJjXshAJAlyU17z28A2GyKLrAJVq5cGZdddln85z//WVNgWbucsrGlltrUNxEmmUzGokWL4tJLL43nnnsufvWrX0XLli0bfB4Am66wMPU3wKuqqjKSo75z6ssJAJArTvnBabF1r+61rr004YWY/PSkDCcCAGi6Ei3y4iujDoh+3zk22vfv2Sj3zCvMj50vHhl9Tj0k5j4+OWb87bEonTW/Ue4NAJBJ+UUNe6O4ipVlce95N8TM597e6OdUlVfG0zeMjbcefDHO/Oel0aFn15T7j/jRSTH10Vdj+RdLGpQRAACgOcrLdgBoKhYuXBgnn3zympLL2hNYaiavrK9mz8Y+1rf2fWv2JJPJePTRR+OUU06JRYsWpfdFA7COvLzUb0+xenWGJrrUc06LFr7EAwByX59dt4/h3xxR69qKZSvitp//LcOJAACarq0P2y2Oful3sc+N5zdayWVtLbtuFdt/47A46tnrY7+/fS/a9Er9w5oAALmmRUGLTX5O2fKVccfXr9ukksvaFs7+LG792lWxYNa8lPsKW7eMQy8a1aAzAAAAmis/BQkbYeHChfGNb3wjZsyYsabkElF7waW24krNvroeqZ67/jk1ZZdp06bFGWecoewCkEFVVakLJvn5mRmWV985lZWZmSwDANBQeS3y4ru/vjBa5Nf+Awb3XH9XLP5icYZTAQA0PXktC2L3a8+MIXf/v2jzlfSXTxIt8qLn8H3j8InXxHYnH5z28wAAGkv16upNfs6EX9wZn0yesVnnLvtsUdz33T9GVXllyn27jz4oWndou1lnAQAANCeZ+WlMaMIqKyvjwgsvjFmzZq1TcFlbbcWUmuvbbrtt9OrVK9q2bRtt27aN4uLiiIgoLS2N5cuXx/Lly+OTTz6JOXPmbDC9Zf37rV92+eCDD+Kiiy6KO+64I1q02PR3HwFg01RUVKRcz1TRpaAg9TkVFam/UQ4AkG0jzx0VfXbZvta1GW+8H4//87EMJwIAaHryWhbE4Lt+GN2G7FLv3uqq1fHFi+/Fl69Mjy9fmxGr5i+M8sWlUbV8VRS0ax2FW7WJVj06Red9dowu+/aProMHRCLF1OCC4lax9x/OiQ4De8cbP7mjEV8VAEB6rN7EN4qb9uSkeGPs841y9ufT58R/byyJw//fSXXuKSgqjD2+dnC88LdHGuVMAACApk7RBerx29/+NqZMmVJvyaXmeu/evWPEiBGx1157xU477bSm2FKf0tLSmD59ekyZMiUefvjhmDlz5pr71xRbatRMlUkmkzFp0qT47W9/Gz/+8Y83+7UCkFp9k1IKCgsykiO/IPU59RVyAACyaete3ePEi0+uda2qsipuvuzPG/y3NwAA60oUtIjBd/yg3pJLdUVVfHj30zHjr4/Gik8W1LqnYnFpVCwujdKPPo8FL74X0yKiTa+useO5w6LP6YdGXoo3Xel71uERyWS8cfmdm/NyAADSblOLLk/+5t+Nev6Ltz0aB3xzWLTpWPfUlgFH763oAgAA8P+r+62YgHj77bfjn//8Z60ll/ULKCNGjIh///vf8dhjj8X5558fe++990aXXCIiiouLY6+99opzzjknHnnkkRg7dmwcf/zx65y3vprz77rrrnj33Xcb+jIB2EilpStSrhcXt8lIjrb1nFNfTgCAbPrOdd+NopZFta49cvv4mP3eRxlOBADQ9Az4f6Oj28G7ptyzYs6C+O/Iq+KNy++ss+RS53M//iLe+Mkd8cyoq2Pl3C9T7u179hHR5xuHbdL9AQAyrXLVxr9R3OxXp8UXMz5t1POryitjygPPptyzzcDto3WHuoswAAAAWxJFF0jh+uuvj+rq6ojYsORSc23nnXeOe+65J66//voYOHBgo509YMCAuPbaa+P++++PAQMGrJniUmPtPKtXr45f//rXjXY2ALVbtHhJyvV2bTe+4Lg52rVL/Q3u+nICAGTLYScdHrseOKjWtS/mfB73/f6eDCcCAGh6Ou21Q/T/zrEp9yyfNT8mHnVFLHpj1madtXDSB/HkkVdE6Uefpdw36GenRpteXTfrLACAdKquWh1ly1du1N4pY55LS4b6ii55LfJi20Hbp+VsAACApkbRBeowadKkmDRp0jpTWyL+N0UlmUzGGWecEWPGjIk99tgjbTkGDhwYDzzwQJx99tm1ll1qPp80aVJMnjw5bTkAiFi0cHHK9a22apeRHO3b11N0qScnAEA2tO+8VZxx+Vl1rt/6s79G+aryDCYCAGiadr385Ei0qPuv+MoXLY8Xvv6bqFhU2ijnVSxcHs9//bdRsaTuKcL5bVrGwJ+d2ijnAQCky6olG/f10SeTZ6Tl/AUz58aqpXV/TRUR0WOX7dJyNgAAQFOj6AJ1+Pe//73BtZqSSyKRiAsuuCAuu+yyyMtL/z9GiUQifvSjH8Ull1yyQdllbbVlBqDxfLlwUcr1li1bRvv26S27dOiwVRQVFaXcs3CRogsAkHvO+eW50Xar2gu7Lz36Ykx66vUMJwIAaHo6DOoTXfbrn3LPe78ridKPPm/Uc0tnzY/3/lCScs82R+5pqgsAkNNWLFpe756VS0rjyw/npy3Dp2+lnrjXsVe3tJ0NAJuj2qPJPgCaKkUXqEV5eXlMnDhxnULJ2iWX4cOHxwUXXJDxXOecc04cf/zxG5RdarJNnDgxKioqMp4LYEvxySdz693TrVuXtGbo1q1zvXs2JicAQCbtffg+ccAxg2tdW7FsRdz2879lOBEAQNPU+5SDU66vnPtlfHj302k5e9YdE2Pl3IV1rida5EWfrx+WlrMBABrD0nl1fy1TY8HM9P492xcffJpyvX33Tmk9HwAAoKlQdIFavPHGG7Fy5cqIiA1KJZ06dYqf//zn2YoWV1xxRXTu/H8/5FxTcKmxcuXKmDx5craiATR7K1asjC+/TD3VpddXtklrhu169Uy5/vnnC2LlylVpzQAAsKnO+uk361y797d3x+LPU3+NBQDA/+ly4M4p1+c89EpUV1Sl5ezqiqqYM/7VlHu6DRmQlrMBABrD4jlf1LunbNnKtGYoW7oi5Xqrrdqk9XwAAICmIj/bASAXTZo0aYNrNYWXb3/729GmTfa+sdCmTZs455xz4pprrlmngFNjypQpsf/++2chGcCW4aPZn0Tnzh3rXO/bt3c8OfG5tJ2//fbbpVyfPXtO2s4GAGiodh3a1Xp9xbIVUVlRGUNPPqLRzuqzy/Yp17v37l7veVNfeSfmz57faJkAABpDUad20a5vj5R7Pnv2nbRm+PzZt6PfecPqXG+/81civ7hVVJV6IxYAIPcs+iT7RZdV9dy/oFVRWs8HAABoKhRdoBYzZsxY8/HaZZL8/Pw44YQTshFpHaNGjYrf/OY3UVVVtUHZ5f33389SKoAtw3vvzYi999qtzvUdd0z9g5Wbq777T33PnwMAQNPRpl2b+M51F2T0zJ322jl22iv1O6HfeMkNii4AQM5p85Uu9e5Z9MastGZYNCX1/fPyW0Rxn26x5O3Zac0BANAQX8z4tN49lWUVac1QVc/981rkpfV8AACApsJ/HUEtPv103W9u1Exz2X333aO4uDhLqf6nuLg49thjj0gmk+tcTyaTMWeOd/IHSKc33kj9rpi777ZLWs/fY/ddU66/+ea7aT0fAAAAgOwo7JD67ydWl1dG1fL0TlKpXLYyqiuqUu4p6tA2rRkAABpq3tTZUb26OuWelm1bpzVDUT33T3fRBgAAoKlQdIFafPHFFxtMSomI2Hnn1O/4mknrZ6nJ+8UX9Y/aBaDh6iu6DBo0IPLy0vMlVosWLWLgwNR/Fim6AAAAADRPhVu1Sblesbg0IznKFy9PuV5fIQcAIFsqVpTFlx+lnuLbsl16iy6t2tfzNd3K8rSeDwAA0FQoukAtVq5cWev1rbfeOsNJ6lZXlrqyA9A4Jk1+O1atqvudMdu2LY499xiYlrP32Xv3aNOm7m+ur1q1KiZPSV3EAQAAAKBpStbz7uN5hfkZydGiqDD1hvWm0QMA5JJPXn8/5Xqbzu3Sen5xp9T3X/bZorSeDwAA0FQoukAtystrf4eM1q3T+84dm6JVq1a1Xq+oMMYWIJ3Ky8vjpZcmpdwzdOhBaTn7sMMGp1x/4YXX6vwzDAAAAICmraqed/cu3KpNRN6G0+obUyK/Rb2TZapW+XsKACB3ffDc2ynXu/bdJgpa1lPs3Qw9BvZJub5k7pdpOxsANke1R5N9ADRVii5Qi8LC2r9pUVZWluEkdasrS13ZAWg8E596LuX6yJFHp+XcUaOOSbn+5MRn03IuAAAAANlX9sXSlOuJvLxo1b1jWjNszP3LF6TOCQCQTbNefDdWV62uc71FQX5sU08ZpaEKWhZGt349U+75bNonaTkbAACgqcnMDHNoYlq1alVrkeTzzz/PQpraLViwoNbrdU16AaDxjC2ZENdec3md63vuMTB23HH7mDFjVqOdOWBAvxi46851rldXV8fYkgmNdh4AQGM6fddTMnbWSd8/JU7+/ql1rj/9wFNx0w9uyFgeAIDGsmLOF/Xu6XrggPj436nfpGVzdBsyoN49Kz6p/e8vAAByQdmylTHz+Xei36G71bmn75BdY/Zr0xv97D4H7hIt8luk3PPpmzMb/VwAAICmyEQXqEWnTp0imUxucP3999/PQprarZ+lJm+nTp2yEQdgi/Lhhx/HK69MTrnnu985q1HPvOC7Z6dcf/nlSfHxx5826pkAAAAA5I6KRaWxcu7ClHu2PnRgWjNs/dVBKddXfb44yhcuS2sGAIDN9cbY1MXgPU86JPLqKaQ0xD6nD025vnjOF/Hlh/Mb/VwAAICmSNEFatGz57qjYhOJRCSTyZg8eXJUVFRkKdX/VFRUxOuvvx6JRGKd64lEYoPsAKTHP+6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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"conf_matrix = pd.DataFrame(confusion_matrix(subm['label'], subm['pred1']), columns=genres, index=genres)\n",
"\n",
- "plt.figure(dpi=600)\n",
+ "plt.figure(dpi=200)\n",
"display(sns.heatmap(conf_matrix, annot=True).set( xlabel=\"Prediction\", ylabel=\"Actual\"))\n"
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:03:45.906250089Z",
- "start_time": "2024-02-15T21:03:44.557085792Z"
- }
+ "collapsed": false
},
- "execution_count": 225
+ "execution_count": null
},
{
"cell_type": "code",
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy for top 2 predictions: 0.9125\n"
- ]
- },
- {
- "data": {
- "text/plain": "[Text(0.5, 23.52222222222222, 'Prediction'),\n Text(50.722222222222214, 0.5, 'Actual')]"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 2 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"subm_top_2 = subm.copy()\n",
"subm_top_2[\"top_2\"] = subm.apply(lambda row: row.get(\"pred2\") if row.get(\"label\") == row.get(\"pred2\") else row.get(\"pred1\"), axis=1)\n",
@@ -892,44 +592,20 @@
"display(sns.heatmap(conf_matrix_top_2, annot=True).set( xlabel=\"Prediction\", ylabel=\"Actual\"))\n"
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T21:03:53.275357129Z",
- "start_time": "2024-02-15T21:03:52.681904599Z"
- }
+ "collapsed": false
},
- "execution_count": 226
+ "execution_count": null
},
{
"cell_type": "code",
- "outputs": [
- {
- "data": {
- "text/plain": "[Text(0.5, 1.0, 'Correlation heatmap of prediction probabilities')]"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "<Figure size 640x480 with 2 Axes>",
- "image/png": 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"
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"display(sns.heatmap(proba_df.corr(numeric_only=True), vmin=-1, vmax=1, annot=True).set(title=\"Correlation heatmap of prediction probabilities\"))"
],
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T20:27:10.163394639Z",
- "start_time": "2024-02-15T20:27:09.325436118Z"
- }
+ "collapsed": false
},
- "execution_count": 183
+ "execution_count": null
},
{
"cell_type": "markdown",
@@ -939,9 +615,7 @@
"The confusion matrix shows the true labels on the y-axis, the predicted values on the x-axis.\n",
"Classical music was predicted well, with 1 wrong classification for electronic. \n",
"The most misclassifications has pop, with a true positive rate of 44.44%, due to wrong classifications towards electronic (4) and rock (6).\n",
- "A high correlation between rock and pop can also be seen in the correlation plot between prediction probabilities.\n",
- "\n",
- "The resulting accuracy score of 68.75% shows "
+ "A high correlation between rock and pop can also be seen in the correlation plot between prediction probabilities.\n"
],
"metadata": {
"collapsed": false
@@ -949,13 +623,9 @@
},
{
"cell_type": "code",
- "execution_count": 143,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T19:02:53.766796976Z",
- "start_time": "2024-02-15T19:02:53.720058687Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -966,13 +636,9 @@
},
{
"cell_type": "code",
- "execution_count": 144,
+ "execution_count": null,
"metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-02-15T19:02:55.357022703Z",
- "start_time": "2024-02-15T19:02:55.341344888Z"
- }
+ "collapsed": false
},
"outputs": [],
"source": [
@@ -981,9 +647,10 @@
" metadata = yaml.safe_load(file)\n",
"\n",
" nb_config_ml = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " nb_location=NB_LOCATION,\n",
+ " main_location=NB_LOCATION,\n",
" entities=[\n",
- " ml_model_entity := InvenioEntity.new(\n",
+ " ml_model_entity := InvenioRDMEntity.new(\n",
" name=\"Standalone Machine Learning model\",\n",
" description=\"An ml model representing the trained clf\",\n",
" location=LOCAL_PATH / \"clf.pickle\",\n",
@@ -992,7 +659,7 @@
" record_metadata=metadata,\n",
" type=\"clf\"\n",
" ),\n",
- " test_result_entity := DbRepoEntity.new(\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",
diff --git a/poetry.lock b/poetry.lock
index 6ff5611781b3ffcc5dfa9d167584dcfb7bf5e5ac..6b4ab90d63bb7255e665569a32f547f3d929da00 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -123,13 +123,13 @@ files = [
[[package]]
name = "anyio"
-version = "4.2.0"
+version = "4.3.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
files = [
- {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
- {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
+ {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+ {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
]
[package.dependencies]
@@ -2149,13 +2149,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
[[package]]
name = "nbconvert"
-version = "7.16.0"
-description = "Converting Jupyter Notebooks"
+version = "7.16.1"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script. nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
optional = false
python-versions = ">=3.8"
files = [
- {file = "nbconvert-7.16.0-py3-none-any.whl", hash = "sha256:ad3dc865ea6e2768d31b7eb6c7ab3be014927216a5ece3ef276748dd809054c7"},
- {file = "nbconvert-7.16.0.tar.gz", hash = "sha256:813e6553796362489ae572e39ba1bff978536192fb518e10826b0e8cadf03ec8"},
+ {file = "nbconvert-7.16.1-py3-none-any.whl", hash = "sha256:3188727dffadfdc9c6a1c7250729063d7bc78b355ad7aa023138afa030d1cd07"},
+ {file = "nbconvert-7.16.1.tar.gz", hash = "sha256:e79e6a074f49ba3ed29428ed86487bf51509d9aab613bd8522ac08f6d28fd7fd"},
]
[package.dependencies]
@@ -2917,19 +2917,23 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pydantic-settings"
-version = "2.1.0"
+version = "2.2.0"
description = "Settings management using Pydantic"
optional = false
python-versions = ">=3.8"
files = [
- {file = "pydantic_settings-2.1.0-py3-none-any.whl", hash = "sha256:7621c0cb5d90d1140d2f0ef557bdf03573aac7035948109adf2574770b77605a"},
- {file = "pydantic_settings-2.1.0.tar.gz", hash = "sha256:26b1492e0a24755626ac5e6d715e9077ab7ad4fb5f19a8b7ed7011d52f36141c"},
+ {file = "pydantic_settings-2.2.0-py3-none-any.whl", hash = "sha256:5f7bcaf9ad4419559dc5ac155c0324a9aeb2547c60471ee7c7d026f467a6b515"},
+ {file = "pydantic_settings-2.2.0.tar.gz", hash = "sha256:648d0a76673e69c51278979cba2e83cf16a23d57519bfd7e553d1c3f37db5560"},
]
[package.dependencies]
pydantic = ">=2.3.0"
python-dotenv = ">=0.21.0"
+[package.extras]
+toml = ["tomlkit (>=0.12)"]
+yaml = ["pyyaml (>=6.0.1)"]
+
[[package]]
name = "pygments"
version = "2.17.2"
@@ -3961,13 +3965,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
[[package]]
name = "urllib3"
-version = "2.2.0"
+version = "2.2.1"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
files = [
- {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
- {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
+ {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+ {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
]
[package.extras]
diff --git a/resource/1_audio_files/record_metadata.yml b/resource/1_audio_files/record_metadata.yml
index dcd7ee64a23661cfa716f460567fbd62ae10683c..dc60b18140369c4aa89f8d30ea827b21429d2f42 100644
--- a/resource/1_audio_files/record_metadata.yml
+++ b/resource/1_audio_files/record_metadata.yml
@@ -1,5 +1,5 @@
access:
- files: public
+ files: restricted
record: public
files:
default_preview: null
@@ -24,13 +24,13 @@ metadata:
publisher: TU Wien
related_identifiers:
- identifier: https://www2.projects.science.uu.nl/memotion/emotifydata/
- relation:
+ relation_type:
id: isderivedfrom
resource_type:
id: sound
scheme: url
- identifier: https://gitlab.tuwien.ac.at/martin.weise/fairnb
- relation:
+ relation_type:
id: isderivedfrom
resource_type:
id: software
diff --git a/resource/5_ml_model/ml_model_entity_metadata.yml b/resource/5_ml_model/ml_model_entity_metadata.yml
index 9bd2ee301255182d3ff998a5df48432b75241659..eecb96526706b6dee2aa79b1bc22f3d7cb0b7afd 100644
--- a/resource/5_ml_model/ml_model_entity_metadata.yml
+++ b/resource/5_ml_model/ml_model_entity_metadata.yml
@@ -21,7 +21,7 @@ metadata:
publisher: TU Wien
related_identifiers:
- identifier: https://gitlab.tuwien.ac.at/martin.weise/fairnb
- relation:
+ relation_type:
id: isderivedfrom
resource_type:
id: software
diff --git a/test/integration/test_download_entities.py b/test/integration/test_download_entities.py
index 55d1c4bc87ea33ede59ca378fdc600150d523ebd..dbacc5e27757e03ebd4d207ff34590d59853837e 100644
--- a/test/integration/test_download_entities.py
+++ b/test/integration/test_download_entities.py
@@ -2,7 +2,7 @@ import yaml
from pytest import fixture
from fairnb.api.dbrepo import DBRepoConnector
-from fairnb.entity.dbrepo_entity import DbRepoEntity
+from fairnb.entity.dbrepo_entity import DBRepoEntity
from definitions import BASE_PATH
@@ -22,7 +22,7 @@ def connector():
def test_download_dbrepo_existing_works(connector: DBRepoConnector):
- entity = DbRepoEntity.existing(
+ entity = DBRepoEntity.existing(
id="1",
location=BASE_PATH / "tmp" / "test" / "dbrepo_entity.csv",
dbrepo_connector=connector,