diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..271516eb50446c9779c3ddec1ef910301cd765ef
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,7 @@
+config
+notes
+.venv
+.vscode
+.idea
+.mypy_cache
+.pytest_cache
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..7cf049731bbb2dbbcec88b5e2c2ac101f202a491
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,18 @@
+FROM jupyter/minimal-notebook:python-3.10
+# TODO: add venv instead of image with correct version
+# https://jupyter-docker-stacks.readthedocs.io/en/latest/using/recipes.html#add-a-custom-conda-environment-and-jupyter-kernel
+
+RUN rmdir work
+
+COPY . .
+RUN pip install poetry
+# RUN poetry config virtualenvs.in-project true
+RUN poetry config virtualenvs.create false
+RUN poetry install
+# RUN source .venv/bin/activate
+
+USER root
+RUN fix-permissions /home/jovyan
+
+# Switch back to jovyan to avoid accidental container runs as root
+USER ${NB_UID}
\ No newline at end of file
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..581febaf6d532839c2139656541db13f6c33733b
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,11 @@
+
+
+docker-build:
+ docker build --tag fairnb .
+
+docker-run:
+ docker run -it -p 8888:8888 fairnb
+
+docker-save:
+ docker save -o tmp/fairnb_image.tar fairnb
+ tar -zcvf tmp/fairnb_image.tar.gz tmp/fairnb_image.tar
\ No newline at end of file
diff --git a/dbrepo_ismir/entity/dbrepo_entity.py b/dbrepo_ismir/entity/dbrepo_entity.py
index a909c27fbcabc4f0485482894ccf03c9f8b48810..d9eef9db956c1d554cd4e85607be472595ca9d6c 100644
--- a/dbrepo_ismir/entity/dbrepo_entity.py
+++ b/dbrepo_ismir/entity/dbrepo_entity.py
@@ -48,7 +48,7 @@ class DbRepoEntity(Entity):
def download(self):
if not self.metadata:
- self.download_metadata()
+ self.download_provenance()
df = self.dbrepo_connector.download_table_as_df(str(self.table_id))
df = df[df['entity_id'] == self.id] # save only entity, not whole table
@@ -80,7 +80,7 @@ class DbRepoEntity(Entity):
platform="dbrepo",
)
- self.upload_metadata(metadata)
+ self.upload_provenance(metadata)
df[
"entity_id"
] = (
diff --git a/dbrepo_ismir/entity/entity.py b/dbrepo_ismir/entity/entity.py
index 0c218ee26efb8b01259d4828f9db3ad685d0dbbb..665a592b0d20cef87c28e74c74efb43582e63b2f 100644
--- a/dbrepo_ismir/entity/entity.py
+++ b/dbrepo_ismir/entity/entity.py
@@ -9,21 +9,19 @@ import pandas as pd
from dbrepo_ismir.api.dbrepo import DBRepoConnector
from dbrepo_ismir.entity.entity_provenance import EntityProvenance
+
PROVENANCE_TABLE_NAME = "entity_provenance"
DEPENDENCY_TABLE_NAME = "entity_dependencies"
LOG = logging.getLogger(__name__)
-
# TODO: Upload Datetime objects as Timestamps instead of str
-
@dataclass
class Entity(ABC):
""" A O-Prov Entity class used to represent an Entity created by a notebook.
Subtypes of this class implement the specific upload and download logic for the
platform the entity was persisted on.
"""
-
location: Path = field(init=True)
dbrepo_connector: DBRepoConnector = field(init=True)
name: str = field(init=True, default=None)
@@ -37,13 +35,13 @@ class Entity(ABC):
@classmethod
@abstractmethod
def new(cls, *args, **kwargs):
- """Create a new Artefact which exists at creation time only locally at 'self.location'."""
+ """Create a new entity which exists at creation time only locally at 'self.location'."""
raise NotImplementedError
@classmethod
@abstractmethod
def existing(cls, *args, **kwargs):
- """Use an existing Artefact which is already uploaded to the database. """
+ """Use an existing entity which is already uploaded to the database. """
raise NotImplementedError
def __post_init__(self):
@@ -63,7 +61,7 @@ class Entity(ABC):
self.dependency_table_id = self.dbrepo_connector.get_table(
DEPENDENCY_TABLE_NAME
)["id"]
- self.download_metadata()
+ self.download_provenance()
@abstractmethod
def download(self) -> EntityProvenance:
@@ -91,7 +89,7 @@ class Entity(ABC):
new_location.write_bytes(self.location.read_bytes()) # works as long file < RAM
return new
- def download_metadata(self) -> EntityProvenance:
+ def download_provenance(self) -> EntityProvenance:
""" Download provenance information by using self.id and assign it to self.metadata"""
assert self.id
@@ -112,14 +110,14 @@ class Entity(ABC):
self.type = self.metadata.type
return self.metadata
- def upload_metadata(self, metadata: EntityProvenance):
- metadata_table = self.create_provenance_table_if_not_exists(metadata)
+ def upload_provenance(self, provenance: EntityProvenance):
+ metadata_table = self.create_provenance_table_if_not_exists(provenance)
self.metadata_table_id = metadata_table["id"]
dependency_table = self.create_dependency_table_if_not_exists()
self.dependency_table_id = dependency_table["id"]
self.dbrepo_connector.upload_data(
- metadata.to_frame().drop("id", axis=1), str(self.metadata_table_id)
+ provenance.to_frame().drop("id", axis=1), str(self.metadata_table_id)
)
df = self.dbrepo_connector.download_table_as_df(str(self.metadata_table_id))
@@ -127,7 +125,7 @@ class Entity(ABC):
# FIXME: create robust version of id retrieval, if possible
row = df.iloc[df["id"].idxmax()] # get the newest row, as it should contain the correct data
meta = EntityProvenance.from_series(row)
- assert meta.creation_time == metadata.creation_time and meta.name == metadata.name
+ assert meta.creation_time == provenance.creation_time and meta.name == provenance.name
self.id = meta.id
self.metadata = meta
diff --git a/dbrepo_ismir/entity/invenio_entity.py b/dbrepo_ismir/entity/invenio_entity.py
index 597becded2cfd2847b16bb4a8ca539ee0d041793..6caad67244cb544f61411546715d6da64c2e0a6f 100644
--- a/dbrepo_ismir/entity/invenio_entity.py
+++ b/dbrepo_ismir/entity/invenio_entity.py
@@ -94,11 +94,11 @@ class InvenioEntity(Entity):
platform="invenio",
)
- self.upload_metadata(metadata)
+ self.upload_provenance(metadata)
self.upload_dependencies(dependencies)
def download(self) -> EntityProvenance:
if not self.metadata:
- self.download_metadata()
+ self.download_provenance()
return self.invenio_manager.download_record(self.location)
diff --git a/dbrepo_ismir/executor.py b/dbrepo_ismir/executor.py
index 358a0c4f0cc2aaf2d033d910b3eb7e11ed3f5ba5..1bfe70795b7ea48a433a61565a1bb0fc928381e1 100644
--- a/dbrepo_ismir/executor.py
+++ b/dbrepo_ismir/executor.py
@@ -6,8 +6,6 @@ from dbrepo_ismir.nb_config import NbConfig
class Executor:
- executeProcessor: ExecutePreprocessor = ExecutePreprocessor(timeout=600, kernel_name='python3')
-
@staticmethod
def download_dependencies(nb_config: NbConfig, require_download: bool = False):
""" Set up the dependencies to allow for later execution """
diff --git a/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb
index 55ff5d3fe1a0928752d2bdd4525965ffec7b298e..24456088a68c3ede3dd0a13b0660d218120040b4 100644
--- a/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/1_audio_files-checkpoint.ipynb
@@ -1,9 +1,38 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4389a8092677254e",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "source": [
+ "# Audio Files\n",
+ "\n",
+ "Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 2,
+ "id": "87ab37c6",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.015854,
+ "end_time": "2023-09-01T11:23:36.114552",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.098698",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"from definitions import BASE_PATH\n",
@@ -15,23 +44,47 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
+ "id": "1b4e6b01",
"metadata": {
+ "papermill": {
+ "duration": 0.01235,
+ "end_time": "2023-09-01T11:23:36.096700",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.084350",
+ "status": "completed"
+ },
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {}\n",
"OUTPUT_PATHS = {\n",
- " \"audio_tar\": (BASE_PATH / \"tmp\" / \"1_audio_files\" / \"output\" / \"emotifymusic.tar.gz\").__str__()\n",
+ " \"audio_tar\": str(BASE_PATH / \"tmp/1_audio_files/output/emotifymusic.tar.gz\")\n",
"}"
]
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {},
+ "execution_count": 4,
+ "id": "1e487573",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 2.541999,
+ "end_time": "2023-09-01T11:23:38.664303",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.122304",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# load provided files\n",
@@ -41,13 +94,27 @@
"dir_path.mkdir(parents=True, exist_ok=True)\n",
"# unzip to dir_path\n",
"with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
- " zfile.extractall(path=dir_path)\n"
+ " zfile.extractall(path=dir_path)"
]
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {},
+ "execution_count": 5,
+ "id": "c3193f35",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 1.066369,
+ "end_time": "2023-09-01T11:23:39.735691",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:38.669322",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"file_paths = list(dir_path.rglob('**/*.*'))\n",
@@ -55,22 +122,28 @@
"flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
"for path in file_paths:\n",
- " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n"
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())"
]
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
+ "execution_count": 6,
+ "id": "3272ea2b",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 15.267255,
+ "end_time": "2023-09-01T11:23:55.005410",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:39.738155",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
"source": [
"tar_path = Path(OUTPUT_PATHS[\"audio_tar\"])\n",
"tar_path.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -81,7 +154,6 @@
}
],
"metadata": {
- "celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@@ -97,9 +169,26 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 20.157944,
+ "end_time": "2023-09-01T11:23:55.227765",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/1_audio_files.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {},
+ "OUTPUT_PATHS": {
+ "audio_tar": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/1_audio_files/output/emotifymusic.tar.gz"
+ }
+ },
+ "start_time": "2023-09-01T11:23:35.069821",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 5
}
diff --git a/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb
index 84a3f978993d3566cf13413af61126ca335e5238..2a1646457063b748a64d7fa7711c8f2ae9686943 100644
--- a/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb
+++ b/notebooks/.ipynb_checkpoints/3_aggregate_features-checkpoint.ipynb
@@ -2,7 +2,17 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {},
+ "id": "f48a4573",
+ "metadata": {
+ "papermill": {
+ "duration": 0.002709,
+ "end_time": "2023-09-01T11:35:09.037422",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.034713",
+ "status": "completed"
+ },
+ "tags": []
+ },
"source": [
"# Aggregate MFCC Features\n",
"\n",
@@ -12,19 +22,57 @@
{
"cell_type": "code",
"execution_count": 1,
+ "id": "389576b8",
"metadata": {
- "collapsed": true
+ "ExecuteTime": {
+ "end_time": "2023-08-14T15:32:41.535589478Z",
+ "start_time": "2023-08-14T15:32:40.986222405Z"
+ },
+ "collapsed": true,
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.044339Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.044011Z",
+ "iopub.status.idle": "2023-09-01T11:35:09.306707Z",
+ "shell.execute_reply": "2023-09-01T11:35:09.305772Z"
+ },
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "papermill": {
+ "duration": 0.268336,
+ "end_time": "2023-09-01T11:35:09.308546",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.040210",
+ "status": "completed"
+ },
+ "tags": []
},
"outputs": [],
"source": [
+ "from pathlib import Path\n",
+ "\n",
"import pandas as pd\n",
"from definitions import BASE_PATH"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
+ "id": "26f640e0",
"metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.315577Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.314983Z",
+ "iopub.status.idle": "2023-09-01T11:35:09.320056Z",
+ "shell.execute_reply": "2023-09-01T11:35:09.318932Z"
+ },
+ "papermill": {
+ "duration": 0.010186,
+ "end_time": "2023-09-01T11:35:09.321555",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.311369",
+ "status": "completed"
+ },
"tags": [
"parameters"
]
@@ -45,20 +93,83 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
+ "execution_count": 3,
+ "id": "12fd5cf6",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.326762Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.326298Z",
+ "iopub.status.idle": "2023-09-01T11:35:09.329659Z",
+ "shell.execute_reply": "2023-09-01T11:35:09.329117Z"
+ },
+ "papermill": {
+ "duration": 0.007292,
+ "end_time": "2023-09-01T11:35:09.330862",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.323570",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {\n",
+ " \"raw_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/input/raw_features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv\"\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c5d9d980",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:09.335772Z",
+ "iopub.status.busy": "2023-09-01T11:35:09.335118Z",
+ "iopub.status.idle": "2023-09-01T11:35:14.097619Z",
+ "shell.execute_reply": "2023-09-01T11:35:14.096620Z"
+ },
+ "papermill": {
+ "duration": 4.766846,
+ "end_time": "2023-09-01T11:35:14.099543",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:09.332697",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# inputs\n",
- "assert INPUT_PATH.exists() and INPUT_PATH.is_dir()\n",
- "\n",
- "raw_features = pd.read_csv(INPUT_PATH / \"raw_features.csv\", index_col=False)"
+ "raw_features = pd.read_csv(INPUT_PATHS[\"raw_features\"], index_col=False)"
]
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {},
+ "execution_count": 5,
+ "id": "99f75f47",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:14.106862Z",
+ "iopub.status.busy": "2023-09-01T11:35:14.106221Z",
+ "iopub.status.idle": "2023-09-01T11:35:18.117596Z",
+ "shell.execute_reply": "2023-09-01T11:35:18.116970Z"
+ },
+ "papermill": {
+ "duration": 4.018079,
+ "end_time": "2023-09-01T11:35:18.120249",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:14.102170",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -136,10 +247,10 @@
" <td>-562.85785</td>\n",
" <td>-96.164795</td>\n",
" <td>-219.259016</td>\n",
- " <td>53.561838</td>\n",
+ " <td>53.561839</td>\n",
" <td>-0.772320</td>\n",
" <td>0.029056</td>\n",
- " <td>259.63270</td>\n",
+ " <td>259.63272</td>\n",
" <td>215.094182</td>\n",
" <td>...</td>\n",
" <td>-27.458416</td>\n",
@@ -147,8 +258,8 @@
" <td>0.484271</td>\n",
" <td>8.660648</td>\n",
" <td>-0.479016</td>\n",
- " <td>-28.989983</td>\n",
- " <td>27.533710</td>\n",
+ " <td>-28.989979</td>\n",
+ " <td>27.533707</td>\n",
" <td>0.952658</td>\n",
" <td>10.477735</td>\n",
" <td>-0.185771</td>\n",
@@ -172,7 +283,7 @@
" <td>8.185075</td>\n",
" <td>0.208425</td>\n",
" <td>-38.095375</td>\n",
- " <td>31.397880</td>\n",
+ " <td>31.397882</td>\n",
" <td>-1.494916</td>\n",
" <td>10.917299</td>\n",
" <td>0.020985</td>\n",
@@ -195,8 +306,8 @@
" <td>-3.781627</td>\n",
" <td>9.191043</td>\n",
" <td>0.260886</td>\n",
- " <td>-22.667440</td>\n",
- " <td>50.992897</td>\n",
+ " <td>-22.667439</td>\n",
+ " <td>50.992905</td>\n",
" <td>1.600777</td>\n",
" <td>10.125545</td>\n",
" <td>0.595763</td>\n",
@@ -212,14 +323,14 @@
" <td>-0.366586</td>\n",
" <td>0.000000</td>\n",
" <td>194.26416</td>\n",
- " <td>148.226647</td>\n",
+ " <td>148.226648</td>\n",
" <td>...</td>\n",
- " <td>-44.843810</td>\n",
+ " <td>-44.843815</td>\n",
" <td>28.490644</td>\n",
" <td>-6.242015</td>\n",
" <td>10.546545</td>\n",
" <td>0.341848</td>\n",
- " <td>-25.040888</td>\n",
+ " <td>-25.040886</td>\n",
" <td>46.878204</td>\n",
" <td>1.844494</td>\n",
" <td>11.160392</td>\n",
@@ -270,7 +381,7 @@
" <td>-24.712723</td>\n",
" <td>23.410387</td>\n",
" <td>-4.502398</td>\n",
- " <td>6.687984</td>\n",
+ " <td>6.687983</td>\n",
" <td>0.238807</td>\n",
" </tr>\n",
" <tr>\n",
@@ -278,21 +389,21 @@
" <td>rock_96.mp3</td>\n",
" <td>rock</td>\n",
" <td>-541.23600</td>\n",
- " <td>27.163334</td>\n",
+ " <td>27.163332</td>\n",
" <td>-119.113996</td>\n",
" <td>58.420684</td>\n",
" <td>-0.957699</td>\n",
- " <td>-7.415961</td>\n",
+ " <td>-7.415959</td>\n",
" <td>210.49246</td>\n",
" <td>125.453699</td>\n",
" <td>...</td>\n",
" <td>-37.584858</td>\n",
- " <td>28.087936</td>\n",
+ " <td>28.087940</td>\n",
" <td>-9.704238</td>\n",
" <td>8.447620</td>\n",
" <td>0.112760</td>\n",
" <td>-38.147890</td>\n",
- " <td>21.814402</td>\n",
+ " <td>21.814400</td>\n",
" <td>-8.249507</td>\n",
" <td>7.807756</td>\n",
" <td>0.071968</td>\n",
@@ -316,7 +427,7 @@
" <td>7.727378</td>\n",
" <td>0.207489</td>\n",
" <td>-29.497524</td>\n",
- " <td>25.410654</td>\n",
+ " <td>25.410656</td>\n",
" <td>-3.356614</td>\n",
" <td>8.170526</td>\n",
" <td>0.160330</td>\n",
@@ -331,16 +442,16 @@
" <td>52.444200</td>\n",
" <td>-1.705641</td>\n",
" <td>0.000000</td>\n",
- " <td>187.04274</td>\n",
+ " <td>187.04272</td>\n",
" <td>96.440874</td>\n",
" <td>...</td>\n",
- " <td>-26.967848</td>\n",
- " <td>8.714737</td>\n",
+ " <td>-26.967852</td>\n",
+ " <td>8.714736</td>\n",
" <td>-9.511491</td>\n",
" <td>5.551820</td>\n",
" <td>-0.025604</td>\n",
- " <td>-23.020084</td>\n",
- " <td>13.948638</td>\n",
+ " <td>-23.020082</td>\n",
+ " <td>13.948639</td>\n",
" <td>-2.664985</td>\n",
" <td>5.051498</td>\n",
" <td>-0.258407</td>\n",
@@ -354,17 +465,17 @@
" <td>-49.380943</td>\n",
" <td>54.045627</td>\n",
" <td>-0.863093</td>\n",
- " <td>-32.930653</td>\n",
+ " <td>-32.930650</td>\n",
" <td>191.73538</td>\n",
" <td>93.971242</td>\n",
" <td>...</td>\n",
" <td>-21.929403</td>\n",
" <td>17.050608</td>\n",
" <td>-5.296691</td>\n",
- " <td>5.894963</td>\n",
+ " <td>5.894962</td>\n",
" <td>0.390705</td>\n",
" <td>-20.983192</td>\n",
- " <td>29.312023</td>\n",
+ " <td>29.312021</td>\n",
" <td>-0.321836</td>\n",
" <td>6.571660</td>\n",
" <td>0.384794</td>\n",
@@ -383,36 +494,36 @@
"4 classical_12.mp3 classical -562.67523 -148.133560 -270.975406 \n",
".. ... ... ... ... ... \n",
"395 rock_95.mp3 rock -553.11010 -5.218835 -193.506047 \n",
- "396 rock_96.mp3 rock -541.23600 27.163334 -119.113996 \n",
+ "396 rock_96.mp3 rock -541.23600 27.163332 -119.113996 \n",
"397 rock_97.mp3 rock -518.49500 58.526745 -66.267744 \n",
"398 rock_98.mp3 rock -518.64307 53.555115 -45.734517 \n",
"399 rock_99.mp3 rock -544.70310 75.612130 -49.380943 \n",
"\n",
" 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n",
"0 51.142183 -0.468374 0.000000 178.75162 111.332342 ... -44.098070 \n",
- "1 53.561838 -0.772320 0.029056 259.63270 215.094182 ... -27.458416 \n",
+ "1 53.561839 -0.772320 0.029056 259.63272 215.094182 ... -27.458416 \n",
"2 83.381622 -2.587179 0.000000 190.47589 112.471713 ... -27.335688 \n",
"3 76.246992 -2.402418 0.000000 159.42575 99.853645 ... -31.774948 \n",
- "4 52.191182 -0.366586 0.000000 194.26416 148.226647 ... -44.843810 \n",
+ "4 52.191182 -0.366586 0.000000 194.26416 148.226648 ... -44.843815 \n",
".. ... ... ... ... ... ... ... \n",
"395 76.869437 -0.201055 -89.948746 201.18045 111.724191 ... -27.043941 \n",
- "396 58.420684 -0.957699 -7.415961 210.49246 125.453699 ... -37.584858 \n",
+ "396 58.420684 -0.957699 -7.415959 210.49246 125.453699 ... -37.584858 \n",
"397 65.635619 -0.898026 -58.824410 175.20135 99.288265 ... -29.620445 \n",
- "398 52.444200 -1.705641 0.000000 187.04274 96.440874 ... -26.967848 \n",
- "399 54.045627 -0.863093 -32.930653 191.73538 93.971242 ... -21.929403 \n",
+ "398 52.444200 -1.705641 0.000000 187.04272 96.440874 ... -26.967852 \n",
+ "399 54.045627 -0.863093 -32.930650 191.73538 93.971242 ... -21.929403 \n",
"\n",
" 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n",
"0 47.308060 -3.713503 16.553984 0.230691 -46.794480 49.352516 -2.282116 \n",
- "1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n",
- "2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397880 -1.494916 \n",
- "3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n",
- "4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n",
+ "1 29.811110 0.484271 8.660648 -0.479016 -28.989979 27.533707 0.952658 \n",
+ "2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397882 -1.494916 \n",
+ "3 31.500881 -3.781627 9.191043 0.260886 -22.667439 50.992905 1.600777 \n",
+ "4 28.490644 -6.242015 10.546545 0.341848 -25.040886 46.878204 1.844494 \n",
".. ... ... ... ... ... ... ... \n",
"395 22.451445 -7.234634 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n",
- "396 28.087936 -9.704238 8.447620 0.112760 -38.147890 21.814402 -8.249507 \n",
- "397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410654 -3.356614 \n",
- "398 8.714737 -9.511491 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n",
- "399 17.050608 -5.296691 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n",
+ "396 28.087940 -9.704238 8.447620 0.112760 -38.147890 21.814400 -8.249507 \n",
+ "397 26.325895 -5.722825 7.727378 0.207489 -29.497524 25.410656 -3.356614 \n",
+ "398 8.714736 -9.511491 5.551820 -0.025604 -23.020082 13.948639 -2.664985 \n",
+ "399 17.050608 -5.296691 5.894962 0.390705 -20.983192 29.312021 -0.321836 \n",
"\n",
" 39_std 39_skew \n",
"0 15.285639 0.171462 \n",
@@ -421,7 +532,7 @@
"3 10.125545 0.595763 \n",
"4 11.160392 0.503120 \n",
".. ... ... \n",
- "395 6.687984 0.238807 \n",
+ "395 6.687983 0.238807 \n",
"396 7.807756 0.071968 \n",
"397 8.170526 0.160330 \n",
"398 5.051498 -0.258407 \n",
@@ -430,7 +541,7 @@
"[400 rows x 202 columns]"
]
},
- "execution_count": 24,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -456,15 +567,32 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "metadata": {},
+ "execution_count": 6,
+ "id": "4ac5c765",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:18.127758Z",
+ "iopub.status.busy": "2023-09-01T11:35:18.127051Z",
+ "iopub.status.idle": "2023-09-01T11:35:18.220446Z",
+ "shell.execute_reply": "2023-09-01T11:35:18.219871Z"
+ },
+ "papermill": {
+ "duration": 0.100061,
+ "end_time": "2023-09-01T11:35:18.222876",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:18.122815",
+ "status": "completed"
+ },
+ "tags": []
+ },
"outputs": [],
"source": [
"# outputs\n",
- "OUTPUT_PATH.mkdir(parents=True, exist_ok=True)\n",
+ "aggregated_features_path = Path(OUTPUT_PATHS[\"aggregated_features\"]).resolve()\n",
+ "aggregated_features_path.parent.mkdir(parents=True, exist_ok=True)\n",
"\n",
"output = mfcc_merged\n",
- "output.to_csv(OUTPUT_PATH / \"features.csv\", index=False)"
+ "output.to_csv(aggregated_features_path, index=False)"
]
}
],
@@ -485,9 +613,28 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 10.352537,
+ "end_time": "2023-09-01T11:35:18.542818",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/3_aggregate_features.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {
+ "raw_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/input/raw_features.csv"
+ },
+ "OUTPUT_PATHS": {
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/3_aggregate_features/output/features.csv"
+ }
+ },
+ "start_time": "2023-09-01T11:35:08.190281",
+ "version": "2.4.0"
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 5
}
diff --git a/notebooks/.ipynb_checkpoints/4_split-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/4_split-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e4d2c5b8d040a4d02b422248156afd19de044258
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/4_split-checkpoint.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e92b4fe9",
+ "metadata": {
+ "papermill": {
+ "duration": 0.004009,
+ "end_time": "2023-09-01T11:35:21.835314",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:21.831305",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# Split the Features into Train and Test Set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5f1fae44",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:21.844307Z",
+ "iopub.status.busy": "2023-09-01T11:35:21.844022Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.144905Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.144381Z"
+ },
+ "papermill": {
+ "duration": 0.308442,
+ "end_time": "2023-09-01T11:35:22.147872",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:21.839430",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pathlib import Path\n",
+ "from definitions import BASE_PATH"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "01de1b27",
+ "metadata": {
+ "collapsed": false,
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.156002Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.155641Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.160059Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.159355Z"
+ },
+ "jupyter": {
+ "outputs_hidden": false
+ },
+ "papermill": {
+ "duration": 0.010206,
+ "end_time": "2023-09-01T11:35:22.161506",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.151300",
+ "status": "completed"
+ },
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Tagged with 'parameters'\n",
+ "from definitions import BASE_PATH\n",
+ "\n",
+ "INPUT_PATHS: dict[str, str] = {\n",
+ " \"features\": (BASE_PATH / \"tmp\" / \"4_split\" / \"input\" / \"features.csv\").__str__()\n",
+ "}\n",
+ "OUTPUT_PATHS: dict[str, str] = {\n",
+ " \"split\": (BASE_PATH / \"tmp\" / \"4_split\" / \"output\" / \"split.csv\").__str__()\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "205bb941",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:21.808793Z",
+ "iopub.status.busy": "2023-09-01T11:35:21.808502Z",
+ "iopub.status.idle": "2023-09-01T11:35:21.824152Z",
+ "shell.execute_reply": "2023-09-01T11:35:21.822789Z"
+ },
+ "papermill": {
+ "duration": 0.023269,
+ "end_time": "2023-09-01T11:35:21.827306",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:21.804037",
+ "status": "completed"
+ },
+ "tags": [
+ "injected-parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a4cc6800",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.190493Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.190038Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.217115Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.216124Z"
+ },
+ "papermill": {
+ "duration": 0.03203,
+ "end_time": "2023-09-01T11:35:22.218934",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.186904",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# INPUT\n",
+ "\n",
+ "for path in INPUT_PATHS.values():\n",
+ " assert Path(path).exists()\n",
+ "\n",
+ "features = pd.read_csv(INPUT_PATHS[\"features\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a186d0c4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.225158Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.224866Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.233993Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.232970Z"
+ },
+ "papermill": {
+ "duration": 0.014722,
+ "end_time": "2023-09-01T11:35:22.236276",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.221554",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "train = features.sample(frac=0.8).sort_index()\n",
+ "test = features.drop(train.index)\n",
+ "\n",
+ "split_true = pd.DataFrame({\n",
+ " \"filename\": train.filename,\n",
+ " \"train\": True\n",
+ "})\n",
+ "split_false = pd.DataFrame({\n",
+ " \"filename\": test.filename,\n",
+ " \"train\": False\n",
+ "})\n",
+ "\n",
+ "split_concat = pd.concat([split_true, split_false])\\\n",
+ " .sort_values(\"filename\")\\\n",
+ " .reset_index(drop=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "091e0641",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.248578Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.248298Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.260910Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.260154Z"
+ },
+ "papermill": {
+ "duration": 0.022698,
+ "end_time": "2023-09-01T11:35:22.264468",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.241770",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .dataframe tbody tr th {\n",
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+ " .dataframe thead th {\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>filename</th>\n",
+ " <th>train</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>classical_1.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>classical_10.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>classical_100.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>classical_11.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>classical_12.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>395</th>\n",
+ " <td>rock_95.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>396</th>\n",
+ " <td>rock_96.mp3</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>397</th>\n",
+ " <td>rock_97.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>398</th>\n",
+ " <td>rock_98.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>399</th>\n",
+ " <td>rock_99.mp3</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>400 rows × 2 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " filename train\n",
+ "0 classical_1.mp3 True\n",
+ "1 classical_10.mp3 True\n",
+ "2 classical_100.mp3 True\n",
+ "3 classical_11.mp3 True\n",
+ "4 classical_12.mp3 True\n",
+ ".. ... ...\n",
+ "395 rock_95.mp3 True\n",
+ "396 rock_96.mp3 False\n",
+ "397 rock_97.mp3 True\n",
+ "398 rock_98.mp3 True\n",
+ "399 rock_99.mp3 True\n",
+ "\n",
+ "[400 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "split_concat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "7b11b8bb",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2023-09-01T11:35:22.274622Z",
+ "iopub.status.busy": "2023-09-01T11:35:22.274234Z",
+ "iopub.status.idle": "2023-09-01T11:35:22.281519Z",
+ "shell.execute_reply": "2023-09-01T11:35:22.280717Z"
+ },
+ "papermill": {
+ "duration": 0.01433,
+ "end_time": "2023-09-01T11:35:22.283192",
+ "exception": false,
+ "start_time": "2023-09-01T11:35:22.268862",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# output\n",
+ "OUTPUT_PATH = Path(OUTPUT_PATHS[\"split\"])\n",
+ "OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "output = split_concat\n",
+ "output.to_csv(OUTPUT_PATH, index=False)"
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Tags",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 1.989508,
+ "end_time": "2023-09-01T11:35:22.603293",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
+ "output_path": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/notebooks/4_split.ipynb",
+ "parameters": {
+ "INPUT_PATHS": {
+ "aggregated_features": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv"
+ },
+ "OUTPUT_PATHS": {
+ "split": "/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv"
+ }
+ },
+ "start_time": "2023-09-01T11:35:20.613785",
+ "version": "2.4.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/.ipynb_checkpoints/6_report-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/6_report-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9e60cbbd55990c547d0733cd2b15e91efb2eab9e
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/6_report-checkpoint.ipynb
@@ -0,0 +1,34 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/.ipynb_checkpoints/standalone-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/standalone-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..06486e7a7e3bcf45db51c363e2b6804d6d9a0ff2
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/standalone-checkpoint.ipynb
@@ -0,0 +1,1051 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Standalone Notebook\n",
+ "\n",
+ "Notebook containing the same functionality as main.ipynb, but it includes all steps in one notebook and does not spin up separate Jupyter Kernels and uploads the entities directly."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "from concurrent.futures import ThreadPoolExecutor\n",
+ "from contextlib import contextmanager, redirect_stderr, redirect_stdout\n",
+ "\n",
+ "import librosa\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import yaml\n",
+ "from matplotlib import pyplot as plt\n",
+ "from pandas import DataFrame, Index\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.svm import SVC\n",
+ "\n",
+ "from dbrepo_ismir.entity.dbrepo_entity import DbRepoEntity\n",
+ "from dbrepo_ismir.entity.invenio_entity import InvenioEntity\n",
+ "from dbrepo_ismir.executor import Executor\n",
+ "from dbrepo_ismir.nb_config import NbConfig\n",
+ "from dbrepo_ismir.util import Util\n",
+ "from definitions import BASE_PATH, RESOURCE_PATH\n",
+ "import tarfile\n",
+ "import zipfile\n",
+ "import os\n",
+ "from pathlib import Path\n",
+ "from definitions import CONFIG_PATH"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:23:50.320823230Z",
+ "start_time": "2023-09-06T16:23:48.109782272Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [],
+ "source": [
+ "# experiment executor setup\n",
+ "executor = Executor()\n",
+ "util = Util.get_instance() # util caches loaded credentials -> via Singleton\n",
+ "connector = util.get_dbrepo_connector(CONFIG_PATH / \"dbrepo_config.yml\")\n",
+ "# connector = None\n",
+ "invenio_connector = util.get_invenio_connector(CONFIG_PATH / \"invenio_config.yml\")\n",
+ "\n",
+ "NOTEBOOK_PATH = BASE_PATH / \"notebooks\"\n",
+ "LOCAL_PATH = BASE_PATH / \"tmp\" / \"standalone\"\n",
+ "NB_LOCATION = NOTEBOOK_PATH / \"standalone.ipynb\"\n",
+ "\n",
+ "ONLY_LOCAL = True"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:23:50.458322232Z",
+ "start_time": "2023-09-06T16:23:50.353619129Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1. Audio Files\n",
+ "\n",
+ "Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [],
+ "source": [
+ "tar_path = LOCAL_PATH / \"emotifymusic.tar.gz\"\n",
+ "\n",
+ "# load provided files\n",
+ "zip_path = BASE_PATH / \"resource\" / \"1_audio_files\" / \"emotifymusic.zip\"\n",
+ "dir_path = LOCAL_PATH / \"music\" / \"original\"\n",
+ "\n",
+ "dir_path.mkdir(parents=True, exist_ok=True)\n",
+ "# unzip to dir_path\n",
+ "with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
+ " zfile.extractall(path=dir_path)\n",
+ "\n",
+ "file_paths = list(dir_path.rglob('**/*.*'))\n",
+ "flattened_dir_path = LOCAL_PATH / \"music\" / \"flattened\"\n",
+ "flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "for path in file_paths:\n",
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n",
+ "\n",
+ "tar_path.parent.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "with tarfile.open(tar_path, \"w:gz\") as file:\n",
+ " file.add(flattened_dir_path, arcname=os.path.sep)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:24:08.646424970Z",
+ "start_time": "2023-09-06T16:23:50.456742546Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "outputs": [],
+ "source": [
+ "if not ONLY_LOCAL:\n",
+ " metadata = yaml.safe_load(open(RESOURCE_PATH / \"1_audio_files\" / \"record_metadata.yml\", \"r\"))\n",
+ "\n",
+ " nb_config_audio_files = NbConfig(\n",
+ " nb_location=NB_LOCATION,\n",
+ " entities=[\n",
+ " audio_files_entity := InvenioEntity.new(\n",
+ " name = \"audio_tar\",\n",
+ " description = \"Raw music files\",\n",
+ " location = tar_path,\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"audio_tar\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[]\n",
+ " )\n",
+ "\n",
+ " executor.upload_entities(nb_config_audio_files)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:24:08.657491220Z",
+ "start_time": "2023-09-06T16:24:08.653629315Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2. Feature Extraction of Base audio Files from Invenio\n",
+ "\n",
+ "Load the audio files from the tar, and extract the MFCC features from them and store them in a dataframe."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "outputs": [],
+ "source": [
+ "DEFAULT_SAMPLING_RATE = 22050\n",
+ "\n",
+ "with tarfile.open(tar_path, \"r:gz\") as archive:\n",
+ " archive.extractall(path=(path_out := tar_path.with_suffix(\"\").with_suffix(\"\")))\n",
+ "\n",
+ "files = list(path_out.rglob(\"**/*.*\"))\n",
+ "\n",
+ "\n",
+ "@contextmanager\n",
+ "def suppress_stdout_stderr():\n",
+ " \"\"\"A context manager that redirects stdout and stderr to devnull\"\"\"\n",
+ " with open(os.devnull, 'w') as fnull:\n",
+ " with redirect_stderr(fnull) as err, redirect_stdout(fnull) as out:\n",
+ " yield err, out\n",
+ "\n",
+ "\n",
+ "def generate_mfcc_feature(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE, number_mfccs: int = 40):\n",
+ " x, _ = load_mp3(filepath, sr=sr)\n",
+ " assert sr == _\n",
+ " mfcc = librosa.feature.mfcc(x, sr=sr, n_mfcc=number_mfccs)\n",
+ "\n",
+ " # transpose to use mfcc bands as columns instead of rows\n",
+ " return pd.DataFrame(mfcc).transpose()\n",
+ "\n",
+ "\n",
+ "def load_mp3(filepath: Path, sr: int = DEFAULT_SAMPLING_RATE):\n",
+ " x, sr = librosa.load(filepath, sr=sr) # extract wave (x) with sample rate (sr)\n",
+ " return x, sr\n",
+ "\n",
+ "\n",
+ "with suppress_stdout_stderr(), ThreadPoolExecutor(6) as executor:\n",
+ " dataframes = list(executor.map(\n",
+ " lambda args: generate_mfcc_feature(args), files)\n",
+ " )\n",
+ "for file, dataframe in zip(files, dataframes):\n",
+ " dataframe[\"sample\"] = dataframe.index.to_numpy(copy=True)\n",
+ " dataframe[\"filename\"] = file.name\n",
+ " dataframe[\"label\"] = file.name.split('_')[0] # extract genre from file name\n",
+ "\n",
+ "dataframe_concat = pd.concat(dataframes)\n",
+ "columns_old = list(dataframe_concat.columns)\n",
+ "columns = columns_old[-3:] + columns_old[:-3]\n",
+ "dataframe_concat = dataframe_concat[columns]\n",
+ "\n",
+ "raw_features = dataframe_concat"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:32:07.450652088Z",
+ "start_time": "2023-09-06T16:24:08.668477833Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "[<matplotlib.lines.Line2D at 0x7efe441870a0>]"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "<Figure size 640x480 with 1 Axes>",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "example_mfcc = raw_features[raw_features.filename == \"rock_50.mp3\"].sort_values(\"sample\").iloc[:,:]\n",
+ "plt.plot(example_mfcc[15])\n",
+ "# plt.plot(example_mfcc[4])\n",
+ "\n",
+ "# librosa.display.waveshow(audio)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:32:08.167783042Z",
+ "start_time": "2023-09-06T16:32:07.535299813Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 3. Aggregate MFCC Features"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [],
+ "source": [
+ "# allow for direct entry if features were already created in earlier run\n",
+ "raw_features.to_csv(LOCAL_PATH / \"raw_features.csv\", index=False)\n",
+ "\n",
+ "if \"raw_features\" not in globals():\n",
+ " raw_features = pd.read_csv(LOCAL_PATH / \"raw_features.csv\")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:37.098344632Z",
+ "start_time": "2023-09-06T16:32:08.174101151Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " filename label 0_min 0_max 0_mean \\\n0 classical_1.mp3 classical -530.784363 -163.308350 -302.203156 \n1 classical_10.mp3 classical -562.857849 -96.164795 -219.259018 \n2 classical_100.mp3 classical -536.237366 -61.608826 -177.804108 \n3 classical_11.mp3 classical -536.457458 -120.429665 -222.126312 \n4 classical_12.mp3 classical -562.675232 -148.133560 -270.975403 \n.. ... ... ... ... ... \n395 rock_95.mp3 rock -553.110107 -5.218835 -193.506042 \n396 rock_96.mp3 rock -541.236023 27.163334 -119.113991 \n397 rock_97.mp3 rock -518.494995 58.526745 -66.267746 \n398 rock_98.mp3 rock -518.643066 53.555115 -45.734516 \n399 rock_99.mp3 rock -544.703125 75.612129 -49.380943 \n\n 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n0 51.142183 -0.468374 0.000000 178.751617 111.332344 ... -44.098068 \n1 53.561838 -0.772320 0.029056 259.632690 215.094193 ... -27.458416 \n2 83.381622 -2.587179 0.000000 190.475891 112.471710 ... -27.335688 \n3 76.246992 -2.402419 0.000000 159.425751 99.853645 ... -31.774948 \n4 52.191182 -0.366587 0.000000 194.264160 148.226654 ... -44.843811 \n.. ... ... ... ... ... ... ... \n395 76.869437 -0.201055 -89.948746 201.180450 111.724190 ... -27.043941 \n396 58.420684 -0.957699 -7.415961 210.492462 125.453690 ... -37.584858 \n397 65.635619 -0.898026 -58.824409 175.201355 99.288261 ... -29.620445 \n398 52.444200 -1.705641 0.000000 187.042740 96.440872 ... -26.967848 \n399 54.045627 -0.863093 -32.930653 191.735382 93.971237 ... -21.929403 \n\n 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n0 47.308060 -3.713503 16.553984 0.230691 -46.794479 49.352516 -2.282116 \n1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397881 -1.494916 \n3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n.. ... ... ... ... ... ... ... \n395 22.451445 -7.234633 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n396 28.087936 -9.704238 8.447620 0.112760 -38.147888 21.814402 -8.249507 \n397 26.325895 -5.722826 7.727378 0.207489 -29.497524 25.410654 -3.356615 \n398 8.714737 -9.511492 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n399 17.050608 -5.296690 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n\n 39_std 39_skew \n0 15.285639 0.171462 \n1 10.477735 -0.185771 \n2 10.917299 0.020984 \n3 10.125545 0.595763 \n4 11.160392 0.503120 \n.. ... ... \n395 6.687984 0.238807 \n396 7.807756 0.071968 \n397 8.170526 0.160330 \n398 5.051498 -0.258407 \n399 6.571660 0.384794 \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>...</td>\n <td>-44.843811</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "meta_columns = [\"sample\", \"filename\", \"label\"]\n",
+ "mfcc_aggregated = raw_features \\\n",
+ " .drop(meta_columns, axis=1, errors='ignore') \\\n",
+ " .groupby(raw_features.filename).agg(['min', 'max', 'mean', 'std', 'skew'])\n",
+ "\n",
+ "mfcc_meta = pd.DataFrame(raw_features['label'].groupby(raw_features.filename).last())\n",
+ "mfcc_meta.columns = pd.MultiIndex.from_arrays([['label'], ['']]) # needed for merge\n",
+ "mfcc_merged = pd.merge(mfcc_meta, mfcc_aggregated, left_index=True, right_index=True)\n",
+ "\n",
+ "# reduce multi index to single index\n",
+ "one_level_cols = ['_'.join([str(el) for el in col]) for col in mfcc_merged.columns[1:]]\n",
+ "one_level_cols.insert(0, \"label\")\n",
+ "\n",
+ "mfcc_merged.columns = pd.Index(one_level_cols)\n",
+ "mfcc_merged = mfcc_merged.reset_index()\n",
+ "mfcc_merged"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:52.624562481Z",
+ "start_time": "2023-09-06T16:33:40.167149213Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 4. Split the Features into Train and Test Set"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " filename train\n0 classical_1.mp3 True\n1 classical_10.mp3 False\n2 classical_100.mp3 False\n3 classical_11.mp3 False\n4 classical_12.mp3 True\n.. ... ...\n395 rock_95.mp3 True\n396 rock_96.mp3 True\n397 rock_97.mp3 True\n398 rock_98.mp3 True\n399 rock_99.mp3 True\n\n[400 rows x 2 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>train</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>True</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 2 columns</p>\n</div>"
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "features = mfcc_merged\n",
+ "train = features.sample(frac=0.8).sort_index()\n",
+ "test = features.drop(train.index)\n",
+ "\n",
+ "split_true = pd.DataFrame({\n",
+ " \"filename\": train.filename,\n",
+ " \"train\": True\n",
+ "})\n",
+ "split_false = pd.DataFrame({\n",
+ " \"filename\": test.filename,\n",
+ " \"train\": False\n",
+ "})\n",
+ "\n",
+ "split = pd.concat([split_true, split_false]) \\\n",
+ " .sort_values(\"filename\") \\\n",
+ " .reset_index(drop=True)\n",
+ "split"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:52.625265280Z",
+ "start_time": "2023-09-06T16:33:52.600299846Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 5: Machine Learning model training and evaluation"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561838 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163334 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847563 \nclassical_10.mp3 -0.772320 0.029056 259.632690 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916949 \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 \n... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415961 210.492462 125.453690 31.908869 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \nrock_98.mp3 -1.705641 0.000000 187.042740 96.440872 24.137702 \nrock_99.mp3 -0.863093 -32.930653 191.735382 93.971237 33.410220 \n\n ... 38_max 38_mean 38_std 38_skew 39_min \\\nfilename ... \nclassical_1.mp3 ... 47.308060 -3.713503 16.553984 0.230691 -46.794479 \nclassical_10.mp3 ... 29.811110 0.484271 8.660648 -0.479016 -28.989983 \nclassical_100.mp3 ... 27.610388 -0.333233 8.185075 0.208425 -38.095375 \nclassical_11.mp3 ... 31.500881 -3.781627 9.191043 0.260886 -22.667440 \nclassical_12.mp3 ... 28.490644 -6.242015 10.546545 0.341848 -25.040888 \n... ... ... ... ... ... ... \nrock_95.mp3 ... 22.451445 -7.234633 8.471853 0.753855 -24.712723 \nrock_96.mp3 ... 28.087936 -9.704238 8.447620 0.112760 -38.147888 \nrock_97.mp3 ... 26.325895 -5.722826 7.727378 0.207489 -29.497524 \nrock_98.mp3 ... 8.714737 -9.511492 5.551820 -0.025604 -23.020084 \nrock_99.mp3 ... 17.050608 -5.296690 5.894963 0.390705 -20.983192 \n\n 39_max 39_mean 39_std 39_skew train \nfilename \nclassical_1.mp3 49.352516 -2.282116 15.285639 0.171462 True \nclassical_10.mp3 27.533710 0.952658 10.477735 -0.185771 False \nclassical_100.mp3 31.397881 -1.494916 10.917299 0.020984 False \nclassical_11.mp3 50.992897 1.600777 10.125545 0.595763 False \nclassical_12.mp3 46.878204 1.844494 11.160392 0.503120 True \n... ... ... ... ... ... \nrock_95.mp3 23.410387 -4.502398 6.687984 0.238807 True \nrock_96.mp3 21.814402 -8.249507 7.807756 0.071968 True \nrock_97.mp3 25.410654 -3.356615 8.170526 0.160330 True \nrock_98.mp3 13.948638 -2.664985 5.051498 -0.258407 True \nrock_99.mp3 29.312023 -0.321836 6.571660 0.384794 True \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n <th>train</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847563</td>\n <td>...</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916949</td>\n <td>...</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n <td>True</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908869</td>\n <td>...</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410220</td>\n <td>...</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "joined = pd.merge(features, split, on=\"filename\").set_index(\"filename\")\n",
+ "joined"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:52.968965270Z",
+ "start_time": "2023-09-06T16:33:52.652167547Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \nclassical_13.mp3 classical -637.720642 -177.713959 -361.834045 71.310080 \nclassical_14.mp3 classical -531.049438 -100.790543 -188.970749 58.287371 \nclassical_15.mp3 classical -555.129944 -96.139236 -209.245819 45.350121 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163334 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std ... \\\nfilename ... \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847563 ... \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 ... \nclassical_13.mp3 0.008326 0.000000 257.162842 211.556549 20.347035 ... \nclassical_14.mp3 -3.246618 0.000000 157.947922 86.563927 17.911136 ... \nclassical_15.mp3 -3.574710 0.000000 140.918640 109.309990 14.171102 ... \n... ... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 ... \nrock_96.mp3 -0.957699 -7.415961 210.492462 125.453690 31.908869 ... \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 ... \nrock_98.mp3 -1.705641 0.000000 187.042740 96.440872 24.137702 ... \nrock_99.mp3 -0.863093 -32.930653 191.735382 93.971237 33.410220 ... \n\n 38_min 38_max 38_mean 38_std 38_skew \\\nfilename \nclassical_1.mp3 -44.098068 47.308060 -3.713503 16.553984 0.230691 \nclassical_12.mp3 -44.843811 28.490644 -6.242015 10.546545 0.341848 \nclassical_13.mp3 -24.728806 18.424036 -0.275737 7.026148 -0.640964 \nclassical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \nclassical_15.mp3 -42.808113 24.146545 -7.260053 9.862490 0.097765 \n... ... ... ... ... ... \nrock_95.mp3 -27.043941 22.451445 -7.234633 8.471853 0.753855 \nrock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \nrock_97.mp3 -29.620445 26.325895 -5.722826 7.727378 0.207489 \nrock_98.mp3 -26.967848 8.714737 -9.511492 5.551820 -0.025604 \nrock_99.mp3 -21.929403 17.050608 -5.296690 5.894963 0.390705 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \nclassical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \nclassical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \nclassical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \nclassical_15.mp3 -31.394997 35.685539 -0.949139 11.141700 0.249278 \n... ... ... ... ... ... \nrock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \nrock_96.mp3 -38.147888 21.814402 -8.249507 7.807756 0.071968 \nrock_97.mp3 -29.497524 25.410654 -3.356615 8.170526 0.160330 \nrock_98.mp3 -23.020084 13.948638 -2.664985 5.051498 -0.258407 \nrock_99.mp3 -20.983192 29.312023 -0.321836 6.571660 0.384794 \n\n[320 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847563</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>-44.843811</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>classical_13.mp3</th>\n <td>classical</td>\n <td>-637.720642</td>\n <td>-177.713959</td>\n <td>-361.834045</td>\n <td>71.310080</td>\n <td>0.008326</td>\n <td>0.000000</td>\n <td>257.162842</td>\n <td>211.556549</td>\n <td>20.347035</td>\n <td>...</td>\n <td>-24.728806</td>\n <td>18.424036</td>\n <td>-0.275737</td>\n <td>7.026148</td>\n <td>-0.640964</td>\n <td>-24.319565</td>\n <td>18.439262</td>\n <td>-2.147022</td>\n <td>8.171929</td>\n <td>0.009566</td>\n </tr>\n <tr>\n <th>classical_14.mp3</th>\n <td>classical</td>\n <td>-531.049438</td>\n <td>-100.790543</td>\n <td>-188.970749</td>\n <td>58.287371</td>\n <td>-3.246618</td>\n <td>0.000000</td>\n <td>157.947922</td>\n <td>86.563927</td>\n <td>17.911136</td>\n <td>...</td>\n <td>-36.261154</td>\n <td>38.335831</td>\n <td>-5.770759</td>\n <td>12.254058</td>\n <td>0.805707</td>\n <td>-40.597336</td>\n <td>32.816467</td>\n <td>-0.543406</td>\n <td>11.467829</td>\n <td>-0.187037</td>\n </tr>\n <tr>\n <th>classical_15.mp3</th>\n <td>classical</td>\n <td>-555.129944</td>\n <td>-96.139236</td>\n <td>-209.245819</td>\n <td>45.350121</td>\n <td>-3.574710</td>\n <td>0.000000</td>\n <td>140.918640</td>\n <td>109.309990</td>\n <td>14.171102</td>\n <td>...</td>\n <td>-42.808113</td>\n <td>24.146545</td>\n <td>-7.260053</td>\n <td>9.862490</td>\n <td>0.097765</td>\n <td>-31.394997</td>\n <td>35.685539</td>\n <td>-0.949139</td>\n <td>11.141700</td>\n <td>0.249278</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908869</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410220</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>320 rows × 201 columns</p>\n</div>"
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train: DataFrame = joined[joined[\"train\"] == True].drop(\"train\", axis=1)\n",
+ "train"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.010226965Z",
+ "start_time": "2023-09-06T16:33:52.744721352Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561838 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_19.mp3 classical -543.642334 -106.038223 -216.909943 61.317534 \nclassical_20.mp3 classical -605.991516 -161.119308 -263.483093 49.157298 \n... ... ... ... ... ... \nrock_57.mp3 rock -543.735168 50.739136 -70.208893 83.040454 \nrock_66.mp3 rock -520.185791 21.333998 -79.359444 44.616105 \nrock_75.mp3 rock -519.826965 54.035805 -32.218468 33.789999 \nrock_81.mp3 rock -532.139099 52.119076 -117.146126 76.883343 \nrock_90.mp3 rock -501.955994 9.573563 -137.388382 46.025847 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_10.mp3 -0.772320 0.029056 259.632690 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916949 \nclassical_19.mp3 -3.473125 0.000000 151.947662 93.405411 22.029233 \nclassical_20.mp3 -0.856221 0.000000 191.926758 141.393814 17.754779 \n... ... ... ... ... ... \nrock_57.mp3 -2.913490 -51.877323 177.711395 89.957848 29.532071 \nrock_66.mp3 -2.708660 0.000000 162.490845 115.182426 18.106840 \nrock_75.mp3 -1.231267 1.666233 164.635895 93.935715 21.886208 \nrock_81.mp3 -0.656551 -44.119019 168.675858 101.038620 31.198018 \nrock_90.mp3 -0.004000 -7.494962 190.229202 112.531166 33.245804 \n\n ... 38_min 38_max 38_mean 38_std 38_skew \\\nfilename ... \nclassical_10.mp3 ... -27.458416 29.811110 0.484271 8.660648 -0.479016 \nclassical_100.mp3 ... -27.335688 27.610388 -0.333233 8.185075 0.208425 \nclassical_11.mp3 ... -31.774948 31.500881 -3.781627 9.191043 0.260886 \nclassical_19.mp3 ... -27.029385 30.682745 3.342259 8.420860 0.043171 \nclassical_20.mp3 ... -24.911243 38.551231 -2.274261 9.671005 0.719436 \n... ... ... ... ... ... ... \nrock_57.mp3 ... -30.258139 9.919489 -6.048107 5.045001 -0.187751 \nrock_66.mp3 ... -23.582970 16.230869 -4.445108 6.836216 -0.005944 \nrock_75.mp3 ... -29.449886 9.328630 -7.874899 6.538823 -0.428034 \nrock_81.mp3 ... -36.623711 23.897625 -3.552371 9.184054 -0.304160 \nrock_90.mp3 ... -23.657921 24.251360 -4.784957 6.215656 0.480860 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_10.mp3 -28.989983 27.533710 0.952658 10.477735 -0.185771 \nclassical_100.mp3 -38.095375 31.397881 -1.494916 10.917299 0.020984 \nclassical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \nclassical_19.mp3 -25.900257 36.766388 2.389575 10.099726 0.140336 \nclassical_20.mp3 -30.311798 29.272329 0.289613 9.590299 -0.244191 \n... ... ... ... ... ... \nrock_57.mp3 -19.538643 21.089222 -1.995280 5.352349 0.480205 \nrock_66.mp3 -16.087088 22.686642 2.065789 6.279558 0.069703 \nrock_75.mp3 -21.944729 18.833591 -2.557417 5.737269 -0.007298 \nrock_81.mp3 -34.576202 36.869560 -1.597456 10.409478 0.058469 \nrock_90.mp3 -21.904373 18.819710 -1.302765 5.389064 -0.064191 \n\n[80 rows x 201 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916949</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>classical_19.mp3</th>\n <td>classical</td>\n <td>-543.642334</td>\n <td>-106.038223</td>\n <td>-216.909943</td>\n <td>61.317534</td>\n <td>-3.473125</td>\n <td>0.000000</td>\n <td>151.947662</td>\n <td>93.405411</td>\n <td>22.029233</td>\n <td>...</td>\n <td>-27.029385</td>\n <td>30.682745</td>\n <td>3.342259</td>\n <td>8.420860</td>\n <td>0.043171</td>\n <td>-25.900257</td>\n <td>36.766388</td>\n <td>2.389575</td>\n <td>10.099726</td>\n <td>0.140336</td>\n </tr>\n <tr>\n <th>classical_20.mp3</th>\n <td>classical</td>\n <td>-605.991516</td>\n <td>-161.119308</td>\n <td>-263.483093</td>\n <td>49.157298</td>\n <td>-0.856221</td>\n <td>0.000000</td>\n <td>191.926758</td>\n <td>141.393814</td>\n <td>17.754779</td>\n <td>...</td>\n <td>-24.911243</td>\n <td>38.551231</td>\n <td>-2.274261</td>\n <td>9.671005</td>\n <td>0.719436</td>\n <td>-30.311798</td>\n <td>29.272329</td>\n <td>0.289613</td>\n <td>9.590299</td>\n <td>-0.244191</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_57.mp3</th>\n <td>rock</td>\n <td>-543.735168</td>\n <td>50.739136</td>\n <td>-70.208893</td>\n <td>83.040454</td>\n <td>-2.913490</td>\n <td>-51.877323</td>\n <td>177.711395</td>\n <td>89.957848</td>\n <td>29.532071</td>\n <td>...</td>\n <td>-30.258139</td>\n <td>9.919489</td>\n <td>-6.048107</td>\n <td>5.045001</td>\n <td>-0.187751</td>\n <td>-19.538643</td>\n <td>21.089222</td>\n <td>-1.995280</td>\n <td>5.352349</td>\n <td>0.480205</td>\n </tr>\n <tr>\n <th>rock_66.mp3</th>\n <td>rock</td>\n <td>-520.185791</td>\n <td>21.333998</td>\n <td>-79.359444</td>\n <td>44.616105</td>\n <td>-2.708660</td>\n <td>0.000000</td>\n <td>162.490845</td>\n <td>115.182426</td>\n <td>18.106840</td>\n <td>...</td>\n <td>-23.582970</td>\n <td>16.230869</td>\n <td>-4.445108</td>\n <td>6.836216</td>\n <td>-0.005944</td>\n <td>-16.087088</td>\n <td>22.686642</td>\n <td>2.065789</td>\n <td>6.279558</td>\n <td>0.069703</td>\n </tr>\n <tr>\n <th>rock_75.mp3</th>\n <td>rock</td>\n <td>-519.826965</td>\n <td>54.035805</td>\n <td>-32.218468</td>\n <td>33.789999</td>\n <td>-1.231267</td>\n <td>1.666233</td>\n <td>164.635895</td>\n <td>93.935715</td>\n <td>21.886208</td>\n <td>...</td>\n <td>-29.449886</td>\n <td>9.328630</td>\n <td>-7.874899</td>\n <td>6.538823</td>\n <td>-0.428034</td>\n <td>-21.944729</td>\n <td>18.833591</td>\n <td>-2.557417</td>\n <td>5.737269</td>\n <td>-0.007298</td>\n </tr>\n <tr>\n <th>rock_81.mp3</th>\n <td>rock</td>\n <td>-532.139099</td>\n <td>52.119076</td>\n <td>-117.146126</td>\n <td>76.883343</td>\n <td>-0.656551</td>\n <td>-44.119019</td>\n <td>168.675858</td>\n <td>101.038620</td>\n <td>31.198018</td>\n <td>...</td>\n <td>-36.623711</td>\n <td>23.897625</td>\n <td>-3.552371</td>\n <td>9.184054</td>\n <td>-0.304160</td>\n <td>-34.576202</td>\n <td>36.869560</td>\n <td>-1.597456</td>\n <td>10.409478</td>\n <td>0.058469</td>\n </tr>\n <tr>\n <th>rock_90.mp3</th>\n <td>rock</td>\n <td>-501.955994</td>\n <td>9.573563</td>\n <td>-137.388382</td>\n <td>46.025847</td>\n <td>-0.004000</td>\n <td>-7.494962</td>\n <td>190.229202</td>\n <td>112.531166</td>\n <td>33.245804</td>\n <td>...</td>\n <td>-23.657921</td>\n <td>24.251360</td>\n <td>-4.784957</td>\n <td>6.215656</td>\n <td>0.480860</td>\n <td>-21.904373</td>\n <td>18.819710</td>\n <td>-1.302765</td>\n <td>5.389064</td>\n <td>-0.064191</td>\n </tr>\n </tbody>\n</table>\n<p>80 rows × 201 columns</p>\n</div>"
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test: DataFrame = joined[joined[\"train\"] == False].drop(\"train\", axis=1)\n",
+ "test"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.064138270Z",
+ "start_time": "2023-09-06T16:33:52.930093810Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "( 0_min 0_max 0_mean 0_std 0_skew \\\n filename \n classical_1.mp3 -530.784363 -163.308350 -302.203156 51.142183 -0.468374 \n classical_12.mp3 -562.675232 -148.133560 -270.975403 52.191182 -0.366587 \n classical_13.mp3 -637.720642 -177.713959 -361.834045 71.310080 0.008326 \n classical_14.mp3 -531.049438 -100.790543 -188.970749 58.287371 -3.246618 \n classical_15.mp3 -555.129944 -96.139236 -209.245819 45.350121 -3.574710 \n ... ... ... ... ... ... \n rock_95.mp3 -553.110107 -5.218835 -193.506042 76.869437 -0.201055 \n rock_96.mp3 -541.236023 27.163334 -119.113991 58.420684 -0.957699 \n rock_97.mp3 -518.494995 58.526745 -66.267746 65.635619 -0.898026 \n rock_98.mp3 -518.643066 53.555115 -45.734516 52.444200 -1.705641 \n rock_99.mp3 -544.703125 75.612129 -49.380943 54.045627 -0.863093 \n \n 1_min 1_max 1_mean 1_std 1_skew ... \\\n filename ... \n classical_1.mp3 0.000000 178.751617 111.332344 24.847563 -0.402642 ... \n classical_12.mp3 0.000000 194.264160 148.226654 19.305008 -0.533256 ... \n classical_13.mp3 0.000000 257.162842 211.556549 20.347035 -1.050120 ... \n classical_14.mp3 0.000000 157.947922 86.563927 17.911136 0.244245 ... \n classical_15.mp3 0.000000 140.918640 109.309990 14.171102 -2.617227 ... \n ... ... ... ... ... ... ... \n rock_95.mp3 -89.948746 201.180450 111.724190 36.463584 -0.443224 ... \n rock_96.mp3 -7.415961 210.492462 125.453690 31.908869 -0.547468 ... \n rock_97.mp3 -58.824409 175.201355 99.288261 25.158417 -0.568056 ... \n rock_98.mp3 0.000000 187.042740 96.440872 24.137702 -0.145216 ... \n rock_99.mp3 -32.930653 191.735382 93.971237 33.410220 0.040112 ... \n \n 38_min 38_max 38_mean 38_std 38_skew \\\n filename \n classical_1.mp3 -44.098068 47.308060 -3.713503 16.553984 0.230691 \n classical_12.mp3 -44.843811 28.490644 -6.242015 10.546545 0.341848 \n classical_13.mp3 -24.728806 18.424036 -0.275737 7.026148 -0.640964 \n classical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \n classical_15.mp3 -42.808113 24.146545 -7.260053 9.862490 0.097765 \n ... ... ... ... ... ... \n rock_95.mp3 -27.043941 22.451445 -7.234633 8.471853 0.753855 \n rock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \n rock_97.mp3 -29.620445 26.325895 -5.722826 7.727378 0.207489 \n rock_98.mp3 -26.967848 8.714737 -9.511492 5.551820 -0.025604 \n rock_99.mp3 -21.929403 17.050608 -5.296690 5.894963 0.390705 \n \n 39_min 39_max 39_mean 39_std 39_skew \n filename \n classical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \n classical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \n classical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \n classical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \n classical_15.mp3 -31.394997 35.685539 -0.949139 11.141700 0.249278 \n ... ... ... ... ... ... \n rock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \n rock_96.mp3 -38.147888 21.814402 -8.249507 7.807756 0.071968 \n rock_97.mp3 -29.497524 25.410654 -3.356615 8.170526 0.160330 \n rock_98.mp3 -23.020084 13.948638 -2.664985 5.051498 -0.258407 \n rock_99.mp3 -20.983192 29.312023 -0.321836 6.571660 0.384794 \n \n [320 rows x 200 columns],\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]))"
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# remove labels\n",
+ "X = train.drop(['label'], axis=1, errors='ignore')\n",
+ "\n",
+ "columns: Index = X.columns\n",
+ "classnames = np.sort(np.unique(joined.label.values)) # -> [\"classical\", \"electronic\", \"pop\", \"rock\"]\n",
+ "\n",
+ "# map classname to an index and create dicts for easy lookup in O(1)\n",
+ "classname2index = {}\n",
+ "index2classname = {}\n",
+ "\n",
+ "for i, classname in enumerate(classnames):\n",
+ " classname2index[classname] = i\n",
+ " index2classname[i] = classname\n",
+ "\n",
+ "# map label to label index\n",
+ "y = np.array([classname2index[classname] for classname in train.label.values])\n",
+ "\n",
+ "(X, y)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.384132460Z",
+ "start_time": "2023-09-06T16:33:52.974140158Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(320, 200)\n",
+ "(80, 200)\n",
+ "0.25\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_test = test.drop(['label'], axis=1, errors='ignore')\n",
+ "\n",
+ "print(X.shape)\n",
+ "print(X_test.shape)\n",
+ "print(X_test.shape[0] / X.shape[0]) # fraction of test sample\n",
+ "\n",
+ "y_test = np.array([classname2index[classname] for classname in test.label.values])\n",
+ "y_test"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.385211474Z",
+ "start_time": "2023-09-06T16:33:53.017055988Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "array([[ 0.43337449, -1.7728706 , -1.32763933, ..., -0.68466574,\n 3.57466709, 0.10865617],\n [-0.42729588, -1.58097286, -0.98456168, ..., 0.50149939,\n 1.74587552, 1.04983892],\n [-2.45262044, -1.95504141, -1.98276254, ..., -0.64583406,\n 0.42103984, -0.35077496],\n ...,\n [ 0.76503978, 1.03241718, 1.2644193 , ..., -0.99352289,\n 0.42041778, 0.07706691],\n [ 0.76104364, 0.96954681, 1.49000365, ..., -0.7947189 ,\n -0.96229984, -1.11123088],\n [ 0.05773511, 1.24847596, 1.44994288, ..., -0.12119682,\n -0.2883865 , 0.71405216]])"
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Standardize for PCA\n",
+ "scaler = StandardScaler()\n",
+ "X_standardized = scaler.fit_transform(X.values)\n",
+ "X_test_standardized = scaler.transform(X_test.values)\n",
+ "\n",
+ "X_standardized"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.387124363Z",
+ "start_time": "2023-09-06T16:33:53.035337727Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.8569547942728654\n",
+ "(320, 50)\n",
+ "(80, 50)\n",
+ "(320,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Reduce Dimensions via PCA\n",
+ "pca = PCA(n_components=50).fit(X_standardized)\n",
+ "X_pca = pca.transform(X_standardized)\n",
+ "X_test_pca = pca.transform(X_test_standardized)\n",
+ "\n",
+ "print(sum(pca.explained_variance_ratio_))\n",
+ "print(X_pca.shape)\n",
+ "print(X_test_pca.shape)\n",
+ "print(y.shape)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.648047288Z",
+ "start_time": "2023-09-06T16:33:53.076945689Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.78125\n",
+ "[[ 2.54932913 -0.8297137 -2.64117401 ... 0.0628467 -1.08498817\n",
+ " -0.54600181]\n",
+ " [ 5.60625198 -1.68012408 -3.12748987 ... -0.79584617 -0.8702528\n",
+ " 0.32833321]\n",
+ " [-4.29091225 -0.05226949 -4.71139064 ... 0.01077759 0.51144486\n",
+ " -0.02390549]\n",
+ " ...\n",
+ " [-7.89826346 1.0391027 -5.76202999 ... -0.43377044 -1.18091018\n",
+ " -0.44692849]\n",
+ " [-8.30381697 -1.3222363 2.37698638 ... 0.11685826 -0.13683289\n",
+ " -1.10917816]\n",
+ " [-4.76845573 -7.78718752 3.16067256 ... -1.23649128 0.39003957\n",
+ " 0.95120336]]\n",
+ "[3 0 3 2 3 0 1 2 0 3 0 0 0 1 2 1 2 3 2 1 1 0 3 0 0 0 3 1 1 3 3 2 3 1 2 1 0\n",
+ " 1 0 1 3 0 0 0 0 3 3 3 0 3 3 3 1 2 2 0 1 2 1 2 3 2 1 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit SVM:\n",
+ "\n",
+ "X_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size = 0.2, random_state=4, shuffle = True)\n",
+ "\n",
+ "clf = SVC(kernel='rbf', probability=True)\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_val), y_val))\n",
+ "print(X_val)\n",
+ "print(y_val)\n"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:53.908081165Z",
+ "start_time": "2023-09-06T16:33:53.275197554Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.7068627450980391\n",
+ "{'C': 4, 'gamma': 0.01}\n",
+ "SVC(C=4, gamma=0.01)\n",
+ "0.78125\n"
+ ]
+ }
+ ],
+ "source": [
+ "# grid for C, gamma\n",
+ "C_grid = [0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+ "gamma_grid = [0.001, 0.01, 0.1, 1, 10]\n",
+ "param_grid = {'C': C_grid, 'gamma': gamma_grid}\n",
+ "\n",
+ "grid = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5, scoring=\"accuracy\")\n",
+ "grid.fit(X_train, y_train)\n",
+ "\n",
+ "# Find the best model\n",
+ "print(grid.best_score_)\n",
+ "print(grid.best_params_)\n",
+ "print(grid.best_estimator_)\n",
+ "print(accuracy_score(grid.predict(X_val), y_val))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.350344280Z",
+ "start_time": "2023-09-06T16:33:53.459579739Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.78125\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Optimal model\n",
+ "\n",
+ "clf = SVC(kernel='rbf', C=4, gamma=0.01, probability=True)\n",
+ "clf.fit(X_train, y_train)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_val), y_val))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.440250619Z",
+ "start_time": "2023-09-06T16:34:01.354593939Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.7875\n",
+ "[[9.98186250e-01 7.07416061e-04 7.80359226e-04 3.25974952e-04]\n",
+ " [9.96457336e-01 8.61299907e-04 1.23630417e-03 1.44505986e-03]\n",
+ " [9.88168745e-01 3.07061855e-03 3.28921925e-03 5.47141688e-03]\n",
+ " [9.99725418e-01 2.34111856e-05 1.88388067e-04 6.27825592e-05]\n",
+ " [9.95553472e-01 1.24175862e-04 9.79386045e-04 3.34296563e-03]\n",
+ " [9.91956058e-01 5.30538055e-03 1.54189494e-03 1.19666681e-03]\n",
+ " [9.48805820e-01 4.51553061e-03 3.54571301e-02 1.12215196e-02]\n",
+ " [9.39011874e-01 1.25859524e-02 3.91467574e-02 9.25541627e-03]\n",
+ " [9.98779810e-01 3.54844256e-04 4.83538402e-04 3.81807584e-04]\n",
+ " [9.90943320e-01 6.68565763e-03 1.21029168e-03 1.16073074e-03]\n",
+ " [1.70650977e-01 4.12405105e-01 2.54547776e-01 1.62396142e-01]\n",
+ " [7.61283180e-01 5.85153569e-02 1.20185726e-01 6.00157375e-02]\n",
+ " [9.99812599e-01 5.80059091e-05 6.23304798e-05 6.70645603e-05]\n",
+ " [9.94666111e-01 3.87806861e-04 1.39823341e-03 3.54784853e-03]\n",
+ " [9.96732326e-01 2.09863248e-03 5.52203710e-04 6.16837643e-04]\n",
+ " [7.83508670e-01 2.25171519e-02 1.45607359e-01 4.83668199e-02]\n",
+ " [1.08947087e-01 1.43062693e-02 6.87834391e-01 1.88912253e-01]\n",
+ " [8.95644592e-01 1.19426746e-02 7.53917300e-02 1.70210036e-02]\n",
+ " [9.99765501e-01 4.27079401e-05 5.63234537e-05 1.35467724e-04]\n",
+ " [6.96168233e-01 2.32101342e-01 5.44296450e-02 1.73007797e-02]\n",
+ " [9.74327858e-01 6.29911950e-03 1.38415586e-02 5.53146410e-03]\n",
+ " [1.97208847e-03 9.39267826e-01 8.53321167e-03 5.02268735e-02]\n",
+ " [1.54495619e-03 9.67010528e-01 2.65155983e-02 4.92891741e-03]\n",
+ " [2.97549129e-03 7.41850329e-01 1.38927973e-01 1.16246207e-01]\n",
+ " [4.09082904e-03 4.48959962e-01 4.03601661e-01 1.43347548e-01]\n",
+ " [1.44331850e-03 8.96264314e-01 7.01120834e-02 3.21802844e-02]\n",
+ " [1.82609926e-02 6.93756720e-01 2.33339552e-01 5.46427349e-02]\n",
+ " [8.24945729e-02 6.49069944e-01 2.17144236e-01 5.12912463e-02]\n",
+ " [9.23976812e-02 7.76732703e-01 1.02546091e-01 2.83235249e-02]\n",
+ " [8.05320661e-01 1.41289007e-01 3.51733662e-02 1.82169657e-02]\n",
+ " [6.32741836e-04 8.05861715e-01 2.26476412e-02 1.70857902e-01]\n",
+ " [2.57706086e-02 4.08707961e-01 4.56859671e-01 1.08661759e-01]\n",
+ " [2.38704115e-03 8.17061213e-01 1.24634438e-01 5.59173084e-02]\n",
+ " [7.85891844e-04 3.23912838e-01 5.91400091e-02 6.16161261e-01]\n",
+ " [8.17398003e-03 5.92856683e-01 1.36843385e-01 2.62125952e-01]\n",
+ " [2.01727314e-03 9.18622935e-01 9.53308788e-03 6.98267044e-02]\n",
+ " [1.02655478e-01 4.15450834e-01 3.07215353e-01 1.74678335e-01]\n",
+ " [4.21319476e-04 3.08866780e-01 5.37141483e-01 1.53570417e-01]\n",
+ " [7.37060683e-04 6.26005330e-01 3.32306955e-01 4.09506550e-02]\n",
+ " [3.57947383e-03 3.38994309e-01 5.85661778e-01 7.17644389e-02]\n",
+ " [7.81396204e-04 9.05484100e-01 4.24574488e-02 5.12770549e-02]\n",
+ " [1.38673280e-02 8.44602740e-01 3.48480447e-02 1.06681887e-01]\n",
+ " [2.38678656e-02 3.16318564e-02 8.06543692e-01 1.37956586e-01]\n",
+ " [2.77447879e-02 1.76362240e-01 2.28042151e-01 5.67850821e-01]\n",
+ " [3.68498883e-03 1.93765262e-02 3.74744135e-01 6.02194350e-01]\n",
+ " [1.08250700e-01 1.31283644e-01 4.52307411e-01 3.08158244e-01]\n",
+ " [1.06117414e-03 4.57675502e-03 1.21562074e-01 8.72799997e-01]\n",
+ " [9.44284923e-03 6.01444265e-02 6.88640823e-01 2.41771902e-01]\n",
+ " [3.04315346e-03 4.91841391e-03 6.73229859e-01 3.18808574e-01]\n",
+ " [3.47407145e-03 3.38133523e-01 4.27311082e-01 2.31081323e-01]\n",
+ " [6.38991934e-02 2.34946580e-02 4.38116434e-01 4.74489715e-01]\n",
+ " [3.65337631e-02 1.47529551e-01 6.56699181e-01 1.59237505e-01]\n",
+ " [3.45369958e-02 4.86056971e-02 4.50222236e-01 4.66635071e-01]\n",
+ " [1.02874384e-01 1.14593225e-01 4.12828659e-01 3.69703732e-01]\n",
+ " [7.01929891e-04 4.72310828e-03 6.33659414e-01 3.60915547e-01]\n",
+ " [1.19280091e-03 2.91859540e-03 6.63499318e-01 3.32389286e-01]\n",
+ " [3.22247158e-04 1.86224604e-03 7.86294266e-01 2.11521241e-01]\n",
+ " [1.62556011e-02 1.10337495e-01 6.90066111e-01 1.83340793e-01]\n",
+ " [5.83937991e-03 8.91483148e-03 8.23557657e-01 1.61688132e-01]\n",
+ " [1.40771587e-03 1.99973215e-03 7.69667401e-01 2.26925151e-01]\n",
+ " [2.85627492e-03 4.09235838e-02 5.47033952e-01 4.09186189e-01]\n",
+ " [5.00914955e-02 5.66591605e-02 8.19154779e-01 7.40945653e-02]\n",
+ " [3.49728526e-04 1.03301315e-02 3.45356722e-01 6.43963418e-01]\n",
+ " [7.86355678e-03 5.17956142e-02 8.13005815e-01 1.27335014e-01]\n",
+ " [2.24811753e-04 1.50400751e-03 5.92965001e-01 4.05306180e-01]\n",
+ " [1.50292599e-03 1.18077604e-02 7.95371303e-01 1.91318010e-01]\n",
+ " [9.71722011e-04 1.93522506e-03 4.85836249e-01 5.11256804e-01]\n",
+ " [2.71508043e-04 8.47075628e-03 6.65359707e-02 9.24721765e-01]\n",
+ " [3.78695110e-04 7.86603624e-03 3.13831725e-01 6.77923544e-01]\n",
+ " [1.56018964e-03 5.04504439e-02 3.75946622e-01 5.72042744e-01]\n",
+ " [4.13000514e-03 4.44190116e-03 1.76929372e-01 8.14498722e-01]\n",
+ " [7.03938811e-04 5.20938587e-01 2.13900864e-01 2.64456610e-01]\n",
+ " [1.26489155e-03 3.03436964e-02 2.15768512e-01 7.52622900e-01]\n",
+ " [1.39383300e-04 2.39767850e-03 7.56550144e-01 2.40912794e-01]\n",
+ " [8.86305936e-04 4.62779469e-04 1.80744510e-01 8.17906405e-01]\n",
+ " [4.22976399e-04 5.51964764e-02 2.85832649e-01 6.58547898e-01]\n",
+ " [6.85422618e-04 1.63669725e-03 1.51783518e-01 8.45894362e-01]\n",
+ " [1.88958698e-03 1.04946968e-02 1.31240172e-01 8.56375545e-01]\n",
+ " [3.17948589e-03 2.18481451e-02 1.25011239e-01 8.49961130e-01]\n",
+ " [8.82291635e-04 1.06487654e-02 3.11812728e-01 6.76656214e-01]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit entire training sets\n",
+ "clf.fit(X_pca, y)\n",
+ "\n",
+ "print(accuracy_score(clf.predict(X_test_pca), y_test))\n",
+ "print(clf.predict_proba(X_test_pca))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.583086439Z",
+ "start_time": "2023-09-06T16:34:01.473457338Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " label pred1 pred2 pred3 pred4\n",
+ "filename \n",
+ "classical_10.mp3 classical classical pop electronic rock\n",
+ "classical_100.mp3 classical classical pop rock electronic\n",
+ "classical_11.mp3 classical classical rock pop electronic\n",
+ "classical_19.mp3 classical classical pop rock electronic\n",
+ "classical_20.mp3 classical classical rock pop electronic\n",
+ "classical_21.mp3 classical classical electronic pop rock\n",
+ "classical_24.mp3 classical classical pop rock electronic\n",
+ "classical_27.mp3 classical classical pop electronic rock\n",
+ "classical_28.mp3 classical classical pop electronic rock\n",
+ "classical_29.mp3 classical classical electronic pop rock\n",
+ "classical_40.mp3 classical electronic pop rock classical\n",
+ "classical_49.mp3 classical classical pop electronic rock\n",
+ "classical_5.mp3 classical classical pop electronic rock\n",
+ "classical_51.mp3 classical classical rock pop electronic\n",
+ "classical_58.mp3 classical classical electronic pop rock\n",
+ "classical_69.mp3 classical classical pop rock electronic\n",
+ "classical_71.mp3 classical pop rock classical electronic\n",
+ "classical_8.mp3 classical classical pop rock electronic\n",
+ "classical_92.mp3 classical classical rock pop electronic\n",
+ "classical_97.mp3 classical classical electronic pop rock\n",
+ "classical_98.mp3 classical classical pop electronic rock\n",
+ "electronic_10.mp3 electronic electronic rock pop classical\n",
+ "electronic_100.mp3 electronic electronic pop rock classical\n",
+ "electronic_14.mp3 electronic electronic pop rock classical\n",
+ "electronic_2.mp3 electronic electronic pop rock classical\n",
+ "electronic_23.mp3 electronic electronic pop rock classical\n",
+ "electronic_27.mp3 electronic electronic pop rock classical\n",
+ "electronic_3.mp3 electronic electronic pop classical rock\n",
+ "electronic_32.mp3 electronic electronic pop classical rock\n",
+ "electronic_47.mp3 electronic classical electronic pop rock\n",
+ "electronic_48.mp3 electronic electronic rock pop classical\n",
+ "electronic_51.mp3 electronic pop electronic rock classical\n",
+ "electronic_52.mp3 electronic electronic pop rock classical\n",
+ "electronic_54.mp3 electronic rock electronic pop classical\n",
+ "electronic_55.mp3 electronic electronic rock pop classical\n",
+ "electronic_56.mp3 electronic electronic rock pop classical\n",
+ "electronic_57.mp3 electronic electronic pop rock classical\n",
+ "electronic_75.mp3 electronic pop electronic rock classical\n",
+ "electronic_77.mp3 electronic electronic pop rock classical\n",
+ "electronic_84.mp3 electronic pop electronic rock classical\n",
+ "electronic_86.mp3 electronic electronic rock pop classical\n",
+ "electronic_95.mp3 electronic electronic rock pop classical\n",
+ "pop_10.mp3 pop pop rock electronic classical\n",
+ "pop_100.mp3 pop rock pop electronic classical\n",
+ "pop_2.mp3 pop rock pop electronic classical\n",
+ "pop_34.mp3 pop pop rock electronic classical\n",
+ "pop_37.mp3 pop rock pop electronic classical\n",
+ "pop_41.mp3 pop pop rock electronic classical\n",
+ "pop_42.mp3 pop pop rock electronic classical\n",
+ "pop_47.mp3 pop pop electronic rock classical\n",
+ "pop_52.mp3 pop rock pop classical electronic\n",
+ "pop_57.mp3 pop pop rock electronic classical\n",
+ "pop_6.mp3 pop rock pop electronic classical\n",
+ "pop_61.mp3 pop pop rock electronic classical\n",
+ "pop_62.mp3 pop pop rock electronic classical\n",
+ "pop_64.mp3 pop pop rock electronic classical\n",
+ "pop_65.mp3 pop pop rock electronic classical\n",
+ "pop_7.mp3 pop pop rock electronic classical\n",
+ "pop_72.mp3 pop pop rock electronic classical\n",
+ "pop_73.mp3 pop pop rock electronic classical\n",
+ "pop_8.mp3 pop pop rock electronic classical\n",
+ "pop_86.mp3 pop pop rock electronic classical\n",
+ "pop_87.mp3 pop rock pop electronic classical\n",
+ "pop_92.mp3 pop pop rock electronic classical\n",
+ "pop_94.mp3 pop pop rock electronic classical\n",
+ "pop_97.mp3 pop pop rock electronic classical\n",
+ "rock_11.mp3 rock rock pop electronic classical\n",
+ "rock_22.mp3 rock rock pop electronic classical\n",
+ "rock_25.mp3 rock rock pop electronic classical\n",
+ "rock_26.mp3 rock rock pop electronic classical\n",
+ "rock_4.mp3 rock rock pop electronic classical\n",
+ "rock_41.mp3 rock electronic rock pop classical\n",
+ "rock_45.mp3 rock rock pop electronic classical\n",
+ "rock_48.mp3 rock pop rock electronic classical\n",
+ "rock_54.mp3 rock rock pop classical electronic\n",
+ "rock_57.mp3 rock rock pop electronic classical\n",
+ "rock_66.mp3 rock rock pop electronic classical\n",
+ "rock_75.mp3 rock rock pop electronic classical\n",
+ "rock_81.mp3 rock rock pop electronic classical\n",
+ "rock_90.mp3 rock rock pop electronic classical\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit the entire training sets\n",
+ "\n",
+ "def convert_to_labels(preds, i2c, k=3):\n",
+ " ans = []\n",
+ " ids = []\n",
+ " for p in preds:\n",
+ " idx = np.argsort(p)[::-1]\n",
+ " ids.append([i for i in idx[:k]])\n",
+ " ans.append([i2c[i] for i in idx[:k]])\n",
+ "\n",
+ " return ans, ids\n",
+ "\n",
+ "clf.fit(X_pca, y)\n",
+ "prediction_lists, percentage_lists = convert_to_labels(clf.predict_proba(X_test_pca), index2classname, k=4)\n",
+ "\n",
+ "# # Write to outputs\n",
+ "subm = pd.DataFrame(index=test.index)\n",
+ "subm['label'] = test.label.values\n",
+ "subm['pred1'] = [prediction_list[0] for prediction_list in prediction_lists]\n",
+ "subm['pred2'] = [prediction_list[1] for prediction_list in prediction_lists]\n",
+ "subm['pred3'] = [prediction_list[2] for prediction_list in prediction_lists]\n",
+ "subm['pred4'] = [prediction_list[3] for prediction_list in prediction_lists]\n",
+ "\n",
+ "pd.set_option('display.max_rows', None)\n",
+ "print(subm)\n",
+ "pd.reset_option('display.max_rows')"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.741253646Z",
+ "start_time": "2023-09-06T16:34:01.567005786Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "outputs": [],
+ "source": [
+ "with open(LOCAL_PATH / \"clf.pickle\", \"wb\") as file:\n",
+ " pickle.dump(clf, file)\n",
+ "subm.to_csv(LOCAL_PATH / \"submission.csv\", index=False)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.742181308Z",
+ "start_time": "2023-09-06T16:34:01.725149499Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "outputs": [],
+ "source": [
+ "if not ONLY_LOCAL:\n",
+ " with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
+ " metadata = yaml.safe_load(file)\n",
+ "\n",
+ " nb_config_ml = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " entities=[\n",
+ " ml_model_entity := InvenioEntity.new(\n",
+ " name=\"Standalone Machine Learning model\",\n",
+ " description=\"An ml model representing the trained clf\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"ml_model.pickle\",\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"clf\"\n",
+ " ),\n",
+ " test_result_entity := DbRepoEntity.new(\n",
+ " name=\"Standalone Test Result Entity\",\n",
+ " description=\"Result of tests on ml model\",\n",
+ " table_name=\"test_result\",\n",
+ " table_description=\"Test results of genre prediction on ml model\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"test_result.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " type=\"submission\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " audio_files_entity\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " executor.upload_entities(nb_config_ml)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:34:01.832515470Z",
+ "start_time": "2023-09-06T16:34:01.741700194Z"
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/1_audio_files.ipynb b/notebooks/1_audio_files.ipynb
index 4803e8e1c94d9cdca630e0dbd607ec59c1b74607..24456088a68c3ede3dd0a13b0660d218120040b4 100644
--- a/notebooks/1_audio_files.ipynb
+++ b/notebooks/1_audio_files.ipynb
@@ -2,45 +2,17 @@
"cells": [
{
"cell_type": "markdown",
+ "id": "4389a8092677254e",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
"source": [
"# Audio Files\n",
"\n",
"Bundle the provided audio files (400, in MP3) in a tar, encrypt it using gzip and store it in the output folder."
- ],
- "metadata": {
- "collapsed": false
- },
- "id": "4389a8092677254e"
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "1b4e6b01",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-09-01T11:23:36.088100Z",
- "iopub.status.busy": "2023-09-01T11:23:36.087181Z",
- "iopub.status.idle": "2023-09-01T11:23:36.095275Z",
- "shell.execute_reply": "2023-09-01T11:23:36.094811Z"
- },
- "papermill": {
- "duration": 0.01235,
- "end_time": "2023-09-01T11:23:36.096700",
- "exception": false,
- "start_time": "2023-09-01T11:23:36.084350",
- "status": "completed"
- },
- "tags": [
- "injected-parameters"
- ]
- },
- "outputs": [],
- "source": [
- "# Parameters\n",
- "INPUT_PATHS = {}\n",
- "OUTPUT_PATHS = {\n",
- " \"audio_tar\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/1_audio_files/output/emotifymusic.tar.gz\"\n",
- "}\n"
]
},
{
@@ -49,11 +21,8 @@
"id": "87ab37c6",
"metadata": {
"collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-09-01T11:23:36.102334Z",
- "iopub.status.busy": "2023-09-01T11:23:36.101625Z",
- "iopub.status.idle": "2023-09-01T11:23:36.110013Z",
- "shell.execute_reply": "2023-09-01T11:23:36.109240Z"
+ "jupyter": {
+ "outputs_hidden": false
},
"papermill": {
"duration": 0.015854,
@@ -76,14 +45,36 @@
{
"cell_type": "code",
"execution_count": 3,
+ "id": "1b4e6b01",
+ "metadata": {
+ "papermill": {
+ "duration": 0.01235,
+ "end_time": "2023-09-01T11:23:36.096700",
+ "exception": false,
+ "start_time": "2023-09-01T11:23:36.084350",
+ "status": "completed"
+ },
+ "tags": [
+ "parameters"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Parameters\n",
+ "INPUT_PATHS = {}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"audio_tar\": str(BASE_PATH / \"tmp/1_audio_files/output/emotifymusic.tar.gz\")\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
"id": "1e487573",
"metadata": {
"collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-09-01T11:23:36.128421Z",
- "iopub.status.busy": "2023-09-01T11:23:36.128029Z",
- "iopub.status.idle": "2023-09-01T11:23:38.662698Z",
- "shell.execute_reply": "2023-09-01T11:23:38.662041Z"
+ "jupyter": {
+ "outputs_hidden": false
},
"papermill": {
"duration": 2.541999,
@@ -103,20 +94,17 @@
"dir_path.mkdir(parents=True, exist_ok=True)\n",
"# unzip to dir_path\n",
"with zipfile.ZipFile(zip_path, \"r\") as zfile:\n",
- " zfile.extractall(path=dir_path)\n"
+ " zfile.extractall(path=dir_path)"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "c3193f35",
"metadata": {
"collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-09-01T11:23:38.674226Z",
- "iopub.status.busy": "2023-09-01T11:23:38.673855Z",
- "iopub.status.idle": "2023-09-01T11:23:39.733494Z",
- "shell.execute_reply": "2023-09-01T11:23:39.732779Z"
+ "jupyter": {
+ "outputs_hidden": false
},
"papermill": {
"duration": 1.066369,
@@ -134,20 +122,17 @@
"flattened_dir_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
"for path in file_paths:\n",
- " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())\n"
+ " (flattened_dir_path / path.relative_to(dir_path).as_posix().replace('/', '_')).write_bytes(path.read_bytes())"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"id": "3272ea2b",
"metadata": {
"collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-09-01T11:23:39.742620Z",
- "iopub.status.busy": "2023-09-01T11:23:39.741545Z",
- "iopub.status.idle": "2023-09-01T11:23:55.002485Z",
- "shell.execute_reply": "2023-09-01T11:23:55.001071Z"
+ "jupyter": {
+ "outputs_hidden": false
},
"papermill": {
"duration": 15.267255,
@@ -170,7 +155,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/notebooks/3_aggregate_features.ipynb b/notebooks/3_aggregate_features.ipynb
index a76cd0743ab7442eb805aeb288b7fa71638ffebc..2a1646457063b748a64d7fa7711c8f2ae9686943 100644
--- a/notebooks/3_aggregate_features.ipynb
+++ b/notebooks/3_aggregate_features.ipynb
@@ -35,6 +35,9 @@
"iopub.status.idle": "2023-09-01T11:35:09.306707Z",
"shell.execute_reply": "2023-09-01T11:35:09.305772Z"
},
+ "jupyter": {
+ "outputs_hidden": true
+ },
"papermill": {
"duration": 0.268336,
"end_time": "2023-09-01T11:35:09.308546",
@@ -76,18 +79,16 @@
},
"outputs": [],
"source": [
- "# 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",
- "INPUT_PATHS: dict[str, str] = {}\n",
- "OUTPUT_PATHS: dict[str, str] = {}"
+ "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",
+ "}"
]
},
{
@@ -636,4 +637,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/notebooks/4_split.ipynb b/notebooks/4_split.ipynb
index bf05bfed1ab18066f75006ee4d4f60ba6c6b4336..e4d2c5b8d040a4d02b422248156afd19de044258 100644
--- a/notebooks/4_split.ipynb
+++ b/notebooks/4_split.ipynb
@@ -1,38 +1,5 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "205bb941",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-09-01T11:35:21.808793Z",
- "iopub.status.busy": "2023-09-01T11:35:21.808502Z",
- "iopub.status.idle": "2023-09-01T11:35:21.824152Z",
- "shell.execute_reply": "2023-09-01T11:35:21.822789Z"
- },
- "papermill": {
- "duration": 0.023269,
- "end_time": "2023-09-01T11:35:21.827306",
- "exception": false,
- "start_time": "2023-09-01T11:35:21.804037",
- "status": "completed"
- },
- "tags": [
- "injected-parameters"
- ]
- },
- "outputs": [],
- "source": [
- "# Parameters\n",
- "INPUT_PATHS = {\n",
- " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv\"\n",
- "}\n",
- "OUTPUT_PATHS = {\n",
- " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"\n",
- "}\n"
- ]
- },
{
"cell_type": "markdown",
"id": "e92b4fe9",
@@ -89,6 +56,9 @@
"iopub.status.idle": "2023-09-01T11:35:22.160059Z",
"shell.execute_reply": "2023-09-01T11:35:22.159355Z"
},
+ "jupyter": {
+ "outputs_hidden": false
+ },
"papermill": {
"duration": 0.010206,
"end_time": "2023-09-01T11:35:22.161506",
@@ -96,7 +66,9 @@
"start_time": "2023-09-01T11:35:22.151300",
"status": "completed"
},
- "tags": []
+ "tags": [
+ "parameters"
+ ]
},
"outputs": [],
"source": [
@@ -113,60 +85,35 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "85fd7edd",
+ "execution_count": 1,
+ "id": "205bb941",
"metadata": {
- "collapsed": false,
"execution": {
- "iopub.execute_input": "2023-09-01T11:35:22.169267Z",
- "iopub.status.busy": "2023-09-01T11:35:22.168949Z",
- "iopub.status.idle": "2023-09-01T11:35:22.172527Z",
- "shell.execute_reply": "2023-09-01T11:35:22.171833Z"
+ "iopub.execute_input": "2023-09-01T11:35:21.808793Z",
+ "iopub.status.busy": "2023-09-01T11:35:21.808502Z",
+ "iopub.status.idle": "2023-09-01T11:35:21.824152Z",
+ "shell.execute_reply": "2023-09-01T11:35:21.822789Z"
},
"papermill": {
- "duration": 0.009521,
- "end_time": "2023-09-01T11:35:22.174242",
+ "duration": 0.023269,
+ "end_time": "2023-09-01T11:35:21.827306",
"exception": false,
- "start_time": "2023-09-01T11:35:22.164721",
+ "start_time": "2023-09-01T11:35:21.804037",
"status": "completed"
},
- "tags": []
+ "tags": [
+ "injected-parameters"
+ ]
},
"outputs": [],
"source": [
"# Parameters\n",
- "INPUT_PATHS = {\"features\": \"/home/lukas/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/tmp/4_split/input/features.csv\"}\n",
- "OUTPUT_PATHS = {\"split\": \"/home/lukas/OneDrive-TU/Uni/Bachelorarbeit/repos/dbrepo-ismir/tmp/4_split/output/split.csv\"}\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "95bb5d4a",
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-09-01T11:35:22.180866Z",
- "iopub.status.busy": "2023-09-01T11:35:22.180523Z",
- "iopub.status.idle": "2023-09-01T11:35:22.183496Z",
- "shell.execute_reply": "2023-09-01T11:35:22.183004Z"
- },
- "papermill": {
- "duration": 0.007831,
- "end_time": "2023-09-01T11:35:22.184753",
- "exception": false,
- "start_time": "2023-09-01T11:35:22.176922",
- "status": "completed"
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# if not INPUT_PATHS[\"features\"]:\n",
- "# INPUT_PATHS[\"features\"] = (BASE_PATH / \"tmp\" / \"4_split\" / \"input\" / \"features.csv\").__str__()\n",
- "#\n",
- "# if not OUTPUT_PATHS[\"split\"]:\n",
- "# OUTPUT_PATHS[\"split\"] = (BASE_PATH / \"tmp\" / \"4_split\" / \"output\" / \"split.csv\").__str__()"
+ "INPUT_PATHS = {\n",
+ " \"aggregated_features\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/input/features.csv\"\n",
+ "}\n",
+ "OUTPUT_PATHS = {\n",
+ " \"split\": \"/home/lukas/Programming/uni/bachelorarbeit/dbrepo-ismir/tmp/4_split/output/split.csv\"\n",
+ "}\n"
]
},
{
diff --git a/notebooks/6_report.ipynb b/notebooks/6_report.ipynb
index 9e60cbbd55990c547d0733cd2b15e91efb2eab9e..6accbc10905d20f111ce5fc695465cedf76a00a9 100644
--- a/notebooks/6_report.ipynb
+++ b/notebooks/6_report.ipynb
@@ -4,7 +4,10 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "collapsed": true
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
},
"outputs": [],
"source": []
@@ -12,23 +15,23 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
}
diff --git a/notebooks/standalone.ipynb b/notebooks/standalone.ipynb
index 7ab6b18dfd6cc68f976e102c59f22f5d16d59951..06486e7a7e3bcf45db51c363e2b6804d6d9a0ff2 100644
--- a/notebooks/standalone.ipynb
+++ b/notebooks/standalone.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 1,
"outputs": [],
"source": [
"import pickle\n",
@@ -47,8 +47,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T13:57:59.609325923Z",
- "start_time": "2023-09-05T13:57:59.177097526Z"
+ "end_time": "2023-09-06T16:23:50.320823230Z",
+ "start_time": "2023-09-06T16:23:48.109782272Z"
}
}
},
@@ -66,13 +66,15 @@
"\n",
"NOTEBOOK_PATH = BASE_PATH / \"notebooks\"\n",
"LOCAL_PATH = BASE_PATH / \"tmp\" / \"standalone\"\n",
- "NB_LOCATION = NOTEBOOK_PATH / \"standalone.ipynb\"\n"
+ "NB_LOCATION = NOTEBOOK_PATH / \"standalone.ipynb\"\n",
+ "\n",
+ "ONLY_LOCAL = True"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T11:51:20.864136489Z",
- "start_time": "2023-09-05T11:51:20.725755134Z"
+ "end_time": "2023-09-06T16:23:50.458322232Z",
+ "start_time": "2023-09-06T16:23:50.353619129Z"
}
}
},
@@ -118,8 +120,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T11:51:41.563812538Z",
- "start_time": "2023-09-05T11:51:24.636708361Z"
+ "end_time": "2023-09-06T16:24:08.646424970Z",
+ "start_time": "2023-09-06T16:23:50.456742546Z"
}
}
},
@@ -128,31 +130,32 @@
"execution_count": 4,
"outputs": [],
"source": [
- "metadata = yaml.safe_load(open(RESOURCE_PATH / \"1_audio_files\" / \"record_metadata.yml\", \"r\"))\n",
+ "if not ONLY_LOCAL:\n",
+ " metadata = yaml.safe_load(open(RESOURCE_PATH / \"1_audio_files\" / \"record_metadata.yml\", \"r\"))\n",
"\n",
- "nb_config_audio_files = NbConfig(\n",
- " nb_location=NB_LOCATION,\n",
- " entities=[\n",
- " audio_files_entity := InvenioEntity.new(\n",
- " name = \"audio_tar\",\n",
- " description = \"Raw music files\",\n",
- " location = tar_path,\n",
- " dbrepo_connector=connector,\n",
- " invenio_connector=invenio_connector,\n",
- " record_metadata=metadata,\n",
- " type=\"audio_tar\"\n",
- " )\n",
- " ],\n",
- " dependencies=[]\n",
- ")\n",
+ " nb_config_audio_files = NbConfig(\n",
+ " nb_location=NB_LOCATION,\n",
+ " entities=[\n",
+ " audio_files_entity := InvenioEntity.new(\n",
+ " name = \"audio_tar\",\n",
+ " description = \"Raw music files\",\n",
+ " location = tar_path,\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"audio_tar\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[]\n",
+ " )\n",
"\n",
- "executor.upload_entities(nb_config_audio_files)"
+ " executor.upload_entities(nb_config_audio_files)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T11:54:32.749919104Z",
- "start_time": "2023-09-05T11:52:04.362976432Z"
+ "end_time": "2023-09-06T16:24:08.657491220Z",
+ "start_time": "2023-09-06T16:24:08.653629315Z"
}
}
},
@@ -221,27 +224,27 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T11:57:15.688222149Z",
- "start_time": "2023-09-05T11:54:42.905095095Z"
+ "end_time": "2023-09-06T16:32:07.450652088Z",
+ "start_time": "2023-09-06T16:24:08.668477833Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 6,
"outputs": [
{
"data": {
- "text/plain": "[<matplotlib.lines.Line2D at 0x7f3aec8bd780>]"
+ "text/plain": "[<matplotlib.lines.Line2D at 0x7efe441870a0>]"
},
- "execution_count": 46,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": "<Figure size 640x480 with 1 Axes>",
- "image/png": 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"
+ "image/png": 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"
},
"metadata": {},
"output_type": "display_data"
@@ -249,15 +252,16 @@
],
"source": [
"example_mfcc = raw_features[raw_features.filename == \"rock_50.mp3\"].sort_values(\"sample\").iloc[:,:]\n",
- "plt.plot(example_mfcc[39])\n",
+ "plt.plot(example_mfcc[15])\n",
+ "# plt.plot(example_mfcc[4])\n",
"\n",
"# librosa.display.waveshow(audio)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T13:58:04.288291098Z",
- "start_time": "2023-09-05T13:58:03.937511714Z"
+ "end_time": "2023-09-06T16:32:08.167783042Z",
+ "start_time": "2023-09-06T16:32:07.535299813Z"
}
}
},
@@ -270,14 +274,33 @@
"collapsed": false
}
},
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [],
+ "source": [
+ "# allow for direct entry if features were already created in earlier run\n",
+ "raw_features.to_csv(LOCAL_PATH / \"raw_features.csv\", index=False)\n",
+ "\n",
+ "if \"raw_features\" not in globals():\n",
+ " raw_features = pd.read_csv(LOCAL_PATH / \"raw_features.csv\")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-09-06T16:33:37.098344632Z",
+ "start_time": "2023-09-06T16:32:08.174101151Z"
+ }
+ }
+ },
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"data": {
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<td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561839</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632721</td>\n <td>215.094193</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989979</td>\n <td>27.533707</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397882</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667439</td>\n <td>50.992905</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>...</td>\n <td>-44.843815</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040886</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687983</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163332</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415959</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087940</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814400</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410656</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042725</td>\n <td>96.440872</td>\n <td>...</td>\n <td>-26.967852</td>\n <td>8.714736</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020082</td>\n <td>13.948639</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930649</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894962</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312021</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ "text/plain": " filename label 0_min 0_max 0_mean \\\n0 classical_1.mp3 classical -530.784363 -163.308350 -302.203156 \n1 classical_10.mp3 classical -562.857849 -96.164795 -219.259018 \n2 classical_100.mp3 classical -536.237366 -61.608826 -177.804108 \n3 classical_11.mp3 classical -536.457458 -120.429665 -222.126312 \n4 classical_12.mp3 classical -562.675232 -148.133560 -270.975403 \n.. ... ... ... ... ... \n395 rock_95.mp3 rock -553.110107 -5.218835 -193.506042 \n396 rock_96.mp3 rock -541.236023 27.163334 -119.113991 \n397 rock_97.mp3 rock -518.494995 58.526745 -66.267746 \n398 rock_98.mp3 rock -518.643066 53.555115 -45.734516 \n399 rock_99.mp3 rock -544.703125 75.612129 -49.380943 \n\n 0_std 0_skew 1_min 1_max 1_mean ... 38_min \\\n0 51.142183 -0.468374 0.000000 178.751617 111.332344 ... -44.098068 \n1 53.561838 -0.772320 0.029056 259.632690 215.094193 ... -27.458416 \n2 83.381622 -2.587179 0.000000 190.475891 112.471710 ... -27.335688 \n3 76.246992 -2.402419 0.000000 159.425751 99.853645 ... -31.774948 \n4 52.191182 -0.366587 0.000000 194.264160 148.226654 ... -44.843811 \n.. ... ... ... ... ... ... ... \n395 76.869437 -0.201055 -89.948746 201.180450 111.724190 ... -27.043941 \n396 58.420684 -0.957699 -7.415961 210.492462 125.453690 ... -37.584858 \n397 65.635619 -0.898026 -58.824409 175.201355 99.288261 ... -29.620445 \n398 52.444200 -1.705641 0.000000 187.042740 96.440872 ... -26.967848 \n399 54.045627 -0.863093 -32.930653 191.735382 93.971237 ... -21.929403 \n\n 38_max 38_mean 38_std 38_skew 39_min 39_max 39_mean \\\n0 47.308060 -3.713503 16.553984 0.230691 -46.794479 49.352516 -2.282116 \n1 29.811110 0.484271 8.660648 -0.479016 -28.989983 27.533710 0.952658 \n2 27.610388 -0.333233 8.185075 0.208425 -38.095375 31.397881 -1.494916 \n3 31.500881 -3.781627 9.191043 0.260886 -22.667440 50.992897 1.600777 \n4 28.490644 -6.242015 10.546545 0.341848 -25.040888 46.878204 1.844494 \n.. ... ... ... ... ... ... ... \n395 22.451445 -7.234633 8.471853 0.753855 -24.712723 23.410387 -4.502398 \n396 28.087936 -9.704238 8.447620 0.112760 -38.147888 21.814402 -8.249507 \n397 26.325895 -5.722826 7.727378 0.207489 -29.497524 25.410654 -3.356615 \n398 8.714737 -9.511492 5.551820 -0.025604 -23.020084 13.948638 -2.664985 \n399 17.050608 -5.296690 5.894963 0.390705 -20.983192 29.312023 -0.321836 \n\n 39_std 39_skew \n0 15.285639 0.171462 \n1 10.477735 -0.185771 \n2 10.917299 0.020984 \n3 10.125545 0.595763 \n4 11.160392 0.503120 \n.. ... ... \n395 6.687984 0.238807 \n396 7.807756 0.071968 \n397 8.170526 0.160330 \n398 5.051498 -0.258407 \n399 6.571660 0.384794 \n\n[400 rows x 202 columns]",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>filename</th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>...</th>\n <th>38_min</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>classical_1.mp3</td>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>1</th>\n <td>classical_10.mp3</td>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>2</th>\n <td>classical_100.mp3</td>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>3</th>\n <td>classical_11.mp3</td>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>4</th>\n <td>classical_12.mp3</td>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>...</td>\n <td>-44.843811</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>395</th>\n <td>rock_95.mp3</td>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>396</th>\n <td>rock_96.mp3</td>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>397</th>\n <td>rock_97.mp3</td>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>398</th>\n <td>rock_98.mp3</td>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>399</th>\n <td>rock_99.mp3</td>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
},
"execution_count": 8,
"metadata": {},
@@ -305,8 +328,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:01:52.228042870Z",
- "start_time": "2023-09-05T12:01:48.012959201Z"
+ "end_time": "2023-09-06T16:33:52.624562481Z",
+ "start_time": "2023-09-06T16:33:40.167149213Z"
}
}
},
@@ -321,14 +344,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"outputs": [
{
"data": {
- "text/plain": " filename train\n0 classical_1.mp3 True\n1 classical_10.mp3 True\n2 classical_100.mp3 True\n3 classical_11.mp3 True\n4 classical_12.mp3 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 False\n\n[400 rows x 2 columns]",
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+ "text/plain": " filename train\n0 classical_1.mp3 True\n1 classical_10.mp3 False\n2 classical_100.mp3 False\n3 classical_11.mp3 False\n4 classical_12.mp3 True\n.. ... ...\n395 rock_95.mp3 True\n396 rock_96.mp3 True\n397 rock_97.mp3 True\n398 rock_98.mp3 True\n399 rock_99.mp3 True\n\n[400 rows x 2 columns]",
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},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -355,8 +378,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:31:04.089580231Z",
- "start_time": "2023-09-05T12:31:04.047436419Z"
+ "end_time": "2023-09-06T16:33:52.625265280Z",
+ "start_time": "2023-09-06T16:33:52.600299846Z"
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@@ -371,14 +394,14 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"outputs": [
{
"data": {
- "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561839 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163332 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847562 \nclassical_10.mp3 -0.772320 0.029056 259.632721 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916948 \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 \n... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415959 210.492462 125.453690 31.908870 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \nrock_98.mp3 -1.705641 0.000000 187.042725 96.440872 24.137702 \nrock_99.mp3 -0.863093 -32.930649 191.735382 93.971237 33.410221 \n\n ... 38_max 38_mean 38_std 38_skew 39_min \\\nfilename ... \nclassical_1.mp3 ... 47.308060 -3.713503 16.553984 0.230691 -46.794479 \nclassical_10.mp3 ... 29.811110 0.484271 8.660648 -0.479016 -28.989979 \nclassical_100.mp3 ... 27.610388 -0.333233 8.185075 0.208425 -38.095375 \nclassical_11.mp3 ... 31.500881 -3.781627 9.191043 0.260886 -22.667439 \nclassical_12.mp3 ... 28.490644 -6.242015 10.546545 0.341848 -25.040886 \n... ... ... ... ... ... ... \nrock_95.mp3 ... 22.451445 -7.234633 8.471853 0.753855 -24.712723 \nrock_96.mp3 ... 28.087940 -9.704238 8.447620 0.112760 -38.147888 \nrock_97.mp3 ... 26.325895 -5.722826 7.727378 0.207489 -29.497524 \nrock_98.mp3 ... 8.714736 -9.511492 5.551820 -0.025604 -23.020082 \nrock_99.mp3 ... 17.050608 -5.296690 5.894962 0.390705 -20.983192 \n\n 39_max 39_mean 39_std 39_skew train \nfilename \nclassical_1.mp3 49.352516 -2.282116 15.285639 0.171462 True \nclassical_10.mp3 27.533707 0.952658 10.477735 -0.185771 True \nclassical_100.mp3 31.397882 -1.494916 10.917299 0.020984 True \nclassical_11.mp3 50.992905 1.600777 10.125545 0.595763 True \nclassical_12.mp3 46.878204 1.844494 11.160392 0.503120 True \n... ... ... ... ... ... \nrock_95.mp3 23.410387 -4.502398 6.687983 0.238807 True \nrock_96.mp3 21.814400 -8.249507 7.807756 0.071968 True \nrock_97.mp3 25.410656 -3.356615 8.170526 0.160330 True \nrock_98.mp3 13.948639 -2.664985 5.051498 -0.258407 True \nrock_99.mp3 29.312021 -0.321836 6.571660 0.384794 False \n\n[400 rows x 202 columns]",
- "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>0_min</th>\n <th>0_max</th>\n <th>0_mean</th>\n <th>0_std</th>\n <th>0_skew</th>\n <th>1_min</th>\n <th>1_max</th>\n <th>1_mean</th>\n <th>1_std</th>\n <th>...</th>\n <th>38_max</th>\n <th>38_mean</th>\n <th>38_std</th>\n <th>38_skew</th>\n <th>39_min</th>\n <th>39_max</th>\n <th>39_mean</th>\n <th>39_std</th>\n <th>39_skew</th>\n <th>train</th>\n </tr>\n <tr>\n <th>filename</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847562</td>\n <td>...</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561839</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632721</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989979</td>\n <td>27.533707</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397882</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916948</td>\n <td>...</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667439</td>\n <td>50.992905</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n <td>True</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040886</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n <td>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>6.687983</td>\n <td>0.238807</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163332</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415959</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908870</td>\n <td>...</td>\n <td>28.087940</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814400</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410656</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042725</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>8.714736</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020082</td>\n <td>13.948639</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n <td>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>-32.930649</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410221</td>\n <td>...</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894962</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312021</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561838 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163334 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847563 \nclassical_10.mp3 -0.772320 0.029056 259.632690 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916949 \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 \n... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415961 210.492462 125.453690 31.908869 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \nrock_98.mp3 -1.705641 0.000000 187.042740 96.440872 24.137702 \nrock_99.mp3 -0.863093 -32.930653 191.735382 93.971237 33.410220 \n\n ... 38_max 38_mean 38_std 38_skew 39_min \\\nfilename ... \nclassical_1.mp3 ... 47.308060 -3.713503 16.553984 0.230691 -46.794479 \nclassical_10.mp3 ... 29.811110 0.484271 8.660648 -0.479016 -28.989983 \nclassical_100.mp3 ... 27.610388 -0.333233 8.185075 0.208425 -38.095375 \nclassical_11.mp3 ... 31.500881 -3.781627 9.191043 0.260886 -22.667440 \nclassical_12.mp3 ... 28.490644 -6.242015 10.546545 0.341848 -25.040888 \n... ... ... ... ... ... ... \nrock_95.mp3 ... 22.451445 -7.234633 8.471853 0.753855 -24.712723 \nrock_96.mp3 ... 28.087936 -9.704238 8.447620 0.112760 -38.147888 \nrock_97.mp3 ... 26.325895 -5.722826 7.727378 0.207489 -29.497524 \nrock_98.mp3 ... 8.714737 -9.511492 5.551820 -0.025604 -23.020084 \nrock_99.mp3 ... 17.050608 -5.296690 5.894963 0.390705 -20.983192 \n\n 39_max 39_mean 39_std 39_skew train \nfilename \nclassical_1.mp3 49.352516 -2.282116 15.285639 0.171462 True \nclassical_10.mp3 27.533710 0.952658 10.477735 -0.185771 False \nclassical_100.mp3 31.397881 -1.494916 10.917299 0.020984 False \nclassical_11.mp3 50.992897 1.600777 10.125545 0.595763 False \nclassical_12.mp3 46.878204 1.844494 11.160392 0.503120 True \n... ... ... ... ... ... \nrock_95.mp3 23.410387 -4.502398 6.687984 0.238807 True \nrock_96.mp3 21.814402 -8.249507 7.807756 0.071968 True \nrock_97.mp3 25.410654 -3.356615 8.170526 0.160330 True \nrock_98.mp3 13.948638 -2.664985 5.051498 -0.258407 True \nrock_99.mp3 29.312023 -0.321836 6.571660 0.384794 True \n\n[400 rows x 202 columns]",
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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>False</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916949</td>\n <td>...</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n <td>False</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n <td>True</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908869</td>\n <td>...</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n <td>True</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410220</td>\n <td>...</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n<p>400 rows × 202 columns</p>\n</div>"
},
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -390,21 +413,21 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:31:42.301103695Z",
- "start_time": "2023-09-05T12:31:42.271662140Z"
+ "end_time": "2023-09-06T16:33:52.968965270Z",
+ "start_time": "2023-09-06T16:33:52.652167547Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 11,
"outputs": [
{
"data": {
- "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561839 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \n... ... ... ... ... ... \nrock_94.mp3 rock -531.794250 39.474583 -78.520462 51.256666 \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163332 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847562 \nclassical_10.mp3 -0.772320 0.029056 259.632721 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916948 \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 \n... ... ... ... ... ... \nrock_94.mp3 -0.846796 -15.139265 177.080322 79.627045 33.557076 \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 \nrock_96.mp3 -0.957699 -7.415959 210.492462 125.453690 31.908870 \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 \nrock_98.mp3 -1.705641 0.000000 187.042725 96.440872 24.137702 \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 ... -44.843815 28.490644 -6.242015 10.546545 0.341848 \n... ... ... ... ... ... ... \nrock_94.mp3 ... -34.662369 26.375679 -4.778466 6.754501 0.157858 \nrock_95.mp3 ... -27.043941 22.451445 -7.234633 8.471853 0.753855 \nrock_96.mp3 ... -37.584858 28.087940 -9.704238 8.447620 0.112760 \nrock_97.mp3 ... -29.620445 26.325895 -5.722826 7.727378 0.207489 \nrock_98.mp3 ... -26.967852 8.714736 -9.511492 5.551820 -0.025604 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \nclassical_10.mp3 -28.989979 27.533707 0.952658 10.477735 -0.185771 \nclassical_100.mp3 -38.095375 31.397882 -1.494916 10.917299 0.020984 \nclassical_11.mp3 -22.667439 50.992905 1.600777 10.125545 0.595763 \nclassical_12.mp3 -25.040886 46.878204 1.844494 11.160392 0.503120 \n... ... ... ... ... ... \nrock_94.mp3 -22.063726 29.165359 1.443975 6.737420 -0.092049 \nrock_95.mp3 -24.712723 23.410387 -4.502398 6.687983 0.238807 \nrock_96.mp3 -38.147888 21.814400 -8.249507 7.807756 0.071968 \nrock_97.mp3 -29.497524 25.410656 -3.356615 8.170526 0.160330 \nrock_98.mp3 -23.020082 13.948639 -2.664985 5.051498 -0.258407 \n\n[320 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847562</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561839</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632721</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989979</td>\n <td>27.533707</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397882</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916948</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667439</td>\n <td>50.992905</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>-44.843815</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040886</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>...</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_94.mp3</th>\n <td>rock</td>\n <td>-531.794250</td>\n <td>39.474583</td>\n <td>-78.520462</td>\n <td>51.256666</td>\n <td>-0.846796</td>\n <td>-15.139265</td>\n <td>177.080322</td>\n <td>79.627045</td>\n <td>33.557076</td>\n <td>...</td>\n <td>-34.662369</td>\n <td>26.375679</td>\n <td>-4.778466</td>\n <td>6.754501</td>\n <td>0.157858</td>\n <td>-22.063726</td>\n <td>29.165359</td>\n <td>1.443975</td>\n <td>6.737420</td>\n <td>-0.092049</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687983</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163332</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415959</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908870</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087940</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814400</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410656</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042725</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>-26.967852</td>\n <td>8.714736</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020082</td>\n <td>13.948639</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n </tbody>\n</table>\n<p>320 rows × 201 columns</p>\n</div>"
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_1.mp3 classical -530.784363 -163.308350 -302.203156 51.142183 \nclassical_12.mp3 classical -562.675232 -148.133560 -270.975403 52.191182 \nclassical_13.mp3 classical -637.720642 -177.713959 -361.834045 71.310080 \nclassical_14.mp3 classical -531.049438 -100.790543 -188.970749 58.287371 \nclassical_15.mp3 classical -555.129944 -96.139236 -209.245819 45.350121 \n... ... ... ... ... ... \nrock_95.mp3 rock -553.110107 -5.218835 -193.506042 76.869437 \nrock_96.mp3 rock -541.236023 27.163334 -119.113991 58.420684 \nrock_97.mp3 rock -518.494995 58.526745 -66.267746 65.635619 \nrock_98.mp3 rock -518.643066 53.555115 -45.734516 52.444200 \nrock_99.mp3 rock -544.703125 75.612129 -49.380943 54.045627 \n\n 0_skew 1_min 1_max 1_mean 1_std ... \\\nfilename ... \nclassical_1.mp3 -0.468374 0.000000 178.751617 111.332344 24.847563 ... \nclassical_12.mp3 -0.366587 0.000000 194.264160 148.226654 19.305008 ... \nclassical_13.mp3 0.008326 0.000000 257.162842 211.556549 20.347035 ... \nclassical_14.mp3 -3.246618 0.000000 157.947922 86.563927 17.911136 ... \nclassical_15.mp3 -3.574710 0.000000 140.918640 109.309990 14.171102 ... \n... ... ... ... ... ... ... \nrock_95.mp3 -0.201055 -89.948746 201.180450 111.724190 36.463584 ... \nrock_96.mp3 -0.957699 -7.415961 210.492462 125.453690 31.908869 ... \nrock_97.mp3 -0.898026 -58.824409 175.201355 99.288261 25.158417 ... \nrock_98.mp3 -1.705641 0.000000 187.042740 96.440872 24.137702 ... \nrock_99.mp3 -0.863093 -32.930653 191.735382 93.971237 33.410220 ... \n\n 38_min 38_max 38_mean 38_std 38_skew \\\nfilename \nclassical_1.mp3 -44.098068 47.308060 -3.713503 16.553984 0.230691 \nclassical_12.mp3 -44.843811 28.490644 -6.242015 10.546545 0.341848 \nclassical_13.mp3 -24.728806 18.424036 -0.275737 7.026148 -0.640964 \nclassical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \nclassical_15.mp3 -42.808113 24.146545 -7.260053 9.862490 0.097765 \n... ... ... ... ... ... \nrock_95.mp3 -27.043941 22.451445 -7.234633 8.471853 0.753855 \nrock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \nrock_97.mp3 -29.620445 26.325895 -5.722826 7.727378 0.207489 \nrock_98.mp3 -26.967848 8.714737 -9.511492 5.551820 -0.025604 \nrock_99.mp3 -21.929403 17.050608 -5.296690 5.894963 0.390705 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \nclassical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \nclassical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \nclassical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \nclassical_15.mp3 -31.394997 35.685539 -0.949139 11.141700 0.249278 \n... ... ... ... ... ... \nrock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \nrock_96.mp3 -38.147888 21.814402 -8.249507 7.807756 0.071968 \nrock_97.mp3 -29.497524 25.410654 -3.356615 8.170526 0.160330 \nrock_98.mp3 -23.020084 13.948638 -2.664985 5.051498 -0.258407 \nrock_99.mp3 -20.983192 29.312023 -0.321836 6.571660 0.384794 \n\n[320 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_1.mp3</th>\n <td>classical</td>\n <td>-530.784363</td>\n <td>-163.308350</td>\n <td>-302.203156</td>\n <td>51.142183</td>\n <td>-0.468374</td>\n <td>0.000000</td>\n <td>178.751617</td>\n <td>111.332344</td>\n <td>24.847563</td>\n <td>...</td>\n <td>-44.098068</td>\n <td>47.308060</td>\n <td>-3.713503</td>\n <td>16.553984</td>\n <td>0.230691</td>\n <td>-46.794479</td>\n <td>49.352516</td>\n <td>-2.282116</td>\n <td>15.285639</td>\n <td>0.171462</td>\n </tr>\n <tr>\n <th>classical_12.mp3</th>\n <td>classical</td>\n <td>-562.675232</td>\n <td>-148.133560</td>\n <td>-270.975403</td>\n <td>52.191182</td>\n <td>-0.366587</td>\n <td>0.000000</td>\n <td>194.264160</td>\n <td>148.226654</td>\n <td>19.305008</td>\n <td>...</td>\n <td>-44.843811</td>\n <td>28.490644</td>\n <td>-6.242015</td>\n <td>10.546545</td>\n <td>0.341848</td>\n <td>-25.040888</td>\n <td>46.878204</td>\n <td>1.844494</td>\n <td>11.160392</td>\n <td>0.503120</td>\n </tr>\n <tr>\n <th>classical_13.mp3</th>\n <td>classical</td>\n <td>-637.720642</td>\n <td>-177.713959</td>\n <td>-361.834045</td>\n <td>71.310080</td>\n <td>0.008326</td>\n <td>0.000000</td>\n <td>257.162842</td>\n <td>211.556549</td>\n <td>20.347035</td>\n <td>...</td>\n <td>-24.728806</td>\n <td>18.424036</td>\n <td>-0.275737</td>\n <td>7.026148</td>\n <td>-0.640964</td>\n <td>-24.319565</td>\n <td>18.439262</td>\n <td>-2.147022</td>\n <td>8.171929</td>\n <td>0.009566</td>\n </tr>\n <tr>\n <th>classical_14.mp3</th>\n <td>classical</td>\n <td>-531.049438</td>\n <td>-100.790543</td>\n <td>-188.970749</td>\n <td>58.287371</td>\n <td>-3.246618</td>\n <td>0.000000</td>\n <td>157.947922</td>\n <td>86.563927</td>\n <td>17.911136</td>\n <td>...</td>\n <td>-36.261154</td>\n <td>38.335831</td>\n <td>-5.770759</td>\n <td>12.254058</td>\n <td>0.805707</td>\n <td>-40.597336</td>\n <td>32.816467</td>\n <td>-0.543406</td>\n <td>11.467829</td>\n <td>-0.187037</td>\n </tr>\n <tr>\n <th>classical_15.mp3</th>\n <td>classical</td>\n <td>-555.129944</td>\n <td>-96.139236</td>\n <td>-209.245819</td>\n <td>45.350121</td>\n <td>-3.574710</td>\n <td>0.000000</td>\n <td>140.918640</td>\n <td>109.309990</td>\n <td>14.171102</td>\n <td>...</td>\n <td>-42.808113</td>\n <td>24.146545</td>\n <td>-7.260053</td>\n <td>9.862490</td>\n <td>0.097765</td>\n <td>-31.394997</td>\n <td>35.685539</td>\n <td>-0.949139</td>\n <td>11.141700</td>\n <td>0.249278</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_95.mp3</th>\n <td>rock</td>\n <td>-553.110107</td>\n <td>-5.218835</td>\n <td>-193.506042</td>\n <td>76.869437</td>\n <td>-0.201055</td>\n <td>-89.948746</td>\n <td>201.180450</td>\n <td>111.724190</td>\n <td>36.463584</td>\n <td>...</td>\n <td>-27.043941</td>\n <td>22.451445</td>\n <td>-7.234633</td>\n <td>8.471853</td>\n <td>0.753855</td>\n <td>-24.712723</td>\n <td>23.410387</td>\n <td>-4.502398</td>\n <td>6.687984</td>\n <td>0.238807</td>\n </tr>\n <tr>\n <th>rock_96.mp3</th>\n <td>rock</td>\n <td>-541.236023</td>\n <td>27.163334</td>\n <td>-119.113991</td>\n <td>58.420684</td>\n <td>-0.957699</td>\n <td>-7.415961</td>\n <td>210.492462</td>\n <td>125.453690</td>\n <td>31.908869</td>\n <td>...</td>\n <td>-37.584858</td>\n <td>28.087936</td>\n <td>-9.704238</td>\n <td>8.447620</td>\n <td>0.112760</td>\n <td>-38.147888</td>\n <td>21.814402</td>\n <td>-8.249507</td>\n <td>7.807756</td>\n <td>0.071968</td>\n </tr>\n <tr>\n <th>rock_97.mp3</th>\n <td>rock</td>\n <td>-518.494995</td>\n <td>58.526745</td>\n <td>-66.267746</td>\n <td>65.635619</td>\n <td>-0.898026</td>\n <td>-58.824409</td>\n <td>175.201355</td>\n <td>99.288261</td>\n <td>25.158417</td>\n <td>...</td>\n <td>-29.620445</td>\n <td>26.325895</td>\n <td>-5.722826</td>\n <td>7.727378</td>\n <td>0.207489</td>\n <td>-29.497524</td>\n <td>25.410654</td>\n <td>-3.356615</td>\n <td>8.170526</td>\n <td>0.160330</td>\n </tr>\n <tr>\n <th>rock_98.mp3</th>\n <td>rock</td>\n <td>-518.643066</td>\n <td>53.555115</td>\n <td>-45.734516</td>\n <td>52.444200</td>\n <td>-1.705641</td>\n <td>0.000000</td>\n <td>187.042740</td>\n <td>96.440872</td>\n <td>24.137702</td>\n <td>...</td>\n <td>-26.967848</td>\n <td>8.714737</td>\n <td>-9.511492</td>\n <td>5.551820</td>\n <td>-0.025604</td>\n <td>-23.020084</td>\n <td>13.948638</td>\n <td>-2.664985</td>\n <td>5.051498</td>\n <td>-0.258407</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930653</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410220</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894963</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312023</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>320 rows × 201 columns</p>\n</div>"
},
- "execution_count": 17,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -416,21 +439,21 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:34:10.121233546Z",
- "start_time": "2023-09-05T12:34:10.072104927Z"
+ "end_time": "2023-09-06T16:33:53.010226965Z",
+ "start_time": "2023-09-06T16:33:52.744721352Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 12,
"outputs": [
{
"data": {
- "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_13.mp3 classical -637.720642 -177.713959 -361.834045 71.310080 \nclassical_16.mp3 classical -602.367676 -92.236809 -246.956161 58.781397 \nclassical_21.mp3 classical -579.357117 -141.929260 -244.335068 43.217757 \nclassical_38.mp3 classical -530.257507 -119.940697 -306.263885 66.061075 \nclassical_62.mp3 classical -553.599792 -95.301186 -252.895355 57.645350 \n... ... ... ... ... ... \nrock_84.mp3 rock -553.272583 33.457363 -112.009064 65.035953 \nrock_90.mp3 rock -501.955994 9.573564 -137.388382 46.025847 \nrock_91.mp3 rock -533.061218 25.355713 -158.489578 74.151701 \nrock_92.mp3 rock -532.891113 13.948147 -206.891678 80.812274 \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_13.mp3 0.008326 0.000000 257.162842 211.556549 20.347035 ... \nclassical_16.mp3 -1.276497 0.000000 242.027344 207.742188 15.827642 ... \nclassical_21.mp3 -0.850777 0.000000 170.296844 129.317596 15.089849 ... \nclassical_38.mp3 -0.184762 0.000000 193.589020 132.254929 25.031131 ... \nclassical_62.mp3 -0.208828 0.000000 240.396759 204.584930 16.741947 ... \n... ... ... ... ... ... ... \nrock_84.mp3 -0.535031 -6.800635 195.284622 105.075165 32.223748 ... \nrock_90.mp3 -0.004000 -7.494959 190.229218 112.531166 33.245804 ... \nrock_91.mp3 -0.529297 -29.862532 204.165237 107.615341 39.961011 ... \nrock_92.mp3 0.090287 -47.724571 179.765060 109.955002 37.880477 ... \nrock_99.mp3 -0.863093 -32.930649 191.735382 93.971237 33.410221 ... \n\n 38_min 38_max 38_mean 38_std 38_skew \\\nfilename \nclassical_13.mp3 -24.728806 18.424034 -0.275736 7.026148 -0.640964 \nclassical_16.mp3 -38.999924 20.457050 -3.002112 8.130004 -1.282625 \nclassical_21.mp3 -18.908131 37.419128 4.746007 8.177644 0.081555 \nclassical_38.mp3 -32.308792 24.287951 -5.733211 9.073842 0.387693 \nclassical_62.mp3 -31.041054 33.676777 -4.153248 10.451528 0.519830 \n... ... ... ... ... ... \nrock_84.mp3 -28.911598 27.619001 -5.295718 6.987569 0.206062 \nrock_90.mp3 -23.657921 24.251358 -4.784957 6.215656 0.480860 \nrock_91.mp3 -25.712143 15.506594 -7.065026 6.016990 0.236868 \nrock_92.mp3 -37.614220 21.420666 -8.287362 7.851784 -0.080285 \nrock_99.mp3 -21.929403 17.050608 -5.296690 5.894962 0.390705 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_13.mp3 -24.319565 18.439264 -2.147022 8.171929 0.009566 \nclassical_16.mp3 -32.711815 23.339695 -6.099672 8.291237 0.088775 \nclassical_21.mp3 -28.569780 28.691933 -0.164881 9.291105 0.026442 \nclassical_38.mp3 -37.738308 33.270340 -5.117373 10.625547 0.343650 \nclassical_62.mp3 -32.060997 24.601665 -1.845992 8.890266 -0.242848 \n... ... ... ... ... ... \nrock_84.mp3 -21.169910 31.117376 -0.642526 6.866395 0.398194 \nrock_90.mp3 -21.904375 18.819710 -1.302765 5.389064 -0.064191 \nrock_91.mp3 -28.482529 20.222202 -1.086115 6.034919 0.097198 \nrock_92.mp3 -41.547260 25.628897 -9.046778 8.779821 0.071449 \nrock_99.mp3 -20.983192 29.312021 -0.321836 6.571660 0.384794 \n\n[80 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_13.mp3</th>\n <td>classical</td>\n <td>-637.720642</td>\n <td>-177.713959</td>\n <td>-361.834045</td>\n <td>71.310080</td>\n <td>0.008326</td>\n <td>0.000000</td>\n <td>257.162842</td>\n <td>211.556549</td>\n <td>20.347035</td>\n <td>...</td>\n <td>-24.728806</td>\n <td>18.424034</td>\n <td>-0.275736</td>\n <td>7.026148</td>\n <td>-0.640964</td>\n <td>-24.319565</td>\n <td>18.439264</td>\n <td>-2.147022</td>\n <td>8.171929</td>\n <td>0.009566</td>\n </tr>\n <tr>\n <th>classical_16.mp3</th>\n <td>classical</td>\n <td>-602.367676</td>\n <td>-92.236809</td>\n <td>-246.956161</td>\n <td>58.781397</td>\n <td>-1.276497</td>\n <td>0.000000</td>\n <td>242.027344</td>\n <td>207.742188</td>\n <td>15.827642</td>\n <td>...</td>\n <td>-38.999924</td>\n <td>20.457050</td>\n <td>-3.002112</td>\n <td>8.130004</td>\n <td>-1.282625</td>\n <td>-32.711815</td>\n <td>23.339695</td>\n <td>-6.099672</td>\n <td>8.291237</td>\n <td>0.088775</td>\n </tr>\n <tr>\n <th>classical_21.mp3</th>\n <td>classical</td>\n <td>-579.357117</td>\n <td>-141.929260</td>\n <td>-244.335068</td>\n <td>43.217757</td>\n <td>-0.850777</td>\n <td>0.000000</td>\n <td>170.296844</td>\n <td>129.317596</td>\n <td>15.089849</td>\n <td>...</td>\n <td>-18.908131</td>\n <td>37.419128</td>\n <td>4.746007</td>\n <td>8.177644</td>\n <td>0.081555</td>\n <td>-28.569780</td>\n <td>28.691933</td>\n <td>-0.164881</td>\n <td>9.291105</td>\n <td>0.026442</td>\n </tr>\n <tr>\n <th>classical_38.mp3</th>\n <td>classical</td>\n <td>-530.257507</td>\n <td>-119.940697</td>\n <td>-306.263885</td>\n <td>66.061075</td>\n <td>-0.184762</td>\n <td>0.000000</td>\n <td>193.589020</td>\n <td>132.254929</td>\n <td>25.031131</td>\n <td>...</td>\n <td>-32.308792</td>\n <td>24.287951</td>\n <td>-5.733211</td>\n <td>9.073842</td>\n <td>0.387693</td>\n <td>-37.738308</td>\n <td>33.270340</td>\n <td>-5.117373</td>\n <td>10.625547</td>\n <td>0.343650</td>\n </tr>\n <tr>\n <th>classical_62.mp3</th>\n <td>classical</td>\n <td>-553.599792</td>\n <td>-95.301186</td>\n <td>-252.895355</td>\n <td>57.645350</td>\n <td>-0.208828</td>\n <td>0.000000</td>\n <td>240.396759</td>\n <td>204.584930</td>\n <td>16.741947</td>\n <td>...</td>\n <td>-31.041054</td>\n <td>33.676777</td>\n <td>-4.153248</td>\n <td>10.451528</td>\n <td>0.519830</td>\n <td>-32.060997</td>\n <td>24.601665</td>\n <td>-1.845992</td>\n <td>8.890266</td>\n <td>-0.242848</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_84.mp3</th>\n <td>rock</td>\n <td>-553.272583</td>\n <td>33.457363</td>\n <td>-112.009064</td>\n <td>65.035953</td>\n <td>-0.535031</td>\n <td>-6.800635</td>\n <td>195.284622</td>\n <td>105.075165</td>\n <td>32.223748</td>\n <td>...</td>\n <td>-28.911598</td>\n <td>27.619001</td>\n <td>-5.295718</td>\n <td>6.987569</td>\n <td>0.206062</td>\n <td>-21.169910</td>\n <td>31.117376</td>\n <td>-0.642526</td>\n <td>6.866395</td>\n <td>0.398194</td>\n </tr>\n <tr>\n <th>rock_90.mp3</th>\n <td>rock</td>\n <td>-501.955994</td>\n <td>9.573564</td>\n <td>-137.388382</td>\n <td>46.025847</td>\n <td>-0.004000</td>\n <td>-7.494959</td>\n <td>190.229218</td>\n <td>112.531166</td>\n <td>33.245804</td>\n <td>...</td>\n <td>-23.657921</td>\n <td>24.251358</td>\n <td>-4.784957</td>\n <td>6.215656</td>\n <td>0.480860</td>\n <td>-21.904375</td>\n <td>18.819710</td>\n <td>-1.302765</td>\n <td>5.389064</td>\n <td>-0.064191</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>-29.862532</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>15.506594</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 <tr>\n <th>rock_92.mp3</th>\n <td>rock</td>\n <td>-532.891113</td>\n <td>13.948147</td>\n <td>-206.891678</td>\n <td>80.812274</td>\n <td>0.090287</td>\n <td>-47.724571</td>\n <td>179.765060</td>\n <td>109.955002</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>25.628897</td>\n <td>-9.046778</td>\n <td>8.779821</td>\n <td>0.071449</td>\n </tr>\n <tr>\n <th>rock_99.mp3</th>\n <td>rock</td>\n <td>-544.703125</td>\n <td>75.612129</td>\n <td>-49.380943</td>\n <td>54.045627</td>\n <td>-0.863093</td>\n <td>-32.930649</td>\n <td>191.735382</td>\n <td>93.971237</td>\n <td>33.410221</td>\n <td>...</td>\n <td>-21.929403</td>\n <td>17.050608</td>\n <td>-5.296690</td>\n <td>5.894962</td>\n <td>0.390705</td>\n <td>-20.983192</td>\n <td>29.312021</td>\n <td>-0.321836</td>\n <td>6.571660</td>\n <td>0.384794</td>\n </tr>\n </tbody>\n</table>\n<p>80 rows × 201 columns</p>\n</div>"
+ "text/plain": " label 0_min 0_max 0_mean 0_std \\\nfilename \nclassical_10.mp3 classical -562.857849 -96.164795 -219.259018 53.561838 \nclassical_100.mp3 classical -536.237366 -61.608826 -177.804108 83.381622 \nclassical_11.mp3 classical -536.457458 -120.429665 -222.126312 76.246992 \nclassical_19.mp3 classical -543.642334 -106.038223 -216.909943 61.317534 \nclassical_20.mp3 classical -605.991516 -161.119308 -263.483093 49.157298 \n... ... ... ... ... ... \nrock_57.mp3 rock -543.735168 50.739136 -70.208893 83.040454 \nrock_66.mp3 rock -520.185791 21.333998 -79.359444 44.616105 \nrock_75.mp3 rock -519.826965 54.035805 -32.218468 33.789999 \nrock_81.mp3 rock -532.139099 52.119076 -117.146126 76.883343 \nrock_90.mp3 rock -501.955994 9.573563 -137.388382 46.025847 \n\n 0_skew 1_min 1_max 1_mean 1_std \\\nfilename \nclassical_10.mp3 -0.772320 0.029056 259.632690 215.094193 18.388131 \nclassical_100.mp3 -2.587179 0.000000 190.475891 112.471710 27.277553 \nclassical_11.mp3 -2.402419 0.000000 159.425751 99.853645 21.916949 \nclassical_19.mp3 -3.473125 0.000000 151.947662 93.405411 22.029233 \nclassical_20.mp3 -0.856221 0.000000 191.926758 141.393814 17.754779 \n... ... ... ... ... ... \nrock_57.mp3 -2.913490 -51.877323 177.711395 89.957848 29.532071 \nrock_66.mp3 -2.708660 0.000000 162.490845 115.182426 18.106840 \nrock_75.mp3 -1.231267 1.666233 164.635895 93.935715 21.886208 \nrock_81.mp3 -0.656551 -44.119019 168.675858 101.038620 31.198018 \nrock_90.mp3 -0.004000 -7.494962 190.229202 112.531166 33.245804 \n\n ... 38_min 38_max 38_mean 38_std 38_skew \\\nfilename ... \nclassical_10.mp3 ... -27.458416 29.811110 0.484271 8.660648 -0.479016 \nclassical_100.mp3 ... -27.335688 27.610388 -0.333233 8.185075 0.208425 \nclassical_11.mp3 ... -31.774948 31.500881 -3.781627 9.191043 0.260886 \nclassical_19.mp3 ... -27.029385 30.682745 3.342259 8.420860 0.043171 \nclassical_20.mp3 ... -24.911243 38.551231 -2.274261 9.671005 0.719436 \n... ... ... ... ... ... ... \nrock_57.mp3 ... -30.258139 9.919489 -6.048107 5.045001 -0.187751 \nrock_66.mp3 ... -23.582970 16.230869 -4.445108 6.836216 -0.005944 \nrock_75.mp3 ... -29.449886 9.328630 -7.874899 6.538823 -0.428034 \nrock_81.mp3 ... -36.623711 23.897625 -3.552371 9.184054 -0.304160 \nrock_90.mp3 ... -23.657921 24.251360 -4.784957 6.215656 0.480860 \n\n 39_min 39_max 39_mean 39_std 39_skew \nfilename \nclassical_10.mp3 -28.989983 27.533710 0.952658 10.477735 -0.185771 \nclassical_100.mp3 -38.095375 31.397881 -1.494916 10.917299 0.020984 \nclassical_11.mp3 -22.667440 50.992897 1.600777 10.125545 0.595763 \nclassical_19.mp3 -25.900257 36.766388 2.389575 10.099726 0.140336 \nclassical_20.mp3 -30.311798 29.272329 0.289613 9.590299 -0.244191 \n... ... ... ... ... ... \nrock_57.mp3 -19.538643 21.089222 -1.995280 5.352349 0.480205 \nrock_66.mp3 -16.087088 22.686642 2.065789 6.279558 0.069703 \nrock_75.mp3 -21.944729 18.833591 -2.557417 5.737269 -0.007298 \nrock_81.mp3 -34.576202 36.869560 -1.597456 10.409478 0.058469 \nrock_90.mp3 -21.904373 18.819710 -1.302765 5.389064 -0.064191 \n\n[80 rows x 201 columns]",
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</thead>\n <tbody>\n <tr>\n <th>classical_10.mp3</th>\n <td>classical</td>\n <td>-562.857849</td>\n <td>-96.164795</td>\n <td>-219.259018</td>\n <td>53.561838</td>\n <td>-0.772320</td>\n <td>0.029056</td>\n <td>259.632690</td>\n <td>215.094193</td>\n <td>18.388131</td>\n <td>...</td>\n <td>-27.458416</td>\n <td>29.811110</td>\n <td>0.484271</td>\n <td>8.660648</td>\n <td>-0.479016</td>\n <td>-28.989983</td>\n <td>27.533710</td>\n <td>0.952658</td>\n <td>10.477735</td>\n <td>-0.185771</td>\n </tr>\n <tr>\n <th>classical_100.mp3</th>\n <td>classical</td>\n <td>-536.237366</td>\n <td>-61.608826</td>\n <td>-177.804108</td>\n <td>83.381622</td>\n <td>-2.587179</td>\n <td>0.000000</td>\n <td>190.475891</td>\n <td>112.471710</td>\n <td>27.277553</td>\n <td>...</td>\n <td>-27.335688</td>\n <td>27.610388</td>\n <td>-0.333233</td>\n <td>8.185075</td>\n <td>0.208425</td>\n <td>-38.095375</td>\n <td>31.397881</td>\n <td>-1.494916</td>\n <td>10.917299</td>\n <td>0.020984</td>\n </tr>\n <tr>\n <th>classical_11.mp3</th>\n <td>classical</td>\n <td>-536.457458</td>\n <td>-120.429665</td>\n <td>-222.126312</td>\n <td>76.246992</td>\n <td>-2.402419</td>\n <td>0.000000</td>\n <td>159.425751</td>\n <td>99.853645</td>\n <td>21.916949</td>\n <td>...</td>\n <td>-31.774948</td>\n <td>31.500881</td>\n <td>-3.781627</td>\n <td>9.191043</td>\n <td>0.260886</td>\n <td>-22.667440</td>\n <td>50.992897</td>\n <td>1.600777</td>\n <td>10.125545</td>\n <td>0.595763</td>\n </tr>\n <tr>\n <th>classical_19.mp3</th>\n <td>classical</td>\n <td>-543.642334</td>\n <td>-106.038223</td>\n <td>-216.909943</td>\n <td>61.317534</td>\n <td>-3.473125</td>\n <td>0.000000</td>\n <td>151.947662</td>\n <td>93.405411</td>\n <td>22.029233</td>\n <td>...</td>\n <td>-27.029385</td>\n <td>30.682745</td>\n <td>3.342259</td>\n <td>8.420860</td>\n <td>0.043171</td>\n <td>-25.900257</td>\n <td>36.766388</td>\n <td>2.389575</td>\n <td>10.099726</td>\n <td>0.140336</td>\n </tr>\n <tr>\n <th>classical_20.mp3</th>\n <td>classical</td>\n <td>-605.991516</td>\n <td>-161.119308</td>\n <td>-263.483093</td>\n <td>49.157298</td>\n <td>-0.856221</td>\n <td>0.000000</td>\n <td>191.926758</td>\n <td>141.393814</td>\n <td>17.754779</td>\n <td>...</td>\n <td>-24.911243</td>\n <td>38.551231</td>\n <td>-2.274261</td>\n <td>9.671005</td>\n <td>0.719436</td>\n <td>-30.311798</td>\n <td>29.272329</td>\n <td>0.289613</td>\n <td>9.590299</td>\n <td>-0.244191</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>rock_57.mp3</th>\n <td>rock</td>\n <td>-543.735168</td>\n <td>50.739136</td>\n <td>-70.208893</td>\n <td>83.040454</td>\n <td>-2.913490</td>\n <td>-51.877323</td>\n <td>177.711395</td>\n <td>89.957848</td>\n <td>29.532071</td>\n <td>...</td>\n <td>-30.258139</td>\n <td>9.919489</td>\n <td>-6.048107</td>\n <td>5.045001</td>\n <td>-0.187751</td>\n <td>-19.538643</td>\n <td>21.089222</td>\n <td>-1.995280</td>\n <td>5.352349</td>\n <td>0.480205</td>\n </tr>\n <tr>\n <th>rock_66.mp3</th>\n <td>rock</td>\n <td>-520.185791</td>\n <td>21.333998</td>\n <td>-79.359444</td>\n <td>44.616105</td>\n <td>-2.708660</td>\n <td>0.000000</td>\n <td>162.490845</td>\n <td>115.182426</td>\n <td>18.106840</td>\n <td>...</td>\n <td>-23.582970</td>\n <td>16.230869</td>\n <td>-4.445108</td>\n <td>6.836216</td>\n <td>-0.005944</td>\n <td>-16.087088</td>\n <td>22.686642</td>\n <td>2.065789</td>\n <td>6.279558</td>\n <td>0.069703</td>\n </tr>\n <tr>\n <th>rock_75.mp3</th>\n <td>rock</td>\n <td>-519.826965</td>\n <td>54.035805</td>\n <td>-32.218468</td>\n <td>33.789999</td>\n <td>-1.231267</td>\n <td>1.666233</td>\n <td>164.635895</td>\n <td>93.935715</td>\n <td>21.886208</td>\n <td>...</td>\n <td>-29.449886</td>\n <td>9.328630</td>\n <td>-7.874899</td>\n <td>6.538823</td>\n <td>-0.428034</td>\n <td>-21.944729</td>\n <td>18.833591</td>\n <td>-2.557417</td>\n <td>5.737269</td>\n <td>-0.007298</td>\n </tr>\n <tr>\n <th>rock_81.mp3</th>\n <td>rock</td>\n <td>-532.139099</td>\n <td>52.119076</td>\n <td>-117.146126</td>\n <td>76.883343</td>\n <td>-0.656551</td>\n <td>-44.119019</td>\n <td>168.675858</td>\n <td>101.038620</td>\n <td>31.198018</td>\n <td>...</td>\n <td>-36.623711</td>\n <td>23.897625</td>\n <td>-3.552371</td>\n <td>9.184054</td>\n <td>-0.304160</td>\n <td>-34.576202</td>\n <td>36.869560</td>\n <td>-1.597456</td>\n <td>10.409478</td>\n <td>0.058469</td>\n </tr>\n <tr>\n <th>rock_90.mp3</th>\n <td>rock</td>\n <td>-501.955994</td>\n <td>9.573563</td>\n <td>-137.388382</td>\n <td>46.025847</td>\n <td>-0.004000</td>\n <td>-7.494962</td>\n <td>190.229202</td>\n <td>112.531166</td>\n <td>33.245804</td>\n <td>...</td>\n <td>-23.657921</td>\n <td>24.251360</td>\n <td>-4.784957</td>\n <td>6.215656</td>\n <td>0.480860</td>\n <td>-21.904373</td>\n <td>18.819710</td>\n <td>-1.302765</td>\n <td>5.389064</td>\n <td>-0.064191</td>\n </tr>\n </tbody>\n</table>\n<p>80 rows × 201 columns</p>\n</div>"
},
- "execution_count": 18,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -442,20 +465,20 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:34:22.259983472Z",
- "start_time": "2023-09-05T12:34:22.242630498Z"
+ "end_time": "2023-09-06T16:33:53.064138270Z",
+ "start_time": "2023-09-06T16:33:52.930093810Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 13,
"outputs": [
{
"data": {
- "text/plain": "( 0_min 0_max 0_mean 0_std 0_skew \\\n filename \n classical_1.mp3 -530.784363 -163.308350 -302.203156 51.142183 -0.468374 \n classical_10.mp3 -562.857849 -96.164795 -219.259018 53.561839 -0.772320 \n classical_100.mp3 -536.237366 -61.608826 -177.804108 83.381622 -2.587179 \n classical_11.mp3 -536.457458 -120.429665 -222.126312 76.246992 -2.402419 \n classical_12.mp3 -562.675232 -148.133560 -270.975403 52.191182 -0.366587 \n ... ... ... ... ... ... \n rock_94.mp3 -531.794250 39.474583 -78.520462 51.256666 -0.846796 \n rock_95.mp3 -553.110107 -5.218835 -193.506042 76.869437 -0.201055 \n rock_96.mp3 -541.236023 27.163332 -119.113991 58.420684 -0.957699 \n rock_97.mp3 -518.494995 58.526745 -66.267746 65.635619 -0.898026 \n rock_98.mp3 -518.643066 53.555115 -45.734516 52.444200 -1.705641 \n \n 1_min 1_max 1_mean 1_std 1_skew \\\n filename \n classical_1.mp3 0.000000 178.751617 111.332344 24.847562 -0.402642 \n classical_10.mp3 0.029056 259.632721 215.094193 18.388131 -1.528750 \n classical_100.mp3 0.000000 190.475891 112.471710 27.277553 -1.318523 \n classical_11.mp3 0.000000 159.425751 99.853645 21.916948 -1.176922 \n classical_12.mp3 0.000000 194.264160 148.226654 19.305008 -0.533256 \n ... ... ... ... ... ... \n rock_94.mp3 -15.139265 177.080322 79.627045 33.557076 0.103628 \n rock_95.mp3 -89.948746 201.180450 111.724190 36.463584 -0.443224 \n rock_96.mp3 -7.415959 210.492462 125.453690 31.908870 -0.547468 \n rock_97.mp3 -58.824409 175.201355 99.288261 25.158417 -0.568056 \n rock_98.mp3 0.000000 187.042725 96.440872 24.137702 -0.145216 \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 ... -44.843815 28.490644 -6.242015 10.546545 0.341848 \n ... ... ... ... ... ... ... \n rock_94.mp3 ... -34.662369 26.375679 -4.778466 6.754501 0.157858 \n rock_95.mp3 ... -27.043941 22.451445 -7.234633 8.471853 0.753855 \n rock_96.mp3 ... -37.584858 28.087940 -9.704238 8.447620 0.112760 \n rock_97.mp3 ... -29.620445 26.325895 -5.722826 7.727378 0.207489 \n rock_98.mp3 ... -26.967852 8.714736 -9.511492 5.551820 -0.025604 \n \n 39_min 39_max 39_mean 39_std 39_skew \n filename \n classical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \n classical_10.mp3 -28.989979 27.533707 0.952658 10.477735 -0.185771 \n classical_100.mp3 -38.095375 31.397882 -1.494916 10.917299 0.020984 \n classical_11.mp3 -22.667439 50.992905 1.600777 10.125545 0.595763 \n classical_12.mp3 -25.040886 46.878204 1.844494 11.160392 0.503120 \n ... ... ... ... ... ... \n rock_94.mp3 -22.063726 29.165359 1.443975 6.737420 -0.092049 \n rock_95.mp3 -24.712723 23.410387 -4.502398 6.687983 0.238807 \n rock_96.mp3 -38.147888 21.814400 -8.249507 7.807756 0.071968 \n rock_97.mp3 -29.497524 25.410656 -3.356615 8.170526 0.160330 \n rock_98.mp3 -23.020082 13.948639 -2.664985 5.051498 -0.258407 \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, 0, 0, 0, 0, 0, 0, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 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, 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, 2, 2, 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]))"
+ "text/plain": "( 0_min 0_max 0_mean 0_std 0_skew \\\n filename \n classical_1.mp3 -530.784363 -163.308350 -302.203156 51.142183 -0.468374 \n classical_12.mp3 -562.675232 -148.133560 -270.975403 52.191182 -0.366587 \n classical_13.mp3 -637.720642 -177.713959 -361.834045 71.310080 0.008326 \n classical_14.mp3 -531.049438 -100.790543 -188.970749 58.287371 -3.246618 \n classical_15.mp3 -555.129944 -96.139236 -209.245819 45.350121 -3.574710 \n ... ... ... ... ... ... \n rock_95.mp3 -553.110107 -5.218835 -193.506042 76.869437 -0.201055 \n rock_96.mp3 -541.236023 27.163334 -119.113991 58.420684 -0.957699 \n rock_97.mp3 -518.494995 58.526745 -66.267746 65.635619 -0.898026 \n rock_98.mp3 -518.643066 53.555115 -45.734516 52.444200 -1.705641 \n rock_99.mp3 -544.703125 75.612129 -49.380943 54.045627 -0.863093 \n \n 1_min 1_max 1_mean 1_std 1_skew ... \\\n filename ... \n classical_1.mp3 0.000000 178.751617 111.332344 24.847563 -0.402642 ... \n classical_12.mp3 0.000000 194.264160 148.226654 19.305008 -0.533256 ... \n classical_13.mp3 0.000000 257.162842 211.556549 20.347035 -1.050120 ... \n classical_14.mp3 0.000000 157.947922 86.563927 17.911136 0.244245 ... \n classical_15.mp3 0.000000 140.918640 109.309990 14.171102 -2.617227 ... \n ... ... ... ... ... ... ... \n rock_95.mp3 -89.948746 201.180450 111.724190 36.463584 -0.443224 ... \n rock_96.mp3 -7.415961 210.492462 125.453690 31.908869 -0.547468 ... \n rock_97.mp3 -58.824409 175.201355 99.288261 25.158417 -0.568056 ... \n rock_98.mp3 0.000000 187.042740 96.440872 24.137702 -0.145216 ... \n rock_99.mp3 -32.930653 191.735382 93.971237 33.410220 0.040112 ... \n \n 38_min 38_max 38_mean 38_std 38_skew \\\n filename \n classical_1.mp3 -44.098068 47.308060 -3.713503 16.553984 0.230691 \n classical_12.mp3 -44.843811 28.490644 -6.242015 10.546545 0.341848 \n classical_13.mp3 -24.728806 18.424036 -0.275737 7.026148 -0.640964 \n classical_14.mp3 -36.261154 38.335831 -5.770759 12.254058 0.805707 \n classical_15.mp3 -42.808113 24.146545 -7.260053 9.862490 0.097765 \n ... ... ... ... ... ... \n rock_95.mp3 -27.043941 22.451445 -7.234633 8.471853 0.753855 \n rock_96.mp3 -37.584858 28.087936 -9.704238 8.447620 0.112760 \n rock_97.mp3 -29.620445 26.325895 -5.722826 7.727378 0.207489 \n rock_98.mp3 -26.967848 8.714737 -9.511492 5.551820 -0.025604 \n rock_99.mp3 -21.929403 17.050608 -5.296690 5.894963 0.390705 \n \n 39_min 39_max 39_mean 39_std 39_skew \n filename \n classical_1.mp3 -46.794479 49.352516 -2.282116 15.285639 0.171462 \n classical_12.mp3 -25.040888 46.878204 1.844494 11.160392 0.503120 \n classical_13.mp3 -24.319565 18.439262 -2.147022 8.171929 0.009566 \n classical_14.mp3 -40.597336 32.816467 -0.543406 11.467829 -0.187037 \n classical_15.mp3 -31.394997 35.685539 -0.949139 11.141700 0.249278 \n ... ... ... ... ... ... \n rock_95.mp3 -24.712723 23.410387 -4.502398 6.687984 0.238807 \n rock_96.mp3 -38.147888 21.814402 -8.249507 7.807756 0.071968 \n rock_97.mp3 -29.497524 25.410654 -3.356615 8.170526 0.160330 \n rock_98.mp3 -23.020084 13.948638 -2.664985 5.051498 -0.258407 \n rock_99.mp3 -20.983192 29.312023 -0.321836 6.571660 0.384794 \n \n [320 rows x 200 columns],\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]))"
},
- "execution_count": 21,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -483,14 +506,14 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:35:41.361012240Z",
- "start_time": "2023-09-05T12:35:41.337225717Z"
+ "end_time": "2023-09-06T16:33:53.384132460Z",
+ "start_time": "2023-09-06T16:33:52.974140158Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 14,
"outputs": [
{
"name": "stdout",
@@ -503,9 +526,9 @@
},
{
"data": {
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+ "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
},
- "execution_count": 23,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -523,20 +546,20 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:36:30.952947854Z",
- "start_time": "2023-09-05T12:36:30.944158345Z"
+ "end_time": "2023-09-06T16:33:53.385211474Z",
+ "start_time": "2023-09-06T16:33:53.017055988Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 15,
"outputs": [
{
"data": {
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@@ -552,20 +575,20 @@
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{
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{
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@@ -586,35 +609,35 @@
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{
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{
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"text": [
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- "{'C': 2, 'gamma': 0.01}\n",
- "SVC(C=2, gamma=0.01)\n",
- "0.71875\n"
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@@ -671,20 +694,20 @@
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+ " [3.47407145e-03 3.38133523e-01 4.27311082e-01 2.31081323e-01]\n",
+ " [6.38991934e-02 2.34946580e-02 4.38116434e-01 4.74489715e-01]\n",
+ " [3.65337631e-02 1.47529551e-01 6.56699181e-01 1.59237505e-01]\n",
+ " [3.45369958e-02 4.86056971e-02 4.50222236e-01 4.66635071e-01]\n",
+ " [1.02874384e-01 1.14593225e-01 4.12828659e-01 3.69703732e-01]\n",
+ " [7.01929891e-04 4.72310828e-03 6.33659414e-01 3.60915547e-01]\n",
+ " [1.19280091e-03 2.91859540e-03 6.63499318e-01 3.32389286e-01]\n",
+ " [3.22247158e-04 1.86224604e-03 7.86294266e-01 2.11521241e-01]\n",
+ " [1.62556011e-02 1.10337495e-01 6.90066111e-01 1.83340793e-01]\n",
+ " [5.83937991e-03 8.91483148e-03 8.23557657e-01 1.61688132e-01]\n",
+ " [1.40771587e-03 1.99973215e-03 7.69667401e-01 2.26925151e-01]\n",
+ " [2.85627492e-03 4.09235838e-02 5.47033952e-01 4.09186189e-01]\n",
+ " [5.00914955e-02 5.66591605e-02 8.19154779e-01 7.40945653e-02]\n",
+ " [3.49728526e-04 1.03301315e-02 3.45356722e-01 6.43963418e-01]\n",
+ " [7.86355678e-03 5.17956142e-02 8.13005815e-01 1.27335014e-01]\n",
+ " [2.24811753e-04 1.50400751e-03 5.92965001e-01 4.05306180e-01]\n",
+ " [1.50292599e-03 1.18077604e-02 7.95371303e-01 1.91318010e-01]\n",
+ " [9.71722011e-04 1.93522506e-03 4.85836249e-01 5.11256804e-01]\n",
+ " [2.71508043e-04 8.47075628e-03 6.65359707e-02 9.24721765e-01]\n",
+ " [3.78695110e-04 7.86603624e-03 3.13831725e-01 6.77923544e-01]\n",
+ " [1.56018964e-03 5.04504439e-02 3.75946622e-01 5.72042744e-01]\n",
+ " [4.13000514e-03 4.44190116e-03 1.76929372e-01 8.14498722e-01]\n",
+ " [7.03938811e-04 5.20938587e-01 2.13900864e-01 2.64456610e-01]\n",
+ " [1.26489155e-03 3.03436964e-02 2.15768512e-01 7.52622900e-01]\n",
+ " [1.39383300e-04 2.39767850e-03 7.56550144e-01 2.40912794e-01]\n",
+ " [8.86305936e-04 4.62779469e-04 1.80744510e-01 8.17906405e-01]\n",
+ " [4.22976399e-04 5.51964764e-02 2.85832649e-01 6.58547898e-01]\n",
+ " [6.85422618e-04 1.63669725e-03 1.51783518e-01 8.45894362e-01]\n",
+ " [1.88958698e-03 1.04946968e-02 1.31240172e-01 8.56375545e-01]\n",
+ " [3.17948589e-03 2.18481451e-02 1.25011239e-01 8.49961130e-01]\n",
+ " [8.82291635e-04 1.06487654e-02 3.11812728e-01 6.76656214e-01]]\n"
]
}
],
@@ -806,14 +829,14 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:39:08.469582448Z",
- "start_time": "2023-09-05T12:39:08.423483139Z"
+ "end_time": "2023-09-06T16:34:01.583086439Z",
+ "start_time": "2023-09-06T16:34:01.473457338Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 21,
"outputs": [
{
"name": "stdout",
@@ -821,86 +844,86 @@
"text": [
" label pred1 pred2 pred3 pred4\n",
"filename \n",
- "classical_13.mp3 classical classical electronic rock pop\n",
- "classical_16.mp3 classical classical electronic rock pop\n",
+ "classical_10.mp3 classical classical pop electronic rock\n",
+ "classical_100.mp3 classical classical pop rock electronic\n",
+ "classical_11.mp3 classical classical rock pop electronic\n",
+ "classical_19.mp3 classical classical pop rock electronic\n",
+ "classical_20.mp3 classical classical rock pop electronic\n",
"classical_21.mp3 classical classical electronic pop rock\n",
- "classical_38.mp3 classical pop classical rock electronic\n",
- "classical_62.mp3 classical classical rock electronic pop\n",
- "classical_65.mp3 classical classical electronic pop rock\n",
- "classical_67.mp3 classical classical electronic pop rock\n",
- "classical_68.mp3 classical classical pop electronic rock\n",
- "classical_7.mp3 classical classical electronic rock pop\n",
+ "classical_24.mp3 classical classical pop rock electronic\n",
+ "classical_27.mp3 classical classical pop electronic rock\n",
+ "classical_28.mp3 classical classical pop electronic rock\n",
+ "classical_29.mp3 classical classical electronic pop rock\n",
+ "classical_40.mp3 classical electronic pop rock classical\n",
+ "classical_49.mp3 classical classical pop electronic rock\n",
+ "classical_5.mp3 classical classical pop electronic rock\n",
+ "classical_51.mp3 classical classical rock pop electronic\n",
+ "classical_58.mp3 classical classical electronic pop rock\n",
+ "classical_69.mp3 classical classical pop rock electronic\n",
+ "classical_71.mp3 classical pop rock classical electronic\n",
"classical_8.mp3 classical classical pop rock electronic\n",
- "classical_80.mp3 classical classical electronic pop rock\n",
- "classical_84.mp3 classical classical pop electronic rock\n",
- "classical_98.mp3 classical classical pop rock electronic\n",
+ "classical_92.mp3 classical classical rock pop electronic\n",
+ "classical_97.mp3 classical classical electronic pop rock\n",
+ "classical_98.mp3 classical classical pop electronic rock\n",
+ "electronic_10.mp3 electronic electronic rock pop classical\n",
"electronic_100.mp3 electronic electronic pop rock classical\n",
- "electronic_13.mp3 electronic electronic pop rock classical\n",
- "electronic_18.mp3 electronic electronic rock pop classical\n",
- "electronic_19.mp3 electronic classical electronic pop rock\n",
- "electronic_21.mp3 electronic electronic pop rock classical\n",
- "electronic_22.mp3 electronic electronic pop rock classical\n",
- "electronic_28.mp3 electronic electronic pop rock classical\n",
- "electronic_34.mp3 electronic electronic pop rock classical\n",
- "electronic_37.mp3 electronic electronic pop rock classical\n",
- "electronic_45.mp3 electronic electronic classical pop rock\n",
- "electronic_46.mp3 electronic electronic pop classical rock\n",
+ "electronic_14.mp3 electronic electronic pop rock classical\n",
+ "electronic_2.mp3 electronic electronic pop rock classical\n",
+ "electronic_23.mp3 electronic electronic pop rock classical\n",
+ "electronic_27.mp3 electronic electronic pop rock classical\n",
+ "electronic_3.mp3 electronic electronic pop classical rock\n",
+ "electronic_32.mp3 electronic electronic pop classical rock\n",
"electronic_47.mp3 electronic classical electronic pop rock\n",
+ "electronic_48.mp3 electronic electronic rock pop classical\n",
+ "electronic_51.mp3 electronic pop electronic rock classical\n",
+ "electronic_52.mp3 electronic electronic pop rock classical\n",
"electronic_54.mp3 electronic rock electronic pop classical\n",
- "electronic_6.mp3 electronic electronic pop rock classical\n",
- "electronic_60.mp3 electronic electronic pop rock classical\n",
- "electronic_69.mp3 electronic electronic pop rock classical\n",
- "electronic_7.mp3 electronic electronic pop rock classical\n",
- "electronic_72.mp3 electronic electronic rock pop classical\n",
- "electronic_74.mp3 electronic electronic pop rock classical\n",
- "electronic_82.mp3 electronic electronic pop rock classical\n",
- "electronic_90.mp3 electronic electronic pop classical rock\n",
- "pop_1.mp3 pop pop classical rock electronic\n",
- "pop_11.mp3 pop pop rock electronic classical\n",
- "pop_14.mp3 pop rock pop electronic classical\n",
- "pop_17.mp3 pop pop rock electronic classical\n",
- "pop_27.mp3 pop pop rock classical electronic\n",
- "pop_45.mp3 pop electronic rock pop classical\n",
- "pop_48.mp3 pop pop electronic rock classical\n",
- "pop_5.mp3 pop rock pop electronic classical\n",
- "pop_50.mp3 pop pop rock electronic classical\n",
- "pop_51.mp3 pop rock pop electronic classical\n",
- "pop_54.mp3 pop pop electronic rock classical\n",
- "pop_57.mp3 pop pop rock classical electronic\n",
- "pop_59.mp3 pop pop rock electronic classical\n",
+ "electronic_55.mp3 electronic electronic rock pop classical\n",
+ "electronic_56.mp3 electronic electronic rock pop classical\n",
+ "electronic_57.mp3 electronic electronic pop rock classical\n",
+ "electronic_75.mp3 electronic pop electronic rock classical\n",
+ "electronic_77.mp3 electronic electronic pop rock classical\n",
+ "electronic_84.mp3 electronic pop electronic rock classical\n",
+ "electronic_86.mp3 electronic electronic rock pop classical\n",
+ "electronic_95.mp3 electronic electronic rock pop classical\n",
+ "pop_10.mp3 pop pop rock electronic classical\n",
+ "pop_100.mp3 pop rock pop electronic classical\n",
+ "pop_2.mp3 pop rock pop electronic classical\n",
+ "pop_34.mp3 pop pop rock electronic classical\n",
+ "pop_37.mp3 pop rock pop electronic classical\n",
+ "pop_41.mp3 pop pop rock electronic classical\n",
+ "pop_42.mp3 pop pop rock electronic classical\n",
+ "pop_47.mp3 pop pop electronic rock classical\n",
+ "pop_52.mp3 pop rock pop classical electronic\n",
+ "pop_57.mp3 pop pop rock electronic classical\n",
"pop_6.mp3 pop rock pop electronic classical\n",
+ "pop_61.mp3 pop pop rock electronic classical\n",
"pop_62.mp3 pop pop rock electronic classical\n",
+ "pop_64.mp3 pop pop rock electronic classical\n",
"pop_65.mp3 pop pop rock electronic classical\n",
+ "pop_7.mp3 pop pop rock electronic classical\n",
+ "pop_72.mp3 pop pop rock electronic classical\n",
"pop_73.mp3 pop pop rock electronic classical\n",
- "pop_74.mp3 pop pop rock classical electronic\n",
- "pop_75.mp3 pop pop rock electronic classical\n",
- "pop_84.mp3 pop pop rock electronic classical\n",
- "pop_96.mp3 pop pop rock electronic classical\n",
- "rock_1.mp3 rock rock pop electronic classical\n",
- "rock_11.mp3 rock pop rock classical electronic\n",
- "rock_16.mp3 rock rock pop electronic classical\n",
- "rock_17.mp3 rock pop rock electronic classical\n",
- "rock_2.mp3 rock pop rock electronic classical\n",
- "rock_29.mp3 rock rock pop electronic classical\n",
- "rock_30.mp3 rock electronic rock pop classical\n",
+ "pop_8.mp3 pop pop rock electronic classical\n",
+ "pop_86.mp3 pop pop rock electronic classical\n",
+ "pop_87.mp3 pop rock pop electronic classical\n",
+ "pop_92.mp3 pop pop rock electronic classical\n",
+ "pop_94.mp3 pop pop rock electronic classical\n",
+ "pop_97.mp3 pop pop rock electronic classical\n",
+ "rock_11.mp3 rock rock pop electronic classical\n",
+ "rock_22.mp3 rock rock pop electronic classical\n",
+ "rock_25.mp3 rock rock pop electronic classical\n",
+ "rock_26.mp3 rock rock pop electronic classical\n",
+ "rock_4.mp3 rock rock pop electronic classical\n",
+ "rock_41.mp3 rock electronic rock pop classical\n",
"rock_45.mp3 rock rock pop electronic classical\n",
"rock_48.mp3 rock pop rock electronic classical\n",
- "rock_50.mp3 rock electronic rock pop classical\n",
- "rock_53.mp3 rock rock pop electronic classical\n",
- "rock_56.mp3 rock rock pop electronic classical\n",
- "rock_67.mp3 rock rock pop electronic classical\n",
- "rock_7.mp3 rock rock pop electronic classical\n",
+ "rock_54.mp3 rock rock pop classical electronic\n",
+ "rock_57.mp3 rock rock pop electronic classical\n",
+ "rock_66.mp3 rock rock pop electronic classical\n",
"rock_75.mp3 rock rock pop electronic classical\n",
- "rock_77.mp3 rock rock pop electronic classical\n",
- "rock_80.mp3 rock rock pop electronic classical\n",
"rock_81.mp3 rock rock pop electronic classical\n",
- "rock_82.mp3 rock rock pop electronic classical\n",
- "rock_83.mp3 rock rock pop electronic classical\n",
- "rock_84.mp3 rock rock pop electronic classical\n",
- "rock_90.mp3 rock rock pop electronic classical\n",
- "rock_91.mp3 rock rock pop electronic classical\n",
- "rock_92.mp3 rock rock pop electronic classical\n",
- "rock_99.mp3 rock rock pop electronic classical\n"
+ "rock_90.mp3 rock rock pop electronic classical\n"
]
}
],
@@ -935,14 +958,14 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T12:41:54.370229130Z",
- "start_time": "2023-09-05T12:41:54.309015774Z"
+ "end_time": "2023-09-06T16:34:01.741253646Z",
+ "start_time": "2023-09-06T16:34:01.567005786Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 22,
"outputs": [],
"source": [
"with open(LOCAL_PATH / \"clf.pickle\", \"wb\") as file:\n",
@@ -952,53 +975,54 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T13:26:41.594046705Z",
- "start_time": "2023-09-05T13:26:41.548514835Z"
+ "end_time": "2023-09-06T16:34:01.742181308Z",
+ "start_time": "2023-09-06T16:34:01.725149499Z"
}
}
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 23,
"outputs": [],
"source": [
- "with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
- " metadata = yaml.safe_load(file)\n",
+ "if not ONLY_LOCAL:\n",
+ " with open(RESOURCE_PATH / \"5_ml_model\" / \"ml_model_entity_metadata.yml\", \"r\") as file:\n",
+ " metadata = yaml.safe_load(file)\n",
"\n",
- "nb_config_ml = NbConfig(\n",
- " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
- " entities=[\n",
- " ml_model_entity := InvenioEntity.new(\n",
- " name=\"Standalone Machine Learning model\",\n",
- " description=\"An ml model representing the trained clf\",\n",
- " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"ml_model.pickle\",\n",
- " dbrepo_connector=connector,\n",
- " invenio_connector=invenio_connector,\n",
- " record_metadata=metadata,\n",
- " type=\"clf\"\n",
- " ),\n",
- " test_result_entity := DbRepoEntity.new(\n",
- " name=\"Standalone Test Result Entity\",\n",
- " description=\"Result of tests on ml model\",\n",
- " table_name=\"test_result\",\n",
- " table_description=\"Test results of genre prediction on ml model\",\n",
- " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"test_result.csv\",\n",
- " dbrepo_connector=connector,\n",
- " type=\"submission\"\n",
- " )\n",
- " ],\n",
- " dependencies=[\n",
- " audio_files_entity\n",
- " ]\n",
- ")\n",
+ " nb_config_ml = NbConfig(\n",
+ " nb_location=NOTEBOOK_PATH / \"5_ml_model.ipynb\",\n",
+ " entities=[\n",
+ " ml_model_entity := InvenioEntity.new(\n",
+ " name=\"Standalone Machine Learning model\",\n",
+ " description=\"An ml model representing the trained clf\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"ml_model.pickle\",\n",
+ " dbrepo_connector=connector,\n",
+ " invenio_connector=invenio_connector,\n",
+ " record_metadata=metadata,\n",
+ " type=\"clf\"\n",
+ " ),\n",
+ " test_result_entity := DbRepoEntity.new(\n",
+ " name=\"Standalone Test Result Entity\",\n",
+ " description=\"Result of tests on ml model\",\n",
+ " table_name=\"test_result\",\n",
+ " table_description=\"Test results of genre prediction on ml model\",\n",
+ " location=LOCAL_PATH / \"5_ml_model\" / \"output\" / \"test_result.csv\",\n",
+ " dbrepo_connector=connector,\n",
+ " type=\"submission\"\n",
+ " )\n",
+ " ],\n",
+ " dependencies=[\n",
+ " audio_files_entity\n",
+ " ]\n",
+ " )\n",
"\n",
- "executor.upload_entities(nb_config_ml)"
+ " executor.upload_entities(nb_config_ml)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-09-05T13:26:58.187077116Z",
- "start_time": "2023-09-05T13:26:58.172572891Z"
+ "end_time": "2023-09-06T16:34:01.832515470Z",
+ "start_time": "2023-09-06T16:34:01.741700194Z"
}
}
}