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from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from src.utils.config_loader import AUDIO_SCALER, LYRICS_SCALER #, PCA_SCALER
from sklearn.decomposition import IncrementalPCA
from src.utils.config_loader import PCA_MODEL
import joblib
import numpy as np
import logging
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
def dataset_splitter(X: np.ndarray, Y: np.ndarray, ids: np.ndarray = None):
"""
Splits X, Y (and optional ids) into train/val/test sets.
Saves metadata CSVs for each split if ids are provided.
Parameters
----------
X : np.array
Feature vectors
Y : np.array
Labels
ids : np.array, optional
Identifiers (filenames or row indices)
save_metadata : bool
Whether to save split metadata CSVs
outdir : str
Directory to save metadata CSVs
Returns
-------
data : dict
A dictionary of np.arrays: {train, val, test}
Each value is a tuple (X_split, y_split, ids_split if provided)
"""
logger.info(f"Dataset shape: {X.shape}, Labels: {len(Y)}")
logger.info(f"Class distribution: {np.bincount(Y)}")
# First split: train vs test
X_train, X_test, y_train, y_test = train_test_split(
X, Y, ids, test_size=0.1, random_state=42, stratify=Y
)
# Second split: train vs val
X_train, X_val, y_train, y_val = train_test_split(
X_train, y_train, test_size=0.2222, random_state=42, stratify=y_train
)
logger.info(
f"Train: {X_train.shape}, Validation: {X_val.shape}, Test: {X_test.shape}"
)
data = {
"train": (X_train, y_train),
"val": (X_val, y_val),
"test": (X_test, y_test),
}
return data
def scale_pca(data: dict):
"""
Script that scales the splits, and applies PCA to the lyrics vector.
Parameters
----------
data : dictionary
Dictionary containing the splits
Returns
-------
data : dict{np.array}
A dictionary of np.arrays, containing the train/test/val split.
"""
# Destructure the dictionary to get data split
X_train, y_train = data["train"]
X_val, y_val = data["val"]
X_test, y_test = data["test"]
# Segment the concatenated embedding to audio and lyrics
X_train_audio, X_train_lyrics = X_train[:, :384], X_train[:, 384:]
X_test_audio, X_test_lyrics = X_test[:, :384], X_test[:, 384:]
X_val_audio, X_val_lyrics = X_val[:, :384], X_val[:, 384:]
# Fit the scalers into the train data, return scalers for fitting of test and validation
audio_scaler, lyric_scaler = dataset_scaler(X_train_audio, X_train_lyrics)
# Transform the rest of the splits using the scalers
X_train_audio = audio_scaler.transform(X_train_audio)
X_test_audio = audio_scaler.transform(X_test_audio)
X_val_audio = audio_scaler.transform(X_val_audio)
X_train_lyrics = lyric_scaler.transform(X_train_lyrics)
X_test_lyrics = lyric_scaler.transform(X_test_lyrics)
X_val_lyrics = lyric_scaler.transform(X_val_lyrics)
# Fit PCA on TRAINING lyrics only
ipca = IncrementalPCA(n_components=512)
batch_size = 1000
for i in range(0, X_train_lyrics.shape[0], batch_size):
ipca.partial_fit(X_train_lyrics[i : i + batch_size])
# Transform in batches
X_train_lyrics = ipca.transform(X_train_lyrics)
X_test_lyrics = ipca.transform(X_test_lyrics)
X_val_lyrics = ipca.transform(X_val_lyrics)
# NOTE: Scaling after PCA produces underperforming models compared to non-scaling.
# One can toggle it on for experimentation/testing purposes.
# pca_lyric_scaler = StandardScaler().fit(X_train_lyrics)
# X_train_lyrics = pca_lyric_scaler.transform(X_train_lyrics)
# X_test_lyrics = pca_lyric_scaler.transform(X_test_lyrics)
# X_val_lyrics = pca_lyric_scaler.transform(X_val_lyrics)
# Concatenate them back to their original form, but scaled
X_train = np.concatenate([X_train_audio, X_train_lyrics], axis=1)
X_test = np.concatenate([X_test_audio, X_test_lyrics], axis=1)
X_val = np.concatenate([X_val_audio, X_val_lyrics], axis=1)
joblib.dump(ipca, PCA_MODEL)
# Save the trained scalers for prediction
# joblib.dump(pca_lyric_scaler, PCA_SCALER)
data = {
"train": (X_train, y_train),
"val": (X_val, y_val),
"test": (X_test, y_test),
}
return data
def scale_pca_lyrics(data: dict):
"""
Script that scales the splits, and applies PCA to the lyrics vector.
Parameters
----------
data : dictionary
Dictionary containing the splits
Returns
-------
data : dict{np.array}
A dictionary of np.arrays, containing the train/test/val split.
"""
# Destructure the dictionary to get data split
X_train, y_train = data["train"]
X_val, y_val = data["val"]
X_test, y_test = data["test"]
lyric_scaler = StandardScaler().fit(X_train)
joblib.dump(lyric_scaler, LYRICS_SCALER)
X_train = lyric_scaler.transform(X_train)
X_test = lyric_scaler.transform(X_test)
X_val = lyric_scaler.transform(X_val)
# Fit PCA on TRAINING lyrics only
ipca = IncrementalPCA(n_components=512)
batch_size = 1000
for i in range(0, X_train.shape[0], batch_size):
ipca.partial_fit(X_train[i : i + batch_size])
# Transform in batches
X_train = ipca.transform(X_train)
X_test = ipca.transform(X_test)
X_val = ipca.transform(X_val)
joblib.dump(ipca, PCA_MODEL)
data = {
"train": (X_train, y_train),
"val": (X_val, y_val),
"test": (X_test, y_test),
}
return data
def scale(data: dict):
"""
Script that scales the splits, and applies PCA to the lyrics vector.
Parameters
----------
data : dictionary
Dictionary containing the splits
Returns
-------
data : dict{np.array}
A dictionary of np.arrays, containing the train/test/val split.
"""
# Destructure the dictionary to get data split
X_train, y_train = data["train"]
X_val, y_val = data["val"]
X_test, y_test = data["test"]
audio_scaler = StandardScaler(with_mean=False).fit(X_train)
joblib.dump(audio_scaler, AUDIO_SCALER)
# Transform the rest of the splits using the scalers
X_train = audio_scaler.transform(X_train)
X_test = audio_scaler.transform(X_test)
X_val = audio_scaler.transform(X_val)
data = {
"train": (X_train, y_train),
"val": (X_val, y_val),
"test": (X_test, y_test),
}
return data
def dataset_scaler(audio: np.ndarray, lyrics: np.ndarray):
"""
Method to scale both audio and lyric vectors using Z-Score.
This allows us to have both vectors with a mean of 0, and ranges up and down based on the
standard deviation without compromising the information it contains.
This also saves the scalers through joblib, which will be loaded in the predict script.
Parameters
----------
audio : np.array
Array of audio features
lyrics : np.array
Array of lyric features
Returns
-------
scaled_audio : np.array
Array of scaled audio features
scaleds : np.array
Array of scaled lyric features
"""
# Apply scalers to have similar-ranged data for both audio and lyrics training values
audio_scaler = StandardScaler().fit(audio)
lyric_scaler = StandardScaler().fit(lyrics)
# Save the trained scalers for prediction
joblib.dump(audio_scaler, AUDIO_SCALER)
joblib.dump(lyric_scaler, LYRICS_SCALER)
return audio_scaler, lyric_scaler
def instance_scaler(audio: np.ndarray, lyrics: np.ndarray):
"""
Method to scale the single input audio and lyrics
Parameters
----------
audio : np.array
Instance of an audio feature
lyrics : np.array
Instance of a lyric feature
Returns
-------
scaled_audio : np.array
Array of scaled audio feature
scaleds : np.array
Array of scaled lyric feature
"""
# Apply scalers to the single inputs
audio_scaler = joblib.load(AUDIO_SCALER)
lyric_scaler = joblib.load(LYRICS_SCALER)
scaled_audio = audio_scaler.transform(audio)
scaled_lyric = lyric_scaler.transform(lyrics)
return scaled_audio, scaled_lyric
def audio_instance_scaler(audio: np.ndarray):
"""
Method to scale the single input audio
Parameters
----------
audio : np.array
Instance of an audio feature
Returns
-------
scaled_audio : np.array
Array of scaled audio feature
"""
# Apply scaler to the single inputs
audio_scaler = joblib.load(AUDIO_SCALER)
scaled_audio = audio_scaler.transform(audio)
return scaled_audio
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