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import warnings
from loguru import logger
from numpy import ndarray
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import (
accuracy_score,
classification_report,
f1_score,
precision_score,
recall_score,
)
from sklearn.model_selection import GridSearchCV
from sklearn.multioutput import MultiOutputClassifier
from sklearn.pipeline import Pipeline
from ..baseModel import BaseModel
warnings.filterwarnings("ignore")
class RandomForestTfIdf(BaseModel):
"""
Sklearn implementation of BaseModel with integrated Grid Search.
Builds a TF-IDF + RandomForest pipeline for multi-output text classification.
"""
def __init__(self, language, path=None):
"""
Initialize the RandomForestTfIdf model with configuration parameters.
Args:
language (str): Language for the model.
path (str, optional): Path to load a pre-trained model. Defaults to None.
If None, a new model is initialized.
"""
self.params = {"stop_words": "english", "random_state": 42, "cv_folds": 5}
self.grid_params = {
"clf__estimator__n_estimators": [50, 100, 200],
"clf__estimator__max_depth": [None, 10, 20],
"tfidf__max_features": [3000, 5000, 8000],
}
super().__init__(language, path)
def setup_model(self):
"""
Initialize the scikit-learn pipeline with TF-IDF vectorizer and RandomForest classifier.
"""
base_estimator = RandomForestClassifier(
random_state=self.params["random_state"], n_jobs=-1
)
self.pipeline = Pipeline(
[
(
"tfidf",
TfidfVectorizer(ngram_range=(1, 2), stop_words=self.params["stop_words"]),
),
("clf", MultiOutputClassifier(base_estimator, n_jobs=-1)),
]
)
self.model = self.pipeline
logger.info("Scikit-learn pipeline initialized.")
def train(self, X_train, y_train) -> dict[str, any]:
"""
Train the model using Grid Search to find the best hyperparameters.
Args:
X_train: Input training data.
y_train: True labels for training data.
"""
if self.model is None:
raise ValueError(
"Model pipeline is not initialized. Call setup_model() before training."
)
logger.info(f"Starting training for: {self.language.upper()}")
logger.info("Performing Grid Search for best hyperparameters...")
grid_search = GridSearchCV(
self.pipeline,
param_grid=self.grid_params,
cv=self.params["cv_folds"],
scoring="f1_weighted",
n_jobs=-1,
verbose=1,
)
grid_search.fit(X_train, y_train)
logger.success(f"Best params found: {grid_search.best_params_}")
parameters_to_log = {
"max_features": grid_search.best_params_["tfidf__max_features"],
"n_estimators": grid_search.best_params_["clf__estimator__n_estimators"],
"max_depth": grid_search.best_params_["clf__estimator__max_depth"],
}
self.model = grid_search.best_estimator_
logger.success(f"Training for {self.language.upper()} completed.")
return parameters_to_log
def evaluate(self, X_test, y_test) -> dict[str, any]:
"""
Evaluate model on test data and return metrics.
Args:
X_test: Input test data.
y_test: True labels for test data.
"""
y_pred = self.predict(X_test)
report = classification_report(y_test, y_pred, zero_division=0)
print("\n" + "=" * 50)
print("CLASSIFICATION REPORT")
print(report)
print("=" * 50 + "\n")
metrics = {
"accuracy": accuracy_score(y_test, y_pred),
"precision": precision_score(y_test, y_pred, average="macro", zero_division=0),
"recall": recall_score(y_test, y_pred, average="macro", zero_division=0),
"f1_score": f1_score(y_test, y_pred, average="weighted"),
}
logger.info(
f"Evaluation completed — Accuracy: {metrics['accuracy']:.3f}, F1: {metrics['f1_score']:.3f}"
)
return metrics
def predict(self, X) -> ndarray:
"""
Make predictions using the trained model.
Args:
X: Input data for prediction.
Returns:
Predictions made by the model.
"""
if self.model is None:
raise ValueError("Model is not trained. Call train() or load() before prediction.")
return self.model.predict(X)
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