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import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import StratifiedGroupKFold
from skopt import BayesSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
import joblib
from skopt.space import Real, Integer, Categorical
from sklearn.metrics import classification_report, accuracy_score
import json
from sklearn.preprocessing import LabelEncoder
#from _config import config
class TrainModel(BaseEstimator, TransformerMixin):
def __init__(self, classifier, train_label, target):
#self.config = config
#self.target = config.get("target_label", None) # User-defined target label in config
self.classifier = classifier
self.train_label = train_label
self.target = target
self.label_encoder = LabelEncoder()
#self.selected_domains = self.config.get("selected_domains", "All domains") # Default to all domains if None
#if not self.target:
# raise ValueError("No target label specified in the config. Please set 'target_label'.")
def get_default_param_space(self, classifier):
""" Returns the default hyperparameter space for a given classifier. """
if classifier == 'xgboost':
return {
'learning_rate': Real(0.01, 0.3, prior='log-uniform'),
'n_estimators': Integer(100, 1000),
'max_depth': Integer(3, 10),
'min_child_weight': (1, 10),
'subsample': (0.5, 1.0),
'colsample_bytree': (0.5, 1.0),
'gamma': (0, 10),
'reg_alpha': (0, 10),
'reg_lambda': (0, 10),
}
elif classifier == 'svm':
return {
'C': Real(0.1, 10, prior='log-uniform'),
'kernel': Categorical(['linear', 'rbf'])
}
elif classifier == 'randomforest':
return {
'n_estimators': Integer(100, 1000),
'max_depth': Integer(3, 10)
}
else:
raise ValueError(f"Unsupported classifier type: {classifier}")
def fit(self, X, y=None):
# Ensure the target column exists in the dataset
if self.target not in X.columns:
raise ValueError(f"Target label '{self.target}' not found in the dataset.")
# Fit the label encoder on the target column
print(f"Encoding the target labels for '{self.target}'...")
self.label_encoder.fit(X[self.target])
# Print the mapping between original labels and encoded labels
original_labels = list(self.label_encoder.classes_)
encoded_labels = list(range(len(original_labels)))
label_mapping = dict(zip(encoded_labels, original_labels))
print(f"Label encoding complete. Mapping: {label_mapping}")
# Transform the target column and add it as 'encoded_target'
X['encoded_target'] = self.label_encoder.transform(X[self.target])
# Value counts for the encoded target
value_counts = X['encoded_target'].value_counts().to_dict()
print(f"Value counts for encoded target: {value_counts}")
print(X.columns)
# Pop unnecessary columns (groupid, emotion labels not being used, etc.)
groups = X.pop('groupid')
print(f"Group IDs popped from the dataset.")
# Pop the label columns which aren't used
self.train_label = self.train_label.split(",")
for label in self.train_label:
X.pop(label)
print(f"Label columns popped from the dataset.")
# Pop the encoded target as Y
y = X.pop('encoded_target')
print(f"Encoded target column popped from the dataset.")
print(X.columns)
# Store the feature names for later use
feature_names = X.columns.tolist()
print(f"hallo")
# Choose classifier
classifier = self.classifier
if classifier == 'xgboost':
model = XGBClassifier(objective='multi:softmax', random_state=42)
elif classifier == 'svm':
model = SVC(probability=True)
elif classifier == 'randomforest':
model = RandomForestClassifier(random_state=42)
else:
raise ValueError(f"Unsupported classifier type: {classifier}")
print(f"Training the model using {classifier}...")
# Use user-defined param_space if provided, otherwise use default
print(f"Classifier: {classifier}")
default_param_space = self.get_default_param_space(classifier)
param_space = default_param_space
# Hyperparameter tuning using Bayesian optimization
sgkf = StratifiedGroupKFold(n_splits=5)
print(f"Parameter space being used: {param_space}")
if param_space is None:
raise ValueError("Parameter space cannot be None. Please check the classifier configuration.")
opt = BayesSearchCV(
estimator=model,
search_spaces=param_space,
cv=sgkf,
n_iter=5,
n_jobs=-1,
n_points=1,
verbose=1,
scoring='accuracy'
)
print("Hyperparameter tuning in progress...")
print(X.describe(),X.columns)
print(f"stop")
# Fit the model using the encoded target
opt.fit(X, y, groups=groups)
self.best_model = opt.best_estimator_
print(f"Best parameters found: {opt.best_params_}")
# Print classification metrics
y_pred = self.best_model.predict(X)
accuracy = accuracy_score(y, y_pred)
report = classification_report(y, y_pred, target_names=self.label_encoder.classes_, output_dict=True)
# Save classification report
classification_report_json = report
with open(f'classification_report_{self.target}.json', 'w') as f:
json.dump(classification_report_json, f, indent=4)
print(f"Accuracy: {accuracy}")
print(f"Classification Report:\n{report}")
# Save the best model with the target label in the file name
model_name = f"{classifier}_best_model_{self.target}.pkl"
joblib.dump(self.best_model, model_name)
print("Model saved successfully.")
# Save model metadata
model_metadata = {
"best_params": opt.best_params_,
"accuracy": accuracy,
"classification_report": classification_report_json,
"label_mapping": label_mapping,
"model_name": model_name,
"value_counts": value_counts,
#"selected_domains": self.selected_domains,
#"include_magnitude": self.config.get("include_magnitude", True)
}
if hasattr(self.best_model, "feature_importances_"):
feature_importances = self.best_model.feature_importances_
# Convert feature importances to native Python floats
feature_importance_dict = {feature: float(importance) for feature, importance in zip(feature_names, feature_importances)}
model_metadata["feature_importances"] = feature_importance_dict
print("Feature Importances:")
for feature, importance in feature_importance_dict.items():
print(f"{feature}: {importance:.4f}")
# Save metadata with the target name in the file name
metadata_file = f"{classifier}_model_metadata_{self.target}.json"
with open(metadata_file, "w") as f:
json.dump(model_metadata, f, indent=4)
print(f"Model metadata saved to {metadata_file}.")
# Save file paths internally for later retrieval
self.model_file = f"{classifier}_best_model_{self.target}.pkl"
self.metadata_file = f"{classifier}_model_metadata_{self.target}.json"
return self
def get_output_files(self):
return self.model_file, self.metadata_file
def transform(self, X):
return X # Placeholder for transform step (not needed for training) |