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| import pandas as pd
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| import numpy as np
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| import pickle
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| from sklearn.linear_model import LogisticRegression
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| from sklearn.ensemble import RandomForestClassifier
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| from xgboost import XGBClassifier
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| from tensorflow.keras.models import Sequential
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| from tensorflow.keras.layers import Dense, Dropout
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| from sklearn.metrics import (
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| accuracy_score, precision_score, recall_score, f1_score,
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| roc_auc_score, confusion_matrix, classification_report
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| )
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| import matplotlib.pyplot as plt
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| import seaborn as sns
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| import warnings
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|
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|
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| warnings.filterwarnings('ignore')
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|
|
| class ModelTrainer:
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| def __init__(self):
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| self.models = {
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| 'Logistic Regression': LogisticRegression(max_iter=1000, class_weight='balanced'),
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| 'Random Forest': RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=42),
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| 'XGBoost': XGBClassifier(scale_pos_weight=10, n_estimators=100, random_state=42, use_label_encoder=False, eval_metric='logloss')
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| }
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| self.neural_net = None
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|
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| def train_models(self, X_train, y_train):
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| """Train multiple machine learning models"""
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| trained_models = {}
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|
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| for name, model in self.models.items():
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| print(f"Training {name}...")
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| model.fit(X_train, y_train)
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| trained_models[name] = model
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|
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| return trained_models
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|
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| def train_neural_network(self, X_train, y_train, input_dim):
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| """Train a neural network model"""
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| model = Sequential([
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| Dense(64, activation='relu', input_dim=input_dim),
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| Dropout(0.3),
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| Dense(32, activation='relu'),
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| Dropout(0.3),
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| Dense(16, activation='relu'),
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| Dense(1, activation='sigmoid')
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| ])
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|
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| model.compile(
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| optimizer='adam',
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| loss='binary_crossentropy',
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| metrics=['accuracy']
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| )
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|
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| history = model.fit(
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| X_train, y_train,
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| epochs=20,
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| batch_size=64,
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| validation_split=0.2,
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| verbose=1
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| )
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|
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| self.neural_net = model
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| return model, history
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|
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| def evaluate_model(self, model, X_test, y_test, model_name="Model"):
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| """Evaluate model performance with various metrics"""
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| if model_name == "Neural Network":
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| y_pred_proba = model.predict(X_test)
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| y_pred = (y_pred_proba > 0.5).astype(int)
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| else:
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| y_pred = model.predict(X_test)
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| y_pred_proba = model.predict_proba(X_test)[:, 1]
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|
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|
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| accuracy = accuracy_score(y_test, y_pred)
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| precision = precision_score(y_test, y_pred)
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| recall = recall_score(y_test, y_pred)
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| f1 = f1_score(y_test, y_pred)
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| auc = roc_auc_score(y_test, y_pred_proba)
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|
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|
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| cm = confusion_matrix(y_test, y_pred)
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|
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|
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| report = classification_report(y_test, y_pred)
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|
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| results = {
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| 'model_name': model_name,
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| 'accuracy': accuracy,
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| 'precision': precision,
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| 'recall': recall,
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| 'f1_score': f1,
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| 'auc': auc,
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| 'confusion_matrix': cm,
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| 'classification_report': report,
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| 'y_test': y_test,
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| 'y_pred_proba': y_pred_proba
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| }
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|
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| return results
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|
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| def save_model(self, model, file_path):
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| """Save the trained model to a file"""
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| if isinstance(model, Sequential):
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| model.save(file_path)
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| else:
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| with open(file_path, 'wb') as f:
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| pickle.dump(model, f)
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|
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| def load_model(self, file_path, model_type='sklearn'):
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| """Load a trained model from a file"""
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| if model_type == 'keras':
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| from tensorflow.keras.models import load_model
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| return load_model(file_path)
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| else:
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| with open(file_path, 'rb') as f:
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| return pickle.load(f) |
