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import logging
import pandas as pd
from typing import Optional, Tuple, Dict, Literal, Any
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
from src.enums import Feature
from src.repository.transaction_repo import get_multiple_rows
from src.entity.model import Model
logger = logging.getLogger(__name__)
def preprocess_data(df: pd.DataFrame, test_size: float = 0.2) -> Tuple:
"""
Split the dataframe into X (features) and y (target).
Args:
df (pd.DataFrame): Input dataframe.
Returns:
Tuple: Split data (X_train, X_test, y_train, y_test).
"""
# Format data for the model
df_model = df
features = [f.name for f in Feature.get_all_features()]
X = df_model[features]
y = df_model['is_fraud']
# Split the data
if test_size > 0:
return train_test_split(X, y, test_size=test_size, stratify=df_model.is_fraud, random_state=42)
else:
return X, pd.DataFrame(), y, pd.DataFrame()
def train_model(
pipeline: Pipeline,
X_train: pd.DataFrame,
y_train: pd.DataFrame) -> Pipeline:
"""
Train the pipeline
"""
# Start the timer
import time
start_time = time.time()
print("Training the model...")
pipeline.fit(X_train, y_train)
print(f"Model trained in {time.time() - start_time:.2f} seconds")
return pipeline
all_algorithms = Literal[
'XGBoost',
'RandomForest',
'SVM',
'GradientBoosting',
'MLP',
'LightGBM',
'XGBRF',
'DecisionTree',
'ExtraTrees',
'Bagging',
]
def create_and_train_model(data: pd.DataFrame,
evaluate: bool = False,
algo: all_algorithms = 'MLP') -> Pipeline:
"""
Create and train a model on the given data
"""
if evaluate:
test_size = 0.2
else:
test_size = 0.0
# Split the data
X_train, X_test, y_train, y_test = preprocess_data(df=data, test_size=test_size)
# Train the model
pipeline = create_pipeline(algo)
pipeline = train_model(pipeline, X_train, y_train)
if evaluate:
evaluation_results = evaluate_model(pipeline, X_test, y_test)
logging.info(f"Evaluation results for {algo}:")
logging.info(f"F1 Score: {evaluation_results['f1_score']}\n")
logging.info(f"Confusion Matrix:\n{evaluation_results['confusion_matrix']}\n")
return pipeline
def evaluate_model(pipeline: Pipeline, X_test: pd.DataFrame, y_test: pd.Series) -> Dict:
"""
Evaluates the model
"""
y_pred = pipeline.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
return {
"accuracy": accuracy,
"f1_score": f1,
"confusion_matrix": cm
}
def train_model_from_scratch(limit: Optional[int] = None,
evaluate: bool = False,
algo: all_algorithms = 'MLP',
output_path: str = './data/model.pkl') -> Pipeline:
"""
Train a model from scratch
"""
# Load data
data = get_multiple_rows(limit=limit)
# Train the model
pipeline = create_and_train_model(data=data, evaluate=evaluate, algo=algo)
# Save the model
metadata = {
"model_name": algo,
"version": "1.0",
"training_data_size": len(data),
"training_datetime": pd.Timestamp.now().isoformat(),
}
Model.save_model(pipeline, metadata, output_path)
return pipeline
def create_pipeline(algo: all_algorithms = 'XGBoost') -> Pipeline:
"""
Creates a machine learning pipeline.
Returns:
Pipeline: A scikit-learn pipeline object.
"""
# Define the features, numerical and categorical
cat_features = [f.name for f in Feature.get_features_by_type('category')]
num_features = [f.name for f in Feature.get_features_by_type('number')]
# Pipeline for numerical variables
num_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='mean')),
('scaler', StandardScaler())
])
# Pipeline for categorical variables
cat_transformer = OneHotEncoder(handle_unknown='ignore')
# Preprocessor
preprocessor = ColumnTransformer(
transformers=[
('num', num_transformer, num_features),
('cat', cat_transformer, cat_features)
]
)
# Choose the classifier based on the algorithm
if algo == 'XGBoost':
from xgboost import XGBClassifier
classifier = XGBClassifier(eval_metric='logloss')
elif algo == 'RandomForest':
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
elif algo == 'SVM':
from sklearn.svm import SVC
classifier = SVC(probability=False)
elif algo == 'GradientBoosting':
from sklearn.ensemble import GradientBoostingClassifier
classifier = GradientBoostingClassifier()
elif algo == 'MLP':
from sklearn.neural_network import MLPClassifier
classifier = MLPClassifier(max_iter=1000, verbose=True)
elif algo == 'LightGBM':
from lightgbm import LGBMClassifier
classifier = LGBMClassifier()
elif algo == 'XGBRF':
from xgboost import XGBRFClassifier
classifier = XGBRFClassifier(eval_metric='logloss')
elif algo == 'DecisionTree':
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
elif algo == 'ExtraTrees':
from sklearn.ensemble import ExtraTreesClassifier
classifier = ExtraTreesClassifier()
elif algo == 'Bagging':
from sklearn.ensemble import BaggingClassifier
classifier = BaggingClassifier()
else:
raise ValueError(f"Unknown algorithm: {algo}")
# Full pipeline
pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('classifier', classifier)
])
return pipeline
def predict(
pipeline: Pipeline,
job: str,
city: str,
state: str,
category: str,
amt: float,
city_pop: int
) -> Dict[str, Any]:
# Built a DataFrame with the new match
transaction = pd.DataFrame([{
Feature.CUSTOMER_CITY.name: city,
Feature.CUSTOMER_CITY_POP.name: city_pop,
Feature.CUSTOMER_JOB.name: job,
Feature.CUSTOMER_STATE.name: state,
Feature.TRANSACTION_AMOUNT.name: amt,
Feature.TRANSACTION_CATEGORY.name: category
}])
# Use the pipeline to make a prediction
prediction = pipeline.predict(transaction)[0]
proba = pipeline.predict_proba(transaction)[0]
# Print the result
logging.info(f"Is fraud: {'True' if prediction == 1 else 'False'}")
print(f"Probability of fraud: {proba}")
# Return the result
return {"result": prediction.item(), "fraud_probability": proba[1].item()} |