update for model_training script
This commit is contained in:
Ayomide
+94
-50
@@ -1,52 +1,105 @@
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from flask import Flask, render_template, request
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from flask import Flask, render_template, request, jsonify
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import joblib
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, Any, Tuple, Union
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from dataclasses import dataclass
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import logging
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from config import MODEL_PATH, FEATURE_CONFIG
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# Initialize logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = Flask(__name__)
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# Load the model
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try:
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model = joblib.load('models/fraud_model.pkl')
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print("Model loaded successfully")
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except Exception as e:
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print(f"Error loading model: {e}")
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@dataclass
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class PredictionResult:
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is_fraud: bool
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probability: float
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confidence: str # "high", "medium", "low"
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class FraudDetectionService:
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def __init__(self, model_path: Path):
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self.model = self._load_model(model_path)
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self.required_features = FEATURE_CONFIG['numeric_features'] + FEATURE_CONFIG['categorical_features']
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def _load_model(self, model_path: Path) -> Any:
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"""Load trained model with error handling"""
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try:
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model = joblib.load(model_path)
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if not hasattr(model, 'predict'):
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raise ValueError("Invalid model object - missing predict method")
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logger.info("Model loaded successfully")
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return model
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except Exception as e:
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logger.error(f"Model loading failed: {str(e)}")
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raise
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def preprocess_input(data):
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# Convert to DataFrame
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df = pd.DataFrame([data])
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def preprocess_input(self, input_data: Dict[str, Any]) -> pd.DataFrame:
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"""Convert and validate input data"""
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df = pd.DataFrame([input_data])
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# Ensure numeric fields are properly converted
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numeric_fields = ['amt', 'city_pop', 'lat', 'long', 'merch_lat', 'merch_long']
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for field in numeric_fields:
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# Type conversion
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for field in FEATURE_CONFIG['numeric_features']:
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if field in df.columns:
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df[field] = pd.to_numeric(df[field], errors='coerce')
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# Convert and extract datetime features
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# Handle datetime features
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if 'trans_date_trans_time' in df.columns:
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df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])
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df['hour'] = df['trans_date_trans_time'].dt.hour
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df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek
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df['month'] = df['trans_date_trans_time'].dt.month
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# Calculate age from dob
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df['dob'] = pd.to_numeric(pd.to_datetime(df['dob']).astype(np.int64) / 10**9)
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df['age'] = (pd.Timestamp.now().timestamp() - df['dob']) / (365 * 24 * 3600)
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# Age calculation
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if 'dob' in df.columns:
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df['dob'] = pd.to_datetime(df['dob'])
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df['age'] = (pd.Timestamp.now() - df['dob']).dt.days / 365.25
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# Calculate distance safely
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# Geospatial features
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if all(col in df.columns for col in ['lat', 'long', 'merch_lat', 'merch_long']):
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df['distance'] = np.sqrt(
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(df['lat'].astype(float) - df['merch_lat'].astype(float))**2 +
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(df['long'].astype(float) - df['merch_long'].astype(float))**2
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)
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# Ensure all expected columns are present
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expected_columns = ['amt', 'city_pop', 'hour', 'day_of_week', 'month', 'age', 'distance',
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'category', 'gender', 'job', 'merchant']
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for col in expected_columns:
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if col not in df.columns:
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df[col] = 0 # Default value if missing
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# Ensure all expected features exist
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for feature in self.required_features:
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if feature not in df.columns:
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df[feature] = 0 # Safe default
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logger.warning(f"Missing feature filled with default: {feature}")
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return df.drop(['trans_date_trans_time', 'dob'], axis=1, errors='ignore')
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return df.drop(FEATURE_CONFIG['drop_columns'], axis=1, errors='ignore')
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def predict(self, input_data: Dict[str, Any]) -> PredictionResult:
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"""Make fraud prediction with probabilities"""
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try:
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processed_data = self.preprocess_input(input_data)
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probabilities = self.model.predict_proba(processed_data)
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fraud_prob = probabilities[0][1] * 100
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prediction = self.model.predict(processed_data)[0]
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confidence = "high" if fraud_prob > 75 else "medium" if fraud_prob > 25 else "low"
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return PredictionResult(
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is_fraud=bool(prediction),
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probability=round(fraud_prob, 2),
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confidence=confidence
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)
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except Exception as e:
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logger.error(f"Prediction failed: {str(e)}")
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raise
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# Initialize service
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try:
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fraud_service = FraudDetectionService(MODEL_PATH)
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except Exception as e:
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logger.critical(f"Service initialization failed: {str(e)}")
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raise
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@app.route('/')
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def home():
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@@ -55,36 +108,27 @@ def home():
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@app.route('/predict', methods=['POST'])
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def predict():
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try:
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# Get data from form
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data = request.form.to_dict()
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print("Received data:", data) # Debugging
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input_data = request.form.to_dict()
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logger.info(f"Received prediction request: {input_data}")
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# Preprocess the input
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processed_data = preprocess_input(data)
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print("Processed data:", processed_data) # Debugging
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result = fraud_service.predict(input_data)
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# Get prediction probabilities
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probabilities = model.predict_proba(processed_data)
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print("Raw probabilities:", probabilities) # Debugging
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# Extract fraud probability (class 1)
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fraud_probability = probabilities[0][1] * 100 # Convert to percentage
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# Make prediction
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prediction = model.predict(processed_data)[0]
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result = {
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'prediction': bool(prediction),
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'probability': float(fraud_probability),
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'is_fraud': bool(prediction)
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response = {
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"is_fraud": result.is_fraud,
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"probability": result.probability,
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"confidence": result.confidence,
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"status": "success"
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}
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print("Prediction result:", result) # Debugging
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return render_template('index.html', prediction=result)
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return render_template('index.html', prediction=response)
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except Exception as e:
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print("Prediction error:", str(e)) # Debugging
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return f"Error: {str(e)}", 400
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logger.error(f"Prediction error: {str(e)}")
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return jsonify({
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"status": "error",
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"message": "Failed to process prediction",
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"error": str(e)
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}), 400
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if __name__ == '__main__':
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app.run(debug=True)
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app.run(host='0.0.0.0', port=5000, debug=True)
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+99
-41
@@ -1,24 +1,31 @@
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import os
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from pathlib import Path
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from typing import Dict, Any, List
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from sklearn.ensemble import RandomForestClassifier
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# Directory Paths
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BASE_DIR = Path(__file__).parent.parent
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DATA_DIR = BASE_DIR / 'data'
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RAW_DATA_DIR = DATA_DIR / 'raw'
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PROCESSED_DATA_DIR = DATA_DIR / 'processed'
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MODELS_DIR = BASE_DIR / 'models'
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REPORTS_DIR = BASE_DIR / 'reports'
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FIGURES_DIR = REPORTS_DIR / 'figures'
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class Config:
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"""Centralized configuration for the fraud detection system"""
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# File Paths
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TRAIN_DATA_PATH = RAW_DATA_DIR / 'fraudTrain.csv'
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TEST_DATA_PATH = RAW_DATA_DIR / 'fraudTest.csv'
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TRAIN_PROCESSED_PATH = PROCESSED_DATA_DIR / 'train_processed.csv'
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TEST_PROCESSED_PATH = PROCESSED_DATA_DIR / 'test_processed.csv'
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MODEL_PATH = MODELS_DIR / 'fraud_model.pkl'
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# ========== Directory Structure ==========
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BASE_DIR = Path(__file__).parent.parent
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DATA_DIR = BASE_DIR / 'data'
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RAW_DATA_DIR = DATA_DIR / 'raw'
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PROCESSED_DATA_DIR = DATA_DIR / 'processed'
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MODELS_DIR = BASE_DIR / 'models'
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REPORTS_DIR = BASE_DIR / 'reports'
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FIGURES_DIR = REPORTS_DIR / 'figures'
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LOGS_DIR = BASE_DIR / 'logs'
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# Model Configuration
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MODEL_PARAMS = {
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# ========== File Paths ==========
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TRAIN_DATA_PATH = RAW_DATA_DIR / 'fraudTrain.csv'
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TEST_DATA_PATH = RAW_DATA_DIR / 'fraudTest.csv'
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TRAIN_PROCESSED_PATH = PROCESSED_DATA_DIR / 'train_processed.csv'
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TEST_PROCESSED_PATH = PROCESSED_DATA_DIR / 'test_processed.csv'
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MODEL_PATH = MODELS_DIR / 'fraud_model.pkl'
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ERROR_LOG_PATH = LOGS_DIR / 'errors.log'
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# ========== Model Configuration ==========
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MODEL_PARAMS: Dict[str, Any] = {
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'classifier': RandomForestClassifier,
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'classifier_params': {
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'n_estimators': 100,
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@@ -27,38 +34,89 @@ MODEL_PARAMS = {
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'class_weight': 'balanced',
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'random_state': 42,
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'n_jobs': -1
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},
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'numeric_features': ['amt', 'city_pop', 'hour', 'day_of_week', 'month', 'age', 'distance'],
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'categorical_features': ['category', 'gender', 'job', 'merchant']
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}
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}
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}
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# Feature Engineering Configuration
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FEATURE_CONFIG = {
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# ========== Feature Engineering ==========
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NUMERIC_FEATURES: List[str] = [
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'amt', 'city_pop', 'hour',
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'day_of_week', 'month', 'age',
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'distance'
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]
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CATEGORICAL_FEATURES: List[str] = [
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'category', 'gender', 'job', 'merchant'
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]
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FEATURE_CONFIG: Dict[str, Any] = {
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'numeric_features': NUMERIC_FEATURES,
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'categorical_features': CATEGORICAL_FEATURES,
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'time_features': ['hour', 'day_of_week', 'month'],
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'demographic_features': ['age'],
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'geographic_features': ['distance'],
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'drop_columns': ['trans_date_trans_time', 'trans_num', 'dob', 'unix_time']
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}
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'behavioral_features': ['txn_count_24h', 'time_since_last_txn'],
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'drop_columns': ['trans_date_trans_time', 'trans_num', 'dob', 'unix_time'],
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'required_features': NUMERIC_FEATURES + CATEGORICAL_FEATURES
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}
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# Evaluation Metrics
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EVAL_METRICS = {
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'threshold': 0.5,
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'metrics': ['precision', 'recall', 'f1', 'roc_auc', 'average_precision'],
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'target_names': ['Legitimate', 'Fraud']
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}
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# ========== Prediction Configuration ==========
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PREDICTION_THRESHOLDS: Dict[str, float] = {
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'high_risk': 0.75,
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'medium_risk': 0.25,
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'low_risk': 0.01
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}
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# Flask App Configuration
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class FlaskConfig:
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DEBUG = True
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SECRET_KEY = 'your-secret-key-here'
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MAX_CONTENT_LENGTH = 16 * 1024 * 1024 # 16MB upload limit
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# ========== Flask Configuration ==========
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class FlaskConfig:
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DEBUG = os.getenv('FLASK_DEBUG', 'True') == 'True'
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SECRET_KEY = os.getenv('FLASK_SECRET_KEY', 'your-secret-key-here')
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MAX_CONTENT_LENGTH = int(os.getenv('MAX_UPLOAD_SIZE', 16 * 1024 * 1024)) # 16MB default
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JSONIFY_PRETTYPRINT_REGULAR = True
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SERVER_NAME = os.getenv('FLASK_SERVER_NAME', None)
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# Create directories if they don't exist
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for directory in [RAW_DATA_DIR, PROCESSED_DATA_DIR, MODELS_DIR, FIGURES_DIR]:
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# ========== Logging Configuration ==========
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LOGGING_CONFIG: Dict[str, Any] = {
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'version': 1,
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'formatters': {
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'default': {
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'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',
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}
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},
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'handlers': {
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'file': {
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'class': 'logging.FileHandler',
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'filename': ERROR_LOG_PATH,
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'formatter': 'default'
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},
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'console': {
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'class': 'logging.StreamHandler',
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'formatter': 'default'
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}
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},
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'root': {
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'level': os.getenv('LOG_LEVEL', 'INFO'),
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'handlers': ['file', 'console']
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}
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}
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# ========== Constants ==========
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DATE_FORMAT: str = '%Y-%m-%d %H:%M:%S'
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RANDOM_STATE: int = 42
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TEST_SIZE: float = 0.2
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@classmethod
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def init_directories(cls):
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"""Ensure all required directories exist"""
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required_dirs = [
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cls.RAW_DATA_DIR,
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cls.PROCESSED_DATA_DIR,
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cls.MODELS_DIR,
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cls.FIGURES_DIR,
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cls.LOGS_DIR
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]
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for directory in required_dirs:
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directory.mkdir(parents=True, exist_ok=True)
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# Constants
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DATE_FORMAT = '%Y-%m-%d %H:%M:%S'
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RANDOM_STATE = 42
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TEST_SIZE = 0.2
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# Initialize directories on import
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Config.init_directories()
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@@ -1,14 +1,17 @@
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from config import TRAIN_DATA_PATH, TEST_DATA_PATH
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# Load data with proper dtype specification
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def load_data(filepath):
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return pd.read_csv(filepath, low_memory=False)
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train_df = load_data('data/raw/fraudTrain.csv')
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test_df = load_data('data/raw/fraudTest.csv')
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train_df = load_data(TRAIN_DATA_PATH)
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test_df = load_data(TEST_DATA_PATH)
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# Data cleaning function
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def clean_data(df):
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@@ -0,0 +1,11 @@
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# exceptions.py
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class FraudDetectionError(Exception):
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"""Base exception class"""
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class DataValidationError(FraudDetectionError):
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"""Raised when data fails validation"""
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class ModelServeError(FraudDetectionError):
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"""API prediction failures"""
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+2
-24
@@ -7,6 +7,7 @@ from sklearn.compose import ColumnTransformer
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from sklearn.pipeline import Pipeline
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import joblib
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from datetime import datetime
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from config import TRAIN_PROCESSED_PATH, TEST_PROCESSED_PATH
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def load_data(train_file, test_file):
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@@ -20,25 +21,6 @@ def load_data(train_file, test_file):
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return train_df, test_df
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def feature_engineering(df):
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# Convert transaction time to datetime
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df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])
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# Extract time features
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df['hour'] = df['trans_date_trans_time'].dt.hour
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df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek
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df['month'] = df['trans_date_trans_time'].dt.month
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# Calculate age from dob
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df['dob'] = pd.to_datetime(df['dob'])
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df['age'] = (pd.to_datetime('today') - df['dob']).dt.days // 365
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# Calculate distance between user and merchant
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df['distance'] = np.sqrt((df['lat']-df['merch_lat'])**2 + (df['long']-df['merch_long'])**2)
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return df
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def train_model(train_df, test_df):
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# Define features and target
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X_train = train_df.drop(['is_fraud', 'trans_date_trans_time', 'trans_num', 'dob', 'unix_time'], axis=1)
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@@ -79,11 +61,7 @@ def train_model(train_df, test_df):
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def main():
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# Load data
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train_df, test_df = load_data('data/raw/fraudTrain.csv', 'data/raw/fraudTest.csv')
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# Feature engineering
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train_df = feature_engineering(train_df)
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test_df = feature_engineering(test_df)
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train_df, test_df = load_data(TRAIN_PROCESSED_PATH, TEST_PROCESSED_PATH)
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# Print dataset sizes after cleaning
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print(f"Training samples after cleaning: {len(train_df)}")
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