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df31aa1 5059de5 df31aa1 5059de5 df31aa1 5059de5 df31aa1 5059de5 df31aa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, precision_score, recall_score, f1_score
import pickle
import os
from feature_engineering import FeatureEngineer
class CTRModelTrainer:
def __init__(self):
self.feature_engineer = FeatureEngineer()
self.model = None
def prepare_training_data(self):
"""Prepare training data from notification feedback"""
conn = self.feature_engineer.conn
# If no database connection, generate synthetic data
if conn is None:
print("Database not available. Generating synthetic notification data...")
return self._generate_synthetic_ctr_data()
query = """
SELECT
nf.clicked::int as label,
COALESCE(uf.recency_score, 0.0) as recency_score,
COALESCE(uf.frequency_score, 0.0) as frequency_score,
COALESCE(pp.conscientiousness, 0.5) as conscientiousness,
COALESCE(pp.openness, 0.5) as openness,
CASE WHEN sn.notification_type = 'reminder' THEN 1 ELSE 0 END as is_reminder,
CASE WHEN sn.notification_type = 'milestone' THEN 1 ELSE 0 END as is_milestone,
COALESCE(sn.priority_score, 0.5) as priority_score,
EXTRACT(HOUR FROM sn.sent_at) / 24.0 as time_of_day
FROM notification_feedback nf
JOIN smart_notifications sn ON sn.id = nf.notification_id
LEFT JOIN user_features uf ON uf.user_id = nf.user_id
LEFT JOIN personality_profiles pp ON pp.user_id = nf.user_id
WHERE sn.sent_at IS NOT NULL
"""
try:
df = pd.read_sql(query, conn)
except Exception as e:
print(f"Database query failed ({e}). Generating synthetic data instead.")
return self._generate_synthetic_ctr_data()
if df.empty:
print("No notification feedback data available. Generating synthetic data...")
return self._generate_synthetic_ctr_data()
X = df.drop('label', axis=1).fillna(0.5)
y = df['label']
return X, y
def train(self):
"""Train CTR prediction model"""
print("Preparing training data...")
X, y = self.prepare_training_data()
if X is None or len(X) < 100:
print(f"Not enough training data for CTR model (need 100+, have {len(X) if X is not None else 0})")
return
print(f"Training on {len(X)} samples")
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# Train model
self.model = GradientBoostingClassifier(
n_estimators=100,
max_depth=3,
learning_rate=0.1,
random_state=42
)
self.model.fit(X_train, y_train)
# Evaluate
self.evaluate(X_test, y_test)
# Save model
os.makedirs('models/personalization', exist_ok=True)
with open('models/personalization/notification_ctr_model.pkl', 'wb') as f:
pickle.dump(self.model, f)
print("CTR model saved successfully")
def evaluate(self, X_test, y_test):
"""Evaluate model performance"""
# Predictions
y_pred = self.model.predict(X_test)
y_pred_proba = self.model.predict_proba(X_test)[:, 1]
# Metrics
auc = roc_auc_score(y_test, y_pred_proba)
precision = precision_score(y_test, y_pred, zero_division=0)
recall = recall_score(y_test, y_pred, zero_division=0)
f1 = f1_score(y_test, y_pred, zero_division=0)
print(f"AUC: {auc:.3f}")
print(f"Precision: {precision:.3f}")
print(f"Recall: {recall:.3f}")
print(f"F1 Score: {f1:.3f}")
# Feature importance
feature_names = X_test.columns
importances = self.model.feature_importances_
print("\nFeature Importance:")
for name, importance in sorted(zip(feature_names, importances), key=lambda x: x[1], reverse=True):
print(f" {name}: {importance:.3f}")
def _generate_synthetic_ctr_data(self):
"""Generate synthetic notification click-through data"""
np.random.seed(42)
n_samples = 3000
rows = []
for _ in range(n_samples):
# Generate features
recency = np.random.uniform(0, 1)
frequency = np.random.uniform(0, 1)
conscientiousness = np.random.beta(2, 2)
openness = np.random.beta(2, 2)
neuroticism = np.random.beta(2, 2)
is_reminder = np.random.choice([0, 1], p=[0.6, 0.4])
is_milestone = np.random.choice([0, 1], p=[0.8, 0.2])
priority_score = np.random.uniform(0.1, 1.0)
time_of_day = np.random.uniform(0, 1)
# Predict probability of click based on features
p_click = (
0.15 + 0.2 * conscientiousness + 0.1 * openness +
0.15 * priority_score + 0.05 * (1 - abs(time_of_day - 0.5)) -
0.1 * (frequency > 0.8) + 0.05 * is_milestone
)
p_click = np.clip(p_click, 0.02, 0.95)
clicked = int(np.random.random() < p_click)
rows.append({
'label': clicked,
'recency_score': recency,
'frequency_score': frequency,
'conscientiousness': conscientiousness,
'openness': openness,
'neuroticism': neuroticism,
'is_reminder': is_reminder,
'is_milestone': is_milestone,
'priority_score': priority_score,
'time_of_day': time_of_day,
})
df = pd.DataFrame(rows)
print(f"Generated {len(df)} synthetic notification samples")
X = df.drop('label', axis=1).fillna(0.5)
y = df['label']
return X, y
if __name__ == "__main__":
trainer = CTRModelTrainer()
trainer.train()
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