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import tensorflow as tf
import numpy as np
from datetime import datetime
import json
import logging
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class IntelligentRoutingModel:
def __init__(self):
self.model = None
self.feature_columns = [
'category_encoded',
'floor_number',
'current_workload',
'past_resolution_rate',
'number_of_requests',
'total_delays'
]
# Category encoding
self.categories = ['electricity', 'internet', 'plumber', 'water_cooler', 'sweeper', 'carpenter']
self.category_encoding = {cat: i for i, cat in enumerate(self.categories)}
logging.info(f"Initialized IntelligentRoutingModel with {len(self.categories)} categories")
def preprocess_data(self, data):
"""Enhanced data preprocessing"""
features = []
labels = []
for sample in data:
# Encode category
category_encoded = [0] * len(self.categories)
category_idx = self.category_encoding[sample['category']]
category_encoded[category_idx] = 1
# Process each staff member
for staff in sample['current_staff_status']:
if staff['department'] == sample['category']:
# Normalize numerical features
normalized_workload = staff['current_workload'] / 5.0
# Calculate time-based features
submission_time = datetime.strptime(sample['submission_timestamp'], "%Y-%m-%dT%H:%M:%SZ")
hour_of_day = submission_time.hour / 24.0
# Calculate distance (simplified)
distance = abs(int(staff.get('current_floor', 0)) -
int(sample['floor_number'])) / 4.0
# Create enhanced feature vector
feature = np.array([
*category_encoded,
sample['floor_number'] / 4.0, # Normalized floor number
normalized_workload,
staff['past_resolution_rate'],
sample['floor_metrics']['number_of_requests'] / 30.0,
sample['floor_metrics']['total_delays'] / 5.0,
hour_of_day,
1.0 if staff['availability_status'] == 'Available' else 0.0,
distance # Normalized distance
])
features.append(feature)
# Enhanced label creation
is_good_match = (
staff['availability_status'] == 'Available' and
staff['current_workload'] < 4 and
staff['past_resolution_rate'] > 0.85 and
distance < 0.5 # Prefer closer staff
)
labels.append(1 if is_good_match else 0)
if not features:
raise ValueError("No valid features found in the dataset")
# Convert to numpy arrays
features = np.array(features)
labels = np.array(labels)
# Log shapes for debugging
logging.info(f"Preprocessed data shapes - Features: {features.shape}, Labels: {labels.shape}")
return features, labels
def build_model(self):
"""Build an improved neural network model"""
# Calculate input dimension based on features
input_dim = (
len(self.categories) + # One-hot encoded categories
8 # Additional features: floor_number, workload, resolution_rate,
# requests, delays, hour_of_day, availability, distance
)
logging.info(f"Building model with input dimension: {input_dim}")
model = tf.keras.Sequential([
# Input layer with regularization
tf.keras.layers.Dense(128, activation='relu', input_shape=(input_dim,),
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.3),
# Hidden layers with skip connections
tf.keras.layers.Dense(64, activation='relu',
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(32, activation='relu',
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Dropout(0.2),
# Output layer
tf.keras.layers.Dense(1, activation='sigmoid')
])
# Use a more sophisticated optimizer with learning rate scheduling
initial_learning_rate = 0.001
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=1000,
decay_rate=0.9,
staircase=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
model.compile(
optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
tf.keras.metrics.AUC()]
)
# Print model summary
model.summary()
self.model = model
return model
def train(self, train_data_path, epochs=20, batch_size=32):
"""Enhanced training process"""
logging.info(f"Starting model training with {epochs} epochs")
# Load and preprocess data
with open(train_data_path) as f:
train_data = json.load(f)
X, y = self.preprocess_data(train_data)
# Create validation split
from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# Handle class imbalance
from sklearn.utils.class_weight import compute_class_weight
class_weights = compute_class_weight(
'balanced',
classes=np.unique(y_train),
y=y_train
)
class_weight_dict = {i: weight for i, weight in enumerate(class_weights)}
# Build model
self.build_model()
# Add callbacks
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor='val_loss',
patience=5,
restore_best_weights=True
),
tf.keras.callbacks.ReduceLROnPlateau(
monitor='val_loss',
factor=0.5,
patience=3,
min_lr=0.00001
)
]
# Train model
history = self.model.fit(
X_train, y_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(X_val, y_val),
class_weight=class_weight_dict,
callbacks=callbacks,
verbose=1
)
return history
def predict(self, input_data):
"""Make predictions for input data"""
logging.info(f"Making prediction for grievance {input_data['grievance_id']}")
# Preprocess input
X = self.preprocess_data([input_data])
# Make prediction
prediction = self.model.predict(X)[0][0]
logging.info(f"Raw prediction: {prediction}")
# Find best matching staff
best_staff = None
highest_score = -1
for staff in input_data['current_staff_status']:
if staff['department'] == input_data['category']:
score = prediction * staff['past_resolution_rate'] * (1 / (staff['current_workload'] + 1))
logging.debug(f"Staff {staff['staff_id']} score: {score}")
if score > highest_score:
highest_score = score
best_staff = staff
if not best_staff:
logging.warning("No suitable staff found for the grievance")
return None
# Generate response
assignment_time = datetime.utcnow()
response = {
"grievance_id": input_data['grievance_id'],
"assigned_staff_id": best_staff['staff_id'],
"assignment_timestamp": assignment_time.strftime("%Y-%m-%dT%H:%M:%SZ"),
"expected_resolution_time": "1 hour",
"floor_number": input_data['floor_number'],
"hostel_name": input_data['hostel_name'],
"student_room_no": input_data['student_room_no']
}
logging.info(f"Generated response: {response}")
return response
def save_model(self, path):
"""Save the trained model"""
logging.info(f"Saving model to {path}")
self.model.save(path)
logging.info("Model saved successfully")
def load_model(self, path):
"""Load a trained model"""
logging.info(f"Loading model from {path}")
self.model = tf.keras.models.load_model(path)
logging.info("Model loaded successfully")
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