extra session feature extraction

experiments/procesing/steps/session.py

@@ -0,0 +1,114 @@
+"""
+Session feature extraction for S_t component of state space.
+Computes behavioral signals from interaction data already in pipeline.
+"""
+import pandas as pd
+import numpy as np
+from typing import Optional, Dict, Any
+from collections import Counter
+from procesing.steps.base import BaseContextStep
+
+
+class ExtractSessionFeaturesStep(BaseContextStep):
+    """
+    Extract session-level behavioral features from interaction logs.
+
+    Input: interactions_df (user-interactions from earlier pipeline step)
+    Output: session_features DataFrame [sessionId, feature_1, feature_2, ...]
+
+    Features computed:
+        - total_interactions: count of all events
+        - page_views, item_views, searches, cart_adds: event type counts
+        - hovers: hover event counts
+        - unique_products_viewed: distinct product IDs
+        - interaction_velocity: events per minute
+        - session_duration_sec: time span of session
+        - avg_time_between_events: mean inter-event time
+        - product_view_depth: max views for single product (attention signal)
+    """
+
+    def transform(self, interactions_df: pd.DataFrame) -> pd.DataFrame:
+        if interactions_df.empty:
+            return pd.DataFrame()
+
+        # ensure timestamp column
+        if 'ts' in interactions_df.columns:
+            interactions_df = interactions_df.copy()
+            interactions_df['ts'] = pd.to_datetime(interactions_df['ts'])
+
+        # group by session and compute features
+        session_features = []
+        for session_id, session_df in interactions_df.groupby('sessionId'):
+            features = self._extract_features_for_session(session_id, session_df)
+            session_features.append(features)
+
+        return pd.DataFrame(session_features)
+
+    def _extract_features_for_session(self, session_id: str, session_df: pd.DataFrame) -> Dict[str, Any]:
+        """Compute features for single session."""
+        features = {'sessionId': session_id}
+
+        # basic counts
+        features['total_interactions'] = len(session_df)
+
+        event_counts = session_df['eventName'].value_counts().to_dict()
+        features['page_views'] = event_counts.get('page_view', 0) + event_counts.get('view_item_page', 0)
+        features['item_views'] = event_counts.get('view_item_page', 0)
+        features['searches'] = event_counts.get('search', 0)
+        features['cart_adds'] = event_counts.get('add_item_to_cart', 0)
+
+        # hover events
+        hover_events = ['hover_over_title', 'hover_over_paragraph', 'hover_over_link', 'hover_over_button']
+        features['hovers'] = sum(event_counts.get(ev, 0) for ev in hover_events)
+
+        # product-level signals
+        product_ids = session_df['productId'].dropna()
+        features['unique_products_viewed'] = product_ids.nunique()
+
+        if len(product_ids) > 0:
+            product_view_counts = Counter(product_ids)
+            features['product_view_depth'] = max(product_view_counts.values())
+        else:
+            features['product_view_depth'] = 0
+
+        # temporal features
+        if 'ts' in session_df.columns:
+            timestamps = session_df['ts'].sort_values()
+            features['session_duration_sec'] = (timestamps.max() - timestamps.min()).total_seconds()
+
+            if features['session_duration_sec'] > 0:
+                features['interaction_velocity'] = (features['total_interactions'] / features['session_duration_sec']) * 60
+            else:
+                features['interaction_velocity'] = 0.0
+
+            # inter-event timing
+            if len(timestamps) > 1:
+                time_diffs = timestamps.diff().dropna().dt.total_seconds()
+                features['avg_time_between_events'] = time_diffs.mean()
+                features['std_time_between_events'] = time_diffs.std()
+            else:
+                features['avg_time_between_events'] = 0.0
+                features['std_time_between_events'] = 0.0
+        else:
+            features['session_duration_sec'] = 0.0
+            features['interaction_velocity'] = 0.0
+            features['avg_time_between_events'] = 0.0
+            features['std_time_between_events'] = 0.0
+
+        # cart/conversion signals
+        features['cart_to_view_ratio'] = features['cart_adds'] / features['item_views'] if features['item_views'] > 0 else 0.0
+
+        return features
+
+
+class FilterSessionInteractionsStep(BaseContextStep):
+    """
+    Filter interactions DataFrame to specific session.
+
+    Input: (interactions_df, session_id)
+    Output: interactions_df filtered to session_id
+    """
+
+    def transform(self, data: tuple) -> pd.DataFrame:
+        interactions_df, session_id = data
+        return interactions_df[interactions_df['sessionId'] == session_id].copy()