first implementation of elasticity demand computation

experiments/procesing/pipeline.py

@@ -1,22 +1,82 @@
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
+import pandas as pd
 
-from extract import KafkaDataFetcher, ExperimentJoiner, EventTitleAugmenter
+from extract import KafkaDataFetcher, ExperimentJoiner, EventTitleAugmenter, chunk_shared_data
 from mapping import SessionTransitionProbMatrixTransformer, render_graph
-from demand import DemandEstimator
+from demand import DemandEstimator, ChunkInteractionsIntoSteps
+from elasticity import TemporalElasticityEstimator, aggregate_price_logs
+
+
+
+# elasticity pipeline components (not sklearn compatible, manual orchestration)
+def elasticity_pipeline(interactions_df, price_logs_df, window_size='30s', store_mode='hotel'):
+    """
+    Compute price elasticity from interaction and price data.
+
+    Args:
+        interactions_df: raw interaction data from demand_data_pipeline
+        price_logs_df: price log data from price_data_pipeline
+        window_size: time window for chunking
+        store_mode: 'hotel' or 'airline'
+
+    Returns:
+        df with [productId, elasticity, std_error, n_obs]
+    """
+    # step 1: chunk interactions into time windows
+    chunker = ChunkInteractionsIntoSteps(window_size=window_size, return_metadata=True)
+    interaction_chunks = chunker.transform(interactions_df)
+    print(len(interaction_chunks))
+
+    if not interaction_chunks:
+        return None
+
+    # step 2: compute demand per window
+    demand_estimator = DemandEstimator(store_mode=store_mode)
+    demand_chunks = []
+    for chunk in interaction_chunks:
+        demand_vector = demand_estimator.transform(chunk['data'])
+        demand_chunks.append({
+            'window_start': chunk['window_start'],
+            'window_end': chunk['window_end'],
+            'demand_vector': demand_vector
+        })
+
+    # step 3: aggregate price logs into windows
+    price_chunks = aggregate_price_logs(price_logs_df, window_size=window_size)
+
+    # step 4: compute elasticity
+    elasticity_estimator = TemporalElasticityEstimator(method='point', min_observations=2)
+    elasticity_df = elasticity_estimator.transform(demand_chunks, price_chunks)
+
+    return elasticity_df
 
 
 # exposable pipelines
-etl_pipeline = Pipeline([
-    ('kafka_fetch', KafkaDataFetcher()),
+interaction_pipeline = Pipeline([
+    ('kafka_fetch', KafkaDataFetcher(topic='user-interactions')),
     ('experiment_join', ExperimentJoiner()),
     ('event_augment', EventTitleAugmenter()),
 ])
+
+price_data_pipeline = Pipeline([
+    ('kafka_fetch', KafkaDataFetcher(topic='price-logs')),
+])
+
 pricing_pipeline = Pipeline([
     ('demand_estimation', DemandEstimator()),
 ])
-
 if __name__ == "__main__":
-    processed_data = etl_pipeline.fit_transform(None)
-    pricing = pricing_pipeline.fit_transform(processed_data)
-    print(pricing)
+    # fetch both datasets
+    interaction_data = interaction_pipeline.fit_transform(None)
+    pricing_data = price_data_pipeline.fit_transform(None)
+    if interaction_data.empty or pricing_data.empty:
+        print("Insufficient data for elasticity computation"); exit(0)
+    # compute elasticity via unified pipeline
+    window_size = "30s"
+    elasticity_results = elasticity_pipeline(interaction_data, pricing_data, window_size=window_size)
+
+    if elasticity_results is not None and not elasticity_results.empty:
+        print(elasticity_results.to_string(index=False))
+    else:
+        print("\nInsufficient data for elasticity computation")