chore: cleaning elasticity references

experiments/procesing/__init__.py

@@ -12,7 +12,6 @@ from procesing.steps import (
     ComputeDemandStep,
     ComputeDemandForChunksStep,
     AggregatePriceLogsStep,
-    ComputeElasticityStep,
     # StateSpace,
     # BuildStateSpaceStep,
     FitPricingFunctionStep,
@@ -41,7 +40,6 @@ __all__ = [
     'ComputeDemandStep',
     'ComputeDemandForChunksStep',
     'AggregatePriceLogsStep',
-    'ComputeElasticityStep',
     # 'StateSpace',
     # 'BuildStateSpaceStep',
     'FitPricingFunctionStep',

experiments/procesing/pipelines.py

@@ -12,7 +12,6 @@ from procesing.steps import (
     ChunkByTimeWindowStep,
     ComputeDemandForChunksStep,
     AggregatePriceLogsStep,
-    ComputeElasticityStep,
     # BuildStateSpaceStep,
     FitPricingFunctionStep,
     PredictPricesStep,
@@ -39,7 +38,6 @@ def price_extraction_pipeline(context: PipelineContext):
 def product_features_pipeline(context: PipelineContext,
                                     interactions_df: pd.DataFrame,
                                     price_logs_df: pd.DataFrame):
-    # elasticity_step = ComputeElasticityStep(context)
     demand_step = ComputeDemandStep(context)
     price_step = AggregatePriceLogsStep(context)
     join_step = JoinProductFeaturesStep(context)

experiments/procesing/steps/__init__.py

@@ -4,7 +4,7 @@ from procesing.steps.join import JoinExperimentsStep, JoinProductFeaturesStep
 from procesing.steps.augment import CreatePriceBucketsStep, AugmentEventNamesStep, AugmentInteractionsStep
 from procesing.steps.chunk import ChunkByTimeWindowStep
 from procesing.steps.demand import ComputeDemandStep, ComputeDemandForChunksStep
-from procesing.steps.elasticity import AggregatePriceLogsStep, ComputeElasticityStep
+from procesing.steps.elasticity import AggregatePriceLogsStep
 from procesing.steps.pricing import FitPricingFunctionStep, PredictPricesStep
 from procesing.steps.session import ExtractSessionFeaturesStep, _extract_features_for_session
 
@@ -22,7 +22,6 @@ __all__ = [
     'ComputeDemandStep',
     'ComputeDemandForChunksStep',
     'AggregatePriceLogsStep',
-    'ComputeElasticityStep',
     'FitPricingFunctionStep',
     'PredictPricesStep',
     'ExtractSessionFeaturesStep',

experiments/procesing/steps/elasticity.py

@@ -40,185 +40,3 @@ class AggregatePriceLogsStep(BaseContextStep):
         # fill 0s with base_price from products
         base_price_map = products.set_index('id')['base_price'].to_dict()
         return avg_prices
-
-
-
-class ComputeElasticityStep(BaseContextStep):
-    """
-    Compute price elasticity from demand and price chunks.
-    Input: (demand_chunks, price_chunks)
-    Output: elasticity_df [productId, elasticity, std_error, n_obs]
-    """
-
-    def transform(self, chunk_tuple: tuple):
-        demand_chunks, price_chunks = chunk_tuple
-
-        method = self.context.config.get('elasticity_method', 'point')
-        min_obs = self.context.config.get('min_observations', 2)
-
-        products = self.context.products
-        all_product_ids = products['id'].unique()
-
-        # align chunks by window_start
-        # aligned = self._align_chunks(demand_chunks, price_chunks)
-
-        if None:
-            return pd.DataFrame({
-                'productId': all_product_ids,
-                'elasticity': 0.0,
-                'std_error': 0.0,
-                'n_obs': 0
-            })
-
-        # build time series per product
-        product_series = self._build_timeseries(aligned)
-
-        # compute elasticity per product
-        elasticities = []
-        for pid, series in product_series.items():
-            if len(series) < min_obs:
-                elasticities.append({
-                    'productId': pid,
-                    'elasticity': 0.0,
-                    'std_error': 0.0,
-                    'n_obs': len(series)
-                })
-                continue
-
-            elast = self._compute_elasticity(series, method)
-            elasticities.append({
-                'productId': pid,
-                'elasticity': elast['value'],
-                'std_error': elast.get('std_error', 0.0),
-                'n_obs': len(series)
-            })
-
-        result_df = pd.DataFrame(elasticities)
-
-        # fill missing products with zero elasticity
-        observed_pids = set(result_df['productId'])
-        missing_pids = [p for p in all_product_ids if p not in observed_pids]
-
-        if missing_pids:
-            missing_df = pd.DataFrame({
-                'productId': missing_pids,
-                'elasticity': 0.0,
-                'std_error': 0.0,
-                'n_obs': 0
-            })
-            result_df = pd.concat([result_df, missing_df], ignore_index=True)
-
-        return result_df
-
-    def _align_chunks(self, demand_chunks: List[Dict], price_chunks: List[Dict]):
-        """Align demand and price chunks by window_start"""
-        price_lookup = {c['window_start']: c for c in price_chunks}
-        aligned = []
-
-        for dc in demand_chunks:
-            ws = dc['window_start']
-            if ws in price_lookup:
-                aligned.append({
-                    'window_start': ws,
-                    'window_end': dc['window_end'],
-                    'demand': dc['demand_vector'],
-                    'prices': price_lookup[ws]['price_vector']
-                })
-
-        return aligned
-
-    def _build_timeseries(self, aligned: List[Dict]):
-        """Build time series [timestamp, price, quantity] per product"""
-        series_by_product = {}
-
-        for chunk in aligned:
-            merged = chunk['demand'].merge(chunk['prices'], on='productId', how='inner')
-
-            for _, row in merged.iterrows():
-                pid = row['productId']
-                if pid not in series_by_product:
-                    series_by_product[pid] = []
-
-                series_by_product[pid].append({
-                    'timestamp': chunk['window_start'],
-                    'price': row['price'],
-                    'quantity': row['demand_score']
-                })
-
-        return series_by_product
-
-    def _compute_elasticity(self, series: List[Dict], method: str):
-        """Compute point or arc elasticity"""
-        prices = np.array([s['price'] for s in series])
-        quantities = np.array([s['quantity'] for s in series])
-
-        # filter out zero/negative values
-        valid = (prices > 0) & (quantities > 0)
-        if valid.sum() < 2:
-            return {'value': 0.0, 'std_error': 0.0}
-
-        prices = prices[valid]
-        quantities = quantities[valid]
-
-        if method == 'point':
-            return self._point_elasticity(prices, quantities)
-        elif method == 'arc':
-            return self._arc_elasticity(prices, quantities)
-        else:
-            raise ValueError(f"Unknown elasticity method: {method}")
-
-    def _point_elasticity(self, prices: np.ndarray, quantities: np.ndarray):
-        """Point elasticity via log-log regression: log(Q) = a + b*log(P), elasticity = b"""
-        if len(prices) < 2:
-            return {'value': 0.0, 'std_error': 0.0}
-
-        log_p = np.log(prices)
-        log_q = np.log(quantities)
-
-        if log_p.std() == 0:
-            return {'value': 0.0, 'std_error': 0.0}
-
-        cov = np.cov(log_p, log_q)[0, 1]
-        var = np.var(log_p)
-        b = cov / var
-
-        # std error estimate
-        if len(prices) > 2:
-            residuals = log_q - (log_q.mean() + b * (log_p - log_p.mean()))
-            mse = (residuals ** 2).sum() / (len(prices) - 2)
-            se_b = np.sqrt(mse / (len(prices) * var))
-        else:
-            se_b = 0.0
-
-        return {'value': b, 'std_error': se_b}
-
-    def _arc_elasticity(self, prices: np.ndarray, quantities: np.ndarray):
-        """Arc elasticity: average period-over-period elasticity"""
-        elasticities = []
-
-        for i in range(1, len(prices)):
-            p1, p2 = prices[i-1], prices[i]
-            q1, q2 = quantities[i-1], quantities[i]
-
-            p_avg = (p1 + p2) / 2
-            q_avg = (q1 + q2) / 2
-
-            if p_avg == 0 or q_avg == 0:
-                continue
-
-            delta_p = p2 - p1
-            delta_q = q2 - q1
-
-            if delta_p == 0:
-                continue
-
-            e = (delta_q / q_avg) / (delta_p / p_avg)
-            elasticities.append(e)
-
-        if not elasticities:
-            return {'value': 0.0, 'std_error': 0.0}
-
-        return {
-            'value': np.mean(elasticities),
-            'std_error': np.std(elasticities) / np.sqrt(len(elasticities))
-        }