Airflow addition (#28)

* introducing airflow to run pipeline

* chore: updating dag with upload to registry

* introducing complete provider (non refactored and noisy)

* chore: removing old shit

* generic pricing baselines

* feature: super simple model registry (to be updated maybe third party OS software)

* chore: refactoring the providers docker config and requirements

* chore: refactored and broke down components (braking

* exporting all

* local pipeline excution working

* fix: fixing import structures from nonrelativistic

* chore: enables cross comm pickling with fully e2e pipeline compilation

* docs: what the pipeline is like now

* pipelines local running and pipeline high level definition

* cleaning old pipeline and vectorization

* leaked but fixing, not so important

* test: started with pipeline step testing

* chore: cleaning up provider of prices

* test: extra tests wit hsemantic meaning checks

* migrating pricers

* feature: introducing pricing predictors (pricers)

* chore: e2e is done with new pipeline

* extra session feature extraction

* feature: experiemntal sessin pricer and metrics(vibe)

* chore: redefined and connected pricers (#29)

experiments/procesing/__init__.py

@@ -1,19 +1,55 @@
-from .extract import (
-    KafkaDataFetcher,
-    ExperimentJoiner,
-    EventTitleAugmenter,
+from procesing.context import PipelineContext
+from procesing.providers import DataProvider, SupabaseProvider, BackendAPIProvider
+from procesing.steps import (
+    BaseContextStep,
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    FetchExperimentsStep,
+    JoinExperimentsStep,
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep,
+    ChunkByTimeWindowStep,
+    ComputeDemandStep,
+    ComputeDemandForChunksStep,
+    AggregatePriceLogsStep,
+    ComputeElasticityStep,
+    StateSpace,
+    BuildStateSpaceStep,
+    FitPricingFunctionStep,
+    PredictPricesStep,
+)
+from procesing.pipelines import (
+    interaction_extraction_pipeline,
+    price_extraction_pipeline,
+    elasticity_computation_pipeline,
+    pricing_pipeline,
+    full_pipeline,
 )
-from .demand import DemandEstimator
-from .mapping import SessionTransitionProbMatrixTransformer, render_graph
-from .pipeline import etl_pipeline, pricing_pipeline
 
 __all__ = [
-    'KafkaDataFetcher',
-    'ExperimentJoiner',
-    'EventTitleAugmenter',
-    'DemandEstimator',
-    'SessionTransitionProbMatrixTransformer',
-    'render_graph',
-    'etl_pipeline',
+    'PipelineContext',
+    'DataProvider',
+    'SupabaseProvider',
+    'BackendAPIProvider',
+    'BaseContextStep',
+    'FetchInteractionsStep',
+    'FetchPriceLogsStep',
+    'FetchExperimentsStep',
+    'JoinExperimentsStep',
+    'CreatePriceBucketsStep',
+    'AugmentEventNamesStep',
+    'ChunkByTimeWindowStep',
+    'ComputeDemandStep',
+    'ComputeDemandForChunksStep',
+    'AggregatePriceLogsStep',
+    'ComputeElasticityStep',
+    'StateSpace',
+    'BuildStateSpaceStep',
+    'FitPricingFunctionStep',
+    'PredictPricesStep',
+    'interaction_extraction_pipeline',
+    'price_extraction_pipeline',
+    'elasticity_computation_pipeline',
     'pricing_pipeline',
+    'full_pipeline',
 ]

experiments/procesing/context.py

@@ -0,0 +1,34 @@
+from typing import Any, Dict
+import pandas as pd
+from procesing.providers.base import DataProvider
+
+class PipelineContext:
+    """
+    Context for pipeline execution holding config, provider, and cached data.
+    Enables dependency injection and eliminates global state.
+    """
+
+    def __init__(self,
+                 provider: DataProvider,
+                 store_mode: str,
+                 window_size: str = '30s',
+                 **config):
+        self.provider = provider
+        self.store_mode = store_mode
+        self.window_size = window_size
+        self.config = config
+        self._cache: Dict[str, Any] = {}
+
+    def get_cached(self, key: str, default=None):
+        return self._cache.get(key, default)
+
+    def cache(self, key: str, value):
+        self._cache[key] = value
+        return value
+
+    @property
+    def products(self) -> pd.DataFrame:
+        """Lazy-load and cache product catalog, single fetch per pipeline run"""
+        if 'products' not in self._cache:
+            self._cache['products'] = self.provider.fetch_products(self.store_mode)
+        return self._cache['products']

experiments/procesing/demand.py

@@ -1,119 +0,0 @@
-from sklearn.base import BaseEstimator, TransformerMixin
-import numpy as np
-import pandas as pd
-from supabase import create_client, Client
-from typing import Optional, Literal
-import os
-import logging
-log = logging.getLogger(__name__)
-
-SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
-SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
-
-supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
-
-class ChunkInteractionsIntoSteps(BaseEstimator, TransformerMixin):
-    """
-    Split interaction data into time windows for temporal analysis.
-    Returns a list of dataframes, one per time window.
-    """
-    def __init__(self,
-                 window_size:str='1h',
-                 ts_col:str='ts',
-                 return_metadata:bool=True):
-        """
-        Args:
-            window_size: pandas freq string ('1h', '30T', '1D', etc)
-            ts_col: timestamp column name
-            return_metadata: if True, return dict with metadata per chunk
-        """
-        self.window_size = window_size
-        self.ts_col = ts_col
-        self.return_metadata = return_metadata
-
-    def fit(self, X):
-        return self
-
-    def transform(self, interactions: pd.DataFrame):
-        """
-        Returns:
-            if return_metadata=False: list of dataframes, one per window
-            if return_metadata=True: list of dicts with keys:
-                - 'data': dataframe for this window
-                - 'window_start': start timestamp
-                - 'window_end': end timestamp
-                - 'window_idx': integer index
-        """
-        if interactions.empty:
-            return []
-
-        df = interactions.copy()
-
-        # ensure timestamp is datetime
-        if not pd.api.types.is_datetime64_any_dtype(df[self.ts_col]):
-            df[self.ts_col] = pd.to_datetime(df[self.ts_col])
-
-        # sort by time
-        df = df.sort_values(self.ts_col)
-
-        # assign window
-        df['_window'] = df[self.ts_col].dt.floor(self.window_size)
-
-        # group by window
-        chunks = []
-        for idx, (window_start, group) in enumerate(df.groupby('_window')):
-            chunk_data = group.drop(columns=['_window'])
-
-            if self.return_metadata:
-                chunks.append({
-                    'data': chunk_data,
-                    'window_start': window_start,
-                    'window_end': window_start + pd.Timedelta(self.window_size),
-                    'window_idx': idx
-                })
-            else:
-                chunks.append(chunk_data)
-
-        return chunks
-
-
-class DemandEstimator(BaseEstimator, TransformerMixin):
-    def __init__(self,
-                 store_mode:str='hotel',
-                 session_filter:str="",
-                 experiment_filter:str=""):
-        self.store=store_mode
-        self.session_filter=session_filter if len(session_filter)>0 else None
-        self.experiment_filter=experiment_filter if len(experiment_filter)>0 else None
-    def fit(self, X):
-        return self
-
-    def transform(self, interactions : pd.DataFrame):
-        if interactions.empty:
-            return pd.DataFrame(columns=["productId", "demand_score"])
-        if self.session_filter:
-            interactions = interactions[interactions['sessionId'] == self.session_filter]
-        if self.experiment_filter:
-            interactions = interactions[interactions['experimentId'] == self.experiment_filter]
-
-        products=supabase.table(f'{self.store}_products').select("id, room_type, date_index, metadata, availability").execute()
-        products = pd.DataFrame(products.data)
-        unique_products = products['id'].unique()
-        log.info(f"Demand estimator found {len(unique_products)} in data")
-
-        # filter out rows without productId
-        interactions_with_products = interactions.dropna(subset=['productId'])
-
-        if interactions_with_products.empty:
-            # no interactions with products, return all zeros
-            return pd.DataFrame({
-                'productId': unique_products,
-                'demand_score': 0
-            })
-
-        # TODO: improve demand score calculation rather than just counting interactions (use weights..)
-        # while maintaining simplicity of a simple cross tab approach
-        product_demand = pd.crosstab(interactions_with_products['productId'], "no_of_interactions")
-        product_demand = product_demand.reindex(unique_products, fill_value=0).reset_index()
-        product_demand.columns = ['productId', 'demand_score']
-        return product_demand

experiments/procesing/elasticity.py

@@ -130,25 +130,24 @@ class TemporalElasticityEstimator(BaseEstimator, TransformerMixin):
 
     def _build_product_timeseries(self, aligned_chunks):
         """Build time series [price, quantity] per product."""
-        series_by_product = {}
-
+        # vectorize chunk merging instead of iterating rows
+        all_merged = []
         for chunk in aligned_chunks:
-            demand_df = chunk['demand']
-            price_df = chunk['prices']
+            merged = chunk['demand'].merge(chunk['prices'], on='productId', how='inner')
+            merged['timestamp'] = chunk['window_start']
+            all_merged.append(merged[['productId', 'timestamp', 'price', 'demand_score']])
 
-            # merge on productId
-            merged = demand_df.merge(price_df, on='productId', how='inner')
+        if not all_merged:
+            return {}
 
-            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']
-                })
+        # concat all chunks and group by productId in one pass
+        combined = pd.concat(all_merged, ignore_index=True)
+        series_by_product = {
+            pid: group[['timestamp', 'price', 'demand_score']].rename(
+                columns={'demand_score': 'quantity'}
+            ).to_dict('records')
+            for pid, group in combined.groupby('productId')
+        }
 
         return series_by_product
 

experiments/procesing/extract.py

@@ -1,207 +0,0 @@
-import pandas as pd
-import json
-import numpy as np
-import os
-import requests
-from dotenv import load_dotenv
-from sklearn.base import BaseEstimator, TransformerMixin
-from supabase import create_client, Client
-from typing import Tuple, List, Dict
-load_dotenv()
-
-BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:5000")
-SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL")
-SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
-N_PRICE_BUCKETS = 5
-
-supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
-
-
-class KafkaDataFetcher(BaseEstimator, TransformerMixin):
-    def __init__(self, topic: str = "user-interactions"):
-        self.topic = topic # also can be price-logs
-    def fit(self, X=None, y=None):
-        return self
-
-    def transform(self, X=None):
-        resp = requests.get(f"{BACKEND_URL}/api/kafka/dump?topic={self.topic}")
-        resp.raise_for_status()
-        data = resp.json()
-
-        if not data.get('success') or not data.get('data'):
-            return pd.DataFrame()
-
-        df = pd.DataFrame(data['data'])
-        if self.topic == 'user-interactions':
-            if 'metadata' in df.columns: # explode metadata col json
-                df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
-            df = df.dropna(subset=['eventName'])
-            # remape dateIndex
-            df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
-        return df
-
-
-class ExperimentJoiner(BaseEstimator, TransformerMixin):
-    def fit(self, X=None, y=None):
-        return self
-
-    def transform(self, df):
-        if df.empty or 'experimentId' not in df.columns:
-            return df
-
-        unique_exp_ids = df['experimentId'].dropna().unique()
-        if len(unique_exp_ids) == 0:
-            return df
-
-        resp = supabase.table('experiments').select(
-            'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, task:tasks(task_name, task_description, task_def_of_done)'
-        ).in_('id', unique_exp_ids.tolist()).execute()
-
-        if not resp.data:
-            return df
-
-        exp_df = pd.DataFrame(resp.data)
-
-        # flatten task nested object if present
-        if 'task' in exp_df.columns and exp_df['task'].notnull().any():
-            task_normalized = pd.json_normalize(exp_df['task'].dropna())
-            task_normalized.index = exp_df[exp_df['task'].notnull()].index
-            exp_df = exp_df.drop(columns=['task']).join(task_normalized, rsuffix='_task')
-
-        # rename experiment columns for clarity
-        exp_df = exp_df.rename(columns={
-            'id': 'experimentId',
-            'subject_name': 'exp_subject',
-            'xp_human_only': 'exp_human_only',
-            'xp_market_mode': 'exp_market_mode',
-            'xp_task_id': 'exp_task_id'
-        })
-
-        df = df.merge(exp_df, on='experimentId', how='left')
-        return df
-
-
-class EventTitleAugmenter(BaseEstimator, TransformerMixin):
-    def fit(self, X=None, y=None):
-        return self
-
-    def transform(self, df):
-        # from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
-        # we want metadata schema to create product specific event names
-
-        # only create price buckets if we have enough unique prices
-        if df["metadata_price"].notnull().sum() > 0:
-            try:
-                price_buckets = pd.qcut(
-                    df["metadata_price"],
-                    q=N_PRICE_BUCKETS,
-                    labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
-                    duplicates='drop'  # handle duplicate bin edges
-                )
-            except ValueError:
-                # fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
-                price_buckets = df["metadata_price"].apply(lambda x: f"P_{int(x)}" if pd.notnull(x) else "")
-        else:
-            price_buckets = pd.Series([""] * len(df), index=df.index)
-
-        # metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
-        # TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
-        df["metadata_schema"] = np.where(
-            df["productId"].notnull() & df["metadata_price"].notnull(),
-            "_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
-            ""
-        )
-        df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
-        return df
-
-
-def chunk_shared_data(interactions_df: pd.DataFrame,
-                      price_logs_df: pd.DataFrame,
-                      window_size: str = '30s',
-                      ts_col: str = 'ts') -> Tuple[List[Dict], List[Dict]]:
-    """
-    Chunk interaction and price data into aligned time windows.
-
-    Args:
-        interactions_df: interaction data with timestamp column
-        price_logs_df: price log data with timestamp column
-        window_size: pandas freq string ('30s', '1min', '1h', etc)
-        ts_col: name of timestamp column
-
-    Returns:
-        tuple of (interaction_chunks, price_chunks) where each is list of dicts:
-        {
-            'window_start': timestamp,
-            'window_end': timestamp,
-            'data': dataframe for this window
-        }
-    """
-    if interactions_df.empty and price_logs_df.empty:
-        return [], []
-
-    # convert timestamps to datetime
-    interactions_df = interactions_df.copy()
-    price_logs_df = price_logs_df.copy()
-
-    if not interactions_df.empty:
-        if not pd.api.types.is_datetime64_any_dtype(interactions_df[ts_col]):
-            interactions_df[ts_col] = pd.to_datetime(interactions_df[ts_col])
-
-    if not price_logs_df.empty:
-        if not pd.api.types.is_datetime64_any_dtype(price_logs_df[ts_col]):
-            price_logs_df[ts_col] = pd.to_datetime(price_logs_df[ts_col])
-
-    # find global time bounds
-    times = []
-    if not interactions_df.empty:
-        times.extend([interactions_df[ts_col].min(), interactions_df[ts_col].max()])
-    if not price_logs_df.empty:
-        times.extend([price_logs_df[ts_col].min(), price_logs_df[ts_col].max()])
-
-    if not times:
-        return [], []
-
-    earliest = min(times)
-    latest = max(times)
-
-    # create shared time windows
-    windows = pd.date_range(start=earliest, end=latest, freq=window_size)
-
-    if len(windows) < 2:
-        return [], []
-
-    # chunk both datasets
-    interaction_chunks = []
-    price_chunks = []
-
-    for i in range(len(windows) - 1):
-        window_start = windows[i]
-        window_end = windows[i + 1]
-
-        # filter interactions in this window
-        if not interactions_df.empty:
-            mask = (interactions_df[ts_col] >= window_start) & (interactions_df[ts_col] < window_end)
-            interaction_chunk = interactions_df[mask]
-        else:
-            interaction_chunk = pd.DataFrame()
-
-        interaction_chunks.append({
-            'window_start': window_start,
-            'window_end': window_end,
-            'data': interaction_chunk
-        })
-
-        # filter price logs in this window
-        if not price_logs_df.empty:
-            mask = (price_logs_df[ts_col] >= window_start) & (price_logs_df[ts_col] < window_end)
-            price_chunk = price_logs_df[mask]
-        else:
-            price_chunk = pd.DataFrame()
-
-        price_chunks.append({
-            'window_start': window_start,
-            'window_end': window_end,
-            'data': price_chunk
-        })
-
-    return interaction_chunks, price_chunks

experiments/procesing/mapping.py

@@ -1,158 +0,0 @@
-import numpy as np
-import pandas as pd
-from sklearn.base import BaseEstimator, TransformerMixin
-
-def build_transition_prob_matrix(df: pd.DataFrame):
-    df = df.dropna(subset=['eventName'])
-    events = df['eventName'].tolist()
-    labels = pd.Index(events).unique().tolist()
-    idx = {e:i for i,e in enumerate(labels)}
-    M = np.zeros((len(labels), len(labels)), dtype=float)
-    for a, b in zip(events, events[1:]):
-        M[idx[a], idx[b]] += 1
-    row_sums = M.sum(axis=1, keepdims=True)
-    with np.errstate(divide='ignore', invalid='ignore'):
-        P = np.divide(M, row_sums, where=row_sums>0)  # row-normalized
-    return P, labels
-
-# https://medium.com/data-science/time-series-data-markov-transition-matrices-7060771e362b
-from graphviz import Digraph
-import numpy as np
-import pandas as pd
-
-def _as_prob_df(matrix, labels=None):
-    """Return a square DataFrame with index=columns=labels."""
-    if isinstance(matrix, pd.DataFrame):
-        # Ensure square and aligned
-        assert (matrix.index == matrix.columns).all(), "Index/columns must match."
-        return matrix
-    matrix = np.asarray(matrix, dtype=float)
-    assert matrix.shape[0] == matrix.shape[1], "Matrix must be square."
-    if labels is None:
-        raise ValueError("labels are required when matrix is not a DataFrame")
-    assert len(labels) == matrix.shape[0], "labels length must match matrix size."
-    return pd.DataFrame(matrix, index=list(labels), columns=list(labels))
-
-def _df_to_edgelist(P: pd.DataFrame, threshold=0.0, round_digits=2):
-    """Build weighted edges > threshold."""
-    edges = []
-    for src in P.index:
-        for dst in P.columns:
-            w = float(P.loc[src, dst])
-            if w > threshold:
-                edges.append((str(src), str(dst), f"{w:.{round_digits}f}"))
-    return edges
-
-def render_graph(fname, matrix, ls_index=None, threshold=0.0, fmt="svg", view=False):
-    """
-    fname: output file stem (no extension)
-    matrix: NumPy array or pandas DataFrame of transition PROBABILITIES
-    ls_index: ordered labels (required if matrix is not a DataFrame)
-    threshold: hide edges with weight <= threshold
-    fmt: 'svg'|'png'|'pdf' etc.
-    view: open after rendering
-    """
-    P = _as_prob_df(matrix, labels=ls_index)
-    edges = _df_to_edgelist(P, threshold=threshold)
-
-    g = Digraph(format=fmt)
-    g.attr(rankdir="LR", size="30")
-    g.attr("node", shape="circle")
-
-    # ensure isolated nodes appear
-    for node in P.index:
-        g.node(str(node), width="1", height="1")
-
-    for src, dst, label in edges:
-        g.edge(src, dst, label=label)
-
-    g.render(fname, view=view, cleanup=True)
-    return g
-
-
-class TransitionProbMatrixTransformer(BaseEstimator, TransformerMixin):
-    def __init__(self, threshold=0.0):
-        self.threshold = threshold
-        self.P_ = None
-        self.labels_ = None
-
-    def fit(self, X: pd.DataFrame, y=None):
-        P, labels = build_transition_prob_matrix(X)
-        self.P_ = P
-        self.labels_ = labels
-        return self
-
-    def transform(self, X: pd.DataFrame = None):
-        return self.P_, self.labels_
-
-    def render(self, fname: str, fmt="svg", view=False):
-        if self.P_ is None or self.labels_ is None:
-            raise ValueError("Transformer has not been fitted yet.")
-        return render_graph(
-            fname,
-            self.P_,
-            ls_index=self.labels_,
-            threshold=self.threshold,
-            fmt=fmt,
-            view=view
-        )
-
-
-class SessionTransitionProbMatrixTransformer(BaseEstimator, TransformerMixin):
-    def __init__(self, threshold=0.0, session_col='sessionId'):
-        self.threshold = threshold
-        self.session_col = session_col
-        self.session_matrices_ = None
-
-    def fit(self, X: pd.DataFrame, y=None):
-        if self.session_col not in X.columns:
-            raise ValueError(f"Column '{self.session_col}' not found in DataFrame")
-
-        session_matrices = {}
-        for session_id, grp in X.groupby(self.session_col):
-            if len(grp) > 1:  # need at least 2 events for transitions
-                P, labels = build_transition_prob_matrix(grp)
-                session_matrices[session_id] = {'matrix': P, 'labels': labels}
-
-        self.session_matrices_ = session_matrices
-        return self
-
-    def transform(self, X: pd.DataFrame = None):
-        if self.session_matrices_ is None:
-            raise ValueError("Transformer has not been fitted yet.")
-        return pd.Series(self.session_matrices_)
-
-    def render_session(self, session_id: str, fname: str, fmt="svg", view=False):
-        if self.session_matrices_ is None:
-            raise ValueError("Transformer has not been fitted yet.")
-        if session_id not in self.session_matrices_:
-            raise ValueError(f"Session '{session_id}' not found in fitted data.")
-
-        sess_data = self.session_matrices_[session_id]
-        return render_graph(
-            fname,
-            sess_data['matrix'],
-            ls_index=sess_data['labels'],
-            threshold=self.threshold,
-            fmt=fmt,
-            view=view
-        )
-if __name__ == "__main__":
-    # Example usage
-    data = {
-        'eventName': [
-            'A', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'B', 'C',
-            'A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A'
-        ]
-    }
-    df = pd.DataFrame(data)
-
-    transformer = TransitionProbMatrixTransformer(threshold=0.1)
-    transformer.fit(df)
-    P, labels = transformer.transform(None)
-
-    print("Transition Probability Matrix:")
-    print(pd.DataFrame(P, index=labels, columns=labels))
-
-    # Render the graph
-    transformer.render("transition_graph", fmt="svg", view=False)

experiments/procesing/metrics.py

@@ -0,0 +1,245 @@
+"""
+Revenue and KPI benchmark framework for pricing strategies.
+
+Computes session-level and aggregate metrics to compare pricing functions:
+    - Revenue: R_T = Σ P_t^T · Q_t
+    - Conversion rate
+    - Average order value (AOV)
+    - Agent exploitation loss: L_agent = R_oracle - R_observed
+"""
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass, field, asdict
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class SessionMetrics:
+    """KPIs for single session."""
+    session_id: str
+    experiment_id: Optional[str] = None
+
+    # interaction metrics
+    total_interactions: int = 0
+    page_views: int = 0
+    item_views: int = 0
+    searches: int = 0
+    cart_adds: int = 0
+
+    # revenue metrics
+    items_purchased: int = 0
+    total_revenue: float = 0.0
+    avg_item_price: float = 0.0
+    conversion_rate: float = 0.0
+
+    # pricing signals
+    total_price_shown: float = 0.0  # sum of all prices displayed
+    avg_markup: float = 0.0  # avg (price / base_price)
+
+    # behavioral features (for agent detection)
+    interaction_velocity: float = 0.0  # interactions per minute
+    session_duration_sec: float = 0.0
+    unique_products_viewed: int = 0
+
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass
+class AggregateMetrics:
+    """Aggregate KPIs across sessions/experiments."""
+    experiment_id: Optional[str] = None
+    n_sessions: int = 0
+
+    # revenue aggregates
+    total_revenue: float = 0.0
+    avg_revenue_per_session: float = 0.0
+    median_revenue_per_session: float = 0.0
+
+    # conversion aggregates
+    total_conversions: int = 0
+    conversion_rate: float = 0.0  # purchases / sessions
+
+    # pricing aggregates
+    avg_markup: float = 0.0
+    median_markup: float = 0.0
+
+    # agent exploitation metrics
+    estimated_agent_sessions: int = 0  # sessions flagged as agent-driven
+    agent_revenue: float = 0.0
+    human_revenue: float = 0.0
+    agent_loss: float = 0.0  # L_agent = R_oracle - R_observed (if available)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+
+class MetricsComputer:
+    """Compute session and aggregate metrics from interaction/price logs."""
+
+    @staticmethod
+    def compute_session_metrics(
+        session_id: str,
+        interactions: pd.DataFrame,
+        price_logs: pd.DataFrame,
+        purchases: Optional[pd.DataFrame] = None,
+        experiment_id: Optional[str] = None
+    ) -> SessionMetrics:
+        """
+        Compute metrics for single session.
+
+        Args:
+            session_id: session identifier
+            interactions: user-interactions events for this session
+            price_logs: price-logs for this session
+            purchases: purchase events (if available)
+            experiment_id: experiment identifier
+        """
+        metrics = SessionMetrics(session_id=session_id, experiment_id=experiment_id)
+
+        if interactions.empty:
+            return metrics
+
+        # interaction counts
+        event_counts = interactions['eventName'].value_counts().to_dict()
+        metrics.total_interactions = len(interactions)
+        metrics.page_views = event_counts.get('page_view', 0) + event_counts.get('view_item_page', 0)
+        metrics.item_views = event_counts.get('view_item_page', 0)
+        metrics.searches = event_counts.get('search', 0)
+        metrics.cart_adds = event_counts.get('add_item_to_cart', 0)
+
+        # unique products viewed
+        metrics.unique_products_viewed = interactions['productId'].dropna().nunique()
+
+        # session duration
+        if 'ts' in interactions.columns:
+            timestamps = pd.to_datetime(interactions['ts'])
+            metrics.session_duration_sec = (timestamps.max() - timestamps.min()).total_seconds()
+            if metrics.session_duration_sec > 0:
+                metrics.interaction_velocity = (metrics.total_interactions / metrics.session_duration_sec) * 60
+
+        # revenue from purchases
+        if purchases is not None and not purchases.empty:
+            metrics.items_purchased = len(purchases)
+            metrics.total_revenue = purchases['price'].sum() if 'price' in purchases.columns else 0.0
+            metrics.avg_item_price = metrics.total_revenue / metrics.items_purchased if metrics.items_purchased > 0 else 0.0
+            metrics.conversion_rate = 1.0 if metrics.items_purchased > 0 else 0.0
+
+        # pricing metrics
+        if not price_logs.empty:
+            metrics.total_price_shown = price_logs['price'].sum()
+            # compute markup if base_price available in price logs or join with product catalog
+            if 'base_price' in price_logs.columns:
+                valid_markup = price_logs[price_logs['base_price'] > 0]
+                if not valid_markup.empty:
+                    metrics.avg_markup = (valid_markup['price'] / valid_markup['base_price']).mean()
+
+        return metrics
+
+    @staticmethod
+    def compute_aggregate_metrics(
+        session_metrics_list: List[SessionMetrics],
+        experiment_id: Optional[str] = None,
+        agent_detector_fn: Optional[callable] = None
+    ) -> AggregateMetrics:
+        """
+        Aggregate metrics across sessions.
+
+        Args:
+            session_metrics_list: list of SessionMetrics
+            experiment_id: experiment identifier
+            agent_detector_fn: optional function to classify session as agent (returns bool)
+        """
+        agg = AggregateMetrics(experiment_id=experiment_id)
+        agg.n_sessions = len(session_metrics_list)
+
+        if agg.n_sessions == 0:
+            return agg
+
+        df = pd.DataFrame([m.to_dict() for m in session_metrics_list])
+
+        # revenue aggregates
+        agg.total_revenue = df['total_revenue'].sum()
+        agg.avg_revenue_per_session = df['total_revenue'].mean()
+        agg.median_revenue_per_session = df['total_revenue'].median()
+
+        # conversion aggregates
+        agg.total_conversions = (df['items_purchased'] > 0).sum()
+        agg.conversion_rate = agg.total_conversions / agg.n_sessions
+
+        # pricing aggregates
+        valid_markups = df[df['avg_markup'] > 0]
+        if not valid_markups.empty:
+            agg.avg_markup = valid_markups['avg_markup'].mean()
+            agg.median_markup = valid_markups['avg_markup'].median()
+
+        # agent detection (if detector provided)
+        if agent_detector_fn is not None:
+            agent_flags = [agent_detector_fn(m) for m in session_metrics_list]
+            agg.estimated_agent_sessions = sum(agent_flags)
+
+            agent_revenue = sum(m.total_revenue for m, is_agent in zip(session_metrics_list, agent_flags) if is_agent)
+            human_revenue = sum(m.total_revenue for m, is_agent in zip(session_metrics_list, agent_flags) if not is_agent)
+
+            agg.agent_revenue = agent_revenue
+            agg.human_revenue = human_revenue
+
+        return agg
+
+    @staticmethod
+    def compare_pricing_strategies(
+        experiments: Dict[str, List[SessionMetrics]],
+        baseline_experiment_id: Optional[str] = None
+    ) -> pd.DataFrame:
+        """
+        Compare multiple pricing strategies/experiments.
+
+        Args:
+            experiments: dict mapping experiment_id -> list of SessionMetrics
+            baseline_experiment_id: experiment to use as baseline for comparison
+
+        Returns:
+            DataFrame with comparative metrics
+        """
+        results = []
+        baseline_agg = None
+
+        for exp_id, session_metrics in experiments.items():
+            agg = MetricsComputer.compute_aggregate_metrics(session_metrics, experiment_id=exp_id)
+            result = agg.to_dict()
+
+            if exp_id == baseline_experiment_id:
+                baseline_agg = agg
+
+            results.append(result)
+
+        df = pd.DataFrame(results)
+
+        # add relative metrics if baseline exists
+        if baseline_agg is not None:
+            df['revenue_lift_pct'] = ((df['total_revenue'] - baseline_agg.total_revenue) / baseline_agg.total_revenue * 100)
+            df['conversion_lift_pct'] = ((df['conversion_rate'] - baseline_agg.conversion_rate) / baseline_agg.conversion_rate * 100)
+
+        return df
+
+
+def simple_agent_detector(session_metrics: SessionMetrics, velocity_threshold: float = 5.0) -> bool:
+    """
+    Simple heuristic agent detector based on interaction velocity.
+
+    Args:
+        session_metrics: SessionMetrics instance
+        velocity_threshold: interactions per minute threshold (default: 5.0)
+
+    Returns:
+        True if session likely agent-driven
+    """
+    # agents tend to have higher interaction velocity and lower session duration
+    if session_metrics.interaction_velocity > velocity_threshold:
+        return True
+    # agents often view many products quickly without converting
+    if session_metrics.unique_products_viewed > 10 and session_metrics.conversion_rate == 0:
+        return True
+    return False

experiments/procesing/pipeline.py

@@ -1,90 +0,0 @@
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import StandardScaler
-import pandas as pd
-import logging
-log = logging.getLogger(__name__)
-
-from extract import KafkaDataFetcher, ExperimentJoiner, EventTitleAugmenter, chunk_shared_data
-from mapping import SessionTransitionProbMatrixTransformer, render_graph
-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)
-    log.info(f"Chunked interactions into {len(interaction_chunks)} windows of size {window_size}")
-
-    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 # each has a full list of all products, even if demand is 0
-        })
-    # [q_chunk1, q_chunk2, ...]
-
-    # 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, store_mode=store_mode)
-
-    return elasticity_df
-
-
-# exposable pipelines
-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')),
-])
-
-# interaction_data + price_data -> elasticity (demand)
-# elasticity -> pricing
-
-pricing_pipeline = Pipeline([
-    ('demand_estimation', DemandEstimator()),
-])
-if __name__ == "__main__":
-    # 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)
-    elasticity_value_array = elasticity_results['elasticity'].values if elasticity_results is not None else np.array([])
-    print(elasticity_value_array)
-
-    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")

experiments/procesing/pipelines.py

@@ -0,0 +1,138 @@
+from sklearn.pipeline import Pipeline
+import pandas as pd
+from procesing.context import PipelineContext
+from procesing.providers import SupabaseProvider, BackendAPIProvider
+from typing import Union
+from procesing.steps import (
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    FetchExperimentsStep,
+    JoinExperimentsStep,
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep,
+    ChunkByTimeWindowStep,
+    ComputeDemandForChunksStep,
+    AggregatePriceLogsStep,
+    ComputeElasticityStep,
+    BuildStateSpaceStep,
+    FitPricingFunctionStep,
+    PredictPricesStep,
+)
+
+def interaction_extraction_pipeline(context: PipelineContext):
+    """Pipeline for extracting and augmenting interaction data"""
+    return Pipeline([
+        ('fetch', FetchInteractionsStep(context)),
+        ('create_buckets', CreatePriceBucketsStep(context)),
+        ('augment_events', AugmentEventNamesStep(context)),
+    ])
+
+
+def price_extraction_pipeline(context: PipelineContext):
+    """Pipeline for extracting price logs"""
+    return Pipeline([
+        ('fetch', FetchPriceLogsStep(context)),
+    ])
+
+
+def elasticity_computation_pipeline(context: PipelineContext,
+                                    interactions_df: pd.DataFrame,
+                                    price_logs_df: pd.DataFrame):
+    """
+    Compute elasticity from interactions and price logs.
+    Manual orchestration needed for branching logic.
+    """
+    # branch 1: chunk interactions and compute demand
+    chunk_step = ChunkByTimeWindowStep(context)
+    interaction_chunks = chunk_step.transform(interactions_df)
+
+    demand_step = ComputeDemandForChunksStep(context)
+    demand_chunks = demand_step.transform(interaction_chunks)
+
+    # branch 2: aggregate price logs
+    price_step = AggregatePriceLogsStep(context)
+    price_chunks = price_step.transform(price_logs_df)
+
+    # convergence: compute elasticity
+    elasticity_step = ComputeElasticityStep(context)
+    elasticity_df = elasticity_step.transform((demand_chunks, price_chunks))
+
+    return elasticity_df
+
+
+def pricing_pipeline(context: PipelineContext, elasticity_df: pd.DataFrame):
+    """
+    Generate optimal prices from elasticity estimates.
+    """
+    # build state space
+    state_step = BuildStateSpaceStep(context)
+    state_space = state_step.transform(elasticity_df)
+
+    # fit pricing function
+    fit_step = FitPricingFunctionStep(context)
+    pricer = fit_step.transform(elasticity_df)
+
+    # predict prices
+    predict_step = PredictPricesStep(context)
+    prices_df = predict_step.transform((pricer, state_space))
+
+    return prices_df
+
+
+def full_pipeline(context: PipelineContext):
+    """
+    Complete end-to-end pipeline: data extraction -> elasticity -> pricing
+    Returns: (elasticity_df, prices_df)
+    """
+    # extract interactions
+    interaction_pipe = interaction_extraction_pipeline(context)
+    interactions_df = interaction_pipe.fit_transform(None)
+
+    # extract price logs
+    price_pipe = price_extraction_pipeline(context)
+    price_logs_df = price_pipe.fit_transform(None)
+
+    if interactions_df.empty or price_logs_df.empty:
+        return None, None
+
+    # compute elasticity
+    elasticity_df = elasticity_computation_pipeline(
+        context,
+        interactions_df,
+        price_logs_df
+    )
+
+    if elasticity_df is None or elasticity_df.empty:
+        return elasticity_df, None
+
+    # generate prices
+    prices_df = pricing_pipeline(context, elasticity_df)
+
+    return elasticity_df, prices_df
+
+
+if __name__ == '__main__':
+
+    class Provider(SupabaseProvider, BackendAPIProvider):
+        def __init__(self, backend_url: str):
+            SupabaseProvider.__init__(self)
+            BackendAPIProvider.__init__(self, backend_url=backend_url)
+    # example run
+    context = PipelineContext(
+        provider=Provider(backend_url="http://localhost:5000"),
+        store_mode='hotel',
+    )
+
+    elasticity_df, prices_df = full_pipeline(context)
+
+    if elasticity_df is not None and not elasticity_df.empty:
+        print("Elasticity Estimates:")
+        print(elasticity_df.to_string(index=False))
+    else:
+        print("No elasticity estimates computed.")
+
+    if prices_df is not None and not prices_df.empty:
+        print("\nPredicted Prices:")
+        print(prices_df.to_string(index=False))
+    else:
+        print("No prices predicted.")

experiments/procesing/pricers/__init__.py

@@ -0,0 +1,13 @@
+from procesing.pricers.base import PricingFunction
+from procesing.pricers.elasticity import ElasticityBasedPricer
+from procesing.pricers.simple import StaticPricer, RandomPricer
+from procesing.pricers.session_aware import SessionAwarePricer, ProductSpecificSessionPricer
+
+__all__ = [
+    'PricingFunction',
+    'ElasticityBasedPricer',
+    'StaticPricer',
+    'RandomPricer',
+    'SessionAwarePricer',
+    'ProductSpecificSessionPricer'
+]

experiments/procesing/pricers/base.py

@@ -0,0 +1,70 @@
+from abc import ABC, abstractmethod
+from typing import Optional, Dict, Any, List
+import numpy as np
+import pandas as pd
+
+
+class PricingFunction(ABC):
+    """
+    Abstract base for pricing functions.
+
+    Defines mapping: f(Q_t, P_t, S_t, H_t) -> P_{t+1}
+
+    Where:
+        Q_t ∈ R^n: demand vector at time t
+        P_t ∈ R^n: price vector at time t
+        S_t: session features (behavioral signals, interactions)
+        H_t = {Q_{t-k}, P_{t-k}, S_{t-k}}: historical state trajectory
+
+    Objective:
+        maximize E[R_T] = E[Σ P_t^T · Q_t]
+        subject to:
+            Q_t = g(P_t, S_t)  (demand response via elasticity)
+            P_t ≥ C  (cost floor)
+            minimize L_agent = R_oracle - R_observed
+    """
+
+    @abstractmethod
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        """
+        Offline training on historical data.
+
+        Args:
+            historical_data: DataFrame with elasticity, prices, demand signals
+            **kwargs: additional training parameters
+        """
+        pass
+
+    @abstractmethod
+    def predict(self, state_space) -> np.ndarray:
+        """
+        Generate optimal prices given current state.
+
+        Args:
+            state_space: StateSpace object containing Q_t, P_t, S_t, H_t
+
+        Returns:
+            P_{t+1}: price vector in R^n
+        """
+        pass
+
+    def update(self, observation: Dict[str, Any]):
+        """
+        Online learning update (optional).
+
+        Args:
+            observation: dict with {state, action, reward, next_state}
+                - state: StateSpace before pricing decision
+                - action: prices shown (P_t)
+                - reward: revenue/conversion signal
+                - next_state: StateSpace after user interaction
+        """
+        pass  # default: no online learning
+
+    def get_params(self) -> Dict[str, Any]:
+        """Return pricing function parameters for serialization."""
+        return {}
+
+    def set_params(self, params: Dict[str, Any]):
+        """Load pricing function parameters from dict."""
+        pass

experiments/procesing/pricers/elasticity.py

@@ -0,0 +1,59 @@
+import numpy as np
+import pandas as pd
+from procesing.pricers.base import PricingFunction
+
+
+class ElasticityBasedPricer(PricingFunction):
+    """
+    Pricing based on demand elasticity estimates.
+    f(Q, S) = base_price * (1 + alpha * elasticity * demand_deviation)
+    """
+
+    def __init__(self, alpha: float = 0.1, price_floor: float = 0.0, price_ceil: float = np.inf):
+        self.alpha = alpha
+        self.price_floor = price_floor
+        self.price_ceil = price_ceil
+        self.elasticity = None
+        self.base_prices = None
+        self.mean_demand = None
+
+    def fit(self, historical_data: pd.DataFrame):
+        """
+        Calibrate from historical elasticity estimates.
+        Expects: [productId, elasticity, base_price, mean_demand]
+        """
+        if 'elasticity' not in historical_data.columns:
+            raise ValueError("historical_data must contain 'elasticity' column")
+
+        self.elasticity = historical_data['elasticity'].values
+        self.base_prices = (historical_data['base_price'].values
+                           if 'base_price' in historical_data.columns
+                           else np.ones(len(historical_data)) * 100)
+        self.mean_demand = (historical_data['mean_demand'].values
+                           if 'mean_demand' in historical_data.columns
+                           else np.ones(len(historical_data)) * 10)
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """
+        Adjust prices based on demand deviation and elasticity.
+        Higher demand -> increase price (but less for elastic goods)
+        """
+        if self.elasticity is None:
+            raise ValueError("Must call fit() before predict()")
+
+        demand = np.asarray(state_space.demand)
+        if len(demand) != len(self.elasticity):
+            raise ValueError(f"Demand vector size {len(demand)} != elasticity size {len(self.elasticity)}")
+
+        # compute demand deviation from mean
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+
+        # adjust price: if demand high and elastic, don't increase much
+        # if demand high and inelastic, increase more
+        price_multiplier = 1 + self.alpha * np.abs(self.elasticity) * demand_dev
+        prices = self.base_prices * price_multiplier
+
+        # enforce bounds
+        prices = np.clip(prices, self.price_floor, self.price_ceil)
+        return prices

experiments/procesing/pricers/session_aware.py

@@ -0,0 +1,172 @@
+"""
+Session-aware pricing functions that leverage behavioral features S_t.
+These pricers aim to minimize L_agent = R_oracle - R_observed.
+"""
+import numpy as np
+import pandas as pd
+from procesing.pricers.base import PricingFunction
+from procesing.pricers.elasticity import ElasticityBasedPricer
+
+
+class SessionAwarePricer(PricingFunction):
+    """
+    Extends elasticity-based pricing with session behavioral signals.
+
+    f(Q, P, S) = base_price * elasticity_factor * session_factor
+
+    Where session_factor adjusts for:
+        - interaction_velocity (agent detection proxy)
+        - product_view_depth (interest signal)
+        - cart_to_view_ratio (conversion intent)
+
+    Strategy: charge higher prices to suspected agents (high velocity)
+    to recover oracle revenue from reconnaissance sessions.
+    """
+
+    def __init__(self,
+                 alpha: float = 0.1,
+                 beta_velocity: float = 0.05,
+                 beta_attention: float = 0.03,
+                 agent_velocity_threshold: float = 5.0,
+                 agent_markup: float = 1.2,
+                 price_floor: float = 0.0,
+                 price_ceil: float = np.inf):
+        """
+        Args:
+            alpha: elasticity sensitivity
+            beta_velocity: interaction velocity weight
+            beta_attention: product attention weight
+            agent_velocity_threshold: velocity above which to apply agent markup
+            agent_markup: price multiplier for suspected agent sessions
+            price_floor, price_ceil: price bounds
+        """
+        self.alpha = alpha
+        self.beta_velocity = beta_velocity
+        self.beta_attention = beta_attention
+        self.agent_velocity_threshold = agent_velocity_threshold
+        self.agent_markup = agent_markup
+        self.price_floor = price_floor
+        self.price_ceil = price_ceil
+
+        # fitted parameters
+        self.elasticity = None
+        self.base_prices = None
+        self.mean_demand = None
+
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        """Calibrate from historical elasticity data."""
+        if 'elasticity' not in historical_data.columns:
+            raise ValueError("historical_data must contain 'elasticity'")
+
+        self.elasticity = historical_data['elasticity'].values
+        self.base_prices = (historical_data['base_price'].values
+                           if 'base_price' in historical_data.columns
+                           else np.ones(len(historical_data)) * 100)
+        self.mean_demand = (historical_data['mean_demand'].values
+                           if 'mean_demand' in historical_data.columns
+                           else np.ones(len(historical_data)) * 10)
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """Generate prices with session awareness."""
+        if self.elasticity is None:
+            raise ValueError("Must call fit() before predict()")
+
+        demand = np.asarray(state_space.demand)
+        n_products = len(demand)
+
+        # base elasticity-driven pricing
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+        elasticity_factor = 1 + self.alpha * np.abs(self.elasticity) * demand_dev
+
+        # session-aware adjustments
+        session_factor = np.ones(n_products)
+
+        if not state_space.session_features.empty:
+            sf = state_space.session_features.iloc[0]  # single session features
+
+            # agent detection via velocity
+            velocity = sf.get('interaction_velocity', 0.0)
+            if velocity > self.agent_velocity_threshold:
+                # suspected agent: apply markup to recover oracle revenue
+                session_factor *= self.agent_markup
+
+            # attention signal: higher view depth -> user interested -> can charge more
+            view_depth = sf.get('product_view_depth', 0)
+            if view_depth > 0:
+                attention_boost = 1 + self.beta_attention * np.log1p(view_depth)
+                session_factor *= attention_boost
+
+            # cart presence: if user has items in cart, slightly increase prices
+            cart_to_view = sf.get('cart_to_view_ratio', 0.0)
+            if cart_to_view > 0.1:
+                session_factor *= (1 + 0.02)  # small boost for conversion intent
+
+        prices = self.base_prices * elasticity_factor * session_factor
+        prices = np.clip(prices, self.price_floor, self.price_ceil)
+
+        return prices
+
+
+class ProductSpecificSessionPricer(PricingFunction):
+    """
+    Session-aware pricer with product-specific demand signals.
+
+    Uses S_t to extract per-product interaction counts and adjusts pricing
+    for products the user has already viewed/hovered.
+
+    Strategy: products viewed multiple times = high interest -> price up
+    """
+
+    def __init__(self,
+                 alpha: float = 0.1,
+                 view_boost: float = 0.02,
+                 max_view_boost: float = 0.15,
+                 price_floor: float = 0.0,
+                 price_ceil: float = np.inf):
+        self.alpha = alpha
+        self.view_boost = view_boost
+        self.max_view_boost = max_view_boost
+        self.price_floor = price_floor
+        self.price_ceil = price_ceil
+
+        self.elasticity = None
+        self.base_prices = None
+        self.mean_demand = None
+        self.product_ids = None
+
+    def fit(self, historical_data: pd.DataFrame, **kwargs):
+        if 'elasticity' not in historical_data.columns or 'productId' not in historical_data.columns:
+            raise ValueError("historical_data must contain 'elasticity' and 'productId'")
+
+        self.elasticity = historical_data['elasticity'].values
+        self.base_prices = (historical_data['base_price'].values
+                           if 'base_price' in historical_data.columns
+                           else np.ones(len(historical_data)) * 100)
+        self.mean_demand = (historical_data['mean_demand'].values
+                           if 'mean_demand' in historical_data.columns
+                           else np.ones(len(historical_data)) * 10)
+        self.product_ids = historical_data['productId'].values
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        if self.elasticity is None:
+            raise ValueError("Must call fit() before predict()")
+
+        demand = np.asarray(state_space.demand)
+        n_products = len(demand)
+
+        # base pricing
+        demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
+        base_prices = self.base_prices * (1 + self.alpha * np.abs(self.elasticity) * demand_dev)
+
+        # product-specific session adjustments
+        if not state_space.session_features.empty and state_space.product_ids is not None:
+            # extract product interaction counts from session metadata
+            # (this would require session features to include per-product signals)
+            # for now, use uniform boost as placeholder
+            # TODO: extend session feature extraction to include product-specific counts
+            pass
+
+        prices = np.clip(base_prices, self.price_floor, self.price_ceil)
+        return prices

experiments/procesing/pricers/simple.py

@@ -0,0 +1,48 @@
+import numpy as np
+import pandas as pd
+from procesing.pricers.base import PricingFunction
+
+
+class StaticPricer(PricingFunction):
+    """Static pricing: always return fixed base prices"""
+
+    def __init__(self, base_prices: np.ndarray = None):
+        self.base_prices = base_prices
+
+    def fit(self, historical_data: pd.DataFrame):
+        """Extract base prices from historical data"""
+        if 'base_price' in historical_data.columns:
+            self.base_prices = historical_data['base_price'].values
+        elif 'price' in historical_data.columns:
+            self.base_prices = historical_data['price'].values
+        else:
+            raise ValueError("historical_data must contain 'base_price' or 'price' column")
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """Return static base prices regardless of state"""
+        if self.base_prices is None:
+            raise ValueError("Must call fit() or provide base_prices in constructor")
+        return self.base_prices.copy()
+
+
+class RandomPricer(PricingFunction):
+    """Random pricing within bounds (for baseline comparison)"""
+
+    def __init__(self, price_min: float = 50.0, price_max: float = 500.0, seed: int = None):
+        self.price_min = price_min
+        self.price_max = price_max
+        self.seed = seed
+        self.n_products = None
+        self.rng = np.random.default_rng(seed)
+
+    def fit(self, historical_data: pd.DataFrame):
+        """Learn number of products"""
+        self.n_products = len(historical_data)
+        return self
+
+    def predict(self, state_space) -> np.ndarray:
+        """Generate random prices"""
+        if self.n_products is None:
+            self.n_products = len(state_space.demand)
+        return self.rng.uniform(self.price_min, self.price_max, size=self.n_products)

experiments/procesing/pricing.py

@@ -35,8 +35,9 @@ from sklearn.base import BaseEstimator, TransformerMixin
 import numpy as np
 import pandas as pd
 import os
+from dotenv import load_dotenv
+load_dotenv()
 from supabase import create_client, Client
-from pipeline import interaction_pipeline, price_data_pipeline, elasticity_pipeline
 
 SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL", "")
 SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY", "")
@@ -79,18 +80,136 @@ class PricingFunction(BaseEstimator, TransformerMixin, ABC):
 class SimpleLinearPricingFunction(PricingFunction):
     def __init__(self, price_sensitivity: float = -0.1):
         super().__init__()
-        self.price_sensitivity = price_sensitivity  # simple coefficient
+        self.price_sensitivity = price_sensitivity
 
     def fit(self, historical_data):
         return self
 
     def transform(self, state_space: StateSpace) -> np.ndarray:
-        # Simple linear adjustment: P_{t+1} = P_t + sensitivity * Q_t
-        new_prices = state_space.prices + self.price_sensitivity * state_space.demand # this is not great
+        new_prices = state_space.prices + self.price_sensitivity * state_space.demand
         return np.maximum(new_prices, 0)
 
+
+class ElasticityBasedPricingFunction(PricingFunction):
+    """
+    Revenue-maximizing pricing using elasticity estimates.
+
+    For each product, optimal price P* maximizes R = P * Q(P)
+    where Q(P) follows power law: Q(P) = Q_0 * (P/P_0)^ε
+
+    Taking derivative dR/dP = 0 gives optimal markup:
+    P* = P_0 * (1 + 1/ε) if ε < -1 (elastic)
+
+    For inelastic demand (|ε| < 1), we apply bounded markup.
+    """
+
+    def __init__(self,
+                 cost_floor: float = 0.5,
+                 max_markup: float = 2.0,
+                 min_markup: float = 1.0,
+                 inelastic_markup: float = 1.3):
+        super().__init__()
+        self.cost_floor = cost_floor  # prices as fraction of base
+        self.max_markup = max_markup  # max price = base * max_markup
+        self.min_markup = min_markup  # min price = base * min_markup
+        self.inelastic_markup = inelastic_markup  # default for |ε| < 1
+        self.elasticity_map = {}  # productId -> elasticity
+
+    def fit(self, elasticity_df: pd.DataFrame):
+        """
+        Args:
+            elasticity_df: df with [productId, elasticity, std_error, n_obs]
+        """
+        if elasticity_df is not None and not elasticity_df.empty:
+            self.elasticity_map = dict(zip(
+                elasticity_df['productId'],
+                elasticity_df['elasticity']
+            ))
+        return self
+
+    def transform(self, state_space: StateSpace, product_ids: np.ndarray = None) -> np.ndarray:
+        """
+        Args:
+            state_space: current state (prices = base prices)
+            product_ids: array of productIds aligned with state_space.prices
+
+        Returns:
+            optimized prices P_{t+1}
+        """
+        base_prices = state_space.prices
+
+        if product_ids is None:
+            # fallback: use positional index as productId (not ideal)
+            product_ids = np.arange(len(base_prices))
+
+        new_prices = np.zeros_like(base_prices)
+
+        for i, (base_p, pid) in enumerate(zip(base_prices, product_ids)):
+            elasticity = self.elasticity_map.get(pid, 0.0)
+
+            if elasticity < -1:  # elastic demand
+                # optimal markup: (1 + 1/ε)
+                markup = 1 + (1 / elasticity)
+                optimal_p = base_p * markup
+            elif elasticity > -1 and elasticity < 0:  # inelastic
+                # conservative markup
+                optimal_p = base_p * self.inelastic_markup
+            else:  # ε ≥ 0 (demand increases with price, or no data)
+                # no elasticity data or anomalous, keep base price
+                optimal_p = base_p
+
+            # apply bounds
+            optimal_p = np.clip(
+                optimal_p,
+                base_p * self.min_markup,
+                base_p * self.max_markup
+            )
+            optimal_p = max(optimal_p, self.cost_floor)
+
+            new_prices[i] = optimal_p
+
+        return new_prices
+
+
+class ContextualElasticityPricing(PricingFunction):
+    """
+    Revenue optimization with contextual adjustments based on session features.
+
+    Combines elasticity-based pricing with surge/demand-based multipliers.
+    """
+
+    def __init__(self,
+                 base_pricer: ElasticityBasedPricingFunction = None,
+                 demand_sensitivity: float = 0.1,
+                 surge_threshold: float = 0.7):
+        super().__init__()
+        self.base_pricer = base_pricer or ElasticityBasedPricingFunction()
+        self.demand_sensitivity = demand_sensitivity
+        self.surge_threshold = surge_threshold
+
+    def fit(self, elasticity_df: pd.DataFrame):
+        self.base_pricer.fit(elasticity_df)
+        return self
+
+    def transform(self, state_space: StateSpace, product_ids: np.ndarray = None) -> np.ndarray:
+        # get base optimal prices from elasticity
+        base_optimal = self.base_pricer.transform(state_space, product_ids)
+
+        # compute surge multiplier from demand
+        if len(state_space.demand) > 0:
+            demand_normalized = state_space.demand / (state_space.demand.max() + 1e-8)
+            surge_multiplier = 1 + self.demand_sensitivity * np.maximum(
+                demand_normalized - self.surge_threshold, 0
+            )
+        else:
+            surge_multiplier = np.ones_like(base_optimal)
+
+        return base_optimal * surge_multiplier
+
 # Example usage:
 if __name__ == "__main__":
+    from pipeline import interaction_pipeline, price_data_pipeline, elasticity_pipeline
+
     store_mode = 'hotel'
     interaction_data = interaction_pipeline.fit_transform(None)
     price_data = price_data_pipeline.fit_transform(None)

experiments/procesing/providers/__init__.py

@@ -0,0 +1,5 @@
+from procesing.providers.base import DataProvider
+from procesing.providers.supabase import SupabaseProvider
+from procesing.providers.backend import BackendAPIProvider
+
+__all__ = ['DataProvider', 'SupabaseProvider', 'BackendAPIProvider']

experiments/procesing/providers/backend.py

@@ -0,0 +1,19 @@
+import os
+import pandas as pd
+import requests
+from typing import List
+from procesing.providers.base import DataProvider
+
+class BackendAPIProvider(DataProvider):
+    """Concrete backend API implementation"""
+    def __init__(self, backend_url: str = None):
+        self.backend_url = backend_url or os.getenv("BACKEND_URL", "http://localhost:5000")
+    def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
+        resp = requests.get(f"{self.backend_url}/api/kafka/dump?topic={topic}")
+        resp.raise_for_status()
+        data = resp.json()
+
+        if not data.get('success') or not data.get('data'):
+            return pd.DataFrame()
+
+        return pd.DataFrame(data['data'])

experiments/procesing/providers/base.py

@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+from typing import List
+import pandas as pd
+
+class DataProvider(ABC):
+    """Abstract interface for data access, enables DI and testing"""
+
+    @abstractmethod
+    def fetch_products(self, store_mode: str) -> pd.DataFrame:
+        """Fetch product catalog for given store mode"""
+        pass
+
+    @abstractmethod
+    def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
+        """Fetch experiment metadata for given IDs"""
+        pass
+
+    @abstractmethod
+    def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
+        """Fetch data from Kafka topic via backend API"""
+        pass

experiments/procesing/providers/supabase.py

@@ -0,0 +1,35 @@
+import os
+import pandas as pd
+import requests
+from typing import List
+from supabase import create_client, Client
+from procesing.providers.base import DataProvider
+from dotenv import load_dotenv
+
+class SupabaseProvider(DataProvider):
+    """Concrete Supabase + backend API implementation"""
+
+    def __init__(self,
+                 supabase_url: str = None,
+                 supabase_key: str = None,):
+        load_dotenv()
+        self.supabase_url = supabase_url or os.getenv("NEXT_PUBLIC_SUPABASE_URL")
+        self.supabase_key = supabase_key or os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
+        self.supabase: Client = create_client(self.supabase_url, self.supabase_key)
+
+    def fetch_products(self, store_mode: str) -> pd.DataFrame:
+        resp = self.supabase.table(f'{store_mode}_products').select(
+            "id, room_type, date_index, metadata, availability"
+        ).execute()
+        return pd.DataFrame(resp.data) if resp.data else pd.DataFrame()
+
+    def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
+        if not experiment_ids:
+            return pd.DataFrame()
+
+        resp = self.supabase.table('experiments').select(
+            'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, '
+            'task:tasks(task_name, task_description, task_def_of_done)'
+        ).in_('id', experiment_ids).execute()
+
+        return pd.DataFrame(resp.data) if resp.data else pd.DataFrame()

experiments/procesing/steps/__init__.py

@@ -0,0 +1,27 @@
+from procesing.steps.base import BaseContextStep
+from procesing.steps.fetch import FetchInteractionsStep, FetchPriceLogsStep, FetchExperimentsStep
+from procesing.steps.join import JoinExperimentsStep
+from procesing.steps.augment import CreatePriceBucketsStep, AugmentEventNamesStep
+from procesing.steps.chunk import ChunkByTimeWindowStep
+from procesing.steps.demand import ComputeDemandStep, ComputeDemandForChunksStep
+from procesing.steps.elasticity import AggregatePriceLogsStep, ComputeElasticityStep
+from procesing.steps.pricing import StateSpace, BuildStateSpaceStep, FitPricingFunctionStep, PredictPricesStep
+
+__all__ = [
+    'BaseContextStep',
+    'FetchInteractionsStep',
+    'FetchPriceLogsStep',
+    'FetchExperimentsStep',
+    'JoinExperimentsStep',
+    'CreatePriceBucketsStep',
+    'AugmentEventNamesStep',
+    'ChunkByTimeWindowStep',
+    'ComputeDemandStep',
+    'ComputeDemandForChunksStep',
+    'AggregatePriceLogsStep',
+    'ComputeElasticityStep',
+    'StateSpace',
+    'BuildStateSpaceStep',
+    'FitPricingFunctionStep',
+    'PredictPricesStep',
+]

experiments/procesing/steps/augment.py

@@ -0,0 +1,53 @@
+import numpy as np
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class CreatePriceBucketsStep(BaseContextStep):
+    """Create price bucket labels from price data"""
+
+    def transform(self, df: pd.DataFrame):
+        if df.empty or 'metadata_price' not in df.columns:
+            df['price_bucket'] = ""
+            return df
+
+        n_buckets = self.context.config.get('n_price_buckets', 5)
+
+        if df['metadata_price'].notnull().sum() > 0:
+            try:
+                price_buckets = pd.qcut(
+                    df['metadata_price'],
+                    q=n_buckets,
+                    labels=[f"PB_{i+1}" for i in range(n_buckets)],
+                    duplicates='drop'
+                )
+            except ValueError:
+                # fallback for insufficient unique values
+                price_buckets = df['metadata_price'].apply(
+                    lambda x: f"P_{int(x)}" if pd.notnull(x) else ""
+                )
+        else:
+            price_buckets = pd.Series([""] * len(df), index=df.index)
+
+        df['price_bucket'] = price_buckets
+        return df
+
+
+class AugmentEventNamesStep(BaseContextStep):
+    """Augment event names with product and price bucket schema"""
+
+    def transform(self, df: pd.DataFrame):
+        if df.empty:
+            return df
+
+        # Create schema: _productId@price_bucket
+        has_product = df.get('productId', pd.Series()).notnull()
+        has_bucket = df.get('price_bucket', pd.Series()).notnull()
+
+        df['metadata_schema'] = np.where(
+            has_product & has_bucket,
+            "_" + df['productId'].astype(str) + "@" + df['price_bucket'].astype(str),
+            ""
+        )
+
+        df['eventName'] = df['eventName'] + df['metadata_schema']
+        return df

experiments/procesing/steps/base.py

@@ -0,0 +1,31 @@
+from abc import ABC, abstractmethod
+from sklearn.base import BaseEstimator, TransformerMixin
+from procesing.context import PipelineContext
+
+class BaseContextStep(BaseEstimator, TransformerMixin, ABC):
+    """
+    Base for all pipeline steps.
+    Each step is stateless, context-driven, and performs ONE transformation.
+    """
+
+    def __init__(self, context: PipelineContext):
+        self.context = context
+
+    def fit(self, X=None, y=None):
+        """Most steps don't need training"""
+        return self
+
+    @abstractmethod
+    def transform(self, X):
+        """Transform input using context. Must be implemented by subclass."""
+        pass
+
+    def get_params(self, deep=True):
+        """sklearn compatibility"""
+        return {'context': self.context}
+
+    def set_params(self, **params):
+        """sklearn compatibility"""
+        if 'context' in params:
+            self.context = params['context']
+        return self

experiments/procesing/steps/chunk.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class ChunkByTimeWindowStep(BaseContextStep):
+    """
+    Chunk dataframe into time windows.
+    Returns list of dicts with window metadata.
+    """
+
+    def transform(self, df: pd.DataFrame):
+        if df.empty:
+            return []
+
+        df = df.copy()
+        ts_col = self.context.config.get('ts_col', 'ts')
+        window_size = self.context.window_size
+
+        # ensure datetime
+        if not pd.api.types.is_datetime64_any_dtype(df[ts_col]):
+            df[ts_col] = pd.to_datetime(df[ts_col])
+
+        df = df.sort_values(ts_col)
+        df['_window'] = df[ts_col].dt.floor(window_size)
+
+        chunks = []
+        for idx, (window_start, group) in enumerate(df.groupby('_window')):
+            chunks.append({
+                'window_start': window_start,
+                'window_end': window_start + pd.Timedelta(window_size),
+                'window_idx': idx,
+                'data': group.drop(columns=['_window'])
+            })
+
+        return chunks

experiments/procesing/steps/demand.py

@@ -0,0 +1,61 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class ComputeDemandStep(BaseContextStep):
+    """
+    Compute demand vector for a single time window or dataframe.
+    Input: single chunk dict OR raw dataframe
+    Output: demand dataframe with [productId, demand_score]
+    """
+
+    def transform(self, chunk):
+        # handle both chunk dict and raw dataframe
+        if isinstance(chunk, dict):
+            interactions = chunk['data']
+            window_meta = {k: v for k, v in chunk.items() if k != 'data'}
+        else:
+            interactions = chunk
+            window_meta = {}
+
+        products = self.context.products
+        unique_products = products['id'].unique()
+
+        # apply filters if configured
+        session_filter = self.context.config.get('session_filter')
+        experiment_filter = self.context.config.get('experiment_filter')
+
+        if session_filter and 'sessionId' in interactions.columns:
+            interactions = interactions[interactions['sessionId'] == session_filter]
+        if experiment_filter and 'experimentId' in interactions.columns:
+            interactions = interactions[interactions['experimentId'] == experiment_filter]
+
+        interactions_with_products = interactions.dropna(subset=['productId'])
+
+        if interactions_with_products.empty:
+            demand_df = pd.DataFrame({
+                'productId': unique_products,
+                'demand_score': 0
+            })
+        else:
+            # crosstab for simple demand count
+            demand_df = pd.crosstab(
+                interactions_with_products['productId'],
+                'count'
+            ).reindex(unique_products, fill_value=0).reset_index()
+            demand_df.columns = ['productId', 'demand_score']
+
+        # attach window metadata if present
+        if window_meta:
+            return {**window_meta, 'demand_vector': demand_df}
+        return demand_df
+
+
+class ComputeDemandForChunksStep(BaseContextStep):
+    """Apply ComputeDemandStep to list of chunks"""
+
+    def transform(self, chunks: list):
+        if not chunks:
+            return []
+
+        demand_step = ComputeDemandStep(self.context)
+        return [demand_step.transform(chunk) for chunk in chunks]

experiments/procesing/steps/elasticity.py

@@ -0,0 +1,253 @@
+import numpy as np
+import pandas as pd
+from typing import Dict, List
+from procesing.steps.base import BaseContextStep
+
+class AggregatePriceLogsStep(BaseContextStep):
+    """
+    Aggregate price logs into time windows using VECTORIZED operations.
+    Input: price_logs_df
+    Output: list of price chunks with [productId, price]
+    """
+
+    def transform(self, price_logs_df: pd.DataFrame):
+        if price_logs_df.empty:
+            return []
+
+        df = price_logs_df.copy()
+        ts_col = self.context.config.get('ts_col', 'ts')
+        window_size = self.context.window_size
+
+        # ensure datetime
+        if not pd.api.types.is_datetime64_any_dtype(df[ts_col]):
+            df[ts_col] = pd.to_datetime(df[ts_col])
+
+        df = df.sort_values([ts_col, 'productId'])
+        products = self.context.products
+        unique_products = products['id'].unique()
+
+        # VECTORIZED: group by product, resample by time window, compute mean
+        df_indexed = df.set_index(ts_col)
+
+        windowed = (
+            df_indexed
+            .groupby('productId')['price']
+            .resample(window_size)
+            .mean()
+            .reset_index()
+        )
+
+        # forward fill missing windows (carry last known price)
+        windowed = windowed.sort_values([ts_col, 'productId'])
+        windowed['price'] = windowed.groupby('productId')['price'].ffill()
+        windowed = windowed.dropna(subset=['price'])
+
+        # group into chunks by window
+        chunks = []
+        for window_start, group in windowed.groupby(ts_col):
+            price_vector = group[['productId', 'price']].copy()
+
+            # fill missing products with last known price before this window
+            missing_products = set(unique_products) - set(price_vector['productId'])
+            if missing_products:
+                for pid in missing_products:
+                    last_price = df_indexed[
+                        (df_indexed['productId'] == pid) &
+                        (df_indexed.index < window_start)
+                    ]['price']
+
+                    if not last_price.empty:
+                        price_vector = pd.concat([
+                            price_vector,
+                            pd.DataFrame({'productId': [pid], 'price': [last_price.iloc[-1]]})
+                        ], ignore_index=True)
+
+            if not price_vector.empty:
+                chunks.append({
+                    'window_start': window_start,
+                    'window_end': window_start + pd.Timedelta(window_size),
+                    'price_vector': price_vector
+                })
+
+        return chunks
+
+
+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 not aligned:
+            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))
+        }

experiments/procesing/steps/fetch.py

@@ -0,0 +1,46 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class FetchInteractionsStep(BaseContextStep):
+    """Fetch raw interaction data from Kafka topic"""
+
+    def transform(self, X=None):
+        df = self.context.provider.fetch_kafka_topic('user-interactions')
+
+        if df.empty:
+            return df
+
+        # Explode metadata JSON column
+        if 'metadata' in df.columns:
+            df = df.join(
+                pd.json_normalize(df.pop('metadata'), sep='.').add_prefix('metadata_')
+            )
+
+        df = df.dropna(subset=['eventName'])
+
+        # Remap dateIndex if present
+        if 'metadata_dateIndex' in df.columns:
+            df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
+
+        return df
+
+
+class FetchPriceLogsStep(BaseContextStep):
+    """Fetch price log data from Kafka topic"""
+
+    def transform(self, X=None):
+        return self.context.provider.fetch_kafka_topic('price-logs')
+
+
+class FetchExperimentsStep(BaseContextStep):
+    """Fetch experiment metadata for given interaction data"""
+
+    def transform(self, interactions_df: pd.DataFrame):
+        if interactions_df.empty or 'experimentId' not in interactions_df.columns:
+            return pd.DataFrame()
+
+        exp_ids = interactions_df['experimentId'].dropna().unique().tolist()
+        if not exp_ids:
+            return pd.DataFrame()
+
+        return self.context.provider.fetch_experiments(exp_ids)

experiments/procesing/steps/join.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from procesing.steps.base import BaseContextStep
+
+class JoinExperimentsStep(BaseContextStep):
+    """Join experiment metadata to interactions"""
+
+    def transform(self, data: tuple):
+        """
+        Args:
+            data: (interactions_df, experiments_df)
+        Returns:
+            merged interactions dataframe
+        """
+        interactions_df, experiments_df = data
+
+        if experiments_df.empty:
+            return interactions_df
+
+        # Flatten nested task field if present
+        if 'task' in experiments_df.columns and experiments_df['task'].notnull().any():
+            task_norm = pd.json_normalize(experiments_df['task'].dropna())
+            task_norm.index = experiments_df[experiments_df['task'].notnull()].index
+            experiments_df = experiments_df.drop('task', axis=1).join(task_norm, rsuffix='_task')
+
+        # Rename for clarity
+        experiments_df = experiments_df.rename(columns={
+            'id': 'experimentId',
+            'subject_name': 'exp_subject',
+            'xp_human_only': 'exp_human_only',
+            'xp_market_mode': 'exp_market_mode',
+            'xp_task_id': 'exp_task_id'
+        })
+
+        return interactions_df.merge(experiments_df, on='experimentId', how='left')

experiments/procesing/steps/pricing.py

@@ -0,0 +1,149 @@
+import numpy as np
+import pandas as pd
+from typing import Optional, List, Dict, Any
+from dataclasses import dataclass, field
+from procesing.steps.base import BaseContextStep
+from procesing.pricers import ElasticityBasedPricer
+
+@dataclass
+class StateSpace:
+    """
+    State representation for pricing functions.
+
+    Components:
+        Q_t: demand ∈ R^n (current demand signal per product)
+        P_t: prices ∈ R^n (current/base prices)
+        S_t: session_features (behavioral signals, interaction data)
+        H_t: history = {Q_{t-k}, P_{t-k}, S_{t-k}} for k in [1, history_length]
+
+    Additionally stores:
+        - product_ids: product identifiers (n,)
+        - elasticity: price elasticity per product (n,)
+        - metadata: arbitrary context (experiment_id, timestamp, etc.)
+    """
+    demand: np.ndarray  # Q_t ∈ R^n
+    prices: np.ndarray  # P_t ∈ R^n
+    session_features: pd.DataFrame = field(default_factory=pd.DataFrame)  # S_t
+
+    # augmented state components
+    product_ids: Optional[np.ndarray] = None
+    elasticity: Optional[np.ndarray] = None
+
+    # historical trajectory H_t = {(Q_{t-k}, P_{t-k}, S_{t-k})}
+    history: List[Dict[str, Any]] = field(default_factory=list)
+
+    # metadata for context
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self):
+        """Validate dimensions."""
+        n = len(self.demand)
+        assert len(self.prices) == n, "demand and prices must have same dimension"
+        if self.elasticity is not None:
+            assert len(self.elasticity) == n, "elasticity must match dimension"
+        if self.product_ids is not None:
+            assert len(self.product_ids) == n, "product_ids must match dimension"
+
+    @property
+    def n_products(self) -> int:
+        """Number of products in state space."""
+        return len(self.demand)
+
+    def add_history(self, q: np.ndarray, p: np.ndarray, s: pd.DataFrame, max_length: int = 10):
+        """Append historical state to trajectory H_t."""
+        self.history.append({'demand': q, 'prices': p, 'session_features': s})
+        if len(self.history) > max_length:
+            self.history.pop(0)
+
+    def get_history_window(self, k: int = 5) -> List[Dict[str, Any]]:
+        """Retrieve last k historical states."""
+        return self.history[-k:] if len(self.history) >= k else self.history
+
+
+class BuildStateSpaceStep(BaseContextStep):
+    """
+    Build state space from elasticity, demand, and price data.
+
+    Input: elasticity_df [productId, elasticity, ...], optional demand_df
+    Output: StateSpace instance with Q_t, P_t, elasticity, product_ids
+    """
+
+    def transform(self, elasticity_df: pd.DataFrame, demand_df: Optional[pd.DataFrame] = None):
+        products = self.context.products
+
+        # extract base prices from product metadata
+        products_with_prices = products.copy()
+        if 'metadata' in products_with_prices.columns:
+            products_with_prices['base_price'] = products_with_prices['metadata'].apply(
+                lambda m: m.get('base_price', 0) if isinstance(m, dict) else 0
+            )
+        else:
+            products_with_prices['base_price'] = 0
+
+        # merge with elasticity
+        merged = products_with_prices[['id', 'base_price']].rename(
+            columns={'id': 'productId'}
+        ).merge(
+            elasticity_df[['productId', 'elasticity']],
+            on='productId',
+            how='left'
+        ).fillna({'elasticity': 0.0, 'base_price': 0.0})
+
+        # merge with demand if provided, else use default
+        if demand_df is not None and 'demand' in demand_df.columns:
+            merged = merged.merge(
+                demand_df[['productId', 'demand']],
+                on='productId',
+                how='left'
+            ).fillna({'demand': 0.0})
+            demand_vector = merged['demand'].values
+        else:
+            # default: uniform demand or use elasticity as proxy
+            demand_vector = np.ones(len(merged)) * 10.0
+
+        return StateSpace(
+            demand=demand_vector,
+            prices=merged['base_price'].values,
+            session_features=pd.DataFrame(),
+            product_ids=merged['productId'].values,
+            elasticity=merged['elasticity'].values,
+            metadata={'timestamp': pd.Timestamp.now().isoformat()}
+        )
+
+
+class FitPricingFunctionStep(BaseContextStep):
+    """
+    Fit pricing function using elasticity data.
+    Input: elasticity_df
+    Output: fitted pricing function instance
+    """
+
+    def transform(self, elasticity_df: pd.DataFrame):
+        pricing_class = self.context.config.get('pricing_function_class', ElasticityBasedPricer)
+        pricing_params = self.context.config.get('pricing_function_params', {})
+
+        pricer = pricing_class(**pricing_params)
+        pricer.fit(elasticity_df)
+
+        return pricer
+
+
+class PredictPricesStep(BaseContextStep):
+    """
+    Predict optimal prices using fitted pricing function.
+    Input: (pricer, state_space)
+    Output: prices_df [productId, predicted_price]
+    """
+
+    def transform(self, data: tuple):
+        pricer, state_space = data
+
+        products = self.context.products
+        product_ids = products['id'].values
+
+        predicted_prices = pricer.predict(state_space)
+
+        return pd.DataFrame({
+            'productId': product_ids,
+            'predicted_price': predicted_prices
+        })

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()

experiments/procesing/tests/conftest.py

@@ -0,0 +1,271 @@
+import pytest
+import pandas as pd
+from typing import List
+from procesing.providers.base import DataProvider
+from procesing.context import PipelineContext
+
+
+class MockProvider(DataProvider):
+    """Mock provider for testing, holds in-memory fixtures"""
+
+    def __init__(self, products_df=None, experiments_df=None, kafka_data=None):
+        self._products = products_df if products_df is not None else pd.DataFrame()
+        self._experiments = experiments_df if experiments_df is not None else pd.DataFrame()
+        self._kafka_data = kafka_data if kafka_data is not None else {}
+
+    def fetch_products(self, store_mode: str) -> pd.DataFrame:
+        return self._products.copy()
+
+    def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
+        if self._experiments.empty:
+            return pd.DataFrame()
+        return self._experiments[
+            self._experiments['id'].isin(experiment_ids)
+        ].copy()
+
+    def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
+        return self._kafka_data.get(topic, pd.DataFrame()).copy()
+
+
+@pytest.fixture
+def mock_products():
+    """Standard product catalog fixture with realistic IDs from test data"""
+    return pd.DataFrame({
+        'id': [
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ],
+        'name': ['Junior Suite', 'Superior Room', 'Deluxe Room'],
+        'base_price': [200.0, 150.0, 180.0]
+    })
+
+
+@pytest.fixture
+def mock_interactions_raw_kafka():
+    """Raw Kafka message structure for interactions, matches production format"""
+    return [
+        {
+            'partitionID': 0, 'offset': 203, 'timestamp': 1764102082676,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'eventName': 'learn_more_about_item',
+                    'page': '/hotel/products/d018efc1-25e9-4284-b276-80386e048b25',
+                    'productId': 'd018efc1-25e9-4284-b276-80386e048b25',
+                    'metadata': {'type': 'hotel', 'dateIndex': 1, 'roomType': 'Junior Suite'},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:22.674Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 204, 'timestamp': 1764102086982,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'eventName': 'page_view',
+                    'page': '/hotel/products',
+                    'productId': None,
+                    'metadata': {'referrer': ''},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:26.947Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 205, 'timestamp': 1764102091825,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'eventName': 'hover_over_title',
+                    'page': '/hotel/products',
+                    'productId': '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+                    'metadata': {'elementText': 'Superior Room', 'dateIndex': 1, 'dwellTime': 1200},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:31.823Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 206, 'timestamp': 1764102094193,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': 'bbbbcccc-dddd-eeee-ffff-000011112222',
+                    'eventName': 'hover_over_paragraph',
+                    'page': '/hotel/products',
+                    'productId': '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+                    'metadata': {'elementText': 'price', 'dateIndex': 1, 'dwellTime': 1307},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:34.191Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 207, 'timestamp': 1764102101970,
+            'value': {
+                'payload': {
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': 'bbbbcccc-dddd-eeee-ffff-000011112222',
+                    'eventName': 'hover_over_paragraph',
+                    'page': '/hotel/products',
+                    'productId': 'd018efc1-25e9-4284-b276-80386e048b25',
+                    'metadata': {'elementText': 'price', 'dateIndex': 1, 'dwellTime': 1201},
+                    'storeMode': 'hotel',
+                    'ts': '2025-11-25T20:21:41.967Z'
+                }
+            }
+        }
+    ]
+
+
+@pytest.fixture
+def mock_interactions(mock_interactions_raw_kafka):
+    """Processed interaction DataFrame (what provider.fetch_kafka_topic returns)"""
+    records = [msg['value']['payload'] for msg in mock_interactions_raw_kafka]
+    df = pd.DataFrame(records)
+    df['timestamp'] = pd.to_datetime(df['ts'])
+    return df
+
+
+@pytest.fixture
+def mock_price_logs_raw_kafka():
+    """Raw Kafka message structure for price logs, matches production format"""
+    return [
+        {
+            'partitionID': 0, 'offset': 32, 'timestamp': 1764104757969,
+            'value': {
+                'payload': {
+                    'productId': '2cd7f756-fc65-4ba0-ab01-74521c1fff43',
+                    'price': 162.47,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:57.967Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 33, 'timestamp': 1764104757995,
+            'value': {
+                'payload': {
+                    'productId': '2ddabbfc-4127-48fc-86dc-ebc4c677efa2',
+                    'price': 743.49,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:57.993Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 34, 'timestamp': 1764104758011,
+            'value': {
+                'payload': {
+                    'productId': '2cd7f756-fc65-4ba0-ab01-74521c1fff43',
+                    'price': 163.87,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:58.009Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 35, 'timestamp': 1764104758050,
+            'value': {
+                'payload': {
+                    'productId': '2ddabbfc-4127-48fc-86dc-ebc4c677efa2',
+                    'price': 397.46,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:05:58.049Z'
+                }
+            }
+        },
+        {
+            'partitionID': 0, 'offset': 36, 'timestamp': 1764104768865,
+            'value': {
+                'payload': {
+                    'productId': '2cd7f756-fc65-4ba0-ab01-74521c1fff43',
+                    'price': 401.66,
+                    'sessionId': 'd423ce8a-77aa-4c9a-94d4-d1adddcc3472',
+                    'experimentId': '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35',
+                    'storeMode': 'shop',
+                    'ts': '2025-11-25T21:06:08.864Z'
+                }
+            }
+        }
+    ]
+
+
+@pytest.fixture
+def mock_price_logs(mock_price_logs_raw_kafka):
+    """Processed price logs DataFrame (what provider.fetch_kafka_topic returns)"""
+    # extract payloads and flatten
+    records = [msg['value']['payload'] for msg in mock_price_logs_raw_kafka]
+    df = pd.DataFrame(records)
+    df['timestamp'] = pd.to_datetime(df['ts'])
+    return df
+
+
+@pytest.fixture
+def mock_experiments():
+    """Standard experiment metadata fixture matching Supabase schema"""
+    return pd.DataFrame({
+        'id': ['53aefd07-f66a-4d7f-ba8b-7ea1fc562d35', 'bbbbcccc-dddd-eeee-ffff-000011112222'],
+        'created_at': pd.to_datetime(['2025-11-25T20:00:00Z', '2025-11-26T10:00:00Z']),
+        'subject_name': ['Session A', 'Session B'],
+        'xp_human_only': [True, False],
+        'xp_market_mode': ['hotel', 'shop'],
+        'xp_task_id': [None, None]
+    })
+
+
+@pytest.fixture
+def mock_provider(mock_products, mock_experiments, mock_interactions, mock_price_logs):
+    """Fully configured mock provider"""
+    return MockProvider(
+        products_df=mock_products,
+        experiments_df=mock_experiments,
+        kafka_data={
+            'user-interactions': mock_interactions,
+            'price-logs': mock_price_logs
+        }
+    )
+
+
+@pytest.fixture
+def pipeline_context(mock_provider):
+    """Standard pipeline context for testing"""
+    return PipelineContext(
+        provider=mock_provider,
+        store_mode='hotel',
+        window_size='30s',
+        n_price_buckets=3
+    )
+
+
+@pytest.fixture
+def empty_provider():
+    """Provider with no data, for edge case testing"""
+    return MockProvider(
+        products_df=pd.DataFrame(columns=['id', 'name', 'base_price']),
+        experiments_df=pd.DataFrame(columns=['id', 'created_at', 'subject_name', 'xp_human_only', 'xp_market_mode', 'xp_task_id']),
+        kafka_data={'user-interactions': pd.DataFrame(), 'price-logs': pd.DataFrame()}
+    )
+
+
+@pytest.fixture
+def empty_context(empty_provider):
+    """Context with empty provider"""
+    return PipelineContext(
+        provider=empty_provider,
+        store_mode='hotel',
+        window_size='30s'
+    )

experiments/procesing/tests/test_augement.py

@@ -0,0 +1,45 @@
+import pytest
+import random
+import pandas as pd
+from procesing.steps import (
+    CreatePriceBucketsStep,
+    AugmentEventNamesStep
+)
+
+def test_bucketing(pipeline_context):
+    step = CreatePriceBucketsStep(context=pipeline_context)
+
+    # Test with normal price data
+    df = pd.DataFrame({
+        'metadata_price': random.sample(range(10, 1000), 100)
+    })
+    result = step.transform(df)
+    assert 'price_bucket' in result.columns
+    # test if is categorical
+    assert isinstance(result['price_bucket'].dtype, pd.CategoricalDtype)
+    assert result['price_bucket'].nunique() == 3 # as per context config
+    # distribution check
+    counts = result['price_bucket'].value_counts()
+    assert all(counts > 0)
+    assert counts.max() - counts.min() <= 10  # roughly equal distribution for 100 samples
+    # Test with empty DataFrame
+    df = pd.DataFrame()
+    result = step.transform(df)
+    assert 'price_bucket' in result.columns
+    assert result.empty
+
+
+def test_augment_names(pipeline_context):
+    df = pd.DataFrame({
+        'eventName': ['click', 'view', 'purchase'],
+        'productId': ['prod_1', 'prod_2', None],
+        'price_bucket': ['PB_1', None, 'PB_3']
+    })
+    step = AugmentEventNamesStep(context=pipeline_context)
+    result = step.transform(df)
+    expected_event_names = [
+        'click_prod_1@PB_1',
+        'view',
+        'purchase'
+    ]
+    assert result['eventName'].tolist() == expected_event_names

experiments/procesing/tests/test_demand.py

@@ -0,0 +1,49 @@
+import pytest
+import random
+import pandas as pd
+from procesing.steps import (
+    ComputeDemandStep
+)
+
+def test_compute_demand(pipeline_context):
+    step = ComputeDemandStep(context=pipeline_context)
+
+    # Test with normal interaction data
+    df = pd.DataFrame({
+        'ts': pd.date_range(start='2023-01-01', periods=100, freq='h'),
+        'productId': random.choices([
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ], k=100),
+        'eventName': random.choices(['view', 'click', 'purchase'], k=100)
+    })
+    result = step.transform(df)
+    assert type(result) == pd.DataFrame
+    assert not result.empty
+    assert set(result['productId']) == set(pipeline_context.products['id'])
+    assert all(result['demand_score'] > 100/3 -10)
+
+
+def test_compute_demand_skewed(pipeline_context):
+    step = ComputeDemandStep(context=pipeline_context)
+
+    # Test with normal interaction data
+    df = pd.DataFrame({
+        'ts': pd.date_range(start='2023-01-01', periods=100, freq='h'),
+        'productId': random.choices([
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ], weights=[0.7, 0.2, 0.1], k=100),
+        'eventName': random.choices(['view', 'click', 'purchase'], k=100)
+    })
+    result = step.transform(df)
+    assert type(result) == pd.DataFrame
+    assert not result.empty
+    assert set(result['productId']) == set(pipeline_context.products['id'])
+    # test for skewness
+    scores = result.set_index('productId')['demand_score'].to_dict()
+    assert scores['d018efc1-25e9-4284-b276-80386e048b25'] > \
+           scores['51266ddb-5b07-47b7-89ee-5b5cae94bb11'] > \
+           scores['2cd7f756-fc65-4ba0-ab01-74521c1fff43']

experiments/procesing/tests/test_elasticity.py

@@ -0,0 +1,353 @@
+import pytest
+import pandas as pd
+import numpy as np
+from procesing.steps import (
+    AggregatePriceLogsStep,
+    ComputeElasticityStep
+)
+
+
+def test_aggregate_price_logs_basic(pipeline_context):
+    """Test basic price aggregation into time windows"""
+    step = AggregatePriceLogsStep(pipeline_context)
+
+    # Create price logs with known window structure
+    df = pd.DataFrame({
+        'ts': pd.date_range(start='2023-01-01 10:00:00', periods=100, freq='10s'),
+        'productId': np.tile([
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+        ], 34)[:100],
+        'price': np.random.uniform(100, 200, 100)
+    })
+
+    result = step.transform(df)
+    assert isinstance(result, list)
+    assert len(result) > 0
+    # each chunk should have window metadata and price vector
+    for chunk in result:
+        assert 'window_start' in chunk
+        assert 'window_end' in chunk
+        assert 'price_vector' in chunk
+        assert isinstance(chunk['price_vector'], pd.DataFrame)
+        assert 'productId' in chunk['price_vector'].columns
+        assert 'price' in chunk['price_vector'].columns
+
+
+def test_aggregate_price_logs_handles_gaps(pipeline_context):
+    """Test that price aggregation forward-fills missing windows"""
+    step = AggregatePriceLogsStep(pipeline_context)
+
+    # create sparse data with gaps
+    df = pd.DataFrame({
+        'ts': pd.to_datetime([
+            '2023-01-01 10:00:00',
+            '2023-01-01 10:00:05',
+            '2023-01-01 10:02:00',  # gap of ~2 mins
+            '2023-01-01 10:02:30'
+        ]),
+        'productId': [
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            'd018efc1-25e9-4284-b276-80386e048b25',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+            '51266ddb-5b07-47b7-89ee-5b5cae94bb11'
+        ],
+        'price': [100, 102, 150, 153]
+    })
+
+    result = step.transform(df)
+    assert isinstance(result, list)
+    # should have multiple windows despite gaps
+    assert len(result) >= 2
+
+
+def test_compute_elasticity_with_known_relationship(pipeline_context):
+    """Test elasticity computation with known price-demand relationship"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    # simulate elastic demand: when price ↑10%, demand ↓15% (elasticity ~ -1.5)
+    base_price = 100
+    base_demand = 50
+
+    demand_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [base_demand]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [base_demand * 0.85]  # 15% decrease
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:01:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [base_demand * 0.70]  # further decrease
+            })
+        }
+    ]
+
+    price_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [base_price]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [base_price * 1.10]  # 10% increase
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:01:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [base_price * 1.20]  # 20% increase
+            })
+        }
+    ]
+
+    result = step.transform((demand_chunks, price_chunks))
+    assert isinstance(result, pd.DataFrame)
+    assert not result.empty
+    assert 'productId' in result.columns
+    assert 'elasticity' in result.columns
+    assert 'n_obs' in result.columns
+
+    # check elasticity is negative (normal good)
+    product_elast = result[result['productId'] == 'd018efc1-25e9-4284-b276-80386e048b25']
+    assert len(product_elast) == 1
+    assert product_elast.iloc[0]['elasticity'] < 0
+    # should be roughly elastic (< -1)
+    assert product_elast.iloc[0]['n_obs'] == 3
+
+
+def test_compute_elasticity_inelastic_product(pipeline_context):
+    """Test with inelastic demand: price changes, demand barely moves"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    base_price = 150
+    base_demand = 40
+
+    demand_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'demand_score': [base_demand]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'demand_score': [base_demand * 0.98]  # tiny 2% decrease
+            })
+        }
+    ]
+
+    price_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'price': [base_price]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'price_vector': pd.DataFrame({
+                'productId': ['51266ddb-5b07-47b7-89ee-5b5cae94bb11'],
+                'price': [base_price * 1.20]  # 20% increase
+            })
+        }
+    ]
+
+    result = step.transform((demand_chunks, price_chunks))
+    product_elast = result[result['productId'] == '51266ddb-5b07-47b7-89ee-5b5cae94bb11']
+    assert len(product_elast) == 1
+    # inelastic: elasticity between 0 and -1
+    assert -1 < product_elast.iloc[0]['elasticity'] < 0
+
+
+def test_compute_elasticity_multiple_products(pipeline_context):
+    """Test elasticity computation across multiple products simultaneously"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    products = [
+        'd018efc1-25e9-4284-b276-80386e048b25',
+        '51266ddb-5b07-47b7-89ee-5b5cae94bb11',
+        '2cd7f756-fc65-4ba0-ab01-74521c1fff43'
+    ]
+
+    # create 5 time windows with all 3 products
+    demand_chunks = []
+    price_chunks = []
+
+    for i in range(5):
+        ts = pd.Timestamp('2023-01-01 10:00:00') + pd.Timedelta(f'{i*30}s')
+
+        demand_chunks.append({
+            'window_start': ts,
+            'window_end': ts + pd.Timedelta('30s'),
+            'demand_vector': pd.DataFrame({
+                'productId': products,
+                'demand_score': [
+                    50 * (0.9 ** i),  # elastic: decreases as price rises
+                    40 * (0.98 ** i), # inelastic: barely changes
+                    30 * (0.85 ** i)  # very elastic
+                ]
+            })
+        })
+
+        price_chunks.append({
+            'window_start': ts,
+            'window_end': ts + pd.Timedelta('30s'),
+            'price_vector': pd.DataFrame({
+                'productId': products,
+                'price': [
+                    100 * (1.05 ** i),
+                    150 * (1.10 ** i),
+                    120 * (1.08 ** i)
+                ]
+            })
+        })
+
+    result = step.transform((demand_chunks, price_chunks))
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == 3  # all products should have elasticity
+    assert set(result['productId']) == set(products)
+    assert all(result['n_obs'] == 5)
+    assert all(result['elasticity'] < 0)  # all normal goods
+
+
+def test_compute_elasticity_insufficient_data(pipeline_context):
+    """Test behavior with insufficient observations"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    # only 1 observation
+    demand_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+        'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+        'demand_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'demand_score': [50]
+        })
+    }]
+
+    price_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+        'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+        'price_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'price': [100]
+        })
+    }]
+
+    result = step.transform((demand_chunks, price_chunks))
+    # should still return result but with low n_obs
+    product_elast = result[result['productId'] == 'd018efc1-25e9-4284-b276-80386e048b25']
+    assert len(product_elast) == 1
+    assert product_elast.iloc[0]['n_obs'] == 1
+    assert product_elast.iloc[0]['elasticity'] == 0.0  # not enough data
+
+
+def test_compute_elasticity_misaligned_chunks(pipeline_context):
+    """Test with non-overlapping demand and price windows"""
+    step = ComputeElasticityStep(pipeline_context)
+
+    demand_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+        'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+        'demand_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'demand_score': [50]
+        })
+    }]
+
+    price_chunks = [{
+        'window_start': pd.Timestamp('2023-01-01 11:00:00'),  # different time
+        'window_end': pd.Timestamp('2023-01-01 11:00:30'),
+        'price_vector': pd.DataFrame({
+            'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+            'price': [100]
+        })
+    }]
+
+    result = step.transform((demand_chunks, price_chunks))
+    # should handle gracefully with no aligned data
+    assert isinstance(result, pd.DataFrame)
+    assert all(result['n_obs'] == 0)
+
+
+def test_elasticity_arc_method(pipeline_context):
+    """Test arc elasticity computation method"""
+    # configure context for arc method
+    pipeline_context.config['elasticity_method'] = 'arc'
+    step = ComputeElasticityStep(pipeline_context)
+
+    demand_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [100]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'demand_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'demand_score': [80]
+            })
+        }
+    ]
+
+    price_chunks = [
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:00'),
+            'window_end': pd.Timestamp('2023-01-01 10:00:30'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [100]
+            })
+        },
+        {
+            'window_start': pd.Timestamp('2023-01-01 10:00:30'),
+            'window_end': pd.Timestamp('2023-01-01 10:01:00'),
+            'price_vector': pd.DataFrame({
+                'productId': ['d018efc1-25e9-4284-b276-80386e048b25'],
+                'price': [110]
+            })
+        }
+    ]
+
+    result = step.transform((demand_chunks, price_chunks))
+    product_elast = result[result['productId'] == 'd018efc1-25e9-4284-b276-80386e048b25']
+    assert len(product_elast) == 1
+    assert product_elast.iloc[0]['elasticity'] < 0
+    # reset config
+    pipeline_context.config['elasticity_method'] = 'point'

experiments/procesing/tests/test_fetch.py

@@ -0,0 +1,51 @@
+import pytest
+import pandas as pd
+from procesing.steps import (
+    FetchInteractionsStep,
+    FetchPriceLogsStep,
+    FetchExperimentsStep,
+)
+
+
+def test_fetch_interactions_data(pipeline_context):
+    step = FetchInteractionsStep(pipeline_context)
+    data = step.transform(None)
+    assert data is not None
+    assert isinstance(data, pd.DataFrame)
+    expected_cols = [
+        "eventName",
+        "dateIndex",
+        "experimentId",
+        "storeMode",
+        "metadata_elementText"
+    ]
+    for expected in expected_cols:
+        assert expected in data.columns
+
+def test_fetch_price_logs(pipeline_context):
+    step = FetchPriceLogsStep(pipeline_context)
+    data = step.transform(None)
+    assert data is not None
+    assert isinstance(data, pd.DataFrame)
+    expected_cols = [
+        "price",
+        "productId"
+    ]
+    for expected in expected_cols:
+        assert expected in data.columns
+    prices = data['price'].to_list()
+    assert min(prices) >= 0
+    assert max(prices) <= 9999
+
+
+def test_experiments_fetching(pipeline_context):
+    interactions = FetchInteractionsStep(pipeline_context).transform(None)
+    assert interactions is not None
+    experiments = FetchExperimentsStep(pipeline_context)
+    experiment_data = experiments.transform(interactions)
+    assert experiment_data is not None
+    assert isinstance(experiment_data, pd.DataFrame)
+    assert not experiment_data.empty
+    assert 'id' in experiment_data.columns
+    assert len(experiment_data) == 2
+    assert '53aefd07-f66a-4d7f-ba8b-7ea1fc562d35' in experiment_data['id'].values

experiments/procesing/tests/test_pricing.py

@@ -0,0 +1,87 @@
+import pytest
+import pandas as pd
+
+from procesing.pricers import (
+    StaticPricer,
+    RandomPricer,
+    ElasticityBasedPricer
+)
+
+
+def test_static_pricer_fit_and_predict():
+    # Sample historical data
+    historical_data = pd.DataFrame({
+        'product_id': [1, 2, 3],
+        'base_price': [100.0, 150.0, 200.0]
+    })
+
+    # Initialize and fit StaticPricer
+    pricer = StaticPricer()
+    pricer.fit(historical_data)
+
+    # Predict prices
+    predicted_prices = pricer.predict(None)
+
+    # Assert that predicted prices match base prices
+    expected_prices = historical_data['base_price'].values
+    assert all(predicted_prices == expected_prices), "Predicted prices do not match base prices"
+
+
+def test_random_pricer_fit_and_predict():
+    # Sample historical data
+    historical_data = pd.DataFrame({
+        'product_id': [1, 2, 3],
+        'base_price': [100.0, 150.0, 200.0]
+    })
+
+    # Initialize and fit RandomPricer
+    pricer = RandomPricer(price_min=50.0, price_max=250.0, seed=42)
+    pricer.fit(historical_data)
+
+    # Predict prices
+    predicted_prices = pricer.predict(None)
+
+    # Assert that predicted prices are within bounds
+    assert predicted_prices.min() >= 50.0, "Predicted prices are below minimum bound"
+    assert predicted_prices.max() <= 250.0, "Predicted prices are above maximum bound"
+    # distribution check (not so strict)
+    assert len(set(predicted_prices)) > 1, "Predicted prices are not varied enough"
+    assert len(predicted_prices) == len(historical_data), "Number of predicted prices does not match number of products"
+
+def test_elasticity_based_pricer_fit_and_predict():
+    # Sample historical data
+    historical_data = pd.DataFrame({
+        'productId': [1, 2, 3],
+        'elasticity': [-1.5, -0.5, -2.0],
+        'base_price': [100.0, 150.0, 200.0],
+        'mean_demand': [10, 20, 15]
+    })
+
+    # Initialize and fit ElasticityBasedPricer
+    pricer = ElasticityBasedPricer(alpha=0.1, price_floor=50.0, price_ceil=300.0)
+    pricer.fit(historical_data)
+
+    # Create a mock state space with demand deviations
+    class MockStateSpace:
+        def __init__(self, demand):
+            self.demand = demand
+
+    # Simulate demand higher than mean for all products
+    state_space = MockStateSpace(demand=[15, 25, 20])
+
+    # Predict prices
+    predicted_prices = pricer.predict(state_space)
+
+    # Assert that predicted prices are within bounds
+    assert predicted_prices.min() >= 50.0, "Predicted prices are below minimum bound"
+    assert predicted_prices.max() <= 300.0, "Predicted prices are above maximum bound"
+    assert len(predicted_prices) == len(historical_data), "Number of predicted prices does not match number of products"
+
+    # now we gotta check semantic validity
+    # since demand is higher than mean, prices should generally increase
+    for i, row in historical_data.iterrows():
+        base_price = row['base_price']
+        elasticity = row['elasticity']
+        expected_increase = base_price * (1 + 0.1 * abs(elasticity) * ((state_space.demand[i] - row['mean_demand']) / row['mean_demand']))
+        assert predicted_prices[i] >= base_price, f"Predicted price for product {row['productId']} did not increase as expected"
+        assert abs(predicted_prices[i] - expected_increase) < 1e-5, f"Predicted price for product {row['productId']} does not match expected calculation within 1e-5 tolerance"