proper pipeline to handle data and build matrices

experiments/procesing/extract.py

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+from kafka import KafkaConsumer
+import pandas as pd
+import json
+import numpy as np
+import os
+from dotenv import load_dotenv
+from sklearn.base import BaseEstimator, TransformerMixin
+# import matplotlib.pyplot as plt
+# from IPython.display import display, SVG, Image
+load_dotenv()
+
+
+KAFKA_HOST=os.getenv("KAFKA_HOST", "localhost")
+KAFKA_PORT=os.getenv("KAFKA_PORT", 9092)
+TOPIC = os.getenv("KAFKA_TOPIC", "user-interactions")
+N_PRICE_BUCKETS = 5
+
+def get_data_from_kafka() -> pd.DataFrame:
+    consumer = KafkaConsumer(
+        TOPIC,
+        enable_auto_commit=True,
+        value_deserializer=lambda x: json.loads(x.decode('utf-8')),
+        auto_offset_reset='earliest',
+        bootstrap_servers=[f"{KAFKA_HOST}:{KAFKA_PORT}"]
+    )
+    messages=consumer.poll(timeout_ms=1000,max_records=10000)
+    df = []
+    for m in messages.values():
+        for i in m:
+            df.append(i.value)
+    df = pd.DataFrame(df)
+    """
+ 0   sessionId             73 non-null     object
+ 1   eventName             73 non-null     object
+ 2   page                  73 non-null     object
+ 3   productId             67 non-null     object
+ 4   storeMode             73 non-null     object
+ 5   userAgent             73 non-null     object
+ 6   ts                    73 non-null     object
+ 7   metadata_referrer     6 non-null      object
+ 8   metadata_roomType     45 non-null     object
+ 9   metadata_price        45 non-null     float64
+ 10  metadata_nights       45 non-null     float64
+ 11  metadata_elementText  22 non-null     object
+ 12  metadata_dwellTime    22 non-null     float64
+    """
+    # explode metadata col json
+    df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
+    df = df.dropna(subset=['eventName'])
+    return df
+
+
+def join_with_experiments(df: pd.DataFrame) -> pd.DataFrame:
+    # TODO: Get experiments db from supabase and join on session_id
+    return df
+
+
+def augment_event_titles(df: pd.DataFrame) -> pd.DataFrame:
+    # from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
+    # we want metadata schema to create product specific event names
+    price_buckets = pd.qcut(
+        df["metadata_price"],
+        q=N_PRICE_BUCKETS,
+        labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)]
+    )
+    # 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 extract() -> pd.DataFrame:
+    df = get_data_from_kafka()
+    df = join_with_experiments(df)
+    df = augment_event_titles(df)
+    return df
+
+
+class DataExtractor(BaseEstimator, TransformerMixin):
+    def fit(self, X=None, y=None):
+        return self
+
+    def transform(self, X=None):
+        return extract()
+
+
+if __name__ == "__main__":
+    df = extract()
+    print(df.head())
+    print(df.tail())
+    print(df.info())

experiments/procesing/mapping.py

@@ -0,0 +1,158 @@
+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/pipeline.py

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+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from extract import DataExtractor
+from mapping import SessionTransitionProbMatrixTransformer, render_graph
+
+
+if __name__ == "__main__":
+    steps = [
+        ('data_extraction', DataExtractor()),
+        ('transition_matrix', SessionTransitionProbMatrixTransformer(threshold=0.05)),
+    ]
+    pipeline = Pipeline(steps)
+    result = pipeline.fit_transform(None)
+    print(f"Number of sessions: {len(result)}\n")
+
+    for session_id, sess_data in result.items():
+        fname = f"session_{session_id}"
+        render_graph(fname, sess_data['matrix'], ls_index=sess_data['labels'], threshold=0.05, fmt="svg", view=False)
+        print(f"Rendered {fname}.svg")