proper pipeline to handle data and build matrices

experiments/procesing/pipeline.py

@@ -0,0 +1,19 @@
+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")