catchup: rogue scripts

experiments/procesing/tests/test_session.py

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+import pytest
+import pandas as pd
+import numpy as np
+from procesing.steps.session import (
+    TemporalFeatureStep,
+    BehavioralFeatureStep,
+    ProductFeatureStep,
+    UserAgentFeatureStep,
+    ExtractSessionFeaturesStep,
+    JoinLabelsStep,
+    ValidateDataStep,
+)
+
+
+# TemporalFeatureStep tests
+def test_temporal_empty(pipeline_context):
+    result = TemporalFeatureStep(pipeline_context).transform(pd.DataFrame())
+    assert 'sessionId' in result.columns
+    assert result.empty
+
+
+def test_temporal_basic(pipeline_context, session_interactions):
+    result = TemporalFeatureStep(pipeline_context).transform(session_interactions)
+    assert 'session_duration_sec' in result.columns
+    assert 'interaction_velocity' in result.columns
+    assert 'max_velocity_5min' in result.columns
+    assert result['total_interactions'].sum() == len(session_interactions)
+
+
+def test_temporal_timeout(pipeline_context):
+    df = pd.DataFrame({
+        'sessionId': ['s1', 's1'],
+        'ts': ['2025-01-01T10:00:00Z', '2025-01-01T11:00:00Z'],  # 1 hour gap
+    })
+    result = TemporalFeatureStep(pipeline_context, timeout_sec=900).transform(df)
+    assert result.iloc[0]['session_duration_sec'] == 0  # gap exceeds timeout
+
+
+# BehavioralFeatureStep tests
+def test_behavioral_empty(pipeline_context):
+    result = BehavioralFeatureStep(pipeline_context).transform(pd.DataFrame())
+    assert 'sessionId' in result.columns
+
+
+def test_behavioral_counts(pipeline_context, session_interactions):
+    result = BehavioralFeatureStep(pipeline_context).transform(session_interactions)
+    assert 'page_views' in result.columns
+    assert 'item_views' in result.columns
+    assert 'hover_events' in result.columns
+    assert result['total_events'].sum() == len(session_interactions)
+
+
+def test_behavioral_hover_prefix(pipeline_context):
+    df = pd.DataFrame({
+        'sessionId': ['s1', 's1'],
+        'eventName': ['hover_over_custom', 'hover_over_button'],
+        'page': ['/products', '/products'],
+    })
+    result = BehavioralFeatureStep(pipeline_context).transform(df)
+    assert result.iloc[0]['hover_events'] == 2
+
+
+# ProductFeatureStep tests
+def test_product_empty(pipeline_context):
+    result = ProductFeatureStep(pipeline_context).transform(pd.DataFrame())
+    assert 'sessionId' in result.columns
+
+
+def test_product_features(pipeline_context, session_interactions):
+    result = ProductFeatureStep(pipeline_context).transform(session_interactions)
+    assert 'unique_products_viewed' in result.columns
+    assert 'price_range' in result.columns
+    assert result['unique_products_viewed'].sum() > 0
+
+
+# UserAgentFeatureStep tests
+def test_ua_empty(pipeline_context):
+    result = UserAgentFeatureStep(pipeline_context).transform(pd.DataFrame())
+    assert 'sessionId' in result.columns
+
+
+def test_ua_headless_detection(pipeline_context):
+    df = pd.DataFrame({
+        'sessionId': ['s1', 's2'],
+        'userAgent': ['Mozilla/5.0 Chrome/120', 'HeadlessChrome/120'],
+    })
+    result = UserAgentFeatureStep(pipeline_context).transform(df)
+    assert 'is_headless' in result.columns
+    headless = dict(zip(result['sessionId'], result['is_headless']))
+    assert headless['s1'] == False
+    assert headless['s2'] == True
+
+
+def test_ua_browser_family(pipeline_context):
+    df = pd.DataFrame({
+        'sessionId': ['s1', 's2', 's3'],
+        'userAgent': ['Mozilla/5.0 Firefox/120', 'Safari/605.1.15', 'Unknown'],
+    })
+    result = UserAgentFeatureStep(pipeline_context).transform(df)
+    browsers = dict(zip(result['sessionId'], result['browser_family']))
+    assert browsers['s1'] == 'Firefox'
+    assert browsers['s2'] == 'Safari'
+    assert browsers['s3'] == 'Other'
+
+
+def test_ua_automation_detection(pipeline_context):
+    df = pd.DataFrame({
+        'sessionId': ['s1', 's2'],
+        'userAgent': ['Selenium WebDriver', 'Normal Chrome/120'],
+    })
+    result = UserAgentFeatureStep(pipeline_context).transform(df)
+    auto = dict(zip(result['sessionId'], result['is_automation']))
+    assert auto['s1'] == True
+    assert auto['s2'] == False
+
+
+# ExtractSessionFeaturesStep tests
+def test_extract_empty(pipeline_context):
+    result = ExtractSessionFeaturesStep(pipeline_context).transform(pd.DataFrame())
+    assert result.empty
+
+
+def test_extract_merges_all(pipeline_context, session_interactions):
+    result = ExtractSessionFeaturesStep(pipeline_context).transform(session_interactions)
+    expected = ['session_duration_sec', 'total_events', 'unique_products_viewed', 'is_headless']
+    for col in expected:
+        assert col in result.columns
+    assert 'experimentId' in result.columns
+
+
+# JoinLabelsStep tests
+def test_join_labels_tuple_input(pipeline_context):
+    features = pd.DataFrame({'sessionId': ['s1'], 'experimentId': ['exp1'], 'total_events': [5]})
+    experiments = pd.DataFrame({'id': ['exp1'], 'xp_human_only': [True]})
+    result = JoinLabelsStep(pipeline_context).transform((features, experiments))
+    assert 'is_agent' in result.columns
+    assert result.iloc[0]['is_agent'] == False
+
+
+def test_join_labels_empty_experiments(pipeline_context):
+    features = pd.DataFrame({'sessionId': ['s1'], 'experimentId': ['exp1']})
+    result = JoinLabelsStep(pipeline_context).transform((features, pd.DataFrame()))
+    assert pd.isna(result.iloc[0]['is_agent'])
+
+
+# ValidateDataStep tests
+def test_validate_empty(pipeline_context):
+    ValidateDataStep(pipeline_context).transform(pd.DataFrame())
+    report = pipeline_context.get_cached('validation_report')
+    assert report['status'] == 'empty'
+
+
+def test_validate_missing_cols(pipeline_context):
+    df = pd.DataFrame({'sessionId': ['s1'], 'ts': ['2025-01-01']})
+    ValidateDataStep(pipeline_context).transform(df)
+    report = pipeline_context.get_cached('validation_report')
+    assert report['status'] == 'invalid'
+    assert 'eventName' in report['missing_cols']
+
+
+def test_validate_valid(pipeline_context, session_interactions):
+    ValidateDataStep(pipeline_context).transform(session_interactions)
+    report = pipeline_context.get_cached('validation_report')
+    assert report['status'] == 'valid'
+    assert report['sessions'] > 0