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chore: syntax cleaning and code quality

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This commit is contained in:
Daniel Rosel
2025-12-12 11:44:11 +01:00
parent f2646b6fa1
commit a4fd4e7aea
2 changed files with 23 additions and 20 deletions
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40
experiments/procesing/steps/session.py
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@@ -29,11 +29,11 @@ class TemporalFeatureStep(BaseContextStep):
self.timeout_sec = timeout_sec
self.velocity_window = velocity_window
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
df = X.copy()
if df.empty or 'ts' not in df.columns:
return pd.DataFrame(columns=['sessionId'])
return pd.DataFrame(columns=pd.Series(['sessionId']))
df = df.copy()
df['ts_dt'] = pd.to_datetime(df['ts'])
df = df.sort_values(['sessionId', 'ts_dt'])
df['time_diff'] = df.groupby('sessionId')['ts_dt'].diff().dt.total_seconds()
@@ -53,19 +53,18 @@ class TemporalFeatureStep(BaseContextStep):
(agg['total_interactions'] / agg['session_duration_sec']) * 60, 0)
vel = df.set_index('ts_dt').groupby('sessionId').resample(self.velocity_window, include_groups=False).size()
agg = agg.merge(vel.groupby('sessionId').max().rename('max_velocity_5min'),
on='sessionId', how='left').fillna({'max_velocity_5min': 0})
agg = agg.merge(vel.groupby('sessionId').max().rename('max_velocity_5min'),on='sessionId', how='left').fillna({'max_velocity_5min': 0}) # warns but its a series so whatevs
return agg
class BehavioralFeatureStep(BaseContextStep):
"""Vectorized event counts and ratios per session."""
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
df = X.copy()
if df.empty or 'eventName' not in df.columns:
return pd.DataFrame(columns=['sessionId'])
return pd.DataFrame(columns=pd.Series(['sessionId']))
df = df.copy()
for cat, events in EVENT_CATS.items():
df[f'is_{cat}'] = df['eventName'].isin(events)
df['is_hover'] = df['is_hover'] | df['eventName'].str.startswith('hover_over_')
@@ -86,11 +85,10 @@ class BehavioralFeatureStep(BaseContextStep):
class ProductFeatureStep(BaseContextStep):
"""Vectorized product interaction features: diversity, depth, price sensitivity."""
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
df = X.copy()
if df.empty:
return pd.DataFrame(columns=['sessionId'])
df = df.copy()
return pd.DataFrame(columns=pd.Series(['sessionId']))
price_col = next((c for c in ['metadata_base_price', 'metadata_price', 'base_price'] if c in df.columns), None)
df['price_seen'] = pd.to_numeric(df[price_col], errors='coerce') if price_col else np.nan
@@ -111,9 +109,10 @@ class ProductFeatureStep(BaseContextStep):
class UserAgentFeatureStep(BaseContextStep):
"""Parse userAgent into bot-detection signals."""
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
def transform(self, X: pd.DataFrame) -> pd.DataFrame|pd.Series:
df = X.copy()
if df.empty or 'userAgent' not in df.columns:
return pd.DataFrame(columns=['sessionId'])
return pd.DataFrame(columns=pd.Series(['sessionId']))
ua = df.groupby('sessionId')['userAgent'].first().reset_index()
ua['is_headless'] = ua['userAgent'].str.contains(HEADLESS_RE, na=False)
@@ -135,9 +134,10 @@ class ExtractSessionFeaturesStep(BaseContextStep):
Output: session-level feature matrix
"""
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
if df.empty:
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
if X.empty:
return pd.DataFrame()
df = X.copy()
# run all feature steps and merge on sessionId
temporal = TemporalFeatureStep(self.context).transform(df)
@@ -165,7 +165,8 @@ class JoinLabelsStep(BaseContextStep):
Output: labeled feature matrix with is_agent column
"""
def transform(self, data) -> pd.DataFrame:
def transform(self, X : tuple) -> pd.DataFrame:
data = X;
if isinstance(data, tuple):
features_df, experiments_df = data
else:
@@ -199,7 +200,8 @@ class ValidateDataStep(BaseContextStep):
"""
REQUIRED = ['sessionId', 'eventName', 'ts']
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
def transform(self, X: pd.DataFrame) -> pd.DataFrame:
df = X.copy()
report = {'status': 'valid', 'rows': len(df), 'sessions': 0}
if df.empty:
report['status'] = 'empty'
@@ -235,7 +237,7 @@ def _extract_features_for_session(session_df: pd.DataFrame, session_timeout_sec:
session_df['sessionId'] = 'tmp'
# use a dummy context for the steps
class DummyCtx: config = {}
class DummyCtx: config = {} # should maybe inherit but whatever
ctx = DummyCtx()
t = TemporalFeatureStep(ctx, timeout_sec=session_timeout_sec).transform(session_df)