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6 catalog data and mode mappers (#25)
* supabase product proxy and rendering * minor pipeline refactor * refactoring and demand estimation * trackion of date index searching * fixing changes of imports * data seeding * chore: airline basic refactor * feat: huge push of product changes and item review with cart * refactored design * chore: moving route elsewhere and align * fix: build of web/ * chore: fixing paper build * fixing chars
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Daniel Alves Rösel
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GitHub
GitHub
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894ce87a5d
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8b76d24ade
@@ -15,106 +15,98 @@ N_PRICE_BUCKETS = 5
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supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
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def get_data_from_kafka() -> pd.DataFrame:
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"""fetch all events from backend dump endpoint"""
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resp = requests.get(f"{BACKEND_URL}/api/kafka/dump")
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resp.raise_for_status()
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data = resp.json()
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if not data.get('success') or not data.get('data'):
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return pd.DataFrame()
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df = pd.DataFrame(data['data'])
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# explode metadata col json
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if 'metadata' in df.columns:
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df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
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df = df.dropna(subset=['eventName'])
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return df
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def join_with_experiments(df: pd.DataFrame) -> pd.DataFrame:
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if df.empty or 'experimentId' not in df.columns:
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return df
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unique_exp_ids = df['experimentId'].dropna().unique()
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if len(unique_exp_ids) == 0:
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return df
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resp = supabase.table('experiments').select(
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'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, task:tasks(task_name, task_description, task_def_of_done)'
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).in_('id', unique_exp_ids.tolist()).execute()
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if not resp.data:
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return df
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exp_df = pd.DataFrame(resp.data)
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# flatten task nested object if present
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if 'task' in exp_df.columns and exp_df['task'].notnull().any():
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task_normalized = pd.json_normalize(exp_df['task'].dropna())
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task_normalized.index = exp_df[exp_df['task'].notnull()].index
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exp_df = exp_df.drop(columns=['task']).join(task_normalized, rsuffix='_task')
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# rename experiment columns for clarity
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exp_df = exp_df.rename(columns={
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'id': 'experimentId',
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'subject_name': 'exp_subject',
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'xp_human_only': 'exp_human_only',
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'xp_market_mode': 'exp_market_mode',
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'xp_task_id': 'exp_task_id'
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})
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df = df.merge(exp_df, on='experimentId', how='left')
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return df
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def augment_event_titles(df: pd.DataFrame) -> pd.DataFrame:
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# from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
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# we want metadata schema to create product specific event names
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# only create price buckets if we have enough unique prices
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if df["metadata_price"].notnull().sum() > 0:
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try:
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price_buckets = pd.qcut(
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df["metadata_price"],
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q=N_PRICE_BUCKETS,
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labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
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duplicates='drop' # handle duplicate bin edges
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)
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except ValueError:
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# fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
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price_buckets = df["metadata_price"].apply(lambda x: f"P_{int(x)}" if pd.notnull(x) else "")
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else:
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price_buckets = pd.Series([""] * len(df), index=df.index)
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# metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
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# TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
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df["metadata_schema"] = np.where(
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df["productId"].notnull() & df["metadata_price"].notnull(),
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"_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
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""
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)
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df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
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return df
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def extract() -> pd.DataFrame:
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df = get_data_from_kafka()
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df = join_with_experiments(df)
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df = augment_event_titles(df)
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return df
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class DataExtractor(BaseEstimator, TransformerMixin):
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class KafkaDataFetcher(BaseEstimator, TransformerMixin):
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def fit(self, X=None, y=None):
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return self
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def transform(self, X=None):
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return extract()
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resp = requests.get(f"{BACKEND_URL}/api/kafka/dump")
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resp.raise_for_status()
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data = resp.json()
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if not data.get('success') or not data.get('data'):
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return pd.DataFrame()
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df = pd.DataFrame(data['data'])
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# explode metadata col json
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if 'metadata' in df.columns:
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df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
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df = df.dropna(subset=['eventName'])
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# remape dateIndex
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df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
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return df
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if __name__ == "__main__":
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df = extract()
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print(df.head())
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print(df.tail())
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print(df.info())
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class ExperimentJoiner(BaseEstimator, TransformerMixin):
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def fit(self, X=None, y=None):
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return self
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def transform(self, df):
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if df.empty or 'experimentId' not in df.columns:
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return df
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unique_exp_ids = df['experimentId'].dropna().unique()
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if len(unique_exp_ids) == 0:
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return df
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resp = supabase.table('experiments').select(
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'id, subject_name, xp_human_only, xp_market_mode, xp_task_id, task:tasks(task_name, task_description, task_def_of_done)'
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).in_('id', unique_exp_ids.tolist()).execute()
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if not resp.data:
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return df
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exp_df = pd.DataFrame(resp.data)
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# flatten task nested object if present
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if 'task' in exp_df.columns and exp_df['task'].notnull().any():
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task_normalized = pd.json_normalize(exp_df['task'].dropna())
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task_normalized.index = exp_df[exp_df['task'].notnull()].index
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exp_df = exp_df.drop(columns=['task']).join(task_normalized, rsuffix='_task')
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# rename experiment columns for clarity
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exp_df = exp_df.rename(columns={
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'id': 'experimentId',
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'subject_name': 'exp_subject',
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'xp_human_only': 'exp_human_only',
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'xp_market_mode': 'exp_market_mode',
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'xp_task_id': 'exp_task_id'
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})
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df = df.merge(exp_df, on='experimentId', how='left')
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return df
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class EventTitleAugmenter(BaseEstimator, TransformerMixin):
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def fit(self, X=None, y=None):
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return self
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def transform(self, df):
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# from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
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# we want metadata schema to create product specific event names
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# only create price buckets if we have enough unique prices
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if df["metadata_price"].notnull().sum() > 0:
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try:
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price_buckets = pd.qcut(
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df["metadata_price"],
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q=N_PRICE_BUCKETS,
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labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
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duplicates='drop' # handle duplicate bin edges
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)
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except ValueError:
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# fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
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price_buckets = df["metadata_price"].apply(lambda x: f"P_{int(x)}" if pd.notnull(x) else "")
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else:
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price_buckets = pd.Series([""] * len(df), index=df.index)
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# metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
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# TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
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df["metadata_schema"] = np.where(
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df["productId"].notnull() & df["metadata_price"].notnull(),
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"_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
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""
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)
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df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
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return df
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