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Add e2e tests for historical feature retrieval
Signed-off-by: Khor Shu Heng <khor.heng@gojek.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| import os | ||
| import tempfile | ||
| import uuid | ||
| from datetime import datetime, timedelta | ||
| from urllib.parse import urlparse | ||
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| import numpy as np | ||
| import pandas as pd | ||
| import pytest | ||
| from google.protobuf.duration_pb2 import Duration | ||
| from pandas._testing import assert_frame_equal | ||
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| from feast import Client, Entity, Feature, FeatureTable, FileSource, ValueType | ||
| from feast.data_format import ParquetFormat | ||
| from feast.staging.storage_client import get_staging_client | ||
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| @pytest.fixture(scope="function") | ||
| def staging_path(pytestconfig, tmp_path): | ||
| if pytestconfig.getoption("env") == "local": | ||
| return f"file://{tmp_path}" | ||
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| staging_path = pytestconfig.getoption("staging_path") | ||
| return os.path.join(staging_path, str(uuid.uuid4())) | ||
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| def test_historical_features(feast_client: Client, staging_path: str): | ||
| customer_entity = Entity( | ||
| name="customer_id", description="Customer", value_type=ValueType.INT64 | ||
| ) | ||
| feast_client.apply_entity(customer_entity) | ||
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| max_age = Duration() | ||
| max_age.FromSeconds(2 * 86400) | ||
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| transactions_feature_table = FeatureTable( | ||
| name="transactions", | ||
| entities=["customer_id"], | ||
| features=[ | ||
| Feature("daily_transactions", ValueType.DOUBLE), | ||
| Feature("total_transactions", ValueType.DOUBLE), | ||
| ], | ||
| batch_source=FileSource( | ||
| "event_timestamp", | ||
| "created_timestamp", | ||
| ParquetFormat(), | ||
| os.path.join(staging_path, "transactions"), | ||
| ), | ||
| max_age=max_age, | ||
| ) | ||
|
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| feast_client.apply_feature_table(transactions_feature_table) | ||
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| retrieval_date = ( | ||
| datetime.utcnow() | ||
| .replace(hour=0, minute=0, second=0, microsecond=0) | ||
| .replace(tzinfo=None) | ||
| ) | ||
| retrieval_outside_max_age_date = retrieval_date + timedelta(1) | ||
| event_date = retrieval_date - timedelta(2) | ||
| creation_date = retrieval_date - timedelta(1) | ||
|
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||
| customers = [1001, 1002, 1003, 1004, 1005] | ||
| daily_transactions = [np.random.rand(seed=0) * 10 for _ in customers] | ||
| total_transactions = [np.random.rand(seed=0) * 100 for _ in customers] | ||
|
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| transactions_df = pd.DataFrame( | ||
| { | ||
| "event_timestamp": [event_date for _ in customers], | ||
| "created_timestamp": [creation_date for _ in customers], | ||
| "customer_id": customers, | ||
| "daily_transactions": daily_transactions, | ||
| "total_transactions": total_transactions, | ||
| } | ||
| ) | ||
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| feast_client.ingest(transactions_feature_table, transactions_df) | ||
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| feature_refs = ["transactions:daily_transactions"] | ||
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| customer_df = pd.DataFrame( | ||
| { | ||
| "event_timestamp": [retrieval_date for _ in customers] | ||
| + [retrieval_outside_max_age_date for _ in customers], | ||
| "customer_id": customers + customers, | ||
| } | ||
| ) | ||
|
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||
| with tempfile.TemporaryDirectory() as tempdir: | ||
| df_export_path = os.path.join(tempdir, "customers.parquets") | ||
| customer_df.to_parquet(df_export_path) | ||
| scheme, _, remote_path, _, _, _ = urlparse(staging_path) | ||
| staging_client = get_staging_client(scheme) | ||
| staging_client.upload_file(df_export_path, None, remote_path) | ||
| customer_source = FileSource( | ||
| "event_timestamp", | ||
| "event_timestamp", | ||
| ParquetFormat(), | ||
| os.path.join(staging_path, os.path.basename(df_export_path)), | ||
| ) | ||
|
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| job = feast_client.get_historical_features(feature_refs, customer_source) | ||
| output_dir = job.get_output_file_uri() | ||
|
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| _, _, joined_df_destination_path, _, _, _ = urlparse(output_dir) | ||
| joined_df = pd.read_parquet(joined_df_destination_path) | ||
|
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| expected_joined_df = pd.DataFrame( | ||
| { | ||
| "event_timestamp": [retrieval_date for _ in customers] | ||
| + [retrieval_outside_max_age_date for _ in customers], | ||
| "customer_id": customers + customers, | ||
| "transactions__daily_transactions": daily_transactions | ||
| + [None] * len(customers), | ||
| } | ||
| ) | ||
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| assert_frame_equal( | ||
| joined_df.sort_values(by=["customer_id", "event_timestamp"]).reset_index( | ||
| drop=True | ||
| ), | ||
| expected_joined_df.sort_values( | ||
| by=["customer_id", "event_timestamp"] | ||
| ).reset_index(drop=True), | ||
| ) |
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