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test_features_enricher.py
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import os
from datetime import date
from typing import List, Optional
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from requests_mock.mocker import Mocker
from upgini.dataset import Dataset
from upgini.errors import ValidationError
from upgini.features_enricher import FeaturesEnricher, hash_input
from upgini.http import _RestClient
from upgini.metadata import (
CVType,
FeaturesMetadataV2,
HitRateMetrics,
ModelEvalSet,
ModelTaskType,
ProviderTaskMetadataV2,
RuntimeParameters,
SearchKey,
)
from upgini.normalizer.normalize_utils import Normalizer
from upgini.resource_bundle import bundle
from upgini.search_task import SearchTask
from upgini.utils.datetime_utils import DateTimeSearchKeyConverter
from .utils import (
mock_default_requests,
mock_get_metadata,
mock_get_task_metadata_v2,
mock_initial_and_validation_summary,
mock_initial_progress,
mock_initial_search,
mock_initial_summary,
mock_raw_features,
mock_validation_progress,
mock_validation_raw_features,
mock_validation_search,
mock_validation_summary,
)
segment_header = bundle.get("quality_metrics_segment_header")
train_segment = bundle.get("quality_metrics_train_segment")
eval_1_segment = bundle.get("quality_metrics_eval_segment").format(1)
eval_2_segment = bundle.get("quality_metrics_eval_segment").format(2)
match_rate_header = bundle.get("quality_metrics_match_rate_header")
rows_header = bundle.get("quality_metrics_rows_header")
target_mean_header = bundle.get("quality_metrics_mean_target_header")
baseline_rocauc = bundle.get("quality_metrics_baseline_header").format("roc_auc")
enriched_rocauc = bundle.get("quality_metrics_enriched_header").format("roc_auc")
baseline_gini = bundle.get("quality_metrics_baseline_header").format("GINI")
enriched_gini = bundle.get("quality_metrics_enriched_header").format("GINI")
uplift = bundle.get("quality_metrics_uplift_header")
feature_name_header = bundle.get("features_info_name")
shap_value_header = bundle.get("features_info_shap")
hitrate_header = bundle.get("features_info_hitrate")
SearchTask.PROTECT_FROM_RATE_LIMIT = False
SearchTask.POLLING_DELAY_SECONDS = 0
pd.set_option("mode.chained_assignment", "raise")
pd.set_option("display.max_columns", 1000)
def test_search_keys_validation(requests_mock: Mocker):
url = "http://fake_url2"
mock_default_requests(requests_mock, url)
error_message = bundle.get("date_and_datetime_simultanious")
with pytest.raises(Exception, match=error_message):
FeaturesEnricher(
search_keys={"d1": SearchKey.DATE, "dt2": SearchKey.DATETIME},
endpoint=url,
logs_enabled=False,
)
error_message = bundle.get("postal_code_without_country")
with pytest.raises(Exception, match=error_message):
FeaturesEnricher(search_keys={"postal_code": SearchKey.POSTAL_CODE}, endpoint=url, logs_enabled=False)
def test_features_enricher(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_initial_progress(requests_mock, url, search_task_id)
mock_validation_progress(requests_mock, url, validation_search_task_id)
ads_search_task_id = mock_initial_and_validation_summary(
requests_mock,
url,
search_task_id,
validation_search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"]
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
eval1_target = eval1_df["target"].reset_index(drop=True)
eval2_df = df[11000:12000]
eval2_features = eval2_df.drop(columns="target")
eval2_target = eval2_df["target"]
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
calculate_metrics=False,
keep_input=True,
)
assert enriched_train_features.shape == (10000, 5)
metrics = enricher.calculate_metrics()
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment, eval_1_segment, eval_2_segment],
rows_header: [10000, 1000, 1000],
target_mean_header: [0.5044, 0.487, 0.486],
baseline_gini: ["-0.001 ± 0.019", "0.002 ± 0.006", "0.006 ± 0.013"],
enriched_gini: ["0.007 ± 0.037", "-0.044 ± 0.044", "-0.038 ± 0.013"],
uplift: [0.00855379495023283, -0.046191606776180685, -0.044711053465917375],
}
)
print("Expected metrics: ")
print(expected_metrics)
print("Actual metrics: ")
print(metrics)
assert metrics is not None
assert_frame_equal(expected_metrics, metrics, atol=1e-6)
print(enricher.features_info)
assert enricher.feature_names_ == ["feature"]
assert enricher.feature_importances_ == [0.0078]
assert len(enricher.features_info) == 1
first_feature_info = enricher.features_info.iloc[0]
assert first_feature_info[feature_name_header] == "feature"
assert first_feature_info[shap_value_header] == 0.0078
def test_eval_set_with_diff_order_of_columns(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
mock_initial_progress(requests_mock, url, search_task_id)
ads_search_task_id = mock_initial_summary(requests_mock, url, search_task_id)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns="SystemRecordId_473310000", inplace=True)
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"]
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
# shuffle columns
eval1_features = eval1_features[list(eval1_features.columns)]
eval1_target = eval1_df["target"].reset_index(drop=True)
eval2_df = df[11000:12000]
eval2_features = eval2_df.drop(columns="target")
# Add feature that doesn't exist in train df
eval2_features["new_feature"] = "test"
eval2_target = eval2_df["target"]
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
with pytest.raises(ValidationError, match=bundle.get("eval_x_and_x_diff_shape")):
enricher.fit(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
calculate_metrics=False,
keep_input=True,
)
enricher.fit(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target)],
calculate_metrics=False,
keep_input=True,
)
def test_features_enricher_with_index_and_column_same_names(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_initial_progress(requests_mock, url, search_task_id)
mock_validation_progress(requests_mock, url, validation_search_task_id)
ads_search_task_id = mock_initial_and_validation_summary(
requests_mock,
url,
search_task_id,
validation_search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
df = df.drop_duplicates(subset="rep_date")
df = df.set_index("rep_date")
df["rep_date"] = df.index
train_features = df.drop(columns="target")
train_target = df["target"]
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
min_rows_count = Dataset.MIN_ROWS_COUNT
Dataset.MIN_ROWS_COUNT = 5
try:
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
calculate_metrics=False,
keep_input=True,
)
assert enriched_train_features.shape == (6, 5)
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
calculate_metrics=False,
keep_input=False,
)
assert enriched_train_features.shape == (6, 3)
finally:
Dataset.MIN_ROWS_COUNT = min_rows_count
def test_saved_features_enricher(requests_mock: Mocker):
url = "http://fake_url2"
if pd.__version__ >= "2.2.0":
print("Use features for pandas 2.2")
features_file = "validation_features_v3_with_entity_system_record_id.parquet"
elif pd.__version__ >= "2.0.0":
print("Use features for pandas 2.0")
features_file = "validation_features_v2_with_entity_system_record_id.parquet"
else:
print("Use features for pandas 1.*")
features_file = "validation_features_v1_with_entity_system_record_id.parquet"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)), f"test_data/binary/{features_file}"
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_initial_progress(requests_mock, url, search_task_id)
mock_validation_progress(requests_mock, url, validation_search_task_id)
ads_search_task_id = mock_initial_and_validation_summary(
requests_mock,
url,
search_task_id,
validation_search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="numeric", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features, metrics_calculation=True)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
mock_validation_raw_features(
requests_mock, url, validation_search_task_id, path_to_mock_features, metrics_calculation=True
)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"].copy()
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
eval1_target = eval1_df["target"].reset_index(drop=True).copy()
eval2_df = df[11000:12000]
eval2_features = eval2_df.drop(columns="target")
eval2_target = eval2_df["target"].copy()
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
search_id=search_task_id,
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
enriched_train_features = enricher.transform(
train_features,
)
print(enriched_train_features)
assert enriched_train_features.shape == (10000, 5)
metrics = enricher.calculate_metrics(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
)
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment, eval_1_segment, eval_2_segment],
rows_header: [10000, 1000, 1000],
target_mean_header: [0.5044, 0.487, 0.486],
baseline_gini: ["0.007 ± 0.016", "0.004 ± 0.006", "0.011 ± 0.017"],
enriched_gini: ["0.010 ± 0.019", "-0.010 ± 0.021", "-0.017 ± 0.023"],
uplift: [0.003129003609412328, -0.014280053316041673, -0.027921890762357758],
}
)
print("Expected metrics: ")
print(expected_metrics)
print("Actual metrics: ")
print(metrics)
assert metrics is not None
assert_frame_equal(expected_metrics, metrics, atol=1e-6)
print(enricher.features_info)
assert enricher.feature_names_ == ["feature"]
assert enricher.feature_importances_ == [0.0052]
assert len(enricher.features_info) == 1
first_feature_info = enricher.features_info.iloc[0]
assert first_feature_info[feature_name_header] == "feature"
assert first_feature_info[shap_value_header] == 0.0052
# Check imbalanced target metrics
random = np.random.RandomState(42)
train_random_indices = random.choice(train_target.index, size=9500, replace=False)
train_target.loc[train_random_indices] = 0
metrics = enricher.calculate_metrics(train_features, train_target)
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment],
rows_header: [10000],
target_mean_header: [0.049],
baseline_gini: ["-0.014 ± 0.045"],
enriched_gini: ["0.005 ± 0.059"],
uplift: [0.018997790411857896],
}
)
print("Expected metrics: ")
print(expected_metrics)
print("Actual metrics: ")
print(metrics)
assert metrics is not None
assert_frame_equal(expected_metrics, metrics, atol=1e-6)
def test_features_enricher_with_demo_key(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
mock_initial_progress(requests_mock, url, search_task_id)
ads_search_task_id = mock_initial_summary(
requests_mock,
url,
search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_validation_summary(
requests_mock,
url,
search_task_id,
ads_search_task_id,
validation_search_task_id,
)
mock_validation_progress(requests_mock, url, validation_search_task_id)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"].to_frame()
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
eval1_target = eval1_df["target"].to_frame().reset_index(drop=True)
eval2_df = df[11000:12000]
eval2_features = eval2_df.drop(columns="target")
eval2_target = eval2_df["target"].to_frame()
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
keep_input=True,
calculate_metrics=False,
)
assert enriched_train_features.shape == (10000, 5)
metrics = enricher.calculate_metrics()
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment, eval_1_segment, eval_2_segment],
rows_header: [10000, 1000, 1000],
target_mean_header: [0.5044, 0.487, 0.486],
baseline_gini: ["0.020 ± 0.024", "0.018 ± 0.028", "0.027 ± 0.031"],
enriched_gini: ["0.008 ± 0.023", "0.018 ± 0.038", "-0.005 ± 0.032"],
uplift: [-0.011899, 0.000176, -0.031963],
}
)
print("Expected metrics: ")
print(expected_metrics)
print("Actual metrics: ")
print(metrics)
assert metrics is not None
assert_frame_equal(expected_metrics, metrics, atol=1e-6)
print(enricher.features_info)
assert enricher.feature_names_ == ["feature"]
assert enricher.feature_importances_ == [0.0085]
assert len(enricher.features_info) == 1
first_feature_info = enricher.features_info.iloc[0]
assert first_feature_info[feature_name_header] == "feature"
assert first_feature_info[shap_value_header] == 0.0085
def test_features_enricher_with_diff_size_xy(requests_mock: Mocker):
url = "http://fake_url2"
mock_default_requests(requests_mock, url)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"].to_frame()
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
eval1_target = eval1_df["target"].to_frame()
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
with pytest.raises(ValidationError, match=bundle.get("x_and_y_diff_size").format(1000, 500)):
enricher.fit(train_features.head(1000), train_target.head(500))
with pytest.raises(ValidationError, match=bundle.get("x_and_y_diff_size_eval_set").format(1000, 500)):
enricher.fit(train_features, train_target, [(eval1_features, eval1_target.head(500))])
def test_features_enricher_with_numpy(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
mock_initial_progress(requests_mock, url, search_task_id)
ads_search_task_id = mock_initial_summary(
requests_mock,
url,
search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_validation_progress(requests_mock, url, validation_search_task_id)
mock_validation_summary(
requests_mock,
url,
search_task_id,
ads_search_task_id,
validation_search_task_id,
)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
train_df = df.head(10000).reset_index(drop=True)
train_features = train_df.drop(columns="target").values
train_target = train_df["target"].values
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target").values
eval1_target = eval1_df["target"].values
eval2_df = df[11000:12000].reset_index(drop=True)
eval2_features = eval2_df.drop(columns="target").values
eval2_target = eval2_df["target"].values
enricher = FeaturesEnricher(
search_keys={0: SearchKey.PHONE, 1: SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
calculate_metrics=False,
keep_input=True,
)
assert enriched_train_features.shape == (10000, 5)
metrics = enricher.calculate_metrics()
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment, eval_1_segment, eval_2_segment],
rows_header: [10000, 1000, 1000],
target_mean_header: [0.5044, 0.487, 0.486],
baseline_gini: ["-0.001 ± 0.019", "0.002 ± 0.006", "0.006 ± 0.013"],
enriched_gini: ["0.007 ± 0.037", "-0.044 ± 0.044", "-0.038 ± 0.013"],
uplift: [0.00855379495023283, -0.046191606776180685, -0.044711053465917375],
}
)
print("Expected metrics: ")
print(expected_metrics)
print("Actual metrics: ")
print(metrics)
assert metrics is not None
assert_frame_equal(expected_metrics, metrics, atol=1e-6)
print(enricher.features_info)
assert enricher.feature_names_ == ["feature"]
assert enricher.feature_importances_ == [0.0078]
assert len(enricher.features_info) == 1
first_feature_info = enricher.features_info.iloc[0]
assert first_feature_info[feature_name_header] == "feature"
assert first_feature_info[shap_value_header] == 0.0078
enricher.transform(train_features)
def test_features_enricher_with_named_index(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
mock_initial_progress(requests_mock, url, search_task_id)
ads_search_task_id = mock_initial_summary(
requests_mock,
url,
search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_validation_progress(requests_mock, url, validation_search_task_id)
mock_validation_summary(
requests_mock,
url,
search_task_id,
ads_search_task_id,
validation_search_task_id,
)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
df.index.name = "custom_index_name"
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"].to_list()
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
eval1_target = eval1_df["target"].reset_index(drop=True).to_list()
eval2_df = df[11000:12000]
eval2_features = eval2_df.drop(columns="target")
eval2_target = eval2_df["target"].to_list()
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
calculate_metrics=False,
keep_input=True,
)
assert enriched_train_features.shape == (10000, 5)
assert enriched_train_features.index.name == "custom_index_name"
metrics = enricher.calculate_metrics()
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment, eval_1_segment, eval_2_segment],
rows_header: [10000, 1000, 1000],
target_mean_header: [0.5044, 0.487, 0.486],
baseline_gini: ["-0.001 ± 0.019", "0.002 ± 0.006", "0.006 ± 0.013"],
enriched_gini: ["0.007 ± 0.037", "-0.044 ± 0.044", "-0.038 ± 0.013"],
uplift: [0.00855379495023283, -0.046191606776180685, -0.044711053465917375],
}
)
print("Expected metrics: ")
print(expected_metrics)
print("Actual metrics: ")
print(metrics)
assert metrics is not None
assert_frame_equal(expected_metrics, metrics, atol=1e-6)
print(enricher.features_info)
assert enricher.feature_names_ == ["feature"]
assert enricher.feature_importances_ == [0.0078]
assert len(enricher.features_info) == 1
first_feature_info = enricher.features_info.iloc[0]
assert first_feature_info[feature_name_header] == "feature"
assert first_feature_info[shap_value_header] == 0.0078
def test_features_enricher_with_index_column(requests_mock: Mocker):
url = "http://fake_url2"
path_to_mock_features = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/binary/mock_features_with_entity_system_record_id.parquet",
)
mock_default_requests(requests_mock, url)
search_task_id = mock_initial_search(requests_mock, url)
mock_initial_progress(requests_mock, url, search_task_id)
ads_search_task_id = mock_initial_summary(
requests_mock,
url,
search_task_id,
)
mock_get_metadata(requests_mock, url, search_task_id)
mock_get_task_metadata_v2(
requests_mock,
url,
ads_search_task_id,
ProviderTaskMetadataV2(
features=[FeaturesMetadataV2(name="feature", type="NUMERIC", source="ads", hit_rate=99.0, shap_value=10.1)],
hit_rate_metrics=HitRateMetrics(
etalon_row_count=10000, max_hit_count=9990, hit_rate=0.999, hit_rate_percent=99.9
),
eval_set_metrics=[
ModelEvalSet(
eval_set_index=1,
hit_rate=1.0,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=1000, hit_rate=1.0, hit_rate_percent=100.0
),
),
ModelEvalSet(
eval_set_index=2,
hit_rate=0.99,
hit_rate_metrics=HitRateMetrics(
etalon_row_count=1000, max_hit_count=990, hit_rate=0.99, hit_rate_percent=99.0
),
),
],
),
)
mock_raw_features(requests_mock, url, search_task_id, path_to_mock_features)
validation_search_task_id = mock_validation_search(requests_mock, url, search_task_id)
mock_validation_progress(requests_mock, url, validation_search_task_id)
mock_validation_summary(
requests_mock,
url,
search_task_id,
ads_search_task_id,
validation_search_task_id,
)
mock_validation_raw_features(requests_mock, url, validation_search_task_id, path_to_mock_features)
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/binary/data.csv")
df = pd.read_csv(path, sep=",")
df.drop(columns=["SystemRecordId_473310000", "client_feature"], inplace=True)
df = df.reset_index()
train_df = df.head(10000)
train_features = train_df.drop(columns="target")
train_target = train_df["target"].to_list()
eval1_df = df[10000:11000].reset_index(drop=True)
eval1_features = eval1_df.drop(columns="target")
eval1_target = eval1_df["target"].reset_index(drop=True).to_list()
eval2_df = df[11000:12000]
eval2_features = eval2_df.drop(columns="target")
eval2_target = eval2_df["target"].to_list()
enricher = FeaturesEnricher(
search_keys={"phone_num": SearchKey.PHONE, "rep_date": SearchKey.DATE},
endpoint=url,
api_key="fake_api_key",
date_format="%Y-%m-%d",
cv=CVType.time_series,
logs_enabled=False,
)
enriched_train_features = enricher.fit_transform(
train_features,
train_target,
eval_set=[(eval1_features, eval1_target), (eval2_features, eval2_target)],
calculate_metrics=False,
keep_input=True,
)
assert enriched_train_features.shape == (10000, 5)
assert "index" not in enriched_train_features.columns
metrics = enricher.calculate_metrics()
expected_metrics = pd.DataFrame(
{
segment_header: [train_segment, eval_1_segment, eval_2_segment],
rows_header: [10000, 1000, 1000],
target_mean_header: [0.5044, 0.487, 0.486],
baseline_gini: ["-0.001 ± 0.019", "0.002 ± 0.006", "0.006 ± 0.013"],
enriched_gini: ["0.007 ± 0.037", "-0.044 ± 0.044", "-0.038 ± 0.013"],