def test_model_comparison_give_non_null_performance_with_classification():
# Given
features = pd.DataFrame(
{"numeric_feature": np.random.normal(size=n_samples)})
comparison_dataset = ComparisonDataset(TaskName.CLASSIFICATION, features,
categorical_target,
cross_validation_n_folds)
# When
comparison = ModelComparison(
comparison_dataset).get_models_scores_and_training_time()
# Then
for model_name, performance_and_training_time in comparison.items():
performance = performance_and_training_time[MODEL_SCORE]
assert_that(~np.isnan(performance),
reason=f"Null performance value for model {model_name}")
def test_model_comparison_give_non_null_performance_and_categorical_feature():
# Given
features = pd.DataFrame({
"string_feature":
np.random.choice(["Paris", "London", "Madrid", "Roma"], n_samples),
"numeric_feature":
np.random.normal(size=n_samples)
})
comparison_dataset = ComparisonDataset(TaskName.REGRESSION, features,
categorical_target,
cross_validation_n_folds)
# When
comparison = ModelComparison(
comparison_dataset).get_models_scores_and_training_time()
# Then
for model_name, performance_and_training_time in comparison.items():
performance = performance_and_training_time[MODEL_SCORE]
assert_that(~np.isnan(performance),
reason=f"Null performance value for model {model_name}")
def test_encode_datetime_columns_as_int():
# Given
date_column = "date_column"
other_string_column = "other_string_column"
other_numeric_column = "other_numeric_column"
df = pd.DataFrame({
date_column: ['2017-02-04 18:41:00'],
other_numeric_column: [1],
other_string_column: ["something"]
})
# When
parsed_df = ModelComparison._encode_date_columns_as_int(df, 1)
# Then
parsed_df_dtypes = parsed_df.dtypes
df_dtypes = df.dtypes
assert_that(parsed_df_dtypes[date_column], is_("float"))
assert_that(parsed_df_dtypes[other_string_column],
is_(df_dtypes[other_string_column]))
assert_that(parsed_df_dtypes[other_numeric_column],
is_(df_dtypes[other_numeric_column]))