def test_plot_records_based_equiv_average_chisquare(
df_treatment, df_pool, col_name, equivalence_feature_ids,
equivalence_feature_matrix):
df_treatment["col2"] = df_treatment[col_name] * 2
df_treatment["col3"] = df_treatment[col_name] * 3
df_pool["col2"] = df_pool[col_name] * 2
df_pool["col3"] = df_pool[col_name] * 3
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
stratified_sampling_obj.add_column("col3")
bin_selection = StratifiedSamplingBinSelector(
stratified_sampling_obj,
df_treatment,
df_pool,
min_n_bins=2,
max_n_bins=3,
random_seed=1,
equivalence_method='chisquare',
equivalence_feature_ids=equivalence_feature_ids,
equivalence_feature_matrix=equivalence_feature_matrix)
bin_selection.plot_records_based_equiv_average(plot=False)
results = bin_selection.results_as_json()
assert 'bins_selected_str' in list(results['n_bin_results'][0].keys())
def test_stratified_sampling_fit_and_sample_records_equivalence_idempotent_check(
df_treatment, df_pool, col_name, equivalence_feature_ids,
equivalence_feature_matrix):
df_treatment["col2"] = df_treatment[col_name] * 2
df_treatment["col3"] = df_treatment[col_name] * 3
df_pool["col2"] = df_pool[col_name] * 2
df_pool["col3"] = df_pool[col_name] * 3
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
stratified_sampling_obj.add_column("col3")
StratifiedSamplingBinSelector(
stratified_sampling_obj,
df_treatment,
df_pool,
min_n_bins=2,
max_n_bins=3,
random_seed=1,
equivalence_method='chisquare',
equivalence_feature_ids=equivalence_feature_ids,
equivalence_feature_matrix=equivalence_feature_matrix)
sample1 = stratified_sampling_obj.data_sample.df.index.values
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
stratified_sampling_obj.add_column("col3")
StratifiedSamplingBinSelector(
stratified_sampling_obj,
df_treatment,
df_pool,
min_n_bins=2,
max_n_bins=3,
random_seed=1,
equivalence_method='chisquare',
equivalence_feature_ids=equivalence_feature_ids,
equivalence_feature_matrix=equivalence_feature_matrix)
sample2 = stratified_sampling_obj.data_sample.df.index.values
assert set(sample1) == set(sample2)
def test_plot_records_based_equiv_average(df_treatment, df_pool, col_name,
equivalence_feature_ids,
equivalence_feature_matrix):
df_treatment["col2"] = df_treatment[col_name] * 2
df_treatment["col3"] = df_treatment[col_name] * 3
df_pool["col2"] = df_pool[col_name] * 2
df_pool["col3"] = df_pool[col_name] * 3
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
stratified_sampling_obj.add_column("col3")
bin_selection = StratifiedSamplingBinSelector(
stratified_sampling_obj,
df_treatment,
df_pool,
min_n_bins=2,
max_n_bins=3,
random_seed=1,
equivalence_method='euclidean',
equivalence_feature_ids=equivalence_feature_ids,
equivalence_feature_matrix=equivalence_feature_matrix)
bin_selection.plot_records_based_equiv_average(plot=False)
bin_selection.results_as_json()
def test_stratified_sampling_fit_and_sample_records_equivalence_too_many_bins(
df_treatment, df_pool, col_name, equivalence_feature_ids,
equivalence_feature_matrix):
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
## attempting to estimate both n_bins and n_samples
with pytest.raises(ModelSamplingException):
model_w_selected_bins = StratifiedSamplingBinSelector(
stratified_sampling_obj,
df_treatment,
df_pool,
min_n_bins=1000,
max_n_bins=1002,
random_seed=1,
equivalence_method='chisquare',
relax_n_samples_approx_constraint=False,
equivalence_feature_ids=equivalence_feature_ids,
equivalence_feature_matrix=equivalence_feature_matrix)
def test_stratified_sampling_fit_and_sample_records_equivalence(
df_treatment, df_pool, col_name, equivalence_feature_ids,
equivalence_feature_matrix):
stratified_sampling_obj = StratifiedSampling()
df_pool["col2"] = df_pool[col_name]
df_treatment["col2"] = df_treatment[col_name]
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
## attempting to estimate both n_bins and n_samples
StratifiedSamplingBinSelector(
stratified_sampling_obj,
df_treatment,
df_pool,
min_n_bins=4,
max_n_bins=6,
random_seed=1,
equivalence_method='chisquare',
equivalence_feature_ids=equivalence_feature_ids,
equivalence_feature_matrix=equivalence_feature_matrix)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()