def test_stratified_sampling_fit_and_sample_n_samples_approx_limit(
df_treatment, df_pool, col_name):
stratified_sampling_obj = StratifiedSampling()
col_name = "col1"
df_treatment = pd.DataFrame([{
"id": f"id_{x}",
col_name: x
} for x in (list(np.arange(0, 2, 0.1)) + list(np.arange(2, 4, 0.5)) +
list(np.arange(4, 6, 1)) + list(np.arange(6, 10, 0.2)))])
df_pool = pd.DataFrame([{
"id": f"id_{x}",
col_name: x
} for x in np.arange(0, 20, 0.01)])
stratified_sampling_obj.add_column(col_name)
n_samples_approx = 40
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
n_samples_approx=n_samples_approx,
random_seed=1,
min_n_sampled_to_n_treatment_ratio=None,
)
output = stratified_sampling_obj.data_sample.df
assert output["_bin_label"].nunique() == 2
bins_df = stratified_sampling_obj.diagnostics().count_bins()
assert abs(len(output) - n_samples_approx) <= 1
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_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 diagnostics_obj(df_treatment, df_pool, col_name):
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name, n_bins=4)
stratified_sampling_obj.fit_and_sample(
df_treatment, df_pool, n_samples_approx=len(df_treatment), random_seed=1
)
return stratified_sampling_obj.diagnostics()
def test_stratified_sampling_fit_and_sample_random_seed_check():
# perturb was returning different values since it was writing over the
# df rather than using a copy
df_comparison = pd.DataFrame([{
"id":
f"id-{x}",
"baseline_annual_kwh":
np.random.random() * 10000,
"baseline_bd_pct_heating_load":
np.random.random(),
} for x in range(0, 200000)])
df_treatment = pd.DataFrame([{
"id":
f"id-{x}",
"baseline_annual_kwh":
np.random.random() * 10000,
"baseline_bd_pct_heating_load":
np.random.random(),
} for x in range(0, 500)])
n_samples_approx = 500
random_seed = 1
stratification_params = [
"baseline_annual_kwh", "baseline_bd_pct_heating_load"
]
model = StratifiedSampling(treatment_label="treatment",
pool_label="comparison",
output_name="control")
[model.add_column(col) for col in stratification_params]
model.fit(df_treatment, min_n_treatment_per_bin=0)
model.sample(df_comparison,
n_samples_approx=n_samples_approx,
random_seed=random_seed)
for run_num in range(0, 10):
model_temp = StratifiedSampling(treatment_label="treatment",
pool_label="comparison",
output_name="control")
[model_temp.add_column(col) for col in stratification_params]
model_temp.fit(df_treatment, min_n_treatment_per_bin=0)
model_temp.sample(df_comparison,
n_samples_approx=n_samples_approx,
random_seed=random_seed)
pd.testing.assert_frame_equal(
model_temp.data_sample.df[stratification_params + ["id"]],
model.data_sample.df[stratification_params + ["id"]],
)
assert (len(
set(model_temp.data_sample.df["id"].values) -
set(model.data_sample.df["id"].values)) == 0)
def test_stratified_sampling_fit_and_sample():
stratified_sampling_obj = StratifiedSampling()
df_treatment = pd.DataFrame([{
"id": f"id_{x}",
"col1": x
} for x in range(0, 10)])
df_pool = pd.DataFrame([{
"id": f"id_{x}",
"col1": x / 2.0
} for x in range(0, 1000)])
stratified_sampling_obj.add_column("col1")
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
n_samples_approx=10,
random_seed=1,
min_n_sampled_to_n_treatment_ratio=None,
)
sample1 = stratified_sampling_obj.data_sample.df.index.values
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
n_samples_approx=10,
random_seed=1,
min_n_sampled_to_n_treatment_ratio=None,
)
sample2 = stratified_sampling_obj.data_sample.df.index.values
assert set(sample1) == set(sample2)
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
n_samples_approx=10,
random_seed=1,
min_n_sampled_to_n_treatment_ratio=None,
)
sample1 = stratified_sampling_obj.data_sample.df.index.values
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
n_samples_approx=10,
random_seed=5,
min_n_sampled_to_n_treatment_ratio=None,
)
sample2 = stratified_sampling_obj.data_sample.df.index.values
assert set(sample1) != set(sample2)
def test_stratified_sampling_fit_and_sample_too_many_bins(
df_treatment, df_pool, col_name):
df_treatment["col2"] = df_treatment[col_name].astype(int)
df_pool["col2"] = df_pool[col_name].astype(int)
df_treatment["col3"] = df_treatment[col_name].astype(int) * 2
df_pool["col3"] = df_pool[col_name].astype(int) / 2
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
stratified_sampling_obj.add_column("col3")
## attempting to estimate both n_bins and n_samples
with pytest.raises(ValueError):
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
random_seed=1)
def test_stratified_sampling_fit_and_sample_n_samples_approx_limit(
df_treatment, df_pool, col_name):
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
n_samples_approx = 40
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
n_samples_approx=n_samples_approx,
random_seed=1)
output = stratified_sampling_obj.data_sample.df
assert output["_bin_label"].nunique() == 2
bins_df = stratified_sampling_obj.diagnostics().count_bins()
assert (bins_df["n_sampled"] /
bins_df["n_pct_sampled"]).round() == n_samples_approx
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_upper_limit_n_samples_approx(
df_treatment, df_pool, col_name):
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
## attempting to estimate both n_bins and n_samples
with pytest.raises(ModelSamplingException):
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
random_seed=1,
n_samples_approx=1000)
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
random_seed=1,
n_samples_approx=1000,
relax_n_samples_approx_constraint=True,
)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()
assert not output.empty
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()
def test_stratified_sampling_fit_and_sample_n_samples_approx_variations(
df_treatment, df_pool, col_name):
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
## attempting to estimate both n_bins and n_samples
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
random_seed=1)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()
assert len(bins_df) == 3
## enforcing 1 bin
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name, n_bins=1)
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
random_seed=1)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()
## enforcing 4 bins
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name, n_bins=4)
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
random_seed=1)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()
assert len(bins_df) == 4
## enforcing n_samples_approx=40
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.fit_and_sample(
df_treatment,
df_pool,
n_samples_approx=40,
random_seed=1,
min_n_sampled_to_n_treatment_ratio=None,
)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()
# should be within 1 of n_samples_approx
assert abs(len(output) - 40) <= 1
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_stratified_sampling_fit_and_sample_dont_require_equivalence(
df_treatment, df_pool, col_name):
df_treatment["col2"] = df_treatment[col_name].astype(int)
df_pool["col2"] = df_pool[col_name].astype(int)
df_treatment["col3"] = df_treatment[col_name].astype(int) * 2
df_pool["col3"] = df_pool[col_name].astype(int) / 2
stratified_sampling_obj = StratifiedSampling()
stratified_sampling_obj.add_column(col_name)
stratified_sampling_obj.add_column("col2")
stratified_sampling_obj.add_column("col3",
auto_bin_require_equivalence=False)
## attempting to estimate both n_bins and n_samples
stratified_sampling_obj.fit_and_sample(df_treatment,
df_pool,
random_seed=1)
output = stratified_sampling_obj.data_sample.df
bins_df = stratified_sampling_obj.diagnostics().count_bins()
assert not output.empty