def test_non_ones_weight():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
pool2 = Pool(pool.get_features(), pool.get_label(), weight=np.arange(1, pool.num_row()+1))
model = CatBoostClassifier(iterations=2, random_seed=0)
model.fit(pool2)
model.save_model(OUTPUT_MODEL_PATH)
return local_canonical_file(OUTPUT_MODEL_PATH)
def test_fit_data():
pool = Pool(CLOUDNESS_TRAIN_FILE, column_description=CLOUDNESS_CD_FILE)
eval_pool = Pool(CLOUDNESS_TEST_FILE, column_description=CLOUDNESS_CD_FILE)
base_model = CatBoostClassifier(iterations=2,
random_seed=0,
loss_function="MultiClass")
base_model.fit(pool)
baseline = np.array(
base_model.predict(pool, prediction_type='RawFormulaVal'))
eval_baseline = np.array(
base_model.predict(eval_pool, prediction_type='RawFormulaVal'))
eval_pool.set_baseline(eval_baseline)
model = CatBoostClassifier(iterations=2,
random_seed=0,
loss_function="MultiClass")
data = map_cat_features(pool.get_features(),
pool.get_cat_feature_indices())
model.fit(data,
pool.get_label(),
pool.get_cat_feature_indices(),
sample_weight=np.arange(1,
pool.num_row() + 1),
baseline=baseline,
use_best_model=True,
eval_set=eval_pool)
model.save_model(OUTPUT_MODEL_PATH)
return compare_canonical_models(OUTPUT_MODEL_PATH)
def test_non_ones_weight():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
weight = np.arange(1, pool.num_row() + 1)
pool.set_weight(weight)
model = CatBoostClassifier(iterations=2, random_seed=0)
model.fit(pool)
model.save_model(OUTPUT_MODEL_PATH)
return compare_canonical_models(OUTPUT_MODEL_PATH)
def test_zero_baseline():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
baseline = np.zeros(pool.num_row())
pool.set_baseline(baseline)
model = CatBoostClassifier(iterations=2, random_seed=0)
model.fit(pool)
model.save_model(OUTPUT_MODEL_PATH)
return compare_canonical_models(OUTPUT_MODEL_PATH)
def test_non_ones_weight():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
weight = np.arange(1, pool.num_row()+1)
pool.set_weight(weight)
model = CatBoostClassifier(iterations=2, random_seed=0)
model.fit(pool)
model.save_model(OUTPUT_MODEL_PATH)
return compare_canonical_models(OUTPUT_MODEL_PATH)
def test_zero_baseline():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
baseline = np.zeros(pool.num_row())
pool.set_baseline(baseline)
model = CatBoostClassifier(iterations=2, random_seed=0)
model.fit(pool)
model.save_model(OUTPUT_MODEL_PATH)
return compare_canonical_models(OUTPUT_MODEL_PATH)
def test_zero_baseline():
pool = Pool(TRAIN_FILE, column_description=CD_FILE)
baseline = np.zeros((pool.num_row(), 2))
pool = Pool(pool.get_features(), pool.get_label(), baseline=baseline)
model = CatBoostClassifier(iterations=2, random_seed=0)
model.fit(pool)
model.save_model(OUTPUT_MODEL_PATH)
return local_canonical_file(OUTPUT_MODEL_PATH)
def aggregate_results(name,
modes=["single", "ens", "virt"],
algorithms=['sgb-fixed', 'sglb-fixed'],
num_models=10):
results = [] # metric values for all algorithms and all folds
for mode in modes:
for alg in algorithms:
if alg == "rf":
train_pool, y_train, test_pool, y_test, enc = process_classification_dataset(
name)
# process ood data
cd = read_cd("datasets/" + name + "/pool.cd",
data_file="datasets/" + name + "/test")
try:
label_ind = cd['column_type_to_indices']['Label']
except:
label_ind = cd['column_type_to_indices']['Target']
ood_test_pool = np.loadtxt("datasets/ood/" + name,
delimiter="\t",
dtype="object")
ood_test_pool = enc.transform(ood_test_pool).astype("float64")
ood_test_pool = np.delete(ood_test_pool, label_ind, 1)
ood_size = len(ood_test_pool)
else:
test_pool = Pool(data="datasets/" + name + "/test",
column_description="datasets/" + name +
"/pool.cd")
ood_test_pool = Pool(data="datasets/ood/" + name,
column_description="datasets/" + name +
"/pool.cd")
ood_size = ood_test_pool.num_row()
y_test = test_pool.get_label()
test_size = len(y_test)
domain_labels = np.concatenate(
[np.zeros(test_size), np.ones(ood_size)])
y_test_norm = normalize_test_labels(y_test)
values = defaultdict(
) # metric values for all folds for given algorithm
if mode == "single":
# use 0th model from ensemble as a single model
model = load_model(name, alg, 0)
preds = model.predict(test_pool)
preds_proba = model.predict_proba(test_pool)
values["error"] = (preds != y_test).astype(int)
values["nll"] = nll_class(y_test_norm, preds_proba)
values["TU_prr"] = prr_class(y_test_norm, preds_proba,
entropy(preds_proba), False)
values["KU_prr"] = float("nan")
values["KU_auc"] = float("nan")
ood_preds_proba = model.predict_proba(ood_test_pool)
in_measure = entropy(preds_proba)
out_measure = entropy(ood_preds_proba)
values["TU_auc"] = ood_detect(domain_labels,
in_measure,
out_measure,
mode="ROC")
if mode == "ens":
all_preds = [] # predictions of all models in ensemble
all_preds_ood = []
for i in range(num_models):
model = load_model(name, alg, i)
preds = model.predict_proba(test_pool)
all_preds.append(preds)
preds = model.predict_proba(ood_test_pool)
all_preds_ood.append(preds)
all_preds = np.array(all_preds)
preds_proba = np.mean(all_preds, axis=0)
all_preds_ood = np.array(all_preds_ood)
preds = np.argmax(preds_proba, axis=1)
values["error"] = (preds != y_test_norm).astype(int)
values["nll"] = nll_class(y_test_norm, preds_proba)
TU = entropy_of_expected_class(all_preds)
DU = expected_entropy_class(all_preds)
KU = TU - DU
TU_ood = entropy_of_expected_class(all_preds_ood)
DU_ood = expected_entropy_class(all_preds_ood)
KU_ood = TU_ood - DU_ood
values["TU_prr"] = prr_class(y_test_norm, preds_proba, TU,
False)
values["KU_prr"] = prr_class(y_test_norm, preds_proba, KU,
False)
values["TU_auc"] = ood_detect(domain_labels,
TU,
TU_ood,
mode="ROC")
values["KU_auc"] = ood_detect(domain_labels,
KU,
KU_ood,
mode="ROC")
if mode == "virt":
if alg in ["sgb", "sgb-fixed"
]: # we do not evaluate virtual sgb model
continue
# generate virtual ensemble from 0th model
model = load_model(name, alg, 0)
all_preds = virtual_ensembles_predict(test_pool, model, alg)
preds_proba = np.mean(all_preds, axis=0)
preds = np.argmax(preds_proba, axis=1)
values["error"] = (preds != y_test_norm).astype(int)
values["nll"] = nll_class(y_test_norm, preds_proba)
TU = entropy_of_expected_class(all_preds)
DU = expected_entropy_class(all_preds)
KU = TU - DU
all_preds_ood = virtual_ensembles_predict(
ood_test_pool, model, alg)
TU_ood = entropy_of_expected_class(all_preds_ood)
DU_ood = expected_entropy_class(all_preds_ood)
KU_ood = TU_ood - DU_ood
values["TU_prr"] = prr_class(y_test_norm, preds_proba, TU,
False)
values["KU_prr"] = prr_class(y_test_norm, preds_proba, KU,
False)
values["TU_auc"] = ood_detect(domain_labels,
TU,
TU_ood,
mode="ROC")
values["KU_auc"] = ood_detect(domain_labels,
KU,
KU_ood,
mode="ROC")
if mode == "virt" and alg in [
"sgb", "sgb-fixed"
]: # we do not evaluate virtual sgb model
continue
results.append(values)
return np.array(results)
def test_fit_data():
pool = Pool(CLOUDNESS_TRAIN_FILE, column_description=CLOUDNESS_CD_FILE)
eval_pool = Pool(CLOUDNESS_TEST_FILE, column_description=CLOUDNESS_CD_FILE)
base_model = CatBoostClassifier(iterations=2, random_seed=0, loss_function="MultiClass")
base_model.fit(pool)
baseline = np.array(base_model.predict(pool, prediction_type='RawFormulaVal'))
eval_baseline = np.array(base_model.predict(eval_pool, prediction_type='RawFormulaVal'))
eval_pool.set_baseline(eval_baseline)
model = CatBoostClassifier(iterations=2, random_seed=0, loss_function="MultiClass")
data = map_cat_features(pool.get_features(), pool.get_cat_feature_indices())
model.fit(data, pool.get_label(), pool.get_cat_feature_indices(), sample_weight=np.arange(1, pool.num_row()+1), baseline=baseline, use_best_model=True, eval_set=eval_pool)
model.save_model(OUTPUT_MODEL_PATH)
return compare_canonical_models(OUTPUT_MODEL_PATH)
