def test_probas(n_samples=1000):
trainX, trainY = generate_sample(n_samples, 10, 0.6)
testX, testY = generate_sample(n_samples, 10, 0.6)
params = {
'n_neighbors': 10,
'n_estimators': 10,
'uniform_variables': ['column0'],
'base_estimator': DecisionTreeClassifier(max_depth=5)
}
for algorithm in ['SAMME', 'SAMME.R']:
uboost_classifier = uBoostClassifier(
algorithm=algorithm,
efficiency_steps=3, **params)
bdt_classifier = uBoostBDT(algorithm=algorithm, **params)
for classifier in [bdt_classifier, uboost_classifier]:
classifier.fit(trainX, trainY)
proba1 = classifier.predict_proba(testX)
proba2 = list(classifier.staged_predict_proba(testX))[-1]
assert np.allclose(proba1, proba2, atol=0.001),\
"staged_predict doesn't coincide with the predict for proba."
score1 = bdt_classifier.predict_score(testX)
score2 = list(bdt_classifier.staged_predict_score(testX))[-1]
assert np.allclose(score1, score2),\
"staged_score doesn't coincide with the score."
assert len(bdt_classifier.feature_importances_) == trainX.shape[1]
def test_cuts(n_samples=1000):
base_classifier = DecisionTreeClassifier(min_samples_leaf=10, max_depth=6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
uniform_variables = ['column0']
for algorithm in ['SAMME', 'SAMME.R']:
for target_efficiency in [0.1, 0.3, 0.5, 0.7, 0.9]:
uBDT = uBoostBDT(
uniform_variables=uniform_variables,
target_efficiency=target_efficiency,
n_neighbors=20, n_estimators=20,
algorithm=algorithm,
base_estimator=base_classifier)
uBDT.fit(trainX, trainY)
passed = sum(trainY) * target_efficiency
assert uBDT.score_cut == uBDT.score_cuts_[-1],\
'something wrong with computed cuts'
for score, cut in zip(uBDT.staged_predict_score(trainX[trainY > 0.5]),
uBDT.score_cuts_):
passed_upper = np.sum(score > cut - 1e-7)
passed_lower = np.sum(score > cut + 1e-7)
assert passed_lower <= passed <= passed_upper, "wrong stage cuts"
def test_quality(n_samples=3000):
testX, testY = generate_sample(n_samples, 10, 0.6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
params = {
'n_neighbors': 10,
'n_estimators': 10,
'uniform_features': ['column0'],
'uniform_label': 1,
'base_estimator': DecisionTreeClassifier(min_samples_leaf=20, max_depth=5)
}
for algorithm in ['SAMME', 'SAMME.R']:
uboost_classifier = uBoostClassifier(
algorithm=algorithm, efficiency_steps=5, **params)
bdt_classifier = uBoostBDT(algorithm=algorithm, **params)
for classifier in [bdt_classifier, uboost_classifier]:
classifier.fit(trainX, trainY)
predict_proba = classifier.predict_proba(testX)
predict = classifier.predict(testX)
assert roc_auc_score(testY, predict_proba[:, 1]) > 0.7, \
"quality is awful"
print("Accuracy = %.3f" % accuracy_score(testY, predict))
def test_probas(n_samples=1000):
trainX, trainY = generate_sample(n_samples, 10, 0.6)
testX, testY = generate_sample(n_samples, 10, 0.6)
params = {
'n_neighbors': 10,
'n_estimators': 10,
'uniform_features': ['column0'],
'uniform_label': 1,
'base_estimator': DecisionTreeClassifier(max_depth=5)
}
for algorithm in ['SAMME', 'SAMME.R']:
uboost_classifier = uBoostClassifier(
algorithm=algorithm,
efficiency_steps=3, **params)
bdt_classifier = uBoostBDT(algorithm=algorithm, **params)
for classifier in [bdt_classifier, uboost_classifier]:
classifier.fit(trainX, trainY)
proba1 = classifier.predict_proba(testX)
proba2 = list(classifier.staged_predict_proba(testX))[-1]
assert np.allclose(proba1, proba2, atol=0.001), \
"staged_predict doesn't coincide with the predict for proba."
score1 = bdt_classifier.decision_function(testX)
score2 = list(bdt_classifier.staged_decision_function(testX))[-1]
assert np.allclose(score1, score2), \
"staged_score doesn't coincide with the score."
assert len(bdt_classifier.feature_importances_) == trainX.shape[1]
def test_cuts(n_samples=1000):
base_classifier = DecisionTreeClassifier(min_samples_leaf=10, max_depth=6)
trainX, trainY = generate_sample(n_samples, 10, 0.6)
uniform_features = ['column0']
for algorithm in ['SAMME', 'SAMME.R']:
for target_efficiency in [0.1, 0.3, 0.5, 0.7, 0.9]:
uBDT = uBoostBDT(
uniform_features=uniform_features,
uniform_label=1,
target_efficiency=target_efficiency,
n_neighbors=20, n_estimators=20,
algorithm=algorithm,
base_estimator=base_classifier)
uBDT.fit(trainX, trainY)
passed = sum(trainY) * target_efficiency
assert uBDT.score_cut == uBDT.score_cuts_[-1], \
'something wrong with computed cuts'
for score, cut in zip(uBDT.staged_decision_function(trainX[trainY > 0.5]),
uBDT.score_cuts_):
passed_upper = np.sum(score > cut - 1e-7)
passed_lower = np.sum(score > cut + 1e-7)
assert passed_lower <= passed <= passed_upper, "wrong stage cuts"
def train_uBoost(X, y, w, cfg, uniforming_rate):
"""
...
"""
# Create base classifier
base_tree = DecisionTreeClassifier(**cfg['DecisionTreeClassifier'])
# Update training configuration
these_cfg = dict(**cfg['uBoost'])
these_cfg['uniforming_rate'] = uniforming_rate
# Create uBoost classifier
uboost = uBoostBDT(base_estimator=base_tree, **these_cfg)
# Fit uBoost classifier
uboost.fit(X, y, sample_weight=w)
return uboost
