def test_classifier_with_dataframe():
try:
from rep.estimators import SklearnClassifier
clf = SklearnClassifier(GradientBoostingClassifier(n_estimators=1))
X, y = generate_sample(n_samples=100, n_features=4)
for X_ in [X, pandas.DataFrame(X)]:
lookup = LookupClassifier(clf, n_bins=16).fit(X_, y)
lookup.predict_proba(X)
except ImportError:
print('expected fail: yandex/rep not installed')
def test_lookup(n_samples=10000, n_features=7, n_bins=8):
X, y = generate_sample(n_samples=n_samples, n_features=n_features, distance=0.6)
base_estimator = GradientBoostingClassifier()
clf = LookupClassifier(base_estimator=base_estimator, n_bins=n_bins, keep_trained_estimator=True).fit(X, y)
p = clf.predict_proba(X)
assert roc_auc_score(y, p[:, 1]) > 0.8, 'quality of classification is too low'
assert p.shape == (n_samples, 2)
assert numpy.allclose(p.sum(axis=1), 1), 'probabilities are not summed up to 1'
# checking conversions
lookup_size = n_bins ** n_features
lookup_indices = numpy.arange(lookup_size, dtype=int)
bins_indices = clf.convert_lookup_index_to_bins(lookup_indices=lookup_indices)
lookup_indices2 = clf.convert_bins_to_lookup_index(bins_indices=bins_indices)
assert numpy.allclose(lookup_indices, lookup_indices2), 'something wrong with conversions'
assert len(clf._lookup_table) == n_bins ** n_features, 'wrong size of lookup table'
# checking speed
X = pandas.concat([X] * 10)
start = time.time()
p1 = clf.trained_estimator.predict_proba(clf.transform(X))
time_old = time.time() - start
start = time.time()
p2 = clf.predict_proba(X)
time_new = time.time() - start
print(time_old, ' now takes ', time_new)
assert numpy.allclose(p1, p2), "pipeline doesn't work as expected"
def test_sizes(n_samples=10000, n_features=4, n_bins=8):
X, y = generate_sample(n_samples=n_samples, n_features=n_features, distance=0.6)
base_estimator = GradientBoostingClassifier(n_estimators=1)
clf = LookupClassifier(base_estimator=base_estimator, n_bins=n_bins).fit(X, y)
bin_indices = clf.transform(X)
assert numpy.allclose(numpy.max(bin_indices, axis=0) + 1, n_bins)
maximals = OrderedDict()
for column in X.columns:
maximals[column] = numpy.random.randint(low=n_bins // 2, high=n_bins)
clf = LookupClassifier(base_estimator=base_estimator, n_bins=maximals).fit(X, y)
bin_indices = clf.transform(X)
assert numpy.allclose(numpy.max(bin_indices, axis=0) + 1, list(maximals.values()))
assert numpy.allclose(numpy.min(bin_indices, axis=0), 0)
def test_lookup(n_samples=10000, n_features=7, n_bins=8):
X, y = generate_sample(n_samples=n_samples, n_features=n_features, distance=0.6)
base_estimator = GradientBoostingClassifier()
clf = LookupClassifier(base_estimator=base_estimator, n_bins=n_bins, keep_trained_estimator=True).fit(X, y)
p = clf.predict_proba(X)
assert roc_auc_score(y, p[:, 1]) > 0.8, 'quality of classification is too low'
assert p.shape == (n_samples, 2)
assert numpy.allclose(p.sum(axis=1), 1), 'probabilities are not summed up to 1'
# checking conversions
lookup_size = n_bins ** n_features
lookup_indices = numpy.arange(lookup_size, dtype=int)
bins_indices = clf.convert_lookup_index_to_bins(lookup_indices=lookup_indices)
lookup_indices2 = clf.convert_bins_to_lookup_index(bins_indices=bins_indices)
assert numpy.allclose(lookup_indices, lookup_indices2), 'something wrong with conversions'
assert len(clf._lookup_table) == n_bins ** n_features, 'wrong size of lookup table'
# checking speed
X = pandas.concat([X] * 10)
start = time.time()
p1 = clf.trained_estimator.predict_proba(clf.transform(X))
time_old = time.time() - start
start = time.time()
p2 = clf.predict_proba(X)
time_new = time.time() - start
print(time_old, ' now takes ', time_new)
assert numpy.allclose(p1, p2), "pipeline doesn't work as expected"
def test_sizes(n_samples=10000, n_features=4, n_bins=8):
X, y = generate_sample(n_samples=n_samples, n_features=n_features, distance=0.6)
base_estimator = GradientBoostingClassifier(n_estimators=1)
clf = LookupClassifier(base_estimator=base_estimator, n_bins=n_bins).fit(X, y)
bin_indices = clf.transform(X)
assert numpy.allclose(numpy.max(bin_indices, axis=0) + 1, n_bins)
maximals = OrderedDict()
for column in X.columns:
maximals[column] = numpy.random.randint(low=n_bins // 2, high=n_bins)
clf = LookupClassifier(base_estimator=base_estimator, n_bins=maximals).fit(X, y)
bin_indices = clf.transform(X)
assert numpy.allclose(numpy.max(bin_indices, axis=0) + 1, list(maximals.values()))
assert numpy.allclose(numpy.min(bin_indices, axis=0), 0)
def test_raising_exception():
X, y = generate_sample(n_samples=100, n_features=10)
LookupClassifier(GradientBoostingClassifier(), n_bins=16).fit(X, y)
