def test_gb_ranking(n_samples=1000):
"""
Testing RankingLossFunction
"""
distance = 0.6
testX, testY = generate_sample(n_samples, 10, distance)
trainX, trainY = generate_sample(n_samples, 10, distance)
rank_variable = 'column1'
trainX[rank_variable] = numpy.random.randint(0, 3, size=len(trainX))
testX[rank_variable] = numpy.random.randint(0, 3, size=len(testX))
rank_loss1 = losses.RankBoostLossFunction(request_column=rank_variable,
update_iterations=1)
rank_loss2 = losses.RankBoostLossFunction(request_column=rank_variable,
update_iterations=2)
rank_loss3 = losses.RankBoostLossFunction(request_column=rank_variable,
update_iterations=10)
for loss in [rank_loss1, rank_loss2, rank_loss3]:
clf = UGradientBoostingRegressor(loss=loss, min_samples_split=20, max_depth=5, learning_rate=0.2,
subsample=0.7, n_estimators=25, train_features=None) \
.fit(trainX[:n_samples], trainY[:n_samples])
result = roc_auc_score(testY, clf.predict(testX))
assert result >= 0.8, "The quality is too poor: {} with loss: {}".format(
result, loss)
def test_gb_regression(n_samples=1000):
X, _ = generate_sample(n_samples, 10, distance=0.6)
y = numpy.tanh(X.sum(axis=1))
clf = UGradientBoostingRegressor(loss=MSELossFunction())
clf.fit(X, y)
y_pred = clf.predict(X)
zeromse = 0.5 * mean_squared_error(y, y * 0.)
assert mean_squared_error(y, y_pred) < zeromse, 'something wrong with regression quality'
def test_gb_regression(n_samples=1000):
X, _ = generate_sample(n_samples, 10, distance=0.6)
y = numpy.tanh(X.sum(axis=1))
clf = UGradientBoostingRegressor(loss=MSELossFunction())
clf.fit(X, y)
y_pred = clf.predict(X)
zeromse = 0.5 * mean_squared_error(y, y * 0.)
assert mean_squared_error(y, y_pred) < zeromse, 'something wrong with regression quality'
def test_constant_fitting(n_samples=1000, n_features=5):
"""
Testing if initial constant fitted properly
"""
X, y = generate_sample(n_samples=n_samples, n_features=n_features)
y = y.astype(numpy.float) + 1000.
for loss in [MSELossFunction(), losses.MAELossFunction()]:
gb = UGradientBoostingRegressor(loss=loss, n_estimators=10)
gb.fit(X, y)
p = gb.predict(X)
assert mean_squared_error(p, y) < 0.5
def test_constant_fitting(n_samples=1000, n_features=5):
"""
Testing if initial constant fitted properly
"""
X, y = generate_sample(n_samples=n_samples, n_features=n_features)
y = y.astype(numpy.float) + 1000.
for loss in [MSELossFunction(), losses.MAELossFunction()]:
gb = UGradientBoostingRegressor(loss=loss, n_estimators=10)
gb.fit(X, y)
p = gb.predict(X)
assert mean_squared_error(p, y) < 0.5
def test_gradient_boosting(n_samples=1000):
"""
Testing workability of GradientBoosting with different loss function
"""
# Generating some samples correlated with first variable
distance = 0.6
testX, testY = generate_sample(n_samples, 10, distance)
trainX, trainY = generate_sample(n_samples, 10, distance)
# We will try to get uniform distribution along this variable
uniform_features = ['column0']
loss1 = LogLossFunction()
loss2 = AdaLossFunction()
loss3 = losses.CompositeLossFunction()
loss4 = losses.KnnAdaLossFunction(uniform_features=uniform_features,
uniform_label=1)
loss5 = losses.KnnAdaLossFunction(uniform_features=uniform_features,
uniform_label=[0, 1])
loss6bin = losses.BinFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=0)
loss7bin = losses.BinFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=[0, 1])
loss6knn = losses.KnnFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=1)
loss7knn = losses.KnnFlatnessLossFunction(uniform_features,
fl_coefficient=2.,
uniform_label=[0, 1])
for loss in [
loss1, loss2, loss3, loss4, loss5, loss6bin, loss7bin, loss6knn,
loss7knn
]:
clf = UGradientBoostingClassifier(loss=loss, min_samples_split=20, max_depth=5, learning_rate=0.2,
subsample=0.7, n_estimators=25, train_features=None) \
.fit(trainX[:n_samples], trainY[:n_samples])
result = clf.score(testX, testY)
assert result >= 0.7, "The quality is too poor: {} with loss: {}".format(
result, loss)
trainX['fake_request'] = numpy.random.randint(0, 4, size=len(trainX))
for loss in [
losses.MSELossFunction(),
losses.MAELossFunction(),
losses.RankBoostLossFunction(request_column='fake_request')
]:
print(loss)
clf = UGradientBoostingRegressor(loss=loss,
max_depth=3,
n_estimators=50,
learning_rate=0.01,
subsample=0.5,
train_features=list(
trainX.columns[1:]))
clf.fit(trainX, trainY)
roc_auc = roc_auc_score(testY, clf.predict(testX))
assert roc_auc >= 0.7, "The quality is too poor: {} with loss: {}".format(
roc_auc, loss)
def test_gb_ranking(n_samples=1000):
distance = 0.6
testX, testY = generate_sample(n_samples, 10, distance)
trainX, trainY = generate_sample(n_samples, 10, distance)
rank_variable = 'column1'
trainX[rank_variable] = numpy.random.randint(0, 3, size=len(trainX))
testX[rank_variable] = numpy.random.randint(0, 3, size=len(testX))
rank_loss1 = RankBoostLossFunction(request_column=rank_variable, update_iterations=1)
rank_loss2 = RankBoostLossFunction(request_column=rank_variable, update_iterations=2)
rank_loss3 = RankBoostLossFunction(request_column=rank_variable, update_iterations=10)
for loss in [rank_loss1, rank_loss2, rank_loss3]:
clf = UGradientBoostingRegressor(loss=loss, min_samples_split=20, max_depth=5, learning_rate=0.2,
subsample=0.7, n_estimators=25, train_features=None) \
.fit(trainX[:n_samples], trainY[:n_samples])
result = roc_auc_score(testY, clf.predict(testX))
assert result >= 0.8, "The quality is too poor: {} with loss: {}".format(result, loss)
