本文整理汇总了Python中sklearn.datasets.make_friedman1方法的典型用法代码示例。如果您正苦于以下问题:Python datasets.make_friedman1方法的具体用法?Python datasets.make_friedman1怎么用?Python datasets.make_friedman1使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.datasets
的用法示例。
在下文中一共展示了datasets.make_friedman1方法的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_staged_predict
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_staged_predict():
# Test whether staged decision function eventually gives
# the same prediction.
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1, noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test = X[200:]
clf = GradientBoostingRegressor()
# test raise ValueError if not fitted
assert_raises(ValueError, lambda X: np.fromiter(
clf.staged_predict(X), dtype=np.float64), X_test)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# test if prediction for last stage equals ``predict``
for y in clf.staged_predict(X_test):
assert_equal(y.shape, y_pred.shape)
assert_array_almost_equal(y_pred, y)
示例2: test_rfe_min_step
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_rfe_min_step():
n_features = 10
X, y = make_friedman1(n_samples=50, n_features=n_features, random_state=0)
n_samples, n_features = X.shape
estimator = SVR(kernel="linear")
# Test when floor(step * n_features) <= 0
selector = RFE(estimator, step=0.01)
sel = selector.fit(X, y)
assert_equal(sel.support_.sum(), n_features // 2)
# Test when step is between (0,1) and floor(step * n_features) > 0
selector = RFE(estimator, step=0.20)
sel = selector.fit(X, y)
assert_equal(sel.support_.sum(), n_features // 2)
# Test when step is an integer
selector = RFE(estimator, step=5)
sel = selector.fit(X, y)
assert_equal(sel.support_.sum(), n_features // 2)
示例3: __init__
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def __init__(self, numFeatures, numSamples, randomSeed):
"""
:param numFeatures: total number of features to be used (at least 5)
:param numSamples: number of samples in dataset
:param randomSeed: random seed value used for reproducible results
"""
self.numFeatures = numFeatures
self.numSamples = numSamples
self.randomSeed = randomSeed
# generate test data:
self.X, self.y = datasets.make_friedman1(n_samples=self.numSamples, n_features=self.numFeatures,
noise=self.NOISE, random_state=self.randomSeed)
# divide the data to a training set and a validation set:
self.X_train, self.X_validation, self.y_train, self.y_validation = \
model_selection.train_test_split(self.X, self.y, test_size=self.VALIDATION_SIZE, random_state=self.randomSeed)
self.regressor = GradientBoostingRegressor(random_state=self.randomSeed)
示例4: test_stacked_regressor
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_stacked_regressor(self):
bclf = LinearRegression()
clfs = [RandomForestRegressor(n_estimators=50, random_state=1),
GradientBoostingRegressor(n_estimators=25, random_state=1),
Ridge(random_state=1)]
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1,
noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
sr = StackedRegressor(bclf,
clfs,
n_folds=3,
verbose=0,
oob_score_flag=True)
sr.fit(X_train, y_train)
mse = mean_squared_error(y_test, sr.predict(X_test))
assert_less(mse, 6.0)
示例5: test_fwls_regressor
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_fwls_regressor(self):
feature_func = lambda x: np.ones(x.shape)
bclf = LinearRegression()
clfs = [RandomForestRegressor(n_estimators=50, random_state=1),
GradientBoostingRegressor(n_estimators=25, random_state=1),
Ridge(random_state=1)]
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1,
noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
sr = FWLSRegressor(bclf,
clfs,
feature_func,
n_folds=3,
verbose=0,
oob_score_flag=True)
sr.fit(X_train, y_train)
mse = mean_squared_error(y_test, sr.predict(X_test))
assert_less(mse, 6.0)
示例6: test_regressor
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_regressor(self):
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1,
noise=1.0)
X_train, y_train = X[:200], y[:200]
index = [i for i in range(200)]
rf = RandomForestRegressor()
jrf = JoblibedRegressor(rf, "rfr", cache_dir='')
jrf.fit(X_train, y_train, index)
prediction = jrf.predict(X_train, index)
mse = mean_squared_error(y_train, prediction)
assert_less(mse, 6.0)
rf = RandomForestRegressor(n_estimators=20)
jrf = JoblibedRegressor(rf, "rfr", cache_dir='')
jrf.fit(X_train, y_train, index)
prediction2 = jrf.predict(X_train, index)
assert_allclose(prediction, prediction2)
示例7: test_staged_predict
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_staged_predict():
# Test whether staged decision function eventually gives
# the same prediction.
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1, noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test = X[200:]
clf = GradientBoostingRegressor()
# test raise ValueError if not fitted
assert_raises(ValueError, lambda X: np.fromiter(
clf.staged_predict(X), dtype=np.float64), X_test)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# test if prediction for last stage equals ``predict``
for y in clf.staged_predict(X_test):
assert_equal(y.shape, y_pred.shape)
assert_array_equal(y_pred, y)
示例8: run
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def run():
"""Run profiling."""
lc = LayerGenerator().get_sequential('stack', False, False)
cm = CMLog(verbose=False)
cm.monitor()
sleep(5)
t1 = int(np.floor(perf_counter() - cm._t0) * 10)
sleep(0.1)
x, z = make_friedman1(int(5 * 1e6))
sleep(5)
t2 = int(np.floor(perf_counter() - cm._t0) * 10)
sleep(0.1)
lc.fit(x, z)
t3 = int(np.floor(perf_counter() - cm._t0) * 10)
sleep(5)
while not hasattr(cm, 'cpu'):
cm.collect()
sleep(1)
return cm, t1, t2, t3
示例9: test_make_friedman1
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_make_friedman1():
X, y = make_friedman1(n_samples=5, n_features=10, noise=0.0,
random_state=0)
assert_equal(X.shape, (5, 10), "X shape mismatch")
assert_equal(y.shape, (5,), "y shape mismatch")
assert_array_almost_equal(y,
10 * np.sin(np.pi * X[:, 0] * X[:, 1])
+ 20 * (X[:, 2] - 0.5) ** 2
+ 10 * X[:, 3] + 5 * X[:, 4])
示例10: test_regression_synthetic
# 需要导入模块: from sklearn import datasets [as 别名]
# 或者: from sklearn.datasets import make_friedman1 [as 别名]
def test_regression_synthetic():
# Test on synthetic regression datasets used in Leo Breiman,
# `Bagging Predictors?. Machine Learning 24(2): 123-140 (1996).
random_state = check_random_state(1)
regression_params = {'n_estimators': 100, 'max_depth': 4,
'min_samples_split': 2, 'learning_rate': 0.1,
'loss': 'ls'}
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=random_state,
noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
for presort in True, False:
clf = GradientBoostingRegressor(presort=presort)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert_less(mse, 5.0)
# Friedman2
X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
for presort in True, False:
regression_params['presort'] = presort
clf = GradientBoostingRegressor(**regression_params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert_less(mse, 1700.0)
# Friedman3
X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
for presort in True, False:
regression_params['presort'] = presort
clf = GradientBoostingRegressor(**regression_params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert_less(mse, 0.015)