本文整理汇总了Python中sklearn.linear_model.LassoLarsCV方法的典型用法代码示例。如果您正苦于以下问题:Python linear_model.LassoLarsCV方法的具体用法?Python linear_model.LassoLarsCV怎么用?Python linear_model.LassoLarsCV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.linear_model
的用法示例。
在下文中一共展示了linear_model.LassoLarsCV方法的14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_few_fit_shapes
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_few_fit_shapes():
"""test_few.py: fit and predict return correct shapes """
np.random.seed(202)
# load example data
boston = load_boston()
d = pd.DataFrame(data=boston.data)
print("feature shape:",boston.data.shape)
learner = FEW(generations=1, population_size=5,
mutation_rate=0.2, crossover_rate=0.8,
ml = LassoLarsCV(), min_depth = 1, max_depth = 3,
sel = 'epsilon_lexicase', tourn_size = 2,
random_state=0, verbosity=0,
disable_update_check=False, fit_choice = 'mse')
score = learner.fit(boston.data[:300], boston.target[:300])
print("learner:",learner._best_estimator)
yhat_test = learner.predict(boston.data[300:])
test_score = learner.score(boston.data[300:],boston.target[300:])
print("train score:",score,"test score:",test_score,
"test r2:",r2_score(boston.target[300:],yhat_test))
assert yhat_test.shape == boston.target[300:].shape
示例2: test_few_with_parents_weight
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_few_with_parents_weight():
"""test_few.py: few performs without error with parent pressure for selection"""
np.random.seed(1006987)
boston = load_boston()
d = np.column_stack((boston.data,boston.target))
np.random.shuffle(d)
features = d[:,0:-1]
target = d[:,-1]
print("feature shape:",boston.data.shape)
learner = FEW(generations=1, population_size=5,
mutation_rate=1, crossover_rate=1,
ml = LassoLarsCV(), min_depth = 1, max_depth = 3,
sel = 'tournament', fit_choice = 'r2',tourn_size = 2, random_state=0, verbosity=0,
disable_update_check=False, weight_parents=True)
learner.fit(features[:300], target[:300])
few_score = learner.score(features[:300], target[:300])
test_score = learner.score(features[300:],target[300:])
print("few score:",few_score)
print("few test score:",test_score)
示例3: test_lars_cv_max_iter
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_lars_cv_max_iter(recwarn):
warnings.simplefilter('always')
with np.errstate(divide='raise', invalid='raise'):
X = diabetes.data
y = diabetes.target
rng = np.random.RandomState(42)
x = rng.randn(len(y))
X = diabetes.data
X = np.c_[X, x, x] # add correlated features
lars_cv = linear_model.LassoLarsCV(max_iter=5, cv=5)
lars_cv.fit(X, y)
# Check that there is no warning in general and no ConvergenceWarning
# in particular.
# Materialize the string representation of the warning to get a more
# informative error message in case of AssertionError.
recorded_warnings = [str(w) for w in recwarn]
assert recorded_warnings == []
示例4: test_estimatorclasses_positive_constraint
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_estimatorclasses_positive_constraint():
# testing the transmissibility for the positive option of all estimator
# classes in this same function here
default_parameter = {'fit_intercept': False}
estimator_parameter_map = {'LassoLars': {'alpha': 0.1},
'LassoLarsCV': {},
'LassoLarsIC': {}}
for estname in estimator_parameter_map:
params = default_parameter.copy()
params.update(estimator_parameter_map[estname])
estimator = getattr(linear_model, estname)(positive=False, **params)
estimator.fit(X, y)
assert estimator.coef_.min() < 0
estimator = getattr(linear_model, estname)(positive=True, **params)
estimator.fit(X, y)
assert min(estimator.coef_) >= 0
示例5: test_lasso_cv
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_lasso_cv():
X, y, X_test, y_test = build_dataset()
max_iter = 150
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter).fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
# Check that the lars and the coordinate descent implementation
# select a similar alpha
lars = LassoLarsCV(normalize=False, max_iter=30).fit(X, y)
# for this we check that they don't fall in the grid of
# clf.alphas further than 1
assert np.abs(np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1
# check that they also give a similar MSE
mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
clf.mse_path_[5].mean(), significant=2)
# test set
assert_greater(clf.score(X_test, y_test), 0.99)
示例6: fit_ensemble
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def fit_ensemble(x,y):
fit_type = jhkaggle.jhkaggle_config['FIT_TYPE']
if 1:
if fit_type == jhkaggle.const.FIT_TYPE_BINARY_CLASSIFICATION:
blend = SGDClassifier(loss="log", penalty="elasticnet") # LogisticRegression()
else:
# blend = SGDRegressor()
#blend = LinearRegression()
#blend = RandomForestRegressor(n_estimators=10, n_jobs=-1, max_depth=5, criterion='mae')
blend = LassoLarsCV(normalize=True)
#blend = ElasticNetCV(normalize=True)
#blend = LinearRegression(normalize=True)
blend.fit(x, y)
else:
blend = LogisticRegression()
blend.fit(x, y)
return blend
示例7: test_lasso_cv
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_lasso_cv():
X, y, X_test, y_test = build_dataset()
max_iter = 150
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter).fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
clf = LassoCV(n_alphas=10, eps=1e-3, max_iter=max_iter, precompute=True)
clf.fit(X, y)
assert_almost_equal(clf.alpha_, 0.056, 2)
# Check that the lars and the coordinate descent implementation
# select a similar alpha
lars = LassoLarsCV(normalize=False, max_iter=30).fit(X, y)
# for this we check that they don't fall in the grid of
# clf.alphas further than 1
assert_true(np.abs(
np.searchsorted(clf.alphas_[::-1], lars.alpha_) -
np.searchsorted(clf.alphas_[::-1], clf.alpha_)) <= 1)
# check that they also give a similar MSE
mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
np.testing.assert_approx_equal(mse_lars(clf.alphas_[5]).mean(),
clf.mse_path_[5].mean(), significant=2)
# test set
assert_greater(clf.score(X_test, y_test), 0.99)
示例8: test_few_at_least_as_good_as_default
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_few_at_least_as_good_as_default():
"""test_few.py: few performs at least as well as the default ML """
np.random.seed(1006987)
boston = load_boston()
d = np.column_stack((boston.data,boston.target))
np.random.shuffle(d)
features = d[:,0:-1]
target = d[:,-1]
print("feature shape:",boston.data.shape)
learner = FEW(generations=1, population_size=5,
ml = LassoLarsCV(), min_depth = 1, max_depth = 3,
sel = 'tournament')
learner.fit(features[:300], target[:300])
few_score = learner.score(features[:300], target[:300])
few_test_score = learner.score(features[300:],target[300:])
lasso = LassoLarsCV()
lasso.fit(features[:300], target[:300])
lasso_score = lasso.score(features[:300], target[:300])
lasso_test_score = lasso.score(features[300:],target[300:])
print("few score:",few_score,"lasso score:",lasso_score)
print("few test score:",few_test_score,"lasso test score:",
lasso_test_score)
assert round(few_score,8) >= round(lasso_score,8)
print("lasso coefficients:",lasso.coef_)
# assert False
示例9: test_lars_cv
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_lars_cv():
# Test the LassoLarsCV object by checking that the optimal alpha
# increases as the number of samples increases.
# This property is not actually guaranteed in general and is just a
# property of the given dataset, with the given steps chosen.
old_alpha = 0
lars_cv = linear_model.LassoLarsCV()
for length in (400, 200, 100):
X = diabetes.data[:length]
y = diabetes.target[:length]
lars_cv.fit(X, y)
np.testing.assert_array_less(old_alpha, lars_cv.alpha_)
old_alpha = lars_cv.alpha_
assert not hasattr(lars_cv, 'n_nonzero_coefs')
示例10: _fit_model
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def _fit_model(x, y, names, operators, **kw):
steps = [("trafo", LibTrafo(names, operators)), ("lasso", LassoLarsCV(**kw))]
model = Pipeline(steps).fit(x, y)
return model, model.score(x, y)
示例11: test_model_lasso_lars_cv
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_model_lasso_lars_cv(self):
model, X = fit_regression_model(linear_model.LassoLarsCV())
model_onnx = convert_sklearn(
model, "lasso lars cv",
[("input", FloatTensorType([None, X.shape[1]]))])
self.assertIsNotNone(model_onnx)
dump_data_and_model(
X,
model,
model_onnx,
basename="SklearnLassoLarsCV-Dec4",
allow_failure="StrictVersion("
"onnxruntime.__version__)"
"<= StrictVersion('0.2.1')",
)
示例12: test_objectmapper
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.linear_model.ARDRegression, lm.ARDRegression)
self.assertIs(df.linear_model.BayesianRidge, lm.BayesianRidge)
self.assertIs(df.linear_model.ElasticNet, lm.ElasticNet)
self.assertIs(df.linear_model.ElasticNetCV, lm.ElasticNetCV)
self.assertIs(df.linear_model.HuberRegressor, lm.HuberRegressor)
self.assertIs(df.linear_model.Lars, lm.Lars)
self.assertIs(df.linear_model.LarsCV, lm.LarsCV)
self.assertIs(df.linear_model.Lasso, lm.Lasso)
self.assertIs(df.linear_model.LassoCV, lm.LassoCV)
self.assertIs(df.linear_model.LassoLars, lm.LassoLars)
self.assertIs(df.linear_model.LassoLarsCV, lm.LassoLarsCV)
self.assertIs(df.linear_model.LassoLarsIC, lm.LassoLarsIC)
self.assertIs(df.linear_model.LinearRegression, lm.LinearRegression)
self.assertIs(df.linear_model.LogisticRegression, lm.LogisticRegression)
self.assertIs(df.linear_model.LogisticRegressionCV, lm.LogisticRegressionCV)
self.assertIs(df.linear_model.MultiTaskLasso, lm.MultiTaskLasso)
self.assertIs(df.linear_model.MultiTaskElasticNet, lm.MultiTaskElasticNet)
self.assertIs(df.linear_model.MultiTaskLassoCV, lm.MultiTaskLassoCV)
self.assertIs(df.linear_model.MultiTaskElasticNetCV, lm.MultiTaskElasticNetCV)
self.assertIs(df.linear_model.OrthogonalMatchingPursuit, lm.OrthogonalMatchingPursuit)
self.assertIs(df.linear_model.OrthogonalMatchingPursuitCV, lm.OrthogonalMatchingPursuitCV)
self.assertIs(df.linear_model.PassiveAggressiveClassifier, lm.PassiveAggressiveClassifier)
self.assertIs(df.linear_model.PassiveAggressiveRegressor, lm.PassiveAggressiveRegressor)
self.assertIs(df.linear_model.Perceptron, lm.Perceptron)
self.assertIs(df.linear_model.RandomizedLasso, lm.RandomizedLasso)
self.assertIs(df.linear_model.RandomizedLogisticRegression, lm.RandomizedLogisticRegression)
self.assertIs(df.linear_model.RANSACRegressor, lm.RANSACRegressor)
self.assertIs(df.linear_model.Ridge, lm.Ridge)
self.assertIs(df.linear_model.RidgeClassifier, lm.RidgeClassifier)
self.assertIs(df.linear_model.RidgeClassifierCV, lm.RidgeClassifierCV)
self.assertIs(df.linear_model.RidgeCV, lm.RidgeCV)
self.assertIs(df.linear_model.SGDClassifier, lm.SGDClassifier)
self.assertIs(df.linear_model.SGDRegressor, lm.SGDRegressor)
self.assertIs(df.linear_model.TheilSenRegressor, lm.TheilSenRegressor)
示例13: test_lars_cv
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_lars_cv():
# Test the LassoLarsCV object by checking that the optimal alpha
# increases as the number of samples increases.
# This property is not actually guaranteed in general and is just a
# property of the given dataset, with the given steps chosen.
old_alpha = 0
lars_cv = linear_model.LassoLarsCV()
for length in (400, 200, 100):
X = diabetes.data[:length]
y = diabetes.target[:length]
lars_cv.fit(X, y)
np.testing.assert_array_less(old_alpha, lars_cv.alpha_)
old_alpha = lars_cv.alpha_
assert_false(hasattr(lars_cv, 'n_nonzero_coefs'))
示例14: test_lars_cv_max_iter
# 需要导入模块: from sklearn import linear_model [as 别名]
# 或者: from sklearn.linear_model import LassoLarsCV [as 别名]
def test_lars_cv_max_iter():
with warnings.catch_warnings(record=True) as w:
X = diabetes.data
y = diabetes.target
rng = np.random.RandomState(42)
x = rng.randn(len(y))
X = np.c_[X, x, x] # add correlated features
lars_cv = linear_model.LassoLarsCV(max_iter=5)
lars_cv.fit(X, y)
assert_true(len(w) == 0)