Python sklearn.cross_decomposition.PLSRegression() Examples
The following are 7
code examples of sklearn.cross_decomposition.PLSRegression().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
sklearn.cross_decomposition
, or try the search function
.
.
Example #1
| Source File: test_sklearn_pls_regression.py From sklearn-onnx with MIT License | 7 votes |
|
def test_model_pls_regression(self):
X = numpy.array([[0., 0., 1.], [1., 0., 0.],
[2., 2., 2.], [2., 5., 4.]],
numpy.float32)
Y = numpy.array([[0.1, -0.2], [0.9, 1.1], [6.2, 5.9],
[11.9, 12.3]],
numpy.float32)
pls2 = PLSRegression(n_components=2)
pls2.fit(X, Y)
model_onnx = convert_sklearn(
pls2, "scikit-learn pls",
[("input", FloatTensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X, pls2, model_onnx, methods=['predict'],
basename="SklearnPLSRegression",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')")
Example #2
| Source File: test_sklearn_pls_regression.py From sklearn-onnx with MIT License | 6 votes |
|
def test_model_pls_regression64(self):
X = numpy.array([[0., 0., 1.], [1., 0., 0.],
[2., 2., 2.], [2., 5., 4.]],
numpy.float64)
Y = numpy.array([[0.1, -0.2], [0.9, 1.1], [6.2, 5.9],
[11.9, 12.3]],
numpy.float64)
pls2 = PLSRegression(n_components=2)
pls2.fit(X, Y)
model_onnx = convert_sklearn(
pls2, "scikit-learn pls64",
[("input", DoubleTensorType([None, X.shape[1]]))],
dtype=numpy.float64)
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X, pls2, model_onnx, methods=['predict'],
basename="SklearnPLSRegression64",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')")
Example #3
| Source File: test_sklearn_pls_regression.py From sklearn-onnx with MIT License | 6 votes |
|
def test_model_pls_regressionInt64(self):
X = numpy.array([[0., 0., 1.], [1., 0., 0.],
[2., 2., 2.], [2., 5., 4.]],
numpy.int64)
Y = numpy.array([[0.1, -0.2], [0.9, 1.1], [6.2, 5.9],
[11.9, 12.3]],
numpy.int64)
pls2 = PLSRegression(n_components=2)
pls2.fit(X, Y)
model_onnx = convert_sklearn(
pls2, "scikit-learn plsint64",
[("input", Int64TensorType([None, X.shape[1]]))])
self.assertTrue(model_onnx is not None)
dump_data_and_model(
X, pls2, model_onnx, methods=['predict'],
basename="SklearnPLSRegressionInt64",
allow_failure="StrictVersion("
"onnxruntime.__version__)<= StrictVersion('0.2.1')")
Example #4
| Source File: test_cross_decomposition.py From pandas-ml with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def test_PLSRegression(self):
n = 1000
q = 3
p = 10
X = np.random.normal(size=n * p).reshape((n, p))
B = np.array([[1, 2] + [0] * (p - 2)] * q).T
# each Yj = 1*X1 + 2*X2 + noize
Y = np.dot(X, B) + np.random.normal(size=n * q).reshape((n, q)) + 5
df = pdml.ModelFrame(X, target=Y)
pls1 = df.cross_decomposition.PLSRegression(n_components=3)
df.fit(pls1)
result = df.predict(pls1)
pls2 = cd.PLSRegression(n_components=3)
pls2.fit(X, Y)
expected = pls2.predict(X)
self.assertIsInstance(result, pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result.values, expected)
Example #5
| Source File: feature_extraction.py From CatLearn with GNU General Public License v3.0 | 5 votes |
|
def pls(components, train_matrix, target, test_matrix):
"""Projection of latent structure routine.
Parameters
----------
components : int
The number of components to be returned.
train_matrix : array
The training features.
test_matrix : array
The test features.
Returns
-------
new_train : array
Extracted training features.
new_test : array
Extracted test features.
"""
msg = 'The number of components must be a positive int greater than 0.'
assert components > 0, msg
pls = PLSRegression(n_components=components)
model = pls.fit(X=train_matrix, Y=target)
new_train = model.transform(train_matrix)
new_test = model.transform(test_matrix)
return new_train, new_test
Example #6
| Source File: test_cross_decomposition.py From pandas-ml with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.cross_decomposition.PLSRegression, cd.PLSRegression)
self.assertIs(df.cross_decomposition.PLSCanonical, cd.PLSCanonical)
self.assertIs(df.cross_decomposition.CCA, cd.CCA)
self.assertIs(df.cross_decomposition.PLSSVD, cd.PLSSVD)
Example #7
| Source File: trainlinearmodel.py From qsar-tools with Apache License 2.0 | 4 votes |
|
def trainmodels(m, x, y, iter=1000):
'''For the model type m, train a model on x->y using built-in CV to
parameterize. Return both this model and an unfit model that can be used for CV.
Note for PLS we cheat a little bit since there isn't a built-in CV trainer.
'''
if m == 'pls':
#have to manually cross-validate to choose number of components
kf = KFold(n_splits=3)
bestscore = -10000
besti = 0
for i in range(1,min(100,len(x[0]))):
#try larger number of components until average CV perf decreases
pls = PLSRegression(i)
scores = []
#TODO: parallelize below
for train,test in kf.split(x):
xtrain = x[train]
ytrain = y[train]
xtest = x[test]
ytest = y[test]
pls.fit(xtrain,ytrain)
score = scoremodel(pls,xtest,ytest)
scores.append(score)
ave = np.mean(scores)
if ave < bestscore*0.95: #getting significantly worse
break
elif ave > bestscore:
bestscore = ave
besti = i
model = PLSRegression(besti)
model.fit(x,y)
unfit = PLSRegression(besti) #choose number of components using full data - iffy
print("PLS components =",besti)
elif m == 'lasso':
model = LassoCV(n_jobs=-1,max_iter=iter)
model.fit(x,y)
unfit = LassoCV(n_jobs=-1,max_iter=iter) #(alpha=model.alpha_)
print("LASSO alpha =",model.alpha_)
return (model,unfit)
elif m == 'ridge':
model = RidgeCV()
model.fit(x,y)
print("Ridge alpha =",model.alpha_)
unfit = RidgeCV()
else:
model = ElasticNetCV(n_jobs=-1,l1_ratio=[.1, .5, .7, .9, .95, .99, 1],max_iter=iter)
model.fit(x,y)
print("Elastic alpha =",model.alpha_," l1_ratio =",model.l1_ratio_)
unfit = ElasticNetCV(n_jobs=-1,max_iter=iter)
return (model,unfit)
