Python cross_decomposition.PLSRegression方法代碼示例

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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')") 

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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')") 

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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')") 

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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) 

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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 

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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) 

# 需要導入模塊: from sklearn import cross_decomposition [as 別名]
# 或者: from sklearn.cross_decomposition import PLSRegression [as 別名]
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)