hugobuddel / orange3

Orange/regression/linear.py

import sklearn.linear_model as skl_linear_model

The import sklearn.linear_model could not be resolved.

import sklearn.pipeline as skl_pipeline

The import sklearn.pipeline could not be resolved.

import sklearn.preprocessing as skl_preprocessing

The import sklearn.preprocessing could not be resolved.

from Orange.regression import Learner, Model, SklLearner


__all__ = ["LinearRegressionLearner", "RidgeRegressionLearner",
           "LassoRegressionLearner", "SGDRegressionLearner",
           "ElasticNetLearner", "ElasticNetCVLearner",
           "PolynomialLearner"]


class LinearRegressionLearner(SklLearner):
    __wraps__ = skl_linear_model.LinearRegression
    name = 'linreg'

    def __init__(self, preprocessors=None):
        super().__init__(preprocessors=preprocessors)
    

Trailing whitespace

    def fit(self, X, Y, W):
        sk = skl_linear_model.LinearRegression()
        sk.fit(X, Y)
        return LinearModel(sk)


class RidgeRegressionLearner(SklLearner):
    __wraps__ = skl_linear_model.Ridge
    name = 'ridge'

    def __init__(self, alpha=1.0, fit_intercept=True,

The argument alpha seems to be unused.

The argument fit_intercept seems to be unused.

                 normalize=False, copy_X=True, max_iter=None,

The argument normalize seems to be unused.

The argument copy_X seems to be unused.

The argument max_iter seems to be unused.

                 tol=0.001, solver='auto', preprocessors=None):

The argument solver seems to be unused.

The argument tol seems to be unused.

        super().__init__(preprocessors=preprocessors)
        self.params = vars()


class LassoRegressionLearner(SklLearner):
    __wraps__ = skl_linear_model.Lasso
    name = 'lasso'

    def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,

The argument alpha seems to be unused.

The argument normalize seems to be unused.

The argument fit_intercept seems to be unused.

                 precompute=False, copy_X=True, max_iter=1000,

The argument precompute seems to be unused.

The argument copy_X seems to be unused.

The argument max_iter seems to be unused.

                 tol=0.0001, warm_start=False, positive=False,

The argument positive seems to be unused.

The argument warm_start seems to be unused.

The argument tol seems to be unused.

                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()


class ElasticNetLearner(SklLearner):
    __wraps__ = skl_linear_model.ElasticNet
    name = 'elastic'

    def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,

The argument alpha seems to be unused.

The argument l1_ratio seems to be unused.

The argument fit_intercept seems to be unused.

                 normalize=False, precompute=False, max_iter=1000,

The argument normalize seems to be unused.

The argument max_iter seems to be unused.

The argument precompute seems to be unused.

                 copy_X=True, tol=0.0001, warm_start=False, positive=False,

The argument warm_start seems to be unused.

The argument positive seems to be unused.

The argument tol seems to be unused.

The argument copy_X seems to be unused.

                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()


class ElasticNetCVLearner(SklLearner):
    __wraps__ = skl_linear_model.ElasticNetCV
    name = 'elasticCV'

    def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None,

The argument alphas seems to be unused.

The argument eps seems to be unused.

The argument n_alphas seems to be unused.

The argument l1_ratio seems to be unused.

                 fit_intercept=True, normalize=False, precompute='auto',

The argument precompute seems to be unused.

The argument normalize seems to be unused.

The argument fit_intercept seems to be unused.

                 max_iter=1000, tol=0.0001, cv=None, copy_X=True,

The argument copy_X seems to be unused.

The argument max_iter seems to be unused.

The argument tol seems to be unused.

The argument cv seems to be unused.

                 verbose=0, n_jobs=1, positive=False, preprocessors=None):

The argument positive seems to be unused.

The argument n_jobs seems to be unused.

The argument verbose seems to be unused.

        super().__init__(preprocessors=preprocessors)
        self.params = vars()


class SGDRegressionLearner(SklLearner):
    __wraps__ = skl_linear_model.SGDRegressor
    name = 'sgd'

    def __init__(self, loss='squared_loss', alpha=0.0001, epsilon=0.1,

The argument alpha seems to be unused.

The argument epsilon seems to be unused.

The argument loss seems to be unused.

                 eta0=0.01, l1_ratio=0.15, penalty='l2', power_t=0.25,

The argument eta0 seems to be unused.

The argument penalty seems to be unused.

The argument l1_ratio seems to be unused.

The argument power_t seems to be unused.

                 learning_rate='invscaling', n_iter=5, fit_intercept=True,

The argument learning_rate seems to be unused.

The argument n_iter seems to be unused.

The argument fit_intercept seems to be unused.

                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y, W):
        sk = self.__wraps__(**self.params)
        clf = skl_pipeline.Pipeline(
            [('scaler', skl_preprocessing.StandardScaler()), ('sgd', sk)])
        clf.fit(X, Y.ravel())
        return LinearModel(clf)


class PolynomialLearner(Learner):
    name = 'poly learner'

    def __init__(self, learner, degree=1, preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.degree = degree
        self.learner = learner
    

Trailing whitespace

    def fit(self, X, Y, W):

Signature differs from overridden 'fit' method

        polyfeatures = skl_preprocessing.PolynomialFeatures(self.degree)
        X = polyfeatures.fit_transform(X)
        clf = self.learner
        if W is None or not self.supports_weights:
            model = clf.fit(X, Y, None)
        else:
            model = clf.fit(X, Y, sample_weight=W.reshape(-1))
        return PolynomialModel(model, polyfeatures)


class LinearModel(Model):
    supports_multiclass = True

    def __init__(self, model):

The __init__ method of the super-class ModelRegression is not called.

        self.model = model

    def predict(self, X):
        vals = self.model.predict(X)
        if len(vals.shape) == 1:
            # Prevent IndexError for 1D array
            return vals
        elif vals.shape[1] == 1:
            return vals.ravel()
        else:
            return vals

    def __str__(self):
        return 'LinearModel {}'.format(self.model)

class PolynomialModel(Model):
    supports_multiclass = True

    def __init__(self, model, polyfeatures):

The __init__ method of the super-class ModelRegression is not called.

        self.model = model
        self.polyfeatures = polyfeatures

    def predict(self, X):
        X = self.polyfeatures.fit_transform(X)
        return self.model.predict(X)

    def __str__(self):
        return 'PolynomialModel {}'.format(self.model)