[MRG] DOC Examples added to the rest of linear models (scikit-learn#11975)

Author: adrinjalali

sklearn/linear_model/base.py

@@ -399,6 +399,23 @@ class LinearRegression(LinearModel, RegressorMixin):
     intercept_ : array
         Independent term in the linear model.
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.linear_model import LinearRegression
+    >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
+    >>> # y = 1 * x_0 + 2 * x_1 + 3
+    >>> y = np.dot(X, np.array([1, 2])) + 3
+    >>> reg = LinearRegression().fit(X, y)
+    >>> reg.score(X, y)
+    1.0
+    >>> reg.coef_
+    array([1., 2.])
+    >>> reg.intercept_ # doctest: +ELLIPSIS
+    3.0000...
+    >>> reg.predict(np.array([[3, 5]]))
+    array([16.])
+
     Notes
     -----
     From the implementation point of view, this is just plain Ordinary

sklearn/linear_model/coordinate_descent.py

@@ -1368,6 +1368,17 @@ class LassoCV(LinearModelCV, RegressorMixin):
         number of iterations run by the coordinate descent solver to reach
         the specified tolerance for the optimal alpha.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import LassoCV
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(noise=4, random_state=0)
+    >>> reg = LassoCV(cv=5, random_state=0).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9993...
+    >>> reg.predict(X[:1,])
+    array([-78.4951...])
+
     Notes
     -----
     For an example, see
@@ -2235,6 +2246,19 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
         number of iterations run by the coordinate descent solver to reach
         the specified tolerance for the optimal alpha.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import MultiTaskLassoCV
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(n_targets=2, noise=4, random_state=0)
+    >>> reg = MultiTaskLassoCV(cv=5, random_state=0).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9994...
+    >>> reg.alpha_
+    0.5713...
+    >>> reg.predict(X[:1,])
+    array([[153.7971...,  94.9015...]])
+
     See also
     --------
     MultiTaskElasticNet

sklearn/linear_model/huber.py

@@ -192,6 +192,29 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
         A boolean mask which is set to True where the samples are identified
         as outliers.
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.linear_model import HuberRegressor, LinearRegression
+    >>> from sklearn.datasets import make_regression
+    >>> np.random.seed(0)
+    >>> X, y, coef = make_regression(
+    ...     n_samples=200, n_features=2, noise=4.0, coef=True, random_state=0)
+    >>> X[:4] = np.random.uniform(10, 20, (4, 2))
+    >>> y[:4] = np.random.uniform(10, 20, 4)
+    >>> huber = HuberRegressor().fit(X, y)
+    >>> huber.score(X, y) # doctest: +ELLIPSIS
+    -7.284608623514573
+    >>> huber.predict(X[:1,])
+    array([806.7200...])
+    >>> linear = LinearRegression().fit(X, y)
+    >>> print("True coefficients:", coef)
+    True coefficients: [20.4923...  34.1698...]
+    >>> print("Huber coefficients:", huber.coef_)
+    Huber coefficients: [17.7906... 31.0106...]
+    >>> print("Linear Regression coefficients:", linear.coef_)
+    Linear Regression coefficients: [-1.9221...  7.0226...]
+
     References
     ----------
     .. [1] Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics

sklearn/linear_model/least_angle.py

@@ -1070,6 +1070,19 @@ class LarsCV(Lars):
     n_iter_ : array-like or int
         the number of iterations run by Lars with the optimal alpha.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import LarsCV
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(n_samples=200, noise=4.0, random_state=0)
+    >>> reg = LarsCV(cv=5).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9996...
+    >>> reg.alpha_
+    0.0254...
+    >>> reg.predict(X[:1,])
+    array([154.0842...])
+
     See also
     --------
     lars_path, LassoLars, LassoLarsCV
@@ -1290,6 +1303,19 @@ class LassoLarsCV(LarsCV):
     n_iter_ : array-like or int
         the number of iterations run by Lars with the optimal alpha.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import LassoLarsCV
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(noise=4.0, random_state=0)
+    >>> reg = LassoLarsCV(cv=5).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9992...
+    >>> reg.alpha_
+    0.0484...
+    >>> reg.predict(X[:1,])
+    array([-77.8723...])
+
     Notes
     -----
 

sklearn/linear_model/omp.py

@@ -583,6 +583,17 @@ class OrthogonalMatchingPursuit(LinearModel, RegressorMixin):
     n_iter_ : int or array-like
         Number of active features across every target.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import OrthogonalMatchingPursuit
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(noise=4, random_state=0)
+    >>> reg = OrthogonalMatchingPursuit().fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9991...
+    >>> reg.predict(X[:1,])
+    array([-78.3854...])
+
     Notes
     -----
     Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
@@ -814,6 +825,20 @@ class OrthogonalMatchingPursuitCV(LinearModel, RegressorMixin):
         Number of active features across every target for the model refit with
         the best hyperparameters got by cross-validating across all folds.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import OrthogonalMatchingPursuitCV
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(n_features=100, n_informative=10,
+    ...                        noise=4, random_state=0)
+    >>> reg = OrthogonalMatchingPursuitCV(cv=5).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9991...
+    >>> reg.n_nonzero_coefs_
+    10
+    >>> reg.predict(X[:1,])
+    array([-78.3854...])
+
     See also
     --------
     orthogonal_mp

sklearn/linear_model/ransac.py

@@ -186,6 +186,18 @@ class RANSACRegressor(BaseEstimator, MetaEstimatorMixin, RegressorMixin):
 
         .. versionadded:: 0.19
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import RANSACRegressor
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(
+    ...     n_samples=200, n_features=2, noise=4.0, random_state=0)
+    >>> reg = RANSACRegressor(random_state=0).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9885...
+    >>> reg.predict(X[:1,])
+    array([-31.9417...])
+
     References
     ----------
     .. [1] https://en.wikipedia.org/wiki/RANSAC

sklearn/linear_model/theil_sen.py

@@ -276,6 +276,18 @@ class TheilSenRegressor(LinearModel, RegressorMixin):
         Number of combinations taken into account from 'n choose k', where n is
         the number of samples and k is the number of subsamples.
 
+    Examples
+    --------
+    >>> from sklearn.linear_model import TheilSenRegressor
+    >>> from sklearn.datasets import make_regression
+    >>> X, y = make_regression(
+    ...     n_samples=200, n_features=2, noise=4.0, random_state=0)
+    >>> reg = TheilSenRegressor(random_state=0).fit(X, y)
+    >>> reg.score(X, y) # doctest: +ELLIPSIS
+    0.9884...
+    >>> reg.predict(X[:1,])
+    array([-31.5871...])
+
     References
     ----------
     - Theil-Sen Estimators in a Multiple Linear Regression Model, 2009