8000 Merge pull request #1366 from agramfort/move_isotonic · erg/scikit-learn@9432b4e · GitHub
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Merge pull request scikit-learn#1366 from agramfort/move_isotonic
API : move isotonic regression out of linear_model
2 parents 2468cbf + dcbddc7 commit 9432b4e

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‎doc/modules/classes.rst‎

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hmm.MultinomialHMM
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hmm.GMMHMM
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.. _isotonic_ref:
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:mod:`sklearn.isotonic`: Isotonic regression
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============================================
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.. automodule:: sklearn.isotonic
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:no-members:
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:no-inherited-members:
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**User guide:** See the :ref:`isotonic` section for further details.
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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isotonic.IsotonicRegression
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.. autosummary::
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:toctree: generated
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:template: function.rst
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isotonic.isotonic_regression
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.. _kernel_approximation_ref:
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linear_model.BayesianRidge
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linear_model.ElasticNet
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linear_model.ElasticNetCV
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linear_model.IsotonicRegression
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linear_model.Lars
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linear_model.LarsCV
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linear_model.Lasso
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:toctree: generated/
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:template: function.rst
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linear_model.isotonic_regression
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linear_model.lars_path
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linear_model.lasso_path
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linear_model.lasso_stability_path

‎doc/modules/isotonic.rst‎

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.. _isotonic:
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===================
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Isotonic regression
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===================
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.. currentmodule:: sklearn.isotonic
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The :class:`IsotonicRegression` fits a non-decreasing function to the data.
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It solves the following problem:
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minimize :math:`\sum_i w_i (y_i - \hat{y}_i)^2`
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subject to :math:`\hat{y}_{min} = \hat{y}_1 \le \hat{y}_2 ... \le \hat{y}_n = \hat{y}_{max}`
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where each :math:`w_i` is strictly positive and each :math:`y_i` is an
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arbitrary real number. It yields the vector which is composed of non-decreasing
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elements the closest in terms of mean squared error. In practice this list
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of elements forms a function that is piecewise linear.
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.. figure:: ../auto_examples/linear_model/images/plot_isotonic_regression_1.png
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:target: ../auto_examples/linear_model/images/plot_isotonic_regression.html
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:align: center

‎doc/modules/linear_model.rst‎

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thus be used to perform feature selection, as detailed in
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:ref:`l1_feature_selection`.
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Isotonic regression
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====================
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The :class:`IsotonicRegression` fits a non-decreasing function to the data.
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It solves the following problem:
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minimize :math:`\sum_i w_i (y_i - \hat{y}_i)^2`
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subject to :math:`\hat{y}_{min} = \hat{y}_1 \le \hat{y}_2 ... \le \hat{y}_n = \hat{y}_{max}`
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where each :math:`w_i` is strictly positive and each :math:`y_i` is an
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arbitrary real number. It yields the vector which is composed of non-decreasing
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elements the closest in terms of mean squared error. In practice this list
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of elements forms a function that is piecewise linear.
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.. figure:: ../auto_examples/linear_model/images/plot_isotonic_regression_1.png
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:target: ../auto_examples/linear_model/images/plot_isotonic_regression.html
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:align: center
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Stochastic Gradient Descent - SGD
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=================================
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‎doc/supervised_learning.rst‎

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modules/feature_selection.rst
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modules/label_propagation.rst
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modules/lda_qda.rst
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modules/isotonic.rst

‎examples/linear_model/plot_isotonic_regression.py‎ renamed to ‎examples/plot_isotonic_regression.py‎

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import pylab as pl
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from matplotlib.collections import LineCollection
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from sklearn.linear_model import IsotonicRegression, LinearRegression
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from sklearn.linear_model import LinearRegression
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from sklearn.isotonic import IsotonicRegression
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from sklearn.utils import check_random_state
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n = 100

‎sklearn/__init__.py‎

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'feature_selection', 'semi_supervised',
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'gaussian_process', 'grid_search', 'hmm', 'lda', 'linear_model',
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'metrics', 'mixture', 'naive_bayes', 'neighbors', 'pipeline',
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'preprocessing', 'qda', 'svm', 'test', 'clone', 'pls']
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'preprocessing', 'qda', 'svm', 'test', 'clone', 'pls',
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'isotonic']
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def setup_module(module):

‎sklearn/linear_model/isotonic_regression_.py‎ renamed to ‎sklearn/isotonic.py‎

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import numpy as np
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from scipy import interpolate
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from ..base import BaseEstimator, TransformerMixin, RegressorMixin
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from ..utils import as_float_array, check_arrays
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from .base import BaseEstimator, TransformerMixin, RegressorMixin
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from .utils import as_float_array, check_arrays
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def isotonic_regression(y, weight=None, y_min=None, y_max=None):

‎sklearn/linear_model/__init__.py‎

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from .perceptron import Perceptron
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from .randomized_l1 import RandomizedLasso, RandomizedLogisticRegression, \
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lasso_stability_path
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from .isotonic_regression_ import IsotonicRegression, isotonic_regression
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__all__ = ['ARDRegression',
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'BayesianRidge',

‎sklearn/manifold/mds.py‎

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from ..utils import check_random_state, check_arrays
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from ..externals.joblib import Parallel
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from ..externals.joblib import delayed
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from ..linear_model.isotonic_regression_ import isotonic_regression
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from ..isotonic import isotonic_regression
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def _smacof_single(similarities, metric=True, n_components=2, init=None,

‎sklearn/tests/test_common.py‎

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Normalizer, OneHotEncoder
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from sklearn.cluster import WardAgglomeration, AffinityPropagation, \
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SpectralClustering
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from sklearn.linear_model import IsotonicRegression
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from sklearn.isotonic import IsotonicRegression
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dont_test = [Pipeline, FeatureUnion, GridSearchCV, SparseCoder,
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EllipticEnvelope, EllipticEnvelop, DictVectorizer, LabelBinarizer,

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