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DOC Ensures BayesianGaussianMixture docstring passes numpydoc validation #20384

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Jun 28, 2021
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DOC Ensures BayesianGaussianMixture docstring passes numpydoc validation #20384

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25efb19
Ensure numpydoc validation
nstefani-ml Jun 26, 2021
40b5311
PR suggested changes
nico-stefani Jun 26, 2021
980fbcf
Update sklearn/mixture/_base.py
glemaitre Jun 28, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@
"AgglomerativeClustering",
"BaggingClassifier",
"BaggingRegressor",
"BayesianGaussianMixture",
"BayesianRidge",
"BernoulliNB",
"BernoulliRBM",
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17 changes: 13 additions & 4 deletions sklearn/mixture/_base.py
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Expand Up @@ -183,9 +183,13 @@ def fit(self, X, y=None):
List of n_features-dimensional data points. Each row
corresponds to a single data point.

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
self
self : object
The fitted mixture.
"""
self.fit_predict(X, y)
return self
Expand All @@ -209,6 +213,9 @@ def fit_predict(self, X, y=None):
List of n_features-dimensional data points. Each row
corresponds to a single data point.

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
labels : array, shape (n_samples,)
Expand Down Expand Up @@ -351,6 +358,9 @@ def score(self, X, y=None):
List of n_features-dimensional data points. Each row
corresponds to a single data point.

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
log_likelihood : float
Expand Down Expand Up @@ -407,11 +417,10 @@ def sample(self, n_samples=1):
Returns
-------
X : array, shape (n_samples, n_features)
Randomly generated sample
Randomly generated sample.

y : array, shape (nsamples,)
Component labels

Component labels.
"""
check_is_fitted(self)

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34 changes: 17 additions & 17 deletions sklearn/mixture/_bayesian_mixture.py
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Expand Up @@ -133,7 +133,7 @@ class BayesianGaussianMixture(BaseMixture):
'dirichlet_process' (using the Stick-breaking representation),
'dirichlet_distribution' (can favor more uniform weights).

weight_concentration_prior : float | None, default=None.
weight_concentration_prior : float or None, default=None
The dirichlet concentration of each component on the weight
distribution (Dirichlet). This is commonly called gamma in the
literature. The higher concentration puts more mass in
Expand All @@ -142,22 +142,22 @@ class BayesianGaussianMixture(BaseMixture):
mixture weights simplex. The value of the parameter must be greater
than 0. If it is None, it's set to ``1. / n_components``.

mean_precision_prior : float | None, default=None.
mean_precision_prior : float or None, default=None
The precision prior on the mean distribution (Gaussian).
Controls the extent of where means can be placed. Larger
values concentrate the cluster means around `mean_prior`.
The value of the parameter must be greater than 0.
If it is None, it is set to 1.

mean_prior : array-like, shape (n_features,), default=None.
mean_prior : array-like, shape (n_features,), default=None
The prior on the mean distribution (Gaussian).
If it is None, it is set to the mean of X.

degrees_of_freedom_prior : float | None, default=None.
degrees_of_freedom_prior : float or None, default=None
The prior of the number of degrees of freedom on the covariance
distributions (Wishart). If it is None, it's set to `n_features`.

covariance_prior : float or array-like, default=None.
covariance_prior : float or array-like, default=None
The prior on the covariance distribution (Wishart).
If it is None, the emiprical covariance prior is initialized using the
covariance of X. The shape depends on `covariance_type`::
Expand Down Expand Up @@ -297,18 +297,6 @@ class BayesianGaussianMixture(BaseMixture):

.. versionadded:: 0.24

Examples
--------
>>> import numpy as np
>>> from sklearn.mixture import BayesianGaussianMixture
>>> X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [12, 4], [10, 7]])
>>> bgm = BayesianGaussianMixture(n_components=2, random_state=42).fit(X)
>>> bgm.means_
array([[2.49... , 2.29...],
[8.45..., 4.52... ]])
>>> bgm.predict([[0, 0], [9, 3]])
array([0, 1])

See Also
--------
GaussianMixture : Finite Gaussian mixture fit with EM.
Expand All @@ -328,6 +316,18 @@ class BayesianGaussianMixture(BaseMixture):
.. [3] `Blei, David M. and Michael I. Jordan. (2006). "Variational
inference for Dirichlet process mixtures". Bayesian analysis 1.1
<https://www.cs.princeton.edu/courses/archive/fall11/cos597C/reading/BleiJordan2005.pdf>`_

Examples
--------
>>> import numpy as np
>>> from sklearn.mixture import BayesianGaussianMixture
>>> X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [12, 4], [10, 7]])
>>> bgm = BayesianGaussianMixture(n_components=2, random_state=42).fit(X)
>>> bgm.means_
array([[2.49... , 2.29...],
[8.45..., 4.52... ]])
>>> bgm.predict([[0, 0], [9, 3]])
array([0, 1])
"""

def __init__(
Expand Down