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[WIP] DOC Document default values for bayes.py (scikit-learn#14518)
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@qdeffense @amueller
qdeffense authored and amueller committed Aug 14, 2019
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Showing 1 changed file with 58 additions and 64 deletions.
122 changes: 58 additions & 64 deletions sklearn/linear_model/bayes.py
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Expand Up @@ -30,74 +30,70 @@ class BayesianRidge(LinearModel, RegressorMixin):
Parameters
----------
n_iter : int, optional
Maximum number of iterations. Default is 300. Should be greater than
or equal to 1.
n_iter : int, default=300
Maximum number of iterations. Should be greater than or equal to 1.
tol : float, optional
Stop the algorithm if w has converged. Default is 1.e-3.
tol : float, default=1e-3
Stop the algorithm if w has converged.
alpha_1 : float, optional
alpha_1 : float, default=1e-6
Hyper-parameter : shape parameter for the Gamma distribution prior
over the alpha parameter. Default is 1.e-6
over the alpha parameter.
alpha_2 : float, optional
alpha_2 : float, default=1e-6
Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the alpha parameter.
Default is 1.e-6.
lambda_1 : float, optional
lambda_1 : float, default=1e-6
Hyper-parameter : shape parameter for the Gamma distribution prior
over the lambda parameter. Default is 1.e-6.
over the lambda parameter.
lambda_2 : float, optional
lambda_2 : float, default=1e-6
Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the lambda parameter.
Default is 1.e-6
alpha_init : float
alpha_init : float, default=None
Initial value for alpha (precision of the noise).
If not set, alpha_init is 1/Var(y).
.. versionadded:: 0.22
lambda_init : float
lambda_init : float, default=None
Initial value for lambda (precision of the weights).
If not set, lambda_init is 1.
.. versionadded:: 0.22
compute_score : boolean, optional
compute_score : bool, default=False
If True, compute the log marginal likelihood at each iteration of the
optimization. Default is False.
optimization.
fit_intercept : boolean, optional, default True
fit_intercept : bool, default=True
Whether to calculate the intercept for this model.
The intercept is not treated as a probabilistic parameter
and thus has no associated variance. If set
to False, no intercept will be used in calculations
(i.e. data is expected to be centered).
normalize : boolean, optional, default False
normalize : bool, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.
copy_X : boolean, optional, default True
copy_X : bool, default=True
If True, X will be copied; else, it may be overwritten.
verbose : boolean, optional, default False
verbose : bool, default=False
Verbose mode when fitting the model.
Attributes
----------
coef_ : array, shape = (n_features,)
Coefficients of the regression model (mean of distribution).
coef_ : array-like of shape (n_features,)
Coefficients of the regression model (mean of distribution)
intercept_ : float
Independent term in decision function. Set to 0.0 if
Expand All @@ -109,10 +105,10 @@ class BayesianRidge(LinearModel, RegressorMixin):
lambda_ : float
Estimated precision of the weights.
sigma_ : array, shape = (n_features, n_features)
Estimated variance-covariance matrix of the weights.
sigma_ : array-like of shape (n_features, n_features)
Estimated variance-covariance matrix of the weights
scores_ : array, shape = (n_iter_ + 1,)
scores_ : array-like of shape (n_iter_ + 1,)
If computed_score is True, value of the log marginal likelihood (to be
maximized) at each iteration of the optimization. The array starts
with the value of the log marginal likelihood obtained for the initial
Expand Down Expand Up @@ -173,12 +169,12 @@ def fit(self, X, y, sample_weight=None):
Parameters
----------
X : numpy array of shape [n_samples,n_features]
X : ndarray of shape (n_samples,n_features)
Training data
y : numpy array of shape [n_samples]
y : ndarray of shape (n_samples,)
Target values. Will be cast to X's dtype if necessary
sample_weight : numpy array of shape [n_samples]
sample_weight : ndarray of shape (n_samples,), default=None
Individual weights for each sample
.. versionadded:: 0.20
Expand Down Expand Up @@ -297,18 +293,18 @@ def predict(self, X, return_std=False):
Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samples.
return_std : boolean, optional
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
Returns
-------
y_mean : array, shape = (n_samples,)
y_mean : array-like of shape (n_samples,)
Mean of predictive distribution of query points.
y_std : array, shape = (n_samples,)
y_std : array-like of shape (n_samples,)
Standard deviation of predictive distribution of query points.
"""
y_mean = self._decision_function(X)
Expand Down Expand Up @@ -392,68 +388,66 @@ class ARDRegression(LinearModel, RegressorMixin):
Parameters
----------
n_iter : int, optional
Maximum number of iterations. Default is 300
n_iter : int, default=300
Maximum number of iterations.
tol : float, optional
Stop the algorithm if w has converged. Default is 1.e-3.
tol : float, default=1e-3
Stop the algorithm if w has converged.
alpha_1 : float, optional
alpha_1 : float, default=1e-6
Hyper-parameter : shape parameter for the Gamma distribution prior
over the alpha parameter. Default is 1.e-6.
over the alpha parameter.
alpha_2 : float, optional
alpha_2 : float, default=1e-6
Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the alpha parameter. Default is 1.e-6.
Gamma distribution prior over the alpha parameter.
lambda_1 : float, optional
lambda_1 : float, default=1e-6
Hyper-parameter : shape parameter for the Gamma distribution prior
over the lambda parameter. Default is 1.e-6.
over the lambda parameter.
lambda_2 : float, optional
lambda_2 : float, default=1e-6
Hyper-parameter : inverse scale parameter (rate parameter) for the
Gamma distribution prior over the lambda parameter. Default is 1.e-6.
Gamma distribution prior over the lambda parameter.
compute_score : boolean, optional
compute_score : bool, default=False
If True, compute the objective function at each step of the model.
Default is False.
threshold_lambda : float, optional
threshold_lambda : float, default=10 000
threshold for removing (pruning) weights with high precision from
the computation. Default is 1.e+4.
the computation.
fit_intercept : boolean, optional
fit_intercept : bool, default=True
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be centered).
Default is True.
normalize : boolean, optional, default False
normalize : bool, default=False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.
copy_X : boolean, optional, default True.
copy_X : bool, default=True
If True, X will be copied; else, it may be overwritten.
verbose : boolean, optional, default False
verbose : bool, default=False
Verbose mode when fitting the model.
Attributes
----------
coef_ : array, shape = (n_features)
coef_ : array-like of shape (n_features,)
Coefficients of the regression model (mean of distribution)
alpha_ : float
estimated precision of the noise.
lambda_ : array, shape = (n_features)
lambda_ : array-like of shape (n_features,)
estimated precisions of the weights.
sigma_ : array, shape = (n_features, n_features)
sigma_ : array-like of shape (n_features, n_features)
estimated variance-covariance matrix of the weights
scores_ : float
Expand Down Expand Up @@ -512,10 +506,10 @@ def fit(self, X, y):
Parameters
----------
X : array-like, shape = [n_samples, n_features]
X : array-like of shape (n_samples, n_features)
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array, shape = [n_samples]
y : array-like of shape (n_samples,)
Target values (integers). Will be cast to X's dtype if necessary
Returns
Expand Down Expand Up @@ -621,18 +615,18 @@ def predict(self, X, return_std=False):
Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samples.
return_std : boolean, optional
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
Returns
-------
y_mean : array, shape = (n_samples,)
y_mean : array-like of shape (n_samples,)
Mean of predictive distribution of query points.
y_std : array, shape = (n_samples,)
y_std : array-like of shape (n_samples,)
Standard deviation of predictive distribution of query points.
"""
y_mean = self._decision_function(X)
Expand Down

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