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Inspection

PartialDependenceDisplay

Module Sklearn.​Inspection.​PartialDependenceDisplay wraps Python class sklearn.inspection.PartialDependenceDisplay.

type t

create

constructor and attributes create
val create :
  pd_results:[>`ArrayLike] Np.Obj.t ->
  features:[`List_of_int_ of Py.Object.t | `Tuples of (int * int) list] ->
  feature_names:string list ->
  target_idx:int ->
  pdp_lim:Dict.t ->
  deciles:Dict.t ->
  unit ->
  t

Partial Dependence Plot (PDP) visualization.

It is recommended to use :func:~sklearn.inspection.plot_partial_dependence to create a :class:~sklearn.inspection.PartialDependenceDisplay. All parameters are stored as attributes.

Read more in :ref:sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py and the :ref:User Guide <visualizations>.

.. versionadded:: 0.22

Parameters

  • pd_results : list of (ndarray, ndarray) Results of :func:~sklearn.inspection.partial_dependence for features. Each tuple corresponds to a (averaged_predictions, grid).

  • features : list of (int,) or list of (int, int) Indices of features for a given plot. A tuple of one integer will plot a partial dependence curve of one feature. A tuple of two integers will plot a two-way partial dependence curve as a contour plot.

  • feature_names : list of str Feature names corresponding to the indices in features.

  • target_idx : int

    • In a multiclass setting, specifies the class for which the PDPs should be computed. Note that for binary classification, the positive class (index 1) is always used.
    • In a multioutput setting, specifies the task for which the PDPs should be computed.

    Ignored in binary classification or classical regression settings.

  • pdp_lim : dict Global min and max average predictions, such that all plots will have the same scale and y limits. pdp_lim[1] is the global min and max for single partial dependence curves. pdp_lim[2] is the global min and max for two-way partial dependence curves.

  • deciles : dict Deciles for feature indices in features.

Attributes

  • bounding_ax_ : matplotlib Axes or None If ax is an axes or None, the bounding_ax_ is the axes where the grid of partial dependence plots are drawn. If ax is a list of axes or a numpy array of axes, bounding_ax_ is None.

  • axes_ : ndarray of matplotlib Axes If ax is an axes or None, axes_[i, j] is the axes on the i-th row and j-th column. If ax is a list of axes, axes_[i] is the i-th item in ax. Elements that are None correspond to a nonexisting axes in that position.

  • lines_ : ndarray of matplotlib Artists If ax is an axes or None, lines_[i, j] is the partial dependence curve on the i-th row and j-th column. If ax is a list of axes, lines_[i] is the partial dependence curve corresponding to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a line plot.

  • deciles_vlines_ : ndarray of matplotlib LineCollection If ax is an axes or None, vlines_[i, j] is the line collection representing the x axis deciles of the i-th row and j-th column. If ax is a list of axes, vlines_[i] corresponds to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a PDP plot. .. versionadded:: 0.23

  • deciles_hlines_ : ndarray of matplotlib LineCollection If ax is an axes or None, vlines_[i, j] is the line collection representing the y axis deciles of the i-th row and j-th column. If ax is a list of axes, vlines_[i] corresponds to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a 2-way plot. .. versionadded:: 0.23

  • contours_ : ndarray of matplotlib Artists If ax is an axes or None, contours_[i, j] is the partial dependence plot on the i-th row and j-th column. If ax is a list of axes, contours_[i] is the partial dependence plot corresponding to the i-th item in ax. Elements that are None correspond to a nonexisting axes or an axes that does not include a contour plot.

  • figure_ : matplotlib Figure Figure containing partial dependence plots.

plot

method plot
val plot :
  ?ax:Py.Object.t ->
  ?n_cols:int ->
  ?line_kw:Dict.t ->
  ?contour_kw:Dict.t ->
  [> tag] Obj.t ->
  Py.Object.t

Plot partial dependence plots.

Parameters

  • ax : Matplotlib axes or array-like of Matplotlib axes, default=None

    • If a single axis is passed in, it is treated as a bounding axes and a grid of partial dependence plots will be drawn within these bounds. The n_cols parameter controls the number of columns in the grid.
    • If an array-like of axes are passed in, the partial dependence plots will be drawn directly into these axes.
    • If None, a figure and a bounding axes is created and treated as the single axes case.
  • n_cols : int, default=3 The maximum number of columns in the grid plot. Only active when ax is a single axes or None.

  • line_kw : dict, default=None Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots.

  • contour_kw : dict, default=None Dict with keywords passed to the matplotlib.pyplot.contourf call for two-way partial dependence plots.

Returns

  • display: :class:~sklearn.inspection.PartialDependenceDisplay

bounding_ax_

attribute bounding_ax_
val bounding_ax_ : t -> Py.Object.t
val bounding_ax_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

axes_

attribute axes_
val axes_ : t -> Py.Object.t
val axes_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

lines_

attribute lines_
val lines_ : t -> Py.Object.t
val lines_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

deciles_vlines_

attribute deciles_vlines_
val deciles_vlines_ : t -> Py.Object.t
val deciles_vlines_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

deciles_hlines_

attribute deciles_hlines_
val deciles_hlines_ : t -> Py.Object.t
val deciles_hlines_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

contours_

attribute contours_
val contours_ : t -> Py.Object.t
val contours_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

figure_

attribute figure_
val figure_ : t -> Py.Object.t
val figure_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string
val to_string: t -> string

Print the object to a human-readable representation.

show

method show
val show: t -> string

Print the object to a human-readable representation.

pp

method pp
val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

partial_dependence

function partial_dependence
val partial_dependence :
  ?response_method:[`Auto | `Predict_proba | `Decision_function] ->
  ?percentiles:Py.Object.t ->
  ?grid_resolution:int ->
  ?method_:string ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  x:Py.Object.t ->
  features:Py.Object.t ->
  unit ->
  ([>`ArrayLike] Np.Obj.t * Py.Object.t)

Partial dependence of features.

Partial dependence of a feature (or a set of features) corresponds to the average response of an estimator for each possible value of the feature.

Read more in the :ref:User Guide <partial_dependence>.

.. warning::

  • For :class:~sklearn.ensemble.GradientBoostingClassifier and :class:~sklearn.ensemble.GradientBoostingRegressor, the 'recursion' method (used by default) will not account for the init predictor of the boosting process. In practice, this will produce the same values as 'brute' up to a constant offset in the target response, provided that init is a constant estimator (which is the default). However, if init is not a constant estimator, the partial dependence values are incorrect for 'recursion' because the offset will be sample-dependent. It is preferable to use the 'brute' method. Note that this only applies to :class:~sklearn.ensemble.GradientBoostingClassifier and :class:~sklearn.ensemble.GradientBoostingRegressor, not to :class:~sklearn.ensemble.HistGradientBoostingClassifier and :class:~sklearn.ensemble.HistGradientBoostingRegressor.

Parameters

  • estimator : BaseEstimator A fitted estimator object implementing :term:predict, :term:predict_proba, or :term:decision_function. Multioutput-multiclass classifiers are not supported.

  • X : {array-like or dataframe} of shape (n_samples, n_features) X is used to generate a grid of values for the target features (where the partial dependence will be evaluated), and also to generate values for the complement features when the method is 'brute'.

  • features : array-like of {int, str} The feature (e.g. [0]) or pair of interacting features (e.g. [(0, 1)]) for which the partial dependency should be computed.

  • response_method : 'auto', 'predict_proba' or 'decision_function', optional (default='auto') Specifies whether to use :term:predict_proba or :term:decision_function as the target response. For regressors this parameter is ignored and the response is always the output of :term:predict. By default, :term:predict_proba is tried first and we revert to :term:decision_function if it doesn't exist. If method is 'recursion', the response is always the output of :term:decision_function.

  • percentiles : tuple of float, optional (default=(0.05, 0.95)) The lower and upper percentile used to create the extreme values for the grid. Must be in [0, 1].

  • grid_resolution : int, optional (default=100) The number of equally spaced points on the grid, for each target feature.

  • method : str, optional (default='auto') The method used to calculate the averaged predictions:

    • 'recursion' is only supported for some tree-based estimators (namely :class:~sklearn.ensemble.GradientBoostingClassifier, :class:~sklearn.ensemble.GradientBoostingRegressor, :class:~sklearn.ensemble.HistGradientBoostingClassifier, :class:~sklearn.ensemble.HistGradientBoostingRegressor, :class:~sklearn.tree.DecisionTreeRegressor, :class:~sklearn.ensemble.RandomForestRegressor, ) but is more efficient in terms of speed. With this method, the target response of a classifier is always the decision function, not the predicted probabilities.

    • 'brute' is supported for any estimator, but is more computationally intensive.

    • 'auto': the 'recursion' is used for estimators that support it, and 'brute' is used otherwise.

    Please see :ref:this note <pdp_method_differences> for differences between the 'brute' and 'recursion' method.

Returns

  • averaged_predictions : ndarray, shape (n_outputs, len(values[0]), len(values[1]), ...) The predictions for all the points in the grid, averaged over all samples in X (or over the training data if method is 'recursion'). n_outputs corresponds to the number of classes in a multi-class setting, or to the number of tasks for multi-output regression. For classical regression and binary classification n_outputs==1. n_values_feature_j corresponds to the size values[j].

  • values : seq of 1d ndarrays The values with which the grid has been created. The generated grid is a cartesian product of the arrays in values. len(values) == len(features). The size of each array values[j] is either grid_resolution, or the number of unique values in X[:, j], whichever is smaller.

Examples

>>> X = [[0, 0, 2], [1, 0, 0]]
>>> y = [0, 1]
>>> from sklearn.ensemble import GradientBoostingClassifier
>>> gb = GradientBoostingClassifier(random_state=0).fit(X, y)
>>> partial_dependence(gb, features=[0], X=X, percentiles=(0, 1),
...                    grid_resolution=2) # doctest: +SKIP
(array([[-4.52...,  4.52...]]), [array([ 0.,  1.])])

See also

  • sklearn.inspection.plot_partial_dependence: Plot partial dependence

permutation_importance

function permutation_importance
val permutation_importance :
  ?scoring:[`Score of [`Explained_variance | `R2 | `Max_error | `Neg_median_absolute_error | `Neg_mean_absolute_error | `Neg_mean_squared_error | `Neg_mean_squared_log_error | `Neg_root_mean_squared_error | `Neg_mean_poisson_deviance | `Neg_mean_gamma_deviance | `Accuracy | `Roc_auc | `Roc_auc_ovr | `Roc_auc_ovo | `Roc_auc_ovr_weighted | `Roc_auc_ovo_weighted | `Balanced_accuracy | `Average_precision | `Neg_log_loss | `Neg_brier_score | `Adjusted_rand_score | `Homogeneity_score | `Completeness_score | `V_measure_score | `Mutual_info_score | `Adjusted_mutual_info_score | `Normalized_mutual_info_score | `Fowlkes_mallows_score | `Precision | `Precision_macro | `Precision_micro | `Precision_samples | `Precision_weighted | `Recall | `Recall_macro | `Recall_micro | `Recall_samples | `Recall_weighted | `F1 | `F1_macro | `F1_micro | `F1_samples | `F1_weighted | `Jaccard | `Jaccard_macro | `Jaccard_micro | `Jaccard_samples | `Jaccard_weighted] | `Callable of Py.Object.t] ->
  ?n_repeats:int ->
  ?n_jobs:int ->
  ?random_state:int ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  x:[`Arr of [>`ArrayLike] Np.Obj.t | `DataFrame of Py.Object.t] ->
  y:[`Arr of [>`ArrayLike] Np.Obj.t | `None] ->
  unit ->
  (Py.Object.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t * [>`ArrayLike] Np.Obj.t)

Permutation importance for feature evaluation [BRE]_.

  • The :term:estimator is required to be a fitted estimator. X can be the data set used to train the estimator or a hold-out set. The permutation importance of a feature is calculated as follows. First, a baseline metric, defined by :term:scoring, is evaluated on a (potentially different) dataset defined by the X. Next, a feature column from the validation set is permuted and the metric is evaluated again. The permutation importance is defined to be the difference between the baseline metric and metric from permutating the feature column.

Read more in the :ref:User Guide <permutation_importance>.

Parameters

  • estimator : object An estimator that has already been :term:fitted and is compatible

  • with :term:scorer.

  • X : ndarray or DataFrame, shape (n_samples, n_features) Data on which permutation importance will be computed.

  • y : array-like or None, shape (n_samples, ) or (n_samples, n_classes) Targets for supervised or None for unsupervised.

  • scoring : string, callable or None, default=None Scorer to use. It can be a single string (see :ref:scoring_parameter) or a callable (see :ref:scoring). If None, the estimator's default scorer is used.

  • n_repeats : int, default=5 Number of times to permute a feature.

  • n_jobs : int or None, default=None The number of jobs to use for the computation. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.

  • random_state : int, RandomState instance, default=None Pseudo-random number generator to control the permutations of each feature. Pass an int to get reproducible results across function calls.

  • See :term: Glossary <random_state>.

Returns

  • result : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes.

  • importances_mean : ndarray, shape (n_features, ) Mean of feature importance over n_repeats.

  • importances_std : ndarray, shape (n_features, ) Standard deviation over n_repeats.

  • importances : ndarray, shape (n_features, n_repeats) Raw permutation importance scores.

References

.. [BRE] L. Breiman, 'Random Forests', Machine Learning, 45(1), 5-32, 2001. https://doi.org/10.1023/A:1010933404324

Examples

>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.inspection import permutation_importance
>>> X = [[1, 9, 9],[1, 9, 9],[1, 9, 9],
...      [0, 9, 9],[0, 9, 9],[0, 9, 9]]
>>> y = [1, 1, 1, 0, 0, 0]
>>> clf = LogisticRegression().fit(X, y)
>>> result = permutation_importance(clf, X, y, n_repeats=10,
...                                 random_state=0)
>>> result.importances_mean
array([0.4666..., 0.       , 0.       ])
>>> result.importances_std
array([0.2211..., 0.       , 0.       ])

plot_partial_dependence

function plot_partial_dependence
val plot_partial_dependence :
  ?feature_names:[`Dtype_str of Py.Object.t | `Ss of string list] ->
  ?target:int ->
  ?response_method:[`Auto | `Predict_proba | `Decision_function] ->
  ?n_cols:int ->
  ?grid_resolution:int ->
  ?percentiles:Py.Object.t ->
  ?method_:string ->
  ?n_jobs:int ->
  ?verbose:int ->
  ?fig:Py.Object.t ->
  ?line_kw:Dict.t ->
  ?contour_kw:Dict.t ->
  ?ax:Py.Object.t ->
  estimator:[>`BaseEstimator] Np.Obj.t ->
  x:Py.Object.t ->
  features:[`S of string | `PyObject of Py.Object.t] ->
  unit ->
  Py.Object.t

Partial dependence plots.

The len(features) plots are arranged in a grid with n_cols columns. Two-way partial dependence plots are plotted as contour plots. The deciles of the feature values will be shown with tick marks on the x-axes for one-way plots, and on both axes for two-way plots.

Read more in the :ref:User Guide <partial_dependence>.

.. note::

:func:`plot_partial_dependence` does not support using the same axes
with multiple calls. To plot the the partial dependence for multiple
estimators, please pass the axes created by the first call to the
second call::

  >>> from sklearn.inspection import plot_partial_dependence
  >>> from sklearn.datasets import make_friedman1
  >>> from sklearn.linear_model import LinearRegression
  >>> X, y = make_friedman1()
  >>> est = LinearRegression().fit(X, y)
  >>> disp1 = plot_partial_dependence(est, X)  # doctest: +SKIP
  >>> disp2 = plot_partial_dependence(est, X,
  ...                                 ax=disp1.axes_)  # doctest: +SKIP

.. warning::

  • For :class:~sklearn.ensemble.GradientBoostingClassifier and :class:~sklearn.ensemble.GradientBoostingRegressor, the 'recursion' method (used by default) will not account for the init predictor of the boosting process. In practice, this will produce the same values as 'brute' up to a constant offset in the target response, provided that init is a constant estimator (which is the default). However, if init is not a constant estimator, the partial dependence values are incorrect for 'recursion' because the offset will be sample-dependent. It is preferable to use the 'brute' method. Note that this only applies to :class:~sklearn.ensemble.GradientBoostingClassifier and :class:~sklearn.ensemble.GradientBoostingRegressor, not to :class:~sklearn.ensemble.HistGradientBoostingClassifier and :class:~sklearn.ensemble.HistGradientBoostingRegressor.

Parameters

  • estimator : BaseEstimator A fitted estimator object implementing :term:predict, :term:predict_proba, or :term:decision_function. Multioutput-multiclass classifiers are not supported.

  • X : {array-like or dataframe} of shape (n_samples, n_features) X is used to generate a grid of values for the target features (where the partial dependence will be evaluated), and also to generate values for the complement features when the method is 'brute'.

  • features : list of {int, str, pair of int, pair of str} The target features for which to create the PDPs. If features[i] is an int or a string, a one-way PDP is created; if features[i] is a tuple, a two-way PDP is created. Each tuple must be of size 2. if any entry is a string, then it must be in feature_names.

  • feature_names : array-like of shape (n_features,), dtype=str, default=None Name of each feature; feature_names[i] holds the name of the feature with index i. By default, the name of the feature corresponds to their numerical index for NumPy array and their column name for pandas dataframe.

  • target : int, optional (default=None)

    • In a multiclass setting, specifies the class for which the PDPs should be computed. Note that for binary classification, the positive class (index 1) is always used.
    • In a multioutput setting, specifies the task for which the PDPs should be computed.

    Ignored in binary classification or classical regression settings.

  • response_method : 'auto', 'predict_proba' or 'decision_function', optional (default='auto') Specifies whether to use :term:predict_proba or :term:decision_function as the target response. For regressors this parameter is ignored and the response is always the output of :term:predict. By default, :term:predict_proba is tried first and we revert to :term:decision_function if it doesn't exist. If method is 'recursion', the response is always the output of :term:decision_function.

  • n_cols : int, optional (default=3) The maximum number of columns in the grid plot. Only active when ax is a single axis or None.

  • grid_resolution : int, optional (default=100) The number of equally spaced points on the axes of the plots, for each target feature.

  • percentiles : tuple of float, optional (default=(0.05, 0.95)) The lower and upper percentile used to create the extreme values for the PDP axes. Must be in [0, 1].

  • method : str, optional (default='auto') The method used to calculate the averaged predictions:

    • 'recursion' is only supported for some tree-based estimators (namely :class:~sklearn.ensemble.GradientBoostingClassifier, :class:~sklearn.ensemble.GradientBoostingRegressor, :class:~sklearn.ensemble.HistGradientBoostingClassifier, :class:~sklearn.ensemble.HistGradientBoostingRegressor, :class:~sklearn.tree.DecisionTreeRegressor, :class:~sklearn.ensemble.RandomForestRegressor but is more efficient in terms of speed. With this method, the target response of a classifier is always the decision function, not the predicted probabilities.

    • 'brute' is supported for any estimator, but is more computationally intensive.

    • 'auto': the 'recursion' is used for estimators that support it, and 'brute' is used otherwise.

    Please see :ref:this note <pdp_method_differences> for differences between the 'brute' and 'recursion' method.

  • n_jobs : int, optional (default=None) The number of CPUs to use to compute the partial dependences. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.

  • verbose : int, optional (default=0) Verbose output during PD computations.

  • fig : Matplotlib figure object, optional (default=None) A figure object onto which the plots will be drawn, after the figure has been cleared. By default, a new one is created.

    .. deprecated:: 0.22 fig will be removed in 0.24.

  • line_kw : dict, optional Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots.

  • contour_kw : dict, optional Dict with keywords passed to the matplotlib.pyplot.contourf call. For two-way partial dependence plots.

  • ax : Matplotlib axes or array-like of Matplotlib axes, default=None

    • If a single axis is passed in, it is treated as a bounding axes and a grid of partial dependence plots will be drawn within these bounds. The n_cols parameter controls the number of columns in the grid.
    • If an array-like of axes are passed in, the partial dependence plots will be drawn directly into these axes.
    • If None, a figure and a bounding axes is created and treated as the single axes case.

    .. versionadded:: 0.22

Returns

  • display: :class:~sklearn.inspection.PartialDependenceDisplay

Examples

>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> plot_partial_dependence(clf, X, [0, (0, 1)]) #doctest: +SKIP

See also

  • sklearn.inspection.partial_dependence: Return raw partial dependence values