PartialDependenceDisplay
Partial Dependence Plot (PDP).
This can also display individual partial dependencies which are often referred to as: Individual Condition Expectation (ICE).
It is recommended to use from\_estimator to create a PartialDependenceDisplay. All parameters are stored as attributes.
Read more in Advanced Plotting With Partial Dependence and the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new PartialDependenceDisplay(opts?: object): PartialDependenceDisplay;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.deciles? | any | Deciles for feature indices in features. |
opts.feature_names? | any | Feature names corresponding to the indices in features. |
opts.features? | any | 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. |
opts.is_categorical? | any | Whether each target feature in features is categorical or not. The list should be same size as features. If undefined, all features are assumed to be continuous. |
opts.kind? | "average" | "individual" | "both" | Whether to plot the partial dependence averaged across all the samples in the dataset or one line per sample or both. Default Value 'average' |
opts.pd_results? | any | Results of partial\_dependence for features. |
opts.pdp_lim? | any | 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. If undefined, the limit will be inferred from the global minimum and maximum of all predictions. |
opts.random_state? | number | Controls the randomness of the selected samples when subsamples is not undefined. See Glossary for details. |
opts.subsample? | number | Sampling for ICE curves when kind is ‘individual’ or ‘both’. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to be used to plot ICE curves. If int, represents the maximum absolute number of samples to use. Note that the full dataset is still used to calculate partial dependence when kind='both'. Default Value 1000 |
opts.target_idx? | number | 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. |
Returns
Defined in: generated/inspection/PartialDependenceDisplay.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/inspection/PartialDependenceDisplay.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/inspection/PartialDependenceDisplay.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/inspection/PartialDependenceDisplay.ts:23 (opens in a new tab)
id
string
Defined in: generated/inspection/PartialDependenceDisplay.ts:20 (opens in a new tab)
opts
any
Defined in: generated/inspection/PartialDependenceDisplay.ts:21 (opens in a new tab)
Accessors
axes_
If ax is an axes or undefined, 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 undefined correspond to a nonexisting axes in that position.
Signature
axes_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:484 (opens in a new tab)
bars_
If ax is an axes or undefined, bars\_\[i, j\] is the partial dependence bar plot on the i-th row and j-th column (for a categorical feature). If ax is a list of axes, bars\_\[i\] is the partial dependence bar plot corresponding to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a bar plot.
Signature
bars_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:619 (opens in a new tab)
bounding_ax_
If ax is an axes or undefined, 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 undefined.
Signature
bounding_ax_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:457 (opens in a new tab)
contours_
If ax is an axes or undefined, 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 undefined correspond to a nonexisting axes or an axes that does not include a contour plot.
Signature
contours_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:592 (opens in a new tab)
deciles_hlines_
If ax is an axes or undefined, 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 undefined correspond to a nonexisting axes or an axes that does not include a 2-way plot.
Signature
deciles_hlines_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:565 (opens in a new tab)
deciles_vlines_
If ax is an axes or undefined, 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 undefined correspond to a nonexisting axes or an axes that does not include a PDP plot.
Signature
deciles_vlines_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:538 (opens in a new tab)
figure_
Figure containing partial dependence plots.
Signature
figure_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:673 (opens in a new tab)
heatmaps_
If ax is an axes or undefined, heatmaps\_\[i, j\] is the partial dependence heatmap on the i-th row and j-th column (for a pair of categorical features) . If ax is a list of axes, heatmaps\_\[i\] is the partial dependence heatmap corresponding to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a heatmap.
Signature
heatmaps_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:646 (opens in a new tab)
lines_
If ax is an axes or undefined, 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 undefined correspond to a nonexisting axes or an axes that does not include a line plot.
Signature
lines_(): Promise<any>;Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:511 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/inspection/PartialDependenceDisplay.ts:88 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/inspection/PartialDependenceDisplay.ts:92 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/inspection/PartialDependenceDisplay.ts:153 (opens in a new tab)
from_estimator()
Partial dependence (PD) and individual conditional expectation (ICE) plots.
Partial dependence plots, individual conditional expectation plots or an overlay of both of them can be plotted by setting the kind parameter. 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 User Guide.
Signature
from_estimator(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | 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'. |
opts.ax? | any | 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. |
opts.categorical_features? | number | ArrayLike | Indicates the categorical features. |
opts.centered? | boolean | If true, the ICE and PD lines will start at the origin of the y-axis. By default, no centering is done. Default Value false |
opts.contour_kw? | any | Dict with keywords passed to the matplotlib.pyplot.contourf call. For two-way partial dependence plots. |
opts.estimator? | any | A fitted estimator object implementing predict, predict_proba, or decision_function. Multioutput-multiclass classifiers are not supported. |
opts.feature_names? | ArrayLike | 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. |
opts.features? | string | The target features for which to create the PDPs. If features\[i\] is an integer or a string, a one-way PDP is created; if features\[i\] is a tuple, a two-way PDP is created (only supported with kind='average'). Each tuple must be of size 2. If any entry is a string, then it must be in feature\_names. |
opts.grid_resolution? | number | The number of equally spaced points on the axes of the plots, for each target feature. Default Value 100 |
opts.ice_lines_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For ICE lines in the one-way partial dependence plots. The key value pairs defined in ice\_lines\_kw takes priority over line\_kw. |
opts.kind? | "average" | "individual" | "both" | Whether to plot the partial dependence averaged across all the samples in the dataset or one line per sample or both. Default Value 'average' |
opts.line_kw? | any | Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots. It can be used to define common properties for both ice\_lines\_kw and pdp\_line\_kw. |
opts.method? | string | The method used to calculate the averaged predictions: Default Value 'auto' |
opts.n_cols? | number | The maximum number of columns in the grid plot. Only active when ax is a single axis or undefined. Default Value 3 |
opts.n_jobs? | number | The number of CPUs to use to compute the partial dependences. Computation is parallelized over features specified by the features parameter. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
opts.pd_line_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For partial dependence in one-way partial dependence plots. The key value pairs defined in pd\_line\_kw takes priority over line\_kw. |
opts.percentiles? | any | The lower and upper percentile used to create the extreme values for the PDP axes. Must be in [0, 1]. |
opts.random_state? | number | Controls the randomness of the selected samples when subsamples is not undefined and kind is either 'both' or 'individual'. See Glossary for details. |
opts.response_method? | "auto" | "predict_proba" | "decision_function" | Specifies whether to use predict_proba or decision_function as the target response. For regressors this parameter is ignored and the response is always the output of predict. By default, predict_proba is tried first and we revert to decision_function if it doesn’t exist. If method is 'recursion', the response is always the output of decision_function. Default Value 'auto' |
opts.subsample? | number | Sampling for ICE curves when kind is ‘individual’ or ‘both’. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to be used to plot ICE curves. If int, represents the absolute number samples to use. Note that the full dataset is still used to calculate averaged partial dependence when kind='both'. Default Value 1000 |
opts.target? | number | 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. |
opts.verbose? | number | Verbose output during PD computations. Default Value 0 |
Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:174 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/inspection/PartialDependenceDisplay.ts:101 (opens in a new tab)
plot()
Plot partial dependence plots.
Signature
plot(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.ax? | any | 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. |
opts.bar_kw? | any | Dict with keywords passed to the matplotlib.pyplot.bar call for one-way categorical partial dependence plots. |
opts.centered? | boolean | If true, the ICE and PD lines will start at the origin of the y-axis. By default, no centering is done. Default Value false |
opts.contour_kw? | any | Dict with keywords passed to the matplotlib.pyplot.contourf call for two-way partial dependence plots. |
opts.heatmap_kw? | any | Dict with keywords passed to the matplotlib.pyplot.imshow call for two-way categorical partial dependence plots. |
opts.ice_lines_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For ICE lines in the one-way partial dependence plots. The key value pairs defined in ice\_lines\_kw takes priority over line\_kw. |
opts.line_kw? | any | Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots. |
opts.n_cols? | number | The maximum number of columns in the grid plot. Only active when ax is a single axes or undefined. Default Value 3 |
opts.pd_line_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For partial dependence in one-way partial dependence plots. The key value pairs defined in pd\_line\_kw takes priority over line\_kw. |
opts.pdp_lim? | any | 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. If undefined (default), the limit will be inferred from the global minimum and maximum of all predictions. |
Returns
Promise<any>
Defined in: generated/inspection/PartialDependenceDisplay.ts:365 (opens in a new tab)