Module artemis.visualizer

Classes

class PartialDependenceVisualizer (model: sklearn.base.BaseEstimator, X: pandas.core.frame.DataFrame)

Visualizer of 1-dimensianal and 2-dimensional partial dependence plots. It wraps scikit-learn PartialDependenceDisplay.from_estimator() method, so only models implementing predict functions in scikit-learn API are supported.

Attributes

model : sklearn.BaseEstimator

Model for which partial dependence plot will be generated.

X : pd.DataFrame

Data used to calculate partial dependence functions.

Constructor for PartialDependenceVisualizer

Parameters

model : sklearn.BaseEstimator

Model for which partial dependence plot will be generated.

X : pd.DataFrame

Data used to calculate partial dependence functions.

Expand source code

class PartialDependenceVisualizer:
    """
    Visualizer of 1-dimensianal and 2-dimensional partial dependence plots. 
    It wraps scikit-learn PartialDependenceDisplay.from_estimator() method, 
    so only models implementing predict functions in scikit-learn API are supported.

    Attributes
    ----------
    model : sklearn.BaseEstimator 
        Model for which partial dependence plot will be generated.
    X : pd.DataFrame
        Data used to calculate partial dependence functions.
    """
    def __init__(self, model: BaseEstimator, X: pd.DataFrame):
        """Constructor for PartialDependenceVisualizer

        Parameters
        ----------
        model : sklearn.BaseEstimator 
            Model for which partial dependence plot will be generated.
        X : pd.DataFrame
            Data used to calculate partial dependence functions.
        """
        self.model = model
        self.X = X

    def plot(self,
             features: List[Union[int, str, Tuple[int, int], Tuple[str, str]]],
             grid_resolution: int = 100,
             title: str = "Partial Dependence",
             figsize: Tuple[float, float] = (12, 6),
             **kwargs):
        """Plot partial dependence plot.

        Parameters
        ----------
        features : int, str, (int, int), or (str, str) 
            Features for which partial dependence plot will be generated. 
            If one feature is provided, 1-dimensional PDP will be returned, two features -- 2-dimensional PDP.
        grid_resolution : int
            The number of equally spaced points on the axes of the plots, for each target feature. Default is 100. 
        title : str 
            Title of plot. Default is 'Partial Dependence'.
        figsize : (float, float) 
            Size of plot. Default is (12, 6).
        **kwargs : Other Parameters
            Additional parameters for plot. Passed to PartialDependenceDisplay.from_estimator() method.
        """
        fig, ax = plt.subplots(figsize=figsize)
        PartialDependenceDisplay.from_estimator(self.model,
                                                self.X,
                                                features,
                                                grid_resolution=grid_resolution,
                                                _ax=ax,
                                                **kwargs)
        ax.set_title(title)

Methods

def plot(self, features: List[Union[int, str, Tuple[int, int], Tuple[str, str]]], grid_resolution: int = 100, title: str = 'Partial Dependence', figsize: Tuple[float, float] = (12, 6), **kwargs)

Plot partial dependence plot.

Parameters

features : int, str, (int, int), or (str, str)

Features for which partial dependence plot will be generated. If one feature is provided, 1-dimensional PDP will be returned, two features – 2-dimensional PDP.

grid_resolution : int

The number of equally spaced points on the axes of the plots, for each target feature. Default is 100.

title : str

Title of plot. Default is 'Partial Dependence'.

figsize : (float, float)

Size of plot. Default is (12, 6).

**kwargs : Other Parameters

Additional parameters for plot. Passed to PartialDependenceDisplay.from_estimator() method.

Expand source code

def plot(self,
         features: List[Union[int, str, Tuple[int, int], Tuple[str, str]]],
         grid_resolution: int = 100,
         title: str = "Partial Dependence",
         figsize: Tuple[float, float] = (12, 6),
         **kwargs):
    """Plot partial dependence plot.

    Parameters
    ----------
    features : int, str, (int, int), or (str, str) 
        Features for which partial dependence plot will be generated. 
        If one feature is provided, 1-dimensional PDP will be returned, two features -- 2-dimensional PDP.
    grid_resolution : int
        The number of equally spaced points on the axes of the plots, for each target feature. Default is 100. 
    title : str 
        Title of plot. Default is 'Partial Dependence'.
    figsize : (float, float) 
        Size of plot. Default is (12, 6).
    **kwargs : Other Parameters
        Additional parameters for plot. Passed to PartialDependenceDisplay.from_estimator() method.
    """
    fig, ax = plt.subplots(figsize=figsize)
    PartialDependenceDisplay.from_estimator(self.model,
                                            self.X,
                                            features,
                                            grid_resolution=grid_resolution,
                                            _ax=ax,
                                            **kwargs)
    ax.set_title(title)