effects

`plot_all_partial_dependence(df, df_target, model='GradientBoostingRegressor', nrows=5, ncols=6, figsize=(20, 15))` ¶

Generates Partial Dependence Plots (PDPs) for every feature in a DataFrame against a target variable, arranged in a grid.

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	DataFrame containing the features.	required
`df_target`	`Series`	Series containing the target variable.	required
`model`	`str`	Name of the model class to use (e.g., “GradientBoostingRegressor”). Defaults to “GradientBoostingRegressor”.	`'GradientBoostingRegressor'`
`nrows`	`int`	Number of rows in the grid of subplots. Defaults to 5.	`5`
`ncols`	`int`	Number of columns in the grid of subplots. Defaults to 6.	`6`
`figsize`	`tuple`	Figure size (width, height) in inches. Defaults to (20, 15).	`(20, 15)`

Returns:

Type	Description
`None`	None

Examples:

>>> form spotpython.utils.effects import plot_all_partial_dependence
>>> from sklearn.datasets import load_boston
>>> import pandas as pd
>>> data = load_boston()
>>> df = pd.DataFrame(data.data, columns=data.feature_names)
>>> df_target = pd.Series(data.target, name="target")
>>> plot_all_partial_dependence(df, df_target, model="GradientBoostingRegressor", nrows=5, ncols=6, figsize=(20, 15))

Source code in spotpython/utils/effects.py

def plot_all_partial_dependence(df, df_target, model="GradientBoostingRegressor", nrows=5, ncols=6, figsize=(20, 15)) -> None:
    """
    Generates Partial Dependence Plots (PDPs) for every feature in a DataFrame against a target variable,
    arranged in a grid.

    Args:
        df (pd.DataFrame): DataFrame containing the features.
        df_target (pd.Series): Series containing the target variable.
        model (str, optional): Name of the model class to use (e.g., "GradientBoostingRegressor").
                               Defaults to "GradientBoostingRegressor".
        nrows (int, optional): Number of rows in the grid of subplots. Defaults to 5.
        ncols (int, optional): Number of columns in the grid of subplots. Defaults to 6.
        figsize (tuple, optional): Figure size (width, height) in inches. Defaults to (20, 15).

    Returns:
        None

    Examples:
        >>> form spotpython.utils.effects import plot_all_partial_dependence
        >>> from sklearn.datasets import load_boston
        >>> import pandas as pd
        >>> data = load_boston()
        >>> df = pd.DataFrame(data.data, columns=data.feature_names)
        >>> df_target = pd.Series(data.target, name="target")
        >>> plot_all_partial_dependence(df, df_target, model="GradientBoostingRegressor", nrows=5, ncols=6, figsize=(20, 15))

    """

    # Separate features and target
    X = df
    y = df_target  # Target variable is now a Series

    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Instantiate the model
    if model == "GradientBoostingRegressor":
        gb_model = GradientBoostingRegressor(random_state=42)
    elif model == "RandomForestRegressor":
        from sklearn.ensemble import RandomForestRegressor

        gb_model = RandomForestRegressor(random_state=42)
    elif model == "DecisionTreeRegressor":
        from sklearn.tree import DecisionTreeRegressor

        gb_model = DecisionTreeRegressor(random_state=42)
    else:
        raise ValueError(f"Unsupported model: {model}")

    # Train model
    gb_model.fit(X_train, y_train)

    # Create subplots
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
    axes = axes.flatten()  # Flatten the 2D array of axes for easy iteration

    # Generate PDP for each feature
    features = X.columns
    for i, feature in enumerate(features):
        ax = axes[i]  # Select the axis for the current feature
        PartialDependenceDisplay.from_estimator(gb_model, X_train, [feature], ax=ax)
        ax.set_title(feature)  # Set the title of the subplot to the feature name

    # Remove empty subplots if the number of features is less than nrows * ncols
    for i in range(len(features), nrows * ncols):
        fig.delaxes(axes[i])

    plt.tight_layout()  # Adjust subplot parameters for a tight layout
    plt.show()

`screening(X, fun, xi, p, labels, range=None, print=False)` ¶

Generates a DataFrame with elementary effect screening metrics.

This function calculates the mean and standard deviation of the elementary effects for a given set of design variables and returns the results as a Pandas DataFrame.