validation

plot_actual_vs_predicted(y_test, y_pred, title=None, show=True, filename=None)

Plot actual vs. predicted values.

Parameters:

Name	Type	Description	Default
y_test	ndarray	True values.	required
y_pred	ndarray	Predicted values.	required
title	str	Title of the plot. Defaults to None.	None
show	bool	If True, the plot is shown. Defaults to True.	True
filename	str	Name of the file to save the plot. Defaults to None.	None

Returns:

Type	Description
NoneType	None

Examples:

>>> from sklearn.datasets import load_diabetes
    from sklearn.linear_model import LinearRegression
    from spotpython.plot.validation import plot_actual_vs_predicted
    X, y = load_diabetes(return_X_y=True)
    lr = LinearRegression()
    lr.fit(X, y)
    y_pred = lr.predict(X)
    plot_actual_vs_predicted(y, y_pred)

Source code in spotpython/plot/validation.py

def plot_actual_vs_predicted(y_test, y_pred, title=None, show=True, filename=None) -> None:
    """Plot actual vs. predicted values.

    Args:
        y_test (np.ndarray):
            True values.
        y_pred (np.ndarray):
            Predicted values.
        title (str, optional):
            Title of the plot. Defaults to None.
        show (bool, optional):
            If True, the plot is shown. Defaults to True.
        filename (str, optional):
            Name of the file to save the plot. Defaults to None.

    Returns:
        (NoneType): None

    Examples:
        >>> from sklearn.datasets import load_diabetes
            from sklearn.linear_model import LinearRegression
            from spotpython.plot.validation import plot_actual_vs_predicted
            X, y = load_diabetes(return_X_y=True)
            lr = LinearRegression()
            lr.fit(X, y)
            y_pred = lr.predict(X)
            plot_actual_vs_predicted(y, y_pred)
    """
    fig, axs = plt.subplots(ncols=2, figsize=(8, 4))
    PredictionErrorDisplay.from_predictions(
        y_test,
        y_pred=y_pred,
        kind="actual_vs_predicted",
        subsample=100,
        ax=axs[0],
        random_state=0,
        scatter_kwargs={"alpha": 0.5},
    )
    axs[0].set_title("Actual vs. Predicted values")
    PredictionErrorDisplay.from_predictions(
        y_test,
        y_pred=y_pred,
        kind="residual_vs_predicted",
        subsample=100,
        ax=axs[1],
        random_state=0,
    )
    axs[1].set_title("Residuals vs. Predicted Values")
    if title is not None:
        fig.suptitle(title)
    plt.tight_layout()
    if filename is not None:
        plt.savefig(filename)
    if show:
        plt.show()

plot_confusion_matrix(model=None, fun_control=None, df=None, title=None, target_names=None, y_true_name=None, y_pred_name=None, show=False, ax=None)

Plotting a confusion matrix. If a model and the fun_control dictionary are passed, the confusion matrix is computed. If a dataframe is passed, the confusion matrix is computed from the dataframe. In this case, the names of the columns with the true and the predicted values must be specified. Default the dataframe is None.

Parameters:

Name	Type	Description	Default
model	Any	Sklearn model. The model to be used for cross-validation. Defaults to None.	None
fun_control	Dict	Dictionary containing the data and the target column. Defaults to None.	None
title	str	Title of the plot. Defaults to None.	None
df	DataFrame	Dataframe containing the predictions and the target column. Defaults to None.	None
target_names	List[str]	List of target names. Defaults to None.	None
y_true_name	str	Name of the column with the true values if a dataframe is specified. Defaults to None.	None
y_pred_name	str	Name of the column with the predicted values if a dataframe is specified. Defaults to None.	None
show	bool	If True, the plot is shown. Defaults to False.	False
ax	AxesSubplot	Axes to plot the confusion matrix. Defaults to None.	None

Returns:

Type	Description
NoneType	None

Source code in spotpython/plot/validation.py

def plot_confusion_matrix(
    model=None,
    fun_control=None,
    df=None,
    title=None,
    target_names=None,
    y_true_name=None,
    y_pred_name=None,
    show=False,
    ax=None,
):
    """
    Plotting a confusion matrix. If a model and the fun_control dictionary are passed,
    the confusion matrix is computed. If a dataframe is passed, the confusion matrix is
    computed from the dataframe. In this case, the names of the columns with the true and
    the predicted values must be specified. Default the dataframe is None.

    Args:
        model (Any, optional):
            Sklearn model. The model to be used for cross-validation. Defaults to None.
        fun_control (Dict, optional):
            Dictionary containing the data and the target column. Defaults to None.
        title (str, optional):
            Title of the plot. Defaults to None.
        df (pd.DataFrame, optional):
            Dataframe containing the predictions and the target column. Defaults to None.
        target_names (List[str], optional):
            List of target names. Defaults to None.
        y_true_name (str, optional):
            Name of the column with the true values if a dataframe is specified. Defaults to None.
        y_pred_name (str, optional):
            Name of the column with the predicted values if a dataframe is specified. Defaults to None.
        show (bool, optional):
            If True, the plot is shown. Defaults to False.
        ax (matplotlib.axes._subplots.AxesSubplot, optional):
            Axes to plot the confusion matrix. Defaults to None.

    Returns:
        (NoneType): None

    """
    if df is not None:
        # assign the column y_true_name from df to y_true
        y_true = df[y_true_name]
        # assign the column y_pred_name from df to y_pred
        y_pred = df[y_pred_name]
    else:
        X_train, y_train = get_Xy_from_df(fun_control["train"], fun_control["target_column"])
        X_test, y_true = get_Xy_from_df(fun_control["test"], fun_control["target_column"])
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
    if ax is None:
        fig, ax = plt.subplots(figsize=(10, 5))
    ConfusionMatrixDisplay.from_predictions(y_true=y_true, y_pred=y_pred, ax=ax, colorbar=False)
    if target_names is not None:
        ax.xaxis.set_ticklabels(target_names)
        ax.yaxis.set_ticklabels(target_names)
    if title is not None:
        _ = ax.set_title(title)
    if show:
        plt.show()

plot_cv_predictions(model, fun_control, show=True)

Plots cross-validated predictions for regression.

Uses sklearn.model_selection.cross_val_predict together with sklearn.metrics.PredictionErrorDisplay to visualize prediction errors. It is based on the example from the scikit-learn documentation: https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html#sphx-glr-download-auto-examples-model-selection-plot-cv-predict-py

Parameters:

Name	Type	Description	Default
model	Any	Sklearn model. The model to be used for cross-validation.	required
fun_control	Dict	Dictionary containing the data and the target column.	required
show	bool	If True, the plot is shown. Defaults to True.	True

Returns:

Type	Description
NoneType	None

Examples:

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import LinearRegression
>>> X, y = load_diabetes(return_X_y=True)
>>> lr = LinearRegression()
>>> plot_cv_predictions(lr, fun_control)

Source code in spotpython/plot/validation.py

def plot_cv_predictions(model: Any, fun_control: Dict, show=True) -> None:
    """
    Plots cross-validated predictions for regression.

    Uses `sklearn.model_selection.cross_val_predict` together with
    `sklearn.metrics.PredictionErrorDisplay` to visualize prediction errors.
    It is based on the example from the scikit-learn documentation:
    https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html#sphx-glr-download-auto-examples-model-selection-plot-cv-predict-py

    Args:
        model (Any):
            Sklearn model. The model to be used for cross-validation.
        fun_control (Dict):
            Dictionary containing the data and the target column.
        show (bool, optional):
            If True, the plot is shown. Defaults to True.

    Returns:
        (NoneType): None

    Examples:
        >>> from sklearn.datasets import load_diabetes
        >>> from sklearn.linear_model import LinearRegression
        >>> X, y = load_diabetes(return_X_y=True)
        >>> lr = LinearRegression()
        >>> plot_cv_predictions(lr, fun_control)
    """
    X_test, y_test = get_Xy_from_df(fun_control["test"], fun_control["target_column"])
    # cross_val_predict returns an array of the same size of y
    # where each entry is a prediction obtained by cross validation.
    y_pred = cross_val_predict(model, X_test, y_test, cv=10)
    fig, axs = plt.subplots(ncols=2, figsize=(8, 4))
    PredictionErrorDisplay.from_predictions(
        y_test,
        y_pred=y_pred,
        kind="actual_vs_predicted",
        subsample=100,
        ax=axs[0],
        random_state=0,
    )
    axs[0].set_title("Actual vs. Predicted values")
    PredictionErrorDisplay.from_predictions(
        y_test,
        y_pred=y_pred,
        kind="residual_vs_predicted",
        subsample=100,
        ax=axs[1],
        random_state=0,
    )
    axs[1].set_title("Residuals vs. Predicted Values")
    fig.suptitle("Plotting cross-validated predictions")
    plt.tight_layout()
    if show:
        plt.show()

plot_roc(model_list, fun_control, alpha=0.8, model_names=None, show=True)

Plots ROC curves for a list of models using the Visualization API from scikit-learn.

Parameters:

Name	Type	Description	Default
model_list	List[BaseEstimator]	A list of scikit-learn models to plot ROC curves for.	required
fun_control	Dict[str, Union[str, DataFrame]]	A dictionary containing the train and test dataframes and the target column name.	required
alpha	float	The alpha value for the ROC curve. Defaults to 0.8.	0.8
model_names	List[str]	A list of names for the models. Defaults to None.	None
show	bool	If True, the plot is shown. Defaults to True.	True

Returns:

Type	Description
NoneType	None

Examples:

>>> from sklearn.datasets import load_iris
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.tree import DecisionTreeClassifier
>>> iris = load_iris()
>>> X_train = iris.data[:100]
>>> y_train = iris.target[:100]
>>> X_test = iris.data[100:]
>>> y_test = iris.target[100:]
>>> train_df = pd.DataFrame(X_train, columns=iris.feature_names)
>>> train_df['target'] = y_train
>>> test_df = pd.DataFrame(X_test, columns=iris.feature_names)
>>> test_df['target'] = y_test
>>> fun_control = {"train": train_df, "test": test_df, "target_column": "target"}
>>> model_list = [LogisticRegression(), DecisionTreeClassifier()]
>>> model_names = ["Logistic Regression", "Decision Tree"]
>>> plot_roc(model_list, fun_control, model_names=model_names)

Source code in spotpython/plot/validation.py

def plot_roc(
    model_list: List[BaseEstimator],
    fun_control: Dict[str, Union[str, pd.DataFrame]],
    alpha: float = 0.8,
    model_names: List[str] = None,
    show=True,
) -> None:
    """
    Plots ROC curves for a list of models using the Visualization API from scikit-learn.

    Args:
        model_list (List[BaseEstimator]):
            A list of scikit-learn models to plot ROC curves for.
        fun_control (Dict[str, Union[str, pd.DataFrame]]):
            A dictionary containing the train and test dataframes and the target column name.
        alpha (float, optional):
            The alpha value for the ROC curve. Defaults to 0.8.
        model_names (List[str], optional):
            A list of names for the models. Defaults to None.
        show (bool, optional):
            If True, the plot is shown. Defaults to True.

    Returns:
        (NoneType): None

    Examples:
        >>> from sklearn.datasets import load_iris
        >>> from sklearn.linear_model import LogisticRegression
        >>> from sklearn.tree import DecisionTreeClassifier
        >>> iris = load_iris()
        >>> X_train = iris.data[:100]
        >>> y_train = iris.target[:100]
        >>> X_test = iris.data[100:]
        >>> y_test = iris.target[100:]
        >>> train_df = pd.DataFrame(X_train, columns=iris.feature_names)
        >>> train_df['target'] = y_train
        >>> test_df = pd.DataFrame(X_test, columns=iris.feature_names)
        >>> test_df['target'] = y_test
        >>> fun_control = {"train": train_df, "test": test_df, "target_column": "target"}
        >>> model_list = [LogisticRegression(), DecisionTreeClassifier()]
        >>> model_names = ["Logistic Regression", "Decision Tree"]
        >>> plot_roc(model_list, fun_control, model_names=model_names)
    """
    X_train, y_train = get_Xy_from_df(fun_control["train"], fun_control["target_column"])
    X_test, y_test = get_Xy_from_df(fun_control["test"], fun_control["target_column"])
    ax = plt.gca()
    for i, model in enumerate(model_list):
        model.fit(X_train, y_train)
        if model_names is not None:
            model_name = model_names[i]
        else:
            model_name = None
        y_pred = model.predict(X_test)
        RocCurveDisplay.from_predictions(y_test, y_pred, ax=ax, alpha=alpha, name=model_name)
    if show:
        plt.show()

plot_roc_from_dataframes(df_list, alpha=0.8, model_names=None, target_column=None, show=True, title='', tkagg=False)

Plot ROC curve for a list of dataframes from model evaluations.

Parameters:

Name	Type	Description	Default
df_list	List[DataFrame]	List of dataframes with results from models.	required
alpha	float	Transparency of the plotted lines.	0.8
model_names	List[str]	List of model names.	None
target_column	str	Name of the target column.	None
show	bool	If True, the plot is shown.	True
title	str	Title of the plot.	''
tkagg	bool	If True, the TkAgg backend is used. Default is False.	False

Returns:

Type	Description
None	None

Examples:

>>> import pandas as pd
    from spotpython.plot.validation import plot_roc_from_dataframes
    df1 = pd.DataFrame({"y": [1, 0, 0, 1], "Prediction": [1,0,0,0]})
    df2 = pd.DataFrame({"y": [1, 0, 0, 1], "Prediction": [1,0,1,1]})
    df_list = [df1, df2]
    model_names = ["Model 1", "Model 2"]
    plot_roc_from_dataframes(df_list, model_names=model_names, target_column="y")

Source code in spotpython/plot/validation.py

def plot_roc_from_dataframes(
    df_list: List[pd.DataFrame],
    alpha: float = 0.8,
    model_names: List[str] = None,
    target_column: str = None,
    show: bool = True,
    title: str = "",
    tkagg: bool = False,
) -> None:
    """
    Plot ROC curve for a list of dataframes from model evaluations.

    Args:
        df_list:
            List of dataframes with results from models.
        alpha:
            Transparency of the plotted lines.
        model_names:
            List of model names.
        target_column:
            Name of the target column.
        show:
            If True, the plot is shown.
        title:
            Title of the plot.
        tkagg:
            If True, the TkAgg backend is used.
            Default is False.

    Returns:
        None

    Examples:
        >>> import pandas as pd
            from spotpython.plot.validation import plot_roc_from_dataframes
            df1 = pd.DataFrame({"y": [1, 0, 0, 1], "Prediction": [1,0,0,0]})
            df2 = pd.DataFrame({"y": [1, 0, 0, 1], "Prediction": [1,0,1,1]})
            df_list = [df1, df2]
            model_names = ["Model 1", "Model 2"]
            plot_roc_from_dataframes(df_list, model_names=model_names, target_column="y")

    """
    if tkagg:
        matplotlib.use("TkAgg")
    fig, ax = plt.subplots(figsize=(10, 5))
    for i, df in enumerate(df_list):
        y_test = df[target_column]
        y_pred = df["Prediction"]
        if model_names is not None:
            model_name = model_names[i]
        else:
            model_name = None
        RocCurveDisplay.from_predictions(y_test, y_pred, ax=ax, alpha=alpha, name=model_name)
    # add a title to the plot
    ax.set_title(title)
    if show:
        plt.show()