plot

set_dark_theme(custom_style=None)

Set aspects of the visual theme for all matplotlib plots. This function changes the global defaults for all plots using the matplotlib rcParams system. The theme includes specific colors for figure and axes backgrounds, gridlines, text, labels, and ticks. It also sets the font size and line width.

Parameters:

Name	Type	Description	Default
custom_style	dict	Optional dictionary containing custom styles to be added or override the default dark theme. It is applied after the default theme is set by using the plt.rcParams.update() method.	None

Returns:

Type	Description
None

Source code in skforecast\plot\plot.py

def set_dark_theme(
    custom_style: Optional[dict] = None
) -> None:
    """
    Set aspects of the visual theme for all matplotlib plots.
    This function changes the global defaults for all plots using the matplotlib
    rcParams system. The theme includes specific colors for figure and axes
    backgrounds, gridlines, text, labels, and ticks. It also sets the font size
    and line width.

    Parameters
    ----------
    custom_style : dict, default None
        Optional dictionary containing custom styles to be added or override the
        default dark theme. It is applied after the default theme is set by
        using the `plt.rcParams.update()` method.

    Returns
    -------
    None

    """

    plt.style.use('fivethirtyeight')
    dark_style = {
        'figure.facecolor': '#212946',
        'axes.facecolor': '#212946',
        'savefig.facecolor': '#212946',
        'axes.grid': True,
        'axes.grid.which': 'both',
        'axes.spines.left': False,
        'axes.spines.right': False,
        'axes.spines.top': False,
        'axes.spines.bottom': False,
        'grid.color': '#2A3459',
        'grid.linewidth': '1',
        'text.color': '0.9',
        'axes.labelcolor': '0.9',
        'xtick.color': '0.9',
        'ytick.color': '0.9',
        'font.size': 10,
        'lines.linewidth': 1.5
    }

    if custom_style is not None:
        dark_style.update(custom_style)

    plt.rcParams.update(dark_style)

plot_residuals(residuals=None, y_true=None, y_pred=None, fig=None, **fig_kw)

Parameters:

Name	Type	Description	Default
residuals	pandas Series, numpy ndarray	Values of residuals. If None, residuals are calculated internally using y_true and y_true.	`None`.
y_true	pandas Series, numpy ndarray	Ground truth (correct) values. Ignored if residuals is not None.	`None`.
y_pred	pandas Series, numpy ndarray	Values of predictions. Ignored if residuals is not None.	`None`.
fig	Figure	Pre-existing fig for the plot. Otherwise, call matplotlib.pyplot.figure() internally.	`None`.
fig_kw	dict	Other keyword arguments are passed to matplotlib.pyplot.figure()	{}

Returns:

Name	Type	Description
fig	Figure	Matplotlib Figure.

Source code in skforecast\plot\plot.py

def plot_residuals(
    residuals: Union[np.ndarray, pd.Series] = None,
    y_true: Union[np.ndarray, pd.Series] = None,
    y_pred: Union[np.ndarray, pd.Series] = None,
    fig: matplotlib.figure.Figure = None,
    **fig_kw
) -> matplotlib.figure.Figure:
    """
    Parameters
    ----------
    residuals : pandas Series, numpy ndarray, default `None`.
        Values of residuals. If `None`, residuals are calculated internally using
        `y_true` and `y_true`.
    y_true : pandas Series, numpy ndarray, default `None`.
        Ground truth (correct) values. Ignored if residuals is not `None`.
    y_pred : pandas Series, numpy ndarray, default `None`. 
        Values of predictions. Ignored if residuals is not `None`.
    fig : matplotlib.figure.Figure, default `None`. 
        Pre-existing fig for the plot. Otherwise, call matplotlib.pyplot.figure()
        internally.
    fig_kw : dict
        Other keyword arguments are passed to matplotlib.pyplot.figure()

    Returns
    -------
    fig: matplotlib.figure.Figure
        Matplotlib Figure.

    """

    if residuals is None and (y_true is None or y_pred is None):
        raise ValueError(
            "If `residuals` argument is None then, `y_true` and `y_pred` must be provided."
        )

    if residuals is None:
        residuals = y_true - y_pred

    if fig is None:
        fig = plt.figure(constrained_layout=True, **fig_kw)

    gs  = matplotlib.gridspec.GridSpec(2, 2, figure=fig)
    ax1 = plt.subplot(gs[0, :])
    ax2 = plt.subplot(gs[1, 0])
    ax3 = plt.subplot(gs[1, 1])

    ax1.plot(residuals)
    sns.histplot(residuals, kde=True, bins=30, ax=ax2)
    plot_acf(residuals, ax=ax3, lags=60)

    ax1.set_title("Residuals")
    ax2.set_title("Distribution")
    ax3.set_title("Autocorrelation")

    return fig

plot_multivariate_time_series_corr(corr, ax=None, **fig_kw)

Heatmap plot of a correlation matrix.

Parameters:

Name	Type	Description	Default
corr	pandas DataFrame	correlation matrix	required
ax	Axes	Pre-existing ax for the plot. Otherwise, call matplotlib.pyplot.subplots() internally.	`None`.
fig_kw	dict	Other keyword arguments are passed to matplotlib.pyplot.subplots()	{}

Returns:

Name	Type	Description
fig	Figure	Matplotlib Figure.

Source code in skforecast\plot\plot.py

def plot_multivariate_time_series_corr(
    corr: pd.DataFrame,
    ax: matplotlib.axes.Axes = None,
    **fig_kw
) -> matplotlib.figure.Figure:
    """
    Heatmap plot of a correlation matrix.

    Parameters
    ----------
    corr : pandas DataFrame
        correlation matrix
    ax : matplotlib.axes.Axes, default `None`. 
        Pre-existing ax for the plot. Otherwise, call matplotlib.pyplot.subplots() 
        internally.
    fig_kw : dict
        Other keyword arguments are passed to matplotlib.pyplot.subplots()

    Returns
    -------
    fig: matplotlib.figure.Figure
        Matplotlib Figure.

    """

    if ax is None:
        fig, ax = plt.subplots(1, 1, **fig_kw)

    sns.heatmap(
        corr,
        annot=True,
        linewidths=.5,
        ax=ax,
        cmap=sns.color_palette("viridis", as_cmap=True)
    )

    ax.set_xlabel('Time series')

    return fig

plot_prediction_distribution(bootstrapping_predictions, bw_method=None, **fig_kw)

Ridge plot of bootstrapping predictions. This plot is very useful to understand the uncertainty of forecasting predictions.

Parameters:

Name	Type	Description	Default
bootstrapping_predictions	pandas DataFrame	Bootstrapping predictions created with Forecaster.predict_bootstrapping.	required
bw_method	(str, scalar, Callable)	The method used to calculate the estimator bandwidth. This can be 'scott', 'silverman', a scalar constant or a Callable. If None (default), 'scott' is used. See scipy.stats.gaussian_kde for more information.	`None`
fig_kw	dict	All additional keyword arguments are passed to the pyplot.figure call.	{}

Returns:

Name	Type	Description
fig	Figure	Matplotlib Figure.

Source code in skforecast\plot\plot.py

def plot_prediction_distribution(
    bootstrapping_predictions: pd.DataFrame,
    bw_method: Optional[Any] = None,
    **fig_kw
) -> matplotlib.figure.Figure:
    """
    Ridge plot of bootstrapping predictions. This plot is very useful to understand 
    the uncertainty of forecasting predictions.

    Parameters
    ----------
    bootstrapping_predictions : pandas DataFrame
        Bootstrapping predictions created with `Forecaster.predict_bootstrapping`.
    bw_method : str, scalar, Callable, default `None`
        The method used to calculate the estimator bandwidth. This can be 'scott', 
        'silverman', a scalar constant or a Callable. If None (default), 'scott' 
        is used. See scipy.stats.gaussian_kde for more information.
    fig_kw : dict
        All additional keyword arguments are passed to the `pyplot.figure` call.

    Returns
    -------
    fig : matplotlib.figure.Figure
        Matplotlib Figure.

    """

    index = bootstrapping_predictions.index.astype(str).to_list()[::-1]
    palette = sns.cubehelix_palette(len(index), rot=-.25, light=.7, reverse=False)
    fig, axs = plt.subplots(len(index), 1, sharex=True, **fig_kw)
    if not isinstance(axs, np.ndarray):
        axs = np.array([axs])

    for i, step in enumerate(index):
        plot = (
            bootstrapping_predictions.loc[step, :]
            .plot.kde(ax=axs[i], bw_method=bw_method, lw=0.5)
        )

        # Fill density area
        x = plot.get_children()[0]._x
        y = plot.get_children()[0]._y
        axs[i].fill_between(x, y, color=palette[i])
        prediction_mean = bootstrapping_predictions.loc[step, :].mean()

        # Closest point on x to the prediction mean
        idx = np.abs(x - prediction_mean).argmin()
        axs[i].vlines(x[idx], ymin=0, ymax=y[idx], linestyle="dashed", color='w')

        axs[i].spines['top'].set_visible(False)
        axs[i].spines['right'].set_visible(False)
        axs[i].spines['bottom'].set_visible(False)
        axs[i].spines['left'].set_visible(False)
        axs[i].set_yticklabels([])
        axs[i].set_yticks([])
        axs[i].set_ylabel(step, rotation='horizontal')
        axs[i].set_xlabel('prediction')

    fig.subplots_adjust(hspace=-0)
    fig.suptitle('Forecasting distribution per step')

    return fig

plot_prediction_intervals(predictions, y_true, target_variable, initial_x_zoom=None, title=None, xaxis_title=None, yaxis_title=None, ax=None)

Plot predicted intervals vs real values using matplotlib.

Parameters:

Name	Type	Description	Default
predictions	pandas DataFrame	Predicted values and intervals. Expected columns are 'pred', 'lower_bound' and 'upper_bound'.	required
y_true	pandas DataFrame	Real values of target variable.	required
target_variable	str	Name of target variable.	required
initial_x_zoom	list	Initial zoom of x-axis, by default None.	`None`
title	str	Title of the plot, by default None.	`None`
xaxis_title	str	Title of x-axis, by default None.	`None`
yaxis_title	str	Title of y-axis, by default None.	`None`
ax	matplotlib axes	Axes where to plot, by default None.	`None`

Returns:

Type	Description
None

Source code in skforecast\plot\plot.py

def plot_prediction_intervals(
    predictions: pd.DataFrame,
    y_true: pd.DataFrame,
    target_variable: str,
    initial_x_zoom: list = None,
    title: str = None,
    xaxis_title: str = None,
    yaxis_title: str = None,
    ax: plt.Axes = None
):
    """
    Plot predicted intervals vs real values using matplotlib.

    Parameters
    ----------
    predictions : pandas DataFrame
        Predicted values and intervals. Expected columns are 'pred', 'lower_bound'
        and 'upper_bound'.
    y_true : pandas DataFrame
        Real values of target variable.
    target_variable : str
        Name of target variable.
    initial_x_zoom : list, default `None`
        Initial zoom of x-axis, by default None.
    title : str, default `None`
        Title of the plot, by default None.
    xaxis_title : str, default `None`
        Title of x-axis, by default None.
    yaxis_title : str, default `None`
        Title of y-axis, by default None.
    ax : matplotlib axes, default `None`
        Axes where to plot, by default None.

    Returns
    -------
    None

    """

    if ax is None:
        fig, ax = plt.subplots(figsize=(7, 3))

    y_true.loc[predictions.index, target_variable].plot(ax=ax, label='Real value')
    predictions['pred'].plot(ax=ax, label='prediction')
    ax.fill_between(
        predictions.index,
        predictions['lower_bound'],
        predictions['upper_bound'],
        color = '#444444',
        alpha = 0.3,
    )
    ax.set_ylabel(yaxis_title)
    ax.set_xlabel(xaxis_title)
    ax.set_title(title)
    ax.legend()

    if initial_x_zoom is not None:
        ax.set_xlim(initial_x_zoom)