plot

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

Parameters:

Name	Type	Description	Default
residuals	Union[numpy.ndarray, pandas.core.series.Series]	Values of residuals. If None, residuals are calculated internally using y_true and y_true.	None
y_true	Union[numpy.ndarray, pandas.core.series.Series]	Ground truth (correct) values. Ignored if residuals is not None.	None
y_pred	Union[numpy.ndarray, pandas.core.series.Series]	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
kwargs	None	Other keyword arguments are passed to matplotlib.pyplot.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,
    **kwargs
) -> None:
    """
    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.

    kwargs
        Other keyword arguments are passed to matplotlib.pyplot.figure()

    Returns
    -------
        None

    """

    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_pred - y_true

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

    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")

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

Heatmap plot of a correlation matrix.

Parameters:

Name	Type	Description	Default
corr	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()	{}

Source code in skforecast/plot/plot.py

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

    Parameters
    ----------
    corr : pd.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
    -------
    None
    """

    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')
    fig.show()