Skip to content

plot

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

Parameters:

Name Type Description Default
residuals pandas Series, numpy ndarray, default

Values of residuals. If None, residuals are calculated internally using y_true and y_true.

 None
y_true pandas Series, numpy ndarray, default

Ground truth (correct) values. Ignored if residuals is not None.

 None
y_pred pandas Series, numpy ndarray, default

Values of predictions. Ignored if residuals is not None.

 None
fig matplotlib.figure.Figure, default

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 matplotlib.figure.Figure

Matplotlib Figure.
Source code in skforecast/plot/plot.py 
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
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_pred - y_true

    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 matplotlib.axes.Axes, default

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 matplotlib.figure.Figure

Matplotlib Figure.
Source code in skforecast/plot/plot.py 
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
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, default

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 matplotlib.figure.Figure

Matplotlib Figure.
Source code in skforecast/plot/plot.py 
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
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