Initial prototype of plot_lm

arviz/plots/__init__.py

@@ -13,6 +13,7 @@
 from .jointplot import plot_joint
 from .kdeplot import plot_kde
 from .khatplot import plot_khat
+from .lmplot import plot_lm
 from .loopitplot import plot_loo_pit
 from .mcseplot import plot_mcse
 from .pairplot import plot_pair
@@ -38,6 +39,7 @@
     "plot_joint",
     "plot_kde",
     "plot_khat",
+    "plot_lm",
     "plot_loo_pit",
     "plot_mcse",
     "plot_pair",

arviz/plots/backends/bokeh/lmplot.py

@@ -0,0 +1,170 @@
+"""Bokeh linear regression plot."""
+import numpy as np
+from bokeh.models.annotations import Legend
+
+from ...hdiplot import plot_hdi
+
+from ...plot_utils import _scale_fig_size
+from .. import show_layout
+from . import backend_kwarg_defaults, create_axes_grid
+
+
+def plot_lm(
+    x,
+    y,
+    y_model,
+    y_hat,
+    num_samples,
+    kind_pp,
+    kind_model,
+    length_plotters,
+    xjitter,
+    rows,
+    cols,
+    y_kwargs,
+    y_hat_plot_kwargs,
+    y_hat_fill_kwargs,
+    y_model_plot_kwargs,
+    y_model_fill_kwargs,
+    backend_kwargs,
+    show,
+    figsize,
+    textsize,
+    axes,
+    legend,
+    grid,  # pylint: disable=unused-argument
+):
+    """Bokeh linreg plot."""
+    if backend_kwargs is None:
+        backend_kwargs = {}
+
+    backend_kwargs = {
+        **backend_kwarg_defaults(),
+        **backend_kwargs,
+    }
+
+    figsize, *_ = _scale_fig_size(figsize, textsize, rows, cols)
+    if axes is None:
+        axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs)
+
+    if y_kwargs is None:
+        y_kwargs = {}
+    y_kwargs.setdefault("fill_color", "red")
+    y_kwargs.setdefault("line_width", 0)
+    y_kwargs.setdefault("size", 3)
+
+    if y_hat_plot_kwargs is None:
+        y_hat_plot_kwargs = {}
+    y_hat_plot_kwargs.setdefault("fill_color", "orange")
+    y_hat_plot_kwargs.setdefault("line_width", 0)
+
+    if y_hat_fill_kwargs is None:
+        y_hat_fill_kwargs = {}
+    y_hat_fill_kwargs.setdefault("color", "orange")
+
+    if y_model_plot_kwargs is None:
+        y_model_plot_kwargs = {}
+    y_model_plot_kwargs.setdefault("line_color", "black")
+    y_model_plot_kwargs.setdefault("line_alpha", 0.5)
+    y_model_plot_kwargs.setdefault("line_width", 0.5)
+
+    if y_model_fill_kwargs is None:
+        y_model_fill_kwargs = {}
+    y_model_fill_kwargs.setdefault("color", "black")
+    y_model_fill_kwargs.setdefault("alpha", 0.5)
+
+    for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)):
+
+        _, _, _, y_plotters = y[i]
+        _, _, _, x_plotters = x[i]
+        legend_it = []
+        observed_legend = ax_i.circle(x_plotters, y_plotters, **y_kwargs)
+        legend_it.append(("Observed", [observed_legend]))
+
+        if y_hat is not None:
+            _, _, _, y_hat_plotters = y_hat[i]
+            if kind_pp == "samples":
+                posterior_legend = []
+                for j in range(num_samples):
+                    if xjitter is True:
+                        jitter_scale = x_plotters[1] - x_plotters[0]
+                        scale_high = jitter_scale * 0.2
+                        x_plotters_jitter = x_plotters + np.random.uniform(
+                            low=-scale_high, high=scale_high, size=len(x_plotters)
+                        )
+                        posterior_circle = ax_i.circle(
+                            x_plotters_jitter,
+                            y_hat_plotters[..., j],
+                            alpha=0.2,
+                            **y_hat_plot_kwargs,
+                        )
+                    else:
+                        posterior_circle = ax_i.circle(
+                            x_plotters, y_hat_plotters[..., j], alpha=0.2, **y_hat_plot_kwargs
+                        )
+                    posterior_legend.append(posterior_circle)
+                legend_it.append(("Posterior predictive samples", posterior_legend))
+
+            else:
+                plot_hdi(
+                    x_plotters,
+                    y_hat_plotters,
+                    ax=ax_i,
+                    backend="bokeh",
+                    fill_kwargs=y_hat_fill_kwargs,
+                    show=False,
+                )
+
+        if y_model is not None:
+            _, _, _, y_model_plotters = y_model[i]
+            if kind_model == "lines":
+
+                model_legend = ax_i.multi_line(
+                    [np.tile(x_plotters, (num_samples, 1))],
+                    [np.transpose(y_model_plotters)],
+                    **y_model_plot_kwargs,
+                )
+                legend_it.append(("Uncertainty in mean", [model_legend]))
+
+                y_model_mean = np.mean(y_model_plotters, axis=1)
+                x_plotters_edge = [min(x_plotters), max(x_plotters)]
+                y_model_mean_edge = [min(y_model_mean), max(y_model_mean)]
+                mean_legend = ax_i.line(
+                    x_plotters_edge, y_model_mean_edge, line_color="yellow", line_width=2
+                )
+                legend_it.append(("Mean", [mean_legend]))
+
+            else:
+                plot_hdi(
+                    x_plotters,
+                    y_model_plotters,
+                    fill_kwargs=y_model_fill_kwargs,
+                    ax=ax_i,
+                    backend="bokeh",
+                    show=False,
+                )
+
+                y_model_mean = np.mean(y_model_plotters, axis=(0, 1))
+                x_plotters_edge = [min(x_plotters), max(x_plotters)]
+                y_model_mean_edge = [min(y_model_mean), max(y_model_mean)]
+                mean_legend = ax_i.line(
+                    x_plotters_edge,
+                    y_model_mean_edge,
+                    line_color="yellow",
+                    line_width=2,
+                )
+                legend_it.append(("Mean", [mean_legend]))
+
+        if legend:
+            legend = Legend(
+                items=legend_it,
+                location="top_left",
+                orientation="vertical",
+            )
+            ax_i.add_layout(legend)
+            if textsize is not None:
+                ax_i.legend.label_text_font_size = f"{textsize}pt"
+            ax_i.legend.click_policy = "hide"
+
+    show_layout(axes, show)
+    return axes

arviz/plots/backends/matplotlib/lmplot.py

@@ -0,0 +1,138 @@
+"""Matplotlib plot linear regression figure."""
+import matplotlib.pyplot as plt
+import numpy as np
+
+from ...plot_utils import _scale_fig_size
+from ...hdiplot import plot_hdi
+from . import create_axes_grid, matplotlib_kwarg_dealiaser, backend_show, backend_kwarg_defaults
+
+
+def plot_lm(
+    x,
+    y,
+    y_model,
+    y_hat,
+    num_samples,
+    kind_pp,
+    kind_model,
+    xjitter,
+    length_plotters,
+    rows,
+    cols,
+    y_kwargs,
+    y_hat_plot_kwargs,
+    y_hat_fill_kwargs,
+    y_model_plot_kwargs,
+    y_model_fill_kwargs,
+    backend_kwargs,
+    show,
+    figsize,
+    textsize,
+    axes,
+    legend,
+    grid,
+):
+    """Matplotlib Linear Regression."""
+    if backend_kwargs is None:
+        backend_kwargs = {}
+
+    backend_kwargs = {
+        **backend_kwarg_defaults(),
+        **backend_kwargs,
+    }
+
+    figsize, _, _, xt_labelsize, _, _ = _scale_fig_size(figsize, textsize, rows, cols)
+    backend_kwargs.setdefault("figsize", figsize)
+    backend_kwargs.setdefault("squeeze", False)
+
+    if axes is None:
+        _, axes = create_axes_grid(length_plotters, rows, cols, backend_kwargs=backend_kwargs)
+
+    for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]):
+
+        # All the kwargs are defined here beforehand
+        y_kwargs = matplotlib_kwarg_dealiaser(y_kwargs, "plot")
+        y_kwargs.setdefault("color", "C3")
+        y_kwargs.setdefault("marker", ".")
+        y_kwargs.setdefault("markersize", 15)
+        y_kwargs.setdefault("linewidth", 0)
+        y_kwargs.setdefault("zorder", 10)
+        y_kwargs.setdefault("label", "observed_data")
+
+        y_hat_plot_kwargs = matplotlib_kwarg_dealiaser(y_hat_plot_kwargs, "plot")
+        y_hat_plot_kwargs.setdefault("color", "C1")
+        y_hat_plot_kwargs.setdefault("alpha", 0.3)
+        y_hat_plot_kwargs.setdefault("markersize", 10)
+        y_hat_plot_kwargs.setdefault("marker", ".")
+        y_hat_plot_kwargs.setdefault("linewidth", 0)
+
+        y_hat_fill_kwargs = matplotlib_kwarg_dealiaser(y_hat_fill_kwargs, "fill_between")
+        y_hat_fill_kwargs.setdefault("color", "C1")
+
+        y_model_plot_kwargs = matplotlib_kwarg_dealiaser(y_model_plot_kwargs, "plot")
+        y_model_plot_kwargs.setdefault("color", "k")
+        y_model_plot_kwargs.setdefault("alpha", 0.5)
+        y_model_plot_kwargs.setdefault("linewidth", 0.5)
+        y_model_plot_kwargs.setdefault("zorder", 9)
+
+        y_model_fill_kwargs = matplotlib_kwarg_dealiaser(y_model_fill_kwargs, "fill_between")
+        y_model_fill_kwargs.setdefault("color", "k")
+        y_model_fill_kwargs.setdefault("linewidth", 0.5)
+        y_model_fill_kwargs.setdefault("zorder", 9)
+        y_model_fill_kwargs.setdefault("alpha", 0.5)
+
+        y_var_name, _, _, y_plotters = y[i]
+        x_var_name, _, _, x_plotters = x[i]
+        ax_i.plot(x_plotters, y_plotters, **y_kwargs)
+        ax_i.set_xlabel(x_var_name)
+        ax_i.set_ylabel(y_var_name)
+
+        if y_hat is not None:
+            _, _, _, y_hat_plotters = y_hat[i]
+            if kind_pp == "samples":
+                for j in range(num_samples):
+                    if xjitter is True:
+                        jitter_scale = x_plotters[1] - x_plotters[0]
+                        scale_high = jitter_scale * 0.2
+                        x_plotters_jitter = x_plotters + np.random.uniform(
+                            low=-scale_high, high=scale_high, size=len(x_plotters)
+                        )
+                        ax_i.plot(x_plotters_jitter, y_hat_plotters[..., j], **y_hat_plot_kwargs)
+                    else:
+                        ax_i.plot(x_plotters, y_hat_plotters[..., j], **y_hat_plot_kwargs)
+                ax_i.plot([], **y_hat_plot_kwargs, label="Posterior predictive samples")
+            else:
+                plot_hdi(x_plotters, y_hat_plotters, ax=ax_i, **y_hat_fill_kwargs)
+                ax_i.plot(
+                    [], color=y_hat_fill_kwargs["color"], label="Posterior predictive samples"
+                )
+
+        if y_model is not None:
+            _, _, _, y_model_plotters = y_model[i]
+            if kind_model == "lines":
+                for j in range(num_samples):
+                    ax_i.plot(x_plotters, y_model_plotters[..., j], **y_model_plot_kwargs)
+                ax_i.plot([], **y_model_plot_kwargs, label="Uncertainty in mean")
+
+                y_model_mean = np.mean(y_model_plotters, axis=1)
+                ax_i.plot(x_plotters, y_model_mean, color="y", lw=0.8, zorder=11, label="Mean")
+            else:
+                plot_hdi(
+                    x_plotters,
+                    y_model_plotters,
+                    fill_kwargs=y_model_fill_kwargs,
+                    ax=ax_i,
+                )
+                ax_i.plot([], color=y_model_fill_kwargs["color"], label="Uncertainty in mean")
+
+                y_model_mean = np.mean(y_model_plotters, axis=(0, 1))
+                ax_i.plot(x_plotters, y_model_mean, color="y", lw=0.8, zorder=11, label="Mean")
+
+        if legend:
+            ax_i.legend(fontsize=xt_labelsize, loc="upper left")
+        if grid:
+            ax_i.grid(True)
+
+    if backend_show(show):
+        plt.show()
+    return axes