#76 improve DID plotting + improve data pre-processing

Author: drbenvincent

causalpy/pymc_experiments.py

@@ -238,6 +238,8 @@ def __init__(
 
         # TODO: check that data in column self.group_variable_name has TWO levels
 
+        # TODO: check we have `unit` as a predictor column which is an vector of labels of unique units
+
         # TODO: `treated` is a deterministic function of group and time, so this should be a function rather than supplied data
 
         # DEVIATION FROM SKL EXPERIMENT CODE =============================
@@ -303,18 +305,17 @@ def plot(self):
 
         # Plot raw data
         # NOTE: This will not work when there is just ONE unit in each group
-        # sns.lineplot(
-        #     self.data,
-        #     x=self.time_variable_name,
-        #     y=self.outcome_variable_name,
-        #     hue=self.group_variable_name,
-        #     # units="unit",
-        #     estimator=None,
-        #     alpha=0.25,
-        #     ax=ax,
-        # )
+        sns.lineplot(
+            self.data,
+            x=self.time_variable_name,
+            y=self.outcome_variable_name,
+            hue=self.group_variable_name,
+            units="unit",  # NOTE: assumes we have a `unit` predictor variable
+            estimator=None,
+            alpha=0.5,
+            ax=ax,
+        )
         # Plot model fit to control group
-        # NOTE: This will not work when there is just ONE unit in each group
         parts = ax.violinplot(
             az.extract(
                 self.y_pred_control, group="posterior_predictive", var_names="mu"
@@ -330,7 +331,6 @@ def plot(self):
             pc.set_alpha(0.5)
 
         # Plot model fit to treatment group
-        # NOTE: This will not work when there is just ONE unit in each group
         parts = ax.violinplot(
             az.extract(
                 self.y_pred_treatment, group="posterior_predictive", var_names="mu"
@@ -340,20 +340,41 @@ def plot(self):
             showmedians=False,
             widths=0.2,
         )
-        # # Plot counterfactual - post-test for treatment group IF no treatment had occurred.
-        # # NOTE: This will not work when there is just ONE unit in each group
-        # parts = ax.violinplot(
-        #     az.extract(
-        #         self.y_pred_counterfactual,
-        #         group="posterior_predictive",
-        #         var_names="mu",
-        #     ).values.T,
-        #     positions=self.x_pred_counterfactual[self.time_variable_name].values,
-        #     showmeans=False,
-        #     showmedians=False,
-        #     widths=0.2,
-        # )
+        for pc in parts["bodies"]:
+            pc.set_facecolor("C1")
+            pc.set_edgecolor("None")
+            pc.set_alpha(0.5)
+        # Plot counterfactual - post-test for treatment group IF no treatment had occurred.
+        parts = ax.violinplot(
+            az.extract(
+                self.y_pred_counterfactual,
+                group="posterior_predictive",
+                var_names="mu",
+            ).values.T,
+            positions=self.x_pred_counterfactual[self.time_variable_name].values,
+            showmeans=False,
+            showmedians=False,
+            widths=0.2,
+        )
+        for pc in parts["bodies"]:
+            pc.set_facecolor("C2")
+            pc.set_edgecolor("None")
+            pc.set_alpha(0.5)
         # arrow to label the causal impact
+        self._plot_causal_impact_arrow(ax)
+        # formatting
+        ax.set(
+            xticks=self.x_pred_treatment[self.time_variable_name].values,
+            title=self._causal_impact_summary_stat(),
+        )
+        ax.legend(fontsize=LEGEND_FONT_SIZE)
+        return (fig, ax)
+
+    def _plot_causal_impact_arrow(self, ax):
+        """
+        draw a vertical arrow between `y_pred_counterfactual` and `y_pred_counterfactual`
+        """
+        # Calculate y values to plot the arrow between
         y_pred_treatment = (
             self.y_pred_treatment["posterior_predictive"]
             .mu.isel({"obs_ind": 1})
@@ -363,32 +384,28 @@ def plot(self):
         y_pred_counterfactual = (
             self.y_pred_counterfactual["posterior_predictive"].mu.mean().data
         )
+        # Calculate the x position to plot at
+        diff = np.ptp(self.x_pred_treatment[self.time_variable_name].values)
+        x = np.max(self.x_pred_treatment[self.time_variable_name].values) + 0.1 * diff
+        # Plot the arrow
         ax.annotate(
             "",
-            xy=(1.15, y_pred_counterfactual),
+            xy=(x, y_pred_counterfactual),
             xycoords="data",
-            xytext=(1.15, y_pred_treatment),
+            xytext=(x, y_pred_treatment),
             textcoords="data",
-            arrowprops={"arrowstyle": "<->", "color": "green", "lw": 3},
+            arrowprops={"arrowstyle": "<-", "color": "green", "lw": 3},
         )
+        # Plot text annotation next to arrow
         ax.annotate(
             "causal\nimpact",
-            xy=(1.15, np.mean([y_pred_counterfactual, y_pred_treatment])),
+            xy=(x, np.mean([y_pred_counterfactual, y_pred_treatment])),
             xycoords="data",
             xytext=(5, 0),
             textcoords="offset points",
             color="green",
             va="center",
         )
-        # formatting
-        ax.set(
-            # xlim=[-0.15, 1.25],
-            xticks=self.x_pred_treatment[self.time_variable_name].values,
-            # xticklabels=["pre", "post"],
-            title=self._causal_impact_summary_stat(),
-        )
-        ax.legend(fontsize=LEGEND_FONT_SIZE)
-        return (fig, ax)
 
     def _causal_impact_summary_stat(self):
         percentiles = self.causal_impact.quantile([0.03, 1 - 0.03]).values