zero failing tests

Author: drbenvincent

causalpy/expt_diff_in_diff.py

@@ -19,7 +19,7 @@
 from causalpy.data_validation import DiDDataValidator
 from causalpy.experiments import ExperimentalDesign
 from causalpy.pymc_models import PyMCModel
-from causalpy.utils import round_num
+from causalpy.utils import convert_to_string
 
 
 class DifferenceInDifferences(ExperimentalDesign, DiDDataValidator):
@@ -168,7 +168,7 @@ def __init__(
             # TODO: THIS IS NOT YET CORRECT
             self.causal_impact = (
                 self.y_pred_treatment[1] - self.y_pred_counterfactual[0]
-            )
+            )[0]
         # ******************************************************************************
 
     def plot(self, round_to=None):
@@ -197,10 +197,13 @@ def summary(self, round_to=None) -> None:
 
     def _causal_impact_summary_stat(self, round_to=None) -> str:
         """Computes the mean and 94% credible interval bounds for the causal impact."""
-        percentiles = self.causal_impact.quantile([0.03, 1 - 0.03]).values
-        ci = (
-            "$CI_{94\\%}$"
-            + f"[{round_num(percentiles[0], round_to)}, {round_num(percentiles[1], round_to)}]"
-        )
-        causal_impact = f"{round_num(self.causal_impact.mean(), round_to)}, "
-        return f"Causal impact = {causal_impact + ci}"
+
+        return f"Causal impact = {convert_to_string(self.causal_impact, round_to=round_to)}"
+
+        # percentiles = self.causal_impact.quantile([0.03, 1 - 0.03]).values
+        # ci = (
+        #     "$CI_{94\\%}$"
+        #     + f"[{round_num(percentiles[0], round_to)}, {round_num(percentiles[1], round_to)}]"
+        # )
+        # causal_impact = f"{round_num(self.causal_impact.mean(), round_to)}, "
+        # return f"Causal impact = {causal_impact + ci}"

causalpy/plotting.py

@@ -586,7 +586,7 @@ def plot_difference_in_differences(results, round_to=None):
             xlim=[-0.05, 1.1],
             xticks=[0, 1],
             xticklabels=["pre", "post"],
-            title=f"Causal impact = {round_num(results.causal_impact[0], round_to)}",
+            title=f"Causal impact = {round_num(results.causal_impact, round_to)}",
         )
         ax.legend(fontsize=LEGEND_FONT_SIZE)
         return (fig, ax)

causalpy/utils.py

@@ -67,17 +67,20 @@ def _format_sig_figs(value, default=None):
     return max(int(np.log10(np.abs(value))) + 1, default)
 
 
-# function `convert_to_string` which accepts either a float or an xarray object. We want
-# to return a string. If the input is a float, we want to return a string with 2 decimal
-# places. If the input is an xarray object, we want to calculate the mean and return a
-# string with 2 decimal places.
 def convert_to_string(x: Union[float, xr.DataArray], round_to=2) -> str:
+    """Utility function which takes in nunmeric inputs and returns a string."""
     if isinstance(x, float):
+        # In the case of a float, we return the number rounded to 2 decimal places
         return f"{x:.2f}"
-    else:
+    elif isinstance(x, xr.DataArray):
+        # In the case of an xarray object, we return the mean and 94% CI
         percentiles = x.quantile([0.03, 1 - 0.03]).values
         ci = (
             r"$CI_{94\%}$"
             + f"[{round_num(percentiles[0], round_to)}, {round_num(percentiles[1], round_to)}]"
         )
         return f"{x.mean().values:.2f}" + ci
+    else:
+        raise ValueError(
+            "Type not supported. Please provide a float or an xarray object."
+        )