renamed get_plot_data_bayesian to _get_plot_data_bayesian, get_plot_data_ols to _get_plot_data_ols, bayesian_plot to _bayesian_plot, and ols_plot to _ols_plot

Author: lpoug

causalpy/experiments/base.py

@@ -60,45 +60,45 @@ def print_coefficients(self, round_to=None):
     def plot(self, *args, **kwargs) -> tuple:
         """Plot the model.
 
-        Internally, this function dispatches to either `bayesian_plot` or `ols_plot`
+        Internally, this function dispatches to either `_bayesian_plot` or `_ols_plot`
         depending on the model type.
         """
         if isinstance(self.model, PyMCModel):
-            return self.bayesian_plot(*args, **kwargs)
+            return self._bayesian_plot(*args, **kwargs)
         elif isinstance(self.model, RegressorMixin):
-            return self.ols_plot(*args, **kwargs)
+            return self._ols_plot(*args, **kwargs)
         else:
             raise ValueError("Unsupported model type")
 
     @abstractmethod
-    def bayesian_plot(self, *args, **kwargs):
+    def _bayesian_plot(self, *args, **kwargs):
         """Abstract method for plotting the model."""
-        raise NotImplementedError("bayesian_plot method not yet implemented")
+        raise NotImplementedError("_bayesian_plot method not yet implemented")
 
     @abstractmethod
-    def ols_plot(self, *args, **kwargs):
+    def _ols_plot(self, *args, **kwargs):
         """Abstract method for plotting the model."""
-        raise NotImplementedError("ols_plot method not yet implemented")
+        raise NotImplementedError("_ols_plot method not yet implemented")
 
     def get_plot_data(self, *args, **kwargs) -> pd.DataFrame:
         """Recover the data of a PrePostFit experiment along with the prediction and causal impact information.
 
-        Internally, this function dispatches to either `get_plot_data_bayesian` or `get_plot_data_ols`
+        Internally, this function dispatches to either `_get_plot_data_bayesian` or `_get_plot_data_ols`
         depending on the model type.
         """
         if isinstance(self.model, PyMCModel):
-            return self.get_plot_data_bayesian(*args, **kwargs)
+            return self._get_plot_data_bayesian(*args, **kwargs)
         elif isinstance(self.model, RegressorMixin):
-            return self.get_plot_data_ols(*args, **kwargs)
+            return self._get_plot_data_ols(*args, **kwargs)
         else:
             raise ValueError("Unsupported model type")
 
     @abstractmethod
-    def get_plot_data_bayesian(self, *args, **kwargs):
+    def _get_plot_data_bayesian(self, *args, **kwargs):
         """Abstract method for recovering plot data."""
-        raise NotImplementedError("get_plot_data_bayesian method not yet implemented")
+        raise NotImplementedError("_get_plot_data_bayesian method not yet implemented")
 
     @abstractmethod
-    def get_plot_data_ols(self, *args, **kwargs):
+    def _get_plot_data_ols(self, *args, **kwargs):
         """Abstract method for recovering plot data."""
-        raise NotImplementedError("get_plot_data_ols method not yet implemented")
+        raise NotImplementedError("_get_plot_data_ols method not yet implemented")

causalpy/experiments/diff_in_diff.py

@@ -229,7 +229,7 @@ def _causal_impact_summary_stat(self, round_to=None) -> str:
         """Computes the mean and 94% credible interval bounds for the causal impact."""
         return f"Causal impact = {convert_to_string(self.causal_impact, round_to=round_to)}"
 
-    def bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
+    def _bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
         """
         Plot the results
 
@@ -367,7 +367,7 @@ def _plot_causal_impact_arrow(results, ax):
         )
         return fig, ax
 
-    def ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
+    def _ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
         """Generate plot for difference-in-differences"""
         round_to = kwargs.get("round_to")
         fig, ax = plt.subplots()

causalpy/experiments/prepostfit.py

@@ -123,7 +123,7 @@ def summary(self, round_to=None) -> None:
         print(f"Formula: {self.formula}")
         self.print_coefficients(round_to)
 
-    def bayesian_plot(
+    def _bayesian_plot(
         self, round_to=None, **kwargs
     ) -> tuple[plt.Figure, List[plt.Axes]]:
         """
@@ -231,7 +231,7 @@ def bayesian_plot(
 
         return fig, ax
 
-    def ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, List[plt.Axes]]:
+    def _ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, List[plt.Axes]]:
         """
         Plot the results
 
@@ -303,7 +303,7 @@ def ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, List[plt.Axes]]
 
         return (fig, ax)
 
-    def get_plot_data_bayesian(self, hdi_prob: float = 0.94) -> pd.DataFrame:
+    def _get_plot_data_bayesian(self, hdi_prob: float = 0.94) -> pd.DataFrame:
         """
         Recover the data of a PrePostFit experiment along with the prediction and causal impact information.
         """
@@ -343,7 +343,7 @@ def get_plot_data_bayesian(self, hdi_prob: float = 0.94) -> pd.DataFrame:
         else:
             raise ValueError("Unsupported model type")
 
-    def get_plot_data_ols(self) -> pd.DataFrame:
+    def _get_plot_data_ols(self) -> pd.DataFrame:
         """
         Recover the data of a PrePostFit experiment along with the prediction and causal impact information.
         """
@@ -436,7 +436,7 @@ class SyntheticControl(PrePostFit):
     supports_ols = True
     supports_bayes = True
 
-    def bayesian_plot(self, *args, **kwargs) -> tuple[plt.Figure, List[plt.Axes]]:
+    def _bayesian_plot(self, *args, **kwargs) -> tuple[plt.Figure, List[plt.Axes]]:
         """
         Plot the results
 
@@ -447,7 +447,7 @@ def bayesian_plot(self, *args, **kwargs) -> tuple[plt.Figure, List[plt.Axes]]:
             Whether to plot the control units as well. Defaults to False.
         """
         # call the super class method
-        fig, ax = super().bayesian_plot(*args, **kwargs)
+        fig, ax = super()._bayesian_plot(*args, **kwargs)
 
         # additional plotting functionality for the synthetic control experiment
         plot_predictors = kwargs.get("plot_predictors", False)

causalpy/experiments/prepostnegd.py

@@ -200,7 +200,7 @@ def summary(self, round_to=None) -> None:
         print(self._causal_impact_summary_stat(round_to))
         self.print_coefficients(round_to)
 
-    def bayesian_plot(
+    def _bayesian_plot(
         self, round_to=None, **kwargs
     ) -> tuple[plt.Figure, List[plt.Axes]]:
         """Generate plot for ANOVA-like experiments with non-equivalent group designs."""

causalpy/experiments/regression_discontinuity.py

@@ -218,7 +218,7 @@ def summary(self, round_to=None) -> None:
         print("\n")
         self.print_coefficients(round_to)
 
-    def bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
+    def _bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
         """Generate plot for regression discontinuity designs."""
         fig, ax = plt.subplots()
         # Plot raw data
@@ -267,7 +267,7 @@ def bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
         )
         return (fig, ax)
 
-    def ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
+    def _ols_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
         """Generate plot for regression discontinuity designs."""
         fig, ax = plt.subplots()
         # Plot raw data

causalpy/experiments/regression_kink.py

@@ -189,7 +189,7 @@ def summary(self, round_to=None) -> None:
         )
         self.print_coefficients(round_to)
 
-    def bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
+    def _bayesian_plot(self, round_to=None, **kwargs) -> tuple[plt.Figure, plt.Axes]:
         """Generate plot for regression kink designs."""
         fig, ax = plt.subplots()
         # Plot raw data