_input_validation -> input_validation

Author: drbenvincent

causalpy/exp_inverse_propensity_weighting.py

@@ -84,7 +84,7 @@ def __init__(
         self.formula = formula
         self.outcome_variable = outcome_variable
         self.weighting_scheme = weighting_scheme
-        self._input_validation()
+        self.input_validation()
 
         t, X = dmatrices(formula, self.data)
         self._t_design_info = t.design_info
@@ -97,7 +97,7 @@ def __init__(
         self.coords = COORDS
         self.model.fit(X=self.X, t=self.t, coords=COORDS)
 
-    def _input_validation(self):
+    def input_validation(self):
         """Validate the input data and model formula for correctness"""
         treatment = self.formula.split("~")[0]
         test = treatment.strip() in self.data.columns

causalpy/expt_diff_in_diff.py

@@ -83,7 +83,7 @@ def __init__(
         self.formula = formula
         self.time_variable_name = time_variable_name
         self.group_variable_name = group_variable_name
-        self._input_validation()
+        self.input_validation()
 
         y, X = dmatrices(formula, self.data)
         self._y_design_info = y.design_info
@@ -177,7 +177,7 @@ def __init__(
         else:
             raise ValueError("Model type not recognized")
 
-    def _input_validation(self):
+    def input_validation(self):
         """Validate the input data and model formula for correctness"""
         if "post_treatment" not in self.formula:
             raise FormulaException(

causalpy/expt_instrumental_variable.py

@@ -105,7 +105,7 @@ def __init__(
         self.formula = formula
         self.instruments_formula = instruments_formula
         self.model = model
-        self._input_validation()
+        self.input_validation()
 
         y, X = dmatrices(formula, self.data)
         self._y_design_info = y.design_info
@@ -139,7 +139,7 @@ def __init__(
             X=self.X, Z=self.Z, y=self.y, t=self.t, coords=COORDS, priors=self.priors
         )
 
-    def _input_validation(self):
+    def input_validation(self):
         """Validate the input data and model formula for correctness"""
         treatment = self.instruments_formula.split("~")[0]
         test = treatment.strip() in self.instruments_data.columns

causalpy/expt_prepostfit.py

@@ -42,7 +42,7 @@ def __init__(
         **kwargs,
     ) -> None:
         super().__init__(model=model, **kwargs)
-        self._input_validation(data, treatment_time)
+        self.input_validation(data, treatment_time)
         self.treatment_time = treatment_time
         # set experiment type - usually done in subclasses
         self.expt_type = "Pre-Post Fit"
@@ -91,7 +91,7 @@ def __init__(
             self.post_impact
         )
 
-    def _input_validation(self, data, treatment_time):
+    def input_validation(self, data, treatment_time):
         """Validate the input data and model formula for correctness"""
         if isinstance(data.index, pd.DatetimeIndex) and not isinstance(
             treatment_time, pd.Timestamp

causalpy/expt_prepostnegd.py

@@ -90,7 +90,7 @@ def __init__(
         self.formula = formula
         self.group_variable_name = group_variable_name
         self.pretreatment_variable_name = pretreatment_variable_name
-        self._input_validation()
+        self.input_validation()
 
         y, X = dmatrices(formula, self.data)
         self._y_design_info = y.design_info
@@ -142,7 +142,7 @@ def __init__(
             {"coeffs": self._get_treatment_effect_coeff()}
         )
 
-    def _input_validation(self) -> None:
+    def input_validation(self) -> None:
         """Validate the input data and model formula for correctness"""
         if not _is_variable_dummy_coded(self.data[self.group_variable_name]):
             raise DataException(

causalpy/expt_regression_discontinuity.py

@@ -92,7 +92,7 @@ def __init__(
         self.treatment_threshold = treatment_threshold
         self.epsilon = epsilon
         self.bandwidth = bandwidth
-        self._input_validation()
+        self.input_validation()
 
         if self.bandwidth is not np.inf:
             fmin = self.treatment_threshold - self.bandwidth
@@ -171,7 +171,7 @@ def __init__(
             ) - np.squeeze(self.pred_discon[0])
         # ******************************************************************************
 
-    def _input_validation(self):
+    def input_validation(self):
         """Validate the input data and model formula for correctness"""
         if "treated" not in self.formula:
             raise FormulaException(

causalpy/expt_regression_kink.py

@@ -53,7 +53,7 @@ def __init__(
         self.kink_point = kink_point
         self.epsilon = epsilon
         self.bandwidth = bandwidth
-        self._input_validation()
+        self.input_validation()
 
         if self.bandwidth is not np.inf:
             fmin = self.kink_point - self.bandwidth
@@ -101,7 +101,7 @@ def __init__(
             mu_kink_left, mu_kink, mu_kink_right, epsilon
         )
 
-    def _input_validation(self):
+    def input_validation(self):
         """Validate the input data and model formula for correctness"""
         if "treated" not in self.formula:
             raise FormulaException(