obs_indx -> obs_ind (see #459)

Author: drbenvincent

causalpy/experiments/diff_in_diff.py

@@ -104,7 +104,7 @@ def __init__(
 
         # fit model
         if isinstance(self.model, PyMCModel):
-            COORDS = {"coeffs": self.labels, "obs_indx": np.arange(self.X.shape[0])}
+            COORDS = {"coeffs": self.labels, "obs_ind": np.arange(self.X.shape[0])}
             self.model.fit(X=self.X, y=self.y, coords=COORDS)
         elif isinstance(self.model, RegressorMixin):
             self.model.fit(X=self.X, y=self.y)

causalpy/experiments/interrupted_time_series.py

@@ -110,7 +110,7 @@ def __init__(
 
         # fit the model to the observed (pre-intervention) data
         if isinstance(self.model, PyMCModel):
-            COORDS = {"coeffs": self.labels, "obs_indx": np.arange(self.pre_X.shape[0])}
+            COORDS = {"coeffs": self.labels, "obs_ind": np.arange(self.pre_X.shape[0])}
             self.model.fit(X=self.pre_X, y=self.pre_y, coords=COORDS)
         elif isinstance(self.model, RegressorMixin):
             self.model.fit(X=self.pre_X, y=self.pre_y)

causalpy/experiments/prepostnegd.py

@@ -113,7 +113,7 @@ def __init__(
 
         # fit the model to the observed (pre-intervention) data
         if isinstance(self.model, PyMCModel):
-            COORDS = {"coeffs": self.labels, "obs_indx": np.arange(self.X.shape[0])}
+            COORDS = {"coeffs": self.labels, "obs_ind": np.arange(self.X.shape[0])}
             self.model.fit(X=self.X, y=self.y, coords=COORDS)
         elif isinstance(self.model, RegressorMixin):
             raise NotImplementedError("Not implemented for OLS model")

causalpy/experiments/regression_discontinuity.py

@@ -124,7 +124,7 @@ def __init__(
         # fit model
         if isinstance(self.model, PyMCModel):
             # fit the model to the observed (pre-intervention) data
-            COORDS = {"coeffs": self.labels, "obs_indx": np.arange(self.X.shape[0])}
+            COORDS = {"coeffs": self.labels, "obs_ind": np.arange(self.X.shape[0])}
             self.model.fit(X=self.X, y=self.y, coords=COORDS)
         elif isinstance(self.model, RegressorMixin):
             self.model.fit(X=self.X, y=self.y)

causalpy/experiments/regression_kink.py

@@ -84,7 +84,7 @@ def __init__(
         self.y, self.X = np.asarray(y), np.asarray(X)
         self.outcome_variable_name = y.design_info.column_names[0]
 
-        COORDS = {"coeffs": self.labels, "obs_indx": np.arange(self.X.shape[0])}
+        COORDS = {"coeffs": self.labels, "obs_ind": np.arange(self.X.shape[0])}
         self.model.fit(X=self.X, y=self.y, coords=COORDS)
 
         # score the goodness of fit to all data

causalpy/experiments/synthetic_control.py

@@ -134,7 +134,7 @@ def __init__(
             COORDS = {
                 "control_units": self.control_units,
                 "treated_units": self.treated_units,
-                "obs_indx": np.arange(self.datapre.shape[0]),
+                "obs_ind": np.arange(self.datapre.shape[0]),
             }
             self.model.fit(
                 X=self.datapre_control.to_numpy(),

causalpy/pymc_models.py

@@ -208,7 +208,7 @@ class LinearRegression(PyMCModel):
     >>> lr = LinearRegression(sample_kwargs={"progressbar": False})
     >>> lr.fit(X, y, coords={
     ...                 'coeffs': ['x', 'treated'],
-    ...                 'obs_indx': np.arange(rd.shape[0])
+    ...                 'obs_ind': np.arange(rd.shape[0])
     ...                },
     ... )
     Inference data...
@@ -448,7 +448,7 @@ class PropensityScore(PyMCModel):
     >>> ps = PropensityScore(sample_kwargs={"progressbar": False})
     >>> ps.fit(X, t, coords={
     ...                 'coeffs': ['age', 'race'],
-    ...                 'obs_indx': np.arange(df.shape[0])
+    ...                 'obs_ind': np.arange(df.shape[0])
     ...                },
     ... )
     Inference...