statsmodels in own file

marginaleffects/model_statsmodels.py

@@ -0,0 +1,99 @@
+import re
+import numpy as np
+import polars as pl
+import warnings
+import patsy
+from .model_abstract import ModelAbstract
+
+
+class ModelStatsmodels(ModelAbstract):
+    def get_coef(self):
+        return np.array(self.model.params)
+
+    def get_modeldata(self):
+        df = self.model.model.data.frame
+        if not isinstance(df, pl.DataFrame):
+            df = pl.from_pandas(df)
+        return df
+
+    def get_response_name(self):
+        return self.model.model.endog_names
+
+    def get_vcov(self, vcov=True):
+        if isinstance(vcov, bool):
+            if vcov is True:
+                V = self.model.cov_params()
+            else:
+                V = None
+        elif isinstance(vcov, str):
+            lab = f"cov_{vcov}"
+            if hasattr(self.model, lab):
+                V = getattr(self.model, lab)
+            else:
+                raise ValueError(f"The model object has no {lab} attribute.")
+        else:
+            raise ValueError(
+                '`vcov` must be a boolean or a string like "HC3", which corresponds to an attribute of the model object such as "vcov_HC3".'
+            )
+
+        if V is not None:
+            V = np.array(V)
+            if V.shape != (len(self.coef), len(self.coef)):
+                raise ValueError(
+                    "vcov must be a square numpy array with dimensions equal to the length of self.coef"
+                )
+
+        return V
+
+    def get_variables_names(self, variables, newdata):
+        if variables is None:
+            variables = self.model.model.exog_names
+            variables = [re.sub("\[.*\]", "", x) for x in variables]
+            variables = [x for x in variables if x in newdata.columns]
+            variables = pl.Series(variables).unique().to_list()
+        if isinstance(variables, (str, dict)):
+            variables = [variables] if isinstance(variables, str) else variables
+        elif isinstance(variables, list) and all(
+            isinstance(var, str) for var in variables
+        ):
+            pass
+        else:
+            raise ValueError(
+                "`variables` must be None, a dict, string, or list of strings"
+            )
+        good = [x for x in variables if x in newdata.columns]
+        bad = [x for x in variables if x not in newdata.columns]
+        if len(bad) > 0:
+            bad = ", ".join(bad)
+            warnings.warn(f"Variable(s) not in newdata: {bad}")
+        if len(good) == 0:
+            raise ValueError("There is no valid column name in `variables`.")
+        return variables
+
+    def get_predict(self, params, newdata: pl.DataFrame):
+        if isinstance(newdata, np.ndarray):
+            exog = newdata
+        else:
+            y, exog = patsy.dmatrices(self.model.model.formula, newdata.to_pandas())
+        p = self.model.model.predict(params, exog)
+        if p.ndim == 1:
+            p = pl.DataFrame({"rowid": range(newdata.shape[0]), "estimate": p})
+        elif p.ndim == 2:
+            colnames = {f"column_{i}": str(i) for i in range(p.shape[1])}
+            p = (
+                pl.DataFrame(p)
+                .rename(colnames)
+                .with_columns(
+                    pl.Series(range(p.shape[0]), dtype=pl.Int32).alias("rowid")
+                )
+                .melt(id_vars="rowid", variable_name="group", value_name="estimate")
+            )
+        else:
+            raise ValueError(
+                "The `predict()` method must return an array with 1 or 2 dimensions."
+            )
+        p = p.with_columns(pl.col("rowid").cast(pl.Int32))
+        return p
+
+    def get_formula(self):
+        return self.model.model.formula