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@mikekeith52
Created June 7, 2022 23:20
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pd.options.display.max_rows = None
all_xvars = f.get_regressor_names()
final_dropped = pd.DataFrame({"Var": all_xvars})
for i, v in f.export("model_summaries").iterrows():
model = v["ModelNickname"]
Xvars = v["Xvars"]
dropped_vars = [x for x in f.get_regressor_names() if x not in Xvars]
if not dropped_vars:
continue
tmp_dropped = pd.DataFrame(
{"Var": dropped_vars, f"dropped in {model}": [1] * len(dropped_vars)}
)
final_dropped = final_dropped.merge(tmp_dropped, on="Var", how="left").fillna(0)
final_dropped["total times dropped"] = final_dropped.iloc[:, 1:].sum(axis=1)
final_dropped = final_dropped.loc[final_dropped["total times dropped"] > 0]
final_dropped = final_dropped.sort_values("total times dropped", ascending=False)
final_dropped = final_dropped.reset_index(drop=True)
final_dropped.iloc[:, 1:] = final_dropped.iloc[:, 1:].astype(int)
final_dropped
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