for col in train.columns:
if train[col].nunique() >= 5 and train[col].nunique(
) < 200 and col != "target":
cat_embed_cols.append(col)
num_cols = [
c for c in train.columns if c not in cat_embed_cols + ["target"]
]
wide_cols = []
for col in train.columns:
if train[col].nunique() < 40 and col != "target":
wide_cols.append(col)
prepare_wide = WidePreprocessor(wide_cols)
X_wide_train = prepare_wide.fit_transform(train)
X_wide_valid = prepare_wide.transform(valid)
prepare_tab = TabPreprocessor(
embed_cols=cat_embed_cols,
continuous_cols=num_cols,
for_tabtransformer=True,
scale=False,
)
X_tab_train = prepare_tab.fit_transform(train)
X_tab_valid = prepare_tab.transform(valid)
y_train = train.target.values
y_valid = valid.target.values
wide = Wide(wide_dim=np.unique(X_wide_train).shape[0])
def test_notfittederror():
processor = WidePreprocessor(wide_cols, cross_cols)
with pytest.raises(NotFittedError):
processor.transform(df_letters)