def skl_imputation(X, nominal_attributes):
# Init
n_rows, n_cols = X.shape
i_list = []
# Make imputers
for c in range(n_cols):
# Generate config
i_config = dict(missing_values=np.nan)
if c in nominal_attributes:
# Nominal attribute
out_kind = "nominal"
i_config["strategy"] = "most_frequent"
else:
# Numeric attribute
i_config["strategy"] = "mean"
out_kind = "numeric"
# Initialize imputer
i = SimpleImputer(**i_config)
i.fit(X[:, [c]])
i.out_kind = out_kind
i_list.append(i)
return i_list
def nan_imputation(X, nominal_attributes):
# Init
n_rows, n_cols = X.shape
i_list = []
# Make imputers
for c in range(n_cols):
i_config = dict(missing_values=np.nan, strategy="constant", fill_value=np.nan)
# Initialize imputer
i = SimpleImputer(**i_config)
i.fit(X[:, [c]])
if c in nominal_attributes:
i.out_kind = "nominal"
else:
i.out_kind = "numeric"
i_list.append(i)
return i_list
