Hi! Thanks for this fantastic package. I'm struggling to find a solution that would handle NaNs. I'm passing a catboost model as an estimator to HSTreeClassifier, which correctly handles missing values.
from catboost import CatBoostClassifier
from imodels import HSTreeClassifier
clf = CatBoostClassifier()
model = HSTreeClassifier(estimator_=clf)
model = model.fit(X_train, y_train)
Error message
ValueError: Input contains NaN
File <command-4108227421121860>, line 6
4 clf = CatBoostClassifier()
5 model = HSTreeClassifier(estimator_=clf)
----> 6 model = model.fit(X_train, y_train, feature_names=_FEATURES)
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-23c4ea58-433a-40f0-89be-b3d953b89efe/lib/python3.11/site-packages/imodels/tree/hierarchical_shrinkage.py:82, in HSTree.fit(self, X, y, sample_weight, *args, **kwargs)
78 def fit(self, X, y, sample_weight=None, *args, **kwargs):
79 # remove feature_names if it exists (note: only works as keyword-arg)
80 # None returned if not passed
81 feature_names = kwargs.pop("feature_names", None)
---> 82 X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
83 if feature_names is not None:
84 self.feature_names = feature_names
File /local_disk0/.ephemeral_nfs/envs/pythonEnv-23c4ea58-433a-40f0-89be-b3d953b89efe/lib/python3.11/site-packages/imodels/util/arguments.py:26, in check_fit_arguments(model, X, y, feature_names)
24 if scipy.sparse.issparse(X):
25 X = X.toarray()
---> 26 X, y = check_X_y(X, y)
27 _, model.n_features_in_ = X.shape
28 assert len(model.feature_names_) == model.n_features_in_, 'feature_names should be same size as X.shape[1]'
File /databricks/python/lib/python3.11/site-packages/sklearn/utils/validation.py:1147, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
1142 estimator_name = _check_estimator_name(estimator)
1143 raise ValueError(
1144 f"{estimator_name} requires y to be passed, but the target y is None"
1145 )
-> 1147 X = check_array(
1148 X,
1149 accept_sparse=accept_sparse,
1150 accept_large_sparse=accept_large_sparse,
1151 dtype=dtype,
1152 order=order,
1153 copy=copy,
1154 force_all_finite=force_all_finite,
1155 ensure_2d=ensure_2d,
1156 allow_nd=allow_nd,
1157 ensure_min_samples=ensure_min_samples,
1158 ensure_min_features=ensure_min_features,
1159 estimator=estimator,
1160 input_name="X",
1161 )
1163 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
1165 check_consistent_length(X, y)
File /databricks/python/lib/python3.11/site-packages/sklearn/utils/validation.py:959, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
953 raise ValueError(
954 "Found array with dim %d. %s expected <= 2."
955 % (array.ndim, estimator_name)
956 )
958 if force_all_finite:
--> 959 _assert_all_finite(
960 array,
961 input_name=input_name,
962 estimator_name=estimator_name,
963 allow_nan=force_all_finite == "allow-nan",
964 )
966 if ensure_min_samples > 0:
967 n_samples = _num_samples(array)
File /databricks/python/lib/python3.11/site-packages/sklearn/utils/validation.py:109, in _assert_all_finite(X, allow_nan, msg_dtype, estimator_name, input_name)
107 if X.dtype == np.dtype("object") and not allow_nan:
108 if _object_dtype_isnan(X).any():
--> 109 raise ValueError("Input contains NaN")
111 # We need only consider float arrays, hence can early return for all else.
112 if not xp.isdtype(X.dtype, ("real floating", "complex floating")):
Hi! Thanks for this fantastic package. I'm struggling to find a solution that would handle
NaNs. I'm passing a catboost model as an estimator toHSTreeClassifier, which correctly handles missing values.Error message