Update: I have non NAN values so fillna is not an issue. Clean dataset.

I'm having this error occur when I try to predict using my grid best params. I get a score when fit it onto the training data. I get this error however when I try and predict on the X_test. Very confused.

I'm attempting to use a pipeline and gridsearch combined for my dataset. Code works up to the training part and score.

It's a clean dataset and has no NAN values.

My code is

classifiers = [AdaBoostClassifier(), 

num_cols = X_train.select_dtypes("number").columns
cat_cols = X_train.select_dtypes("object").columns

categorical_transformation = make_pipeline(MinMaxScaler(),

integer_features = list(X_train.columns[X_train.dtypes == 'int64'])
continuous_features = list(X_train.columns[X_train.dtypes == 'float64'])

int_transformation = make_pipeline(MinMaxScaler(),
float_transformation = make_pipeline(MinMaxScaler(),

preprocessor = make_column_transformer((int_transformation, integer_features),
                                       (float_transformation, float_feature))

for classifier in classifiers:
    pipe = make_pipeline(preprocessor, classifier)
    grid = GridSearchCV(pipe, cv=5, scoring="recall", param_grid = {})
    grid.fit(X_train, y_train)
    # RandomForestClassifier()
    # 0.9996252992392879

pipe = make_pipeline(preprocessor, LogisticRegression())
param_grid_logreg = {"logisticregression__C": [0.1, 1, 10, 100, 1000]}

grid_logreg = GridSearchCV(estimator = pipe, param_grid=param_grid_logreg, cv=5)

grid_logreg.fit(X_train, y_train)

print("Best score:", grid_logreg.best_score_)
print("Best parameters:", grid_logreg.best_params_)
# Best score: 0.9337686658306279
# Best parameters: {'logisticregression__C': 0.1}

log_reg_best_model = grid_logreg.best_estimator_
log_reg_best_model.score(X_train, y_train)
# 0.9983211913323731



ValueError: Input contains infinity or a value too large for dtype('float64').

2 Answers 2


I solved the issue in the end.

The issue was with the order of my pipeline - I'd placed the PowerTransformer at the end of the pipeline which gave the infinite values. Placing the MinMaxScaler after it solved this :)


The error raises with both, either you have NaNs or infinite values:

From documentation:

def _assert_all_finite(X):
    """Like assert_all_finite, but only for ndarray."""
    X = np.asanyarray(X)
    # First try an O(n) time, O(1) space solution for the common case that
    # everything is finite; fall back to O(n) space np.isfinite to prevent
    # false positives from overflow in sum method.
    if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
            and not np.isfinite(X).all()):
        raise ValueError("Input contains NaN, infinity"
                         " or a value too large for %r." % X.dtype)

You could simply run X_test.describe() and check what the max and min values are, if you have -np.inf or np.inf as min or max values respect. you could replace them.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.