sklearn error ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

Question

This might happen inside scikit, and it depends on what you're doing. I recommend reading the documentation for the functions you're using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria.

EDIT: How could I miss that:

np.isnan(mat.any()) #and gets False
np.isfinite(mat.all()) #and gets True

is obviously wrong. Right would be:

np.any(np.isnan(mat))

and

np.all(np.isfinite(mat))

You want to check wheter any of the element is NaN, and not whether the return value of the any function is a number...

I got the same error message when using sklearn with pandas. My solution is to reset the index of my dataframe df before running any sklearn code:

df = df.reset_index()

I encountered this issue many times when I removed some entries in my df, such as

df = df[df.label=='desired_one']

This is my function (based on this) to clean the dataset of nan, Inf, and missing cells (for skewed datasets):

import pandas as pd
import numpy as np

def clean_dataset(df):
    assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
    df.dropna(inplace=True)
    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)
    return df[indices_to_keep].astype(np.float64)

This is the check on which it fails:

• https://github.com/scikit-learn/scikit-learn/blob/0.17.X/sklearn/utils/validation.py#L51

Which says

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)

So make sure that you have non NaN values in your input. And all those values are actually float values. None of the values should be Inf either.

In most cases getting rid of infinite and null values solve this problem.

get rid of infinite values.

df.replace([np.inf, -np.inf], np.nan, inplace=True)

get rid of null values the way you like, specific value such as 999, mean, or create your own function to impute missing values

df.fillna(999, inplace=True)