The Dimensions of my input array were skewed, as my input csv had empty spaces.

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.

I had the same error, and in my case X and y were dataframes so I had to convert them to matrices first:

X = X.values.astype(np.float)
y = y.values.astype(np.float)

Edit: The originally suggested X.as_matrix() is Deprecated

With this version of python 3:

/opt/anaconda3/bin/python --version
Python 3.6.0 :: Anaconda 4.3.0 (64-bit)

Looking at the details of the error, I found the lines of codes causing the failure:

/opt/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in _assert_all_finite(X)
     56             and not np.isfinite(X).all()):
     57         raise ValueError("Input contains NaN, infinity"
---> 58                          " or a value too large for %r." % X.dtype)
     59 
     60 

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

From this, I was able to extract the correct way to test what was going on with my data using the same test which fails given by the error message: np.isfinite(X)

Then with a quick and dirty loop, I was able to find that my data indeed contains nans:

print(p[:,0].shape)
index = 0
for i in p[:,0]:
    if not np.isfinite(i):
        print(index, i)
    index +=1

(367340,)
4454 nan
6940 nan
10868 nan
12753 nan
14855 nan
15678 nan
24954 nan
30251 nan
31108 nan
51455 nan
59055 nan
...

Now all I have to do is remove the values at these indexes.

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(axis=1)
    return df[indices_to_keep].astype(np.float64)

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']

I had the error after trying to select a subset of rows:

df = df.reindex(index=my_index)

Turns out that my_index contained values that were not contained in df.index, so the reindex function inserted some new rows and filled them with nan.

i got the same error. it worked with df.fillna(-99999, inplace=True) before doing any replacement, substitution etc

In my case the problem was that many scikit functions return numpy arrays, which are devoid of pandas index. So there was an index mismatch when I used those numpy arrays to build new DataFrames and then I tried to mix them with the original data.

try

mat.sum()

If the sum of your data is infinity (greater that the max float value which is 3.402823e+38) you will get that error.

see the _assert_all_finite function in validation.py from the scikit source code:

if is_float and np.isfinite(X.sum()):
    pass
elif is_float:
    msg_err = "Input contains {} or a value too large for {!r}."
    if (allow_nan and np.isinf(X).any() or
            not allow_nan and not np.isfinite(X).all()):
        type_err = 'infinity' if allow_nan else 'NaN, infinity'
        # print(X.sum())
        raise ValueError(msg_err.format(type_err, X.dtype))

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)

Remove all infinite values:

(and replace with min or max for that column)

import numpy as np

# generate example matrix
matrix = np.random.rand(5,5)
matrix[0,:] = np.inf
matrix[2,:] = -np.inf
>>> matrix
array([[       inf,        inf,        inf,        inf,        inf],
       [0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
       [      -inf,       -inf,       -inf,       -inf,       -inf],
       [0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
       [0.90272002, 0.37357483, 0.92952479, 0.072105  , 0.20837798]])

# find min and max values for each column, ignoring nan, -inf, and inf
mins = [np.nanmin(matrix[:, i][matrix[:, i] != -np.inf]) for i in range(matrix.shape[1])]
maxs = [np.nanmax(matrix[:, i][matrix[:, i] != np.inf]) for i in range(matrix.shape[1])]

# go through matrix one column at a time and replace  + and -infinity 
# with the max or min for that column
for i in range(matrix.shape[1]):
    matrix[:, i][matrix[:, i] == -np.inf] = mins[i]
    matrix[:, i][matrix[:, i] == np.inf] = maxs[i]

>>> matrix
array([[0.90272002, 0.37357483, 0.95222639, 0.37570528, 0.68779902],
       [0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
       [0.72877665, 0.06580068, 0.7427659 , 0.00833664, 0.20837798],
       [0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
       [0.90272002, 0.37357483, 0.92952479, 0.072105  , 0.20837798]])

I would like to propose a solution for numpy that worked well for me. The line

from numpy import inf
inputArray[inputArray == inf] = np.finfo(np.float64).max

substitues all infite values of a numpy array with the maximum float64 number.

None of the answers here worked for me. This was what worked.

Test_y = np.nan_to_num(Test_y)

It replaces the infinity values with high finite values and the nan values with numbers

dataset = dataset.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

This worked for me

If you’re running an estimator, it could be that your learning rate is too high. I passed in the wrong array to a grid search by accident and ended up training with a learning rate of 500, which I could see causing issues with the training process.

Basically it’s not necessarily only your inputs that have to all be valid, but the intermediate data as well.

Problem seems to occur in DecisionTreeClassifier input check, Try

X_train = X_train.replace((np.inf, -np.inf, np.nan), 0).reset_index(drop=True)

I had the same issue, in my case the answer was simply that I had a cell in my CSV with no value ("x,y,z,,"). Putting a default value in fixed it for me.

Using isneginf may help.
http://docs.scipy.org/doc/numpy/reference/generated/numpy.isneginf.html#numpy.isneginf

x[numpy.isneginf(x)] = 0 #0 is the value you want to replace with

Note: This solution only applies if you consciously want to keep NaN entries in your dataset.

This error happened to me when I was using some of the scikit-learn functionality (in my case: GridSearchCV). Under the hood I was using an xgboost XGBClassifier which handles NaN data gracefully. However, GridSearchCV was using sklearn.utils.validation module that encforced lack of missing data in the input data by calling _assert_all_finite function. This was ultimately causing an error:

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

Sidenote: _assert_all_finite accepts an allow_nan argument, which, if set to True, would not be causing issues. However, scikit-learn API does not allow us to have control over this argument.

Solution

My solution was to use patch module to silence the _assert_all_finite function so that it does not raise ValueError. Here is a snippet

import sklearn
with mock.patch("sklearn.utils.validation._assert_all_finite"):
    # your code that raises ValueError

this will replace the _assert_all_finite by a dummy mock function so it won’t get executed.

Please note that patching is not a recommended practice and might result in unpredictable behaviour!

EDIT:
This Pull Request should resolve the issue (though the fix has not been released as of Jan 2022)

I found that after calling pct_change on a new column that nan existed in one of rows. I remove the nan row with the following code

df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.reset_index()

Puff !! In my case the problem was about NaN values…

You can list your columns that had NaN with this function

your_data.isnull().sum()

and then you can fill these NAN values in your dataset file.

Here is the code for how to "Replace NaN with zero and infinity with large finite numbers."

your_data[:] = np.nan_to_num(your_data)

from numpy.nan_to_num

After a long time of dealing with this problem, I realized that this is because in splits of training and testing sets there are columns of data which are the same for all data rows. Then some calculations in some algorithms may lead to infinity results. If the data that you are using is in a way that close rows are more likely to be similar then shuffling the data can help. This is a bug with scikit. I’m using version 0.23.2.

If you happen to use the "kc_house_data.csv" dataset (which some commenters and many data-science newcomers seem to use, because it’s presented in lots of popular course material), the data is faulty and the true source for the error.

To fix it, as of 2022:

• Delete the last (empty) line in the csv file
• There are two lines that contain one empty data value "x,x,,x,x" – to fix it, don’t delete the comma, instead add a random integer value like 2000, so it looks like this "x,x,2000,x,x"

Don’t forget to save and reload in your project.

All the other answers are helpful and correct, but not in this case:

If you use kc_house_data.csv you need to fix the data in the file, nothing else will help, the empty data field will shift the other data around randomly and generate weird bugs that are hard to track to the source!

In my case the algorithm required data to be between (0,1) noninclusive. My quite brutal solutions was to add a small random number to all desired values:

y_train = pd.DataFrame(y_train).applymap(lambda x: x + np.random.rand()/100000.0)["col_name"]
y_train[y_train >= 1] = 0.999999

while y_train is in the range of [0,1].

This is definitely not suitable for all cases, as you are messing with your input data but can be a solution if you have sparse data and only need a quick forecast

sklearn=1.1.2

python=3.9

In my case the PowerScaler with standardize=True is causing the problem. As @TomaszBartkowiak already explained, the assertion is raised in sklearn.utils.validation._asser_all_finite which seems to be used in many places before aggregations like np.sum in my case.

I check all the conditions manually (dtypes, nan, inf, -inf) and found that no reason why the assertion still fails. So i simply temporarily comment out the check in _asser_all_finit line 108:

...
is_float = X.dtype.kind in "fc"
if True:#is_float and (np.isfinite(_safe_accumulator_op(np.sum, X))):
    pass
elif is_float:
...

After the successful execution of PowerScaler i change the code back. This is quick and dirty, but if you are really confident with your data and this happens seeming for no reason, you can solve it this way. But in general speaking the probability is very high that the data does contains INF/-INF somewhere. So better dig deeper. In case of Scaler you can easily find the columns with INF/-INF in output, so that you can go back and check these columns again in the input data. I don’t know why though why the checks didn’t work in the first place before using the Scaler …