Scikit-learn - ValueError: Input contains NaN, infinity or a value too large for dtype('float32') with Random Forest

Question

First, I have checked the different posts concerning this error and none of them can solve my issue.

So I am using RandomForest and I am able to generate the forest and to do a prediction but sometimes during the generation of the forest, I get the following error.

ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

This error occurs with the same dataset. Sometimes the dataset creates an error during the training and most of the time not. The error sometimes occurs at the start and sometimes in the middle of the training.

Here's my code :

import pandas as pd
from sklearn import ensemble
import numpy as np

def azureml_main(dataframe1 = None, dataframe2 = None):

    # Execution logic goes here

    Input = dataframe1.values[:,:]
    InputData = Input[:,:15]
    InputTarget = Input[:,16:]

    limitTrain = 2175

    clf = ensemble.RandomForestClassifier(n_estimators = 10000, n_jobs = 4 );

    features=np.empty([len(InputData),10])
    j=0
    for i in range (0,14):
        if (i == 1 or i == 4 or i == 5 or i == 6 or i == 8 or i == 9 or  i == 10 or i == 11 or i == 13 or i == 14):
            features[:,j] = (InputData[:, i])
            j += 1     
        
    clf.fit(features[:limitTrain,:],np.asarray(InputTarget[:limitTrain,1],dtype = np.float32))

    res = clf.predict_proba(features[limitTrain+1:,:])

    listreu = np.empty([len(res),5])
    for i in range(len(res)):
        if(res[i,0] > 0.5):
            listreu[i,4] = 0;
        elif(res[i,1] > 0.5):
            listreu[i,4] = 1;
        elif(res[i,2] > 0.5):
            listreu[i,4] = 2;
        else:
            listreu[i,4] = 3;
    

    listreu[:,0] = features[limitTrain+1:,0]
    listreu[:,1] = InputData[limitTrain+1:,2]
    listreu[:,2] = InputData[limitTrain+1:,3]
    listreu[:,3] = features[limitTrain+1:,1]



    # Return value must be of a sequence of pandas.DataFrame
    return pd.DataFrame(listreu),

I ran my code locally and on Azure ML Studio and the error occurs in both cases.

I am sure that it is not due to my dataset since most of the time I don't get the error and I am generating the dataset myself from different input.

This is a part of the dataset I use

EDIT: I probably found out that I had 0 value which were not real 0 value. The values were like

3.0x10^-314

Solution 1

Since I correct the problem of the edit, I have no more errors. I just replace 3.0x10^-314 values with zeros.

Solution 2

I would presume somewhere in you dataframe you sometimes have nan values.

these can simply be removed using

dataframe1 = dataframe1.dropna()

However, with this approach you could be losing some valueable training data so it may be worth looking into .fillna() or sklearn.preprocessing.Imputer in order to augment some values for the nan cells in the df.

Without seeing the source of dataframe1 it is hard to give a full / complete answer but it is possible that some sort of train, test split is going on resulting in the dataframe being passed only having nan values some of the time.

Comments

[Thomas]

Since I am generating my own dataset I know that it is impossible that there are NaN values in the dataset.

[Kieran Lavelle]

Have you tried the above to verify that? Something somewhere is likely being cast to a nan without you knowing.

[Thomas]

I am trying to use it but I can not tell you right now if it is working since I am not getting the error 100%

[Kieran Lavelle]

@ThomasR thats fine, just reply once tested for a reasonable number of attempts.

[Kieran Lavelle]

In that case try features=np.empty([len(InputData),10]).astype(np.float64)

[Thomas]

Let us continue this discussion in chat.

[Swarit Agarwal]

This isn't the case