Scikit-learn RandomForestClassifier error

Question

I am using Python 3.5 and I have NumPy, SciPy, and matplotlib installed and imported.

When I try:

# Import the random forest package
from sklearn.ensemble import RandomForestClassifier

# Create the random forest object which will include all the parameters
# for the fit
forest = RandomForestClassifier(n_estimators = 1)

# Fit the training data to the Survived labels and create the decision trees
forest = forest.fit(train_data[0::,1::],train_data[0::,0])

# Take the same decision trees and run it on the test data
output = forest.predict(test_data)

(test_data and train_data are both float arrays) I get the following error:

C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\utils\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
  if 'order' in inspect.getargspec(np.copy)[0]:
C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\base.py:175: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
  args, varargs, kw, default = inspect.getargspec(init)
C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\base.py:175: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
  args, varargs, kw, default = inspect.getargspec(init)
C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\base.py:175: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
  args, varargs, kw, default = inspect.getargspec(init)
C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\base.py:175: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
  args, varargs, kw, default = inspect.getargspec(init)
Traceback (most recent call last):
  File "C:/Users/Uri/PycharmProjects/titanic1/fdsg.py", line 54, in <module>
    output = forest.predict(test_data)
  File "C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\ensemble\forest.py", line 461, in predict
    X = check_array(X, ensure_2d=False, accept_sparse="csr")
  File "C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\utils\validation.py", line 352, in check_array
    _assert_all_finite(array)
  File "C:\Users\Uri\AppData\Local\Programs\Python\Python35-32\lib\site-packages\sklearn\utils\validation.py", line 52, in _assert_all_finite
    " or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Process finished with exit code 1

Does it not import RandomForrestClassifier just fine though? — Dimitris Fasarakis Hilliard
– Dimitris Fasarakis Hilliard, Commented Oct 27, 2015 at 8:31
Seems like a warning, not an error. I run into such warnings regularly when some library uses deprecated numpy functions. Or maybe you didn't provide the full stack trace. — kurtosis
– kurtosis, Commented Oct 27, 2015 at 8:41
Exactly, if it imports, you should be fine. Dont worry too much about it. — Dimitris Fasarakis Hilliard
– Dimitris Fasarakis Hilliard, Commented Oct 27, 2015 at 8:49
Add your code to the question body by pressing edit and not in the comments. I added the code in your comment for now, update it if necessary. You need to specify if the error is something other than DeprecationWarning (which is a Warning). — Dimitris Fasarakis Hilliard
– Dimitris Fasarakis Hilliard, Commented Oct 27, 2015 at 8:58
ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). is telling you you have invalid values in your data. — Imanol Luengo
– Imanol Luengo, Commented Oct 27, 2015 at 23:47

Euclides · Accepted Answer · 2015-10-28 09:34:11Z

1

from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import Imputer
import numpy as np

X = np.random.randint(0, (2**31)-1, (500, 4)).astype(object)
y = np.random.randint(0, 2, 500)
clf = RandomForestClassifier()
print(X.max())
clf.fit(X, y) # OK
print("First fit OK")

# 1 - First case your data has null values
X[0,0] = np.nan # replaces of of the cells by a null value
#clf.fit(X, y) # gives you the same error

# to solve NAN values you can use the Imputer class:
imp = Imputer(strategy='median')
X_ok = imp.fit_transform(X)
clf.fit(X_ok, y)

# 2 - Second case your data has huge integers
X[0,0] = 2**128 # the same happens if you have a huge integer
#clf.fit(X, y) # gives you the same error
# to solve this you can clip your values to some cap
X_ok = X.clip(-2**63, 2**63) # I used 2**63 for example, but you should realize what makes sense to your application
clf.fit(X_ok, y)

answered Oct 28, 2015 at 9:34

Euclides

2871 silver badge5 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

AbtPst Over a year ago

nice! but i guess his error is due to ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

Collectives™ on Stack Overflow

Scikit-learn RandomForestClassifier error

1 Answer 1

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related