Scikit-learn RandomForestClassifier error

Question

Я использую Python 3.5, и у меня установлены и импортированы NumPy, SciPy и matplotlib.

Когда я пытаюсь:

# 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 и train_data оба поплавка массивы) я получаю следующее сообщение об ошибке:

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 

Answer 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)