amueller

amueller

  # Standard scientific Python imports
  import pylab as pl
  import numpy as np
  from time import time
      
  # Import datasets, classifiers and performance metrics
  from sklearn import datasets, svm, pipeline
  from sklearn.kernel_approximation import (RBFSampler,
                                            Nystroem)
  from sklearn.utils import shuffle

amueller

amueller

import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from sklearn.decomposition import RandomizedPCA
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle

mnist = fetch_mldata("MNIST original")
X_train, y_train = mnist.data[:60000] / 255., mnist.target[:60000]

amueller

# Standard scientific Python imports
import pylab as pl
import numpy as np
from time import time

# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, pipeline
from sklearn.kernel_approximation import (RBFSampler,
                                          Nystroem)
from sklearn.utils import shuffle

amueller

amueller

======================================================================
FAIL: Check that oob prediction is as acurate as
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/home/andy/sklearn_tests/sklearn/ensemble/tests/test_forest.py", line 198, in test_oob_score_classification
    assert_almost_equal(training_score, clf.oob_score_)
  File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 468, in assert_almost_equal
    raise AssertionError(msg)

amueller

import os                                                                                                                              
from glob import glob                                                                                                                  
                                                                                                                                       
import numpy as np                                                                                                                     
import matplotlib.pyplot as plt                                                                                                        
                                                                                                                                       
from sklearn.svm import LinearSVC                                                                                                      
from sklearn.metrics import accuracy_score, confusion_matrix            

amueller

amueller

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
X, y = make_blobs()

KMeans(n_jobs=2).fit(X)
print("done")