CODEWITHSUNDEEP
pythonscikit-learnrandom-forest
joek
joek

Reputation: 33

Unexpected exception when combining random forest trees

Using the information described in this question, Combining random forest models in scikit learn ,I have attempted to combine several random forest classifiers into a single classifier using python2.7.10 and sklearn 0.16.1, but get this exception in some cases:

    Traceback (most recent call last):
      File "sktest.py", line 50, in <module>
        predict(rf)
      File "sktest.py", line 46, in predict
        Y = rf.predict(X)
      File "/python-2.7.10/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line 462, in predict
        proba = self.predict_proba(X)
      File "/python-2.7.10/lib/python2.7/site-packages/sklearn/ensemble/forest.py", line 520, in predict_proba
        proba += all_proba[j]
    ValueError: non-broadcastable output operand with shape (39,1) doesn't match the broadcast shape (39,2)

The application is to create a number of random forest classifiers on many processors and combine these objects into a single classifier available to all processors.

The test code to produce this exception is shown below, it creates 5 classifiers with a random number of arrays of 10 features. If yfrac is changed to 0.5, the code will not give an exception. Is this a valid method of combining classifier objects? Also, this same exception is created when using warm_start to add trees to an existing RandomForestClassifier when n_estimators is increased and data added via fit.

from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from numpy import zeros,random,logical_or,where,array

random.seed(1) 

def generate_rf(X_train, y_train, X_test, y_test, numTrees=50):
  rf = RandomForestClassifier(n_estimators=numTrees, n_jobs=-1)
  rf.fit(X_train, y_train)
  print "rf score ", rf.score(X_test, y_test)
  return rf

def combine_rfs(rf_a, rf_b):
  rf_a.estimators_ += rf_b.estimators_
  rf_a.n_estimators = len(rf_a.estimators_)
  return rf_a

def make_data(ndata, yfrac=0.5):
  nx = int(random.uniform(10,100))

  X = zeros((nx,ndata))
  Y = zeros(nx)

  for n in range(ndata):
    rnA = random.random()*10**(random.random()*5)
    X[:,n] = random.uniform(-rnA,rnA, nx)
    Y = logical_or(Y,where(X[:,n] > yfrac*rnA, 1.,0.))

  return X, Y

def train(ntrain=5, ndata=10, test_frac=0.2, yfrac=0.5):
  rfs = []
  for u in range(ntrain):
    X, Y = make_data(ndata, yfrac=yfrac)

    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_frac)

    #Train the random forest and add to list
    rfs.append(generate_rf(X_train, Y_train, X_test, Y_test))

  # Combine the block classifiers into a single classifier
  return reduce(combine_rfs, rfs)

def predict(rf, ndata=10):
  X, Y = make_data(ndata)
  Y = rf.predict(X)

if __name__ == "__main__":
  rf = train(yfrac = 0.42)
  predict(rf)

Upvotes: 3

Views: 494

Answers (1)

Andreus
Andreus

Reputation: 2487

Your first RandomForest only gets positive cases, while other RandomForests get both cases. As a result, their DecisionTree results are incompatible with each other. Run your code with this replaced train() function:

def train(ntrain=5, ndata=10, test_frac=0.2, yfrac=0.5):
  rfs = []
  for u in range(ntrain):
    X, Y = make_data(ndata, yfrac=yfrac)

    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_frac)

    assert Y_train.sum() != 0
    assert Y_train.sum() != len( Y_train )
    #Train the random forest and add to list
    rfs.append(generate_rf(X_train, Y_train, X_test, Y_test))

  # Combine the block classifiers into a single classifier
  return reduce(combine_rfs, rfs)

Use a StratifiedShuffleSplit cross-validation generator rather than train_test_split, and check to make sure each RF gets both (all) classes in the training set.

Upvotes: 2

Related Questions