from util import get_split_training_dataset
from metrics import suite
from sklearn.naive_bayes import GaussianNB
def train(Xtrain, Ytrain):
""" Use entirety of provided X, Y to predict
Default Arguments
Xtrain -- Training data
Ytrain -- Training prediction
Named Arguments
C -- regularization parameter
Returns
classifier -- a tree fitted to Xtrain and Ytrain
"""
classifier = GaussianNB()
classifier.fit(Xtrain, Ytrain)
return classifier
if __name__ == "__main__":
# Let's take our training data and train a decision tree
# on a subset. Scikit-learn provides a good module for cross-
# validation.
Xt, Xv, Yt, Yv = get_split_training_dataset()
Classifier = train(Xt, Yt)
print "Naive Bayes Classifier"
suite(Yv, Classifier.predict(Xv))
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.grid_search import GridSearchCV
def train(Xtrain, Ytrain, n=250, d=None):
""" Use entirety of provided X, Y to train random forest
Arguments
Xtrain -- Training data
Ytrain -- Training prediction
Returns
classifier
"""
classifier = ExtraTreesClassifier(n_estimators=n,
max_depth=d,
min_samples_split=1,
random_state=0,
max_features=36)
classifier.fit(Xtrain, Ytrain)
return classifier
if __name__ == "__main__":
# Let's take our training data and train a random forest
# on a subset.
Xt, Xv, Yt, Yv = get_split_training_dataset()
Classifier = train(Xt, Yt)
print "Extra Random Trees Ensemble Classifier"
suite(Yv, Classifier.predict(Xv))