random forest

rf.py

from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction import DictVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from operator import itemgetter
from sklearn.metrics import classification_report
from random import shuffle
from scipy.stats import mode
import numpy as np
import os
import re
import flask

def gather_data():
    """
    Iterate through data files on disk and return a shuffled array of strings,
    ready to be vectorized and split into training and test sets
    """
    data = []

    with open('train', 'r') as f:
        for index, line in enumerate(f):
            if index == 0:
                continue
            line = line.replace('YES', '1')
            line = line.replace('NO', '-1')
            line = line.split(',')
            newline = []
            for x in line:
                try:
                    newline.append(float(x))
                except:
                    newline.append(10000)
            data.append(newline)

    return data

def classify(algorithm, **kwargs):
    """
    Run data through any classifier, printing out results as well.
    """
    print "\n" + algorithm.__name__
    classifier = algorithm(**kwargs).fit(data_train, label_train)
    label_predicted = classifier.predict(data_test)
    print classification_report(label_test, label_predicted)

# Prepare the data and vectorize
labels = []
with open('trainLabels', 'r') as f:
    for index, line in enumerate(f):
        if index == 0:
            continue
        line = line.split(',')
        line = [int(x) for x in line]
        line.pop(0)
        labels.append(line)

data = gather_data()
zipped = []

for index, d in enumerate(data):
    label = labels[index]
    entry = {}
    for i, field in enumerate(d):
        entry[i] = field
    zipped.append(entry)

training_size = int(round(len(zipped) * 0.35))

print 'Training set size: ' + str(training_size)

data_train_orig = np.array( [x for x in zipped[0:training_size]] )
label_train = np.array( [x for x in labels[0:training_size]] )

data_test_orig = np.array( [x for x in zipped[training_size + 1 : len(zipped)]] )
label_test = np.array( [x for x in labels[training_size + 1 : len(zipped)]] )

v = DictVectorizer(sparse=False)
data_train = v.fit_transform(data_train_orig)
data_test = v.transform(data_test_orig)

# Run the classifiers
# classify(MultinomialNB)
#classify(LinearSVC)
classify(RandomForestClassifier, n_jobs=8)
# classify(LogisticRegression)