train_model_PYthon

train_model_Python.py

import numpy as np
from sklearn.ensemble import RandomForestClassifier
import sys
try: import cPickle as pickle   # python2
except: import pickle           # python3
from scipy import sparse
from numpy import loadtxt
import feather as ft

if len(sys.argv) != 4:
    sys.stderr.write('Arguments error. Usage:\n')
    sys.stderr.write('\tpython train_model.py INPUT_MATRIX_FILE SEED OUTPUT_MODEL_FILE\n')
    sys.exit(1)

input = sys.argv[1]
seed = int(sys.argv[2])
output = sys.argv[3]

df = ft.read_dataframe(input)
labels = df.loc[:,'label']
x = df.loc[:, df.columns != 'label']

clf = RandomForestClassifier(n_estimators=100, n_jobs=2, random_state=seed)
clf.fit(x, labels.ix[:,0])

with open(output, 'wb') as fd:
    pickle.dump(clf, fd)