import numpy as np
import matplotlib.pyplot as pt
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
train_data=pd.read_csv("data/train.csv").as_matrix()
test_data=pd.read_csv("data/test.csv").as_matrix()
clf=ExtraTreesClassifier()
total=train_data.shape[0]
total_test=test_data.shape[0]/home/oem/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
"""Entry point for launching an IPython kernel.
/home/oem/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
#training data
xtrain=train_data[0:total, 1:]
train_label=train_data[0:total, 0]
clf.fit(xtrain, train_label)
ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
oob_score=False, random_state=None, verbose=0, warm_start=False)
xtest=test_data[0:total_test, 0:]
p=clf.predict(xtest)
output=[]
for i in range(0, total_test):
curr_list=[]
curr_list.append(i + 1)
curr_list.append(p[i])
output.append(curr_list)df=pd.DataFrame(output, columns=['ImageId', 'Label'])
df.to_csv("output.csv", index=False)