In [1]:

    

# -*- coding: utf-8 -*-
"""
Created on Wed Mar  2 22:16:47 2016

@author: jim
"""

import pandas as pd
import numpy as np
import csv
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn import ensemble


    

In [2]:

    

print('Load data...')
train = pd.read_csv("../../data/train.csv")
target = train['target'].values
train = train.drop(['ID','target'],axis=1)
test = pd.read_csv("../../data/test.csv")
id_test = test['ID'].values
test = test.drop(['ID'],axis=1)


    

    




Load data...

In [3]:

    

print('Clearing...')
for (train_name, train_series), (test_name, test_series) in zip(train.iteritems(),test.iteritems()):
    if train_series.dtype == 'O':
        #for objects: factorize
        train[train_name], tmp_indexer = pd.factorize(train[train_name])
        test[test_name] = tmp_indexer.get_indexer(test[test_name])
        #but now we have -1 values (NaN)
    else:
        #for int or float: fill NaN
        tmp_len = len(train[train_series.isnull()])
        if tmp_len>0:
            #print "mean", train_series.mean()
            train.loc[train_series.isnull(), train_name] = -9999 #train_series.mean()
        #and Test
        tmp_len = len(test[test_series.isnull()])
        if tmp_len>0:
            test.loc[test_series.isnull(), test_name] = -9999 #train_series.mean()  #TODO

X_train = train
X_test = test


    

    




Clearing...

In [4]:

    

extc = ExtraTreesClassifier(n_estimators=700,max_features= 50,criterion= 'entropy',min_samples_split= 5,
                            max_depth= 50, min_samples_leaf= 5)      

extc.fit(X_train,target)


    

    
Out[4]:





ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='entropy',
           max_depth=50, max_features=50, max_leaf_nodes=None,
           min_samples_leaf=5, min_samples_split=5,
           min_weight_fraction_leaf=0.0, n_estimators=700, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False)

In [5]:

    

print('Predict...')
y_pred = extc.predict_proba(X_test)
#print y_pred

pd.DataFrame({"ID": id_test, "PredictedProb": y_pred[:,1]}).to_csv('extra_trees.csv',index=False)


    

    




Predict...