#!/usr/bin/env python
from __future__ import division
from sklearn.ensemble import RandomForestClassifier

__author__ = '<NAME>'

#import libraries
import pandas as pd
import os
import numpy as np
import random
import math
from sklearn.preprocessing import scale
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import SGDClassifier
from sklearn import cross_validation
random.seed(666)

data_path = os.path.join("..", "data")

training = pd.read_csv(os.path.join(data_path, "train.csv"))
testing = pd.read_csv(os.path.join(data_path, "test.csv"))

target = training["target"]

train = training.drop(["target", "id"], axis=1)

# def fun(x):
#     return math.log(x + 1)

# train_log = train.applymap(fun)
# train_log_scale = scale(train_log.values.astype(float), axis=0)

id = testing["id"]

test = testing.drop(["id"], axis=1)

# testing_log = testing.applymap(fun)

# testing_log_scale = scale(testing_log.values.astype(float), axis=0)

clf = RandomForestClassifier(n_estimators=1000, n_jobs=3)
# forest = GradientBoostingClassifier(n_estimators=1000, subsample=0.5)
# forest = SGDClassifier(n_jobs=3, loss="log")

print "Estimating"
clf = RandomForestClassifier(n_estimators=10, n_jobs=3)
scores = cross_validation.cross_val_score(clf, train.values, target.values, cv=5, scoring="log_loss")
print "Estimated log_loss = ", -np.mean(scores)


print "Training"
fit = clf.fit(train.values, target.values)
# fit = forest.fit(train, target)
def predicting(testing, n):
    '''
    There are problems with memory that I have when I am trying to predict all testing set
    at a time. But I believe I can split testing set, predict on each part and merge after this.

    :param testing: set that needs to be predicted
    :param n
    :return:
    '''
    frames = np.array_split(testing, n)

    prediction = map(fit.predict_proba, frames)

    return np.concatenate(prediction)


print "Predicting"
prediction = fit.predict_proba(test.values)
# prediction = predicting(testing_log_scale, 10)


temp = pd.DataFrame(prediction)

temp = temp.rename(columns = {0: "Class_1",
                              1: "Class_2",
                              2: "Class_3",
                              3: "Class_4",
                              4: "Class_5",
                              5: "Class_6",
                              6: "Class_7",
                              7: "Class_8",
                              8: "Class_9"})

temp["id"] = id

# temp.to_csv(os.path.join("..", "data", "sgdc_log_scale.csv"), index=False)
temp.to_csv(os.path.join("..", "data", "rf_10_squared.csv"), index=False)
#!/usr/bin/env python
from __future__ import division
from sklearn.ensemble import RandomForestClassifier

__author__ = '<NAME>'

#import libraries
import pandas as pd
import os
import numpy as np
import random
import math
from sklearn.preprocessing import scale
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import SGDClassifier
from sklearn import cross_validation
random.seed(666)

data_path = os.path.join("..", "data")

training = pd.read_csv(os.path.join(data_path, "train.csv"))
testing = pd.read_csv(os.path.join(data_path, "test.csv"))

target = training["target"]

train = training.drop(["target", "id"], axis=1)

# def fun(x):
#     return math.log(x + 1)

# train_log = train.applymap(fun)
# train_log_scale = scale(train_log.values.astype(float), axis=0)

id = testing["id"]

test = testing.drop(["id"], axis=1)

# testing_log = testing.applymap(fun)

# testing_log_scale = scale(testing_log.values.astype(float), axis=0)

clf = RandomForestClassifier(n_estimators=1000, n_jobs=3)
# forest = GradientBoostingClassifier(n_estimators=1000, subsample=0.5)
# forest = SGDClassifier(n_jobs=3, loss="log")

print "Estimating"
clf = RandomForestClassifier(n_estimators=10, n_jobs=3)
scores = cross_validation.cross_val_score(clf, train.values, target.values, cv=5, scoring="log_loss")
print "Estimated log_loss = ", -np.mean(scores)


print "Training"
fit = clf.fit(train.values, target.values)
# fit = forest.fit(train, target)
def predicting(testing, n):
    '''
    There are problems with memory that I have when I am trying to predict all testing set
    at a time. But I believe I can split testing set, predict on each part and merge after this.

    :param testing: set that needs to be predicted
    :param n
    :return:
    '''
    frames = np.array_split(testing, n)

    prediction = map(fit.predict_proba, frames)

    return np.concatenate(prediction)


print "Predicting"
prediction = fit.predict_proba(test.values)
# prediction = predicting(testing_log_scale, 10)


temp = pd.DataFrame(prediction)

temp = temp.rename(columns = {0: "Class_1",
                              1: "Class_2",
                              2: "Class_3",
                              3: "Class_4",
                              4: "Class_5",
                              5: "Class_6",
                              6: "Class_7",
                              7: "Class_8",
                              8: "Class_9"})

temp["id"] = id

# temp.to_csv(os.path.join("..", "data", "sgdc_log_scale.csv"), index=False)
temp.to_csv(os.path.join("..", "data", "rf_10_squared.csv"), index=False)
#!/usr/bin/env python #import libraries # def fun(x): #     return math.log(x + 1) # train_log = train.applymap(fun) # train_log_scale = scale(train_log.values.astype(float), axis=0) # testing_log = testing.applymap(fun) # testing_log_scale = scale(testing_log.values.astype(float), axis=0) # forest = GradientBoostingClassifier(n_estimators=1000, subsample=0.5) # forest = SGDClassifier(n_jobs=3, loss="log") # fit = forest.fit(train, target) There are problems with memory that I have when I am trying to predict all testing set     at a time. But I believe I can split testing set, predict on each part and merge after this.      :param testing: set that needs to be predicted     :param n     :return: # prediction = predicting(testing_log_scale, 10) # temp.to_csv(os.path.join("..", "data", "sgdc_log_scale.csv"), index=False)