# Stacking Starter based on Allstate Faron's Script
#https://www.kaggle.com/mmueller/allstate-claims-severity/stacking-starter/run/390867
# Preprocessing from Alexandru Papiu
#https://www.kaggle.com/apapiu/house-prices-advanced-regression-techniques/regularized-linear-models

import pandas as pd
import numpy as np
from scipy.stats import skew
import xgboost as xgb
from sklearn.cross_validation import KFold
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, Lasso
from math import sqrt


TARGET = 'SalePrice'
NFOLDS = 4
SEED = 0
NROWS = None
SUBMISSION_FILE = '../input/sample_submission.csv'


## Load the data ##
train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/test.csv")

ntrain = train.shape[0]
ntest = test.shape[0]

## Preprocessing ##

y_train = np.log(train[TARGET]+1)


train.drop([TARGET], axis=1, inplace=True)


all_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],
                      test.loc[:,'MSSubClass':'SaleCondition']))


#log transform skewed numeric features:
numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index

skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness
skewed_feats = skewed_feats[skewed_feats > 0.75]
skewed_feats = skewed_feats.index

all_data[skewed_feats] = np.log1p(all_data[skewed_feats])

all_data = pd.get_dummies(all_data)

#filling NA's with the mean of the column:
all_data = all_data.fillna(all_data.mean())

#creating matrices for sklearn:

x_train = np.array(all_data[:train.shape[0]])
x_test = np.array(all_data[train.shape[0]:])

kf = KFold(ntrain, n_folds=NFOLDS, shuffle=True, random_state=SEED)


class SklearnWrapper(object):
    def __init__(self, clf, seed=0, params=None):
        params['random_state'] = seed
        self.clf = clf(**params)

    def train(self, x_train, y_train):
        self.clf.fit(x_train, y_train)

    def predict(self, x):
        return self.clf.predict(x)


class XgbWrapper(object):
    def __init__(self, seed=0, params=None):
        self.param = params
        self.param['seed'] = seed
        self.nrounds = params.pop('nrounds', 250)

    def train(self, x_train, y_train):
        dtrain = xgb.DMatrix(x_train, label=y_train)
        self.gbdt = xgb.train(self.param, dtrain, self.nrounds)

    def predict(self, x):
        return self.gbdt.predict(xgb.DMatrix(x))


def get_oof(clf):
    oof_train = np.zeros((ntrain,))
    oof_test = np.zeros((ntest,))
    oof_test_skf = np.empty((NFOLDS, ntest))

    for i, (train_index, test_index) in enumerate(kf):
        x_tr = x_train[train_index]
        y_tr = y_train[train_index]
        x_te = x_train[test_index]

        clf.train(x_tr, y_tr)

        oof_train[test_index] = clf.predict(x_te)
        oof_test_skf[i, :] = clf.predict(x_test)

    oof_test[:] = oof_test_skf.mean(axis=0)
    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)


et_params = {
    'n_jobs': 16,
    'n_estimators': 100,
    'max_features': 0.5,
    'max_depth': 12,
    'min_samples_leaf': 2,
}

rf_params = {
    'n_jobs': 16,
    'n_estimators': 100,
    'max_features': 0.2,
    'max_depth': 12,
    'min_samples_leaf': 2,
}

xgb_params = {
    'seed': 0,
    'colsample_bytree': 0.7,
    'silent': 1,
    'subsample': 0.7,
    'learning_rate': 0.075,
    'objective': 'reg:linear',
    'max_depth': 4,
    'num_parallel_tree': 1,
    'min_child_weight': 1,
    'eval_metric': 'rmse',
    'nrounds': 500
}



rd_params={
    'alpha': 10
}


ls_params={
    'alpha': 0.005
}


xg = XgbWrapper(seed=SEED, params=xgb_params)
et = SklearnWrapper(clf=ExtraTreesRegressor, seed=SEED, params=et_params)
rf = SklearnWrapper(clf=RandomForestRegressor, seed=SEED, params=rf_params)
rd = SklearnWrapper(clf=Ridge, seed=SEED, params=rd_params)
ls = SklearnWrapper(clf=Lasso, seed=SEED, params=ls_params)

xg_oof_train, xg_oof_test = get_oof(xg)
et_oof_train, et_oof_test = get_oof(et)
rf_oof_train, rf_oof_test = get_oof(rf)
rd_oof_train, rd_oof_test = get_oof(rd)
ls_oof_train, ls_oof_test = get_oof(ls)

print("XG-CV: {}".format(sqrt(mean_squared_error(y_train, xg_oof_train))))
print("ET-CV: {}".format(sqrt(mean_squared_error(y_train, et_oof_train))))
print("RF-CV: {}".format(sqrt(mean_squared_error(y_train, rf_oof_train))))
print("RD-CV: {}".format(sqrt(mean_squared_error(y_train, rd_oof_train))))
print("LS-CV: {}".format(sqrt(mean_squared_error(y_train, ls_oof_train))))


x_train = np.concatenate((xg_oof_train, et_oof_train, rf_oof_train, rd_oof_train, ls_oof_train), axis=1)
x_test = np.concatenate((xg_oof_test, et_oof_test, rf_oof_test, rd_oof_test, ls_oof_test), axis=1)

print(x_train)
print("{},{}".format(x_train.shape, x_test.shape))

dtrain = xgb.DMatrix(x_train, label=y_train)
dtest = xgb.DMatrix(x_test)

xgb_params = {
    'seed': 0,
    'colsample_bytree': 0.8,
    'silent': 1,
    'subsample': 0.6,
    'learning_rate': 0.01,
    'objective': 'reg:linear',
    'max_depth': 1,
    'num_parallel_tree': 1,
    'min_child_weight': 1,
    'eval_metric': 'rmse',
}

res = xgb.cv(xgb_params, dtrain, num_boost_round=1000, nfold=4, seed=SEED, stratified=False,
             early_stopping_rounds=25, verbose_eval=10, show_stdv=True)

best_nrounds = res.shape[0] - 1
cv_mean = res.iloc[-1, 0]
cv_std = res.iloc[-1, 1]

print('Ensemble-CV: {0}+{1}'.format(cv_mean, cv_std))

gbdt = xgb.train(xgb_params, dtrain, best_nrounds)

#submission = pd.read_csv(SUBMISSION_FILE)
#submission.iloc[:, 1] = gbdt.predict(dtest)
#saleprice = np.exp(submission['SalePrice'])-1
#submission['SalePrice'] = saleprice
#submission.to_csv('xgstacker_starter.sub.csv', index=None)