PrivacyScanData/BI/BusinessIntelligence-Kaggle/Submissions/submission-6/kaggle-6.py

# -*- coding: utf-8 -*-

# This code is initially based on the Kaggle kernel from Sergei Neviadomski, which can be found in the following link
# https://www.kaggle.com/neviadomski/how-to-get-to-top-25-with-simple-model-sklearn/notebook
# and the Kaggle kernel from Pedro Marcelino, which can be found in the link below
# https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python/notebook

# Adding needed libraries and reading data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import ensemble, tree, linear_model
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.utils import shuffle
from scipy import stats
from scipy.stats import norm, skew
import xgboost as xgb

import warnings
warnings.filterwarnings('ignore')

train = pd.read_csv("../../train.csv")
test = pd.read_csv("../../test.csv")

# Prints R2 and RMSE scores
def get_score(prediction, labels):
    print('R2: {}'.format(r2_score(prediction, labels)))
    print('RMSE: {}'.format(np.sqrt(mean_squared_error(prediction, labels))))
    print('RMSLE: {}'.format(np.sqrt(np.square(np.log(prediction + 1) - np.log(labels + 1)).mean())))

# Shows scores for train and validation sets
def train_test(estimator, x_trn, x_tst, y_trn, y_tst):
    prediction_train = estimator.predict(x_trn)
    # Printing estimator
    print(estimator)
    # Printing train scores
    get_score(prediction_train, y_trn)
    prediction_test = estimator.predict(x_tst)
    # Printing test scores
    print("Test")
    get_score(prediction_test, y_tst)

# Deleting outliers
train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)

#We use the numpy fuction log1p which applies log(1+x) to all elements of the column
train["SalePrice"] = np.log1p(train["SalePrice"])

#Check the new distribution
sns.distplot(train['SalePrice'] , fit=norm);

# Get the fitted parameters used by the function
(mu, sigma) = norm.fit(train['SalePrice'])
print( '\n mu = {:.2f} and sigma = {:.2f}\n'.format(mu, sigma))

#Now plot the distribution
plt.legend(['Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )'.format(mu, sigma)],
            loc='best')
plt.ylabel('Frequency')
plt.title('SalePrice distribution')

#Get also the QQ-plot
fig = plt.figure()
res = stats.probplot(train['SalePrice'], plot=plt)
plt.show()

# Splitting to features and lables and deleting variables I don't need
train_labels = train.pop('SalePrice')

# Test set does not even have a 'SalePrice' column, so both sets can be concatenated
features = pd.concat([train, test], keys=['train', 'test'])

'''
# I decided to get rid of features that have more than half of missing information or do not correlate to SalePrice
features.drop(['Utilities', 'RoofMatl', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'Heating', 'LowQualFinSF',
               'BsmtFullBath', 'BsmtHalfBath', 'Functional', 'GarageYrBlt', 'GarageArea', 'GarageCond', 'WoodDeckSF',
               'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC', 'Fence', 'MiscFeature', 'MiscVal'],
              axis=1, inplace=True)
'''

features.drop(['Utilities'], axis=1, inplace=True)

# Checking for missing data, showing every variable with at least one missing value in train set
total_missing_data = features.isnull().sum().sort_values(ascending=False)
missing_data_percent = (features.isnull().sum()/features.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total_missing_data, missing_data_percent], axis=1, keys=['Total', 'Percent'])
print(missing_data[missing_data['Percent']> 0])

# Converting OverallCond and OverallQual to str
features.OverallCond = features.OverallCond.astype(str)
features.OverallQual = features.OverallQual.astype(str)

# MSSubClass as str
features['MSSubClass'] = features['MSSubClass'].astype(str)

# MSZoning NA in pred. filling with most popular values
features['MSZoning'] = features['MSZoning'].fillna(features['MSZoning'].mode()[0])

# LotFrontage NA filling with median according to its OverallQual value
median = features.groupby('OverallQual')['LotFrontage'].transform('median')
features['LotFrontage'] = features['LotFrontage'].fillna(median)

# Alley NA in all. NA means no access
features['Alley'] = features['Alley'].fillna('NOACCESS')

# MasVnrArea NA filling with median according to its OverallQual value
median = features.groupby('OverallQual')['MasVnrArea'].transform('median')
features['MasVnrArea'] = features['MasVnrArea'].fillna(median)

# MasVnrType NA in all. filling with most popular values
features['MasVnrType'] = features['MasVnrType'].fillna(features['MasVnrType'].mode()[0])

# BsmtQual, BsmtCond, BsmtExposure, BsmtFinType1, BsmtFinType2
# NA in all. NA means No basement
for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):
    features[col] = features[col].fillna('NoBSMT')

# TotalBsmtSF  NA in pred. I suppose NA means 0
features['TotalBsmtSF'] = features['TotalBsmtSF'].fillna(0)

# Electrical NA in pred. filling with most popular values
features['Electrical'] = features['Electrical'].fillna(features['Electrical'].mode()[0])

# KitchenAbvGr to categorical
features['KitchenAbvGr'] = features['KitchenAbvGr'].astype(str)

# KitchenQual NA in pred. filling with most popular values
features['KitchenQual'] = features['KitchenQual'].fillna(features['KitchenQual'].mode()[0])

# FireplaceQu  NA in all. NA means No Fireplace
features['FireplaceQu'] = features['FireplaceQu'].fillna('NoFP')

# Garage-like features NA in all. NA means No Garage
for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageYrBlt', 'GarageCond'):
    features[col] = features[col].fillna('NoGRG')

# GarageCars and GarageArea NA in pred. I suppose NA means 0
for col in ('GarageCars', 'GarageArea'):
    features[col] = features[col].fillna(0.0)

# SaleType NA in pred. filling with most popular values
features['SaleType'] = features['SaleType'].fillna(features['SaleType'].mode()[0])

# PoolQC NA in all. NA means No Pool
features['PoolQC'] = features['PoolQC'].fillna('NoPool')

# MiscFeature NA in all. NA means None
features['MiscFeature'] = features['MiscFeature'].fillna('None')

# Fence NA in all. NA means no fence
features['Fence'] = features['Fence'].fillna('NoFence')

# BsmtHalfBath and BsmtFullBath NA means 0
for col in ('BsmtHalfBath', 'BsmtFullBath'):
    features[col] = features[col].fillna(0)

# Functional NA means Typ
features['Functional'] = features['Functional'].fillna('Typ')

# NA in Bsmt SF variables means not that type of basement, 0 square feet
for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF'):
    features[col] = features[col].fillna(0)

# NA in Exterior1st and Exterior2nd filled with the most common value
for col in ('Exterior1st', 'Exterior2nd'):
    features[col] = features[col].fillna(features[col].mode()[0])

# Year and Month to categorical
features['YrSold'] = features['YrSold'].astype(str)
features['MoSold'] = features['MoSold'].astype(str)

# Adding total sqfootage feature and removing Basement, 1st and 2nd floor features
features['TotalSF'] = features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF']
features.drop(['TotalBsmtSF', '1stFlrSF', '2ndFlrSF'], axis=1, inplace=True)

## Standardizing numeric features
numeric_features = features.loc[:,['LotFrontage', 'LotArea', 'GrLivArea', 'TotalSF']]
numeric_features_standardized = (numeric_features - numeric_features.mean())/numeric_features.std()

#ax = sns.pairplot(numeric_features_standardized)

# Getting Dummies from Condition1 and Condition2
conditions = set([x for x in features['Condition1']] + [x for x in features['Condition2']])
dummies = pd.DataFrame(data=np.zeros((len(features.index), len(conditions))),
                       index=features.index, columns=conditions)
for i, cond in enumerate(zip(features['Condition1'], features['Condition2'])):
    dummies.ix[i, cond] = 1
features = pd.concat([features, dummies.add_prefix('Condition_')], axis=1)
features.drop(['Condition1', 'Condition2'], axis=1, inplace=True)

# Getting Dummies from Exterior1st and Exterior2nd
exteriors = set([x for x in features['Exterior1st']] + [x for x in features['Exterior2nd']])
dummies = pd.DataFrame(data=np.zeros((len(features.index), len(exteriors))),
                       index=features.index, columns=exteriors)
for i, ext in enumerate(zip(features['Exterior1st'], features['Exterior2nd'])):
    dummies.ix[i, ext] = 1
features = pd.concat([features, dummies.add_prefix('Exterior_')], axis=1)
features.drop(['Exterior1st', 'Exterior2nd'], axis=1, inplace=True)

# Getting Dummies from all other categorical vars
for col in features.dtypes[features.dtypes == 'object'].index:
    for_dummy = features.pop(col)
    features = pd.concat([features, pd.get_dummies(for_dummy, prefix=col)], axis=1)

### Copying features
features_standardized = features.copy()

### Replacing numeric features by standardized values
features_standardized.update(numeric_features_standardized)

### Splitting features
train_features = features.loc['train'].drop('Id', axis=1).select_dtypes(include=[np.number]).values
test_features = features.loc['test'].drop('Id', axis=1).select_dtypes(include=[np.number]).values

### Splitting standardized features
train_features_st = features_standardized.loc['train'].drop('Id', axis=1).select_dtypes(include=[np.number]).values
test_features_st = features_standardized.loc['test'].drop('Id', axis=1).select_dtypes(include=[np.number]).values

### Shuffling train sets
train_features_st, train_features, train_labels = shuffle(train_features_st, train_features, train_labels, random_state = 5)

### Splitting
x_train, x_test, y_train, y_test = train_test_split(train_features, train_labels, test_size=0.1, random_state=200)
x_train_st, x_test_st, y_train_st, y_test_st = train_test_split(train_features_st, train_labels, test_size=0.1, random_state=200)

'''
Elastic Net
'''
ENSTest = linear_model.ElasticNetCV(alphas=[0.0001, 0.0005, 0.001, 0.01, 0.1, 1, 10], l1_ratio=[.01, .1, .5, .9, .99], max_iter=5000).fit(x_train_st, y_train_st)
train_test(ENSTest, x_train_st, x_test_st, y_train_st, y_test_st)

# Average R2 score and standard deviation of 5-fold cross-validation
scores = cross_val_score(ENSTest, train_features_st, train_labels, cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

'''
Gradient Boosting
'''
GBest = ensemble.GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05, max_depth=3, max_features='sqrt',
                                               min_samples_leaf=15, min_samples_split=10, loss='huber').fit(x_train, y_train)
train_test(GBest, x_train, x_test, y_train, y_test)

# Average R2 score and standart deviation of 5-fold cross-validation
scores = cross_val_score(GBest, train_features_st, train_labels, cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

'''
XGBoost
'''
XGBest = xgb.XGBRegressor(max_depth=3, learning_rate=0.05, n_estimators=3000).fit(x_train, y_train)
train_test(XGBest, x_train, x_test, y_train, y_test)

# Average R2 score and standart deviation of 5-fold cross-validation
scores = cross_val_score(XGBest, train_features_st, train_labels, cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

# Retraining models
GB_model = GBest.fit(train_features, train_labels)
ENST_model = ENSTest.fit(train_features_st, train_labels)
XGB_model = XGBest.fit(train_features, train_labels)

## Getting our SalePrice estimation
Final_labels = (np.exp(GB_model.predict(test_features)) + np.exp(ENST_model.predict(test_features_st))
                + np.exp(XGB_model.predict(test_features))) / 3

## Saving to CSV
pd.DataFrame({'Id': test.Id, 'SalePrice': Final_labels}).to_csv('submission-6.csv', index =False)