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LMG_Ensembling_same_Model_v01.py
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LMG_Ensembling_same_Model_v01.py
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import requests
import numpy as np
import scipy as sp
import sys
import platform
import pandas as pd
from time import time
from operator import itemgetter
from sklearn.cross_validation import StratifiedShuffleSplit, KFold
import re
import time as tm
import warnings
from math import sqrt, exp, log
from csv import DictReader
from sklearn.metrics import log_loss
from sklearn.utils import shuffle
from sklearn.grid_search import GridSearchCV , RandomizedSearchCV
from sklearn.feature_extraction.text import TfidfVectorizer ,TfidfTransformer
from sklearn.feature_extraction import DictVectorizer
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.neighbors import KNeighborsRegressor,RadiusNeighborsRegressor
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, SGDRegressor, LogisticRegression
from scipy.stats import randint as sp_randint
from sklearn import decomposition, pipeline, metrics
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor,RadiusNeighborsRegressor
#import xgboost as xgb
#from sklearn.feature_extraction.text im
#from lasagne import layers
# from lasagne.nonlinearities import softmax, rectify
# from lasagne.updates import nesterov_momentum,sgd,adagrad
# from lasagne.nonlinearities import identity
# from nolearn.lasagne import NeuralNetport TfidfVectorizer,TfidfTransformer
import collections
from sklearn.cross_validation import train_test_split
########################################################################################################################
#Liberty Mutual Group: Property Inspection Prediction
########################################################################################################################
# This program will use teh best parms for each model and ensemble it in multiple ways to see the best one
########################################################################################################################
########################################################################################################################
#Gini Scorer - Scorer
########################################################################################################################
def gini(solution, submission):
df = zip(solution, submission, range(len(solution)))
df = sorted(df, key=lambda x: (x[1],-x[2]), reverse=True)
rand = [float(i+1)/float(len(df)) for i in range(len(df))]
totalPos = float(sum([x[0] for x in df]))
cumPosFound = [df[0][0]]
for i in range(1,len(df)):
cumPosFound.append(cumPosFound[len(cumPosFound)-1] + df[i][0])
Lorentz = [float(x)/totalPos for x in cumPosFound]
Gini = [Lorentz[i]-rand[i] for i in range(len(df))]
return sum(Gini)
########################################################################################################################
#Normalized Gini Scorer
########################################################################################################################
def normalized_gini(solution, submission):
normalized_gini = gini(solution, submission)/gini(solution, solution)
return normalized_gini
########################################################################################################################
#Cross Validation and model fitting
########################################################################################################################
def Nfold_Cross_Valid(X, y, ss , clf):
print("***************Starting Kfold Cross validation*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
X =np.array(X)
scores=[]
DS_Blend_Train = np.zeros((X.shape[0], 1))
#ss = StratifiedShuffleSplit(y, n_iter=10,test_size=0.3, random_state=42, indices=None)
#ss = KFold(len(y), n_folds=10,shuffle=True,indices=False)
i = 1
for trainCV, testCV in ss:
X_train, X_test= X[trainCV], X[testCV]
y_train, y_test= y[trainCV], y[testCV]
clf.fit(X_train, y_train)
y_pred=clf.predict(X_test)
DS_Blend_Train[testCV, 0] = y_pred
scores.append(normalized_gini(y_test,y_pred))
print(" %d-iteration... %s " % (i,scores))
i = i + 1
#Average ROC from cross validation
scores=np.array(scores)
print ("Normal CV Score:",np.mean(scores))
print("***************Ending Kfold Cross validation*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
return DS_Blend_Train
########################################################################################################################
#Create models from multiple ensembling...
########################################################################################################################
def Get_Models_for_Stacking():
print("***************Starting Get_Models_for_Ensembling*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
t0 = time()
clfs=[]
# Get different Random Forest models
for i in range(2):
if (np.isnan(Parm_RF_DS['max_depth'][i])):
max_depth_val = None
else:
max_depth_val = int(Parm_RF_DS['max_depth'][i])
clfs.append(RandomForestRegressor(n_estimators=200
,min_samples_leaf=Parm_RF_DS['min_samples_leaf'][i]
,max_features=None
,bootstrap=Parm_RF_DS['bootstrap'][i]
,min_samples_split=Parm_RF_DS['min_samples_split'][i]
,max_depth=max_depth_val
,n_jobs=-1))
print("***************Ending Get_Models_for_Ensembling*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
return clfs
########################################################################################################################
#Data cleansing , feature scalinng , splitting
########################################################################################################################
def Data_Munging(Train_DS,Actual_DS):
print("***************Starting Data cleansing*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
y = Train_DS.Hazard.values
Train_DS = Train_DS.drop(['Hazard'], axis = 1)
Train_DS = Train_DS.drop(['T2_V10','T2_V7','T1_V13','T1_V10'], axis = 1)
Actual_DS = Actual_DS.drop(['T2_V10','T2_V7','T1_V13','T1_V10'], axis = 1)
# global columns
columns = Train_DS.columns
col_types = (Train_DS.dtypes).reset_index(drop=True)
####################################################################################################################
#perform De-vectorizer
Train_Dict_DS = Train_DS.T.to_dict().values()
Actual_Dict_DS = Actual_DS.T.to_dict().values()
vec = DictVectorizer(sparse=False)
Train_Dict_DS = vec.fit_transform(Train_Dict_DS)
Actual_Dict_DS = vec.transform(Actual_Dict_DS)
# for i in range(Train_DS.shape[1]):
# if col_types[i] != 'object':
# Train_Dict_DS = np.delete(Train_Dict_DS, i, 1)
# Actual_Dict_DS = np.delete(Actual_Dict_DS, i, 1)
####################################################################################################################
Train_DS = np.array(Train_DS)
Actual_DS = np.array(Actual_DS)
print("Starting label encoding")
# label encode the categorical variables
for i in range(Train_DS.shape[1]):
if col_types[i] =='object':
lbl = preprocessing.LabelEncoder()
lbl.fit(list(Train_DS[:,i]) + list(Actual_DS[:,i]))
Train_DS[:,i] = lbl.transform(Train_DS[:,i])
Actual_DS[:,i] = lbl.transform(Actual_DS[:,i])
####################################################################################################################
#Merge De-vectorizer
Train_DS = np.append(Train_DS,Train_Dict_DS,1)
Actual_DS = np.append(Actual_DS,Actual_Dict_DS,1)
Train_DS = Train_DS.astype(float)
Actual_DS = Actual_DS.astype(float)
print("starting TFID conversion...")
tfv = TfidfTransformer()
tfv.fit(Train_DS)
Train_DS1 = tfv.transform(Train_DS).toarray()
Actual_DS1 = tfv.transform(Actual_DS).toarray()
Train_DS = np.append(Train_DS,Train_DS1,1)
Actual_DS = np.append(Actual_DS,Actual_DS1,1)
Train_DS = np.log( 1 + Train_DS)
Actual_DS = np.log( 1 + Actual_DS)
#Setting Standard scaler for data
stdScaler = StandardScaler()
stdScaler.fit(Train_DS,y)
Train_DS = stdScaler.transform(Train_DS)
Actual_DS = stdScaler.transform(Actual_DS)
Train_DS, y = shuffle(Train_DS, y, random_state=21)
print(np.shape(Train_DS))
print(np.shape(Actual_DS))
print("***************Ending Data cleansing*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
return Train_DS, y, Actual_DS
########################################################################################################################
#Ensemble with fold
########################################################################################################################
def Ensemble_with_nfold(Train_DS, y , Actual_DS, Sample_DS):
print("***************Starting Ensemble_with_nfold*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
t0 = time()
#define CV plan
ss = list(KFold(len(y), n_folds=5,shuffle=True,indices=False))
clfs = Get_Models_for_Stacking()
scores = []
Ensemble_DS_output = np.zeros((Actual_DS.shape[0], len(clfs)))
Ensemble_DS_Train = np.zeros((Train_DS.shape[0], len(clfs)))
for j, clf in enumerate(clfs):
print(" Training Model...%d" % (j+1))
Ensemble_DS_Train[:,j] = Nfold_Cross_Valid(Train_DS, y, ss , clf)
#Fit and predict the model
clf.fit(Train_DS, y)
Ensemble_DS_output[:,j] = np.array(clf.predict(Actual_DS))
#Do what ever Ensembling you want
pred_Train = Ensemble_DS_Train.mean(1)
Ens_score = normalized_gini(y,pred_Train)
print(" Ensemble output of Train is... %d " % (Ens_score))
pred_Actual = Ensemble_DS_output.mean(1)
#Get the predictions for actual data set
preds = pd.DataFrame(pred_Actual, index=Sample_DS.Id.values, columns=Sample_DS.columns[1:])
preds.to_csv(file_path+'output/Submission_Ensembling.csv', index_label='Id')
print("***************Ending Ensemble_with_nfold*************** at Time: %s" %(tm.strftime("%H:%M:%S")))
return Ensemble_DS_output
########################################################################################################################
#Main module #
########################################################################################################################
def main(argv):
pd.set_option('display.width', 200)
pd.set_option('display.height', 500)
warnings.filterwarnings("ignore")
global file_path, gini_scorer,Parm_RF_DS, Parm_XGB_DS
# Normalized Gini Scorer
gini_scorer = metrics.make_scorer(normalized_gini, greater_is_better = True)
if(platform.system() == "Windows"):
file_path = 'C:/Python/Others/data/Kaggle/Liberty_Mutual_Group/'
else:
file_path = '/home/roshan/Desktop/DS/Others/data/Kaggle/Liberty_Mutual_Group/'
Full_run = True
########################################################################################################################
#Read the input file , munging and splitting the data to train and test
########################################################################################################################
Sample_DS = pd.read_csv(file_path+'sample_submission.csv',sep=',')
Parm_RF_DS = pd.read_csv(file_path+'Parms_DS_RF2.csv',sep=',', index_col=0)
Parm_XGB_DS = pd.read_csv(file_path+'Parms_DS_XGB_1000_2.csv',sep=',', index_col=0)
Train_DS = pd.read_csv(file_path+'train.csv',sep=',', index_col=0)
Actual_DS = pd.read_csv(file_path+'test.csv',sep=',', index_col=0)
Train_DS, y, Actual_DS = Data_Munging(Train_DS,Actual_DS)
Ensemble_DS_output = Ensemble_with_nfold(Train_DS, y , Actual_DS, Sample_DS)
########################################################################################################################
#Main program starts here #
########################################################################################################################
if __name__ == "__main__":
main(sys.argv)