def TrTeGlobalModel(lossFunc, inputData, alpha, lamda):
# Train and Test the expert absolute labels.
# inputData is a dictionary and contains the following keys:
# featSin is the N by d matrix and N is the number of concensus labels or one expert.
# featCmp is the M by d matrix and M is the the number of comparisons on singe expert.
# labelRSD is the (N,) array contains +1 or -1.
# labelAbs is the (N, numOfExpLbl) array contains +1 or -1, N is the number of samples for a singe expert and numOfExperLbl is the number of expert who labeled the N images.
# labelCmp is the (M, numOfExpCmp) array contains +1 or -1, M is the M is the the number of comparisons for singe expert, numOfExpCmp is the number of expert who label the comparison data.
# indexLblTrain is a list range in (0,N-1) that contains the training label data index for one expert or RSD. indexLbl Test is for testing.
# indexCmpTrain is a list range in (0,M-1) that contains the training comparison data for one expert.
data = dataPrepare(inputData)
expAbs = data.ExpAbs()
featTrLblAbs = expAbs['featTrainLblAbs']
labelTrLblAbs = expAbs['labelTrainLblAbs']
featTeLblSinAbs = expAbs['featTestLblSinAbs']
featTrCmpAbs = expAbs['featTrainCmpAbs']
labelTrCmpAbs = expAbs['labelTrainCmpAbs']
featTeCmpSinAbs = expAbs['featTestCmpSinAbs']
if lossFunc == 'LogLog':
beta, const = Log_Log(featTrLblAbs,
labelTrLblAbs,
featTrCmpAbs,
labelTrCmpAbs,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'LogSVM':
beta, const = Log_SVM(featTrLblAbs,
labelTrLblAbs,
featTrCmpAbs,
labelTrCmpAbs,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMLog':
beta, const = SVM_Log(featTrLblAbs,
labelTrLblAbs,
featTrCmpAbs,
labelTrCmpAbs,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMSVM':
beta, const = SVM_SVM(featTrLblAbs,
labelTrLblAbs,
featTrCmpAbs,
labelTrCmpAbs,
absWeight=alpha,
lamda=lamda)
else:
sys.exit(
'Please choose the correct loss function from one of {Log_Log,Log_SVM,SVM_Log,SVM_SVM}'
)
scoreLblTestSin = np.dot(featTeLblSinAbs, np.array(beta)) + const
scoreCmpTestSin = np.dot(featTeCmpSinAbs, np.array(beta)) + const
beta = np.array(beta).T
return beta, const, scoreLblTestSin, scoreCmpTestSin
def LogSVMTrTeExpBias(inputData,
alpha,
lamda,
penaltyTimes=100,
penaltyType='l1',
numMaxIters=10000):
# Train and Test the expert absolute labels.
# inputData is a dictionary and contains the following keys:
# featSin is the N by d matrix and N is the number of concensus labels or one expert.
# featCmp is the M by d matrix and M is the the number of comparisons on singe expert.
# labelRSD is the (N,) array contains +1 or -1.
# labelAbs is the (N, numOfExpLbl) array contains +1 or -1, N is the number of samples for a singe expert and numOfExperLbl is the number of expert who labeled the N images.
# labelCmp is the (M, numOfExpCmp) array contains +1 or -1, M is the M is the the number of comparisons for singe expert, numOfExpCmp is the number of expert who label the comparison data.
# indexLblTrain is a list range in (0,N-1) that contains the training label data index for one expert or RSD. indexLbl Test is for testing.
# indexCmpTrain is a list range in (0,M-1) that contains the training comparison data for one expert.
data = dataPrepare(inputData)
expBias = data.ExpAbsBias(penaltyTimes=penaltyTimes)
featTrLblBias = expBias['featTrainLblBias']
labelTrLblBias = expBias['labelTrainLblBias']
featTeLblListBias = expBias['featTestLblBiasList']
featTrCmpBias = expBias['featTrainCmpBias']
labelTrCmpBias = expBias['labelTrainCmpBias']
featTeCmpSinBias = expBias['featTestCmpSinBias']
numOfExpLbl = inputData['numOfExpLbl']
numOfExpCmp = inputData['numOfExpCmp']
scoreLblListTest = [None] * numOfExpLbl
if penaltyType == 'l1':
beta, const = Log_SVM(featTrLblBias,
labelTrLblBias,
featTrCmpBias,
labelTrCmpBias,
absWeight=alpha,
lamda=lamda)
for expLbl in range(numOfExpLbl):
scoreLblListTest[expLbl] = np.dot(featTeLblListBias[expLbl].copy(),
np.array(beta)) + const
scoreCmpTestSin = np.dot(featTeCmpSinBias, np.array(beta)) + const
beta = np.array(beta).T
else:
sys.exit('The penalty type must be either l1 or l2')
return beta, const, scoreLblListTest, scoreCmpTestSin
def TrTeExpertBiasModel(lossFunc, inputData, alpha, lamda, penaltyTimes=100):
# Train and Test the expert absolute labels.
# inputData is a dictionary and contains the following keys:
# featSin is the N by d matrix and N is the number of concensus labels or one expert.
# featCmp is the M by d matrix and M is the the number of comparisons on singe expert.
# labelRSD is the (N,) array contains +1 or -1.
# labelAbs is the (N, numOfExpLbl) array contains +1 or -1, N is the number of samples for a singe expert and numOfExperLbl is the number of expert who labeled the N images.
# labelCmp is the (M, numOfExpCmp) array contains +1 or -1, M is the M is the the number of comparisons for singe expert, numOfExpCmp is the number of expert who label the comparison data.
# indexLblTrain is a list range in (0,N-1) that contains the training label data index for one expert or RSD. indexLbl Test is for testing.
# indexCmpTrain is a list range in (0,M-1) that contains the training comparison data for one expert.
data = dataPrepare(inputData)
expBias = data.ExpAbsBias(penaltyTimes=penaltyTimes)
featTrLblBias = expBias['featTrainLblBias']
labelTrLblBias = expBias['labelTrainLblBias']
featTeLblListBias = expBias['featTestLblBiasList']
featTrCmpBias = expBias['featTrainCmpBias']
labelTrCmpBias = expBias['labelTrainCmpBias']
featTeCmpSinBias = expBias['featTestCmpSinBias']
numOfExpLbl = inputData['numOfExpLbl']
numOfExpCmp = inputData['numOfExpCmp']
scoreLblListTest = [None] * numOfExpLbl
if lossFunc == 'LogLog':
beta, const = Log_Log(featTrLblBias,
labelTrLblBias,
featTrCmpBias,
labelTrCmpBias,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'LogSVM':
beta, const = Log_SVM(featTrLblBias,
labelTrLblBias,
featTrCmpBias,
labelTrCmpBias,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMLog':
beta, const = SVM_Log(featTrLblBias,
labelTrLblBias,
featTrCmpBias,
labelTrCmpBias,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMSVM':
beta, const = SVM_SVM(featTrLblBias,
labelTrLblBias,
featTrCmpBias,
labelTrCmpBias,
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'Boost':
NAbs, _ = featTrLblBias.shape()
NCmp, _ = featTrCmpBias.shape()
est = GradientBoostingRegressor(n_estimators=100,
learning_rate=0.1,
max_depth=5,
random_state=1,
loss='ls').fit()
else:
sys.exit(
'Please choose the correct loss function from one of {Log_Log,Log_SVM,SVM_Log,SVM_SVM}'
)
for expLbl in range(numOfExpLbl):
scoreLblListTest[expLbl] = np.dot(featTeLblListBias[expLbl].copy(),
np.array(beta)) + const
scoreCmpTestSin = np.dot(featTeCmpSinBias, np.array(beta)) + const
beta = np.array(beta).T
return beta, const, scoreLblListTest, scoreCmpTestSin
def TrTeExpertModel(lossFunc, inputData, alpha, lamda, cmpExpOrder):
# Train and Test the each experts data.
# inputData is a dictionary and contains the following keys:
# featSin is the N by d matrix and N is the number of concensus labels or one expert.
# featCmp is the M by d matrix and M is the the number of comparisons on singe expert.
# labelRSD is the (N,) array contains +1 or -1.
# labelAbs is the (N, numOfExpLbl) array contains +1 or -1, N is the number of samples for a singe expert and numOfExperLbl is the number of expert who labeled the N images.
# labelCmp is the (M, numOfExpCmp) array contains +1 or -1, M is the M is the the number of comparisons for singe expert, numOfExpCmp is the number of expert who label the comparison data.
# indexLblTrain is a list range in (0,N-1) that contains the training label data index for one expert or RSD. indexLbl Test is for testing.
# indexCmpTrain is a list range in (0,M-1) that contains the training comparison data for one expert.
data = dataPrepare(inputData)
expUnique = data.ExpUnique()
featTrLblUniqueList = expUnique['featTrainLblUniqueList']
labelTrLblUniqueList = expUnique['labelTrainLblUniqueList']
featTeLblUniqueList = expUnique['featTestLblUniqueList']
featTrCmpUniqueList = expUnique['featTrainCmpUniqueList']
labelTrCmpUniqueList = expUnique['labelTrainCmpUnique']
featTeCmpUniqueList = expUnique['featTestCmpUniqueList']
numOfExpLbl = inputData['numOfExpLbl']
numOfExpCmp = inputData['numOfExpCmp']
betaList = [None] * numOfExpLbl
constList = [None] * numOfExpLbl
scoreLblListTest = [None] * numOfExpLbl
scoreCmpListTest = [None] * numOfExpLbl
if alpha != 0.0:
for exp in range(numOfExpLbl):
if not exp in cmpExpOrder:
if lossFunc == 'LogLog':
beta, const = Logistic(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
lamda,
alpha=alpha)
elif lossFunc == 'LogSVM':
beta, const = Logistic(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
lamda,
alpha=alpha)
elif lossFunc == 'SVMLog':
beta, const = SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
lamda,
alpha=alpha)
elif lossFunc == 'SVMSVM':
beta, const = SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
lamda,
alpha=alpha)
else:
sys.exit(
'Please choose the correct loss function from one of {Log_Log,Log_SVM,SVM_Log,SVM_SVM}'
)
betaList[exp] = np.array(beta).T
constList[exp] = const
scoreLblListTest[exp] = np.dot(featTeLblUniqueList[exp].copy(),
np.array(beta)) + const
elif exp in cmpExpOrder:
if lossFunc == 'LogLog':
beta, const = Log_Log(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'LogSVM':
beta, const = Log_SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMLog':
beta, const = SVM_Log(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMSVM':
beta, const = SVM_SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
else:
sys.exit(
'Please choose the correct loss function from one of {Log_Log,Log_SVM,SVM_Log,SVM_SVM}'
)
scoreLblListTest[exp] = np.dot(featTeLblUniqueList[exp].copy(),
np.array(beta)) + const
scoreCmpListTest[exp] = np.dot(featTeCmpUniqueList[exp].copy(),
np.array(beta)) + const
betaList[exp] = np.array(beta).T
constList[exp] = const
else:
sys.exit('The expert order is wrong.')
else:
for exp in cmpExpOrder:
if lossFunc == 'LogLog':
beta, const = Log_Log(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'LogSVM':
beta, const = Log_SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMLog':
beta, const = SVM_Log(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
elif lossFunc == 'SVMSVM':
beta, const = SVM_SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
else:
sys.exit(
'Please choose the correct loss function from one of {Log_Log,Log_SVM,SVM_Log,SVM_SVM}'
)
scoreLblListTest[exp] = np.dot(featTeLblUniqueList[exp].copy(),
np.array(beta)) + const
scoreCmpListTest[exp] = np.dot(featTeCmpUniqueList[exp].copy(),
np.array(beta)) + const
betaList[exp] = np.array(beta).T
constList[exp] = const
return betaList, constList, scoreLblListTest, scoreCmpListTest
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import dataPreparation as dp
data = pd.read_csv('train.csv')
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(data, test_size=0.2, random_state=42)
test_X = test_set.drop('Survived', axis=1)
test_y = test_set['Survived'].copy()
#time for data manipulation
features = dp.dataPrepare(train_set.drop('Survived',
axis=1)) #used module dataPreparation
attributes = train_set['Survived'].copy()
#its time for model.
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
rf = RandomForestClassifier()
grid = [{
'n_estimators': [3, 10, 30, 35, 40],
'max_features': [2, 3, 4, 5, 6],
'bootstrap': [False, True],
'n_estimators': [2, 3, 10, 15, 20],
'max_depth': [1, 2, 3],
'criterion': ['gini', 'entropy']
}]
model = GridSearchCV(rf, grid, scoring='accuracy', cv=5)
def LogSVMTrTeExpUnique(inputData,
alpha,
lamda,
cmpExpOrder,
penaltyType='l1',
numMaxIters=10000):
# Train and Test the each experts data.
# inputData is a dictionary and contains the following keys:
# featSin is the N by d matrix and N is the number of concensus labels or one expert.
# featCmp is the M by d matrix and M is the the number of comparisons on singe expert.
# labelRSD is the (N,) array contains +1 or -1.
# labelAbs is the (N, numOfExpLbl) array contains +1 or -1, N is the number of samples for a singe expert and numOfExperLbl is the number of expert who labeled the N images.
# labelCmp is the (M, numOfExpCmp) array contains +1 or -1, M is the M is the the number of comparisons for singe expert, numOfExpCmp is the number of expert who label the comparison data.
# indexLblTrain is a list range in (0,N-1) that contains the training label data index for one expert or RSD. indexLbl Test is for testing.
# indexCmpTrain is a list range in (0,M-1) that contains the training comparison data for one expert.
data = dataPrepare(inputData)
expUnique = data.ExpUnique()
featTrLblUniqueList = expUnique['featTrainLblUniqueList']
labelTrLblUniqueList = expUnique['labelTrainLblUniqueList']
featTeLblUniqueList = expUnique['featTestLblUniqueList']
featTrCmpUniqueList = expUnique['featTrainCmpUniqueList']
labelTrCmpUniqueList = expUnique['labelTrainCmpUnique']
featTeCmpUniqueList = expUnique['featTestCmpUniqueList']
numOfExpLbl = inputData['numOfExpLbl']
numOfExpCmp = inputData['numOfExpCmp']
betaList = [None] * numOfExpLbl
constList = [None] * numOfExpLbl
scoreLblListTest = [None] * numOfExpLbl
scoreCmpListTest = [None] * numOfExpLbl
if penaltyType == 'l1':
if alpha != 0.0:
for exp in range(numOfExpLbl):
if not exp in cmpExpOrder:
beta, const = Logistic(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
alpha=alpha,
lamda=lamda)
betaList[exp] = np.array(beta).T
constList[exp] = const
scoreLblListTest[exp] = np.dot(
featTeLblUniqueList[exp].copy(),
np.array(beta)) + const
elif exp in cmpExpOrder:
beta, const = Log_SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
scoreLblListTest[exp] = np.dot(
featTeLblUniqueList[exp].copy(),
np.array(beta)) + const
scoreCmpListTest[exp] = np.dot(
featTeCmpUniqueList[exp].copy(),
np.array(beta)) + const
betaList[exp] = np.array(beta).T
constList[exp] = const
else:
sys.exit('The expert order is wrong.')
else:
for exp in cmpExpOrder:
beta, const = Log_SVM(featTrLblUniqueList[exp],
labelTrLblUniqueList[exp],
featTrCmpUniqueList[exp],
labelTrCmpUniqueList[exp],
absWeight=alpha,
lamda=lamda)
scoreLblListTest[exp] = np.dot(featTeLblUniqueList[exp].copy(),
np.array(beta)) + const
scoreCmpListTest[exp] = np.dot(featTeCmpUniqueList[exp].copy(),
np.array(beta)) + const
betaList[exp] = np.array(beta).T
constList[exp] = const
else:
sys.exit('The penalty type must be either l1')
return betaList, constList, scoreLblListTest, scoreCmpListTest