def CrossValidationWithSampling():
runObj = run_util.run()
## Load data
((X_train,Y_train),(X_test)) = loadData(runObj)
## Sample train data
RSObj=RS.randomSampling()
(X_train,Y_train) = RSObj.getRandomSample(X_train,Y_train)
(X_train,Y_train) = runObj.randomShuffleData(X_train,Y_train)
print('\n Size of X_train : {0}').format(X_train.shape)
print('\n Size of Y_train : {0}').format(Y_train.shape)
X_train.reset_index(drop=True,inplace=True)
Y_train.reset_index(drop=True,inplace=True)
## Cross Validation
kf = KFold(X_train.shape[0], n_folds=5)
for train_index, test_index in kf:
X_train_CV, X_test_CV = X_train.ix[train_index], X_train.ix[test_index]
Y_train_CV, Y_test_CV = Y_train.ix[train_index], Y_train.ix[test_index]
print('\n Size of X_train_CV : {0}').format(X_train_CV.shape)
print('\n Size of Y_train_CV : {0}').format(Y_train_CV.shape)
print('\n Size of X_test_CV : {0}').format(X_test_CV.shape)
print('\n Size of Y_test_CV : {0}').format(Y_test_CV.shape)
## Run classifiers
runObj.runClassifier(X_train_CV,Y_train_CV,X_test_CV,Y_test_CV)
def RFEWithSampling():
runObj = run_util.run()
## Load data
((X_train,Y_train),(X_test)) = loadData(runObj)
## Sample train data
RSObj=RS.randomSampling()
(X_train,Y_train)=RSObj.getRandomSample(X_train,Y_train)
(X_train,Y_train) = runObj.randomShuffleData(X_train,Y_train)
print('\n Size of Sample X_train : {0}').format(X_train.shape)
print('\n Size of Sample Y_train : {0}').format(Y_train.shape)
X_train.reset_index(drop=True,inplace=True)
Y_train.reset_index(drop=True,inplace=True)
## RFE Feature Selection
print('\n RFE Feature Selection starts...')
selected_columns = AF.RFE_featureSelection(X_train,Y_train)
print('\n RFE Feature Selection ends...')
X_train = X_train[selected_columns]
X_test = X_train[selected_columns]
## Split data into 66% train and 33% test
((X_train_S,Y_train_S), (X_test_S,Y_test_S)) = runObj.splitDataset(X_train,Y_train)
print('\n Size of X_train_S : {0}').format(X_train_S.shape)
print('\n Size of Y_train_S : {0}').format(Y_train_S.shape)
print('\n Size of X_test_S : {0}').format(X_test_S.shape)
print('\n Size of Y_test_S : {0}').format(Y_test_S.shape)
## Run classifiers
runObj.runClassifier(X_train_S,Y_train_S,X_test_S,Y_test_S)
def costValuesBasedPrediction():
runObj = run_util.run()
## Load data
((X_train,Y_train),(X_test)) = loadData(runObj)
## Select feature related to cost
costFeatures = ['MMRAcquisitionAuctionAveragePrice','VehBCost']
X_train = X_train[costFeatures]
print('\n Size of X_train only cost features : {0}').format(X_train.shape)
## Sample train data
RSObj=RS.randomSampling()
(X_train,Y_train) = RSObj.getRandomSample(X_train,Y_train)
(X_train,Y_train) = runObj.randomShuffleData(X_train,Y_train)
print('\n Size of X_train : {0}').format(X_train.shape)
print('\n Size of Y_train : {0}').format(Y_train.shape)
X_train.reset_index(drop=True,inplace=True)
Y_train.reset_index(drop=True,inplace=True)
## Split data into 66% train and 33% test
((X_train_S,Y_train_S), (X_test_S,Y_test_S)) = runObj.splitDataset(X_train,Y_train)
print('\n Size of X_train_S : {0}').format(X_train_S.shape)
print('\n Size of Y_train_S : {0}').format(Y_train_S.shape)
print('\n Size of X_test_S : {0}').format(X_test_S.shape)
print('\n Size of Y_test_S : {0}').format(Y_test_S.shape)
## Run classifiers
runObj.runClassifier(X_train_S,Y_train_S,X_test_S,Y_test_S)
def handpickWithoutSampling():
runObj = run_util.run()
## Load data
((X_train,Y_train),(X_test)) = loadData(runObj)
## Split data into 66% train and 33% test
((X_train_S,Y_train_S), (X_test_S,Y_test_S)) = runObj.splitDataset(X_train,Y_train)
print('\n Size of X_train_S : {0}').format(X_train_S.shape)
print('\n Size of Y_train_S : {0}').format(Y_train_S.shape)
print('\n Size of X_test_S : {0}').format(X_test_S.shape)
print('\n Size of Y_test_S : {0}').format(Y_test_S.shape)
## Run classifiers
runObj.runClassifier(X_train_S,Y_train_S,X_test_S,Y_test_S)
def BoostingWithSampling():
runObj = run_util.run()
## Load data
((X_train,Y_train),(X_test)) = loadData(runObj)
## Sample train data
RSObj=RS.randomSampling()
(X_train,Y_train)=RSObj.getRandomSample(X_train,Y_train)
(X_train,Y_train) = runObj.randomShuffleData(X_train,Y_train)
print('\n Size of X_train : {0}').format(X_train.shape)
print('\n Size of Y_train : {0}').format(Y_train.shape)
X_train.reset_index(drop=True,inplace=True)
Y_train.reset_index(drop=True,inplace=True)
## Boosting Feature Selection
print('\n Boosting Feature Selection starts...')
selected_columns_dict = AF.Boosting_featureSelection(X_train,Y_train)
print('\n Boosting Feature Selection ends...')
selected_columns=[]
for col, imp in selected_columns_dict.iteritems():
if(imp > 0.005):
selected_columns.append(col)
print('\n Selected Cols : {0}').format(len(selected_columns))
X_train = X_train[selected_columns]
X_test = X_train[selected_columns]
## Split data into 66% train and 33% test
((X_train_S,Y_train_S), (X_test_S,Y_test_S)) = runObj.splitDataset(X_train,Y_train)
print('\n Size of X_train_S : {0}').format(X_train_S.shape)
print('\n Size of Y_train_S : {0}').format(Y_train_S.shape)
print('\n Size of X_test_S : {0}').format(X_test_S.shape)
print('\n Size of Y_test_S : {0}').format(Y_test_S.shape)
## Run classifiers
runObj.runClassifier(X_train_S,Y_train_S,X_test_S,Y_test_S)
def handpickWithSampling():
runObj = run_util.run()
## Load data
((X_train,Y_train),(X_test)) = loadData(runObj)
## Sample train data
RSObj=RS.randomSampling()
(X_train,Y_train) = RSObj.getRandomSample(X_train,Y_train)
(X_train,Y_train) = runObj.randomShuffleData(X_train,Y_train)
print('\n Size of X_train : {0}').format(X_train.shape)
print('\n Size of Y_train : {0}').format(Y_train.shape)
X_train.reset_index(drop=True,inplace=True)
Y_train.reset_index(drop=True,inplace=True)
## Split data into 66% train and 33% test
((X_train_S,Y_train_S), (X_test_S,Y_test_S)) = runObj.splitDataset(X_train,Y_train)
print('\n Size of X_train_S : {0}').format(X_train_S.shape)
print('\n Size of Y_train_S : {0}').format(Y_train_S.shape)
print('\n Size of X_test_S : {0}').format(X_test_S.shape)
print('\n Size of Y_test_S : {0}').format(Y_test_S.shape)
## Run classifiers
runObj.runClassifier(X_train_S,Y_train_S,X_test_S,Y_test_S)
def visualizePieChart(featureList): runObj = run_util.run() X = loadData(runObj) V_util.visualizePieChart(X,featureList)
def visualizeCategorial(featureList): runObj = run_util.run() X = loadData(runObj) X = X[featureList] V_util.visualizeCategorial(X)
def visualizeCategoricalClassLabel(featureList): runObj = run_util.run() X = loadData(runObj) V_util.visualizeCategoricalClassLabel(X,featureList)
def visualizeAll(): runObj = run_util.run() X = loadData(runObj) V_util.visualizeAllFeatures(X)
def visualizePieChart(featureList):
runObj = run_util.run()
X = loadData(runObj)
V_util.visualizePieChart(X, featureList)
def visualizeCategoricalClassLabel(featureList):
runObj = run_util.run()
X = loadData(runObj)
V_util.visualizeCategoricalClassLabel(X, featureList)
def visualizeCategorial(featureList):
runObj = run_util.run()
X = loadData(runObj)
X = X[featureList]
V_util.visualizeCategorial(X)
def visualizeAll():
runObj = run_util.run()
X = loadData(runObj)
V_util.visualizeAllFeatures(X)