def Trainig():
if (os.path.exists("PreProcessing Data/TrainingDataSet.csv")):
data = pd.read_csv("PreProcessing Data/TrainingDataSet.csv")
print("Training DataSet Already Existing")
Y = data['Rate'] # Label
X = data.drop(columns=["Rate"], inplace=False)
else:
print("Starting Training Data PreProcessing")
pre_processing = Data_Preprocessing.Pre_Processing()
X, Y = pre_processing.PreProcessing_Trainig()
highX = X[Y == 2]
highY = Y[Y == 2]
intermediateX = X[Y == 1]
intermediateY = Y[Y == 1]
lowX = X[Y == 0]
lowY = Y[Y == 0]
HX_train, HX_test, HY_train, HY_test = train_test_split(highX,
highY,
test_size=0.2,
shuffle=True)
IX_train, IX_test, IY_train, IY_test = train_test_split(intermediateX,
intermediateY,
test_size=0.2,
shuffle=True)
LX_train, LX_test, LY_train, LY_test = train_test_split(lowX,
lowY,
test_size=0.2,
shuffle=True)
X_train = np.concatenate((HX_train, IX_train, LX_train))
y_train = np.concatenate((HY_train, IY_train, LY_train))
X_test = np.concatenate((HX_test, IX_test, LX_test))
y_test = np.concatenate((HY_test, IY_test, LY_test))
print("Start Classification Techinques")
# X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, shuffle=True)
np.save('modelsPCA/traindata.npy', X_train)
np.save('modelsPCA/trainlabel.npy', y_train)
caller(X_train, y_train, X_test, y_test, 0)
PCA_algorithm(X_train, y_train, X_test, y_test)
import Classification
import Classification_Testing
import Data_Preprocessing
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
print("Appstore games Classification")
input = int(input("Press 1 To Train Models Or 2 To Test Model :: "))
if input == 1:
Classification.Trainig()
else:
pre_processing = Data_Preprocessing.Pre_Processing()
X, Y = pre_processing.PreProcessing_Testing()
print("Testing Before PCA Algorithm")
Testing = Classification_Testing.Classification_Testing(X, Y)
Testing.OneVsOnelinear(0)
Testing.OneVsOne_LinearSVC(0)
Testing.OneVsOne_ploy(0)
Testing.OneVsOne_rbf(0)
Testing.adaBoost(0)
Testing.decisionTree(0)
Testing.KNN(0)
xtrain = np.load('modelsPCA/traindata.npy', allow_pickle=True)
ytrain = np.load('modelsPCA/trainlabel.npy', allow_pickle=True)
Testing.PCA_algorithm(xtrain, ytrain)