def starcraft_sp_test():
# Create DataLoader instance to load and format data
dataLoader = DataLoader()
logging.info("Program started")
logging.info("Loading starcraft data")
# Read skillcraft dataset, the class index is the second column
dataLoader.read(filename="data/SkillCraft1_Dataset.csv",
classIndex=1,
numOfFeatures=15)
# Normalize data values from 0 - 1
#dataLoader.normalize()
# Create new labels to fit into binary classification
dataLoader.scaleToBinary(5)
# Spectral Clustering
# Binary
clustering(dataLoader.x_train,
dataLoader.y_train,
writer_starcraft,
'starcraft-binary',
multiple=True,
binary=True)
# Multiclass
#clustering(dataLoader.x_train, dataLoader.multi_y_train, writer_starcraft, 'starcraft-multiclass', multiple=True, binary=False)
# Write all the results
writer_starcraft.save()
def starcraft_svm_test():
# Create DataLoader instance to load and format data
dataLoader = DataLoader()
logging.info("Program started")
logging.info("Loading starcraft data")
# Read skillcraft dataset, the class index is the second column
dataLoader.read(filename="data/SkillCraft1_Dataset.csv",
classIndex=1,
numOfFeatures=15)
multi_label_count = dataLoader.labelCount(8)
# Creates plots for a few of the data features
# dataLoader.visualize()
# Normalize data values from 0 - 1
#dataLoader.normalize()
# Create new labels to fit into binary classification
dataLoader.scaleToBinary(5)
label_count = dataLoader.binaryLabelCount(5)
logging.info("Number of examples per class")
logging.info("Casual - (1): " + str(label_count[0]))
logging.info("Hardcore - (-1): " + str(label_count[1]))
label_count = dataLoader.labelCount(8)
logDataCount(label_count)
"""
# Create SVM
svm = SVM()
# Train and predict for binary svm
logging.info("Running SVM for binary classification")
# Train for binary single run with these objects
logging.info("Single binary SVM")
svm.train(dataLoader.x_train, dataLoader.y_train, dataLoader.x_test, dataLoader.y_test)
# Train and test binary svm multiple times for all available binary variables
logging.info("Multiple runs with different parameters - binary SVM")
svm.train(dataLoader.x_train, dataLoader.y_train, dataLoader.x_test, dataLoader.y_test, iterate=True)
# Save binary results to excel sheet
logging.info("Saving binary SVM results")
svm.results.to_excel(writer_starcraft, sheet_name='binary-svm')
# MULTI CLASS SVM
logging.info("Running SVM for multiclass classification")
# Train and predict for multi-class data using the linear svm from liblinear implementation
logging.info("Running SVM for multiclass classification with liblinear implementation")
svm.train(dataLoader.x_train, dataLoader.multi_y_train, dataLoader.x_test, dataLoader.multi_y_test, binary=False)
logging.info("Saving multiclass liblinear results")
svm.results.to_excel(writer_starcraft, sheet_name='multiclass-liblinear')
# Train for multi-class single run with these objects using the libsvm implementation
logging.info("Running SVM for multiclass classification with libsvm implementation")
svm.train(dataLoader.x_train, dataLoader.multi_y_train, dataLoader.x_test, dataLoader.multi_y_test, binary=False, linear=False)
logging.info("Saving multiclass libsvm results")
svm.results.to_excel(writer_starcraft, sheet_name='multiclass-libsvm')
# Train and test multi-class svm multiple times for all available multi-class variables
logging.info("Running SVM for multiclass classification for all available multi-class variables")
svm.train(dataLoader.x_train, dataLoader.multi_y_train, dataLoader.x_test, dataLoader.multi_y_test, iterate=True, binary=False)
logging.info("Saving multiclass multiple-runs results")
svm.results.to_excel(writer_starcraft, sheet_name='multiclass-multiple-variables')
# Train and test multi-class svm multiple times with KPCA-LDA
logging.info("Running SVM for multiclass classification with KPCA-LDA")
svm.train(dataLoader.x_train, dataLoader.multi_y_train, dataLoader.x_test, dataLoader.multi_y_test, iterate=True, binary=False, decomposition=True)
logging.info("Saving multiclass multiple-runs results")
svm.results.to_excel(writer_starcraft, sheet_name='multiclass-kpca-lda')
# KNN and NC
nearest(dataLoader.x_train, dataLoader.y_train, dataLoader.x_test, dataLoader.y_test, dataLoader.multi_y_train, dataLoader.multi_y_test, writer_starcraft)
"""
clustering(dataLoader.x_train, dataLoader.y_train, dataLoader.x_test,
dataLoader.y_test)
# Write all the results
writer_starcraft.save()
