def plt_multiclass(logger, **args):
result_dir = '../results/'
logger.info(f'Results directory: {result_dir}')
if not os.path.exists(result_dir):
os.makedirs(result_dir)
logger.debug(
f'Results directory does not exist. Creating the directory')
log_fname = logger.handlers[0].baseFilename.split('/')[-1]
result_fname = log_fname.replace('_log_',
'_results_').replace('.log', '.results')
logger.debug(f'Results filepath: {result_dir}{result_fname}')
result_fh = open(f'{result_dir}{result_fname}', 'w')
print('---PROGRESSIVE ELM - MULTI-CLASS---')
print('---PROGRESSIVE ELM - MULTI-CLASS---', file=result_fh)
filename = args['filename']
header = args['header']
label_location = args['label']
scale_type = args['scale']
test_ratio = args['testratio']
print(f'Dataset file: {filename}', file=result_fh)
logger.debug(
f'Dataset File: {filename}, Scaling Type: {scale_type}, Test ratio: {test_ratio}'
)
logger.debug(
f'Header attribute in pandas read_csv: {header}, label location in csv: {label_location}'
)
# Hyper-parameters
hidden_dim = args['neurons']
N0 = args['initial']
batch_size = args['batch']
logger.debug(
f'Hidden layer neurons: {hidden_dim}, Number of samples in initial block: {N0}, '
f'Batch size for training: {batch_size}')
# Load and Pre-process Data
logger.info('Loading and preprocessing data...')
data = Data()
data.set_logger(logger)
print(f'Loading data from file: {filename}')
data.load_csv(fname=filename, header=header)
data.get_feats_labels(label_column=label_location)
data.scale_features(scale_type=scale_type)
data.split_train_test(test_ratio=test_ratio)
logger.info('Loading and preprocessing data done')
# PLT-ELM Model
logger.info('Creating PLT ELM model')
try:
plt = PLT(data.num_feats, hidden_dim)
except:
logger.error('---> !!! Error in creating PLT class object !!!')
raise ValueError
plt.set_logger(logger)
data.set_initial_batch_size(N0)
data.set_training_batch_size(batch_size)
print('Begin Training...')
logger.info('Begin Training...')
for batch_id, X, y in data.fetch_train_data():
if batch_id == 0:
plt.initial_batch(X, y)
continue
plt.train(X, y)
print('Training Complete')
logger.info('Training Complete')
print('Begin Testing...')
logger.info('Begin Testing...')
report, accuracy = plt.test(data.test_data['X'], data.test_data['y'])
print('Testing Complete')
logger.info('Testing Complete')
print(f'Classification Report: \n{report}\nAccuracy: {accuracy}')
print(f'Classification Report: \n{report}\nAccuracy: {accuracy}',
file=result_fh)
logger.info(f'\nClassification Report: \n{report}')
logger.info(f'\nAccuracy: {accuracy}')
pass
def plt_multiclass(logger, **args):
"""
Receives arguments from main.py and executes progressive multi-class ELM
:param logger: Logger object that handles logging status
:param args: command line arguments from main.py
:return: None (Generates results file)
"""
# CONFIGURE RESULTS FILE, DIRECTORY
result_dir = '../results/' # Results directory
logger.info(f'Results directory: {result_dir}')
if not os.path.exists(result_dir):
os.makedirs(result_dir) # Create directory if doesn't exist
logger.debug(
f'Results directory does not exist. Creating the directory')
log_fname = logger.handlers[0].baseFilename.split('/')[-1]
result_fname = log_fname.replace('_log_', '_results_').replace(
'.log', '.results') # Result filename
logger.debug(f'Results filepath: {result_dir}{result_fname}')
result_fh = open(f'{result_dir}{result_fname}', 'w')
# BEGIN EXECUTION
print('---PROGRESSIVE ELM - MULTI-CLASS---')
print('---PROGRESSIVE ELM - MULTI-CLASS---', file=result_fh)
# Read Arguments
filename = args['filename']
header = args['header']
label_location = args['label']
scale_type = args['scale']
test_ratio = args['testratio']
print(f'Dataset file: {filename}', file=result_fh)
logger.debug(
f'Dataset File: {filename}, Scaling Type: {scale_type}, Test ratio: {test_ratio}'
)
logger.debug(
f'Header attribute in pandas read_csv: {header}, label location in csv: {label_location}'
)
# Hyper-parameters of the model
hidden_dim = args['neurons']
N0 = args['initial']
batch_size = args['batch']
logger.debug(
f'Hidden layer neurons: {hidden_dim}, Number of samples in initial block: {N0}, '
f'Batch size for training: {batch_size}')
# Load and Pre-process Data
logger.info('Loading and preprocessing data...')
data = Data() # Create an object of class Data to handle data operations
data.set_logger(logger) # Set logger for Data object
print(f'Loading data from file: {filename}...')
data.load_csv(fname=filename, header=header) # Load dataset to data object
data.get_feats_labels(
label_column=label_location) # Extract features and labels
data.scale_features(scale_type=scale_type) # Scale features
data.split_train_test(test_ratio=test_ratio) # Split to train, test
logger.info('Loading and preprocessing data done')
# PLT-ELM Model
print('Creating PLT ELM model...')
logger.info('Creating PLT ELM model')
try:
plt = PLT(data.num_feats, hidden_dim) # Create an object of class PLT
except:
logger.error('---> !!! Error in creating PLT class object !!!')
raise ValueError
plt.set_logger(logger) # Set logger for plt object
data.set_initial_batch_size(N0) # Set initial batch size
data.set_training_batch_size(batch_size) # Set training batch size
# Training
print('Begin Training...')
logger.info('Begin Training...')
for batch_id, X, y in data.fetch_train_data(): # Fetch data sequentially
if batch_id == 0:
plt.initial_batch(X, y)
continue
plt.train(X, y) # Train model
print('Training Complete')
logger.info('Training Complete')
# Testing
print('Begin Testing...')
logger.info('Begin Testing...')
report, accuracy = plt.test(data.test_data['X'],
data.test_data['y']) # Test model
print('Testing Complete')
logger.info('Testing Complete')
# Evaluation Metrics
print(f'Classification Report: \n{report}\nAccuracy: {accuracy}')
print(f'Classification Report: \n{report}\nAccuracy: {accuracy}',
file=result_fh)
logger.info(f'\nClassification Report: \n{report}')
logger.info(f'\nAccuracy: {accuracy}')
# Close results file handler
result_fh.close()
pass
