def run_opt_single( conf, iteration, globals ):
'''
Evaluate the algorithms for a single split
--------
conf: dict
Configuration dictionary
slice: int
Optional index for the window slice
'''
print( 'run test opt single' )
algorithms = create_algorithms_dict( conf['algorithms'] )
for k, a in algorithms.items():
aclass = type(a)
if not aclass in globals:
globals[aclass] = { 'key': '', 'best': -1 }
metrics = create_metric_list( conf['metrics'] )
metric_opt = create_metric( conf['optimize'] )
metrics = metric_opt + metrics
evaluation = load_evaluation( conf['evaluation'] )
train_eval = True
if 'train_eval' in conf['data']:
train_eval = conf['data']['train_eval']
if 'opts' in conf['data']:
train, test = dl.load_data_session( conf['data']['folder'], conf['data']['prefix'], train_eval=train_eval, **conf['data']['opts'] )
else:
train, test = dl.load_data_session( conf['data']['folder'], conf['data']['prefix'], train_eval=train_eval )
for m in metrics:
m.init( train )
results = {}
for k, a in algorithms.items():
eval_algorithm(train, test, k, a, evaluation, metrics, results, conf, iteration=iteration, out=False)
write_results_csv( results, conf, iteration=iteration )
for k, a in algorithms.items():
aclass = type(a)
current_value = results[k][0][1]
if globals[aclass]['best'] < current_value:
print( 'found new best configuration' )
print( k )
print( 'improvement from {} to {}'.format(globals[aclass]['best'], current_value) )
send_message( 'improvement for {} from {} to {} in test {}'.format(k, globals[aclass]['best'], current_value, iteration ) )
globals[aclass]['best'] = current_value
globals[aclass]['key'] = k
globals['results'].append( results )
del algorithms
del metrics
del evaluation
del results
gc.collect()
def run_single(conf, slice=None):
'''
Evaluate the algorithms for a single split
--------
conf: dict
Configuration dictionary
slice: int
Optional index for the window slice
'''
print('run test single')
algorithms = create_algorithms_dict(conf['algorithms'])
metrics = create_metric_list(conf['metrics'])
evaluation = load_evaluation(conf['evaluation'])
if 'opts' in conf['data']:
train, test = dl.load_data_session(conf['data']['folder'],
conf['data']['prefix'],
slice_num=slice,
**conf['data']['opts'])
else:
train, test = dl.load_data_session(conf['data']['folder'],
conf['data']['prefix'],
slice_num=slice)
buys = pd.DataFrame()
if 'buys' in conf['data'] and 'file_buys' in conf['data']:
buys = dl.load_buys(
conf['data']['folder'],
conf['data']['file_buys']) # load buy actions in addition
for m in metrics:
m.init(train)
if hasattr(m, 'set_buys'):
m.set_buys(buys, test)
results = {}
for k, a in algorithms.items():
eval_algorithm(train,
test,
k,
a,
evaluation,
metrics,
results,
conf,
slice=slice,
iteration=slice)
print_results(results)
write_results_csv(results, conf, iteration=slice)
def run_single(conf, slice=None):
'''
Evaluate the algorithms for a single split
--------
conf: dict
Configuration dictionary
slice: int
Optional index for the window slice
'''
print('run test single')
algorithms = create_algorithms_dict(conf['algorithms'])
metrics = create_metric_list(conf['metrics'])
evaluation = load_evaluation(conf['evaluation'])
buys = pd.DataFrame()
if 'type' in conf['data']:
if conf['data']['type'] == 'hdf': # hdf5 file
if 'opts' in conf['data']:
# ( path, file, sessions_train=None, sessions_test=None, slice_num=None, train_eval=False )
train, test = dl.load_data_session_hdf(conf['data']['folder'],
conf['data']['prefix'],
slice_num=slice,
**conf['data']['opts'])
else:
train, test = dl.load_data_session_hdf(conf['data']['folder'],
conf['data']['prefix'],
slice_num=slice)
# elif conf['data']['type'] == 'csv': # csv file
else: # csv file (default)
if 'opts' in conf['data']:
train, test = dl.load_data_session(conf['data']['folder'],
conf['data']['prefix'],
slice_num=slice,
**conf['data']['opts'])
else:
train, test = dl.load_data_session(conf['data']['folder'],
conf['data']['prefix'],
slice_num=slice)
if 'buys' in conf['data'] and 'file_buys' in conf['data']:
buys = dl.load_buys(
conf['data']['folder'],
conf['data']['file_buys']) # load buy actions in addition
# else:
# raise RuntimeError('Unknown data type: {}'.format(conf['data']['type']))
for m in metrics:
m.init(train)
if hasattr(m, 'set_buys'):
m.set_buys(buys, test)
results = {}
for k, a in algorithms.items():
eval_algorithm(train,
test,
k,
a,
evaluation,
metrics,
results,
conf,
slice=slice,
iteration=slice)
print_results(results)
write_results_csv(results, conf, iteration=slice)