def test_evaluate_nfold_fail_due_to_invalid_data():
X = 'not a numpy array...'
y = '...nor a pandas data structure'
model = GaussianNB()
num_folds = 3
with pytest.raises(ValueError):
evaluate_nfold(X, y, model, num_folds)
def test_evaluate_nfold_fail_due_to_invalid_bootstripping_param(classification_data):
X, y = classification_data
num_folds = 3
bootstrapping = 'non-Boolean'
with pytest.raises(ValueError):
evaluate_nfold(X, y, GaussianNB(), num_folds,
bootstrapping=bootstrapping)
def test_evaluate_nfold_with_numpy_arrays(classification_data):
X, y = classification_data
model = GaussianNB()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.5,
random_state=1)
model.fit(X_train, y_train)
num_folds = 1
scores = evaluate_nfold(X_test, y_test, model,
num_folds)
assert len(scores) == num_folds
assert all((scores[i]>=0) & (scores[i]<=1) \
for i in range(len(scores))) == True
num_folds = 3
scores = evaluate_nfold(X_test, y_test, model,
num_folds)
assert len(scores) == num_folds
assert all((scores[i]>=0) & (scores[i]<=1) \
for i in range(len(scores))) == True
def test_evaluate_nfold_bootstrapping(classification_data):
X, y = classification_data
model = GaussianNB()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.5,
random_state=1)
model.fit(X_train, y_train)
num_folds = 3
scores = evaluate_nfold(pd.DataFrame(X_test), pd.DataFrame(y_test),
model, num_folds, bootstrapping=True)
assert len(scores) == num_folds
assert all((scores[i]>=0) & (scores[i]<=1) \
for i in range(len(scores))) == True
def main():
start_time_main = time.time()
print_info('Reading config files...', ':')
run_config = config_file_to_dict(config_path + 'run_params.conf')
data_config = config_file_to_dict(config_path + 'data_params.conf')
model_config = config_file_to_dict(config_path + 'model_params.conf')
if run_mode_user in run_config:
frac_train_sample = run_config[run_mode_user]['frac_train_sample']
num_test_samples = run_config[run_mode_user]['num_test_samples']
num_CV_folds = run_config[run_mode_user]['num_CV_folds']
do_optimize_params = run_config[run_mode_user]['do_optimize_params']
n_iter = run_config[run_mode_user]['n_iter']
print_info('Chosen run mode is {}: {}'.format(
run_mode_user, run_config[run_mode_user]))
else:
raise KeyError('{} is not a valid run mode setting ' \
'(use, e.g., "run_params")'.format(
run_mode_user))
# collection of performance measures to be applied to the test set(s)
scoring_funcs = ['accuracy_score', 'precision_score', 'recall_score', \
'f1_score']
final_results_labels = [
'dataset', 'model', 'model_params', 'num_test_sets', 'num_CV_folds',
'elapsed_time_train', 'elapsed_time_test'
]
final_results_labels += ['test_{}_1fold'.format(i) for i in scoring_funcs]
final_results_labels += ['train_{}'.format(i) for i in scoring_funcs]
final_results_labels += ['test_{}'.format(i) for i in scoring_funcs]
final_results_labels += [
'test_{}_bootstrap'.format(i) for i in scoring_funcs
]
final_results_labels += [
'test_{}_diff_max'.format(i) for i in scoring_funcs
]
final_results_labels += [
'test_{}_diff_max_bootstrap'.format(i) for i in scoring_funcs
]
final_results_labels += [
'test_{}_diff_mean'.format(i) for i in scoring_funcs
]
final_results_labels += [
'test_{}_diff_std'.format(i) for i in scoring_funcs
]
final_results_labels += [
'test_{}_diff_mean_bootstrap'.format(i) for i in scoring_funcs
]
final_results_labels += [
'test_{}_diff_std_bootstrap'.format(i) for i in scoring_funcs
]
final_results = pd.DataFrame(columns=final_results_labels)
# loop over all sections of the data params config file
for d_cnt, d in enumerate(data_config):
print_info(
'Processing dataset: {} ({} of {})'.format(d, d_cnt + 1,
len(data_config)), '=',
50)
current_data_results = {}
current_data_params = data_config[d]
check_data_config_requirements(current_data_params)
print_info('Loading data...', ':')
data = load_data(current_data_params)
print_info('Preparing target vector...', ':')
X = data.drop(current_data_params['data_target_col'], axis=1)
y = data[current_data_params['data_target_col']]
y = parse_target_labels(
y, current_data_params['data_target_positive_label'],
current_data_params['data_target_negative_label'])
del data
print_info('Dimensions of feature matrix X: {}'.format(X.shape))
print_info('Dimensions of target vector y: {}'.format(y.shape))
print_info('Splitting the data: splitting off the training sample...',
':')
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=1 -
frac_train_sample)
del X, y
print_info('Preprocessing the data...', ':')
pm = PreprocessingManager(om.get_session_folder())
for func in current_data_params['data_preprocessing']:
X_train = getattr(pm, func)(X_train, False)
X_test = getattr(pm, func)(X_test, True)
print_class_counts(y_train, 'training', background=0, signal=1)
print_class_counts(y_test, 'test', background=0, signal=1)
# hyperparameter optimization, if required
if num_CV_folds is None:
print_info('Optimizing the number of cross-validation folds...',
':')
num_CV_folds = get_optimal_CV_n_folds(X_train.as_matrix(),
y_train.as_matrix())
for mod in model_config:
print_info('Training model: {}'.format(mod), '-', 50)
try:
model_params = model_config[mod]
model = supported_models[mod](**model_params)
except KeyError:
raise KeyError('Model {} not supported. Choose a valid input ' \
'from this list: {}'.format(mod, supported_models))
fitkwargs = {'X': X_train, 'y': y_train}
if do_optimize_params:
print_info('Optimizing hyperparameters...', ':')
model = hyperparameter_search(model, n_iter, num_CV_folds)
if mod != 'GaussianNB':
fitkwargs['callback'] = DeltaXStopper(1e-2)
start_time_train = time.time()
print_info('Fitting the model...', ':')
model.fit(**fitkwargs)
elapsed_time_train = time.time() - start_time_train
model_parameters = get_search_results(model)
# evaluate model on the training sample
print_info('Evaluating the model on the training sample...', ':')
for scoring_func in scoring_funcs:
try:
model_scores_train = evaluate_nfold(X_train,
y_train,
model,
1,
scoring=scoring_func)
current_data_results['train_{}'.format(
scoring_func)] = model_scores_train[0]
except ValueError:
warnings.warn('ValueError when evaluating with {}. ' \
'Ignoring and continuing...'.format(
scoring_func))
# evaluate model on the test sample(s)
print_info('Evaluating the model on the test sample(s)...', ':')
test_performance_1fold = -1 # must be initialized with a negative number
for t in range(1, num_test_samples + 1):
start_time_test = time.time()
for scoring_func in scoring_funcs:
try:
model_scores_test = evaluate_nfold(
X_test,
y_test,
model,
t,
scoring=scoring_func,
bootstrapping=False)
model_scores_test_bootstrap = evaluate_nfold(
X_test,
y_test,
model,
t,
scoring=scoring_func,
bootstrapping=True)
if test_performance_1fold < 0:
test_performance_1fold = model_scores_test[0]
else:
pass
current_data_results['test_{}_1fold'.format(
scoring_func)] = test_performance_1fold
current_data_results['test_{}'.format(
scoring_func)] = str(model_scores_test)
current_data_results['test_{}_bootstrap'.format(
scoring_func)] = str(model_scores_test_bootstrap)
current_data_results['test_{}_diff_max'.format(
scoring_func
)] = max(model_scores_test) - min(model_scores_test)
current_data_results[
'test_{}_diff_max_bootstrap'.format(
scoring_func
)] = max(model_scores_test_bootstrap) - min(
model_scores_test_bootstrap)
scores_mean, scores_std = performance_difference(
model_scores_test)
current_data_results['test_{}_diff_mean'.format(
scoring_func)] = scores_mean
current_data_results['test_{}_diff_std'.format(
scoring_func)] = scores_std
scores_mean_bootstrap, scores_std_bootstrap = performance_difference(
model_scores_test_bootstrap)
current_data_results[
'test_{}_diff_mean_bootstrap'.format(
scoring_func)] = scores_mean_bootstrap
current_data_results[
'test_{}_diff_std_bootstrap'.format(
scoring_func)] = scores_std_bootstrap
except ValueError:
warnings.warn('ValueError when evaluating with {}. ' \
'Ignoring and continuing...'.format(
scoring_func))
current_data_results['test_{}_1fold'.format(
scoring_func)] = -1
#current_data_results['test_{}'.format(
# scoring_func)] = "-1"
#current_data_results['test_{}_bootstrap'.format(
# scoring_func)] = "-1"
current_data_results['test_{}_diff_mean'.format(
scoring_func)] = -1
current_data_results['test_{}_diff_std'.format(
scoring_func)] = -1
current_data_results['test_{}_diff_mean_bootstrap'.
format(scoring_func)] = -1
current_data_results['test_{}_diff_std_bootstrap'.
format(scoring_func)] = -1
print_info('Model score differences (mean, std) for {} ' \
'test sample folds: {:.5f}, {:.5f}'.format(
t, scores_mean, scores_std))
model_params_string = ','.join('{}:{}'.format(key, val) \
for key, val in \
sorted(model_parameters.items()))
current_data_results['dataset'] = str(d)
current_data_results['model'] = str(mod)
current_data_results['model_params'] = model_params_string
current_data_results['num_test_sets'] = t
current_data_results['num_CV_folds'] = num_CV_folds
current_data_results['elapsed_time_train'] = elapsed_time_train
current_data_results['elapsed_time_test'] = time.time(
) - start_time_test
final_results = final_results.append(current_data_results,
ignore_index=True)
print_info('Creating results plots...', ':')
scoring_func_plot = 'f1_score'
train_differences = []
current_data_plot_nsplits = final_results.query(
'(dataset=="{}") & (model=="{}")'.format(d, mod))['num_test_sets']
# explicit conversion to floats is necessary for the np.isfinite method,
# which is implicitely called during plotting
current_data_plot_xyvals = [
current_data_plot_nsplits.values.astype(np.float32)
]
current_data_plot_xyvals_bootstrap = [
current_data_plot_nsplits.values.astype(np.float32)
]
current_data_plot_xyvals_max = [
current_data_plot_nsplits.values.astype(np.float32)
]
current_data_plot_xyvals_max_bootstrap = [
current_data_plot_nsplits.values.astype(np.float32)
]
for mod in model_config:
current_data_plot_xyvals.append(
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d, mod))['test_{}_diff_mean'.format(
scoring_func_plot)].values.astype(np.float32))
current_data_plot_xyvals.append(
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d, mod))['test_{}_diff_std'.format(
scoring_func_plot)].values.astype(np.float32))
current_data_plot_xyvals_max.append(
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d, mod))['test_{}_diff_max'.format(
scoring_func_plot)].values.astype(np.float32))
current_data_plot_xyvals_max.append(
np.zeros(current_data_plot_xyvals_max[-1].shape))
current_data_plot_xyvals_max_bootstrap.append(
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d, mod))['test_{}_diff_max_bootstrap'.format(
scoring_func_plot)].values.astype(np.float32))
current_data_plot_xyvals_max_bootstrap.append(
np.zeros(current_data_plot_xyvals_max_bootstrap[-1].shape))
train_differences.append(
abs(final_results.query('(dataset=="{}") & '\
'(model=="{}")'.format(
d,mod))['train_{}'.format(
scoring_func_plot)].iloc[0] -
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d,mod))['test_{}_1fold'.format(scoring_func_plot)].iloc[0])
)
current_data_plot_xyvals_bootstrap.append(
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d, mod))['test_{}_diff_mean_bootstrap'.format(
scoring_func_plot)].values.astype(np.float32))
current_data_plot_xyvals_bootstrap.append(
final_results.query('(dataset=="{}") & (model=="{}")'.format(
d, mod))['test_{}_diff_std_bootstrap'.format(
scoring_func_plot)].values.astype(np.float32))
xmax_list = [None]
for i in range(10, 100, 10):
if num_test_samples > i:
xmax_list.append(i)
for lim in xmax_list:
current_data_plot = plot_performance_diff(
*current_data_plot_xyvals,
labels=[m for m in model_config],
train_difference=train_differences,
xmax=lim,
xlabel='number of samples',
ylabel='mean performance difference')
plot_filename = '{}_performance-diff_num-splits_full'.format(d)
if lim is not None:
plot_filename += '_zoomed-{}'.format(lim)
om.save(current_data_plot, plot_filename)
current_data_plot = plot_performance_diff(
*current_data_plot_xyvals,
labels=[m for m in model_config],
xmax=lim,
xlabel='number of samples',
ylabel='mean performance difference')
plot_filename = '{}_performance-diff_num-splits'.format(d)
if lim is not None:
plot_filename += '_zoomed-{}'.format(lim)
om.save(current_data_plot, plot_filename)
current_data_plot = plot_performance_diff(
*current_data_plot_xyvals_max,
labels=[m for m in model_config],
xmax=lim,
xlabel='number of samples',
ylabel='maximum performance difference')
plot_filename = '{}_performance-diff_max_num-splits'.format(d)
if lim is not None:
plot_filename += '_zoomed-{}'.format(lim)
om.save(current_data_plot, plot_filename)
current_data_plot = plot_performance_diff(
*current_data_plot_xyvals_max_bootstrap,
labels=[m for m in model_config],
xmax=lim,
xlabel='number of samples',
ylabel='maximum performance difference')
plot_filename = '{}_performance-diff_max_num-splits_bootstrap'.format(
d)
if lim is not None:
plot_filename += '_zoomed-{}'.format(lim)
om.save(current_data_plot, plot_filename)
current_data_plot = plot_performance_diff(
*current_data_plot_xyvals_bootstrap,
labels=[m for m in model_config],
train_difference=train_differences,
xmax=lim,
xlabel='number of samples',
ylabel='mean performance difference')
plot_filename = '{}_performance-diff_num-splits_full_bootstrap'.format(
d)
if lim is not None:
plot_filename += '_zoomed-{}'.format(lim)
om.save(current_data_plot, plot_filename)
current_data_plot = plot_performance_diff(
*current_data_plot_xyvals_bootstrap,
labels=[m for m in model_config],
xmax=lim,
xlabel='number of samples',
ylabel='mean performance difference')
plot_filename = '{}_performance-diff_num-splits_bootstrap'.format(
d)
if lim is not None:
plot_filename += '_zoomed-{}'.format(lim)
om.save(current_data_plot, plot_filename)
print_info('Saving the final results...', ':')
om.save(final_results, '{}_final-results'.format(d))
final_results_dict = final_results.to_dict('dict')
final_results_dict['relation'] = str(d) # needed for ARFF
final_results_dict['description'] = u'' # needed for ARFF
om.save(final_results, '{}_final-results'.format(d), to_arff=True)
print_info('\n')
print_info(
'Everything done. (Elapsed overall time: {} seconds)\n'.format(
time.time() - start_time_main))
def test_evaluate_nfold_fail_due_to_invalid_num_folds(classification_data):
X, y = classification_data
num_folds = 'not an integer'
with pytest.raises(ValueError):
evaluate_nfold(X, y, GaussianNB(), num_folds)
def test_evaluate_nfold_fail_due_to_zero_folds(classification_data):
X, y = classification_data
model = GaussianNB()
num_folds = 0
with pytest.raises(ValueError):
evaluate_nfold(X, y, model, num_folds)
def test_evaluate_nfold_fail_due_to_invalid_scoring_func(classification_data):
X, y = classification_data
num_folds = 3
scoring_func = 'invalid function'
with pytest.raises(ValueError):
evaluate_nfold(X, y, GaussianNB(), num_folds, scoring=scoring_func)
