split_train = {l:0 for l in labels}
for l in labels:
split_train[l] = sum(developement_df[l].values)
split_test = {l:0 for l in labels}
for l in labels:
split_test[l] = sum(testing_df[l].values)
n_samples_train = len(developement_df)
n_samples_test = len(testing_df)
# Create the appropriate statistics container for the whole experiment.
training_stats = Statistics()
validation_stats = Statistics()
testing_stats = Statistics()
seeds = create_seeds(iterations)
min_class_freq = min(split_train.values())
cv_folds = min([min_class_freq, cv_folds])
statistics_objects = []
best_params = {l: {'score': 0.0, 'params': {}} for l in labels}
print("Running Supervised Ensemble Classification...")
# def do_iteration(i):
for i in range(iterations):
print("Iteration " + str(i+1))
rng = np.random.RandomState()
rng.seed(seeds[i])
dev_df_i = developement_df.copy(deep=True)
test_df_i = testing_df.copy(deep=True)
folds_i = list(DataFrameStratifiedKFold(
if len(selection) == 0:
print("Please select some features using the command line args. Use --help or -h for help.")
sys.exit(1)
print(selection)
# ---------------------- THRESHOLD TESTING ---------------------------- #
developement_df, _ = prep_data_frames(selection, load_interactome=False)
thresholds = np.arange(0, 1.1, step=0.1)
folds = list(DataFrameStratifiedKFold(
n_splits=cv_folds, shuffle=True, random_state=None
).split(developement_df, y=developement_df['label'].values))
statistics = Statistics()
params = sk_generate_params('lr', columns=None)
labels = get_labels_from_file('data/labels.tsv')
seeds = create_seeds(len(labels))
things = {}
def pr_curve(i):
label = labels[i]
statistics_l = Statistics()
print('Doing label {}'.format(label))
for train_idx, valid_idx in folds:
rng = np.random.RandomState()
rng.seed(seeds[i])
training_fold = developement_df.loc[train_idx, ]
training_fold = training_fold.reset_index(drop=True)
validation_fold = developement_df.loc[valid_idx, ]
validation_fold = validation_fold.reset_index(drop=True)
base_estimators = make_classifiers(method, balanced, labels, random_state=rng)
