def main():
######################
# Prepare filesystem #
######################
mkdir("models")
#################
# Load networks #
#################
networks, dims = load_ppmi_matrices(data_path)
A = minmax_scale(networks[0])
#########################
# Train the autoencoder #
#########################
model_name = [f'{org}', f'{model_type}-{args.layers}']
if ofile_tags != '':
model_name.append(ofile_tags)
model_name = '_'.join(model_name)
stdout("Running for architecture", model_name)
best_model_filename = 'best_model.h5'
autoencoder = DeepAutoencoder(
x_train=A,
x_val=0.1,
layers=layers,
epochs=epochs,
sparse=sparse,
dropout=dropout,
batch_size=batch_size,
activation=activation,
optimizer=optimizer,
# early_stopping=(5, 0.),
save_best_model=best_model_filename,
verbose=2)
autoencoder.train()
history = autoencoder.history.history
with open(os.path.join(models_path, f'{model_name}_training_history.pkl'),
'wb') as f:
pickle.dump(history, f)
plot_loss({model_name: history}, f'{models_path}/{model_name}')
embeddings = autoencoder.encode(A)
embeddings_path = os.path.join(models_path, f'{model_name}_embeddings.mat')
sio.savemat(embeddings_path, {'embeddings': embeddings})
os.remove(best_model_filename)
def main():
print(__doc__)
stdout("Command line arguments", args)
s = STRING()
s.convert_ids_to_numbers()
stdout('Writing networks to', output_path)
s.write()
def load_ppmi_matrices(data_path):
'''Load PPMI matrices.
# Arguments
data_path: str, path to .mat files
# Returns
Ms: List[numpy.ndarray], PPMI matrices
dims: List[int], dimensions of matrices
'''
paths = sorted(glob.glob(os.path.join(data_path, "*.mat")))
stdout('Networks', paths)
Ms = []
for p in paths:
M = _load_ppmi_matrix(p)
Ms.append(minmax_scale(M))
dims = [i.shape[1] for i in Ms]
stdout('Input dims', dims)
return Ms, dims
def main():
######################
# Prepare filesystem #
######################
mkdir(results_path)
#################
# Load networks #
#################
networks, dims = load_ppmi_matrices(data_path)
###############
# Load models #
###############
model_names = sorted(
glob.glob(
os.path.join(os.path.expandvars(models_path),
f'{org}_MDA_arch_*.h5')))
stdout("Model names", model_names)
# For now I am only loading a single model at a time, specified by
# `--architecture`. TODO improve this to handle multiple models at one
# time.
if architecture:
for m in model_names:
if int(m[-4]) == architecture:
mid_model = load_model(m)
model_name = os.path.basename(m).split(".")[0]
else:
raise Warning("`--architecture` must be supplied")
################################
# Calculate network embeddings #
################################
stdout("Calculating embeddings for", model_name)
embeddings = minmax_scale(mid_model.predict(networks))
embeddings_path = os.path.join(os.path.expandvars(results_path),
f'{model_name}_features.mat')
stdout("Writing embeddings to", embeddings_path)
sio.savemat(embeddings_path, {'embeddings': embeddings})
def cross_validation(X,
y,
n_trials=10,
n_jobs=1,
n_threads=1,
random_state=None,
clf_type='LRC',
max_depth=5):
'''Perform model selection via cross validation.
'''
stdout('Number of samples pre-filtering', X.shape)
# Filter samples with no annotations
del_rid = np.where(y.sum(axis=1) == 0)[0]
y = np.delete(y, del_rid, axis=0)
X = np.delete(X, del_rid, axis=0)
stdout('Number of samples post-filtering', X.shape)
# Scoring
scoring = {
'accuracy': 'accuracy',
'f1': 'f1_micro',
'M_AUPR': make_scorer(M_AUPR),
'm_AUPR': make_scorer(m_AUPR)
}
# Split training data
trials = ShuffleSplit(n_splits=n_trials,
test_size=0.2,
random_state=random_state)
# Performance
performance_metrics = ("accuracy", "m_AUPR", "M_AUPR", "f1")
perf = defaultdict(dict)
perf['grid_search'] = {}
# Model selection for optimum hyperparameters
iteration = 0
for train_idx, test_idx in trials.split(X):
# Split data
X_train = X[train_idx]
X_test = X[test_idx]
y_train = y[train_idx]
y_test = y[test_idx]
iteration += 1
stdout('Cross validation trial', iteration)
stdout('Train samples', y_train.shape[0])
stdout('Test samples', y_test.shape[0])
# Classifier
if clf_type == 'SVC':
clf = SVClassifier(n_jobs=n_jobs, random_state=random_state)
grid_search_params = {
'C': [1, 5, 10, 50, 100],
'gamma': [0.001, 0.005, 0.01, 0.05, 0.1],
'kernel': ['rbf']
}
if clf_type == 'LinearSVC':
clf = SVClassifier(n_jobs=n_jobs, random_state=random_state)
grid_search_params = {
'C': np.logspace(-1, 2, 4),
'kernel': ['linear']
}
elif clf_type == 'LRC':
clf = LRClassifier(n_jobs=n_jobs, random_state=random_state)
grid_search_params = {'C': np.logspace(-2, 2, 5)}
elif clf_type == 'RFC':
clf = RFClassifier(n_jobs=n_jobs, random_state=random_state)
grid_search_params = {'max_features': ['auto']}
elif clf_type == 'XGB':
clf = XGBClassifier(learning_rate=0.1,
n_estimators=1000,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
scale_pos_weight=1,
n_jobs=n_jobs,
n_threads=n_threads,
random_state=random_state)
grid_search_params = {
'max_depth': max_depth,
'min_child_weight': range(1, 3)
}
else:
raise ValueError('`clf` must be a class in agape.ml.classifer')
# Perform a grid search over the hyperparameter ranges
stdout('Grid search')
clf.grid_search(X_train,
y_train,
grid_search_params,
scoring=scoring,
refit='m_AUPR',
cv=5,
verbose=10)
# Get the best hyperparameters
clf_params = clf.get_clf().get_params()['estimator'] \
.get_params()['estimator'] \
.get_params()
best_params = {
k: clf_params[k.replace('estimator__', '')]
for k in grid_search_params
}
perf['grid_search'][iteration] = {}
perf['grid_search'][iteration]['best_estimator_'] = {}
for k, v in best_params.items():
k = k.split('__')[-1]
perf['grid_search'][iteration]['best_estimator_'][k] = v
stdout('Optimal parameters', best_params)
perf['grid_search'][iteration]['best_score_'] = \
clf.clf_grid_search.best_score_
stdout('Train dataset AUPR', clf.clf_grid_search.best_score_)
# Train a classifier with the optimal hyperparameters using the full
# training data
clf.fit(X_train, y_train)
# Compute performance on test set
y_pred = clf.predict(X_test)
y_score = clf.predict_proba(X_test)
stdout('Number of positive predictions', len(y_pred.nonzero()[0]))
perf_trial = _Performance(y_test, y_score, y_pred)
stdout('Test dataset')
for pm in performance_metrics:
pm_v = getattr(perf_trial, pm)
stdout(pm, pm_v)
perf[pm][iteration] = pm_v
dummy = DummyClassifier().fit(X_train, y_train).score(X_test, y_test)
perf['dummy'][iteration] = dummy
# Performance across K-fold cross-validation
def calculate_mean_std(metric):
values = list(perf[metric].values())
perf['metrics'][metric]['mean'] = np.mean(values)
perf['metrics'][metric]['std'] = std(values)
for pm in performance_metrics:
perf['metrics'][pm] = {}
calculate_mean_std(pm)
return perf
def main():
######################
# Prepare filesystem #
######################
directory_exists(models_path)
mkdir(results_path)
###################
# Load embeddings #
###################
embeddings_file = glob.glob(os.path.join(models_path, '*.mat'))[0]
model_name = os.path.splitext(os.path.basename(embeddings_file))[0]
print(model_name)
stdout('Loading embeddings', embeddings_file)
embeddings = load_embeddings(embeddings_file)
embeddings = minmax_scale(embeddings)
#######################
# Load GO annotations #
#######################
annotation_dir = os.path.join(data_path, 'annotations')
if validation == 'cerevisiae':
annotation_file = os.path.join(annotation_dir,
'cerevisiae_annotations.mat')
else:
annotation_file = os.path.join(annotation_dir, 'yeast_annotations.mat')
stdout('Loading GO annotations', annotation_file)
GO = sio.loadmat(annotation_file)
####################
# Train classifier #
####################
stdout('Running cross-validation for', level)
annotations = GO[level]
# Silence certain warning messages during cross-validation
for w in (sklearn.exceptions.UndefinedMetricWarning, UserWarning,
RuntimeWarning):
warnings.filterwarnings("ignore", category=w)
# Only use a subset of the data for testing purposes
embeddings = embeddings[:test]
annotations = annotations[:test]
# performance = cross_validation(
# embeddings,
# annotations,
# n_trials=n_trials,
# n_jobs=n_jobs,
# n_threads=n_threads,
# random_state=random_state,
# clf_type=clf_type,
# max_depth=max_depth[level])
performance = cross_validation(embeddings, annotations, n_trials=n_trials)
performance['my_level'] = level
pprint(performance)
fout = f'{model_name}_{level}_{clf_type}_performance.json'
with open(os.path.join(results_path, fout), 'w') as f:
json.dump(performance, f)
default=-1,
type=int,
help='Set sklearn random_state. If -1, then sklearn uses \
the system randomness as a seed. If int, then this \
number will be used as a seed.')
parser.add_argument('--tags', default="", type=str)
parser.add_argument('-j', '--n_jobs', default=1, type=int)
parser.add_argument('-t', '--n_threads', default=1, type=int)
parser.add_argument('-c', '--clf_type', default='LRC', type=str)
parser.add_argument('--test',
default=None,
type=int,
help='If True, then only a subset of the data is used')
args = parser.parse_args()
stdout("Command line arguments", args)
org = args.organism
models_path = os.path.expandvars(args.models_path)
results_path = os.path.expandvars(args.results_path)
data_path = os.path.expandvars(args.data_path)
n_trials = args.n_trials
tags = args.tags
validation = args.validation
n_jobs = args.n_jobs
n_threads = args.n_threads
random_state = args.random_state
level = args.level
clf_type = args.clf_type
test = args.test
def main():
# Prepare filesystem
directory_exists(models_path)
mkdir(results_path)
# Load embeddings
embeddings_file = glob.glob(os.path.join(models_path, '*.mat'))[0]
model_name = os.path.splitext(
os.path.basename(embeddings_file))[0].replace('_embeddings', '')
stdout('Loading embeddings', embeddings_file)
embeddings = load_embeddings(embeddings_file).astype('int32')
# Load annotations
annotation_dir = os.path.join(data_path, 'annotations')
if validation == 'cerevisiae':
annotation_file = os.path.join(
annotation_dir, 'cerevisiae_annotations.mat')
else:
annotation_file = os.path.join(annotation_dir, 'yeast_annotations.mat')
stdout('Loading GO annotations', annotation_file)
annotation_file = sio.loadmat(annotation_file)
# Train classifier
stdout('Running cross-validation for', level)
if validation == 'cv':
if level in ('P', 'F', 'C'):
annotations = np.hstack(
[annotation_file[f'{level}_{i}'] for i in range(1, 4)])
else:
annotations = annotation_file[level]
elif validation == 'cerevisiae':
if level == 'all':
annotations = np.hstack(
[annotation_file[f'level{i}'] for i in range(1, 4)])
else:
annotations = annotation_file[level]
annotations = annotations.astype('int32')
# Silence certain warning messages during cross-validation
for w in (sklearn.exceptions.UndefinedMetricWarning, UserWarning,
RuntimeWarning):
warnings.filterwarnings("ignore", category=w)
# Remove genes with no annotations
x = embeddings
y = annotations
del_rid = np.where(y.sum(axis=1) == 0)[0]
x = np.delete(x, del_rid, axis=0)
y = np.delete(y, del_rid, axis=0)
# Set up CV
performance_metrics = ('accuracy', 'm_AUPR', 'M_AUPR', 'f1')
performance_repeats = defaultdict(dict)
for repeat in range(1, repeats + 1):
performance_repeats[f'repeat_{repeat}'] = defaultdict(dict)
performance = performance_repeats[f'repeat_{repeat}']
trials = ShuffleSplit(n_splits=n_trials, test_size=0.2,
random_state=random_state)
iteration = 0
# CV-folds
for train_idx, test_idx in trials.split(x):
iteration += 1
x_train = x[train_idx]
x_test = x[test_idx]
y_train = y[train_idx]
y_test = y[test_idx]
# Define the MLP architecture
model = MLP(x_train, y_train)
model.compile('adam', 'binary_crossentropy', ['acc'])
# Train the model
callbacks = [EarlyStopping(min_delta=0., patience=20),
ModelCheckpoint('best_model.h5', save_best_only=True)]
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=200,
validation_split=0.2, shuffle=True,
callbacks=callbacks, verbose=2)
performance['history'][iteration] = {}
for tm in history.history:
performance['history'][iteration][tm] = history.history[tm]
# Read the best model from file (defined as the model which minimizes
# the validation loss.
model = load_model('best_model.h5')
# Predict annotations
y_score = model.predict(x_test)
y_pred = y_score.copy()
positive_threshold = .5
y_pred[y_pred < positive_threshold] = 0
y_pred[y_pred > 0] = 1
performance_trial = _Performance(y_test, y_score, y_pred)
for pm in performance_metrics:
performance[pm][iteration] = getattr(performance_trial, pm)
calculate_mean_std(performance, pm)
dummy = DummyClassifier().fit(x_train, y_train).score(x_test, y_test)
performance['dummy'][iteration] = dummy
performance['level'] = level
pprint(performance)
# Save results and training history
fout = f'{model_name}_{level}_{clf_type}'
with open(os.path.join(results_path, f'{fout}.json'), 'w') as f:
json.dump(performance_repeats, f)
# Delete the best model file
os.remove('best_model.h5')
return None
def test_string_object(self):
stdout(self.s, self.o, file=self.f)
assert self.f.getvalue() == 'string:\n object\n\n'
def test_string(self):
stdout(self.s, file=self.f)
assert self.f.getvalue() == 'string\n\n\n'