def evaluate_model_config_train_vs_test(
config,
training_allele_datasets,
testing_allele_datasets,
min_samples_per_allele=5):
binary_encoding = config.embedding_size == 0
print("=== Training Alleles ===")
for (allele_name, dataset) in sorted(training_allele_datasets.items()):
print("%s: count = %d" % (allele_name, len(dataset.Y)))
print("=== Testing Alleles ===")
for (allele_name, dataset) in sorted(testing_allele_datasets.items()):
print(" %s: count = %d" % (allele_name, len(dataset.Y)))
X_train_dict, Y_train_dict, ic50_train_dict = encode_allele_datasets(
allele_datasets=training_allele_datasets,
max_ic50=config.max_ic50,
binary_encoding=binary_encoding)
X_test_dict, Y_test_dict, ic50_test_dict = encode_allele_datasets(
allele_datasets=testing_allele_datasets,
max_ic50=config.max_ic50,
binary_encoding=binary_encoding)
X_train_combined = np.vstack(X_train_dict.values())
Y_train_combined = np.concatenate(list(Y_train_dict.values()))
model = make_model(config)
model.fit(
X_train_combined,
Y_train_combined,
nb_epoch=config.n_pretrain_epochs,
batch_size=config.minibatch_size,
verbose=1)
scores = ScoreCollection()
initial_weights = [w.copy() for w in model.get_weights()]
for allele_name, training_dataset in filter_alleles(
training_allele_datasets,
min_samples_per_allele=min_samples_per_allele):
if allele_name not in X_test_dict:
print("Skipping %s, missing from test datasets" % allele_name)
continue
X_test_allele = X_test_dict[allele_name]
ic50_test_allele = ic50_test_dict[allele_name]
true_log_ic50 = np.log(ic50_test_allele) / np.log(config.max_ic50)
true_log_ic50 = np.maximum(0, 1.0 - true_log_ic50)
true_label = ic50_test_allele <= 500
if true_label.all():
print("Skipping %s since all affinities are <= 500nM" % allele_name)
continue
elif not true_label.any():
print("Skipping %s since all affinities are > 500nM" % allele_name)
continue
model.set_weights(initial_weights)
model.fit(
training_dataset.X,
training_dataset.Y,
nb_epoch=config.n_epochs,
batch_size=config.minibatch_size,
verbose=0)
pred = model.predict(X_test_allele).flatten()
accuracy, auc, f1_score = score_predictions(
predicted_log_ic50=pred,
true_log_ic50=true_log_ic50,
max_ic50=config.max_ic50)
print("-- %s accuracy=%0.4f AUC = %0.4f F1 = %0.4f" % (
allele_name, accuracy, auc, f1_score))
scores.add(allele_name, auc=[auc], accuracy=[accuracy], f1=[f1_score])
return scores.dataframe()
def leave_out_allele_cross_validation(
model,
allele_datasets,
max_ic50,
binary_encoding=False,
n_pretrain_epochs=0,
n_training_epochs=100,
min_samples_per_allele=5,
cv_folds=5,
minibatch_size=128):
"""
Fit the model for every allele in the dataset and return a DataFrame
with the following columns:
allele_name
dataset_size
auc_mean
auc_median
auc_std
auc_min
auc_max
accuracy_mean
accuracy_median
accuracy_std
accuracy_min
accuracy_max
f1_mean
f1_median
f1_std
f1_min
f1_max
"""
scores = ScoreCollection()
X_dict, Y_log_ic50_dict, ic50_dict = encode_allele_datasets(
allele_datasets=allele_datasets,
max_ic50=max_ic50,
binary_encoding=binary_encoding)
initial_weights = [w.copy() for w in model.get_weights()]
for allele_name, dataset in filter_alleles(
allele_datasets, min_samples_per_allele=min_samples_per_allele):
model.set_weights(initial_weights)
X_allele = X_dict[allele_name]
Y_allele = Y_log_ic50_dict[allele_name]
ic50_allele = ic50_dict[allele_name]
if n_pretrain_epochs > 0:
X_other_alleles = np.vstack([
X
for (other_allele, X) in X_dict.items()
if normalize_allele_name(other_allele) != allele_name])
Y_other_alleles = np.concatenate([
y
for (other_allele, y)
in Y_log_ic50_dict.items()
if normalize_allele_name(other_allele) != allele_name])
print("Pre-training X shape: %s" % (X_other_alleles.shape,))
print("Pre-training Y shape: %s" % (Y_other_alleles.shape,))
model.fit(
X_other_alleles,
Y_other_alleles,
nb_epoch=n_pretrain_epochs,
batch_size=minibatch_size,
verbose=0)
print("Cross-validation for %s (%d):" % (allele_name, len(Y_allele)))
aucs, accuracies, f1_scores = kfold_cross_validation_for_single_allele(
allele_name=allele_name,
model=model,
X=X_allele,
Y=Y_allele,
ic50=ic50_allele,
n_training_epochs=n_training_epochs,
cv_folds=cv_folds,
max_ic50=max_ic50,
minibatch_size=minibatch_size)
if len(aucs) == 0:
print("Skipping allele %s" % allele_name)
continue
scores.add(allele_name, auc=aucs, accuracy=accuracies, f1=f1_scores)
return scores.dataframe()
