def wnd_test(X, y):
auc = []
pr = []
sr = []
# X_val2 = X.tolist()
# for i in range(0, len(X_val2)):
# X_val2[i] = X_val2[i][0:3967]
# X = np.array(X_val2)
for i in range(1, 11):
model = keras.models.load_model(
'../saved_models/WnD/WnD' + str(i) + '.h5',
custom_objects={
'masked_loss_function': masked_loss_function,
'masked_accuracy': masked_accuracy
})
score_for_each_drug = ROC_PR.ROC(model,
X,
y, ("wide-n-deep" + "BO_delete"),
True,
bccdc=True)
spec_recall, prec_recall = ROC_PR.PR(model, X, y, bccdc=True)
# print('AUC-ROC:', score_for_each_drug)
# print("recall at 95 spec: ", spec_recall)
# print("precision recall: ", prec_recall)
auc.append(score_for_each_drug)
pr.append(prec_recall)
sr.append(spec_recall)
print(auc)
print(pr)
print(sr)
def run_one_fold(model):
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(
X_train,
y_train,
epochs=epochs,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_val, y_val),
callbacks=[MyCustomCallback()])
score = ROC_PR.ROC_Score(model, X_val, y_val)
score_test = ROC_PR.ROC_Score(model, X_test, y_test)
score_for_each_drug = ROC_PR.ROC(model, X_test, y_test,
("wide-n-deep" + "BO_delete"), True)
spec_recall, prec_recall = ROC_PR.PR(model, X_test, y_test)
print('area under ROC curve for val:', score)
print('area under ROC curve for test:', score_test)
print(score_for_each_drug)
print("recall at 95 spec: ", spec_recall)
print("precision recall: ", prec_recall)
string_random = get_random_string(17)
print(string_random)
model.save('wnd_' + string_random + '.h5')
return score
def lrcn_test(X, y):
auc = []
pr = []
sr = []
for i in range(0, 10):
model = keras.models.load_model(
'../saved_models/LRCN/lrcn_' + str(i) + '.h5',
custom_objects={
'masked_loss_function': masked_loss_function,
'masked_accuracy': masked_accuracy
})
score_for_each_drug = ROC_PR.ROC(model,
X,
y, ("LRCN" + "BO_delete"),
True,
bccdc=True)
spec_recall, prec_recall = ROC_PR.PR(model, X, y, bccdc=True)
# print('AUC-ROC:', score_for_each_drug)
# print("recall at 95 spec: ", spec_recall)
# print("precision recall: ", prec_recall)
auc.append(score_for_each_drug)
pr.append(prec_recall)
sr.append(spec_recall)
print(auc)
print(pr)
print(sr)
def run_ELI5(model, X_train, X_test, X_val, y_train, y_test, y_val):
X_train2 = np.array(X_train).astype(np.float)
X_test2 = np.array(X_test).astype(np.float)
X_val2 = np.array(X_val).astype(np.float)
y_train2 = np.array(y_train).astype(np.float)
y_test2 = np.array(y_test).astype(np.float)
y_val2 = np.array(y_val).astype(np.float)
score = ROC_PR.ROC_Score(model, X_val2, y_val2)
score_test = ROC_PR.ROC_Score(model, X_test2, y_test2)
# score_for_each_drug = ROC_PR.ROC(model, X_test2, y_test2, ("LRCN" + "BO_delete"), True)
spec_recall, prec_recall = ROC_PR.PR(model, X_test2, y_test2)
print('area under ROC curve for val:', score)
print('area under ROC curve for test:', score_test)
print("recall at 95 spec: ", spec_recall)
print("precision recall: ", prec_recall)
def score(X_test, y_test):
return ROC_PR.ROC_Score(model, X_test, y_test)
from eli5.permutation_importance import get_score_importances
feature_score = []
for i in range(0, len(X_test2[0])):
lst = []
lst.append(i)
base_score, score_decreases = get_score_importances(
score, X_test2, y_test2, n_iter=1, columns_to_shuffle=lst)
feature_importances = np.mean(score_decreases, axis=0)
feature_score.append(feature_importances[0])
print(i)
print(feature_score)
def run_one_fold(model):
#
# X_train2 = tf.cast(X_train, tf.float32)
# X_test2 = tf.cast(X_test, tf.float32)
# X_val2 = tf.cast(X_val, tf.float32)
# y_train2 = tf.cast(y_train, tf.float32)
# y_test2 = tf.cast(y_test, tf.float32)
# y_val2 = tf.cast(y_val, tf.float32)
# X_train2 = np.array(X_train)
# X_train2 = tf.convert_to_tensor(X_train2,dtype=tf.float32)
# X_train2 = tf.cast(X_train2, tf.float32)
# y_train2 = np.array(y_train)
# y_train2 = tf.convert_to_tensor(y_train2, dtype=tf.float32)
# y_train2 = np.array(y_train)
# y_train2 = tf.cast(y_train2, tf.float32)
# print(X_train.)
# Train the model for a specified number of epochs.
X_train2 = np.array(X_train).astype(np.float)
X_test2 = np.array(X_test).astype(np.float)
X_val2 = np.array(X_val).astype(np.float)
y_train2 = np.array(y_train).astype(np.float)
y_test2 = np.array(y_test).astype(np.float)
y_val2 = np.array(y_val).astype(np.float)
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
# Train the model with the train dataset.
history = model.fit(
X_train2,
y_train2,
epochs=epochs,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_val2, y_val2))
# Evaluate the model with the eval dataset.
# score = model.evaluate(X_test, y_test, steps=10, verbose=0)
# print('Test loss:', score[0])
# print('Test accuracy:', score[1])
# Return the accuracy.
# print(history.history['val_masked_accuracy'])
score = ROC_PR.ROC_Score(model, X_val2, y_val2)
score_test = ROC_PR.ROC_Score(model, X_test2, y_test2)
score_for_each_drug = ROC_PR.ROC(model, X_test2, y_test2,
("LRCN" + "BO_delete"), True)
spec_recall, prec_recall = ROC_PR.PR(model, X_test2, y_test2)
print('area under ROC curve for val:', score)
print('area under ROC curve for test:', score_test)
print(score_for_each_drug)
print("recall at 95 spec: ", spec_recall)
print("precision recall: ", prec_recall)
global scores
global fold_num
global comp
if len(scores) == 0:
# string_random = get_random_string(17)
# print(string_random)
print(scores)
print(score)
model.save('LRCN' + str(comp) + '_' + str(fold_num) + '.h5')
scores.append(score)
else:
br = 0
for iter in range(0, len(scores)):
if scores[iter] > score:
print(scores)
print(score)
br = 1
break
if br == 0:
print(br)
print(scores)
print(score)
model.save('LRCN' + str(comp) + '_' + str(fold_num) + '.h5')
scores.append(score)
# from lime import lime_tabular
# ins = lime_tabular.LimeTabularExplainer
# explainer = lime_tabular.LimeTabularExplainer.explain_instance(self=ins ,data_row=X_train, predict_fn=model, num_samples=6354)
# explainer = lime_tabular.LimeTabularExplainer.explain_instance
# import lime
# import lime.lime_tabular
# import pandas as pd
# explainer = lime.lime_tabular.LimeTabularExplainer(X_train)
# print(len(X_train))
# exp = explainer.explain_instance(len(X_train), model.predict, num_features=len(X_train[0]))
#
# exp.show_in_notebook(show_table=True)
#
# exp.as_list()
# shap.initjs()
#
# explainer = shap.DeepExplainer(model, X_train2[:100])
# shap_values = explainer.shap_values(X_test2[:10])
# shap.summary_plot(shap_values, X_test2, plot_type='bar')
# worked
# TODO this block worked
# def score(X_test, y_test):
# return ROC_PR.ROC_Score(model, X_test, y_test)
#
# from eli5.permutation_importance import get_score_importances
#
# feature_score = []
#
# for i in range(0, len(X_test2[0])):
# lst = []
# lst.append(i)
# base_score, score_decreases = get_score_importances(score, X_test2, y_test2, n_iter=1, columns_to_shuffle=lst)
# feature_importances = np.mean(score_decreases, axis=0)
# feature_score.append(feature_importances[0])
# print(feature_score)
#
# print(feature_score)
# model.save('model_save.h5')
#
# import deeplift
# from deeplift.conversion import kerasapi_conversion as kc
#
# import keras
# # print(keras.__version__)
# # deeplift_model = kc.convert_sequential_model(model)
# deeplift_model = \
# kc.convert_model_from_saved_files(
# 'model_save.h5',
# nonlinear_mxts_mode=deeplift.layers.NonlinearMxtsMode.DeepLIFT_GenomicsDefault)
#
# find_scores_layer_idx = 0
#
# deeplift_contribs_func = deeplift_model.get_target_contribs_func(
# find_scores_layer_idx=find_scores_layer_idx,
# target_layer_idx=-1)
#
# scores = np.array(deeplift_contribs_func(task_idx=0,
# input_data_list=[X],
# batch_size=10,
# progress_update=1000))
# print(scores)
return score