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Run_hyperas.py
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Run_hyperas.py
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from __future__ import print_function
import numpy as np
from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utils
from hyperas import optim
from hyperas.distributions import choice, uniform
from hyperas.utils import eval_hyperopt_space
from keras.callbacks import TensorBoard
import globalvars
import cwt
# class_weights = {0: 1,
# 1: 3}
#
"""
Created on Sat Feb 16 20:15:16 2019
@author: smk5g5
#Using Hyperas for hyperparameter optimization###
"""
import os
import pickle
gpu_id = '0'
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
os.system('echo $CUDA_VISIBLE_DEVICES')
def data():
"""
Data providing function:
This function is separated from create_model() so that hyperopt
won't reload data for each evaluation run.
"""
from sklearn.utils import shuffle
import numpy as np
import pickle
import pandas as pd
from keras.layers import Input
file1 = open("./imbalanced_testset/trainx1_0", 'rb') #sec str one hot matrix
trainX1 = pickle.load(file1)
file1 = open("./imbalanced_testset/trainx2_0", 'rb') #seq one hot matrix
trainX2 = pickle.load(file1)
file1 = open("./imbalanced_testset/trainy_0", 'rb') #labels
trainY1 = pickle.load(file1)
file1 = open("./imbalanced_testset/valx1_0", 'rb')
valX1 = pickle.load(file1)
file1 = open("./imbalanced_testset/valx2_0", 'rb')
valX2 = pickle.load(file1)
file1 = open("./imbalanced_testset/valy_0", 'rb')
valY1 = pickle.load(file1)
file1 = open("./imbalanced_testset/testx1_0", 'rb')
testX1 = pickle.load(file1)
file1 = open("./imbalanced_testset/testx2_0", 'rb')
testX2 = pickle.load(file1)
file1 = open("./imbalanced_testset/testy_0", 'rb')
testY = pickle.load(file1)
trainX1, trainX2, trainY1 = shuffle(trainX1, trainX2, trainY1)
valX1,valX2, valY1 = shuffle(valX1,valX2, valY1)
testX1,testX2,testY = shuffle(testX1,testX2,testY)
return trainX1, trainX2, trainY1, valX1,valX2, valY1,testX1,testX2,testY
##def step_decay(epoch):
## initial_lrate = 0.1
## drop=0.5
## epochs_drop = 10.0
## lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))
## return lrate
def f1_score(true_class,pred_score):
from sklearn.metrics import precision_score, recall_score, f1_score
for index in range(len(pred_score)):
if pred_score[index] > 0.5:
pred_score[index] = 1
else:
pred_score[index] = 0
myf1 = f1_score(y_true=true_class, y_pred=pred_score)
return myf1
def MCNN(trainX1,trainX2,trainY1,valX1,valX2,valY1,testX1,testX2,testY):
import pickle
from sklearn.metrics import precision_score, recall_score, f1_score
import pandas as pd
#import pirna_kmer as pk
from pandas import DataFrame
from sklearn.model_selection import train_test_split
import numpy as np
#import phy_net as pn
from keras.layers import Input
import keras.utils.np_utils as kutils
#import threading
import time
from keras.utils import np_utils
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization, Activation, Flatten
from keras.optimizers import Adam
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import load_model
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.utils.class_weight import compute_class_weight
from keras.layers import Convolution2D as Conv2D
from keras.layers import MaxPooling2D
from keras.callbacks import EarlyStopping
import json
#from sklearn.metrics import matthews_corrcoef
from keras.models import Model
import tensorflow as tf
## from tensorflow.keras.callbacks import TensorBoard
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, roc_curve, auc, average_precision_score, matthews_corrcoef
from sklearn.metrics import precision_score, recall_score, f1_score
#from sklearn.metrics import accuracy_score, recall_score
remark = '' # The mark written in the result file.
import time
import numpy as np
import matplotlib
import pickle
matplotlib.use('Agg')
import keras.layers.core as core
import keras.layers.convolutional as conv
import keras.models as models
from keras.models import Model
from keras.layers.merge import concatenate
from keras.callbacks import EarlyStopping, ModelCheckpoint, Callback, LearningRateScheduler, History
from keras.layers import Dense, Dropout, Activation, Flatten, Input
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l1, l2, l1_l2
import keras.metrics
import matplotlib.pyplot as plt
from keras.optimizers import Nadam,Adam,RMSprop,SGD
from sklearn.metrics import precision_recall_curve, roc_curve, auc, average_precision_score, matthews_corrcoef
import os
from sklearn import svm
from sklearn.manifold import TSNE
from matplotlib import offsetbox
from sklearn.metrics import accuracy_score, recall_score
import random
## from tensorflow.keras.callbacks import TensorBoard
row1,col1 = trainX1[0].shape
input_1 = Input(shape=(row1,col1))
row2,col2 = trainX2[0].shape
input_2 = Input(shape=(row2,col2))
NAME = "combined_secstr_seq_CNN_model_emboss-{}".format(int(time.time()))
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
onehot_secstr = conv.Conv1D(5, 10, kernel_initializer='glorot_normal',kernel_regularizer=l2({{uniform(0.0001, 0.1)}}), padding='valid', name='0_secstr')(input_1)
onehot_secstr = Dropout({{uniform(0, 1)}})(onehot_secstr)
onehot_secstr = keras.layers.advanced_activations.PReLU(alpha_initializer='zeros', alpha_regularizer=None,alpha_constraint=None, shared_axes=None)(onehot_secstr)
onehot_secstr = core.Flatten()(onehot_secstr)
onehot_secstr2 = conv.Conv1D(9, 4, kernel_initializer='glorot_normal',kernel_regularizer=l2({{uniform(0.0001, 0.1)}}), padding='valid', name='1_secstr')(input_1)
onehot_secstr2 = Dropout({{uniform(0, 1)}})(onehot_secstr2)
onehot_secstr2 = keras.layers.advanced_activations.PReLU(alpha_initializer='zeros', alpha_regularizer=None,alpha_constraint=None, shared_axes=None)(onehot_secstr2)
onehot_secstr2 = core.Flatten()(onehot_secstr2)
output_onehot_sec = concatenate([onehot_secstr, onehot_secstr2], axis=-1)
onehot_x = conv.Conv1D(5, 10, kernel_initializer='glorot_normal',kernel_regularizer=l2({{uniform(0.0001, 0.1)}}), padding='valid', name='0')(input_2)
onehot_x = Dropout({{uniform(0, 1)}})(onehot_x)
onehot_x = keras.layers.advanced_activations.PReLU(alpha_initializer='zeros', alpha_regularizer=None,alpha_constraint=None, shared_axes=None)(onehot_x)
onehot_x = core.Flatten()(onehot_x)
onehot_x2 = conv.Conv1D(9, 4, kernel_initializer='glorot_normal',kernel_regularizer=l2({{uniform(0.0001, 0.1)}}), padding='valid', name='1')(input_2)
onehot_x2 = Dropout({{uniform(0, 1)}})(onehot_x2)
onehot_x2 = keras.layers.advanced_activations.PReLU(alpha_initializer='zeros', alpha_regularizer=None,alpha_constraint=None, shared_axes=None)(onehot_x2)
onehot_x2 = core.Flatten()(onehot_x2)
output_onehot_seq = concatenate([onehot_x, onehot_x2], axis=-1)
final_output = concatenate([output_onehot_sec, output_onehot_seq])
dense_out = Dense({{choice([20,30,50,60,64,70,80,90,100, 128, 256, 512, 1024])}}, kernel_initializer='glorot_normal', activation='softplus', name='dense_concat')(final_output)
out = Dense(2, activation="softmax", kernel_initializer='glorot_normal', name='6')(dense_out)
########## Set Net ##########
cnn = Model(inputs=[input_1,input_2], outputs=out)
cnn.summary()
adam = Adam(lr={{uniform(0.0001, 0.1)}})
nadam = Nadam(lr={{uniform(0.0001, 0.1)}})
rmsprop = RMSprop(lr={{uniform(0.0001, 0.1)}})
sgd = SGD(lr={{uniform(0.0001, 0.1)}})
choiceval = {{choice(['adam', 'sgd', 'rmsprop','nadam'])}}
if choiceval == 'adam':
optim = adam
elif choiceval == 'rmsprop':
optim = rmsprop
elif choiceval=='nadam':
optim = nadam
else:
optim = sgd
globalvars.globalVar += 1
#early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=20, verbose=1, mode='auto')
cnn.compile(loss='binary_crossentropy', optimizer=optim, metrics=[keras.metrics.binary_accuracy]) # Nadam
early_stopping = EarlyStopping(monitor='val_loss', patience=20)
checkpointer = ModelCheckpoint(filepath='%d-secstr_seq_denseconcat.h5' % globalvars.globalVar, verbose=1,save_best_only=True, monitor='val_loss', mode='min')
fitHistory = cnn.fit([trainX1,trainX2], trainY1, batch_size={{choice([32,64,128,256,512])}}, nb_epoch=500,validation_data=([valX1,valX2], valY1),callbacks=[checkpointer,early_stopping,tensorboard],class_weight=cwt.class_weights)
myjson_file = "myhist_" +"_dict" + "_hyperas_model_trial_" +str(globalvars.globalVar)
json.dump(fitHistory.history, open(myjson_file, 'w'))
score, acc = cnn.evaluate([valX1, valX2], valY1, batch_size=32)
pred_proba = cnn.predict([valX1,valX2], batch_size=32)
pred_score = pred_proba[:, 1]
true_class = valY1[:, 1]
f1_sc = f1_score(true_class,pred_score)
print('F1 score:', f1_sc)
print('Test score:', score)
print('accuracy:', acc)
return {'loss': -f1_sc, 'status': STATUS_OK, 'model': cnn}
if __name__ == '__main__':
trials=Trials()
best_run, best_model,space = optim.minimize(model=MCNN,
data=data,
functions=[f1_score],
algo=tpe.suggest,
max_evals=100,
trials=trials,
eval_space=True,
return_space=True)
trainX1,trainX2,trainY1,valX1,valX2,valY1,testX1,testX2,testY = data()
print("Evalutation of best performing model:")
print(best_model.evaluate([valX1, valX2], valY1, verbose=0))
print("Best performing model chosen hyper-parameters:")
print(best_run)
best_model.save('best_hyperas_model_secstr.h5')
hyperas_dict = dict()
for t, trial in enumerate(trials):
vals = trial.get('misc').get('vals')
hyperas_dict[t] = vals
print("Trial %s vals: %s" % (t, vals)) # <-- indices
print(eval_hyperopt_space(space, vals)) # <-- values
##from hyperas.utils import eval_hyperopt_space
##print(eval_hyperopt_space(space, vals))
pickle.dump(hyperas_dict, open( "./hyperas_dict_100trials", "wb" ))