optimization.py

from __future__ import print_function
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

import numpy as np
import pandas as pd
import csv
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import ShuffleSplit
from sklearn.preprocessing import label_binarize
from sklearn.metrics import roc_curve, auc
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import classification_report
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn import svm
import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt
import scikitplot as skplt
from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.model_selection import KFold
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import matplotlib.patches as mpatches
from sklearn import preprocessing

def data():
    """
    Data providing function:

    This function is separated from create_model() so that hyperopt
    won't reload data for each evaluation run.
    """
    ALLdata = pd.read_csv('/Users/althubsw/Desktop/cancer-classification/newdatacmpo.txt', header=None, skiprows=0,
                          sep='\t')
    ALLdata.columns = ['gene', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16',
                       '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32',
                       '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48',
                       '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64',
                       '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80',
                       '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96',
                       '97', '98', '99', '100', 'label']
    print(ALLdata.shape)

    data = ALLdata[
        ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20',
         '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38',
         '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56',
         '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74',
         '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92',
         '93', '94', '95', '96', '97', '98', '99', '100']]
    targetdata = ALLdata['label'].values.ravel()
    # targetdata = label_binarize(targetdata, classes=['AA','AML','BLCA','BRCA','CLL','CM','COREAD','ESCA','GBM','HC','HNSC','LGG','LUAD','LUSC','OV','PRAD','RCCC','SCLC','STAD','THCA','UCEC'])
    # data = np.array(data.values)

    x_train, x_test, y_train, y_test = train_test_split(data, targetdata, test_size=0.2, random_state=0)
    return x_train, y_train, x_test, y_test




def create_model(x_train, y_train, x_test, y_test):
    """
    Model providing function:

    Create Keras model with double curly brackets dropped-in as needed.
    Return value has to be a valid python dictionary with two customary keys:
        - loss: Specify a numeric evaluation metric to be minimized
        - status: Just use STATUS_OK and see hyperopt documentation if not feasible
    The last one is optional, though recommended, namely:
        - model: specify the model just created so that we can later use it again.
    """
    model = Sequential()
    model.add(Dense(512, input_shape=(100,)))
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0, 1)}}))
    model.add(Dense({{choice([256, 512, 1024])}}))
    model.add(Activation({{choice(['relu', 'sigmoid'])}}))
    model.add(Dropout({{uniform(0, 1)}}))

    # If we choose 'four', add an additional fourth layer
    if {{choice(['three', 'four'])}} == 'four':
        model.add(Dense(100))

        # We can also choose between complete sets of layers

        model.add({{choice([Dropout(0.5), Activation('linear')])}})
        model.add(Activation('relu'))

    model.add(Dense(21))
    model.add(Activation('softmax'))

    model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'],
                  optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})

    model.fit(x_train, y_train,
              batch_size={{choice([64, 128])}},
              epochs=1,
              verbose=2,
              validation_data=(x_test, y_test))
    score, acc = model.evaluate(x_test, y_test, verbose=0)
    print('Test accuracy:', acc)
    return {'loss': mse, 'status': STATUS_OK, 'model': model}

if __name__ == '__main__':
    best_run, best_model = optim.minimize(model=create_model,
                                          data=data,
                                          algo=tpe.suggest,
                                          max_evals=5,
                                          trials=Trials())
    X_train, Y_train, X_test, Y_test = data()
    print("Evalutation of best performing model:")
    print(best_model.evaluate(X_test, Y_test))
    print("Best performing model chosen hyper-parameters:")
    print(best_run)