# Modules
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from keras import backend as K
import os


def set_keras_backend(backend):
    if K.backend() != backend:
        os.environ['KERAS_BACKEND'] = backend
        reload(K)
        assert K.backend() == backend


set_keras_backend("theano")
# seed
seed = 7
numpy.random.seed(seed)

# load dataset
dataFrame = pandas.read_csv("iris.csv", header=None)
dataset = dataFrame.values

X = dataset[:, 0:4].astype(float)
Y = dataset[:, 4]

# encode class values
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

dummy_Y = np_utils.to_categorical(encoded_Y)


# baseline model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(8, input_dim=4, kernel_initializer='normal', activation='softplus'))
    model.add(Dense(3, kernel_initializer='normal', activation='softmax'))
    # compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


estimator = KerasClassifier(build_fn=baseline_model, nb_epoch=200, batch_size=5, verbose=0)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)

results = cross_val_score(estimator, X, dummy_Y, cv=kfold)

print("Accuracy: %.2f%% (%.2f%%)" % (results.mean() * 100, results.std() * 100))