import numpy as np
from keras.utils import np_utils

import dataset
import models


def main():
    # parameter
    innerCNNFilters = 512
    innerCNNKernelSize = 2
    cnnDropout = 0.5
    cnnHiddenDims = 1024
    domainFeatures = 512
    flowFeatures = 3
    numCiscoFeatures = 30
    windowSize = 10
    maxLen = 40
    embeddingSize = 100
    kernel_size = 2
    drop_out = 0.5
    filters = 2
    hidden_dims = 100
    vocabSize = 40
    threshold = 3
    minFlowsPerUser = 10
    numEpochs = 100
    timesNeg = -1

    char_dict = dataset.get_character_dict()
    user_flow_df = dataset.get_user_flow_data()

    print("create training dataset")
    (X_tr, y_tr, hits_tr, names_tr) = dataset.create_dataset_from_flows(
        user_flow_df, char_dict,
        maxLen=maxLen, threshold=threshold, windowSize=windowSize)

    pos_idx = np.where(y_tr == 1.0)[0]
    neg_idx = np.where(y_tr == 0.0)[0]

    use_idx = np.concatenate((pos_idx, neg_idx))

    y_tr = y_tr[use_idx]
    # hits_tr = hits_tr[use_idx]
    # names_tr = names_tr[use_idx]
    for i in range(len(X_tr)):
        X_tr[i] = X_tr[i][use_idx]

    # TODO: WTF? I don't get it...
    shared_cnn = models.get_shared_cnn(len(char_dict) + 1, embeddingSize, maxLen,
                                       domainFeatures, kernel_size, domainFeatures, 0.5)

    model = models.get_top_cnn(shared_cnn, flowFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size,
                               cnnHiddenDims, cnnDropout)

    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

    epochNumber = 0
    y_tr = np_utils.to_categorical(y_tr, 2)
    model.fit(x=X_tr, y=y_tr, batch_size=128,
              epochs=epochNumber + 1, shuffle=True, initial_epoch=epochNumber)  # ,
    # validation_data=(testData,testLabel))


if __name__ == "__main__":
    main()