from scipy.stats.stats import pearsonr

import pandas as pd

import numpy as np

from keras import backend as K

from keras.models import Sequential

from keras.layers import Dense, Activation

from keras.layers import Dropout

from keras import regularizers

from keras.activations import relu, elu, linear, softmax, tanh, softplus

from keras.callbacks import EarlyStopping, Callback

from keras.wrappers.scikit_learn import KerasRegressor

from keras.optimizers import Adam, Nadam, sgd,Adadelta, RMSprop

from keras.losses import mean_squared_error, categorical_crossentropy, logcosh

from keras.utils.np_utils import to_categorical

from keras import metrics

from keras import regularizers

#keras Load Model

from keras.models import load_model

import talos as ta

import wrangle as wr

from talos.metrics.keras_metrics import fmeasure_acc

from talos.model.layers import hidden_layers

from talos import live

from talos.model import lr_normalizer, early_stopper, hidden_layers

import os

from CNN.cnn import acc_pearson_r  as acc_pearson_r

def mlp_main(x, y, x_val, y_val, params):

    model_mlp = Sequential()

    nSNP = x.shape[1]

    try:

        out_c= y.shape[1]

    except IndexError:

        out_c=1

    model_mlp.add(Dense(params['first_neuron'], input_dim=nSNP,

                        activation=params['activation'],

                        kernel_initializer='normal', kernel_regularizer=regularizers.l2(params['reg1'])))

    model_mlp.add(Dropout(params['dropout_1']))

    if (params['hidden_layers'] != 0):

        # if we want to also test for number of layers and shapes, that's possible

        for _ in range(params['hidden_layers']):

            model_mlp.add(Dense(params['hidden_neurons'], activation=params['activation'],

                                kernel_regularizer=regularizers.l2(params['reg1'])))

            # hidden_layers(model, params, 1)

            model_mlp.add(Dropout(params['dropout_2']))

    model_mlp.add(Dense(out_c,activation=params['last_activation'],

    kernel_regularizer=regularizers.l2(params['reg2'])))

    if params['optimizer']=='Adam':

        params['optimizer']= Adam

    if params['optimizer']=='Nadam':

        params['optimizer']= Nadam

    if params['optimizer']=='sgd':

        params['optimizer']= sgd

    model_mlp.compile(loss=mean_squared_error,

                     optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])),

                     metrics=[acc_pearson_r])

    #es = EarlyStopping(monitor=mean_squared_error, mode='min', verbose=1)

    # callbacks=[live()] see the output

    # callbacks= es to EarlyStopping

    out_mlp = model_mlp.fit(x, y, validation_split=0.2,

                            verbose=0, batch_size=params['batch_size'],

                            epochs=params['epochs'])

    return out_mlp, model_mlp

def mlp_main_cat(x, y, x_val, y_val, params):

    model_mlp = Sequential()

    nSNP = x.shape[1]

    last_layer= y.shape[1]

    model_mlp.add(Dense(params['first_neuron'], input_dim=nSNP,

                        activation=params['activation'],

                        kernel_initializer='normal', activity_regularizer=regularizers.l1(params['reg1'])))

    model_mlp.add(Dropout(params['dropout_1']))

    if (params['hidden_layers'] != 0):

        # if we want to also test for number of layers and shapes, that's possible

        for _ in range(params['hidden_layers']):

            model_mlp.add(Dense(params['hidden_neurons'], activation=params['activation'],

                                activity_regularizer=regularizers.l2(params['reg1'])))

            # hidden_layers(model, params, 1)

            model_mlp.add(Dropout(params['dropout_2']))

    model_mlp.add(Dense(last_layer,activation='softmax'))

    if params['optimizer']=='Adam':

        params['optimizer']= Adam

    if params['optimizer']=='Nadam':

        params['optimizer']= Nadam

    if params['optimizer']=='sgd':

        params['optimizer']= sgd

    model_mlp.compile(loss='categorical_crossentropy',optimizer=params['optimizer'](lr=lr_normalizer(params['lr'],

    params['optimizer'])), metrics=['accuracy'])

    #acc or mean_squared_error in metrics

    # simple early stopping

    # if you monitor is an accuracy parameter (pearson here, you should chose mode="max"), otherwise it would be "min"

    #es = EarlyStopping(monitor=mean_squared_error, mode='min', verbose=1)

    # callbacks=[live()] see the output

    # callbacks= es to EarlyStopping

    out_mlp = model_mlp.fit(x, y, validation_split=0.2,

                            verbose=0, batch_size=params['batch_size'],

                            epochs=params['epochs'])

    return out_mlp, model_mlp