import talos as ta
from talos.metrics.keras_metrics import fmeasure

import pandas as pd

from keras.models import Sequential
from keras.layers import Dropout, Dense

# Keras items
from keras.optimizers import Adam, Nadam
from keras.activations import relu, elu
from keras.losses import binary_crossentropy
from keras.activations import relu, elu, sigmoid
from keras.losses import binary_crossentropy, mse
%matplotlib inline

def breast_cancer_model(x_train, y_train, x_val, y_val, params):

    model = Sequential()
    model.add(Dense(params['first_neuron'], input_dim=x_train.shape[1],
                    activation=params['activation'],
                    kernel_initializer=params['kernel_initializer']))

    model.add(Dropout(params['dropout']))

    model.add(Dense(1, activation=params['last_activation'],
                    kernel_initializer=params['kernel_initializer']))

    model.compile(loss=params['losses'],
                  optimizer=params['optimizer'](),
                  metrics=['acc', fmeasure])

    history = model.fit(x_train, y_train,
                        validation_data=[x_val, y_val],
                        batch_size=params['batch_size'],
                        epochs=params['epochs'],
                        verbose=1)

    return history, model

# then we load the dataset
x, y = ta.datasets.breast_cancer()

# then we can go ahead and set the parameter space
p = {'first_neuron':[9,10],
     'hidden_layers':[0],
     'batch_size': [30],
     'epochs': [100],
     'dropout': [0],
     'kernel_initializer': ['uniform'],
     'optimizer': [Nadam],
     'losses': [mse],
     'activation':[relu],
     'last_activation': [sigmoid]}


# and run the experiment
t = ta.Scan(x=x,
            y=y,
            model=breast_cancer_model,
            grid_downsample=1,
            search_method='linear',
            params=p,
            dataset_name='breast_cancer',
            experiment_no='k')