#!/usr/bin/env python
# coding: utf-8

# <img src='https://raw.githubusercontent.com/autonomio/hyperio/master/logo.png' width=250px>

# # Predicting Diabetes with Keras

# ### A Very Short Introduction to Hyperparameter Optimization with Talos

# The goal of a Talos experiment, is to find a set of suitable hyperparameters for Keras model. In order to do this, you need to have three things: 
# 
# - Keras model 
# - Talos hyperparameter dictionary
# - Talos experiment configuration
# 
# Below we will briefly overview each.

# ### The Keras Model
# 
# As a model, any Keras model will do. Let's consider as an example a very simple model that makes a prediction on the classic *Pima Indians Diabetes* dataset. A brief overview of the dataset can be found [here](https://www.kaggle.com/uciml/pima-indians-diabetes-database) and the dataset we will use can be found [here](https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv). The below model does not require you to separately download the file.

# In[ ]:


from numpy import loadtxt

dataset = loadtxt("https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv", delimiter=",")
x = dataset[:,0:8]
y = dataset[:, 8]


# In[ ]:


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

def diabetes():
    
    model = Sequential()
    model.add(Dense(12, input_dim=8, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.fit(X, Y, epochs=100, batch_size=10, verbose=0)
    
    return model


# ### Talos Hyperparameter Dictionary

# Let's prepare for an experiment where we will optimize against three common attributes:
# 
# - neurons on the first layer
# - activations
# - batch_sizes

# In[ ]:


from tensorflow.keras.activations import relu, elu

p = {
    'first_neuron': [12, 24, 48],
    'activation': ['relu', 'elu'],
    'batch_size': [10, 20, 30]
}


# ### Configuring the Keras Model for Talos

# In order to prepare a Keras model for a Talos experiment, we need to do four things:
# 
# - add input parameters to the function
# - replace the hyperparameter inputs with references to params dictionary
# - make sure model.fit() stores the history object
# - modify the output of the model
# 
# These steps are always the same.

# In[ ]:


# add input parameters to the function
def diabetes(x_train, y_train, x_val, y_val, params):
    
    # replace the hyperparameter inputs with references to params dictionary 
    model = Sequential()
    model.add(Dense(params['first_neuron'], input_dim=8, activation=params['activation']))
    #model.add(Dense(8, activation=params['activation']))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    
    # make sure history object is returned by model.fit()
    out = model.fit(x=x, 
                    y=y,
                    validation_data=[x_val, y_val],
                    epochs=100,
                    batch_size=params['batch_size'],
                    verbose=0)
    
    # modify the output model
    return out, model


# That's it, there is nothing more to it. A more complicated experiment would just entail more of the same in terms of the way the params dictionary references are made. Otherwise the changes would always be exactly the same.

# ### Talos Experiment

# The Talos experiment is performed through the Scan() command. In case you don't have Talos installed already, you can do that now.

# In[ ]:


import talos


# While many configurations are possible, the only things that you absolutely must input to a Talos experiment are: 
# 
# - x
# - y
# - params (the dictionary 'p' we created above)
# - model (the 'diabetes' we created above)

# In[ ]:


t = talos.Scan(x=x, y=y, params=p, model=diabetes, experiment_name='diabetes')