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

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

# ## How to recover best model from experiment log?
# Due to system error or other reason where scan_object is no longer available, it's still possible to get best model/s using nothing but the experiment log. In the below notebook you will learn exactly how.
# 
# 

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


# First we'll have to perform the `Scan()` experiment to produce the experiment log. Because the experiment log is stored on local machine, interrupted `Scan()` or other reason will not affect its availability. The experiment log is updated after each permutation; it contains an up-to-date record of the experiment.

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# load the data
x, y = talos.templates.datasets.iris()
x_train, y_train, x_val, y_val = wrangle.array_split(x, y, .3)

# set the parameter space boundary
p = {'activation':['relu', 'elu'],
     'optimizer': ['Nadam', 'Adam'],
     'losses': ['logcosh'],
     'shapes': ['brick'],
     'first_neuron': [16, 32, 64, 128],
     'hidden_layers':[0, 1, 2, 3],
     'dropout': [.2, .3, .4],
     'batch_size': [20, 30, 40, 50],
     'epochs': [10]}

# define the input model
def iris_model(x_train, y_train, x_val, y_val, params):

    model = Sequential()
    model.add(Dense(params['first_neuron'], input_dim=4, activation=params['activation']))

    talos.utils.hidden_layers(model, params, 3)

    model.add(Dense(3, activation='softmax'))
    model.compile(optimizer=params['optimizer'], loss=params['losses'], metrics=['acc'])

    out = model.fit(x_train, y_train, callbacks=[talos.utils.ExperimentLogCallback('minimal_iris', params)],
                     batch_size=params['batch_size'],
                     epochs=params['epochs'],
                     validation_data=[x_val, y_val],
                     verbose=0)

    return out, model

# start the experiment
scan_object = talos.Scan(x=x_train,
                         y=y_train,
                         x_val=x_val,
                         y_val=y_val,
                         model=iris_model,
                         experiment_name='minimal_iris',
                         params=p,
                         round_limit=10)


# Now we can assume the case where we no longer have access to the `scan_object`. In this `Scan(...experiment_name...)` was set to "reactivate" so we'll find a folder with that name in the present working directory. Next we have to find out what is the name of the experiment log.

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# get the name of the experiment log
get_ipython().system('ls -lhtr minimal_iris')


# What you want to do, is get the name of the `.csv` file you want to use, and use it as part of the input for `experiment_log` in the next step.

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from talos.utils.recover_best_model import recover_best_model

results, models = recover_best_model(x_train=x_train,
                                     y_train=y_train,
                                     x_val=x_val,
                                     y_val=y_val,
                                     experiment_log='minimal_iris/012620102735.csv',
                                     input_model=iris_model,
                                     n_models=5,
                                     task='multi_label')


# Now we can access the cross-validation results:

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results


# We can also access the models and make predictions with them:

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models[0].predict(x_val)