deploy.py

import os
import pandas as pd
import shutil
import numpy as np

from ..utils.best_model import best_model, activate_model

# NOTE: this has some overlap with code in evaluate.py
# and needs to be cleaned up.
# TODO: needs to also deploy the hyperparameter configuration
# and some kind of summary of the experiment and then finally
# pack everything into a zip file


class Deploy:

    '''Functionality for deploying a model to a filename'''

    def __init__(self, scan_object, model_name, metric='val_acc', asc=False):

        self.scan_object = scan_object
        os.mkdir(model_name)
        self.path = model_name + '/' + model_name
        self.model_name = model_name
        self.metric = metric
        self.asc = asc
        self.data = scan_object.data
        self.best_model = best_model(scan_object, metric, asc)
        self.model = activate_model(scan_object, self.best_model)

        # runtime
        self.save_model_as()
        self.save_details()
        self.save_data()
        self.save_results()
        self.save_params()
        self.save_readme()
        self.package()

    def save_model_as(self):

        '''Model Saver
        WHAT: Saves a trained model so it can be loaded later
        for predictions by predictor().
        '''

        model_json = self.model.to_json()
        with open(self.path + "_model.json", "w") as json_file:
            json_file.write(model_json)

        self.model.save_weights(self.path + "_model.h5")
        print("Deploy package" + " " + self.model_name + " " + "have been saved.")

    def save_details(self):

        self.scan_object.details.to_csv(self.path + '_details.txt')

    def save_data(self):

        x = pd.DataFrame(self.scan_object.x[:100])
        y = pd.DataFrame(self.scan_object.y[:100])

        x.to_csv(self.path + '_x.csv', header=None, index=None)
        y.to_csv(self.path + '_y.csv', header=None, index=None)

    def save_results(self):

        self.scan_object.data.to_csv(self.path + '_results.csv')

    def save_params(self):

        np.save(self.path + '_params', self.scan_object.params)

    def save_readme(self):

        txt = 'To activate the assets in the Talos deploy package: \n\n   from talos.commands.restore import Restore \n   a = Restore(\'path_to_asset\')\n\nNow you will have an object similar to the Scan object, which can be used with other Talos commands as you would be able to with the Scan object'

        text_file = open(self.path.split('/')[0] + '/README.txt', "w")
        text_file.write(txt)
        text_file.close()

    def package(self):

        shutil.make_archive(self.model_name, 'zip', self.model_name)
        shutil.rmtree(self.model_name)