data_utils.py

# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the \"License\");
# you may not use this file except in compliance with the License.\n",
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an \"AS IS\" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from google.cloud import storage
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data import random_split


class SonarDataset(Dataset):
    def __init__(self, csv_file):
        self.dataframe = pd.read_csv(csv_file, header=None)

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        # When iterating through the dataset get the features and targets
        features = self.dataframe.iloc[idx, :-1].values.astype(dtype='float64')

        # Convert the targets to binary values:
        # R = rock --> 0
        # M = mine --> 1
        target = self.dataframe.iloc[idx, -1:].values
        if target[0] == 'R':
            target[0] = 0
        elif target[0] == 'M':
            target[0] = 1
        target = target.astype(dtype='float64')

        # Load the data as a tensor
        data = {'features': torch.from_numpy(features),
                'target': target}
        return data


def load_data(test_split, seed, batch_size):
    """Loads the data"""
    sonar_dataset = SonarDataset('./sonar.all-data')
    # Create indices for the split
    dataset_size = len(sonar_dataset)
    test_size = int(test_split * dataset_size)
    train_size = dataset_size - test_size

    train_dataset, test_dataset = random_split(sonar_dataset,
                                               [train_size, test_size])

    train_loader = DataLoader(
        train_dataset.dataset,
        batch_size=batch_size,
        shuffle=True)
    test_loader = DataLoader(
        test_dataset.dataset,
        batch_size=batch_size,
        shuffle=True)

    return train_loader, test_loader


def save_model(job_dir, model_name):
    """Saves the model to Google Cloud Storage"""
    # Example: job_dir = 'gs://BUCKET_ID/hptuning_sonar/1'
    job_dir = job_dir.replace('gs://', '')  # Remove the 'gs://'
    # Get the Bucket Id
    bucket_id = job_dir.split('/')[0]
    # Get the path. Example: 'hptuning_sonar/1'
    bucket_path = job_dir[len('{}/'.format(bucket_id)):]

    # Upload the model to GCS
    bucket = storage.Client().bucket(bucket_id)
    blob = bucket.blob('{}/{}'.format(
        bucket_path,
        model_name))
    blob.upload_from_filename(model_name)