BigQuery TensorFlow 리더의 엔드 투 엔드 예제

TensorFlow.org에서 보기 Google Colab에서 실행하기 GitHub에서소스 보기 노트북 다운로드하기

개요

이 가이드에서는 Keras 순차 API를 사용하여 신경망을 훈련하기 위한 BigQuery TensorFlow 리더의 사용 방법을 보여줍니다.

데이터세트

이 튜토리얼에서는 UC Irvine 머신러닝 리포지토리에서 제공하는 United States Census Income 데이터세트를 사용합니다. 이 데이터세트에는 연령, 학력, 결혼 상태, 직업 및 연간 수입이 $50,000 이상인지 여부를 포함하여 1994년 인구 조사 데이터베이스에 수록된 사람들에 대한 정보가 포함되어 있습니다.

설정

GCP 프로젝트 설정하기

노트북 환경과 관계없이 다음 단계가 필요합니다.

  1. GCP 프로젝트를 선택하거나 만듭니다.
  2. 프로젝트에 결제가 사용 설정되어 있는지 확인하세요.
  3. BigQuery Storage API 사용
  4. 아래 셀에 프로젝트 ID를 입력합니다. 그런 다음 셀을 실행하여 Cloud SDK가 이 노트북의 모든 명령에 올바른 프로젝트를 사용하는지 확인합니다.

참고: Jupyter는 앞에 !가 붙은 줄을 셸 명령으로 실행하고 앞에 $가 붙은 Python 변수를 이러한 명령에 보간하여 넣습니다.

필수 패키지를 설치하고 런타임을 다시 시작합니다.

try:
  # Use the Colab's preinstalled TensorFlow 2.x
  %tensorflow_version 2.x 
except:
  pass

pip install fastavro
pip install tensorflow-io==0.9.0
 
pip install google-cloud-bigquery-storage

인증합니다.

from google.colab import auth
auth.authenticate_user()
print('Authenticated')

프로젝트 ID를 설정합니다.

PROJECT_ID = "<YOUR PROJECT>"
! gcloud config set project $PROJECT_ID
%env GCLOUD_PROJECT=$PROJECT_ID

Python 라이브러리를 가져오고 상수를 정의합니다.

from __future__ import absolute_import, division, print_function, unicode_literals

import os
from six.moves import urllib
import tempfile

import numpy as np
import pandas as pd
import tensorflow as tf

from google.cloud import bigquery
from google.api_core.exceptions import GoogleAPIError

LOCATION = 'us'

# Storage directory
DATA_DIR = os.path.join(tempfile.gettempdir(), 'census_data')

# Download options.
DATA_URL = 'https://storage.googleapis.com/cloud-samples-data/ml-engine/census/data'
TRAINING_FILE = 'adult.data.csv'
EVAL_FILE = 'adult.test.csv'
TRAINING_URL = '%s/%s' % (DATA_URL, TRAINING_FILE)
EVAL_URL = '%s/%s' % (DATA_URL, EVAL_FILE)

DATASET_ID = 'census_dataset'
TRAINING_TABLE_ID = 'census_training_table'
EVAL_TABLE_ID = 'census_eval_table'

CSV_SCHEMA = [
      bigquery.SchemaField("age", "FLOAT64"),
      bigquery.SchemaField("workclass", "STRING"),
      bigquery.SchemaField("fnlwgt", "FLOAT64"),
      bigquery.SchemaField("education", "STRING"),
      bigquery.SchemaField("education_num", "FLOAT64"),
      bigquery.SchemaField("marital_status", "STRING"),
      bigquery.SchemaField("occupation", "STRING"),
      bigquery.SchemaField("relationship", "STRING"),
      bigquery.SchemaField("race", "STRING"),
      bigquery.SchemaField("gender", "STRING"),
      bigquery.SchemaField("capital_gain", "FLOAT64"),
      bigquery.SchemaField("capital_loss", "FLOAT64"),
      bigquery.SchemaField("hours_per_week", "FLOAT64"),
      bigquery.SchemaField("native_country", "STRING"),
      bigquery.SchemaField("income_bracket", "STRING"),
  ]

UNUSED_COLUMNS = ["fnlwgt", "education_num"]

BigQuery로 인구 조사 데이터 가져오기

BigQuery에 데이터를 로드하는 도우미 메서드를 정의합니다.

def create_bigquery_dataset_if_necessary(dataset_id):
  # Construct a full Dataset object to send to the API.
  client = bigquery.Client(project=PROJECT_ID)
  dataset = bigquery.Dataset(bigquery.dataset.DatasetReference(PROJECT_ID, dataset_id))
  dataset.location = LOCATION

  try:
    dataset = client.create_dataset(dataset)  # API request
    return True
  except GoogleAPIError as err:
    if err.code != 409: # http_client.CONFLICT
      raise
  return False

def load_data_into_bigquery(url, table_id):
  create_bigquery_dataset_if_necessary(DATASET_ID)
  client = bigquery.Client(project=PROJECT_ID)
  dataset_ref = client.dataset(DATASET_ID)
  table_ref = dataset_ref.table(table_id)
  job_config = bigquery.LoadJobConfig()
  job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE
  job_config.source_format = bigquery.SourceFormat.CSV
  job_config.schema = CSV_SCHEMA

  load_job = client.load_table_from_uri(
      url, table_ref, job_config=job_config
  )
  print("Starting job {}".format(load_job.job_id))

  load_job.result()  # Waits for table load to complete.
  print("Job finished.")

  destination_table = client.get_table(table_ref)
  print("Loaded {} rows.".format(destination_table.num_rows))

BigQuery에서 인구 조사 데이터를 로드합니다.

load_data_into_bigquery(TRAINING_URL, TRAINING_TABLE_ID)
load_data_into_bigquery(EVAL_URL, EVAL_TABLE_ID)

Starting job 2ceffef8-e6e4-44bb-9e86-3d97b0501187
Job finished.
Loaded 32561 rows.
Starting job bf66f1b3-2506-408b-9009-c19f4ae9f58a
Job finished.
Loaded 16278 rows.

가져온 데이터를 확인합니다.

수행할 작업: 를 PROJECT_ID로 바꿉니다.

참고: --use_bqstorage_api는 BigQueryStorage API를 사용하여 데이터를 가져오고 사용 권한이 있는지 확인합니다. 프로젝트에 이 부분이 활성화되어 있는지 확인합니다(https://cloud.google.com/bigquery/docs/reference/storage/#enabling_the_api).

%%bigquery --use_bqstorage_api
SELECT * FROM `<YOUR PROJECT>.census_dataset.census_training_table` LIMIT 5

BigQuery 리더를 사용하여 TensorFlow DataSet에 인구 조사 데이터 로드하기

BigQuery에서 인구 조사 데이터를 읽고 TensorFlow DataSet로 변환합니다.

from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
from tensorflow_io.bigquery import BigQueryClient
from tensorflow_io.bigquery import BigQueryReadSession

def transofrom_row(row_dict):
  # Trim all string tensors
  trimmed_dict = { column:
                  (tf.strings.strip(tensor) if tensor.dtype == 'string' else tensor) 
                  for (column,tensor) in row_dict.items()
                  }
  # Extract feature column
  income_bracket = trimmed_dict.pop('income_bracket')
  # Convert feature column to 0.0/1.0
  income_bracket_float = tf.cond(tf.equal(tf.strings.strip(income_bracket), '>50K'), 
                 lambda: tf.constant(1.0), 
                 lambda: tf.constant(0.0))
  return (trimmed_dict, income_bracket_float)

def read_bigquery(table_name):
  tensorflow_io_bigquery_client = BigQueryClient()
  read_session = tensorflow_io_bigquery_client.read_session(
      "projects/" + PROJECT_ID,
      PROJECT_ID, table_name, DATASET_ID,
      list(field.name for field in CSV_SCHEMA 
           if not field.name in UNUSED_COLUMNS),
      list(dtypes.double if field.field_type == 'FLOAT64' 
           else dtypes.string for field in CSV_SCHEMA
           if not field.name in UNUSED_COLUMNS),
      requested_streams=2)

  dataset = read_session.parallel_read_rows()
  transformed_ds = dataset.map (transofrom_row)
  return transformed_ds

BATCH_SIZE = 32

training_ds = read_bigquery(TRAINING_TABLE_ID).shuffle(10000).batch(BATCH_SIZE)
eval_ds = read_bigquery(EVAL_TABLE_ID).batch(BATCH_SIZE)

특성 열 정의하기

def get_categorical_feature_values(column):
  query = 'SELECT DISTINCT TRIM({}) FROM `{}`.{}.{}'.format(column, PROJECT_ID, DATASET_ID, TRAINING_TABLE_ID)
  client = bigquery.Client(project=PROJECT_ID)
  dataset_ref = client.dataset(DATASET_ID)
  job_config = bigquery.QueryJobConfig()
  query_job = client.query(query, job_config=job_config)
  result = query_job.to_dataframe()
  return result.values[:,0]

from tensorflow import feature_column

feature_columns = []

# numeric cols
for header in ['capital_gain', 'capital_loss', 'hours_per_week']:
  feature_columns.append(feature_column.numeric_column(header))

# categorical cols
for header in ['workclass', 'marital_status', 'occupation', 'relationship',
               'race', 'native_country', 'education']:
  categorical_feature = feature_column.categorical_column_with_vocabulary_list(
        header, get_categorical_feature_values(header))
  categorical_feature_one_hot = feature_column.indicator_column(categorical_feature)
  feature_columns.append(categorical_feature_one_hot)

# bucketized cols
age = feature_column.numeric_column('age')
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)

feature_layer = tf.keras.layers.DenseFeatures(feature_columns)

모델 빌드 및 훈련하기

모델을 빌드합니다.

Dense = tf.keras.layers.Dense
model = tf.keras.Sequential(
  [
    feature_layer,
      Dense(100, activation=tf.nn.relu, kernel_initializer='uniform'),
      Dense(75, activation=tf.nn.relu),
      Dense(50, activation=tf.nn.relu),
      Dense(25, activation=tf.nn.relu),
      Dense(1, activation=tf.nn.sigmoid)
  ])

# Compile Keras model
model.compile(
    loss='binary_crossentropy', 
    metrics=['accuracy'])

모델을 훈련합니다.

model.fit(training_ds, epochs=5)

WARNING:tensorflow:Layer sequential is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4276: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:4331: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
Epoch 1/5
1018/1018 [==============================] - 17s 17ms/step - loss: 0.5985 - accuracy: 0.8105
Epoch 2/5
1018/1018 [==============================] - 10s 10ms/step - loss: 0.3670 - accuracy: 0.8324
Epoch 3/5
1018/1018 [==============================] - 11s 10ms/step - loss: 0.3487 - accuracy: 0.8393
Epoch 4/5
1018/1018 [==============================] - 11s 10ms/step - loss: 0.3398 - accuracy: 0.8435
Epoch 5/5
1018/1018 [==============================] - 11s 11ms/step - loss: 0.3377 - accuracy: 0.8455
<tensorflow.python.keras.callbacks.History at 0x7f978f5b91d0>

모델 평가하기

모델을 평가합니다.

loss, accuracy = model.evaluate(eval_ds)
print("Accuracy", accuracy)

509/509 [==============================] - 8s 15ms/step - loss: 0.3338 - accuracy: 0.8398
Accuracy 0.8398452

몇 가지 무작위 샘플을 평가합니다.

sample_x = {
    'age' : np.array([56, 36]), 
    'workclass': np.array(['Local-gov', 'Private']), 
    'education': np.array(['Bachelors', 'Bachelors']), 
    'marital_status': np.array(['Married-civ-spouse', 'Married-civ-spouse']), 
    'occupation': np.array(['Tech-support', 'Other-service']), 
    'relationship': np.array(['Husband', 'Husband']), 
    'race': np.array(['White', 'Black']), 
    'gender': np.array(['Male', 'Male']), 
    'capital_gain': np.array([0, 7298]), 
    'capital_loss': np.array([0, 0]), 
    'hours_per_week': np.array([40, 36]), 
    'native_country': np.array(['United-States', 'United-States'])
  }

model.predict(sample_x)

array([[0.5541261],
       [0.6209938]], dtype=float32)

리소스