Сквозной пример для читателя BigQuery TensorFlow

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Обзор

В этом учебнике показано , как использовать считыватель BigQuery TensorFlow для обучения нейронной сети с использованием последовательного API Keras.

Набор данных

В этом руководстве используется Перепись дохода Dataset США , предоставленного UC Irvine Machine Learning Repository . Этот набор данных содержит информацию о людях из базы данных переписи 1994 года, включая возраст, образование, семейное положение, профессию и то, зарабатывают ли они более 50 000 долларов в год.

Настраивать

Настройте свой проект GCP

Следующие шаги необходимы независимо от среды вашего ноутбука.

  1. Выберите или создайте проект GCP.
  2. Убедитесь, что для вашего проекта включена оплата.
  3. Включить API хранилища BigQuery
  4. Введите идентификатор вашего проекта в ячейку ниже. Затем запустите ячейку, чтобы убедиться, что Cloud SDK использует правильный проект для всех команд в этой записной книжке.

Установите необходимые пакеты и перезапустите среду выполнения.

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

Аутентификация

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

Установите свой ID ПРОЕКТА

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

Импортируйте библиотеки Python, определите константы

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"]

Импорт данных переписи в BigQuery

Определите вспомогательные методы для загрузки данных в BigQuery.

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))

Загрузите данные переписи в BigQuery.

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.

Подтвердите, что данные были импортированы

TODO: замените <ВАШ ПРОЕКТ> на ваш PROJECT_ID

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

Загрузите данные переписи в набор данных TensorFlow с помощью средства чтения BigQuery.

Чтение и преобразование данных cesnus из BigQuery в набор данных TensorFlow.

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 transform_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(transform_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)

Определить столбцы функций

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)

Построить и обучить модель

Построить модель

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'])

Модель поезда

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>

Оценить модель

Оценить модель

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

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

Оценить пару случайных выборок

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)

Ресурсы