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Kernel: Python 3

Copyright 2019 The TensorFlow IO Authors.

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Ejemplo de principio a fin para el lector BigQuery de TensorFlow

Ver en TensorFlow.org

Ejecutar en Google Colab

Ver código fuente en GitHub

Descargar el bloc de notas

Descripción general

En este tutorial se muestra cómo usar el lector BigQuery de TensorFlow para entrenar redes neuronales con la API secuencial de Keras.

Conjunto de datos

Este tutorial usa el conjunto de datos de sueldos del censo de Estados Unidos proporcionado por el repositorio de aprendizaje automático de UC Irvine. Este conjunto de datos contiene información sobre personas de una base de datos del censo de 1994, incluida la edad, la educación, el estado civil, la ocupación y si ganan más de $50 000 al año.

Preparación

Configure su proyecto de GCP

Se deben seguir siguientes pasos, independientemente del entorno de su cuaderno.

Seleccione o cree un proyecto de GCP.
Asegúrese de que la facturación esté habilitada para su proyecto.
Habilite la API de almacenamiento de BigQuery
Ingrese su ID de proyecto en la celda a continuación. Luego ejecute la celda para asegurarse de que Cloud SDK use el proyecto correcto para todos los comandos en este cuaderno.

Nota: Jupyter ejecuta líneas con ! por delante como comandos de shell e interpola variables de Python con $ por delante en estos comandos.

Instale los paquetes necesarios y reinicie el tiempo de ejecución

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

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!pip install fastavro
!pip install tensorflow-io==0.9.0

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!pip install google-cloud-bigquery-storage

Autentique

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

Establezca su ID de PROYECTO

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PROJECT_ID = "<YOUR PROJECT>" #@param {type:"string"}
! gcloud config set project $PROJECT_ID
%env GCLOUD_PROJECT=$PROJECT_ID

Importe bibliotecas de Python y defina constantes

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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"]

Importar datos del censo a BigQuery

Defina métodos ayudantes para cargar datos en BigQuery

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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

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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))

Cargue datos del censo en BigQuery.

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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.

Confirme que se hayan importado los datos.

TAREA: reemplace <YOUR PROJECT> con su ID de PROYECTO

Nota: --use_bqstorage_api obtendrá datos mediante la API de BigQueryStorage y se asegurará de que tenga la autorización para usarlos. Asegúrese de habilitarlo para su proyecto: https://cloud.google.com/bigquery/docs/reference/storage/#enabling_the_api

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

##Load census data in TensorFlow DataSet using BigQuery reader

Lea y transforme datos del censo de BigQuery en el conjunto de datos de TensorFlow

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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

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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)

##Define feature columns

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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]

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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)

##Build and train model

Genere el modelo

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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'])

Entrene el modelo

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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>

##Evaluate model

Evalúe el modelo

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

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

Evalúe un par de muestras aleatorias

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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)

Copyright 2019 The TensorFlow IO Authors.

Ejemplo de principio a fin para el lector BigQuery de TensorFlow

Descripción general

Conjunto de datos

Preparación

Importar datos del censo a BigQuery

Recursos

Product

Resources

Company