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tensorflow
GitHub Repository: tensorflow/docs-l10n
 Path: blob/master/site/ja/io/tutorials/elasticsearch.ipynb
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Kernel: Python 3
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Tensorflow-IO を使用した Elasticsearch からの構造化データのストリーミング

概要

このチュートリアルでは、 Elasticsearch クラスターからtf.data.Dataset にデータをストリーミングし、トレーニングと推論のために tf.keras と組み合わせて使用することに焦点を当てています。

Elasticseach は主に、構造化データ、非構造化データ、地理空間データ、数値データなどの保存をサポートする分散検索エンジンです。このチュートリアルでは、構造化レコードを含むデータセットを使用します。

注意: 基本的な Elasticsearch ストレージの知識があれば、簡単にこのチュートリアルを進めることができます。

セットアップパッケージ

elasticsearch パッケージは、デモンストレーション目的でのみ、Elasticsearch インデックス内のデータを準備および保存するために使用されます。多数のノードがある実際の運用クラスターでは、クラスターが logstash などのコネクターからデータを受信している可能性があります。

データが Elasticsearch クラスターで利用可能になると、データをモデルにストリーミングするために tensorflow-io のみが必要です。

必要な tensorflow-io および elasticsearch パッケージをインストールする

!pip install tensorflow-io
!pip install elasticsearch
Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.6/dist-packages (0.16.0) Requirement already satisfied: tensorflow<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-io) (2.3.0) Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.1.2) Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.18.5) Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.4.1) Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.10.0) Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.3.0) Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (2.10.0) Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.33.2) Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.35.1) Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.6.3) Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.15.0) Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.2.0) Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4.0,>=2.3.0->tensorflow-io) (1.1.0) Requirement 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pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (0.4.8) Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < "3.8"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.4.0) Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow<2.4.0,>=2.3.0->tensorflow-io) (3.1.0) Requirement already satisfied: elasticsearch in /usr/local/lib/python3.6/dist-packages (7.9.1) Requirement already satisfied: urllib3>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from elasticsearch) (1.24.3) Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from elasticsearch) (2020.6.20)

パッケージをインポートする

import os
import time
from sklearn.model_selection import train_test_split
from elasticsearch import Elasticsearch
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
import tensorflow_io as tfio

インポートした TensorFlow と TensorFlow-IO を確認する

print("tensorflow-io version: {}".format(tfio.__version__))
print("tensorflow version: {}".format(tf.__version__))
tensorflow-io version: 0.16.0 tensorflow version: 2.3.0

Elasticsearch インスタンスをダウンロードしてセットアップする

デモの目的で、elasticsearch パッケージのオープンソースバージョンが使用されます。

%%bash

wget -q https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz
wget -q https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512
tar -xzf elasticsearch-oss-7.9.2-linux-x86_64.tar.gz
sudo chown -R daemon:daemon elasticsearch-7.9.2/
shasum -a 512 -c elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512
elasticsearch-oss-7.9.2-linux-x86_64.tar.gz: OK

インスタンスをデーモンプロセスとして実行します

%%bash --bg

sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch
Starting job # 0 in a separate thread. 
# Sleep for few seconds to let the instance start.
time.sleep(20)

インスタンスが開始されたら、プロセスリストで elasticsearch の grep を実行して、利用できることを確認します。

%%bash

ps -ef | grep elasticsearch
root 144 142 0 21:24 ? 00:00:00 sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch daemon 145 144 86 21:24 ? 00:00:17 /content/elasticsearch-7.9.2/jdk/bin/java -Xshare:auto -Des.networkaddress.cache.ttl=60 -Des.networkaddress.cache.negative.ttl=10 -XX:+AlwaysPreTouch -Xss1m -Djava.awt.headless=true -Dfile.encoding=UTF-8 -Djna.nosys=true -XX:-OmitStackTraceInFastThrow -XX:+ShowCodeDetailsInExceptionMessages -Dio.netty.noUnsafe=true -Dio.netty.noKeySetOptimization=true -Dio.netty.recycler.maxCapacityPerThread=0 -Dio.netty.allocator.numDirectArenas=0 -Dlog4j.shutdownHookEnabled=false -Dlog4j2.disable.jmx=true -Djava.locale.providers=SPI,COMPAT -Xms1g -Xmx1g -XX:+UseG1GC -XX:G1ReservePercent=25 -XX:InitiatingHeapOccupancyPercent=30 -Djava.io.tmpdir=/tmp/elasticsearch-16913031424109346409 -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=data -XX:ErrorFile=logs/hs_err_pid%p.log -Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,pid,tags:filecount=32,filesize=64m -XX:MaxDirectMemorySize=536870912 -Des.path.home=/content/elasticsearch-7.9.2 -Des.path.conf=/content/elasticsearch-7.9.2/config -Des.distribution.flavor=oss -Des.distribution.type=tar -Des.bundled_jdk=true -cp /content/elasticsearch-7.9.2/lib/* org.elasticsearch.bootstrap.Elasticsearch root 382 380 0 21:24 ? 00:00:00 grep elasticsearch

ベースエンドポイントにクエリを実行して、クラスターに関する情報を取得します。

%%bash

curl -sX GET "localhost:9200/"
{ "name" : "d1bc7d054c69", "cluster_name" : "elasticsearch", "cluster_uuid" : "P8YXfKqYS-OS3k9CdMmlsw", "version" : { "number" : "7.9.2", "build_flavor" : "oss", "build_type" : "tar", "build_hash" : "d34da0ea4a966c4e49417f2da2f244e3e97b4e6e", "build_date" : "2020-09-23T00:45:33.626720Z", "build_snapshot" : false, "lucene_version" : "8.6.2", "minimum_wire_compatibility_version" : "6.8.0", "minimum_index_compatibility_version" : "6.0.0-beta1" }, "tagline" : "You Know, for Search" }

データセットを探索する

このチュートリアルでは、PetFinder データセットをダウンロードして、データを elasticsearch に手動でフィードします。この分類問題の目的は、ペットが引き取られるかどうかを予測することです。

dataset_url = 'http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip'
csv_file = 'datasets/petfinder-mini/petfinder-mini.csv'
tf.keras.utils.get_file('petfinder_mini.zip', dataset_url,
                        extract=True, cache_dir='.')
pf_df = pd.read_csv(csv_file)
Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/petfinder-mini.zip 1671168/1668792 [==============================] - 0s 0us/step 
pf_df.head()

チュートリアルの目的で、ラベル列に変更が加えられます。 0 はペットが引き取られなかったことを示し、1 は引き取られたことを示すようになります。

# In the original dataset "4" indicates the pet was not adopted.
pf_df['target'] = np.where(pf_df['AdoptionSpeed']==4, 0, 1)

# Drop un-used columns.
pf_df = pf_df.drop(columns=['AdoptionSpeed', 'Description'])


# Number of datapoints and columns
len(pf_df), len(pf_df.columns)
(11537, 14)

データセットを分割する

train_df, test_df = train_test_split(pf_df, test_size=0.3, shuffle=True)
print("Number of training samples: ",len(train_df))
print("Number of testing sample: ",len(test_df))
Number of training samples: 8075 Number of testing sample: 3462

トレーニングとテストデータを elasticsearch インデックスに保存する

ローカル elasticsearch クラスターにデータを保存すると、トレーニングと推論の目的で継続的にリモートデータを取得するための環境がシミュレートされます。

ES_NODES = "http://localhost:9200"

def prepare_es_data(index, doc_type, df):
  records = df.to_dict(orient="records")
  es_data = []
  for idx, record in enumerate(records):
    meta_dict = {
          "index": {
              "_index": index, 
              "_type": doc_type, 
              "_id": idx
          }
      }
    es_data.append(meta_dict)
    es_data.append(record)

  return es_data

def index_es_data(index, es_data):
  es_client = Elasticsearch(hosts = [ES_NODES])
  if es_client.indices.exists(index):
      print("deleting the '{}' index.".format(index))
      res = es_client.indices.delete(index=index)
      print("Response from server: {}".format(res))

  print("creating the '{}' index.".format(index))
  res = es_client.indices.create(index=index)
  print("Response from server: {}".format(res))

  print("bulk index the data")
  res = es_client.bulk(index=index, body=es_data, refresh = True)
  print("Errors: {}, Num of records indexed: {}".format(res["errors"], len(res["items"])))
  

train_es_data = prepare_es_data(index="train", doc_type="pet", df=train_df)
test_es_data = prepare_es_data(index="test", doc_type="pet", df=test_df)

index_es_data(index="train", es_data=train_es_data)
time.sleep(3)
index_es_data(index="test", es_data=test_es_data)
creating the 'train' index. Response from server: {'acknowledged': True, 'shards_acknowledged': True, 'index': 'train'} bulk index the data
/usr/local/lib/python3.6/dist-packages/elasticsearch/connection/base.py:190: ElasticsearchDeprecationWarning: [types removal] Specifying types in bulk requests is deprecated. warnings.warn(message, category=ElasticsearchDeprecationWarning)
Errors: False, Num of records indexed: 8075 creating the 'test' index. Response from server: {'acknowledged': True, 'shards_acknowledged': True, 'index': 'test'} bulk index the data Errors: False, Num of records indexed: 3462

tfio データセットを準備する

データがクラスターで利用可能になると、インデックスからデータをストリーミングするために tensorflow-io のみが必要です。この目的のために、elasticsearch.ElasticsearchIODataset クラスが使用されます。このクラスは tf.data.Dataset を継承しているため、tf.data.Dataset のすべての便利な機能をすぐに利用できます。

トレーニングデータセット

BATCH_SIZE=32
HEADERS = {"Content-Type": "application/json"}

train_ds = tfio.experimental.elasticsearch.ElasticsearchIODataset(
        nodes=[ES_NODES],
        index="train",
        doc_type="pet",
        headers=HEADERS
    )

# Prepare a tuple of (features, label)
train_ds = train_ds.map(lambda v: (v, v.pop("target")))
train_ds = train_ds.batch(BATCH_SIZE)
Connection successful: http://localhost:9200/_cluster/health

テストデータセット

test_ds = tfio.experimental.elasticsearch.ElasticsearchIODataset(
        nodes=[ES_NODES],
        index="test",
        doc_type="pet",
        headers=HEADERS
    )

# Prepare a tuple of (features, label)
test_ds = test_ds.map(lambda v: (v, v.pop("target")))
test_ds = test_ds.batch(BATCH_SIZE)
Connection successful: http://localhost:9200/_cluster/health

Keras 前処理レイヤーを定義する

構造化データのチュートリアルに従って、Keras 前処理レイヤーを使用することをお勧めします。より直感的で、モデルと簡単に統合できるためです。ただし、標準の feature_columns も使用できます。

構造化データの分類での preprocessing_layers の理解を深めるには、構造化データのチュートリアルを参照してください。

def get_normalization_layer(name, dataset):
  # Create a Normalization layer for our feature.
  normalizer = preprocessing.Normalization()

  # Prepare a Dataset that only yields our feature.
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the statistics of the data.
  normalizer.adapt(feature_ds)

  return normalizer

def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):
  # Create a StringLookup layer which will turn strings into integer indices
  if dtype == 'string':
    index = preprocessing.StringLookup(max_tokens=max_tokens)
  else:
    index = preprocessing.IntegerLookup(max_values=max_tokens)

  # Prepare a Dataset that only yields our feature
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the set of possible values and assign them a fixed integer index.
  index.adapt(feature_ds)

  # Create a Discretization for our integer indices.
  encoder = preprocessing.CategoryEncoding(max_tokens=index.vocab_size())

  # Prepare a Dataset that only yields our feature.
  feature_ds = feature_ds.map(index)

  # Learn the space of possible indices.
  encoder.adapt(feature_ds)

  # Apply one-hot encoding to our indices. The lambda function captures the
  # layer so you can use them, or include them in the functional model later.
  return lambda feature: encoder(index(feature))

バッチをフェッチし、サンプルレコードの機能を観察します。これは、tf.keras モデルをトレーニングするための keras 前処理レイヤーを定義するのに役立ちます。

ds_iter = iter(train_ds)
features, label = next(ds_iter)
{key: value.numpy()[0] for key,value in features.items()}
{'Age': 2, 'Breed1': b'Tabby', 'Color1': b'Black', 'Color2': b'Cream', 'Fee': 0, 'FurLength': b'Short', 'Gender': b'Male', 'Health': b'Healthy', 'MaturitySize': b'Small', 'PhotoAmt': 4, 'Sterilized': b'No', 'Type': b'Cat', 'Vaccinated': b'No'}

機能のサブセットを選択します。

all_inputs = []
encoded_features = []

# Numeric features.
for header in ['PhotoAmt', 'Fee']:
  numeric_col = tf.keras.Input(shape=(1,), name=header)
  normalization_layer = get_normalization_layer(header, train_ds)
  encoded_numeric_col = normalization_layer(numeric_col)
  all_inputs.append(numeric_col)
  encoded_features.append(encoded_numeric_col)

# Categorical features encoded as string.
categorical_cols = ['Type', 'Color1', 'Color2', 'Gender', 'MaturitySize',
                    'FurLength', 'Vaccinated', 'Sterilized', 'Health', 'Breed1']
for header in categorical_cols:
  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='string')
  encoding_layer = get_category_encoding_layer(header, train_ds, dtype='string',
                                               max_tokens=5)
  encoded_categorical_col = encoding_layer(categorical_col)
  all_inputs.append(categorical_col)
  encoded_features.append(encoded_categorical_col)

モデルをビルド、コンパイル、およびトレーニングする

# Set the parameters

OPTIMIZER="adam"
LOSS=tf.keras.losses.BinaryCrossentropy(from_logits=True)
METRICS=['accuracy']
EPOCHS=10


# Convert the feature columns into a tf.keras layer
all_features = tf.keras.layers.concatenate(encoded_features)

# design/build the model
x = tf.keras.layers.Dense(32, activation="relu")(all_features)
x = tf.keras.layers.Dropout(0.5)(x)
x = tf.keras.layers.Dense(64, activation="relu")(x)
x = tf.keras.layers.Dropout(0.5)(x)
output = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(all_inputs, output)

tf.keras.utils.plot_model(model, rankdir='LR', show_shapes=True)
Image in a Jupyter notebook
# compile the model
model.compile(optimizer=OPTIMIZER, loss=LOSS, metrics=METRICS)

# fit the model
model.fit(train_ds, epochs=EPOCHS)
Epoch 1/10
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:543: UserWarning: Input dict contained keys ['Age'] which did not match any model input. They will be ignored by the model. [n for n in tensors.keys() if n not in ref_input_names])
253/253 [==============================] - 4s 14ms/step - loss: 0.6169 - accuracy: 0.6042 Epoch 2/10 253/253 [==============================] - 4s 14ms/step - loss: 0.5634 - accuracy: 0.6937 Epoch 3/10 253/253 [==============================] - 4s 15ms/step - loss: 0.5573 - accuracy: 0.6981 Epoch 4/10 253/253 [==============================] - 4s 15ms/step - loss: 0.5528 - accuracy: 0.7087 Epoch 5/10 253/253 [==============================] - 4s 14ms/step - loss: 0.5512 - accuracy: 0.7173 Epoch 6/10 253/253 [==============================] - 4s 15ms/step - loss: 0.5456 - accuracy: 0.7219 Epoch 7/10 253/253 [==============================] - 4s 15ms/step - loss: 0.5397 - accuracy: 0.7283 Epoch 8/10 253/253 [==============================] - 4s 14ms/step - loss: 0.5385 - accuracy: 0.7331 Epoch 9/10 253/253 [==============================] - 4s 15ms/step - loss: 0.5355 - accuracy: 0.7326 Epoch 10/10 253/253 [==============================] - 4s 15ms/step - loss: 0.5412 - accuracy: 0.7321
<tensorflow.python.keras.callbacks.History at 0x7f5c235112e8>

テストデータを推測する

res = model.evaluate(test_ds)
print("test loss, test acc:", res)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:543: UserWarning: Input dict contained keys ['Age'] which did not match any model input. They will be ignored by the model. [n for n in tensors.keys() if n not in ref_input_names])
109/109 [==============================] - 2s 15ms/step - loss: 0.5344 - accuracy: 0.7421 test loss, test acc: [0.534355640411377, 0.7420566082000732]

注: このチュートリアルの目的は、elasticsearch からデータをストリーミングしてtf.kerasモデルを直接トレーニングする Tensorflow-IO の機能を示すことであるため、モデルの精度を向上させることは現在の範囲外です。ただし、ユーザーはデータセットを調べ、特徴列とモデルアーキテクチャを試して、分類パフォーマンスを向上させることができます。