Menggunakan Dataproc, BigQuery, dan Apache Spark ML untuk Machine Learning

Konektor BigQuery untuk Apache Spark memungkinkan Data Scientist memadukan kecanggihan mesin SQL yang dapat diskalakan secara lancar dari BigQuery dengan kemampuan Machine Learning Apache Spark. Dalam tutorial ini, kami menunjukkan cara menggunakan Dataproc, BigQuery, dan Apache Spark ML untuk melakukan machine learning pada set data.

Membuat subset data natalitas BigQuery

Di bagian ini, Anda akan membuat set data di project, lalu membuat tabel di set data yang akan Anda salin ke subset data tingkat kelahiran dari set data BigQuery natality yang tersedia untuk publik. Nanti dalam tutorial ini, Anda akan menggunakan data subset dalam tabel ini untuk memprediksi berat lahir sebagai fungsi dari usia ibu, usia ayah, dan minggu kehamilan.

Anda dapat membuat subset data menggunakan konsol Google Cloud atau menjalankan skrip Python di mesin lokal Anda.

Konsol

  1. Buat set data di project Anda.

    1. Buka UI Web BigQuery.
    2. Di panel navigasi kiri, klik nama project Anda, lalu klik BUAT SET DATA.
    3. Dalam dialog Create dataset:
      1. Untuk Dataset ID, masukkan "natality_regression".
      2. Untuk Lokasi data, Anda dapat memilih lokasi untuk set data. Lokasi nilai defaultnya adalah US multi-region. Setelah set data dibuat, lokasi tidak dapat diubah.
      3. Untuk Default table expiration, pilih salah satu opsi berikut:
        • Tidak pernah (default): Anda harus menghapus tabel secara manual.
        • Jumlah hari: Tabel akan dihapus setelah jumlah hari yang ditentukan sejak waktu pembuatannya.
      4. Untuk Enkripsi, pilih salah satu opsi berikut:
      5. Klik Create dataset.

  2. Jalankan kueri terhadap set data publik kelahiran, lalu simpan hasil kueri dalam tabel baru di set data Anda.

    1. Salin dan tempel kueri berikut ke Editor Kueri, lalu klik Jalankan. 
      CREATE OR REPLACE TABLE natality_regression.regression_input as
SELECT
weight_pounds,
mother_age,
father_age,
gestation_weeks,
weight_gain_pounds,
apgar_5min
FROM
`bigquery-public-data.samples.natality`
WHERE
weight_pounds IS NOT NULL
AND mother_age IS NOT NULL
AND father_age IS NOT NULL
AND gestation_weeks IS NOT NULL
AND weight_gain_pounds IS NOT NULL
AND apgar_5min IS NOT NULL
    2. Setelah kueri selesai (dalam waktu sekitar satu menit), hasilnya akan disimpan sebagai tabel BigQuery "regression_input" dalam set data natality_regression di project Anda.

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Dataproc menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi Python API Dataproc.

Untuk melakukan autentikasi ke Dataproc, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

  1. Lihat Menyiapkan Lingkungan Pengembangan Python untuk mengetahui petunjuk tentang cara menginstal Python dan Library Klien Google Cloud untuk Python (diperlukan untuk menjalankan kode). Sebaiknya instal dan gunakan virtualenv Python.

  2. Salin dan tempel kode natality_tutorial.py, di bawah, ke dalam shell python di komputer lokal Anda. Tekan tombol <return> di shell untuk menjalankan kode guna membuat set data BigQuery "natality_regression" di projectGoogle Cloud default Anda dengan tabel "regression_input" yang diisi dengan subset data natalitypublik.

    """Create a Google BigQuery linear regression input table.

In the code below, the following actions are taken:
* A new dataset is created "natality_regression."
* A query is run against the public dataset,
    bigquery-public-data.samples.natality, selecting only the data of
    interest to the regression, the output of which is stored in a new
    "regression_input" table.
* The output table is moved over the wire to the user's default project via
    the built-in BigQuery Connector for Spark that bridges BigQuery and
    Cloud Dataproc.
"""

from google.cloud import bigquery

# Create a new Google BigQuery client using Google Cloud Platform project
# defaults.
client = bigquery.Client()

# Prepare a reference to a new dataset for storing the query results.
dataset_id = "natality_regression"
dataset_id_full = f"{client.project}.{dataset_id}"

dataset = bigquery.Dataset(dataset_id_full)

# Create the new BigQuery dataset.
dataset = client.create_dataset(dataset)

# Configure the query job.
job_config = bigquery.QueryJobConfig()

# Set the destination table to where you want to store query results.
# As of google-cloud-bigquery 1.11.0, a fully qualified table ID can be
# used in place of a TableReference.
job_config.destination = f"{dataset_id_full}.regression_input"

# Set up a query in Standard SQL, which is the default for the BigQuery
# Python client library.
# The query selects the fields of interest.
query = """
    SELECT
        weight_pounds, mother_age, father_age, gestation_weeks,
        weight_gain_pounds, apgar_5min
    FROM
        `bigquery-public-data.samples.natality`
    WHERE
        weight_pounds IS NOT NULL
        AND mother_age IS NOT NULL
        AND father_age IS NOT NULL
        AND gestation_weeks IS NOT NULL
        AND weight_gain_pounds IS NOT NULL
        AND apgar_5min IS NOT NULL
"""

# Run the query.
client.query_and_wait(query, job_config=job_config)  # Waits for the query to finish

  3. Konfirmasi pembuatan set data natality_regression dan tabel regression_input.

Menjalankan regresi linear

Di bagian ini, Anda akan menjalankan regresi linear PySpark dengan mengirimkan tugas ke layanan Dataproc menggunakan konsol Google Cloud atau dengan menjalankan perintah gcloud dari terminal lokal.

Konsol

  1. Salin dan tempel kode berikut ke dalam file natality_sparkml.py baru di mesin lokal Anda. 

    """Run a linear regression using Apache Spark ML.

In the following PySpark (Spark Python API) code, we take the following actions:

  * Load a previously created linear regression (BigQuery) input table
    into our Cloud Dataproc Spark cluster as an RDD (Resilient
    Distributed Dataset)
  * Transform the RDD into a Spark Dataframe
  * Vectorize the features on which the model will be trained
  * Compute a linear regression using Spark ML

"""
from pyspark.context import SparkContext
from pyspark.ml.linalg import Vectors
from pyspark.ml.regression import LinearRegression
from pyspark.sql.session import SparkSession
# The imports, above, allow us to access SparkML features specific to linear
# regression as well as the Vectors types.


# Define a function that collects the features of interest
# (mother_age, father_age, and gestation_weeks) into a vector.
# Package the vector in a tuple containing the label (`weight_pounds`) for that
# row.
def vector_from_inputs(r):
  return (r["weight_pounds"], Vectors.dense(float(r["mother_age"]),
                                            float(r["father_age"]),
                                            float(r["gestation_weeks"]),
                                            float(r["weight_gain_pounds"]),
                                            float(r["apgar_5min"])))

sc = SparkContext()
spark = SparkSession(sc)

# Read the data from BigQuery as a Spark Dataframe.
natality_data = spark.read.format("bigquery").option(
    "table", "natality_regression.regression_input").load()
# Create a view so that Spark SQL queries can be run against the data.
natality_data.createOrReplaceTempView("natality")

# As a precaution, run a query in Spark SQL to ensure no NULL values exist.
sql_query = """
SELECT *
from natality
where weight_pounds is not null
and mother_age is not null
and father_age is not null
and gestation_weeks is not null
"""
clean_data = spark.sql(sql_query)

# Create an input DataFrame for Spark ML using the above function.
training_data = clean_data.rdd.map(vector_from_inputs).toDF(["label",
                                                             "features"])
training_data.cache()

# Construct a new LinearRegression object and fit the training data.
lr = LinearRegression(maxIter=5, regParam=0.2, solver="normal")
model = lr.fit(training_data)
# Print the model summary.
print("Coefficients:" + str(model.coefficients))
print("Intercept:" + str(model.intercept))
print("R^2:" + str(model.summary.r2))
model.summary.residuals.show()

  2. Salin file natality_sparkml.py lokal ke bucket Cloud Storage di project Anda. 

    gcloud storage cp natality_sparkml.py gs://bucket-name

  3. Jalankan regresi dari halaman Dataproc Submit a job.

    1. Di kolom Main python file, masukkan URI gs:// dari bucket Cloud Storage tempat salinan file natality_sparkml.py Anda berada.

    2. Pilih PySpark sebagai Jenis tugas.

    3. Masukkan gs://spark-lib/bigquery/spark-bigquery-latest_2.12.jar di kolom File JAR. Hal ini membuat spark-bigquery-connector tersedia untuk aplikasi PySpark saat runtime agar dapat membaca data BigQuery ke dalam DataFrame Spark.

    4. Isi kolom Job ID, Region, dan Cluster.

    5. Klik Submit untuk menjalankan tugas di cluster Anda.

Setelah tugas selesai, ringkasan model output regresi linear akan muncul di jendela detail Tugas Dataproc.

gcloud

  1. Salin dan tempel kode berikut ke dalam file natality_sparkml.py baru di mesin lokal Anda. 

    """Run a linear regression using Apache Spark ML.

In the following PySpark (Spark Python API) code, we take the following actions:

  * Load a previously created linear regression (BigQuery) input table
    into our Cloud Dataproc Spark cluster as an RDD (Resilient
    Distributed Dataset)
  * Transform the RDD into a Spark Dataframe
  * Vectorize the features on which the model will be trained
  * Compute a linear regression using Spark ML

"""
from pyspark.context import SparkContext
from pyspark.ml.linalg import Vectors
from pyspark.ml.regression import LinearRegression
from pyspark.sql.session import SparkSession
# The imports, above, allow us to access SparkML features specific to linear
# regression as well as the Vectors types.


# Define a function that collects the features of interest
# (mother_age, father_age, and gestation_weeks) into a vector.
# Package the vector in a tuple containing the label (`weight_pounds`) for that
# row.
def vector_from_inputs(r):
  return (r["weight_pounds"], Vectors.dense(float(r["mother_age"]),
                                            float(r["father_age"]),
                                            float(r["gestation_weeks"]),
                                            float(r["weight_gain_pounds"]),
                                            float(r["apgar_5min"])))

sc = SparkContext()
spark = SparkSession(sc)

# Read the data from BigQuery as a Spark Dataframe.
natality_data = spark.read.format("bigquery").option(
    "table", "natality_regression.regression_input").load()
# Create a view so that Spark SQL queries can be run against the data.
natality_data.createOrReplaceTempView("natality")

# As a precaution, run a query in Spark SQL to ensure no NULL values exist.
sql_query = """
SELECT *
from natality
where weight_pounds is not null
and mother_age is not null
and father_age is not null
and gestation_weeks is not null
"""
clean_data = spark.sql(sql_query)

# Create an input DataFrame for Spark ML using the above function.
training_data = clean_data.rdd.map(vector_from_inputs).toDF(["label",
                                                             "features"])
training_data.cache()

# Construct a new LinearRegression object and fit the training data.
lr = LinearRegression(maxIter=5, regParam=0.2, solver="normal")
model = lr.fit(training_data)
# Print the model summary.
print("Coefficients:" + str(model.coefficients))
print("Intercept:" + str(model.intercept))
print("R^2:" + str(model.summary.r2))
model.summary.residuals.show()

  2. Salin file natality_sparkml.py lokal ke bucket Cloud Storage di project Anda. 

    gcloud storage cp natality_sparkml.py gs://bucket-name

  3. Kirimkan tugas Pyspark ke layanan Dataproc dengan menjalankan perintah gcloud, yang ditampilkan di bawah, dari jendela terminal di komputer lokal Anda.

    1. Nilai tanda --jars membuat spark-bigquery-connector tersedia untuk tugas PySpark saat runtime agar dapat membaca data BigQuery ke dalam DataFrame Spark. 
      gcloud dataproc jobs submit pyspark \
    gs://your-bucket/natality_sparkml.py \
    --cluster=cluster-name \
    --region=region \
    --jars=gs://spark-lib/bigquery/spark-bigquery-with-dependencies_SCALA_VERSION-CONNECTOR_VERSION.jar

Output regresi linear (ringkasan model) akan muncul di jendela terminal saat tugas selesai. 

<<< # Cetak ringkasan model.
... print "Coefficients:" + str(model.coefficients)
Coefficients:[0.0166657454602,-0.00296751984046,0.235714392936,0.00213002070133,-0.00048577251587]
<<< print "Intercept:" + str(model.intercept)
Intercept:-2.26130330748
<<< print "R^2:" + str(model.summary.r2)
R^2:0.295200579035
<<< model.summary.residuals.show()
+--------------------+
|           residuals|
+--------------------+
| -0.7234737533344147|
|  -0.985466980630501|
| -0.6669710598385468|
|  1.4162434829714794|
|-0.09373154375186754|
|-0.15461747949235072|
| 0.32659061654192545|
|  1.5053877697929803|
|  -0.640142797263989|
|   1.229530260294963|
|-0.03776160295256...|
| -0.5160734239126814|
| -1.5165972740062887|
|  1.3269085258245008|
|  1.7604670124710626|
|  1.2348130901905972|
|   2.318660276655887|
|  1.0936947030883175|
|  1.0169768511417363|
| -1.7744915698181583|
+--------------------+
only showing top 20 rows.