<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 LAB 1b
Step1:  Note
Step2:  Lab Task #1
Step3:  The source dataset
Our dataset is hosted in BigQuery. The CDC's Natality data has details on US births from 1969 to 2008 and is a publically available dataset, meaning anyone with a GCP account has access. Click here to access the dataset.
The natality dataset is relatively large at almost 138 million rows and 31 columns, but simple to understand. weight_pounds is the target, the continuous value we’ll train a model to predict.
Create a BigQuery Dataset and Google Cloud Storage Bucket
A BigQuery dataset is a container for tables, views, and models built with BigQuery ML. Let's create one called babyweight if we have not already done so in an earlier lab. We'll do the same for a GCS bucket for our project too.
Step4:  Create the training and evaluation data tables
Since there is already a publicly available dataset, we can simply create the training and evaluation data tables using this raw input data. First we are going to create a subset of the data limiting our columns to weight_pounds, is_male, mother_age, plurality, and gestation_weeks as well as some simple filtering and a column to hash on for repeatable splitting.
Note
Step5:  Lab Task #3
Step6:  Lab Task #4
Step7:  Split augmented dataset into eval dataset
Exercise
Step8:  Verify table creation
Verify that you created the dataset and training data table.
Step9:  Lab Task #5
Step10:  Verify CSV creation
Verify that we correctly created the CSV files in our bucket.
<ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
Explanation: LAB 1b:  Prepare babyweight dataset.
Learning Objectives
Setup up the environment
Preprocess natality dataset
Augment natality dataset
Create the train and eval tables in BigQuery
Export data from BigQuery to GCS in CSV format
Introduction
In this notebook, we will prepare the babyweight dataset for model development and training to predict the weight of a baby before it is born.  We will use BigQuery to perform data augmentation and preprocessing which will be used for AutoML Tables, BigQuery ML, and Keras models trained on Cloud AI Platform.
In this lab, we will set up the environment, create the project dataset, preprocess and augment natality dataset, create the train and eval tables in BigQuery, and export data from BigQuery to GCS in CSV format.
Each learning objective will correspond to a #TODO in this student lab notebook -- try to complete this notebook first and then review the solution notebook.
Set up environment variables and load necessary libraries
Check that the Google BigQuery library is installed and if not, install it.
End of explanation
import os
from google.cloud import bigquery
Explanation: Note: Restart your kernel to use updated packages.
Kindly ignore the deprecation warnings and incompatibility errors related to google-cloud-storage.
Import necessary libraries.
End of explanation
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
# TODO: Change environment variables
PROJECT = "cloud-training-demos"  # REPLACE WITH YOUR PROJECT NAME
BUCKET = "BUCKET"  # REPLACE WITH YOUR BUCKET NAME, DEFAULT BUCKET WILL BE PROJECT ID
REGION = "us-central1"  # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["BUCKET"] = PROJECT if BUCKET == "BUCKET" else BUCKET # DEFAULT BUCKET WILL BE PROJECT ID
os.environ["REGION"] = REGION
if PROJECT == "cloud-training-demos":
    print("Don't forget to update your PROJECT name! Currently:", PROJECT)
Explanation: Lab Task #1: Set environment variables.
Set environment variables so that we can use them throughout the entire lab. We will be using our project name for our bucket, so you only need to change your project and region.
End of explanation
%%bash
## Create a BigQuery dataset for babyweight if it doesn't exist
datasetexists=$(bq ls -d | grep -w # TODO: Add dataset name)
if [ -n "$datasetexists" ]; then
    echo -e "BigQuery dataset already exists, let's not recreate it."
else
    echo "Creating BigQuery dataset titled: babyweight"
    
    bq --location=US mk --dataset \
        --description "Babyweight" \
        $PROJECT:# TODO: Add dataset name
    echo "Here are your current datasets:"
    bq ls
fi
    
## Create GCS bucket if it doesn't exist already...
exists=$(gsutil ls -d | grep -w gs://${BUCKET}/)
if [ -n "$exists" ]; then
    echo -e "Bucket exists, let's not recreate it."
    
else
    echo "Creating a new GCS bucket."
    gsutil mb -l ${REGION} gs://${BUCKET}
    echo "Here are your current buckets:"
    gsutil ls
fi
Explanation: The source dataset
Our dataset is hosted in BigQuery. The CDC's Natality data has details on US births from 1969 to 2008 and is a publically available dataset, meaning anyone with a GCP account has access. Click here to access the dataset.
The natality dataset is relatively large at almost 138 million rows and 31 columns, but simple to understand. weight_pounds is the target, the continuous value we’ll train a model to predict.
Create a BigQuery Dataset and Google Cloud Storage Bucket
A BigQuery dataset is a container for tables, views, and models built with BigQuery ML. Let's create one called babyweight if we have not already done so in an earlier lab. We'll do the same for a GCS bucket for our project too.
End of explanation
%%bigquery
CREATE OR REPLACE TABLE
    babyweight.babyweight_data AS
SELECT
    # TODO: Add selected raw features and preprocessed features
FROM
    publicdata.samples.natality
WHERE
    # TODO: Add filters
Explanation: Create the training and evaluation data tables
Since there is already a publicly available dataset, we can simply create the training and evaluation data tables using this raw input data. First we are going to create a subset of the data limiting our columns to weight_pounds, is_male, mother_age, plurality, and gestation_weeks as well as some simple filtering and a column to hash on for repeatable splitting.
Note:  The dataset in the create table code below is the one created previously, e.g. "babyweight".
Lab Task #2: Preprocess and filter dataset
We have some preprocessing and filtering we would like to do to get our data in the right format for training.
Preprocessing:
* Cast is_male from BOOL to STRING
* Cast plurality from INTEGER to STRING where [1, 2, 3, 4, 5] becomes ["Single(1)", "Twins(2)", "Triplets(3)", "Quadruplets(4)", "Quintuplets(5)"]
* Add hashcolumn hashing on year and month
Filtering:
* Only want data for years later than 2000
* Only want baby weights greater than 0
* Only want mothers whose age is greater than 0
* Only want plurality to be greater than 0
* Only want the number of weeks of gestation to be greater than 0
End of explanation
%%bigquery
CREATE OR REPLACE TABLE
    babyweight.babyweight_augmented_data AS
SELECT
    weight_pounds,
    is_male,
    mother_age,
    plurality,
    gestation_weeks,
    hashmonth
FROM
    babyweight.babyweight_data
UNION ALL
SELECT
    # TODO: Replace is_male and plurality as indicated above
FROM
    babyweight.babyweight_data
Explanation: Lab Task #3: Augment dataset to simulate missing data
Now we want to augment our dataset with our simulated babyweight data by setting all gender information to Unknown and setting plurality of all non-single births to Multiple(2+).
End of explanation
%%bigquery
CREATE OR REPLACE TABLE
    babyweight.babyweight_data_train AS
SELECT
    weight_pounds,
    is_male,
    mother_age,
    plurality,
    gestation_weeks
FROM
    babyweight.babyweight_augmented_data
WHERE
    # TODO: Modulo hashmonth to be approximately 75% of the data
Explanation: Lab Task #4: Split augmented dataset into train and eval sets
Using hashmonth, apply a modulo to get approximately a 75/25 train/eval split.
Split augmented dataset into train dataset
Exercise:   RUN the query to create the training data table.
End of explanation
%%bigquery
CREATE OR REPLACE TABLE
    babyweight.babyweight_data_eval AS
SELECT
    weight_pounds,
    is_male,
    mother_age,
    plurality,
    gestation_weeks
FROM
    babyweight.babyweight_augmented_data
WHERE
    # TODO: Modulo hashmonth to be approximately 25% of the data
Explanation: Split augmented dataset into eval dataset
Exercise:   RUN the query to create the evaluation data table.
End of explanation
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_train
LIMIT 0
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_eval
LIMIT 0
Explanation: Verify table creation
Verify that you created the dataset and training data table.
End of explanation
# Construct a BigQuery client object.
client = bigquery.Client()
dataset_name = # TODO: Add dataset name
# Create dataset reference object
dataset_ref = client.dataset(
    dataset_id=dataset_name, project=client.project)
# Export both train and eval tables
for step in [# TODO: Loop over train and eval]:
    destination_uri = os.path.join(
        "gs://", BUCKET, dataset_name, "data", "{}*.csv".format(step))
    table_name = "babyweight_data_{}".format(step)
    table_ref = dataset_ref.table(table_name)
    extract_job = client.extract_table(
        table_ref,
        destination_uri,
        # Location must match that of the source table.
        location="US",
    )  # API request
    extract_job.result()  # Waits for job to complete.
    print("Exported {}:{}.{} to {}".format(
        client.project, dataset_name, table_name, destination_uri))
Explanation: Lab Task #5: Export from BigQuery to CSVs in GCS
Use BigQuery Python API to export our train and eval tables to Google Cloud Storage in the CSV format to be used later for TensorFlow/Keras training. We'll want to use the dataset we've been using above as well as repeat the process for both training and evaluation data.
End of explanation
%%bash
gsutil ls gs://${BUCKET}/babyweight/data/*.csv
%%bash
gsutil cat gs://${BUCKET}/babyweight/data/train000000000000.csv | head -5
%%bash
gsutil cat gs://${BUCKET}/babyweight/data/eval000000000000.csv | head -5
Explanation: Verify CSV creation
Verify that we correctly created the CSV files in our bucket.
End of explanation
<END_TASK>