PROJECT = 'cloud-training-demos'  # Replace with your PROJECT
BUCKET = 'cloud-training-demos'  # Replace with your BUCKET
REGION = 'us-central1'            # Choose an available region for Cloud MLE

import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION

%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION

%load_ext google.cloud.bigquery

"""
Explanation: Predicting Babyweight Using BigQuery ML
Learning Objectives
- Explore the machine learning capabilities of BigQuery
- Learn how to train a linear regression model in BigQuery
- Examine the TRAINING_INFO produced by training a model
- Make predictions with a trained model in BigQuery using ML.PREDICT
Introduction
BQML is a great option when a linear model will suffice, or when you want a quick benchmark to beat. But, for more complex models such as neural networks you will need to pull the data out of BigQuery and into an ML Framework like TensorFlow. That being said, what BQML gives up in complexity, it gains in ease of use. 
Please see this notebook for more context on this problem and how the features were chosen.
We'll start as usual by setting our environment variables.
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
    *
FROM
    publicdata.samples.natality
WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
LIMIT 10

"""
Explanation: Exploring the data
Here, we will be taking natality data and training on features to predict the birth weight.
The CDC's Natality data has details on US births from 1969 to 2008 and is available in BigQuery as a public data set. More details: https://bigquery.cloud.google.com/table/publicdata:samples.natality?tab=details
Lets start by looking at the data since 2000 with useful values; i.e. those greater than zero!
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
    weight_pounds, -- this is the label; because it is continuous, we need to use regression
    CAST(is_male AS STRING) AS is_male,
    mother_age,
    CAST(plurality AS STRING) AS plurality,
    gestation_weeks,
    FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth
FROM
  publicdata.samples.natality
WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
LIMIT 10

"""
Explanation: Define features
Looking over the data set, there are a few columns of interest that could be leveraged into features for a reasonable prediction of approximate birth weight.
Further, some feature engineering may be accomplished with the BigQuery CAST function -- in BQML, all strings are considered categorical features and all numeric types are considered continuous ones.
The hashmonth is added so that we can repeatably split the data without leakage -- we want all babies that share a birthday to be either in training set or in test set and not spread between them (otherwise, there would be information leakage when it comes to triplets, etc.)
End of explanation
"""


%%bash
bq --location=US mk -d demo

"""
Explanation: Train a model in BigQuery
With the relevant columns chosen to accomplish predictions, it is then possible to create (train) the model in BigQuery. First, a dataset will be needed store the model. (if this throws an error in Datalab, simply create the dataset from the BigQuery console).
End of explanation
"""


%%bigquery --project $PROJECT
CREATE or REPLACE MODEL demo.babyweight_model_asis
OPTIONS
  (model_type='linear_reg', labels=['weight_pounds']) AS
  
WITH natality_data AS (
  SELECT
    weight_pounds,-- this is the label; because it is continuous, we need to use regression
    CAST(is_male AS STRING) AS is_male,
    mother_age,
    CAST(plurality AS STRING) AS plurality,
    gestation_weeks,
    FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
)

SELECT
    weight_pounds,
    is_male,
    mother_age,
    plurality,
    gestation_weeks
FROM
    natality_data
WHERE
  ABS(MOD(hashmonth, 4)) < 3  -- select 75% of the data as training

"""
Explanation: With the demo dataset ready, it is possible to create a linear regression model to train the model.
This will take approximately 4 minutes to run and will show Done when complete.
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
  *
FROM
  ML.TRAINING_INFO(MODEL `demo.babyweight_model_asis`)

"""
Explanation: Explore the training statistics
During the model training (and after the training), it is possible to see the model's training evaluation statistics. For each training run, a table named &lt;model_name&gt;_eval is created. This table has basic performance statistics for each iteration.
While the new model is training, review the training statistics in the BigQuery UI to see the below model training: https://bigquery.cloud.google.com/. Since these statistics are updated after each iteration of model training, you will see different values for each refresh while the model is training.
The training details may also be viewed after the training completes from this notebook.
End of explanation
"""


from google.cloud import bigquery
bq = bigquery.Client(project=PROJECT)

df = bq.query("SELECT * FROM ML.TRAINING_INFO(MODEL demo.babyweight_model_asis)").to_dataframe()

# plot both lines in same graph
import matplotlib.pyplot as plt
plt.plot( 'iteration', 'loss', data=df, marker='o', color='orange', linewidth=2)
plt.plot( 'iteration', 'eval_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')
plt.xlabel('iteration')
plt.ylabel('loss')
plt.legend();

"""
Explanation: Some of these columns are obvious; although, what do the non-specific ML columns mean (specific to BQML)?


training_run - Will be zero for a newly created model. If the model is re-trained using warm_start, this will increment for each re-training.


iteration - Number of the associated training_run, starting with zero for the first iteration.


duration_ms - Indicates how long the iteration took (in ms).


Note, you can also see these stats by refreshing the BigQuery UI window, finding the &lt;model_name&gt; table, selecting on it, and then the Training Stats sub-header.
Let's plot the training and evaluation loss to see if the model has an overfit.
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
  *
FROM
  ml.PREDICT(MODEL demo.babyweight_model_asis,
      (SELECT
        weight_pounds,
        CAST(is_male AS STRING) AS is_male,
        mother_age,
        CAST(plurality AS STRING) AS plurality,
        gestation_weeks
      FROM
        publicdata.samples.natality
      WHERE
        year > 2000
        AND gestation_weeks > 0
        AND mother_age > 0
        AND plurality > 0
        AND weight_pounds > 0
    ))
LIMIT 100

"""
Explanation: As you can see, the training loss and evaluation loss are essentially identical. We do not seem to be overfitting.
Make a prediction with BQML using the trained model
With a trained model, it is now possible to make a prediction on the values. The only difference from the second query above is the reference to the model. The data has been limited (LIMIT 100) to reduce amount of data returned.
When the ml.predict function is leveraged, output prediction column name for the model is predicted_&lt;label_column_name&gt;.
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
    weight_pounds,
    CAST(is_male AS STRING) AS is_male,
    IF(mother_age < 18, 'LOW',
         IF(mother_age > 45, 'HIGH',
            CAST(mother_age AS STRING))) AS mother_age,
    CAST(plurality AS STRING) AS plurality,
    CAST(gestation_weeks AS STRING) AS gestation_weeks,
    FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
LIMIT 25

"""
Explanation: More advanced...
In the original example, we were taking into account the idea that if no ultrasound has been performed, some of the features (e.g. is_male) will not be known. Therefore, we augmented the dataset with such masked features and trained a single model to deal with both these scenarios.
In addition, during data exploration, we learned that the data size set for mothers older than 45 was quite sparse, so we will discretize the mother age.
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
    weight_pounds,
    'Unknown' AS is_male,
    IF(mother_age < 18, 'LOW',
         IF(mother_age > 45, 'HIGH',
            CAST(mother_age AS STRING))) AS mother_age,
    IF(plurality > 1, 'Multiple', 'Single') AS plurality,
    CAST(gestation_weeks AS STRING) AS gestation_weeks,
    FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
LIMIT 25

"""
Explanation: On the same dataset, will also suppose that it is unknown whether the child is male or female (on the same dataset) to simulate that an ultrasound was not been performed.
End of explanation
"""


%%bigquery --project $PROJECT
WITH with_ultrasound AS (
  SELECT
    weight_pounds,
    CAST(is_male AS STRING) AS is_male,
    IF(mother_age < 18, 'LOW',
         IF(mother_age > 45, 'HIGH',
            CAST(mother_age AS STRING))) AS mother_age,
    CAST(plurality AS STRING) AS plurality,
    CAST(gestation_weeks AS STRING) AS gestation_weeks,
    FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
),

without_ultrasound AS (
  SELECT
    weight_pounds,
    'Unknown' AS is_male,
    IF(mother_age < 18, 'LOW',
         IF(mother_age > 45, 'HIGH',
            CAST(mother_age AS STRING))) AS mother_age,
    IF(plurality > 1, 'Multiple', 'Single') AS plurality,
    CAST(gestation_weeks AS STRING) AS gestation_weeks,
    FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
),

preprocessed AS (
  SELECT * from with_ultrasound
  UNION ALL
  SELECT * from without_ultrasound
)

SELECT
    weight_pounds,
    is_male,
    mother_age,
    plurality,
    gestation_weeks
FROM
    preprocessed
WHERE
  ABS(MOD(hashmonth, 4)) < 3
LIMIT 25

"""
Explanation: Bringing these two separate data sets together, there is now a dataset for male or female children determined with ultrasound or unknown if without.
End of explanation
"""


%%bigquery --project $PROJECT
CREATE or REPLACE MODEL demo.babyweight_model_fc
OPTIONS
  (model_type='linear_reg', labels=['weight_pounds']) AS
  
WITH with_ultrasound AS (
  SELECT
    weight_pounds,
    CAST(is_male AS STRING) AS is_male,
    IF(mother_age < 18, 'LOW',
         IF(mother_age > 45, 'HIGH',
            CAST(mother_age AS STRING))) AS mother_age,
    CAST(plurality AS STRING) AS plurality,
    CAST(gestation_weeks AS STRING) AS gestation_weeks,
    ABS(FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING)))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
),

without_ultrasound AS (
  SELECT
    weight_pounds,
    'Unknown' AS is_male,
    IF(mother_age < 18, 'LOW',
         IF(mother_age > 45, 'HIGH',
            CAST(mother_age AS STRING))) AS mother_age,
    IF(plurality > 1, 'Multiple', 'Single') AS plurality,
    CAST(gestation_weeks AS STRING) AS gestation_weeks,
    ABS(FARM_FINGERPRINT(CONCAT(CAST(YEAR AS STRING), CAST(month AS STRING)))) AS hashmonth
  FROM
    publicdata.samples.natality
  WHERE
    year > 2000
    AND gestation_weeks > 0
    AND mother_age > 0
    AND plurality > 0
    AND weight_pounds > 0
),

preprocessed AS (
  SELECT * from with_ultrasound
  UNION ALL
  SELECT * from without_ultrasound
)

SELECT
    weight_pounds,
    is_male,
    mother_age,
    plurality,
    gestation_weeks
FROM
    preprocessed
WHERE
  ABS(MOD(hashmonth, 4)) < 3

"""
Explanation: Create a new model
With a data set which has been feature engineered, it is ready to create model with the CREATE or REPLACE MODEL statement
This will take 5-10 minutes and will show Done when complete.
End of explanation
"""


bq = bigquery.Client(project=PROJECT)

df = bq.query("SELECT * FROM ML.TRAINING_INFO(MODEL demo.babyweight_model_fc)").to_dataframe()
# plot both lines in same graph
import matplotlib.pyplot as plt
plt.plot( 'iteration', 'loss', data=df, marker='o', color='orange', linewidth=2)
plt.plot( 'iteration', 'eval_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')
plt.xlabel('iteration')
plt.ylabel('loss')
plt.legend();

"""
Explanation: Training statistics
While the new model is training, review the training statistics in the BigQuery UI to see the below model training: https://bigquery.cloud.google.com/
The training details may also be viewed after the training completes from this notebook.
End of explanation
"""


%%bigquery --project $PROJECT
SELECT
  *
FROM
  ml.PREDICT(MODEL demo.babyweight_model_fc,
      (SELECT
          'True' AS is_male,
          '28' AS mother_age,
          '1' AS plurality,
          '38' AS gestation_weeks
    ))

"""
Explanation: Make a prediction with the new model
Perhaps it is of interest to make a prediction of the baby's weight given a number of other factors: Male, Mother is 28 years old, Mother will only have one child, and the baby was born after 38 weeks of pregnancy.
To make this prediction, these values will be passed into the SELECT statement.
End of explanation
"""


