PROJECT = !gcloud config get-value project
PROJECT = PROJECT[0]

%env PROJECT=$PROJECT

"""
Explanation: Basic Feature Engineering in BQML
Learning Objectives

Create SQL statements to evaluate the model
Extract temporal features
Perform a feature cross on temporal features

Overview
In this lab, we utilize feature engineering to improve the prediction of the fare amount for a taxi ride in New York City.  We will use BigQuery ML to build a taxifare prediction model, using feature engineering to improve and create a final model.
In this Notebook we set up the environment, create the project dataset, create a feature engineering table, create and evaluate a baseline model, extract temporal features, perform a feature cross on temporal features, and evaluate model performance throughout the process.
End of explanation
"""


%%bash
# Create a BigQuery dataset for feat_eng if it doesn't exist
datasetexists=$(bq ls -d | grep -w feat_eng)

if [ -n "$datasetexists" ]; then
    echo -e "BigQuery dataset already exists, let's not recreate it."

else
    echo "Creating BigQuery dataset titled: feat_eng"
    
    bq --location=US mk --dataset \
        --description 'Taxi Fare' \
        $PROJECT:feat_eng
    echo "\nHere are your current datasets:"
    bq ls
fi    

"""
Explanation: The source dataset
Our dataset is hosted in BigQuery. The taxi fare data is a publically available dataset, meaning anyone with a GCP account has access. Click here to access the dataset.
The Taxi Fare dataset is relatively large at 55 million training rows, but simple to understand, with only six features. The fare_amount is the target, the continuous value we’ll train a model to predict.
Create a BigQuery Dataset
A BigQuery dataset is a container for tables, views, and models built with BigQuery ML. Let's create one called feat_eng 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
  feat_eng.feateng_training_data AS
SELECT
  (tolls_amount + fare_amount) AS fare_amount,
  passenger_count*1.0 AS passengers,
  pickup_datetime,
  pickup_longitude AS pickuplon,
  pickup_latitude AS pickuplat,
  dropoff_longitude AS dropofflon,
  dropoff_latitude AS dropofflat
FROM
  `nyc-tlc.yellow.trips`
WHERE
  MOD(ABS(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING))), 10000) = 1
  AND fare_amount >= 2.5
  AND passenger_count > 0
  AND pickup_longitude > -78
  AND pickup_longitude < -70
  AND dropoff_longitude > -78
  AND dropoff_longitude < -70
  AND pickup_latitude > 37
  AND pickup_latitude < 45
  AND dropoff_latitude > 37
  AND dropoff_latitude < 45

"""
Explanation: Create the training data table
Since there is already a publicly available dataset, we can simply create the training data table using this raw input data.  Note the WHERE clause in the below query:  This clause allows us to TRAIN a portion of the data (e.g. one hundred thousand rows versus one million rows), which keeps your query costs down.  If you need a refresher on using MOD() for repeatable splits see this post.  

Note:  The dataset in the create table code below is the one created previously, e.g. feat_eng.  The table name is feateng_training_data.  Run the query to create the table.
End of explanation
"""


%%bigquery

# LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT
  *
FROM
  feat_eng.feateng_training_data
LIMIT
  0

"""
Explanation: Verify table creation
Verify that you created the dataset.
End of explanation
"""


%%bigquery

CREATE OR REPLACE MODEL
  feat_eng.baseline_model OPTIONS (model_type='linear_reg',
    input_label_cols=['fare_amount']) AS
SELECT
  fare_amount,
  passengers,
  pickup_datetime,
  pickuplon,
  pickuplat,
  dropofflon,
  dropofflat
FROM
  feat_eng.feateng_training_data

"""
Explanation: Baseline Model:  Create the baseline model
Next, you create a linear regression baseline model with no feature engineering.  Recall that a model in BigQuery ML represents what an ML system has learned from the training data.  A baseline model is a solution to a problem without applying any machine learning techniques.  
When creating a BQML model, you must specify the model type (in our case linear regression) and the input label (fare_amount).  Note also that we are using the training data table as the data source.
Now we create the SQL statement to create the baseline model.
End of explanation
"""


%%bigquery
# Eval statistics on the held out data.

SELECT
  *,
  SQRT(loss) AS rmse
FROM
  ML.TRAINING_INFO(MODEL feat_eng.baseline_model)

%%bigquery

SELECT
  *
FROM
  ML.EVALUATE(MODEL feat_eng.baseline_model)

"""
Explanation: REMINDER:  The query takes several minutes to complete. After the first iteration is complete, your model (baseline_model) appears in the navigation panel of the BigQuery web UI. Because the query uses a CREATE MODEL statement to create a model, you do not see query results.
You can observe the model as it's being trained by viewing the Model stats tab in the BigQuery web UI. As soon as the first iteration completes, the tab is updated. The stats continue to update as each iteration completes.
Once the training is done, visit the BigQuery Cloud Console and look at the model that has been trained. Then, come back to this notebook.
Evaluate the baseline model
Note that BigQuery automatically split the data we gave it, and trained on only a part of the data and used the rest for evaluation.  After creating your model, you evaluate the performance of the regressor using the ML.EVALUATE function. The ML.EVALUATE function evaluates the predicted values against the actual data.
NOTE: The results are also displayed in the BigQuery Cloud Console under the Evaluation tab.
Review the learning and eval statistics for the baseline_model.
End of explanation
"""


# TODO: Your code here

"""
Explanation: NOTE: Because you performed a linear regression, the results include the following columns:

mean_absolute_error
mean_squared_error
mean_squared_log_error
median_absolute_error
r2_score
explained_variance

Resource for an explanation of the Regression Metrics.
Mean squared error (MSE) - Measures the difference between the values our model predicted using the test set and the actual values. You can also think of it as the distance between your regression (best fit) line and the predicted values. 
Root mean squared error (RMSE) - The primary evaluation metric for this ML problem is the root mean-squared error. RMSE measures the difference between the predictions of a model, and the observed values. A large RMSE is equivalent to a large average error, so smaller values of RMSE are better. One nice property of RMSE is that the error is given in the units being measured, so you can tell very directly how incorrect the model might be on unseen data.
R2:  An important metric in the evaluation results is the R2 score. The R2 score is a statistical measure that determines if the linear regression predictions approximate the actual data. Zero (0) indicates that the model explains none of the variability of the response data around the mean.  One (1) indicates that the model explains all the variability of the response data around the mean.
Exercise. Next, we write a SQL query to take the SQRT() of the mean squared error as your loss metric for evaluation for the benchmark_model.
End of explanation
"""


%%bigquery

CREATE OR REPLACE MODEL
  feat_eng.model_1 OPTIONS (model_type='linear_reg',
    input_label_cols=['fare_amount']) AS
SELECT
  fare_amount,
  passengers,
  pickup_datetime,
    
  # TODO: Your code here

  pickuplon,
  pickuplat,
  dropofflon,
  dropofflat
FROM
  feat_eng.feateng_training_data

"""
Explanation: Model 1:  EXTRACT dayofweek from the pickup_datetime feature.


As you recall, dayofweek is an enum representing the 7 days of the week. This factory allows the enum to be obtained from the int value. The int value follows the ISO-8601 standard, from 1 (Monday) to 7 (Sunday).


If you were to extract the dayofweek from pickup_datetime using BigQuery SQL, the dataype returned would be integer.


Exercise. Next, we create a model titled "model_1" from the benchmark model and extract out the DayofWeek.
End of explanation
"""


%%bigquery

SELECT
  *,
  SQRT(loss) AS rmse
FROM
  ML.TRAINING_INFO(MODEL feat_eng.model_1)

%%bigquery

SELECT
  *
FROM
  ML.EVALUATE(MODEL feat_eng.model_1)

"""
Explanation: Once the training is done, visit the BigQuery Cloud Console and look at the model that has been trained. Then, come back to this notebook.
Next, two distinct SQL statements show the TRAINING and EVALUATION metrics of model_1.
End of explanation
"""


%%bigquery

SELECT
  SQRT(mean_squared_error) AS rmse
FROM
  ML.EVALUATE(MODEL feat_eng.model_1)

"""
Explanation: Here we run a SQL query to take the SQRT() of the mean squared error as your loss metric for evaluation for the benchmark_model.
End of explanation
"""


%%bigquery

CREATE OR REPLACE MODEL
  feat_eng.model_2 OPTIONS (model_type='linear_reg',
    input_label_cols=['fare_amount']) AS
SELECT
  fare_amount,
  passengers,

   # TODO: Your code here
    
  pickuplon,
  pickuplat,
  dropofflon,
  dropofflat
FROM
  `feat_eng.feateng_training_data`

"""
Explanation: Model 2:  EXTRACT hourofday from the pickup_datetime feature
As you recall, pickup_datetime is stored as a TIMESTAMP, where the Timestamp format is retrieved in the standard output format – year-month-day hour:minute:second (e.g. 2016-01-01 23:59:59).  Hourofday returns the integer number representing the hour number of the given date.
Hourofday is best thought of as a discrete ordinal variable (and not a categorical feature), as the hours can be ranked (e.g. there is a natural ordering of the values).  Hourofday has an added characteristic of being cyclic, since 12am follows 11pm and precedes 1am.
Exercise. Next, we create a model titled "model_2" and EXTRACT the hourofday from the pickup_datetime feature to improve our model's rmse.
End of explanation
"""


%%bigquery

SELECT
  *
FROM
  ML.EVALUATE(MODEL feat_eng.model_2)

%%bigquery

SELECT
  SQRT(mean_squared_error) AS rmse
FROM
  ML.EVALUATE(MODEL feat_eng.model_2)

"""
Explanation: Evaluate the model.
End of explanation
"""


%%bigquery

CREATE OR REPLACE MODEL
  feat_eng.model_3 OPTIONS (model_type='linear_reg',
    input_label_cols=['fare_amount']) AS
SELECT
  fare_amount,
  passengers,

  # TODO: Your code here
    
  pickuplon,
  pickuplat,
  dropofflon,
  dropofflat
FROM
  `feat_eng.feateng_training_data`

"""
Explanation: Model 3:  Feature cross dayofweek and hourofday using CONCAT
First, let’s allow the model to learn traffic patterns by creating a new feature that combines the time of day and day of week (this is called a feature cross. 
Note:  BQML by default assumes that numbers are numeric features, and strings are categorical features.  We need to convert both the dayofweek and hourofday features to strings because the model (Neural Network) will automatically treat any integer as a numerical value rather than a categorical value.  Thus, if not cast as a string, the dayofweek feature will be interpreted as numeric values (e.g. 1,2,3,4,5,6,7) and hour ofday will also be interpreted as numeric values (e.g. the day begins at midnight, 00:00, and the last minute of the day begins at 23:59 and ends at 24:00).  As such, there is no way to distinguish the "feature cross" of hourofday and dayofweek "numerically".  Casting the dayofweek and hourofday as strings ensures that each element will be treated like a label and will get its own coefficient associated with it.
Exercise. Create the SQL statement to feature cross the dayofweek and hourofday using the CONCAT function.  Name the model "model_3"
End of explanation
"""


%%bigquery

SELECT
  *
FROM
  ML.EVALUATE(MODEL feat_eng.model_3)

%%bigquery

SELECT
  SQRT(mean_squared_error) AS rmse
FROM
  ML.EVALUATE(MODEL feat_eng.model_3)

"""
Explanation: Next we evaluate the model.
End of explanation
"""


