Learning Objectives
In this notebook, 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 lab we set up the environment, create the project dataset, create a feature engineering table, create and evaluate a benchmark model, extract numeric features, perform a feature cross and evaluate model performance.
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. NOTE TO SELF: UPDATE HYPERLINK.
In [47]:
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
In [48]:
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-west1-b" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["REGION"] = REGION
os.environ["BUCKET"] = PROJECT # DEFAULT BUCKET WILL BE PROJECT ID
if PROJECT == "your-gcp-project-here":
print("Don't forget to update your PROJECT name! Currently:", PROJECT)
Check that the Google BigQuery library is installed and if not, install it.
In [ ]:
!pip freeze | grep google-cloud-bigquery==1.6.1 || pip install google-cloud-bigquery==1.6.1
In [75]:
%%bash
## Create a BigQuery dataset for feat_eng_TEST 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' \
$PROJECT:feat_eng
echo "\nHere are your current datasets:"
bq ls
fi
## Create GCS bucket if it doesn't exist already...
exists=$(gsutil ls -d | grep -w gs://${PROJECT}/)
if [ -n "$exists" ]; then
echo -e "Bucket exists, let's not recreate it."
else
echo "Creating a new GCS bucket."
gsutil mb -l ${REGION} gs://${PROJECT}
echo "\nHere are your current buckets:"
gsutil ls
fi
Since there is already a publicly available dataset, we can simply create the training data table. Note the WHERE clause in the below query: This clause allows us to TRAIN a portion of the data (e.g. one million rows versus a billion rows), which keeps your query costs down.
In [76]:
%%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
Out[76]:
In [154]:
%%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
Out[154]:
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.
In [ ]:
%%bigquery
# TODO:
In [84]:
%%bigquery
# SOLUTION
CREATE OR REPLACE MODEL feat_eng.benchmark_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
Out[84]:
REMINDER: The query takes several minutes to complete. After the first iteration is complete, your model (benchmark_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.
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.
In [115]:
%%bigquery
#Eval statistics on the held out data.
SELECT *, SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL feat_eng.benchmark_model)
Out[115]:
In [112]:
%%bigquery
SELECT * FROM ML.EVALUATE(MODEL feat_eng.benchmark_model)
Out[112]:
NOTE: Because you performed a linear regression, the results include the following columns:
Resource for an explanation of the regression metrics: 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. 0 indicates that the model explains none of the variability of the response data around the mean. 1 indicates that the model explains all the variability of the response data around the mean.
In [ ]:
%%bigquery
# TODO
In [110]:
%%bigquery
#SOLUTION
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.benchmark_model)
Out[110]:
In [ ]:
# TODO
In [103]:
%%bigquery
#SOLUTION
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,
EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
pickuplon,
pickuplat,
dropofflon,
dropofflat FROM feat_eng.feateng_training_data
Out[103]:
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.
In [ ]:
#Create the SQL statements to extract Model_1 TRAINING metrics.
# TODO: Your code goes here
In [ ]:
#Create the SQL statements to extract Model_1 EVALUATION metrics.
# TODO: Your code goes here
In [104]:
%%bigquery
SELECT *, SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL feat_eng.model_1)
Out[104]:
In [108]:
%%bigquery
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_1)
Out[108]:
In [ ]:
#Create the SQL statement to EVALUATE Model_1 here.
# TODO: Your code goes here
In [117]:
%%bigquery
#SOLUTION
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_1)
Out[117]:
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). Hourofaday returns the integer number representing the hour number of the given date.
In [ ]:
# TODO: Your code goes here
In [121]:
%%bigquery
#SOLUTION
CREATE OR REPLACE MODEL feat_eng.model_2
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
pickuplon,
pickuplat,
dropofflon,
dropofflat
FROM `feat_eng.feateng_training_data`
Out[121]:
In [ ]:
# TODO: Your code goes here
In [ ]:
# TODO: Your code goes here
In [122]:
%%bigquery
#SOLUTION
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_2)
Out[122]:
In [123]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_2)
Out[123]:
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).
Modify model_2 to create a feature cross that combines the time of day and day of week. Note: CAST DAYOFWEEK and HOUR as strings. Name the model "model_3".
In this lab, we will modify the SQL to first use the CONCAT function to concatenate (feature cross) the dayofweek and hourofday features. Then, we will use the ML.FEATURE_CROSS, BigQuery's new pre-processing feature cross function.
Note: BQML by default assumes that numbers are numeric features, and strings are categorical features. We need to convert these features to strings because the Neural Network will treat 1,2,3,4,5,6,7 as numeric values. Thus, there is no way to distinguish the time of day and day of week "numerically."
In [ ]:
# TODO: Your code goes here
In [124]:
%%bigquery
#SOLUTION
CREATE OR REPLACE MODEL feat_eng.revised_model_3
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
#EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
#EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS hourofday,
pickuplon,
pickuplat,
dropofflon,
dropofflat
FROM `feat_eng.feateng_training_data`
Out[124]:
In [4]:
%%bigquery
#SOLUTION
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_3)
Out[4]:
In [5]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_3)
Out[5]:
BigQuery ML now has ML.FEATURE_CROSS, a pre-processing function that performs a feature cross.
ML.FEATURE_CROSS generates a STRUCT feature with all combinations of crossed categorical features, except for 1-degree items (the original features) and self-crossing items.
Syntax: ML.FEATURE_CROSS(STRUCT(features), degree)
The feature parameter is a categorical features separated by comma to be crossed. The maximum number of input features is 10. Unnamed feature is not allowed in features. Duplicates are not allowed in features.
Degree(optional): The highest degree of all combinations. Degree should be in the range of [1, 4]. Default to 2.
Output: The function outputs a STRUCT of all combinations except for 1-degree items (the original features) and self-crossing items, with field names as concatenation of original feature names and values as the concatenation of the column string values.
In [ ]:
%%bigquery
CREATE OR REPLACE MODEL feat_eng.model_4
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
#EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
#EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
#CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
#CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS hourofday,
ML.FEATURE_CROSS(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
pickuplon,
pickuplat,
dropofflon,
dropofflat
FROM `feat_eng.feateng_training_data`
In [18]:
%%bigquery
#SOLUTION
CREATE OR REPLACE MODEL feat_eng.model_4
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
#EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
#EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
#CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
#CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS hourofday,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
pickuplon,
pickuplat,
dropofflon,
dropofflat
FROM `feat_eng.feateng_training_data`
Out[18]:
In [19]:
%%bigquery
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_4)
Out[19]:
In [20]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_4)
Out[20]:
Learning Objectives
In this notebook, we derive coordinate features, feature cross coordinate features, evaluate model performance, and cleanup the code.
Pickup coordinate:
Dropoff coordinate:
NOTES:
The pick-up and drop-off longitude and latitude data are crucial to predicting the fare amount as fare amounts in NYC taxis are largely determined by the distance traveled. Assuch, we need to teach the model the Euclidean distance between the pick-up and drop-off points.
Recall that latitude and longitude allows us to specify any location on Earth using a set of coordinates. In our training data set, we restricted our data points to only pickups and drop offs within NYC. NYC has an approximate longitude range of -74.05 to -73.75 and a latitude range of 40.63 to 40.85.
The dataset contains information regarding the pickup and drop off coordinates. However, there is no information regarding the distance between the pickup and drop off points. Therefore, we create a new feature that calculates the distance between each pair of pickup and drop off points. We can do this using the Euclidean Distance, which is the straight-line distance between any two coordiante points.
We need to convert those coordinates into a single column of a spatial data type. We will use the The ST_Distance function, which returns the minimum distance between two spatial objects.
In [ ]:
# TODO
In [21]:
%%bigquery
#Solution
CREATE OR REPLACE MODEL feat_eng.model_5
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
#EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
#EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
#CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
#CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS hourofday,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
#pickuplon,
#pickuplat,
#dropofflon,
#dropofflat,
ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat))
AS euclidean
FROM `feat_eng.feateng_training_data`
Out[21]:
In [ ]:
# TODO: Your code goes here
In [22]:
%%bigquery
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_5)
Out[22]:
In [23]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_5)
Out[23]:
In this section, we feature cross the pick-up and drop-off locations so that the model can learn pick-up-drop-off pairs that will require tolls.
This step takes the geographic point corresponding to the pickup point and grids to a 0.1-degree-latitude/longitude grid (approximately 8km x 11km in New York—we should experiment with finer resolution grids as well). Then, it concatenates the pickup and dropoff grid points to learn “corrections” beyond the Euclidean distance associated with pairs of pickup and dropoff locations.
Because the lat and lon by themselves don't have meaning, but only in conjunction, it may be useful to treat the fields as a pair instead of just using them as numeric values. However, lat and lon are continuous numbers, so we have to discretize them first. That's what SnapToGrid does.
REMINDER: The ST_GEOGPOINT creates a GEOGRAPHY with a single point. ST_GEOGPOINT creates a point from the specified FLOAT64 longitude and latitude parameters and returns that point in a GEOGRAPHY value. The ST_Distance function returns the minimum distance between two spatial objectsa. It also returns meters for geographies and SRID units for geometrics.
In [ ]:
%%bigquery
#TODO
CREATE OR REPLACE MODEL feat_eng.model_6
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
#EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
#EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
#CONCAT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING),
#CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING)) AS hourofday,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
#pickuplon,
#pickuplat,
#dropofflon,
#dropofflat,
ST_AsText(ST_SnapToGrid(ST_GeogPoint(pickuplat,pickuplon,pickuplat), 0.05)),
ST_AsText(ST_GeogPoint(dropofflon, dropofflat,dropofflon), 0.04)) AS pickup_and_dropoff
FROM `feat_eng.feateng_training_data`
In [142]:
%%bigquery
#SOLUTION
CREATE OR REPLACE MODEL feat_eng.model_6
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
#pickup_datetime,
#EXTRACT(DAYOFWEEK FROM pickup_datetime) AS dayofweek,
#EXTRACT(HOUR FROM pickup_datetime) AS hourofday,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
#pickuplon,
#pickuplat,
#dropofflon,
#dropofflat,
ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat))
AS euclidean,
CONCAT(ST_AsText(ST_SnapToGrid(ST_GeogPoint(pickuplon, pickuplat), 0.01)),
ST_AsText(ST_SnapToGrid(ST_GeogPoint(dropofflon, dropofflat), 0.01)))
AS pickup_and_dropoff
FROM `feat_eng.feateng_training_data`
Out[142]:
In [7]:
%%bigquery
#SOLUTION
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_6)
Out[7]:
In [144]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_6)
Out[144]:
In [24]:
%%bigquery
#Solution
CREATE OR REPLACE MODEL feat_eng.model_6
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
SELECT
fare_amount,
passengers,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
ST_Distance(ST_GeogPoint(pickuplon, pickuplat), ST_GeogPoint(dropofflon, dropofflat))
AS euclidean,
CONCAT(ST_AsText(ST_SnapToGrid(ST_GeogPoint(pickuplon, pickuplat), 0.01)),
ST_AsText(ST_SnapToGrid(ST_GeogPoint(dropofflon, dropofflat), 0.01)))
AS pickup_and_dropoff
FROM `feat_eng.feateng_training_data`
Out[24]:
Learning Objectives
In this notebook, we apply the BUCKETIZE function, the TRANSFORM clause, L2 Regularization, and perform model evaluation.
Here are some of the preprocessing functions in BigQuery ML:
Bucketize is a pre-processing function that creates "buckets" (e.g bins) - e.g. it bucketizes a continuous numerical feature into a string feature with bucket names as the value.
ML.BUCKETIZE(feature, split_points)
feature: A numerical column.
split_points: Array of numerical points to split the continuous values in feature into buckets. With n split points (s1, s2 … sn), there will be n+1 buckets generated.
Output: The function outputs a STRING for each row, which is the bucket name. bucketname is in the format of bin
Currently, our model uses the ST_GeogPoint function to derive the pickup and dropoff feature. In this lab, we use the BUCKETIZE function to create the pickup and dropoff feature.
In [ ]:
#TODO
In [1]:
%%bigquery
#SOLUTION
CREATE OR REPLACE MODEL feat_eng.model_7
OPTIONS
(model_type='linear_reg',
input_label_cols=['fare_amount'])
AS
AS
SELECT
fare_amount,
passengers,
SQRT( (pickuplon-dropofflon)*(pickuplon-dropofflon) + (pickuplat-dropofflat)*(pickuplat-dropofflat) ) AS euclidean,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
CONCAT(
ML.BUCKETIZE(pickuplon, GENERATE_ARRAY(-78, -70, 0.01)),
ML.BUCKETIZE(pickuplat, GENERATE_ARRAY(37, 45, 0.01)),
ML.BUCKETIZE(dropofflon, GENERATE_ARRAY(-78, -70, 0.01)),
ML.BUCKETIZE(dropofflat, GENERATE_ARRAY(37, 45, 0.01))
) AS pickup_and_dropoff
FROM `feat_eng.feateng_training_data`
Out[1]:
In [2]:
%%bigquery
SELECT *, SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL feat_eng.model_7)
Out[2]:
In [3]:
%%bigquery
SELECT * FROM ML.EVALUATE(MODEL feat_eng.model_7)
Out[3]:
In [4]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.model_7)
Out[4]:
Before we perform our prediction, we should encapsulate the entire feature set in a TRANSFORM clause. BigQuery ML now supports defining data transformations during model creation, which will be automatically applied during prediction and evaluation. This is done through the TRANSFORM clause in the existing CREATE MODEL statement. By using the TRANSFORM clause, user specified transforms during training will be automatically applied during model serving (prediction, evaluation, etc.)
In our case, we are using the TRANSFORM clause to separate out the raw input data from the TRANSFORMED features. The input columns of the TRANSFORM clause is the query_expr (AS SELECT part). The output columns of TRANSFORM from select_list are used in training. These transformed columns are post-processed with standardization for numerics and one-hot encoding for categorical variables by default.
The advantage of encapsulating features in the TRANSFORM is the client code doing the PREDICT doesn't change. Our model improvement is transparent to client code. Note that the TRANSFORM clause MUST be placed after the CREATE statement.
Sometimes, the training RMSE is quite reasonable, but the evaluation RMSE illustrate more error. Given the severity of the delta between the EVALUATION RMSE and the TRAINING RMSE, it may be an indication of overfitting. When we do feature crosses, we run into the risk of overfitting (for example, when a particular day-hour combo doesn't have enough taxirides).
In [ ]:
#TODO
In [12]:
%%bigquery
#SOLUTION
CREATE OR REPLACE MODEL feat_eng.final_model
TRANSFORM(
fare_amount,
SQRT( (pickuplon-dropofflon)*(pickuplon-dropofflon) + (pickuplat-dropofflat)*(pickuplat-dropofflat) ) AS euclidean,
ML.FEATURE_CROSS(STRUCT(CAST(EXTRACT(DAYOFWEEK FROM pickup_datetime) AS STRING) AS dayofweek,
CAST(EXTRACT(HOUR FROM pickup_datetime) AS STRING) AS hourofday)) AS day_hr,
CONCAT(
ML.BUCKETIZE(pickuplon, GENERATE_ARRAY(-78, -70, 0.01)),
ML.BUCKETIZE(pickuplat, GENERATE_ARRAY(37, 45, 0.01)),
ML.BUCKETIZE(dropofflon, GENERATE_ARRAY(-78, -70, 0.01)),
ML.BUCKETIZE(dropofflat, GENERATE_ARRAY(37, 45, 0.01))
) AS pickup_and_dropoff
)
OPTIONS(input_label_cols=['fare_amount'], model_type='linear_reg', l2_reg=0.1)
AS
SELECT * FROM feat_eng.feateng_training_data
Out[12]:
In [7]:
%%bigquery
SELECT *, SQRT(loss) AS rmse FROM ML.TRAINING_INFO(MODEL feat_eng.final_model)
Out[7]:
In [8]:
%%bigquery
SELECT * FROM ML.EVALUATE(MODEL feat_eng.final_model)
Out[8]:
In [9]:
%%bigquery
SELECT SQRT(mean_squared_error) AS rmse FROM ML.EVALUATE(MODEL feat_eng.final_model)
Out[9]:
Learning Objectives
In this notebook, we create prediction models, evaluate model performance, and examine the role of feature engineering on the ML problem.
Now that you have evaluated your model, the next step is to use it to predict an outcome. You use your model to predict the taxifare amount. The ML.PREDICT function is used to predict results using your model: feat_eng.final_model.
Since this is a regression model (predicting a continuous numerical value), the best way to see how it performed is to evaluate the difference between the value predicted by the model and the benchmark score. We can do this with an ML.PREDICT query.
In [ ]:
%%bigquery
#TODO
SELECT * FROM ML.EVALUATE(MODEL feat_eng.benchmark_model, (
-73.982683 AS pickuplon,
40.742104 AS pickuplat,
-73.983766 AS dropofflon,
40.755174 AS dropofflat,
3.0 AS passengers,
TIMESTAMP('2019-06-03 04:21:29.769443 UTC) AS pickup_datetime
))
In [9]:
%%bigquery
#SOLUTION
# This is the prediction query FOR heading 1.3 miles uptown in New York City on 2019-06-03 at 04:21:29.769443 UTC time with 3 passengers.
SELECT * FROM ML.PREDICT(MODEL feat_eng.final_model, (
SELECT
-73.982683 AS pickuplon,
40.742104 AS pickuplat,
-73.983766 AS dropofflon,
40.755174 AS dropofflat,
3.0 AS passengers,
TIMESTAMP('2019-06-03 04:21:29.769443 UTC') AS pickup_datetime
))
Out[9]:
In [29]:
#TODO
In [10]:
%%bigquery
#SOLUTION - remove passengers
SELECT * FROM ML.PREDICT(MODEL feat_eng.final_model, (
SELECT
-73.982683 AS pickuplon,
40.742104 AS pickuplat,
-73.983766 AS dropofflon,
40.755174 AS dropofflat,
TIMESTAMP('2019-06-03 04:21:29.769443 UTC') AS pickup_datetime
))
Out[10]:
Our ML problem was to develop a model to predict taxi fare based on distance -- from one point to another in New York City. Using feature engineering, we were able to predict a taxi fare of $6.08 in New York City, with an R2 score of .75, and an RMSE of 4.653 based upon the distance travelled.
Create a RMSE summary table: benchmark_model = 8.299407 model_1 = 9.431 --EXTRACT DayOfWeek from the pickup_datetime feature model_2 = 8.408 --EXTRACT hourofday from the pickup_datetime feature model_3 = 9.657 --Feature cross dayofweek and hourofday -Feature Cross does lead ot overfitting model_4 = 9.657 --Apply the ML.FEATURE_CROSS clause to categorical features model_5 = 5.588 --Feature cross coordinate features to create a Euclidean feature model_6 = 5.906 --Feature cross pick-up and drop-off locations features model_7 = 5.750 --Apply the BUCKETIZE function final_model = 4.653 --Apply the TRANSFORM clause and L2 Regularization
| Model | RMSE | Description |
|---|---|---|
| model_1 | 9.431 | --EXTRACT DayOfWeek from the pickup_datetime feature |
| model_2 | 8.408 | --EXTRACT hourofday from the pickup_datetime feature |
| model_3 | 9.657 | --Feature cross dayofweek and hourofday -Feature Cross does lead to overfitting |
| model_4 | 9.657 | --Apply the ML.FEATURE_CROSS clause to categorical features |
| model_5 | 5.588 | --Feature cross coordinate features to create a Euclidean feature |
| model_6 | 5.906 | --Feature cross pick-up and drop-off locations features |
| model_7 | 5.75 | --Apply the BUCKETIZE function |
| final_model | 4.653 | --Apply the TRANSFORM clause and L2 Regularization |
In [11]:
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
models = ('m1', 'm2', 'm3', 'm4', 'm5', 'm6','m7', 'final_model')
y_pos = np.arange(len(models))
rmse = [9.431,8.408,9.657,9.657,5.588,5.906,5.759,4.653]
plt.bar(y_pos, rmse, align='center', alpha=0.5)
plt.xticks(y_pos, models)
plt.ylabel('RMSE')
plt.title('RMSE Model Summary')
plt.show()
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