CoCalc -- Use Spark to recommend mitigation for car rental company.ipynb

GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cloud/notebooks/python_sdk/deployments/spark/cars-4-you/Use Spark to recommend mitigation for car rental company.ipynb
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Kernel: test2

This notebook contains steps and code to create a predictive model, and deploy it on WML. This notebook introduces commands for pipeline creation, model training, model persistance to watsonx.ai Runtime repository, model deployment, and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.11 and Apache® Spark 3.4

You will use car_rental_training dataset.

Learning goals

The learning goals of this notebook are:

Load a CSV file into an Apache® Spark DataFrame.
Explore data.
Prepare data for training and evaluation.
Create an Apache® Spark machine learning pipeline.
Train and evaluate a model.
Persist a pipeline and model in watsonx.ai Runtime repository.
Deploy a model for online scoring using Wastson Machine Learning API.
Score sample scoring data using the watsonx.ai API.

This notebook contains the following parts:

Note: This notebook works correctly with kernel Python 3.11 with Spark 3.4, please do not change kernel.

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

Create a watsonx.ai Runtime Service instance (a free plan is offered and information about how to create the instance can be found here).

Install and import the `ibm-watsonx-ai` and dependecies

Note: ibm-watsonx-ai documentation can be found here.

In [ ]:

!pip install wget | tail -n 1
!pip install pyspark==3.4.3 | tail -n 1
!pip install -U ibm-watsonx-ai | tail -n 1

Connection to watsonx.ai Runtime

Authenticate the watsonx.ai Runtime service on IBM Cloud. You need to provide platform api_key and instance location.

You can use IBM Cloud CLI to retrieve platform API Key and instance location.

API Key can be generated in the following way:

ibmcloud login
ibmcloud iam api-key-create API_KEY_NAME

In result, get the value of api_key from the output.

Location of your watsonx.ai Runtime instance can be retrieved in the following way:

ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance INSTANCE_NAME

In result, get the value of location from the output.

Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the watsonx.ai Runtime docs. You can check your instance location in your watsonx.ai Runtime Service instance details.

You can also get service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, then copy the created key and paste it below.

Action: Enter your api_key and location in the following cell.

In [1]:

api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'

In [1]:

from ibm_watsonx_ai import Credentials

credentials = Credentials(
    api_key=api_key,
    url='https://' + location + '.ml.cloud.ibm.com'
)

In [4]:

from ibm_watsonx_ai import APIClient

client = APIClient(credentials)

Working with spaces

First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use Deployment Spaces Dashboard to create one.

Click New Deployment Space
Create an empty space
Select Cloud Object Storage
Select watsonx.ai Runtime instance and press Create
Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: Assign space ID below

In [2]:

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

In [ ]:

client.spaces.list(limit=10)

To be able to interact with all resources available in watsonx.ai Runtime, you need to set space which you will be using.

In [5]:

client.set.default_space(space_id)

Out[5]:

'SUCCESS'

Note: Please restart the kernel (Kernel -> Restart)

Test Spark

In [6]:

try:
    from pyspark.sql import SparkSession
except:
    print('Error: Spark runtime is missing. If you are using Watson Studio change the notebook runtime to Spark.')
    raise

2. Load and explore data

In this section you will load the data as an Apache Spark DataFrame and perform a basic exploration.

Read data into Spark DataFrame from DB2 database and show sample record.

Load data

In [7]:

import os
from wget import download

sample_dir = 'spark_sample_model'
if not os.path.isdir(sample_dir):
    os.mkdir(sample_dir)
    
filename = os.path.join(sample_dir, 'car_rental_training_data.csv')
if not os.path.isfile(filename):
    filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/cars-4-you/car_rental_training_data.csv', out=sample_dir)

In [ ]:

spark = SparkSession.builder.getOrCreate()

df_data = spark.read\
  .format('org.apache.spark.sql.execution.datasources.csv.CSVFileFormat')\
  .option('header', 'true')\
  .option('inferSchema', 'true')\
  .option("delimiter", ";")\
  .load(filename)
df_data.take(3)

Explore data

In [9]:

df_data.printSchema()

Out[9]:

root
 |-- ID: integer (nullable = true)
 |-- Gender: string (nullable = true)
 |-- Status: string (nullable = true)
 |-- Children: integer (nullable = true)
 |-- Age: double (nullable = true)
 |-- Customer_Status: string (nullable = true)
 |-- Car_Owner: string (nullable = true)
 |-- Customer_Service: string (nullable = true)
 |-- Satisfaction: integer (nullable = true)
 |-- Business_Area: string (nullable = true)
 |-- Action: string (nullable = true)

As you can see, the data contains eleven fields. Action field is the one you would like to predict using feedback data in Customer_Service field.

In [10]:

print("Number of records: " + str(df_data.count()))

Out[10]:

Number of records: 486

As you can see, the data set contains 486 records.

In [11]:

df_data.select('Business_area').groupBy('Business_area').count().show()

Out[11]:

+--------------------+-----+
|       Business_area|count|
+--------------------+-----+
|Service: Accessib...|   26|
|Product: Functioning|  150|
|   Service: Attitude|   24|
|Service: Orders/C...|   32|
|Product: Availabi...|   42|
|Product: Pricing ...|   24|
|Product: Information|    8|
|  Service: Knowledge|  180|
+--------------------+-----+

In [12]:

df_data.select('Action').groupBy('Action').count().show(truncate=False)

Out[12]:

+-------------------------+-----+
|Action                   |count|
+-------------------------+-----+
|NA                       |274  |
|Voucher                  |42   |
|Premium features         |30   |
|On-demand pickup location|56   |
|Free Upgrade             |84   |
+-------------------------+-----+

3. Create an Apache Spark machine learning model

In this section you will learn how to:

3.1 Prepare data for training a model

In this subsection you will split your data into: train and test data set.

In [13]:

train_data, test_data = df_data.randomSplit([0.8, 0.2], 24)

print("Number of training records: " + str(train_data.count()))
print("Number of testing records : " + str(test_data.count()))

Out[13]:

Number of training records: 401
Number of testing records : 85

3.2 Create the pipeline

In this section you will create an Apache Spark machine learning pipeline and then train the model.

In [15]:

from pyspark.ml.feature import StringIndexer, IndexToString, VectorAssembler
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml import Pipeline, Model

In the following step, use the StringIndexer transformer to convert all the string fields to numeric ones.

In [16]:

string_indexer_gender = StringIndexer(inputCol="Gender", outputCol="gender_ix")
string_indexer_customer_status = StringIndexer(inputCol="Customer_Status", outputCol="customer_status_ix")
string_indexer_status = StringIndexer(inputCol="Status", outputCol="status_ix")
string_indexer_owner = StringIndexer(inputCol="Car_Owner", outputCol="owner_ix")
string_business_area = StringIndexer(inputCol="Business_Area", outputCol="area_ix")

In [17]:

assembler = VectorAssembler(inputCols=["gender_ix", "customer_status_ix", "status_ix", "owner_ix", "area_ix", "Children", "Age", "Satisfaction"], outputCol="features")

In [18]:

string_indexer_action = StringIndexer(inputCol="Action", outputCol="label").fit(df_data)

In [19]:

label_action_converter = IndexToString(inputCol="prediction", outputCol="predictedLabel", labels=string_indexer_action.labels)

In [20]:

dt_action = DecisionTreeClassifier()

In [21]:

pipeline_action = Pipeline(stages=[string_indexer_gender, string_indexer_customer_status, string_indexer_status, string_indexer_action, string_indexer_owner, string_business_area, assembler, dt_action, label_action_converter])

In [22]:

model_action = pipeline_action.fit(train_data)

In [23]:

predictions_action = model_action.transform(test_data)
predictions_action.select('Business_Area','Action','probability','predictedLabel').show(2)

Out[23]:

+--------------------+--------------------+--------------------+--------------------+
|       Business_Area|              Action|         probability|      predictedLabel|
+--------------------+--------------------+--------------------+--------------------+
|Service: Accessib...|        Free Upgrade|[0.0,1.0,0.0,0.0,...|        Free Upgrade|
|   Service: Attitude|On-demand pickup ...|[0.0,0.15625,0.59...|On-demand pickup ...|
+--------------------+--------------------+--------------------+--------------------+
only showing top 2 rows

In [24]:

evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions_action)

print("Accuracy = %g" % accuracy)

Out[24]:

Accuracy = 0.870588

4. Persist model

In this section you will learn how to store your pipeline and model in watsonx.ai Runtime repository by using python client libraries.

Note: Apache® Spark 3.4 is required.

Save training data in your Cloud Object Storage

ibm-cos-sdk library allows Python developers to manage Cloud Object Storage (COS).

In [25]:

import ibm_boto3
from ibm_botocore.client import Config

Action: Put credentials from Object Storage Service in Bluemix here.

In [26]:

cos_credentials = {
                  "apikey": "***",
                  "cos_hmac_keys": {
                    "access_key_id": "***",
                    "secret_access_key": "***"
                  },
                  "endpoints": "***",
                  "iam_apikey_description": "***",
                  "iam_apikey_name": "***",
                  "iam_role_crn": "***",
                  "iam_serviceid_crn": "***",
                  "resource_instance_id": "***"
                }

In [27]:

connection_apikey = cos_credentials['apikey']
connection_resource_instance_id = cos_credentials["resource_instance_id"]
connection_access_key_id = cos_credentials['cos_hmac_keys']['access_key_id']
connection_secret_access_key = cos_credentials['cos_hmac_keys']['secret_access_key']

Action: Define the service endpoint we will use.
Tip: You can find this information in Endpoints section of your Cloud Object Storage intance's dashbord.

In [28]:

service_endpoint = 'https://s3.us.cloud-object-storage.appdomain.cloud'

You also need IBM Cloud authorization endpoint to be able to create COS resource object.

In [29]:

auth_endpoint = 'https://iam.cloud.ibm.com/identity/token'

We create COS resource to be able to write data to Cloud Object Storage.

In [30]:

cos = ibm_boto3.resource('s3',
                         ibm_api_key_id=cos_credentials['apikey'],
                         ibm_service_instance_id=cos_credentials['resource_instance_id'],
                         ibm_auth_endpoint=auth_endpoint,
                         config=Config(signature_version='oauth'),
                         endpoint_url=service_endpoint)

Now you will create bucket in COS and copy training dataset for model from car_rental_training_data.csv.

In [31]:

from uuid import uuid4

bucket_id = str(uuid4())

score_filename = "car_rental_training_data.csv"
buckets = ["car-rental-" + bucket_id]

In [38]:

for bucket in buckets:
    if not cos.Bucket(bucket) in cos.buckets.all():
        print('Creating bucket "{}"...'.format(bucket))
        try:
            cos.create_bucket(Bucket=bucket)
        except ibm_boto3.exceptions.ibm_botocore.client.ClientError as e:
            print('Error: {}.'.format(e.response['Error']['Message']))

Out[38]:

Creating bucket "car-rental-d839a4ec-530a-4ab4-95f5-c0fbbfeaefcf"...

In [39]:

bucket_obj = cos.Bucket(buckets[0])

print('Uploading data {}...'.format(score_filename))
with open(filename, 'rb') as f:
    bucket_obj.upload_fileobj(f, score_filename)
print('{} is uploaded.'.format(score_filename))

Out[39]:

Uploading data car_rental_training_data.csv...
car_rental_training_data.csv is uploaded.

Create connections to a COS bucket

In [40]:

datasource_type = client.connections.get_datasource_type_id_by_name('bluemixcloudobjectstorage')

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: "COS connection - spark",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: datasource_type,
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': buckets[0],
        'access_key': connection_access_key_id,
        'secret_key': connection_secret_access_key,
        'iam_url': auth_endpoint,
        'url': service_endpoint
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

Out[40]:

Creating connections...
SUCCESS

Note: The above connection can be initialized alternatively with api_key and resource_instance_id. The above cell can be replaced with:

conn_meta_props= {
    client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {db_name} ",
    client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_id_by_name(db_name),
    client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
    client.connections.ConfigurationMetaNames.PROPERTIES: {
        'bucket': bucket_name,
        'api_key': cos_credentials['apikey'],
        'resource_instance_id': cos_credentials['resource_instance_id'],
        'iam_url': 'https://iam.cloud.ibm.com/identity/token',
        'url': 'https://s3.us.cloud-object-storage.appdomain.cloud'
    }
}

conn_details = client.connections.create(meta_props=conn_meta_props)

In [41]:

connection_id = client.connections.get_id(conn_details)

4.2 Save the pipeline and model

In [42]:

training_data_references = [
                {
                    "id":"car-rental-training",
                    "type": "connection_asset",
                    "connection": {
                        "id": connection_id
                    },
                    "location": {
                        "bucket": buckets[0],
                        "file_name": score_filename,
                    }
                }
            ]

In [43]:

saved_model = client.repository.store_model(
    model=model_action, 
    meta_props={
        client.repository.ModelMetaNames.NAME:"CARS4U - Action Recommendation Model",
        client.repository.ModelMetaNames.TYPE: "mllib_3.4",
        client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: client.software_specifications.get_id_by_name('spark-mllib_3.4'),
        client.repository.ModelMetaNames.TRAINING_DATA_REFERENCES: training_data_references,
        client.repository.ModelMetaNames.LABEL_FIELD: "Action",
    },  
    training_data=train_data, 
    pipeline=pipeline_action)

Out[43]:

Get saved model metadata from watsonx.ai Runtime.

In [44]:

published_model_id = client.repository.get_model_id(saved_model)

print("Model Id: " + str(published_model_id))

Out[44]:

Model Id: 2699d051-7cd4-4241-ab63-f1963f31ec7a

Model Id can be used to retrive latest model version from watsonx.ai Runtime instance.

Below you can see stored model details.

In [ ]:

client.repository.get_model_details(published_model_id)

5. Deploy model in the IBM Cloud

You can use following command to create online deployment in cloud.

In [46]:

deployment_details = client.deployments.create(
    published_model_id, 
    meta_props={
        client.deployments.ConfigurationMetaNames.NAME: "CARS4U - Action Recommendation model deployment",
        client.deployments.ConfigurationMetaNames.ONLINE: {}
    }
)

Out[46]:

#######################################################################################

Synchronous deployment creation for uid: '2699d051-7cd4-4241-ab63-f1963f31ec7a' started

#######################################################################################

initializing
Note: online_url and serving_urls are deprecated and will be removed in a future release. Use inference instead.

ready

------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='412b6b5d-c6ce-48de-a5cf-7a0c1c5abacf'
------------------------------------------------------------------------------------------------

In [ ]:

deployment_details

6. Score

In [47]:

fields = ['ID', 'Gender', 'Status', 'Children', 'Age', 'Customer_Status','Car_Owner', 'Customer_Service', 'Business_Area', 'Satisfaction']
values = [3785, 'Male', 'S', 1, 17, 'Inactive', 'Yes', 'The car should have been brought to us instead of us trying to find it in the lot.', 'Product: Information', 0]

In [48]:

import json

payload_scoring = {"input_data": [{"fields": fields,"values": [values]}]}
scoring_response = client.deployments.score(client.deployments.get_id(deployment_details), payload_scoring)

print(json.dumps(scoring_response, indent=3))

Out[48]:

{
   "predictions": [
      {
         "fields": [
            "ID",
            "Gender",
            "Status",
            "Children",
            "Age",
            "Customer_Status",
            "Car_Owner",
            "Customer_Service",
            "Business_Area",
            "Satisfaction",
            "gender_ix",
            "customer_status_ix",
            "status_ix",
            "label",
            "owner_ix",
            "area_ix",
            "features",
            "rawPrediction",
            "probability",
            "prediction",
            "predictedLabel"
         ],
         "values": [
            [
               3785,
               "Male",
               "S",
               1,
               17.0,
               "Inactive",
               "Yes",
               "The car should have been brought to us instead of us trying to find it in the lot.",
               "Product: Information",
               0,
               0.0,
               1.0,
               1.0,
               0.0,
               1.0,
               7.0,
               [
                  0.0,
                  1.0,
                  1.0,
                  1.0,
                  7.0,
                  1.0,
                  17.0,
                  0.0
               ],
               [
                  0.0,
                  0.0,
                  7.0,
                  0.0,
                  0.0
               ],
               [
                  0.0,
                  0.0,
                  1.0,
                  0.0,
                  0.0
               ],
               2.0,
               "On-demand pickup location"
            ]
         ]
      }
   ]
}

7. Clean up

If you want to clean up all created assets:

experiments
trainings
pipelines
model definitions
models
functions
deployments

please follow up this sample notebook.

8. Summary and next steps

You successfully completed this notebook! You learned how to use Apache Spark machine learning as well as watsonx.ai Runtime for model creation and deployment. Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Amadeusz Masny, Python Software Developer at watsonx.ai

Mateusz Szewczyk, Software Engineer at watsonx.ai

Learning goals

Contents

1. Set up the environment

Install and import the `ibm-watsonx-ai` and dependecies

Connection to watsonx.ai Runtime

Working with spaces

Test Spark

2. Load and explore data

Load data

Explore data

3. Create an Apache Spark machine learning model

3.1 Prepare data for training a model

3.2 Create the pipeline

4. Persist model

Save training data in your Cloud Object Storage

Create connections to a COS bucket

4.2 Save the pipeline and model

5. Deploy model in the IBM Cloud

6. Score

7. Clean up

8. Summary and next steps

Authors

Product

Resources

Company

Use Spark to recommend mitigation for car rental company with ibm-watsonx-ai

Learning goals

Contents

1. Set up the environment

Install and import the ibm-watsonx-ai and dependecies

Connection to watsonx.ai Runtime

Working with spaces

Test Spark

2. Load and explore data

Load data

Explore data

3. Create an Apache Spark machine learning model

3.1 Prepare data for training a model

3.2 Create the pipeline

4. Persist model

Save training data in your Cloud Object Storage

Create connections to a COS bucket

4.2 Save the pipeline and model

5. Deploy model in the IBM Cloud

6. Score

7. Clean up

8. Summary and next steps

Authors

Use Spark to recommend mitigation for car rental company with `ibm-watsonx-ai`

Install and import the `ibm-watsonx-ai` and dependecies