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GitHub Repository: ibm/watson-machine-learning-samples
 Path: blob/master/cpd3.5/notebooks/python_sdk/deployments/spark/Use Spark to predict credit risk.ipynb
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Kernel: Python 3

Use Spark to predict credit risk with ibm-watson-machine-learning

This notebook introduces commands for model persistance to Watson Machine Learning repository, model deployment, and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.6 and Apache® Spark 2.4.

You will use German Credit Risk 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.

  • Persist a pipeline and model in Watson Machine Learning repository from tar.gz files.

  • Deploy a model for online scoring using Wastson Machine Learning API.

  • Score sample scoring data using the Watson Machine Learning API.

  • Explore and visualize prediction result using the plotly package.

Contents

This notebook contains the following parts:

  1. Set up

  2. Load and explore data

  3. Persist model

  4. Predict locally

  5. Deploy and score

  6. Clean up

  7. Summary and next steps

1. Set up the environment

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

  • Contact with your Cloud Pack for Data administrator and ask him for your account credentials

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform url, your username and password.

username = 'PASTE YOUR USERNAME HERE'
password = 'PASTE YOUR PASSWORD HERE'
url = 'PASTE THE PLATFORM URL HERE'

wml_credentials = {
    "username": username,
    "password": password,
    "url": url,
    "instance_id": 'openshift',
    "version": '3.5'
}

Install and import the ibm-watson-machine-learning package

Note: ibm-watson-machine-learning documentation can be found here.

!pip install -U ibm-watson-machine-learning

from ibm_watson_machine_learning import APIClient

client = APIClient(wml_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 {PLATFORM_URL}/ml-runtime/spaces?context=icp4data to create one.

  • Click New Deployment Space

  • Create an empty space

  • Go to space Settings tab

  • 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

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

client.spaces.list(limit=10)

To be able to interact with all resources available in Watson Machine Learning, you need to set space which you will be using.

client.set.default_space(space_id)
'SUCCESS'

Test Spark

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.

The csv file for German Credit Risk is available on the same repository as this notebook. Load the file to Apache® Spark DataFrame using code below.

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, 'credit_risk_training.csv')
if not os.path.isfile(filename):
    filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd3.5/data/credit_risk/credit_risk_training.csv', out=sample_dir)

spark = SparkSession.builder.getOrCreate()

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

Explore the loaded data by using the following Apache® Spark DataFrame methods:

  • print schema

  • print top ten records

  • count all records

df_data.printSchema()
root |-- CheckingStatus: string (nullable = true) |-- LoanDuration: integer (nullable = true) |-- CreditHistory: string (nullable = true) |-- LoanPurpose: string (nullable = true) |-- LoanAmount: integer (nullable = true) |-- ExistingSavings: string (nullable = true) |-- EmploymentDuration: string (nullable = true) |-- InstallmentPercent: integer (nullable = true) |-- Sex: string (nullable = true) |-- OthersOnLoan: string (nullable = true) |-- CurrentResidenceDuration: integer (nullable = true) |-- OwnsProperty: string (nullable = true) |-- Age: integer (nullable = true) |-- InstallmentPlans: string (nullable = true) |-- Housing: string (nullable = true) |-- ExistingCreditsCount: integer (nullable = true) |-- Job: string (nullable = true) |-- Dependents: integer (nullable = true) |-- Telephone: string (nullable = true) |-- ForeignWorker: string (nullable = true) |-- Risk: string (nullable = true)

As you can see, the data contains 21 fields. Risk field is the one we would like to predict (label).

df_data.show(n=5, truncate=False, vertical=True)
-RECORD 0------------------------------------------ CheckingStatus | 0_to_200 LoanDuration | 31 CreditHistory | credits_paid_to_date LoanPurpose | other LoanAmount | 1889 ExistingSavings | 100_to_500 EmploymentDuration | less_1 InstallmentPercent | 3 Sex | female OthersOnLoan | none CurrentResidenceDuration | 3 OwnsProperty | savings_insurance Age | 32 InstallmentPlans | none Housing | own ExistingCreditsCount | 1 Job | skilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk -RECORD 1------------------------------------------ CheckingStatus | less_0 LoanDuration | 18 CreditHistory | credits_paid_to_date LoanPurpose | car_new LoanAmount | 462 ExistingSavings | less_100 EmploymentDuration | 1_to_4 InstallmentPercent | 2 Sex | female OthersOnLoan | none CurrentResidenceDuration | 2 OwnsProperty | savings_insurance Age | 37 InstallmentPlans | stores Housing | own ExistingCreditsCount | 2 Job | skilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk -RECORD 2------------------------------------------ CheckingStatus | less_0 LoanDuration | 15 CreditHistory | prior_payments_delayed LoanPurpose | furniture LoanAmount | 250 ExistingSavings | less_100 EmploymentDuration | 1_to_4 InstallmentPercent | 2 Sex | male OthersOnLoan | none CurrentResidenceDuration | 3 OwnsProperty | real_estate Age | 28 InstallmentPlans | none Housing | own ExistingCreditsCount | 2 Job | skilled Dependents | 1 Telephone | yes ForeignWorker | no Risk | No Risk -RECORD 3------------------------------------------ CheckingStatus | 0_to_200 LoanDuration | 28 CreditHistory | credits_paid_to_date LoanPurpose | retraining LoanAmount | 3693 ExistingSavings | less_100 EmploymentDuration | greater_7 InstallmentPercent | 3 Sex | male OthersOnLoan | none CurrentResidenceDuration | 2 OwnsProperty | savings_insurance Age | 32 InstallmentPlans | none Housing | own ExistingCreditsCount | 1 Job | skilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk -RECORD 4------------------------------------------ CheckingStatus | no_checking LoanDuration | 28 CreditHistory | prior_payments_delayed LoanPurpose | education LoanAmount | 6235 ExistingSavings | 500_to_1000 EmploymentDuration | greater_7 InstallmentPercent | 3 Sex | male OthersOnLoan | none CurrentResidenceDuration | 3 OwnsProperty | unknown Age | 57 InstallmentPlans | none Housing | own ExistingCreditsCount | 2 Job | skilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | Risk only showing top 5 rows 
print("Number of records: " + str(df_data.count()))
Number of records: 5000

As you can see, the data set contains 5000 records.

2.1 Prepare data

In this subsection you will split your data into: train, test and predict datasets.

splitted_data = df_data.randomSplit([0.8, 0.18, 0.02], 24)
train_data = splitted_data[0]
test_data = splitted_data[1]
predict_data = splitted_data[2]

print("Number of training records: " + str(train_data.count()))
print("Number of testing records : " + str(test_data.count()))
print("Number of prediction records : " + str(predict_data.count()))
Number of training records: 4016 Number of testing records : 881 Number of prediction records : 103

As you can see our data has been successfully split into three datasets:

  • The train data set, which is the largest group, is used for training.

  • The test data set will be used for model evaluation and is used to test the assumptions of the model.

  • The predict data set will be used for prediction.

3. Persist model

In this section you will learn how to store your pipeline and model in Watson Machine Learning repository by using python client libraries.

Note: Apache® Spark 2.4 is required.

3.1: Save pipeline and model

In this subsection you will learn how to save pipeline and model artifacts to your Watson Machine Learning instance.

Download pipeline and model archives

import os
from wget import download

sample_dir = 'spark_sample_model'
if not os.path.isdir(sample_dir):
    os.mkdir(sample_dir)
    
pipeline_filename = os.path.join(sample_dir, 'credit_risk_spark_pipeline.tar.gz')
if not os.path.isfile(pipeline_filename):
    pipeline_filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd3.5/models/spark/credit-risk/model/credit_risk_spark_pipeline.tar.gz', out=sample_dir)
model_filename = os.path.join(sample_dir, 'credit_risk_spark_model.gz')
if not os.path.isfile(model_filename):
    model_filename = download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd3.5/models/spark/credit-risk/model/credit_risk_spark_model.gz', out=sample_dir)

Store piepline and model

To be able to store your Spark model, you need to provide a training data reference, this will allow to read the model schema automatically.

training_data_references = [
                {
                    "type": "fs",
                    "connection": {},
                    "location": {},
                    "schema": {
                    "id": "training_schema",
                    "fields": [
                      {
                        "metadata": {},
                        "name": "CheckingStatus",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "LoanDuration",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "CreditHistory",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "LoanPurpose",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "LoanAmount",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "ExistingSavings",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "EmploymentDuration",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "InstallmentPercent",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "Sex",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "OthersOnLoan",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "CurrentResidenceDuration",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "OwnsProperty",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "Age",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "InstallmentPlans",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "Housing",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "ExistingCreditsCount",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "Job",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "Dependents",
                        "nullable": True,
                        "type": "integer"
                      },
                      {
                        "metadata": {},
                        "name": "Telephone",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {},
                        "name": "ForeignWorker",
                        "nullable": True,
                        "type": "string"
                      },
                      {
                        "metadata": {
                          "modeling_role": "target"
                        },
                        "name": "Risk",
                        "nullable": True,
                        "type": "string"
                      }
                    ]
                  }
                }
]

published_model_details = client.repository.store_model(
    model=model_filename, 
    meta_props={
        client.repository.ModelMetaNames.NAME:'Credit Risk model',
        client.repository.ModelMetaNames.TYPE: "mllib_2.4",
        client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: client.software_specifications.get_id_by_name('spark-mllib_2.4'),
        client.repository.ModelMetaNames.TRAINING_DATA_REFERENCES: training_data_references,
        client.repository.ModelMetaNames.LABEL_FIELD: "Risk",
    }, 
    training_data=train_data, 
    pipeline=pipeline_filename)

model_uid = client.repository.get_model_uid(published_model_details)
print(model_uid)
e96692a9-cc64-408d-9b1c-05da622c508a 
client.repository.get_model_details(model_uid)

Get saved model metadata from Watson Machine Learning.

Tip: Use client.repository.ModelMetaNames.show() to get the list of available props.

client.repository.ModelMetaNames.show()
------------------------ ---- -------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ META_PROP NAME TYPE REQUIRED SCHEMA NAME str Y DESCRIPTION str N INPUT_DATA_SCHEMA list N {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]} TRAINING_DATA_REFERENCES list N [{'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}, 'schema(optional)': {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]}}] OUTPUT_DATA_SCHEMA dict N {'id(required)': 'string', 'fields(required)': [{'name(required)': 'string', 'type(required)': 'string', 'nullable(optional)': 'string'}]} LABEL_FIELD str N TRANSFORMED_LABEL_FIELD str N TAGS list N ['string', 'string'] SIZE dict N {'in_memory(optional)': 'string', 'content(optional)': 'string'} SPACE_UID str N PIPELINE_UID str N RUNTIME_UID str N TYPE str Y CUSTOM dict N DOMAIN str N HYPER_PARAMETERS dict N METRICS list N IMPORT dict N {'name(optional)': 'string', 'type(required)': 'string', 'connection(required)': {'endpoint_url(required)': 'string', 'access_key_id(required)': 'string', 'secret_access_key(required)': 'string'}, 'location(required)': {'bucket': 'string', 'path': 'string'}} TRAINING_LIB_UID str N MODEL_DEFINITION_UID str N SOFTWARE_SPEC_UID str N TF_MODEL_PARAMS dict N ------------------------ ---- -------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

3.2: Load model

In this subsection you will learn how to load back saved model from specified instance of Watson Machine Learning.

loaded_model = client.repository.load(model_uid)

You can print for example model name to make sure that model has been loaded correctly.

print(type(loaded_model))
<class 'pyspark.ml.pipeline.PipelineModel'>

4. Predict locally

In this section you will learn how to score test data using loaded model.

4.1: Make local prediction using previously loaded model and test data

In this subsection you will score predict_data data set.

predictions = loaded_model.transform(predict_data)

Preview the results by calling the show() method on the predictions DataFrame.

predictions.show(5)
+--------------+------------+--------------------+-----------+----------+---------------+------------------+------------------+------+------------+------------------------+------------+---+----------------+-------+--------------------+----------+----------+---------+-------------+-------+-----------------+----------------+--------------+------------------+---------------------+------+---------------+---------------+-------------------+----------+------+------------+----------------+-----+--------------------+--------------------+--------------------+----------+--------------+ |CheckingStatus|LoanDuration| CreditHistory|LoanPurpose|LoanAmount|ExistingSavings|EmploymentDuration|InstallmentPercent| Sex|OthersOnLoan|CurrentResidenceDuration|OwnsProperty|Age|InstallmentPlans|Housing|ExistingCreditsCount| Job|Dependents|Telephone|ForeignWorker| Risk|CheckingStatus_IX|CreditHistory_IX|LoanPurpose_IX|ExistingSavings_IX|EmploymentDuration_IX|Sex_IX|OthersOnLoan_IX|OwnsProperty_IX|InstallmentPlans_IX|Housing_IX|Job_IX|Telephone_IX|ForeignWorker_IX|label| features| rawPrediction| probability|prediction|predictedLabel| +--------------+------------+--------------------+-----------+----------+---------------+------------------+------------------+------+------------+------------------------+------------+---+----------------+-------+--------------------+----------+----------+---------+-------------+-------+-----------------+----------------+--------------+------------------+---------------------+------+---------------+---------------+-------------------+----------+------+------------+----------------+-----+--------------------+--------------------+--------------------+----------+--------------+ | 0_to_200| 10|credits_paid_to_date| repairs| 2224| less_100| 4_to_7| 3| male| none| 3| car_other| 29| none| own| 1| skilled| 1| none| yes| Risk| 2.0| 1.0| 5.0| 0.0| 1.0| 0.0| 0.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 1.0|(20,[0,1,2,4,7,13...|[16.7095445704993...|[0.83547722852496...| 0.0| No Risk| | 0_to_200| 10| no_credits| car_new| 250| less_100| less_1| 2|female| none| 2| real_estate| 22| none| own| 1| skilled| 1| none| yes|No Risk| 2.0| 4.0| 0.0| 0.0| 3.0| 1.0| 0.0| 2.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|(20,[0,1,4,5,7,13...|[18.7487477403696...|[0.93743738701848...| 0.0| No Risk| | 0_to_200| 12|credits_paid_to_date| car_used| 250| greater_1000| 1_to_4| 2|female| none| 2| real_estate| 24| bank| rent| 1|unemployed| 1| none| yes|No Risk| 2.0| 1.0| 2.0| 3.0| 0.0| 1.0| 0.0| 2.0| 2.0| 1.0| 3.0| 0.0| 0.0| 0.0|[2.0,1.0,2.0,3.0,...|[17.8941841848564...|[0.89470920924282...| 0.0| No Risk| | 0_to_200| 13|all_credits_paid_...| car_new| 250| less_100| less_1| 1| male| none| 2| real_estate| 25| stores| rent| 1| skilled| 1| none| yes|No Risk| 2.0| 3.0| 0.0| 0.0| 3.0| 0.0| 0.0| 2.0| 1.0| 1.0| 0.0| 0.0| 0.0| 0.0|[2.0,3.0,0.0,0.0,...|[18.7487477403696...|[0.93743738701848...| 0.0| No Risk| | 0_to_200| 13|prior_payments_de...| radio_tv| 2453| less_100| 4_to_7| 3| male| none| 2| car_other| 39| none| own| 2| skilled| 1| none| yes|No Risk| 2.0| 0.0| 3.0| 0.0| 1.0| 0.0| 0.0| 1.0| 0.0| 0.0| 0.0| 0.0| 0.0| 0.0|(20,[0,2,4,7,13,1...|[16.1402858904161...|[0.80701429452080...| 0.0| No Risk| +--------------+------------+--------------------+-----------+----------+---------------+------------------+------------------+------+------------+------------------------+------------+---+----------------+-------+--------------------+----------+----------+---------+-------------+-------+-----------------+----------------+--------------+------------------+---------------------+------+---------------+---------------+-------------------+----------+------+------------+----------------+-----+--------------------+--------------------+--------------------+----------+--------------+ only showing top 5 rows

By tabulating a count, you can see which product line is the most popular.

predictions.select("predictedLabel").groupBy("predictedLabel").count().show(truncate=False)
+--------------+-----+ |predictedLabel|count| +--------------+-----+ |No Risk |82 | |Risk |21 | +--------------+-----+

5. Deploy and score

In this section you will learn how to create online scoring and to score a new data record using ibm-watson-machine-learning.

Note: You can also use REST API to deploy and score. For more information about REST APIs, see the Swagger Documentation.

5.1: Create online scoring endpoint

Now you can create an online scoring endpoint.

Create online deployment for published model

deployment_details = client.deployments.create(
    model_uid, 
    meta_props={
        client.deployments.ConfigurationMetaNames.NAME: "Credit Risk model deployment",
        client.deployments.ConfigurationMetaNames.ONLINE: {}
    }
)
####################################################################################### Synchronous deployment creation for uid: 'e96692a9-cc64-408d-9b1c-05da622c508a' started ####################################################################################### initializing. ready ------------------------------------------------------------------------------------------------ Successfully finished deployment creation, deployment_uid='3f22f536-f395-4d03-a08f-6311765a0e42' ------------------------------------------------------------------------------------------------ 
deployment_details
{'entity': {'asset': {'id': 'e96692a9-cc64-408d-9b1c-05da622c508a'}, 'custom': {}, 'deployed_asset_type': 'model', 'hardware_spec': {'id': 'Not_Applicable', 'name': 'S', 'num_nodes': 1}, 'name': 'Credit Risk model deployment', 'online': {}, 'space_id': '83b00166-9047-4159-b777-83dcb498e7ab', 'status': {'online_url': {'url': 'https://wmlgmc-cpd-wmlgmc.apps.wmlautoai.cp.fyre.ibm.com/ml/v4/deployments/3f22f536-f395-4d03-a08f-6311765a0e42/predictions'}, 'state': 'ready'}}, 'metadata': {'created_at': '2020-12-08T12:12:57.722Z', 'id': '3f22f536-f395-4d03-a08f-6311765a0e42', 'modified_at': '2020-12-08T12:12:57.722Z', 'name': 'Credit Risk model deployment', 'owner': '1000330999', 'space_id': '83b00166-9047-4159-b777-83dcb498e7ab'}}

Now, you can send new scoring records (new data) for which you would like to get predictions. To do that, execute the following sample code:

fields = ["CheckingStatus", "LoanDuration", "CreditHistory", "LoanPurpose", "LoanAmount", "ExistingSavings",
                  "EmploymentDuration", "InstallmentPercent", "Sex", "OthersOnLoan", "CurrentResidenceDuration",
                  "OwnsProperty", "Age", "InstallmentPlans", "Housing", "ExistingCreditsCount", "Job", "Dependents",
                  "Telephone", "ForeignWorker"]
values = [
    ["no_checking", 13, "credits_paid_to_date", "car_new", 1343, "100_to_500", "1_to_4", 2, "female", "none", 3,
     "savings_insurance", 46, "none", "own", 2, "skilled", 1, "none", "yes"],
    ["no_checking", 24, "prior_payments_delayed", "furniture", 4567, "500_to_1000", "1_to_4", 4, "male", "none",
     4, "savings_insurance", 36, "none", "free", 2, "management_self-employed", 1, "none", "yes"],
    ["0_to_200", 26, "all_credits_paid_back", "car_new", 863, "less_100", "less_1", 2, "female", "co-applicant",
     2, "real_estate", 38, "none", "own", 1, "skilled", 1, "none", "yes"],
    ["0_to_200", 14, "no_credits", "car_new", 2368, "less_100", "1_to_4", 3, "female", "none", 3, "real_estate",
     29, "none", "own", 1, "skilled", 1, "none", "yes"],
    ["0_to_200", 4, "no_credits", "car_new", 250, "less_100", "unemployed", 2, "female", "none", 3,
     "real_estate", 23, "none", "rent", 1, "management_self-employed", 1, "none", "yes"],
    ["no_checking", 17, "credits_paid_to_date", "car_new", 832, "100_to_500", "1_to_4", 2, "male", "none", 2,
     "real_estate", 42, "none", "own", 1, "skilled", 1, "none", "yes"],
    ["no_checking", 33, "outstanding_credit", "appliances", 5696, "unknown", "greater_7", 4, "male",
     "co-applicant", 4, "unknown", 54, "none", "free", 2, "skilled", 1, "yes", "yes"],
    ["0_to_200", 13, "prior_payments_delayed", "retraining", 1375, "100_to_500", "4_to_7", 3, "male", "none", 3,
     "real_estate", 37, "none", "own", 2, "management_self-employed", 1, "none", "yes"]
]

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

client.deployments.score(deployment_id, payload_scoring)
{'predictions': [{'fields': ['CheckingStatus', 'LoanDuration', 'CreditHistory', 'LoanPurpose', 'LoanAmount', 'ExistingSavings', 'EmploymentDuration', 'InstallmentPercent', 'Sex', 'OthersOnLoan', 'CurrentResidenceDuration', 'OwnsProperty', 'Age', 'InstallmentPlans', 'Housing', 'ExistingCreditsCount', 'Job', 'Dependents', 'Telephone', 'ForeignWorker', 'CheckingStatus_IX', 'CreditHistory_IX', 'LoanPurpose_IX', 'ExistingSavings_IX', 'EmploymentDuration_IX', 'Sex_IX', 'OthersOnLoan_IX', 'OwnsProperty_IX', 'InstallmentPlans_IX', 'Housing_IX', 'Job_IX', 'Telephone_IX', 'ForeignWorker_IX', 'features', 'rawPrediction', 'probability', 'prediction', 'predictedLabel'], 'values': [['no_checking', 13, 'credits_paid_to_date', 'car_new', 1343, '100_to_500', '1_to_4', 2, 'female', 'none', 3, 'savings_insurance', 46, 'none', 'own', 2, 'skilled', 1, 'none', 'yes', 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, [20, [1, 3, 5, 13, 14, 15, 16, 17, 18, 19], [1.0, 1.0, 1.0, 13.0, 1343.0, 2.0, 3.0, 46.0, 2.0, 1.0]], [13.539659419607977, 6.46034058039202], [0.676982970980399, 0.32301702901960105], 0.0, 'No Risk'], ['no_checking', 24, 'prior_payments_delayed', 'furniture', 4567, '500_to_1000', '1_to_4', 4, 'male', 'none', 4, 'savings_insurance', 36, 'none', 'free', 2, 'management_self-employed', 1, 'none', 'yes', 0.0, 0.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0, [20, [2, 3, 9, 10, 13, 14, 15, 16, 17, 18, 19], [1.0, 2.0, 2.0, 2.0, 24.0, 4567.0, 4.0, 4.0, 36.0, 2.0, 1.0]], [12.785167512823998, 7.214832487176002], [0.6392583756411999, 0.3607416243588001], 0.0, 'No Risk'], ['0_to_200', 26, 'all_credits_paid_back', 'car_new', 863, 'less_100', 'less_1', 2, 'female', 'co-applicant', 2, 'real_estate', 38, 'none', 'own', 1, 'skilled', 1, 'none', 'yes', 2.0, 3.0, 0.0, 0.0, 3.0, 1.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, [2.0, 3.0, 0.0, 0.0, 3.0, 1.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 26.0, 863.0, 2.0, 2.0, 38.0, 1.0, 1.0], [17.542315254085228, 2.457684745914773], [0.8771157627042614, 0.12288423729573865], 0.0, 'No Risk'], ['0_to_200', 14, 'no_credits', 'car_new', 2368, 'less_100', '1_to_4', 3, 'female', 'none', 3, 'real_estate', 29, 'none', 'own', 1, 'skilled', 1, 'none', 'yes', 2.0, 4.0, 0.0, 0.0, 0.0, 1.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, [20, [0, 1, 5, 7, 13, 14, 15, 16, 17, 18, 19], [2.0, 4.0, 1.0, 2.0, 14.0, 2368.0, 3.0, 3.0, 29.0, 1.0, 1.0]], [16.74917151322493, 3.2508284867750716], [0.8374585756612465, 0.16254142433875357], 0.0, 'No Risk'], ['0_to_200', 4, 'no_credits', 'car_new', 250, 'less_100', 'unemployed', 2, 'female', 'none', 3, 'real_estate', 23, 'none', 'rent', 1, 'management_self-employed', 1, 'none', 'yes', 2.0, 4.0, 0.0, 0.0, 4.0, 1.0, 0.0, 2.0, 0.0, 1.0, 2.0, 0.0, 0.0, [2.0, 4.0, 0.0, 0.0, 4.0, 1.0, 0.0, 2.0, 0.0, 1.0, 2.0, 0.0, 0.0, 4.0, 250.0, 2.0, 3.0, 23.0, 1.0, 1.0], [18.519458793482613, 1.4805412065173857], [0.9259729396741306, 0.07402706032586928], 0.0, 'No Risk'], ['no_checking', 17, 'credits_paid_to_date', 'car_new', 832, '100_to_500', '1_to_4', 2, 'male', 'none', 2, 'real_estate', 42, 'none', 'own', 1, 'skilled', 1, 'none', 'yes', 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, [20, [1, 3, 7, 13, 14, 15, 16, 17, 18, 19], [1.0, 1.0, 2.0, 17.0, 832.0, 2.0, 2.0, 42.0, 1.0, 1.0]], [15.520847400508805, 4.4791525994911945], [0.7760423700254402, 0.22395762997455973], 0.0, 'No Risk'], ['no_checking', 33, 'outstanding_credit', 'appliances', 5696, 'unknown', 'greater_7', 4, 'male', 'co-applicant', 4, 'unknown', 54, 'none', 'free', 2, 'skilled', 1, 'yes', 'yes', 0.0, 2.0, 4.0, 4.0, 2.0, 0.0, 1.0, 3.0, 0.0, 2.0, 0.0, 1.0, 0.0, [0.0, 2.0, 4.0, 4.0, 2.0, 0.0, 1.0, 3.0, 0.0, 2.0, 0.0, 1.0, 0.0, 33.0, 5696.0, 4.0, 4.0, 54.0, 2.0, 1.0], [1.8811314591199693, 18.11886854088003], [0.09405657295599847, 0.9059434270440014], 1.0, 'Risk'], ['0_to_200', 13, 'prior_payments_delayed', 'retraining', 1375, '100_to_500', '4_to_7', 3, 'male', 'none', 3, 'real_estate', 37, 'none', 'own', 2, 'management_self-employed', 1, 'none', 'yes', 2.0, 0.0, 8.0, 1.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 2.0, 0.0, 0.0, [2.0, 0.0, 8.0, 1.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 2.0, 0.0, 0.0, 13.0, 1375.0, 3.0, 3.0, 37.0, 2.0, 1.0], [16.185863861771526, 3.8141361382284735], [0.8092931930885763, 0.19070680691142367], 0.0, 'No Risk']]}]}

6. 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.

7. Summary and next steps

You successfully completed this notebook! You learned how to use Apache Spark machine learning as well as Watson Machine Learning 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 in Watson Machine Learning at IBM

Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License.