Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
ibm
GitHub Repository: ibm/watson-machine-learning-samples
 Path: blob/master/cpd4.5/notebooks/python_sdk/deployments/spark/Use Spark and batch deployment to predict customer churn.ipynb
6405 views
Kernel: Python 3 (ipykernel)

Use Spark and batch deployment to predict customer churn with ibm-watson-machine-learning

This notebook contains steps and code to develop a predictive model, and start scoring new data. This notebook introduces commands for getting data and for basic data cleaning and exploration, pipeline creation, model training, model persistance to Watson Machine Learning repository, model deployment, and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.9 and Apache® Spark 3.0.

You will use a data set, Telco Customer Churn, which details anonymous customer data from a telecommunication company. Use the details of this data set to predict customer churn which is very critical to business as it's easier to retain existing customers rather than acquiring new ones.

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 Watson Machine Learning repository.

  • Explore and visualize prediction results using the plotly package.

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

Contents

This notebook contains the following parts:

  1. Set up the environment

  2. Load and explore data

  3. Create spark ml model

  4. Persist model

  5. Predict locally and visualize

  6. Deploy and score

  7. Clean up

  8. 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 api_key.

username = 'PASTE YOUR USERNAME HERE'
api_key = 'PASTE YOUR API_KEY HERE'
url = 'PASTE THE PLATFORM URL HERE'

wml_credentials = {
    "username": username,
    "apikey": api_key,
    "url": url,
    "instance_id": 'openshift',
    "version": '4.5'
}

Alternatively you can use username and password to authenticate WML services.

wml_credentials = {
    "username": ***,
    "password": ***,
    "url": ***,
    "instance_id": 'openshift',
    "version": '4.5'
}

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. 

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, 'WA_FnUseC_TelcoCustomerChurn.csv')
if not os.path.isfile(filename):
    filename = download("https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd4.0/data/customer_churn/WA_FnUseC_TelcoCustomerChurn.csv", out=sample_dir)
100% [........................................................] 970456 / 970456
spark = SparkSession.builder.getOrCreate()

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

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

  • print schema

  • count all records

  • show distribution of label classes

df_data.printSchema()

print("Number of fields: %3g" % len(df_data.schema))
root |-- customerID: string (nullable = true) |-- gender: string (nullable = true) |-- SeniorCitizen: integer (nullable = true) |-- Partner: string (nullable = true) |-- Dependents: string (nullable = true) |-- tenure: integer (nullable = true) |-- PhoneService: string (nullable = true) |-- MultipleLines: string (nullable = true) |-- InternetService: string (nullable = true) |-- OnlineSecurity: string (nullable = true) |-- OnlineBackup: string (nullable = true) |-- DeviceProtection: string (nullable = true) |-- TechSupport: string (nullable = true) |-- StreamingTV: string (nullable = true) |-- StreamingMovies: string (nullable = true) |-- Contract: string (nullable = true) |-- PaperlessBilling: string (nullable = true) |-- PaymentMethod: string (nullable = true) |-- MonthlyCharges: double (nullable = true) |-- TotalCharges: double (nullable = true) |-- Churn: string (nullable = true) Number of fields: 21

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

print("Total number of records: " + str(df_data.count()))
Total number of records: 7043

Data set contains 7043 records.

Now you will check if all records have complete data.

df_complete = df_data.dropna()

print("Number of records with complete data: %3g" % df_complete.count())
Number of records with complete data: 7032

You can see that there are some missing values you can investigate that all missing values are present in TotalCharges feature. We will use dataset with missing values removed for model training and evaluation.

Now you will inspect distribution of classes in label column.

df_complete.groupBy('Churn').count().show()
+-----+-----+ |Churn|count| +-----+-----+ | No| 5163| | Yes| 1869| +-----+-----+

3. Create an Apache® Spark machine learning model

In this section you will learn how to prepare data, create an Apache® Spark machine learning pipeline, and train a model.

3.1: Prepare data

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

(train_data, test_data, predict_data) = df_complete.randomSplit([0.8, 0.18, 0.02], 24)

print("Number of records for training: " + str(train_data.count()))
print("Number of records for evaluation: " + str(test_data.count()))
print("Number of records for prediction: " + str(predict_data.count()))
Number of records for training: 5638 Number of records for evaluation: 1261 Number of records for prediction: 133

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.

3.2: Create pipeline and train a model

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

In the first step you need to import the Apache® Spark machine learning packages that will be needed in the subsequent steps.

from pyspark.ml.feature import StringIndexer, IndexToString, RFormula
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml import Pipeline, Model

In the following step, convert all the predictors to features vector and label feature convert to numeric.

lab = StringIndexer(inputCol = 'Churn', outputCol = 'label')
features = RFormula(formula = "~ gender + SeniorCitizen +  Partner + Dependents + tenure + PhoneService + MultipleLines + InternetService + OnlineSecurity + OnlineBackup + DeviceProtection + TechSupport + StreamingTV + StreamingMovies + Contract + PaperlessBilling + PaymentMethod + MonthlyCharges + TotalCharges - 1")

Next, define estimators you want to use for classification. Logistic Regression is used in the following example.

lr = LogisticRegression(maxIter = 10)

Let's build the pipeline now. A pipeline consists of transformers and an estimator.

pipeline_lr = Pipeline(stages = [features, lab , lr])

Now, you can train your Logistic Regression model using the previously defined pipeline and train data."

model_lr = pipeline_lr.fit(train_data)

You can check your model accuracy now. To evaluate the model, use test data.

predictions = model_lr.transform(test_data)
evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction", metricName="accuracy")
accuracy = evaluator.evaluate(predictions)

print("Test dataset:")
print("Accuracy = %3.2f" % accuracy)
Test dataset: Accuracy = 0.80

You can tune your model now to achieve better accuracy. For simplicity of this example tuning section is omitted.

4. Persist model

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

Note: Apache® Spark 3.0 is required.

4.1: Save pipeline and model

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

saved_model = client.repository.store_model(
    model=model_lr, 
    meta_props={
        client.repository.ModelMetaNames.NAME:'Customer Churn model',
        client.repository.ModelMetaNames.TYPE: "mllib_3.2",
        client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: client.software_specifications.get_id_by_name('spark-mllib_3.2'),
        client.repository.ModelMetaNames.LABEL_FIELD: "Churn",
    },  
    training_data=train_data, 
    pipeline=pipeline_lr)

Get saved model metadata from Watson Machine Learning.

published_model_ID = client.repository.get_model_id(saved_model)

print("Model Id: " + str(published_model_ID))
Model Id: 6dfdb7d0-3a4e-4a34-803a-0c75c1d4d12a

Model Id can be used to retrive latest model version from Watson Machine Learning instance.

4.2: 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(published_model_ID)

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

As you can see the name is correct. You have already learned how save and load the model from Watson Machine Learning repository.

5. Predict locally

In this section you will learn how to load data from batch scoring and visualize the prediction results with plotly package.

5.1: 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, truncate=False, vertical=True)
-RECORD 0----------------------------------------------------------------------------------------------------------------------------------------- customerID | 0117-LFRMW gender | Male SeniorCitizen | 0 Partner | Yes Dependents | Yes tenure | 37 PhoneService | No MultipleLines | No phone service InternetService | DSL OnlineSecurity | Yes OnlineBackup | Yes DeviceProtection | Yes TechSupport | No StreamingTV | No StreamingMovies | No Contract | Month-to-month PaperlessBilling | No PaymentMethod | Bank transfer (automatic) MonthlyCharges | 40.2 TotalCharges | 1448.8 Churn | Yes features | (31,[0,5,10,12,14,16,17,19,21,23,28,29,30],[1.0,37.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,40.2,1448.8]) label | 1.0 rawPrediction | [1.9626194004455484,-1.9626194004455484] probability | [0.8768161521616983,0.1231838478383017] prediction | 0.0 -RECORD 1----------------------------------------------------------------------------------------------------------------------------------------- customerID | 0121-SNYRK gender | Male SeniorCitizen | 0 Partner | No Dependents | No tenure | 50 PhoneService | No MultipleLines | No phone service InternetService | DSL OnlineSecurity | Yes OnlineBackup | No DeviceProtection | No TechSupport | Yes StreamingTV | No StreamingMovies | No Contract | One year PaperlessBilling | Yes PaymentMethod | Mailed check MonthlyCharges | 35.4 TotalCharges | 1748.9 Churn | No features | (31,[0,3,4,5,10,12,13,15,18,19,21,25,27,29,30],[1.0,1.0,1.0,50.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,35.4,1748.9]) label | 0.0 rawPrediction | [2.7913815222317555,-2.7913815222317555] probability | [0.9422083170753114,0.05779168292468861] prediction | 0.0 -RECORD 2----------------------------------------------------------------------------------------------------------------------------------------- customerID | 0314-TKOSI gender | Female SeniorCitizen | 0 Partner | No Dependents | No tenure | 6 PhoneService | Yes MultipleLines | No InternetService | DSL OnlineSecurity | Yes OnlineBackup | Yes DeviceProtection | No TechSupport | No StreamingTV | No StreamingMovies | No Contract | Month-to-month PaperlessBilling | No PaymentMethod | Mailed check MonthlyCharges | 55.15 TotalCharges | 322.9 Churn | No features | (31,[1,3,4,5,6,7,10,12,14,15,17,19,21,23,27,29,30],[1.0,1.0,1.0,6.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,55.15,322.9]) label | 0.0 rawPrediction | [1.4099918130909657,-1.4099918130909657] probability | [0.8037646523405363,0.1962353476594637] prediction | 0.0 -RECORD 3----------------------------------------------------------------------------------------------------------------------------------------- customerID | 0365-TRTPY gender | Female SeniorCitizen | 0 Partner | No Dependents | No tenure | 37 PhoneService | Yes MultipleLines | Yes InternetService | Fiber optic OnlineSecurity | Yes OnlineBackup | Yes DeviceProtection | Yes TechSupport | No StreamingTV | No StreamingMovies | No Contract | Month-to-month PaperlessBilling | No PaymentMethod | Bank transfer (automatic) MonthlyCharges | 91.2 TotalCharges | 3382.3 Churn | No features | (31,[1,3,4,5,6,8,9,12,14,16,17,19,21,23,28,29,30],[1.0,1.0,1.0,37.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,91.2,3382.3]) label | 0.0 rawPrediction | [1.2214169620331363,-1.2214169620331363] probability | [0.7723128122911829,0.22768718770881713] prediction | 0.0 -RECORD 4----------------------------------------------------------------------------------------------------------------------------------------- customerID | 0384-LPITE gender | Male SeniorCitizen | 0 Partner | No Dependents | No tenure | 40 PhoneService | No MultipleLines | No phone service InternetService | DSL OnlineSecurity | Yes OnlineBackup | Yes DeviceProtection | Yes TechSupport | No StreamingTV | Yes StreamingMovies | Yes Contract | One year PaperlessBilling | No PaymentMethod | Credit card (automatic) MonthlyCharges | 62.05 TotalCharges | 2511.55 Churn | No features | (31,[0,3,4,5,10,12,14,16,17,20,22,29,30],[1.0,1.0,1.0,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,62.05,2511.55]) label | 0.0 rawPrediction | [1.9757055839213993,-1.9757055839213993] probability | [0.8782226299073993,0.12177737009260067] prediction | 0.0 only showing top 5 rows

By tabulating a count, you can see the split between labels.

predictions.select("prediction").groupBy("prediction").count().show(truncate=False)
+----------+-----+ |prediction|count| +----------+-----+ |0.0 |104 | |1.0 |29 | +----------+-----+

6. Deploy model and score

In this section you will learn how to create batch deployment and to score a new data record by using the Watson Machine Learning REST API. For more information about REST APIs, see the Swagger Documentation.

6.1 Prepare scoring data for batch job

Get data for prediction

First, download scoring data into notebook's filesystem

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, 'scoreInput.csv')
if not os.path.isfile(filename):
    filename = download("https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd4.0/data/customer_churn/scoreInput.csv", out=sample_dir)
100% [............................................................] 1643 / 1643

6.2: Create batch deployment

Now you can create a batch scoring endpoint. Execute the following sample code that uses the published_model_ID value to create the scoring endpoint for predictions.

Create batch deployment for published model

meta_data = {
    client.deployments.ConfigurationMetaNames.NAME: "Customer Churn batch deployment",
    client.deployments.ConfigurationMetaNames.BATCH: {},
    client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {
        "name": "S",
        "num_nodes": 1
    }
}

deployment_details = client.deployments.create(published_model_ID, meta_props=meta_data)
####################################################################################### Synchronous deployment creation for uid: '6dfdb7d0-3a4e-4a34-803a-0c75c1d4d12a' started ####################################################################################### ready. ------------------------------------------------------------------------------------------------ Successfully finished deployment creation, deployment_uid='67247040-4f16-43c8-b9e7-4b45aa01e427' ------------------------------------------------------------------------------------------------

Batch deployment has been created.

You can retrieve now your deployment ID.

deployment_id = client.deployments.get_id(deployment_details)

You can also list all deployments in your space.

client.deployments.list()

If you want to get additional information on your deployment, you can do it as below.

client.deployments.get_details(deployment_id)
{'entity': {'asset': {'id': '7b5b7c8c-09fb-4cb2-96a6-382a70a6b309'}, 'batch': {}, 'custom': {}, 'deployed_asset_type': 'model', 'hardware_spec': {'id': 'e7ed1d6c-2e89-42d7-aed5-863b972c1d2b', 'name': 'S', 'num_nodes': 1}, 'name': 'Customer Churn batch deployment', 'space_id': '83b00166-9047-4159-b777-83dcb498e7ab', 'status': {'state': 'ready'}}, 'metadata': {'created_at': '2020-12-08T12:12:03.473Z', 'id': '82f2f0e9-c0e5-400f-87ca-6e168e13c5f2', 'modified_at': '2020-12-08T12:12:03.473Z', 'name': 'Customer Churn batch deployment', 'owner': '1000330999', 'space_id': '83b00166-9047-4159-b777-83dcb498e7ab'}}

Create and run Batch job

Tip: To install pandas execute !pip install pandas

import pandas as pd

score_input = pd.read_csv(filename).astype('object')

job_payload_ref = {
    client.deployments.ScoringMetaNames.INPUT_DATA: [
        {
            "fields": score_input.columns.tolist(),
            "values": [score_input.loc[0].tolist()]
        }
    ]
}

job = client.deployments.create_job(deployment_id, meta_props=job_payload_ref)

Now, your job has been submitted to Spark runtime.

You can retrieve now your job ID.

job_id = client.deployments.get_job_uid(job)

You can also list all jobs in your space.

client.deployments.list_jobs()
------------------------------------ --------- ------------------------ ------------------------------------ JOB-UID STATE CREATED DEPLOYMENT-ID ae7716c4-d8f0-4b46-829e-b61ae77b0559 queued 2022-02-03T12:22:15.122Z 67247040-4f16-43c8-b9e7-4b45aa01e427 4f565247-bf47-45ef-82c0-b6d2140e52d2 completed 2022-02-02T13:52:44.823Z ed305dab-6e70-4f00-a144-7de6122f8dee 79cb30e4-57b2-4383-8e51-034615465df4 failed 2022-02-02T13:51:36.835Z ed305dab-6e70-4f00-a144-7de6122f8dee 5294fb65-22ac-4682-9a87-46668081431b completed 2022-01-31T11:16:03.939Z c51f0d13-ed0f-41df-b5cb-781221105680 ------------------------------------ --------- ------------------------ ------------------------------------

If you want to get additional information on your job, you can do it as below.

client.deployments.get_job_details(job_id)
{'entity': {'deployment': {'id': '67247040-4f16-43c8-b9e7-4b45aa01e427'}, 'platform_job': {'job_id': 'e22ad116-ac98-4cdb-8654-eafb7b34e509', 'run_id': '948d4cf9-24a4-4f2c-8fcf-528b1f85d873'}, 'scoring': {'input_data': [{'fields': ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn', 'SampleWeight'], 'values': [['9237-HQITU', 'Female', 0, 'No', 'No', 2, 'Yes', 'No', 'Fiber optic', 'No', 'No', 'No', 'No', 'No', 'No', 'Month-to-month', 'Yes', 'Electronic check', 70.7, 151.65, 'Yes', 1.0]]}], 'status': {'completed_at': '', 'running_at': '', 'state': 'queued'}}}, 'metadata': {'created_at': '2022-02-03T12:22:15.122Z', 'id': 'ae7716c4-d8f0-4b46-829e-b61ae77b0559', 'name': 'name_6184a553-5758-4602-97c3-31d8f0747951', 'space_id': '779349f5-b119-496d-9a2b-3fcd6df73f91'}}

Monitor job execution

Here you can check status of your batch scoring. When status of Spark job is completed the results will be written to scoring_output file in Object Storage.

import time

elapsed_time = 0
while client.deployments.get_job_status(job_id).get('state') != 'completed' and elapsed_time < 300:
    print(f" Current state: {client.deployments.get_job_status(job_id).get('state')}")
    elapsed_time += 10
    time.sleep(10)
if client.deployments.get_job_status(job_id).get('state') == 'completed':
    print(f" Current state: {client.deployments.get_job_status(job_id).get('state')}")
    job_details_do = client.deployments.get_job_details(job_id)
    print(job_details_do)
else:
    print("Job hasn't completed successfully in 5 minutes.")
Current state: queued Current state: queued Current state: queued Current state: queued Current state: completed {'entity': {'deployment': {'id': '67247040-4f16-43c8-b9e7-4b45aa01e427'}, 'platform_job': {'job_id': 'e22ad116-ac98-4cdb-8654-eafb7b34e509', 'run_id': '948d4cf9-24a4-4f2c-8fcf-528b1f85d873'}, 'scoring': {'input_data': [{'fields': ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn', 'SampleWeight'], 'values': [['9237-HQITU', 'Female', 0, 'No', 'No', 2, 'Yes', 'No', 'Fiber optic', 'No', 'No', 'No', 'No', 'No', 'No', 'Month-to-month', 'Yes', 'Electronic check', 70.7, 151.65, 'Yes', 1.0]]}], 'predictions': [{'fields': ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn', 'SampleWeight', 'features', 'label', 'rawPrediction', 'probability', 'prediction'], 'values': [['9237-HQITU', 'Female', 0, 'No', 'No', 2, 'Yes', 'No', 'Fiber optic', 'No', 'No', 'No', 'No', 'No', 'No', 'Month-to-month', 'Yes', 'Electronic check', 70.7, 151.65, 'Yes', 1.0, [31, [1, 3, 4, 5, 6, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 26, 29, 30], [1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 70.7, 151.65]], 1.0, [-0.7082988149434561, 0.7082988149434561], [0.32997484880952394, 0.6700251511904761], 1.0]]}], 'status': {'completed_at': '2022-02-03T12:23:09.000Z', 'running_at': '2022-02-03T12:23:08.000Z', 'state': 'completed'}}}, 'metadata': {'created_at': '2022-02-03T12:22:15.122Z', 'id': 'ae7716c4-d8f0-4b46-829e-b61ae77b0559', 'modified_at': '2022-02-03T12:23:09.611Z', 'name': 'name_6184a553-5758-4602-97c3-31d8f0747951', 'space_id': '779349f5-b119-496d-9a2b-3fcd6df73f91'}}

Get scored data

import json

print(json.dumps(client.deployments.get_job_details(job_id), indent=2))
{ "entity": { "deployment": { "id": "67247040-4f16-43c8-b9e7-4b45aa01e427" }, "platform_job": { "job_id": "e22ad116-ac98-4cdb-8654-eafb7b34e509", "run_id": "948d4cf9-24a4-4f2c-8fcf-528b1f85d873" }, "scoring": { "input_data": [ { "fields": [ "customerID", "gender", "SeniorCitizen", "Partner", "Dependents", "tenure", "PhoneService", "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies", "Contract", "PaperlessBilling", "PaymentMethod", "MonthlyCharges", "TotalCharges", "Churn", "SampleWeight" ], "values": [ [ "9237-HQITU", "Female", 0, "No", "No", 2, "Yes", "No", "Fiber optic", "No", "No", "No", "No", "No", "No", "Month-to-month", "Yes", "Electronic check", 70.7, 151.65, "Yes", 1.0 ] ] } ], "predictions": [ { "fields": [ "customerID", "gender", "SeniorCitizen", "Partner", "Dependents", "tenure", "PhoneService", "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies", "Contract", "PaperlessBilling", "PaymentMethod", "MonthlyCharges", "TotalCharges", "Churn", "SampleWeight", "features", "label", "rawPrediction", "probability", "prediction" ], "values": [ [ "9237-HQITU", "Female", 0, "No", "No", 2, "Yes", "No", "Fiber optic", "No", "No", "No", "No", "No", "No", "Month-to-month", "Yes", "Electronic check", 70.7, 151.65, "Yes", 1.0, [ 31, [ 1, 3, 4, 5, 6, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 26, 29, 30 ], [ 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 70.7, 151.65 ] ], 1.0, [ -0.7082988149434561, 0.7082988149434561 ], [ 0.32997484880952394, 0.6700251511904761 ], 1.0 ] ] } ], "status": { "completed_at": "2022-02-03T12:23:09.000Z", "running_at": "2022-02-03T12:23:08.000Z", "state": "completed" } } }, "metadata": { "created_at": "2022-02-03T12:22:15.122Z", "id": "ae7716c4-d8f0-4b46-829e-b61ae77b0559", "modified_at": "2022-02-03T12:23:09.611Z", "name": "name_6184a553-5758-4602-97c3-31d8f0747951", "space_id": "779349f5-b119-496d-9a2b-3fcd6df73f91" } }

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 Watson Machine Learning for model creation and deployment.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Author

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.