Use Spark to predict credit risk with ibm-watsonx-ai¶
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 3.11 and Apache® Spark 3.4.
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 the environment¶
Before you use the sample code in this notebook, you must perform the following setup tasks:
- Create a Watson Machine Learning (WML) Service instance (a free plan is offered and information about how to create the instance can be found here).
In [ ]:
!pip install wget
!pip install pyspark==3.4.3 | tail -n 1
!pip install -U ibm-watsonx-ai | tail -n 1
Connection to WML¶
Authenticate the Watson Machine Learning 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 WML instance can be retrieved in the following way:
ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance WML_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 Watson Machine Learning docs. You can check your instance location in your Watson Machine Learning (WML) 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 [2]:
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 Watson Machine Learning instance and press Create
- Copy
space_idand 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 [3]:
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 Watson Machine Learning, you need to set space which you will be using.
In [4]:
client.set.default_space(space_id)
Out[4]:
'SUCCESS'
Test Spark¶
In [5]:
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.
In [6]:
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/cloud/data/credit_risk/credit_risk_training.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')\
.load(filename)
Explore the loaded data by using the following Apache® Spark DataFrame methods:
- print schema
- print top ten records
- count all records
In [8]:
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).
In [9]:
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
In [10]:
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.
In [11]:
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: 4005 Number of testing records : 901 Number of prediction records : 94
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 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 [12]:
import ibm_boto3
from ibm_botocore.client import Config
Action: Put credentials from Object Storage Service in Bluemix here.
In [13]:
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 [14]:
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 [15]:
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 [16]:
auth_endpoint = 'https://iam.cloud.ibm.com/identity/token'
We create COS resource to be able to write data to Cloud Object Storage.
In [17]:
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 credit_risk_training.csv.
In [18]:
from uuid import uuid4
bucket_id = str(uuid4())
score_filename = "credit_risk_training.csv"
buckets = ["credit-risk-" + bucket_id]
In [19]:
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']))
Creating bucket "credit-risk-d85ccca2-1cd3-4a2f-88bf-bfa34ec9ec59"...
In [20]:
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))
Uploading data credit_risk_training.csv... credit_risk_training.csv is uploaded.
Create connections to a COS bucket¶
In [21]:
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)
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 [22]:
connection_id = client.connections.get_id(conn_details)
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
In [23]:
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/cloud/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/cloud/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.
In [24]:
training_data_references = [
{
"type": "connection_asset",
"connection": {
"id": connection_id,
},
"location": {
"bucket": buckets[0],
"file_name": score_filename,
},
"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"
}
]
}
}
]
In [25]:
published_model_details = client.repository.store_model(
model=model_filename,
meta_props={
client.repository.ModelMetaNames.NAME:'Credit Risk 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: "Risk",
},
training_data=train_data,
pipeline=pipeline_filename)
In [26]:
model_id = client.repository.get_model_id(published_model_details)
print(model_id)
6129f828-f9e4-4145-a394-4e48cb5282a7
In [27]:
client.repository.get_model_details(model_id)
Out[27]:
{'entity': {'hybrid_pipeline_software_specs': [],
'label_column': 'Risk',
'schemas': {'input': [{'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'}],
'id': '1',
'type': 'struct'}],
'output': []},
'software_spec': {'id': 'dabb5e1b-e1c7-5ef5-95ea-34a8223e436c',
'name': 'spark-mllib_3.4'},
'training_data_references': [{'connection': {'id': '4125ce75-c759-443f-814a-24cf634f1784'},
'location': {'bucket': 'credit-risk-d85ccca2-1cd3-4a2f-88bf-bfa34ec9ec59',
'file_name': 'credit_risk_training.csv'},
'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'}],
'id': 'training_schema'},
'type': 'connection_asset'}],
'type': 'mllib_3.4'},
'metadata': {'created_at': '2024-03-06T13:45:42.482Z',
'id': '6129f828-f9e4-4145-a394-4e48cb5282a7',
'modified_at': '2024-03-06T13:45:44.434Z',
'name': 'Credit Risk model',
'owner': 'IBMid-55000091VC',
'resource_key': '558df75c-9eca-4a81-b346-d91e80222675',
'space_id': '93ee84d1-b7dd-42b4-b2ca-121bc0c86315'},
'system': {'warnings': []}}
Get saved model metadata from Watson Machine Learning.
Tip: Use client.repository.ModelMetaNames.show() to get the list of available props.
In [28]:
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'}]}}]
TEST_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'}
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
FAIRNESS_INFO 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.
In [29]:
loaded_model = client.repository.load(model_id)
You can print for example model name to make sure that model has been loaded correctly.
In [30]:
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.
In [31]:
predictions = loaded_model.transform(predict_data)
24/03/06 14:46:06 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.
Preview the results by calling the show() method on the predictions DataFrame.
In [32]:
predictions.show(5, vertical=True)
-RECORD 0---------------------------------------- CheckingStatus | 0_to_200 LoanDuration | 4 CreditHistory | all_credits_paid_... LoanPurpose | education LoanAmount | 936 ExistingSavings | less_100 EmploymentDuration | less_1 InstallmentPercent | 2 Sex | male OthersOnLoan | none CurrentResidenceDuration | 2 OwnsProperty | savings_insurance Age | 41 InstallmentPlans | bank Housing | rent ExistingCreditsCount | 1 Job | unskilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk CheckingStatus_IX | 2.0 CreditHistory_IX | 3.0 LoanPurpose_IX | 7.0 ExistingSavings_IX | 0.0 EmploymentDuration_IX | 3.0 Sex_IX | 0.0 OthersOnLoan_IX | 0.0 OwnsProperty_IX | 0.0 InstallmentPlans_IX | 2.0 Housing_IX | 1.0 Job_IX | 1.0 Telephone_IX | 0.0 ForeignWorker_IX | 0.0 label | 0.0 features | [2.0,3.0,7.0,0.0,... rawPrediction | [17.8582417779453... probability | [0.89291208889726... prediction | 0.0 predictedLabel | No Risk -RECORD 1---------------------------------------- CheckingStatus | 0_to_200 LoanDuration | 4 CreditHistory | all_credits_paid_... LoanPurpose | furniture LoanAmount | 250 ExistingSavings | less_100 EmploymentDuration | 1_to_4 InstallmentPercent | 2 Sex | female OthersOnLoan | none CurrentResidenceDuration | 1 OwnsProperty | real_estate Age | 19 InstallmentPlans | bank Housing | rent ExistingCreditsCount | 1 Job | unskilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk CheckingStatus_IX | 2.0 CreditHistory_IX | 3.0 LoanPurpose_IX | 1.0 ExistingSavings_IX | 0.0 EmploymentDuration_IX | 0.0 Sex_IX | 1.0 OthersOnLoan_IX | 0.0 OwnsProperty_IX | 2.0 InstallmentPlans_IX | 2.0 Housing_IX | 1.0 Job_IX | 1.0 Telephone_IX | 0.0 ForeignWorker_IX | 0.0 label | 0.0 features | [2.0,3.0,1.0,0.0,... rawPrediction | [18.6217190953704... probability | [0.93108595476852... prediction | 0.0 predictedLabel | No Risk -RECORD 2---------------------------------------- CheckingStatus | 0_to_200 LoanDuration | 7 CreditHistory | all_credits_paid_... LoanPurpose | car_new LoanAmount | 3091 ExistingSavings | less_100 EmploymentDuration | less_1 InstallmentPercent | 3 Sex | female OthersOnLoan | none CurrentResidenceDuration | 2 OwnsProperty | savings_insurance Age | 28 InstallmentPlans | none Housing | own ExistingCreditsCount | 2 Job | skilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk CheckingStatus_IX | 2.0 CreditHistory_IX | 3.0 LoanPurpose_IX | 0.0 ExistingSavings_IX | 0.0 EmploymentDuration_IX | 3.0 Sex_IX | 1.0 OthersOnLoan_IX | 0.0 OwnsProperty_IX | 0.0 InstallmentPlans_IX | 0.0 Housing_IX | 0.0 Job_IX | 0.0 Telephone_IX | 0.0 ForeignWorker_IX | 0.0 label | 0.0 features | (20,[0,1,4,5,13,1... rawPrediction | [17.0900288758532... probability | [0.85450144379266... prediction | 0.0 predictedLabel | No Risk -RECORD 3---------------------------------------- CheckingStatus | 0_to_200 LoanDuration | 9 CreditHistory | prior_payments_de... LoanPurpose | furniture LoanAmount | 250 ExistingSavings | less_100 EmploymentDuration | less_1 InstallmentPercent | 1 Sex | male OthersOnLoan | none CurrentResidenceDuration | 3 OwnsProperty | savings_insurance Age | 31 InstallmentPlans | none Housing | own ExistingCreditsCount | 1 Job | skilled Dependents | 1 Telephone | yes ForeignWorker | yes Risk | No Risk CheckingStatus_IX | 2.0 CreditHistory_IX | 0.0 LoanPurpose_IX | 1.0 ExistingSavings_IX | 0.0 EmploymentDuration_IX | 3.0 Sex_IX | 0.0 OthersOnLoan_IX | 0.0 OwnsProperty_IX | 0.0 InstallmentPlans_IX | 0.0 Housing_IX | 0.0 Job_IX | 0.0 Telephone_IX | 1.0 ForeignWorker_IX | 0.0 label | 0.0 features | (20,[0,2,4,11,13,... rawPrediction | [17.3233263549217... probability | [0.86616631774608... prediction | 0.0 predictedLabel | No Risk -RECORD 4---------------------------------------- CheckingStatus | 0_to_200 LoanDuration | 10 CreditHistory | prior_payments_de... LoanPurpose | car_new LoanAmount | 1797 ExistingSavings | greater_1000 EmploymentDuration | 4_to_7 InstallmentPercent | 3 Sex | male OthersOnLoan | none CurrentResidenceDuration | 3 OwnsProperty | savings_insurance Age | 28 InstallmentPlans | none Housing | own ExistingCreditsCount | 1 Job | skilled Dependents | 1 Telephone | none ForeignWorker | yes Risk | No Risk CheckingStatus_IX | 2.0 CreditHistory_IX | 0.0 LoanPurpose_IX | 0.0 ExistingSavings_IX | 3.0 EmploymentDuration_IX | 1.0 Sex_IX | 0.0 OthersOnLoan_IX | 0.0 OwnsProperty_IX | 0.0 InstallmentPlans_IX | 0.0 Housing_IX | 0.0 Job_IX | 0.0 Telephone_IX | 0.0 ForeignWorker_IX | 0.0 label | 0.0 features | (20,[0,3,4,13,14,... rawPrediction | [16.7833363666722... probability | [0.83916681833361... prediction | 0.0 predictedLabel | No Risk only showing top 5 rows
By tabulating a count, you can see which product line is the most popular.
In [33]:
predictions.select("predictedLabel").groupBy("predictedLabel").count().show(truncate=False)
+--------------+-----+ |predictedLabel|count| +--------------+-----+ |No Risk |71 | |Risk |23 | +--------------+-----+
5. Deploy and score in a Cloud¶
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¶
In [34]:
deployment_details = client.deployments.create(
model_id,
meta_props={
client.deployments.ConfigurationMetaNames.NAME: "Credit Risk model deployment",
client.deployments.ConfigurationMetaNames.ONLINE: {}
}
)
####################################################################################### Synchronous deployment creation for uid: '6129f828-f9e4-4145-a394-4e48cb5282a7' 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='2491e383-0f9b-4330-9f47-aa5169974676' ------------------------------------------------------------------------------------------------
In [ ]:
deployment_details
In [36]:
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)
Out[36]:
{'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]],
[14.298844113312466, 5.701155886687534],
[0.7149422056656233, 0.2850577943343767],
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]],
[13.06676919836477, 6.93323080163523],
[0.6533384599182386, 0.34666154008176153],
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],
[17.26065854270966, 2.739341457290342],
[0.863032927135483, 0.1369670728645171],
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]],
[17.80975737817367, 2.190242621826325],
[0.8904878689086837, 0.10951213109131627],
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],
[18.38016125542918, 1.6198387445708178],
[0.9190080627714591, 0.08099193722854091],
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]],
[16.887908351466088, 3.112091648533915],
[0.8443954175733043, 0.15560458242669573],
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],
[2.252422102778986, 17.747577897221014],
[0.1126211051389493, 0.8873788948610507],
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],
[15.860708756328307, 4.1392912436716935],
[0.7930354378164154, 0.20696456218358467],
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
Mateusz Szewczyk, Software Engineer at Watson Machine Learning
Copyright © 2020-2024 IBM. This notebook and its source code are released under the terms of the MIT License.