GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cloud/notebooks/python_sdk/monitoring/German credit risk prediction with Scikit for model monitoring.ipynb
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Kernel: Python 3 (ipykernel)

German credit risk prediciton with Scikit-learn for model monitoring

This notebook should be run in a Watson Studio project, using Default Python 3.11 runtime environment. It requires service credentials for the following Cloud services:

watsonx.ai Runtime

The notebook will train, create and deploy a German Credit Risk model.

Learning goals

In this notebook, you will learn how to:

Explore data
Prepare data for training and evaluation
Create a scikit-learn pipeline
Train and evaluate a model
Store a model in the watsonx.ai Runtime repository
Deploy and score the model

1. Set up the environment

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

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

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

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

In [ ]:

!pip install -U ibm-watsonx-ai | tail -n 1
!pip install "scikit-learn==1.3.2" | tail -n 1

Connection to watsonx.ai Runtime

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

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

API Key can be generated in the following way:

ibmcloud login
ibmcloud iam api-key-create API_KEY_NAME

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

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

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

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

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

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

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

In [ ]:

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

In [2]:

from ibm_watsonx_ai import Credentials

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

In [3]:

from ibm_watsonx_ai import APIClient

client = APIClient(credentials)

Working with spaces

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

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

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

Action: Assign space ID below

In [4]:

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

In [ ]:

client.spaces.list(limit=10)

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

In [5]:

client.set.default_space(space_id)

Out[5]:

'SUCCESS'

Connections to COS

In next cell we read the COS credentials from the space.

In [10]:

cos_credentials = client.spaces.get_details(space_id=space_id)['entity']['storage']['properties']

Run the notebook

At this point, the notebook is ready to run. You can either run the cells one at a time, or click the Kernel option above and select Restart and Run All to run all the cells.

In this section you will learn how to train Scikit-learn model and next deploy it as web-service using watsonx.ai Runtime service.

Load the training data from github

In [ ]:

!rm german_credit_data_biased_training.csv
!wget https://raw.githubusercontent.com/pmservice/ai-openscale-tutorials/master/assets/historical_data/german_credit_risk/wml/german_credit_data_biased_training.csv

In [12]:

import numpy as np
import pandas as pd 

training_data_file_name = "german_credit_data_biased_training.csv"
data_df = pd.read_csv(training_data_file_name)

Explore data

In [13]:

data_df.head()

Out[13]:

In [14]:

print('Columns: ', list(data_df.columns))
print('Number of columns: ', len(data_df.columns))

Out[14]:

Columns:  ['CheckingStatus', 'LoanDuration', 'CreditHistory', 'LoanPurpose', 'LoanAmount', 'ExistingSavings', 'EmploymentDuration', 'InstallmentPercent', 'Sex', 'OthersOnLoan', 'CurrentResidenceDuration', 'OwnsProperty', 'Age', 'InstallmentPlans', 'Housing', 'ExistingCreditsCount', 'Job', 'Dependents', 'Telephone', 'ForeignWorker', 'Risk']
Number of columns:  21

As you can see, the data contains twenty one fields. Risk field is the one you would like to predict using feedback data.

In [15]:

print('Number of records: ', data_df.Risk.count())

Out[15]:

Number of records:  5000

In [16]:

target_count = data_df.groupby('Risk')['Risk'].count()
target_count

Out[16]:

Risk
No Risk    3330
Risk       1670
Name: Risk, dtype: int64

Visualize data

In [18]:

target_count.plot.pie(figsize=(8, 8));

Out[18]:

Save training data to Cloud Object Storage

In [22]:

import ibm_boto3
from ibm_botocore.client import Config

cos_client = ibm_boto3.resource("s3",
    ibm_api_key_id=cos_credentials['credentials']['editor']['api_key'],
    ibm_service_instance_id=cos_credentials['resource_crn'],
    ibm_auth_endpoint='https://iam.cloud.ibm.com/identity/token',
    config=Config(signature_version="oauth"),
    endpoint_url=cos_credentials['endpoint_url']
)

In [23]:

with open(training_data_file_name, "rb") as file_data:
    cos_client.Object(cos_credentials['bucket_name'], training_data_file_name).upload_fileobj(
        Fileobj=file_data
    )

Create a model

In this section you will learn how to:

Prepare data for training a model
Create machine learning pipeline
Train a model

In [32]:

MODEL_NAME = "Scikit German Risk Model WML V4"

DEPLOYMENT_NAME = "Scikit German Risk Deployment WML V4"

You will start with importing required libraries

In [24]:

from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split

Splitting the data into train and test

In [25]:

train_data, test_data = train_test_split(data_df, test_size=0.2)

Preparing the pipeline

In [26]:

features_idx = np.s_[0:-1]
all_records_idx = np.s_[:]
first_record_idx = np.s_[0]

In this step you will encode target column labels into numeric values. You can use inverse_transform to decode numeric predictions into labels.

In [27]:

string_fields = [type(fld) is str for fld in train_data.iloc[first_record_idx, features_idx]]
ct = ColumnTransformer([("ohe", OneHotEncoder(), list(np.array(train_data.columns)[features_idx][string_fields]))])
clf_linear = SGDClassifier(loss='log', penalty='l2', max_iter=1000, tol=1e-5)

pipeline_linear = Pipeline([('ct', ct), ('clf_linear', clf_linear)])

Train a model

In [28]:

risk_model = pipeline_linear.fit(train_data.drop('Risk', axis=1), train_data.Risk)

Evaluate the model

In [29]:

from sklearn.metrics import roc_auc_score

predictions = risk_model.predict(test_data.drop('Risk', axis=1))
indexed_preds = [0 if prediction=='No Risk' else 1 for prediction in predictions]

real_observations = test_data.Risk.replace('Risk', 1)
real_observations = real_observations.replace('No Risk', 0).values

auc = roc_auc_score(real_observations, indexed_preds)
print(auc)

Out[29]:

0.7140884968445209

Publish the model

In this section, the notebook uses the supplied watsonx.ai Runtime credentials to save the model (including the pipeline) to the watsonx.ai Runtime instance. Previous versions of the model are removed so that the notebook can be run again, resetting all data for another demo.

In [33]:

software_spec_id = client.software_specifications.get_id_by_name("runtime-24.1-py3.11")
print("Software Specification ID: {}".format(software_spec_id))
model_props = {
    client.repository.ModelMetaNames.NAME: "{}".format(MODEL_NAME),
    client.repository.ModelMetaNames.TYPE: 'scikit-learn_1.3',
    client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: software_spec_id
}

Out[33]:

Software Specification ID: 336b29df-e0e1-5e7d-b6a5-f6ab722625b2

In [35]:

print("Storing model ...")

published_model_details = client.repository.store_model(model=risk_model, meta_props=model_props, training_data=data_df.drop(["Risk"], axis=1), training_target=data_df.Risk)
model_id = client.repository.get_model_id(published_model_details)
print("Done")
print("Model ID: {}".format(model_id))

Out[35]:

Storing model ...
Done
Model ID: 727c0550-b0aa-47c7-8ebc-dec52f2ff578

Deploy and score

The next section of the notebook deploys the model as a RESTful web service in watsonx.ai Runtime. The deployed model will have a scoring URL you can use to send data to the model for predictions.

In [36]:

print("Deploying model...")
metadata = {
    client.deployments.ConfigurationMetaNames.NAME: DEPLOYMENT_NAME,
    client.deployments.ConfigurationMetaNames.ONLINE: {}
}
deployment = client.deployments.create(model_id, meta_props=metadata)
deployment_id = client.deployments.get_id(deployment)
    
print("Model id: {}".format(model_id))
print("Deployment id: {}".format(deployment_id))

Out[36]:

Deploying model...

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

Synchronous deployment creation for uid: '727c0550-b0aa-47c7-8ebc-dec52f2ff578' 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='e27bd69c-47e3-4375-8a67-a0d0322f6053'
------------------------------------------------------------------------------------------------

Model id: 727c0550-b0aa-47c7-8ebc-dec52f2ff578
Deployment id: e27bd69c-47e3-4375-8a67-a0d0322f6053

Score the model

In [37]:

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"],
        ]

scoring_payload = {"input_data": [{"fields": fields, "values": values}]}

In [38]:

predictions = client.deployments.score(deployment_id, scoring_payload)
predictions

Out[38]:

{'predictions': [{'fields': ['prediction', 'probability'],
   'values': [['No Risk', [0.569000245132717, 0.43099975486728304]],
    ['No Risk', [0.7041741561003128, 0.2958258438996873]]]}]}

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.

Summary and next steps

You successfully completed this notebook!

You have finished the hands-on lab for IBM watsonx.ai. You created, published and deployed Scikit-Learn german credit risk model.

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

You can now run the model monitoring notebook. You need to pass deployed model id in mentioned notebook

Authors

Lukasz Cmielowski, PhD, is an Automation Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

Szymon Kucharczyk, Software Engineer at IBM watsonx.ai.

Mateusz Szewczyk, Software Engineer at watsonx.ai.

German credit risk prediciton with Scikit-learn for model monitoring

Learning goals

Contents

1. Set up the environment

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

Connection to watsonx.ai Runtime

Working with spaces

Connections to COS

Run the notebook

Load the training data from github

Explore data

Visualize data

Save training data to Cloud Object Storage

Create a model

You will start with importing required libraries

Splitting the data into train and test

Preparing the pipeline

Train a model

Evaluate the model

Publish the model

Deploy and score

Score the model

Clean up

Summary and next steps

Authors

Product

Resources

Company

German credit risk prediciton with Scikit-learn for model monitoring

Learning goals

Contents

1. Set up the environment

Install and import the ibm-watsonx-ai and dependecies

Connection to watsonx.ai Runtime

Working with spaces

Connections to COS

Run the notebook

Load the training data from github

Explore data

Visualize data

Save training data to Cloud Object Storage

Create a model

You will start with importing required libraries

Splitting the data into train and test

Preparing the pipeline

Train a model

Evaluate the model

Publish the model

Deploy and score

Score the model

Clean up

Summary and next steps

Authors

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