German credit scoring [scikit-learn]¶

Giskard is an open-source framework for testing all ML models, from LLMs to tabular models. Don’t hesitate to give the project a star on GitHub ⭐️ if you find it useful!

In this notebook, you’ll learn how to create comprehensive test suites for your model in a few lines of code, thanks to Giskard’s open-source Python library.

Use-case:

Binary classification. Whether to give a customer credit or not.
Model: LogisticRegression
Dataset

Outline:

Detect vulnerabilities automatically with Giskard’s scan
Automatically generate & curate a comprehensive test suite to test your model beyond accuracy-related metrics
Upload your model to the Giskard Hub to:
- Debug failing tests & diagnose issues
- Compare models & decide which one to promote
- Share your results & collect feedback from non-technical team members

Install dependencies¶

Make sure to install the giskard

[22]:

%pip install giskard --upgrade

Import libraries¶

[1]:

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

from giskard import Model, Dataset, scan, testing, GiskardClient, Suite

Define constants¶

[2]:

# Constants.
COLUMN_TYPES = {
    "account_check_status": "category",
    "duration_in_month": "numeric",
    "credit_history": "category",
    "purpose": "category",
    "credit_amount": "numeric",
    "savings": "category",
    "present_employment_since": "category",
    "installment_as_income_perc": "numeric",
    "sex": "category",
    "personal_status": "category",
    "other_debtors": "category",
    "present_residence_since": "numeric",
    "property": "category",
    "age": "category",
    "other_installment_plans": "category",
    "housing": "category",
    "credits_this_bank": "numeric",
    "job": "category",
    "people_under_maintenance": "numeric",
    "telephone": "category",
    "foreign_worker": "category",
}

TARGET_COLUMN_NAME = "default"

COLUMNS_TO_SCALE = [key for key in COLUMN_TYPES.keys() if COLUMN_TYPES[key] == "numeric"]
COLUMNS_TO_ENCODE = [key for key in COLUMN_TYPES.keys() if COLUMN_TYPES[key] == "category"]

# Paths.
DATA_URL = "https://raw.githubusercontent.com/Giskard-AI/giskard-examples/main/datasets/credit_scoring_classification_model_dataset/german_credit_prepared.csv"

Dataset preparation¶

Load data¶

[3]:

df = pd.read_csv(DATA_URL, keep_default_na=False, na_values=["_GSK_NA_"])

Train-test split¶

[4]:

X_train, X_test, Y_train, Y_test = train_test_split(df.drop(columns=TARGET_COLUMN_NAME), df[TARGET_COLUMN_NAME],
                                                    test_size=0.2, random_state=0, stratify=df[TARGET_COLUMN_NAME])

Wrap dataset with Giskard¶

To prepare for the vulnerability scan, make sure to wrap your dataset using Giskard’s Dataset class. More details here.

[5]:

raw_data = pd.concat([X_test, Y_test], axis=1)
giskard_dataset = Dataset(
    df=raw_data,
    # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable (target).
    target=TARGET_COLUMN_NAME,  # Ground truth variable.
    name='German credit scoring dataset',  # Optional.
    cat_columns=COLUMNS_TO_ENCODE
    # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.
)

Model building¶

Define preprocessing pipeline¶

[6]:

numeric_transformer = Pipeline(steps=[
    ("imputer", SimpleImputer(strategy="median")),
    ("scaler", StandardScaler())
])

categorical_transformer = Pipeline([
    ("imputer", SimpleImputer(strategy="constant", fill_value="missing")),
    ("onehot", OneHotEncoder(handle_unknown="ignore", sparse_output=False)),
])

preprocessor = ColumnTransformer(transformers=[
    ("num", numeric_transformer, COLUMNS_TO_SCALE),
    ("cat", categorical_transformer, COLUMNS_TO_ENCODE),
])

Build estimator¶

[ ]:

pipeline = Pipeline(steps=[
    ("preprocessor", preprocessor),
    ("classifier", LogisticRegression(max_iter=100))
])

pipeline.fit(X_train, Y_train)

pred_train = pipeline.predict(X_train)
pred_test = pipeline.predict(X_test)

print(classification_report(Y_test, pred_test))

Wrap model with Giskard¶

To prepare for the vulnerability scan, make sure to wrap your model using Giskard’s Model class. You can choose to either wrap the prediction function (preferred option) or the model object. More details here.

[ ]:

giskard_model = Model(
    model=pipeline,
    # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.
    model_type="classification",  # Either regression, classification or text_generation.
    name="Credit scoring classifier",  # Optional.
    classification_labels=pipeline.classes_.tolist(),
    # Their order MUST be identical to the prediction_function's output order.
    feature_names=list(COLUMN_TYPES.keys()),  # Default: all columns of your dataset.
)

# Validate wrapped model.
print(classification_report(Y_test, pipeline.classes_[giskard_model.predict(giskard_dataset).raw_prediction]))

Detect vulnerabilities in your model¶

Scan your model for vulnerabilities with Giskard¶

Giskard’s scan allows you to detect vulnerabilities in your model automatically. These include performance biases, unrobustness, data leakage, stochasticity, underconfidence, ethical issues, and more. For detailed information about the scan feature, please refer to our scan documentation.

[ ]:

results = scan(giskard_model, giskard_dataset)

[10]:

display(results)

Generate comprehensive test suites automatically for your model¶

Generate test suites from the scan¶

The objects produced by the scan can be used as fixtures to generate a test suite that integrate all detected vulnerabilities. Test suites allow you to evaluate and validate your model’s performance, ensuring that it behaves as expected on a set of predefined test cases, and to identify any regressions or issues that might arise during development or updates.

[11]:

test_suite = results.generate_test_suite("My first test suite")
test_suite.run()

Executed 'Precision on data slice “`other_installment_plans` == "bank"”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x143e15ea0>, 'threshold': 0.7540625}:
               Test failed
               Metric: 0.6


Executed 'Precision on data slice “`account_check_status` == "0 <= ... < 200 DM"”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x143d7d600>, 'threshold': 0.7540625}:
               Test failed
               Metric: 0.6


Executed 'Precision on data slice “`present_employment_since` == "... < 1 year"”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x1440253c0>, 'threshold': 0.7540625}:
               Test failed
               Metric: 0.65


Executed 'Recall on data slice “`personal_status` == "divorced"”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x143d7f910>, 'threshold': 0.8617857142857143}:
               Test failed
               Metric: 0.8


Executed 'Precision on data slice “`duration_in_month` >= 16.500”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x143d7fd60>, 'threshold': 0.7540625}:
               Test failed
               Metric: 0.71


Executed 'Precision on data slice “`property` == "if not A121/A122 : car or other, not in attribute 6"”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x143d7d0f0>, 'threshold': 0.7540625}:
               Test failed
               Metric: 0.72


Executed 'Precision on data slice “`sex` == "female"”' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12dc2e860>, 'dataset': <giskard.datasets.base.Dataset object at 0x12daddb10>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x143b84700>, 'threshold': 0.7540625}:
               Test failed
               Metric: 0.74

[11]:

Test suite failed. To debug your failing test and diagnose the issue, please run the Giskard hub (see documentation)

Test Precision on data slice “`other_installment_plans` == "bank"”

Measured Metric = 0.6 Failed

model a4d8f299-351e-4722-8176-9b37fa35a9e8