IEEE Fraud detection adversarial validation [LGBM]¶

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:

IEEE Fraud detection train/test data binary classification task.
Reference notebook
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

[ ]:

%pip install giskard --upgrade

Troubleshooting¶

If you encounter a segmentation fault on macOS at any point during this tutorial, check: https://docs.giskard.ai/en/stable/community/contribution_guidelines/dev-environment.html#fatal-python-error-segmentation-fault-when-running-pytest-on-macos

Import libraries¶

[1]:

import os
from pathlib import Path
from urllib.request import urlretrieve

import numpy as np
import pandas as pd
from lightgbm import LGBMClassifier
from pandas.api.types import union_categoricals
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

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

Define constants¶

[2]:

# Constants.
TARGET_COLUMN = 'isTest'
IDX_LABEL = 'TransactionID'

# Paths.
DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/fraud_detection_classification_dataset/{}"
DATA_PATH = Path.home() / ".giskard" / "fraud_detection_classification_dataset"

Dataset preparation¶

Load and preprocess data¶

[3]:

def fetch_from_ftp(url: str, file: Path) -> None:
    """Helper to fetch data from the FTP server."""
    if not file.parent.exists():
        file.parent.mkdir(parents=True, exist_ok=True)

    if not file.exists():
        print(f"Downloading data from {url}")
        urlretrieve(url, file)

    print(f"Data was loaded!")


def fetch_dataset():
    files_to_fetch = ["train_transaction.csv", "train_identity.csv", "test_transaction.csv", "test_identity.csv"]
    for file_name in files_to_fetch:
        fetch_from_ftp(DATA_URL.format(file_name), DATA_PATH / file_name)


# Define data-types of transactions features.
DATA_TYPES_TRANSACTION = {
    'TransactionID': 'int32',
    'isFraud': 'int8',
    'TransactionDT': 'int32',
    'TransactionAmt': 'float32',
    'ProductCD': 'category',
    'card1': 'int16',
    'card2': 'float32',
    'card3': 'float32',
    'card4': 'category',
    'card5': 'float32',
    'card6': 'category',
    'addr1': 'float32',
    'addr2': 'float32',
    'dist1': 'float32',
    'dist2': 'float32',
    'P_emaildomain': 'category',
    'R_emaildomain': 'category',
}

C_COLS = [f'C{i}' for i in range(1, 15)]
D_COLS = [f'D{i}' for i in range(1, 16)]
M_COLS = [f'M{i}' for i in range(1, 10)]
V_COLS = [f'V{i}' for i in range(1, 340)]

DATA_TYPES_TRANSACTION.update((c, 'float32') for c in C_COLS)
DATA_TYPES_TRANSACTION.update((c, 'float32') for c in D_COLS)
DATA_TYPES_TRANSACTION.update((c, 'float32') for c in V_COLS)
DATA_TYPES_TRANSACTION.update((c, 'category') for c in M_COLS)

# Define datatypes of identity features.
DATA_TYPES_ID = {
    'TransactionID': 'int32',
    'DeviceType': 'category',
    'DeviceInfo': 'category',
}

ID_COLS = [f'id_{i:02d}' for i in range(1, 39)]
ID_CATS = [
    'id_12', 'id_15', 'id_16', 'id_23', 'id_27', 'id_28', 'id_29', 'id_30',
    'id_31', 'id_33', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38'
]

DATA_TYPES_ID.update(((c, 'float32') for c in ID_COLS))
DATA_TYPES_ID.update(((c, 'category') for c in ID_CATS))

# Define list of all categorical features.
CATEGORICALS = [f_name for (f_name, f_type) in dict(DATA_TYPES_TRANSACTION, **DATA_TYPES_ID).items() if
                f_type == "category"]


def read_set(_type):
    """Read both transactions and identity data."""
    print(f"Reading transactions data...")
    _df = pd.read_csv(os.path.join(DATA_PATH, f'{_type}_transaction.csv'),
                      index_col=IDX_LABEL, dtype=DATA_TYPES_TRANSACTION, nrows=250)

    print(f"Reading identity data...")
    _df = _df.join(pd.read_csv(os.path.join(DATA_PATH, f'{_type}_identity.csv'),
                               index_col=IDX_LABEL, dtype=DATA_TYPES_ID))
    return _df


def read_dataset():
    """Read whole data."""
    fetch_dataset()

    print(f"Reading train data...")
    train_set = read_set('train')

    print(f"Reading test data...")
    test_set = read_set('test')

    return train_set, test_set

[4]:

def preprocess_dataset(train_set, test_set):
    """Unite train and test into common dataframe."""
    # Create a new target column and remove a former one from the train data.
    print("Start data preprocessing...")
    train_set.pop('isFraud')
    train_set['isTest'] = 0
    test_set['isTest'] = 1

    # Preprocess categorical features.
    n_train = train_set.shape[0]
    for c in train_set.columns:
        s = train_set[c]
        if hasattr(s, 'cat'):
            u = union_categoricals([train_set[c], test_set[c]], sort_categories=True)
            train_set[c] = u[:n_train]
            test_set[c] = u[n_train:]

    # Unite train and test data.
    united = pd.concat([train_set, test_set])

    # Add additional features.
    united['TimeInDay'] = united.TransactionDT % 86400
    united['Cents'] = united.TransactionAmt % 1

    # Remove useless columns.
    united.drop("TransactionDT", axis=1, inplace=True)

    print(f"Dataset merged and preprocessed! Resulted shape: {united.shape}")

    return united

[ ]:

united_dataset = preprocess_dataset(*read_dataset())

Train-test split¶

[6]:

X_train, X_test, y_train, y_test = train_test_split(united_dataset.drop(TARGET_COLUMN, axis=1),
                                                    united_dataset[TARGET_COLUMN], test_size=0.25)

Wrap dataset with Giskard¶

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

[7]:

raw_dataset = pd.concat([X_test, y_test], axis=1)
giskard_dataset = Dataset(
    df=raw_dataset,
    # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable (target).
    target=TARGET_COLUMN,  # Ground truth variable.
    name="fraud_detection_adversarial_dataset",  # Optional.
    cat_columns=CATEGORICALS
    # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.
)

Model building¶

Build estimator¶

[ ]:

# Define parameters of an estimator.
ESTIMATOR_PARAMS = {
    'num_leaves': 64,
    'objective': 'binary',
    'min_data_in_leaf': 10,
    'learning_rate': 0.1,
    'feature_fraction': 0.5,
    'bagging_fraction': 0.9,
    'bagging_freq': 1,
    'max_cat_to_onehot': 128,
    'metric': 'auc',
    'n_jobs': -1,
    'seed': 42,
    'subsample_for_bin': united_dataset.shape[0]
}

estimator = LGBMClassifier(**ESTIMATOR_PARAMS)
estimator.fit(X_train, y_train)

train_metric = roc_auc_score(y_train, estimator.predict_proba(X_train)[:, 1].T)
test_metric = roc_auc_score(y_test, estimator.predict_proba(X_test)[:, 1].T)

print(f"Train ROC-AUC score: {train_metric:.2f}")
print(f"Test ROC-AUC score: {test_metric:.2f}")

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.

[9]:

def prediction_function(df: pd.DataFrame) -> np.ndarray:
    return estimator.predict_proba(df)

[ ]:

giskard_model = Model(
    model=prediction_function,
    # 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="train_test_data_classifier",  # Optional.
    classification_labels=[0, 1],  # Their order MUST be identical to the prediction_function's output order.
    feature_names=X_train.columns,  # Default: all columns of your dataset.
)

# Validate wrapped model.
wrapped_test_metric = roc_auc_score(y_test, giskard_model.predict(giskard_dataset).raw[:, 1].T)
print(f"Wrapped Test ROC-AUC score: {wrapped_test_metric:.2f}")

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)

[12]:

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.

[13]:

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

Executed 'Precision on data slice “`D15` >= 4.000 AND `D15` < 344.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be228f0>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.7


Executed 'Precision on data slice “`D4` >= 81.000”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12b6744f0>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.75


Executed 'Accuracy on data slice “`D2` >= 108.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be232b0>, 'threshold': 0.8664000000000001}:
               Test failed
               Metric: 0.79


Executed 'Accuracy on data slice “`C6` >= 1.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be8ba30>, 'threshold': 0.8664000000000001}:
               Test failed
               Metric: 0.8


Executed 'Accuracy on data slice “`D11` >= 65.000”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12b6747c0>, 'threshold': 0.8664000000000001}:
               Test failed
               Metric: 0.81


Executed 'Precision on data slice “`V310` >= 48.475”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12b95b2b0>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.79


Executed 'Precision on data slice “`C11` >= 1.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be22200>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.79


Executed 'Accuracy on data slice “`D5` >= 13.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12b676650>, 'threshold': 0.8664000000000001}:
               Test failed
               Metric: 0.81


Executed 'Precision on data slice “`C1` >= 2.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be21ea0>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.8


Executed 'Precision on data slice “`V283` < 0.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be211e0>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.8


Executed 'Precision on data slice “`V282` < 0.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be8a470>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.8


Executed 'Accuracy on data slice “`D3` >= 13.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be8bc10>, 'threshold': 0.8664000000000001}:
               Test failed
               Metric: 0.82


Executed 'Precision on data slice “`V285` >= 0.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12b6779a0>, 'threshold': 0.8444444444444444}:
               Test failed
               Metric: 0.81


Executed 'Recall on data slice “`addr1` >= 312.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12b677760>, 'threshold': 0.8603773584905661}:
               Test failed
               Metric: 0.83


Executed 'Accuracy on data slice “`D10` >= 222.000”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x12ba078b0>, 'dataset': <giskard.datasets.base.Dataset object at 0x12b8fac20>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x12be88e50>, 'threshold': 0.8664000000000001}:
               Test failed
               Metric: 0.84

[13]:

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

Test Precision on data slice “`D15` >= 4.000 AND `D15` < 344.500”

Measured Metric = 0.7 Failed