M5 Sales prediction [LGBM]ยถ
Giskard is an open-source framework for testing all ML models, from LLMs to tabular models. Donโt hesitate to give the project a star on GitHub โญ๏ธ if you find it useful!
In this notebook, youโll learn how to create comprehensive test suites for your model in a few lines of code, thanks to Giskardโs open-source Python library.
Use-case:
Prediction of the productsโ demand for the next 28 days.
Outline:
Detect vulnerabilities automatically with Giskardโs 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 dependenciesยถ
Make sure to install the giskard
[16]:
%pip install giskard --upgrade
Import librariesยถ
[2]:
from pathlib import Path
from typing import Tuple
from urllib.request import urlretrieve
import pandas as pd
from sklearn import preprocessing
from lightgbm import LGBMRegressor
from sklearn.metrics import r2_score
from giskard import Dataset, Model, testing, GiskardClient, scan, Suite
Define constantsยถ
[3]:
# Constants.
ID_COLUMN = "id"
DATE_COLUMN = "date"
TARGET_COLUMN = "demand"
SPLIT_DATE = "2016-03-27"
# Paths.
DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/m5_sales_prediction_dataset/{}"
DATA_PATH = Path.home() / ".giskard" / "m5_sales_prediction_dataset"
DATA_FILES = ["calendar.csv", "sales_train_validation.csv", "sell_prices.csv"]
Dataset preparationยถ
Load and preprocess dataยถ
[ ]:
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() -> None:
"""Fetch all necessary datafiles."""
for file_name in DATA_FILES:
source = DATA_URL.format(file_name)
destination = DATA_PATH / file_name
fetch_from_ftp(source, destination)
def load_data(n_series_use: int = 100) -> Tuple[pd.DataFrame, ...]:
"""Load necessary data files."""
fetch_dataset()
calendar_df = pd.read_csv(DATA_PATH / "calendar.csv")
prices_df = pd.read_csv(DATA_PATH / 'sell_prices.csv')
sales_df = pd.read_csv(DATA_PATH / 'sales_train_validation.csv')
sales_df = sales_df.iloc[:n_series_use]
return calendar_df, prices_df, sales_df
dfs = load_data()
[ ]:
def preprocess_data(calendar_df: pd.DataFrame, prices_df: pd.DataFrame, sales_df: pd.DataFrame) -> pd.DataFrame:
"""Preprocess and create df with the whole data."""
# Melt the sales data: translate columnar demand representation into single target vector.
data = pd.melt(sales_df,
id_vars=[ID_COLUMN, 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id'],
var_name='day', value_name=TARGET_COLUMN)
data = data.drop(columns=ID_COLUMN)
# Add the calendar data.
calendar_df = calendar_df.drop(['weekday', 'wday', 'month', 'year'], axis=1)
data = pd.merge(data, calendar_df, how ='left', left_on=['day'], right_on=['d'])
data = data.drop(columns=['d', 'day'])
# Add the sell price data.
data = data.merge(prices_df, on=['store_id', 'item_id', 'wm_yr_wk'], how='left')
# Fill NaN values.
nan_features = ['event_name_1', 'event_type_1', 'event_name_2', 'event_type_2']
for feature in nan_features:
data[feature] = data[feature].fillna('unknown')
# Encode categorical features.
to_encode = ['item_id', 'dept_id', 'cat_id', 'store_id', 'state_id', 'event_name_1', 'event_type_1', 'event_name_2', 'event_type_2']
for feature in to_encode:
encoder = preprocessing.LabelEncoder()
data[feature] = encoder.fit_transform(data[feature])
print(f'Final dataset has {data.shape[0]} rows and {data.shape[1]} columns')
return data
df = preprocess_data(*dfs)
Train-test splitยถ
[ ]:
def drop_after_split(x: pd.DataFrame) -> pd.DataFrame:
"""Drops useless data after train-test split."""
to_drop = [DATE_COLUMN, TARGET_COLUMN]
x = x.drop(columns=to_drop)
return x
def train_val_split(data: pd.DataFrame) -> Tuple[pd.DataFrame, ...]:
"""Perform train/val split, where the split point is the date '2016-03-27'. Validation records are 28 days for each product."""
x_train = data[data[DATE_COLUMN] <= SPLIT_DATE]
y_train = x_train[TARGET_COLUMN]
x_train = drop_after_split(x_train)
print(f"Train samples: {len(x_train)}")
x_val = data[data[DATE_COLUMN] > SPLIT_DATE]
y_val = x_val[TARGET_COLUMN]
x_val = drop_after_split(x_val)
print(f"Valid samples: {len(x_val)}")
return x_train, y_train, x_val, y_val
X_train, Y_train, X_val, Y_val = train_val_split(df)
Wrap dataset with Giskardยถ
To prepare for the vulnerability scan, make sure to wrap your dataset using Giskardโs Dataset class. More details here.
[7]:
raw_data = pd.concat([X_val, Y_val], 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, # Ground truth variable
name="M5 products timeseries dataset", # Optional
cat_columns=X_val.select_dtypes(int).columns.tolist() # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.
)
Model buildingยถ
Build estimatorยถ
[ ]:
ESTIMATOR_PARAMS = {
'n_estimators': 300,
'seed': 0,
'n_jobs': -1,
"verbose": -1,
}
regressor = LGBMRegressor(**ESTIMATOR_PARAMS)
regressor.fit(X_train, Y_train)
# Validate estimator.
y_train_pred = regressor.predict(X_train)
train_score = r2_score(Y_train, y_train_pred)
print(f'Train R2-score: {train_score: .2f}')
y_val_pred = regressor.predict(X_val)
val_score = r2_score(Y_val, y_val_pred)
print(f'Val R2-score: {val_score: .2f}')
Wrap model with Giskardยถ
To prepare for the vulnerability scan, make sure to wrap your model using Giskardโs Model class. You can choose to either wrap the prediction function (preferred option) or the model object. More details here.
[ ]:
giskard_model = Model(
model=regressor, # 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="regression", # Either regression, classification or text_generation.
name="M5 sales timeseries regressor", # Optional
feature_names=X_val.columns # Default: all columns of your dataset
)
Detect vulnerabilities in your modelยถ
Scan your model for vulnerabilities with Giskardยถ
Giskardโs 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)
[11]:
display(results)
Generate comprehensive test suites automatically for your modelยถ
Generate test suites from the scanยถ
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 modelโs 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.
[12]:
test_suite = results.generate_test_suite("My first test suite")
test_suite.run()
Executed 'MSE on data slice โ`snap_TX` == 1โ' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12653d090>, 'dataset': <giskard.datasets.base.Dataset object at 0x12615f700>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x13c3b5cc0>, 'threshold': 7.1383301822423295}:
Test failed
Metric: 8.92
Executed 'MSE on data slice โ`snap_WI` == 1โ' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12653d090>, 'dataset': <giskard.datasets.base.Dataset object at 0x12615f700>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x13c3b5f00>, 'threshold': 7.1383301822423295}:
Test failed
Metric: 8.64
Executed 'MSE on data slice โ`wm_yr_wk` == 1.161e+04โ' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12653d090>, 'dataset': <giskard.datasets.base.Dataset object at 0x12615f700>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x13c3b5090>, 'threshold': 7.1383301822423295}:
Test failed
Metric: 8.63
Executed 'MSE on data slice โ`snap_CA` == 1โ' with arguments {'model': <giskard.models.sklearn.SKLearnModel object at 0x12653d090>, 'dataset': <giskard.datasets.base.Dataset object at 0x12615f700>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x13c3b5a80>, 'threshold': 7.1383301822423295}:
Test failed
Metric: 7.92
[12]:
Customize your suite by loading objects from the Giskard catalogยถ
The Giskard open source catalog will enable to load:
Tests such as metamorphic, performance, prediction & data drift, statistical tests, etc
Slicing functions such as detectors of toxicity, hate, emotion, etc
Transformation functions such as generators of typos, paraphrase, style tune, etc
To create custom tests, refer to this page.
For demo purposes, we will load a simple unit test (test_r2) that checks if the test R2 score is above the given threshold. For more examples of tests and functions, refer to the Giskard catalog.
[ ]:
test_suite.add_test(testing.test_r2(model=giskard_model, dataset=giskard_dataset, threshold=0.7)).run()
Debug and interact with your tests in the Giskard Hubยถ
At this point, youโve created a test suite that is highly specific to your domain & use-case. Failing tests can be a pain to debug, which is why we encourage you to head over to the Giskard Hub.
Play around with a demo of the Giskard Hub on HuggingFace Spaces using this link.
More than just debugging tests, the Giskard Hub allows you to:
Compare models to decide which model to promote
Automatically create additional domain-specific tests through our automated model insights feature
Share your test results with team members and decision makers
The Giskard Hub can be deployed easily on HuggingFace Spaces.
Hereโs a sneak peek of automated model insights on a credit scoring classification model.
Upload your test suite to the Giskard Hubยถ
The entry point to the Giskard Hub is the upload of your test suite. Uploading the test suite will automatically save the model, dataset, tests, slicing & transformation functions to the Giskard Hub.
[ ]:
# Create a Giskard client after having install the Giskard server (see documentation)
api_key = "<Giskard API key>" #This can be found in the Settings tab of the Giskard hub
#hf_token = "<Your Giskard Space token>" #If the Giskard Hub is installed on HF Space, this can be found on the Settings tab of the Giskard Hub
client = GiskardClient(
url="http://localhost:19000", # Option 1: Use URL of your local Giskard instance.
# url="<URL of your Giskard hub Space>", # Option 2: Use URL of your remote HuggingFace space.
key=api_key,
# hf_token=hf_token # Use this token to access a private HF space.
)
project_key = "my_project"
my_project = client.create_project(project_key, "PROJECT_NAME", "DESCRIPTION")
# Upload to the project you just created
test_suite.upload(client, project_key)
Download a test suite from the Giskard Hubยถ
After curating your test suites with additional tests on the Giskard Hub, you can easily download them back into your environment. This allows you to:
Check for regressions after training a new model
Automate the test suite execution in a CI/CD pipeline
Compare several models during the prototyping phase
[ ]:
test_suite_downloaded = Suite.download(client, project_key, suite_id=...)
test_suite_downloaded.run()