First Example with Modeva

Installation

# To install the required package, use the following command:
# !pip install modeva

Authentication

# To get authentication, use the following command: (To get full access please replace the token to your own token)
# from modeva.utils.authenticate import authenticate
# authenticate(auth_code='eaaa4301-b140-484c-8e93-f9f633c8bacb')

Import modeva modules.

from modeva import DataSet
from modeva import TestSuite
from modeva.models import MoXGBRegressor, MoLGBMRegressor

Load BikeSharing Dataset

ds = DataSet()
ds.load(name="BikeSharing")
ds.set_random_split()

✓ Auth code found in local storage.
Authenticating Modeva...
✓ License is active and valid.
✓ Authenticated successfully!

Fit XGB and LGBM models

model1 = MoXGBRegressor(name="XGB")
model1.fit(ds.train_x, ds.train_y)

model2 = MoLGBMRegressor(name="LGBM-2", max_depth=2, verbose=-1)
model2.fit(ds.train_x, ds.train_y)

MoLGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
                importance_type='split', learning_rate=0.1, max_depth=2,
                min_child_samples=20, min_child_weight=0.001,
                min_split_gain=0.0, n_estimators=100, n_jobs=None,
                num_leaves=31, objective=None, random_state=None, reg_alpha=0.0,
                reg_lambda=0.0, subsample=1.0, subsample_for_bin=200000,
                subsample_freq=0, verbose=-1)

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Model Explainability (PDP for hr)

ts = TestSuite(ds, model1)
results = ts.explain_pdp("hr")
results.plot()

Model Explainability (PDP for season)

results = ts.explain_pdp("season")
results.plot()

Diagnostics (accuracy)

results = ts.diagnose_accuracy_table()
results.plot()

Diagnostics (slicing accuracy)

results = ts.diagnose_slicing_accuracy(features=(("hr", ), ("season", )), method="uniform",
                                       bins=10, metric="MSE")
results.plot()

Model comparison (slicing accuracy)

tsc = TestSuite(ds, models=[model1, model2])
results = tsc.compare_slicing_accuracy(features="hr", method="quantile",
                                       bins=10, metric="MSE")
results.plot()