Residual Analysis (Classification)

Evaluate model residuals.

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 MoLGBMClassifier

Load BikeSharing Dataset

ds = DataSet()
ds.load(name="TaiwanCredit")
ds.set_random_split()
ds.set_target("FlagDefault")

Fit a LGBM model

model = MoLGBMClassifier(name="LGBM-2", max_depth=2, verbose=-1, random_state=0)
model.fit(ds.train_x, ds.train_y.ravel())

MoLGBMClassifier(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=0, reg_alpha=0.0,
                 reg_lambda=0.0, subsample=1.0, subsample_for_bin=200000,
                 subsample_freq=0, verbose=-1)

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Analyzes residuals feature importance

ts = TestSuite(ds, model)
results = ts.diagnose_residual_interpret(dataset="train")
results.plot()

Visualize the residual against predictor

results = ts.diagnose_residual_analysis(features="PAY_1", dataset="train")
results.plot()

Visualize the residual against response variable

results = ts.diagnose_residual_analysis(features="FlagDefault", dataset="train")
results.plot()

Visualize the residual against model prediction (predict proba)

results = ts.diagnose_residual_analysis(use_prediction=True, dataset="train")
results.plot()

Interpret residual by a XGB depth-2 model

results = ts.diagnose_residual_interpret(dataset='test', n_estimators=100, max_depth=2)

XGB-2 feature performance

results.plot("feature_importance")

XGB-2 effect performance

results.plot("effect_importance")

Further interpretation (main effect plot)

ts_residual = results.value["TestSuite"]
ts_residual.interpret_effects("PAY_1", dataset="test").plot()

Further interpretation (local interpretation)

ts_residual.interpret_local_fi(sample_index=20).plot()

Random forest-based residual clustering analysis (absolute residual)

results = ts.diagnose_residual_cluster(
    dataset="test",
    response_type="abs_residual",
    metric="AUC",
    n_clusters=10,
    cluster_method="rf",
    sample_size=2000,
    n_estimators=100,
    max_depth=5,
)
results.table

	AUC	Size	abs_residual
8	0.7154	546.0	0.4159
1	0.6771	505.0	0.4157
7	0.7873	303.0	0.3967
3	0.5774	395.0	0.3713
0	0.5578	828.0	0.3080
4	0.5983	580.0	0.2636
5	0.6240	397.0	0.2324
6	0.5773	353.0	0.2108
2	0.5649	572.0	0.1948
9	0.6507	1521.0	0.1457

Residual value for each cluster

results.plot("cluster_residual")

Performance metric for each cluster

results.plot("cluster_performance")

Feature importance of the random forest model

results.plot("feature_importance")

Analyze data drift for a specific cluster

data_results = ds.data_drift_test(
    **results.value["clusters"][0]["data_info"],
    distance_metric="PSI",
    psi_method="uniform",
    psi_bins=10
)
data_results.plot("summary")

data_results.plot(name=('density', 'PAY_1'))

Random forest-based residual clustering analysis (perturbed residual)

results = ts.diagnose_residual_cluster(
    dataset="test",
    response_type="abs_residual_perturb",
    metric="AUC",
    n_clusters=10,
    cluster_method="rf",
    sample_size=2000,
    n_estimators=100,
    max_depth=5,
)
results.table

	AUC	Size	abs_residual_perturb
8	0.6163	402.0	0.4647
0	0.5702	267.0	0.4251
4	0.7641	451.0	0.3942
6	0.5864	1374.0	0.3819
7	0.5843	324.0	0.3788
5	0.5081	207.0	0.3488
9	0.5652	816.0	0.3040
3	0.6097	518.0	0.2948
1	0.6311	1305.0	0.2389
2	0.5840	336.0	0.1384

Random forest-based residual clustering analysis (prediction interval width)

results = ts.diagnose_residual_cluster(
    dataset="test",
    response_type="pi_width",
    metric="AUC",
    n_clusters=10,
    cluster_method="rf",
    sample_size=2000,
    n_estimators=100,
    max_depth=5,
)
results.table

	AUC	Size	pi_width
3	0.5111	62.0	1.9839
5	0.4744	73.0	1.9589
0	0.7062	436.0	1.9472
6	0.5538	68.0	1.2941
2	0.5236	110.0	1.2000
4	0.4581	94.0	1.1809
7	0.5564	56.0	1.1786
1	0.6386	355.0	1.0873
8	0.5405	109.0	1.0459
9	0.6352	1637.0	1.0006

Compare residuals cluster of multiple models

benchmark = MoLGBMClassifier(name="LGBM-5", max_depth=5, verbose=-1, random_state=0)
benchmark.fit(ds.train_x, ds.train_y.ravel())

tsc = TestSuite(ds, models=[model, benchmark])
results = tsc.compare_residual_cluster(dataset="test")
results.table

	LGBM-2		LGBM-5
	AUC	size	AUC	size
0	0.2120	268.0	0.2768	268.0
7	0.5834	784.0	0.5862	784.0
2	0.5985	644.0	0.6451	644.0
9	0.3919	455.0	0.4526	455.0
3	0.3846	643.0	0.4567	643.0
8	0.3664	700.0	0.4727	700.0
6	0.4594	562.0	0.5297	562.0
1	0.2993	705.0	0.4142	705.0
5	0.3790	794.0	0.3205	794.0
4	NaN	445.0	NaN	445.0

results.plot("cluster_performance")