Binary Classification: California Housing Dataset

This document outlines a typical workflow for estimating performance of a model without access to ground truth, detecting performance issues and identifying potential root causes for these issues. Below, one can find an example use of NannyML on the modified California Housing Prices dataset.

See what modifications were made to the data to make it suitable for the use case here.

Load and prepare data

Let’s load the dataset from NannyML datasets:

>>> import pandas as pd
>>> import nannyml as nml
>>> # load data
>>> reference, analysis, analysis_targets = nml.datasets.load_modified_california_housing_dataset()
>>> reference.head(3)

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude	timestamp	partition	clf_target	y_pred_proba	y_pred	identifier
0	9.8413	32	7.17004	1.01484	4353	2.93725	34.22	-118.19	2020-10-01 00:00:00	reference	1	0.99	1	0
1	8.3695	37	7.45875	1.06271	941	3.10561	34.22	-118.21	2020-10-01 01:00:00	reference	1	1	1	1
2	8.72	44	6.16318	1.04603	668	2.79498	34.2	-118.18	2020-10-01 02:00:00	reference	1	1	1	2

Let’s extract metadata.

>>> # extract metadata, add target column name
>>> metadata = nml.extract_metadata(reference, exclude_columns=['identifier'], model_type='classification_binary')
>>> metadata.target_column_name = 'clf_target'
>>> metadata.timestamp_column_name = 'timestamp'

Performance Estimation

Let’s estimate performance for reference and analysis partitions:

>>> # fit performance estimator and estimate for combined reference and analysis
>>> cbpe = nml.CBPE(model_metadata=metadata, chunk_period='M', metrics=['roc_auc'])
>>> cbpe.fit(reference_data=reference)
>>> est_perf = cbpe.estimate(pd.concat([reference, analysis]))

UserWarning: The resulting list of chunks contains 1 underpopulated chunks.They contain too few records to be statistically relevant and might negatively influence the quality of calculations.Please consider splitting your data in a different way or continue at your own risk.

Some chunks are too small, let’s quickly check:

>>> est_perf.data['end_index'] - est_perf.data['start_index']

0     743
1     719
2     743
3     743
4     671
5     743
6     719
7     743
8     719
9     743
10    743
11    719
12    743
13    719
14    743
15    743
16    671
17    743
18    719
19    215
dtype: int64

The last one is smaller than the others due to the selected chunking method. Let’s remove it for clarity of visualizations.

>>> est_perf.data = est_perf.data[:-1].copy()
>>> est_perf.data.tail(2)

	key	start_index	end_index	start_date	end_date	partition	confidence_roc_auc	realized_roc_auc	estimated_roc_auc	upper_threshold_roc_auc	lower_threshold_roc_auc	alert_roc_auc
17	2022-03	12384	13127	2022-03-01 00:00:00	2022-03-31 23:59:59.999999999	analysis	0.051046	nan	0.829077	0.708336	1	False
18	2022-04	13128	13847	2022-04-01 00:00:00	2022-04-30 23:59:59.999999999	analysis	0.051046	nan	0.910661	0.708336	1	False

Let’s plot the estimated performance:

>>> fig = est_perf.plot(kind='performance', metric='roc_auc')
>>> fig.show()

CBPE estimates a significant performance drop in the chunk corresponding to the month of September.

Comparison with the actual performance

Let’s use the ground truth that we have to calculate ROC AUC on relevant chunks and compare:

>>> from sklearn.metrics import roc_auc_score
>>> import matplotlib.pyplot as plt
>>> # add ground truth to analysis
>>> analysis_full = pd.merge(analysis,analysis_targets, on = 'identifier')
>>> df_all = pd.concat([reference, analysis_full]).reset_index(drop=True)
>>> df_all['timestamp'] = pd.to_datetime(df_all['timestamp'])
>>> # calculate actual ROC AUC
>>> target_col = metadata.target_column_name
>>> pred_score_col = 'y_pred_proba'
>>> actual_performance = []
>>> for idx in est_perf.data.index:
>>>     start_date, end_date = est_perf.data.loc[idx, 'start_date'], est_perf.data.loc[idx, 'end_date']
>>>     sub = df_all[df_all['timestamp'].between(start_date, end_date)]
>>>     actual_perf = roc_auc_score(sub[target_col], sub[pred_score_col])
>>>     est_perf.data.loc[idx, 'actual_roc_auc'] = actual_perf
>>> # plot
>>> first_analysis = est_perf.data[est_perf.data['partition']=='analysis']['key'].values[0]
>>> plt.plot(est_perf.data['key'], est_perf.data['estimated_roc_auc'], label='estimated AUC')
>>> plt.plot(est_perf.data['key'], est_perf.data['actual_roc_auc'], label='actual ROC AUC')
>>> plt.xticks(rotation=90)
>>> plt.axvline(x=first_analysis, label='First analysis chunk', linestyle=':', color='grey')
>>> plt.ylabel('ROC AUC')
>>> plt.legend()
>>> plt.show()

The significant drop at the first few chunks of the analysis period was estimated accurately. After that, the overall trend seems to be well represented. The estimation of performance has a lower variance than actual performance.

Drift detection

The next step is to dig deeper to find out what might be responsible for this drop in ROC AUC. Let’s do it using univariate drift detection.

>>> univariate_calculator = nml.UnivariateStatisticalDriftCalculator(model_metadata=metadata, chunk_period='M').fit(reference_data=reference)
>>> univariate_results = univariate_calculator.calculate(data=analysis)
>>> nml.Ranker.by('alert_count').rank(univariate_results, metadata)

	feature	number_of_alerts	rank
0	Latitude	12	1
1	AveOccup	12	2
2	Longitude	12	3
3	HouseAge	12	4
4	MedInc	11	5
5	AveRooms	11	6
6	AveBedrms	8	7
7	Population	8	8

It looks like there is a lot of drift in this dataset. Since we have 12 chunks in the analysis period, top 4 features drifted in all analyzed chunks. Let’s look at the magnitude of this drift by looking at the KS distance statistics.

>>> # get columns with d statistics only
>>> d_stat_cols = [x for x in univariate_results.data if 'dstat' in x]
>>> univariate_results.data[d_stat_cols].mean().sort_values(ascending=False)

Longitude_dstat	0.836534
Latitude_dstat	0.799592
HouseAge_dstat	0.173479
MedInc_dstat	0.158278
AveOccup_dstat	0.133803
AveRooms_dstat	0.110907
AveBedrms_dstat	0.0786656
Population_dstat	0.0713122

The mean value of D-statistic for Longitude and Latitude on analysis chunks is the largest. Let’s plot their distributions for the analysis period.

>>> for label in ['Longitude', 'Latitude']:
>>>     fig = univariate_results.plot(
>>>         kind='feature_distribution',
>>>         feature_label=label)
>>>     fig.show()

Indeed, distributions of these variables are completely different in each chunk. This was expected, as the original dataset has observations from nearby locations next to each other. Let’s see it on a scatter plot:

>>> analysis_res = est_perf.data[est_perf.data['partition']=='analysis']
>>> plt.figure(figsize=(8,6))
>>> for idx in analysis_res.index[:10]:
>>>     start_date, end_date = analysis_res.loc[idx, 'start_date'], analysis_res.loc[idx, 'end_date']
>>>     sub = df_all[df_all['timestamp'].between(start_date, end_date)]
>>>     plt.scatter(sub['Latitude'], sub['Longitude'], s=5, label="Chunk {}".format(str(idx)))
>>> plt.legend()
>>> plt.xlabel('Latitude')
>>> plt.ylabel('Longitude')

In summary, NannyML estimated the performance (ROC AUC) of a model without accessing the target data. The estimate is quite accurate. Next, the potential root causes of the drop in performance were indicated by detecting data drift. This was achieved using univariate methods that identify features which drifted the most.