Average

Just The Code

>>> import nannyml as nml
>>> from IPython.display import display

>>> reference_df, analysis_df, analysis_targets_df = nml.load_synthetic_car_loan_dataset()
>>> display(reference_df.head())

>>> feature_column_names = [
...     'car_value', 'debt_to_income_ratio', 'driver_tenure'
>>> ]
>>> calc = nml.SummaryStatsAvgCalculator(
...     column_names=feature_column_names,
>>> )

>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> display(results.filter(period='all').to_df())

>>> for column_name in results.column_names:
...     results.filter(column_names=column_name).plot().show()

Walkthrough

The Mean value calculation is straightforward. For each chunk NannyML calculates the mean for all selected numerical columns. The resulting values from the reference data chunks are used to calculate the alert thresholds. The mean value results from the analysis chunks are compared against those thresholds and generate alerts if applicable.

We begin by loading the synthetic car loan dataset provided by the NannyML package.

>>> import nannyml as nml
>>> from IPython.display import display

>>> reference_df, analysis_df, analysis_targets_df = nml.load_synthetic_car_loan_dataset()
>>> display(reference_df.head())

	id	car_value	salary_range	debt_to_income_ratio	loan_length	repaid_loan_on_prev_car	size_of_downpayment	driver_tenure	repaid	timestamp	y_pred_proba	y_pred
0	0	39811	40K - 60K €	0.63295	19	False	40%	0.212653	1	2018-01-01 00:00:00.000	0.99	1
1	1	12679	40K - 60K €	0.718627	7	True	10%	4.92755	0	2018-01-01 00:08:43.152	0.07	0
2	2	19847	40K - 60K €	0.721724	17	False	0%	0.520817	1	2018-01-01 00:17:26.304	1	1
3	3	22652	20K - 20K €	0.705992	16	False	10%	0.453649	1	2018-01-01 00:26:09.456	0.98	1
4	4	21268	60K+ €	0.671888	21	True	30%	5.69526	1	2018-01-01 00:34:52.608	0.99	1

The SummaryStatsAvgCalculator class implements the functionality needed for mean values calculations. We need to instantiate it with appropriate parameters:

• column_names: A list with the names of columns to be evaluated.

• timestamp_column_name (Optional): The name of the column in the reference data that contains timestamps.

• chunk_size (Optional): The number of observations in each chunk of data used. Only one chunking argument needs to be provided. For more information about chunking configurations check out the chunking tutorial.

• chunk_number (Optional): The number of chunks to be created out of data provided for each period.

• chunk_period (Optional): The time period based on which we aggregate the provided data in order to create chunks.

• chunker (Optional): A NannyML Chunker object that will handle the aggregation provided data in order to create chunks.

• threshold (Optional): The threshold strategy used to calculate the alert threshold limits. For more information about thresholds, check out the thresholds tutorial.

>>> feature_column_names = [
...     'car_value', 'debt_to_income_ratio', 'driver_tenure'
>>> ]
>>> calc = nml.SummaryStatsAvgCalculator(
...     column_names=feature_column_names,
>>> )

Next, the fit() method needs to be called on the reference data, which provides the baseline that the analysis data will be compared with for alert generation. Then the calculate() method will calculate the data quality results on the data provided to it.

The results can be filtered to only include a certain data period, method or column by using the filter method. You can evaluate the result data by converting the results into a DataFrame, by calling the to_df() method. By default this will return a DataFrame with a multi-level index. The first level represents the column, the second level represents resulting information such as the data quality metric values, the alert thresholds or the associated sampling error.

>>> calc.fit(reference_df)
>>> results = calc.calculate(analysis_df)
>>> display(results.filter(period='all').to_df())

	chunk key	chunk_index	start_index	end_index	start_date	end_date	period	car_value value	sampling_error	upper_confidence_boundary	lower_confidence_boundary	upper_threshold	lower_threshold	alert	debt_to_income_ratio value	sampling_error	upper_confidence_boundary	lower_confidence_boundary	upper_threshold	lower_threshold	alert	driver_tenure value	sampling_error	upper_confidence_boundary	lower_confidence_boundary	upper_threshold	lower_threshold	alert
0	[0:4999]	0	0	4999			reference	29660.5	288.466	30525.9	28795.1	30193.2	29096.1	False	0.585079	0.00219605	0.591667	0.578491	0.590895	0.580378	False	4.61614	0.0325543	4.71381	4.51848	4.66547	4.53461	False
1	[5000:9999]	1	5000	9999			reference	29617.7	288.466	30483.1	28752.3	30193.2	29096.1	False	0.582728	0.00219605	0.589316	0.57614	0.590895	0.580378	False	4.61693	0.0325543	4.71459	4.51927	4.66547	4.53461	False
2	[10000:14999]	2	10000	14999			reference	29577.6	288.466	30443	28712.2	30193.2	29096.1	False	0.586344	0.00219605	0.592932	0.579756	0.590895	0.580378	False	4.57162	0.0325543	4.66928	4.47396	4.66547	4.53461	False
3	[15000:19999]	3	15000	19999			reference	29458	288.466	30323.4	28592.6	30193.2	29096.1	False	0.584026	0.00219605	0.590614	0.577438	0.590895	0.580378	False	4.6291	0.0325543	4.72676	4.53144	4.66547	4.53461	False
4	[20000:24999]	4	20000	24999			reference	29436.6	288.466	30302	28571.2	30193.2	29096.1	False	0.585748	0.00219605	0.592337	0.57916	0.590895	0.580378	False	4.5947	0.0325543	4.69237	4.49704	4.66547	4.53461	False
5	[25000:29999]	5	25000	29999			reference	29943.3	288.466	30808.7	29077.9	30193.2	29096.1	False	0.584041	0.00219605	0.590629	0.577452	0.590895	0.580378	False	4.61304	0.0325543	4.7107	4.51537	4.66547	4.53461	False
6	[30000:34999]	6	30000	34999			reference	29918.4	288.466	30783.8	29053	30193.2	29096.1	False	0.587836	0.00219605	0.594424	0.581248	0.590895	0.580378	False	4.57001	0.0325543	4.66767	4.47235	4.66547	4.53461	False
7	[35000:39999]	7	35000	39999			reference	29725.6	288.466	30591	28860.2	30193.2	29096.1	False	0.588643	0.00219605	0.595231	0.582055	0.590895	0.580378	False	4.58064	0.0325543	4.6783	4.48297	4.66547	4.53461	False
8	[40000:44999]	8	40000	44999			reference	29733.2	288.466	30598.6	28867.8	30193.2	29096.1	False	0.584906	0.00219605	0.591494	0.578318	0.590895	0.580378	False	4.62703	0.0325543	4.72469	4.52937	4.66547	4.53461	False
9	[45000:49999]	9	45000	49999			reference	29375.8	288.466	30241.2	28510.4	30193.2	29096.1	False	0.587012	0.00219605	0.5936	0.580424	0.590895	0.580378	False	4.58119	0.0325543	4.67885	4.48353	4.66547	4.53461	False
10	[0:4999]	0	0	4999			analysis	29961.2	288.466	30826.6	29095.8	30193.2	29096.1	False	0.589539	0.00219605	0.596127	0.582951	0.590895	0.580378	False	4.51218	0.0325543	4.60984	4.41452	4.66547	4.53461	True
11	[5000:9999]	1	5000	9999			analysis	29876.1	288.466	30741.5	29010.7	30193.2	29096.1	False	0.584504	0.00219605	0.591092	0.577916	0.590895	0.580378	False	4.58621	0.0325543	4.68387	4.48854	4.66547	4.53461	False
12	[10000:14999]	2	10000	14999			analysis	29877.3	288.466	30742.7	29011.9	30193.2	29096.1	False	0.583473	0.00219605	0.590061	0.576885	0.590895	0.580378	False	4.65065	0.0325543	4.74832	4.55299	4.66547	4.53461	False
13	[15000:19999]	3	15000	19999			analysis	29983.8	288.466	30849.2	29118.4	30193.2	29096.1	False	0.585695	0.00219605	0.592283	0.579106	0.590895	0.580378	False	4.62346	0.0325543	4.72112	4.52579	4.66547	4.53461	False
14	[20000:24999]	4	20000	24999			analysis	29231.8	288.466	30097.2	28366.4	30193.2	29096.1	False	0.589847	0.00219605	0.596435	0.583259	0.590895	0.580378	False	4.5877	0.0325543	4.68537	4.49004	4.66547	4.53461	False
15	[25000:29999]	5	25000	29999			analysis	48378.3	288.466	49243.7	47512.9	30193.2	29096.1	True	0.586023	0.00219605	0.592611	0.579435	0.590895	0.580378	False	4.5831	0.0325543	4.68076	4.48543	4.66547	4.53461	False
16	[30000:34999]	6	30000	34999			analysis	49061.2	288.466	49926.6	48195.8	30193.2	29096.1	True	0.586636	0.00219605	0.593224	0.580048	0.590895	0.580378	False	4.5939	0.0325543	4.69157	4.49624	4.66547	4.53461	False
17	[35000:39999]	7	35000	39999			analysis	48814.5	288.466	49679.9	47949.1	30193.2	29096.1	True	0.586345	0.00219605	0.592933	0.579757	0.590895	0.580378	False	4.5507	0.0325543	4.64836	4.45303	4.66547	4.53461	False
18	[40000:44999]	8	40000	44999			analysis	49046.1	288.466	49911.5	48180.7	30193.2	29096.1	True	0.584932	0.00219605	0.59152	0.578344	0.590895	0.580378	False	4.59686	0.0325543	4.69453	4.4992	4.66547	4.53461	False
19	[45000:49999]	9	45000	49999			analysis	48706.3	288.466	49571.7	47840.9	30193.2	29096.1	True	0.585008	0.00219605	0.591596	0.57842	0.590895	0.580378	False	4.60281	0.0325543	4.70048	4.50515	4.66547	4.53461	False

More information on accessing the information contained in the Result can be found on the Working with results page.

The next step is visualizing the results, which is done using the plot() method. It is recommended to filter results for each column and plot separately.

>>> for column_name in results.column_names:
...     results.filter(column_names=column_name).plot().show()

Insights

We see that only the car_value column exhibits a change in mean value.

What Next

We can also inspect the dataset for other Summary Statistics such as Standard Deviation. We can also look for any Data Drift present in the dataset using Detecting Data Drift functionality of NannyML.