accuracy_measure

hana_ml.algorithms.pal.tsa.accuracy_measure.accuracy_measure(data, evaluation_metric)

Measures are used to check the accuracy of the forecast made by PAL algorithms.

Parameters
dataDataFrame

Input data, should have two or three columns:

  • If data contains 2 columns, then the first column is of actual data while the second one is of forecasted data.

  • If data contains 3 columns, then the 1st column must be the ID column, 2nd column be the actual data, while the 3rd column be the forecasted data.

evaluation_metricstr of ListOfStrings

Specifies the accuracy measure name(s), with valid options listed as follows:

  • 'mpe': mean percentage error

  • 'mse': mean square error

  • 'rmse': root mean square error

  • 'et': error total

  • 'mad': mean absolute deviation

  • 'mase': out-of-sample mean absolute scaled error

  • 'wmape': weighted mean absolute percentage error

  • 'smape': symmetric mean absolute percentage error

  • 'mape': mean absolute percentage error

  • 'spec': stock-keeping-oriented prediction error costs

Note

If evaluation_metric is specified as 'spec' or contains 'spec' as one of its element, then data must have 3 columns(i.e. contain an ID column).

Returns
DataFrame

Result of the forecast accuracy measurement, structured as follows:

  • STAT_NAME: Name of accuracy measures.

  • STAT_VALUE: Value of accuracy measures.

Examples

Data for accuracy measurement:

>>> df.collect()
    ACTUAL  FORECAST
0   1130.0    1270.0
1   2410.0    2340.0
2   2210.0    2310.0
3   2500.0    2340.0
4   2432.0    2348.0
5   1980.0    1890.0
6   2045.0    2100.0
7   2340.0    2231.0
8   2460.0    2401.0
9   2350.0    2310.0
10  2345.0    2340.0
11  2650.0    2560.0

Perform accuracy measurement on the input dataframe:

>>> res = accuracy_measure(data=df,
                           evaluation_metric=['mse', 'rmse', 'mpe', 'et',
                                              'mad', 'mase', 'wmape', 'smape',
                                              'mape'])
>>> res.collect()
  STAT_NAME   STAT_VALUE
0        ET   412.000000
1       MAD    83.500000
2      MAPE     0.041063
3      MASE     0.287931
4       MPE     0.008390
5       MSE  8614.000000
6      RMSE    92.811637
7     SMAPE     0.040876
8     WMAPE     0.037316