weighted_score_table

hana_ml.algorithms.pal.wst.weighted_score_table(data, maps, weights, key, features=None, thread_ratio=None)

Perform the weighted_score_table to weight the score by the importance of each criterion. The alternative with the highest total score should be the best alternative.

Parameters

dataDataFrame

Input data.

mapsDataFrame

Every attribute (except ID) in the input data table maps to two columns in the map Function table: Key column and Value column.

The Value column must be of DOUBLE type.

weightsDataFrame

This table has three columns.

When the data table has n attributes (except ID), the weights table will have n rows.

keystr

Name of the ID column.

featuresstr/ListOfStrings, optional

Name of the feature columns.

If not given, the feature columns should be all columns in the DataFrame except the ID column.

thread_ratiofloat, optional

Specifies the ratio of total number of threads that can be used by this function.

The value range is from 0 to 1, where 0 means only using 1 thread, and 1 means using at most all the currently available threads.

Values outside the range will be ignored and this function heuristically determines the number of threads to use.

Default to 0.

Returns

DataFrame

The result value of weight for each score.

Examples

Data to analyze:

>>> df_train.collect()
    ID   GENDER   INCOME    HEIGHT
0   0    male      5000     1.73
1   1    male      9000     1.80
2   2    female    6000     1.55
3   3    male      15000    1.65
4   4    female    2000     1.70
5   5    female    12000    1.65
6   6    male      1000     1.65
7   7    male      8000     1.60
8   8    female    5500     1.85
9   9    female    9500     1.85

>>> df_map.collect()
    GENDER  VAL1   INCOME   VAL2   HEIGHT  VAL3
0     male   2.0        0    0.0      1.5   0.0
1   female   1.5     5500    1.0      1.6   1.0
2     None   0.0     9000    2.0     1.71   2.0
3     None   0.0    12000    3.0     1.80   3.0

>>> df_weight.collect()
    WEIGHT  ISDIS   ROWNUM
0      0.5      1        2
1      2.0     -1        4
2      1.0     -1        4

Perform weighted_score_table:

>>> res = weighted_score_table(data=df_train, maps=df_map, weights=df_weight,
                               key='ID', thread_ratio=0.3)
>>> res.collect()
   ID  SCORE
0   0   3.00
1   1   8.00
2   2   2.75
3   3   8.00
4   4   1.75
5   5   7.75
6   6   2.00
7   7   4.00
8   8   5.75
9   9   7.75