weighted_score_table

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

A weighted score table is a method of evaluating alternatives when the importance of each criterion differs. In a weighted score table, each alternative is given a score for each criterion. These scores are then weighted by the importance of each criterion. All of an alternative's weighted scores are then added together to calculate its total weighted score. The alternative with the highest total score should be the best alternative. You can use weighted score tables to make predictions about future customer behavior. You first create a model based on historical data in the data mining application, and then apply the model to new data to make the prediction. The prediction, which is the output of the model, is called a score. You can create a single score for your customers by taking into account different dimensions.

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 or a list of str, 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

Adjusts the percentage of available threads to use, from 0 to 1. A value of 0 indicates the use of a single thread, while 1 implies the use of all possible current 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

Input DataFrame df_train, df_map, df_weight:

>>> df_train.collect()
    ID   GENDER    INCOME   HEIGHT
0   0    male      5000     1.73
1   1    male      9000     1.80
...
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
...
9   9   7.75