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ks_test_fl()

  • Άρθρο

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

The function ks_test_fl() is a UDF (user-defined function) that performs the Kolmogorov Smirnov Test.

Prerequisites

  • The Python plugin must be enabled on the cluster. This is required for the inline Python used in the function.

Syntax

T | invoke ks_test_fl(data1, data2, test_statistic,p_value)

Learn more about syntax conventions.

Parameters

Name Type Required Description
data1 string ✔️ The name of the column containing the first set of data to be used for the test.
data2 string ✔️ The name of the column containing the second set of data to be used for the test.
test_statistic string ✔️ The name of the column to store test statistic value for the results.
p_value string ✔️ The name of the column to store p-value for the results.

Function definition

You can define the function by either embedding its code as a query-defined function, or creating it as a stored function in your database, as follows:

Define the function using the following let statement. No permissions are required.

Important

A let statement can't run on its own. It must be followed by a tabular expression statement. To run a working example of ks_test_fl(), see Example.

let ks_test_fl = (tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string)
{
    let kwargs = bag_pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value);
    let code = ```if 1:
        from scipy import stats
        data1 = kargs["data1"]
        data2 = kargs["data2"]
        test_statistic = kargs["test_statistic"]
        p_value = kargs["p_value"]
        def func(row):
            statistics = stats.ks_2samp(row[data1], row[data2])
            return statistics[0], statistics[1]
        result = df
        result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")
    ```;
    tbl
    | evaluate python(typeof(*), code, kwargs)
};
// Write your query to use the function here.

Example

The following example uses the invoke operator to run the function.

To use a query-defined function, invoke it after the embedded function definition.

let ks_test_fl = (tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string)
{
    let kwargs = bag_pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value);
    let code = ```if 1:
        from scipy import stats
        data1 = kargs["data1"]
        data2 = kargs["data2"]
        test_statistic = kargs["test_statistic"]
        p_value = kargs["p_value"]
        def func(row):
            statistics = stats.ks_2samp(row[data1], row[data2])
            return statistics[0], statistics[1]
        result = df
        result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")
    ```;
    tbl
    | evaluate python(typeof(*), code, kwargs)
};
datatable(id:string, sample1:dynamic, sample2:dynamic) [
'Test #1', dynamic([23.64, 20.57, 20.42]), dynamic([27.1, 22.12, 33.56]),
'Test #2', dynamic([20.85, 21.89, 23.41]), dynamic([35.09, 30.02, 26.52]),
'Test #3', dynamic([20.13, 20.5, 21.7, 22.02]), dynamic([32.2, 32.79, 33.9, 34.22])
]
| extend test_stat= 0.0, p_val = 0.0
| invoke ks_test_fl('sample1', 'sample2', 'test_stat', 'p_val')

Output

id sample1 sample2 test_stat p_val
Test #1 [23.64, 20.57, 20.42] [27.1, 22.12, 33.56] 0.66666666666666674 0.3197243332709643
Test #2 [20.85, 21.89, 23.41] [35.09, 30.02, 26.52] 1 0.03262165165202116
Test #3 [20.13, 20.5, 21.7, 22.02] [32.2, 32.79, 33.9, 34.22] 1 0.01106563701580386