quantize_fl()

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Applies to: ✅ Azure Data Explorer ✅ Azure Monitor ✅ Microsoft Sentinel

The function quantize_fl() is a user-defined function (UDF) that bins metric columns. It quantizes metric columns to categorical labels, based on the K-Means algorithm.

Prerequisites

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

Syntax

T | invoke quantize_fl(num_bins, in_cols, out_cols [, labels ])

Learn more about syntax conventions.

Parameters

Name Type Required Description
num_bins int ✔️ The required number of bins.
in_cols dynamic ✔️ An array containing the names of the columns to quantize.
out_cols dynamic ✔️ An array containing the names of the respective output columns for the binned values.
labels dynamic An array containing the label names. If unspecified, bin ranges will be used.

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 quantize_fl(), see Example.

let quantize_fl=(tbl:(*), num_bins:int, in_cols:dynamic, out_cols:dynamic, labels:dynamic=dynamic(null))
{
    let kwargs = bag_pack('num_bins', num_bins, 'in_cols', in_cols, 'out_cols', out_cols, 'labels', labels);
    let code = ```if 1:
        
        from sklearn.preprocessing import KBinsDiscretizer
        
        num_bins = kargs["num_bins"]
        in_cols = kargs["in_cols"]
        out_cols = kargs["out_cols"]
        labels = kargs["labels"]
        
        result = df
        binner = KBinsDiscretizer(n_bins=num_bins, encode="ordinal", strategy="kmeans")
        df_in = df[in_cols]
        bdata = binner.fit_transform(df_in)
        if labels is None:
            for i in range(len(out_cols)):    # loop on each column and convert it to binned labels
                ii = np.round(binner.bin_edges_[i], 3)
                labels = [str(ii[j-1]) + '-' + str(ii[j]) for j in range(1, num_bins+1)]
                result.loc[:,out_cols[i]] = np.take(labels, bdata[:, i].astype(int))
        else:
            result[out_cols] = np.take(labels, bdata.astype(int))
    ```;
    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 quantize_fl=(tbl:(*), num_bins:int, in_cols:dynamic, out_cols:dynamic, labels:dynamic=dynamic(null))
{
    let kwargs = bag_pack('num_bins', num_bins, 'in_cols', in_cols, 'out_cols', out_cols, 'labels', labels);
    let code = ```if 1:
        
        from sklearn.preprocessing import KBinsDiscretizer
        
        num_bins = kargs["num_bins"]
        in_cols = kargs["in_cols"]
        out_cols = kargs["out_cols"]
        labels = kargs["labels"]
        
        result = df
        binner = KBinsDiscretizer(n_bins=num_bins, encode="ordinal", strategy="kmeans")
        df_in = df[in_cols]
        bdata = binner.fit_transform(df_in)
        if labels is None:
            for i in range(len(out_cols)):    # loop on each column and convert it to binned labels
                ii = np.round(binner.bin_edges_[i], 3)
                labels = [str(ii[j-1]) + '-' + str(ii[j]) for j in range(1, num_bins+1)]
                result.loc[:,out_cols[i]] = np.take(labels, bdata[:, i].astype(int))
        else:
            result[out_cols] = np.take(labels, bdata.astype(int))
    ```;
    tbl
    | evaluate python(typeof(*), code, kwargs)
};
//
union 
(range x from 1 to 5 step 1),
(range x from 10 to 15 step 1),
(range x from 20 to 25 step 1)
| extend x_label='', x_bin=''
| invoke quantize_fl(3, pack_array('x'), pack_array('x_label'), pack_array('Low', 'Med', 'High'))
| invoke quantize_fl(3, pack_array('x'), pack_array('x_bin'), dynamic(null))

Output

x x_label x_bin
1 Low 1.0-7.75
2 Low 1.0-7.75
3 Low 1.0-7.75
4 Low 1.0-7.75
5 Low 1.0-7.75
20 High 17.5-25.0
21 High 17.5-25.0
22 High 17.5-25.0
23 High 17.5-25.0
24 High 17.5-25.0
25 High 17.5-25.0
10 Med 7.75-17.5
11 Med 7.75-17.5
12 Med 7.75-17.5
13 Med 7.75-17.5
14 Med 7.75-17.5
15 Med 7.75-17.5
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