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predict_fl()

  • [アーティクル]

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

The function predict_fl() is a user-defined function (UDF) that predicts using an existing trained machine learning model. This model was built using Scikit-learn, serialized to string, and saved in a standard table.

Prerequisites

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

Syntax

T | invoke predict_fl(models_tbl, model_name, features_cols, pred_col)

Learn more about syntax conventions.

Parameters

Name Type Required Description
models_tbl string ✔️ The name of the table that contains all serialized models. The table must have the following columns:
name: the model name
timestamp: time of model training
model: string representation of the serialized model
model_name string ✔️ The name of the specific model to use.
features_cols synamic ✔️ An array containing the names of the features columns that are used by the model for prediction.
pred_col string ✔️ The name of the column that stores the predictions.

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 predict_fl(), see Example.

let predict_fl=(samples:(*), models_tbl:(name:string, timestamp:datetime, model:string), model_name:string, features_cols:dynamic, pred_col:string)
{
    let model_str = toscalar(models_tbl | where name == model_name | top 1 by timestamp desc | project model);
    let kwargs = bag_pack('smodel', model_str, 'features_cols', features_cols, 'pred_col', pred_col);
    let code = ```if 1:
        
        import pickle
        import binascii
        
        smodel = kargs["smodel"]
        features_cols = kargs["features_cols"]
        pred_col = kargs["pred_col"]
        bmodel = binascii.unhexlify(smodel)
        clf1 = pickle.loads(bmodel)
        df1 = df[features_cols]
        predictions = clf1.predict(df1)
        
        result = df
        result[pred_col] = pd.DataFrame(predictions, columns=[pred_col])
        
    ```;
    samples
    | evaluate python(typeof(*), code, kwargs)
};
// Write your code 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 predict_fl=(samples:(*), models_tbl:(name:string, timestamp:datetime, model:string), model_name:string, features_cols:dynamic, pred_col:string)
{
    let model_str = toscalar(models_tbl | where name == model_name | top 1 by timestamp desc | project model);
    let kwargs = bag_pack('smodel', model_str, 'features_cols', features_cols, 'pred_col', pred_col);
    let code = ```if 1:
        
        import pickle
        import binascii
        
        smodel = kargs["smodel"]
        features_cols = kargs["features_cols"]
        pred_col = kargs["pred_col"]
        bmodel = binascii.unhexlify(smodel)
        clf1 = pickle.loads(bmodel)
        df1 = df[features_cols]
        predictions = clf1.predict(df1)
        
        result = df
        result[pred_col] = pd.DataFrame(predictions, columns=[pred_col])
        
    ```;
    samples
    | evaluate python(typeof(*), code, kwargs)
};
//
// Predicts room occupancy from sensors measurements, and calculates the confusion matrix
//
// Occupancy Detection is an open dataset from UCI Repository at https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+
// It contains experimental data for binary classification of room occupancy from Temperature,Humidity,Light and CO2.
// Ground-truth labels were obtained from time stamped pictures that were taken every minute
//
OccupancyDetection 
| where Test == 1
| extend pred_Occupancy=false
| invoke predict_fl(ML_Models, 'Occupancy', pack_array('Temperature', 'Humidity', 'Light', 'CO2', 'HumidityRatio'), 'pred_Occupancy')
| summarize n=count() by Occupancy, pred_Occupancy

Output

Occupancy pred_Occupancy n
TRUE TRUE 3006
FALSE TRUE 112
TRUE FALSE 15
FALSE FALSE 9284

Model asset

Get sample dataset and pre-trained model with Python plugin enabled.

//dataset
.set OccupancyDetection <| cluster('help').database('Samples').OccupancyDetection

//model
.set ML_Models <| datatable(name:string, timestamp:datetime, model:string) [
'Occupancy', datetime(now), '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'
]