predict_fl()predict_fl()

函数 predict_fl() 使用经过训练的现有机器学习模型进行预测。The function predict_fl() predicts using an existing trained machine learning model. 此模型使用 Scikit-learn 构建，已序列化为字符串，并已保存在标准 Azure 数据资源管理器表中。This model was built using Scikit-learn, serialized to string, and saved in a standard Azure Data Explorer table.

备注

语法Syntax

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

参数Arguments

  • models_tbl：包含所有序列化模型的表的名称。models_tbl : The name of the table containing all serialized models. 此表必须包含以下列：This table must contain the following columns:
    • name：模型名称name : the model name
    • timestamp：模型训练时间timestamp : time of model training
    • model：序列化模型的字符串表示形式model : string representation of the serialized model
  • model_name：要使用的特定模型的名称。model_name : The name of the specific model to use.
  • features_cols：动态数组，其中包含模型用于预测的特征列的名称。features_cols : Dynamic array containing the names of the features columns that are used by the model for prediction.
  • pred_col：存储预测的列的名称。pred_col : The name of the column that stores the predictions.

使用情况Usage

predict_fl() 是用户定义的表格函数，需使用 invoke 运算符进行应用。predict_fl() is a user-defined tabular function to be applied using the invoke operator. 可以在查询中嵌入该函数的代码，或者在数据库中安装该函数。You can either embed its code in your query, or install it in your database. 用法选项有两种：临时使用和持久使用。There are two usage options: ad hoc and persistent usage. 请参阅下面选项卡上的示例。See the below tabs for examples.

如果是临时使用，请使用 let 语句嵌入代码。For ad hoc usage, embed the code using the let statement. 不需要权限。No permission is required.

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 = pack('smodel', model_str, 'features_cols', features_cols, 'pred_col', pred_col);
    let code =
    '\n'
    'import pickle\n'
    'import binascii\n'
    '\n'
    'smodel = kargs["smodel"]\n'
    'features_cols = kargs["features_cols"]\n'
    'pred_col = kargs["pred_col"]\n'
    'bmodel = binascii.unhexlify(smodel)\n'
    'clf1 = pickle.loads(bmodel)\n'
    'df1 = df[features_cols]\n'
    'predictions = clf1.predict(df1)\n'
    '\n'
    'result = df\n'
    'result[pred_col] = pd.DataFrame(predictions, columns=[pred_col])'
    '\n'
    ;
    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

混淆矩阵：Confusion matrix:

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