Menerapkan dan membuat prediksi dengan model ONNX dan pembelajaran mesin SQL

Dalam mulai cepat ini, Anda akan mempelajari cara melatih model, mengonversinya ke ONNX, menyebarkannya ke Azure SQL Edge, lalu menjalankan PREDICT asli pada data menggunakan model ONNX yang diunggah.

Mulai cepat ini didasarkan pada scikit-learn dan menggunakan himpunan data Boston Housing.

Sebelum Anda mulai

• Jika Anda menggunakan Azure SQL Edge, dan Anda belum menerapkan modul Azure SQL Edge, ikuti langkah-langkah penerapan SQL Edge menggunakan portal Microsoft Azure.

• Pasang Azure Data Studio.

• Pasang paket Python yang diperlukan untuk mulai cepat ini:

  1. Buka Buku Catatan Baru yang tersambung ke Python 3 Kernel.
  2. Pilih Kelola Paket
  3. Di tab Terpasang, cari paket Python berikut ini dalam daftar paket yang terpasang. Jika salah satu paket ini tidak diinstal, pilih tab Tambahkan Baru , cari paket, dan pilih Instal.
     • scikit-learn
     • numpy
     • onnxmltools
     • onnxruntime
     • pyodbc
     • setuptools
     • skl2onnx
     • sqlalchemy

• Untuk setiap bagian skrip di bagian berikut, masukkan dalam sel di buku catatan Azure Data Studio dan jalankan sel.

Melatih alur

Pisahkan himpunan data untuk menggunakan fitur untuk memprediksi nilai median rumah.

import numpy as np
import onnxmltools
import onnxruntime as rt
import pandas as pd
import skl2onnx
import sklearn
import sklearn.datasets

from sklearn.datasets import load_boston
boston = load_boston()
boston

df = pd.DataFrame(data=np.c_[boston['data'], boston['target']], columns=boston['feature_names'].tolist() + ['MEDV'])

target_column = 'MEDV'

# Split the data frame into features and target
x_train = pd.DataFrame(df.drop([target_column], axis = 1))
y_train = pd.DataFrame(df.iloc[:,df.columns.tolist().index(target_column)])

print("\n*** Training dataset x\n")
print(x_train.head())

print("\n*** Training dataset y\n")
print(y_train.head())

Output:

*** Training dataset x

        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \
0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0
1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0
2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0
3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0
4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0

    PTRATIO       B  LSTAT
0     15.3  396.90   4.98
1     17.8  396.90   9.14
2     17.8  392.83   4.03
3     18.7  394.63   2.94
4     18.7  396.90   5.33

*** Training dataset y

0    24.0
1    21.6
2    34.7
3    33.4
4    36.2
Name: MEDV, dtype: float64

Buat alur untuk melatih model LinearRegression. Anda juga dapat menggunakan model regresi lainnya.

from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import RobustScaler

continuous_transformer = Pipeline(steps=[('scaler', RobustScaler())])

# All columns are numeric - normalize them
preprocessor = ColumnTransformer(
    transformers=[
        ('continuous', continuous_transformer, [i for i in range(len(x_train.columns))])])

model = Pipeline(
    steps=[
        ('preprocessor', preprocessor),
        ('regressor', LinearRegression())])

# Train the model
model.fit(x_train, y_train)

Periksa keakuratan model lalu hitung skor R2 dan rata-rata kesalahan kuadrat.

# Score the model
from sklearn.metrics import r2_score, mean_squared_error
y_pred = model.predict(x_train)
sklearn_r2_score = r2_score(y_train, y_pred)
sklearn_mse = mean_squared_error(y_train, y_pred)
print('*** Scikit-learn r2 score: {}'.format(sklearn_r2_score))
print('*** Scikit-learn MSE: {}'.format(sklearn_mse))

Output:

*** Scikit-learn r2 score: 0.7406426641094094
*** Scikit-learn MSE: 21.894831181729206

Mengonversi model ke ONNX

Mengonversi jenis data ke jenis data SQL yang didukung. Konversi ini juga diperlukan untuk dataframe lain.

from skl2onnx.common.data_types import FloatTensorType, Int64TensorType, DoubleTensorType

def convert_dataframe_schema(df, drop=None, batch_axis=False):
    inputs = []
    nrows = None if batch_axis else 1
    for k, v in zip(df.columns, df.dtypes):
        if drop is not None and k in drop:
            continue
        if v == 'int64':
            t = Int64TensorType([nrows, 1])
        elif v == 'float32':
            t = FloatTensorType([nrows, 1])
        elif v == 'float64':
            t = DoubleTensorType([nrows, 1])
        else:
            raise Exception("Bad type")
        inputs.append((k, t))
    return inputs

Menggunakan skl2onnx, konversikan model LinearRegression ke format ONNX dan simpan secara lokal.

# Convert the scikit model to onnx format
onnx_model = skl2onnx.convert_sklearn(model, 'Boston Data', convert_dataframe_schema(x_train), final_types=[('variable1',FloatTensorType([1,1]))])
# Save the onnx model locally
onnx_model_path = 'boston1.model.onnx'
onnxmltools.utils.save_model(onnx_model, onnx_model_path)

Menguji model ONNX

Setelah mengonversi model ke format ONNX, cetak model untuk menunjukkan sedikit atau tidak ada degradasi dalam performa.

import onnxruntime as rt
sess = rt.InferenceSession(onnx_model_path)

y_pred = np.full(shape=(len(x_train)), fill_value=np.nan)

for i in range(len(x_train)):
    inputs = {}
    for j in range(len(x_train.columns)):
        inputs[x_train.columns[j]] = np.full(shape=(1,1), fill_value=x_train.iloc[i,j])

    sess_pred = sess.run(None, inputs)
    y_pred[i] = sess_pred[0][0][0]

onnx_r2_score = r2_score(y_train, y_pred)
onnx_mse = mean_squared_error(y_train, y_pred)

print()
print('*** Onnx r2 score: {}'.format(onnx_r2_score))
print('*** Onnx MSE: {}\n'.format(onnx_mse))
print('R2 Scores are equal' if sklearn_r2_score == onnx_r2_score else 'Difference in R2 scores: {}'.format(abs(sklearn_r2_score - onnx_r2_score)))
print('MSE are equal' if sklearn_mse == onnx_mse else 'Difference in MSE scores: {}'.format(abs(sklearn_mse - onnx_mse)))
print()

Output:

*** Onnx r2 score: 0.7406426691136831
*** Onnx MSE: 21.894830759270633

R2 Scores are equal
MSE are equal

Menyisipkan model ONNX

Simpan model di Azure SQL Edge, dalam tabel models dalam database onnx. Dalam string koneksi, tentukan alamat server, nama pengguna, dan kata sandi.

import pyodbc

server = '' # SQL Server IP address
username = '' # SQL Server username
password = '' # SQL Server password

# Connect to the master DB to create the new onnx database
connection_string = "Driver={ODBC Driver 17 for SQL Server};Server=" + server + ";Database=master;UID=" + username + ";PWD=" + password + ";"

conn = pyodbc.connect(connection_string, autocommit=True)
cursor = conn.cursor()

database = 'onnx'
query = 'DROP DATABASE IF EXISTS ' + database
cursor.execute(query)
conn.commit()

# Create onnx database
query = 'CREATE DATABASE ' + database
cursor.execute(query)
conn.commit()

# Connect to onnx database
db_connection_string = "Driver={ODBC Driver 17 for SQL Server};Server=" + server + ";Database=" + database + ";UID=" + username + ";PWD=" + password + ";"

conn = pyodbc.connect(db_connection_string, autocommit=True)
cursor = conn.cursor()

table_name = 'models'

# Drop the table if it exists
query = f'drop table if exists {table_name}'
cursor.execute(query)
conn.commit()

# Create the model table
query = f'create table {table_name} ( ' \
    f'[id] [int] IDENTITY(1,1) NOT NULL, ' \
    f'[data] [varbinary](max) NULL, ' \
    f'[description] varchar(1000))'
cursor.execute(query)
conn.commit()

# Insert the ONNX model into the models table
query = f"insert into {table_name} ([description], [data]) values ('Onnx Model',?)"

model_bits = onnx_model.SerializeToString()

insert_params  = (pyodbc.Binary(model_bits))
cursor.execute(query, insert_params)
conn.commit()

Muat data

Muat data ke SQL.

Pertama, buat dua tabel, fitur, dan target, untuk menyimpan subset himpunan data perumahan Boston.

• Fitur berisi semua data yang digunakan untuk memprediksi target, nilai median.
• Target berisi nilai median untuk setiap rekaman dalam himpunan data.

import sqlalchemy
from sqlalchemy import create_engine
import urllib

db_connection_string = "Driver={ODBC Driver 17 for SQL Server};Server=" + server + ";Database=" + database + ";UID=" + username + ";PWD=" + password + ";"

conn = pyodbc.connect(db_connection_string)
cursor = conn.cursor()

features_table_name = 'features'

# Drop the table if it exists
query = f'drop table if exists {features_table_name}'
cursor.execute(query)
conn.commit()

# Create the features table
query = \
    f'create table {features_table_name} ( ' \
    f'    [CRIM] float, ' \
    f'    [ZN] float, ' \
    f'    [INDUS] float, ' \
    f'    [CHAS] float, ' \
    f'    [NOX] float, ' \
    f'    [RM] float, ' \
    f'    [AGE] float, ' \
    f'    [DIS] float, ' \
    f'    [RAD] float, ' \
    f'    [TAX] float, ' \
    f'    [PTRATIO] float, ' \
    f'    [B] float, ' \
    f'    [LSTAT] float, ' \
    f'    [id] int)'

cursor.execute(query)
conn.commit()

target_table_name = 'target'

# Create the target table
query = \
    f'create table {target_table_name} ( ' \
    f'    [MEDV] float, ' \
    f'    [id] int)'

x_train['id'] = range(1, len(x_train)+1)
y_train['id'] = range(1, len(y_train)+1)

print(x_train.head())
print(y_train.head())

Pada akhirnya, gunakan sqlalchemy untuk menyisipkan dataframe panda x_train dan y_train ke dalam tabel features dan target, masing-masing.

db_connection_string = 'mssql+pyodbc://' + username + ':' + password + '@' + server + '/' + database + '?driver=ODBC+Driver+17+for+SQL+Server'
sql_engine = sqlalchemy.create_engine(db_connection_string)
x_train.to_sql(features_table_name, sql_engine, if_exists='append', index=False)
y_train.to_sql(target_table_name, sql_engine, if_exists='append', index=False)

Sekarang Anda bisa menampilkan data dalam database.

Jalankan PREDICT menggunakan model ONNX

Dengan model di SQL, jalankan PREDICT asli pada data menggunakan model ONNX yang diunggah.

USE onnx

DECLARE @model VARBINARY(max) = (
        SELECT DATA
        FROM dbo.models
        WHERE id = 1
        );

WITH predict_input
AS (
    SELECT TOP (1000) [id],
        CRIM,
        ZN,
        INDUS,
        CHAS,
        NOX,
        RM,
        AGE,
        DIS,
        RAD,
        TAX,
        PTRATIO,
        B,
        LSTAT
    FROM [dbo].[features]
    )
SELECT predict_input.id,
    p.variable1 AS MEDV
FROM PREDICT(MODEL = @model, DATA = predict_input, RUNTIME = ONNX) WITH (variable1 FLOAT) AS p;

Konten terkait

• Pembelajaran mesin dan AI dengan ONNX di SQL Edge