Azure DatabricksのML Quickstartやーる

投稿日 2022年4月26日
著者 azure-recipe-user
カテゴリー Azure
カテゴリー Databricks

はじめに

DatabricksのML Quickstartやっていきます

ML Quickstart: Model Training – Databricks

Cluster

導入

1．Azure Databricksで新しいノートブックを作成する

2．ライブラリのインストール

%pip install mlflow
%pip install numpy
%pip install pandas
%pip install scikit-learn
%pip install hyperopt

				
					
				1
2
3
4
5
6

						%pip install mlflow
%pip install numpy
%pip install pandas
%pip install scikit-learn
%pip install hyperopt
 

					

			

3．ライブラリのインポート

import mlflow
import numpy as np
import pandas as pd
import sklearn.datasets
import sklearn.metrics
import sklearn.model_selection
import sklearn.ensemble
 
from hyperopt import fmin, tpe, hp, SparkTrials, Trials, STATUS_OK
from hyperopt.pyll import scope

				
					
				1
2
3
4
5
6
7
8
9
10
11

						import mlflow
import numpy as np
import pandas as pd
import sklearn.datasets
import sklearn.metrics
import sklearn.model_selection
import sklearn.ensemble
 
from hyperopt import fmin, tpe, hp, SparkTrials, Trials, STATUS_OK
from hyperopt.pyll import scope
 

					

			

4．ワインのクオリティデータセットをダウンロード

Wine Quality Dataset

UCI Machine Learning Repository: Wine Quality Data Set

5．Create Tableから、winequality_white.csvとwinequality_red.csvをアップロード

6．データセットの読み込み

white_wine = pd.read_csv("/dbfs/FileStore/tables/winequality_white.csv", sep=';')
red_wine = pd.read_csv("/dbfs/FileStore/tables/winequality_red.csv", sep=';')
white_wine['is_red'] = 0.0
red_wine['is_red'] = 1.0
data_df = pd.concat([white_wine, red_wine], axis=0)
 
# Define classification labels based on the wine quality
data_labels = data_df['quality'] &gt;= 7
data_df = data_df.drop(['quality'], axis=1)
 
# Split 80/20 train-test
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
  data_df,
  data_labels,
  test_size=0.2,
  random_state=1
)

				
					
				1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

						white_wine = pd.read_csv("/dbfs/FileStore/tables/winequality_white.csv", sep=';')
red_wine = pd.read_csv("/dbfs/FileStore/tables/winequality_red.csv", sep=';')
white_wine['is_red'] = 0.0
red_wine['is_red'] = 1.0
data_df = pd.concat([white_wine, red_wine], axis=0)
 
# Define classification labels based on the wine quality
data_labels = data_df['quality'] &gt;= 7
data_df = data_df.drop(['quality'], axis=1)
 
# Split 80/20 train-test
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
  data_df,
  data_labels,
  test_size=0.2,
  random_state=1
)
 

					

			

7．autologgingの有効化

# Enable MLflow autologging for this notebook
mlflow.autolog()

				1
2
3

						# Enable MLflow autologging for this notebook
mlflow.autolog()

JavaPackageがないと怒られました

2022/04/12 15:46:01 INFO mlflow.tracking.fluent: Autologging successfully enabled for sklearn.
2022/04/12 15:46:01 WARNING mlflow.utils.autologging_utils: Encountered unexpected error during spark autologging: Exception while attempting to initialize JVM-side state for Spark datasource autologging. Please create a new Spark session and ensure you have the mlflow-spark JAR attached to your Spark session as described in http://mlflow.org/docs/latest/tracking.html#automatic-logging-from-spark-experimental. Exception:
'JavaPackage' object is not callable
2022/04/12 15:46:01 WARNING mlflow.tracking.fluent: Exception raised while enabling autologging for pyspark: Exception while attempting to initialize JVM-side state for Spark datasource autologging. Please create a new Spark session and ensure you have the mlflow-spark JAR attached to your Spark session as described in http://mlflow.org/docs/latest/tracking.html#automatic-logging-from-spark-experimental. Exception:
'JavaPackage' object is not callable
2022/04/12 15:46:01 WARNING mlflow.utils.autologging_utils: Encountered unexpected error during spark autologging: Exception while attempting to initialize JVM-side state for Spark datasource autologging. Please create a new Spark session and ensure you have the mlflow-spark JAR attached to your Spark session as described in http://mlflow.org/docs/latest/tracking.html#automatic-logging-from-spark-experimental. Exception:
'JavaPackage' object is not callable
2022/04/12 15:46:01 INFO mlflow.tracking.fluent: Autologging successfully enabled for pyspark.ml.

				
					
				1
2
3
4
5
6
7
8
9

						2022/04/12 15:46:01 INFO mlflow.tracking.fluent: Autologging successfully enabled for sklearn.
2022/04/12 15:46:01 WARNING mlflow.utils.autologging_utils: Encountered unexpected error during spark autologging: Exception while attempting to initialize JVM-side state for Spark datasource autologging. Please create a new Spark session and ensure you have the mlflow-spark JAR attached to your Spark session as described in http://mlflow.org/docs/latest/tracking.html#automatic-logging-from-spark-experimental. Exception:
'JavaPackage' object is not callable
2022/04/12 15:46:01 WARNING mlflow.tracking.fluent: Exception raised while enabling autologging for pyspark: Exception while attempting to initialize JVM-side state for Spark datasource autologging. Please create a new Spark session and ensure you have the mlflow-spark JAR attached to your Spark session as described in http://mlflow.org/docs/latest/tracking.html#automatic-logging-from-spark-experimental. Exception:
'JavaPackage' object is not callable
2022/04/12 15:46:01 WARNING mlflow.utils.autologging_utils: Encountered unexpected error during spark autologging: Exception while attempting to initialize JVM-side state for Spark datasource autologging. Please create a new Spark session and ensure you have the mlflow-spark JAR attached to your Spark session as described in http://mlflow.org/docs/latest/tracking.html#automatic-logging-from-spark-experimental. Exception:
'JavaPackage' object is not callable
2022/04/12 15:46:01 INFO mlflow.tracking.fluent: Autologging successfully enabled for pyspark.ml.
 

					

			

8．MLFlowを開始

with mlflow.start_run(run_name='gradient_boost') as run:
  model = sklearn.ensemble.GradientBoostingClassifier(random_state=0)
  
  # Models, parameters, and training metrics are tracked automatically
  model.fit(X_train, y_train)
 
  predicted_probs = model.predict_proba(X_test)
  roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1])
  
  # The AUC score on test data is not automatically logged, so log it manually
  mlflow.log_metric("test_auc", roc_auc)
  print("Test AUC of: {}".format(roc_auc))

				
					
				1
2
3
4
5
6
7
8
9
10
11
12
13

						with mlflow.start_run(run_name='gradient_boost') as run:
  model = sklearn.ensemble.GradientBoostingClassifier(random_state=0)
  
  # Models, parameters, and training metrics are tracked automatically
  model.fit(X_train, y_train)
 
  predicted_probs = model.predict_proba(X_test)
  roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1])
  
  # The AUC score on test data is not automatically logged, so log it manually
  mlflow.log_metric("test_auc", roc_auc)
  print("Test AUC of: {}".format(roc_auc))
 

					

			

Test AUC of: 0.8834365701533531

				1
2

						Test AUC of: 0.8834365701533531

9．n_estimatorsを追加して再度MLFlowを実行

# Start a new run and assign a run_name for future reference
with mlflow.start_run(run_name='gradient_boost') as run:
  model_2 = sklearn.ensemble.GradientBoostingClassifier(
    random_state=0, 
    
    # Try a new parameter setting for n_estimators
    n_estimators=200,
  )
  model_2.fit(X_train, y_train)
 
  predicted_probs = model_2.predict_proba(X_test)
  roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1])
  mlflow.log_metric("test_auc", roc_auc)
  print("Test AUC of: {}".format(roc_auc))

				
					
				1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

						# Start a new run and assign a run_name for future reference
with mlflow.start_run(run_name='gradient_boost') as run:
  model_2 = sklearn.ensemble.GradientBoostingClassifier(
    random_state=0, 
    
    # Try a new parameter setting for n_estimators
    n_estimators=200,
  )
  model_2.fit(X_train, y_train)
 
  predicted_probs = model_2.predict_proba(X_test)
  roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1])
  mlflow.log_metric("test_auc", roc_auc)
  print("Test AUC of: {}".format(roc_auc))
 

					

			

Test AUC of: 0.8914761673151751

				1
2

						Test AUC of: 0.8914761673151751

10．別のノートブックやジョブから利用できる

# After a model has been logged, you can load it in different notebooks or jobs
# mlflow.pyfunc.load_model makes model prediction available under a common API
model_loaded = mlflow.pyfunc.load_model(
  'runs:/{run_id}/model'.format(
    run_id=run.info.run_id
  )
)
 
predictions_loaded = model_loaded.predict(X_test)
predictions_original = model_2.predict(X_test)
 
# The loaded model should match the original
assert(np.array_equal(predictions_loaded, predictions_original))

				
					
				1
2
3
4
5
6
7
8
9
10
11
12
13
14

						# After a model has been logged, you can load it in different notebooks or jobs
# mlflow.pyfunc.load_model makes model prediction available under a common API
model_loaded = mlflow.pyfunc.load_model(
  'runs:/{run_id}/model'.format(
    run_id=run.info.run_id
  )
)
 
predictions_loaded = model_loaded.predict(X_test)
predictions_original = model_2.predict(X_test)
 
# The loaded model should match the original
assert(np.array_equal(predictions_loaded, predictions_original))
 

					

			

11．ハイパーパラメータチューニング

# Define the search space to explore
search_space = {
  'n_estimators': scope.int(hp.quniform('n_estimators', 20, 1000, 1)),
  'learning_rate': hp.loguniform('learning_rate', -3, 0),
  'max_depth': scope.int(hp.quniform('max_depth', 2, 5, 1)),
}
 
def train_model(params):
  # Enable autologging on each worker
  mlflow.autolog()
  with mlflow.start_run(nested=True):
    model_hp = sklearn.ensemble.GradientBoostingClassifier(
      random_state=0,
      **params
    )
    model_hp.fit(X_train, y_train)
    predicted_probs = model_hp.predict_proba(X_test)
    # Tune based on the test AUC
    # In production settings, you could use a separate validation set instead
    roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1])
    mlflow.log_metric('test_auc', roc_auc)
    
    # Set the loss to -1*auc_score so fmin maximizes the auc_score
    return {'status': STATUS_OK, 'loss': -1*roc_auc}
 
# SparkTrials distributes the tuning using Spark workers
# Greater parallelism speeds processing, but each hyperparameter trial has less information from other trials
# On smaller clusters or Databricks Community Edition try setting parallelism=2
spark_trials = SparkTrials(
  parallelism=8
)
 
with mlflow.start_run(run_name='gb_hyperopt') as run:
  # Use hyperopt to find the parameters yielding the highest AUC
  best_params = fmin(
    fn=train_model, 
    space=search_space, 
    algo=tpe.suggest, 
    max_evals=32,
    trials=spark_trials)

				
					
				1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

						# Define the search space to explore
search_space = {
  'n_estimators': scope.int(hp.quniform('n_estimators', 20, 1000, 1)),
  'learning_rate': hp.loguniform('learning_rate', -3, 0),
  'max_depth': scope.int(hp.quniform('max_depth', 2, 5, 1)),
}
 
def train_model(params):
  # Enable autologging on each worker
  mlflow.autolog()
  with mlflow.start_run(nested=True):
    model_hp = sklearn.ensemble.GradientBoostingClassifier(
      random_state=0,
      **params
    )
    model_hp.fit(X_train, y_train)
    predicted_probs = model_hp.predict_proba(X_test)
    # Tune based on the test AUC
    # In production settings, you could use a separate validation set instead
    roc_auc = sklearn.metrics.roc_auc_score(y_test, predicted_probs[:,1])
    mlflow.log_metric('test_auc', roc_auc)
    
    # Set the loss to -1*auc_score so fmin maximizes the auc_score
    return {'status': STATUS_OK, 'loss': -1*roc_auc}
 
# SparkTrials distributes the tuning using Spark workers
# Greater parallelism speeds processing, but each hyperparameter trial has less information from other trials
# On smaller clusters or Databricks Community Edition try setting parallelism=2
spark_trials = SparkTrials(
  parallelism=8
)
 
with mlflow.start_run(run_name='gb_hyperopt') as run:
  # Use hyperopt to find the parameters yielding the highest AUC
  best_params = fmin(
    fn=train_model, 
    space=search_space, 
    algo=tpe.suggest, 
    max_evals=32,
    trials=spark_trials)
 

					

			

12．ベストスコアのモデルで実行

# Sort runs by their test auc; in case of ties, use the most recent run
best_run = mlflow.search_runs(
  order_by=['metrics.test_auc DESC', 'start_time DESC'],
  max_results=10,
).iloc[0]
print('Best Run')
print('AUC: {}'.format(best_run["metrics.test_auc"]))
print('Num Estimators: {}'.format(best_run["params.n_estimators"]))
print('Max Depth: {}'.format(best_run["params.max_depth"]))
print('Learning Rate: {}'.format(best_run["params.learning_rate"]))
 
best_model_pyfunc = mlflow.pyfunc.load_model(
  'runs:/{run_id}/model'.format(
    run_id=best_run.run_id
  )
)
best_model_predictions = best_model_pyfunc.predict(X_test[:5])
print("Test Predictions: {}".format(best_model_predictions))

				
					
				1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

						# Sort runs by their test auc; in case of ties, use the most recent run
best_run = mlflow.search_runs(
  order_by=['metrics.test_auc DESC', 'start_time DESC'],
  max_results=10,
).iloc[0]
print('Best Run')
print('AUC: {}'.format(best_run["metrics.test_auc"]))
print('Num Estimators: {}'.format(best_run["params.n_estimators"]))
print('Max Depth: {}'.format(best_run["params.max_depth"]))
print('Learning Rate: {}'.format(best_run["params.learning_rate"]))
 
best_model_pyfunc = mlflow.pyfunc.load_model(
  'runs:/{run_id}/model'.format(
    run_id=best_run.run_id
  )
)
best_model_predictions = best_model_pyfunc.predict(X_test[:5])
print("Test Predictions: {}".format(best_model_predictions))
 

					

			

Best Run
AUC: 0.9142824444953079
Num Estimators: 782
Max Depth: 5
Learning Rate: 0.08908078790759665
Test Predictions: [False False False  True False]

				
					
				1
2
3
4
5
6
7

						Best Run
AUC: 0.9142824444953079
Num Estimators: 782
Max Depth: 5
Learning Rate: 0.08908078790759665
Test Predictions: [False False False  True False]
 

					

			

お疲れ様でした。

参考文献

10 分間チュートリアル: Azure Databricks での機械学習の概要 – Azure Databricks

Azure Databricks での機械学習の概要 – Azure Databricks

ノートブック（Databricks ML Quickstart: Model Training）

ML Quickstart: Model Training – Databricks

Azure Databricksの導入ならナレコムにおまかせください。

導入から活用方法までサポートします。お気軽にご相談ください。

Azure Databricksソリューションページはこちら

この記事を書いた人

azure-recipe-user

記事一覧

Azure DatabricksのML Quickstartやーる

はじめに

Cluster

導入

参考文献

Azure Databricksの導入ならナレコムにおまかせください。

この記事を書いた人

azure-recipe-user

【Azure】Auto MLで作成したモデルをPythonからデプロイする方法

Azureデータ分析入門 #5 【Databricks → Power BI Desktop】

【Databricks Data+AI Summit 2023】キーノートday1

Azure Policyの割り当て手順