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@illumination-k(illumination -k)

optunaでハイパーパラメータ最適化

Last updated at Posted at 2019-09-26

TL;DR

ハイパーパラメータの最適化は機械学習をするうえで避けては通れない道ですが、なかなか時間を取られます。ライブラリとしてはscikit-learnのgrid-searchなどをはじめとしていろいろありますが、今回はOptunaというPreferred Networksのライブラリを使用してみます。
Preferred Networksといえば深層学習フレームワークのChainerが有名ですが、ベイズ最適化を活用したOptunaというハイパーパラメータ最適化ライブラリも作成されています。Optunaを使ってKaggleで準優勝したとか。
ということでOptunaを使ってハイパーパラメータ最適化を試してみたいと思います。

概要

大好きなscikit-learnのワインデータセットを使って分類を試してみたいと思います。

Optunaを使う流れとしては以下のようなものになります。

  1. Classifierクラスを作成します（既存のClassifierを使う分にはSkip可。ここを工夫すれば非常に自由度が高い最適化を行えます。例えば、前処理部分の最適化やどの学習器を使えばいいのかなどがわかります。）
  2. どういった最適化を行うか、といったObjectiveクラスを作成します。
  3. 訓練と評価をします。

では、実際に試してみます。

0. インストール

pip install optuna

1. Classifierクラスの作成

説明が前後しますが、Objectクラスはgenerate_paramsメソッドを持ち、パラメーターを作成します。このメソッドによって作成されたパラメーターがClassifierクラスに渡されます。なので、Classifierクラスはアトリビュートとしてparamsを持つことにします。
今回は正規化処理として、何もしない、StandardScaler、MinMaxScalerのどれがいいか、また学習器としてSVCとRandomForestのどれが優れているか、またそのハイパーパラメーターは何かについて探索します。
今回はscikit-learnのような感じで実行できるように、Classifierクラスのメソッドを以下のように作成します。

method 説明
_fit_and_predict_core fit及びpredictのときに呼ばれる予測のコア部分
fit fitさせます（雑）
predict [データ数]行 × [次元数]列の特徴量行列 X を引数にして、データ数分の予測ラベルを返す
predict_proba [データ数]行 × [次元数]列の特徴量行列 X を引数にして、各データがそれぞれのクラスに所属する確率を返す

実装

渡されるパラメーターに従って前処理クラス、学習器クラスを作ってそれを_fit_and_predict_coreで使用します。
また、何もしない前処理である、NoScalerクラスを作成しています。passするだけですが、ほかの前処理クラスと同様のメソッドが必要なため、fitとtransformが必要になります。

class NoScaler(BaseEstimator, TransformerMixin):
    def __init__(self):
        pass
    
    def fit(self, x, y=None):
        return self
    
    def transform(self, x, y=None):
        return x

class Classifier:
    def __init__(self, params):
        self.params = params
        if params['standardize'] == 'StandardScaler':
            self.standardizer = StandardScaler()
        elif params['standardize'] == 'MinMaxScaler':
            self.standardizer = MinMaxScaler()
        elif params['standardize'] == 'NoScaler':
            self.standardizer = NoScaler()
            
        if params['classifier_name'] == 'RandomForest':
            self.classifier = RandomForestClassifier(**params['classifier_params'])
        elif params['classifier_name'] == 'SVC':
            self.classifier = SVC(**params['classifier_params'])
        
    def _fit_and_predict_core(self, x, y=None, fitting=False, proba=False):
        if fitting == True:
            self.standardizer.fit(x)
        self.standardizer.transform(x)
        
        if fitting == True:
            self.classifier.fit(x, y)
        if y is None:
            if proba:
                return self.classifier.predict_proba(x)
            else:
                return self.classifier.predict(x)
        return None
    
    def fit(self, x, y):
        self._fit_and_predict_core(x, y, fitting=True)
        return self
    
    def predict(self, x):
        pred_y = self._fit_and_predict_core(x)
        return pred_y
        
    def predict_proba(self, x):
        pred_y = self._fit_and_predict_core(x, proba=True)
        return pred_y

2. Objectiveクラスを作成する

Objectiveクラスはパラメータの作成と最適化する基準が必要です。最適化する際に基準とするものを__call__メソッドに記述します。__call__ではsklearnのStratifiedKFoldを使ってデータを分割して訓練とValidationを行っています。
また、generate_paramsメソッドを使ってどの前処理クラスを使うか、どの学習器を使ってそのパラメーターをどの範囲から最適化するかを決定します。__call__メソッドの返り値が最適化する対象になります。
paramsは辞書型として、前処理、学習器をtrial.suggestcategorialメソッドを使って提案し、それぞれkey=standize, classifier_nameとします。また、学習器のハイパーパラメータをkey=classifier_paramsに格納します。
このパラメーターを使ってf1_scoreを最大化する方向に最適化していきます。

実装

class Objective:
    def __init__(self, x, y, label_index):
        self.x = x
        self.y = y
        self.label_index = label_index
        self.best_score = 0
        self.best_params = None
        
    def __call__(self, trial):
        x = self.x
        y = self.y
        params = self.generate_params(trial, x)
        
        classifier = Classifier(params)
        
        skf = StratifiedKFold(n_splits=5, random_state=2019, shuffle=True)
        
        ts = []
        ys = []
        
        for train, test in skf.split(x, y):
            train_y = y.iloc[train].values
            test_y = y.iloc[test].values
            train_x = x.iloc[train].values
            test_x = x.iloc[test].values
            
            classifier.fit(train_x, train_y)
            pred_y = classifier.predict(test_x).reshape(-1).astype(np.int)
            ts.append(test_y)
            ys.append(pred_y)
            
        y_true = self.label_index[np.concatenate(ts)]
        y_pred = self.label_index[np.concatenate(ys)]
        
        f1_score = metrics.f1_score(y_true, y_pred, average='macro')
        if self.best_score < f1_score:
            self.best_score = f1_score
            self.best_params = params
        return f1_score
    
    def generate_params(self, trial, x):
        params = {}
        
        params['standardize'] = trial.suggest_categorical('standardize', ['NoScaler', 'StandardScaler', 'MinMaxScaler'])
        params['classifier_name'] = trial.suggest_categorical('classifier_name', ['RandomForest', 'SVC'])
        
        classifier_params = {}
        if params['classifier_name'] == 'SVC':
            classifier_params['kernel'] = trial.suggest_categorical('svc_kernel',
                                                             ['linear', 'rbf'])
            classifier_params['C'] = trial.suggest_loguniform('svc_c', 1e-10, 1e10)
            if classifier_params['kernel'] == 'rbf':
                classifier_params['gamma'] = trial.suggest_categorical('svc_gamma',
                                                           ['auto', 'scale'])
            else:
                classifier_params['gamma'] = 'auto'

        elif params['classifier_name'] == 'RandomForest':
            classifier_params['n_estimators'] = trial.suggest_categorical(
                'rf_n_estimators', [5, 10, 20, 30, 50, 100])
            classifier_params['max_features'] = trial.suggest_categorical(
                'rf_max_features', ['auto', 0.2, 0.4, 0.6, 0.8])
            classifier_params['max_depth'] = int(
                trial.suggest_loguniform('rf_max_depth', 2, 32))
            classifier_params['n_jobs'] = -1
        else:
            raise RuntimeError('unspport classifier', params['classifier_name'])
        params['classifier_params'] = classifier_params
        return params

3. 訓練と評価

遅いですが、必要なライブラリをインポートし、Wineのデータセットのダウンロードを行います。また、正解ラベルのindexを作成しておきます。

import numpy as np
import pandas as pd
import optuna
from sklearn import datasets, metrics
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.base import BaseEstimator, TransformerMixin

wine = datasets.load_wine()
wine_df = pd.DataFrame(wine.data, columns=wine.feature_names)
wine_label = pd.Series(wine.target)
label, label_index = pd.factorize(wine_label)

学習

optunaの学習は非常に簡単で、studyクラスのインスタンスを作成し、optimizeメソッドを実行するだけです。studyインスタンス作成の際に、最適化する方向をdirection=maximizeで決定しています。F1_scoreの最大化を行っていますが、損失関数などを最適化するならminimizeを指定します。

objective = Objective(x=wine_df, y=wine_label, label_index=label_index)
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=30)

ということで、学習結果になります。SVCでもRnadomForestでもさすがの精度ですね。最初はSVCだったのが最終的にRandomForestが選ばれ、正規化方法も最終的には何もしないNoScalerが選ばれています。

[I 2019-09-25 11:27:31,892] Finished trial#0 resulted in value: 0.9624935249935249. Current best value is 0.9624935249935249 with parameters: {'standardize': 'StandardScaler', 'classifier_name': 'SVC', 'svc_kernel': 'linear', 'svc_c': 0.025216155955058838, 'max_iter': 10000000}.
[I 2019-09-25 11:27:32,482] Finished trial#1 resulted in value: 0.9388331603827972. Current best value is 0.9624935249935249 with parameters: {'standardize': 'StandardScaler', 'classifier_name': 'SVC', 'svc_kernel': 'linear', 'svc_c': 0.025216155955058838, 'max_iter': 10000000}.
[I 2019-09-25 11:27:32,578] Finished trial#2 resulted in value: 0.37333515781791643. Current best value is 0.9624935249935249 with parameters: {'standardize': 'StandardScaler', 'classifier_name': 'SVC', 'svc_kernel': 'linear', 'svc_c': 0.025216155955058838, 'max_iter': 10000000}.
[I 2019-09-25 11:27:33,823] Finished trial#3 resulted in value: 0.9780237518736309. Current best value is 0.9780237518736309 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 30, 'rf_max_features': 'auto', 'rf_max_depth': 7.949794224946728}.
[I 2019-09-25 11:27:35,034] Finished trial#4 resulted in value: 0.9457183021463584. Current best value is 0.9780237518736309 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 30, 'rf_max_features': 'auto', 'rf_max_depth': 7.949794224946728}.
[I 2019-09-25 11:27:36,241] Finished trial#5 resulted in value: 0.9547126096815538. Current best value is 0.9780237518736309 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 30, 'rf_max_features': 'auto', 'rf_max_depth': 7.949794224946728}.
[I 2019-09-25 11:27:37,607] Finished trial#6 resulted in value: 0.9832019138590686. Current best value is 0.9832019138590686 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 100, 'rf_max_features': 0.2, 'rf_max_depth': 24.44778629409185}.
[I 2019-09-25 11:27:38,818] Finished trial#7 resulted in value: 0.9663462486443852. Current best value is 0.9832019138590686 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 100, 'rf_max_features': 0.2, 'rf_max_depth': 24.44778629409185}.
[I 2019-09-25 11:27:40,047] Finished trial#8 resulted in value: 0.9228335043843066. Current best value is 0.9832019138590686 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 100, 'rf_max_features': 0.2, 'rf_max_depth': 24.44778629409185}.
/usr/local/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
[I 2019-09-25 11:27:40,153] Finished trial#9 resulted in value: 0.19009370816599733. Current best value is 0.9832019138590686 with parameters: {'standardize': 'MinMaxScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 100, 'rf_max_features': 0.2, 'rf_max_depth': 24.44778629409185}.
[I 2019-09-25 11:27:41,437] Finished trial#10 resulted in value: 0.9890005486730775. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:42,673] Finished trial#11 resulted in value: 0.967066267066267. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:43,901] Finished trial#12 resulted in value: 0.9774360337020184. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:45,155] Finished trial#13 resulted in value: 0.9664591260232905. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:46,446] Finished trial#14 resulted in value: 0.9722206731660932. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:47,819] Finished trial#15 resulted in value: 0.972829939395289. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:49,108] Finished trial#16 resulted in value: 0.9832019138590686. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:50,493] Finished trial#17 resulted in value: 0.9832019138590686. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:51,778] Finished trial#18 resulted in value: 0.9832019138590686. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:53,159] Finished trial#19 resulted in value: 0.9890005486730775. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:53,766] Finished trial#20 resulted in value: 0.9388331603827972. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:55,011] Finished trial#21 resulted in value: 0.9890005486730775. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:56,243] Finished trial#22 resulted in value: 0.966985815823735. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:57,447] Finished trial#23 resulted in value: 0.9444828778969215. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:27:58,670] Finished trial#24 resulted in value: 0.961842823264262. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:28:00,039] Finished trial#25 resulted in value: 0.9722588647110046. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:28:01,333] Finished trial#26 resulted in value: 0.9780237518736309. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:28:02,564] Finished trial#27 resulted in value: 0.9670016460192324. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:28:03,786] Finished trial#28 resulted in value: 0.9456877801649823. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.
[I 2019-09-25 11:28:03,891] Finished trial#29 resulted in value: 0.7024265061979896. Current best value is 0.9890005486730775 with parameters: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}.

評価

最後に、作成したモデルについて評価を行います。Objective内の訓練時と同様にデータを分割して内部的に保存されたbest_paramsを使ってClassirierインスタンスを作成し、作成されたモデルの評価を行います。

skf = StratifiedKFold(n_splits=5, random_state=44, shuffle=True)
x = wine_df
y = wine_label

print(study.best_trial)
best_score = objective.best_score
print('best score:', best_score)
best_params = objective.best_params
print('best params:', best_params)

ts = []
ys = []

for train, test in skf.split(x, y):
    train_y = y.iloc[train].values
    test_y = y.iloc[test].values
    train_x = x.iloc[train]
    test_x = x.iloc[test]
    model = Classifier(best_params)
    model.fit(train_x, train_y)
    predict = model.predict(test_x).reshape(-1).astype(np.int)
    ts.append(test_y)
    ys.append(predict)
y_true = label_index[np.concatenate(ts)]
y_pred = label_index[np.concatenate(ys)]
print(metrics.confusion_matrix(y_true=y_true, y_pred=y_pred))
print(metrics.classification_report(y_true=y_true, y_pred=y_pred))

FrozenTrial(number=10, state=<TrialState.COMPLETE: 1>, value=0.9890005486730775, datetime_start=datetime.datetime(2019, 9, 25, 11, 27, 40, 155549), datetime_complete=datetime.datetime(2019, 9, 25, 11, 27, 41, 434767), params={'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'rf_n_estimators': 50, 'rf_max_features': 0.2, 'rf_max_depth': 8.269534929659784}, distributions={'standardize': CategoricalDistribution(choices=('NoScaler', 'StandardScaler', 'MinMaxScaler')), 'classifier_name': CategoricalDistribution(choices=('RandomForest', 'SVC')), 'rf_n_estimators': CategoricalDistribution(choices=(5, 10, 20, 30, 50, 100)), 'rf_max_features': CategoricalDistribution(choices=('auto', 0.2, 0.4, 0.6, 0.8)), 'rf_max_depth': LogUniformDistribution(low=2, high=32)}, user_attrs={}, system_attrs={'_number': 10}, intermediate_values={}, params_in_internal_repr={'standardize': 0, 'classifier_name': 0, 'rf_n_estimators': 4, 'rf_max_features': 1, 'rf_max_depth': 8.269534929659784}, trial_id=10)
best score: 0.9890005486730775
best params: {'standardize': 'NoScaler', 'classifier_name': 'RandomForest', 'classifier_params': {'n_estimators': 50, 'max_features': 0.2, 'max_depth': 8, 'n_jobs': -1}}
[[59  0  0]
 [ 1 68  2]
 [ 0  0 48]]
              precision    recall  f1-score   support

           0       0.98      1.00      0.99        59
           1       1.00      0.96      0.98        71
           2       0.96      1.00      0.98        48

    accuracy                           0.98       178
   macro avg       0.98      0.99      0.98       178
weighted avg       0.98      0.98      0.98       178

まとめ

簡単に最適化できますね。Classifierを好きにカスタマイズできるのも素晴らしいところな気がします。
Trialを増やせばさらに最適化できますが、無限にパラメーターを増やせるのでしっかり最適化するにはマシンパワー要りそうです。

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