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第11章:ランダムフォレスト - ロングショート戦略 第3節: ランダム木をチューニング

カナヲ定量分析

はじめに

この記事では、ランダムフォレストの分類モデルと回帰モデルの両方に対して、モデルの最適化のやり方、特徴量の重要度の比較をやっていきたいと思います。

インポートと設定

import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

import os, sys
import numpy as np

from numpy.random import choice
import pandas as pd
from scipy.stats import spearmanr

from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.metrics import make_scorer
import joblib

import matplotlib.pyplot as plt
import seaborn as sns

from pathlib import Path
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from utils import MultipleTimeSeriesCV
sns.set_style('white')
np.random.seed(seed=42)
results_path = Path('results', 'random_forest')
if not results_path.exists():
   results_path.mkdir(parents=True)

データ取得

with pd.HDFStore('data.h5') as store:
   data =store['us/equities/monthly']
data.info()
'''
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 56756 entries, ('A', Timestamp('2010-12-31 00:00:00')) to ('ZION', Timestamp('2017-11-30 00:00:00'))
Data columns (total 27 columns):
#   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
0   atr            56756 non-null  float64
1   bb_down        56756 non-null  float64
2   bb_high        56756 non-null  float64
3   bb_low         56756 non-null  float64
4   bb_mid         56756 non-null  float64
5   bb_up          56756 non-null  float64
6   macd           56756 non-null  float64
7   natr           56756 non-null  float64
8   rsi            56756 non-null  float64
9   sector         56756 non-null  object 
10  return_1m      56756 non-null  float64
11  return_3m      56756 non-null  float64
12  return_6m      56756 non-null  float64
13  return_12m     56756 non-null  float64
14  beta           56756 non-null  float64
15  SMB            56756 non-null  float64
16  HML            56756 non-null  float64
17  RMW            56756 non-null  float64
18  CMA            56756 non-null  float64
19  momentum_3     56756 non-null  float64
20  momentum_6     56756 non-null  float64
21  momentum_3_6   56756 non-null  float64
22  momentum_12    56756 non-null  float64
23  momentum_3_12  56756 non-null  float64
24  year           56756 non-null  int64  
25  month          56756 non-null  int64  
26  target         56756 non-null  float64
dtypes: float64(24), int64(2), object(1)
memory usage: 12.0+ MB
'''
y = data.target
y_binary = (y > 0).astype(int)
X = pd.get_dummies(data.drop('target', axis=1))

ランダムフォレスト

交差検定パラメーター

n_splits = 10
train_period_length = 60
test_period_length = 6
lookahead = 1

cv = MultipleTimeSeriesCV(n_splits=n_splits,
                         train_period_length=train_period_length,
                         test_period_length=test_period_length,
                         lookahead=lookahead)

分類器

rf_clf = RandomForestClassifier(n_estimators=100,             # default changed from 10 to 100 in version 0.22 
                               criterion='gini', 
                               max_depth=None, 
                               min_samples_split=2, 
                               min_samples_leaf=1, 
                               min_weight_fraction_leaf=0.0, 
                               max_features='auto',
                               max_leaf_nodes=None, 
                               min_impurity_decrease=0.0, 
                               min_impurity_split=None, 
                               bootstrap=True, 
                               oob_score=True, 
                               n_jobs=-1,
                               random_state=42, 
                               verbose=1)

交差検定のデフォルト設定

cv_score = cross_val_score(estimator=rf_clf,
                          X=X,
                          y=y_binary,
                          scoring='roc_auc',
                          cv=cv,
                          n_jobs=-1,
                          verbose=1)
np.mean(cv_score)
'''
0.5236992279415401
'''

回帰

def rank_correl(y, y_pred):
   return spearmanr(y, y_pred)[0]

ic = make_scorer(rank_correl)
rf_reg = RandomForestRegressor(n_estimators=100, 
                               max_depth=None, 
                               min_samples_split=2, 
                               min_samples_leaf=1, 
                               min_weight_fraction_leaf=0.0, 
                               max_features='auto', 
                               max_leaf_nodes=None, 
                               min_impurity_decrease=0.0, 
                               min_impurity_split=None, 
                               bootstrap=True, 
                               oob_score=False, 
                               n_jobs=-1, 
                               random_state=None, 
                               verbose=0, 
                               warm_start=False)
cv_score = cross_val_score(estimator=rf_reg,
                          X=X,
                          y=y,
                          scoring=ic,
                          cv=cv,
                          n_jobs=-1,
                          verbose=1)
np.mean(cv_score)
'''
0.02360557172927478
'''

パラメーターグリッド

param_grid = {'n_estimators': [50, 100, 250],
             'max_depth': [5, 15, None],
             'min_samples_leaf': [5, 25, 100]}

GridSearchCVのインスタンス

gridsearch_clf = GridSearchCV(estimator=rf_clf,
                             param_grid=param_grid,
                             scoring='roc_auc',
                             n_jobs=-1,
                             cv=cv,
                             refit=True,
                             return_train_score=True,
                             verbose=1)

フィッティング

gridsearch_clf.fit(X=X, y=y_binary)
'''
Fitting 10 folds for each of 27 candidates, totalling 270 fits

[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   43.6s
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  5.5min
[Parallel(n_jobs=-1)]: Done 270 out of 270 | elapsed:  8.8min finished
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    2.5s
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   10.9s
[Parallel(n_jobs=-1)]: Done 250 out of 250 | elapsed:   14.2s finished

GridSearchCV(cv=<utils.MultipleTimeSeriesCV object at 0x7fbf99d90dd0>,
            estimator=RandomForestClassifier(n_jobs=-1, oob_score=True,
                                             random_state=42, verbose=1),
            n_jobs=-1,
            param_grid={'max_depth': [5, 15, None],
                        'min_samples_leaf': [5, 25, 100],
                        'n_estimators': [50, 100, 250]},
            return_train_score=True, scoring='roc_auc', verbose=1)
'''

結果の保存

joblib.dump(gridsearch_clf, results_path / 'gridsearch_clf.joblib') 
gridsearch_clf = joblib.load(results_path / 'gridsearch_clf.joblib') 
gridsearch_clf.best_params_
'''
{'max_depth': None, 'min_samples_leaf': 5, 'n_estimators': 250}
'''
gridsearch_clf.best_score_
'''
0.5208670763661614
'''

特徴量の重要度

fig, ax = plt.subplots(figsize=(12,5))
(pd.Series(gridsearch_clf.best_estimator_.feature_importances_, 
          index=X.columns)
.sort_values(ascending=False)
.iloc[:20]
.sort_values()
.plot.barh(ax=ax, title='RF Feature Importance'))
sns.despine()
fig.tight_layout();

画像1

回帰バージョン

gridsearch_reg = GridSearchCV(estimator=rf_reg,
                     param_grid=param_grid,
                     scoring=ic,
                     n_jobs=-1,
                     cv=cv,
                     refit=True,
                     return_train_score=True,
                     verbose=1)
gs_reg = gridsearch_reg
gridsearch_reg.fit(X=X, y=y)
'''
Fitting 10 folds for each of 27 candidates, totalling 270 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  3.2min
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed: 24.6min
[Parallel(n_jobs=-1)]: Done 270 out of 270 | elapsed: 39.6min finished

GridSearchCV(cv=<utils.MultipleTimeSeriesCV object at 0x7fbf99d90dd0>,
            estimator=RandomForestRegressor(n_jobs=-1), n_jobs=-1,
            param_grid={'max_depth': [5, 15, None],
                        'min_samples_leaf': [5, 25, 100],
                        'n_estimators': [50, 100, 250]},
            return_train_score=True, scoring=make_scorer(rank_correl),
            verbose=1)
'''

結果の保存

joblib.dump(gridsearch_reg, results_path / 'rf_reg_gridsearch.joblib') 
gridsearch_reg = joblib.load(results_path / 'rf_reg_gridsearch.joblib') 
gridsearch_reg.best_params_
'''
{'max_depth': 5, 'min_samples_leaf': 5, 'n_estimators': 100}
'''
f'{gridsearch_reg.best_score_*100:.2f}'
'''
'4.81'
'''

結果の比較

最適パラメーター

pd.DataFrame({'Regression': pd.Series(gridsearch_reg.best_params_),
             'Classification': pd.Series(gridsearch_clf.best_params_)})

画像2

特徴量の重要度

fi_clf = gridsearch_clf.best_estimator_.feature_importances_
fi_reg = gridsearch_reg.best_estimator_.feature_importances_
idx = [c.replace('_', ' ').upper() for c in X.columns]
fig, axes = plt.subplots(figsize=(14, 4), ncols=2)
(pd.Series(fi_clf, index=idx)
.sort_values(ascending=False)
.iloc[:15]
.sort_values()
.plot.barh(ax=axes[1], title='Classifier'))
(pd.Series(fi_reg, index=idx)
.sort_values(ascending=False)
.iloc[:15]
.sort_values()
.plot.barh(ax=axes[0], title='Regression'))
sns.despine()
fig.tight_layout()

画像3


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