def compare_performance(trees_num, max_depth, bootstrap):
dataset = datasets.load_digits()
X_train, X_test, y_train, y_test = train_test_split(dataset['data'], dataset['target'], test_size=0.3, random_state=0)
# 決定木
print('##### 決定木の性能 #####')
decision_tree = DecisionTreeClassifier(max_depth=max_depth)
dt_lr_start = time.time() # 学習開始時間を記録
decision_tree.fit(X_train, y_train)
dt_lr_time = time.time() - dt_lr_start # 学習時間
dt_est_start = time.time() # 推論開始時間を記録
y_est = decision_tree.predict(X_test)
dt_est_time = time.time() - dt_est_start # 推論時間
print('学習時間 : {:.6f} [sec] 推論時間 : {:.6f} [sec]'.format(dt_lr_time, dt_est_time))
dt_train_accuracy = decision_tree.accuracy_score(X_train, y_train)
dt_test_accuracy = decision_tree.accuracy_score(X_test, y_test)
print('train accuracy : {:.4f} test_accuracy : {:.4f}'.format(dt_train_accuracy, dt_test_accuracy))
# ランダムフォレスト
print('##### ランダムフォレストの性能 #####')
random_forest = RandomForestClassifier(trees_num=trees_num, max_depth=max_depth, bootstrap=bootstrap)
rf_lr_start = time.time() # 学習開始時間を記録
random_forest.fit(X_train, y_train)
rf_lr_time = time.time() - rf_lr_start # 学習時間
rf_est_start = time.time() # 推論開始時間を記録
y_est = random_forest.predict(X_test)
rf_est_time = time.time() - rf_est_start # 推論時間
print('学習時間 : {:.6f} [sec] 推論時間 : {:.6f} [sec]'.format(rf_lr_time, rf_est_time))
rf_train_accuracy = random_forest.accuracy_score(X_train, y_train)
rf_test_accuracy = random_forest.accuracy_score(X_test, y_test)
print('train accuracy : {:.4f} test_accuracy : {:.4f}'.format(rf_train_accuracy, rf_test_accuracy))
def compare_depth():
dataset = datasets.load_digits()
X_train, X_test, y_train, y_test = train_test_split(dataset['data'], dataset['target'], test_size=0.3, random_state=0)
# ランダムフォレストに関して、最良のハイパーパラメータを調べる
trees_num, bootstrap = grid_search_RF()
# 決定木の深度の制限を変えて、調べる
depth_list = [i for i in range(21)] # 深さの制限0~20まで調べる
dt_train_acc_list = []
dt_test_acc_list = []
rf_train_acc_list = []
rf_test_acc_list = []
for depth in tqdm(depth_list):
print('***** max_depth = {} *****'.format(depth))
# 決定木
decision_tree = DecisionTreeClassifier(max_depth=depth)
decision_tree.fit(X_train, y_train)
dt_train_accuracy = decision_tree.accuracy_score(X_train, y_train)
dt_test_accuracy = decision_tree.accuracy_score(X_test, y_test)
# accuracyをリストに追加
dt_train_acc_list.append(dt_train_accuracy)
dt_test_acc_list.append(dt_test_accuracy)
print('決定木 train accuracy : {:.4f} test_accuracy : {:.4f}'.format(dt_train_accuracy, dt_test_accuracy))
# ランダムフォレスト
random_forest = RandomForestClassifier(trees_num=trees_num, max_depth=depth, bootstrap=bootstrap)
random_forest.fit(X_train, y_train)
rf_train_accuracy = random_forest.accuracy_score(X_train, y_train)
rf_test_accuracy = random_forest.accuracy_score(X_test, y_test)
# accuracyをリストに追加
rf_train_acc_list.append(rf_train_accuracy)
rf_test_acc_list.append(rf_test_accuracy)
print('ランダムフォレスト train accuracy : {:.4f} test_accuracy : {:.4f}'.format(rf_train_accuracy, rf_test_accuracy))
# グラフの描画
plt.plot(depth_list, dt_train_acc_list, label='Decision Tree - train accuracy', color='r')
plt.plot(depth_list, dt_test_acc_list, label='Decision Tree - test accuracy', color='g')
plt.plot(depth_list, rf_train_acc_list, label='Random Forest - train accuracy', color='y')
plt.plot(depth_list, rf_test_acc_list, label='Random Forest - test accuracy', color='b')
plt.xlabel('Max Depth')
plt.ylabel('Accuracy')
plt.xlim(0, 20)
plt.xticks(np.arange(0, 21, 2))
plt.ylim(0, 1.0)
plt.legend(loc='lower right')
plt.title('Max Depth of Decision Trees and Accuracy')
# グラフを保存
plt.savefig('figures/mnist/max_depth_&_accuracy.png')
def grid_search_RF():
dataset = datasets.load_digits()
X_train, X_test, y_train, y_test = train_test_split(dataset['data'], dataset['target'], test_size=0.3, random_state=0)
trees_num_list = [16, 32, 64, 128] # ランダムフォレストに含まれる決定木の個数の候補
bootstrap_list = [0.1, 0.3, 0.5, 0.7, 0.9] # ブートストラップ法で復元するデータ量の元のデータ量に対する割合の候補
best_acc = 0
best_trees_num = None
best_bootstrap = None
with tqdm(total=len(trees_num_list)*len(bootstrap_list), desc='Progress') as pbar:
for trees_num in trees_num_list:
for bootstrap in bootstrap_list:
random_forest = RandomForestClassifier(trees_num=trees_num, max_depth=5, bootstrap=bootstrap)
random_forest.fit(X_train, y_train)
acc = random_forest.accuracy_score(X_test, y_test)
if acc > best_acc:
best_acc = acc
best_trees_num = trees_num
best_bootstrap = bootstrap
pbar.update(1)
print('best acc : {:.4f} best trees_num : {} best bootstrap : {}'.format(best_acc, best_trees_num, best_bootstrap))
return best_trees_num, best_bootstrap
def main():
dataset = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(dataset['data'],
dataset['target'],
test_size=0.3,
random_state=0)
# 決定木の深度の制限が1~3、制限なしの各場合について調べる
depth_list = [1, 2, 3, None]
for depth in depth_list:
print('######### max_depth = {} #########'.format(depth))
# 全ての特徴量を使用したときの精度、学習時間、推論時間、汎化性能を調べる
# 決定木
decision_tree = DecisionTreeClassifier(max_depth=depth)
dt_lr_start = time.time() # 学習開始時間を記録
decision_tree.fit(X_train, y_train)
dt_lr_time = time.time() - dt_lr_start # 学習時間
dt_est_start = time.time() # 推論開始時間を記録
y_est = decision_tree.predict(X_test)
dt_est_time = time.time() - dt_est_start # 推論時間
print('決定木 学習時間 : {:.6f} [sec] 推論時間 : {:.6f} [sec]'.format(
dt_lr_time, dt_est_time))
dt_train_accuracy = decision_tree.accuracy_score(X_train, y_train)
dt_test_accuracy = decision_tree.accuracy_score(X_test, y_test)
print('決定木 train accuracy : {:.4f} test_accuracy : {:.4f}'.
format(dt_train_accuracy, dt_test_accuracy))
# ランダムフォレスト
random_forest = RandomForestClassifier(max_depth=depth)
rf_lr_start = time.time() # 学習開始時間を記録
random_forest.fit(X_train, y_train)
rf_lr_time = time.time() - rf_lr_start # 学習時間
rf_est_start = time.time() # 推論開始時間を記録
y_est = random_forest.predict(X_test)
rf_est_time = time.time() - rf_est_start # 推論時間
print('ランダムフォレスト 学習時間 : {:.6f} [sec] 推論時間 : {:.6f} [sec]'.
format(rf_lr_time, rf_est_time))
rf_train_accuracy = random_forest.accuracy_score(X_train, y_train)
rf_test_accuracy = random_forest.accuracy_score(X_test, y_test)
print(
'ランダムフォレスト train accuracy : {:.4f} test_accuracy : {:.4f}'
.format(rf_train_accuracy, rf_test_accuracy))
# 使用する特徴量を2つ(Petal legth, Petal width)に絞って、二次元で可視化
visualize('decision_tree', max_depth=depth)
visualize('random_forest', max_depth=depth)