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 test_forest():
#加载数据
train_set = pd.read_csv('./data_set/seeds.csv')
data_set = np.array(train_set)
X = data_set[:, :-1]
y = data_set[:, -1]
train_X, test_X, train_y, y_true = train_test_split(X,
y,
test_size=1 / 3.,
random_state=7)
# 加载模型
rf_model = RandomForestClassifier(n_estimators=3,
criterion='gini',
max_features='sqrt',
max_depth=20)
rf_model.fit(train_X, train_y) #创建决策树集合
print('rf_model.predict...begin...')
pre_result = rf_model.predict(test_X)
print('训练数据的预测概率向量：')
print(pre_result)
print('真实标签 ：')
print(y_true)
print('训练数据的预测准确度：')
print(accuracy_score(y_true, pre_result))
def test_rf_classification():
iris = datasets.load_iris()
X, y = iris.data, iris.target
print (X.shape, y.shape)
train_X, train_y, test_X, test_y = split_train_test(X, y)
print (train_X.shape, train_y.shape, test_X.shape, test_y.shape)
clf = RandomForestClassifier(n_estimators=100)
clf.fit(train_X, train_y)
preds = clf.predict(test_X)
accuracy = cal_accuracy(test_y, preds)
print ('accuracy: ', accuracy)
def train_and_predict(x_train, y_train, x_test, x_val, y_val):
""" Interface to train and test the new/improved decision tree.
This function is an interface for training and testing the new/improved
decision tree classifier.
x_train and y_train should be used to train your classifier, while
x_test should be used to test your classifier.
x_val and y_val may optionally be used as the validation dataset.
You can just ignore x_val and y_val if you do not need a validation dataset.
Args:
x_train (numpy.ndarray): Training instances, numpy array of shape (N, K)
N is the number of instances
K is the number of attributes
y_train (numpy.ndarray): Class labels, numpy array of shape (N, )
Each element in y is a str
x_test (numpy.ndarray): Test instances, numpy array of shape (M, K)
M is the number of test instances
K is the number of attributes
x_val (numpy.ndarray): Validation instances, numpy array of shape (L, K)
L is the number of validation instances
K is the number of attributes
y_val (numpy.ndarray): Class labels of validation set, numpy array of shape (L, )
"""
#######################################################################
# ** TASK 4.2: COMPLETE THIS FUNCTION **
#######################################################################
# TODO: Train new classifier
forest = RandomForestClassifier()
# Forest is trained on the best hyperparameter set
forest.update_hyperparameters(feature_sel=True,
cross_val=False,
max_tree_depth=13,
min_sample_size=2,
num_trees=20)
forest.fit(x_train, y_train)
# set up an empty (M, ) numpy array to store the predicted labels
# feel free to change this if needed
# TODO: Make predictions on x_test using new classifier
predictions = forest.predict(x_test)
# return result on best classifier option
# remember to change this if you rename the variable
return predictions
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)
def test_random_foerst_fit_predict(self):
model = RandomForestClassifier(n_estimators=100)
features = np.array([
[0, 0],
[0, 0],
[0, 1],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[0, 0],
[0, 0],
[0, 1],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[0, 0],
[0, 0],
[0, 1],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[0, 0],
[0, 0],
[0, 1],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[0, 0],
[0, 0],
[0, 1],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[0, 0],
[0, 0],
[0, 1],
[0, 1],
[0, 1],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
])
labels = np.array([
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
])
model.fit(features, labels)
"""
for tree in model._models:
print("=================================")
from pprint import pprint
pprint(tree._node)
for tree in model._models:
print(tree.predict(np.array([[0, 0], [0, 1], [1, 0], [1, 1]])))
"""
predictions = model.predict(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]))
self.assertEqual(predictions.tolist(), [0, 1, 1, 0])
f"with feature selection and sampling: {acc_val_4}")
results = np.asarray([acc_val_1, acc_val_2, acc_val_3, acc_val_4])
print(f"Best result was achieved with setup {np.argmax(results) + 1}")
"""
# 4.2.4 starts here
# Trees start following the best tree model from improvement 1
forest.update_hyperparameters(feature_sel=True,
cross_val=False,
max_tree_depth=13,
min_sample_size=3,
num_trees=10)
forest.fit(x_train, y_train)
pred_val = forest.predict(x_val)
acc_val_5 = metrics.accuracy(pred_val, y_val)
print(f"10 Best Tree Random Forest Validation Accuracy, ",
f"with feature selection and sampling: {acc_val_5}")
# start by tuning the trees used
param_space = {
"max_tree_depth": [x for x in range(13, 15)],
"min_sample_size": [y for y in range(2, 4)],
"num_trees": [10, 20]
}
best_param = metrics.grid_search(forest,
x_train,
y_train,
x_val,