def test_model_sample_weight():
"""Run the workflow of deep forest with a local buffer."""
case_kwargs = copy.deepcopy(kwargs)
# Training without sample_weight
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
y_pred_no_sample_weight = model.predict(X_test)
# Training with equal sample_weight
model = CascadeForestClassifier(**case_kwargs)
sample_weight = np.ones(y_train.size)
model.fit(X_train, y_train, sample_weight=sample_weight)
y_pred_equal_sample_weight = model.predict(X_test)
# Make sure the same predictions with None and equal sample_weight
assert_array_equal(y_pred_no_sample_weight, y_pred_equal_sample_weight)
model = CascadeForestClassifier(**case_kwargs)
sample_weight = np.where(y_train == 0, 0.1, y_train)
model.fit(X_train, y_train, sample_weight=y_train)
y_pred_skewed_sample_weight = model.predict(X_test)
# Make sure the different predictions with None and equal sample_weight
assert_raises(AssertionError, assert_array_equal,
y_pred_skewed_sample_weight, y_pred_equal_sample_weight)
model.clean() # clear the buffer
def test_model_workflow_in_memory(backend):
"""Run the workflow of deep forest with in-memory mode."""
case_kwargs = copy.deepcopy(kwargs)
case_kwargs.update({"partial_mode": False})
case_kwargs.update({"backend": backend})
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
# Test feature_importances_
if backend == "sklearn":
model.get_layer_feature_importances(0)
else:
with pytest.raises(RuntimeError) as excinfo:
model.get_layer_feature_importances(0)
assert "Please use the sklearn backend" in str(excinfo.value)
# Predictions before saving
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier(**case_kwargs)
model.load(save_dir)
# Make sure the same predictions before and after model serialization
y_pred_after = model.predict(X_test)
assert_array_equal(y_pred_before, y_pred_after)
shutil.rmtree(save_dir)
def test_model_workflow_partial_mode():
"""Run the workflow of deep forest with a local buffer."""
case_kwargs = copy.deepcopy(kwargs)
case_kwargs.update({"partial_mode": True})
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
# Predictions before saving
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier(**case_kwargs)
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test)
# Make sure the same predictions before and after model serialization
assert_array_equal(y_pred_before, y_pred_after)
model.clean() # clear the buffer
shutil.rmtree(save_dir)
def test_model_workflow_in_memory(backend):
"""Run the workflow of deep forest with in-memory mode."""
case_kwargs = copy.deepcopy(kwargs)
case_kwargs.update({"partial_mode": False})
case_kwargs.update({"backend": backend})
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
# Predictions before saving
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier(**case_kwargs)
model.load(save_dir)
# Make sure the same predictions before and after model serialization
y_pred_after = model.predict(X_test)
assert_array_equal(y_pred_before, y_pred_after)
shutil.rmtree(save_dir)
def test_model_input_label_encoder():
"""Test if the model behaves the same with and without label encoding."""
# Load data
X, y = load_digits(return_X_y=True)
y_as_str = np.char.add("label_", y.astype(str))
# Train model on integer labels. Labels should look like: 1, 2, 3, ...
model = CascadeForestClassifier(random_state=1)
model.fit(X, y)
y_pred_int_labels = model.predict(X)
# Train model on string labels. Labels should look like: "label_1", "label_2", "label_3", ...
model = CascadeForestClassifier(random_state=1)
model.fit(X, y_as_str)
y_pred_str_labels = model.predict(X)
# Check if the underlying data are the same
y_pred_int_labels_as_str = np.char.add(
"label_", y_pred_int_labels.astype(str)
)
assert_array_equal(y_pred_str_labels, y_pred_int_labels_as_str)
# Clean up buffer
model.clean()
def test_custom_cascade_layer_workflow_partial_mode():
model = CascadeForestClassifier(partial_mode=True)
n_estimators = 4
estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
model.set_estimator(estimators) # set custom base estimators
predictor = DecisionTreeClassifier()
model.set_predictor(predictor)
model.fit(X_train, y_train)
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier()
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test)
# Make sure the same predictions before and after model serialization
assert_array_equal(y_pred_before, y_pred_after)
model.clean() # clear the buffer
shutil.rmtree(save_dir)
def test_classifier_custom_cascade_layer_workflow_in_memory():
model = CascadeForestClassifier()
n_estimators = 4
estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
model.set_estimator(estimators) # set custom base estimators
predictor = DecisionTreeClassifier()
model.set_predictor(predictor)
model.fit(X_train_clf, y_train_clf)
y_pred_before = model.predict(X_test_clf)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier()
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test_clf)
# Make sure the same predictions before and after model serialization
assert_array_equal(y_pred_before, y_pred_after)
assert (model.get_estimator(0, 0, "custom") is
model._get_layer(0).estimators_["0-0-custom"].estimator_)
model.clean() # clear the buffer
shutil.rmtree(save_dir)
random_state=args.randomseed,
n_jobs=-1)
x_resampled, y_resampled = sm.fit_sample(X, y)
# after over sampleing 读取分类信息并返回数量
np_resampled_y = np.asarray(np.unique(y_resampled, return_counts=True))
df_resampled_y = pd.DataFrame(np_resampled_y.T, columns=['Class', 'Sum'])
print("\nNumber of samples after over sampleing:\n{0}\n".format(
df_resampled_y))
# 初始化 classifier
clf = CascadeForestClassifier(random_state=args.randomseed)
print("\nClassifier parameters:")
print(clf.get_params())
print("\nSMOTE parameters:")
print(sm.get_params())
print("\n")
# 使用SMOTE后数据进行训练
clf.fit(x_resampled, y_resampled)
# 预测测试集
y_pred = clf.predict(X_test)
# 输出测试集统计结果
if (num_categories > 2):
model_evaluation(num_categories, y_test, y_pred)
else:
bi_model_evaluation(y_test, y_pred)
end_time = time.time() # 程序结束时间
print("\n[Finished in: {0:.6f} mins = {1:.6f} seconds]".format(
((end_time - start_time) / 60), (end_time - start_time)))
import numpy as np
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from deepforest import CascadeForestClassifier
from sklearn.model_selection import train_test_split
X, y = load_iris(return_X_y=True)
X = np.array(X)
y = np.array(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = CascadeForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred) * 100
print("Accuracy: {:.3f} %".format(acc))
n_trees=100,
n_jobs=6,
use_predictor="forest")
classification.fit(train_data, train_label.ravel())
prediction = classification.predict_proba(valx)
probability = [prob[1] for prob in prediction]
pre_class = []
for i in probability:
if i > 0.5:
pre_class.append(1)
else:
pre_class.append(0)
prediction = prediction[:, 1]
pred = classification.predict(valx)
print(pred.shape)
print("训练集：", classification.score(train_data, train_label))
print("测试集：", classification.score(test_data, test_label))
a = classification.score(valx, valy)
p = precision_score(valy, pred)
acc = roc_auc_score(valy, pre_class)
roc = roc_auc_score(valy, prediction)
f1_score = f1_score(valy, pred)
cm = confusion_matrix(valy, pred)
recall_score = recall_score(valy, pred)