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Python SMOTE.fit_resample方法代码示例

本文整理汇总了Python中imblearn.over_sampling.SMOTE.fit_resample方法的典型用法代码示例。如果您正苦于以下问题：Python SMOTE.fit_resample方法的具体用法？Python SMOTE.fit_resample怎么用？Python SMOTE.fit_resample使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类imblearn.over_sampling.SMOTE的用法示例。


在下文中一共展示了SMOTE.fit_resample方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_sample_with_nn_svm

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def test_sample_with_nn_svm():
    kind = 'svm'
    nn_k = NearestNeighbors(n_neighbors=6)
    svm = SVC(gamma='scale', random_state=RND_SEED)
    smote = SMOTE(
        random_state=RND_SEED, kind=kind, k_neighbors=nn_k, svm_estimator=svm)
    X_resampled, y_resampled = smote.fit_resample(X, Y)
    X_gt = np.array([[0.11622591, -0.0317206],
                     [0.77481731, 0.60935141],
                     [1.25192108, -0.22367336],
                     [0.53366841, -0.30312976],
                     [1.52091956, -0.49283504],
                     [-0.28162401, -2.10400981],
                     [0.83680821, 1.72827342],
                     [0.3084254, 0.33299982],
                     [0.70472253, -0.73309052],
                     [0.28893132, -0.38761769],
                     [1.15514042, 0.0129463],
                     [0.88407872, 0.35454207],
                     [1.31301027, -0.92648734],
                     [-1.11515198, -0.93689695],
                     [-0.18410027, -0.45194484],
                     [0.9281014, 0.53085498],
                     [-0.14374509, 0.27370049],
                     [-0.41635887, -0.38299653],
                     [0.08711622, 0.93259929],
                     [1.70580611, -0.11219234],
                     [0.47436887, -0.2645749],
                     [1.07844562, -0.19435291],
                     [1.44228238, -1.31256615],
                     [1.25636713, -1.04463226]])
    y_gt = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
                     1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0])
    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
    assert_array_equal(y_resampled, y_gt)
开发者ID:chkoar，项目名称:imbalanced-learn，代码行数:37，代码来源:test_smote.py

示例2: test_fit_resample_nn_obj

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def test_fit_resample_nn_obj():
    kind = 'borderline1'
    nn_m = NearestNeighbors(n_neighbors=11)
    nn_k = NearestNeighbors(n_neighbors=6)
    smote = SMOTE(
        random_state=RND_SEED, kind=kind, k_neighbors=nn_k, m_neighbors=nn_m)
    X_resampled, y_resampled = smote.fit_resample(X, Y)
    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141], [
        1.25192108, -0.22367336
    ], [0.53366841, -0.30312976], [1.52091956, -0.49283504], [
        -0.28162401, -2.10400981
    ], [0.83680821, 1.72827342], [0.3084254, 0.33299982], [
        0.70472253, -0.73309052
    ], [0.28893132, -0.38761769], [1.15514042, 0.0129463], [
        0.88407872, 0.35454207
    ], [1.31301027, -0.92648734], [-1.11515198, -0.93689695], [
        -0.18410027, -0.45194484
    ], [0.9281014, 0.53085498], [-0.14374509, 0.27370049], [
        -0.41635887, -0.38299653
    ], [0.08711622, 0.93259929], [1.70580611, -0.11219234],
                     [0.3765279, -0.2009615], [0.55276636, -0.10550373],
                     [0.45413452, -0.08883319], [1.21118683, -0.22817957]])
    y_gt = np.array([
        0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0
    ])
    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
    assert_array_equal(y_resampled, y_gt)
开发者ID:chkoar，项目名称:imbalanced-learn，代码行数:29，代码来源:test_smote.py

示例3: train_decisiontree_with

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def train_decisiontree_with(configurationname, train_data, k, score_function, undersam=False, oversam=False,
                            export=False):
    assert k > 0
    print("Training with configuration " + configurationname)
    X_train, y_train, id_to_a_train = train_data
    dtc = DecisionTreeClassifier(random_state=0)

    print("Feature Selection")
    # selector = SelectFpr(score_function)
    selector = SelectKBest(score_function, k=k)
    result = selector.fit(X_train, y_train)
    X_train = selector.transform(X_train)

    fitted_ids = [i for i in result.get_support(indices=True)]

    print("Apply Resampling")
    print(Counter(y_train))
    if undersam and not oversam:
        renn = RepeatedEditedNearestNeighbours()
        X_train, y_train = renn.fit_resample(X_train, y_train)
    if oversam and not undersam:
        # feature_indices_array = list(range(len(f_to_id)))
        # smote_nc = SMOTENC(categorical_features=feature_indices_array, random_state=0)
        # X_train, y_train = smote_nc.fit_resample(X_train, y_train)
        sm = SMOTE(random_state=42)
        X_train, y_train = sm.fit_resample(X_train, y_train)
    if oversam and undersam:
        smote_enn = SMOTEENN(random_state=0)
        X_train, y_train = smote_enn.fit_resample(X_train, y_train)
    print(Counter(y_train))

    print("Train Classifier")
    dtc = dtc.fit(X_train, y_train, check_input=True)

    if export:
        export_graphviz(dtc, out_file=DATAP + "/temp/trees/sltree_" + configurationname + ".dot", filled=True)
        transform(fitted_ids, configurationname)

    print("Self Accuracy: " + str(dtc.score(X_train, y_train)))

    return selector, dtc
开发者ID:softlang，项目名称:wikionto，代码行数:43，代码来源:decision_tree.py

示例4: test_sample_regular_half

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def test_sample_regular_half():
    sampling_strategy = {0: 9, 1: 12}
    smote = SMOTE(
        sampling_strategy=sampling_strategy, random_state=RND_SEED)
    X_resampled, y_resampled = smote.fit_resample(X, Y)
    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141], [
        1.25192108, -0.22367336
    ], [0.53366841, -0.30312976], [1.52091956, -0.49283504], [
        -0.28162401, -2.10400981
    ], [0.83680821, 1.72827342], [0.3084254, 0.33299982], [
        0.70472253, -0.73309052
    ], [0.28893132, -0.38761769], [1.15514042, 0.0129463], [
        0.88407872, 0.35454207
    ], [1.31301027, -0.92648734], [-1.11515198, -0.93689695], [
        -0.18410027, -0.45194484
    ], [0.9281014, 0.53085498], [-0.14374509, 0.27370049],
                     [-0.41635887, -0.38299653], [0.08711622, 0.93259929],
                     [1.70580611, -0.11219234], [0.36784496, -0.1953161]])
    y_gt = np.array(
        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0])
    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
    assert_array_equal(y_resampled, y_gt)
开发者ID:chkoar，项目名称:imbalanced-learn，代码行数:24，代码来源:test_smote.py

示例5: test_sample_borderline2

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def test_sample_borderline2():
    kind = 'borderline2'
    smote = SMOTE(random_state=RND_SEED, kind=kind)
    X_resampled, y_resampled = smote.fit_resample(X, Y)
    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141], [
        1.25192108, -0.22367336
    ], [0.53366841, -0.30312976], [1.52091956, -0.49283504], [
        -0.28162401, -2.10400981
    ], [0.83680821, 1.72827342], [0.3084254, 0.33299982], [
        0.70472253, -0.73309052
    ], [0.28893132, -0.38761769], [1.15514042, 0.0129463], [
        0.88407872, 0.35454207
    ], [1.31301027, -0.92648734], [-1.11515198, -0.93689695], [
        -0.18410027, -0.45194484
    ], [0.9281014, 0.53085498], [-0.14374509, 0.27370049],
                     [-0.41635887, -0.38299653], [0.08711622, 0.93259929],
                     [1.70580611, -0.11219234], [0.47436888, -0.2645749],
                     [1.07844561, -0.19435291], [0.33339622, 0.49870937]])
    y_gt = np.array(
        [0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0])
    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
    assert_array_equal(y_resampled, y_gt)
开发者ID:chkoar，项目名称:imbalanced-learn，代码行数:24，代码来源:test_smote.py

示例6: test_sample_regular_with_nn

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def test_sample_regular_with_nn():
    nn_k = NearestNeighbors(n_neighbors=6)
    smote = SMOTE(random_state=RND_SEED, k_neighbors=nn_k)
    X_resampled, y_resampled = smote.fit_resample(X, Y)
    X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141], [
        1.25192108, -0.22367336
    ], [0.53366841, -0.30312976], [1.52091956, -0.49283504], [
        -0.28162401, -2.10400981
    ], [0.83680821, 1.72827342], [0.3084254, 0.33299982], [
        0.70472253, -0.73309052
    ], [0.28893132, -0.38761769], [1.15514042, 0.0129463], [
        0.88407872, 0.35454207
    ], [1.31301027, -0.92648734], [-1.11515198, -0.93689695], [
        -0.18410027, -0.45194484
    ], [0.9281014, 0.53085498], [-0.14374509, 0.27370049], [
        -0.41635887, -0.38299653
    ], [0.08711622, 0.93259929], [1.70580611, -0.11219234],
                     [0.29307743, -0.14670439], [0.84976473, -0.15570176],
                     [0.61319159, -0.11571668], [0.66052536, -0.28246517]])
    y_gt = np.array([
        0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0
    ])
    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
    assert_array_equal(y_resampled, y_gt)
开发者ID:chkoar，项目名称:imbalanced-learn，代码行数:26，代码来源:test_smote.py

示例7: test_smote_error_passing_estimator

# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit_resample [as 别名]
def test_smote_error_passing_estimator(smote_params, err_msg):
    smote = SMOTE(**smote_params)
    with pytest.raises(ValueError, match=err_msg):
        smote.fit_resample(X, Y)
开发者ID:chkoar，项目名称:imbalanced-learn，代码行数:6，代码来源:test_smote.py


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