Python over_sampling.SMOTE属性代码示例

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def create_sampler(sampler_name, random_state=None):

    if sampler_name is None or sampler_name == 'None':
        return None
    if sampler_name.lower() == 'randomundersampler':
        return RandomUnderSampler(random_state=random_state)
    if sampler_name.lower() == 'tomeklinks':
        return TomekLinks(random_state=random_state)
    if sampler_name.lower() == 'enn':
        return EditedNearestNeighbours(random_state=random_state)
    if sampler_name.lower() == 'ncl':
        return NeighbourhoodCleaningRule(random_state=random_state)
    if sampler_name.lower() == 'randomoversampler':
        return RandomOverSampler(random_state=random_state)
    if sampler_name.lower() == 'smote':
        return SMOTE(random_state=random_state)
    if sampler_name.lower() == 'smotetomek':
        return SMOTETomek(random_state=random_state)
    if sampler_name.lower() == 'smoteenn':
        return SMOTEENN(random_state=random_state)
    else:
        raise ValueError('Unsupported value \'%s\' for sampler' % sampler_name) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_smote_limit_case(plot=False):
    """Execute k-means SMOTE with parameters equivalent to SMOTE"""
    kmeans_smote = KMeansSMOTE(
        random_state=RND_SEED,
        imbalance_ratio_threshold=float('Inf'),
        kmeans_args={
            'n_clusters': 1
        }
    )
    smote = SMOTE(random_state=RND_SEED)
    X_resampled, y_resampled = kmeans_smote.fit_sample(X, Y)
    X_resampled_smote, y_resampled_smote = smote.fit_sample(X, Y)

    if plot:
        plot_resampled(X, X_resampled, Y, y_resampled,
                       'smote_limit_case_test_kmeans_smote')
        plot_resampled(X, X_resampled_smote, Y, y_resampled_smote,
                       'smote_limit_case_test_smote')

    assert_array_equal(X_resampled, X_resampled_smote)
    assert_array_equal(y_resampled, y_resampled_smote) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_random_oversampling_limit_case(plot=False):
    """Execute k-means SMOTE with parameters equivalent to random oversampling"""
    kmeans_smote = KMeansSMOTE(
        random_state=RND_SEED,
        imbalance_ratio_threshold=float('Inf'),
        kmeans_args={
            'n_clusters': 1
        },
        smote_args={
            'k_neighbors': 0
        }
    )
    random_oversampler = RandomOverSampler(random_state=RND_SEED)
    X_resampled, y_resampled = kmeans_smote.fit_sample(X, Y)
    X_resampled_random_oversampler, y_resampled_random_oversampler = random_oversampler.fit_sample(
        X, Y)

    if plot:
        plot_resampled(X, X_resampled, Y, y_resampled,
                       'random_oversampling_limit_case_test_kmeans_smote')
        plot_resampled(X, X_resampled_random_oversampler, Y, y_resampled_random_oversampler,
                       'random_oversampling_limit_case_test_random_oversampling')

    assert_array_equal(X_resampled, X_resampled_random_oversampler)
    assert_array_equal(y_resampled, y_resampled_random_oversampler) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_smote_limit_case_multiclass(plot=False):
    """Execute k-means SMOTE with parameters equivalent to SMOTE"""
    kmeans_smote = KMeansSMOTE(
        random_state=RND_SEED,
        imbalance_ratio_threshold=float('Inf'),
        kmeans_args={
            'n_clusters': 1
        },
        smote_args={'k_neighbors':3}
    )
    smote = SMOTE(random_state=RND_SEED, k_neighbors=3)
    X_resampled, y_resampled = kmeans_smote.fit_sample(X_MULTICLASS, Y_MULTICLASS)
    X_resampled_smote, y_resampled_smote = smote.fit_sample(X_MULTICLASS, Y_MULTICLASS)

    if plot:
        plot_resampled(X_MULTICLASS, X_resampled, Y_MULTICLASS, y_resampled,
                       'smote_limit_case_multiclass_test_kmeans_smote')
        plot_resampled(X_MULTICLASS, X_resampled_smote, Y_MULTICLASS, y_resampled_smote,
                       'smote_limit_case_multiclass_test_smote')

    assert_array_equal(X_resampled, X_resampled_smote)
    assert_array_equal(y_resampled, y_resampled_smote) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_multiclass_irt_dict(plot=False):
    """
    Execute k-means SMOTE for multi-class dataset with
    different imbalance ratio thresholds per class.
    """
    kmeans_smote = KMeansSMOTE(
        random_state=RND_SEED,
        kmeans_args={'n_clusters': 10},
        imbalance_ratio_threshold={1: 1, 2: np.inf})
    X_resampled, y_resampled = kmeans_smote.fit_sample(
        X_MULTICLASS, Y_MULTICLASS)

    assert (np.unique(y_resampled, return_counts=True)[1]
            == np.unique(Y_MULTICLASS_EXPECTED, return_counts=True)[1]).all()
    assert (X_resampled.shape == X_MULTICLASS_SHAPE_EXPECTED)
    if plot:
        plot_resampled(X_MULTICLASS, X_resampled, Y_MULTICLASS,
                       y_resampled, 'multiclass_test') 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def __init__(self, **kwargs):
        super(SmoteSampling, self).__init__(SMOTE(**kwargs, random_state=RANDOM_SEED[BALANCE_SMOTE]), BALANCE_SMOTE) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def GetDescription(self):
        return "To Remove the unbalance of the training data set, we used the Synthetic Minority Oversampling " \
               "TEchnique (SMOTE) to make positive/negative samples balance. " 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def __init__(self, operator = None, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=1):
        if operator is None:
            raise ValueError("Operator is a required argument.")

        self._hyperparams = {
            'sampling_strategy': sampling_strategy,
            'random_state': random_state,
            'k_neighbors': k_neighbors,
            'n_jobs': n_jobs}
    
        resampler_instance = OrigModel(**self._hyperparams)
        super(SMOTEImpl, self).__init__(
            operator = operator,
            resampler = resampler_instance) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def over_sample_smote(train_inputs, train_targets):
    sampler = SMOTE(random_state=32)
    train_inputs, train_targets = _sampler_helper(sampler, train_inputs, train_targets)
    return train_inputs, train_targets 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_objectmapper_oversampling(self):
        import imblearn.over_sampling as os
        df = pdml.ModelFrame([])
        self.assertIs(df.imbalance.over_sampling.ADASYN,
                      os.ADASYN)
        self.assertIs(df.imbalance.over_sampling.RandomOverSampler,
                      os.RandomOverSampler)
        self.assertIs(df.imbalance.over_sampling.SMOTE,
                      os.SMOTE) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_sample(self):
        from imblearn.under_sampling import ClusterCentroids, OneSidedSelection
        from imblearn.over_sampling import ADASYN, SMOTE
        from imblearn.combine import SMOTEENN, SMOTETomek

        models = [ClusterCentroids, OneSidedSelection, ADASYN, SMOTE,
                  SMOTEENN, SMOTETomek]

        X = np.random.randn(100, 5)
        y = np.array([0, 1]).repeat([80, 20])

        df = pdml.ModelFrame(X, target=y, columns=list('ABCDE'))

        for model in models:
            mod1 = model(random_state=self.random_state)
            mod2 = model(random_state=self.random_state)

            df.fit(mod1)
            mod2.fit(X, y)

            result = df.fit_resample(mod1)
            expected_X, expected_y = mod2.fit_resample(X, y)

            self.assertIsInstance(result, pdml.ModelFrame)
            tm.assert_numpy_array_equal(result.target.values, expected_y)
            tm.assert_numpy_array_equal(result.data.values, expected_X)
            tm.assert_index_equal(result.columns, df.columns)

            mod1 = model(random_state=self.random_state)
            mod2 = model(random_state=self.random_state)

            result = df.fit_sample(mod1)
            expected_X, expected_y = mod2.fit_sample(X, y)

            self.assertIsInstance(result, pdml.ModelFrame)
            tm.assert_numpy_array_equal(result.target.values, expected_y)
            tm.assert_numpy_array_equal(result.data.values, expected_X)
            tm.assert_index_equal(result.columns, df.columns) 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def _validate_smote_args(self, smote_args, minority_count):
        # determine max number of nearest neighbors considering sample size
        max_k_neighbors =  minority_count - 1
        # check if max_k_neighbors is violated also considering smote's default
        smote = SMOTE(**smote_args)
        if smote.k_neighbors > max_k_neighbors:
            smote_args['k_neighbors'] = max_k_neighbors
            smote = SMOTE(**smote_args)
        return smote_args 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_smoke(plot=False):
    """Execute k-means SMOTE with default parameters"""
    kmeans_smote = KMeansSMOTE(random_state=RND_SEED)
    X_resampled, y_resampled = kmeans_smote.fit_sample(X, Y)

    assert (np.unique(y_resampled, return_counts=True)[1]
            == np.unique(Y_EXPECTED, return_counts=True)[1]).all()
    assert (X_resampled.shape == X_SHAPE_EXPECTED)
    if plot:
        plot_resampled(X, X_resampled, Y, y_resampled, 'smoke_test') 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_smoke_regular_kmeans(plot=False):
    """Execute k-means SMOTE with default parameters using regular k-means (not minibatch)"""
    kmeans_smote = KMeansSMOTE(
        random_state=RND_SEED, use_minibatch_kmeans=False)
    X_resampled, y_resampled = kmeans_smote.fit_sample(X, Y)

    assert (np.unique(y_resampled, return_counts=True)[1]
            == np.unique(Y_EXPECTED, return_counts=True)[1]).all()
    assert (X_resampled.shape == X_SHAPE_EXPECTED)
    if plot:
        plot_resampled(X, X_resampled, Y, y_resampled, 'smoke_test') 

# 需要导入模块: from imblearn import over_sampling [as 别名]
# 或者: from imblearn.over_sampling import SMOTE [as 别名]
def test_smoke_multiclass(plot=False):
    """Execute k-means SMOTE with default parameters for multi-class dataset"""
    kmeans_smote = KMeansSMOTE(random_state=RND_SEED)
    X_resampled, y_resampled = kmeans_smote.fit_sample(X_MULTICLASS, Y_MULTICLASS)

    assert (np.unique(y_resampled, return_counts=True)[1]
            == np.unique(Y_MULTICLASS_EXPECTED, return_counts=True)[1]).all()
    assert (X_resampled.shape == X_MULTICLASS_SHAPE_EXPECTED)
    if plot:
        plot_resampled(X_MULTICLASS, X_resampled, Y_MULTICLASS, y_resampled, 'smoke_multiclass_test')