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_resampled, y_resampled,
'random_oversampling_limit_case_test_kmeans_smote')
plot_resampled(X_resampled_random_oversampler, 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)
def smote(target_df, target_outcome, grouping):
y = target_df[target_outcome]
target_df.drop(target_outcome, axis=1, inplace=True)
target_df[grouping] = [
float((x.partition('_')[2])) for x in target_df[grouping]
]
#target_df.drop(grouping, axis=1, inplace=True)
X = target_df
target_columns = target_df.columns
#target_columns = target_columns.insert(0, grouping)
target_columns = target_columns.insert(len(target_columns), target_outcome)
kmeans_smote = KMeansSMOTE(kmeans_args={'n_clusters': 5},
smote_args={'k_neighbors': 10})
X_resampled, y_resampled = kmeans_smote.fit_sample(X, y)
X_resampled = pd.DataFrame(X_resampled)
y_resampled = pd.DataFrame(y_resampled)
frames = [X_resampled, y_resampled]
total_df = pd.concat(frames, axis=1)
total_df.columns = target_columns
total_df[grouping] = ['p_' + str(x) for x in total_df[grouping]]
return total_df
def test_smote_fallback(plot=False):
"""Assert that regular SMOTE is applied if no minority clusters are found."""
kmeans_smote = KMeansSMOTE(
random_state=RND_SEED,
kmeans_args={
'n_clusters': 1
}
)
smote = SMOTE(random_state=RND_SEED)
with warnings.catch_warnings(record=True) as w:
X_resampled, y_resampled = kmeans_smote.fit_sample(X, Y)
assert len(w) == 1
assert "No minority clusters found" in str(w[0].message)
assert "Performing regular SMOTE" in str(w[0].message)
assert issubclass(w[0].category, UserWarning)
X_resampled_smote, y_resampled_smote = smote.fit_sample(X, Y)
if plot:
plot_resampled(X_resampled, y_resampled,
'smote_fallback_test_kmeans_smote')
plot_resampled(X_resampled_smote, y_resampled_smote,
'smote_fallback_test_smote')
assert_array_equal(X_resampled, X_resampled_smote)
assert_array_equal(y_resampled, y_resampled_smote)
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')
def test_backwards_compatibility(plot=False):
"""Test if deprecated parameter ratio can still be used without error"""
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
kmeans_smote = KMeansSMOTE(random_state=RND_SEED, ratio={0: Y.sum()})
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')
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')
def test_multiclass(plot=False):
"""Execute k-means SMOTE for multi-class dataset with user-defined n_clusters"""
kmeans_smote = KMeansSMOTE(random_state=RND_SEED,
kmeans_args={'n_clusters': 10})
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')
def test_documentation_example():
"""Test basic code example shown in documentation"""
from imblearn.datasets import fetch_datasets
datasets = fetch_datasets(filter_data=['oil'])
X, y = datasets['oil']['data'], datasets['oil']['target']
labels, counts = np.unique(y, return_counts=True)
assert counts[0] > counts[1]
kmeans_smote = KMeansSMOTE(kmeans_args={'n_clusters': 100},
smote_args={'k_neighbors': 10})
X_resampled, y_resampled = kmeans_smote.fit_sample(X, y)
labels, counts = np.unique(y_resampled, return_counts=True)
assert counts[0] == counts[1]
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)
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')
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)
def split_dataset(X_1,
y_1,
X_0,
y_0,
test_size=0.2,
random_seed=0,
smote=True):
if smote:
dataX_1, testX_1, datay_1, testy_1 = train_test_split(
X_1, y_1, test_size=0.16, random_state=random_seed)
dataX_0, testX_0, datay_0, testy_0 = train_test_split(
X_0, y_0, test_size=0.082, random_state=random_seed)
test_X, test_y = shuffled(array_joint(testX_1, testX_0),
array_joint(testy_1, testy_0),
random_seed=random_seed)
# print(len(testy_1), len(testy_0), len(testy_1) + len(testy_0))
addX, addy = shuffled(array_joint(dataX_1, dataX_0),
array_joint(datay_1, datay_0),
random_seed=random_seed)
kmeans_smote = KMeansSMOTE(kmeans_args={'n_clusters': 2},
smote_args={'k_neighbors': 2},
random_state=random_seed)
X_resampled, y_resampled = kmeans_smote.fit_sample(addX, addy)
# datay_1 = np.ones(len(smote_dataX_1), dtype=np.int16)
train_X, train_y = shuffled(X_resampled,
y_resampled,
random_seed=random_seed)
else:
dataX_1, testX_1, datay_1, testy_1 = train_test_split(
X_1, y_1, test_size=0.2, random_state=random_seed)
dataX_0, testX_0, datay_0, testy_0 = train_test_split(
X_0, y_0, test_size=0.067, random_state=random_seed)
test_X, test_y = shuffled(array_joint(testX_1, testX_0),
array_joint(testy_1, testy_0),
random_seed=random_seed)
train_X, train_y = shuffled(array_joint(dataX_1, dataX_0),
array_joint(datay_1, datay_0),
random_seed=random_seed)
return train_X, test_X, train_y, test_y
from kmeans_smote import KMeansSMOTE
from TestDataGeneration.data_generation import DataGenerator
from experiment import Experiment
if __name__ == "__main__":
total_number = 10000
ratio = 0.9
dg1 = DataGenerator(total_number=total_number, ratio=ratio)
data_train, label_train = dg1.generate()
dg2 = DataGenerator(total_number=total_number, ratio=ratio)
data_test, label_test = dg2.generate()
Ratio = "minority"
kmeans_args = {"n_clusters": 20}
imbalance_ratio_threshold = 20
smote_args = {"k_neighbors": 10}
kmeans_smote = KMeansSMOTE(
ratio=Ratio,
kmeans_args=kmeans_args,
smote_args=smote_args,
imbalance_ratio_threshold=imbalance_ratio_threshold)
X_resampled, Y_resampled = kmeans_smote.fit_sample(data_train, label_train)
exp = Experiment(data=X_resampled, label=Y_resampled)
n_neighbors = [1, 3, 5, 7, 9, 11, 13, 15, 17]
for neighbor in n_neighbors:
true_posi, false_posi, true_neg, false_neg = exp.get_confusion_matrix(
data=data_test, label=label_test, n_neighbors=neighbor)
print("true_posi:", true_posi, "false_posi:", false_posi, "true_neg:",
true_neg, "false_neg:", false_neg)
ly = LabelEncoder()
y = ly.fit_transform(y)
import numpy as np
#from imblearn.datasets import fetch_datasets
from kmeans_smote import KMeansSMOTE
[
print('Class {} has {} instances'.format(label, count))
for label, count in zip(*np.unique(y, return_counts=True))
]
kmeans_smote = KMeansSMOTE(sampling_strategy='minority',
kmeans_args={'n_clusters': 100},
smote_args={'k_neighbors': 10})
X_resampled, y_resampled = kmeans_smote.fit_sample(X, y)
[
print('Class {} has {} instances after oversampling'.format(label, count))
for label, count in zip(*np.unique(y_resampled, return_counts=True))
]
#Splitting Training and Test Set
#Since we have a very small dataset, we will train our model with all availabe data.
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X_resampled,
y_resampled,
test_size=0.2)
from sklearn.svm import SVC
svc1 = SVC(C=50, kernel='rbf', gamma=1)