class Config:
model_names = [
"RBF SVM", "Decision Tree", "Random Forest", "Neural Net", "LDA",
"LogReg", "SVC", "KNN"
]
no_of_splits = 5
Groups = {
'Group_1': {
'ProWSyn': sv.ProWSyn(),
'AND_SMOTE': sv.AND_SMOTE(),
'SMOTE': sv.SMOTE()
},
'Group_2': {
'G_SMOTE': sv.G_SMOTE(),
'Random_SMOTE': sv.Random_SMOTE()
},
'Group_3': {
'SMOTE_TomekLinks': sv.SMOTE_TomekLinks(proportion=1.0),
'VIS_RST': sv.VIS_RST()
},
'Group_4': {
'CBSO': sv.CBSO(),
'SMOBD': sv.SMOBD(),
'A_SUWO': sv.A_SUWO()
}
}
classifiers = {
"RBF SVM":
SVC(gamma=2, C=1, max_iter=1000),
"Decision Tree":
DecisionTreeClassifier(max_depth=5),
"Random Forest":
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
"Neural Net":
MLPClassifier(alpha=1, max_iter=1000),
"LDA":
LinearDiscriminantAnalysis(),
"LogReg":
LogisticRegression(),
"SVC":
SVC(kernel="linear", C=0.025),
"KNN":
KNeighborsClassifier(n_neighbors=3)
}
## setting up directories
raw_data_dir = r'C:\Users\shubh\Desktop\Methods\raw_data'
X_filename = r'X_4_feature_3_sec_Acc_m_+Gyr_m1_Scale.npy'
y_filename = r'y_4_feature_3_sec_Acc_m_+Gyr_m1_Scale.npy'
saving_dir = r'C:\Users\shubh\Desktop\Methods\k_fold_data'
oversampled_data_dir = r'C:\Users\shubh\Desktop\Methods\oversampled_data'
data_shape = 16
similarity_score_dir = r'C:\Users\shubh\Desktop\Methods\similarity_score'
oversampled_data_based_on_similarity_dir = r'C:\Users\shubh\Desktop\Methods\oversampled_data_based_on_similarity'
model_results = r'C:\Users\shubh\Desktop\Methods\model_results'
without_smote_results_dir = r'C:\Users\shubh\Desktop\Methods\without smote results'
final_result_dir = r'C:\Users\shubh\Desktop\Methods\final_result'
# user inputs
groups_to_analyse = ['Group_1']
def test_normal(self):
X = np.vstack([data_min, data_maj])
y = np.hstack(
[np.repeat(1, len(data_min)),
np.repeat(0, len(data_maj))])
samplers = sv.get_all_oversamplers()
for s in samplers:
logging.info("testing %s" % str(s))
X_samp, y_samp = s().sample(X, y)
self.assertTrue(len(X_samp) > 0)
samplers_plus = [
sv.polynom_fit_SMOTE(topology='star'),
sv.polynom_fit_SMOTE(topology='bus'),
sv.polynom_fit_SMOTE(topology='mesh'),
sv.polynom_fit_SMOTE(topology='poly_2'),
sv.Stefanowski(strategy='weak_amp'),
sv.Stefanowski(strategy='weak_amp_relabel'),
sv.Stefanowski(strategy='strong_amp'),
sv.G_SMOTE(method='non-linear_2.0'),
sv.SMOTE_PSOBAT(method='pso'),
sv.AHC(strategy='maj'),
sv.AHC(strategy='minmaj'),
sv.SOI_CJ(method='jittering'),
sv.ADG(kernel='rbf_1'),
sv.SMOTE_IPF(voting='consensus'),
sv.ASMOBD(smoothing='sigmoid')
]
for s in samplers_plus:
logging.info("testing %s" % str(s.__class__.__name__))
X_samp, y_samp = s.sample(X, y)
self.assertTrue(len(X_samp) > 0)
nf = sv.get_all_noisefilters()
for n in nf:
logging.info("testing %s" % str(n))
X_nf, y_nf = n().remove_noise(X, y)
self.assertTrue(len(X_nf) > 0)
def test_normal():
data_min = np.array([[5.7996138, -0.25574582], [3.0637093, 2.11750874],
[4.91444087, -0.72380123], [1.06414164, 0.08694243],
[2.59071708, 0.75283568], [3.44834937, 1.46118085],
[2.8036378, 0.69553702], [3.57901791, 0.71870743],
[3.81529064, 0.62580927], [3.05005506, 0.33290343],
[1.83674689, 1.06998465], [2.08574889, -0.32686821],
[3.49417022, -0.92155623], [2.33920982, -1.59057568],
[1.95332431, -0.84533309], [3.35453368, -1.10178101],
[4.20791149, -1.41874985], [2.25371221, -1.45181929],
[2.87401694, -0.74746037], [1.84435381, 0.15715329]])
data_maj = np.array([[-1.40972752, 0.07111486], [-1.1873495, -0.20838002],
[0.51978825, 2.1631319], [-0.61995016, -0.45111475],
[2.6093289, -0.40993063], [-0.06624482, -0.45882838],
[-0.28836659, -0.59493865], [0.345051, 0.05188811],
[1.75694985, 0.16685025], [0.52901288, -0.62341735],
[0.09694047, -0.15811278], [-0.37490451, -0.46290818],
[-0.32855088,
-0.20893795], [-0.98508364, -0.32003935],
[0.07579831, 1.36455355], [-1.44496689, -0.44792395],
[1.17083343, -0.15804265], [1.73361443, -0.06018163],
[-0.05139342, 0.44876765], [0.33731075, -0.06547923],
[-0.02803696, 0.5802353], [0.20885408, 0.39232885],
[0.22819482, 2.47835768], [1.48216063, 0.81341279],
[-0.6240829, -0.90154291], [0.54349668, 1.4313319],
[-0.65925018, 0.78058634], [-1.65006105, -0.88327625],
[-1.49996313, -0.99378106], [0.31628974, -0.41951526],
[0.64402186, 1.10456105], [-0.17725369, -0.67939216],
[0.12000555, -1.18672234], [2.09793313, 1.82636262],
[-0.11711376, 0.49655609], [1.40513236, 0.74970305],
[2.40025472, -0.5971392], [-1.04860983, 2.05691699],
[0.74057019, -1.48622202], [1.32230881, -2.36226588],
[-1.00093975,
-0.44426212], [-2.25927766, -0.55860504],
[-1.12592836, -0.13399132], [0.14500925, -0.89070934],
[0.90572513, 1.23923502], [-1.25416346, -1.49100593],
[0.51229813, 1.54563048], [-1.36854287, 0.0151081],
[0.08169257, -0.69722099], [-0.73737846, 0.42595479],
[0.02465411, -0.36742946], [-1.14532211, -1.23217124],
[0.98038343, 0.59259824], [-0.20721222, 0.68062552],
[-2.21596433, -1.96045872], [-1.20519292, -1.8900018],
[0.47189299, -0.4737293], [1.18196143, 0.85320018],
[0.03255894, -0.77687178], [0.32485141, -0.34609381]])
X = np.vstack([data_min, data_maj])
y = np.hstack([np.repeat(1, len(data_min)), np.repeat(0, len(data_maj))])
samplers = sv.get_all_oversamplers()
for s in samplers:
logging.info("testing %s" % str(s))
X_samp, y_samp = s().sample(X, y)
assert len(X_samp) > 0
samplers_plus = [
sv.polynom_fit_SMOTE(topology='star'),
sv.polynom_fit_SMOTE(topology='bus'),
sv.polynom_fit_SMOTE(topology='mesh'),
sv.polynom_fit_SMOTE(topology='poly_2'),
sv.Stefanowski(strategy='weak_amp'),
sv.Stefanowski(strategy='weak_amp_relabel'),
sv.Stefanowski(strategy='strong_amp'),
sv.G_SMOTE(method='non-linear_2.0'),
sv.SMOTE_PSOBAT(method='pso'),
sv.AHC(strategy='maj'),
sv.AHC(strategy='minmaj'),
sv.SOI_CJ(method='jittering'),
sv.ADG(kernel='rbf_1'),
sv.SMOTE_IPF(voting='consensus'),
sv.ASMOBD(smoothing='sigmoid')
]
for s in samplers_plus:
logging.info("testing %s" % str(s.__class__.__name__))
X_samp, y_samp = s.sample(X, y)
assert len(X_samp) > 0
nf = sv.get_all_noisefilters()
for n in nf:
logging.info("testing %s" % str(n))
X_nf, y_nf = n().remove_noise(X, y)
assert len(X_nf) > 0
def evaluate_trial(resampler_name, fold):
RESULTS_PATH = Path(__file__).parents[0] / 'results_final'
RANDOM_STATE = 42
resamplers = {
'SMOTE': sv.SMOTE(random_state=RANDOM_STATE),
'polynom-fit-SMOTE': sv.polynom_fit_SMOTE(random_state=RANDOM_STATE),
'Lee': sv.Lee(random_state=RANDOM_STATE),
'SMOBD': sv.SMOBD(random_state=RANDOM_STATE),
'G-SMOTE': sv.G_SMOTE(random_state=RANDOM_STATE),
'LVQ-SMOTE': sv.LVQ_SMOTE(random_state=RANDOM_STATE),
'Assembled-SMOTE': sv.Assembled_SMOTE(random_state=RANDOM_STATE),
'SMOTE-TomekLinks': sv.SMOTE_TomekLinks(random_state=RANDOM_STATE),
'RBO': RBO(random_state=RANDOM_STATE),
'PA': PA(random_state=RANDOM_STATE)
}
for dataset_name in datasets.names():
classifiers = {
'CART': DecisionTreeClassifier(random_state=RANDOM_STATE),
'KNN': KNeighborsClassifier(n_neighbors=3),
'SVM': SVC(kernel='rbf', random_state=RANDOM_STATE),
'MLP': MLPClassifier(random_state=RANDOM_STATE)
}
trial_name = f'{dataset_name}_{fold}_{resampler_name}'
trial_path = RESULTS_PATH / f'{trial_name}.csv'
if trial_path.exists():
continue
logging.info(f'Evaluating {trial_name}...')
dataset = datasets.load(dataset_name)
(X_train, y_train), (X_test,
y_test) = dataset[fold][0], dataset[fold][1]
resampler = resamplers[resampler_name]
assert len(np.unique(y_train)) == len(np.unique(y_test)) == 2
X_train, y_train = resampler.sample(X_train, y_train)
rows = []
for classifier_name in classifiers.keys():
classifier = classifiers[classifier_name]
clf = classifier.fit(X_train, y_train)
predictions = clf.predict(X_test)
scoring_functions = {
'Precision': metrics.precision,
'Recall': metrics.recall,
'AUC': metrics.auc,
'G-mean': metrics.g_mean
}
for scoring_function_name in scoring_functions.keys():
score = scoring_functions[scoring_function_name](y_test,
predictions)
row = [
dataset_name, fold, classifier_name, resampler_name,
scoring_function_name, score
]
rows.append(row)
columns = [
'Dataset', 'Fold', 'Classifier', 'Resampler', 'Metric', 'Score'
]
RESULTS_PATH.mkdir(exist_ok=True, parents=True)
pd.DataFrame(rows, columns=columns).to_csv(trial_path, index=False)
def test_reproducibility(self):
X = np.vstack([data_min, data_maj])
y = np.hstack(
[np.repeat(1, len(data_min)),
np.repeat(0, len(data_maj))])
samplers = sv.get_all_oversamplers()
for s in samplers:
logging.info("testing %s" % str(s))
X_orig = X.copy()
y_orig = y.copy()
X_samp_a, y_samp_a = s(random_state=5).sample(X, y)
sampler = s(random_state=5)
X_samp_b, y_samp_b = sampler.sample(X, y)
X_samp_c, y_samp_c = s(**sampler.get_params()).sample(X, y)
self.assertTrue(np.array_equal(X_samp_a, X_samp_b))
self.assertTrue(np.array_equal(X_samp_a, X_samp_c))
self.assertTrue(np.array_equal(X_orig, X))
samplers_plus = [
sv.polynom_fit_SMOTE(topology='star', random_state=5),
sv.polynom_fit_SMOTE(topology='bus', random_state=5),
sv.polynom_fit_SMOTE(topology='mesh', random_state=5),
sv.polynom_fit_SMOTE(topology='poly_2', random_state=5),
sv.Stefanowski(strategy='weak_amp', random_state=5),
sv.Stefanowski(strategy='weak_amp_relabel', random_state=5),
sv.Stefanowski(strategy='strong_amp', random_state=5),
sv.G_SMOTE(method='non-linear_2.0', random_state=5),
sv.SMOTE_PSOBAT(method='pso', random_state=5),
sv.AHC(strategy='maj', random_state=5),
sv.AHC(strategy='minmaj', random_state=5),
sv.SOI_CJ(method='jittering', random_state=5),
sv.ADG(kernel='rbf_1', random_state=5),
sv.SMOTE_IPF(voting='consensus', random_state=5),
sv.ASMOBD(smoothing='sigmoid', random_state=5)
]
for s in samplers_plus:
logging.info("testing %s" % str(s.__class__.__name__))
X_orig = X.copy()
y_orig = y.copy()
X_samp_a, y_samp_a = s.sample(X, y)
sc = s.__class__(**s.get_params())
X_samp_b, y_samp_b = sc.sample(X, y)
self.assertTrue(np.array_equal(X_samp_a, X_samp_b))
self.assertTrue(np.array_equal(X_orig, X))
nf = sv.get_all_noisefilters()
for n in nf:
logging.info("testing %s" % str(n))
X_orig, y_orig = X.copy(), y.copy()
nf = n()
X_nf_a, y_nf_a = nf.remove_noise(X, y)
nf_b = n(**nf.get_params())
X_nf_b, y_nf_b = nf_b.remove_noise(X, y)
self.assertTrue(np.array_equal(X_nf_a, X_nf_b))
self.assertTrue(np.array_equal(X_orig, X))
