def apply_sampling(X_data, Y_data, sampling, n_states, maxlen):
ratio = float(np.count_nonzero(Y_data == 1)) / \
float(np.count_nonzero(Y_data == 0))
X_data = np.reshape(X_data, (len(X_data), n_states * maxlen))
# 'Random over-sampling'
if sampling == 'OverSampler':
OS = OverSampler(ratio=ratio, verbose=True)
# 'Random under-sampling'
elif sampling == 'UnderSampler':
OS = UnderSampler(verbose=True)
# 'Tomek under-sampling'
elif sampling == 'TomekLinks':
OS = TomekLinks(verbose=True)
# Oversampling
elif sampling == 'SMOTE':
OS = SMOTE(ratio=1, verbose=True, kind='regular')
# Oversampling - Undersampling
elif sampling == 'SMOTETomek':
OS = SMOTETomek(ratio=ratio, verbose=True)
# Undersampling
elif sampling == 'OneSidedSelection':
OS = OneSidedSelection(verbose=True)
# Undersampling
elif sampling == 'CondensedNearestNeighbour':
OS = CondensedNearestNeighbour(verbose=True)
# Undersampling
elif sampling == 'NearMiss':
OS = NearMiss(version=1, verbose=True)
# Undersampling
elif sampling == 'NeighbourhoodCleaningRule':
OS = NeighbourhoodCleaningRule(verbose=True)
# ERROR: WRONG SAMPLER, TERMINATE
else:
print('Wrong sampling variable you have set... Exiting...')
sys.exit()
# print('shape ' + str(X.shape))
X_data, Y_data = OS.fit_transform(X_data, Y_data)
return X_data, Y_data
colnames = ['old_index','job_id', 'task_idx','sched_cls', 'priority', 'cpu_requested',
'mem_requested', 'disk', 'violation']
tain_path = r'/home/askrey/Dropbox/Project_step_by_step/3_create_database/csvs/frull_db_2.csv'
X = pd.read_csv(tain_path, header = None, index_col = False ,names = colnames, skiprows = [0], usecols = [3,4,5,6,7])
y = pd.read_csv(tain_path, header = None, index_col = False ,names = colnames, skiprows = [0], usecols = [8])
y = y['violation'].values
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.333, random_state=0)
main_x = X.values
main_y = y
verbose = False
# 'One-Sided Selection'
OSS = OneSidedSelection(size_ngh=51, n_seeds_S=51, verbose=verbose)
x, y = OSS.fit_transform(main_x, main_y)
ratio = float(np.count_nonzero(y==1)) / float(np.count_nonzero(y==0))
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.333, random_state=0)
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
clf = RandomForestClassifier(n_estimators=10)
scores = cross_val_score(clf, X_test, y_test)
y_pred = clf.fit(X_train, y_train).predict(X_test)
y_score = clf.fit(X_train, y_train).predict_proba(X_test)[:,1]
