def test_multiclass():
dataset = datasets.load_wine()
oversampler = sv.MulticlassOversampling(sv.distance_SMOTE())
X_samp, y_samp = oversampler.sample(dataset['data'], dataset['target'])
assert len(X_samp) > 0
oversampler = sv.MulticlassOversampling(sv.distance_SMOTE(),
strategy='equalize_1_vs_many')
X_samp, y_samp = oversampler.sample(dataset['data'], dataset['target'])
def over_sample(X, y, len_data, random_seed=None): # -> 没有使用
"""
对样本较少的类别进行过采样，增加样本数目，实现样本平衡
"""
oversampler = sv.MulticlassOversampling(
sv.distance_SMOTE(random_state=random_seed))
X_samp, y_samp = oversampler.sample(X, y)
return X_samp, y_samp
# In[4]:
# printing the number of samples
for i in np.unique(y):
print("class %d - samples: %d" % (i, np.sum(y == i)))
# ## Oversampling
#
# In this section multiclass oversampling is driven by the binary oversampler ```distance_SMOTE```.
# In[5]:
# chosing an oversampler supporting multiclass oversampling
oversampler = sv.MulticlassOversampling(sv.distance_SMOTE())
# In[6]:
X_samp, y_samp = oversampler.sample(X, y)
# ## Illustrating the outcome
# In[7]:
# printing the number of samples
for i in np.unique(y_samp):
print("class %d - samples: %d" % (i, np.sum(y_samp == i)))
# In[8]:
data_x_tst = pd.read_csv('nepal_earthquake_tst.csv')
# Comprueba balanceo de clases
# GraficoComprobarVar(data_y, "damage_grade")
# Comprueba valores perdidos
#ComprobarValPer(data_x)
# Calcula la matriz de correlación
#MatrizCorrelacion(data_x)
# Elimina etiquetas
eliminaLabels(data_x, data_y, data_x_tst, ['building_id'])
# Preprocesado category to number
X = catToNum(data_x).values
y = np.ravel(data_y.values)
X_tst = catToNum(data_x_tst).values
oversampler = sv.MulticlassOversampling(sv.distance_SMOTE(proportion=0.75))
X_sample, y_sample = oversampler.sample(X, y)
'''
print("------ RandomForest...")
rfm = RandomForestClassifier(max_features = 'sqrt', criterion='gini', n_estimators=500, \
max_depth=25, random_state=76592621, \
n_jobs=-1)
# Hago la validación cruzada para el algoritmo
#rfm, y_train_clf, y_test_clf = validacion_cruzada(rfm, X_sample, y_sample)
print("------ Generando submission...")
submission(X_sample, y_sample, X_tst, rfm)
'''
print("------ Catboost...")
cbc = CatBoostClassifier(n_estimators=450,
np.random.seed(random_seed)
# In[3]:
libras = imb_datasets.fetch_datasets()['libras_move']
X, y = libras['data'], libras['target']
# In[4]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
# ## Fitting a pipeline
# In[5]:
oversampler = sv.MulticlassOversampling(sv.distance_SMOTE())
classifier = KNeighborsClassifier(n_neighbors=5)
# In[6]:
model = Pipeline([('scale', StandardScaler()),
('clf', sv.OversamplingClassifier(oversampler, classifier))])
# In[7]:
model.fit(X, y)
# ## Grid search
# In[8]:
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score, f1_score
from sklearn import metrics
import smote_variants as sv
from sklearn.linear_model import LogisticRegression
from sklearn import svm
import heapq
# prepare the smote data --> X_samp, y_samp
df = pd.read_csv("original_data.csv")
data = np.array(df)
X = data[:,:-1]
y = data[:, -1]
oversampler = sv.distance_SMOTE()
X_samp, y_samp = oversampler.sample(X, y)
X_samp = np.round(X_samp)
y_samp = np.round(y_samp)
X_samp, y_samp = X_samp[len(X):], y_samp[len(y):]
# set the count of smote data
SmoteNum = 11
X_samp = X_samp[:SmoteNum,:]
y_samp = y_samp[:SmoteNum]
# init arrays for saving test scores and train scores
meanAUC = np.array([])
meanPrecision = np.array([])
meanRecall = np.array([])
meanAccuracy = np.array([])
meanF1score = np.array([])
validator= RepeatedStratifiedKFold(n_repeats= 8,
n_splits= 5))
print(results.T[['sampler', 'auc', 'gacc']])
np.random.seed(random_seed)
results= sv.cross_validate(dataset= libras,
sampler= sv.NoSMOTE(),
classifier= KNeighborsClassifier(),
validator= RepeatedStratifiedKFold(n_repeats= 8,
n_splits= 5))
print(results.T[['sampler', 'auc', 'gacc']])
#%% running multiclass oversampling
np.random.seed(random_seed)
mc_oversampler= sv.MulticlassOversampling(sv.distance_SMOTE(), strategy= 'equalize_1_vs_many_successive')
X_os, y_os= mc_oversampler.sample(wine['data'], wine['target'])
plot_mc(wine['data'], wine['target'], 'wine', 0, 1, 2, 'wine.eps')
plot_mc(X_os, y_os, 'wine oversampled by distance-SMOTE', 0, 1, 2, 'wine_distance_smote.eps')
#%% oversampler evaluation
import os.path
ecoli['name']= 'ecoli'
cache_path= os.path.join(os.path.expanduser('~'), 'smote_cache')
np.random.seed(random_seed)
results= sv.evaluate_oversamplers(datasets= [ecoli],
samplers= [sv.SPY,
