class cca:
def __init__(self, n_components=1, ccatype=None):
self.n_components = n_components
self.ccatype = ccatype
def derive_transform(self, A, B):
self.model = CCA(n_components=self.n_components, scale=False).fit(A, B)
if self.ccatype == 'new':
# http://onlinelibrary.wiley.com/doi/10.1002/cem.2637/abstract
F1 = np.linalg.pinv(self.model.x_scores_).dot(self.model.y_scores_)
F2 = np.linalg.pinv(self.model.y_scores_).dot(B)
P = ct.multi_dot((self.model.x_weights_, F1, F2))
self.proj_to_B = P
else:
return self.model
def apply_transform(self, C):
C = np.array(C)
if self.ccatype == 'new':
return C.dot(self.proj_to_B)
else:
if len(C.shape) == 1:
C = C.reshape(1, -1)
return self.model.predict(C)
class _CCAImpl:
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if y is not None:
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
def predict(self, X):
return self._wrapped_model.predict(X)
class BiomeCCA():
def __init__(self, args):
self.CCA = CCA(n_components=args.latent_size)
def fit(self,X1_train, X2_train, y_train, X1_val, X2_val, y_val, args,):
if args.latent_size > min(X1_train.shape[1], X2_train.shape[1]):
print("Warning: auto reduce latent size")
self.CCA = CCA(n_components=min(X1_train.shape[1], X2_train.shape[1]))
return self.CCA.fit(X1_train, X2_train)
def transform(self,x1_train, x2_train, y, args):
return self.CCA.transform(x1_train, x2_train)
def predict(self,X1_val, X2_val, y_val, args):
return self.CCA.predict(X1_val)
def get_transformation(self):
return self.CCA.coef_
def param_l0(self):
return {"Encoder":self.CCA.coef_.shape[0]*self.CCA.n_components,
"Decoder":self.CCA.n_components*self.CCA.coef_.shape[1]}
def get_graph(self):
return ([],[])
def cca(self, X, y):
cca_model = CCA(n_components=self.n_comp, scale=False)
cca_model.fit(X, y)
X_c, y_c = cca_model.transform(X, y)
y_predict = cca_model.predict(X, copy=True)
# R2=cca_model.score(X, y, sample_weight=None)
# loading 为每个原始变量与对应典型变量的相关性*
loading_x = cca_model.x_loadings_
loading_y = cca_model.y_loadings_
# weight即为线性组合的系数，可能可以用来将降维的变量投射到原始空间
# 注意：如果scale参数设置为Ture，则weight是原始数据经过标准化后得到的weight
weight_x = cca_model.x_weights_
weight_y = cca_model.y_weights_
# weight_orig=np.dot(y_c[0,:],weight_y.T)
# coef为X对y的系数，可以用来预测y（np.dot,矩阵乘法）
coef = cca_model.coef_
# 此算法中rotations==weight
# rotation_y=cca_model.y_rotations_
# rotation_x=cca_model.x_rotations_
# score(X,y)返回R squre
# 求某个典型变量对本组变量的协方差解释度（covariance explained by each canonical variate or component）
cov_x = np.cov(X_c.T)
cov_y = np.cov(y_c.T)
# np.diag(cov_x)
eigvals_x, _ = np.linalg.eig(cov_x)
eigvals_y, _ = np.linalg.eig(cov_y)
explain_x = pow(eigvals_x, 2) / np.sum(pow(eigvals_x, 2))
explain_y = pow(eigvals_y, 2) / np.sum(pow(eigvals_y, 2))
# np.sort(explain)
return (cca_model,\
X_c,y_c,\
loading_x,loading_y,\
weight_x,weight_y,\
explain_x,explain_y,\
coef,y_predict)
def main(args):
(training_file, label_file, test_file, test_label, u_file) = args
X_training = load_feat(training_file)
n = len(X_training)
U = load_feat(u_file)
y_training = [int(line.strip()) for line in open(label_file)]
U = np.asarray(U)
X_training = np.asarray(X_training)
#X = preprocessing.normalize(X, norm='l2')
y_training = np.asarray(y_training)
X_test = load_feat(test_file)
y_test = [int(line.strip()) for line in open(test_label)]
X_test = np.asarray(X_test)
#test_X = preprocessing.normalize(test_X, norm='l2')
y_test = np.asarray(y_test)
cca = CCA(n_components=100)
(X_cca, U_cca) = cca.fit_transform(X_training, U[:n])
X_test_cca = cca.predict(X_test)
svr = SVC()
svr.fit(X_cca, y_training)
pred = svr.predict(X_test_cca)
print pred
print test_y
print accuracy_score(y_test, pred)
with open(test_file + '.cca.2.pred', 'w') as output:
for p in pred:
print >>output, p
#svm_model.fit(X, y)
#pickle.dump(lr, open(model_file, "wb"))
return
return
def main(args):
(training_file, label_file, test_file, test_label, u_file) = args
X_training = load_feat(training_file)
n = len(X_training)
U = load_feat(u_file)
y_training = [int(line.strip()) for line in open(label_file)]
U = np.asarray(U)
X_training = np.asarray(X_training)
#X = preprocessing.normalize(X, norm='l2')
y_training = np.asarray(y_training)
X_test = load_feat(test_file)
y_test = [int(line.strip()) for line in open(test_label)]
X_test = np.asarray(X_test)
#test_X = preprocessing.normalize(test_X, norm='l2')
y_test = np.asarray(y_test)
cca = CCA(n_components=100)
(X_cca, U_cca) = cca.fit_transform(X_training, U[:n])
X_test_cca = cca.predict(X_test)
svr = SVC()
svr.fit(X_cca, y_training)
pred = svr.predict(X_test_cca)
print pred
print test_y
print accuracy_score(y_test, pred)
with open(test_file + '.cca.2.pred', 'w') as output:
for p in pred:
print >> output, p
#svm_model.fit(X, y)
#pickle.dump(lr, open(model_file, "wb"))
return
return
def test_cca_implementation():
X = np.random.multivariate_normal(np.random.randint(50,100,(10)).astype('float'),np.identity(10),200)
Y = np.random.multivariate_normal(np.random.randint(80,200,(6)).astype('float'),np.identity(6),200)
X_test = np.random.multivariate_normal(np.random.randint(50,100,(10)).astype('float'),np.identity(10),20)
Y_test = np.random.multivariate_normal(np.random.randint(50,100,(6)).astype('float'),np.identity(6),20)
mdl_test = CCA(n_components = 6)
mdl_test.fit(X,Y)
Y_pred = mdl_test.predict(X)
print Y_pred
print '-'*50
# print Y_test
from sklearn.cross_decomposition import CCA as CCA_sklearn
mdl_actual = CCA_sklearn(n_components = 6)
mdl_actual.fit(X,Y)
print '-'*50
Y_actual = mdl_actual.predict(X)
print Y_actual
def cca(x,
neural_data,
region=None,
brain_region=['IT', 'V4'],
cv=5,
n_components=5,
variation=[0, 3, 6],
sortby='image_id',
train_size=0.75):
# var_lookup = stimulus_set[stimulus_set.variation.isin(variation)].image_id.values
# x = x.where(x.image_id.isin(var_lookup),drop=True)
# nd = neural_data.where(neural_data.image_id.isin(var_lookup),drop=True)
x = x.sortby(sortby)
nd = neural_data.sortby(sortby)
assert list(getattr(x, sortby).values) == list(getattr(nd, sortby).values)
num_images = x.shape[0]
out_recs = []
cv_tr = []
cv_te = []
kf = KFold(n_splits=cv, shuffle=True, random_state=cv)
for tr, te in kf.split(np.arange(num_images)):
cv_tr.append(tr)
cv_te.append(te)
for rand_delta in np.arange(cv):
tr_idx, te_idx, _, _ = train_test_split(
np.arange(num_images),
np.arange(num_images),
train_size=train_size,
random_state=np.random.randint(0, 50) + rand_delta)
cv_tr.append(tr_idx)
cv_te.append(te_idx)
for br in brain_region:
nd_reg = nd.sel(region=br)
if region is None:
region = np.unique(x.region.values)
for reg in region:
if reg == 'pixel':
continue
x_reg = x.sel(region=reg)
depth = np.unique(x_reg.layer.values)[0]
with tqdm(zip(np.arange(cv), cv_tr, cv_te), total=cv) as t:
t.set_description('{}{} x {}{}'.format(reg, x_reg.shape, br,
nd_reg.shape))
r_mean = []
fve_mean = []
cca_mean = []
for n, tr, te in t:
cca = CCA(n_components=n_components)
cca.fit(x_reg.values[tr], nd_reg.values[tr])
u, v = cca.transform(x_reg.values[te], nd_reg.values[te])
y_pred = cca.predict(x_reg.values[te])
y_true = nd_reg.values[te]
fve = explained_variance_score(y_true,
y_pred,
multioutput='raw_values')
r_vals = [
pearsonr(y_pred[:, i], y_true[:, i])
for i in range(y_pred.shape[-1])
]
cca_r = np.mean([
pearsonr(u[:, i], v[:, i])
for i in np.arange(n_components)
])
# r_vals = [pearsonr(ab_vec[0][:,i],ab_vec[1][:,i]) for i in range(ab_vec[0].shape[-1])]
r_mean.append(np.mean([r for r, v in r_vals]))
cca_mean.append(cca_r)
fve_mean.append(np.mean(fve))
for rv, f, nid in zip(r_vals, fve,
nd_reg[te].neuroid_id.values):
out_recs.append({
'region': br,
'layer': reg,
'pearsonr': rv[0],
'cca_r': cca_r,
'fve': f,
'iter': n,
'depth': depth,
'neuroid_id': nid,
})
t.set_postfix(pearson=np.mean(r_mean),
cca=np.mean(cca_mean),
fve=np.mean(fve_mean))
return pd.DataFrame.from_records(out_recs)
def cca_ds(A, B, n_components=1):
model = CCA(n_components=n_components, scale=False).fit(B, A)
return model.coefs, model.predict(B)
class CcaClass:
"""
Name : CCA
Attribute : None
Method : predict, predict_by_cv, save_model
"""
def __init__(self):
# 알고리즘 이름
self._name = 'cca'
# 기본 경로
self._f_path = os.path.abspath(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
os.pardir))
# 경고 메시지 삭제
warnings.filterwarnings('ignore')
# 원본 데이터 로드
data = pd.read_csv(self._f_path +
"/regression/resource/regression_sample.csv",
sep=",",
encoding="utf-8")
# 학습 및 테스트 데이터 분리
self._x = (data["year"] <= 2017)
self._y = (data["year"] >= 2018)
# 학습 데이터 분리
self._x_train, self._y_train = self.preprocessing(data[self._x])
# 테스트 데이터 분리
self._x_test, self._y_test = self.preprocessing(data[self._y])
# 모델 선언
self._model = CCA()
# 모델 학습
self._model.fit(self._x_train, self._y_train)
# 데이터 전처리
def preprocessing(self, data):
# 학습
x = []
# 레이블
y = []
# 기준점(7일)
base_interval = 7
# 기온
temps = list(data["temperature"])
for i in range(len(temps)):
if i < base_interval:
continue
y.append(temps[i])
xa = []
for p in range(base_interval):
d = i + p - base_interval
xa.append(temps[d])
x.append(xa)
return x, y
# 일반 예측
def predict(self, save_img=False, show_chart=False):
# 예측
y_pred = self._model.predict(self._x_test)
# 스코어 정보
score = r2_score(self._y_test, y_pred)
# 리포트 확인
if hasattr(self._model, 'coef_') and hasattr(self._model,
'intercept_'):
print(f'Coef = {self._model.coef_}')
print(f'intercept = {self._model.intercept_}')
print(f'Score = {score}')
# 이미지 저장 여부
if save_img:
self.save_chart_image(y_pred, show_chart)
# 예측 값 & 스코어
return [list(y_pred.tolist()), score]
# CV 예측(Cross Validation)
def predict_by_cv(self):
# Regression 알고리즘은 실 프로젝트 상황에 맞게 Cross Validation 구현
return False
# GridSearchCV 예측
def predict_by_gs(self):
pass
# 모델 저장 및 갱신
def save_model(self, renew=False):
# 모델 저장
if not renew:
# 처음 저장
joblib.dump(self._model,
self._f_path + f'/model/{self._name}_rg.pkl')
else:
# 기존 모델 대체
if os.path.isfile(self._f_path + f'/model/{self._name}_rg.pkl'):
os.rename(
self._f_path + f'/model/{self._name}_rg.pkl',
self._f_path +
f'/model/{str(self._name) + str(time.time())}_rg.pkl')
joblib.dump(self._model,
self._f_path + f'/model/{self._name}_rg.pkl')
# 회귀 차트 저장
def save_chart_image(self, data, show_chart):
# 사이즈
plt.figure(figsize=(15, 10), dpi=100)
# 레이블
plt.plot(self._y_test, c='r')
# 예측 값
plt.plot(data, c='b')
# 이미지로 저장
plt.savefig('./chart_images/tenki-kion-lr.png')
# 차트 확인(Optional)
if show_chart:
plt.show()
def __del__(self):
del self._x_train, self._x_test, self._y_train, self._y_test, self._x, self._y, self._model
