class Solver(BaseSolver):
name = 'Lightning'
install_cmd = 'pip'
requirements = ['sklearn-contrib-lightning']
requirements_import = ['lightning']
requirements_install = [
'git+https://github.com/scikit-learn-contrib/lightning.git'
]
def set_objective(self, X, y, lmbd):
self.X, self.y, self.lmbd = X, y, lmbd
self.clf = CDRegressor(loss='squared',
penalty='l1',
C=1,
alpha=self.lmbd,
tol=1e-15)
def run(self, n_iter):
self.clf.max_iter = n_iter
self.clf.fit(self.X, self.y)
def get_result(self):
return self.clf.coef_.flatten()
class Solver(BaseSolver):
name = 'Lightning'
install_cmd = 'conda'
requirements = [
'pip:git+https://github.com/scikit-learn-contrib/lightning.git'
]
references = [
'M. Blondel, K. Seki and K. Uehara, '
'"Block coordinate descent algorithms for large-scale sparse '
'multiclass classification" '
'Mach. Learn., vol. 93, no. 1, pp. 31-52 (2013)'
]
def set_objective(self, X, y, lmbd):
self.X, self.y, self.lmbd = X, y, lmbd
self.clf = CDRegressor(loss='squared',
penalty='l1',
C=1,
alpha=self.lmbd,
tol=1e-15)
def run(self, n_iter):
self.clf.max_iter = n_iter
self.clf.fit(self.X, self.y)
def get_result(self):
return self.clf.coef_.flatten()
def fc_kernel(X,
Y,
copy_X=True,
W=None,
B=None,
ret_reg=False,
fit_intercept=True):
"""
return: n c
"""
assert copy_X == True
assert len(X.shape) == 2
if dcfgs.ls == cfgs.solvers.gd:
w = Worker()
def wo():
from .GDsolver import fc_GD
a, b = fc_GD(X, Y, W, B, n_iters=1)
return {'a': a, 'b': b}
outputs = w.do(wo)
return outputs['a'], outputs['b']
elif dcfgs.ls == cfgs.solvers.tls:
return tls(X, Y, debug=True)
elif dcfgs.ls == cfgs.solvers.keras:
_reg = keras_kernel()
_reg.fit(X, Y, W, B)
return _reg.coef_, _reg.intercept_
elif dcfgs.ls == cfgs.solvers.lightning:
#_reg = SGDRegressor(eta0=1e-8, intercept_decay=0, alpha=0, verbose=2)
_reg = CDRegressor(n_jobs=-1, alpha=0, verbose=2)
if 0:
_reg.intercept_ = B
_reg.coef_ = W
elif dcfgs.fc_ridge > 0:
_reg = Ridge(alpha=dcfgs.fc_ridge)
else:
_reg = LinearRegression(n_jobs=-1,
copy_X=copy_X,
fit_intercept=fit_intercept)
_reg.fit(X, Y)
if ret_reg:
return _reg
return _reg.coef_, _reg.intercept_
def fc_kernel(X, Y, copy_X=True, W=None, B=None, ret_reg=False,fit_intercept=True):
"""
return: n c
"""
assert copy_X == True
assert len(X.shape) == 2
if dcfgs.ls == cfgs.solvers.gd:
w = Worker()
def wo():
from .GDsolver import fc_GD
a,b=fc_GD(X,Y, W, B, n_iters=1)
return {'a':a, 'b':b}
outputs = w.do(wo)
return outputs['a'], outputs['b']
elif dcfgs.ls == cfgs.solvers.tls:
return tls(X,Y, debug=True)
elif dcfgs.ls == cfgs.solvers.keras:
_reg=keras_kernel()
_reg.fit(X, Y, W, B)
return _reg.coef_, _reg.intercept_
elif dcfgs.ls == cfgs.solvers.lightning:
#_reg = SGDRegressor(eta0=1e-8, intercept_decay=0, alpha=0, verbose=2)
_reg = CDRegressor(n_jobs=-1,alpha=0, verbose=2)
if 0:
_reg.intercept_=B
_reg.coef_=W
elif dcfgs.fc_ridge > 0:
_reg = Ridge(alpha=dcfgs.fc_ridge)
else:
#redprint("fc_kernel entry here")
_reg = LinearRegression(n_jobs=-1 , copy_X=copy_X, fit_intercept=fit_intercept)
#redprint("[in fc_kernel],X.shape=%s,Y.shape=%s"%(str(X.shape),str(Y.shape)))
_reg.fit(X, Y)
#用LinearRegression这个库，拟合从x(66维)到y（64维）的线性隐射
#其中Coefficients是系数部分，所以是个矩阵【64，66】：y=W*x'，intercept是bias
#print('Coefficients.shape:', _reg.coef_.shape)
#print('intercept.shape : ', _reg.intercept_.shape)
if ret_reg:
return _reg
return _reg.coef_, _reg.intercept_
class Solver(BaseSolver):
name = 'Lightning'
install_cmd = 'conda'
requirements = [
'cython',
'pip:git+https://github.com/scikit-learn-contrib/lightning.git'
]
references = [
'M. Blondel, K. Seki and K. Uehara, '
'"Block coordinate descent algorithms for large-scale sparse '
'multiclass classification" '
'Mach. Learn., vol. 93, no. 1, pp. 31-52 (2013)'
]
def skip(self, X, y, lmbd, fit_intercept):
if fit_intercept:
return True, f"{self.name} does not handle fit_intercept"
return False, None
def set_objective(self, X, y, lmbd, fit_intercept):
self.X, self.y, self.lmbd = X, y, lmbd
self.fit_intercept = fit_intercept
self.clf = CDRegressor(
loss='squared', penalty='l1', C=.5, alpha=self.lmbd,
tol=1e-15
)
def run(self, n_iter):
self.clf.max_iter = n_iter
self.clf.fit(self.X, self.y)
def get_result(self):
beta = self.clf.coef_.flatten()
if self.fit_intercept:
beta = np.r_[beta, self.clf.intercept_]
return beta