def _cloglog_inversed(self, ast_to_transform):
return utils.sub(
ast.NumVal(1.0),
ast.ExpExpr(
utils.sub(
ast.NumVal(0.0),
ast.ExpExpr(ast_to_transform))))
def _negativebinomial_inversed(self, ast_to_transform):
alpha = self._get_alpha()
res = utils.sub(
ast.NumVal(1.0),
ast.ExpExpr(utils.sub(ast.NumVal(0.0), ast_to_transform)))
return utils.div(
ast.NumVal(-1.0),
utils.mul(ast.NumVal(alpha), res) if alpha != 1.0 else res)
def _negativebinomial_inversed(self, ast_to_transform):
return utils.div(
ast.NumVal(-1.0),
utils.mul(
ast.NumVal(self.model.model.family.link.alpha),
utils.sub(
ast.NumVal(1.0),
ast.ExpExpr(
utils.sub(
ast.NumVal(0.0),
ast_to_transform)))))
def _rbf_kernel(self, support_vector):
negative_gamma = utils.sub(ast.NumVal(0), ast.NumVal(self._gamma))
elem_wise = [
ast.PowExpr(
utils.sub(ast.NumVal(support_element), ast.FeatureRef(i)),
ast.NumVal(2))
for i, support_element in enumerate(support_vector)
]
kernel = utils.apply_op_to_expressions(ast.BinNumOpType.ADD,
*elem_wise)
kernel = utils.mul(negative_gamma, kernel)
return ast.ExpExpr(kernel)
def __init__(self, model):
super().__init__(model)
supported_kernels = {
"rbf": self._rbf_kernel,
"sigmoid": self._sigmoid_kernel,
"poly": self._poly_kernel,
"linear": self._linear_kernel
}
kernel_type = model.kernel
if kernel_type not in supported_kernels:
raise ValueError("Unsupported kernel type {}".format(kernel_type))
self._kernel_fun = supported_kernels[kernel_type]
n_features = len(model.support_vectors_[0])
gamma = model.gamma
if gamma == "auto" or gamma == "auto_deprecated":
gamma = 1.0 / n_features
self._gamma_expr = ast.NumVal(gamma)
self._neg_gamma_expr = utils.sub(ast.NumVal(0),
ast.NumVal(gamma),
to_reuse=True)
self._output_size = 1
if type(model).__name__ in ("SVC", "NuSVC"):
n_classes = len(model.n_support_)
if n_classes > 2:
self._output_size = n_classes
def log1p(expr):
# Use trick to compute log1p for small values more accurate
# https://www.johndcook.com/blog/2012/07/25/trick-for-computing-log1x/
expr = ast.IdExpr(expr, to_reuse=True)
expr1p = utils.add(ast.NumVal(1.0), expr, to_reuse=True)
expr1pm1 = utils.sub(expr1p, ast.NumVal(1.0), to_reuse=True)
return ast.IfExpr(
utils.eq(expr1pm1, ast.NumVal(0.0)), expr,
utils.div(utils.mul(expr, ast.LogExpr(expr1p)), expr1pm1))
def _logit_inversed(self, ast_to_transform):
return utils.div(
ast.NumVal(1.0),
utils.add(
ast.NumVal(1.0),
ast.ExpExpr(
utils.sub(
ast.NumVal(0.0),
ast_to_transform))))
def tanh(expr):
expr = ast.IdExpr(expr, to_reuse=True)
tanh_expr = utils.sub(
ast.NumVal(1.0),
utils.div(
ast.NumVal(2.0),
utils.add(ast.ExpExpr(utils.mul(ast.NumVal(2.0), expr)),
ast.NumVal(1.0))))
return ast.IfExpr(
utils.gt(expr, ast.NumVal(44.0)), # exp(2*x) <= 2^127
ast.NumVal(1.0),
ast.IfExpr(utils.lt(expr, ast.NumVal(-44.0)), ast.NumVal(-1.0),
tanh_expr))
def __init__(self, model):
super().__init__(model)
kernel_type = model.kernel
supported_kernels = self._get_supported_kernels()
if kernel_type not in supported_kernels:
raise ValueError("Unsupported kernel type {}".format(kernel_type))
self._kernel_fun = supported_kernels[kernel_type]
gamma = self._get_gamma()
self._gamma_expr = ast.NumVal(gamma)
self._neg_gamma_expr = utils.sub(ast.NumVal(0), ast.NumVal(gamma),
to_reuse=True)
self._output_size = self._get_output_size()
def abs(expr):
expr = ast.IdExpr(expr, to_reuse=True)
return ast.IfExpr(utils.lt(expr, ast.NumVal(0)),
utils.sub(ast.NumVal(0.0), expr), expr)
def atan(expr):
expr = ast.IdExpr(expr, to_reuse=True)
expr_abs = ast.AbsExpr(expr, to_reuse=True)
expr_reduced = ast.IdExpr(ast.IfExpr(
utils.gt(expr_abs, ast.NumVal(2.4142135623730950488)),
utils.div(ast.NumVal(1.0), expr_abs),
ast.IfExpr(
utils.gt(expr_abs, ast.NumVal(0.66)),
utils.div(utils.sub(expr_abs, ast.NumVal(1.0)),
utils.add(expr_abs, ast.NumVal(1.0))), expr_abs)),
to_reuse=True)
P0 = ast.NumVal(-8.750608600031904122785e-01)
P1 = ast.NumVal(1.615753718733365076637e+01)
P2 = ast.NumVal(7.500855792314704667340e+01)
P3 = ast.NumVal(1.228866684490136173410e+02)
P4 = ast.NumVal(6.485021904942025371773e+01)
Q0 = ast.NumVal(2.485846490142306297962e+01)
Q1 = ast.NumVal(1.650270098316988542046e+02)
Q2 = ast.NumVal(4.328810604912902668951e+02)
Q3 = ast.NumVal(4.853903996359136964868e+02)
Q4 = ast.NumVal(1.945506571482613964425e+02)
expr2 = utils.mul(expr_reduced, expr_reduced, to_reuse=True)
z = utils.mul(
expr2,
utils.div(
utils.sub(
utils.mul(
expr2,
utils.sub(
utils.mul(
expr2,
utils.sub(
utils.mul(expr2,
utils.sub(utils.mul(expr2, P0), P1)),
P2)), P3)), P4),
utils.add(
Q4,
utils.mul(
expr2,
utils.add(
Q3,
utils.mul(
expr2,
utils.add(
Q2,
utils.mul(
expr2,
utils.add(
Q1,
utils.mul(expr2,
utils.add(Q0,
expr2)))))))))))
z = utils.add(utils.mul(expr_reduced, z), expr_reduced)
ret = utils.mul(
z,
ast.IfExpr(utils.gt(expr_abs, ast.NumVal(2.4142135623730950488)),
ast.NumVal(-1.0), ast.NumVal(1.0)))
ret = utils.add(
ret,
ast.IfExpr(
utils.lte(expr_abs, ast.NumVal(0.66)), ast.NumVal(0.0),
ast.IfExpr(utils.gt(expr_abs, ast.NumVal(2.4142135623730950488)),
ast.NumVal(1.570796326794896680463661649),
ast.NumVal(0.7853981633974483402318308245))))
ret = utils.mul(
ret,
ast.IfExpr(utils.lt(expr, ast.NumVal(0.0)), ast.NumVal(-1.0),
ast.NumVal(1.0)))
return ret
