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