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hinge.py
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hinge.py
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"""
Theano op for multiclass hinge loss courtesy James Bergstra.
http://trac-hg.assembla.com/pylearn/changeset/
d4a35c1c0a232fdec05df19387438d9c1638e06c
"""
# This file seems like it has some overlap with theano.tensor.nnet. Which functions should go
# in which file?
from theano import gof
from theano.gof import Apply
from theano import tensor
from theano.tensor import DisconnectedType
import numpy as np
class MultiHingeMargin(gof.Op):
"""
This is a hinge loss function for multiclass predictions.
For each vector X[i] and label index yidx[i],
output z[i] = 1 - margin
where margin is the difference between X[i, yidx[i]] and the maximum other element of X[i].
"""
default_output = 0
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return tensor.hashtype(self)
def __str__(self):
return self.__class__.__name__
def make_node(self, X, yidx):
X_ = tensor.as_tensor_variable(X)
yidx_ = tensor.as_tensor_variable(yidx)
if X_.type.ndim != 2:
raise TypeError('X must be matrix')
if yidx.type.ndim != 1:
raise TypeError('yidx must be vector')
if 'int' not in str(yidx.type.dtype):
raise TypeError("yidx must be integers, it's a vector of class labels")
hinge_loss = tensor.vector(dtype=X.dtype)
winners = X.type()
return Apply(self, [X_, yidx_], [hinge_loss, winners])
def perform(self, node, input_storage, out):
X, yidx = input_storage
toplabel = X.shape[1]-1
out[0][0] = z = np.zeros_like(X[:,0])
out[1][0] = w = np.zeros_like(X)
for i,Xi in enumerate(X):
yi = yidx[i]
if yi == 0:
next_best = Xi[1:].argmax()+1
elif yi==toplabel:
next_best = Xi[:toplabel].argmax()
else:
next_best0 = Xi[:yi].argmax()
next_best1 = Xi[yi+1:].argmax()+yi+1
next_best = next_best0 if Xi[next_best0]>Xi[next_best1] else next_best1
margin = Xi[yi] - Xi[next_best]
if margin < 1:
z[i] = 1 - margin
w[i,yi] = -1
w[i,next_best] = 1
def grad(self, inputs, g_outs):
z = self(*inputs)
w = z.owner.outputs[1]
gz, gw = g_outs
gX = gz.dimshuffle(0,'x') * w
if gw is None:
gY = None
elif isinstance(gw.type, DisconnectedType):
gY = DisconnectedType()()
else:
raise NotImplementedError()
return [gX, gY]
# def c_code_cache_version(self):
# return (1,)
# def c_code(self, node, name, (X, y_idx), (z,w), sub):
# return '''
# if ((%(X)s->descr->type_num != PyArray_DOUBLE) && (%(X)s->descr->type_num != PyArray_FLOAT))
# {
# PyErr_SetString(PyExc_TypeError, "types should be float or float64");
# %(fail)s;
# }
# if ((%(y_idx)s->descr->type_num != PyArray_INT64)
# && (%(y_idx)s->descr->type_num != PyArray_INT32)
# && (%(y_idx)s->descr->type_num != PyArray_INT16)
# && (%(y_idx)s->descr->type_num != PyArray_INT8))
# {
# PyErr_SetString(PyExc_TypeError, "y_idx not int8, int16, int32, or int64");
# %(fail)s;
# }
# if ((%(X)s->nd != 2)
# || (%(y_idx)s->nd != 1))
# {
# PyErr_SetString(PyExc_ValueError, "rank error");
# %(fail)s;
# }
# if (%(X)s->dimensions[0] != %(y_idx)s->dimensions[0])
# {
# PyErr_SetString(PyExc_ValueError, "dy.shape[0] != sm.shape[0]");
# %(fail)s;
# }
# if ((NULL == %(z)s)
# || (%(z)s->dimensions[0] != %(X)s->dimensions[0]))
# {
# Py_XDECREF(%(z)s);
# %(z)s = (PyArrayObject*) PyArray_SimpleNew(1, PyArray_DIMS(%(X)s),
# type_num_%(X)s);
# if (!%(z)s)
# {
# PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output");
# %(fail)s;
# }
# }
# if ((NULL == %(w)s)
# || (%(w)s->dimensions[0] != %(X)s->dimensions[0])
# || (%(w)s->dimensions[1] != %(X)s->dimensions[1]))
# {
# Py_XDECREF(%(w)s);
# %(w)s = (PyArrayObject*) PyArray_SimpleNew(2, PyArray_DIMS(%(X)s),
# type_num_%(X)s);
# if (!%(w)s)
# {
# PyErr_SetString(PyExc_MemoryError, "failed to alloc dx output");
# %(fail)s;
# }
# }
# for (size_t i = 0; i < %(X)s->dimensions[0]; ++i)
# {
# const dtype_%(X)s* __restrict__ X_i = (dtype_%(X)s*) (%(X)s->data + %(X)s->strides[0] * i);
# npy_intp SX = %(X)s->strides[1]/sizeof(dtype_%(X)s);
# dtype_%(w)s* __restrict__ w_i = (dtype_%(w)s*) (%(w)s->data + %(w)s->strides[0] * i);
# npy_intp Sw = %(w)s->strides[1]/sizeof(dtype_%(w)s);
# const dtype_%(y_idx)s y_i = ((dtype_%(y_idx)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
# dtype_%(X)s X_i_max = X_i[0];
# dtype_%(X)s X_at_y_i = X_i[0];
# size_t X_i_argmax = 0;
# size_t j = 1;
# w_i[0] = 0;
# if (y_i == 0)
# {
# X_i_max = X_i[SX];
# X_i_argmax = 1;
# w_i[Sw] = 0;
# }
# for (; j < %(X)s->dimensions[1]; ++j)
# {
# dtype_%(X)s X_ij = X_i[j*SX];
# if (j == y_i)
# {
# X_at_y_i = X_ij;
# }
# else if (X_ij > X_i_max)
# {
# X_i_max = X_ij;
# X_i_argmax = j;
# }
# w_i[j*Sw] = 0;
# }
# if (0 < 1 - X_at_y_i + X_i_max)
# {
# ((dtype_%(z)s*)(%(z)s->data + %(z)s->strides[0] * i))[0]
# = 1 - X_at_y_i + X_i_max;
# w_i[y_i*Sw] = -1;
# w_i[X_i_argmax*Sw] = 1;
# }
# }
# ''' % dict(locals(), **sub)
multi_hinge_margin = MultiHingeMargin()