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* Add validation for argmaxing in _ml.pyx
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@ -29,22 +29,25 @@ cdef int arg_max(const weight_t* scores, const int n_classes) nogil:
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cdef int arg_max_if_true(const weight_t* scores, const int* is_valid,
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const int n_classes) nogil:
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cdef int i
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cdef int best = 0
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cdef weight_t mode = -900000
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cdef int best = -1
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cdef weight_t mode = 0
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for i in range(n_classes):
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if is_valid[i] and scores[i] > mode:
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if is_valid[i] and (best == -1 or scores[i] > mode):
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mode = scores[i]
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best = i
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return best
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class ValidationError(Exception):
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pass
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cdef int arg_max_if_zero(const weight_t* scores, const int* costs,
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const int n_classes) nogil:
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cdef int i
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cdef int best = 0
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cdef weight_t mode = -900000
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cdef int best = -1
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cdef weight_t mode = 0
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for i in range(n_classes):
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if costs[i] == 0 and scores[i] > mode:
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if costs[i] == 0 and (best == -1 or scores[i] > mode):
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mode = scores[i]
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best = i
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return best
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@ -63,13 +66,18 @@ cdef class Model:
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self._model.load(self.model_loc, freq_thresh=0)
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def predict(self, Example eg):
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assert self.n_classes == eg.c.nr_class
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memset(eg.c.scores, 0, sizeof(weight_t) * eg.c.nr_class)
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self.set_scores(eg.c.scores, eg.c.atoms)
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eg.c.guess = arg_max_if_true(eg.c.scores, eg.c.is_valid, self.n_classes)
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if eg.c.guess == -1:
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raise ValidationError("No valid classes during prediction")
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def train(self, Example eg):
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self.predict(eg)
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eg.c.best = arg_max_if_zero(eg.c.scores, eg.c.costs, self.n_classes)
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if eg.c.best == -1:
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raise ValidationError("No zero-cost classes during training.")
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eg.c.cost = eg.c.costs[eg.c.guess]
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self.update(eg.c.atoms, eg.c.guess, eg.c.best, eg.c.cost)
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