<commit_before># cython: infer_types=True
"""
MALT-style dependency parser
"""
from __future__ import unicode_literals
cimport cython
cimport cython.parallel

from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals

from libc.stdint cimport uint32_t, uint64_t
from libc.string cimport memset, memcpy
from libc.stdlib cimport malloc, calloc, free

import os.path
from collections import Counter
from os import path
import shutil
import json
import sys
from .nonproj import PseudoProjectivity

from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from thinc.structs cimport SparseArrayC
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get

from thinc.structs cimport FeatureC
from thinc.structs cimport ExampleC
from thinc.extra.eg cimport Example

from util import Config

from ..structs cimport TokenC

from ..tokens.doc cimport Doc
from ..strings cimport StringStore

from .transition_system import OracleError
from .transition_system cimport TransitionSystem, Transition

from ..gold cimport GoldParse

from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
from .stateclass cimport StateClass
from ._state cimport StateC

USE_FTRL = False
DEBUG = False
def set_debug(val):
    global DEBUG
    DEBUG = val


def get_templates(name):
    pf = _parse_features
    if name == 'ner':
        return pf.ner
    elif name == 'debug':
        return pf.unigrams
    elif name.startswith('embed'):
        return (pf.words, pf.tags, pf.labels)
    else:
        return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
                pf.tree_shape + pf.trigrams)


cdef class ParserModel(AveragedPerceptron):
    cdef int set_featuresC(self, atom_t* context, FeatureC* features,
            const StateC* state) nogil:
        fill_context(context, state)
        nr_feat = self.extracter.set_features(features, context)
        return nr_feat

    def update(self, Example eg, itn=0):
        '''Does regression on negative cost. Sort of cute?'''
        self.time += 1
        best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
        guess = eg.guess
        if guess == best or best == -1:
            return 0.0
        if USE_FTRL:
            for feat in eg.c.features[:eg.c.nr_feat]:
                self.update_weight_ftrl(feat.key, guess, feat.value * eg.c.costs[guess])
                self.update_weight_ftrl(feat.key, best, -feat.value * eg.c.costs[guess])
        else:
            for feat in eg.c.features[:eg.c.nr_feat]:
                self.update_weight(feat.key, guess, feat.value * eg.c.costs[guess])
                self.update_weight(feat.key, best, -feat.value * eg.c.costs[guess])
        return eg.c.costs[guess]

    def update_from_histories(self, TransitionSystem moves, Doc doc, histories, weight_t min_grad=0.0):
        cdef Pool mem = Pool()
        features = <FeatureC*>mem.alloc(self.nr_feat, sizeof(FeatureC))

        cdef StateClass stcls

        cdef class_t clas
        self.time += 1
        cdef atom_t[CONTEXT_SIZE] atoms
        histories = [(grad, hist) for grad, hist in histories if abs(grad) >= min_grad and hist]
        if not histories:<commit_msg>Enable regression loss for parser<commit_after># cython: infer_types=True
"""
MALT-style dependency parser
"""
from __future__ import unicode_literals
cimport cython
cimport cython.parallel

from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals

from libc.stdint cimport uint32_t, uint64_t
from libc.string cimport memset, memcpy
from libc.stdlib cimport malloc, calloc, free

import os.path
from collections import Counter
from os import path
import shutil
import json
import sys
from .nonproj import PseudoProjectivity

from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from thinc.structs cimport SparseArrayC
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get

from thinc.structs cimport FeatureC
from thinc.structs cimport ExampleC
from thinc.extra.eg cimport Example

from util import Config

from ..structs cimport TokenC

from ..tokens.doc cimport Doc
from ..strings cimport StringStore

from .transition_system import OracleError
from .transition_system cimport TransitionSystem, Transition

from ..gold cimport GoldParse

from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
from .stateclass cimport StateClass
from ._state cimport StateC

USE_FTRL = True
DEBUG = False
def set_debug(val):
    global DEBUG
    DEBUG = val


def get_templates(name):
    pf = _parse_features
    if name == 'ner':
        return pf.ner
    elif name == 'debug':
        return pf.unigrams
    elif name.startswith('embed'):
        return (pf.words, pf.tags, pf.labels)
    else:
        return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
                pf.tree_shape + pf.trigrams)


cdef class ParserModel(AveragedPerceptron):
    cdef int set_featuresC(self, atom_t* context, FeatureC* features,
            const StateC* state) nogil:
        fill_context(context, state)
        nr_feat = self.extracter.set_features(features, context)
        return nr_feat

    def update(self, Example eg, itn=0):
        '''Does regression on negative cost. Sort of cute?'''
        self.time += 1
        cdef int best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
        cdef int guess = eg.guess
        if guess == best or best == -1:
            return 0.0
        cdef FeatureC feat
        cdef int clas
        cdef weight_t gradient
        if USE_FTRL:
            for feat in eg.c.features[:eg.c.nr_feat]:
                for clas in range(eg.c.nr_class):
                    if eg.c.is_valid[clas] and eg.c.scores[clas] >= eg.c.scores[best]:
                        gradient = eg.c.scores[clas] + eg.c.costs[clas]
                        self.update_weight_ftrl(feat.key, clas, feat.value * gradient)
        else:
            for feat in eg.c.features[:eg.c.nr_feat]:
                self.update_weight(feat.key, guess, feat.value * eg.c.costs[guess])
                self.update_weight(feat.key, best, -feat.value * eg.c.costs[guess])
        return eg.c.costs[guess]

    def update_from_histories(self, TransitionSystem moves, Doc doc, histories, weight_t min_grad=0.0):
        cdef Pool mem = Pool()
        features = <FeatureC*>mem.alloc(self.nr_feat, sizeof(FeatureC))

        cdef StateClass stcls

        cdef class_t clas
        self.time += 1
        cdef atom_t[CONTEXT_SIZE] atoms
        histories = [(grad, hist) for grad, hist in histories if abs(grad) >= min_grad and hist]
        if not histories:
{'fraction_non_alphanumeric': 0.08233638282899367, 'fraction_numerical': 0.006755805770584096, 'mean_word_length': 3.0466970387243735, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2191476', 'n_tokens_mistral': 2390, 'n_tokens_neox': 2303, 'n_words': 662}