* Add file to hold GoldParse class

spacy/syntax/conll.pxd

@@ -0,0 +1,7 @@
+from ..structs cimport TokenC
+
+
+cdef class GoldParse:
+    cdef int* heads
+    cdef int* labels
+    cdef int heads_correct(self, TokenC* tokens, bint score_punct=?) except -1

spacy/syntax/conll.pyx

@@ -0,0 +1,150 @@
+cdef class GoldParse:
+    def __init__(self):
+        pass
+
+    cdef int heads_correct(self, TokenC* tokens, bint score_punct=False) except -1:
+        pass
+
+"""
+    @classmethod
+    def from_conll(cls, unicode sent_str):
+        ids = []
+        words = []
+        heads = []
+        labels = []
+        tags = []
+        for i, line in enumerate(sent_str.split('\n')):
+            id_, word, pos_string, head_idx, label = _parse_line(line)
+            words.append(word)
+            if head_idx == -1:
+                head_idx = i
+            ids.append(id_)
+            heads.append(head_idx)
+            labels.append(label)
+            tags.append(pos_string)
+        text = ' '.join(words)
+        return cls(text, [words], ids, words, tags, heads, labels)
+
+    @classmethod
+    def from_docparse(cls, unicode sent_str):
+        words = []
+        heads = []
+        labels = []
+        tags = []
+        ids = []
+        lines = sent_str.strip().split('\n')
+        raw_text = lines.pop(0).strip()
+        tok_text = lines.pop(0).strip()
+        for i, line in enumerate(lines):
+            id_, word, pos_string, head_idx, label = _parse_line(line)
+            if label == 'root':
+                label = 'ROOT'
+            words.append(word)
+            if head_idx < 0:
+                head_idx = id_
+            ids.append(id_)
+            heads.append(head_idx)
+            labels.append(label)
+            tags.append(pos_string)
+        tokenized = [sent_str.replace('<SEP>', ' ').split(' ')
+                     for sent_str in tok_text.split('<SENT>')]
+        return cls(raw_text, tokenized, ids, words, tags, heads, labels)
+
+    cdef int heads_correct(self, TokenC* tokens, bint score_punct=False) except -1:
+        pass
+
+    def align_to_non_gold_tokens(self, tokens):
+        # TODO
+        tags = []
+        heads = []
+        labels = []
+        orig_words = list(words)
+        missed = []
+        for token in tokens:
+            while annot and token.idx > annot[0][0]:
+                miss_id, miss_tag, miss_head, miss_label = annot.pop(0)
+                miss_w = words.pop(0)
+                if not is_punct_label(miss_label):
+                    missed.append(miss_w)
+                    loss += 1
+            if not annot:
+                tags.append(None)
+                heads.append(None)
+                labels.append(None)
+                continue
+            id_, tag, head, label = annot[0]
+            if token.idx == id_:
+                tags.append(tag)
+                heads.append(head)
+                labels.append(label)
+                annot.pop(0)
+                words.pop(0)
+            elif token.idx < id_:
+                tags.append(None)
+                heads.append(None)
+                labels.append(None)
+            else:
+                raise StandardError
+        return loss, tags, heads, labels
+
+
+def is_punct_label(label):
+    return label == 'P' or label.lower() == 'punct'
+
+
+def _map_indices_to_tokens(ids, heads):
+    mapped = []
+    for head in heads:
+        if head not in ids:
+            mapped.append(None)
+        else:
+            mapped.append(ids.index(head))
+    return mapped
+
+
+
+def _parse_line(line):
+    pieces = line.split()
+    if len(pieces) == 4:
+        return 0, pieces[0], pieces[1], int(pieces[2]) - 1, pieces[3]
+    else:
+        id_ = int(pieces[0])
+        word = pieces[1]
+        pos = pieces[3]
+        head_idx = int(pieces[6])
+        label = pieces[7]
+        return id_, word, pos, head_idx, label
+
+
+# TODO
+def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
+    global loss
+    nlp = Language()
+    n_corr = 0
+    pos_corr = 0
+    n_tokens = 0
+    total = 0
+    skipped = 0
+    loss = 0
+    with codecs.open(dev_loc, 'r', 'utf8') as file_:
+        #paragraphs = read_tokenized_gold(file_)
+        paragraphs = read_docparse_gold(file_)
+    for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
+                                                     gold_preproc=gold_preproc):
+        assert len(tokens) == len(labels)
+        nlp.tagger(tokens)
+        nlp.parser(tokens)
+        for i, token in enumerate(tokens):
+            pos_corr += token.tag_ == tag_strs[i]
+            n_tokens += 1
+            if heads[i] is None:
+                skipped += 1
+                continue
+            if is_punct_label(labels[i]):
+                continue
+            n_corr += token.head.i == heads[i]
+            total += 1
+    print loss, skipped, (loss+skipped + total)
+    print pos_corr / n_tokens
+    return float(n_corr) / (total + loss)
+"""