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"""Block util.See prints statistics about the nodes matching a given condition.
Example usage from the command line::
udapy util.See node='node.is_nonprojective()' n=3 \
stats=dir,children,c_upos,p_lemma,deprel,feats_split < in.conllu
Example output::
node.is_nonprojective()
matches 245 out of 35766 nodes (0.7%) in 174 out of 1478 trees (11.8%)
=== dir (2 values) ===
right 193 78% delta=+37%
left 52 21% delta=-33%
=== children (9 values) ===
0 64 26% delta=-38%
2 58 23% delta=+14%
3 38 15% delta= +7%
=== c_upos (15 values) ===
NOUN 118 23% delta= +4%
DET 61 12% delta= -3%
PROPN 47 9% delta= +1%
=== p_lemma (187 values) ===
il 5 2% delta= +1%
fonction 4 1% delta= +1%
écrire 4 1% delta= +1%
=== deprel (22 values) ===
appos 41 16% delta=+15%
conj 41 16% delta=+13%
punct 36 14% delta= +4%
=== feats_split (20 values) ===
Number=Sing 114 21% delta= +2%
Gender=Masc 81 15% delta= +3%
_ 76 14% delta= -6%
In addition to absolute counts for each value, the percentage within matching nodes is printed
and a delta relative to percentage within all nodes.
This helps to highlight what is special about the matching nodes.
"""
from collections import Counter
import re # may be useful in eval, thus pylint: disable=unused-import
from udapi.core.block import Block
STATS = 'dir,edge,depth,children,siblings,p_upos,p_lemma,c_upos,form,lemma,upos,deprel,feats_split'
# We need eval in this block
# pylint: disable=eval-used
class See(Block):
"""Print statistics about the nodes specified by the parameter `node`."""
def __init__(self, node, n=5, stats=STATS, **kwargs):
"""Args:
`node`: Python expression to be evaluated for each node and if True,
the node will be considered "matching".
`n`: Top n values will be printed for each statistic.
`stats`: a list of comma-separated statistics to be printed.
A statistic can be an attribute (`form`, `lemma`) or a pseudo-attribute
(`depth` = depth of a node in dependency tree,
`children` = number of children nodes,
`p_lemma` = lemma of a parent node, etc).
See `udapi.core.Node.get_attrs` for a full list of statistics.
"""
super().__init__(**kwargs)
self.node = node
self.n_limit = n
self.stats = stats.split(',')
self.match = dict()
self.every = dict()
for stat in self.stats:
self.match[stat] = Counter()
self.every[stat] = Counter()
self.overall = Counter()
def process_tree(self, root):
self.overall['trees'] += 1
tree_match = False
for node in root.descendants:
matching = self.process_node(node)
self.overall['nodes'] += 1
if matching:
self.overall['matching_nodes'] += 1
if not tree_match:
self.overall['matching_trees'] += 1
tree_match = True
def process_node(self, node):
matching = eval(self.node)
for stat in self.stats:
for value in node.get_attrs([stat], undefs=''):
self.every[stat][value] += 1
self.every[stat]['T O T A L'] += 1
if matching:
self.match[stat][value] += 1
self.match[stat]['T O T A L'] += 1
return matching
def process_end(self):
print(self.node)
print("matches %d out of %d nodes (%.1f%%) in %d out of %d trees (%.1f%%)"
% (self.overall['matching_nodes'],
self.overall['nodes'],
self.overall['matching_nodes'] * 100 / self.overall['nodes'],
self.overall['matching_trees'],
self.overall['trees'],
self.overall['matching_trees'] * 100 / self.overall['trees']))
for stat in self.stats:
vals = len(self.match[stat].keys()) - 1
print("=== %s (%d value%s) ===" % (stat, vals, 's' if vals > 1 else ''))
match_total = self.match[stat]['T O T A L'] or 1
every_total = self.every[stat]['T O T A L'] or 1
for value, match_count in self.match[stat].most_common(self.n_limit + 1):
if value == 'T O T A L':
continue
every_count = self.every[stat][value]
match_perc = 100 * match_count / match_total
every_perc = 100 * every_count / every_total
print("%15s %5d %3d%% delta=%+3d%%"
% (value, match_count, match_perc, match_perc - every_perc))