コード例 #1
0
        # Work out the minimal span to show all errors
        gold_spans = set([(node.label, node.span[0], node.span[1])
                          for node in gold_tree.get_nodes()])
        test_spans = set([(node.label, node.span[0], node.span[1])
                          for node in test_tree.get_nodes()])
        diff = gold_spans.symmetric_difference(test_spans)
        width = [1e5, -1]
        for span in diff:
            if span[2] - span[1] == 1:
                continue
            if span[1] < width[0]:
                width[0] = span[1]
            if span[2] > width[1]:
                width[1] = span[2]
        mprint('\n\\scalebox{\\derivscale}{', out, 'tex')
        mprint(render_tree.tex_synttree(test_tree, gold_spans, span=width),
               out, 'tex')
        mprint(
            '}\n\\small\n(a) Parser output\n\n\\vspace{3mm}\n\\scalebox{\\derivscale}{',
            out, 'tex')
        mprint(render_tree.tex_synttree(gold_tree, test_spans, span=width),
               out, 'tex')
        mprint('}\n\\small\n(b) Gold tree\n\\pagebreak', out, 'tex')

        mprint("", out, 'all')
    match = stats['out'][0]
    gold = stats['out'][1]
    test = stats['out'][2]
    p, r, f = nlp_eval.calc_prf(match, gold, test)
    mprint("Overall %s: %.2f  %.2f  %.2f" % ('out', p * 100, r * 100, f * 100),
           out, 'out')
コード例 #2
0
			if gold_tree is not None:
				treebanks.remove_trivial_unaries(gold_tree)

		# Print tree
		if out_format == 's':
			print render_tree.text_tree(tree, single_line=True)
		elif out_format == 'm':
			print render_tree.text_tree(tree, single_line=False)
		elif out_format == 'o':
			print render_tree.text_ontonotes(tree)
		elif out_format == 'p':
			print render_tree.text_POS_tagged(tree)
		elif out_format == 't':
			if gold_tree is None:
				print '\\scalebox{\\derivscale}{'
				print render_tree.tex_synttree(tree)
				print '}\n\\small\n\\pagebreak'
			else:
				print '\\scalebox{\\derivscale}{'
				other_spans = gold_tree.span_dict()
				print render_tree.tex_synttree(tree, other_spans)
				print '}\n\\small\n\\pagebreak'
				print '\\scalebox{\\derivscale}{'
				other_spans = tree.span_dict()
				print render_tree.tex_synttree(gold_tree, other_spans)
				print '}\n\\small\n\\pagebreak'
		elif out_format == 'w':
			print render_tree.text_words(tree)
	if out_format == 't':
		print '\\end{document}'
コード例 #3
0
            if gold_tree is not None:
                treebanks.remove_trivial_unaries(gold_tree)

        # Print tree
        if out_format == 's':
            print render_tree.text_tree(tree, single_line=True)
        elif out_format == 'm':
            print render_tree.text_tree(tree, single_line=False)
        elif out_format == 'o':
            print render_tree.text_ontonotes(tree)
        elif out_format == 'p':
            print render_tree.text_POS_tagged(tree)
        elif out_format == 't':
            if gold_tree is None:
                print '\\scalebox{\\derivscale}{'
                print render_tree.tex_synttree(tree)
                print '}\n\\small\n\\pagebreak'
            else:
                print '\\scalebox{\\derivscale}{'
                other_spans = gold_tree.span_dict()
                print render_tree.tex_synttree(tree, other_spans)
                print '}\n\\small\n\\pagebreak'
                print '\\scalebox{\\derivscale}{'
                other_spans = tree.span_dict()
                print render_tree.tex_synttree(gold_tree, other_spans)
                print '}\n\\small\n\\pagebreak'
        elif out_format == 'w':
            print render_tree.text_words(tree)
    if out_format == 't':
        print '\\end{document}'
コード例 #4
0
		stats['out'][2] += test
		p, r, f = nlp_eval.calc_prf(match, gold, test)
		mprint("Eval: %.2f  %.2f  %.2f" % (p*100, r*100, f*100), out, 'out')

		# Work out the minimal span to show all errors
		gold_spans = set([(node.label, node.span[0], node.span[1]) for node in gold_tree.get_nodes()])
		test_spans = set([(node.label, node.span[0], node.span[1]) for node in test_tree.get_nodes()])
		diff = gold_spans.symmetric_difference(test_spans)
		width = [1e5, -1]
		for span in diff:
			if span[2] - span[1] == 1:
				continue
			if span[1] < width[0]:
				width[0] = span[1]
			if span[2] > width[1]:
				width[1] = span[2]
		mprint('\n\\scalebox{\\derivscale}{', out, 'tex')
		mprint(render_tree.tex_synttree(test_tree, gold_spans, span=width), out, 'tex')
		mprint( '}\n\\small\n(a) Parser output\n\n\\vspace{3mm}\n\\scalebox{\\derivscale}{', out, 'tex')
		mprint(render_tree.tex_synttree(gold_tree, test_spans, span=width), out, 'tex')
		mprint( '}\n\\small\n(b) Gold tree\n\\pagebreak', out, 'tex')

		mprint("", out, 'all')
	match = stats['out'][0]
	gold = stats['out'][1]
	test = stats['out'][2]
	p, r, f = nlp_eval.calc_prf(match, gold, test)
	mprint("Overall %s: %.2f  %.2f  %.2f" % ('out', p*100, r*100, f*100), out, 'out')
	
	mprint('\\end{document}', out, 'tex')