def main(args):
output = u""
if args.reportlist:
load_language(args.language, True)
analyzer = Analyzer.instance(args.language)
output = analyzer.reportlist()
elif args.metriclist:
load_language(args.language, True)
analyzer = Analyzer.instance(args.language)
output = analyzer.metriclist(args.language)
elif args.rulelist:
load_language(args.language, True)
analyzer = Analyzer.instance(args.language)
output = analyzer.rulelist(args.language)
elif args.validate:
output = validate(args.files)
elif args.xml:
output = pdf2xml(args)
elif args.report is not "":
output = report(args)
# Write output
if args.outfile is not "":
with open(args.outfile, "w") as f:
f.write(output.encode("utf8"))
f.write(u"\n".encode("utf8"))
else:
sys.stdout.write(output.encode("utf8"))
sys.stdout.write(u"\n".encode("utf8"))
def evaluate(self, node):
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
tagger = corp.tagger(True)
tagged_words = tagger.tag(node.words())
pres_verbs = 0
total_verbs = 0
for w in tagged_words:
if w[1] and w[1].startswith("V"):
#if w[1].startswith(u"VVFIN") or\
# w[1].startswith(u"VAFIN") or\
# w[1].startswith(u"VVINF") or\
# w[1].startswith(u"VVIZU"): # beinhaltet noch vergangenheit!
# pres_verbs += 1
total_verbs += 1
tense = tenses(w[0])
if tense is not []:
tense = [t[0] for t in tense]
past_count = 0
present_count = 0
for t in tense:
if t == "past":
past_count += 1
elif t == "present":
present_count += 1
if present_count > past_count:
pres_verbs += 1
#print w
if total_verbs > 0:
return float(pres_verbs) / total_verbs
return 0.0
def execute(self, docs, args):
if len(docs) < 1:
return u""
output = []
for doc in docs:
output.append(u"# Dokumentbericht")
output.append(u"")
output.append(u"## Metriken")
output.append(u"")
for metric_ID in sorted((_METRIC_EXPECTATIONS.keys())):
output.append(self._execute_metric(metric_ID, doc))
output.append(u"")
output.append(u"## Regeln")
output.append(u"")
rule_IDs = RULE_NAMES
A = Analyzer.instance()
rules = [
A.get(rule=ID) for ID in rule_IDs if A.get(rule=ID) is not None
]
rule_messages = eval_doc(doc, rules)
if len(rule_messages) == 0:
output.append(u"Es liegen keine Regelverletzungen vor!")
else:
for m in rule_messages:
output.append(m)
return u"\n".join(output)
def evaluate(self, node):
A = Analyzer.instance()
#corp = A.get(corpus=u"TIGER")
words = node.words()
word_count = len(words)
word_len = reduce(lambda w, v: w + v, [len(w) for w in words], 0)
if len(words) > 0:
return word_len / float(len(words))
return 0.0
def evaluate(self, node):
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
sents = node.sents(tokenizer=corp.sent_tokenizer())
summ = 0
for s in sents:
s = [w for w in s if w not in NO_WORDS]
summ += len(s)
if len(sents) > 0:
return float(summ) / len(sents)
return 0.0
def evaluate(self, node):
words = [w for w in node.words() if w not in NO_WORDS]
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
sents = node.sents(tokenizer=corp.sent_tokenizer())
char_count = float(sum([len(w) for w in words]))
word_count = float(len(words))
sent_count = float(len(sents))
if word_count > 0.0 and sent_count > 0.0:
return (word_count / sent_count) + 9 * (char_count / word_count)
return 0.0
def evaluate(self, node):
words = node.words()
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
sents_count = len(node.sents(tokenizer=corp.sent_tokenizer()))
count = 0
for w in words:
low = w.lower()
if low in self.IMPERSONAL:
count += 1
if sents_count > 0:
return float(count) / sents_count
return 0.0
def evaluate(self, node):
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
sents = node.sents(tokenizer=corp.sent_tokenizer())
sent_len_diff = 0
last_sent = None
for s in sents:
s = [w for w in s if w not in NO_WORDS]
if last_sent is not None:
sent_len_diff += abs(len(last_sent) - len(s))
last_sent = s
if len(sents) > 1:
return sent_len_diff / float(len(sents) - 1)
return 0.0
def evaluate(self, node):
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
tagger = corp.tagger(True)
words = node.words()
words_no_no_words = [w for w in words if w not in NO_WORDS]
tagged_words = tagger.tag(words)
word_count = len(words_no_no_words)
count = 0
for w in tagged_words:
if w[1] and "ADV-MO" == w[1]:
count += 1
if word_count > 0:
return float(count) / word_count
return 0.0
def evaluate(self, node):
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
fillers = list()
if corp:
fillers = corp.fillers()
words = node.words()
words_no_no_words = [w for w in words if w not in NO_WORDS]
filler_count = 0
for w in words:
if w in fillers:
filler_count += 1
if len(words_no_no_words) > 0:
return float(filler_count) / len(words_no_no_words)
return 0.0
def main(args):
output = u""
if args.reportlist:
load_language(args.language, True)
analyzer = Analyzer.instance(args.language)
output = analyzer.reportlist()
elif args.metriclist:
load_language(args.language, True)
analyzer = Analyzer.instance(args.language)
output = analyzer.metriclist(args.language)
elif args.rulelist:
load_language(args.language, True)
analyzer = Analyzer.instance(args.language)
output = analyzer.rulelist(args.language)
elif args.validate:
output = validate(args.files)
elif args.xml:
output = pdf2xml(args)
elif args.report is not "":
output = report(args)
# Write output
if args.outfile is not "":
with open(args.outfile, "w") as f:
f.write(output.encode("utf8"))
f.write(u"\n".encode("utf8"))
else:
sys.stdout.write(output.encode("utf8"))
sys.stdout.write(u"\n".encode("utf8"))
def evaluate(self, node):
words = node.words()
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
sents_count = len(node.sents(tokenizer=corp.sent_tokenizer()))
tagger = corp.tagger(True)
tagged_words = tagger.tag(words)
count = 0
if len(tagged_words) > 0:
for w in tagged_words:
if w[1] and w[1].startswith("V"):
lemm = lemma(w[0])
if lemm in self.VERBS:
count += 1
return float(count) / sents_count
return 0.0
def evaluate(self, node):
words = node.words()
words_no_no_words = [w for w in words if w not in NO_WORDS]
A = Analyzer.instance()
corp = A.get(corpus="TIGER")
tagger = corp.tagger(True)
tagged_words = tagger.tag(words)
unique_words = set()
if len(tagged_words) > 0 and len(words_no_no_words) > 0:
for w in tagged_words:
if w[0] not in NO_WORDS:
if w[1] and w[1].startswith("V"):
lemm = lemma(w[0])
unique_words.add(lemm)
else:
unique_words.add(w[0])
return float(len(unique_words)) / len(words_no_no_words)
return 0.0
def execute(self, docs, args):
output = list()
metric_names = METRIC_NAMES
A = Analyzer.instance()
metrics = [A.get(metric=m) for m in metric_names]
metrics = [m for m in metrics if m != None]
corp = A.get(corpus=u"TIGER")
results = list()
for m in metrics:
results.append([m.evaluate(d) for d in docs])
stats = [mean_stdev(r, ROUND) for r in results]
if args.latex:
output.append(u"\\begin{tabular}{l|l l|r}")
output.append(u" Metric & mean & stdev & TIGER \\\\")
output.append(u" \\hline")
else:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
output.append(u" * MEAN: der Mittelwert über alle Dokumente")
output.append(u" * STDEV: die dazugehörige Standardabweichung")
output.append(
u" * TIGER: Metrikwert für die deutsche Sprachereferenz,")
output.append(u" den TIGER-Corpus")
output.append(u"")
output.append(u"%s | MEAN | STDEV | TIGER" %
u"METRIC".ljust(METRIC_COL_WIDTH))
output.append(u"%s-+-------+-------+------" %
u"".ljust(METRIC_COL_WIDTH, u"-"))
for i in range(len(metrics)):
# Execute metrics on reference corpus
val = metrics[i].evaluate(corp)
val = round(val, ROUND)
if args.latex:
output.append(u" %s & %s & %s & %s \\\\" %
(metric_names[i].ljust(METRIC_COL_WIDTH),
stats[i][0], stats[i][1], val))
else:
output.append(u"%s | %05.2f | %05.2f | %05.2f" %
(metric_names[i].ljust(METRIC_COL_WIDTH),
stats[i][0], stats[i][1], val))
if args.latex:
output.append(u"\\end{tabular}")
return u"\n".join(output)
def _execute_metric(self, metric_ID, node):
A = Analyzer.instance()
metric = A.get(metric=metric_ID)
val = metric.evaluate(node)
expect = _METRIC_EXPECTATIONS.get(metric_ID, None)
output = u""
if expect is not None:
val_str = str(round(val, ROUND))
if (expect.low is not None) and (expect.high is not None):
output = u" * %s %s (erwartet: zw. %.2f und %.2f)" % (metric_ID, val_str, expect.low, expect.high)
elif expect.low is not None:
output = u" * %s %s (erwartet: min. %.2f)" % (metric_ID, val_str, expect.low)
elif expect.high is not None:
output = u" * %s %s (erwartet: max. %.2f)" % (metric_ID, val_str, expect.high)
if (expect.low is not None) and val < expect.low:
output += u"\n %s" % expect.msg_toolow
elif (expect.high is not None) and val > expect.high:
output += u"\n %s" % expect.msg_toohigh
else:
output += u"\n %s" % expect.msg_ok
return output
def _execute_metric(self, metric_ID, node):
A = Analyzer.instance()
metric = A.get(metric=metric_ID)
val = metric.evaluate(node)
expect = _METRIC_EXPECTATIONS.get(metric_ID, None)
output = ""
if expect is not None:
val_str = str(round(val, ROUND))
if (expect.low is not None) and (expect.high is not None):
output = " * %s %s (erwartet: zw. %.2f und %.2f)" % (metric_ID, val_str, expect.low, expect.high)
elif expect.low is not None:
output = " * %s %s (erwartet: min. %.2f)" % (metric_ID, val_str, expect.low)
elif expect.high is not None:
output = " * %s %s (erwartet: max. %.2f)" % (metric_ID, val_str, expect.high)
if (expect.low is not None) and val < expect.low:
output += "\n %s" % expect.msg_toolow
elif (expect.high is not None) and val > expect.high:
output += "\n %s" % expect.msg_toohigh
else:
output += "\n %s" % expect.msg_ok
return output
def report(args, output=u""):
# Convert files to Documents
dc = DocumentConverter()
docs = list()
for f in args.files:
if op.isfile(f):
if f.lower().endswith(PDF_SUFFIX):
doc = PDF2document(f)
docs.append(doc)
elif f.lower().endswith(XML_SUFFIX):
docs.extend(dc.to_Documents(f))
# Fetch and execute report
load_language(args.language)
analyzer = Analyzer.instance()
rep = analyzer.get(report=args.report)
if rep:
output += rep.execute(docs, args)
pass
else:
output += 'No report named "%s" available!' % args.report
return output
def report(args, output=u""):
# Convert files to Documents
dc = DocumentConverter()
docs = list()
for f in args.files:
if op.isfile(f):
if f.lower().endswith(PDF_SUFFIX):
doc = PDF2document(f)
docs.append(doc)
elif f.lower().endswith(XML_SUFFIX):
docs.extend(dc.to_Documents(f))
# Fetch and execute report
load_language(args.language)
analyzer = Analyzer.instance()
rep = analyzer.get(report=args.report)
if rep:
output += rep.execute(docs, args)
pass
else:
output += 'No report named "%s" available!' % args.report
return output
def execute(self, docs, args):
output = list()
metric_names = METRIC_NAMES
A = Analyzer.instance()
metrics = [A.get(metric=m) for m in metric_names]
metrics = [m for m in metrics if m != None]
corp = A.get(corpus=u"TIGER")
results = list()
for m in metrics:
results.append([m.evaluate(d) for d in docs])
stats = [mean_stdev(r, ROUND) for r in results]
if args.latex:
output.append(u"\\begin{tabular}{l|l l|r}")
output.append(u" Metric & mean & stdev & TIGER \\\\")
output.append(u" \\hline")
else:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
output.append(u" * MEAN: der Mittelwert über alle Dokumente")
output.append(u" * STDEV: die dazugehörige Standardabweichung")
output.append(u" * TIGER: Metrikwert für die deutsche Sprachereferenz,")
output.append(u" den TIGER-Corpus")
output.append(u"")
output.append(u"%s | MEAN | STDEV | TIGER" % u"METRIC".ljust(METRIC_COL_WIDTH))
output.append(u"%s-+-------+-------+------" % u"".ljust(METRIC_COL_WIDTH, u"-"))
for i in range(len(metrics)):
# Execute metrics on reference corpus
val = metrics[i].evaluate(corp)
val = round(val, ROUND)
if args.latex:
output.append(u" %s & %s & %s & %s \\\\" % (metric_names[i].ljust(METRIC_COL_WIDTH), stats[i][0], stats[i][1], val))
else:
output.append(u"%s | %05.2f | %05.2f | %05.2f" % (metric_names[i].ljust(METRIC_COL_WIDTH), stats[i][0], stats[i][1], val))
if args.latex:
output.append(u"\\end{tabular}")
return u"\n".join(output)
def execute(self, docs, args):
if len(docs) < 1:
return u""
output = list()
output.append(u"# Dokumentbericht")
output.append(u"")
output.append(u"## Metriken")
output.append(u"")
doc = docs[0]
for metric_ID in sorted((_METRIC_EXPECTATIONS.keys())):
output.append(self._execute_metric(metric_ID, doc))
output.append(u"")
output.append(u"## Regeln")
output.append(u"")
rule_IDs = RULE_NAMES
A = Analyzer.instance()
rules = [A.get(rule=ID) for ID in rule_IDs if A.get(rule=ID) is not None]
rule_messages = eval_doc(doc, rules)
if len(rule_messages) == 0:
output.append(u"Es liegen keine Regelverletzungen vor!")
else:
for m in rule_messages:
output.append(m)
return u"\n".join(output)
def execute(self, docs, args):
output = list()
if len(docs) < 2 or len(docs) % 2 != 0:
output.append(
u"Error: Need an even number of documents (at least 2) for the document comparison report!"
)
else:
metric_names = METRIC_NAMES
A = Analyzer.instance()
metrics = [A.get(metric=m) for m in metric_names]
metrics = [m for m in metrics if m != None]
if len(docs) == 2:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
output.append(u" * PROGRESS: Vorher- --> Nachher-Wert.")
output.append(u" (+) ... Erhöhung ")
output.append(u" (-) ... Verringerung ")
output.append(u" (=) ... gleichbleibend ")
output.append(u"")
output.append(u"%s | PROGRESS" %
u"METRIC".ljust(METRIC_COL_WIDTH))
output.append(u"%s-+---------------------" %
u"".ljust(METRIC_COL_WIDTH, u"-"))
for m in metrics:
vals = [m.evaluate(doc) for doc in docs]
progress = u"="
if vals[0] > vals[1]:
progress = u"-"
elif vals[0] < vals[1]:
progress = u"+"
output.append(u"%s | %05.2f --> %05.2f (%s)" %
(m.ID.ljust(METRIC_COL_WIDTH), vals[0],
vals[1], progress))
else:
half = len(docs) / 2
if args.latex:
output.append(u"\\begin{tabular}{l|l l|l l|r}")
output.append(
u"\\multirow{2}{*}{\\textbf{Metrik}} & \\multicolumn{2}{|c|}{\\textbf{Erhöhung}} & \\multicolumn{2}{|c|}{\\textbf{Verringerung}} & \\textbf{gleichbleibend} \\\\"
)
output.append(
u" & \\multicolumn{1}{|c}{$\\#$} & \\multicolumn{1}{c|}{$\\Delta$} & \\multicolumn{1}{|c}{$\\#$} & \\multicolumn{1}{c|}{$\\Delta$} & \\multicolumn{1}{c}{$\\#$} \\\\"
)
output.append(u" \\hline")
else:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
output.append(u" * +: Anzahl an Metrikerhöhungen")
output.append(
u" * DELTA+: Durchschnittliche Erhöhung um diesen Wert"
)
output.append(u" * -: Anzahl an Metrikverringerungen")
output.append(
u" * DELTA-: Durchschnittliche Verringerung um diesen Wert"
)
output.append(
u" * =: Anzahl an Dokumentpaaren, bei denen der")
output.append(
u" Metrikwert gleich geblieben ist")
output.append(u"")
output.append(u"%s | + | DELTA+ | - | DELTA- | = " %
u"METRIC".ljust(METRIC_COL_WIDTH))
output.append(u"%s-+----+--------+----+--------+----" %
u"".ljust(METRIC_COL_WIDTH, u"-"))
for m in metrics:
results = list()
for i in range(half):
results.append(
(m.evaluate(docs[i]), m.evaluate(docs[i + half])))
counts = [0, 0, 0] # greater, less, equal
avg_diffs = [0.0, 0.0]
for r in results:
if r[0] > r[1]:
counts[1] += 1
avg_diffs[1] += r[0] - r[1]
elif r[0] < r[1]:
counts[0] += 1
avg_diffs[0] += r[1] - r[0]
else:
counts[2] += 1
if counts[0] > 0:
avg_diffs[0] /= float(counts[0])
avg_diffs[0] = round(avg_diffs[0], ROUND + 1)
if counts[1] > 0:
avg_diffs[1] /= float(counts[1])
avg_diffs[1] = round(avg_diffs[1], ROUND + 1)
if args.latex:
output.append(u" %s & %s & %s & %s & %s & %s \\\\" %
(m.ID, counts[0], avg_diffs[0],
counts[1], avg_diffs[1], counts[2]))
else:
output.append(
u"%s | %02d | %06.3f | %02d | %06.3f | %02d" %
(m.ID.ljust(METRIC_COL_WIDTH), counts[0],
avg_diffs[0], counts[1], avg_diffs[1], counts[2]))
if args.latex:
output.append(u"\\end{tabular}")
return u"\n".join(output)
def execute(self, docs, args):
output = []
metric_names = METRIC_NAMES
A = Analyzer.instance()
metrics = [A.get(metric=m) for m in metric_names]
metrics = [m for m in metrics if m != None]
corp = A.get(corpus=u"TIGER")
results = list()
for m in metrics:
results.append([m.evaluate(d) for d in docs])
exceedances = self.compute_exceedances(metric_names, results)
exceedances_transposed = list(map(list, zip(*exceedances)))
# Metric matrix output
doc_numbers = range(1, len(docs) + 1)
if args.latex:
tabular_format_str = [u" r" for d in docs]
tabular_format_str = u"".join(tabular_format_str)
output.append(u"\\begin{tabular}{l|%s}" % tabular_format_str)
docs_header_str = map(u"& doc%02d ".__mod__, doc_numbers)
docs_header_str = u"".join(docs_header_str)
output.append(u" Metrik %s\\\\" % docs_header_str)
output.append(u" \\hline")
for i in range(len(metrics)):
value_str = u""
for doc_nr in range(len(results[i])):
if exceedances[i][doc_nr] == 1:
value_str = value_str + u"& \emph{%.2f} " % results[i][
doc_nr]
else:
value_str = value_str + u"& %.2f " % results[i][doc_nr]
#value_str = map(u"& %.2f ".__mod__, results[i])
#value_str = u"".join(value_str)
output.append(
u" %s %s\\\\" %
(metric_names[i].ljust(METRIC_COL_WIDTH), value_str))
else:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
docs_header_str = map(u"| doc%02d ".__mod__, doc_numbers)
docs_header_str = u"".join(docs_header_str)
output.append(u"%s%s" %
(u"METRIC".ljust(METRIC_COL_WIDTH), docs_header_str))
dash_length = len(docs_header_str) - 2
if dash_length < 0:
dash_length = 0
output.append(u"%s+%s" % (u"".ljust(
METRIC_COL_WIDTH, u"-"), u"".ljust(dash_length, u"-")))
for i in range(len(metrics)):
value_str = map(u"| %05.2f ".__mod__, results[i])
value_str = u"".join(value_str)
output.append(
u"%s%s" %
(metric_names[i].ljust(METRIC_COL_WIDTH), value_str))
# Exceedances/shortfalls
exceedances_counts = map(sum, exceedances_transposed)
if args.latex:
output.append(u" \\hline")
exceedances_str = map(u"& %d ".__mod__, exceedances_counts)
exceedances_str = u"".join(exceedances_str)
output.append(
u" %s %s\\\\" %
(u"Überschreitungen".ljust(METRIC_COL_WIDTH), exceedances_str))
else:
output.append(u"%s+%s" % (u"".ljust(
METRIC_COL_WIDTH, u"-"), u"".ljust(dash_length, u"-")))
exceedances_str = map(u"| %02d ".__mod__, exceedances_counts)
exceedances_str = u"".join(exceedances_str)
output.append(
u"%s%s" %
(u"Transgressions".ljust(METRIC_COL_WIDTH), exceedances_str))
# Rule violations
rule_IDs = RULE_NAMES
rules = [
A.get(rule=ID) for ID in rule_IDs if A.get(rule=ID) is not None
]
violated_rule_counts = [len(eval_doc(doc, rules)) for doc in docs]
if args.latex:
violated_rule_counts_str = map(u"& %d ".__mod__,
violated_rule_counts)
violated_rule_counts_str = u"".join(violated_rule_counts_str)
output.append(u" %s %s\\\\" %
(u"Regelverletzungen".ljust(METRIC_COL_WIDTH),
violated_rule_counts_str))
output.append(u"\\end{tabular}")
else:
violated_rule_counts_str = map(u"| %02d ".__mod__,
violated_rule_counts)
violated_rule_counts_str = u"".join(violated_rule_counts_str)
output.append(u"%s%s" % (u"Violated rules".ljust(METRIC_COL_WIDTH),
violated_rule_counts_str))
return u"\n".join(output)
def execute(self, docs, args):
output = []
metric_names = METRIC_NAMES
A = Analyzer.instance()
metrics = [A.get(metric=m) for m in metric_names]
metrics = [m for m in metrics if m != None]
corp = A.get(corpus=u"TIGER")
results = list()
for m in metrics:
results.append([m.evaluate(d) for d in docs])
exceedances = self.compute_exceedances(metric_names, results)
exceedances_transposed = list(map(list, zip(*exceedances)))
# Metric matrix output
doc_numbers = range(1, len(docs) + 1)
if args.latex:
tabular_format_str = [u" r" for d in docs]
tabular_format_str = u"".join(tabular_format_str)
output.append(u"\\begin{tabular}{l|%s}" % tabular_format_str)
docs_header_str = map(u"& doc%02d ".__mod__, doc_numbers)
docs_header_str = u"".join(docs_header_str)
output.append(u" Metrik %s\\\\" % docs_header_str)
output.append(u" \\hline")
for i in range(len(metrics)):
value_str = u""
for doc_nr in range(len(results[i])):
if exceedances[i][doc_nr] == 1:
value_str = value_str + u"& \emph{%.2f} " % results[i][doc_nr]
else:
value_str = value_str + u"& %.2f " % results[i][doc_nr]
#value_str = map(u"& %.2f ".__mod__, results[i])
#value_str = u"".join(value_str)
output.append(u" %s %s\\\\" % (metric_names[i].ljust(METRIC_COL_WIDTH), value_str))
else:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
docs_header_str = map(u"| doc%02d ".__mod__, doc_numbers)
docs_header_str = u"".join(docs_header_str)
output.append(u"%s%s" % (u"METRIC".ljust(METRIC_COL_WIDTH), docs_header_str))
dash_length = len(docs_header_str) - 2
if dash_length < 0:
dash_length = 0
output.append(u"%s+%s" % (u"".ljust(METRIC_COL_WIDTH, u"-"), u"".ljust(dash_length, u"-")))
for i in range(len(metrics)):
value_str = map(u"| %05.2f ".__mod__, results[i])
value_str = u"".join(value_str)
output.append(u"%s%s" % (metric_names[i].ljust(METRIC_COL_WIDTH), value_str))
# Exceedances/shortfalls
exceedances_counts = map(sum, exceedances_transposed)
if args.latex:
output.append(u" \\hline")
exceedances_str = map(u"& %d ".__mod__, exceedances_counts)
exceedances_str = u"".join(exceedances_str)
output.append(u" %s %s\\\\" % (u"Überschreitungen".ljust(METRIC_COL_WIDTH), exceedances_str))
else:
output.append(u"%s+%s" % (u"".ljust(METRIC_COL_WIDTH, u"-"), u"".ljust(dash_length, u"-")))
exceedances_str = map(u"| %02d ".__mod__, exceedances_counts)
exceedances_str = u"".join(exceedances_str)
output.append(u"%s%s" % (u"Transgressions".ljust(METRIC_COL_WIDTH), exceedances_str))
# Rule violations
rule_IDs = RULE_NAMES
rules = [A.get(rule=ID) for ID in rule_IDs if A.get(rule=ID) is not None]
violated_rule_counts = [len(eval_doc(doc, rules)) for doc in docs]
if args.latex:
violated_rule_counts_str = map(u"& %d ".__mod__, violated_rule_counts)
violated_rule_counts_str = u"".join(violated_rule_counts_str)
output.append(u" %s %s\\\\" % (u"Regelverletzungen".ljust(METRIC_COL_WIDTH), violated_rule_counts_str))
output.append(u"\\end{tabular}")
else:
violated_rule_counts_str = map(u"| %02d ".__mod__, violated_rule_counts)
violated_rule_counts_str = u"".join(violated_rule_counts_str)
output.append(u"%s%s" % (u"Violated rules".ljust(METRIC_COL_WIDTH), violated_rule_counts_str))
return u"\n".join(output)
def execute(self, docs, args):
output = list()
if len(docs) < 2 or len(docs) % 2 != 0:
output.append(u"Error: Need an even number of documents (at least 2) for the document comparison report!")
else:
metric_names = METRIC_NAMES
A = Analyzer.instance()
metrics = [A.get(metric=m) for m in metric_names]
metrics = [m for m in metrics if m != None]
if len(docs) == 2:
output.append(u"# Bericht \"%s\""% self.ID)
output.append(u"")
output.append(u" * PROGRESS: Vorher- --> Nachher-Wert.")
output.append(u" (+) ... Erhöhung ")
output.append(u" (-) ... Verringerung ")
output.append(u" (=) ... gleichbleibend ")
output.append(u"")
output.append(u"%s | PROGRESS" % u"METRIC".ljust(METRIC_COL_WIDTH))
output.append(u"%s-+---------------------" % u"".ljust(METRIC_COL_WIDTH, u"-"))
for m in metrics:
vals = [m.evaluate(doc) for doc in docs]
progress = u"="
if vals[0] > vals[1]:
progress = u"-"
elif vals[0] < vals[1]:
progress = u"+"
output.append(u"%s | %05.2f --> %05.2f (%s)" % (m.ID.ljust(METRIC_COL_WIDTH), vals[0], vals[1], progress))
else:
half = len(docs) / 2
if args.latex:
output.append(u"\\begin{tabular}{l|l l|l l|r}")
output.append(u"\\multirow{2}{*}{\\textbf{Metrik}} & \\multicolumn{2}{|c|}{\\textbf{Erhöhung}} & \\multicolumn{2}{|c|}{\\textbf{Verringerung}} & \\textbf{gleichbleibend} \\\\")
output.append(u" & \\multicolumn{1}{|c}{$\\#$} & \\multicolumn{1}{c|}{$\\Delta$} & \\multicolumn{1}{|c}{$\\#$} & \\multicolumn{1}{c|}{$\\Delta$} & \\multicolumn{1}{c}{$\\#$} \\\\")
output.append(u" \\hline")
else:
output.append(u"# Bericht \"%s\"" % self.ID)
output.append(u"")
output.append(u" * +: Anzahl an Metrikerhöhungen")
output.append(u" * DELTA+: Durchschnittliche Erhöhung um diesen Wert")
output.append(u" * -: Anzahl an Metrikverringerungen")
output.append(u" * DELTA-: Durchschnittliche Verringerung um diesen Wert")
output.append(u" * =: Anzahl an Dokumentpaaren, bei denen der")
output.append(u" Metrikwert gleich geblieben ist")
output.append(u"")
output.append(u"%s | + | DELTA+ | - | DELTA- | = " % u"METRIC".ljust(METRIC_COL_WIDTH))
output.append(u"%s-+----+--------+----+--------+----" % u"".ljust(METRIC_COL_WIDTH, u"-"))
for m in metrics:
results = list()
for i in range(half):
results.append((m.evaluate(docs[i]), m.evaluate(docs[i + half])))
counts = [0, 0, 0] # greater, less, equal
avg_diffs = [0.0, 0.0]
for r in results:
if r[0] > r[1]:
counts[1] += 1
avg_diffs[1] += r[0] - r[1]
elif r[0] < r[1]:
counts[0] += 1
avg_diffs[0] += r[1] - r[0]
else:
counts[2] += 1
if counts[0] > 0:
avg_diffs[0] /= float(counts[0])
avg_diffs[0] = round(avg_diffs[0], ROUND + 1)
if counts[1] > 0:
avg_diffs[1] /= float(counts[1])
avg_diffs[1] = round(avg_diffs[1], ROUND + 1)
if args.latex:
output.append(u" %s & %s & %s & %s & %s & %s \\\\" % (m.ID, counts[0], avg_diffs[0], counts[1], avg_diffs[1], counts[2]))
else:
output.append(u"%s | %02d | %06.3f | %02d | %06.3f | %02d" % (m.ID.ljust(METRIC_COL_WIDTH), counts[0], avg_diffs[0], counts[1], avg_diffs[1], counts[2]))
if args.latex:
output.append(u"\\end{tabular}")
return u"\n".join(output)
