How do you print accurate scores in python?
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Use the Show Accuracy classification score. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. Read more in the User Guide. Parameters:y_true1d array-like, or label indicator array / sparse matrixGround truth (correct) labels. y_pred1d array-like, or label indicator array / sparse matrixPredicted labels, as returned by a classifier. normalizebool, default=TrueIf Sample weights. Returns:scorefloatIf The best performance is 1 with See also balanced_accuracy_scoreCompute the balanced accuracy to deal with imbalanced datasets. jaccard_score
Compute the Jaccard similarity coefficient score. hamming_lossCompute the average Hamming loss or Hamming distance between two sets of samples. zero_one_lossCompute the Zero-one classification loss. By default, the function will return the percentage of imperfectly predicted subsets. Notes In binary classification, this function is equal to the Examples >>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2 In the multilabel case with binary label indicators: >>> import numpy as np >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5 Examples using sklearn.metrics.accuracy_score¶38 Python code examples are found related to " print scores". You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Example 1 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.6f}".format(
val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.6f}".format(
val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr + " " + niouStr)
# Print intersection-over-union scores for all categorys. Example 2 def print_scores_table(scores, prefix="TARGET"):
prefix_path, scores["File"] = _extract_path_prefix(scores["File"])
path_str = " ({})".format(prefix_path) if prefix_path else ""
max_method_length = max(len(path_str) + 4, max(map(len, scores["File"])))
print("-" * (max_method_length + 13 * 3))
print("Word-level scores for {}:".format(prefix))
print(
"{:{width}} {:9} {:9} {:9}".format(
"File{}".format(path_str),
"F1_mult",
"F1_{}".format(const.LABELS[0]),
"F1_{}".format(const.LABELS[1]),
width=max_method_length,
)
)
for score in np.sort(scores, order=["F1_mult", "File"])[::-1]:
print(
"{:{width}s} {:<9.5f} {:<9.5} {:<9.5f}".format(
*score, width=max_method_length
)
) Example 3 def print_evaluation_scores(json_file):
assert config.BASEDIR and os.path.isdir(config.BASEDIR)
annofile = os.path.join(
config.BASEDIR, 'annotations',
'instances_{}.json'.format(config.VAL_DATASET))
coco = COCO(annofile)
cocoDt = coco.loadRes(json_file)
cocoEval = COCOeval(coco, cocoDt, 'bbox')
if True:#config.CATEGORY_AGNOSTIC:
print("Using category agnostic evaluation mode")
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
if config.MODE_MASK:
cocoEval = COCOeval(coco, cocoDt, 'segm')
if True: #config.CATEGORY_AGNOSTIC:
print("Using category agnostic evaluation mode")
cocoEval.params.useCats = 0
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize() Example 4 def print_scores(self, show_miou=True):
for key, rs in self.running_scores.items():
Log.info('Result for {}'.format(key))
if isinstance(rs, fscore_rslib.F1RunningScore):
FScore, FScore_cls = rs.get_scores()
Log.info('Mean FScore: {}'.format(FScore))
Log.info(
'Class-wise FScore: {}'.format(
', '.join(
'{:.3f}'.format(x)
for x in FScore_cls
)
)
)
elif isinstance(rs, rslib.SimpleCounterRunningScore):
Log.info('ACC: {}\n'.format(rs.get_mean_acc()))
else:
if show_miou and hasattr(rs, 'get_mean_iou'):
Log.info('Mean IOU: {}\n'.format(rs.get_mean_iou()))
Log.info('Pixel ACC: {}\n'.format(rs.get_pixel_acc()))
if hasattr(rs, 'n_classes') and rs.n_classes == 2:
Log.info(
'F1 Score: {} Precision: {} Recall: {}\n'
.format(*rs.get_F1_score())
) Example 5 def print_current_scores(self, epoch, iters, scores):
"""print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
scores (OrderedDict) -- training losses stored in the format of (name, float) pairs
"""
message = '(epoch: %d, giters: %d) ' % (epoch, iters)
for k, v in scores.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(self.score_log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message Example 6 def print_scores(self, metric_types=None):
if metric_types is None:
metric_types = self.metric_types
scores = [np.mean([self.scores[m][trial] for trial in self.trials]) for m in metric_types]
txt = "All: " + " ".join(["{}:{:.04}".format(metric_types[i], scores[i]) for i in range(len(metric_types))])
print(txt) Example 7 def print_df_scores(df_scores, indent=''):
"""Pretty print the scores dataframe.
Parameters
----------
df_scores : pd.DataFrame
the score dataframe
indent : str, default=''
indentation if needed
"""
with option_context("display.width", 160):
df_repr = repr(df_scores)
df_repr_out = []
for line, color_key in zip(df_repr.splitlines(),
[None, None] +
list(df_scores.index.values)):
if line.strip() == 'step':
continue
if color_key is None:
# table header
if IS_COLOR_TERM:
line = stylize(line, fg(fg_colors['title']) + attr('bold'))
if color_key is not None:
tokens = line.split()
tokens_bak = tokens[:]
if 'official_' + color_key in fg_colors:
# line label and official score bold & bright
if IS_COLOR_TERM:
label_color = fg(fg_colors['official_' + color_key])
tokens[0] = stylize(tokens[0], label_color + attr('bold'))
tokens[1] = stylize(tokens[1], label_color + attr('bold'))
if IS_COLOR_TERM and (color_key in fg_colors):
# other scores pale
tokens[2:] = [stylize(token, fg(fg_colors[color_key]))
for token in tokens[2:]]
for token_from, token_to in zip(tokens_bak, tokens):
line = line.replace(token_from, token_to)
line = indent + line
df_repr_out.append(line)
print('\n'.join(df_repr_out)) Example 8 def print_loci_scores(self):
"""Wrapper method to create a csv.DictWriter instance and call
the Locus.print_scores method on it."""
self.define_loci()
# self.available_monolocus_metrics = set(self.monoholder.available_metrics)
if len(self.loci) == 0:
return
for locus in self.loci:
for row in self.loci[locus].print_scores():
yield row Example 9 def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print(args.bold + "categories IoU nIoU" + args.nocol)
print("--------------------------------")
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print("{:<14}: ".format(categoryName) + iouStr + " " + niouStr)
# Evaluate image lists pairwise. Example 10 def print_scores(scores, cg_names, ref_cg):
if ref_cg is None:
for cg_name in cg_names:
bleu, iter_ = max(scores[cg_name], key=lambda item: item[0])
print 'Best score for [{}]: {} at iter {}'.format(cg_name, bleu,
iter_)
else:
bleu, iter_ = max(scores[ref_cg], key=lambda item: item[0])
print 'Best score for [{}]: {} at iter {}'.format(ref_cg, bleu, iter_)
for cg_name in cg_names:
if cg_name != ref_cg:
bleu = [bb for bb, tt in scores[cg_name] if tt == iter_][0]
print 'Best score for [{}]: {} at iter {}'.format(
cg_name, bleu, iter_) Example 11 def print_scores(top_lessons, titles=False):
for lesson in top_lessons:
lesson_id = lesson[0]
num_recs = lesson[2] if titles else lesson[1]
if titles:
title = lesson[1]
out_str = 'Course-ID: {0}, title: {1}, #recs: {2}'.format(lesson_id, title, num_recs)
else:
out_str = 'Course-ID: {0}, #recs: {1}'.format(lesson_id, num_recs)
print(out_str) Example 12 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print args.bold + "classes IoU nIoU" + args.nocol
print "--------------------------------"
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print "{:<14}: ".format(labelName) + iouStr + " " + niouStr
# Print intersection-over-union scores for all categorys. Example 13 def print_scores(self, pred, k=10, only_first_name=True):
"""
Print the scores (or probabilities) for the top-k predicted classes.
:param pred:
Predicted class-labels returned from the predict() function.
:param k:
How many classes to print.
:param only_first_name:
Some class-names are lists of names, if you only want the first name,
then set only_first_name=True.
:return:
Nothing.
"""
# Get a sorted index for the pred-array.
idx = pred.argsort()
# The index is sorted lowest-to-highest values. Take the last k.
top_k = idx[-k:]
# Iterate the top-k classes in reversed order (i.e. highest first).
for cls in reversed(top_k):
# Lookup the class-name.
name = self.name_lookup.cls_to_name(cls=cls, only_first_name=only_first_name)
# Predicted score (or probability) for this class.
score = pred[cls]
# Print the score and class-name.
print("{0:>6.2%} : {1}".format(score, name)) Example 14 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr + " " + niouStr)
# Print intersection-over-union scores for all categorys. Example 15 def print_columns_with_scores(self):
mode = self.mode
self.set_fields_mode()
seen_nid = dict()
for el, score in self._chosen_rank:
if el not in seen_nid:
print(str(el) + " -> " + str(score))
seen_nid[el] = 0
self._mode = mode Example 16 def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print args.bold + "categories IoU nIoU" + args.nocol
print "--------------------------------"
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print "{:<14}: ".format(categoryName) + iouStr + " " + niouStr
# Evaluate image lists pairwise. Example 17 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr + " " + niouStr)
# Print intersection-over-union scores for all categorys. Example 18 def print_scores(scores, etype):
levels = ['easy', 'medium', 'hard', 'extra', 'all']
partial_types = ['select', 'select(no AGG)', 'where', 'where(no OP)', 'group(no Having)',
'group', 'order', 'and/or', 'IUEN', 'keywords']
print("{:20} {:20} {:20} {:20} {:20} {:20}".format("", *levels))
counts = [scores[level]['count'] for level in levels]
print("{:20} {:<20d} {:<20d} {:<20d} {:<20d} {:<20d}".format("count", *counts))
if etype in ["all", "exec"]:
print('===================== EXECUTION ACCURACY =====================')
this_scores = [scores[level]['exec'] for level in levels]
print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format("execution", *this_scores))
if etype in ["all", "match"]:
print('\n====================== EXACT MATCHING ACCURACY =====================')
exact_scores = [scores[level]['exact'] for level in levels]
print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format("exact match", *exact_scores))
print('\n---------------------PARTIAL MATCHING ACCURACY----------------------')
for type_ in partial_types:
this_scores = [scores[level]['partial'][type_]['acc'] for level in levels]
print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format(type_, *this_scores))
print('---------------------- PARTIAL MATCHING RECALL ----------------------')
for type_ in partial_types:
this_scores = [scores[level]['partial'][type_]['rec'] for level in levels]
print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format(type_, *this_scores))
print('---------------------- PARTIAL MATCHING F1 --------------------------')
for type_ in partial_types:
this_scores = [scores[level]['partial'][type_]['f1'] for level in levels]
print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format(type_, *this_scores)) Example 19 def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print(args.bold + "categories IoU nIoU" + args.nocol)
print("--------------------------------")
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print("{:<14}: ".format(categoryName) + iouStr + " " + niouStr)
# Evaluate image lists pairwise. Example 20 def print_scores(self):
"""This method yields dictionary rows that are given to a csv.DictWriter class."""
self.calculate_scores()
if self.regressor is None:
score_keys = sorted(list(self.json_conf["scoring"].keys()) + ["source_score"])
else:
score_keys = sorted(self.regressor.metrics + ["source_score"])
keys = ["tid", "alias", "parent", "score"] + score_keys
for tid in self.scores:
row = dict().fromkeys(keys)
row["tid"] = tid
row["parent"] = self.id
row["alias"] = self.transcripts[tid].alias
if tid in self._not_passing:
row["score"] = 0
else:
row["score"] = round(self.scores[tid]["score"], 2)
calculate_total = (self.regressor is None)
for key in score_keys:
if calculate_total:
assert self.scores[tid][key] != "NA" and self.scores[tid][key] is not None
row[key] = round(self.scores[tid][key], 2)
if calculate_total is True:
score_sum = sum(row[key] for key in score_keys)
if tid not in self._not_passing and self.scores[tid]["score"] > 0:
assert round(score_sum, 2) == round(self.scores[tid]["score"], 2), (
score_sum,
self.transcripts[tid].score,
tid)
else:
assert self.scores[tid]["score"] == 0
yield row Example 21 def print_scores(self, pred, k=10, only_first_name=True):
"""
Print the scores (or probabilities) for the top-k predicted classes.
:param pred:
Predicted class-labels returned from the predict() function.
:param k:
How many classes to print.
:param only_first_name:
Some class-names are lists of names, if you only want the first name,
then set only_first_name=True.
:return:
Nothing.
"""
vals = []
# Get a sorted index for the pred-array.
idx = pred.argsort()
# The index is sorted lowest-to-highest values. Take the last k.
top_k = idx[-k:]
# Iterate the top-k classes in reversed order (i.e. highest first).
for cls in reversed(top_k):
# Lookup the class-name.
name = self.name_lookup.cls_to_name(cls=cls, only_first_name=only_first_name)
# Predicted score (or probability) for this class.
score = pred[cls]
vals.append({"score": float(score), "name": name})
# Print the score and class-name.
# print("{0:>6.2%} : {1}".format(score, name))
return vals Example 22
def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print(args.bold + "categories IoU nIoU" + args.nocol)
print("--------------------------------")
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print("{:<14}: ".format(categoryName) + iouStr + " " + niouStr)
# Evaluate image lists pairwise. Example 23 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr + " " + niouStr)
# Print intersection-over-union scores for all categorys. Example 24 def print_scores(self, metric_types=None):
if metric_types is None:
metric_types = self.metric_types
scores = [np.mean([self.scores[m][trial] for trial in self.trials]) for m in metric_types]
txt = "All: " + " ".join(["{}:{:.04}".format(metric_types[i], scores[i]) for i in range(len(metric_types))])
print(txt) Example 25 def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print(args.bold + "categories IoU nIoU" + args.nocol)
print("--------------------------------")
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print("{:<14}: ".format(categoryName) + iouStr + " " + niouStr)
# Evaluate image lists pairwise. Example 26 def print_cricket_scores():
print "=================CRICKET SCORES=================================="
try:
parsed_scores = _fetch_and_parse()
for i, score in enumerate(parsed_scores):
print beautify(i+1, score)
except:
print beautify(0, 'No cricket scores to display.')
print "***************************************************************" Example 27 def print_subloci_scores(self):
"""Wrapper method to create a csv.DictWriter instance and call the
sublocus.print_metrics method
on it for each sublocus."""
# self.get_sublocus_metrics()
for slocus in self.subloci:
for row in slocus.print_scores():
yield row Example 28 def print_monoholder_scores(self):
"""Wrapper method to create a csv.DictWriter instance and call
the MonosublocusHolder.print_scores method on it."""
self.define_monosubloci()
# self.available_monolocus_metrics = set(self.monoholder.available_metrics)
if len(self.monoholders) == 0:
return
for monoholder in self.monoholders:
for row in monoholder.print_scores():
yield row Example 29 def print_scores(self, pred, k=10, only_first_name=True):
"""
Print the scores (or probabilities) for the top-k predicted classes.
:param pred:
Predicted class-labels returned from the predict() function.
:param k:
How many classes to print.
:param only_first_name:
Some class-names are lists of names, if you only want the first name,
then set only_first_name=True.
:return:
Nothing.
"""
# Get a sorted index for the pred-array.
idx = pred.argsort()
# The index is sorted lowest-to-highest values. Take the last k.
top_k = idx[-k:]
# Iterate the top-k classes in reversed order (i.e. highest first).
for cls in reversed(top_k):
# Lookup the class-name.
name = self.name_lookup.cls_to_name(cls=cls, only_first_name=only_first_name)
# Predicted score (or probability) for this class.
score = pred[cls]
# Print the score and class-name.
print("{0:>6.2%} : {1}".format(score, name)) Example 30 def print_scores(self, pred, k=10, only_first_name=True):
"""
Print the scores (or probabilities) for the top-k predicted classes.
:param pred:
Predicted class-labels returned from the predict() function.
:param k:
How many classes to print.
:param only_first_name:
Some class-names are lists of names, if you only want the first name,
then set only_first_name=True.
:return:
Nothing.
"""
# Get a sorted index for the pred-array.
idx = pred.argsort()
# The index is sorted lowest-to-highest values. Take the last k.
top_k = idx[-k:]
# Iterate the top-k classes in reversed order (i.e. highest first).
for cls in reversed(top_k):
# Lookup the class-name.
name = self.name_lookup.cls_to_name(cls=cls, only_first_name=only_first_name)
# Predicted score (or probability) for this class.
score = pred[cls]
# Print the score and class-name.
print("{0:>6.2%} : {1}".format(score, name)) Example 31 def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print(args.bold + "categories IoU nIoU" + args.nocol)
print("--------------------------------")
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print("{:<14}: ".format(categoryName) + iouStr + " " + niouStr)
# Evaluate image lists pairwise. Example 32 def print_performance_scores(performance_scores, title=None):
"""Prints an ASCII table of performance scores.
Args:
performance_scores: A dictionary of label, score pairs where label is a class tag and
scores is a 4-tuple containing precision, recall, f1 and support
title (str): The title of the table (uppercased).
Preconditions:
Assumes the values of performance_scores are 4-tuples, where the first three items are
float representaions of a percentage and the last item is an count integer.
"""
# create table, give it a title a column names
table = PrettyTable()
if title is not None:
table.title = title.upper()
table.field_names = ['Label', 'Precision', 'Recall', 'F1', 'Support']
# column alignment
table.align['Label'] = 'l'
table.align['Precision'] = 'r'
table.align['Recall'] = 'r'
table.align['F1'] = 'r'
table.align['Support'] = 'r'
# create and add the rows
for label, scores in performance_scores.items():
row = [label]
# convert scores to formatted percentage strings
support = scores[-1]
performance_metrics = ['{:.2%}'.format(x) for x in scores[:-1]]
row_scores = performance_metrics + [support]
row.extend(row_scores)
table.add_row(row)
print(table) Example 33 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr + " " + niouStr)
# Print intersection-over-union scores for all categorys. Example 34 def printClassScoresPytorch(scoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
#niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr )#+ " " + niouStr)
# Print intersection-over-union scores for all classes. Example 35 def printCategoryScores(scoreDict, instScoreDict, args):
if (args.quiet):
return
print(args.bold + "categories IoU nIoU" + args.nocol)
print("--------------------------------")
for categoryName in scoreDict:
if all( label.ignoreInEval for label in category2labels[categoryName] ):
continue
iouStr = getColorEntry(scoreDict[categoryName], args) + "{val:>5.3f}".format(val=scoreDict[categoryName]) + args.nocol
niouStr = getColorEntry(instScoreDict[categoryName], args) + "{val:>5.3f}".format(val=instScoreDict[categoryName]) + args.nocol
print("{:<14}: ".format(categoryName) + iouStr + " " + niouStr)
# Evaluate image lists pairwise. Example 36 def printClassScores(scoreList, instScoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (id2label[label].ignoreInEval):
continue
labelName = str(id2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr + " " + niouStr)
# Print intersection-over-union scores for all categorys.
Example 37 def printClassScoresPytorchTrain(scoreList, args):
if (args.quiet):
return
print(args.bold + "classes IoU nIoU" + args.nocol)
print("--------------------------------")
for label in args.evalLabels:
if (trainId2label[label].ignoreInEval):
continue
labelName = str(trainId2label[label].name)
iouStr = getColorEntry(scoreList[labelName], args) + "{val:>5.3f}".format(val=scoreList[labelName]) + args.nocol
#niouStr = getColorEntry(instScoreList[labelName], args) + "{val:>5.3f}".format(val=instScoreList[labelName]) + args.nocol
print("{:<14}: ".format(labelName) + iouStr )#+ " " + niouStr) Example 38 def print_tables_with_scores(self):
def aggr_certainty_table(group_by_table):
ranked_list = []
# First aggregate bitsets
for x, score in self._chosen_rank:
new_score = score
if x.source_name in group_by_table:
old_score = group_by_table[x.source_name]
new_score = old_score + new_score
group_by_table[x.source_name] = new_score
for table, score_value in group_by_table.items():
value = (table, score_value)
ranked_list.append(value)
ranked_list = sorted(ranked_list, key=lambda a: a[1], reverse=True)
return ranked_list
def aggr_coverage_table(group_by_table):
ranked_list = []
# First aggregate bitsets
for x, score in self._chosen_rank:
coverage_score, new_coverage_set = score
if x.source_name in group_by_table:
old_coverage_set = group_by_table[x.source_name]
new_coverage_set = new_coverage_set | old_coverage_set
group_by_table[x.source_name] = new_coverage_set
for table, bitset in group_by_table.items():
new_score = bitset.count() / bitset.length()
value = (table, new_score)
ranked_list.append(value)
ranked_list = sorted(ranked_list, key=lambda a: a[1], reverse=True)
return ranked_list
mode = self.mode # save state
self.set_fields_mode()
group_by_table = dict()
if self._ranking_criteria == self.RankingCriteria.CERTAINTY:
ranked_list = aggr_certainty_table(group_by_table)
elif self._ranking_criteria == self.RankingCriteria.COVERAGE:
ranked_list = aggr_coverage_table(group_by_table)
for x in ranked_list:
print(x)
self._mode = mode # recover state
How do you find the accuracy score in Python?How to Calculate Balanced Accuracy in Python Using sklearn. Balanced accuracy = (Sensitivity + Specificity) / 2.. Balanced accuracy = (0.75 + 9868) / 2.. Balanced accuracy = 0.8684.. How do you find accuracy score?Accuracy is a metric used in classification problems used to tell the percentage of accurate predictions. We calculate it by dividing the number of correct predictions by the total number of predictions.
What does score () do in Python?Assuming it's sci-kit learn in python then model. score automates the prediction of your data using X_test and compares it with Y_test and by default uses the R-squared metric to so (hence don't need to manually derive y_pred ).
How does Python calculate accuracy and precision?# accuracy: (tp + tn) / (p + n) accuracy = accuracy_score(testy, yhat_classes). print('Accuracy: %f' % accuracy) # precision tp / (tp + fp). precision = precision_score(testy, yhat_classes) print('Precision: %f' % precision). # recall: tp / (tp + fn) ... . print('Recall: %f' % recall) ... . f1 = f1_score(testy, yhat_classes). |
