def calculate_score(solution,
prediction,
task_type,
metric,
num_classes,
all_scoring_functions=False,
logger=None):
if task_type == MULTICLASS_CLASSIFICATION:
# This used to crash on travis-ci; special treatment to find out why
# it crashed!
try:
solution_binary = np.zeros((prediction.shape[0], num_classes))
except IndexError as e:
if logger is not None:
logger.error("Prediction shape: %s, solution "
"shape %s", prediction.shape, solution.shape)
raise e
for i in range(solution_binary.shape[0]):
label = solution[i]
solution_binary[i, label] = 1
solution = solution_binary
elif task_type in [BINARY_CLASSIFICATION, REGRESSION]:
if len(solution.shape) == 1:
solution = solution.reshape((-1, 1))
if task_type not in TASK_TYPES:
raise NotImplementedError(task_type)
if solution.shape != prediction.shape:
raise ValueError('Solution shape %s != prediction shape %s' %
(solution.shape, prediction.shape))
if all_scoring_functions:
score = dict()
if task_type in REGRESSION_TASKS:
cprediction = sanitize_array(prediction)
for metric_ in REGRESSION_METRICS:
score[metric_] = regression_metrics.calculate_score(
metric_, solution, cprediction)
else:
csolution, cprediction = normalize_array(solution, prediction)
for metric_ in CLASSIFICATION_METRICS:
score[metric_] = classification_metrics.calculate_score(
metric_, csolution, cprediction, task_type)
else:
if task_type in REGRESSION_TASKS:
cprediction = sanitize_array(prediction)
score = regression_metrics.calculate_score(metric, solution,
cprediction)
else:
csolution, cprediction = normalize_array(solution, prediction)
score = classification_metrics.calculate_score(metric,
csolution,
cprediction,
task=task_type)
return score
def calculate_score(solution, prediction, task_type, metric, num_classes,
all_scoring_functions=False, logger=None):
if task_type == MULTICLASS_CLASSIFICATION:
# This used to crash on travis-ci; special treatment to find out why
# it crashed!
try:
solution_binary = np.zeros((prediction.shape[0], num_classes))
except IndexError as e:
if logger is not None:
logger.error("Prediction shape: %s, solution "
"shape %s", prediction.shape, solution.shape)
raise e
for i in range(solution_binary.shape[0]):
label = solution[i]
solution_binary[i, label] = 1
solution = solution_binary
elif task_type in [BINARY_CLASSIFICATION, REGRESSION]:
if len(solution.shape) == 1:
solution = solution.reshape((-1, 1))
if task_type not in TASK_TYPES:
raise NotImplementedError(task_type)
if solution.shape != prediction.shape:
raise ValueError('Solution shape %s != prediction shape %s' %
(solution.shape, prediction.shape))
if all_scoring_functions:
score = dict()
if task_type in REGRESSION_TASKS:
cprediction = sanitize_array(prediction)
for metric_ in REGRESSION_METRICS:
score[metric_] = regression_metrics.calculate_score(metric_,
solution,
cprediction)
else:
csolution, cprediction = normalize_array(solution, prediction)
for metric_ in CLASSIFICATION_METRICS:
score[metric_] = classification_metrics.calculate_score(
metric_, csolution, cprediction, task_type)
else:
if task_type in REGRESSION_TASKS:
cprediction = sanitize_array(prediction)
score = regression_metrics.calculate_score(metric,
solution,
cprediction)
else:
csolution, cprediction = normalize_array(solution, prediction)
score = classification_metrics.calculate_score(metric,
csolution,
cprediction,
task=task_type)
return score
def calculate_score(solution, prediction, task_type, metric, num_classes,
all_scoring_functions=False):
if task_type == MULTICLASS_CLASSIFICATION:
solution_binary = np.zeros((prediction.shape[0], num_classes))
for i in range(solution_binary.shape[0]):
label = solution[i]
solution_binary[i, label] = 1
solution = solution_binary
elif task_type in [BINARY_CLASSIFICATION, REGRESSION]:
if len(solution.shape) == 1:
solution = solution.reshape((-1, 1))
if task_type not in TASK_TYPES:
raise NotImplementedError(task_type)
if solution.shape != prediction.shape:
raise ValueError('Solution shape %s != prediction shape %s' %
(solution.shape, prediction.shape))
if all_scoring_functions:
score = dict()
if task_type in REGRESSION_TASKS:
cprediction = sanitize_array(prediction)
for metric_ in REGRESSION_METRIC:
score[metric_] = regression_metrics.calculate_score(metric_,
solution,
cprediction)
else:
csolution, cprediction = normalize_array(solution, prediction)
for metric_ in CLASSIFICATION_METRICS:
score[metric_] = classification_metrics.calculate_score(
metric_, csolution, cprediction, task_type)
else:
if task_type in REGRESSION_TASKS:
cprediction = sanitize_array(prediction)
score = regression_metrics.calculate_score(metric,
solution,
cprediction)
else:
csolution, cprediction = normalize_array(solution, prediction)
score = classification_metrics.calculate_score(metric,
csolution,
cprediction,
task=task_type)
return score