def __init__(self,
thresholds=None,
top_k=None,
class_id=None,
name='class_all_binary_accuracy_tfkeras',
dtype=None):
super().__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils_tf_keras.NEG_INF
self.thresholds = metrics_utils_tf_keras.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
'true_positives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
self.false_positives = self.add_weight(
'false_positives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
self.false_negatives = self.add_weight(
'false_negatives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
self.true_negatives = self.add_weight(
'true_negatives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
def __init__(self,
thresholds=None,
top_k=None,
class_id=None,
name=None,
dtype=None):
super(F1Score, self).__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
'true_positives',
shape=(len(self.thresholds), ),
initializer=tf.keras.initializers.Zeros)
self.false_positives = self.add_weight(
'false_positives',
shape=(len(self.thresholds), ),
initializer=tf.keras.initializers.Zeros)
self.false_negatives = self.add_weight(
'false_negatives',
shape=(len(self.thresholds), ),
initializer=tf.keras.initializers.Zeros)
def __init__(self,
thresholds=None,
top_k=None,
class_id=None,
name=None,
dtype=None):
super(CustomF1, self).__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
"true_positives",
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
self.false_negatives = self.add_weight(
"fasle_negatives",
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
self.false_positives = self.add_weight(
"fasle_positives",
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
def __init__(self,
beta=1,
thresholds=None,
top_k=None,
class_id=None,
name=None,
dtype=None):
name = name or str('f' + str(beta) + 'score')
super().__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.beta = beta
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils_tf_keras.NEG_INF
self.thresholds = metrics_utils_tf_keras.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
'true_positives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
self.false_positives = self.add_weight(
'false_positives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
self.false_negatives = self.add_weight(
'false_negatives',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
def __init__(self,
thresholds=None,
name='BinaryFbeta',
dtype=None,
beta=1):
# super(Metric, self).__init__(name=name, dtype=dtype)
super().__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.thresholds = parse_init_thresholds(thresholds,
default_threshold=0.5)
num_thresholds = len(self.thresholds)
self.true_positives = self.add_weight('true_positives',
shape=(num_thresholds, ),
initializer='zeros')
self.true_negatives = self.add_weight('true_negatives',
shape=(num_thresholds, ),
initializer='zeros')
self.false_positives = self.add_weight('false_positives',
shape=(num_thresholds, ),
initializer='zeros')
self.false_negatives = self.add_weight('false_negatives',
shape=(num_thresholds, ),
initializer='zeros')
self.beta = beta
def __init__(self,
name: str = "f1",
dtype=None,
thresholds: float = None,
top_k=None,
class_id=None,
**kwargs) -> None:
super(F1score, self).__init__(name=name, dtype=dtype, **kwargs)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds=thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
"true_positives",
shape=(len(self.thresholds), ),
initializer=tf.compat.v1.zeros_initializer,
)
self.false_positives = self.add_weight(
"false_positives",
shape=(len(self.thresholds), ),
initializer=tf.compat.v1.zeros_initializer,
)
self.false_negatives = self.add_weight(
"false_negatives",
shape=(len(self.thresholds), ),
initializer=tf.compat.v1.zeros_initializer,
)
self.score = self.add_weight(name="f1", initializer="zeros")
def __init__(self,
thresholds=None,
top_k=None,
class_id=None,
name=None,
dtype=None):
'''
Creates an instance of the F1Score class
:param thresholds: A float value or a python list/tuple of float threshold values in [0, 1].
:param top_k: An int value specifying the top-k predictions to consider when calculating precision
:param class_id: Integer class ID for which we want binary metrics. This must be in the half-open interval
`[0, num_classes)`, where `num_classes` is the last dimension of predictions
:param name: string name of the metric instance
:param dtype: data type of the metric result
'''
super(F1Score, self).__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
'true_positives',
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
self.false_positives = self.add_weight(
'false_positives',
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
self.false_negatives = self.add_weight(
'false_negatives',
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
def __init__(self, thresholds=None, name=None, dtype=None):
"""Creates a `F1Score` instance.
Args:
thresholds: (Optional) Defaults to 0.5. A float value or a python
list/tuple of float threshold values in [0, 1]. A threshold is compared
with prediction values to determine the truth value of predictions
(i.e., above the threshold is `true`, below is `false`). One metric
value is generated for each threshold value.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.
"""
super(F1Score, self).__init__(name=name, dtype=dtype)
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=0.5)
self.tp = self.add_weight(
'true_positives',
shape=(len(self.thresholds),),
initializer=metrics.init_ops.zeros_initializer)
self.fp = self.add_weight(
'false_positives',
shape=(len(self.thresholds),),
initializer=metrics.init_ops.zeros_initializer)
self.fn = self.add_weight(
'false_negatives',
shape=(len(self.thresholds),),
initializer=metrics.init_ops.zeros_initializer)
def __init__(self,
thresholds=None,
top_k=None,
class_id=None,
name='mcc',
dtype=None,
argmax=False):
super(MatthewsCorrCoef, self).__init__(name=name, dtype=dtype)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
self.argmax = argmax
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
'true_positives',
shape=(len(self.thresholds),),
initializer=zeros_initializer)
self.false_positives = self.add_weight(
'false_positives',
shape=(len(self.thresholds),),
initializer=zeros_initializer)
self.false_negatives = self.add_weight(
'false_negatives',
shape=(len(self.thresholds),),
initializer=zeros_initializer)
self.true_negatives = self.add_weight(
'true_negatives',
shape=(len(self.thresholds),),
initializer=zeros_initializer)
def __init__(self,
num_classes=1,
average=None,
beta=1.0,
thresholds=None,
top_k=None,
class_id=None,
sample_weight=None,
name=None,
dtype=None):
# Initialize keras base metric instance
super(FBetaScore, self).__init__(name=name, dtype=dtype)
# Checks
if average not in (None, "micro", "macro", "weighted"):
raise ValueError(
"Unknown average type. Given value '{}' is not in [None, micro, macro, weighted]"
.format(average))
if not isinstance(beta, float):
raise TypeError(
"The value of beta should be a python float, but a '{}' was given"
.format(type(beta)))
if beta <= 0.0:
raise ValueError(
"Beta value should be greater than zero, but a value of '{}' was given"
.format(beta))
if type(top_k) not in (int, ) and top_k is not None:
raise TypeError(
"The value of top_k should be either a python float or None, but a '{}' was given"
.format(type(top_k)))
# Initialize the F-Beta score instance
self.num_classes = num_classes
self.average = average
self.beta = beta
self.init_thresholds = thresholds if top_k is None else 0.0
self.thresholds = metrics_utils.parse_init_thresholds(
self.init_thresholds, default_threshold=0.5)
self.top_k = top_k
self.class_id = class_id
self.sample_weight = sample_weight
self.init_shape = () if self.average == 'micro' else (
self.num_classes, )
self.axis = None if self.average == 'micro' else 0
# Add metric states
self.true_positives = self.add_weight(name='true_positives',
shape=self.init_shape,
initializer='zeros')
self.false_positives = self.add_weight(name='false_positives',
shape=self.init_shape,
initializer='zeros')
self.false_negatives = self.add_weight(name='false_negatives',
shape=self.init_shape,
initializer='zeros')
def __init__(self,
thresholds=None,
top_k=None,
class_id=None,
name=None,
dtype=None,
**kwargs):
"""Creates a `F1` instance.
Args:
thresholds: (Optional) A float value or a python list/tuple of float
threshold values in [0, 1]. A threshold is compared with prediction
values to determine the truth value of predictions (i.e., above the
threshold is `true`, below is `false`). One metric value is generated
for each threshold value. If neither thresholds nor top_k are set, the
default is to calculate recall with `thresholds=0.5`.
top_k: (Optional) Unset by default. An int value specifying the top-k
predictions to consider when calculating recall.
class_id: (Optional) Integer class ID for which we want binary metrics.
This must be in the half-open interval `[0, num_classes)`, where
`num_classes` is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.
"""
super().__init__(name, dtype, **kwargs)
self.init_thresholds = thresholds
self.top_k = top_k
self.class_id = class_id
default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=default_threshold)
self.true_positives = self.add_weight(
'true_positives',
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
self.false_positives = self.add_weight(
'false_positives',
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
self.false_negatives = self.add_weight(
'false_negatives',
shape=(len(self.thresholds), ),
initializer=init_ops.zeros_initializer)
def __init__(self,
confusion_matrix_cond,
thresholds=None,
class_id=None,
multi_label=False,
name=None,
dtype=None):
super(_ConfusionMatrixConditionCount,
self).__init__(name=name, dtype=dtype)
self._confusion_matrix_cond = confusion_matrix_cond
self.init_thresholds = thresholds
self.thresholds = metrics_utils.parse_init_thresholds(
thresholds, default_threshold=0.5)
self.class_id = class_id
self.multi_label = multi_label
self.accumulator = self.add_weight(
'accumulator',
shape=(len(self.thresholds),),
initializer=init_ops.zeros_initializer)
def __init__(self,
confusion_matrix_cond,
thresholds=None,
top_k=None,
class_id=None,
sample_weight=None,
name=None,
dtype=None):
# Initialize keras base metric instance
super(_TopKConfusionMatrixConditionCount, self).__init__(name=name,
dtype=dtype)
# Initialize the top K confusion matrix condition count
self._confusion_matrix_cond = confusion_matrix_cond
self.init_thresholds = thresholds if top_k is None else 0.0
self.thresholds = metrics_utils.parse_init_thresholds(
self.init_thresholds, default_threshold=0.5)
self.top_k = top_k
self.class_id = class_id
self.sample_weight = sample_weight
self.accumulator = self.add_weight('accumulator',
shape=(len(self.thresholds), ),
initializer='zeros')
