From 901159da45695da24a5206125910f02fc50169ce Mon Sep 17 00:00:00 2001
From: Efraim Flashner <efraim@flashner.co.il>
Date: Thu, 23 Apr 2020 15:50:37 +0300
Subject: [PATCH] add keras metrics
---
keras/backend/tensorflow_backend.py | 12 +
keras/metrics.py | 584 ++++++++++++++++++++++++++++
keras/utils/__init__.py | 2 +
keras/utils/losses_utils.py | 177 +++++++++
keras/utils/metrics_utils.py | 278 +++++++++++++
5 files changed, 1053 insertions(+)
create mode 100644 keras/utils/losses_utils.py
create mode 100644 keras/utils/metrics_utils.py
diff --git a/keras/backend/tensorflow_backend.py b/keras/backend/tensorflow_backend.py
index bcb8be0..a2870f5 100644
--- a/keras/backend/tensorflow_backend.py
+++ b/keras/backend/tensorflow_backend.py
@@ -4453,3 +4453,15 @@ def local_conv2d(inputs, kernel, kernel_size, strides, output_shape, data_format
else:
output = permute_dimensions(output, (2, 0, 1, 3))
return output
+
+#get_graph = tf_keras_backend.get_graph
+
+#def is_symbolic(x):
+# return isinstance(x, tf.Tensor) and hasattr(x, 'op')
+
+def size(x, name=None):
+# if is_symbolic(x):
+# with get_graph().as_default():
+# return tf.size(x)
+ return tf.size(x, name=name)
+
diff --git a/keras/metrics.py b/keras/metrics.py
index 8e3df1f..8f57910 100644
--- a/keras/metrics.py
+++ b/keras/metrics.py
@@ -4,8 +4,12 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
+import abc
import six
+import types
+
from . import backend as K
+from .engine.base_layer import Layer
from .losses import mean_squared_error
from .losses import mean_absolute_error
from .losses import mean_absolute_percentage_error
@@ -19,10 +23,201 @@ from .losses import binary_crossentropy
from .losses import kullback_leibler_divergence
from .losses import poisson
from .losses import cosine_proximity
+from .utils import losses_utils
+from .utils import metrics_utils
from .utils.generic_utils import deserialize_keras_object
from .utils.generic_utils import serialize_keras_object
+@six.add_metaclass(abc.ABCMeta)
+class Metric(Layer):
+ """Encapsulates metric logic and state.
+
+ Standalone usage:
+ ```python
+ m = SomeMetric(...)
+ for input in ...:
+ m.update_state(input)
+ m.result()
+ ```
+
+ Usage with the `compile` API:
+ ```python
+ model.compile(optimizer='rmsprop',
+ loss=keras.losses.categorical_crossentropy,
+ metrics=[keras.metrics.CategoricalAccuracy()])
+ ```
+
+ To be implemented by subclasses:
+ * `__init__()`: All state variables should be created in this method by
+ calling `self.add_weight()` like: `self.var = self.add_weight(...)`
+ * `update_state()`: Has all updates to the state variables like:
+ self.var.assign_add(...).
+ * `result()`: Computes and returns a value for the metric
+ from the state variables.
+ """
+
+ def __init__(self, name=None, dtype=None, **kwargs):
+ super(Metric, self).__init__(name=name, dtype=dtype, **kwargs)
+ self.stateful = True # All metric layers are stateful.
+ self.built = True
+ self.dtype = K.floatx() if dtype is None else dtype
+
+ def __new__(cls, *args, **kwargs):
+ obj = super(Metric, cls).__new__(cls)
+ update_state_fn = obj.update_state
+
+ obj.update_state = types.MethodType(
+ metrics_utils.update_state_wrapper(update_state_fn), obj)
+ return obj
+
+ def __call__(self, *args, **kwargs):
+ """Accumulates statistics and then computes metric result value."""
+ update_op = self.update_state(*args, **kwargs)
+ return self.result()
+
+ def get_config(self):
+ """Returns the serializable config of the metric."""
+ return {'name': self.name, 'dtype': self.dtype}
+
+ def reset_states(self):
+ """Resets all of the metric state variables.
+ This function is called between epochs/steps,
+ when a metric is evaluated during training.
+ """
+ K.batch_set_value([(v, 0) for v in self.weights])
+
+ @abc.abstractmethod
+ def update_state(self, *args, **kwargs):
+ """Accumulates statistics for the metric. """
+ raise NotImplementedError('Must be implemented in subclasses.')
+
+ @abc.abstractmethod
+ def result(self):
+ """Computes and returns the metric value tensor.
+ Result computation is an idempotent operation that simply calculates the
+ metric value using the state variables.
+ """
+ raise NotImplementedError('Must be implemented in subclasses.')
+
+ # For use by subclasses #
+ def add_weight(self,
+ name,
+ shape=(),
+ initializer=None,
+ dtype=None):
+ """Adds state variable. Only for use by subclasses."""
+ return super(Metric, self).add_weight(
+ name=name,
+ shape=shape,
+ dtype=self.dtype if dtype is None else dtype,
+ trainable=False,
+ initializer=initializer)
+
+ # End: For use by subclasses ###
+
+
+class Reduce(Metric):
+ """Encapsulates metrics that perform a reduce operation on the values."""
+
+ def __init__(self, reduction, name, dtype=None):
+ """Creates a `Reduce` instance.
+ # Arguments
+ reduction: a metrics `Reduction` enum value.
+ name: string name of the metric instance.
+ dtype: (Optional) data type of the metric result.
+ """
+ super(Reduce, self).__init__(name=name, dtype=dtype)
+ self.reduction = reduction
+ self.total = self.add_weight('total', initializer='zeros')
+ if reduction in [metrics_utils.Reduction.SUM_OVER_BATCH_SIZE,
+ metrics_utils.Reduction.WEIGHTED_MEAN]:
+ self.count = self.add_weight('count', initializer='zeros')
+
+ def update_state(self, values, sample_weight=None):
+ """Accumulates statistics for computing the reduction metric.
+ For example, if `values` is [1, 3, 5, 7] and reduction=SUM_OVER_BATCH_SIZE,
+ then the value of `result()` is 4. If the `sample_weight` is specified as
+ [1, 1, 0, 0] then value of `result()` would be 2.
+ # Arguments
+ values: Per-example value.
+ sample_weight: Optional weighting of each example. Defaults to 1.
+ """
+ values = K.cast(values, self.dtype)
+ if sample_weight is not None:
+ sample_weight = K.cast(sample_weight, self.dtype)
+ # Update dimensions of weights to match with values if possible.
+ values, _, sample_weight = losses_utils.squeeze_or_expand_dimensions(
+ values, sample_weight=sample_weight)
+
+ # Broadcast weights if possible.
+ sample_weight = losses_utils.broadcast_weights(sample_weight, values)
+ values = values * sample_weight
+
+ value_sum = K.sum(values)
+ update_total_op = K.update_add(self.total, value_sum)
+
+ # Exit early if the reduction doesn't have a denominator.
+ if self.reduction == metrics_utils.Reduction.SUM:
+ return update_total_op
+
+ # Update `count` for reductions that require a denominator.
+ if self.reduction == metrics_utils.Reduction.SUM_OVER_BATCH_SIZE:
+ num_values = K.cast(K.size(values), self.dtype)
+ elif self.reduction == metrics_utils.Reduction.WEIGHTED_MEAN:
+ if sample_weight is None:
+ num_values = K.cast(K.size(values), self.dtype)
+ else:
+ num_values = K.sum(sample_weight)
+ else:
+ raise NotImplementedError(
+ 'reduction [%s] not implemented' % self.reduction)
+
+ with K.control_dependencies([update_total_op]):
+ return K.update_add(self.count, num_values)
+
+ def result(self):
+ if self.reduction == metrics_utils.Reduction.SUM:
+ return self.total
+ elif self.reduction in [
+ metrics_utils.Reduction.WEIGHTED_MEAN,
+ metrics_utils.Reduction.SUM_OVER_BATCH_SIZE
+ ]:
+ return self.total / self.count
+ else:
+ raise NotImplementedError(
+ 'reduction [%s] not implemented' % self.reduction)
+
+
+class Sum(Reduce):
+ """Computes the (weighted) sum of the given values.
+
+ For example, if values is [1, 3, 5, 7] then the sum is 16.
+ If the weights were specified as [1, 1, 0, 0] then the sum would be 4.
+
+ This metric creates one variable, `total`, that is used to compute the sum of
+ `values`. This is ultimately returned as `sum`.
+ If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0
+ to mask values.
+
+ Standalone usage:
+ ```python
+ m = keras.metrics.Sum()
+ m.update_state([1, 3, 5, 7])
+ m.result()
+ ```
+ """
+
+ def __init__(self, name='sum', dtype=None):
+ """Creates a `Sum` instance.
+ # Arguments
+ name: (Optional) string name of the metric instance.
+ dtype: (Optional) data type of the metric result.
+ """
+ super(Sum, self).__init__(reduction=metrics_utils.Reduction.SUM,
+ name=name, dtype=dtype)
+
+
def binary_accuracy(y_true, y_pred):
return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)
@@ -49,6 +244,395 @@ def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
return K.mean(K.in_top_k(y_pred, K.cast(K.flatten(y_true), 'int32'), k),
axis=-1)
+class SensitivitySpecificityBase(Metric):
+ """Abstract base class for computing sensitivity and specificity.
+
+ For additional information about specificity and sensitivity, see the
+ following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
+ """
+
+ def __init__(self, value, num_thresholds=200, name=None, dtype=None):
+ super(SensitivitySpecificityBase, self).__init__(name=name, dtype=dtype)
+ if num_thresholds <= 0:
+ raise ValueError('`num_thresholds` must be > 0.')
+ self.value = value
+ 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')
+
+ # Compute `num_thresholds` thresholds in [0, 1]
+ if num_thresholds == 1:
+ self.thresholds = [0.5]
+ else:
+ thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
+ for i in range(num_thresholds - 2)]
+ self.thresholds = [0.0] + thresholds + [1.0]
+
+ def update_state(self, y_true, y_pred, sample_weight=None):
+ return metrics_utils.update_confusion_matrix_variables(
+ {
+ metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
+ metrics_utils.ConfusionMatrix.TRUE_NEGATIVES: self.true_negatives,
+ metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives,
+ metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives,
+ },
+ y_true,
+ y_pred,
+ thresholds=self.thresholds,
+ sample_weight=sample_weight)
+
+ def reset_states(self):
+ num_thresholds = len(self.thresholds)
+ K.batch_set_value(
+ [(v, np.zeros((num_thresholds,))) for v in self.variables])
+
+
+class SensitivityAtSpecificity(SensitivitySpecificityBase):
+ """Computes the sensitivity at a given specificity.
+
+ `Sensitivity` measures the proportion of actual positives that are correctly
+ identified as such (tp / (tp + fn)).
+ `Specificity` measures the proportion of actual negatives that are correctly
+ identified as such (tn / (tn + fp)).
+
+ This metric creates four local variables, `true_positives`, `true_negatives`,
+ `false_positives` and `false_negatives` that are used to compute the
+ sensitivity at the given specificity. The threshold for the given specificity
+ value is computed and used to evaluate the corresponding sensitivity.
+
+ If `sample_weight` is `None`, weights default to 1.
+ Use `sample_weight` of 0 to mask values.
+
+ For additional information about specificity and sensitivity, see the
+ following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
+
+ Usage with the compile API:
+
+ ```python
+ model = keras.Model(inputs, outputs)
+ model.compile(
+ 'sgd',
+ loss='mse',
+ metrics=[keras.metrics.SensitivityAtSpecificity()])
+ ```
+
+ # Arguments
+ specificity: A scalar value in range `[0, 1]`.
+ num_thresholds: (Optional) Defaults to 200. The number of thresholds to
+ use for matching the given specificity.
+ name: (Optional) string name of the metric instance.
+ dtype: (Optional) data type of the metric result.
+ """
+
+ def __init__(self, specificity, num_thresholds=200, name=None, dtype=None):
+ if specificity < 0 or specificity > 1:
+ raise ValueError('`specificity` must be in the range [0, 1].')
+ self.specificity = specificity
+ self.num_thresholds = num_thresholds
+ super(SensitivityAtSpecificity, self).__init__(
+ specificity, num_thresholds=num_thresholds, name=name, dtype=dtype)
+
+ def result(self):
+ # Calculate specificities at all the thresholds.
+ specificities = K.switch(
+ K.greater(self.true_negatives + self.false_positives, 0),
+ (self.true_negatives / (self.true_negatives + self.false_positives)),
+ K.zeros_like(self.thresholds))
+
+ # Find the index of the threshold where the specificity is closest to the
+ # given specificity.
+ min_index = K.argmin(
+ K.abs(specificities - self.value), axis=0)
+ min_index = K.cast(min_index, 'int32')
+
+ # Compute sensitivity at that index.
+ return K.switch(
+ K.greater((self.true_positives[min_index] +
+ self.false_negatives[min_index]), 0),
+ (self.true_positives[min_index] /
+ (self.true_positives[min_index] + self.false_negatives[min_index])),
+ K.zeros_like(self.true_positives[min_index]))
+
+ def get_config(self):
+ config = {
+ 'num_thresholds': self.num_thresholds,
+ 'specificity': self.specificity
+ }
+ base_config = super(SensitivityAtSpecificity, self).get_config()
+ return dict(list(base_config.items()) + list(config.items()))
+
+
+class AUC(Metric):
+ """Computes the approximate AUC (Area under the curve) via a Riemann sum.
+
+ This metric creates four local variables, `true_positives`, `true_negatives`,
+ `false_positives` and `false_negatives` that are used to compute the AUC.
+ To discretize the AUC curve, a linearly spaced set of thresholds is used to
+ compute pairs of recall and precision values. The area under the ROC-curve is
+ therefore computed using the height of the recall values by the false positive
+ rate, while the area under the PR-curve is the computed using the height of
+ the precision values by the recall.
+
+ This value is ultimately returned as `auc`, an idempotent operation that
+ computes the area under a discretized curve of precision versus recall values
+ (computed using the aforementioned variables). The `num_thresholds` variable
+ controls the degree of discretization with larger numbers of thresholds more
+ closely approximating the true AUC. The quality of the approximation may vary
+ dramatically depending on `num_thresholds`. The `thresholds` parameter can be
+ used to manually specify thresholds which split the predictions more evenly.
+
+ For best results, `predictions` should be distributed approximately uniformly
+ in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC
+ approximation may be poor if this is not the case. Setting `summation_method`
+ to 'minoring' or 'majoring' can help quantify the error in the approximation
+ by providing lower or upper bound estimate of the AUC.
+
+ If `sample_weight` is `None`, weights default to 1.
+ Use `sample_weight` of 0 to mask values.
+
+ Usage with the compile API:
+
+ ```python
+ model = keras.Model(inputs, outputs)
+ model.compile('sgd', loss='mse', metrics=[keras.metrics.AUC()])
+ ```
+
+ # Arguments
+ num_thresholds: (Optional) Defaults to 200. The number of thresholds to
+ use when discretizing the roc curve. Values must be > 1.
+ curve: (Optional) Specifies the name of the curve to be computed, 'ROC'
+ [default] or 'PR' for the Precision-Recall-curve.
+ summation_method: (Optional) Specifies the Riemann summation method used
+ (https://en.wikipedia.org/wiki/Riemann_sum): 'interpolation' [default],
+ applies mid-point summation scheme for `ROC`. For PR-AUC, interpolates
+ (true/false) positives but not the ratio that is precision (see Davis
+ & Goadrich 2006 for details); 'minoring' that applies left summation
+ for increasing intervals and right summation for decreasing intervals;
+ 'majoring' that does the opposite.
+ name: (Optional) string name of the metric instance.
+ dtype: (Optional) data type of the metric result.
+ thresholds: (Optional) A list of floating point values to use as the
+ thresholds for discretizing the curve. If set, the `num_thresholds`
+ parameter is ignored. Values should be in [0, 1]. Endpoint thresholds
+ equal to {-epsilon, 1+epsilon} for a small positive epsilon value will
+ be automatically included with these to correctly handle predictions
+ equal to exactly 0 or 1.
+ """
+
+ def __init__(self,
+ num_thresholds=200,
+ curve='ROC',
+ summation_method='interpolation',
+ name=None,
+ dtype=None,
+ thresholds=None):
+ # Validate configurations.
+ if (isinstance(curve, metrics_utils.AUCCurve) and
+ curve not in list(metrics_utils.AUCCurve)):
+ raise ValueError('Invalid curve: "{}". Valid options are: "{}"'.format(
+ curve, list(metrics_utils.AUCCurve)))
+ if isinstance(
+ summation_method,
+ metrics_utils.AUCSummationMethod) and summation_method not in list(
+ metrics_utils.AUCSummationMethod):
+ raise ValueError(
+ 'Invalid summation method: "{}". Valid options are: "{}"'.format(
+ summation_method, list(metrics_utils.AUCSummationMethod)))
+
+ # Update properties.
+ if thresholds is not None:
+ # If specified, use the supplied thresholds.
+ self.num_thresholds = len(thresholds) + 2
+ thresholds = sorted(thresholds)
+ else:
+ if num_thresholds <= 1:
+ raise ValueError('`num_thresholds` must be > 1.')
+
+ # Otherwise, linearly interpolate (num_thresholds - 2) thresholds in
+ # (0, 1).
+ self.num_thresholds = num_thresholds
+ thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
+ for i in range(num_thresholds - 2)]
+
+ # Add an endpoint "threshold" below zero and above one for either
+ # threshold method to account for floating point imprecisions.
+ self.thresholds = [0.0 - K.epsilon()] + thresholds + [1.0 + K.epsilon()]
+
+ if isinstance(curve, metrics_utils.AUCCurve):
+ self.curve = curve
+ else:
+ self.curve = metrics_utils.AUCCurve.from_str(curve)
+ if isinstance(summation_method, metrics_utils.AUCSummationMethod):
+ self.summation_method = summation_method
+ else:
+ self.summation_method = metrics_utils.AUCSummationMethod.from_str(
+ summation_method)
+ super(AUC, self).__init__(name=name, dtype=dtype)
+
+ # Create metric variables
+ self.true_positives = self.add_weight(
+ 'true_positives',
+ shape=(self.num_thresholds,),
+ initializer='zeros')
+ self.true_negatives = self.add_weight(
+ 'true_negatives',
+ shape=(self.num_thresholds,),
+ initializer='zeros')
+ self.false_positives = self.add_weight(
+ 'false_positives',
+ shape=(self.num_thresholds,),
+ initializer='zeros')
+ self.false_negatives = self.add_weight(
+ 'false_negatives',
+ shape=(self.num_thresholds,),
+ initializer='zeros')
+
+ def update_state(self, y_true, y_pred, sample_weight=None):
+ return metrics_utils.update_confusion_matrix_variables({
+ metrics_utils.ConfusionMatrix.TRUE_POSITIVES: self.true_positives,
+ metrics_utils.ConfusionMatrix.TRUE_NEGATIVES: self.true_negatives,
+ metrics_utils.ConfusionMatrix.FALSE_POSITIVES: self.false_positives,
+ metrics_utils.ConfusionMatrix.FALSE_NEGATIVES: self.false_negatives,
+ }, y_true, y_pred, self.thresholds, sample_weight=sample_weight)
+
+ def interpolate_pr_auc(self):
+ """Interpolation formula inspired by section 4 of Davis & Goadrich 2006.
+
+ https://www.biostat.wisc.edu/~page/rocpr.pdf
+
+ Note here we derive & use a closed formula not present in the paper
+ as follows:
+
+ Precision = TP / (TP + FP) = TP / P
+
+ Modeling all of TP (true positive), FP (false positive) and their sum
+ P = TP + FP (predicted positive) as varying linearly within each interval
+ [A, B] between successive thresholds, we get
+
+ Precision slope = dTP / dP
+ = (TP_B - TP_A) / (P_B - P_A)
+ = (TP - TP_A) / (P - P_A)
+ Precision = (TP_A + slope * (P - P_A)) / P
+
+ The area within the interval is (slope / total_pos_weight) times
+
+ int_A^B{Precision.dP} = int_A^B{(TP_A + slope * (P - P_A)) * dP / P}
+ int_A^B{Precision.dP} = int_A^B{slope * dP + intercept * dP / P}
+
+ where intercept = TP_A - slope * P_A = TP_B - slope * P_B, resulting in
+
+ int_A^B{Precision.dP} = TP_B - TP_A + intercept * log(P_B / P_A)
+
+ Bringing back the factor (slope / total_pos_weight) we'd put aside, we get
+
+ slope * [dTP + intercept * log(P_B / P_A)] / total_pos_weight
+
+ where dTP == TP_B - TP_A.
+
+ Note that when P_A == 0 the above calculation simplifies into
+
+ int_A^B{Precision.dTP} = int_A^B{slope * dTP} = slope * (TP_B - TP_A)
+
+ which is really equivalent to imputing constant precision throughout the
+ first bucket having >0 true positives.
+
+ # Returns
+ pr_auc: an approximation of the area under the P-R curve.
+ """
+ dtp = self.true_positives[:self.num_thresholds -
+ 1] - self.true_positives[1:]
+ p = self.true_positives + self.false_positives
+ dp = p[:self.num_thresholds - 1] - p[1:]
+
+ prec_slope = dtp / K.maximum(dp, 0)
+ intercept = self.true_positives[1:] - (prec_slope * p[1:])
+
+ # Logical and
+ pMin = K.expand_dims(p[:self.num_thresholds - 1] > 0, 0)
+ pMax = K.expand_dims(p[1:] > 0, 0)
+ are_different = K.concatenate([pMin, pMax], axis=0)
+ switch_condition = K.all(are_different, axis=0)
+
+ safe_p_ratio = K.switch(
+ switch_condition,
+ p[:self.num_thresholds - 1] / K.maximum(p[1:], 0),
+ K.ones_like(p[1:]))
+
+ numer = prec_slope * (dtp + intercept * K.log(safe_p_ratio))
+ denom = K.maximum(self.true_positives[1:] + self.false_negatives[1:], 0)
+ return K.sum((numer / denom))
+
+ def result(self):
+ if (self.curve == metrics_utils.AUCCurve.PR and
+ (self.summation_method ==
+ metrics_utils.AUCSummationMethod.INTERPOLATION)):
+ # This use case is different and is handled separately.
+ return self.interpolate_pr_auc()
+
+ # Set `x` and `y` values for the curves based on `curve` config.
+ recall = K.switch(
+ K.greater((self.true_positives), 0),
+ (self.true_positives /
+ (self.true_positives + self.false_negatives)),
+ K.zeros_like(self.true_positives))
+ if self.curve == metrics_utils.AUCCurve.ROC:
+ fp_rate = K.switch(
+ K.greater((self.false_positives), 0),
+ (self.false_positives /
+ (self.false_positives + self.true_negatives)),
+ K.zeros_like(self.false_positives))
+ x = fp_rate
+ y = recall
+ else: # curve == 'PR'.
+ precision = K.switch(
+ K.greater((self.true_positives), 0),
+ (self.true_positives / (self.true_positives + self.false_positives)),
+ K.zeros_like(self.true_positives))
+ x = recall
+ y = precision
+
+ # Find the rectangle heights based on `summation_method`.
+ if self.summation_method == metrics_utils.AUCSummationMethod.INTERPOLATION:
+ # Note: the case ('PR', 'interpolation') has been handled above.
+ heights = (y[:self.num_thresholds - 1] + y[1:]) / 2.
+ elif self.summation_method == metrics_utils.AUCSummationMethod.MINORING:
+ heights = K.minimum(y[:self.num_thresholds - 1], y[1:])
+ else: # self.summation_method = metrics_utils.AUCSummationMethod.MAJORING:
+ heights = K.maximum(y[:self.num_thresholds - 1], y[1:])
+
+ # Sum up the areas of all the rectangles.
+ return K.sum((x[:self.num_thresholds - 1] - x[1:]) * heights)
+
+ def reset_states(self):
+ K.batch_set_value(
+ [(v, np.zeros((self.num_thresholds,))) for v in self.variables])
+
+ def get_config(self):
+ config = {
+ 'num_thresholds': self.num_thresholds,
+ 'curve': self.curve.value,
+ 'summation_method': self.summation_method.value,
+ # We remove the endpoint thresholds as an inverse of how the thresholds
+ # were initialized. This ensures that a metric initialized from this
+ # config has the same thresholds.
+ 'thresholds': self.thresholds[1:-1],
+ }
+ base_config = super(AUC, self).get_config()
+ return dict(list(base_config.items()) + list(config.items()))
+
# Aliases
diff --git a/keras/utils/__init__.py b/keras/utils/__init__.py
index 8cc39d5..65af329 100644
--- a/keras/utils/__init__.py
+++ b/keras/utils/__init__.py
@@ -4,6 +4,8 @@ from . import generic_utils
from . import data_utils
from . import io_utils
from . import conv_utils
+from . import losses_utils
+from . import metrics_utils
# Globally-importable utils.
from .io_utils import HDF5Matrix
diff --git a/keras/utils/losses_utils.py b/keras/utils/losses_utils.py
new file mode 100644
index 0000000..617ebb7
--- /dev/null
+++ b/keras/utils/losses_utils.py
@@ -0,0 +1,177 @@
+"""Utilities related to losses."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from .. import backend as K
+
+
+class Reduction(object):
+ """Types of loss reduction.
+
+ Contains the following values:
+
+ * `NONE`: Un-reduced weighted losses with the same shape as input. When this
+ reduction type used with built-in Keras training loops like
+ `fit`/`evaluate`, the unreduced vector loss is passed to the optimizer but
+ the reported loss will be a scalar value.
+ * `SUM`: Scalar sum of weighted losses.
+ * `SUM_OVER_BATCH_SIZE`: Scalar `SUM` divided by number of elements in losses.
+ """
+
+ NONE = 'none'
+ SUM = 'sum'
+ SUM_OVER_BATCH_SIZE = 'sum_over_batch_size'
+
+ @classmethod
+ def all(cls):
+ return (cls.NONE, cls.SUM, cls.SUM_OVER_BATCH_SIZE)
+
+ @classmethod
+ def validate(cls, key):
+ if key not in cls.all():
+ raise ValueError('Invalid Reduction Key %s.' % key)
+
+
+def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None):
+ """Squeeze or expand last dimension if needed.
+
+ 1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1.
+ 2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1
+ from the new rank of `y_pred`.
+ If `sample_weight` is scalar, it is kept scalar.
+
+ # Arguments
+ y_pred: Predicted values, a `Tensor` of arbitrary dimensions.
+ y_true: Optional label `Tensor` whose dimensions match `y_pred`.
+ sample_weight: Optional weight scalar or `Tensor` whose dimensions match
+ `y_pred`.
+
+ # Returns
+ Tuple of `y_pred`, `y_true` and `sample_weight`. Each of them possibly has
+ the last dimension squeezed, `sample_weight` could be extended by one
+ dimension.
+ """
+ if y_true is not None:
+ y_pred_rank = K.ndim(y_pred)
+ y_pred_shape = K.int_shape(y_pred)
+ y_true_rank = K.ndim(y_true)
+ y_true_shape = K.int_shape(y_true)
+
+ if (y_pred_rank - y_true_rank == 1) and (y_pred_shape[-1] == 1):
+ y_pred = K.squeeze(y_pred, -1)
+ elif (y_true_rank - y_pred_rank == 1) and (y_true_shape[-1] == 1):
+ y_true = K.squeeze(y_true, -1)
+
+ if sample_weight is None:
+ return y_pred, y_true
+
+ y_pred_rank = K.ndim(y_pred)
+ weights_rank = K.ndim(sample_weight)
+ if weights_rank != 0:
+ if weights_rank - y_pred_rank == 1:
+ sample_weight = K.squeeze(sample_weight, -1)
+ elif y_pred_rank - weights_rank == 1:
+ sample_weight = K.expand_dims(sample_weight, -1)
+ return y_pred, y_true, sample_weight
+
+
+def _num_elements(losses):
+ """Computes the number of elements in `losses` tensor."""
+ with K.name_scope('num_elements') as scope:
+ return K.cast(K.size(losses, name=scope), losses.dtype)
+
+
+def reduce_weighted_loss(weighted_losses, reduction=Reduction.SUM_OVER_BATCH_SIZE):
+ """Reduces the individual weighted loss measurements."""
+ if reduction == Reduction.NONE:
+ loss = weighted_losses
+ else:
+ loss = K.sum(weighted_losses)
+ if reduction == Reduction.SUM_OVER_BATCH_SIZE:
+ loss = loss / _num_elements(weighted_losses)
+ return loss
+
+
+def broadcast_weights(values, sample_weight):
+ # Broadcast weights if possible.
+ weights_shape = K.int_shape(sample_weight)
+ values_shape = K.int_shape(values)
+
+ if values_shape != weights_shape:
+ weights_rank = K.ndim(sample_weight)
+ values_rank = K.ndim(values)
+
+ # Raise error if ndim of weights is > values.
+ if weights_rank > values_rank:
+ raise ValueError(
+ 'Incompatible shapes: `values` {} vs `sample_weight` {}'.format(
+ values_shape, weights_shape))
+
+ # Expand dim of weights to match ndim of values, if required.
+ for i in range(weights_rank, values_rank):
+ sample_weight = K.expand_dims(sample_weight, axis=i)
+
+ if weights_shape is not None and values_shape is not None:
+ for i in range(weights_rank):
+ if (weights_shape[i] is not None and
+ values_shape[i] is not None and
+ weights_shape[i] != values_shape[i]):
+ # Cannot be broadcasted.
+ if weights_shape[i] != 1:
+ raise ValueError(
+ 'Incompatible shapes: `values` {} vs '
+ '`sample_weight` {}'.format(
+ values_shape, weights_shape))
+ sample_weight = K.repeat_elements(
+ sample_weight, values_shape[i], axis=i)
+ return sample_weight
+
+
+def compute_weighted_loss(losses,
+ sample_weight=None,
+ reduction=Reduction.SUM_OVER_BATCH_SIZE,
+ name=None):
+ """Computes the weighted loss.
+
+ # Arguments
+ losses: `Tensor` of shape `[batch_size, d1, ... dN]`.
+ sample_weight: Optional `Tensor` whose rank is either 0, or the same rank as
+ ` losses`, or be broadcastable to `losses`.
+ reduction: (Optional) Type of Reduction to apply to loss.
+ Default value is `SUM_OVER_BATCH_SIZE`.
+ name: Optional name for the op.
+
+ # Raises
+ ValueError: If the shape of `sample_weight` is not compatible with `losses`.
+
+ # Returns
+ Weighted loss `Tensor` of the same type as `losses`. If `reduction` is
+ `NONE`, this has the same shape as `losses`; otherwise, it is scalar.
+ """
+ Reduction.validate(reduction)
+ if sample_weight is None:
+ sample_weight = 1.0
+ with K.name_scope(name or 'weighted_loss'):
+ input_dtype = K.dtype(losses)
+ losses = K.cast(losses, K.floatx())
+ sample_weight = K.cast(sample_weight, K.floatx())
+
+ # Update dimensions of `sample_weight` to match with `losses` if possible.
+ losses, _, sample_weight = squeeze_or_expand_dimensions(
+ losses, None, sample_weight)
+
+ # Broadcast weights if possible.
+ sample_weight = broadcast_weights(losses, sample_weight)
+
+ # Apply weights to losses.
+ weighted_losses = sample_weight * losses
+
+ # Apply reduction function to the individual weighted losses.
+ loss = reduce_weighted_loss(weighted_losses, reduction)
+ # Convert the result back to the input type.
+ loss = K.cast(loss, input_dtype)
+ return loss
+
diff --git a/keras/utils/metrics_utils.py b/keras/utils/metrics_utils.py
new file mode 100644
index 0000000..e6a5bb0
--- /dev/null
+++ b/keras/utils/metrics_utils.py
@@ -0,0 +1,278 @@
+"""Utilities related to metrics."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from enum import Enum
+
+from .. import backend as K
+from . import losses_utils
+
+NEG_INF = -1e10
+
+class Reduction(object):
+ """Types of metrics reduction.
+ Contains the following values:
+ * `SUM`: Scalar sum of weighted values.
+ * `SUM_OVER_BATCH_SIZE`: Scalar `SUM` of weighted values divided by
+ number of elements in values.
+ * `WEIGHTED_MEAN`: Scalar sum of weighted values divided by sum of weights.
+ """
+
+ SUM = 'sum'
+ SUM_OVER_BATCH_SIZE = 'sum_over_batch_size'
+ WEIGHTED_MEAN = 'weighted_mean'
+
+
+def update_state_wrapper(update_state_fn):
+ """Decorator to wrap metric `update_state()` with `add_update()`.
+ # Arguments
+ update_state_fn: function that accumulates metric statistics.
+ # Returns
+ Decorated function that wraps `update_state_fn()` with `add_update()`.
+ """
+ def decorated(metric_obj, *args, **kwargs):
+ """Decorated function with `add_update()`."""
+
+ update_op = update_state_fn(*args, **kwargs)
+ metric_obj.add_update(update_op)
+ return update_op
+
+ return decorated
+
+def result_wrapper(result_fn):
+ """Decorator to wrap metric `result()` with identity op.
+ Wrapping result in identity so that control dependency between
+ update_op from `update_state` and result works in case result returns
+ a tensor.
+ # Arguments
+ result_fn: function that computes the metric result.
+ # Returns
+ Decorated function that wraps `result()` with identity op.
+ """
+ def decorated(metric_obj, *args, **kwargs):
+ result_t = K.identity(result_fn(*args, **kwargs))
+ metric_obj._call_result = result_t
+ result_t._is_metric = True
+ return result_t
+ return decorated
+
+
+def to_list(x):
+ if isinstance(x, list):
+ return x
+ return [x]
+
+
+def assert_thresholds_range(thresholds):
+ if thresholds is not None:
+ invalid_thresholds = [t for t in thresholds if t is None or t < 0 or t > 1]
+ if invalid_thresholds:
+ raise ValueError(
+ 'Threshold values must be in [0, 1]. Invalid values: {}'.format(
+ invalid_thresholds))
+
+
+def parse_init_thresholds(thresholds, default_threshold=0.5):
+ if thresholds is not None:
+ assert_thresholds_range(to_list(thresholds))
+ thresholds = to_list(default_threshold if thresholds is None else thresholds)
+ return thresholds
+
+class ConfusionMatrix(Enum):
+ TRUE_POSITIVES = 'tp'
+ FALSE_POSITIVES = 'fp'
+ TRUE_NEGATIVES = 'tn'
+ FALSE_NEGATIVES = 'fn'
+
+class AUCCurve(Enum):
+ """Type of AUC Curve (ROC or PR)."""
+ ROC = 'ROC'
+ PR = 'PR'
+
+ @staticmethod
+ def from_str(key):
+ if key in ('pr', 'PR'):
+ return AUCCurve.PR
+ elif key in ('roc', 'ROC'):
+ return AUCCurve.ROC
+ else:
+ raise ValueError('Invalid AUC curve value "%s".' % key)
+
+
+class AUCSummationMethod(Enum):
+ """Type of AUC summation method.
+
+ https://en.wikipedia.org/wiki/Riemann_sum)
+
+ Contains the following values:
+ * 'interpolation': Applies mid-point summation scheme for `ROC` curve. For
+ `PR` curve, interpolates (true/false) positives but not the ratio that is
+ precision (see Davis & Goadrich 2006 for details).
+ * 'minoring': Applies left summation for increasing intervals and right
+ summation for decreasing intervals.
+ * 'majoring': Applies right summation for increasing intervals and left
+ summation for decreasing intervals.
+ """
+ INTERPOLATION = 'interpolation'
+ MAJORING = 'majoring'
+ MINORING = 'minoring'
+
+ @staticmethod
+ def from_str(key):
+ if key in ('interpolation', 'Interpolation'):
+ return AUCSummationMethod.INTERPOLATION
+ elif key in ('majoring', 'Majoring'):
+ return AUCSummationMethod.MAJORING
+ elif key in ('minoring', 'Minoring'):
+ return AUCSummationMethod.MINORING
+ else:
+ raise ValueError('Invalid AUC summation method value "%s".' % key)
+
+def weighted_assign_add(label, pred, weights, var):
+ # Logical and
+ label = K.expand_dims(label, 0)
+ pred = K.expand_dims(pred, 0)
+ are_different = K.concatenate([label, pred], axis=0)
+ label_and_pred = K.all(are_different, axis=0)
+ label_and_pred = K.cast(label_and_pred, dtype=K.floatx())
+ if weights is not None:
+ label_and_pred *= weights
+ return var.assign_add(K.sum(label_and_pred, 1))
+
+def update_confusion_matrix_variables(variables_to_update,
+ y_true,
+ y_pred,
+ thresholds,
+ top_k=None,
+ class_id=None,
+ sample_weight=None):
+ """Returns op to update the given confusion matrix variables.
+ For every pair of values in y_true and y_pred:
+ true_positive: y_true == True and y_pred > thresholds
+ false_negatives: y_true == True and y_pred <= thresholds
+ true_negatives: y_true == False and y_pred <= thresholds
+ false_positive: y_true == False and y_pred > thresholds
+ The results will be weighted and added together. When multiple thresholds are
+ provided, we will repeat the same for every threshold.
+ For estimation of these metrics over a stream of data, the function creates an
+ `update_op` operation that updates the given variables.
+ If `sample_weight` is `None`, weights default to 1.
+ Use weights of 0 to mask values.
+ # Arguments
+ variables_to_update: Dictionary with 'tp', 'fn', 'tn', 'fp' as valid keys
+ and corresponding variables to update as values.
+ y_true: A `Tensor` whose shape matches `y_pred`. Will be cast to `bool`.
+ y_pred: A floating point `Tensor` of arbitrary shape and whose values are in
+ the range `[0, 1]`.
+ thresholds: A float value or a python list or tuple of float thresholds in
+ `[0, 1]`, or NEG_INF (used when top_k is set).
+ top_k: Optional int, indicates that the positive labels should be limited to
+ the top k predictions.
+ class_id: Optional int, limits the prediction and labels to the class
+ specified by this argument.
+ sample_weight: Optional `Tensor` whose rank is either 0, or the same rank as
+ `y_true`, and must be broadcastable to `y_true` (i.e., all dimensions must
+ be either `1`, or the same as the corresponding `y_true` dimension).
+ # Returns
+ Update ops.
+ # Raises
+ ValueError: If `y_pred` and `y_true` have mismatched shapes, or if
+ `sample_weight` is not `None` and its shape doesn't match `y_pred`, or if
+ `variables_to_update` contains invalid keys.
+ """
+ if variables_to_update is None:
+ return
+ y_true = K.cast(y_true, dtype=K.floatx())
+ y_pred = K.cast(y_pred, dtype=K.floatx())
+ if sample_weight is not None:
+ sample_weight = K.cast(sample_weight, dtype=K.floatx())
+
+ if not any(key
+ for key in variables_to_update
+ if key in list(ConfusionMatrix)):
+ raise ValueError(
+ 'Please provide at least one valid confusion matrix '
+ 'variable to update. Valid variable key options are: "{}". '
+ 'Received: "{}"'.format(
+ list(ConfusionMatrix), variables_to_update.keys()))
+
+ invalid_keys = [
+ key for key in variables_to_update if key not in list(ConfusionMatrix)
+ ]
+ if invalid_keys:
+ raise ValueError(
+ 'Invalid keys: {}. Valid variable key options are: "{}"'.format(
+ invalid_keys, list(ConfusionMatrix)))
+
+ if sample_weight is None:
+ y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
+ y_pred, y_true=y_true)
+ else:
+ y_pred, y_true, sample_weight = (
+ losses_utils.squeeze_or_expand_dimensions(
+ y_pred, y_true=y_true, sample_weight=sample_weight))
+
+ if top_k is not None:
+ y_pred = _filter_top_k(y_pred, top_k)
+ if class_id is not None:
+ y_true = y_true[..., class_id]
+ y_pred = y_pred[..., class_id]
+
+ thresholds = to_list(thresholds)
+ num_thresholds = len(thresholds)
+ num_predictions = K.size(y_pred)
+
+ # Reshape predictions and labels.
+ predictions_2d = K.reshape(y_pred, [1, -1])
+ labels_2d = K.reshape(
+ K.cast(y_true, dtype='bool'), [1, -1])
+
+ # Tile the thresholds for every prediction.
+ thresh_tiled = K.tile(
+ K.expand_dims(K.constant(thresholds), 1),
+ K.stack([1, num_predictions]))
+
+ # Tile the predictions for every threshold.
+ preds_tiled = K.tile(predictions_2d, [num_thresholds, 1])
+
+ # Compare predictions and threshold.
+ pred_is_pos = K.greater(preds_tiled, thresh_tiled)
+ pred_is_neg = K.greater(thresh_tiled, preds_tiled)
+
+ # Tile labels by number of thresholds
+ label_is_pos = K.tile(labels_2d, [num_thresholds, 1])
+
+ if sample_weight is not None:
+ weights = losses_utils.broadcast_weights(
+ y_pred, K.cast(sample_weight, dtype=K.floatx()))
+ weights_tiled = K.tile(
+ K.reshape(weights, [1, -1]), [num_thresholds, 1])
+ else:
+ weights_tiled = None
+
+ update_ops = []
+ loop_vars = {
+ ConfusionMatrix.TRUE_POSITIVES: (label_is_pos, pred_is_pos),
+ }
+ update_tn = ConfusionMatrix.TRUE_NEGATIVES in variables_to_update
+ update_fp = ConfusionMatrix.FALSE_POSITIVES in variables_to_update
+ update_fn = ConfusionMatrix.FALSE_NEGATIVES in variables_to_update
+
+ if update_fn or update_tn:
+ loop_vars[ConfusionMatrix.FALSE_NEGATIVES] = (label_is_pos, pred_is_neg)
+
+ if update_fp or update_tn:
+ label_is_neg = K.equal(
+ label_is_pos, K.zeros_like(label_is_pos, dtype=label_is_pos.dtype))
+ loop_vars[ConfusionMatrix.FALSE_POSITIVES] = (label_is_neg, pred_is_pos)
+ if update_tn:
+ loop_vars[ConfusionMatrix.TRUE_NEGATIVES] = (label_is_neg, pred_is_neg)
+
+ for matrix_cond, (label, pred) in loop_vars.items():
+ if matrix_cond in variables_to_update:
+ update_ops.append(
+ weighted_assign_add(label, pred, weights_tiled,
+ variables_to_update[matrix_cond]))
+ return update_ops
+
--
2.26.2
