Tensorflow 自定义指标：SensitivityAtSpecificity

Question

Background

The metric for my machine learning task is weight TPR = 0.4 * TPR1 + 0.3 * TPR2 + 0.3 * TPR3 . Generally, it asks for a model with higher recall rate while disturbing less negative samples.

Some terminology:

• TPR（True Positive Rate, Sensitivity): TPR = TP /（TP + FN）
• FPR（False Positive Rate, 1 - Specificity）: FPR = FP /（FP + TN）
• TP、FN、FP、TN stands for True Positive, False Negative, Fasle Positive and True Negative.
• TPR1：TPR at FPR = 0.001
• TPR2：TPR at FPR = 0.005
• TPR3：TPR at FPR = 0.01

My attempt

Since keras does not have such metric, we need to write our own custome metric. Another word for mention, unlike in lightgbm and xgboost, custom metric in keras is not straight-foward because training process are on tensors instead of pandas/numpy arrays.

In lightgbm/Xgboost, I have this wtpr custom metric, and it works fine:

def tpr_weight_funtion(y_true,y_predict):
    d = pd.DataFrame()
    d['prob'] = list(y_predict)
    d['y'] = list(y_true)
    d = d.sort_values(['prob'], ascending=[0])
    y = d.y
    PosAll = pd.Series(y).value_counts()[1]
    NegAll = pd.Series(y).value_counts()[0]
    pCumsum = d['y'].cumsum()
    nCumsum = np.arange(len(y)) - pCumsum + 1
    pCumsumPer = pCumsum / PosAll
    nCumsumPer = nCumsum / NegAll
    TR1 = pCumsumPer[abs(nCumsumPer-0.001).idxmin()]
    TR2 = pCumsumPer[abs(nCumsumPer-0.005).idxmin()]
    TR3 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()]
    return 0.4 * TR1 + 0.3 * TR2 + 0.3 * TR3

In keras, I write a custom metric below. It works with regular tensor input, but it failed during model fitting with batch Gradient descent :

import keras.backend as K
def keras_wtpr_metric(y_true, y_predict):
    n = y_predict.shape[0]
    
    a = tf.dtypes.cast(y_predict, tf.float32)
    b = tf.dtypes.cast(y_true, tf.float32)
    a = tf.reshape(a,shape = [-1])
    b = tf.reshape(b,shape = [-1])
    d = tf.stack([a,b], axis = 0)
    d = tf.gather(d, tf.argsort(a,direction='DESCENDING'), axis = 1)
    PosAll = tf.math.reduce_sum(b, axis = -1) # the number of positive samples
    NegAll = tf.math.reduce_sum(1-b, axis = -1) # the number of negative samples
    pCumsum = tf.math.cumsum(d[1]) # TP
    pCumsum = tf.dtypes.cast(pCumsum,dtype = tf.float32)
    nCumsum = tf.range(0,n,dtype = tf.float32) - pCumsum + 1 # FP
    pCumsumPer = pCumsum / PosAll # tpr
    nCumsumPer = nCumsum / NegAll # fpr
    TR1 = pCumsumPer[tf.math.argmin(abs(nCumsumPer-0.001))]
    TR2 = pCumsumPer[tf.math.argmin(abs(nCumsumPer-0.005))]
    TR3 = pCumsumPer[tf.math.argmin(abs(nCumsumPer-0.01))]
    return tf.reduce_sum(0.4*TR1+0.3*TR2+0.3*TR3)

My model is:

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(load_breast_cancer().data, load_breast_cancer().target,test_size = 0.3)

model = keras.models.Sequential([
# I have a tabular data
    keras.layers.Dense(256, activation='relu',input_shape = (x_train.shape[1],)), 
    keras.layers.Dense(64, activation = 'relu'),
    keras.layers.Dense(1, activation = 'sigmoid')
])
model.compile(optimizer='adam',loss = 'binary_crossentropy', metrics = [keras_wtpr_metric])
# it seems can not work under batch training, I don't know why
model.fit(x=x_train, y= y_train, batch_size = 2048, epochs = 30,validation_data = [x_test,y_test]) 

Error message is

Epoch 1/30
398/398 [==============================] - 1s 2ms/sample
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-639-da481d44d615> in <module>
      5 ])
      6 model.compile(optimizer='adam',loss = 'binary_crossentropy', metrics = [keras_wtpr_metric])
----> 7 model.fit(x=x_train, y= y_train, batch_size = 2048, epochs = 30,validation_data = [x_test,y_test]) # it seems can not work under batch training, I don't know why

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    726         max_queue_size=max_queue_size,
    727         workers=workers,
--> 728         use_multiprocessing=use_multiprocessing)
    729 
    730   def evaluate(self,

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    322                 mode=ModeKeys.TRAIN,
    323                 training_context=training_context,
--> 324                 total_epochs=epochs)
    325             cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
    326 

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
    121         step=step, mode=mode, size=current_batch_size) as batch_logs:
    122       try:
--> 123         batch_outs = execution_function(iterator)
    124       except (StopIteration, errors.OutOfRangeError):
    125         # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
     84     # `numpy` translates Tensors to values in Eager mode.
     85     return nest.map_structure(_non_none_constant_value,
---> 86                               distributed_function(input_fn))
     87 
     88   return execution_function

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
    455 
    456     tracing_count = self._get_tracing_count()
--> 457     result = self._call(*args, **kwds)
    458     if tracing_count == self._get_tracing_count():
    459       self._call_counter.called_without_tracing()

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
    518         # Lifting succeeded, so variables are initialized and we can run the
    519         # stateless function.
--> 520         return self._stateless_fn(*args, **kwds)
    521     else:
    522       canon_args, canon_kwds = \

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
   1821     """Calls a graph function specialized to the inputs."""
   1822     graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   1824 
   1825   @property

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _filtered_call(self, args, kwargs)
   1139          if isinstance(t, (ops.Tensor,
   1140                            resource_variable_ops.BaseResourceVariable))),
-> 1141         self.captured_inputs)
   1142 
   1143   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1222     if executing_eagerly:
   1223       flat_outputs = forward_function.call(
-> 1224           ctx, args, cancellation_manager=cancellation_manager)
   1225     else:
   1226       gradient_name = self._delayed_rewrite_functions.register()

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    509               inputs=args,
    510               attrs=("executor_type", executor_type, "config_proto", config),
--> 511               ctx=ctx)
    512         else:
    513           outputs = execute.execute_with_cancellation(

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     keras_symbolic_tensors = [

~/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/six.py in raise_from(value, from_value)

InvalidArgumentError:  Incompatible shapes: [0] vs. [398]
     [[node metrics/keras_wtpr_metric/sub_1 (defined at /Users/travis/opt/anaconda3/envs/envpython36/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_distributed_function_681042]

Function call stack:
distributed_function

My question

1. How to write a weighted SensitivityAtSpecificity in keras?
2. Why my keras_wtpr_metric failed?

Some Useful Sources:

1. https://keras.io/api/metrics/#creating-custom-metrics
2. https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SensitivityAtSpecificity

Answer 1

use n = tf.shape(y_predict)[0] intead of n = y_predict.shape[0] for dynamically take into account the batch dimensionality

pass also your validation data in round brackets: validation_data = (x_test,y_test)

here the running notebook: https://colab.research.google.com/drive/1uUb3nAk8CAsLYDJXGraNt1_sYYRYVihX?usp=sharing