宣告：tensorflow的版本1.1.0

class_id是用來讓你確定哪一個類別是正類的，

這是tf.contrib.metrics.streaming_sparse_recall_at_k官方文件

Signature: tf.contrib.metrics.streaming_sparse_recall_at_k(predictions, labels,
k, class_id=None, weights=None, metrics_collections=None, updates_collections=No
ne, name=None)

Computes [email protected] of the predictions with respect to sparse labels.


If `class_id` is not specified, we'll calculate recall as the ratio of true
    positives (i.e., correct predictions, items in the top `k` highest
    `predictions` that are found in the corresponding row in `labels`) to

    actual positives (the full `labels` row).

如果class_id沒有指明的，那麼則使用所有預測正確的樣本數除以樣本總數。這麼一看，[email protected]變成了[email protected]的定義了

If `class_id` is specified, we calculate recall by considering only the rows
    in the batch for which `class_id` is in `labels`, and computing the
    fraction of them for which `class_id` is in the corresponding row in
    `labels`.


`streaming_sparse_recall_at_k` creates two local variables,
`true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
the recall_at_k frequency. This frequency is ultimately returned as
`recall_at_<k>`: an idempotent operation that simply divides
`true_positive_at_<k>` by total (`true_positive_at_<k>` +
`false_negative_at_<k>`).


For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`recall_at_<k>`. Internally, a `top_k` operation computes a `Tensor`
indicating the top `k` `predictions`. Set operations applied to `top_k` and
`labels` calculate the true positives and false negatives weighted by
`weights`. Then `update_op` increments `true_positive_at_<k>` and
`false_negative_at_<k>` using these values.


If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.


Args:
  predictions: Float `Tensor` with shape [D1, ... DN, num_classes] where
    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
    The final dimension contains the logit values for each class. [D1, ... DN]
    must match `labels`.
  labels: `int64` `Tensor` or `SparseTensor` with shape
    [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
    target classes for the associated prediction. Commonly, N=1 and `labels`
    has shape [batch_size, num_labels]. [D1, ... DN] must match `predictions`.
    Values should be in range [0, num_classes), where num_classes is the last
    dimension of `predictions`. Values outside this range always count
    towards `false_negative_at_<k>`.
  k: Integer, k for @k metric.
  class_id: Integer class ID for which we want binary metrics. This should be
    in range [0, num_classes), where num_classes is the last dimension of
    `predictions`. If class_id is outside this range, the method returns NAN.
  weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
    `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
    dimensions must be either `1`, or the same as the corresponding `labels`
    dimension).
  metrics_collections: An optional list of collections that values should
    be added to.
  updates_collections: An optional list of collections that updates should
    be added to.
  name: Name of new update operation, and namespace for other dependent ops.


Returns:
  recall: Scalar `float64` `Tensor` with the value of `true_positives` divided
    by the sum of `true_positives` and `false_negatives`.
  update_op: `Operation` that increments `true_positives` and
    `false_negatives` variables appropriately, and whose value matches
    `recall`.


Raises:
  ValueError: If `weights` is not `None` and its shape doesn't match
  `predictions`, or if either `metrics_collections` or `updates_collections`
  are not a list or tuple.
File:      d:\programdata\anaconda3\lib\site-packages\tensorflow\contrib\metrics
\python\ops\metric_ops.py

Type:      function

這是 tf.contrib.metrics.streaming_sparse_precision_at_k的官方文件
Signature: tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, label
s, k, class_id=None, weights=None, metrics_collections=None, updates_collections
=None, name=None)
Docstring:
Computes [email protected] of the predictions with respect to sparse labels.


If `class_id` is not specified, we calculate precision as the ratio of true
    positives (i.e., correct predictions, items in the top `k` highest
    `predictions` that are found in the corresponding row in `labels`) to
    positives (all top `k` `predictions`).

如果沒有指定class_id,那麼預測正確的樣本數除以所有[email protected]個預測數目，也就是樣本總數*k。因此，隨著K的增大，[email protected]一般會減小。
If `class_id` is specified, we calculate precision by considering only the
    rows in the batch for which `class_id` is in the top `k` highest
    `predictions`, and computing the fraction of them for which `class_id` is
    in the corresponding row in `labels`.


We expect precision to decrease as `k` increases.


`streaming_sparse_precision_at_k` creates two local variables,
`true_positive_at_<k>` and `false_positive_at_<k>`, that are used to compute
the [email protected] frequency. This frequency is ultimately returned as
`precision_at_<k>`: an idempotent operation that simply divides
`true_positive_at_<k>` by total (`true_positive_at_<k>` +
`false_positive_at_<k>`).


For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`precision_at_<k>`. Internally, a `top_k` operation computes a `Tensor`
indicating the top `k` `predictions`. Set operations applied to `top_k` and
`labels` calculate the true positives and false positives weighted by
`weights`. Then `update_op` increments `true_positive_at_<k>` and
`false_positive_at_<k>` using these values.


If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.


Args:
  predictions: Float `Tensor` with shape [D1, ... DN, num_classes] where
    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
    The final dimension contains the logit values for each class. [D1, ... DN]
    must match `labels`.
  labels: `int64` `Tensor` or `SparseTensor` with shape
    [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
    target classes for the associated prediction. Commonly, N=1 and `labels`
    has shape [batch_size, num_labels]. [D1, ... DN] must match
    `predictions`. Values should be in range [0, num_classes), where
    num_classes is the last dimension of `predictions`. Values outside this
    range are ignored.
  k: Integer, k for @k metric.
  class_id: Integer class ID for which we want binary metrics. This should be
    in range [0, num_classes], where num_classes is the last dimension of
    `predictions`. If `class_id` is outside this range, the method returns
    NAN.

sklearn版本為0.19.1

sklearn.metrics.recall_score(y_true,y_pred,labels=None,pos_label=1,average=’binary’,sample_weight=None)

Compute the recall

The recall is the ratiotp/(tp+fn)wheretpis the number of true positives andfnthe number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.

The best value is 1 and the worst value is 0.

Read more in theUser Guide.

Parameters:

y_true: 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred: 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels: list, optional The set of labels to include whenaverage!='binary', and their order ifaverageisNone. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels iny_trueandy_predare used in sorted order. Changed in version 0.17:parameterlabelsimproved for multiclass problem. pos_label: str or int, 1 by default The class to report ifaverage='binary'and the data is binary. If the data are multiclass or multilabel, this will be ignored; settinglabels=[pos_label]andaverage!='binary'will report scores for that label only. average: string, [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’] This parameter is required for multiclass/multilabel targets. IfNone, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: 'binary': Only report results for the class specified bypos_label. This is applicable only if targets (y_{true,pred}) are binary. 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives. 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. 'weighted': Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall. 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs fromaccuracy_score). sample_weight: array-like of shape = [n_samples], optional Sample weights.

Returns:

recall: float (if average is not None) or array of float, shape = [n_unique_labels] Recall of the positive class in binary classification or weighted average of the recall of each class for the multiclass task.

Examples

>>>

>>> from sklearn.metrics import recall_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> recall_score(y_true, y_pred, average='macro')  
0.33...
>>> recall_score(y_true, y_pred, average='micro')  
0.33...
>>> recall_score(y_true, y_pred, average='weighted')  
0.33...
>>> recall_score(y_true, y_pred, average=None)
array([ 1.,  0.,  0.])

需要注意的第一點，y_true和y_pred可以一維矩陣，也可是多維的，但是必須要統一

第二點，micro是計算全域性的TP，FP,FN，macro是計算每個類的TP、FP、FN，並算出算術平均值，weighted和macro類似，只是它是加權平均值，權重是由每個真實樣本的個數決定的。sample，這個是針對每個樣本，目前暫不清楚是如何工作的。

第三點，pos_label 和label是用來確定要計算正類的label。