metrics

问题 I have following code snippet that attempts to do a grid search in which one of the grid parameters are the distance metrics to be used for the KNN algorithm. The example below fails if I use "wminkowski", "seuclidean" or "mahalanobis" distances metrics. # Define the parameter values that should be searched k_range = range(1,31) weights = ['uniform' , 'distance'] algos = ['auto', 'ball_tree', 'kd_tree', 'brute'] leaf_sizes = range(10, 60, 10) metrics = ["euclidean", "manhattan", "chebyshev",

问题 I try to calculate the f1_score but I get some warnings for some cases when I use the sklearn f1_score method. I have a multilabel 5 classes problem for a prediction. import numpy as np from sklearn.metrics import f1_score y_true = np.zeros((1,5)) y_true[0,0] = 1 # => label = [[1, 0, 0, 0, 0]] y_pred = np.zeros((1,5)) y_pred[:] = 1 # => prediction = [[1, 1, 1, 1, 1]] result_1 = f1_score(y_true=y_true, y_pred=y_pred, labels=None, average="weighted") print(result_1) # prints 1.0 result_2 = f1

问题 I try to calculate the f1_score but I get some warnings for some cases when I use the sklearn f1_score method. I have a multilabel 5 classes problem for a prediction. import numpy as np from sklearn.metrics import f1_score y_true = np.zeros((1,5)) y_true[0,0] = 1 # => label = [[1, 0, 0, 0, 0]] y_pred = np.zeros((1,5)) y_pred[:] = 1 # => prediction = [[1, 1, 1, 1, 1]] result_1 = f1_score(y_true=y_true, y_pred=y_pred, labels=None, average="weighted") print(result_1) # prints 1.0 result_2 = f1

问题 I am using Apache Ignite 2.8.0. Actually now i am getting memory metrics by the following(Using Java thick client), ClusterGroup remoteGroup = ignite.cluster().forRemotes(); ClusterMetrics metrics = remoteGroup.metrics(); Is there any way to get the Memory metrics by Python or Java thin client? 回答1: It is not supported currently by thin clients, so you will have to gather this information using thick client or server node. Then you can store it somewhere (in e.g. cache) to be accessible by

问题 I am using Apache Ignite 2.8.0. Actually now i am getting memory metrics by the following(Using Java thick client), ClusterGroup remoteGroup = ignite.cluster().forRemotes(); ClusterMetrics metrics = remoteGroup.metrics(); Is there any way to get the Memory metrics by Python or Java thin client? 回答1: It is not supported currently by thin clients, so you will have to gather this information using thick client or server node. Then you can store it somewhere (in e.g. cache) to be accessible by

问题 While I started using JBeret being the embedded JSR-352 engine in Wildfly, I notice that for some of my workload the chunk pattern does not apply. Simple enough I just wrapped it into batchlets and they are running ok. Now I'd like to collect metrics the same style as chunks do but there seems no method to increase existing metrics introduce new metrics What am I missing? 回答1: Batchlet is used to model task that is a single unit of work. If your workload is iterative in nature and needs

问题 We want to scale our pods horizontally based on the amount of messages in our Kafka Topic. The standard solution is to publish the metrics to the custom metrics API of Kubernetes. However, due to company guidelines we are not allowed to use the custom metrics API of Kubernetes. We are only allowed to use non-admin functionality. Is there a solution for this with kubernetes-nativ features or do we need to implement a customized solution? 回答1: I'm not exactly sure if this would fit your needs

问题 We want to scale our pods horizontally based on the amount of messages in our Kafka Topic. The standard solution is to publish the metrics to the custom metrics API of Kubernetes. However, due to company guidelines we are not allowed to use the custom metrics API of Kubernetes. We are only allowed to use non-admin functionality. Is there a solution for this with kubernetes-nativ features or do we need to implement a customized solution? 回答1: I'm not exactly sure if this would fit your needs

问题 I want to create one metric related to the number of errors but in fact I would like to add some contextual labels ? What is the best way to do that ? gauge ? How to create this kind of metric dynamically during the run of the task (because open method is not possible because too early) ? Thanks in advance David 回答1: You should be able to create a metric whenever you want -- just do it once per operator instance, before you use it. For error reporting you might find side outputs useful as

来源： https://stackoverflow.com/questions/64380057/tf-2-3-0-training-keras-model-using-tf-dataset-with-sample-weights-does-not-appl