evaluator.compute()的含义？

`evaluator.compute()` 是指在评估器（evaluator）上执行计算（compute）过程，用于计算模型在给定数据集上的性能指标。具体来说，它会加载数据集中的数据，将其输入到模型中进行预测，然后与数据集中的真实标签进行比较，计算出模型在该数据集上的性能指标（如准确率、F1 值等）。在完成计算后，它会返回一个包含性能指标的字典或者一个具有多个指标的评估结果对象。

相关问题

如何解决Loading and preparing results... DONE (t=0.01s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=0.44s). Accumulating evaluation results... Traceback (most recent call last): File "tools/train.py", line 133, in <module> main() File "tools/train.py", line 129, in main runner.train() File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1721, in train model = self.train_loop.run() # type: ignore File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/runner/loops.py", line 102, in run self.runner.val_loop.run() File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/runner/loops.py", line 366, in run metrics = self.evaluator.evaluate(len(self.dataloader.dataset)) File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/evaluator/evaluator.py", line 79, in evaluate _results = metric.evaluate(size) File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/mmengine/evaluator/metric.py", line 133, in evaluate _metrics = self.compute_metrics(results) # type: ignore File "/home/wangbei/mmdetection(coco)/mmdet/evaluation/metrics/coco_metric.py", line 512, in compute_metrics coco_eval.accumulate() File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/pycocotools-2.0-py3.8-linux-x86_64.egg/pycocotools/cocoeval.py", line 378, in accumulate tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) File "/home/wangbei/anaconda3/envs/Object_mmdetection/lib/python3.8/site-packages/numpy/__init__.py", line 305, in __getattr__ raise AttributeError(__former_attrs__[attr]) AttributeError: module 'numpy' has no attribute 'float'. `np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 29887 closing signal SIGTERM ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 29886) of binary: /home/wangbei/anaconda3/envs/Object_mmdetection/bin/python

这个错误看起来像是在使用numpy时出现了问题。根据错误信息，似乎是在`pycocotools/cocoeval.py`文件中的`np.float`出现了问题。这是因为在NumPy 1.20中，`np.float`被弃用了。为了解决这个问题，你需要将代码中的`np.float`替换为`float`或`np.float64`。

你可以在`pycocotools/cocoeval.py`文件中找到`tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)`这一行代码，将其中的`np.float`替换为`float`或`np.float64`。如果你不确定应该使用哪个，请根据NumPy版本查看官方文档或参考错误信息中提供的链接。

在修改代码后，重新运行程序，应该就可以解决这个问题了。

scala支持向量机模型计算混淆矩阵和roc曲线

要在Scala中使用支持向量机模型计算混淆矩阵和ROC曲线，您需要使用Spark MLlib库。以下是一个简单的示例：

import org.apache.spark.ml.classification.LinearSVC
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{VectorAssembler, StringIndexer}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder().appName("SVMExample").getOrCreate()

// Load data
val data = spark.read.format("csv").option("header", "true").load("data.csv")

// Convert label column to numeric
val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(data)
val indexed = labelIndexer.transform(data)

// Assemble feature columns into a vector
val assembler = new VectorAssembler().setInputCols(Array("feature1", "feature2")).setOutputCol("features")
val assembled = assembler.transform(indexed)

// Split data into training and test sets
val Array(training, test) = assembled.randomSplit(Array(0.7, 0.3), seed = 12345)

// Train SVM model
val svm = new LinearSVC().setMaxIter(10).setRegParam(0.1).setElasticNetParam(0.0)
val model = svm.fit(training)

// Make predictions on test data
val predictions = model.transform(test)

// Compute evaluation metrics
val evaluator = new BinaryClassificationEvaluator().setLabelCol("indexedLabel").setRawPredictionCol("rawPrediction").setMetricName("areaUnderROC")
val areaUnderROC = evaluator.evaluate(predictions)

val tp = predictions.filter("prediction = 1.0 AND indexedLabel = 1.0").count()
val fp = predictions.filter("prediction = 1.0 AND indexedLabel = 0.0").count()
val tn = predictions.filter("prediction = 0.0 AND indexedLabel = 0.0").count()
val fn = predictions.filter("prediction = 0.0 AND indexedLabel = 1.0").count()

val confusionMatrix = Seq(
  (tp, fp),
  (fn, tn)
)

// Output results
println(s"Area under ROC: $areaUnderROC")
println(s"Confusion matrix:\n${confusionMatrix.mkString("\n")}")

请注意，这些示例假定您已经将数据加载到Spark DataFrame中，并且已经使用StringIndexer和VectorAssembler转换了数据以进行训练和预测。您需要根据您的数据和模型进行相应的更改。