[Python知识库]TP、TN、FP、FN

P/N：表示模型预测的结果积极或消极。

T/F：表示模型预测的结果正确或错误。

所以

TP：表示模型预测为positive是准确的

TN：表示模型预测为negtive是正确的

FP：表示模型预测为positive是错误的

FN：表示模型预测为negtive是错误的

准确率（accuracy）=(TP + TN)/(TP+TN+FP+FN)，表示模型预测准确的概率

查准率-精准率(Precision) = TP/(TP+FP)，表示预测为positve的结果中预测正确所占的比例

查全率-召回率（recall）=TP/(TP+FN)，表示预测postive的结果占应该预测正确的数量的比例

关于对应的代码实现：

import configparser

import tensorflow as tf

from utils.path_utils import get_full_path
from utils.read_batch_data import get_data_by_batch_size


class ModelTest:
    def __init__(self,
                 batch_size=500,
                 sentence_vocb_length=15,
                 model_path ="",
                 fill_vocab='TTTTTT'
                 ):
        self.fill_vocab = fill_vocab
        self.sentence_vocb_length=sentence_vocb_length
        self.batch_size =batch_size
        self.model = tf.keras.models.load_model(model_path)

        self.fasenegtives = tf.keras.metrics.FalseNegatives()
        self.trueNegatives = tf.keras.metrics.TrueNegatives()
        self.falsePositives =tf.keras.metrics.FalsePositives()
        self.truePositives =tf.keras.metrics.TruePositives()

        # 返回结果的顺序性指标

    def test(self):
        # ========================== Create dataset =======================
        for x, y in get_data_by_batch_size("data/train_data/validate_data.txt", self.batch_size,
                                           self.sentence_vocb_length, self.fill_vocab):
            test_x = x
            test_y = y
            y_predict_test = self.model(test_x)
            result1 = tf.reshape(tf.cast(y_predict_test>0.6,dtype=tf.int32),shape=(len(test_y),))
            result2 = tf.reshape(tf.cast(test_y,dtype=tf.int32),shape=(len(test_y),))
            self.fasenegtives.update_state(result2,result1)
            self.trueNegatives.update_state(result2,result1)
            self.falsePositives.update_state(result2,result1)
            self.truePositives.update_state(result2,result1)

if __name__ == '__main__':
    # =============================== GPU ==============================
    gpu = tf.config.experimental.list_physical_devices(device_type='GPU')
    print("gpu message:{}".format(gpu))
    # If you have GPU, and the value is GPU serial number.
    import os
    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
    config = configparser.ConfigParser()
    config.read(get_full_path('conf/config.txt'), encoding="utf-8")
    batch_size = 1000
    sentence_vocb_length = config.getint("lstm_model", "sentence_vocab_lenth")

    train_instance = ModelTest(model_path=get_full_path(config.get("lstm_model","model_path")),batch_size=batch_size,sentence_vocb_length = sentence_vocb_length)

    train_instance.test()
    print("fasenegtives",train_instance.fasenegtives.result().numpy())
    print("trueNegatives",train_instance.trueNegatives.result().numpy())
    print("falsePositives",train_instance.falsePositives.result().numpy())
    print("truePositives",train_instance.truePositives.result().numpy())
    
    print("准确率（accuracy）:",(train_instance.truePositives.result().numpy() + train_instance.trueNegatives.result().numpy())/
          (train_instance.truePositives.result().numpy() + train_instance.trueNegatives.result().numpy() +
          train_instance.fasenegtives.result().numpy()+
          train_instance.falsePositives.result().numpy()))
    
    print("查准率-精准率（precision）:",train_instance.truePositives.result().numpy()/(train_instance.truePositives.result().numpy()+
                                                                               train_instance.falsePositives.result().numpy()))
    print("查全率-召回率（recall）:",train_instance.truePositives.result().numpy()/(train_instance.truePositives.result().numpy()+
                                                                               train_instance.fasenegtives.result().numpy()))