如何将概率添加到 model.predict 输出？

Question

构建了一个功能分类模型

教程只输出预测的类别名称。我希望它输出类别名称及其概率，我只想输出高于某个概率的类别。例如，我只想要超过 .5

的类别

这是用于访问模型的函数：

import pickle
import numpy as np
category_model_path="categorymodel.pkl"
category_transformer_path="categorytransformer.pkl"
sentiment_model_path="sentimentmodel.pkl"
sentiment_transformer_path="sentimenttransformer.pkl"

def get_top_k_predictions(model,X_test,k):
    
    # get probabilities instead of predicted labels, since we want to collect top 3
    np.set_printoptions(suppress=True)
    probs = model.predict_proba(X_test)

    # GET TOP K PREDICTIONS BY PROB - note these are just index
    best_n = np.argsort(probs, axis=1)[:,-k:]
    
    # GET CATEGORY OF PREDICTIONS
    preds=[[model.classes_[predicted_cat] for predicted_cat in prediction] for prediction in best_n]
    
    preds=[ item[::-1] for item in preds]
    
    return preds

category_loaded_model = pickle.load(open(category_model_path, 'rb'))
category_loaded_transformer = pickle.load(open(category_transformer_path, 'rb'))

sentiment_loaded_model = pickle.load(open(sentiment_model_path, 'rb'))
sentiment_loaded_transformer = pickle.load(open(sentiment_transformer_path, 'rb'))

那么这段代码就是用来调用函数的：

category_test_features=category_loaded_transformer.transform(["I absolutley loved the organization "])
get_top_k_predictions(category_loaded_model,category_test_features,2)

这是当前输出：

[['Course Structure', 'Learning Materials']]

概率是在 probs 变量的函数中计算的。我不知道如何只得到超过 .5 的那些并将它们添加到 preds 输出。

Answer 1

best_n 数组包含概率数组 probs 的索引。您可以像获取标签一样使用它。您可以获得这样的标签概率元组：

preds = [
    [(model.classes_[predicted_cat], distribution[predicted_cat])
     for predicted_cat in prediction]
    for distribution, prediction in zip(probs, best_n)]

如果您不想 return 概率而只想过滤它们，您可以这样做：

preds=[
    [model.classes_[predicted_cat]
     for predicted_cat in prediction if distribution[predicted_cat] > 0.5]
    for distribution, prediction in zip(probs, best_n)]

如何将概率添加到 model.predict 输出？

How to add probabilities to model.predict output?

python

classification

machine-learning

text-classification