predict_proba() 中的前 3 个类

Question

我正在研究一个多类文本分类问题，该问题需要具有相应概率的前 3 个预测标签。我可以使用sklearn predict_proba()，但很难像 table A 那样格式化输出。我的代码如下：

cv = StratifiedKFold(n_splits = 10, random_state = 42, shuffle = None)

pipeline_sgd = Pipeline([
     ('vect', CountVectorizer()),
     ('tfdif', TfidfTransformer()),
     ('nb', CalibratedClassifierCV(base_estimator = SGDClassifier(), cv=cv)),
])
Model = pipeline_sgd.fit(X_train, y_train)

n_top_labels = 3
probas = model.predict_probas(test["text"])
top_n_lables_idx = probas.argsort()[::-1][:n_top_lables]
top_n_probs = probas[top_n_lables_idx]
top_n_labels = label_encoder.inverse_transform(top_n_lables_idx.ravel())

results = list(zip(top_n_labels, top_n_probas))

 

输出：

[(A, .80),
 (B, .10),
 (C, .10)]

我在上述输出中遇到的挑战是它没有为我提供每行文本的前 3 个标签/概率。例如，当我对一组新文档（文本）进行推理时，我只得到一个输出，而不是每个文档（行）的输出。

我遇到的第二个挑战是，当我使用 pd.Dataframe(data = results) 将其插入数据帧时，我得到以下信息：

|   | 0 | 1               |
|---|---|-----------------|
| 0 | A | [[.80,.10,.10]] |
| 1 | B | [[.85,.10,.05]] |
| 2 | C | [[.70,.20,.10]] |

应该是：

|   | 0     | 1               |
|---|-------|-----------------|
| 0 | A,B,C | [[.80,.10,.10]] |
| 1 | B,C,A | [[.85,.10,.05]] |
| 2 | C,B,A | [[.70,.20,.10]] |

表 A

| Text                                       | Predicted labels | Probabilities  |
|--------------------------------------------|------------------|----------------|
| Hello  World!                              | A,B,C            | [.80,.10,10]   |
| Have a nice Day!                           | B,C,A            | [.90,.05,05]   |
| It's a wonderful day in the neighborhood.  | C,A,B            | [.80,.10,10]   |

Answer 1

当我运行您的代码时，top_n_probs 的形状非常奇怪，我发现很难找回标签。 argsort 和调用排序值的代码似乎有点奇怪。

下面我写了一个应该可以工作的快速实现。

举个例子dataset：

from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer
from sklearn.calibration import CalibratedClassifierCV
from sklearn.linear_model import SGDClassifier

import pandas as pd
import numpy as np

df = pd.read_csv('./smsspamcollection//SMSSpamCollection', sep='\t', names=["label", "message"])
df['label'][df['label']=='ham'] = np.random.choice(['hamA','hamB'],np.sum(df['label']=='ham'))
X_train = df['message']
y_train = df['label']

我的标签如下所示：

df['label'].value_counts()

hamB    2425
hamA    2400
spam     747

并运行您的代码进行拟合：

cv = StratifiedKFold(n_splits = 10, random_state = 42, shuffle = True)

pipeline_sgd = Pipeline([
     ('vect', CountVectorizer()),
     ('tfdif', TfidfTransformer()),
     ('nb', CalibratedClassifierCV(base_estimator = SGDClassifier(), cv=cv)),
])

model = pipeline_sgd.fit(X_train, y_train)

这应该可行：

n_top_labels = 3
probas = model.predict_proba(X_train[:5])
top_n_lables_idx = np.argsort(-probas)
top_n_probs = np.round(-np.sort(-probas),3)
top_n_labels = [model.classes_[i] for i in top_n_lables_idx]

results = list(zip(top_n_labels, top_n_probs))

pd.DataFrame(results)

    0   1
0   [hamB, hamA, spam]  [0.608, 0.38, 0.012]
1   [hamA, hamB, spam]  [0.605, 0.391, 0.004]
2   [spam, hamB, hamA]  [0.603, 0.212, 0.185]
3   [hamB, hamA, spam]  [0.521, 0.478, 0.001]
4   [hamB, hamA, spam]  [0.645, 0.352, 0.003]

Answer 2

更新已接受的答案。

n = 3

probas = model.predict_proba(X_train)
top_n_lables_idx = np.argsort(-probas, axis=1)[:, :n]
top_n_probs = np.round(-np.sort(-probas),3)[:, :n]
top_n_labels = [model.classes_[i] for i in top_n_lables_idx]
    
results = list(zip(top_n_labels, top_n_probs))

pd.DataFrame(results)

这确保我在两列中都获得前 3 名。