在Dataframe中查找BernoulliNB概率

Question

我有一些训练数据（TRAIN）和一些测试数据（TEST）。每个数据帧的每一行包含一个观察到的类（X）和一些二进制（Y）列。 BernoulliNB基于训练数据预测测试数据中给定Y的X的概率。我试图查找测试数据（Pr）中每行的观察类的概率。

编辑：我使用Antoine Zambelli的建议来修复代码：

from sklearn.naive_bayes import BernoulliNB
BNB = BernoulliNB()

# Training Data
TRAIN = pd.DataFrame({'X' : [1,2,3,9],
                      'Y1': [1,1,0,0],
                      'Y4': [1,0,0,0]})

# Test Data
TEST  = pd.DataFrame({'X' : [5,0,1,1,1,2,2,2,2],
                      'Y1': [1,1,0,1,0,1,0,0,0],
                      'Y2': [1,0,1,0,1,0,1,0,1],
                      'Y3': [1,1,0,1,1,0,0,0,0],
                      'Y4': [1,1,0,1,1,0,0,0,0]})

# Add the information that TRAIN has none of the missing items
diff_cols = set(TEST.columns)-set(TRAIN.columns)
for i in diff_cols:
    TRAIN[i] = 0

# Split the data
Se_Tr_X = TRAIN['X']
Se_Te_X = TEST ['X']
df_Tr_Y = TRAIN .drop('X', axis=1)
df_Te_Y = TEST  .drop('X', axis=1)

# Train: Bernoulli Naive Bayes Classifier
A_F = BNB.fit(df_Tr_Y, Se_Tr_X)

# Test: Predict Probability
Ar_R = BNB.predict_proba(df_Te_Y)
df_R = pd.DataFrame(Ar_R)

# Rename the columns after the classes of X
df_R.columns = BNB.classes_

df_S = df_R .join(TEST)

# Look up the predicted probability of the observed X
# Skip X's that are not in the training data
def get_lu(df):
  def lu(i, j):
    return df.get(j, {}).get(i, np.nan)
  return lu
df_S['Pr'] = [*map(get_lu(df_R), df_S .T, df_S .X)]

这似乎有效，给我结果（df_S）：

这正确地为前两行提供“NaN”，因为训练数据不包含关于类X = 5或X = 0的信息。

Answer 1

好的，这里有几个问题。我在下面有一个完整的工作示例，但首先是那些问题。主要是断言“这正确地为前两行提供了”NaN“。

这与使用分类算法的方式以及它们可以做什么有关。训练数据包含您希望算法知道并能够采取行动的所有信息。测试数据仅在处理时考虑到该信息。即使您（该人）知道测试标签是5并且未包含在训练数据中，该算法也不知道。它只会查看要素数据，然后尝试从这些数据中预测标签。所以它不能返回nan（或5，或任何不在训练集中） - nan来自你的工作从df_R到df_S。

这导致了第二个问题，即行df_Te_Y = TEST .iloc[ : , 1 : ]，该行应该是df_Te_Y = TEST .iloc[ : , 2 : ]，因此它不包括标签数据。标签数据仅出现在训练集中。预测标签将仅从训练数据中出现的标签集中提取。

注意：我已经将类标签更改为Y，并将特征数据更改为X，因为这是文献中的标准。

from sklearn.naive_bayes import BernoulliNB
from sklearn.metrics import accuracy_score
import pandas as pd

BNB = BernoulliNB()

# Training Data
train_df = pd.DataFrame({'Y' : [1,2,3,9], 'X1': [1,1,0,0], 'X2': [0,0,0,0], 'X3': [0,0,0,0], 'X4': [1,0,0,0]})

# Test Data
test_df  = pd.DataFrame({'Y' : [5,0,1,1,1,2,2,2,2],
                      'X1': [1,1,0,1,0,1,0,0,0],
                      'X2': [1,0,1,0,1,0,1,0,1],
                      'X3': [1,1,0,1,1,0,0,0,0],
                      'X4': [1,1,0,1,1,0,0,0,0]})


X = train_df.drop('Y', axis=1)  # Known training data - all but 'Y' column.
Y = train_df['Y']  # Known training labels - just the 'Y' column.

X_te = test_df.drop('Y', axis=1)  # Test data.
Y_te = test_df['Y']  # Only used to measure accuracy of prediction - if desired.

Ar_R = BNB.fit(X, Y).predict_proba(X_te)  # Can be combined to a single line.
df_R = pd.DataFrame(Ar_R)
df_R.columns = BNB.classes_  # Rename as per class labels.

# Columns are class labels and Rows are observations.
# Each entry is a probability of that observation being assigned to that class label.
print(df_R)

predicted_labels = df_R.idxmax(axis=1).values  # For each row, take the column with the highest prob in that row.
print(predicted_labels)  # [1 1 3 1 3 2 3 3 3]

print(accuracy_score(Y_te, predicted_labels))  # Percent accuracy of prediction.

print(BNB.fit(X, Y).predict(X_te))  # [1 1 3 1 3 2 3 3 3], can be used in one line if predicted_label is all we want.
# NOTE: change train_df to have 'Y': [1,2,1,9] and we get predicted_labels = [1 1 9 1 1 1 9 1 9].
# So probabilities have changed.

如果在阅读代码后没有意义，我建议您查看有关聚类算法的一些教程或其他材料。