多类别分类和概率预测

Question

import pandas as pd
import numpy
from sklearn import cross_validation
from sklearn.naive_bayes import GaussianNB

fi = "df.csv"
# Open the file for reading and read in data
file_handler = open(fi, "r")
data = pd.read_csv(file_handler, sep=",")
file_handler.close()

# split the data into training and test data
train, test = cross_validation.train_test_split(data,test_size=0.6, random_state=0)
# initialise Gaussian Naive Bayes
naive_b = GaussianNB()


train_features = train.ix[:,0:127]
train_label = train.iloc[:,127]

test_features = test.ix[:,0:127]
test_label = test.iloc[:,127]

naive_b.fit(train_features, train_label)
test_data = pd.concat([test_features, test_label], axis=1)
test_data["p_malw"] = naive_b.predict_proba(test_features)

print "test_data\n",test_data["p_malw"]
print "Accuracy:", naive_b.score(test_features,test_label)

I have written this code to accept input from a csv file with 128 columns where 127 columns are features and the 128th column is the class label.

I want to predict probability that the sample belongs to each class (There are 5 classes (1-5)) and print it in for of a matrix and determine the class of sample based on the prediction. predict_proba() is not giving the desired output. Please suggest required changes.

Answer 1

GaussianNB.predict_proba returns the probabilities of the samples for each class in the model. In your case, it should return a result with five columns with the same number of rows as in your test data. You can verify which column corresponds to which class using naive_b.classes_ . So, it is not clear why you are saying that this is not the desired output. Perhaps, your problem comes from the fact that you are assigning the output of predict proba to a data frame column. Try:

pred_prob = naive_b.predict_proba(test_features)

instead of

test_data["p_malw"] = naive_b.predict_proba(test_features)

and verify its shape using pred_prob.shape. The second dimension should be 5.

If you want the predicted label for each sample you can use the predict method, followed by confusion matrix to see how many labels have been predicted correctly.

from sklearn.metrics import confusion_matrix

naive_B.fit(train_features, train_label)

pred_label = naive_B.predict(test_features)

confusion_m = confusion_matrix(test_label, pred_label)
confusion_m

Here is some useful reading.

sklearn GaussianNB - http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB.predict_proba

sklearn confusion_matrix - http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html