CODEWITHSUNDEEP
pythonmachine-learningtext-classificationmultilabel-classification
user4069366
user4069366

Reputation:

Issues using scikit to for multi-label data

Im using the following code for Multi-label data classification :-

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn import preprocessing

X_train = np.array(["new york is a hell of a town",
                    "new york was originally dutch",
                    "the big apple is great",
                    "new york is also called the big apple",
                    "nyc is nice",
                    "people abbreviate new york city as nyc",
                    "the capital of great britain is london",
                    "london is in the uk",
                    "london is in england",
                    "london is in great britain",
                    "it rains a lot in london",
                    "london hosts the british museum",
                    "new york is great and so is london",
                    "i like london better than new york"])
y_train_text = [[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[12],[12]]

X_test = np.array(['nice day in nyc',
                   'welcome to london',
                   'london is rainy',
                   'it is raining in britian',
                   'it is raining in britian and the big apple',
                   'it is raining in britian and nyc',
                   'hello welcome to new york. enjoy it here and london too'])
target_names = ['New York', 'London']

lb = preprocessing.MultiLabelBinarizer()
Y = lb.fit_transform(y_train_text)

classifier = Pipeline([
    ('vectorizer', CountVectorizer()),
    ('tfidf', TfidfTransformer()),
    ('clf', OneVsRestClassifier(LinearSVC()))])

classifier.fit(X_train, Y)
predicted = classifier.predict(X_test)

======OUTPUT=====

[1, 0, 0],'New York'
[0, 1, 0],'London'
[0, 1, 0],'London'
[0, 1, 0],'London'
[1, 0, 0],'New York'
[0, 0, 0], 
[0, 0, 0]]

The last two are wrongly predicted , they should both be [0,0,1] for ['New York', 'London']

So I have these questions :- 1.] What exactly is wrong with my code 2.] Is this a proper way to deal with "Multi-label" data ? Or is there any other better approach. Because this and a code or two are all I could find on internet about "Multi-label" data. Whereas for binary classification there are a thousands. Please help me on this

Upvotes: 4

Views: 445

Answers (1)

lejlot
lejlot

Reputation: 66775

12 is not "1" and "2" it is twelve, thus

[[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[12],[12]]

should be

[[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[1, 2],[1, 2]]

Upvotes: 2

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