multi-label-classification | machine-learning tensorflow | Machine Learning library

by hi-zhengcheng Python Version: Current License: No License

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kandi X-RAY | multi-label-classification Summary

multi-label-classification is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. multi-label-classification has no bugs, it has no vulnerabilities and it has low support. However multi-label-classification build file is not available. You can download it from GitHub.

machine-learning tensorflow multi-label-classification

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multi-label-classification has a low active ecosystem.

It has 28 star(s) with 7 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 266 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of multi-label-classification is current.

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multi-label-classification has 0 bugs and 0 code smells.

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multi-label-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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multi-label-classification releases are not available. You will need to build from source code and install.

multi-label-classification has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

multi-label-classification saves you 366 person hours of effort in developing the same functionality from scratch.

It has 874 lines of code, 44 functions and 9 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed multi-label-classification and discovered the below as its top functions. This is intended to give you an instant insight into multi-label-classification implemented functionality, and help decide if they suit your requirements.

ResNet v2
Resnet v2d
Create a resnet block
Stack blocks of blocks into dense tensors
Builds the model
Process an image
Over - sampled image
Build tensorflow inputs
Bottleneck bottleneck function
Subsample inputs into a single dimension
1d convolutional convolutional layer
Generate a tfrecord file
Decode a JPEG image
Converts an image into a sequence example
Runs a single checkpoint
Evaluate a single model
Calculate threshold calibration
Compute the threshold for the roc curve
Resnet v2
Builds a batch readout graph

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multi-label-classification Key Features

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multi-label-classification Examples and Code Snippets

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Community Discussions

Trending Discussions on multi-label-classification

Label not x is present in all training examples

Inverse transform function is not returning correct value

Multi-Label Image Classification

Formatting issues with MultiLabelBinarizer() after reading CSV into Pandas

Imbalanced Dataset for Multi Label Classification

QUESTION

Label not x is present in all training examples

Asked 2019-Dec-20 at 06:16

Hello, I have come across an issue when trying to predict tag/label on my project. I am currently using similar tutorial (with my own data) to predict complain in complaint register based on given tag such as 1 Complaint --> many Genre (Warranty, Refund, Air Conditioning)

DF -> Tag No of Columns -> 4 (original), 2 (clean-up) > genre_new and clean_plot Column Names ->ID, Plot, Title, Genre, genre_new, clean_plot

I used this tutorial: https://www.analyticsvidhya.com/blog/2019/04/predicting-movie-genres-nlp-multi-label-classification/. This is to predict movies with multiple Genre such as 1 movies has multiple Genre

I also found solution on UserWarning: Label not :NUMBER: is present in all training examples

Problem: The issue is likely to be that some tags occur just in a few documents . When you split the dataset into train and test to validate your model, it may happen that some tags are missing from the training data.

Error: label warning and 0 prediction

But I am not sure how to do write this workaround to cater my code as I am not a coder. Please help.

Please refer to my google drive link https://drive.google.com/drive/folders/10yLOVWZPgl1shVwwM5qDy7iyMCm7cS9A?usp=sharing

...

ANSWER

Answered 2019-Dec-20 at 06:16

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn.model_selection import train_test_split

mlb = MultiLabelBinarizer()
vect = CountVectorizer()
tfidf = TfidfTransformer()

lr = LogisticRegression()
clf = OneVsRestClassifier(lr)

df = pd.read_excel("Building Compliants in 2018 for training(1).xls")
df['Genre'] = df['Genre'].apply(lambda x: x.split(','))

y = mlb.fit_transform(df['Genre'])

train_data_vect = vect.fit_transform(df['Plot'])
train_data_tfidf = tfidf.fit_transform(train_data_vect)

x_train, x_test, y_train, y_test=train_test_split(train_data_tfidf,y, test_size=0.25)

clf.fit(x_train,y_train) #train your model on train data
print(clf.score(x_test,y_test)) #check score on test data
#op


Out[29]:
0.3333333333333333

#now for predicting , taking first element of Plot column

text =  df['Plot'][0]
vect_transform = vect.transform([text])
tfidf_transform = tfidf.transform(vect_transform)

clf.predict(tfidf_transform)
#array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0]])

mlb.inverse_transform(clf.predict(tfidf_transform))
#op
[(' Warranty', 'Airconditioning')]

def infer_tags(q):
    q = clean_text(q)
    q = remove_stopwords(q)
    q_vec = tfidf.transform([q])
    q_pred = clf.predict(q_vec)
    #print(q)
    return MultiLabelBinarizer.inverse_transform(q_pred)


 for i in range(100): 
     k = x_test.sample(i).index[2] 
     #print("Trader: ", Tag['Title'][k])
     print("Trader: ", Tag['Title'][k], "\nPredicted genre: ",infer_tags(x_test[k]))
     print("Actual genre: ",Tag['Genre'][k], "\n")

#op
   Traceback (most recent call last):
       File "", line 11, in 
       k = x_test.sample(i).index[2]
       File "C:\Users\LAUJ3\Documents\Python Project\env\lib\site- 
       packages\scipy\sparse\base.py", line 688, in __getattr__
       raise AttributeError(attr + " not found")

    AttributeError: sample not found

Source https://stackoverflow.com/questions/59349516

QUESTION

Inverse transform function is not returning correct value

Asked 2019-Dec-12 at 07:24

I am following tutorial for multi labeling movie genre from https://www.analyticsvidhya.com/blog/2019/04/predicting-movie-genres-nlp-multi-label-classification/

I am using that tutorial to create prediction tag for complaint register. In my case, I am labeling 'Genre' for Complaint Register such as 1 complaint can have many label/tag of Genre). For example: Complaint #1 has multi Genre = Warranty, Air Conditioning.

I am up to the stage where I am invoking multilablebinarizer() function to label the movie 'Genre'

My issue is as following:

The total unique Genre = 55 (Please see screenshot below) image.png

I ran Multilabel_binarizer function and transform "Genre" target variable into y.

Questions:

I encounter y only has (166,49). If my understanding is correct, there is only 49 Genre as opposed to 55 unique Genre
I encounter error message: C:\Users\LAUJ3\Documents\Python Project\env\lib\site-packages\sklearn\multiclass.py:74: UserWarning: Label not 47 is present in all training examples. warnings.warn("Label %s is present in all training examples." %
The inverse_transfrom function of multilabel_binarizer result does not make sense. Expected to see the Genre label instead of Gibberish multilabel_binarizer.inverse_transform(y_pred)[3]

y_pred[3] Out[57]: array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0])

multilabel_binarizer.inverse_transform(y_pred)[3] Out[58]: (' ', ',', 'a', 'c', 'e', 'g', 'i', 'n', 'o', 'r', 't')

I don't know what went wrong. Thanks for your help in advance.

Screenshot

...

ANSWER

Answered 2019-Dec-12 at 07:24

from sklearn.preprocessing import MultiLabelBinarizer

mlb =  MultiLabelBinarizer()
mlb.fit_transform(df['genre'])

print(mlb.classes_)
#op
[' ' '"' '&' "'" ',' '-' '/' '0' '1' '2' '3' '4' '5' '6' '7' '8' '9' ':'
'A' 'B' 'C' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N' 'O' 'P' 'Q' 'R'
'S' 'T' 'V' 'W' 'Z' '[' '\\' ']' '_' 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i'
'j' 'k' 'l' 'm' 'n' 'o' 'p' 'q' 'r' 's' 't' 'u' 'v' 'w' 'x' 'y' 'z' '{'
'}']

Source https://stackoverflow.com/questions/59278190

QUESTION

Multi-Label Image Classification

Asked 2019-Nov-21 at 04:27

I tried myself but couldn't reach the final point that's why posting here, please guide me.

I am working in multi-label image classification and have slightly different scenarios. Actually I am confused, how we will map labels and their attribute with Id etc So we can use for training and testing.
Here is code on which I am working
...

ANSWER

Answered 2019-Nov-21 at 04:27

Base on the above discussion. Here is the solution for the above problem. As I mentioned we have a total of 5 labels and each label have further three tags like (L, M, H) We can perform encoding in this way

Source https://stackoverflow.com/questions/58813194

QUESTION

Formatting issues with MultiLabelBinarizer() after reading CSV into Pandas

Asked 2019-Sep-18 at 23:05

I'd like to use MultiLabelBinarizer() to prepare a column containing labels that apply to a text. For example, predicting which genres a movie might fall under based on the title.

MultiLabelBinarizer() works great when the values are pre-defined as a list in the DataFrame:

...

ANSWER

Answered 2019-Sep-18 at 23:05

Add .str.split(",")

Source https://stackoverflow.com/questions/58001072

QUESTION

Imbalanced Dataset for Multi Label Classification

Asked 2017-May-31 at 13:26

So I trained a deep neural network on a multi label dataset I created (about 20000 samples). I switched softmax for sigmoid and try to minimize (using Adam optimizer) :

...

ANSWER

Answered 2017-May-31 at 13:22

Your problem is not the class imbalance, rather just the lack of data. 26 samples are considered to be a very small dataset for practically any real machine learning task. A class imbalance could be easily handled by ensuring that each minibatch will have at least one sample from every class (this leads to situations when some samples will be used much more frequently than another, but who cares).

However, in the case of presence only 26 samples this approach (and any other) will quickly lead to overfitting. This problem could be partly solved with some form of data augmentation, but there still too few samples to construct something reasonable.

So, my suggestion will be to collect more data.

Source https://stackoverflow.com/questions/44285537

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install multi-label-classification

You can download it from GitHub.
You can use multi-label-classification like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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