Classifier | Supervised Text/Document Classification using Complementary

by rsudharshan Java Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | Classifier Summary

Classifier is a Java library. Classifier has no bugs, it has no vulnerabilities and it has low support. However Classifier build file is not available. You can download it from GitHub.

Automated Document Classifier using Complementary NaiveBayes Algorithm.. Trainer.java Takes unprocessed data set and produces processed dataset as suitable for Mahout file format. Responsible for training Complementary Naive bayes algorithm and build a statistical model. Classifier.java Takes an unclassified data directory and classifies the documents. Creates separate subdirectories for each category and writes the files onto the directory. Setting Up Parameters in settings.properties file Bayesparameters. Gramsize=2 // Ngram size Algorithm=cbayes // our classification algorithm DefaultCategory=unknown // Default Category DataSource=hdfs // Hadoop File System Encoding=UTF-8 // Unicode Alpha=1.0 //Smoothing parameter. TrainSet=/home/developer/dataset_rev/freshrevs/train/ // training set location which containing subdirectories of each category ProcessedSet=/home/developer/dataset_rev/freshrevs/processedTrain/ // Processed Output Directory. ModelPath=/home/developer/dataset_rev/freshrevs/model/ // Path to store and retrieve Model IpDirPath=/home/developer/dataset_rev/freshrevs/test/pos/ // Unclassifed data set OpDirPath=/home/developer/dataset_rev/freshrevs/classified/ // Path to store classified documents.

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Security

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Support

Classifier has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Classifier has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Classifier is current.

Quality

Classifier has 0 bugs and 0 code smells.

Security

Classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Classifier code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Classifier does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Classifier releases are not available. You will need to build from source code and install.

Classifier has no build file. You will be need to create the build yourself to build the component from source.

It has 370 lines of code, 20 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Main entry point
Prepares the class files
Train C Bayes algorithm
Initializes default settings
Classifier
Initializes the classifier context
Get Bayes Parameters
Preprocess a document
Test classifier
Set classifier parameters
Preprocess the supplied document to a single line
Get single line of tokens

Get all kandi verified functions for this library.

Classifier Key Features

No Key Features are available at this moment for Classifier.

Classifier Examples and Code Snippets

Use Decision Tree classifier .

python

Lines of Code : 51

License : No License

Copy

def main():
    Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data()

    # convert to numpy arrays
    Xtrain = np.array(Xtrain)
    Ytrain = np.array(Ytrain)

    # convert Xtrain to indicator matrix
    N = len(Xtrain)
    V = len(word2idx) + 1

Classify the classifier .

python

Lines of Code : 28

License : Permissive (MIT License)

Copy

def classifier(train_data, train_target, classes, point, k=5):
    """
    Classifies the point using the KNN algorithm
    k closest points are found (ranked in ascending order of euclidean distance)
    Params:
    :train_data: Set of points that a

Fit Decision Tree classifier

python

Lines of Code : 21

License : No License

Copy

def fit(self, X, Y, M=None):
    N, D = X.shape
    if M is None:
      M = int(np.sqrt(D))

    self.models = []
    self.features = []
    for b in range(self.B):
      tree = DecisionTreeClassifier()

      # sample features
      features = np.ra

Community Discussions

Trending Discussions on Classifier

Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)

Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'

How to calculate maximum gradient for each layer given a mini-batch

What issue could I have in Gradle managed device setup?

Unpickle instance from Jupyter Notebook in Flask App

nexus-staging-maven-plugin: maven deploy failed: An API incompatibility was encountered while executing

Getting optimal vocab size and embedding dimensionality using GridSearchCV

InternalError when using TPU for training Keras model

How to map function directly over list of lists?

Sklearn: Calibrate a multi-label classification with CalibratedClassifierCV

QUESTION

Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)

Asked 2022-Mar-27 at 23:14

The classifier script I wrote is working fine and recently added weight balancing to the fitting. Since I added the weight estimate function using 'sklearn' library I get the following error :

...

ANSWER

Answered 2022-Mar-27 at 23:14

After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.

Source https://stackoverflow.com/questions/69783897

QUESTION

Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'

Asked 2022-Mar-23 at 04:30

Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?

I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:

...

ANSWER

Answered 2021-Aug-19 at 03:49

This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

update to

Source https://stackoverflow.com/questions/68836551

QUESTION

How to calculate maximum gradient for each layer given a mini-batch

Asked 2022-Mar-14 at 07:58

I try to implement a fully-connected model for classification using the MNIST dataset. A part of the code is the following:

...

ANSWER

Answered 2022-Mar-10 at 08:19

You could start off with a custom training loop using tf.GradientTape:

Source https://stackoverflow.com/questions/71420132

QUESTION

What issue could I have in Gradle managed device setup?

Asked 2022-Mar-07 at 23:47

There was introduced a new feature Gradle managed devices (see for example here: https://developer.android.com/studio/preview/features?hl=fr)

The setup seems to be pretty straightforward, just copy a few lines to the module level build.gradle file and everything should work.

Sadly it is not the case for me and I strive for some advice, please. The code is red and the script doesn't succeed. See my build.gradle.kts file:

The underlined ManagedVirtualDevice shows the following error:

My Android studio version is Android Studio Bumblebee | 2021.1.1 Canary 11 Build #AI-211.7628.21.2111.7676841, built on August 26, 2021.

Syncing Gradle shows this:

...

ANSWER

Answered 2021-Oct-15 at 11:43

Just ran into the same issue - you need to instantiate a ManagedVirtualDevice object and configure it, before adding it to your devices list:

Source https://stackoverflow.com/questions/69159985

QUESTION

Unpickle instance from Jupyter Notebook in Flask App

Asked 2022-Feb-28 at 18:03

I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

I am creating the model on Google Colab.

Code in Jupyter Notebook:

...

ANSWER

Answered 2022-Feb-24 at 11:48

Import GensimWord2VecVectorizer in your Flask Web app python file.

Source https://stackoverflow.com/questions/71231611

QUESTION

nexus-staging-maven-plugin: maven deploy failed: An API incompatibility was encountered while executing

Asked 2022-Feb-11 at 22:39

This worked fine for me be building under Java 8. Now under Java 17.01 I get this when I do mvn deploy.

mvn install works fine. I tried 3.6.3 and 3.8.4 and updated (I think) all my plugins to the newest versions.

Any ideas?

...

ANSWER

Answered 2022-Feb-11 at 22:39

Update: Version 1.6.9 has been released and should fix this issue! 🎉

This is actually a known bug, which is now open for quite a while: OSSRH-66257. There are two known workarounds:

1. Open Modules

As a workaround, use --add-opens to give the library causing the problem access to the required classes:

Source https://stackoverflow.com/questions/70153962

QUESTION

Getting optimal vocab size and embedding dimensionality using GridSearchCV

Asked 2022-Feb-06 at 09:13

I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.

...

ANSWER

Answered 2022-Feb-02 at 08:53

I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.

The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.

So the build_model function becomes:

Source https://stackoverflow.com/questions/70884608

QUESTION

InternalError when using TPU for training Keras model

Asked 2021-Dec-31 at 08:18

I am attempting to fine-tune a BERT model on Google Colab from the Tensorflow Hub using this link.

However, I run into the following error:

...

ANSWER

Answered 2021-Dec-31 at 08:18

As I don't exactly know what changes you have made in the code... I don't have idea about your dataset. But I can see that you are trying to train the whole datset with one epoch and passing the steps per epoch directly. I would recommend to write it like this

set some batch_size 2^n power (for example 16 or 32 or etc) if you don't want to batch the dataset just set batch_size to 1

Source https://stackoverflow.com/questions/70479279

QUESTION

How to map function directly over list of lists?

Asked 2021-Dec-26 at 15:38

I have built a pixel classifier for images, and for each pixel in the image, I want to define to which pre-defined color cluster it belongs. It works, but at some 5 minutes per image, I think I am doing something unpythonic that can for sure be optimized.

How can we map the function directly over the list of lists?

...

ANSWER

Answered 2021-Jul-23 at 07:41

Just quick speedups:

You can omit math.sqrt()
Create dictionary of colors instead of a list (that way you don't have to search for the index each iteration)
use min() instead of sorted()

Source https://stackoverflow.com/questions/68495481

QUESTION

Sklearn: Calibrate a multi-label classification with CalibratedClassifierCV

Asked 2021-Dec-18 at 17:38

I have built a number of sklearn classifier models to perform multi-label classification and I would like to calibrate their predict_proba outputs so that I can obtain confidence scores. I would also like to use metrics such as sklearn.metrics.recall_score to evaluate them.

I have 4 labels to predict and the true labels are multi-hot encoded (e.g. [0, 1, 1, 1]). As a result, CalibratedClassifierCV does not directly accept my data:

...

ANSWER

Answered 2021-Dec-17 at 15:33

In your example, you're using a DecisionTreeClassifier which by default support targets of dimension (n, m) where m > 1.

However if you want to have as result the marginal probability of each class then use the OneVsRestClassifier.

Notice that CalibratedClassifierCV expects target to be 1d so the "trick" is to extend it to support Multilabel Classification with MultiOutputClassifier.

Full Example

Source https://stackoverflow.com/questions/70388422

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

Vulnerabilities

No vulnerabilities reported

Install Classifier

You can download it from GitHub.
You can use Classifier like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Classifier component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

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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