feature-extraction | Sample techniques for a variety of feature | Machine Learning library

 by   tdunning Java Version: Current License: Apache-2.0

kandi X-RAY | feature-extraction Summary

kandi X-RAY | feature-extraction Summary

feature-extraction is a Java library typically used in Artificial Intelligence, Machine Learning applications. feature-extraction has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However feature-extraction build file is not available. You can download it from GitHub.

This repository contains a compendium of useful feature extraction techniques I have learned about over the years. If you have a favorite that I have missed, let me know.

            kandi-support Support

              feature-extraction has a highly active ecosystem.
              It has 28 star(s) with 1 fork(s). There are 3 watchers for this library.
              It had no major release in the last 6 months.
              feature-extraction has no issues reported. There are 1 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of feature-extraction is current.

            kandi-Quality Quality

              feature-extraction has 0 bugs and 0 code smells.

            kandi-Security Security

              feature-extraction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              feature-extraction code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              feature-extraction is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              feature-extraction releases are not available. You will need to build from source code and install.
              feature-extraction has no build file. You will be need to create the build yourself to build the component from source.
              feature-extraction saves you 572 person hours of effort in developing the same functionality from scratch.
              It has 1336 lines of code, 66 functions and 12 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed feature-extraction and discovered the below as its top functions. This is intended to give you an instant insight into feature-extraction implemented functionality, and help decide if they suit your requirements.
            • Returns a sparse matrix containing the data
            • Standard quick sort routine
            • Compresses the table
            • Performs a limited range insertion sort
            • Swaps the elements in the specified array
            • Tangle an array of values
            • Simple quick sort algorithm
            • Resolves the size
            • Vectorizes a string in a dictionary
            • Tokenizes a CharSequence
            • Checks if the data structure is correctly formatted
            • Adds a value to the table
            • Append this coo data to another
            • Initialize the matrix
            • Counts the number of words in a dictionary
            • Tokenize the string as a list
            Get all kandi verified functions for this library.

            feature-extraction Key Features

            No Key Features are available at this moment for feature-extraction.

            feature-extraction Examples and Code Snippets

            No Code Snippets are available at this moment for feature-extraction.

            Community Discussions


            TF-IDF calculation in Dask
            Asked 2020-Sep-23 at 17:04

            Apache Spark comes with a package to do TF-IDF calculations that I find it quite handy: https://spark.apache.org/docs/latest/mllib-feature-extraction.html

            Is there any equivalent, or maybe a way to do this with Dask? If so, can it also be done in horizontally scaled Dask (i.e., cluster with multiple GPUs)



            Answered 2020-Sep-23 at 17:04

            This was also asked on the dask gitter, with the following reply by @stsievert :

            counting/hashing vectorizer are similar. They’re in Dask-ML and are the same as TFIDF without the normalization/function.

            I think this would be a good github issue/feature request.

            Here is the link to the API for HashingVectorizer.

            Source https://stackoverflow.com/questions/64030119


            What features are used in the default transformers pipeline?
            Asked 2020-Aug-18 at 11:24

            I'm looking here at the feature extraction pipeline.

            I initialize with the following:



            Answered 2020-Aug-18 at 11:24

            Unfortunately, as you have rightly stated, the pipelines documentation is rather sparse. However, the source code specifies which models are used by default, see here. Specifically, the model is distilbert-base-cased.

            For a way to use models, see a related answer by me here. You can simply specifiy the model and tokenizer parameters like this:

            Source https://stackoverflow.com/questions/63461821


            Using Hugging Face Transformers library how can you POS_TAG French text
            Asked 2020-Jul-11 at 11:48

            I am trying to POS_TAG French using the Hugging Face Transformers library. In English I was able to do so given a sentence like e.g:

            The weather is really great. So let us go for a walk.

            the result is:



            Answered 2020-Jul-11 at 11:48

            We have ended up training a model for POS Tagging (part of speech tagging) with the Hugging Face Transformers library. The resulting model is available here:


            You can basically see how it assigns POS tags on the webpage mentioned above. If you have the Hugging Face Transformers library installed you can try it out in a Jupyter notebook with this code:

            Source https://stackoverflow.com/questions/62782001


            pyAudioAnalysis Library error: cannot reshape array of size 4400 into shape (220,10)
            Asked 2019-Dec-03 at 17:18

            I'm trying to extract some features of a wav file, I have this:


            I'm using the feature extraction tools of the pyAudioAnalysis library:


            The problem is that I'm getting an error of the reshape() function of numpy. I'm following the guidelines of how to use the feature extractor as in the wiki like this:



            Answered 2017-Oct-05 at 08:21

            I had the same error, but according to https://github.com/tyiannak/pyAudioAnalysis/issues/72, I converted my stereo music to mono and it solved the problem for me.

            Source https://stackoverflow.com/questions/44622007


            I get isnan error when I merge two countvectorizers
            Asked 2019-May-19 at 08:16

            I'm going dialect text classification and I have this code:



            Answered 2019-May-19 at 08:16

            You should union vectorizerN and vectorizerMX, not MX and XN. Change the line to

            Source https://stackoverflow.com/questions/56205728


            How term frequency is calculated in TfidfVectorizer?
            Asked 2019-Apr-24 at 15:21

            I searched a lot for understanding this but I am not able to. I understand that by default TfidfVectorizer will apply l2 normalization on term frequency. This article explain the equation of it. I am using TfidfVectorizer on my text written in Gujarati language. Following is details of output about it:

            My two documents are:



            Answered 2019-Apr-24 at 15:21

            Ok, Now lets go through the documentation I gave in comments step by step:


            Source https://stackoverflow.com/questions/42440621


            CNN features for classification
            Asked 2019-Mar-18 at 22:48

            I am new to deep learning and I hope you guys can help me. The following site uses CNN features for multi-class classification: https://www.mathworks.com/help/deeplearning/examples/feature-extraction-using-alexnet.html

            This example extracts features from fully connected layer and the extracted features are fed to ECOC classifier.

            In this example, regarding to the whole dataset, there are total 15 samples in each category and in the training dataset, there are 11 samples in each category.

            My question are related to the dataset size: If I want to use cnn features for ECOC classification as above example, it must be required to have the number of samples in each category the same? If so, would you like to explain why? If not, would you like to show the reference papers which have used different numbers?

            Thank you.



            Answered 2019-Mar-18 at 22:48

            You may want to have a balanced dataset to prevent your model from learning a wrong probability distribution. If a category represents 95% of your dataset, a model that classifies everything as part of that category, will have an accuracy of 95%.

            Source https://stackoverflow.com/questions/55226034


            How to calculate the number of parameters for convolutional neural network?
            Asked 2019-Feb-19 at 12:41

            I'm using Lasagne to create a CNN for the MNIST dataset. I'm following closely to this example: Convolutional Neural Networks and Feature Extraction with Python.

            The CNN architecture I have at the moment, which doesn't include any dropout layers, is:



            Answered 2018-Jun-27 at 12:46

            Let's first look at how the number of learnable parameters is calculated for each individual type of layer you have, and then calculate the number of parameters in your example.

            • Input layer: All the input layer does is read the input image, so there are no parameters you could learn here.
            • Convolutional layers: Consider a convolutional layer which takes l feature maps at the input, and has k feature maps as output. The filter size is n x m. For example, this will look like this:

              Here, the input has l=32 feature maps as input, k=64 feature maps as output, and the filter size is n=3 x m=3. It is important to understand, that we don't simply have a 3x3 filter, but actually a 3x3x32 filter, as our input has 32 dimensions. And we learn 64 different 3x3x32 filters. Thus, the total number of weights is n*m*k*l. Then, there is also a bias term for each feature map, so we have a total number of parameters of (n*m*l+1)*k.

            • Pooling layers: The pooling layers e.g. do the following: "replace a 2x2 neighborhood by its maximum value". So there is no parameter you could learn in a pooling layer.
            • Fully-connected layers: In a fully-connected layer, all input units have a separate weight to each output unit. For n inputs and m outputs, the number of weights is n*m. Additionally, you have a bias for each output node, so you are at (n+1)*m parameters.
            • Output layer: The output layer is a normal fully-connected layer, so (n+1)*m parameters, where n is the number of inputs and m is the number of outputs.

            The final difficulty is the first fully-connected layer: we do not know the dimensionality of the input to that layer, as it is a convolutional layer. To calculate it, we have to start with the size of the input image, and calculate the size of each convolutional layer. In your case, Lasagne already calculates this for you and reports the sizes - which makes it easy for us. If you have to calculate the size of each layer yourself, it's a bit more complicated:

            • In the simplest case (like your example), the size of the output of a convolutional layer is input_size - (filter_size - 1), in your case: 28 - 4 = 24. This is due to the nature of the convolution: we use e.g. a 5x5 neighborhood to calculate a point - but the two outermost rows and columns don't have a 5x5 neighborhood, so we can't calculate any output for those points. This is why our output is 2*2=4 rows/columns smaller than the input.
            • If one doesn't want the output to be smaller than the input, one can zero-pad the image (with the pad parameter of the convolutional layer in Lasagne). E.g. if you add 2 rows/cols of zeros around the image, the output size will be (28+4)-4=28. So in case of padding, the output size is input_size + 2*padding - (filter_size -1).
            • If you explicitly want to downsample your image during the convolution, you can define a stride, e.g. stride=2, which means that you move the filter in steps of 2 pixels. Then, the expression becomes ((input_size + 2*padding - filter_size)/stride) +1.

            In your case, the full calculations are:

            Source https://stackoverflow.com/questions/42786717


            How to hash a class or function definition?
            Asked 2018-Oct-07 at 14:07

            When experimenting with machine learning, I often reuse models trained previously, by means of pickling/unpickling. However, when working on the feature-extraction part, it's a challenge not to confuse different models. Therefore, I want to add a check that ensures that the model was trained using exactly the same feature-extraction procedure as the test data.


            My idea was the following: Along with the model, I'd include in the pickle dump a hash value which fingerprints the feature-extraction procedure.

            When training a model or using it for prediction/testing, the model wrapper is given a feature-extraction class that conforms to certain protocol. Using hash() on that class won't work, of course, as it isn't persistent across calls. So I thought I could maybe find the source file where the class is defined, and get a hash value from that file.

            However, there might be a way to get a stable hash value from the class’s in-memory contents directly. This would have two advantages: It would also work if no source file can be found. And it would probably ignore irrelevant changes to the source file (eg. fixing a typo in the module docstring). Do classes have a code object that could be used here?



            Answered 2018-Oct-07 at 14:07

            All you’re looking for is a hash procedure that includes all the salient details of the class’s definition. (Base classes can be included by including their definitions recursively.) To minimize false matches, the basic idea is to apply a wide (cryptographic) hash to a serialization of your class. So start with pickle: it supports more types than hash and, when it uses identity, it uses a reproducible identity based on name. This makes it a good candidate for the base case of a recursive strategy: deal with the functions and classes whose contents are important and let it handle any ancillary objects referenced.

            So define a serialization by cases. Call an object special if it falls under any case below but the last.

            • For a tuple deemed to contain special objects:
              1. The character t
              2. The serialization of its len
              3. The serialization of each element, in order
            • For a dict deemed to contain special objects:
              1. The character d
              2. The serialization of its len
              3. The serialization of each name and value, in sorted order
            • For a class whose definition is salient:
              1. The character C
              2. The serialization of its __bases__
              3. The serialization of its vars
            • For a function whose definition is salient:
              1. The character f
              2. The serialization of its __defaults__
              3. The serialization of its __kwdefaults__ (in Python 3)
              4. The serialization of its __closure__ (but with cell values instead of the cells themselves)
              5. The serialization of its vars
              6. The serialization of its __code__
            • For a code object (since pickle doesn’t support them at all):
              1. The character c
              2. The serializations of its co_argcount, co_nlocals, co_flags, co_code, co_consts, co_names, co_freevars, and co_cellvars, in that order; none of these are ever special
            • For a static or class method object:
              1. The character s or m
              2. The serialization of its __func__
            • For a property:
              1. The character p
              2. The serializations of its fget, fset, and fdel, in that order
            • For any other object: pickle.dumps(x,-1)

            (You never actually store all this: just create a hashlib object of your choice in the top-level function, and in the recursive part update it with each piece of the serialization in turn.)

            The type tags are to avoid collisions and in particular to be prefix-free. Binary pickles are already prefix-free. You can base the decision about a container on a deterministic analysis of its contents (even if heuristic) or on context, so long as you’re consistent.

            As always, there is something of an art to balancing false positives against false negatives: for a function, you could include __globals__ (with pruning of objects already serialized to avoid large if not infinite serializations) or just any __name__ found therein. Omitting co_varnames ignores renaming local variables, which is good unless introspection is important; similarly for co_filename and co_name.

            You may need to support more types: look for static attributes and default arguments that don’t pickle correctly (because they contain references to special types) or at all. Note of course that some types (like file objects) are unpicklable because it’s difficult or impossible to serialize them (although unlike pickle you can handle lambdas just like any other function once you’ve done code objects). At some risk of false matches, you can choose to serialize just the type of such objects (as always, prefixed with a character ? to distinguish from actually having the type in that position).

            Source https://stackoverflow.com/questions/46768213


            Can word2vec algorithm be distributed on multi-computers using tensorflow?
            Asked 2018-Sep-28 at 11:12

            I want to train word2vec model on a very big corpus such that embedded words cannot be allocated to RAM.

            I know there are existing solutions for the algorithm parallelization, for example Spark implementation, but I would like to use tensorflow library.

            Is it possible?



            Answered 2017-Jul-03 at 23:18

            The authors of word2vec implemented the algorithm using an asynchronous SGD called: HogWild!. So you might want to look for tensor flow implementation of this algorithm.

            In HogWild!, each thread takes a sample at a time and performs an update to the weights without any synchronization with other threads. These updates from different threads can potentially overwrite each other, leading to data race conditions. But Hogwild! authors show that it works well for very sparse data sets, where many samples are actually near independent since they write to mostly different indices of the model.

            Source https://stackoverflow.com/questions/44863493

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


            No vulnerabilities reported

            Install feature-extraction

            You can download it from GitHub.
            You can use feature-extraction 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 feature-extraction 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 .


            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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