mlcrate | python module of handy tools | Predictive Analytics library

 by   mxbi Python Version: 0.2.0 License: MIT

kandi X-RAY | mlcrate Summary

kandi X-RAY | mlcrate Summary

mlcrate is a Python library typically used in Analytics, Predictive Analytics applications. mlcrate has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install mlcrate' or download it from GitHub, PyPI.

A collection of handy python tools and helper functions, mainly for machine learning-related packages and Kaggle. The methods in this package aren't revolutionary, and most of them are very simple. They are largely bunch of 'macro' functions which I often end up rewriting across multiple projects, and various helper functions for different packages, all in one place and easily accessible as a quality of life improvement. Hopefully, they can be some use to others in the community too. This package has been tested with Python 3.5+, but should work with all versions of Python 3. Python 2 is not officially supported.
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            kandi-support Support

              mlcrate has a low active ecosystem.
              It has 320 star(s) with 42 fork(s). There are 13 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 2 have been closed. On average issues are closed in 65 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of mlcrate is 0.2.0

            kandi-Quality Quality

              mlcrate has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              mlcrate is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              mlcrate releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              mlcrate saves you 124 person hours of effort in developing the same functionality from scratch.
              It has 312 lines of code, 30 functions and 8 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed mlcrate and discovered the below as its top functions. This is intended to give you an instant insight into mlcrate implemented functionality, and help decide if they suit your requirements.
            • Train k - fold clustering
            • Calculate importance from a list of features
            • Write data to the file
            • Flush the file
            • Adds a key to the timer
            • Return a formatted time string
            • Format a duration in seconds
            • Return time since last time
            • Aggregate multiple layers
            • Add a layer to the model
            • Build MLP model
            • Import torch
            • Save a DataFrame to a CSV file
            • Return current time
            • Convert an array to a tensor
            • Rank an array of arrays
            • Convert a tensor
            • Used to mark a function as deprecated
            Get all kandi verified functions for this library.

            mlcrate Key Features

            No Key Features are available at this moment for mlcrate.

            mlcrate Examples and Code Snippets

            No Code Snippets are available at this moment for mlcrate.

            Community Discussions

            QUESTION

            will TensorFlow utilize GPU for predictive Analysis?
            Asked 2020-Nov-21 at 21:35

            GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

            note: predictive Analysis requires no graphics processing.

            ...

            ANSWER

            Answered 2020-Nov-21 at 21:35
            The short answer: yes, it will! The slightly longer answer:

            The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

            The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

            If you want to know more about this, I'd recommend you start here or here.

            some things to consider:
            • how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
            • Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
            • Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.

            Source https://stackoverflow.com/questions/64948197

            QUESTION

            Restructuring Pandas Dataframe for large number of columns
            Asked 2020-Nov-01 at 19:39

            I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

            The current structure is as below.

            What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

            Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.

            ...

            ANSWER

            Answered 2020-Nov-01 at 19:39

            You might want try pivoting your table:

            Source https://stackoverflow.com/questions/64630691

            QUESTION

            Display data from two json files in react native
            Asked 2020-May-17 at 23:55

            I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

            ...

            ANSWER

            Answered 2020-May-17 at 23:55

            The new object to get params in React Navigation 5 is:

            Source https://stackoverflow.com/questions/61859411

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

            Vulnerabilities

            No vulnerabilities reported

            Install mlcrate

            Alternatively, clone the repo and run python setup.py install within the top-level folder to install the bleeding-edge version - this is recommended.

            Support

            If you find any bugs or have any feature suggestions (even general feature requests unrelated to what's already in the package), feel free to open an issue. Pull requests are also very welcome :slightly_smiling_face:.
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            Install
          • PyPI

            pip install mlcrate

          • CLONE
          • HTTPS

            https://github.com/mxbi/mlcrate.git

          • CLI

            gh repo clone mxbi/mlcrate

          • sshUrl

            git@github.com:mxbi/mlcrate.git

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