autonomio | Core functionality for the Autonomio augmented intelligence | Machine Learning library

by autonomio Python Version: v.0.3.0 License: MIT

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kandi X-RAY | autonomio Summary

autonomio is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. autonomio has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Autonomio provides a very high level abstraction layer for rapidly testing research ideas and instantly creating neural network based decision making models. Autonomio is built on top of Keras, using Tensorflow as a backend and spaCy for word vectorization. Autonomio brings deep learning and state-of-the-art linguistic processing accessible to anyone with basic computer skills. This document focus on an overview of Autonomio's capabilities. If you want something higher level visit the website.

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autonomio has a low active ecosystem.

It has 26 star(s) with 8 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 26 open issues and 88 have been closed. On average issues are closed in 26 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of autonomio is v.0.3.0

Quality

autonomio has 0 bugs and 0 code smells.

Security

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

autonomio code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

autonomio is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

autonomio releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

autonomio saves you 353 person hours of effort in developing the same functionality from scratch.

It has 844 lines of code, 25 functions and 16 files.

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

Top functions reviewed by kandi - BETA

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

Performs a hyperscan
Load parameters
Prepare training data
Convert data to pandas DataFrame
Check if all columns are binary
Concatenate data
Fill missing values in cols
Implementation of nans
Compute LSTM
Plot a histogram
Plots the LSTM model
Load LSTM data
Train the model
R Check the value of a register
Simple MLP model
Checkeras accuracy
Make a prediction
Plots the prediction distribution
Convert data to pandas dataframe
Load data from a data file
Generate column names
Plot a parameter scatter plot
Calculate alpha distribution
Drops nan values from the dataframe
Find missing values
Check required dependencies

Get all kandi verified functions for this library.

autonomio Key Features

No Key Features are available at this moment for autonomio.

autonomio Examples and Code Snippets

No Code Snippets are available at this moment for autonomio.

Community Discussions

Trending Discussions on autonomio

Keras EarlyStopping callback working inconsistently

Talos hyperparametr search: how to set metric in evaluation step

Hyper-parameter Optimization with keras models: GridSearchCV or talos?

QUESTION

Keras EarlyStopping callback working inconsistently

Asked 2021-Apr-03 at 16:14

For training my neural network model I use Keras' EarlyStopping callback to minimize train time (via talos.utils.early_stopper wrapper):

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ANSWER

Answered 2021-Apr-03 at 16:14

For some reason, changing monitor from val_loss to val_accuracy (EarlyStopping(monitor="val_accuracy", min_delta=0.01, patience=2, verbose=1, mode='auto') seems to give a more consistent callback.

Source https://stackoverflow.com/questions/66882679

QUESTION

Talos hyperparametr search: how to set metric in evaluation step

Asked 2019-Jan-29 at 15:15

I want to learn about hyperparameter search in talos. Specifically the evaluation of the models. I was going through this example notebook https://nbviewer.jupyter.org/github/autonomio/talos/blob/master/examples/Hyperparameter%20Optimization%20with%20Keras%20for%20the%20Iris%20Prediction.ipynb#seven

No, my question is: In evaluation (7), how do I set a specific evaluation metric? E.g. F1 score for a classification problem. Do they come from Keras or talos? What is the default, if the parameter is not passed? I could not find it in the talos docs. Did I overlook sth?https://autonomio.github.io/docs_talos/#evaluate

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ANSWER

Answered 2019-Jan-29 at 15:15

Evaluation in Talos use f1-score with binary average for binary classification, macro average for multi_label and multi_class, and MAE for regression. These come from sklearn.

The metric argument refers to any metric you've already used in Scan() experiment and is used for first picking the best model/s to evaluate. You can use any Keras or custom metric in Scan() as you would with your Keras models.

Source https://stackoverflow.com/questions/54323554

QUESTION

Hyper-parameter Optimization with keras models: GridSearchCV or talos?

Asked 2018-Nov-30 at 14:11

I want to tune hyper-parameters on keras models and I was exploring the alternatives I had at hand. The first and most obvious one was to use scikit-learn wrappers as shown here (https://keras.io/scikit-learn-api/) thereby being able to use all the faboulous things in the scikit-learn worflow but I also came across this package here (https://github.com/autonomio/talos) that seems very promising and most likely offers a speed boost.

if anyone used them both, could someone point me towards the better solution (flexibility, speed, features)? The sklearn workflow with pipeline and custom estimators provides a world of flexibility but talos seems more directly geared towards keras specifically therefore it must yield some advantages (I guess they would not have made a new standalone package otherwise) which I am not able to see (some benefits are highlighted here https://github.com/autonomio/talos/blob/master/docs/roadmap.rst but such thigns seem to be adequately covered within the scikit-learn framework)

any insights?

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ANSWER

Answered 2018-Jun-08 at 08:20

Personal opinions:

train/valid/test split is a better choice than cross validation for deep learning. (The cost of k training is too high)
random search is a good way to start exploring the hyper-parameters, so it's not really hard to code this yourself but yes talos or hyperas (which is quite famous) could be helpfull.

Source https://stackoverflow.com/questions/50755429

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