Python binding for Khiva library.

Last update: Oct 16, 2022

Overview

Khiva-Python

Build Documentation	Build Linux and Mac OS	Build Windows	Code Coverage

README

This is the Khiva Python binding, it allows the usage of Khiva library from Python.

License

This project is licensed under MPL-v2.

Quick Summary

This Python binding called 'khiva' provides all the functionalities of the KHIVA library for time series analytics.

Set up

In order to use this binding, you need to install Khiva library.

Prerequisites

Python 64-bits

Note: By now, only 64-bit Python versions are supported.

Note Windows' users: Search your 64-bits version here

Install latest version

Install latest stable version of Khiva library. Follow the steps in the "Installation" section of the Khiva repository

To install the Khiva Python binding, we just need to execute the following command:

python setup.py install

Install any release

Install the prerequisites listed in the "Installation" section of the Khiva library repository. Download and install your selected Khiva release from Khiva repository.

Install the Khiva python binding compatible with the Khiva library installed previously. Follow the steps to install the Khiva python binding explained in pypi.

Executing the tests:

All tests can be executed separately, please find them in /tests/unit_tests.

Documentation

This Khiva Python binding follows the standard way of writing documentation of Python by using Sphinx.

In order to generate the documentation (in html format), run the following command under the /docs folder:

make html

Contributing

The rules to contribute to this project are described here

Comments

Access violation

Describe the bug I get a access violation whenever I use khiva python lib

To Reproduce Run a simple program like:

from khiva.library import *
set_backend(KHIVABackend.KHIVA_BACKEND_OPENCL)
set_device(0)

from khiva.array import *
a = Array([1, 2, 3, 4, 5, 6, 7, 8])
a.display()

Expected behavior Application should run without exceptions

Screenshots

(base) C:\Progetti\Lab\Khiva\MyPythonSamples\HelloKhiva>python main.py
array
[8 1 1 1]
    1.0000
    2.0000
    3.0000
    4.0000
    5.0000
    6.0000
    7.0000
    8.0000

Traceback (most recent call last):
  File "main.py", line 7, in <module>
    a.display()
  File "C:\Anaconda3\lib\site-packages\khiva\array.py", line 323, in display
    KhivaLibrary().c_khiva_library.display(ctypes.pointer(self.arr_reference))
OSError: exception: access violation writing 0x00007FFE94CB180A

Environment information:

OS: Windows 10
Version 0.3.0

opened by maiorfi 6

V0.5.0
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opened by raulbocanegra 1
V0.5.0
Make sure you have checked all steps below.

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[ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

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License

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[ ] In case of new functionality, my PR adds documentation that describes how to use it.
opened by raulbocanegra 1
Khiva Python Bindings not passing all unit tests

Describe the bug Khiva Python Bindings not passing all unit tests. Problems include seg fault or is not able to find required library in khiva function call.

To Reproduce Build khiva from source, install. Run install script to generate python bindings. Run unit tests from this repo.

Expected behavior All tests should pass.

Environment information: Ubuntu 18.04.4 LTS Python 3.6.9 Conan version 1.23.0 ArrayFire-v3.6.2

Additional context I joined the Gitter, it may be easier to continue the conversation there. Saw no errors during installation of both khiva and the python bindings. Prerequisites were installed prior to installing khiva.

opened by yuhongsun96 1
update setup.py
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opened by raulbocanegra 1
Feature/error handling
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opened by avilchess 1
Feature/scamp_getChains
Add scamp algorithm

Add get_chains function

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opened by jrecuerda 1
Increase package version
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opened by jrecuerda 1
Add mass and findBestNOccurrences functions
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opened by jrecuerda 1
Fix issue with numpy interop
Reading from an Array:

When an Array of real or complex numbers with more than two dimensions was benig converted to numpy this conversion didn't return an np.array with a proper shape.

Creating an Array:

When an Array of real numbers with more than two dimensions was built. The shape in the device (arrayfire) was not correct.

For complex numbers it occurrs with more than one dimension.

Make sure you have checked all steps below.

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[ ] Here are some details about my PR, including screenshots of any UI changes:

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[ ] My PR adds the following unit tests OR does not need testing for this extremely good reason:

Commits

[ ] My commits have been squashed if they address the same issue. In addition, my commits follow the guidelines from "How to write a good git commit message":

Subject is separated from body by a blank line

Subject is limited to 50 characters

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Body wraps at 72 characters

Body explains "what" and "why", not "how"

License

[ ] Add a Mozilla Public License 2.0 license header to all the new files.

Documentation

[ ] In case of new functionality, my PR adds documentation that describes how to use it.
opened by jrecuerda 1
Fix bug when doing get_data from an Array with one element.
This PR solves one issue when doing get_data().tolist() on a Khiva.Array with one element.

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opened by avilchess 1
Bump numpy from 1.18.1 to 1.22.0
Bumps numpy from 1.18.1 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 1
Improve packaging

Use of Khiva Python requires a lot of previous installation of Arrayfire and other dependencies. We could try to bundle most of the dependencies (Arrayfire at least) in order to make life easier to our users.

opened by raulbocanegra 0

Releases(v0.3.0)

v0.3.0(Jun 11, 2019)
Added

mass (Mueen's Algorithm for Similarity Search) function with a proper public interface.

findBestNOccurrences function.

Improved

STOMP (self-join) performance has been improved by a ~54%.

Fixed

STOMP (self-join) doesn't crash in batched mode for long time series.

Source code(tar.gz)
Source code(zip)
v0.2.2(May 7, 2019)

This is a minor version change, it fixes some bugs.
Source code(tar.gz)
Source code(zip)
v0.2.1(Mar 5, 2019)

Chaged

Fixes in Visvalingam and SAX methods.
Source code(tar.gz)
Source code(zip)
v0.2.0(Feb 27, 2019)
Added

KMeans algorithm.

KShape Algorithm.

Added Ljung-Box test.

SBD distance function.

Changed

Implementation improvement of stomp function and find motifs and discords functions.

Source code(tar.gz)
Source code(zip)

Owner

Shapelets

Accelerated Time Series Analytics

GitHub Repository https://khiva-python.readthedocs.io/

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Python binding for Khiva library.

Related tags

Overview

Khiva-Python

README

License

Quick Summary

Set up

Prerequisites

Install latest version

Install any release

Executing the tests:

Documentation

Contributing

Comments

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

Description

Tests

Commits

License

Documentation

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Releases(v0.3.0)

v0.3.0(Jun 11, 2019)

Added

Improved

Fixed

v0.2.2(May 7, 2019)

v0.2.1(Mar 5, 2019)

Chaged

v0.2.0(Feb 27, 2019)

Added

Changed

Owner

Shapelets

The deployment framework aims to provide a simple, lightweight, fast integrated, pipelined deployment framework that ensures reliability, high concurrency and scalability of services.

Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio