未验证 提交 94e474e3 编写于 作者: P PyCaret 提交者: GitHub

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PyCaret is a free software and an open source low-code machine learning library for supervised and unsupervised machine learning techniques in Python programming language. Its primary objective is to reduce the cycle time from hypothesis to insights and make data scientists more productive in their experiments. It does so by providing a high-level API which is sophisticated yet easy to use and consistent across all modules. PyCaret enables data scientists and analysts to perform iterative end-to-end data science experiments in very efficient way allowing them to reach the conclusions faster. Through the use of its high-level low-code API, the amount of time spent in coding experiments reduce drastically, allowing business to restructure their machine learning workflows and re-evaluate the value chain of data science projects. PyCaret is essentially a python wrapper around several machine learning frameworks and libraries such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy to name a few.
## Current Release
The current release is beta 0.0.58 (as of 23/02/2020). A full public release is expected by end of Feb 2020.
The current release is beta 0.0.59 (as of 23/02/2020). A full public release is expected by end of Feb 2020.
## Who should use PyCaret?
PyCaret is free and open source library which is easy to install and can be setup either locally or on any cloud service within minutes. As such there is no limitation of use however, in our opinion following are the ideal target audience: <br />
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......@@ -27,7 +27,7 @@ def readme():
setup(
name="pycaret",
version="0.0.58",
version="0.0.59",
description="A Python package for supervised and unsupervised machine learning.",
long_description=readme(),
long_description_content_type="text/markdown",
......
......@@ -3,7 +3,7 @@
# License: MIT
def version():
print("0.0.58")
print("0.0.59")
def check_metric(actual, prediction, metric, round=4):
......@@ -94,3 +94,27 @@ def check_metric(actual, prediction, metric, round=4):
return result
def enable_colab():
"""
Function to render plotly visuals in colab.
"""
def configure_plotly_browser_state():
import IPython
display(IPython.core.display.HTML('''
<script src="/static/components/requirejs/require.js"></script>
<script>
requirejs.config({
paths: {
base: '/static/base',
plotly: 'https://cdn.plot.ly/plotly-latest.min.js?noext',
},
});
</script>
'''))
import IPython
IPython.get_ipython().events.register('pre_run_cell', configure_plotly_browser_state)
print('Colab mode activated.')
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