PyCaret

PyCaret Kaggle Notebook (Since April 10, 2022)

Programming, Kaggle, PyCaret, Python

개요

  • PyCaret이 최근 업데이트 되면서 Kaggle에서 설치 오류가 뜨기 시작함.
  • 해결책은 몇가지 있으나, 그 중 Downgrade 해서 설치 할 예정

캐글 대회 시작

  • 캐글 노트북 시작을 하면 다음 코드가 나타난다. 다음 Cell부터 진행한다.
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

/kaggle/input/tabular-playground-series-apr-2022/sample_submission.csv
/kaggle/input/tabular-playground-series-apr-2022/train_labels.csv
/kaggle/input/tabular-playground-series-apr-2022/train.csv
/kaggle/input/tabular-playground-series-apr-2022/test.csv

라이브러리 Downgrade

  • 설치하려고 하는 Library는 PyCaret 2.3.5 버전임 (4월 10일 기준 2.3.10 버전)
  • Scikit-Learn 최신 버전은 1.0대 버전임
  • 이를 낮춰서 진행할 것임
!pip install numpy==1.19.5
!pip install matplotlib==3.4.0
!pip install scikit-learn==0.23.2
!pip install pycaret==2.3.5

Collecting numpy==1.19.5
  Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.8/14.8 MB 33.0 MB/s eta 0:00:00
[?25hInstalling collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 1.21.5
    Uninstalling numpy-1.21.5:
      Successfully uninstalled numpy-1.21.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, which is not installed.
beatrix-jupyterlab 3.1.7 requires google-cloud-bigquery-storage, which is not installed.
thinc 8.0.15 requires typing-extensions<4.0.0.0,>=3.7.4.1; python_version < "3.8", but you have typing-extensions 4.1.1 which is incompatible.
tfx-bsl 1.7.0 requires pyarrow<6,>=1, but you have pyarrow 7.0.0 which is incompatible.
tfx-bsl 1.7.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3,>=1.15.5, but you have tensorflow 2.6.3 which is incompatible.
tensorflow 2.6.3 requires absl-py~=0.10, but you have absl-py 1.0.0 which is incompatible.
tensorflow 2.6.3 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.
tensorflow 2.6.3 requires typing-extensions<3.11,>=3.7, but you have typing-extensions 4.1.1 which is incompatible.
tensorflow 2.6.3 requires wrapt~=1.12.1, but you have wrapt 1.14.0 which is incompatible.
tensorflow-transform 1.7.0 requires pyarrow<6,>=1, but you have pyarrow 7.0.0 which is incompatible.
tensorflow-transform 1.7.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<2.9,>=1.15.5, but you have tensorflow 2.6.3 which is incompatible.
tensorflow-serving-api 2.8.0 requires tensorflow<3,>=2.8.0, but you have tensorflow 2.6.3 which is incompatible.
spacy 3.2.4 requires typing-extensions<4.0.0.0,>=3.7.4; python_version < "3.8", but you have typing-extensions 4.1.1 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.5.1 which is incompatible.
imageio 2.16.1 requires numpy>=1.20.0, but you have numpy 1.19.5 which is incompatible.
featuretools 1.8.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.
apache-beam 2.37.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.4 which is incompatible.
apache-beam 2.37.0 requires httplib2<0.20.0,>=0.8, but you have httplib2 0.20.4 which is incompatible.
apache-beam 2.37.0 requires pyarrow<7.0.0,>=0.15.1, but you have pyarrow 7.0.0 which is incompatible.
Successfully installed numpy-1.19.5
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting matplotlib==3.4.0
  Downloading matplotlib-3.4.0-cp37-cp37m-manylinux1_x86_64.whl (10.3 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 10.3/10.3 MB 21.5 MB/s eta 0:00:00
[?25hRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (3.0.7)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (1.4.0)
Requirement already satisfied: numpy>=1.16 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (1.19.5)
Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (2.8.2)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (0.11.0)
Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (9.0.1)
Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib==3.4.0) (4.1.1)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib==3.4.0) (1.16.0)
Installing collected packages: matplotlib
  Attempting uninstall: matplotlib
    Found existing installation: matplotlib 3.5.1
    Uninstalling matplotlib-3.5.1:
      Successfully uninstalled matplotlib-3.5.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
beatrix-jupyterlab 3.1.7 requires google-cloud-bigquery-storage, which is not installed.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
Successfully installed matplotlib-3.4.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting scikit-learn==0.23.2
  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.8/6.8 MB 14.8 MB/s eta 0:00:00
[?25hRequirement already satisfied: scipy>=0.19.1 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.7.3)
Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.0.1)
Requirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.19.5)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (3.1.0)
Installing collected packages: scikit-learn
  Attempting uninstall: scikit-learn
    Found existing installation: scikit-learn 1.0.2
    Uninstalling scikit-learn-1.0.2:
      Successfully uninstalled scikit-learn-1.0.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.23.2 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
imbalanced-learn 0.9.0 requires scikit-learn>=1.0.1, but you have scikit-learn 0.23.2 which is incompatible.
hypertools 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.
featuretools 1.8.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.
Successfully installed scikit-learn-0.23.2
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting pycaret==2.3.5
  Downloading pycaret-2.3.5-py3-none-any.whl (288 kB)
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[?25hRequirement already satisfied: ipywidgets in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (7.6.5)
Requirement already satisfied: pyLDAvis in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.2.2)
Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.3.5)
Collecting scipy<=1.5.4
  Downloading scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 25.9/25.9 MB 30.0 MB/s eta 0:00:00
[?25hRequirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.2.4)
Requirement already satisfied: IPython in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (7.32.0)
Collecting spacy<2.4.0
  Downloading spacy-2.3.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.4 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 10.4/10.4 MB 41.9 MB/s eta 0:00:00
[?25hRequirement already satisfied: lightgbm>=2.3.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.3.1)
Requirement already satisfied: yellowbrick>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.4)
Requirement already satisfied: textblob in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.17.1)
Requirement already satisfied: plotly>=4.4.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (5.7.0)
Requirement already satisfied: scikit-plot in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.3.7)
Requirement already satisfied: umap-learn in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.5.2)
Collecting gensim<4.0.0
  Downloading gensim-3.8.3-cp37-cp37m-manylinux1_x86_64.whl (24.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.2/24.2 MB 28.5 MB/s eta 0:00:00
[?25hRequirement already satisfied: Boruta in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.3)
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.4.0)
Requirement already satisfied: pandas-profiling>=2.8.0 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.1.0)
Collecting imbalanced-learn==0.7.0
  Downloading imbalanced_learn-0.7.0-py3-none-any.whl (167 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 167.1/167.1 KB 14.8 MB/s eta 0:00:00
[?25hCollecting mlflow
  Downloading mlflow-1.25.1-py3-none-any.whl (16.8 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.8/16.8 MB 33.2 MB/s eta 0:00:00
[?25hCollecting pyod
  Downloading pyod-0.9.9.tar.gz (116 kB)
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[?25h  Preparing metadata (setup.py) ... [?25ldone
[?25hRequirement already satisfied: kmodes>=0.10.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.12.0)
Requirement already satisfied: wordcloud in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.8.1)
Requirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.0.1)
Requirement already satisfied: numpy==1.19.5 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.19.5)
Requirement already satisfied: seaborn in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.11.2)
Requirement already satisfied: mlxtend>=0.17.0 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.19.0)
Requirement already satisfied: scikit-learn==0.23.2 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.23.2)
Requirement already satisfied: cufflinks>=0.17.0 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.17.3)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2->pycaret==2.3.5) (3.1.0)
Requirement already satisfied: setuptools>=34.4.1 in /opt/conda/lib/python3.7/site-packages (from cufflinks>=0.17.0->pycaret==2.3.5) (59.8.0)
Requirement already satisfied: colorlover>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from cufflinks>=0.17.0->pycaret==2.3.5) (0.3.0)
Requirement already satisfied: six>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from cufflinks>=0.17.0->pycaret==2.3.5) (1.16.0)
Requirement already satisfied: smart-open>=1.8.1 in /opt/conda/lib/python3.7/site-packages (from gensim<4.0.0->pycaret==2.3.5) (5.2.1)
Requirement already satisfied: pickleshare in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (0.7.5)
Requirement already satisfied: backcall in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (0.2.0)
Requirement already satisfied: traitlets>=4.2 in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (5.1.1)
Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (4.8.0)
Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (3.0.27)
Requirement already satisfied: pygments in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (2.11.2)
Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (0.18.1)
Requirement already satisfied: decorator in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (5.1.1)
Requirement already satisfied: matplotlib-inline in /opt/conda/lib/python3.7/site-packages (from IPython->pycaret==2.3.5) (0.1.3)
Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.7/site-packages (from ipywidgets->pycaret==2.3.5) (0.2.0)
Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from ipywidgets->pycaret==2.3.5) (1.0.2)
Requirement already satisfied: widgetsnbextension~=3.5.0 in /opt/conda/lib/python3.7/site-packages (from ipywidgets->pycaret==2.3.5) (3.5.2)
Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.7/site-packages (from ipywidgets->pycaret==2.3.5) (5.2.0)
Requirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.7/site-packages (from ipywidgets->pycaret==2.3.5) (6.9.2)
Requirement already satisfied: wheel in /opt/conda/lib/python3.7/site-packages (from lightgbm>=2.3.1->pycaret==2.3.5) (0.37.1)
Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pycaret==2.3.5) (9.0.1)
Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pycaret==2.3.5) (2.8.2)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pycaret==2.3.5) (1.4.0)
Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pycaret==2.3.5) (3.0.7)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pycaret==2.3.5) (0.11.0)
Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->pycaret==2.3.5) (2021.3)
Requirement already satisfied: visions[type_image_path]==0.7.4 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (0.7.4)
Requirement already satisfied: jinja2>=2.11.1 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (3.1.1)
Requirement already satisfied: phik>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (0.12.0)
Requirement already satisfied: requests>=2.24.0 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (2.27.1)
Requirement already satisfied: pydantic>=1.8.1 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (1.8.2)
Requirement already satisfied: markupsafe~=2.0.1 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (2.0.1)
Requirement already satisfied: htmlmin>=0.1.12 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (0.1.12)
Requirement already satisfied: tqdm>=4.48.2 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (4.63.0)
Requirement already satisfied: multimethod>=1.4 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (1.4)
Requirement already satisfied: PyYAML>=5.0.0 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (6.0)
Requirement already satisfied: tangled-up-in-unicode==0.1.0 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (0.1.0)
Requirement already satisfied: missingno>=0.4.2 in /opt/conda/lib/python3.7/site-packages (from pandas-profiling>=2.8.0->pycaret==2.3.5) (0.4.2)
Requirement already satisfied: attrs>=19.3.0 in /opt/conda/lib/python3.7/site-packages (from visions[type_image_path]==0.7.4->pandas-profiling>=2.8.0->pycaret==2.3.5) (21.4.0)
Requirement already satisfied: networkx>=2.4 in /opt/conda/lib/python3.7/site-packages (from visions[type_image_path]==0.7.4->pandas-profiling>=2.8.0->pycaret==2.3.5) (2.5)
Requirement already satisfied: imagehash in /opt/conda/lib/python3.7/site-packages (from visions[type_image_path]==0.7.4->pandas-profiling>=2.8.0->pycaret==2.3.5) (4.2.1)
Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from plotly>=4.4.1->pycaret==2.3.5) (8.0.1)
Collecting catalogue<1.1.0,>=0.0.7
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Building wheels for collected packages: pyod, databricks-cli
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  Attempting uninstall: spacy
    Found existing installation: spacy 3.2.4
    Uninstalling spacy-3.2.4:
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
scattertext 0.1.6 requires gensim>=4.0.0, but you have gensim 3.8.3 which is incompatible.
pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.5.4 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
hypertools 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.
featuretools 1.8.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.
en-core-web-sm 3.2.0 requires spacy<3.3.0,>=3.2.0, but you have spacy 2.3.7 which is incompatible.
en-core-web-lg 3.2.0 requires spacy<3.3.0,>=3.2.0, but you have spacy 2.3.7 which is incompatible.
Successfully installed catalogue-1.0.0 databricks-cli-0.16.6 gensim-3.8.3 gunicorn-20.1.0 imbalanced-learn-0.7.0 mlflow-1.25.1 plac-1.1.3 prometheus-flask-exporter-0.20.1 pycaret-2.3.5 pyod-0.9.9 querystring-parser-1.2.4 scipy-1.5.4 spacy-2.3.7 srsly-1.0.5 thinc-7.4.5 yellowbrick-1.3.post1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv


테스트

  • 분류를 위한 필수 라이브러리를 가져왔다.
from pycaret.classification import *
import warnings
warnings.filterwarnings("ignore")
  • 데이터를 불러오는 코드다.
from pycaret.datasets import get_data
data = get_data('diabetes')

png

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PyCaret Installation on M1 Mac

Programming, Python, PyCaret, M1 Mac

PyCaret Installation on M1 Mac

개요

  • M1 Mac에서 PyCaret을 설치하고 싶었다.
  • PyCaret 은 AutoML 라이브러리이며, 단 몇줄의 코드로 복잡한 기계학습을 학습 및 비교할 수 있도록 구현한 코드라고 볼 수 있다.
  • M1 Mac에서 해당 라이브러리를 사용하려면 크게 2가지 필수 전제 조건이 있다.
    • LightGBM, XGboost 설치

1. PyCaret 설치 방법

  • 일반 인텔 기반의 Mac의 설치는 매우 쉽다. (Intel Mac)
$ brew install lightgbm
  • 그러나, M1 Mac에서는 생각보다 쉽지 않다.
    • 물론, Rosetta로 터미널을 바꾸면 Intel Mac 처럼 쓸 수 있다. 그러나, M1의 GPU를 활용하려면 기존 설치 방법으로는 적용이 어렵다.

(1) Step 01. Xcode Command Line Tools

(2) Step 02. miniforge 설치

  • 본 블로그에서 가장 중요하다.
  • 2021년 12월 기준 시점에서는 반드시 설치를 해야 한다.
    • GPU를 사용하기 위해서는 LightGBM, XGBoost, PyCaret은 Conda 기반으로만 설치가 가능하다.
  • 설치 파일 주소: https://github.com/conda-forge/miniforge
    • 설치 시, 아래 그림과 같이 arm64 Apple Silicon을 선택해서 다운로드 받아야 한다.

install_01.png

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PyCaret, Skorch Using Pipeline

Programming, Machine Learning, Python, PyCaret, Pipeline

개요

  • Scikit-Learn의 Pipeline은 강력하다.
  • PyCaret, Skorch에도 사용이 가능하다.
  • Google Colab에서 시도해보자.

필수 라이브러리 설치

  • pycaret을 설치 한 후에는 반드시 런타임 재시작을 클릭한다.
!pip install pycaret

Collecting pycaret
  Downloading pycaret-2.3.5-py3-none-any.whl (288 kB)
.
.
Successfully installed Boruta-0.3 Mako-1.1.6 PyYAML-6.0 alembic-1.4.1 databricks-cli-0.16.2 docker-5.0.3 funcy-1.17 gitdb-4.0.9 gitpython-3.1.24 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 joblib-1.0.1 kmodes-0.11.1 lightgbm-3.3.1 mlflow-1.22.0 mlxtend-0.19.0 multimethod-1.6 pandas-profiling-3.1.0 phik-0.12.0 prometheus-flask-exporter-0.18.7 pyLDAvis-3.2.2 pycaret-2.3.5 pydantic-1.8.2 pynndescent-0.5.5 pyod-0.9.6 python-editor-1.0.4 querystring-parser-1.2.4 requests-2.26.0 scikit-learn-0.23.2 scikit-plot-0.3.7 scipy-1.5.4 smmap-5.0.0 tangled-up-in-unicode-0.1.0 umap-learn-0.5.2 visions-0.7.4 websocket-client-1.2.3

!pip install -U skorch

Requirement already satisfied: skorch in /usr/local/lib/python3.7/dist-packages (0.11.0)
Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from skorch) (0.8.9)
Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from skorch) (0.23.2)
Requirement already satisfied: tqdm>=4.14.0 in /usr/local/lib/python3.7/dist-packages (from skorch) (4.62.3)
Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from skorch) (1.19.5)
Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from skorch) (1.5.4)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->skorch) (1.0.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->skorch) (3.0.0)

from pycaret.datasets import get_data
data = get_data("electrical_grid")
tau1 tau2 tau3 tau4 p1 p2 p3 p4 g1 g2 g3 g4 stabf
0 2.959060 3.079885 8.381025 9.780754 3.763085 -0.782604 -1.257395 -1.723086 0.650456 0.859578 0.887445 0.958034 unstable
1 9.304097 4.902524 3.047541 1.369357 5.067812 -1.940058 -1.872742 -1.255012 0.413441 0.862414 0.562139 0.781760 stable
2 8.971707 8.848428 3.046479 1.214518 3.405158 -1.207456 -1.277210 -0.920492 0.163041 0.766689 0.839444 0.109853 unstable
3 0.716415 7.669600 4.486641 2.340563 3.963791 -1.027473 -1.938944 -0.997374 0.446209 0.976744 0.929381 0.362718 unstable
4 3.134112 7.608772 4.943759 9.857573 3.525811 -1.125531 -1.845975 -0.554305 0.797110 0.455450 0.656947 0.820923 unstable

PyTorchModel

  • sktorch 라이브러리는 PyTorch 모델과 함께 작동한다.
  • MLP 모델을 작성하는 클래스를 설계한다.
import torch.nn as nn

class Net(nn.Module): 
  def __init__(self, num_inputs=12, num_units_d1 = 200, num_units_d2 = 100):
    super(Net, self).__init__() 

    self.dense0 = nn.Linear(num_inputs, num_units_d1)
    self.nonlin = nn.ReLU()
    self.dropout = nn.Dropout(0.5)
    self.dense1 = nn.Linear(num_units_d1, num_units_d2)
    self.output = nn.Linear(num_units_d2, 2)
    self.softmax = nn.Softmax(dim=-1)

  def forward(self, X, **kwargs):
    X = self.nonlin(self.dense0(X))
    X = self.dropout(X)
    X = self.nonlin(self.dense1(X))
    X = self.softmax(self.output(X))
    return X

Skorch Classifier

  • NeuralNetClassifier 클래스를 PyTorch 클래스와 연동한다.
  • Optimizer 기본값인 SGD를 사용한다. 만약 다른 Optimizer로 변경을 원하면 다음 링크에서 확인한다.
  • Sktorch 5 폴드 교차검증을 수행한다.
    • 학습 데이터는 80%, 나머지 20%는 검증 데이터로 활용한다.
from skorch import NeuralNetClassifier 

net = NeuralNetClassifier(
    module = Net, 
    max_epochs = 30, 
    lr = 0.1, 
    batch_size = 32, 
    train_split = None
)

PyCaret과 신경망 학습 방법

  • SKORCH NN model을 초기화 했다면, 이번에는 PyCaret과 함께 모델을 학습할 수 있다.
  • PyCaret은 기본적으로 Pandas DataFrame을 메인 객체로 사용하다.
  • 그런데, sktorch model을 사용하기 위해서는 pipeline을 구성할 때는 DataFrameTransformer() 함수를 사용해야 한다.
from skorch.helper import DataFrameTransformer
import numpy as np
from sklearn.pipeline import Pipeline

nn_pipe = Pipeline(
    [("transform", DataFrameTransformer()), 
     ("net", net), ]
)

PyCaret Setup

  • Skorch API 대신 PyCaret 모델을 사용해본다.
  • log_experiment가 True를 사용하게 되면 MLFlow를 사용할 수 있다.
  • silent가 True인 경우 중간에 발생하는 press enter to continue 입력 단계를 피할 수 있다.
from pycaret.classification import *
target = "stabf"
clf1 = setup(data = data, 
            target = target,
            train_size = 0.8,
            fold = 5,
            session_id = 123,
            log_experiment = True, 
            experiment_name = 'electrical_grid_1', 
            silent = True)
Description Value
0 session_id 123
1 Target stabf
2 Target Type Binary
3 Label Encoded stable: 0, unstable: 1
4 Original Data (10000, 13)
5 Missing Values False
6 Numeric Features 12
7 Categorical Features 0
8 Ordinal Features False
9 High Cardinality Features False
10 High Cardinality Method None
11 Transformed Train Set (8000, 12)
12 Transformed Test Set (2000, 12)
13 Shuffle Train-Test True
14 Stratify Train-Test False
15 Fold Generator StratifiedKFold
16 Fold Number 5
17 CPU Jobs -1
18 Use GPU False
19 Log Experiment True
20 Experiment Name electrical_grid_1
21 USI 9626
22 Imputation Type simple
23 Iterative Imputation Iteration None
24 Numeric Imputer mean
25 Iterative Imputation Numeric Model None
26 Categorical Imputer constant
27 Iterative Imputation Categorical Model None
28 Unknown Categoricals Handling least_frequent
29 Normalize False
30 Normalize Method None
31 Transformation False
32 Transformation Method None
33 PCA False
34 PCA Method None
35 PCA Components None
36 Ignore Low Variance False
37 Combine Rare Levels False
38 Rare Level Threshold None
39 Numeric Binning False
40 Remove Outliers False
41 Outliers Threshold None
42 Remove Multicollinearity False
43 Multicollinearity Threshold None
44 Remove Perfect Collinearity True
45 Clustering False
46 Clustering Iteration None
47 Polynomial Features False
48 Polynomial Degree None
49 Trignometry Features False
50 Polynomial Threshold None
51 Group Features False
52 Feature Selection False
53 Feature Selection Method classic
54 Features Selection Threshold None
55 Feature Interaction False
56 Feature Ratio False
57 Interaction Threshold None
58 Fix Imbalance False
59 Fix Imbalance Method SMOTE

PyCaret Train Model

  • Random Forest 모델을 사용해본다.
model = create_model("rf")
Accuracy AUC Recall Prec. F1 Kappa MCC
0 0.9244 0.9796 0.9667 0.9189 0.9422 0.8331 0.8353
1 0.9275 0.9793 0.9549 0.9330 0.9438 0.8417 0.8422
2 0.9225 0.9810 0.9608 0.9211 0.9406 0.8294 0.8309
3 0.9081 0.9738 0.9461 0.9130 0.9293 0.7983 0.7993
4 0.9044 0.9738 0.9471 0.9071 0.9267 0.7894 0.7909
Mean 0.9174 0.9775 0.9551 0.9186 0.9365 0.8184 0.8197
SD 0.0093 0.0031 0.0079 0.0087 0.0071 0.0206 0.0206

PyCaret Train Skorch Model

  • 이번에는 Skorch Model을 Pycaret 함수에 넣어서 확인해본다.
skorch_model = create_model(nn_pipe)
Accuracy AUC Recall Prec. F1 Kappa MCC
0 0.8831 0.9644 0.9500 0.8769 0.9120 0.7389 0.7441
1 0.8550 0.9437 0.9569 0.8385 0.8938 0.6685 0.6831
2 0.8369 0.9280 0.9638 0.8146 0.8829 0.6202 0.6446
3 0.8506 0.9347 0.8668 0.8957 0.8810 0.6805 0.6812
4 0.8081 0.9411 0.9765 0.7789 0.8666 0.5400 0.5859
Mean 0.8468 0.9424 0.9428 0.8409 0.8873 0.6496 0.6678
SD 0.0245 0.0123 0.0390 0.0421 0.0151 0.0666 0.0519

Comparing Models

  • 두 모델 중 어떤 모델이 더 좋은지 확인해본다.
best_model = compare_models(include=[skorch_model, model], sort = "AUC")
Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
1 Random Forest Classifier 0.9174 0.9775 0.9551 0.9186 0.9365 0.8184 0.8197 2.114
0 NeuralNetClassifier 0.8426 0.9400 0.9547 0.8281 0.8861 0.6355 0.6565 11.878

Hyperparameter Grid

  • Hyperparameter 튜닝을 적용하도록 한다.
  • 모형 튜닝을 위한 parameter 값은 다음 명령어를 통해서 확인할 수 있다.
skorch_model.get_params().keys()

dict_keys(['memory', 'steps', 'verbose', 'transform', 'net', 'transform__float_dtype', 'transform__int_dtype', 'transform__treat_int_as_categorical', 'net__module', 'net__criterion', 'net__optimizer', 'net__lr', 'net__max_epochs', 'net__batch_size', 'net__iterator_train', 'net__iterator_valid', 'net__dataset', 'net__train_split', 'net__callbacks', 'net__predict_nonlinearity', 'net__warm_start', 'net__verbose', 'net__device', 'net___kwargs', 'net__classes', 'net__callbacks__epoch_timer', 'net__callbacks__train_loss', 'net__callbacks__train_loss__name', 'net__callbacks__train_loss__lower_is_better', 'net__callbacks__train_loss__on_train', 'net__callbacks__valid_loss', 'net__callbacks__valid_loss__name', 'net__callbacks__valid_loss__lower_is_better', 'net__callbacks__valid_loss__on_train', 'net__callbacks__valid_acc', 'net__callbacks__valid_acc__scoring', 'net__callbacks__valid_acc__lower_is_better', 'net__callbacks__valid_acc__on_train', 'net__callbacks__valid_acc__name', 'net__callbacks__valid_acc__target_extractor', 'net__callbacks__valid_acc__use_caching', 'net__callbacks__print_log', 'net__callbacks__print_log__keys_ignored', 'net__callbacks__print_log__sink', 'net__callbacks__print_log__tablefmt', 'net__callbacks__print_log__floatfmt', 'net__callbacks__print_log__stralign'])

net.get_params().keys()

dict_keys(['module', 'criterion', 'optimizer', 'lr', 'max_epochs', 'batch_size', 'iterator_train', 'iterator_valid', 'dataset', 'train_split', 'callbacks', 'predict_nonlinearity', 'warm_start', 'verbose', 'device', '_kwargs', 'classes', 'callbacks__epoch_timer', 'callbacks__train_loss', 'callbacks__train_loss__name', 'callbacks__train_loss__lower_is_better', 'callbacks__train_loss__on_train', 'callbacks__valid_loss', 'callbacks__valid_loss__name', 'callbacks__valid_loss__lower_is_better', 'callbacks__valid_loss__on_train', 'callbacks__valid_acc', 'callbacks__valid_acc__scoring', 'callbacks__valid_acc__lower_is_better', 'callbacks__valid_acc__on_train', 'callbacks__valid_acc__name', 'callbacks__valid_acc__target_extractor', 'callbacks__valid_acc__use_caching', 'callbacks__print_log', 'callbacks__print_log__keys_ignored', 'callbacks__print_log__sink', 'callbacks__print_log__tablefmt', 'callbacks__print_log__floatfmt', 'callbacks__print_log__stralign'])

import torch.optim as optim

custom_grid = {
	'net__max_epochs':[20, 30],
	'net__lr': [0.01, 0.05, 0.1],
	'net__module__num_units_d1': [50, 100, 150],
	'net__module__num_units_d2': [50, 100, 150],
	'net__optimizer': [optim.Adam, optim.SGD, optim.RMSprop]
	}
  • 이번에는 hyperparameter 모델을 적용하여 모델을 빠르게 만들어 본다.
tuned_skorch_model = tune_model(skorch_model, custom_grid = custom_grid)
Accuracy AUC Recall Prec. F1 Kappa MCC
0 0.8762 0.9667 0.9686 0.8562 0.9089 0.7182 0.7316
1 0.8675 0.9477 0.8784 0.9106 0.8942 0.7171 0.7179
2 0.8375 0.9452 0.7835 0.9535 0.8602 0.6706 0.6891
3 0.8575 0.9522 0.8208 0.9490 0.8803 0.7066 0.7180
4 0.7975 0.9315 0.9726 0.7704 0.8597 0.5127 0.5602
Mean 0.8472 0.9487 0.8848 0.8879 0.8807 0.6650 0.6834
SD 0.0280 0.0114 0.0763 0.0684 0.0192 0.0781 0.0631

References

  1. https://pycaret.org/
  2. https://www.analyticsvidhya.com/blog/2020/05/pycaret-machine-learning-model-seconds/
  3. https://github.com/skorch-dev/skorch
  4. https://towardsdatascience.com/skorch-pytorch-models-trained-with-a-scikit-learn-wrapper-62b9a154623e
  5. https://towardsdatascience.com/pycaret-skorch-build-pytorch-neural-networks-using-minimal-code-57079e197f33

[Python] PyCaret Windows 10 아나콘다 설치 방법

Programming, Py-Openaq, R, Python

강의 홍보

1줄 요약

  • 관리자 실행해서 아나콘다 가상 환경을 만든 후, 새로운 패키지를 설치한다.

PyCaret 설치 방법 (Windows 10)

  • 윈도우 10 환경에서 PyCaret 패키지를 설치해봅니다.
  • 아나콘다 설치에 관한 내용은 생략합니다. 다만, 이 때, 필요한 것은 환경변수에 추가가 되어 있어야 합니다.

가상환경 설정

  • 새로운 가상환경을 만듭니다. (이게 제일 편합니다.)
  • 명령프롬프트를 관리자로 실행합니다.

Read more…