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Upgrade python version to include 3.12 and 3.13 (#144)
* update python * update dependencies * mute simpleimputer for now * edit docs * change image * restore tutorials * fix devel tests * change btb to baytune * fix python specification * cap copulas at 0.11 * update readme
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.github/workflows/tests.yml

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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.8]
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python-version: ['3.10']
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os: [ubuntu-latest]
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steps:
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- uses: actions/checkout@v1
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with:
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python-version: ${{ matrix.python-version }}
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- name: Upgrade pip
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run: pip install -U pip setuptools wheel
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run: pip install -U "pip<=24.1" setuptools wheel
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- name: Install lightfm
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run: python -m pip install --no-use-pep517 'lightfm<2'
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- name: Install package
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8]
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python-version: ['3.8', '3.9', '3.10', '3.11']
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os: [ubuntu-20.04, macos-latest]
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steps:
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- uses: actions/checkout@v1
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: ['3.6', '3.7', '3.8', '3.9', '3.10', '3.11']
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python-version: ['3.8', '3.9', '3.10', '3.11', '3.12', '3.13']
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os: [ubuntu-20.04, macos-latest]
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steps:
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- uses: actions/checkout@v1
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8]
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python-version: ['3.8', '3.9', '3.10', '3.11']
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os: [ubuntu-20.04, macos-latest]
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steps:
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- uses: actions/checkout@v1
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8]
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python-version: ['3.8', '3.9', '3.10', '3.11']
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os: [ubuntu-20.04]
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steps:
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- uses: actions/checkout@v1

README.md

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## Requirements
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**MLBlocks** has been developed and tested on [Python 3.6, 3.7, 3.8, 3.9, and 3.10](https://www.python.org/downloads/)
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**MLBlocks** has been developed and tested on [Python 3.8, 3.9, 3.10, 3.11, 3.12, 3.13](https://www.python.org/downloads/)
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## Install with `pip`
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examples/tutorials/7. Tuning a Pipeline.ipynb

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{
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"data": {
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"text/plain": [
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"0.8639171383183359"
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"0.863978563379761"
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]
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},
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"execution_count": 6,
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{
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"data": {
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"text/plain": [
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"0.8686773872402614"
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"0.868554574842"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from btb.tuning import Tunable\n",
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"from baytune.tuning import Tunable\n",
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"\n",
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"tunable = Tunable.from_dict(tunable_hyperparameters)"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"from btb.tuning import GPTuner\n",
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"from baytune.tuning import GPTuner\n",
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"\n",
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"tuner = GPTuner(tunable)"
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]
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"output_type": "stream",
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"text": [
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"scoring pipeline 1\n",
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"New best found: 0.871994161365419\n",
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"scoring pipeline 2\n",
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"New best found: 0.8723319756253888\n",
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"scoring pipeline 3\n",
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"scoring pipeline 4\n",
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"New best found: 0.8642241881762839\n",
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"scoring pipeline 5\n",
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"scoring pipeline 6\n",
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"scoring pipeline 7\n",
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"New best found: 0.8644390957265209\n",
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"scoring pipeline 8\n",
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"New best found: 0.8679095503945804\n",
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"scoring pipeline 9\n",
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"scoring pipeline 10\n"
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]
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"data": {
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"text/plain": [
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"{('mlprimitives.custom.feature_extraction.CategoricalEncoder#1',\n",
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" 'max_labels'): 39,\n",
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" ('sklearn.impute.SimpleImputer#1', 'strategy'): 'most_frequent',\n",
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" ('xgboost.XGBClassifier#1', 'n_estimators'): 70,\n",
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" ('xgboost.XGBClassifier#1', 'max_depth'): 6,\n",
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" ('xgboost.XGBClassifier#1', 'learning_rate'): 0.07406443671152008,\n",
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" ('xgboost.XGBClassifier#1', 'gamma'): 0.9244108160038952,\n",
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" ('xgboost.XGBClassifier#1', 'min_child_weight'): 1}"
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" 'max_labels'): 60,\n",
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" ('sklearn.impute.SimpleImputer#1', 'strategy'): 'mean',\n",
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" ('xgboost.XGBClassifier#1', 'n_estimators'): 190,\n",
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" ('xgboost.XGBClassifier#1', 'max_depth'): 5,\n",
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" ('xgboost.XGBClassifier#1', 'learning_rate'): 0.13575511242790694,\n",
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" ('xgboost.XGBClassifier#1', 'gamma'): 0.6326488945712287,\n",
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" ('xgboost.XGBClassifier#1', 'min_child_weight'): 8}"
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]
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},
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"execution_count": 13,
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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"version": "3.10.15"
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}
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},
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"nbformat": 4,

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