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support xgboost 2.0 (#1219)

Browse Source 
* support xgboost 2.0

* try classes_

* test version

* quote

* use_label_encoder

* Fix xgboost test error

* remove deprecated files

* remove deprecated files

* remove deprecated import

* replace deprecated import in integrate_spark.ipynb

* replace deprecated import in automl_lightgbm.ipynb

* formatted integrate_spark.ipynb

* replace deprecated import

* try fix driver python path

* Update python-package.yml

* replace deprecated reference

* move spark python env var to other section

* Update setup.py, install xgb<2 for MacOS

* Fix typo

* assert

* Try assert xgboost version

* Fail fast

* Keep all test/spark to try fail fast

* No need to skip spark test in Mac or Win

* Remove assert xgb version

* Remove fail fast

* Found root cause, fix test_sparse_matrix_xgboost

* Revert "No need to skip spark test in Mac or Win"

This reverts commit a09034817f.

* remove assertion

---------

Co-authored-by: Li Jiang <bnujli@gmail.com>
Co-authored-by: levscaut <57213911+levscaut@users.noreply.github.com>
Co-authored-by: levscaut <lwd2010530@qq.com>
Co-authored-by: Li Jiang <lijiang1@microsoft.com>
This commit is contained in:
Chi Wang
2023-09-21 23:55:00 -07:00
committed by GitHub
parent 4886cb5689
commit 868e7dd1ca
22 changed files with 576 additions and 440 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
notebook/automl_classification.ipynb
View File

@@ -80,7 +80,7 @@
],
"source": [
"from minio.error import ServerError\n",
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"\n",
"try:\n",
" X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir='./')\n",
@@ -1252,7 +1252,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=settings['log_file_name'], time_budget=240)\n",
"for config in config_history:\n",
@@ -1540,7 +1540,7 @@
"outputs": [],
"source": [
"''' SKLearnEstimator is the super class for a sklearn learner '''\n",
"from flaml.model import SKLearnEstimator\n",
"from flaml.automl.model import SKLearnEstimator\n",
"from flaml import tune\n",
"from flaml.automl.task.task import CLASSIFICATION\n",
"\n",

381
notebook/automl_flight_delays.ipynb
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@@ -37,383 +37,20 @@
"\n",
"In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install the following packages."
"FLAML requires `Python>=3.8`. To run this notebook example, please install the following packages."
]
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": null,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
"data": {
"application/vnd.livy.statement-meta+json": {
"execution_finish_time": "2023-04-09T03:11:05.782522Z",
"execution_start_time": "2023-04-09T03:11:05.7822033Z",
"livy_statement_state": "available",
"parent_msg_id": "18b2ee64-09c4-4ceb-8975-e4ed43d7c41a",
"queued_time": "2023-04-09T03:10:33.571519Z",
"session_id": "7",
"session_start_time": null,
"spark_jobs": null,
"spark_pool": null,
"state": "finished",
"statement_id": -1
},
"text/plain": [
"StatementMeta(, 7, -1, Finished, Available)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting flaml[synapse]==1.1.3\n",
" Using cached FLAML-1.1.3-py3-none-any.whl (224 kB)\n",
"Collecting xgboost==1.6.1\n",
" Using cached xgboost-1.6.1-py3-none-manylinux2014_x86_64.whl (192.9 MB)\n",
"Collecting pandas==1.5.1\n",
" Using cached pandas-1.5.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB)\n",
"Collecting numpy==1.23.4\n",
" Using cached numpy-1.23.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)\n",
"Collecting openml\n",
" Using cached openml-0.13.1-py3-none-any.whl\n",
"Collecting scipy>=1.4.1\n",
" Using cached scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.5 MB)\n",
"Collecting scikit-learn>=0.24\n",
" Using cached scikit_learn-1.2.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
"Collecting lightgbm>=2.3.1\n",
" Using cached lightgbm-3.3.5-py3-none-manylinux1_x86_64.whl (2.0 MB)\n",
"Collecting pyspark>=3.0.0\n",
" Using cached pyspark-3.3.2-py2.py3-none-any.whl\n",
"Collecting optuna==2.8.0\n",
" Using cached optuna-2.8.0-py3-none-any.whl (301 kB)\n",
"Collecting joblibspark>=0.5.0\n",
" Using cached joblibspark-0.5.1-py3-none-any.whl (15 kB)\n",
"Collecting python-dateutil>=2.8.1\n",
" Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)\n",
"Collecting pytz>=2020.1\n",
" Using cached pytz-2023.3-py2.py3-none-any.whl (502 kB)\n",
"Collecting cliff\n",
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"Collecting packaging>=20.0\n",
" Using cached packaging-23.0-py3-none-any.whl (42 kB)\n",
"Collecting cmaes>=0.8.2\n",
" Using cached cmaes-0.9.1-py3-none-any.whl (21 kB)\n",
"Collecting sqlalchemy>=1.1.0\n",
" Using cached SQLAlchemy-2.0.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB)\n",
"Collecting tqdm\n",
" Using cached tqdm-4.65.0-py3-none-any.whl (77 kB)\n",
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"Collecting colorlog\n",
" Using cached colorlog-6.7.0-py2.py3-none-any.whl (11 kB)\n",
"Collecting xmltodict\n",
" Using cached xmltodict-0.13.0-py2.py3-none-any.whl (10.0 kB)\n",
"Collecting requests\n",
" Using cached requests-2.28.2-py3-none-any.whl (62 kB)\n",
"Collecting minio\n",
" Using cached minio-7.1.14-py3-none-any.whl (77 kB)\n",
"Collecting liac-arff>=2.4.0\n",
" Using cached liac_arff-2.5.0-py3-none-any.whl\n",
"Collecting pyarrow\n",
" Using cached pyarrow-11.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (35.0 MB)\n",
"Collecting joblib>=0.14\n",
" Using cached joblib-1.2.0-py3-none-any.whl (297 kB)\n",
"Collecting wheel\n",
" Using cached wheel-0.40.0-py3-none-any.whl (64 kB)\n",
"Collecting py4j==0.10.9.5\n",
" Using cached py4j-0.10.9.5-py2.py3-none-any.whl (199 kB)\n",
"Collecting six>=1.5\n",
" Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)\n",
"Collecting threadpoolctl>=2.0.0\n",
" Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
"Collecting urllib3\n",
" Using cached urllib3-1.26.15-py2.py3-none-any.whl (140 kB)\n",
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" Using cached certifi-2022.12.7-py3-none-any.whl (155 kB)\n",
"Collecting idna<4,>=2.5\n",
" Using cached idna-3.4-py3-none-any.whl (61 kB)\n",
"Collecting charset-normalizer<4,>=2\n",
" Using cached charset_normalizer-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (195 kB)\n",
"Collecting typing-extensions>=4.2.0\n",
" Using cached typing_extensions-4.5.0-py3-none-any.whl (27 kB)\n",
"Collecting greenlet!=0.4.17\n",
" Using cached greenlet-2.0.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (618 kB)\n",
"Collecting importlib-metadata\n",
" Using cached importlib_metadata-6.2.0-py3-none-any.whl (21 kB)\n",
"Collecting importlib-resources\n",
" Using cached importlib_resources-5.12.0-py3-none-any.whl (36 kB)\n",
"Collecting Mako\n",
" Using cached Mako-1.2.4-py3-none-any.whl (78 kB)\n",
"Collecting autopage>=0.4.0\n",
" Using cached autopage-0.5.1-py3-none-any.whl (29 kB)\n",
"Collecting cmd2>=1.0.0\n",
" Using cached cmd2-2.4.3-py3-none-any.whl (147 kB)\n",
"Collecting stevedore>=2.0.1\n",
" Using cached stevedore-5.0.0-py3-none-any.whl (49 kB)\n",
"Collecting PrettyTable>=0.7.2\n",
" Using cached prettytable-3.6.0-py3-none-any.whl (27 kB)\n",
"Collecting PyYAML>=3.12\n",
" Using cached PyYAML-6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (701 kB)\n",
"Collecting attrs>=16.3.0\n",
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"Collecting pyperclip>=1.6\n",
" Using cached pyperclip-1.8.2-py3-none-any.whl\n",
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"Collecting zipp>=0.5\n",
" Using cached zipp-3.15.0-py3-none-any.whl (6.8 kB)\n",
"Collecting pbr!=2.1.0,>=2.0.0\n",
" Using cached pbr-5.11.1-py2.py3-none-any.whl (112 kB)\n",
"Collecting MarkupSafe>=0.9.2\n",
" Using cached MarkupSafe-2.1.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\n",
"Installing collected packages: wcwidth, pytz, pyperclip, py4j, zipp, xmltodict, wheel, urllib3, typing-extensions, tqdm, threadpoolctl, six, PyYAML, pyspark, PrettyTable, pbr, packaging, numpy, MarkupSafe, liac-arff, joblib, idna, greenlet, colorlog, charset-normalizer, certifi, autopage, attrs, stevedore, sqlalchemy, scipy, requests, python-dateutil, pyarrow, minio, Mako, joblibspark, importlib-resources, importlib-metadata, cmd2, cmaes, xgboost, scikit-learn, pandas, cliff, alembic, optuna, openml, lightgbm, flaml\n",
" Attempting uninstall: wcwidth\n",
" Found existing installation: wcwidth 0.2.6\n",
" Uninstalling wcwidth-0.2.6:\n",
" Successfully uninstalled wcwidth-0.2.6\n",
" Attempting uninstall: pytz\n",
" Found existing installation: pytz 2023.3\n",
" Uninstalling pytz-2023.3:\n",
" Successfully uninstalled pytz-2023.3\n",
" Attempting uninstall: pyperclip\n",
" Found existing installation: pyperclip 1.8.2\n",
" Uninstalling pyperclip-1.8.2:\n",
" Successfully uninstalled pyperclip-1.8.2\n",
" Attempting uninstall: py4j\n",
" Found existing installation: py4j 0.10.9.5\n",
" Uninstalling py4j-0.10.9.5:\n",
" Successfully uninstalled py4j-0.10.9.5\n",
" Attempting uninstall: zipp\n",
" Found existing installation: zipp 3.15.0\n",
" Uninstalling zipp-3.15.0:\n",
" Successfully uninstalled zipp-3.15.0\n",
" Attempting uninstall: xmltodict\n",
" Found existing installation: xmltodict 0.13.0\n",
" Uninstalling xmltodict-0.13.0:\n",
" Successfully uninstalled xmltodict-0.13.0\n",
" Attempting uninstall: wheel\n",
" Found existing installation: wheel 0.40.0\n",
" Uninstalling wheel-0.40.0:\n",
" Successfully uninstalled wheel-0.40.0\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 1.26.15\n",
" Uninstalling urllib3-1.26.15:\n",
" Successfully uninstalled urllib3-1.26.15\n",
" Attempting uninstall: typing-extensions\n",
" Found existing installation: typing_extensions 4.5.0\n",
" Uninstalling typing_extensions-4.5.0:\n",
" Successfully uninstalled typing_extensions-4.5.0\n",
" Attempting uninstall: tqdm\n",
" Found existing installation: tqdm 4.65.0\n",
" Uninstalling tqdm-4.65.0:\n",
" Successfully uninstalled tqdm-4.65.0\n",
" Attempting uninstall: threadpoolctl\n",
" Found existing installation: threadpoolctl 3.1.0\n",
" Uninstalling threadpoolctl-3.1.0:\n",
" Successfully uninstalled threadpoolctl-3.1.0\n",
" Attempting uninstall: six\n",
" Found existing installation: six 1.16.0\n",
" Uninstalling six-1.16.0:\n",
" Successfully uninstalled six-1.16.0\n",
" Attempting uninstall: PyYAML\n",
" Found existing installation: PyYAML 6.0\n",
" Uninstalling PyYAML-6.0:\n",
" Successfully uninstalled PyYAML-6.0\n",
" Attempting uninstall: pyspark\n",
" Found existing installation: pyspark 3.3.2\n",
" Uninstalling pyspark-3.3.2:\n",
" Successfully uninstalled pyspark-3.3.2\n",
" Attempting uninstall: PrettyTable\n",
" Found existing installation: prettytable 3.6.0\n",
" Uninstalling prettytable-3.6.0:\n",
" Successfully uninstalled prettytable-3.6.0\n",
" Attempting uninstall: pbr\n",
" Found existing installation: pbr 5.11.1\n",
" Uninstalling pbr-5.11.1:\n",
" Successfully uninstalled pbr-5.11.1\n",
" Attempting uninstall: packaging\n",
" Found existing installation: packaging 23.0\n",
" Uninstalling packaging-23.0:\n",
" Successfully uninstalled packaging-23.0\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.23.4\n",
" Uninstalling numpy-1.23.4:\n",
" Successfully uninstalled numpy-1.23.4\n",
" Attempting uninstall: MarkupSafe\n",
" Found existing installation: MarkupSafe 2.1.2\n",
" Uninstalling MarkupSafe-2.1.2:\n",
" Successfully uninstalled MarkupSafe-2.1.2\n",
" Attempting uninstall: liac-arff\n",
" Found existing installation: liac-arff 2.5.0\n",
" Uninstalling liac-arff-2.5.0:\n",
" Successfully uninstalled liac-arff-2.5.0\n",
" Attempting uninstall: joblib\n",
" Found existing installation: joblib 1.2.0\n",
" Uninstalling joblib-1.2.0:\n",
" Successfully uninstalled joblib-1.2.0\n",
" Attempting uninstall: idna\n",
" Found existing installation: idna 3.4\n",
" Uninstalling idna-3.4:\n",
" Successfully uninstalled idna-3.4\n",
" Attempting uninstall: greenlet\n",
" Found existing installation: greenlet 2.0.2\n",
" Uninstalling greenlet-2.0.2:\n",
" Successfully uninstalled greenlet-2.0.2\n",
" Attempting uninstall: colorlog\n",
" Found existing installation: colorlog 6.7.0\n",
" Uninstalling colorlog-6.7.0:\n",
" Successfully uninstalled colorlog-6.7.0\n",
" Attempting uninstall: charset-normalizer\n",
" Found existing installation: charset-normalizer 3.1.0\n",
" Uninstalling charset-normalizer-3.1.0:\n",
" Successfully uninstalled charset-normalizer-3.1.0\n",
" Attempting uninstall: certifi\n",
" Found existing installation: certifi 2022.12.7\n",
" Uninstalling certifi-2022.12.7:\n",
" Successfully uninstalled certifi-2022.12.7\n",
" Attempting uninstall: autopage\n",
" Found existing installation: autopage 0.5.1\n",
" Uninstalling autopage-0.5.1:\n",
" Successfully uninstalled autopage-0.5.1\n",
" Attempting uninstall: attrs\n",
" Found existing installation: attrs 22.2.0\n",
" Uninstalling attrs-22.2.0:\n",
" Successfully uninstalled attrs-22.2.0\n",
" Attempting uninstall: stevedore\n",
" Found existing installation: stevedore 5.0.0\n",
" Uninstalling stevedore-5.0.0:\n",
" Successfully uninstalled stevedore-5.0.0\n",
" Attempting uninstall: sqlalchemy\n",
" Found existing installation: SQLAlchemy 2.0.9\n",
" Uninstalling SQLAlchemy-2.0.9:\n",
" Successfully uninstalled SQLAlchemy-2.0.9\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.10.1\n",
" Uninstalling scipy-1.10.1:\n",
" Successfully uninstalled scipy-1.10.1\n",
" Attempting uninstall: requests\n",
" Found existing installation: requests 2.28.2\n",
" Uninstalling requests-2.28.2:\n",
" Successfully uninstalled requests-2.28.2\n",
" Attempting uninstall: python-dateutil\n",
" Found existing installation: python-dateutil 2.8.2\n",
" Uninstalling python-dateutil-2.8.2:\n",
" Successfully uninstalled python-dateutil-2.8.2\n",
" Attempting uninstall: pyarrow\n",
" Found existing installation: pyarrow 11.0.0\n",
" Uninstalling pyarrow-11.0.0:\n",
" Successfully uninstalled pyarrow-11.0.0\n",
" Attempting uninstall: minio\n",
" Found existing installation: minio 7.1.14\n",
" Uninstalling minio-7.1.14:\n",
" Successfully uninstalled minio-7.1.14\n",
" Attempting uninstall: Mako\n",
" Found existing installation: Mako 1.2.4\n",
" Uninstalling Mako-1.2.4:\n",
" Successfully uninstalled Mako-1.2.4\n",
" Attempting uninstall: joblibspark\n",
" Found existing installation: joblibspark 0.5.1\n",
" Uninstalling joblibspark-0.5.1:\n",
" Successfully uninstalled joblibspark-0.5.1\n",
" Attempting uninstall: importlib-resources\n",
" Found existing installation: importlib-resources 5.12.0\n",
" Uninstalling importlib-resources-5.12.0:\n",
" Successfully uninstalled importlib-resources-5.12.0\n",
" Attempting uninstall: importlib-metadata\n",
" Found existing installation: importlib-metadata 6.2.0\n",
" Uninstalling importlib-metadata-6.2.0:\n",
" Successfully uninstalled importlib-metadata-6.2.0\n",
" Attempting uninstall: cmd2\n",
" Found existing installation: cmd2 2.4.3\n",
" Uninstalling cmd2-2.4.3:\n",
" Successfully uninstalled cmd2-2.4.3\n",
" Attempting uninstall: cmaes\n",
" Found existing installation: cmaes 0.9.1\n",
" Uninstalling cmaes-0.9.1:\n",
" Successfully uninstalled cmaes-0.9.1\n",
" Attempting uninstall: xgboost\n",
" Found existing installation: xgboost 1.6.1\n",
" Uninstalling xgboost-1.6.1:\n",
" Successfully uninstalled xgboost-1.6.1\n",
" Attempting uninstall: scikit-learn\n",
" Found existing installation: scikit-learn 1.2.2\n",
" Uninstalling scikit-learn-1.2.2:\n",
" Successfully uninstalled scikit-learn-1.2.2\n",
" Attempting uninstall: pandas\n",
" Found existing installation: pandas 1.5.1\n",
" Uninstalling pandas-1.5.1:\n",
" Successfully uninstalled pandas-1.5.1\n",
" Attempting uninstall: cliff\n",
" Found existing installation: cliff 4.2.0\n",
" Uninstalling cliff-4.2.0:\n",
" Successfully uninstalled cliff-4.2.0\n",
" Attempting uninstall: alembic\n",
" Found existing installation: alembic 1.10.3\n",
" Uninstalling alembic-1.10.3:\n",
" Successfully uninstalled alembic-1.10.3\n",
" Attempting uninstall: optuna\n",
" Found existing installation: optuna 2.8.0\n",
" Uninstalling optuna-2.8.0:\n",
" Successfully uninstalled optuna-2.8.0\n",
" Attempting uninstall: openml\n",
" Found existing installation: openml 0.13.1\n",
" Uninstalling openml-0.13.1:\n",
" Successfully uninstalled openml-0.13.1\n",
" Attempting uninstall: lightgbm\n",
" Found existing installation: lightgbm 3.3.5\n",
" Uninstalling lightgbm-3.3.5:\n",
" Successfully uninstalled lightgbm-3.3.5\n",
" Attempting uninstall: flaml\n",
" Found existing installation: FLAML 1.1.3\n",
" Uninstalling FLAML-1.1.3:\n",
" Successfully uninstalled FLAML-1.1.3\n",
"\u001b[31mERROR: 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.\n",
"virtualenv 20.14.0 requires platformdirs<3,>=2, but you have platformdirs 3.2.0 which is incompatible.\n",
"tensorflow 2.4.1 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.\n",
"tensorflow 2.4.1 requires typing-extensions~=3.7.4, but you have typing-extensions 4.5.0 which is incompatible.\n",
"pmdarima 1.8.2 requires numpy~=1.19.0, but you have numpy 1.23.4 which is incompatible.\n",
"koalas 1.8.0 requires numpy<1.20.0,>=1.14, but you have numpy 1.23.4 which is incompatible.\n",
"gevent 21.1.2 requires greenlet<2.0,>=0.4.17; platform_python_implementation == \"CPython\", but you have greenlet 2.0.2 which is incompatible.\n",
"azureml-dataset-runtime 1.34.0 requires pyarrow<4.0.0,>=0.17.0, but you have pyarrow 11.0.0 which is incompatible.\n",
"azureml-core 1.34.0 requires urllib3<=1.26.6,>=1.23, but you have urllib3 1.26.15 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed Mako-1.2.4 MarkupSafe-2.1.2 PrettyTable-3.6.0 PyYAML-6.0 alembic-1.10.3 attrs-22.2.0 autopage-0.5.1 certifi-2022.12.7 charset-normalizer-3.1.0 cliff-4.2.0 cmaes-0.9.1 cmd2-2.4.3 colorlog-6.7.0 flaml-1.1.3 greenlet-2.0.2 idna-3.4 importlib-metadata-6.2.0 importlib-resources-5.12.0 joblib-1.2.0 joblibspark-0.5.1 liac-arff-2.5.0 lightgbm-3.3.5 minio-7.1.14 numpy-1.23.4 openml-0.13.1 optuna-2.8.0 packaging-23.0 pandas-1.5.1 pbr-5.11.1 py4j-0.10.9.5 pyarrow-11.0.0 pyperclip-1.8.2 pyspark-3.3.2 python-dateutil-2.8.2 pytz-2023.3 requests-2.28.2 scikit-learn-1.2.2 scipy-1.10.1 six-1.16.0 sqlalchemy-2.0.9 stevedore-5.0.0 threadpoolctl-3.1.0 tqdm-4.65.0 typing-extensions-4.5.0 urllib3-1.26.15 wcwidth-0.2.6 wheel-0.40.0 xgboost-1.6.1 xmltodict-0.13.0 zipp-3.15.0\n",
"\u001b[33mWARNING: You are using pip version 22.0.4; however, version 23.0.1 is available.\n",
"You should consider upgrading via the '/nfs4/pyenv-bfada21f-d1ed-44b9-a41d-4ff480d237e7/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
"\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
]
},
{
"data": {},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: PySpark kernel has been restarted to use updated packages.\n",
"\n"
]
}
],
"outputs": [],
"source": [
"%pip install flaml[synapse]==1.1.3 xgboost==1.6.1 pandas==1.5.1 numpy==1.23.4 openml --force-reinstall"
"%pip install flaml[automl,synapse] xgboost==1.6.1 pandas==1.5.1 numpy==1.23.4 openml --force-reinstall"
]
},
{
@@ -480,7 +117,7 @@
}
],
"source": [
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir='./')"
]
},
@@ -1389,7 +1026,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=settings['log_file_name'], time_budget=240)\n",
"for config in config_history:\n",
@@ -1861,7 +1498,7 @@
}
],
"source": [
"!pip install rgf-python "
"%pip install rgf-python "
]
},
{
@@ -1898,9 +1535,9 @@
],
"source": [
"''' SKLearnEstimator is the super class for a sklearn learner '''\n",
"from flaml.model import SKLearnEstimator\n",
"from flaml.automl.model import SKLearnEstimator\n",
"from flaml import tune\n",
"from flaml.data import CLASSIFICATION\n",
"from flaml.automl.data import CLASSIFICATION\n",
"\n",
"\n",
"class MyRegularizedGreedyForest(SKLearnEstimator):\n",

8
notebook/automl_lightgbm.ipynb
View File

@@ -28,7 +28,7 @@
"\n",
"In this notebook, we demonstrate how to use FLAML library to tune hyperparameters of LightGBM with a regression example.\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `automl` option (this option is introduced from version 2, for version 1 it is installed by default):\n",
"FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the `automl` option (this option is introduced from version 2, for version 1 it is installed by default):\n",
"```bash\n",
"pip install flaml[automl]\n",
"```"
@@ -87,7 +87,7 @@
}
],
"source": [
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir='./')"
]
},
@@ -509,7 +509,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=settings['log_file_name'], time_budget=60)\n",
"\n",
@@ -852,7 +852,7 @@
" coef[0] * hess + coef[1] * hess_rmse + coef[2] * hess_mae\n",
"\n",
"\n",
"from flaml.model import LGBMEstimator\n",
"from flaml.automl.model import LGBMEstimator\n",
"\n",
"''' create a customized LightGBM learner class with your objective function '''\n",
"class MyLGBM(LGBMEstimator):\n",

10
notebook/automl_nlp.ipynb
View File

@@ -21,7 +21,7 @@
"\n",
"In this notebook, we demonstrate how to use the FLAML library to fine tune an NLP language model with hyperparameter search. We will use [flaml.tune](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function) with the built in GPU in colab for the tuning. However, if you have a machine with more than 1 GPU, you can also use FLAML's [parallel tuning](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) with the ray tune option. \n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `[automl,hf,blendsearch]` option:\n",
"FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the `[automl,hf,blendsearch]` option:\n",
"```bash\n",
"pip install flaml[automl,hf,blendsearch]; \n",
"```"
@@ -2107,7 +2107,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=automl_settings['log_file_name'], time_budget=3000)\n",
"for config in config_history:\n",
@@ -3460,7 +3460,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
@@ -4098,7 +4098,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=automl_settings['log_file_name'], time_budget=3000)\n",
"for config in config_history:\n",
@@ -5136,7 +5136,7 @@
],
"source": [
"\n",
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=automl_settings['log_file_name'], time_budget=3000)\n",
"for config in config_history:\n",

4
notebook/automl_time_series_forecast.ipynb
View File

@@ -22,7 +22,7 @@
"\n",
"In this notebook, we demonstrate how to use FLAML library for time series forecasting tasks: univariate time series forecasting (only time), multivariate time series forecasting (with exogneous variables) and forecasting discrete values.\n",
"\n",
"FLAML requires Python>=3.7. To run this notebook example, please install flaml with the [automl,ts_forecast] option:\n"
"FLAML requires Python>=3.8. To run this notebook example, please install flaml with the [automl,ts_forecast] option:\n"
]
},
{
@@ -1518,7 +1518,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, train_loss_history = \\\n",
" get_output_from_log(filename=settings['log_file_name'], time_budget=180)\n",
"\n",

14
notebook/automl_xgboost.ipynb
View File

@@ -28,7 +28,7 @@
"\n",
"In this notebook, we demonstrate how to use FLAML library to tune hyperparameters of XGBoost with a regression example.\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `automl` option (this option is introduced from version 2, for version 1 it is installed by default):\n",
"FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the `automl` option (this option is introduced from version 2, for version 1 it is installed by default):\n",
"```bash\n",
"pip install flaml[automl]\n",
"```"
@@ -44,6 +44,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
@@ -87,11 +88,12 @@
}
],
"source": [
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir='./')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
@@ -509,6 +511,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
@@ -761,7 +764,7 @@
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"from flaml.automl.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=settings['log_file_name'], time_budget=60)\n",
"\n",
@@ -804,6 +807,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -832,6 +836,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -922,6 +927,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1839,7 +1845,7 @@
" return grad, hess\n",
"\n",
"# create customized XGBoost learners class with your objective function\n",
"from flaml.model import XGBoostEstimator\n",
"from flaml.automl.model import XGBoostEstimator\n",
"\n",
"\n",
"class MyXGB1(XGBoostEstimator):\n",

4
notebook/integrate_azureml.ipynb
View File

@@ -28,7 +28,7 @@
"\n",
"In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library together with AzureML.\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [automl,azureml] option:\n",
"FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the [automl,azureml] option:\n",
"```bash\n",
"pip install flaml[automl,azureml]\n",
"```"
@@ -88,7 +88,7 @@
},
"outputs": [],
"source": [
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir='./')"
]
},

14
notebook/integrate_sklearn.ipynb
View File

@@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12,6 +13,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -40,7 +42,7 @@
"\n",
"In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `[automl]` option (this option is introduced from version 2, for version 1 it is installed by default):\n",
"FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the `[automl]` option (this option is introduced from version 2, for version 1 it is installed by default):\n",
"```bash\n",
"pip install flaml[automl]\n",
"```"
@@ -56,6 +58,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -76,6 +79,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -83,6 +87,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -109,7 +114,7 @@
}
],
"source": [
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"X_train, X_test, y_train, y_test = load_openml_dataset(\n",
" dataset_id=1169, data_dir='./', random_state=1234, dataset_format='array')"
]
@@ -135,6 +140,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -232,6 +238,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -449,7 +456,7 @@
{
"data": {
"text/plain": [
"<flaml.model.XGBoostSklearnEstimator at 0x7f03a5eada00>"
"<flaml.automl.model.XGBoostSklearnEstimator at 0x7f03a5eada00>"
]
},
"execution_count": 10,
@@ -462,6 +469,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [

494
notebook/integrate_spark.ipynb
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4
notebook/zeroshot_lightgbm.ipynb
View File

@@ -28,7 +28,7 @@
"\n",
"In this notebook, we demonstrate a basic use case of zero-shot AutoML with FLAML.\n",
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install the [autozero] option:"
"FLAML requires `Python>=3.8`. To run this notebook example, please install the [autozero] option:"
]
},
{
@@ -130,7 +130,7 @@
}
],
"source": [
"from flaml.data import load_openml_dataset\n",
"from flaml.automl.data import load_openml_dataset\n",
"X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir='./')"
]
},