microsoft
/
FLAML
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support latest xgboost version (#599) 

* support latest xgboost version

* Update test_classification.py

* Update 

Exists problems when installing xgb1.6.1 in py3.6

* cleanup

* xgboost version

* remove time_budget_s in test

* remove redundancy

* stop support of python 3.6

Co-authored-by: zsk <shaokunzhang529@gmail.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
This commit is contained in:
Chi Wang 2022-06-21 18:59:07 -07:00 коммит произвёл GitHub
Родитель c5272ad377
Коммит c45741a67b
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: 4AEE18F83AFDEB23
19 изменённых файлов: 126 добавлений и 95 удалений
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2
.github/workflows/python-package.yml поставляемый
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@ -16,7 +16,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-2019]
python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"]
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v2

4
README.md
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@ -1,7 +1,7 @@
[![PyPI version](https://badge.fury.io/py/FLAML.svg)](https://badge.fury.io/py/FLAML)
![Conda version](https://img.shields.io/conda/vn/conda-forge/flaml)
[![Build](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml/badge.svg)](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
![Python Version](https://img.shields.io/badge/3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)
![Python Version](https://img.shields.io/badge/3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)
[![Downloads](https://pepy.tech/badge/flaml)](https://pepy.tech/project/flaml)
[![Join the chat at https://gitter.im/FLAMLer/community](https://badges.gitter.im/FLAMLer/community.svg)](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
@ -27,7 +27,7 @@ FLAML has a .NET implementation as well from [ML.NET Model Builder](https://dotn
## Installation
FLAML requires **Python version >= 3.6**. It can be installed from pip:
FLAML requires **Python version >= 3.7**. It can be installed from pip:
```bash
pip install flaml

4
flaml/ml.py
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@ -1,5 +1,5 @@
# !
# * Copyright (c) Microsoft Corporation. All rights reserved.
# * Copyright (c) FLAML authors. All rights reserved.
# * Licensed under the MIT License. See LICENSE file in the
# * project root for license information.
import time
@ -39,7 +39,7 @@ from .model import (
TransformersEstimator,
TransformersEstimatorModelSelection,
)
from .data import CLASSIFICATION, group_counts, TS_FORECAST, TS_VALUE_COL
from .data import CLASSIFICATION, group_counts, TS_FORECAST
import logging
logger = logging.getLogger(__name__)

22
flaml/model.py
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@ -155,7 +155,8 @@ class BaseEstimator:
X_train = self._preprocess(X_train)
model = self.estimator_class(**self.params)
if logger.level == logging.DEBUG:
logger.debug(f"flaml.model - {model} fit started")
# xgboost 1.6 doesn't display all the params in the model str
logger.debug(f"flaml.model - {model} fit started with params {self.params}")
model.fit(X_train, y_train, **kwargs)
if logger.level == logging.DEBUG:
logger.debug(f"flaml.model - {model} fit finished")
@ -1111,12 +1112,23 @@ class LGBMEstimator(BaseEstimator):
kwargs.pop("callbacks")
else:
callbacks = self._callbacks(start_time, deadline)
if isinstance(self, XGBoostSklearnEstimator):
from xgboost import __version__
if __version__ >= "1.6.0":
# since xgboost>=1.6.0, callbacks can't be passed in fit()
self.params["callbacks"] = callbacks
callbacks = None
self._fit(
X_train,
y_train,
callbacks=callbacks,
**kwargs,
)
if callbacks is None:
# for xgboost>=1.6.0, pop callbacks to enable pickle
callbacks = self.params.pop("callbacks")
self._model.set_params(callbacks=callbacks[:-1])
best_iteration = (
self._model.get_booster().best_iteration
if isinstance(self, XGBoostSklearnEstimator)
@ -1233,7 +1245,9 @@ class XGBoostEstimator(SKLearnEstimator):
start_time = time.time()
deadline = start_time + budget if budget else np.inf
if issparse(X_train):
self.params["tree_method"] = "auto"
if xgb.__version__ < "1.6.0":
# "auto" fails for sparse input since xgboost 1.6.0
self.params["tree_method"] = "auto"
else:
X_train = self._preprocess(X_train)
if "sample_weight" in kwargs:
@ -1337,9 +1351,11 @@ class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator):
self.estimator_class = xgb.XGBRanker
elif task in CLASSIFICATION:
self.estimator_class = xgb.XGBClassifier
self._xgb_version = xgb.__version__
def fit(self, X_train, y_train, budget=None, **kwargs):
if issparse(X_train):
if issparse(X_train) and self._xgb_version < "1.6.0":
# "auto" fails for sparse input since xgboost 1.6.0
self.params["tree_method"] = "auto"
if kwargs.get("gpu_per_trial"):
self.params["tree_method"] = "gpu_hist"

2
flaml/version.py
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@ -1 +1 @@
__version__ = "1.0.7"
__version__ = "1.0.8"

2
notebook/automl_classification.ipynb
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@ -26,7 +26,7 @@
"\n",
"In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
"\n",
"FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
"```bash\n",
"pip install flaml[notebook]\n",
"```"

2
notebook/automl_lightgbm.ipynb
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@ -27,7 +27,7 @@
"\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>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
"```bash\n",
"pip install flaml[notebook]\n",
"```"

2
notebook/automl_nlp.ipynb
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@ -26,7 +26,7 @@
"\n",
"In this notebook, we demonstrate how to use the FLAML library to fine tune an NLP language model with hyperparameter search. We have tested this notebook on a server with 4 NVidia V100 GPU (32GB) and 400GB CPU Ram.\n",
"\n",
"FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `nlp,ray,notebook` and `blendsearch` option:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `nlp,ray,notebook` and `blendsearch` option:\n",
"```bash\n",
"pip install flaml[nlp,ray,notebook,blendsearch];\n",
"```"

2
notebook/automl_time_series_forecast.ipynb
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@ -21,7 +21,7 @@
"\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>=3.6. To run this notebook example, please install flaml with the notebook and forecast option:\n"
"FLAML requires Python>=3.7. To run this notebook example, please install flaml with the notebook and forecast option:\n"
]
},
{

2
notebook/automl_xgboost.ipynb
Просмотреть файл

@ -27,7 +27,7 @@
"\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>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
"```bash\n",
"pip install flaml[notebook]\n",
"```"

4
notebook/autovw.ipynb
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@ -8,7 +8,7 @@
}
},
"source": [
"Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
"\n",
"Licensed under the MIT License.\n",
"\n",
@ -22,7 +22,7 @@
"\n",
"*ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. To appear in ICML 2021.*\n",
"\n",
"AutoVW is implemented in FLAML. FLAML requires `Python>=3.6`. To run this notebook example, please install:"
"AutoVW is implemented in FLAML. FLAML requires `Python>=3.7`. To run this notebook example, please install:"
]
},
{

4
notebook/integrate_azureml.ipynb
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@ -8,7 +8,7 @@
}
},
"source": [
"Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
"\n",
"Licensed under the MIT License.\n",
"\n",
@ -27,7 +27,7 @@
"\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>=3.6`. To run this notebook example, please install flaml with the [azureml] option:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [azureml] option:\n",
"```bash\n",
"pip install flaml[azureml]\n",
"```"

2
notebook/integrate_sklearn.ipynb
Просмотреть файл

@ -39,7 +39,7 @@
"\n",
"In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
"\n",
"FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
"```bash\n",
"pip install flaml[notebook]\n",
"```"

80
notebook/research/acl2021.ipynb
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@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) 2020-2021. All rights reserved.\n",
"Copyright (c). All rights reserved.\n",
"\n",
"Licensed under the MIT License.\n",
"\n",
@ -22,7 +22,7 @@
"\n",
"*Running this notebook takes about one hour.\n",
"\n",
"FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` and `nlp` options:\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` and `nlp` options:\n",
"\n",
"```bash\n",
"pip install flaml[nlp]==0.7.1 # in higher version of flaml, the API for nlp tasks changed\n",
@ -364,10 +364,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(pid=50964)\u001B[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
"\u001B[2m\u001B[36m(pid=50964)\u001B[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
"\u001B[2m\u001B[36m(pid=50948)\u001B[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n",
"\u001B[2m\u001B[36m(pid=50948)\u001B[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n"
"\u001b[2m\u001b[36m(pid=50964)\u001b[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
"\u001b[2m\u001b[36m(pid=50964)\u001b[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
"\u001b[2m\u001b[36m(pid=50948)\u001b[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n",
"\u001b[2m\u001b[36m(pid=50948)\u001b[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n"
]
},
{
@ -485,12 +485,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(pid=54411)\u001B[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=54411)\u001B[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=54411)\u001B[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=54417)\u001B[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=54417)\u001B[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=54417)\u001B[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n"
"\u001b[2m\u001b[36m(pid=54411)\u001b[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=54411)\u001b[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=54411)\u001b[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=54417)\u001b[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=54417)\u001b[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=54417)\u001b[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n"
]
},
{
@ -590,18 +590,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n"
"\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
"\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
"\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n"
]
},
{
@ -701,21 +701,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n"
"\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
"\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
"\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
"\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n"
]
},
{
@ -806,4 +806,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
}
}

2
notebook/zeroshot_lightgbm.ipynb
Просмотреть файл

@ -19,7 +19,7 @@
"\n",
"In this notebook, we demonstrate a basic use case of zero-shot AutoML with FLAML.\n",
"\n",
"FLAML requires `Python>=3.6`. To run this notebook example, please install flaml and openml:"
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml and openml:"
]
},
{

4
setup.py
Просмотреть файл

@ -16,7 +16,7 @@ __version__ = version["__version__"]
install_requires = [
"NumPy>=1.17.0rc1",
"lightgbm>=2.3.1",
"xgboost>=0.90,<=1.3.3",
"xgboost>=0.90",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
@ -110,5 +110,5 @@ setuptools.setup(
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires=">=3.6",
python_requires=">=3.7",
)

65
test/automl/test_classification.py
Просмотреть файл

@ -85,6 +85,22 @@ class TestClassification(unittest.TestCase):
)
y = pd.Series([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
automl = AutoML()
automl_settings = {
"time_budget": 3,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["xgboost", "catboost", "kneighbor"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
# "verbose": 4,
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
del automl
automl = AutoML()
automl_settings = {
"time_budget": 6,
@ -99,6 +115,7 @@ class TestClassification(unittest.TestCase):
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
del automl
automl = AutoML()
try:
@ -121,21 +138,7 @@ class TestClassification(unittest.TestCase):
"n_concurrent_trials": n_concurrent_trials,
}
automl.fit(X, y, **automl_settings)
automl = AutoML()
automl_settings = {
"time_budget": 3,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["xgboost", "catboost", "kneighbor"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
# "verbose": 4,
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
del automl
automl = AutoML()
automl_settings = {
@ -151,6 +154,7 @@ class TestClassification(unittest.TestCase):
"ensemble": True,
}
automl.fit(X, y, **automl_settings)
del automl
def test_binary(self):
automl_experiment = AutoML()
@ -208,7 +212,7 @@ class TestClassification(unittest.TestCase):
_ = automl_experiment.predict(fake_df)
def test_sparse_matrix_xgboost(self):
automl_experiment = AutoML()
automl = AutoML()
automl_settings = {
"time_budget": 3,
"metric": "ap",
@ -223,15 +227,28 @@ class TestClassification(unittest.TestCase):
import xgboost as xgb
callback = xgb.callback.TrainingCallback()
automl_experiment.fit(
automl.fit(
X_train=X_train, y_train=y_train, callbacks=[callback], **automl_settings
)
print(automl_experiment.predict(X_train))
print(automl_experiment.model)
print(automl_experiment.config_history)
print(automl_experiment.best_model_for_estimator("xgboost"))
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
print(automl.predict(X_train))
print(automl.model)
print(automl.config_history)
print(automl.best_model_for_estimator("xgboost"))
print(automl.best_iteration)
print(automl.best_estimator)
# test an old version of xgboost
import subprocess
import sys
subprocess.check_call(
[sys.executable, "-m", "pip", "install", "xgboost==1.3.3", "--user"]
)
automl = AutoML()
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
subprocess.check_call(
[sys.executable, "-m", "pip", "install", "-U", "xgboost", "--user"]
)
def test_ray_classification(self):
X, y = load_breast_cancer(return_X_y=True)
@ -353,5 +370,5 @@ class TestClassification(unittest.TestCase):
if __name__ == "__main__":
unittest.main()
test = TestClassification()
test.test_preprocess()

14
test/tune/test_reproducibility.py
Просмотреть файл

@ -24,9 +24,7 @@ def _easy_objective(use_raytune, config):
return
def test_tune(
smoke_test=True, externally_setup_searcher=False, use_ray=False, use_raytune=False
):
def test_tune(externally_setup_searcher=False, use_ray=False, use_raytune=False):
from flaml import tune
from flaml.searcher.blendsearch import BlendSearch
@ -95,7 +93,7 @@ def test_tune(
metric="mean_loss",
mode="min",
num_samples=10,
time_budget_s=5,
# time_budget_s=5,
use_ray=use_ray,
config=search_space,
)
@ -107,14 +105,14 @@ def test_tune(
def test_reproducibility():
best_config_1 = test_tune(smoke_test=True)
best_config_2 = test_tune(smoke_test=True)
best_config_1 = test_tune()
best_config_2 = test_tune()
print(best_config_1)
print(best_config_2)
assert best_config_1 == best_config_2, "flaml.tune not reproducible"
best_config_1 = test_tune(smoke_test=True, externally_setup_searcher=True)
best_config_2 = test_tune(smoke_test=True, externally_setup_searcher=True)
best_config_1 = test_tune(externally_setup_searcher=True)
best_config_2 = test_tune(externally_setup_searcher=True)
print(best_config_1)
print(best_config_2)
assert (

2
website/docs/Installation.md
Просмотреть файл

@ -1,6 +1,6 @@
# Installation
FLAML requires **Python version >= 3.6**. It can be installed from pip:
FLAML requires **Python version >= 3.7**. It can be installed from pip:
```bash
pip install flaml