"This tutorial will help you understand how FLAML's AutoML can be used as a transformer in the Sklearn pipeline."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## 1.Introduction\n",
"\n",
"### 1.1 FLAML - Fast and Lightweight AutoML\n",
"\n",
"FLAML is a Python library (https://github.com/microsoft/FLAML) designed to automatically produce accurate machine learning models with low computational cost. It is fast and cheap. The simple and lightweight design makes it easy to use and extend, such as adding new learners. \n",
"\n",
"FLAML can \n",
"- serve as an economical AutoML engine,\n",
"- be used as a fast hyperparameter tuning tool, or \n",
"- be embedded in self-tuning software that requires low latency & resource in repetitive\n",
" tuning tasks.\n",
"\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",
"```bash\n",
"pip install flaml[notebook]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Why are pipelines a silver bullet?\n",
"\n",
"In a typical machine learning workflow we have to apply all the transformations at least twice. \n",
"1. During Training\n",
"2. During Inference\n",
"\n",
"Scikit-learn pipelines provide an easy to use inteface to automate ML workflows by allowing several transformers to be chained together. \n",
"\n",
"The key benefits of using pipelines:\n",
"* Make ML workflows highly readable, enabling fast development and easy review\n",
"* Help to build sequential and parallel processes\n",
"* Allow hyperparameter tuning across the estimators\n",
"* Easier to share and collaborate with multiple users (bug fixes, enhancements etc)\n",
"* Enforce the implementation and order of steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### As FLAML's AutoML module can be used a transformer in the Sklearn's pipeline we can get all the benefits of pipeline and thereby write extremley clean, and resuable code."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"!pip install flaml[notebook];"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Classification Example\n",
"### Load data and preprocess\n",
"\n",
"Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure."
"In the FLAML automl run configuration, users can specify the task type, time budget, error metric, learner list, whether to subsample, resampling strategy type, and so on. All these arguments have default values which will be used if users do not provide them. For example, the default ML learners of FLAML are `['lgbm', 'xgboost', 'catboost', 'rf', 'extra_tree', 'lrl1']`. "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"settings = {\n",
" \"time_budget\": 60, # total running time in seconds\n",
"[flaml.automl: 08-09 19:49:30] {884} INFO - Evaluation method: holdout\n",
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"[flaml.automl: 08-09 19:49:30] {924} INFO - List of ML learners in AutoML Run: ['xgboost', 'catboost', 'lgbm']\n",
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"/Users/budigam.nagaraju/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
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"/Users/budigam.nagaraju/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
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