API docs #6 (#13) and update version to 0.1.2

.github/workflows/python-package.yml

@@ -57,3 +57,28 @@ jobs:
         with:
           file: ./coverage.xml
           flags: unittests
+  
+  docs:
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Setup Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.8'
+      - name: Compile documentation
+        run: |
+            pip install -e .
+            python -m pip install sphinx sphinx_rtd_theme
+            cd docs
+            make html
+      - name: Deploy to GitHub pages
+        if: ${{ github.ref == 'refs/heads/main' }}
+        uses: JamesIves/github-pages-deploy-action@3.6.2
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          BRANCH: gh-pages
+          FOLDER: docs/_build/html
+          CLEAN: true

docs/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/conf.py

@@ -0,0 +1,60 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'FLAML'
+copyright = '2020, FLAML Team'
+author = 'FLAML Team'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.doctest',
+    'sphinx.ext.coverage',
+    'sphinx.ext.mathjax',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.githubpages',
+    'sphinx_rtd_theme',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']

docs/index.rst

@@ -0,0 +1,29 @@
+.. FLAML documentation master file, created by
+   sphinx-quickstart on Mon Dec 14 23:33:24 2020.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+.. Welcome to FLAML's documentation!
+.. =================================
+
+.. .. toctree::
+..    :maxdepth: 2
+..    :caption: Contents:
+
+
+FLAML API Documentation
+=======================
+
+AutoML
+------
+
+.. autoclass:: flaml.AutoML
+   :members:
+
+
+.. Indices and tables
+.. ==================
+
+.. * :ref:`genindex`
+.. * :ref:`modindex`
+.. * :ref:`search`

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

flaml/automl.py

@@ -28,23 +28,9 @@ logger = logging.getLogger(__name__)
 class AutoML:
     '''The AutoML class
 
-    Attributes:
+    Example:
-        model: An object with predict() and predict_proba() method (for
-            classification), storing the best trained model.
-        model_history: A dictionary of iter->model, storing the models when
-            the best model is updated each time
-        config_history: A dictionary of iter->(estimator, config, time), 
-            storing the best estimator, config, and the time when the best
-            model is updated each time
-        classes_: A list of n_classes elements for class labels
-        best_iteration: An integer of the iteration number where the best
-            config is found
-        best_estimator: A string indicating the best estimator found.
-        best_config: A dictionary of the best configuration.
-        best_config_train_time: A float of the seconds taken by training the
-            best config 
 
-    Typical usage example:
+        .. code-block:: python
 
             automl = AutoML()
             automl_settings = {
@@ -55,6 +41,7 @@ class AutoML:
             }
             automl.fit(X_train = X_train, y_train = y_train,
                 **automl_settings)
+
     '''
 
     def __init__(self):
@@ -66,14 +53,24 @@ class AutoML:
 
     @property
     def model_history(self):
+        '''A dictionary of iter->model, storing the models when
+        the best model is updated each time.
+        '''
         return self._model_history
 
     @property
     def config_history(self):
+        '''A dictionary of iter->(estimator, config, time),
+        storing the best estimator, config, and the time when the best
+        model is updated each time.
+        '''
         return self._config_history
 
     @property
     def model(self):
+        '''An object with `predict()` and `predict_proba()` method (for
+        classification), storing the best trained model.
+        '''
         if self._trained_estimator:
             return self._trained_estimator.model
         else:
@@ -81,14 +78,18 @@ class AutoML:
 
     @property
     def best_estimator(self):
+        '''A string indicating the best estimator found.'''
         return self._best_estimator
 
     @property
     def best_iteration(self):
+        '''An integer of the iteration number where the best
+        config is found.'''
         return self._best_iteration
 
     @property
     def best_config(self):
+        '''A dictionary of the best configuration.'''
         return self._selected.best_config[0]
 
     @property
@@ -97,10 +98,13 @@ class AutoML:
 
     @property
     def best_config_train_time(self):
+        '''A float of the seconds taken by training the
+        best config.'''
         return self.best_train_time
 
     @property
     def classes_(self):
+        '''A list of n_classes elements for class labels.'''
         if self.label_transformer:
             return self.label_transformer.classes_.tolist()
         if self._trained_estimator:
@@ -111,10 +115,10 @@ class AutoML:
         '''Predict label from features.
 
         Args:
-            X_test: A numpy array of featurized instances, shape n*m.
+            X_test: A numpy array of featurized instances, shape n * m.
 
         Returns:
-            A numpy array of shape n*1 -- each element is a predicted class
+            A numpy array of shape n * 1 - - each element is a predicted class
             label for an instance.
         '''
         X_test = self.preprocess(X_test)
@@ -132,11 +136,11 @@ class AutoML:
         classification problems.
 
         Args:
-            X_test: A numpy array of featurized instances, shape n*m.
+            X_test: A numpy array of featurized instances, shape n * m.
 
         Returns:
-            A numpy array of shape n*c. c is the # classes. Each element at
+            A numpy array of shape n * c. c is the  # classes. Each element at
-            (i,j) is the probability for instance i to be in class j.
+            (i, j) is the probability for instance i to be in class j.
         '''
         X_test = self.preprocess(X_test)
         proba = self._trained_estimator.predict_proba(X_test)
@@ -396,7 +400,7 @@ class AutoML:
             learner_class: A subclass of BaseEstimator
             size_estimate: A function from a config to its memory size in float
             cost_relative2lgbm: A float number for the training cost ratio with
-                respect to lightgbm (when both use the initial config)
+                respect to lightgbm(when both use the initial config)
         '''
         self._custom_learners[learner_name] = learner_class
         self._eti_ini[learner_name] = cost_relative2lgbm
@@ -450,14 +454,14 @@ class AutoML:
         Args:
             time_budget: A float number of the time budget in seconds
             log_file_name: A string of the log file name
-            X_train: A numpy array of training data in shape n*m
+            X_train: A numpy array of training data in shape n * m
-            y_train: A numpy array of labels in shape n*1
+            y_train: A numpy array of labels in shape n * 1
             task: A string of the task type, e.g.,
                 'classification', 'regression'
             eval_method: A string of resampling strategy, one of
                 ['auto', 'cv', 'holdout']
             split_ratio: A float of the validation data percentage for holdout
-            n_splits: An integer of the number of folds for cross-validation
+            n_splits: An integer of the number of folds for cross - validation
             n_jobs: An integer of the number of threads for training
             train_best: A boolean of whether to train the best config in the
                 time budget; if false, train the last config in the budget
@@ -507,7 +511,8 @@ class AutoML:
                     self._trained_estimator = BaseEstimator()
                     self._trained_estimator.model = None
                     return training_duration
-        if not best: return
+        if not best:
+            return
         best_estimator = best.learner
         best_config = best.config
         sample_size = len(self.y_train_all) if train_full \
@@ -581,17 +586,19 @@ class AutoML:
 
         Args:
             X_train: A numpy array or a pandas dataframe of training data in
-             shape n*m
+             shape n * m
-            y_train: A numpy array or a pandas series of labels in shape n*1
+            y_train: A numpy array or a pandas series of labels in shape n * 1
             dataframe: A dataframe of training data including label column
             label: A str of the label column name
                 Note: If X_train and y_train are provided,
                 dataframe and label are ignored;
                 If not, dataframe and label must be provided.
             metric: A string of the metric name or a function,
-                e.g., 'accuracy','roc_auc','f1','log_loss','mae','mse','r2'
+                e.g., 'accuracy', 'roc_auc', 'f1', 'log_loss', 'mae', 'mse', 'r2'
                 if passing a customized metric function, the function needs to
-                have the follwing signature
+                have the follwing signature:
+
+                .. code-block:: python
 
                     def metric(X_test, y_test, estimator, labels, X_train, y_train):
                         return metric_to_minimize, metrics_to_log
@@ -603,7 +610,12 @@ class AutoML:
             n_jobs: An integer of the number of threads for training
             log_file_name: A string of the log file name
             estimator_list: A list of strings for estimator names, or 'auto'
-                e.g., ['lgbm', 'xgboost', 'catboost', 'rf', 'extra_tree']
+                e.g., 
+
+                .. code-block:: python
+
+                    ['lgbm', 'xgboost', 'catboost', 'rf', 'extra_tree']
+
             time_budget: A float number of the time budget in seconds
             max_iter: An integer of the maximal number of iterations
             sample: A boolean of whether to sample the training data during
@@ -611,11 +623,12 @@ class AutoML:
             eval_method: A string of resampling strategy, one of
                 ['auto', 'cv', 'holdout']
             split_ratio: A float of the valiation data percentage for holdout
-            n_splits: An integer of the number of folds for cross-validation
+            n_splits: An integer of the number of folds for cross - validation
-            log_type: A string of the log type, one of ['better', 'all', 'new']
+            log_type: A string of the log type, one of
+                ['better', 'all', 'new']
                 'better' only logs configs with better loss than previos iters
                 'all' logs all the tried configs
-                'new' only logs non-redundant configs
+                'new' only logs non - redundant configs
             model_history: A boolean of whether to keep the history of best
                 models in the history property. Make sure memory is large
                 enough if setting to True.

flaml/version.py

@@ -1 +1 @@
-__version__="0.1.1"
+__version__ = "0.1.2"