add warmstart test (#298)

* add warmstart test

* remove redundancy

* add more types of hps

* revise comments

* simplify name

* reduce redundancy

test/automl/test_warmstart.py

@@ -0,0 +1,130 @@
+import unittest
+import numpy as np
+from sklearn.datasets import load_iris
+from flaml import AutoML
+from flaml.model import LGBMEstimator
+from flaml import tune
+
+
+class TestWarmStart(unittest.TestCase):
+    def test_fit_w_freezinghp_starting_point(self, as_frame=True):
+        automl_experiment = AutoML()
+        automl_settings = {
+            "time_budget": 1,
+            "metric": "accuracy",
+            "task": "classification",
+            "estimator_list": ["lgbm"],
+            "log_file_name": "test/iris.log",
+            "log_training_metric": True,
+            "n_jobs": 1,
+            "model_history": True,
+        }
+        X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
+        if as_frame:
+            # test drop column
+            X_train.columns = range(X_train.shape[1])
+            X_train[X_train.shape[1]] = np.zeros(len(y_train))
+        automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings)
+        automl_val_accuracy = 1.0 - automl_experiment.best_loss
+        print("Best ML leaner:", automl_experiment.best_estimator)
+        print("Best hyperparmeter config:", automl_experiment.best_config)
+        print("Best accuracy on validation data: {0:.4g}".format(automl_val_accuracy))
+        print(
+            "Training duration of best run: {0:.4g} s".format(
+                automl_experiment.best_config_train_time
+            )
+        )
+        # 1. Get starting points from previous experiments.
+        starting_points = automl_experiment.best_config_per_estimator
+        print("starting_points", starting_points)
+        print("loss of the starting_points", automl_experiment.best_loss_per_estimator)
+        starting_point = starting_points['lgbm']
+        hps_to_freeze = ["colsample_bytree", "reg_alpha", "reg_lambda", "log_max_bin"]
+
+        # 2. Constrct a new class:
+        # a. write the hps you want to freeze as hps with constant 'domain';
+        # b. specify the new search space of the other hps accrodingly.
+
+        class MyPartiallyFreezedLargeLGBM(LGBMEstimator):
+            @classmethod
+            def search_space(cls, **params):
+                # (1) Get the hps in the original search space
+                space = LGBMEstimator.search_space(**params)
+                # (2) Set up the fixed value from hps from the starting point
+                for hp_name in hps_to_freeze:
+                    # if an hp is specifed to be freezed, use tine value provided in the starting_point
+                    # otherwise use the setting from the original search space
+                    if hp_name in starting_point:
+                        space[hp_name] = {
+                            "domain": starting_point[hp_name]
+                        }
+                # (3.1) Configure the search space for hps that are in the original search space
+                #  but you want to change something, for example the range.
+                revised_hps_to_search = {
+                    "n_estimators": {
+                        "domain": tune.lograndint(lower=10, upper=32768),
+                        "init_value": starting_point.get(
+                            "n_estimators"
+                        )
+                        or space["n_estimators"].get("init_value", 10),
+                        "low_cost_init_value": space["n_estimators"].get(
+                            "low_cost_init_value", 10
+                        ),
+                    },
+                    "num_leaves": {
+                        "domain": tune.lograndint(lower=10, upper=3276),
+                        "init_value": starting_point.get(
+                            "num_leaves"
+                        )
+                        or space["num_leaves"].get("init_value", 10),
+                        "low_cost_init_value": space["num_leaves"].get(
+                            "low_cost_init_value", 10
+                        ),
+                    },
+                    # (3.2) Add a new hp which is not in the original search space
+                    "subsample": {
+                        "domain": tune.uniform(lower=0.1, upper=1.0),
+                        "init_value": 0.1,
+                    },
+                }
+                space.update(revised_hps_to_search)
+                return space
+
+        new_estimator_name = "large_lgbm"
+        new_automl_experiment = AutoML()
+        new_automl_experiment.add_learner(
+            learner_name=new_estimator_name, learner_class=MyPartiallyFreezedLargeLGBM
+        )
+        
+        automl_settings_resume = {
+            "time_budget": 3,
+            "metric": "accuracy",
+            "task": "classification",
+            "estimator_list": [new_estimator_name],
+            "log_file_name": "test/iris_resume.log",
+            "log_training_metric": True,
+            "n_jobs": 1,
+            "model_history": True,
+            "log_type": "all",
+            "starting_points": {new_estimator_name: starting_point},
+        }
+
+        new_automl_experiment.fit(
+            X_train=X_train, y_train=y_train, **automl_settings_resume
+        )
+
+        new_automl_val_accuracy = 1.0 - new_automl_experiment.best_loss
+        print("Best ML leaner:", new_automl_experiment.best_estimator)
+        print("Best hyperparmeter config:", new_automl_experiment.best_config)
+        print(
+            "Best accuracy on validation data: {0:.4g}".format(new_automl_val_accuracy)
+        )
+        print(
+            "Training duration of best run: {0:.4g} s".format(
+                new_automl_experiment.best_config_train_time
+            )
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()