Merge pull request #423 from ChengYen-Tang/LightGBM-Hyperparameter

LightGBM hyperparameter

examples/hyperparameter/LightGBM/Readme.md

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+# LightGBM hyperparameter
+
+## Alpha158
+First terminal
+```
+optuna create-study --study LGBM_158 --storage sqlite:///db.sqlite3
+optuna-dashboard --port 5000 --host 0.0.0.0 sqlite:///db.sqlite3
+```
+Second terminal
+```
+python hyperparameter_158.py
+```
+
+## Alpha360
+First terminal
+```
+optuna create-study --study LGBM_360 --storage sqlite:///db.sqlite3
+optuna-dashboard --port 5000 --host 0.0.0.0 sqlite:///db.sqlite3
+```
+Second terminal
+```
+python hyperparameter_360.py
+```

examples/hyperparameter/LightGBM/hyperparameter_158.py

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+import qlib
+from qlib.config import REG_CN
+from qlib.utils import exists_qlib_data, init_instance_by_config
+import optuna
+
+provider_uri = "~/.qlib/qlib_data/cn_data"
+if not exists_qlib_data(provider_uri):
+    print(f"Qlib data is not found in {provider_uri}")
+    sys.path.append(str(scripts_dir))
+    from get_data import GetData
+
+    GetData().qlib_data(target_dir=provider_uri, region="cn")
+qlib.init(provider_uri=provider_uri, region="cn")
+
+market = "csi300"
+benchmark = "SH000300"
+
+data_handler_config = {
+    "start_time": "2008-01-01",
+    "end_time": "2020-08-01",
+    "fit_start_time": "2008-01-01",
+    "fit_end_time": "2014-12-31",
+    "instruments": market,
+}
+dataset_task = {
+    "dataset": {
+        "class": "DatasetH",
+        "module_path": "qlib.data.dataset",
+        "kwargs": {
+            "handler": {
+                "class": "Alpha158",
+                "module_path": "qlib.contrib.data.handler",
+                "kwargs": data_handler_config,
+            },
+            "segments": {
+                "train": ("2008-01-01", "2014-12-31"),
+                "valid": ("2015-01-01", "2016-12-31"),
+                "test": ("2017-01-01", "2020-08-01"),
+            },
+        },
+    },
+}
+dataset = init_instance_by_config(dataset_task["dataset"])
+
+
+def objective(trial):
+    task = {
+        "model": {
+            "class": "LGBModel",
+            "module_path": "qlib.contrib.model.gbdt",
+            "kwargs": {
+                "loss": "mse",
+                "colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1),
+                "learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
+                "subsample": trial.suggest_uniform("subsample", 0, 1),
+                "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
+                "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
+                "max_depth": 10,
+                "num_leaves": trial.suggest_int("num_leaves", 1, 1024),
+                "feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
+                "bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
+                "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
+                "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
+                "min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
+            },
+        },
+    }
+
+    evals_result = dict()
+    model = init_instance_by_config(task["model"])
+    model.fit(dataset, evals_result=evals_result)
+    return min(evals_result["valid"])
+
+
+study = optuna.Study(study_name="LGBM_158", storage="sqlite:///db.sqlite3")
+study.optimize(objective, n_jobs=6)

examples/hyperparameter/LightGBM/hyperparameter_360.py

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+import qlib
+from qlib.config import REG_CN
+from qlib.utils import exists_qlib_data, init_instance_by_config
+import optuna
+
+provider_uri = "~/.qlib/qlib_data/cn_data"
+if not exists_qlib_data(provider_uri):
+    print(f"Qlib data is not found in {provider_uri}")
+    sys.path.append(str(scripts_dir))
+    from get_data import GetData
+
+    GetData().qlib_data(target_dir=provider_uri, region="cn")
+qlib.init(provider_uri=provider_uri, region="cn")
+
+market = "csi300"
+benchmark = "SH000300"
+
+data_handler_config = {
+    "start_time": "2008-01-01",
+    "end_time": "2020-08-01",
+    "fit_start_time": "2008-01-01",
+    "fit_end_time": "2014-12-31",
+    "instruments": market,
+}
+dataset_task = {
+    "dataset": {
+        "class": "DatasetH",
+        "module_path": "qlib.data.dataset",
+        "kwargs": {
+            "handler": {
+                "class": "Alpha360",
+                "module_path": "qlib.contrib.data.handler",
+                "kwargs": data_handler_config,
+            },
+            "segments": {
+                "train": ("2008-01-01", "2014-12-31"),
+                "valid": ("2015-01-01", "2016-12-31"),
+                "test": ("2017-01-01", "2020-08-01"),
+            },
+        },
+    },
+}
+dataset = init_instance_by_config(dataset_task["dataset"])
+
+
+def objective(trial):
+    task = {
+        "model": {
+            "class": "LGBModel",
+            "module_path": "qlib.contrib.model.gbdt",
+            "kwargs": {
+                "loss": "mse",
+                "colsample_bytree": trial.suggest_uniform("colsample_bytree", 0.5, 1),
+                "learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
+                "subsample": trial.suggest_uniform("subsample", 0, 1),
+                "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
+                "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
+                "max_depth": 10,
+                "num_leaves": trial.suggest_int("num_leaves", 1, 1024),
+                "feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
+                "bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
+                "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
+                "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
+                "min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
+            },
+        },
+    }
+
+    evals_result = dict()
+    model = init_instance_by_config(task["model"])
+    model.fit(dataset, evals_result=evals_result)
+    return min(evals_result["valid"])
+
+
+study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
+study.optimize(objective, n_jobs=6)

examples/hyperparameter/LightGBM/requirements.txt

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+pandas==1.1.2
+numpy==1.17.4
+lightgbm==3.1.0
+optuna==2.7.0
+optuna-dashboard==0.4.1