Added LightGBM

dvclive/lgbm.py

@@ -0,0 +1,16 @@
+import dvclive
+
+
+class DvcLiveCallback:
+    def __init__(self, model_file=None):
+        super().__init__()
+        self.model_file = model_file
+
+    def __call__(self, env):
+        for eval_result in env.evaluation_result_list:
+            metric = eval_result[1]
+            value = eval_result[2]
+            dvclive.log(metric, value)
+        if self.model_file:
+            env.model.save_model(self.model_file)
+        dvclive.next_step()

setup.py

@@ -39,8 +39,9 @@ def run(self):
 mmcv = ["mmcv", "torch", "torchvision"]
 tf = ["tensorflow"]
 xgb = ["xgboost"]
+lgbm = ["lightgbm"]
 
-all_libs = mmcv + tf + xgb
+all_libs = mmcv + tf + xgb + lgbm
 
 tests_requires = [
     "pylint==2.5.3",
@@ -71,6 +72,7 @@ def run(self):
         "all": all_libs,
         "tf": tf,
         "xgb": xgb,
+        "lgbm": lgbm,
     },
     keywords="data-science metrics machine-learning developer-tools ai",
     python_requires=">=3.6",

tests/test_lgbm.py

@@ -0,0 +1,71 @@
+import os
+
+import lightgbm as lgbm
+import numpy as np
+import pandas as pd
+import pytest
+from funcy import first
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+
+import dvclive
+from dvclive.lgbm import DvcLiveCallback
+from tests.test_main import read_logs
+
+# pylint: disable=redefined-outer-name, unused-argument
+
+
+@pytest.fixture
+def model_params():
+    return {"objective": "multiclass", "n_estimators": 5, "seed": 0}
+
+
+@pytest.fixture
+def iris_data():
+    iris = datasets.load_iris()
+    x = pd.DataFrame(iris["data"], columns=iris["feature_names"])
+    y = iris["target"]
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, test_size=0.33, random_state=42
+    )
+    return (x_train, y_train), (x_test, y_test)
+
+
+def test_lgbm_integration(tmp_dir, model_params, iris_data):
+    dvclive.init("logs")
+    model = lgbm.LGBMClassifier()
+    model.set_params(**model_params)
+
+    model.fit(
+        iris_data[0][0],
+        iris_data[0][1],
+        eval_set=(iris_data[1][0], iris_data[1][1]),
+        eval_metric=["multi_logloss"],
+        callbacks=[DvcLiveCallback()],
+    )
+
+    assert os.path.exists("logs")
+
+    logs, _ = read_logs("logs")
+    assert len(logs) == 1
+    assert len(first(logs.values())) == 5
+
+
+def test_lgbm_model_file(tmp_dir, model_params, iris_data):
+    dvclive.init("logs")
+    model = lgbm.LGBMClassifier()
+    model.set_params(**model_params)
+
+    model.fit(
+        iris_data[0][0],
+        iris_data[0][1],
+        eval_set=(iris_data[1][0], iris_data[1][1]),
+        eval_metric=["multi_logloss"],
+        callbacks=[DvcLiveCallback("lgbm_model")],
+    )
+
+    preds = model.predict(iris_data[1][0])
+    model2 = lgbm.Booster(model_file="lgbm_model")
+    preds2 = model2.predict(iris_data[1][0])
+    preds2 = np.argmax(preds2, axis=1)
+    assert np.sum(np.abs(preds2 - preds)) == 0