🐛 Fix xgboost error

kaylode · Nov 4, 2023 · 1b1ea74 · 1b1ea74 · github-actions · Nov 4, 2023
1 parent 4fe8224
commit 1b1ea74
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Showing 5 changed files with 9 additions and 9 deletions.
diff --git a/pyproject.toml b/pyproject.toml
@@ -93,7 +93,7 @@ ml = [
   "psycopg2-binary>=2.9.5",
   "gunicorn>=20.1.0",
   "lightgbm>=3.3.3",
-  "xgboost>=1.7.1",
+  "xgboost<=1.7.1",
   "catboost",
   "shap>=0.41.0",
   "lime>=0.2.0.1",

diff --git a/tests/classification/inference.py b/tests/classification/inference.py
@@ -38,7 +38,7 @@ def inference(self):
 
         for idx, batch in enumerate(tqdm(self.dataloader)):
             img_names = batch["img_names"]
-            outputs = self.model.get_prediction(batch)
+            outputs = self.model.predict_step(batch)
             preds = outputs["names"]
             probs = outputs["confidences"]
 

diff --git a/tests/semantic/inference.py b/tests/semantic/inference.py
@@ -49,7 +49,7 @@ def inference(self):
             img_names = batch["img_names"]
             ori_sizes = batch["ori_sizes"]
 
-            outputs = self.model.get_prediction(batch)
+            outputs = self.model.predict_step(batch)
             preds = outputs["masks"]
 
             for (inpt, pred, filename, ori_size) in zip(

diff --git a/tests/tabular/test_tablr.py b/tests/tabular/test_tablr.py
@@ -10,11 +10,11 @@ def test_train_tblr(override_config):
     train_pipeline.fit()
 
 
-# @pytest.mark.order(2)
-# def test_eval_tblr(override_config):
-#     override_config["global"]["pretrained"] = "runs/pytest_tablr/checkpoints/last"
-#     val_pipeline = MLPipeline(override_config)
-#     val_pipeline.evaluate()
+@pytest.mark.order(2)
+def test_eval_tblr(override_config):
+    override_config["global"]["pretrained"] = "runs/pytest_tablr/checkpoints/last"
+    val_pipeline = MLPipeline(override_config)
+    val_pipeline.evaluate()
 
 
 # @pytest.mark.order(2)

diff --git a/theseus/base/models/wrapper.py b/theseus/base/models/wrapper.py
@@ -94,7 +94,7 @@ def validation_step(self, batch, batch_idx):
         self.log_dict(outputs["loss_dict"], prog_bar=True, on_step=True, on_epoch=False)
         return outputs
 
-    def predict_step(self, batch, batch_idx):
+    def predict_step(self, batch, batch_idx=None):
         pred = self.model.get_prediction(batch)
         return pred
number	value	datetime_start	datetime_complete	duration	params_gamma	params_learning_rate	params_max_depth	params_n_estimators	params_reg_alpha	params_reg_lambda	best_key	model_name	state
0	0.7755790944	1699094300961	1699094301099	138	0.7945736611	0.8777502647	4	160	0.1489015551	0.7730128265	bl_acc	xgboost	COMPLETE
1	0.8319171226	1699094301100	1699094301251	150	0.432788513	0.8145506214	5	182	0.9873357615	0.4220072194	bl_acc	xgboost	COMPLETE
2	0.8037481085	1699094301252	1699094301423	170	0.9391836415	0.3691424918	5	409	0.2298818258	0.9618219432	bl_acc	xgboost	COMPLETE
number	value	datetime_start	datetime_complete	duration	params_optimizer.args.lr	best_key	model_name	state
0	1	1699094301030	1699094352745	51715	0.0008913358	bl_acc	efficientnet_b0	COMPLETE
1	0.75	1699094352746	1699094407028	54281	0.0003423306	bl_acc	efficientnet_b0	COMPLETE