Merge pull request #29 from Learnware-LAMDA/dev_spec

[MNT] add "type" when saving specification and move rkme to table folder

README.md

@@ -178,7 +178,7 @@ For example, the following code is designed to work with Reduced Set Kernel Embe
 ```python
 import learnware.specification as specification
 
-user_spec = specification.rkme.RKMEStatSpecification()
+user_spec = specification.RKMEStatSpecification()
 user_spec.load(os.path.join(unzip_path, "rkme.json"))
 user_info = BaseUserInfo(
     semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}

docs/start/client.rst

@@ -123,7 +123,7 @@ You can search learnware by providing a statistical specification. The statistic
 
     import learnware.specification as specification
 
-    user_spec = specification.rkme.RKMEStatSpecification()
+    user_spec = specification.RKMEStatSpecification()
     user_spec.load(os.path.join(unzip_path, "rkme.json"))
     
     specification = learnware.specification.Specification()
@@ -151,7 +151,7 @@ You can provide both semantic and statistical specification to search learnwares
         senarioes=[],
         input_description={}, output_description={})
 
-    stat_spec = specification.rkme.RKMEStatSpecification()
+    stat_spec = specification.RKMEStatSpecification()
     stat_spec.load(os.path.join(unzip_path, "rkme.json"))
     specification = learnware.specification.Specification()
     specification.update_semantic_spec(semantic_spec)

docs/start/quick.rst

@@ -170,7 +170,7 @@ For example, the code below executes learnware search when using Reduced Set Ker
 
     import learnware.specification as specification
 
-    user_spec = specification.rkme.RKMEStatSpecification()
+    user_spec = specification.RKMEStatSpecification()
 
     # unzip_path: directory for unzipped learnware zipfile
     user_spec.load(os.path.join(unzip_path, "rkme.json"))

docs/workflow/identify.rst

@@ -73,7 +73,7 @@ For example, the following code is designed to work with Reduced Kernel Mean Emb
 
     import learnware.specification as specification
 
-    user_spec = specification.rkme.RKMEStatSpecification()
+    user_spec = specification.RKMEStatSpecification()
     user_spec.load(os.path.join("rkme.json"))
     user_info = BaseUserInfo(
         semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}

examples/dataset_pfs_workflow/pfs/pfs_cross_transfer.py

@@ -85,9 +85,7 @@ def get_split_errs(algo):
                 split = train_xs.shape[0] - proportion_list[tmp]
 
                 model.fit(
-                    train_xs[
-                        split:,
-                    ],
+                    train_xs[split:,],
                     train_ys[split:],
                     eval_set=[(val_xs, val_ys)],
                     early_stopping_rounds=50,

examples/workflow_by_code/main.py

@@ -148,7 +148,7 @@ class LearnwareMarketWorkflow:
             with zipfile.ZipFile(zip_path, "r") as zip_obj:
                 zip_obj.extractall(path=unzip_dir)
 
-            user_spec = specification.rkme.RKMEStatSpecification()
+            user_spec = specification.RKMEStatSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
             user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
             (

learnware/specification/__init__.py

@@ -1,3 +1,3 @@
 from .utils import generate_stat_spec
 from .base import Specification, BaseStatSpecification
-from .rkme import RKMEStatSpecification
+from .table import RKMEStatSpecification

learnware/specification/base.py

@@ -6,6 +6,15 @@ from typing import Dict
 class BaseStatSpecification:
     """The Statistical Specification Interface, which provide save and load method"""
 
+    def __init__(self, type: str):
+        """initilize the type of stats specification
+        Parameters
+        ----------
+        type : str
+            the type of the stats specification
+        """
+        self.type = type
+
     def generate_stat_spec_from_data(self, **kwargs):
         """Construct statistical specification from raw dataset
         - kwargs may include the feature, label and model

learnware/specification/table/__init__.py

@@ -0,0 +1 @@
+from .rkme import RKMEStatSpecification

learnware/specification/rkme.py → learnware/specification/table/rkme.py

@@ -20,8 +20,8 @@ try:
 except ImportError:
     _FAISS_INSTALLED = False
 
-from .base import BaseStatSpecification
-from ..logger import get_module_logger
+from ..base import BaseStatSpecification
+from ...logger import get_module_logger
 
 logger = get_module_logger("rkme")
 
@@ -51,6 +51,7 @@ class RKMEStatSpecification(BaseStatSpecification):
         torch.cuda.empty_cache()
         self.device = choose_device(cuda_idx=cuda_idx)
         setup_seed(0)
+        super(RKMEStatSpecification, self).__init__(type=self.__class__.__name__)
 
     def get_beta(self) -> np.ndarray:
         """Move beta(RKME weights) back to memory accessible to the CPU.
@@ -427,6 +428,7 @@ class RKMEStatSpecification(BaseStatSpecification):
             rkme_to_save["beta"] = rkme_to_save["beta"].detach().cpu().numpy()
         rkme_to_save["beta"] = rkme_to_save["beta"].tolist()
         rkme_to_save["device"] = "gpu" if rkme_to_save["cuda_idx"] != -1 else "cpu"
+        rkme_to_save["type"] = self.type
         json.dump(
             rkme_to_save,
             codecs.open(save_path, "w", encoding="utf-8"),

learnware/specification/utils.py

@@ -4,7 +4,7 @@ import pandas as pd
 from typing import Union
 
 from .base import BaseStatSpecification
-from .rkme import RKMEStatSpecification
+from .table import RKMEStatSpecification
 from ..config import C
 
 

tests/test_specification/test_rkme.py

@@ -0,0 +1,31 @@
+import os
+import json
+import unittest
+import tempfile
+import numpy as np
+
+import learnware
+import learnware.specification as specification
+from learnware.specification import RKMEStatSpecification
+
+
+class TestRKME(unittest.TestCase):
+    def test_rkme(self):
+        X = np.random.uniform(-10000, 10000, size=(5000, 200))
+        rkme = specification.utils.generate_rkme_spec(X)
+
+        with tempfile.TemporaryDirectory(prefix="learnware_") as tempdir:
+            rkme_path = os.path.join(tempdir, "rkme.json")
+            rkme.save(rkme_path)
+
+            with open(rkme_path, "r") as f:
+                data = json.load(f)
+                assert data["type"] == "RKMEStatSpecification"
+
+            rkme2 = RKMEStatSpecification()
+            rkme2.load(rkme_path)
+            assert rkme2.type == "RKMEStatSpecification"
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_workflow/test_workflow.py

@@ -155,7 +155,7 @@ class TestAllWorkflow(unittest.TestCase):
             with zipfile.ZipFile(zip_path, "r") as zip_obj:
                 zip_obj.extractall(path=unzip_dir)
 
-            user_spec = specification.rkme.RKMEStatSpecification()
+            user_spec = specification.RKMEStatSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
             user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec})
             (