[MNT] import extended package locally

learnware/reuse/__init__.py

@@ -3,16 +3,9 @@ from .align import AlignLearnware
 
 from ..logger import get_module_logger
 from ..utils import is_torch_available, get_platform, SystemType
-from .utils import is_geatpy_available, is_lightgbm_available
 
 logger = get_module_logger("reuse")
 
-if not is_geatpy_available(verbose=False):
-    EnsemblePruningReuser = None
-    logger.warning("EnsemblePruningReuser is skipped due to 'geatpy' is not installed!")
-else:
-    from .ensemble_pruning import EnsemblePruningReuser
-
 if not is_torch_available(verbose=False):
     AveragingReuser = None
     FeatureAugmentReuser = None
@@ -20,27 +13,10 @@ if not is_torch_available(verbose=False):
     FeatureAlignLearnware = None
     JobSelectorReuser = None
     logger.error(
-        "[AveragingReuser, FeatureAugmentReuser, HeteroMapAlignLearnware, FeatureAlignLearnware, JobSelectorReuser] is skipped due to 'torch' is not installed!"
+        "[AveragingReuser, FeatureAugmentReuser, HeteroMapAlignLearnware, FeatureAlignLearnware, JobSelectorReuser] are skipped due to 'torch' is not installed!"
     )
 else:
     from .averaging import AveragingReuser
     from .feature_augment import FeatureAugmentReuser
     from .hetero import HeteroMapAlignLearnware, FeatureAlignLearnware
-    from .job_selector import JobSelectorReuser
-
-if not is_lightgbm_available(verbose=False) or not is_torch_available(verbose=False):
-    JobSelectorReuser = None
-    uninstall_packages = [
-        value
-        for flag, value in zip(
-            [
-                is_lightgbm_available(verbose=False),
-                is_torch_available(verbose=False),
-            ],
-            ["lightgbm", "torch"],
-        )
-        if flag is False
-    ]
-    logger.error(f"JobSelectorReuser is skipped due to {uninstall_packages} is not installed!")
-else:
-    
+    from .job_selector import JobSelectorReuser

learnware/reuse/ensemble_pruning.py

@@ -1,7 +1,6 @@
 import torch
 import random
 import numpy as np
-import geatpy as ea
 from typing import List
 
 from ..learnware import Learnware
@@ -54,6 +53,13 @@ class EnsemblePruningReuser(BaseReuser):
         np.ndarray
             Binary one-dimensional vector, 1 indicates that the corresponding model is selected.
         """
+        
+        
+        try:
+            import geatpy as ea
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"EnsemblePruningReuser is not available because 'geatpy' is not installed! Please install it manually (only support python_version<3.11).")
+           
         model_num = v_predict.shape[1]
 
         @ea.Problem.single
@@ -138,6 +144,12 @@ class EnsemblePruningReuser(BaseReuser):
         np.ndarray
             Binary one-dimensional vector, 1 indicates that the corresponding model is selected.
         """
+        try:
+            import geatpy as ea
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"EnsemblePruningReuser is not available because 'geatpy' is not installed! Please install it manually (only support python_version<3.11).")
+           
+
         model_num = v_predict.shape[1]
 
         def find_top_two_freq(row):
@@ -252,6 +264,11 @@ class EnsemblePruningReuser(BaseReuser):
         np.ndarray
             Binary one-dimensional vector, 1 indicates that the corresponding model is selected.
         """
+        try:
+            import geatpy as ea
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"EnsemblePruningReuser is not available because 'geatpy' is not installed! Please install it manually (only support python_version<3.11).")
+           
         model_num = v_predict.shape[1]
         v_predict[v_predict == 0.0] = -1
         v_true[v_true == 0.0] = -1

learnware/reuse/job_selector.py

@@ -2,7 +2,6 @@ import torch
 import numpy as np
 
 from typing import List, Union
-from lightgbm import LGBMClassifier, early_stopping
 from sklearn.metrics import accuracy_score
 
 from .base import BaseReuser
@@ -196,7 +195,7 @@ class JobSelectorReuser(BaseReuser):
         val_x: np.ndarray,
         val_y: np.ndarray,
         num_class: int,
-    ) -> LGBMClassifier:
+    ):
         """Train a LGBMClassifier as job selector using the herding data as training instances.
 
         Parameters
@@ -221,6 +220,11 @@ class JobSelectorReuser(BaseReuser):
         LGBMClassifier
             The job selector model.
         """
+        try:
+            from lightgbm import LGBMClassifier, early_stopping
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"JobSelectorReuser is not available because 'lightgbm' is not installed! Please install it manually.")
+           
         score_best = -1
         learning_rate = [0.01]
         max_depth = [66]

learnware/reuse/utils.py

@@ -3,29 +3,6 @@ from ..logger import get_module_logger
 
 logger = get_module_logger("reuse_utils")
 
-
-def is_geatpy_available(verbose=False):
-    try:
-        import geatpy
-    except ModuleNotFoundError as err:
-        if verbose is True:
-            logger.warning(
-                "ModuleNotFoundError: geatpy is not installed, please install geatpy (only support python version<3.11)!"
-            )
-        return False
-    return True
-
-
-def is_lightgbm_available(verbose=False):
-    try:
-        import lightgbm
-    except ModuleNotFoundError as err:
-        if verbose is True:
-            logger.warning("ModuleNotFoundError: lightgbm is not installed, please install lightgbm!")
-        return False
-    return True
-
-
 def fill_data_with_mean(X: np.ndarray) -> np.ndarray:
     """
     Fill missing data (NaN, Inf) in the input array with the mean of the column.

learnware/specification/regular/__init__.py

@@ -1,6 +1,4 @@
 from .base import RegularStatSpecification
-from ...utils import is_torch_available
-
 from .text import RKMETextSpecification
 from .table import RKMETableSpecification, RKMEStatSpecification, rkme_solve_qp
 from .image import RKMEImageSpecification

learnware/specification/regular/image/__init__.py

@@ -1,4 +1,3 @@
-from .utils import is_torch_optimizer_available, is_torchvision_available
 from ....utils import is_torch_available
 from ....logger import get_module_logger
 

learnware/specification/regular/image/rkme.py

@@ -10,7 +10,6 @@ from typing import Any
 
 import numpy as np
 import torch
-import torch_optimizer
 from torch import nn
 from torch.utils.data import TensorDataset, DataLoader
 from tqdm import tqdm
@@ -128,7 +127,7 @@ class RKMEImageSpecification(RegularStatSpecification):
         try:
             from torchvision.transforms import Resize
         except ModuleNotFoundError:
-            raise ModuleNotFoundError(f"RKMEImageSpecification is not available because 'torchvision' is not installed!")
+            raise ModuleNotFoundError(f"RKMEImageSpecification is not available because 'torchvision' is not installed! Please install it manually." )
             
         if X.shape[2] != RKMEImageSpecification.IMAGE_WIDTH or X.shape[3] != RKMEImageSpecification.IMAGE_WIDTH:
             X = Resize((RKMEImageSpecification.IMAGE_WIDTH, RKMEImageSpecification.IMAGE_WIDTH), antialias=None)(X)
@@ -155,7 +154,12 @@ class RKMEImageSpecification(RegularStatSpecification):
         with torch.no_grad():
             x_features = self._generate_random_feature(X_train, random_models=random_models)
         self._update_beta(x_features, nonnegative_beta, random_models=random_models)
-
+        
+        try:
+            import torch_optimizer
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"RKMEImageSpecification is not available because 'torch-optimizer' is not installed! Please install it manually.")
+        
         optimizer = torch_optimizer.AdaBelief([{"params": [self.z]}], lr=step_size, eps=1e-16)
 
         for _ in tqdm(range(steps)) if verbose else range(steps):

learnware/specification/regular/table/__init__.py

@@ -1,5 +1,3 @@
-from .utils import is_fast_pytorch_kmeans_available
-
 from ....utils import is_torch_available
 from ....logger import get_module_logger
 

learnware/specification/regular/table/rkme.py

@@ -9,7 +9,6 @@ import numpy as np
 from qpsolvers import solve_qp, Problem, solve_problem
 from collections import Counter
 from typing import Any, Union
-from fast_pytorch_kmeans import KMeans
 
 from ..base import RegularStatSpecification
 from ....logger import get_module_logger
@@ -143,6 +142,12 @@ class RKMETableSpecification(RegularStatSpecification):
             X = torch.from_numpy(X)
             
         X = X.to(self._device)
+        
+        try:
+            from fast_pytorch_kmeans import KMeans
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"RKMETableSpecification is not available because 'fast_pytorch_kmeans' is not installed! Please install it manually." )
+
         kmeans = KMeans(n_clusters=K, mode='euclidean', max_iter=100, verbose=0)
         kmeans.fit(X)
         self.z = kmeans.centroids.double()

learnware/specification/regular/table/utils.py

@@ -1,15 +0,0 @@
-from ....logger import get_module_logger
-
-logger = get_module_logger("regular_table_spec_utils")
-
-
-def is_fast_pytorch_kmeans_available(verbose=False):
-    try:
-        import fast_pytorch_kmeans
-    except ModuleNotFoundError as err:
-        if verbose is True:
-            logger.warning(
-                "ModuleNotFoundError: fast_pytorch_kmeans is not installed, please install fast_pytorch_kmeans!"
-            )
-        return False
-    return True

learnware/specification/regular/text/__init__.py

@@ -1,6 +1,3 @@
-from .utils import is_sentence_transformers_available
-from ..table.utils import is_fast_pytorch_kmeans_available
-
 from ....utils import is_torch_available
 from ....logger import get_module_logger
 
@@ -8,6 +5,6 @@ logger = get_module_logger("regular_text_spec")
 
 if not is_torch_available(verbose=False):
     RKMETextSpecification = None
-    logger.warning(f"RKMETextSpecification is skipped because 'torch' is not installed!")
+    logger.error(f"RKMETextSpecification is skipped because 'torch' is not installed!")
 else:
     from .rkme import RKMETextSpecification

learnware/specification/regular/text/rkme.py

@@ -1,7 +1,6 @@
 import os
 import langdetect
 import numpy as np
-from sentence_transformers import SentenceTransformer
 
 from ..table import RKMETableSpecification
 from ....logger import get_module_logger
@@ -87,6 +86,12 @@ class RKMETextSpecification(RKMETableSpecification):
             return np.array(miniLM_learnware.predict(X))
 
         logger.info("Load the necessary feature extractor for RKMETextSpecification.")
+        
+        try:
+            from sentence_transformers import SentenceTransformer
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(f"RKMETextSpecification is not available because 'sentence_transformers' is not installed! Please install it manually.")
+           
         if os.path.exists(zip_path):
             X = _get_from_client(zip_path, X)
         else:

learnware/specification/regular/text/utils.py

@@ -1,15 +0,0 @@
-from ....logger import get_module_logger
-
-logger = get_module_logger("regular_text_spec_utils")
-
-
-def is_sentence_transformers_available(verbose=False):
-    try:
-        import sentence_transformers
-    except ModuleNotFoundError as err:
-        if verbose is True:
-            logger.warning(
-                "ModuleNotFoundError: sentence_transformers is not installed, please install sentence_transformers!"
-            )
-        return False
-    return True