fix: correct EXAMPLES_DATA_PATH import

aimclub · Jan 27, 2025 · 6362f51 · 6362f51
1 parent c567c3c
commit 6362f51
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Showing 3 changed files with 51 additions and 63 deletions.
diff --git a/examples/automl_example/custom_strategy/specific_strategy/LoRa_example.py b/examples/automl_example/custom_strategy/specific_strategy/LoRa_example.py
@@ -2,78 +2,66 @@
 import torchvision.transforms as transforms
 from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate
 from fedot_ind.core.repository.config_repository import DEFAULT_COMPUTE_CONFIG, \
-    DEFAULT_AUTOML_LEARNING_CONFIG
+    DEFAULT_AUTOML_LEARNING_CONFIG, DEFAULT_CLF_AUTOML_CONFIG
+from fedot_ind.tools.serialisation.path_lib import EXAMPLES_DATA_PATH
 
 
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.1307,), (0.3081,))
-])
+if __name__ == '__main__':
+    import ssl
+    ssl._create_default_https_context = ssl._create_unverified_context
 
-# Load the MNIST train and test dataset
-train_data = (datasets.MNIST(
-    root="./examples/data",
-    train=True,
-    download=True,
-    transform=transform), 'torchvision_dataset')
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,))
+    ])
 
-test_data = (datasets.MNIST(
-    root="./examples/data",
-    train=False,
-    download=True,
-    transform=transform), 'torchvision_dataset')
+    # Load the MNIST train and test dataset
+    train_data = (datasets.MNIST(
+        root=EXAMPLES_DATA_PATH,
+        train=True,
+        download=True,
+        transform=transform), 'torchvision_dataset')
 
-metric_names = ('f1', 'accuracy', 'precision', 'roc_auc')
+    test_data = (datasets.MNIST(
+        root="EXAMPLES_DATA_PATH",
+        train=False,
+        download=True,
+        transform=transform), 'torchvision_dataset')
 
-lora_params = dict(rank=2,
-                   sampling_share=0.5,
-                   lora_init='random',
-                   epochs=1,
-                   batch_size=10
-                   )
+    METRIC_NAMES = ('f1', 'accuracy', 'precision', 'roc_auc')
 
-api_config = dict(problem='classification',
-                  metric='accuracy',
-                  timeout=0.1,
-                  with_tuning=False,
-                  industrial_strategy='lora_strategy',
-                  industrial_strategy_params=lora_params,
-                  logging_level=20)
 
-AUTOML_LEARNING_STRATEGY = DEFAULT_AUTOML_LEARNING_CONFIG
-COMPUTE_CONFIG = DEFAULT_COMPUTE_CONFIG
-AUTOML_CONFIG = {'task': 'classification',
-                 'use_automl': True,
-                 'optimisation_strategy': {'optimisation_strategy': {'mutation_agent': 'bandit',
-                                                                     'mutation_strategy': 'growth_mutation_strategy'},
-                                           'optimisation_agent': 'Industrial'}}
+    DEFAULT_AUTOML_LEARNING_CONFIG['timeout'] = 0.1
+    AUTOML_LEARNING_STRATEGY = DEFAULT_AUTOML_LEARNING_CONFIG
+    COMPUTE_CONFIG = DEFAULT_COMPUTE_CONFIG
+    AUTOML_CONFIG = DEFAULT_CLF_AUTOML_CONFIG
 
-LEARNING_CONFIG = {'learning_strategy': 'from_scratch',
-                   'learning_strategy_params': AUTOML_LEARNING_STRATEGY,
-                   'optimisation_loss': {'quality_loss': 'accuracy'}}
+    LEARNING_CONFIG = {'learning_strategy': 'from_scratch',
+                       'learning_strategy_params': AUTOML_LEARNING_STRATEGY,
+                       'optimisation_loss': {'quality_loss': 'accuracy'}}
 
-INDUSTRIAL_PARAMS = {'rank': 2,
-                     'sampling_share': 0.5,
-                     'lora_init': 'random',
-                     'epochs': 1,
-                     'batch_size': 10,
-                     'data_type': 'tensor'
-                     }
+    INDUSTRIAL_PARAMS = {'rank': 2,
+                         'sampling_share': 0.5,
+                         'lora_init': 'random',
+                         'epochs': 1,
+                         'batch_size': 10,
+                         'data_type': 'tensor'
+                         }
 
-INDUSTRIAL_CONFIG = {'problem': 'classification',
-                     'strategy': 'lora_strategy',
-                     'strategy_params': INDUSTRIAL_PARAMS
-                     }
+    INDUSTRIAL_CONFIG = {'problem': 'classification',
+                         'strategy': 'lora_strategy',
+                         'strategy_params': INDUSTRIAL_PARAMS
+                         }
 
-API_CONFIG = {'industrial_config': INDUSTRIAL_CONFIG,
-              'automl_config': AUTOML_CONFIG,
-              'learning_config': LEARNING_CONFIG,
-              'compute_config': COMPUTE_CONFIG}
+    API_CONFIG = {'industrial_config': INDUSTRIAL_CONFIG,
+                  'automl_config': AUTOML_CONFIG,
+                  'learning_config': LEARNING_CONFIG,
+                  'compute_config': COMPUTE_CONFIG}
 
-dataset = dict(test_data=test_data, train_data=train_data)
+    dataset_dict = dict(test_data=(test_data[0].data.numpy(), test_data[0].targets.numpy()),
+                        train_data=(train_data[0].data.numpy(), train_data[0].targets.numpy()))
 
-industrial = ApiTemplate(api_config=API_CONFIG,
-                         metric_list=metric_names).eval(dataset=dataset)
-industrial.fit(train_data)
-predict = industrial.predict(test_data)
-_ = 1
+    industrial = ApiTemplate(api_config=API_CONFIG,
+                             metric_list=METRIC_NAMES).eval(dataset=dataset_dict)
+    industrial.fit(train_data)
+    predict = industrial.predict(test_data)
diff --git a/examples/real_world_examples/benchmark_example/detection/ts_anomaly_detection_skab_bench.py b/examples/real_world_examples/benchmark_example/detection/ts_anomaly_detection_skab_bench.py
@@ -2,7 +2,7 @@
 
 from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate
 from fedot_ind.core.repository.config_repository import DEFAULT_COMPUTE_CONFIG, DEFAULT_CLF_AUTOML_CONFIG
-from tools.test_load_data import EXAMPLES_DATA_PATH
+from fedot_ind.tools.serialisation.path_lib import EXAMPLES_DATA_PATH
 
 
 def prepare_skab_benchmark():

diff --git a/fedot_ind/core/architecture/pipelines/abstract_pipeline.py b/fedot_ind/core/architecture/pipelines/abstract_pipeline.py
@@ -14,7 +14,7 @@
 from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
 from fedot_ind.core.repository.model_repository import NEURAL_MODEL
 from fedot_ind.tools.loader import DataLoader
-from tools.test_load_data import EXAMPLES_DATA_PATH
+from fedot_ind.tools.serialisation.path_lib import EXAMPLES_DATA_PATH
 
 BENCHMARK = 'M4'