Code review fix

cases/credit_scoring/le_exp.py

@@ -0,0 +1,94 @@
+import logging
+
+from sklearn.metrics import roc_auc_score as roc_auc
+from sklearn.preprocessing import LabelEncoder
+
+from fedot import Fedot
+from fedot.core.constants import Consts
+from fedot.core.data.data import InputData
+from fedot.core.data.data_split import train_test_data_setup
+from fedot.core.pipelines.pipeline import Pipeline
+from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
+
+
+def calculate_validation_metric(pipeline: Pipeline, dataset_to_validate: InputData) -> float:
+    # the execution of the obtained composite models
+    predicted = pipeline.predict(dataset_to_validate)
+    # the quality assessment for the simulation results
+    roc_auc_value = roc_auc(y_true=dataset_to_validate.target,
+                            y_score=predicted.predict)
+    return roc_auc_value
+
+
+def run_problem(timeout: float = 5.0,
+                visualization=False,
+                target='target',
+                model_type="auto",
+                **composer_args):
+    file_path_train = 'cases/data/mfeat-pixel.csv'
+    full_path_train = fedot_project_root().joinpath(file_path_train)
+
+    data = InputData.from_csv(full_path_train, task='classification', target_columns='class')
+
+    target = data.target
+    encoded = LabelEncoder().fit_transform(target)
+    data.target = encoded
+
+    train, test = train_test_data_setup(data, shuffle=True)
+    print(model_type, Consts.USE_LABEL_ENC_AS_DEFAULT)
+    automl = Fedot(problem='classification',
+                   timeout=timeout,
+                   logging_level=logging.FATAL,
+                   metric='f1',
+                   **composer_args)
+    if model_type != "auto":
+        automl.fit(train, predefined_model=model_type)
+    else:
+        automl.fit(train)
+
+    automl.predict(test)
+    metrics = automl.get_metrics()
+
+    if automl.history and automl.history.generations:
+        print(automl.history.get_leaderboard())
+        automl.history.show()
+
+    if visualization:
+        automl.current_pipeline.show()
+
+    print(f'f1 is {round(metrics["f1"], 3)}')
+
+    return metrics["f1"]
+
+
+if __name__ == '__main__':
+    set_random_seed(42)
+
+    Consts.USE_LABEL_ENC_AS_DEFAULT = True
+
+    run_problem(timeout=1,
+                visualization=False,
+                with_tuning=False, model_type='logit')
+
+    run_problem(timeout=1,
+                visualization=False,
+                with_tuning=False, model_type='xgboost')
+
+    run_problem(timeout=10,
+                visualization=True,
+                with_tuning=True, model_type='auto')
+
+    Consts.USE_LABEL_ENC_AS_DEFAULT = False
+
+    run_problem(timeout=1,
+                visualization=True,
+                with_tuning=True, model_type='logit')
+
+    run_problem(timeout=1,
+                visualization=False,
+                with_tuning=False, model_type='xgboost')
+
+    run_problem(timeout=10,
+                visualization=True,
+                with_tuning=True, model_type='auto')