Import won't work

[TellemHD]

Describe the bug

Importing .sds-FIles doesn't work (Statisch wird kein Fehler erkannt, aber dynamisch bei der Ausführung)

To Reproduce

• EIne test.sds-File mit dem Packagenamen test und einem Segment erstellen
• In einer anderen .sds-Fatei "from test import *" schreiben
• Das Segment ausführen

  2024-05-17 10:38:33.418 [warning] root:Message queue terminated: EOFError()
  2024-05-17 10:45:06.706 [warning] .
  warnings.warn(
  2024-05-17 10:47:48.353 [warning] .
  warnings.warn(
  2024-05-17 10:56:49.427 [debug] root:Received Message: {"type":"program","id":"7f1dda19-c56c-4d88-88bc-4b8c687f589b","data":{"code":{"creditScore":{"gen_creditScore":"# Imports ----------------------------------------------------------------------\r\n\r\nimport safeds_runner\r\nfrom safeds.data.tabular.containers import Table\r\nfrom safeds.data.tabular.transformation import LabelEncoder, OneHotEncoder\r\nfrom safeds.ml.classical.classification import RandomForestClassifier\r\nfrom test.gen_test import calculateAccuracyTemplate\r\n\r\n# Segments ---------------------------------------------------------------------\r\n\r\ndef createClassifierResults(encoded, outputColumn):\r\n    __gen_classifier3 = safeds_runner.memoized_static_call(\r\n        \"safeds.ml.classical.classification.RandomForestClassifier\",\r\n        RandomForestClassifier,\r\n        [],\r\n        {\"number_of_trees\": 200, \"maximum_depth\": None, \"minimum_number_of_samples_in_leaves\": 1},\r\n        []\r\n    )\r\n    __gen_accuracy3 = calculateAccuracyTemplate(encoded, outputColumn, __gen_classifier3)\r\n    __gen_accList = [__gen_accuracy3[0]]\r\n    __gen_nameList = ['RandomForest']\r\n    __gen_f1List = [__gen_accuracy3[1]]\r\n    __gen_precList = [__gen_accuracy3[2]]\r\n    __gen_recList = [__gen_accuracy3[3]]\r\n    __gen_yield_table = safeds_runner.memoized_static_call(\r\n        \"safeds.data.tabular.containers.Table.from_dict\",\r\n        Table.from_dict,\r\n        [{'Name': __gen_nameList, 'Accuracy': __gen_accList, 'F1': __gen_f1List, 'Precision': __gen_precList, 'Recall': __gen_recList}],\r\n        {},\r\n        []\r\n    )\r\n    return __gen_yield_table\r\n\r\ndef calculateAccuracy(data, outputColumn, classifier):\r\n    __gen_receiver_0 = data\r\n    __gen_training, __gen_test = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_0,\r\n        \"split_rows\",\r\n        [0.9],\r\n        {\"shuffle\": True},\r\n        []\r\n    )\r\n    __gen_receiver_1 = __gen_training\r\n    __gen_trainingTable = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_1,\r\n        \"to_tabular_dataset\",\r\n        [outputColumn],\r\n        {\"extra_names\": None},\r\n        []\r\n    )\r\n    __gen_receiver_2 = __gen_test\r\n    __gen_testTable = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_2,\r\n        \"to_tabular_dataset\",\r\n        [outputColumn],\r\n        {\"extra_names\": None},\r\n        []\r\n    )\r\n    __gen_receiver_3 = classifier\r\n    __gen_trained = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_3,\r\n        \"fit\",\r\n        [__gen_trainingTable],\r\n        {},\r\n        []\r\n    )\r\n    __gen_receiver_4 = __gen_trained\r\n    __gen_accuracy = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_4,\r\n        \"accuracy\",\r\n        [__gen_testTable],\r\n        {},\r\n        []\r\n    )\r\n    __gen_receiver_5 = __gen_trained\r\n    __gen_f1 = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_5,\r\n        \"f1_score\",\r\n        [__gen_testTable, 'Good'],\r\n        {},\r\n        []\r\n    )\r\n    __gen_receiver_6 = __gen_trained\r\n    __gen_prec = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_6,\r\n        \"precision\",\r\n        [__gen_testTable, 'Good'],\r\n        {},\r\n        []\r\n    )\r\n    __gen_receiver_7 = __gen_trained\r\n    __gen_rec = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_7,\r\n        \"recall\",\r\n        [__gen_testTable, 'Good'],\r\n        {},\r\n        []\r\n    )\r\n    __gen_yield_result = [__gen_accuracy, __gen_f1, __gen_prec, __gen_rec]\r\n    return __gen_yield_result\r\n\r\n# Pipelines --------------------------------------------------------------------\r\n\r\ndef creditScorePipeline():\r\n    __gen_toRemove = ['ID', 'CustomerID', 'Month', 'Name', 'SSN', 'CreditHistoryAge', 'MonthlyInhandSalary', 'TypeofLoan']\r\n    safeds_runner.save_placeholder('toRemove', __gen_toRemove)\r\n    __gen_toOneHotEncode = ['PaymentBehaviour', 'PaymentofMinAmount', 'Occupation']\r\n    safeds_runner.save_placeholder('toOneHotEncode', __gen_toOneHotEncode)\r\n    __gen_toLabelEncode = ['CreditMix']\r\n    safeds_runner.save_placeholder('toLabelEncode', __gen_toLabelEncode)\r\n    __gen_receiver_0 = safeds_runner.memoized_static_call(\r\n        \"safeds.data.tabular.containers.Table.from_csv_file\",\r\n        Table.from_csv_file,\r\n        [safeds_runner.absolute_path('train.csv')],\r\n        {\"separator\": ','},\r\n        [safeds_runner.file_mtime('train.csv')]\r\n    )\r\n    __gen_table = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_0,\r\n        \"shuffle_rows\",\r\n        [],\r\n        {},\r\n        []\r\n    )\r\n    safeds_runner.save_placeholder('table', __gen_table)\r\n    __gen_receiver_1 = __gen_table\r\n    __gen_receiver_2 = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_1,\r\n        \"remove_columns\",\r\n        [__gen_toRemove],\r\n        {},\r\n        []\r\n    )\r\n    __gen_receiver_3 = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_2,\r\n        \"remove_rows_with_missing_values\",\r\n        [],\r\n        {\"column_names\": None},\r\n        []\r\n    )\r\n    def __gen_lambda_4(r):\r\n        __gen_receiver_5 = r\r\n        __gen_receiver_6 = safeds_runner.memoized_dynamic_call(\r\n            __gen_receiver_5,\r\n            \"get_value\",\r\n            ['Age'],\r\n            {},\r\n            []\r\n        )\r\n        return safeds_runner.memoized_dynamic_call(\r\n            __gen_receiver_6,\r\n            \"lt\",\r\n            [0],\r\n            {},\r\n            []\r\n        )\r\n    __gen_receiver_7 = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_3,\r\n        \"remove_rows\",\r\n        [__gen_lambda_4],\r\n        {},\r\n        []\r\n    )\r\n    def __gen_lambda_8(r):\r\n        __gen_receiver_9 = r\r\n        __gen_receiver_10 = safeds_runner.memoized_dynamic_call(\r\n            __gen_receiver_9,\r\n            \"get_value\",\r\n            ['NumBankAccounts'],\r\n            {},\r\n            []\r\n        )\r\n        return safeds_runner.memoized_dynamic_call(\r\n            __gen_receiver_10,\r\n            \"lt\",\r\n            [0],\r\n            {},\r\n            []\r\n        )\r\n    __gen_removed = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_7,\r\n        \"remove_rows\",\r\n        [__gen_lambda_8],\r\n        {},\r\n        []\r\n    )\r\n    safeds_runner.save_placeholder('removed', __gen_removed)\r\n    __gen_receiver_11 = safeds_runner.memoized_static_call(\r\n        \"safeds.data.tabular.transformation.OneHotEncoder\",\r\n        OneHotEncoder,\r\n        [],\r\n        {\"separator\": '__'},\r\n        []\r\n    )\r\n    __gen_hotEncoded = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_11,\r\n        \"fit_and_transform\",\r\n        [__gen_removed],\r\n        {\"column_names\": __gen_toOneHotEncode},\r\n        []\r\n    )[1]\r\n    safeds_runner.save_placeholder('hotEncoded', __gen_hotEncoded)\r\n    __gen_receiver_12 = safeds_runner.memoized_static_call(\r\n        \"safeds.data.tabular.transformation.LabelEncoder\",\r\n        LabelEncoder,\r\n        [],\r\n        {\"partial_order\": []},\r\n        []\r\n    )\r\n    __gen_labeled = safeds_runner.memoized_dynamic_call(\r\n        __gen_receiver_12,\r\n        \"fit_and_transform\",\r\n        [__gen_hotEncoded],\r\n        {\"column_names\": __gen_toLabelEncode},\r\n        []\r\n    )[1]\r\n    safeds_runner.save_placeholder('labeled', __gen_labeled)\r\n    __gen_result = createClassifierResults(__gen_labeled, 'CreditScore')\r\n    safeds_runner.save_placeholder('result', __gen_result)\r\n","gen_creditScore_creditScorePipeline":"from .gen_creditScore import creditScorePipeline\r\n\r\nif __name__ == '__main__':\r\n    creditScorePipeline()\r\n"}},"main":{"modulepath":"creditScore","module":"creditScore","pipeline":"creditScorePipeline"},"cwd":"c:\\Users\\TimLocke\\OneDrive - Objektkultur Software GmbH\\Desktop\\Uni\\24 Sommersemester\\Projektgruppe Angewandte Softwaretechnologie\\ast-sose24-dsl\\credit_score"}}

Expected behavior

DIe Methdoe sollte ausgeführt werden

Screenshots (optional)

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Additional Context (optional)

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