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����M��f�2����������������������@���s8���d�dl�mZmZmZ�d�dlmZ�eG�dd��de��ZdS�)�����)�formatted_flat_dict�
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    Model level text classification metrics
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        Initializes a new TextClassificationModelMetrics object with values from keyword arguments.
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:param accuracy:
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:param micro_f1:
            The value to assign to the micro_f1 property of this TextClassificationModelMetrics.
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:param micro_precision:
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        :type micro_precision: float

:param micro_recall:
            The value to assign to the micro_recall property of this TextClassificationModelMetrics.
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:param macro_f1:
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:param macro_precision:
            The value to assign to the macro_precision property of this TextClassificationModelMetrics.
        :type macro_precision: float

:param macro_recall:
            The value to assign to the macro_recall property of this TextClassificationModelMetrics.
        :type macro_recall: float

:param weighted_f1:
            The value to assign to the weighted_f1 property of this TextClassificationModelMetrics.
        :type weighted_f1: float

:param weighted_precision:
            The value to assign to the weighted_precision property of this TextClassificationModelMetrics.
        :type weighted_precision: float

:param weighted_recall:
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z'TextClassificationModelMetrics.__init__c�����������������C���s���|�j�S�)z�
        **[Required]** Gets the accuracy of this TextClassificationModelMetrics.
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        Sets the accuracy of this TextClassificationModelMetrics.
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:param accuracy: The accuracy of this TextClassificationModelMetrics.
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        Nr,���)r'���r���r)���r)���r*���r���o���s����
c�����������������C���s���|�j�S�)u����
        **[Required]** Gets the micro_f1 of this TextClassificationModelMetrics.
        F1-score, is a measure of a model’s accuracy on a dataset

:return: The micro_f1 of this TextClassificationModelMetrics.
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        �r���r-���r)���r)���r*���r	���{���s����
z'TextClassificationModelMetrics.micro_f1c�����������������C���s
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        Sets the micro_f1 of this TextClassificationModelMetrics.
        F1-score, is a measure of a model’s accuracy on a dataset

:param micro_f1: The micro_f1 of this TextClassificationModelMetrics.
        :type: float
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        **[Required]** Gets the micro_precision of this TextClassificationModelMetrics.
        Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

:return: The micro_precision of this TextClassificationModelMetrics.
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        �r���r-���r)���r)���r*���r
�������s����
z.TextClassificationModelMetrics.micro_precisionc�����������������C���s
���||�_�dS�)az��
        Sets the micro_precision of this TextClassificationModelMetrics.
        Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

:param micro_precision: The micro_precision of this TextClassificationModelMetrics.
        :type: float
        Nr/���)r'���r
���r)���r)���r*���r
�������s����
c�����������������C���s���|�j�S�)a���
        **[Required]** Gets the micro_recall of this TextClassificationModelMetrics.
        Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

:return: The micro_recall of this TextClassificationModelMetrics.
        :rtype: float
        �r ���r-���r)���r)���r*���r�������s����
z+TextClassificationModelMetrics.micro_recallc�����������������C���s
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        Sets the micro_recall of this TextClassificationModelMetrics.
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:param micro_recall: The micro_recall of this TextClassificationModelMetrics.
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        Nr0���)r'���r���r)���r)���r*���r�������s����
c�����������������C���s���|�j�S�)u����
        **[Required]** Gets the macro_f1 of this TextClassificationModelMetrics.
        F1-score, is a measure of a model’s accuracy on a dataset

:return: The macro_f1 of this TextClassificationModelMetrics.
        :rtype: float
        �r!���r-���r)���r)���r*���r�������s����
z'TextClassificationModelMetrics.macro_f1c�����������������C���s
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        Sets the macro_f1 of this TextClassificationModelMetrics.
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:param macro_f1: The macro_f1 of this TextClassificationModelMetrics.
        :type: float
        Nr1���)r'���r���r)���r)���r*���r�������s����
c�����������������C���s���|�j�S�)al��
        Gets the macro_precision of this TextClassificationModelMetrics.
        Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

:return: The macro_precision of this TextClassificationModelMetrics.
        :rtype: float
        �r"���r-���r)���r)���r*���r
�������s����
z.TextClassificationModelMetrics.macro_precisionc�����������������C���s
���||�_�dS�)az��
        Sets the macro_precision of this TextClassificationModelMetrics.
        Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

:param macro_precision: The macro_precision of this TextClassificationModelMetrics.
        :type: float
        Nr2���)r'���r
���r)���r)���r*���r
�������s����
c�����������������C���s���|�j�S�)a���
        Gets the macro_recall of this TextClassificationModelMetrics.
        Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

:return: The macro_recall of this TextClassificationModelMetrics.
        :rtype: float
        �r#���r-���r)���r)���r*���r�������s����
z+TextClassificationModelMetrics.macro_recallc�����������������C���s
���||�_�dS�)a���
        Sets the macro_recall of this TextClassificationModelMetrics.
        Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

:param macro_recall: The macro_recall of this TextClassificationModelMetrics.
        :type: float
        Nr3���)r'���r���r)���r)���r*���r�������s����
c�����������������C���s���|�j�S�)u����
        Gets the weighted_f1 of this TextClassificationModelMetrics.
        F1-score, is a measure of a model’s accuracy on a dataset

:return: The weighted_f1 of this TextClassificationModelMetrics.
        :rtype: float
        �r$���r-���r)���r)���r*���r�����s����
z*TextClassificationModelMetrics.weighted_f1c�����������������C���s
���||�_�dS�)u����
        Sets the weighted_f1 of this TextClassificationModelMetrics.
        F1-score, is a measure of a model’s accuracy on a dataset

:param weighted_f1: The weighted_f1 of this TextClassificationModelMetrics.
        :type: float
        Nr4���)r'���r���r)���r)���r*���r�����s����
c�����������������C���s���|�j�S�)ar��
        Gets the weighted_precision of this TextClassificationModelMetrics.
        Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

:return: The weighted_precision of this TextClassificationModelMetrics.
        :rtype: float
        �r%���r-���r)���r)���r*���r���#��s����
z1TextClassificationModelMetrics.weighted_precisionc�����������������C���s
���||�_�dS�)a���
        Sets the weighted_precision of this TextClassificationModelMetrics.
        Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)

:param weighted_precision: The weighted_precision of this TextClassificationModelMetrics.
        :type: float
        Nr5���)r'���r���r)���r)���r*���r���/��s����
c�����������������C���s���|�j�S�)a���
        Gets the weighted_recall of this TextClassificationModelMetrics.
        Measures the model's ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

:return: The weighted_recall of this TextClassificationModelMetrics.
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        �r&���r-���r)���r)���r*���r���;��s����
z.TextClassificationModelMetrics.weighted_recallc�����������������C���s
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        Sets the weighted_recall of this TextClassificationModelMetrics.
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:param weighted_recall: The weighted_recall of this TextClassificationModelMetrics.
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❌ Batal

Nama	Ukuran	Dimodifikasi	Permission	Aksi
📎 __init__.pyc	7.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_dominant_language_details.pyc	3.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_dominant_language_result.pyc	3.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_entities_details.pyc	5.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_entities_result.pyc	3.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_key_phrases_details.pyc	3.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_key_phrases_result.pyc	3.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_sentiments_details.pyc	3.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_sentiments_result.pyc	3.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_text_classification_details.pyc	5.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_detect_language_text_classification_result.pyc	3.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_language_translation_details.pyc	5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 batch_language_translation_result.pyc	3.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 change_endpoint_compartment_details.pyc	2.8 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 change_model_compartment_details.pyc	2.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 change_project_compartment_details.pyc	2.8 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 class_metrics.pyc	5.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 classification_multi_class_mode_details.pyc	2.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 classification_multi_label_mode_details.pyc	2.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 classification_type.pyc	3.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 confusion_matrix_details.pyc	2.3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 create_endpoint_details.pyc	8.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 create_model_details.pyc	10.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 create_project_details.pyc	6.8 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 data_science_labeling_dataset.pyc	3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 dataset_details.pyc	3.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_dominant_language_details.pyc	2.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_dominant_language_result.pyc	2.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_entities_details.pyc	2.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_entities_result.pyc	2.5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_key_phrases_details.pyc	2.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_key_phrases_result.pyc	2.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_sentiments_details.pyc	2.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_sentiments_result.pyc	2.5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_text_classification_details.pyc	2.5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detect_language_text_classification_result.pyc	2.8 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 detected_language.pyc	4.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 document_error.pyc	2.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 dominant_language_document.pyc	3.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 dominant_language_document_result.pyc	3.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 endpoint.pyc	15.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 endpoint_collection.pyc	2.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 endpoint_summary.pyc	14.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 entity.pyc	5.8 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 entity_document_result.pyc	4.3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 entity_label_error_analysis.pyc	3.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 entity_metrics.pyc	5.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 error_details.pyc	3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 evaluation_result_collection.pyc	2.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 evaluation_result_summary.pyc	5.5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 evaluation_results.pyc	3.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 hierarchical_entity.pyc	6.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 key_phrase.pyc	2.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 key_phrase_document_result.pyc	4.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 location_details.pyc	3.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 model.pyc	17.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 model_collection.pyc	2.3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 model_details.pyc	4.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 model_summary.pyc	14.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 named_entity_recognition_evaluation_result.pyc	6.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 named_entity_recognition_evaluation_results.pyc	6.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 named_entity_recognition_model_details.pyc	2.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 named_entity_recognition_model_metrics.pyc	12.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 object_list_dataset.pyc	4.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 object_storage_dataset.pyc	3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 pre_deployed_language_models.pyc	4.3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 project.pyc	12.5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 project_collection.pyc	2.3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 project_summary.pyc	12.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 sentiment_aspect.pyc	5.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 sentiment_document_result.pyc	7.3 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 sentiment_sentence.pyc	5.5 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 test_and_validation_dataset_strategy.pyc	4.2 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 test_strategy.pyc	3.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_classification.pyc	3.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_classification_document_result.pyc	4.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_classification_evaluation_results.pyc	6.1 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_classification_model_details.pyc	3.4 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_classification_model_evaluation_result.pyc	6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_classification_model_metrics.pyc	12.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 text_document.pyc	3.9 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 translation_document_result.pyc	5.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 update_endpoint_details.pyc	7.7 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
📎 update_model_details.pyc	5.6 KB	2024-08-12 23:07	0644	✏️ Edit ⬇
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