Metadata-Version: 2.1
Name: take-ai-evaluation
Version: 0.2.2
Summary: Metrics and visualizations for evaluating chatbot's AI utilization.
Home-page: UNKNOWN
Author: squad ROps
Author-email: anaytics.dar@take.net
Maintainer: daresearch
Maintainer-email: anaytics.dar@take.net
License: MIT License
Description: # TakeAiEvaluation 
        
        TakeAiEvaluation is a tool to _provide metrics and visualizations for evaluating a chatbot's AI utilization._
        This currently addresses two types of evaluation: Knowledge Base Quality and Message Base Information.
        
        
        ## Installation
        
        The `take_ai_evaluation` package can be installed from PyPI:
        ```bash
        pip install take_ai_evaluation
        ```
        
        ## Usage
        
        As input, either a `pandas.DataFrame` or a `CSV` file path can be used.
        
        1. Matrix all vs all
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_all_vs_all_confusion_matrix(title='All vs All')
        
        plt.show()
        ```
        
        2. Matrix one vs all
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_one_vs_all_confusion_matrix(intent='Intent', title='All vs All')
        
        plt.show()
        ```
        
        3. Best intent
        - Just the values for the default metric, which is 'accuracy'
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_best_intent()
        
        plt.show()
        ```
        
        - Just the values for 'recall' metric
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_best_intent(metric='recall')
        
        plt.show()
        ```
        
        - As graph
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_best_intent(as_graph=True)
        
        plt.show()
        ```
        
        4. Worst intent
        - Just the values for the default metric, which is 'accuracy'
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_worst_intent()
        
        plt.show()
        ```
        
        - Just the values for 'recall' metric
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_worst_intent(metric='recall')
        
        plt.show()
        ```
        
        - As graph
        ```python
        import matplotlib.pyplot as plt
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_worst_intent(as_graph=True)
        
        plt.show()
        ```
        
        5. Classification Report
        ```python
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_classification_report()
        ```
        
        - As pandas DataFrame
        ```python
        from take_ai_evaluation import AiEvaluation
        
        ai_evaluation = AiEvaluation(analysed_base='knowledge-base.csv', 
                                     sentence_col='id', 
                                     intent_col='intent', 
                                     predict_col='predicted')
        
        ai_evaluation.get_classification_report(as_dataframe=True)
        ```
        
        ## Author
        Take Blip Data&Analytics Research (ROps)
Keywords: ai-knowledge,chatbot,classification,evaluation
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
