take-ai-evaluation 0.2.3

Metrics and visualizations for evaluating chatbot's AI utilization.

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:

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

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

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'

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

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

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'

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

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

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

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

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)