Sanity Check Analysis
- mlbugdetection.sanity.check_type_input_model(model)[source]
Check the type of the input model and returns the model object
- mlbugdetection.sanity.sanity_check(model, samples, target)[source]
- Sanity Test
Analyzes the sanity of a model with samples and return a bool that represents if the tests passed or not.
- Parameters
model (sklearn model or str) – The model to be used for prediction. Could be a model object or a path to a model file.
samples (pandas DataFrame) – The samples (or sample) to be used for prediction, which the model need to predict correctly.
target (str) – The name of the column containing the target variable.
- Return type
bool True if the model is sane, False otherwise.
- mlbugdetection.sanity.sanity_check_with_indexes(model, samples, target)[source]
- Sanity Test With Indexes
Analyzes the sanity of a model with samples and shows a Analysis Report that shows if the tests passed or not. If the tests failed, it will show the indexes of the samples that were misclassified.
- Parameters
model (sklearn model or str) – The model to be used for prediction. Could be a model object or a path to a model file.
samples (pandas DataFrame) – The samples (or sample) to be used for prediction, which the model need to predict correctly.
target (str) – The name of the column containing the target variable.
- Returns
For more information: >>> from mlbugdetection.analysis_report import AnalysisReport >>> help(AnalysisReport)
- Return type
AnalysisReport object with following attributes
- model_namestr
Name of the model being analysed.
- analysed_featurestr
Name of the feature being analysed.
- metricsdictionary
Dictionary with all the calculated metrics, such as:
- ‘sanity’bool
If the model is sane or not.
- ‘sanity_indexes’: List
List of indexes of the samples that were misclassified.