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Utility function for sanity checks that tests if model output increases with the provided input columns
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| def sanity_check_sum(model, dataframe, cols, delta=1): | |
| '''Calculates success rate on basic sanity check. A "delta" value is added | |
| to columns in a dataframe and the newly predicted house price should be higher | |
| than the existing prediction since the addition is supposed to be an added feature | |
| to the house such as bigger area or better condition or view etc. | |
| Args: | |
| model: sklearn or other model with predict() method | |
| dataframe: pandas dataframe with dataset to be test | |
| cols: column or list of columns in dataframe to be incremented by delta parameter | |
| delta (optional): Value added to columns before predicting price on updated dataframe. | |
| Defaults to 1. | |
| Returns: | |
| % of observations where all sanity checks are passed | |
| ''' | |
| if isinstance(cols, str): | |
| cols = [cols] | |
| test_results = [] | |
| for col in cols: | |
| dataframe_pre = dataframe.copy(deep=True) | |
| dataframe_post = dataframe.copy(deep=True) | |
| dataframe_post[col] = dataframe_pre[col] + delta | |
| test_results.append(model.predict(dataframe_post) >= | |
| model.predict(dataframe_pre)) | |
| # Check if test is passed on every column (AND logic) | |
| test_results = np.min(test_results, axis=0) | |
| return round(np.mean(test_results), 4) |
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