def get_important_missing_cols(lmd, prediction_row, pred_col):
if lmd['column_importances'] is None or len(lmd['column_importances']) < 2:
important_cols = [
col for col in lmd['columns'] if col not in lmd['predict_columns']
and not col.startswith('model_')
]
else:
top_30_val = np.percentile(list(lmd['column_importances'].values()),
70)
important_cols = [
col for col in lmd['column_importances']
if lmd['column_importances'][col] >= top_30_val
]
important_missing_cols = []
for col in important_cols:
if col not in prediction_row or prediction_row[col] is None or str(
prediction_row[col]) == '' or value_isnan(prediction_row[col]):
important_missing_cols.append(col)
return important_missing_cols
def explain_prediction(lmd, prediction_row, confidence, pred_col):
'''
Given a row contianing a prediction, it tries to use the information in it plus the metadata generated by the ModelAnalyzer and StatsGenerator in order to explain "why" the prediction took said value.
:param prediction_row: The row that was predicted by the model backend and processed by mindsdb
:return: A, hopefully human readable, string containing the explanation
'''
if lmd['column_importances'] is None or len(lmd['column_importances']) < 2:
important_cols = [
col for col in lmd['columns'] if col not in lmd['predict_columns']
]
useless_cols = []
else:
top_20_val = np.percentile(list(lmd['column_importances'].values()),
80)
bottom_20_val = np.percentile(list(lmd['column_importances'].values()),
20)
important_cols = [
col for col in lmd['column_importances']
if lmd['column_importances'][col] >= top_20_val
]
useless_cols = [
col for col in lmd['column_importances']
if lmd['column_importances'][col] <= bottom_20_val
]
explain_predictions = {}
predicted_val = prediction_row[pred_col]
col_stats = lmd['column_stats'][pred_col]
col_type = col_stats['data_type']
if 'histogram' in col_stats and ['histogram'] is not None:
histogram_x = None
histogram_keys = col_stats['histogram']['x']
if col_type == DATA_TYPES.NUMERIC:
for i in range(len(histogram_keys)):
if i == 0:
if histogram_keys[i] >= predicted_val:
histogram_x = histogram_keys[0]
if i == len(histogram_keys) - 1:
if histogram_keys[i] <= predicted_val:
histogram_x = histogram_keys[0]
else:
if histogram_keys[
i] <= predicted_val or predicted_val > histogram_keys[
i + 1]:
histogram_x = histogram_keys[0]
else:
histogram_x = predicted_val
bucket_occurances = col_stats['histogram']['y'][
col_stats['histogram']['x'].index(histogram_x)]
total_occuraces = sum(col_stats['histogram']['y'])
percentage_bucket_percentage = round(
100 * bucket_occurances / total_occuraces, 2)
column_confidence = confidence * 100
confidence_str = 'very confident'
if confidence < 0.80:
confidence_str = 'confident'
if confidence < 0.60:
confidence_str = 'somewhat confident'
if confidence < 0.40:
confidence_str = 'not very confident'
if confidence < 0.20:
confidence_str = 'not confident'
if percentage_bucket_percentage < 2:
column_explanation = f'A similar value for the predicted column {pred_col} occurs rarely in your dataset'
if column_confidence >= 70:
column_explanation += ', in spite of this, due to the quality of the input data and the model, we are very confident this prediction is correct.'
if column_confidence < 70 and column_confidence > 30:
column_explanation += ', it is partially because of this reason that we are only somewhat confident this prediction is correct.'
if column_confidence <= 30:
column_explanation += ', it is partially because of this reason that we aren\'t confident this prediction is correct.'
elif percentage_bucket_percentage < 12.5:
column_explanation = f'A similar value for the predicted column {pred_col} occurs a moderate amount of times in your dataset'
if column_confidence >= 70:
column_explanation += ', we are very confident this prediction is correct.'
if column_confidence < 70 and column_confidence > 30:
column_explanation += ', we are only somewhat confident this prediction is correct.'
if column_confidence <= 30:
column_explanation += ', you\'r input data might be of sub-par qualkity, since we aren\'t confident this prediction is correct.'
else:
column_explanation = f'A similar value for the predicted column {pred_col} occurs very often in your dataset'
if column_confidence >= 70:
column_explanation += ', it\'s partially because of this plaethora of examples that we can be very confident this prediction is correct.'
if column_confidence < 70 and column_confidence > 30:
column_explanation += ', still we can only be somewhat confident this prediction is correct.'
if column_confidence <= 30:
column_explanation += ', in spite of this, possibly due to poor input data quality, we aren\'t confident this prediction is correct.'
explain_predictions[pred_col] = column_explanation
else:
explain_predictions[
pred_col] = f'Column {pred_col} is of type {col_type} and thus it\'s impossible for us to make statistical inferences about it.'
explain_inputs = {}
for icol in important_cols:
if prediction_row[icol] is None or value_isnan(prediction_row[icol]):
explain_inputs[
icol] = f'The column {icol} is very important for this model to predict correctly. Since it\'s missing it\'s quite likely that the quality of this prediction is lacking because of this.'
else:
explain_inputs[
icol] = f'The value of the column {icol} played a large role in generating this prediction.'
for icol in useless_cols:
if prediction_row[icol] is None or value_isnan(prediction_row[icol]):
explain_inputs[
icol] = f'The fact that {icol} is missing is probably not very relevant for this prediction.'
else:
explain_inputs[
icol] = f'The column {icol} is probably not very relevant for this prediction.'
join_str = '\n\n*'
explanation = join_str + join_str.join(explain_predictions.values())
explanation += join_str + join_str.join(explain_inputs.values())
return explanation