Training predictions¶

Notes

Each PredictionValue dict contains these keys:

• label : describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification and multiclass projects, it is a label from the target feature.
• value : the output of the prediction. For regression projects, it is the predicted value of the target. For classification and multiclass projects, it is the predicted probability that the row belongs to the class identified by the label.

Each PredictionExplanations dictionary contains these keys:

• label (str) : describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation.
• feature (str) : the name of the feature contributing to the prediction
• feature_value (object) : the value the feature took on for this row. The type corresponds to the feature (boolean, integer, number, string)
• strength (float) : algorithm-specific explanation value attributed to feature in this row

ShapMetadata dictionary contains these keys:

• shap_remaining_total (float) : The total of SHAP values for features beyond the max_explanations. This can be identically 0 in all rows, if max_explanations is greater than the number of features and thus all features are returned.
• shap_base_value (float) : the model’s average prediction over the training data. SHAP values are deviations from the base value.
• warnings (dict or None) : SHAP values calculation warnings (e.g., additivity check failures in XGBoost models). Schema described as ShapWarnings.

ShapWarnings dictionary contains these keys:

• mismatch_row_count (int) : the count of rows for which additivity check failed
• max_normalized_mismatch (float) : the maximal relative normalized mismatch value

import datarobot as dr

# Fetch existing training predictions by their id
training_predictions = dr.TrainingPredictions.get(project_id, prediction_id)

# Iterate over predictions
for row in training_predictions.iterate_rows()
    print(row.row_id, row.prediction)

Notes

Each element in shap_warnings has the following schema:

• partition_name (str) : the partition used for the prediction record.
• value (object) : the warnings related to this partition.

The objects in value are:

• mismatch_row_count (int) : the count of rows for which additivity check failed.
• max_normalized_mismatch (float) : the maximal relative normalized mismatch value.

import datarobot as dr

# Request calculation of training predictions
training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL)
training_predictions = training_predictions_job.get_result_when_complete()
print('Training predictions {} are ready'.format(training_predictions.prediction_id))

# Iterate over actual predictions
for row in training_predictions.iterate_rows():
    print(row.row_id, row.partition_id, row.prediction)

import datarobot as dr

# Fetch all training predictions for a project
all_training_predictions = dr.TrainingPredictions.list(project_id)

# Inspect all calculated training predictions
for training_predictions in all_training_predictions:
    print(
        'Prediction {} is made for data subset "{}"'.format(
            training_predictions.prediction_id,
            training_predictions.data_subset,
        )
    )

import datarobot as dr

# Getting training predictions by id
training_predictions = dr.TrainingPredictions.get(project_id, prediction_id)

# Iterate over actual predictions
for row in training_predictions.iterate_rows():
    print(row.row_id, row.partition_id, row.prediction)