ease/grade.py
def get_confidence_value(algorithm,model,grader_feats,score, scores):
    """
    Determines a confidence in a certain score, given proper input parameters
    algorithm- from util_functions.AlgorithmTypes
    model - a trained model
    grader_feats - a row of features used by the model for classification/regression
    score - The score assigned to the submission by a prior model
    """
    min_score=min(numpy.asarray(scores))
    max_score=max(numpy.asarray(scores))
    if algorithm == util_functions.AlgorithmTypes.classification and hasattr(model, "predict_proba"):
        #If classification, predict with probability, which gives you a matrix of confidences per score point
        raw_confidence=model.predict_proba(grader_feats)[0,(float(score)-float(min_score))]
        #TODO: Normalize confidence somehow here
        confidence=raw_confidence
    elif hasattr(model, "predict"):
        raw_confidence = model.predict(grader_feats)[0]
        confidence = max(float(raw_confidence) - math.floor(float(raw_confidence)), math.ceil(float(raw_confidence)) - float(raw_confidence))
    else:
        confidence = 0

    return confidence
def get_confidence_value(algorithm,model,grader_feats,score, scores):
    """
    Determines a confidence in a certain score, given proper input parameters
    algorithm- from util_functions.AlgorithmTypes
    model - a trained model
    grader_feats - a row of features used by the model for classification/regression
    score - The score assigned to the submission by a prior model
    """
    min_score=min(numpy.asarray(scores))
    max_score=max(numpy.asarray(scores))
    if algorithm == util_functions.AlgorithmTypes.classification and hasattr(model, "predict_proba"):
        #If classification, predict with probability, which gives you a matrix of confidences per score point
        raw_confidence=model.predict_proba(grader_feats)[0,(float(score)-float(min_score))]
        #TODO: Normalize confidence somehow here
        confidence=raw_confidence
    elif hasattr(model, "predict"):
        raw_confidence = model.predict(grader_feats)[0]
        confidence = max(float(raw_confidence) - math.floor(float(raw_confidence)), math.ceil(float(raw_confidence)) - float(raw_confidence))
    else:
        confidence = 0

    return confidence
def get_confidence_value ( algorithm , model , grader_feats , score , scores ) : min_score = min ( numpy . asarray ( scores ) ) max_score = max ( numpy . asarray ( scores ) ) if algorithm == util_functions . AlgorithmTypes . classification and hasattr ( model , "predict_proba" ) : #If classification, predict with probability, which gives you a matrix of confidences per score point raw_confidence = model . predict_proba ( grader_feats ) [ 0 , ( float ( score ) - float ( min_score ) ) ] #TODO: Normalize confidence somehow here confidence = raw_confidence elif hasattr ( model , "predict" ) : raw_confidence = model . predict ( grader_feats ) [ 0 ] confidence = max ( float ( raw_confidence ) - math . floor ( float ( raw_confidence ) ) , math . ceil ( float ( raw_confidence ) ) - float ( raw_confidence ) ) else : confidence = 0 return confidence
Determines a confidence in a certain score, given proper input parameters
    algorithm- from util_functions.AlgorithmTypes
    model - a trained model
    grader_feats - a row of features used by the model for classification/regression
    score - The score assigned to the submission by a prior model
Determines a confidence in a certain score given proper input parameters algorithm - from util_functions . AlgorithmTypes model - a trained model grader_feats - a row of features used by the model for classification / regression score - The score assigned to the submission by a prior model