6b4fac6310432e138dea049ee8b606dca5c61d50,dlpy/metrics.py,,accuracy_score,#Any#Any#Any#Any#,26

Before Change

    
    matched_colname = "matched"
    
    conn = castable.get_connection()
    
    // check whether matched_colname is already in the castable, 
    // to avoid duplication or overwrite when creating computedvars. 
    while matched_colname in castable.columns:

After Change

    """

    
    check_results = _check_inputs(y_true, y_pred, castable=castable, 
                                  return_target_dtype=False)
    y_true = check_results[0]
    y_pred = check_results[1]
    castable = check_results[2]
    conn = check_results[3]
    tmp_table_created = check_results[4]
       
    matched_colname = "matched"
    // check whether matched_colname is already in the castable, 
    // to avoid duplication or overwrite when creating computedvars. 
    while matched_colname in castable.columns:
        matched_colname = random_name(name="matched_")  
     
    castbl_params = {}
    castbl_params["computedvars"] = [{"name":matched_colname}]
    code = "if {0}={1} then {2}=1;else {2}=0".format(y_true, y_pred, matched_colname)
    castbl_params["computedvarsprogram"] = code
    castable = conn.CASTable(castable.name, **castbl_params)
    
    if normalize:
        score = castable[matched_colname].mean()
    else:
        score = castable[matched_colname].sum()
    
    if tmp_table_created:  // if tmp_table_created, tbl_name referes to the temporary table name   
        conn.retrieve("table.droptable", _messagelevel="error", name=castable.name) 
    
    return score


def confusion_matrix(y_true, y_pred, castable=None, labels=None):

Instances