d04b0d52e8789d32d71c099e904a0617756884ac,dlpy/metrics.py,,accuracy_score,#Any#Any#Any#Any#Any#,29

Before Change

    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) 

After Change

    // total number of observations
    obs_per_class = conf_mat.sum()
    tot_obs = sum(obs_per_class)
    correct_pred_class = pd.Series(np.diag(conf_mat), index=[conf_mat.index, conf_mat.columns])
    tot_correct_pred_obs = sum(correct_pred_class)

    if normalize:
        score = tot_correct_pred_obs/tot_obs
    else:
        score = tot_correct_pred_obs

    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)

Instances