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Runtime error
| from pandas import DataFrame | |
| from sklearn.metrics import f1_score, recall_score | |
| from zeno import ZenoOptions, MetricReturn, metric, distill, DistillReturn | |
| def accuracy(df, ops: ZenoOptions): | |
| if len(df) == 0: | |
| return MetricReturn(metric=0) | |
| return MetricReturn( | |
| metric=100 * (df[ops.label_column] == df[ops.output_column]).sum() / len(df) | |
| ) | |
| def recall(df, ops: ZenoOptions): | |
| rec = recall_score(df[ops.label_column], df[ops.output_column], average="macro") | |
| if type(rec) == float: | |
| return MetricReturn(metric=100 * float(rec)) | |
| else: | |
| return MetricReturn(metric=0) | |
| def f1(df, ops: ZenoOptions): | |
| f = f1_score(df[ops.label_column], df[ops.output_column], average="macro") | |
| if type(f) == float: | |
| return MetricReturn(metric=100 * f) | |
| else: | |
| return MetricReturn(metric=0) | |
| def incorrect(df: DataFrame, ops: ZenoOptions): | |
| return DistillReturn(distill_output=df[ops.label_column] != df[ops.output_column]) | |
| def output_label(df: DataFrame, ops: ZenoOptions): | |
| return DistillReturn(distill_output=df[ops.output_column]) | |