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| import sys | |
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import evaluate | |
| from evaluate.utils import infer_gradio_input_types, json_to_string_type, parse_readme, parse_test_cases | |
| # from evaluate.utils import launch_gradio_widget # using this directly is erroneous - lets fix this | |
| from fixed_precision import FixedPrecision | |
| from pathlib import Path | |
| added_description = """ | |
| See the 🤗 Space showing off how to combine various metrics here: | |
| [MarioBarbeque/CombinedEvaluationMetrics](https://huggingface.co/spaces/MarioBarbeque/CombinedEvaluationMetrics) | |
| In the specific use case of the `FixedPrecision` metric, one writes the following:\n | |
| ```python | |
| precision = FixedPrecision(average=..., zero_division=...) | |
| precision.add_batch(predictions=..., references=...) | |
| precision.compute() | |
| ```\n | |
| where the `average` parameter can be chosen to configure the way precision scores across labels are averaged. Acceptable values include `[None, 'micro', 'macro', 'weighted']` ( | |
| or `binary` if there exist only two labels). Similarly, the `zero_division` parameter "Sets the value to return when there is a zero division". Options include: | |
| {`“warn”`, `0.0`, `1.0`, `np.nan`}. Since "warn" can still result in an error, we fix to it NaN in this demo.\n | |
| """ | |
| metric = FixedPrecision() | |
| if isinstance(metric.features, list): | |
| (feature_names, feature_types) = zip(*metric.features[0].items()) | |
| else: | |
| (feature_names, feature_types) = zip(*metric.features.items()) | |
| gradio_input_types = infer_gradio_input_types(feature_types) | |
| local_path = Path(sys.path[0]) | |
| # configure these randomly using randint generator and feature names? | |
| test_case_1 = [ {"predictions":[1,2,3,4,5], "references":[1,2,5,4,3]} ] | |
| test_case_2 = [ {"predictions":[9,8,7,6,5], "references":[7,8,9,6,5]} ] | |
| # configure this based on the input type, etc. for launch_gradio_widget | |
| def compute(input_df: pd.DataFrame, method: str): | |
| metric = FixedPrecision(average=method if method != "None" else None, zero_division=np.nan) | |
| cols = [col for col in input_df.columns] | |
| predicted = [int(num) for num in input_df[cols[0]].to_list()] | |
| references = [int(num) for num in input_df[cols[1]].to_list()] | |
| metric.add_batch(predictions=predicted, references=references) | |
| outputs = metric.compute() | |
| return f"The precision score for these predictions is: \n {outputs}" | |
| space = gr.Interface( | |
| fn=compute, | |
| inputs=[ | |
| gr.Dataframe( | |
| headers=feature_names, | |
| col_count=len(feature_names), | |
| row_count=5, | |
| datatype=json_to_string_type(gradio_input_types), | |
| ), | |
| gr.Radio( | |
| ["weighted", "micro", "macro", "None", "binary"], | |
| label="Averaging Method", | |
| info="Method for averaging the precision score across labels. \n `binary` only works if you are evaluating a binary classification model." | |
| ) | |
| ], | |
| outputs=gr.Textbox(label=metric.name), | |
| description=metric.info.description + added_description, | |
| title="FixedPrecision Metric", # think about how to generalize this with the launch_gradio_widget - it seems fine as is really | |
| article=parse_readme(local_path / "README.md"), | |
| examples=[ | |
| [ | |
| parse_test_cases(test_case_1, feature_names, gradio_input_types)[0], # notice how we unpack this for when we fix launch_gradio_widget | |
| "weighted" | |
| ], | |
| [ | |
| parse_test_cases(test_case_2, feature_names, gradio_input_types)[0], | |
| "micro" | |
| ], | |
| ], | |
| cache_examples=False | |
| ) | |
| space.launch() |