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| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """TODO: Add a description here.""" | |
| import evaluate | |
| import datasets | |
| import pandas as pd | |
| import numpy as np | |
| import torch | |
| # TODO: Add BibTeX citation | |
| _CITATION = """\ | |
| @InProceedings{huggingface:module, | |
| title = {A great new module}, | |
| authors={huggingface, Inc.}, | |
| year={2020} | |
| } | |
| """ | |
| # TODO: Add description of the module here | |
| _DESCRIPTION = """\ | |
| This new module is designed to solve this great ML task and is crafted with a lot of care. | |
| """ | |
| # TODO: Add description of the arguments of the module here | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates how good are predictions given some references, using certain scores | |
| Args: | |
| predictions: list of predictions to score. Each predictions | |
| should be a string with tokens separated by spaces. | |
| references: list of reference for each prediction. Each | |
| reference should be a string with tokens separated by spaces. | |
| Returns: | |
| accuracy: description of the first score, | |
| another_score: description of the second score, | |
| Examples: | |
| Examples should be written in doctest format, and should illustrate how | |
| to use the function. | |
| >>> my_new_module = evaluate.load("my_new_module") | |
| >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1]) | |
| >>> print(results) | |
| {'accuracy': 1.0} | |
| """ | |
| # TODO: Define external resources urls if needed | |
| BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt" | |
| class metric_tp_fp_Datasets(evaluate.Metric): | |
| """TODO: Short description of my metric.""" | |
| def _info(self): | |
| # TODO: Specifies the evaluate.EvaluationModuleInfo object | |
| return evaluate.MetricInfo( | |
| # This is the description that will appear on the metrics page. | |
| module_type="metric", | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| # This defines the format of each prediction and reference | |
| features=datasets.Features({ | |
| 'predictions': datasets.features.Sequence(datasets.Value('float32')), | |
| 'references': datasets.features.Sequence(datasets.Value('int32')), | |
| }), | |
| # Homepage of the metric for documentation | |
| homepage="http://module.homepage", | |
| # Additional links to the codebase or references | |
| codebase_urls=["http://github.com/path/to/codebase/of/new_module"], | |
| reference_urls=["http://path.to.reference.url/new_module"] | |
| ) | |
| def _download_and_prepare(self, dl_manager): | |
| """Optional: download external resources useful to compute the scores""" | |
| # TODO: Download external resources if needed | |
| pass | |
| #Prediction strategy function selector######################################## | |
| def predict(self, logits, prediction_strategy): | |
| if prediction_strategy[0] == "argmax_max": | |
| results = self.argmax_max(logits) | |
| elif prediction_strategy[0] == "softmax_threshold": | |
| results = self.softmax_threshold(logits, prediction_strategy[1]) | |
| elif prediction_strategy[0] == "softmax_topk": | |
| results = self.softmax_topk(logits, prediction_strategy[1]) | |
| elif prediction_strategy[0] == "threshold": | |
| results = self.threshold(logits, prediction_strategy[1]) | |
| elif prediction_strategy[0] == "topk": | |
| results = self.topk(logits, prediction_strategy[1]) | |
| return results | |
| #Prediction strategy functions______________________________________________ | |
| def argmax_max(self, logits): | |
| predictions = [] | |
| argmax = torch.argmax(logits, dim=-1) | |
| for prediction in argmax: | |
| predicted_indexes = [prediction.item()] | |
| predictions.append(predicted_indexes) | |
| return predictions | |
| def softmax_threshold(logits, threshold): | |
| predictions = [] | |
| softmax = torch.softmax(logits, dim=-1) | |
| for prediction in softmax: | |
| predicted_indexes =[] | |
| for index, value in enumerate(prediction): | |
| if value >= threshold: | |
| predicted_indexes.append(index) | |
| predictions.append(predicted_indexes) | |
| return predictions | |
| def softmax_topk(self, logits, topk): | |
| softmax = torch.softmax(logits, dim=-1) | |
| predictions = softmax.topk(topk).indices.tolist() | |
| return predictions | |
| def threshold(self, logits, threshold): | |
| predictions = [] | |
| for prediction in logits: | |
| predicted_indexes =[] | |
| for index, value in enumerate(prediction): | |
| if value >= threshold: | |
| predicted_indexes.append(index) | |
| predictions.append(predicted_indexes) | |
| return predictions | |
| def topk(self, logits, topk): | |
| predictions = logits.topk(topk).indices.tolist() | |
| return predictions | |
| #Builds a report with the metrics#################################################### | |
| def metrics_report(self, true_positives = "", false_positives = ""): | |
| classes = true_positives.loc[true_positives["class"] != 'total']["class"].tolist() | |
| samples = [0 for i in range(len(classes))] | |
| results = pd.DataFrame({ | |
| "class": classes, | |
| "N# of True samples": samples, | |
| "N# of False samples": samples, | |
| "True Positives": samples, | |
| "False Positives": samples, | |
| "r": samples, | |
| "p": samples, | |
| "f1": samples, | |
| "acc": samples, | |
| }) | |
| results.loc[len(results.index)] = ["total", 0, 0, 0, 0, 0, 0, 0, 0] | |
| for label in results["class"].tolist(): | |
| if label in true_positives["class"].tolist(): | |
| label_true_samples = true_positives.loc[true_positives["class"] == label, "number of samples"].iloc[0] | |
| label_true_positives = true_positives.loc[true_positives["class"] == label, "coincidence count"].iloc[0] | |
| else: | |
| label_true_samples = 0 | |
| label_true_positives = 0 | |
| if label in false_positives["class"].tolist(): | |
| label_false_samples = false_positives.loc[false_positives["class"] == label, "number of samples"].iloc[0] | |
| label_false_positives = false_positives.loc[false_positives["class"] == label, "coincidence count"].iloc[0] | |
| else: | |
| label_false_samples = 0 | |
| label_false_positives = 0 | |
| r = label_true_positives/label_true_samples | |
| p = label_true_positives/(label_true_positives+label_false_positives) | |
| f1 = 2*r*p/(r+p) | |
| acc = (label_true_positives+(label_false_samples-label_false_positives))/(label_true_samples+label_false_samples) | |
| results.loc[results["class"] == label, "N# of True samples"] = label_true_samples | |
| results.loc[results["class"] == label, "N# of False samples"] = label_false_samples | |
| results.loc[results["class"] == label, "True Positives"] = label_true_positives | |
| results.loc[results["class"] == label, "False Positives"] = label_false_positives | |
| if label != "total": | |
| results.loc[results["class"] == label, "r"] = r | |
| results.loc[results["class"] == label, "p"] = p | |
| results.loc[results["class"] == label, "f1"] = f1 | |
| results.loc[results["class"] == label, "acc"] = acc | |
| else: | |
| results.loc[results["class"] == label, "r"] = "" | |
| results.loc[results["class"] == label, "p"] = "" | |
| results.loc[results["class"] == label, "f1"] = "" | |
| results.loc[results["class"] == label, "acc"] = "" | |
| results.loc[len(results.index)] = ["", "", "", "", "Micro avg.", r , p, f1, acc] | |
| results = results.fillna(0.0) | |
| final_values = results.loc[:len(results.index)-3] | |
| results.loc[len(results.index)] = ["", "", "", "", "Macro avg.", final_values["r"].mean(), final_values["p"].mean(), final_values["f1"].mean(), final_values["acc"].mean()] | |
| return results | |
| #Computes the metric for each prediction strategy############################################## | |
| def _compute(self, predictions, references, prediction_strategies = []): | |
| """Returns the scores""" | |
| # TODO: Compute the different scores of the metric | |
| predictions = torch.from_numpy(np.array(predictions, dtype = 'float32')) | |
| classes = [] | |
| for value in references: | |
| if value[0] not in classes: | |
| classes.append(value[0]) | |
| results = {} | |
| for prediction_strategy in prediction_strategies: | |
| prediction_strategy_name = '-'.join(map(str, prediction_strategy)) | |
| results[prediction_strategy_name] = {} | |
| predicted_labels = self.predict(predictions, prediction_strategy) | |
| samples = [0 for i in range(len(classes))] | |
| TP_data = pd.DataFrame({ | |
| "class": classes, | |
| "number of samples": samples, | |
| "coincidence count": samples, | |
| }) | |
| FP_data = pd.DataFrame({ | |
| "class": classes, | |
| "number of samples": samples, | |
| "coincidence count": samples, | |
| }) | |
| for i, j in zip(predicted_labels, references): | |
| if j[1] == 0: | |
| TP_data.loc[TP_data["class"] == j[0], "number of samples"] += 1 | |
| if len(i) >> 0: | |
| if j[0] in i: | |
| TP_data.loc[TP_data["class"] == j[0], "coincidence count"] += 1 | |
| TP_data = TP_data.sort_values(by=["class"], ignore_index = True) | |
| if j[1] == 2: | |
| FP_data.loc[FP_data["class"] == j[0], "number of samples"] += 1 | |
| if len(i) >> 0: | |
| if j[0] in i: | |
| FP_data.loc[FP_data["class"] == j[0], "coincidence count"] += 1 | |
| FP_data = FP_data.sort_values(by=["class"], ignore_index = True) | |
| TP_data.loc[len(TP_data.index)] =["total", TP_data["number of samples"].sum(), TP_data["coincidence count"].sum()] | |
| FP_data.loc[len(FP_data.index)] =["total", FP_data["number of samples"].sum(), FP_data["coincidence count"].sum()] | |
| report_table = self.metrics_report( | |
| true_positives = TP_data, | |
| false_positives = FP_data | |
| ) | |
| results[prediction_strategy_name] = report_table.rename_axis(prediction_strategy_name, axis='columns') | |
| return results | |