include dataset generator script

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+sourceA.tar.gz filter=lfs diff=lfs merge=lfs -text

aes_enem_dataset.py

@@ -0,0 +1,520 @@
+# Copyright 2023 Andre Barbosa, Igor Caetano Silveira & The HuggingFace Datasets Authors
+#
+# 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: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import math
+import os
+import re
+
+import datasets
+import numpy as np
+import pandas as pd
+from bs4 import BeautifulSoup
+from tqdm.auto import tqdm
+
+np.random.seed(42)  # Set the seed
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+TODO
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "sourceA": "https://huggingface.co/datasets/kamel-usp/aes_enem_dataset/resolve/main/sourceA.tar.gz?download=true",
+}
+
+
+PROMPTS_TO_IGNORE = [
+    "brasileiros-tem-pessima-educacao-argumentativa-segundo-cientista",
+    "carta-convite-discutir-discriminacao-na-escola",
+    "informacao-no-rotulo-de-produtos-transgenicos",
+]
+CSV_HEADER = [
+    "id",
+    "id_prompt",
+    "title",
+    "essay",
+    "grades",
+    "general",
+    "specific",
+    "essay_year",
+]
+
+
+class AesEnemDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="sourceA", version=VERSION, description="TODO"),
+        datasets.BuilderConfig(
+            name="sourceB",
+            version=VERSION,
+            description="TODO",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "sourceA"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if (
+            self.config.name == "sourceA"
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "id_prompt": datasets.Value("string"),
+                    "essay_title": datasets.Value("string"),
+                    "essay_text": datasets.Value("string"),
+                    "grades": datasets.Sequence(datasets.Value("int16")),
+                    "essay_year": datasets.Value("int16"),
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        urls = _URLS[self.config.name]
+        extracted_files = dl_manager.download_and_extract({"sourceA": urls})
+        html_parser = self._process_html_files(extracted_files)
+        self._generate_splits(html_parser.sourceA)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        extracted_files["sourceA"], "sourceA", "train.csv"
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        extracted_files["sourceA"], "sourceA", "validation.csv"
+                    ),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        extracted_files["sourceA"], "sourceA", "test.csv"
+                    ),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _process_html_files(self, paths_dict):
+        html_parser = HTMLParser(paths_dict)
+        html_parser.parse()
+        return html_parser
+
+    def _generate_splits(self, filepath: str, train_size=0.7):
+        def map_year(year):
+            if year <= 2017:
+                return "<=2017"
+            return str(year)
+
+        def normalize_grades(grades):
+            grades = grades.strip("[]").split(", ")
+            grade_mapping = {"0.0": 0, "20": 40}
+
+            # We will remove the rows that match the criteria below
+            if any(
+                single_grade in grades
+                for single_grade in ["50", "100", "150", "0.5", "1.0", "1.5"]
+            ):
+                return None
+            # Use the mapping to transform grades, ignoring the last grade
+            mapped_grades = [
+                int(grade_mapping.get(grade_concept, grade_concept))
+                for grade_concept in grades[:-1]
+            ]
+
+            # Calculate and append the sum of the mapped grades as the last element
+            mapped_grades.append(sum(mapped_grades))
+            return mapped_grades
+
+        df = pd.read_csv(filepath)
+        df["general"] = df["general"].fillna("")
+        df["essay_year"] = df["essay_year"].astype("int")
+        df["mapped_year"] = df["essay_year"].apply(map_year)
+        df["grades"] = df["grades"].apply(normalize_grades)
+        df = df.dropna()
+        buckets = df.groupby("mapped_year")["id_prompt"].unique().to_dict()        
+        df.drop('mapped_year', axis=1, inplace=True)
+        train_set = []
+        val_set = []
+        test_set = []
+        for year, prompts in buckets.items():
+            np.random.shuffle(prompts)
+            num_prompts = len(prompts)
+
+            # All prompts go to the test if less than 3
+            if num_prompts <= 3:
+                train_set.append(df[df["id_prompt"].isin([prompts[0]])])
+                val_set.append(df[df["id_prompt"].isin([prompts[1]])])
+                test_set.append(df[df["id_prompt"].isin([prompts[2]])])
+                continue
+
+            # Determine the number of prompts for each set based on train_size and remaining prompts
+            num_train = math.floor(num_prompts * train_size)
+            num_val_test = num_prompts - num_train
+            num_val = num_val_test // 2
+            num_test = num_val_test - num_val
+
+            # Assign prompts to each set
+            train_set.append(df[df["id_prompt"].isin(prompts[:num_train])])
+            val_set.append(
+                df[df["id_prompt"].isin(prompts[num_train : (num_train + num_val)])]
+            )
+            test_set.append(
+                df[
+                    df["id_prompt"].isin(
+                        prompts[
+                            (num_train + num_val) : (num_train + num_val + num_test)
+                        ]
+                    )
+                ]
+            )
+
+        # Convert lists of groups to DataFrames
+        train_df = pd.concat(train_set)
+        val_df = pd.concat(val_set)
+        test_df = pd.concat(test_set)
+
+        # Data Validation Assertions
+        assert (
+            len(set(train_df["id_prompt"]).intersection(set(val_df["id_prompt"]))) == 0
+        ), "Overlap between train and val id_prompt"
+        assert (
+            len(set(train_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
+        ), "Overlap between train and test id_prompt"
+        assert (
+            len(set(val_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
+        ), "Overlap between val and test id_prompt"
+        dirname = os.path.dirname(filepath)
+        train_df.to_csv(f"{dirname}/train.csv", index=False)
+        val_df.to_csv(f"{dirname}/validation.csv", index=False)
+        test_df.to_csv(f"{dirname}/test.csv", index=False)
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        if self.config.name == "sourceA":
+            with open(filepath, encoding="utf-8") as csvfile:
+                next(csvfile)
+                csv_reader = csv.DictReader(csvfile, fieldnames=CSV_HEADER)
+                for i, row in enumerate(csv_reader):
+                    grades = row["grades"].strip("[]").split(", ")
+                    yield i, {
+                        "id": row["id"],
+                        "id_prompt": row["id_prompt"],
+                        "essay_title": row["title"],
+                        "essay_text": row["essay"],
+                        "grades": grades,
+                        "essay_year": row["essay_year"],
+                    }
+
+
+class HTMLParser:
+    def __init__(self, paths_dict):
+        self.paths_dict = paths_dict
+        self.sourceA = None
+        self.sourceB = None
+
+    def apply_soup(self, filepath, num):
+        # recebe uma URL, salva o HTML dessa página e retorna o soup dela
+        file = open(os.path.join(filepath, num), "r", encoding="utf8")
+        conteudo = file.read()
+        soup = BeautifulSoup(conteudo, "html.parser")
+        return soup
+
+    @staticmethod
+    def _get_title(soup):
+        title = soup.find("div", class_="container-composition")
+        if title is None:
+            title = soup.find("h1", class_="pg-color10").get_text()
+        else:
+            title = title.h2.get_text()
+        title = title.replace("\xa0", "")
+        return title
+
+    @staticmethod
+    def _get_grades(soup):
+        grades = soup.find("section", class_="results-table")
+        final_grades = []
+        if grades is not None:
+            grades = grades.find_all("span", class_="points")
+            assert len(grades) == 6, f"Missing grades: {len(grades)}"
+            for single_grade in grades:
+                grade = int(single_grade.get_text())
+                final_grades.append(grade)
+            assert final_grades[-1] == sum(
+                final_grades[:-1]
+            ), "Grading sum is not making sense"
+        else:
+            grades = soup.find("div", class_="redacoes-corrigidas pg-bordercolor7")
+            grades_sum = float(
+                soup.find("th", class_="noBorder-left").get_text().replace(",", ".")
+            )
+            grades = grades.find_all("td")[:10]
+            for idx in range(1, 10, 2):
+                grade = float(grades[idx].get_text().replace(",", "."))
+                final_grades.append(grade)
+            assert grades_sum == sum(final_grades), "Grading sum is not making sense"
+            final_grades.append(grades_sum)
+        return final_grades
+
+    @staticmethod
+    def _get_general_comment(soup):
+        def get_general_comment_aux(soup):
+            result = soup.find("article", class_="list-item c")
+            if result is not None:
+                result = result.find("div", class_="description")
+                return result.get_text()
+            else:
+                result = soup.find("p", style="margin: 0px 0px 11px;")
+                if result is not None:
+                    return result.get_text()
+                else:
+                    result = soup.find("p", style="margin: 0px;")
+                    if result is not None:
+                        return result.get_text()
+                    else:
+                        result = soup.find(
+                            "p", style="margin: 0px; text-align: justify;"
+                        )
+                        if result is not None:
+                            return result.get_text()
+                        else:
+                            return ""
+
+        text = soup.find("div", class_="text")
+        if text is not None:
+            text = text.find("p")
+            if (text is None) or (len(text.get_text()) < 2):
+                return get_general_comment_aux(soup)
+            return text.get_text()
+        else:
+            return get_general_comment_aux(soup)
+
+    @staticmethod
+    def _get_specific_comment(soup):
+        result = soup.find("div", class_="text")
+        if result is not None:
+            result = result.find_all("li")
+            cms = []
+            if result != []:
+                for item in result:
+                    text = item.get_text()
+                    if text != "\xa0":
+                        cms.append(text)
+                return cms
+            else:
+                result = soup.find("div", class_="text").find_all("p")
+                for item in result:
+                    text = item.get_text()
+                    if text != "\xa0":
+                        cms.append(text)
+                return cms
+        else:
+            result = soup.find_all("article", class_="list-item c")
+            if len(result) < 2:
+                return ["First if"]
+            result = result[1].find_all("p")
+            cms = []
+            for item in result:
+                text = item.get_text()
+                if text != "\xa0":
+                    cms.append(text)
+            return cms
+
+    @staticmethod
+    def _get_essay(soup):
+        essay = soup.find("div", class_="text-composition")
+        if essay is not None:
+            essay = essay.find_all("p")
+            for f in essay:
+                while f.find("span", style="color:#00b050") is not None:
+                    f.find("span", style="color:#00b050").decompose()
+                while f.find("span", class_="certo") is not None:
+                    f.find("span", class_="certo").decompose()
+            result = []
+            for paragraph in essay:
+                result.append(paragraph.get_text())
+            return result
+        else:
+            essay = soup.find("div", {"id": "texto"})
+            essay.find("section", class_="list-items").decompose()
+            essay = essay.find_all("p")
+            for f in essay:
+                while f.find("span", class_="certo") is not None:
+                    f.find("span", class_="certo").decompose()
+            result = []
+            for paragraph in essay:
+                result.append(paragraph.get_text())
+            return result
+
+    @staticmethod
+    def _get_essay_year(soup):
+        pattern = r"redações corrigidas - \w+/\d+"
+        first_occurrence = re.search(pattern, soup.get_text().lower())
+        matched_url = first_occurrence.group(0) if first_occurrence else None
+        year_pattern = r"\d{4}"
+        return re.search(year_pattern, matched_url).group(0)
+
+    def _clean_title(self, title):
+        smaller_index = title.find("[")
+        if smaller_index == -1:
+            return title
+        else:
+            bigger_index = title.find("]")
+            new_title = title[:smaller_index] + title[bigger_index + 1 :]
+            return self._clean_title(new_title.replace("  ", " "))
+
+    def _clean_list(self, list):
+        if list == []:
+            return []
+        else:
+            new_list = []
+            for phrase in list:
+                phrase = (
+                    phrase.replace("\xa0", "").replace(" ,", ",").replace(" .", ".")
+                )
+                while phrase.find("  ") != -1:
+                    phrase = phrase.replace("  ", " ")
+                if len(phrase) > 1:
+                    new_list.append(phrase)
+            return new_list
+
+    def parse(self):
+        for key, filepath in self.paths_dict.items():
+            full_path = os.path.join(filepath, key)
+            if key == "sourceA":
+                self.sourceA = f"{full_path}/sourceA.csv"
+            with open(
+                f"{full_path}/{key}.csv", "w", newline="", encoding="utf8"
+            ) as final_file:
+                writer = csv.writer(final_file)
+                writer.writerow(CSV_HEADER)
+                sub_folders = [
+                    name for name in os.listdir(full_path) if not name.endswith(".csv")
+                ]
+                essay_id = 0
+                essay_title = None
+                essay_text = None
+                essay_grades = None
+                general_comment = None
+                specific_comment = None
+                essay_year = None
+                for prompt_folder in tqdm(
+                    sub_folders,
+                    desc=f"Parsing HTML files from: {key}",
+                    total=len(sub_folders),
+                ):
+                    if prompt_folder in PROMPTS_TO_IGNORE:
+                        continue
+                    prompt = os.path.join(full_path, prompt_folder)
+                    prompt_essays = [name for name in os.listdir(prompt)]
+                    prompt_essays = prompt_essays[:-1]
+                    essay_year = HTMLParser._get_essay_year(
+                        self.apply_soup(prompt, "Prompt.html")
+                    )
+                    for essay in prompt_essays:
+                        soup_text = self.apply_soup(prompt, essay)
+                        if essay == "Prompt.html":
+                            continue
+                        essay_title = self._clean_title(
+                            HTMLParser._get_title(soup_text).replace(";", ",")
+                        )
+                        essay_grades = HTMLParser._get_grades(soup_text)
+                        general_comment = HTMLParser._get_general_comment(
+                            soup_text
+                        ).strip()
+                        specific_comment = HTMLParser._get_specific_comment(soup_text)
+                        if general_comment in specific_comment:
+                            specific_comment.remove(general_comment)
+                            if (len(specific_comment) > 1) and (
+                                len(specific_comment[0]) < 2
+                            ):
+                                specific_comment = specific_comment[1:]
+                        essay_text = self._clean_list(HTMLParser._get_essay(soup_text))
+                        specific_comment = self._clean_list(specific_comment)
+                        writer.writerow(
+                            [
+                                essay,
+                                prompt_folder,
+                                essay_title,
+                                essay_text,
+                                essay_grades,
+                                general_comment,
+                                specific_comment,
+                                essay_year,
+                            ]
+                        )
+                        essay_id += 1