Update snapshot

moodle_extract/extract_moodle_xml.py

+"""
+Contains functions to actually parse moodles xml question format (using `moodle_questions_dataclasses.py`).
+
+If executed as main it tests these methods, and also determines some statistics.
+"""
+from xml.etree import ElementTree
+from common_py.utils import UNREACHABLE
+from typing import Iterable, Tuple
+from itertools import product
+import moodle_questions_dataclasses as mqd
+
+class CollectDebugInfos():
+    "Just a little record keeping for debugging purposes"
+    questionTypesMetaInfo: dict
+
+    def __init__(self):
+        self.questionTypesMetaInfo = {}
+    
+    def collectData(self, question: ElementTree.Element):
+        questionType = question.attrib["type"]
+        if questionType not in self.questionTypesMetaInfo:
+            self.questionTypesMetaInfo[questionType] = {
+                "child_attrib": {},
+            }
+        debug_meta_info = self.questionTypesMetaInfo[questionType]
+        for item in question.findall("./"):
+            if item.tag not in debug_meta_info["child_attrib"]:
+                debug_meta_info["child_attrib"][item.tag] = {}
+
+def matchAndCreateQuestion(question: ElementTree.Element, schoolClassAsked: str | None):
+    common = mqd.CommonQuestionFields.createFromXml(question, schoolClassAsked)
+    match question.attrib["type"]:
+        case "multichoice":
+            return mqd.MultiChoice.createFromXml(question, common)
+        case "truefalse":
+            return mqd.TrueFalse.createFromXml(question, common)
+        case "matching":
+            return mqd.Matching.createFromXml(question, common)
+        case "ddimageortext":
+            return mqd.NotHandled.createFromXml(question, common=common, reason="Very likely to contain/require images")
+        # These questions are only present in PGM1+2
+        case "cloze":
+            return mqd.NotHandled.createFromXml(question, common=common, reason="not a closed question")
+        case "gapselect":
+            return mqd.Gapselect.createFromXml(question, common)
+        # "Move item to the right spot in the sentence", the right choice is encoded in the marker, [[i]] means the ith answer (beginning with 1) is the correct choice.
+        case "ddwtos":
+            return mqd.Ddwtos.createFromXml(question, common)
+        case _:
+            UNREACHABLE("Handle all possible question types")
+
+def extractQuestionsFromMoodleQuizXml(
+    subjectAndXmlPaths: Iterable[Tuple[str, str]]
+) -> mqd.QuestionsDataType:
+    ":param subjectAndXmlPaths: Iterator of Tuple Pairs of (subject, xml_path)"
+    questionsData: mqd.QuestionsDataType = []
+
+    debugInfo = CollectDebugInfos()
+
+    for (subject, xmlPath) in subjectAndXmlPaths:
+        for question in ElementTree.parse(xmlPath).getroot().iter("question"):
+            debugInfo.collectData(question)
+            questionType = question.attrib["type"]
+
+            # "category" questions are not actually questions it appears
+            if not question.find("name"):
+                assert questionType == "category"
+                continue
+            # adding a bunch of assertions to avoid silent failure (also in following match)
+            assert question.find("./questiontext").attrib["format"] == "html"
+            questionsData.append(matchAndCreateQuestion(question, schoolClassAsked=subject))
+    return questionsData
+
+if __name__ == "__main__":
+    subjects = ["ASV", "KI", "MLDM", "PGM_1_2"]
+    questionsData = extractQuestionsFromMoodleQuizXml(
+        map(lambda subject: (subject, f"../raw_data/hft/{subject}/quiz.moodle.xml"), subjects)
+    )
+
+    # pprint(questionsData)
+
+    questionTypes = [
+        "multichoice",
+        "truefalse",
+        "matching",
+        "ddimageortext",
+        "cloze",
+        "gapselect",
+        "ddwtos",
+    ]
+
+    print("Table")
+    for (maybeSubject, maybeType) in product([*subjects, None], [*questionTypes, None]):
+        print(f"\t{maybeSubject}, {maybeType}: {len([itm for itm in questionsData if (not maybeSubject or itm.common.schoolClassAsked == maybeSubject) and (not maybeType or itm.common.questionType == maybeType)])}\n")
+
+    questionsWithoutFiles = [itm for itm in questionsData if not itm.common.questionContainsFile]
+    print("Table")
+    for (maybeSubject, maybeType) in product([*subjects, None], [*questionTypes, None]): #"singlechoice", 
+        print(f"\t{maybeSubject}, {maybeType}: {len([itm for itm in questionsWithoutFiles if (not maybeSubject or itm.common.schoolClassAsked == maybeSubject) and (not maybeType or (itm.common.questionType == maybeType))])}\n") #and (not maybeType.endswith('choice') or (itm.singleChoice if maybeType == 'singlechoice' else not itm.singleChoice))
+        if maybeType and maybeType.endswith("choice"):
+            print(f"single: {len([itm for itm in questionsWithoutFiles if (not maybeSubject or itm.common.schoolClassAsked == maybeSubject) and (not maybeType or (itm.common.questionType == maybeType and itm.singleChoice))])}")
+            print(f"multi: {len([itm for itm in questionsWithoutFiles if (not maybeSubject or itm.common.schoolClassAsked == maybeSubject) and (not maybeType or (itm.common.questionType == maybeType and not itm.singleChoice))])}")
+    # countAll = 0
+    # countTrueFalse = 0
+    # for question in questionsData:
+    #     countAll += 1
+    #     if isinstance(question, TrueFalse):
+    #         countTrueFalse += 1
+
+    # print(countAll, countTrueFalse)
+
+    for questionData in questionsData:
+        match questionData:
+            case mqd.Matching(common=mqd.CommonQuestionFields(questionText = questionText)):
+                pass
+                # UNREACHABLE("testing matching")

moodle_extract/map_generic_question_to_str.py

+"""
+This file contains fairly simple logic that converts generic
+questions to answers as they were found in the enem dataset.
+"""
+from common_py.string_processing import cleanHtml, stripLines, combineStrIter
+from moodle_to_generic_questions import GenericQuestion, AnswerOption
+
+def mapGenericQuestionToAnswerStr(input: GenericQuestion):
+    return stripLines(combineStrIter([f"#{'T' if ans['correct'] else 'F'} {cleanHtml(ans['text'])}" for ans in input.answerOptions]))
+
+def mapGenericQuestionToQuestionStr(input: GenericQuestion):
+    return stripLines(cleanHtml(input.questionText))
+
+def mapGenericQuestionToStr(input: GenericQuestion):
+    return f"{mapGenericQuestionToQuestionStr(input)}{mapGenericQuestionToAnswerStr(input)}"
+
+if __name__=="__main__":
+    print(
+        mapGenericQuestionToStr(GenericQuestion("alaaa", "<h>headertext</h>", [AnswerOption(text="Answertext", correct=False)], "whatever"))
+    )
\ No newline at end of file

moodle_extract/moodle_questions_dataclasses.py

+"""
+This contains only dataclasses describing the questiontypes 
+in moodles xml export -- the ones that were found in my data, theres probably more.
+
+Each dataclass also contains a function that can create itself 
+when given a `xml.etree.ElementTree.Element` that corresponds to itself.
+"""
+
+from dataclasses import dataclass
+import dataclasses
+from xml.etree import ElementTree
+from math import nan
+
+@dataclass
+class CommonQuestionFields():
+    name: str
+    questionText: str
+    "This text is enriched with HTML"
+    questionContainsFile: bool
+    defaultgrade: float
+    penalty: float
+    questionType: str
+    schoolClassAsked: str | None = None
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, schoolClassAsked: str | None = None):
+        questionType = question.attrib["type"]
+        # a way around special casing for close without making other things annoying. This WOULD blow in our face if we were to use it, thankkfully, but we dont want to.
+        try:
+            defaultgrade = float(question.find("./defaultgrade").text)
+        except:
+            assert questionType == "cloze"
+            defaultgrade = nan
+        return CommonQuestionFields(
+            name = question.find("./name/text").text,
+            questionText = question.find("./questiontext/text").text,
+            questionContainsFile = question.find("./questiontext/file") is not None,
+            defaultgrade = defaultgrade,
+            penalty = float(question.find("./penalty").text),
+            schoolClassAsked = schoolClassAsked,
+            questionType=questionType,
+        )
+
+@dataclass
+class MultiChoice():
+    @dataclass
+    class Answer():
+        correctFract: float | None
+        "None is the answer is wrong, float if the answer is correct and contributes its value of the total points"
+        text: str
+
+    singleChoice: bool
+    common: CommonQuestionFields
+    answers: list[Answer]
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, common: CommonQuestionFields):
+        # AFAICT multichoice questions mark correct answers with a fraction != 0, and theres no other marker
+        answers = question.findall("./answer")
+        for ans in answers:
+            assert ans.attrib["format"] == "html"
+            assert ans.find("file") is None
+        answers = [
+            (float(ans.attrib["fraction"]) / 100, ans.find("text").text)
+            for ans in answers
+        ]
+        return MultiChoice(
+            common=common, 
+            # found 2 questions where there was only one correct answer but it was not marked as single question
+            # Originally these were also considered single choice questions, and added with
+            #  or sum([frac == 1 for (frac, _) in answers]) == 1
+            # However, on reconsideration this was not correct.
+            # The KNOWLEDGE that a question only has a single true answer can strongly influence performance.
+            singleChoice=question.find("./single").text == "true",
+            answers= [
+                MultiChoice.Answer(
+                    correctFract= float(ans[0]) if (int(ans[0]) > 0) else None,
+                    text=str(ans[1]),
+                )
+                for ans in answers
+            ]
+        )
+
+
+@dataclass
+class TrueFalse():
+    @dataclass
+    class Answer():
+        correct: bool
+        value: bool
+
+    common: CommonQuestionFields
+    answers: list[Answer]
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, common: CommonQuestionFields):
+        answers = question.findall("./answer")
+        for ans in answers:
+            assert ans.attrib["format"] == "moodle_auto_format"
+            assert ans.find("file") is None
+        answers = [
+            (float(ans.attrib["fraction"]) / 100, ans.find("text").text)
+            for ans in answers
+        ]
+        return TrueFalse(
+            common=common, 
+            answers= [
+                TrueFalse.Answer(
+                    correct = fract > 0.5,
+                    value = text == "true",                            
+                )
+                for (fract, text) in answers
+            ]
+        )
+
+
+@dataclass
+class Matching():
+    @dataclass
+    class SubQuestion():
+        text: str
+        answer: str
+    
+    common: CommonQuestionFields
+    subQuestions: list[SubQuestion]
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, common: CommonQuestionFields):
+        subquestions = question.findall("./subquestion")
+        for subq in subquestions: 
+            assert subq.attrib["format"] == "html"
+            assert subq.find("file") is None
+        return Matching(
+            common=common,
+            subQuestions=[
+                Matching.SubQuestion(text = subq.find("./text").text, answer = subq.find("./answer/text").text) 
+                for subq in subquestions
+            ],
+        )
+
+@dataclass
+class Ddwtos():
+    """
+    `common.questionsText` contains numbered fields `[[i]]`,
+    where i indicates the item to insert in that spot
+    """
+    @dataclass
+    class Dragbox():
+        text: str
+        position: int
+    
+    common: CommonQuestionFields
+    dragboxes: list[Dragbox]
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, common: CommonQuestionFields):
+        dragboxes = question.findall("./dragbox")
+        for item in dragboxes: 
+            assert item.find("file") is None
+        return Ddwtos(
+            common=common,
+            dragboxes=[
+                Ddwtos.Dragbox(
+                    text = item.find("./text").text,
+                    position = idx,
+                )
+                for (idx, item) in enumerate(dragboxes)
+            ],
+        )
+
+@dataclass
+class Gapselect():
+    """
+    `common.questionsText` contains numbered fields `[[i]]`,
+    where i indicates the item to select for that spot
+    """
+    @dataclass
+    class SelectOption():
+        text: str
+        position: int
+    
+    common: CommonQuestionFields
+    selectOptions: list[SelectOption]
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, common: CommonQuestionFields):
+        selectOption = question.findall("./selectoption")
+        for item in selectOption: 
+            assert item.find("file") is None
+        return Gapselect(
+            common=common,
+            selectOptions=[
+                Gapselect.SelectOption(
+                    text = item.find("./text").text,
+                    position = idx,
+                )
+                for (idx, item) in enumerate(selectOption)
+            ],
+        )
+
+@dataclass
+class NotHandled():
+    name: str
+    reason: str
+    xml: ElementTree.Element
+    common: CommonQuestionFields | None
+
+    @staticmethod
+    def createFromXml(question: ElementTree.Element, reason: str, common: CommonQuestionFields | None = None):
+        return NotHandled(
+            name = question.find("./name/text").text, 
+            xml = question,
+            reason = reason,
+            common = common,
+        )
+
+QuestionsDataType = list[MultiChoice | Matching | TrueFalse | Matching | Gapselect | Ddwtos | NotHandled]

moodle_extract/moodle_to_generic_questions.py

+"""
+Contains functions to convert the multliple variants of moodle question types to a more generic one.
+This makes a few choices in how these are mapped!
+"""
+from dataclasses import dataclass
+import moodle_questions_dataclasses as mdt
+from itertools import product
+from common_py.utils import UNREACHABLE
+from pprint import pprint
+from typing import TypedDict
+
+# polars doesnt like lists of dataclasses as elements, 
+# but for earlier processing TypedDict is uncomfortable,
+# as one cant do type-based match on that
+# So I end up using both, oh well.
+class AnswerOption(TypedDict):
+    text: str
+    "Enriched with HTML"
+    # recievedPointsFract: float
+    correct: bool
+
+@dataclass
+class GenericQuestion():
+    name: str
+    "This is the name from the moodle XML export, not the csv"
+    questionText: str
+    "Enriched with HTML"
+    answerOptions: list[AnswerOption]
+    questionType: str
+    "The original question type (to allow filtering in case some types are suspected to be problematic)"
+
+def mapToGenericQuestionFormat(
+    input: mdt.QuestionsDataType,
+):
+    trueMultichoiceOptions = []
+    matchingOptions = []
+    skippedBecauseImage = []
+    skippedForAnotherReason = []
+    output: list[GenericQuestion] = []
+    for elem in input:
+        meta = elem.common
+        match elem:
+            case mdt.NotHandled(name, _):
+                skippedForAnotherReason.append(name)
+            # Skip questions which contain an image (assumes that all types contain meta as first field)
+            case elem if elem.common.questionContainsFile:
+                skippedBecauseImage +=[meta.name]
+                continue
+            case mdt.MultiChoice(answers = answersSrc, singleChoice = singleChoice):
+                answers: list[AnswerOption] = []
+                if singleChoice:
+                    answers.extend([
+                        AnswerOption(
+                            text = ans.text,
+                            # recievedPointsFract=0.0 if ans.correctFract is None else 1.0,
+                            correct=False if ans.correctFract is None else True,
+                        ) for ans in answersSrc
+                    ])
+                else:
+                    trueMultichoiceOptions += [len(answersSrc)]
+                    selections = answersSrc
+                    answers.extend([
+                        AnswerOption(
+                            text = ans.text,
+                            correct = False if ans.correctFract is None else True,
+                            # recievedPointsFract=0,
+                        ) for ans in selections
+                    ])
+                output.append(GenericQuestion(
+                    name=meta.name,
+                    questionText=meta.questionText,
+                    answerOptions=answers,
+                    questionType= "singlechoice" if singleChoice else meta.questionType,
+                ))
+            case mdt.TrueFalse(answers = answersSrc):
+                answers = [
+                    AnswerOption(
+                        text = "Die Aussage stimmt" if ans.correct else "Die Aussage stimmt nicht",
+                        # recievedPointsFract=1.0 if ans.correct else 0.0,
+                        correct = ans.correct,
+                    ) for ans in answersSrc
+                ]
+                output.append(GenericQuestion(
+                    name=meta.name,
+                    questionText=meta.questionText,
+                    answerOptions=answers,
+                    questionType=meta.questionType,
+                ))
+            case mdt.Matching(subQuestions = subQuestionsSrc):
+                answers: list[AnswerOption] = []
+                for ((subqIdx, subquestion), (ansIdx, answer)) in product(
+                    enumerate([ans.text for ans in subQuestionsSrc]), 
+                    enumerate([ans.answer for ans in subQuestionsSrc])
+                ):
+                    answers.append(AnswerOption(
+                        text = f"{answer} passt zu {subquestion}",
+                        correct = subqIdx == ansIdx,
+                    ))
+                # for matchedIndexes in permutations(range(len(subQuestionsSrc))):
+                #     answerTextParts = [
+                #         f"{subQuestionsSrc[answerIdx].answer} passt zu {subQuestionsSrc[textIdx].text}" 
+                #         for (textIdx, answerIdx) in enumerate(matchedIndexes)
+                #     ]
+                #     answerPoints = meta.defaultgrade - sum([
+                #         textIdx != answerIdx
+                #         for (textIdx, answerIdx) in enumerate(matchedIndexes)
+                #     ]) * meta.penalty
+                #     answers.append(AnswerOption(
+                #         text = reduce(lambda rhs, lhs: f"{rhs}, {lhs}", answerTextParts[1:], answerTextParts[0]),
+                #         recievedPointsFract=float(answerPoints) / meta.defaultgrade,
+                #     ))
+                matchingOptions += [len(answers)]
+                output.append(GenericQuestion(
+                    name = meta.name,
+                    questionText=meta.questionText,
+                    answerOptions= answers,
+                    questionType=meta.questionType,
+                ))
+            case mdt.Ddwtos(dragboxes=dragBoxesSrc, common=mdt.CommonQuestionFields(name, _)):
+                skippedForAnotherReason.append(name)
+                # I wont put in that work as this is unlikely to work,
+                # considering that the generated text will be too long
+                continue
+            case mdt.Gapselect(selectOptions = selectOptionsSrc, common=mdt.CommonQuestionFields(name, _)):
+                skippedForAnotherReason.append(name)
+                # I wont put in that work as this is unlikely to work,
+                # considering that the generated text will be too long
+                continue
+            case _:
+                UNREACHABLE("Please be exhaustive (explicitly skip ignored stuff)")
+    print(f"""
+    #options true multichoice" {trueMultichoiceOptions}
+    #options match (also true multichoice) {matchingOptions}
+    #skipped because of pictures {len(skippedBecauseImage)}
+    #skipped because of other reason {len(skippedForAnotherReason)}
+    """)
+    return output
+
+if __name__ == "__main__":
+    from extract_moodle_xml import extractQuestionsFromMoodleQuizXml
+
+    pprint(
+    len(mapToGenericQuestionFormat(extractQuestionsFromMoodleQuizXml(
+        map(lambda subject: (subject, f"../raw_data/hft/{subject}/quiz.moodle.xml"), ["ASV", "KI", "MLDM", "PGM_1_2"])
+    )))
+    )
+
+# FIXME: Determine typical context length for BERT etc, maybe some of the answer serializations are not possible as they result in too much text
+# Alternative would be some other encoding, but its doubtful whether the LLM can learn that encoding with out dataset.
+# EG keeping the [[1]] fields in [[2]] the sentence and providing an order for answers
+# Is there some sparse encoding example in typical pretraining data/tasks?
+
+# TrueFalse: Prepend with "Ist der foldende Satz wahr oder falsch?", use these as variants?
+# Matching: subq.text " passt zu " subq.answer
+# DDWTOS: "Welcher der folgenden sätze ist korrekt?", options with text filled in
+
+# FIXME: Kein true multiple choice in ENEM (Regex /#T((.*?)\n #){1,8}T/, getested an einem gefaktem positive)
+# FIXME: Enthält als einziges itemize|array|footnotesize umgebungen (keine tables)
\ No newline at end of file

moodle_extract/poetry.lock

+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.12.2"
+description = "Screen-scraping library"
+category = "main"
+optional = false
+python-versions = ">=3.6.0"
+files = [
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+    {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"},
+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
+[[package]]
+name = "common-py"
+version = "0.1.0"
+description = ""
+category = "main"
+optional = false
+python-versions = ">=3.10"
+files = []
+develop = true
+
+[package.dependencies]
+beautifulsoup4 = "^4.12.2"
+lxml = "^4.9.2"
+matplotlib = "^3.7.1"
+numpy = ">=1.22,<1.24"
+
+[package.source]
+type = "directory"
+url = "../common_py"
+
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+category = "main"
+optional = false
+python-versions = ">=3.8"
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moodle_extract/pyproject.toml

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qde_model_code/.gitignore

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+hash\[*
\ No newline at end of file

qde_model_code/README.md

+# The model creation and Tests
+
+This repo contains the code that trains on the data created in the other projects, end most of the evaluation of the model too.
+
+As such it it has multiple entrypoints.
+The model creation and the creation of the csv files is done on the cluster, drawing plots etc should be done locally.
+
+## Cluster execution
+Code that executes on the cluster should always start in [its single entrypoint](./cluster_entry.py), and also consider the docuumentation at the start of the file.
+
+To execute the code onto the cluster, either use or inspect the [corresponding shell script](./cluster_push_and_queue.sh). Please refer to the corresponding file in [enem aggregation](../enem_aggregate/execute_cluster.sh) and its [README](../enem_aggregate/README.md) for more detail on how this file works.
+
+You probably want to change `EXEC_DIR` in that shell script, if you want to iterate, so that the results are added to a new directory.
+
+The execution directory will contain the code that generated the results as a nice side effect, that way you can just refer to the code alongside the results if youre no longer certain how these results were generated (if you were clean and always made a new `EXEC_DIR`).
\ No newline at end of file

qde_model_code/analyze_metapar_variations_enem.py

+import numpy as np
+import polars as pl
+from typing import NamedTuple
+from numpy import typing as npt
+from common_py.plotting import *
+from common_py.utils import Statistics
+import re
+import os
+import json
+
+if __name__ == '__main__':
+    analyzedDir = "./results/clean/"
+
+    varied_variable = "learning_rate"
+    varied_variable_pretty = "Learning rate for the combined model"
+
+    train_correlation_coefficient: list[float] = []
+    valid_correlation_coefficient: list[float] = []
+    train_rmse: list[float] = []
+    varied_variable_vals = []
+
+    for modelConfigIdentifier in [
+        match.group(1) 
+        for match
+        in [
+            re.match(r"(hash\[[\d\w-]*\])_config.json", fileName)
+            for fileName
+            in os.listdir(analyzedDir)
+        ]
+        if match
+    ]:
+        basePath = f"{analyzedDir}/{modelConfigIdentifier}"
+        with open(f"{basePath}_config.json") as fp:
+            config = json.load(fp)
+        history = pl.read_csv(f"{basePath}_history.csv")
+        train_loss_sorted = np.sort(history['loss'])
+        val_loss_sorted = np.sort(history['val_loss'])
+
+        target = config["target"]
+
+        varied_variable_vals.append(config["training"][varied_variable])
+
+        target_sqErr_col = f"{target}_err_squared"
+        target_absErr_col = f"{target}_err_abs"
+
+        predictions: pl.DataFrame = (pl.read_csv(f"{basePath}_predictions.csv", dtypes={"split": pl.Categorical})
+            .with_columns(
+            (pl.col(target) - pl.col(f"predicted_{target}")).abs()
+                .alias(target_absErr_col),
+            (pl.col(target) - pl.col(f"predicted_{target}")).pow(2)
+                .alias(target_sqErr_col)
+            )
+        )
+        predictions_valid: pl.DataFrame = (predictions
+            .filter(pl.col("split") == "validate")
+        )
+        predictions_train: pl.DataFrame = (predictions
+            .filter(pl.col("split") == "train")
+        )
+        stat_sqErr_train = Statistics.from_numpy(predictions_train[target_sqErr_col].to_numpy())
+        stat_sqErr_valid = Statistics.from_numpy(predictions_train[target_sqErr_col].to_numpy())
+        train_rmse.append(
+            np.sqrt(stat_sqErr_train.mean)
+        )
+        train_correlation_coefficient.append(
+            np.corrcoef(predictions_train[target].to_numpy(), predictions_train[f"predicted_{target}"].to_numpy())[0,1]
+        )
+        valid_correlation_coefficient.append(
+            np.corrcoef(predictions_valid[target].to_numpy(), predictions_valid[f"predicted_{target}"].to_numpy())[0,1]
+        )
+
+    PlotData(
+        [
+            Plot(train_correlation_coefficient, varied_variable_vals, kind="scatter", label="training data"),
+            Plot(valid_correlation_coefficient, varied_variable_vals, kind="scatter", label="validation data"),
+        ],
+        x_ax=AxData(varied_variable_pretty),
+        y_ax=AxData("Linear correlation coefficient"),
+        save_path=f"{analyzedDir}/correlation_variance.pdf",
+    ).create().draw_and_save()
\ No newline at end of file

qde_model_code/analyze_metapar_variations_hft.py

+"TODO: Not a plot, not the right file"
+
+import numpy as np
+import polars as pl
+from typing import NamedTuple
+from numpy import typing as npt
+from common_py.plotting import *
+from common_py.utils import Statistics
+import re
+import os
+import json
+
+if __name__ == '__main__':
+    analyzedDir = "./results/containerbased_hftv15"
+
+    varied_variable = "seed"
+    varied_variable_pretty = varied_variable
+
+    correlation_coefficient: list[float] = []
+    rmse: list[float] = []
+    varied_variable_vals = []
+
+    for modelConfigIdentifier in [
+        match.group(1) 
+        for match
+        in [
+            re.match(r"(hash\[[\d\w-]*\])_config.json", fileName)
+            for fileName
+            in os.listdir(analyzedDir)
+        ]
+        if match
+    ]:
+        basePath = f"{analyzedDir}/{modelConfigIdentifier}"
+        with open(f"{basePath}_config.json") as fp:
+            config = json.load(fp)
+        history = pl.read_csv(f"{basePath}_history.csv")
+
+        target = config["target"]
+
+        varied_variable_vals.append(config[varied_variable])
+
+        target_sqErr_col = f"{target}_err_squared"
+        target_absErr_col = f"{target}_err_abs"
+
+        predictions: pl.DataFrame = (pl.read_csv(f"{basePath}_predictions.csv", dtypes={"split": pl.Categorical})
+            .with_columns(
+            (pl.col(target) - pl.col(f"predicted_{target}")).abs()
+                .alias(target_absErr_col),
+            (pl.col(target) - pl.col(f"predicted_{target}")).pow(2)
+                .alias(target_sqErr_col)
+            )
+        )
+        stat_sqErr = Statistics.from_numpy(predictions[target_sqErr_col].to_numpy())
+        rmse.append(
+            np.sqrt(stat_sqErr.mean)
+        )
+        correlation_coefficient.append(
+            np.corrcoef(predictions[target].to_numpy(), predictions[f"predicted_{target}"].to_numpy())[0,1]
+        )
+
+    PlotData(
+        [
+            Plot(correlation_coefficient, varied_variable_vals, kind="scatter", label="correlation over levt out data points"),
+        ],
+        x_ax=AxData(varied_variable_pretty),
+        y_ax=AxData("Linear correlation coefficient"),
+        save_path=f"{analyzedDir}/correlation_variance.pdf",
+    ).create().draw_and_save()
\ No newline at end of file

qde_model_code/cluster_entry.py

+"""
+Cluster Entrypoint. 
+Ensure that this only refers to dependencies listed in `pyproject.toml/tool.poetry.dependencies` (implicit main group)
+As well as `pyproject.toml/tool.poetry.group.containerprovided.dependencies`.
+This includes indirect imports!
+
+In particular I dont copy the common_py package on the cluster.
+
+Also note the stuff in comments in the toml file.
+"""
+from training_enem import enem_training
+from training_hft import hft_training
+
+if __name__ == '__main__':
+    for seed in [
+        1,
+        # DEV:
+        2,
+        3,
+    ]:
+        # hft_training(seed)
+        enem_training(seed)
\ No newline at end of file

qde_model_code/cluster_push_and_queue.sh

+#!/bin/sh
+
+set -o errexit
+
+AUTHENTICATED_SSH_HOST="uniClusterIp"
+# Authenticated ssh Host.
+# eg. set (this also ought to work on windows with git bash + per default preinstalled openssh):
+# -------------------
+# # ~/.ssh/config
+# Host uniCluster
+#         HostName bwunicluster.scc.kit.edu
+#         User <YOUR_LOGIN>
+# 
+# Host *
+#         # reuse existing ssh connections (see man ssh_config)
+#         # creates a file according to ControlPath for every (now shared) session
+#         ControlMaster auto
+#         ControlPath ~/.ssh/ssh_mux_%h_%p_%r
+# ----------------
+# and then login once, and keep that connection while executing this.
+EXEC_DIR="enem/eval_paramv3" #(path relative to home)
+# EXEC_DIR="hft_reset_model/lotsof_epochs_mcq_no_retries" #(path relative to home)
+
+# print each (following) command as it gets executed (except commands from an ssh script, didnt find a way to do that at all)
+set -o xtrace
+
+poetry run python create_server_files.py
+
+# (EXEC_DIR gets expanded on the client - the computer that executes this - on purpose)
+ssh ${AUTHENTICATED_SSH_HOST} "mkdir -p ${EXEC_DIR}"
+
+scp -r sources ./*.py train_strategy slurm.sh ${AUTHENTICATED_SSH_HOST}:${EXEC_DIR}/
+
+# The cluster doesnt have poetry, but requirements.txt generated from poetry should suffice
+# poetry also generates an venv by default, which we dont want in all circumstances on the server
+# (eg when we already use a container, I've seen venv interfering with GPU support)
+poetry export --only=main > requirements.txt
+scp requirements.txt ${AUTHENTICATED_SSH_HOST}:${EXEC_DIR}/
+rm requirements.txt
+
+ssh ${AUTHENTICATED_SSH_HOST} <<EOF_SSH
+set -o errexit
+
+cd ${EXEC_DIR}
+sbatch slurm.sh
+exit
+EOF_SSH
\ No newline at end of file

qde_model_code/config.py

+"""
+These configs serve the purpose of being easily serializable into a json file for record keeping,
+as well as being hashable to be able to easily correlate generated files with the config used to generate it
+"""
+
+from dataclasses import dataclass
+import dataclasses
+from typing import Literal
+
+# frozen=True enables a structural hash implementation
+@dataclass(frozen=True)
+class TrainingConfig:
+    batch_size: int = 32
+    epochs: int = 25
+    # earlystop_patience: int = 5
+    earlystop_patience: int = 10
+    learning_rate: float = 1e-4
+    dropout_pretrained: float = 0
+    dropout_regression: float = 0.1
+    freeze_base: bool = False
+    training_kind: Literal["train_validate"] | Literal["leave_one_out"] = "train_validate"
+    "this does nothing atm"
+
+@dataclass(frozen=True)
+class ModelConfig:
+    huggingface_name: str
+    context_length: int = 512
+
+# @dataclass(frozen=True)
+# class OtherConfig:
+#     seed: int = 1
+#     """
+#     This doesnt get applied the dataset split on purpose.
+#     If the seed also influences the split, then we may select for the split instead of the actual model
+#     """
+
+@dataclass(frozen=True)
+class PresetConfig:
+    model: ModelConfig
+    target: str = "correctness"
+    # default values will usually be assigned ONCE per dataclass,
+    # which would mean that all classes share the same training instance.
+    # Because of this issue we use dataclasses.field
+    training: TrainingConfig = dataclasses.field(default_factory=TrainingConfig)
+
+@dataclass(frozen=True)
+class AllConfig:
+    model: ModelConfig
+    training: TrainingConfig = dataclasses.field(default_factory=TrainingConfig)
+    target: str = "correctness"
+    # meta: OtherConfig  = dataclasses.field(default_factory=OtherConfig)
+    seed: int = 1
+    """
+    This doesnt get applied the dataset split on purpose.
+    If the seed also influences the split, 
+    then we may select for the split instead of the model config,
+    meaning we taint the split-off test data
+    """

qde_model_code/create_server_files.py

+import polars as pl
+from functools import reduce
+import re
+from common_py.utils import deprecated
+from common_py.string_processing import cleanHtml, combineStrIter, stripLines
+
+@deprecated("""
+Keeping this for posterity.
+Matching URLs in the Marinho ENEM texts via regex is not an easy task,
+think twice if you want to archive it.
+
+The much easier approach that gets you most of the way is to either manually remove them all,
+or apply undermatching regular excaptions manually piece by piece.
+
+Do ensure that these are actually undermatching,
+and dont remove more text in a few border cases, this was often the case for me.
+""")
+def removeUrls(input: str):
+    # the regex below overmatches some lines
+    # r"Disponivel em : .+? \d\d\d\d (\( adaptado \))?"
+    # ENEM data from Marinho contains URLs, but they are also separated with whitespace, which means regular Regexes for URLs dont work.
+    # also, trying to expand regular regex to match this will end up overmatching
+    # this us damned hopeless. I always find further things that make it hard and dont get removed or require overmatching some required text. Might as well give up.
+    return re.compile(r"https? :? ?/ / ([\w\d]+ \. )+(net|br|com|uk)", re.MULTILINE).sub("url", input)
+
+def removeEnemSignet(input: str):
+    return re.compile(r"\( ENEM (- 2021 )?\)").sub("", input)
+
+def cleanText(input: str, doCleanHtml: bool = False):
+    lineStripped = removeEnemSignet(stripLines(input))
+    return lineStripped if not doCleanHtml else cleanHtml(lineStripped)
+
+def isEnemAnswerLine(arg: str):
+    return arg.startswith(" #F") or arg.startswith(" #T")
+
+def onlyQuestionTextEnem(input: str):
+    return combineStrIter(filter(lambda arg: not isEnemAnswerLine(arg), input.splitlines(keepends=True)))
+
+def onlyAnswerText(input: str, doCleanHtml: bool = False):
+    return combineStrIter(filter(isEnemAnswerLine, input.splitlines(keepends=True)))
+
+def loadEnem(input: pl.DataFrame, num: int | None = None):
+    difficulty_col = "irt_b_marinho"
+    max_irt = input[difficulty_col].max()
+    return (pl
+        .DataFrame([
+            input["question_id"],
+            input["topic"],
+            input["correctness"],
+            input["anulled"],
+            input["qname"],
+            input[difficulty_col].apply(lambda f: f / max_irt).alias("irt_b_marinho_maxnorm") ,
+            input["question_and_answer_text"].apply(cleanText),
+            input["question_and_answer_text"].apply(onlyQuestionTextEnem).apply(cleanText).alias("question_text"),
+            input["question_and_answer_text"].apply(onlyAnswerText).apply(cleanText).alias("answer_text"),
+        ])
+        .filter(pl.col("topic") != "LC")
+        # .filter(pl.col("topic") == "MT")
+        .filter(pl.col("anulled") == 0)
+        .with_columns(
+            # just a little explainer in the file...
+            pl.lit(None).alias(f"irt_b_marinho_maxnorm={difficulty_col}/{max_irt}"),
+        )
+        .unique(subset="question_and_answer_text", keep='any')
+        # .sample(num, seed=123)
+        .sort(pl.col("qname"))
+    )
+
+def loadHft(input: pl.DataFrame):
+    return (input
+        # if you want to update the way answers are formatted, you MUST do so in `moodle_export`, and execute the main entry point afterward
+        .filter(
+            (pl.col("questionType").is_in(pl.Series([
+                # "singlechoice", 
+                "multichoice", 
+                # "truefalse",
+                # "matching", # these and the others below do not have answers if you didnt change the logic!
+                # TODO: actually add these questiontypes, ATM theyre straight up ignored before we get to this repo.
+                # "gapselect",
+                # "ddwtos",
+            ]))) 
+            & (pl.col("retries_allowed") == False)
+        )
+    )
+
+if __name__ == "__main__":
+    hft_dataset = loadHft(
+        pl.read_csv("../moodle_extract/GENERATED/result.csv")
+    )
+
+    loadEnem(
+        pl.read_csv("../enem_aggregate/GENERATED/localGen_result.csv"), 
+        # num=len(hft_dataset),
+    ).write_csv(
+        "./sources/enem_prepared.csv"
+    )
+
+    hft_dataset.write_csv(
+        "./sources/hft_prepared.csv"
+    )
+
+    # loadEnem(
+    #     ingestData("../raw_data/brasilian/with_corresponding/enem2.csv")
+    # ).write_csv(
+    #     "./sources/enem_text_difficulty.csv"
+    # )
\ No newline at end of file

qde_model_code/detect_best_config_overall_enem.py

+"have fun trying to understand the choices in here, i dont have time to document it properly"
+
+import numpy as np
+import polars as pl
+from typing import NamedTuple
+from numpy import typing as npt
+from common_py.plotting import *
+from common_py.utils import Statistics
+import re
+import os
+import json
+import math
+
+if __name__ == '__main__':
+    analyzedTopDir = "./results/clean"
+
+    configIdents: list[str] = []
+    configPaths: list[str] = []
+    configStr: list[str] = []
+    valid_correlation_coefficient: list[float] = []
+    train_correlation_coefficient: list[float] = []
+    test_correlation_coefficient: list[float] = []
+    combined_correlation_coefficient: list[float] = []
+
+    for (path, _, filenames) in os.walk(analyzedTopDir):
+        for modelConfigIdentifier in [
+            match.group(1) 
+            for match
+            in [
+                re.match(r"(hash\[[\d\w-]*\])_config.json", fileName)
+                for fileName
+                in filenames
+            ]
+            if match
+        ]:
+            try:
+                basePath = f"{path}/{modelConfigIdentifier}"
+                # kindof problematic but for now it works
+                if "hft" in basePath:
+                    continue
+
+                with open(f"{basePath}_config.json") as fp:
+                    config = json.load(fp)
+                history = pl.read_csv(f"{basePath}_history.csv")
+
+                target = config["target"]
+
+                target_sqErr_col = f"{target}_err_squared"
+                target_absErr_col = f"{target}_err_abs"
+
+                predictions: pl.DataFrame = (pl.read_csv(f"{basePath}_predictions.csv", dtypes={"split": pl.Categorical})
+                    .with_columns(
+                    (pl.col(target) - pl.col(f"predicted_{target}")).abs()
+                        .alias(target_absErr_col),
+                    (pl.col(target) - pl.col(f"predicted_{target}")).pow(2)
+                        .alias(target_sqErr_col)
+                    )
+                )
+                predictions_valid: pl.DataFrame = (predictions
+                    .filter(pl.col("split") == "validate")
+                )
+                predictions_train: pl.DataFrame = (predictions
+                    .filter(pl.col("split") == "train")
+                )
+                predictions_test: pl.DataFrame = (predictions
+                    .filter(pl.col("split") == "test")
+                )
+                valid_correlation_coeff_val = np.corrcoef(predictions_valid[target].to_numpy(), predictions_valid[f"predicted_{target}"].to_numpy())[0,1]
+                train_correlation_coeff_val = np.corrcoef(predictions_train[target].to_numpy(), predictions_train[f"predicted_{target}"].to_numpy())[0,1]
+                test_correlation_coeff_val = np.corrcoef(predictions_test[target].to_numpy(), predictions_test[f"predicted_{target}"].to_numpy())[0,1]
+                combined_correlation_coeff_val = math.pow(valid_correlation_coeff_val * valid_correlation_coeff_val * train_correlation_coeff_val, 1.0/3)
+                # combined_correlation_coeff_val = (2 * valid_correlation_coeff_val + train_correlation_coeff_val) / 3
+                
+                # any of the above operations might fail, for a multitude of reasons but usually this indicates problematic data
+                # (eg when the pipeline stopped early, leading to "missing" exected files) or at the very least not a good result
+                # (eg a non-convergence that is largely flat, but actually results in a negative correlation, which in turn leads to errors when doing a root during geometric mean calculation), 
+                # so we just skip it.
+                # But because of that I moved all the append operations to the end, so we dont include partial data, which would lead to us combining the wrong values
+                configPaths.append(basePath)
+                configIdents.append(modelConfigIdentifier)
+                configStr.append(str(config))
+                valid_correlation_coefficient.append(
+                    valid_correlation_coeff_val
+                )
+                train_correlation_coefficient.append(
+                    train_correlation_coeff_val
+                )
+                combined_correlation_coefficient.append(
+                    combined_correlation_coeff_val
+                )
+                test_correlation_coefficient.append(
+                    test_correlation_coeff_val
+                )
+            except Exception as ex:
+                print(ex)
+                continue
+
+    df = pl.DataFrame({
+        "combined_corr_coeff": pl.Series(combined_correlation_coefficient),
+        "valid_corr_coeff": pl.Series(valid_correlation_coefficient),
+        "train_corr_coeff": pl.Series(train_correlation_coefficient),
+        "test_corr_coeff": pl.Series(test_correlation_coefficient),
+        "ident": pl.Series(configIdents),
+        "paths": pl.Series(configPaths),
+        "config": pl.Series(configStr),
+    })
+
+    correlation_correlation = np.corrcoef(df["valid_corr_coeff"], df["train_corr_coeff"])[0,1]
+    ";-)"
+
+    print(f"correlation of training vs validation coefficient: {correlation_correlation}")
+
+    df: pl.DataFrame = df.sort("test_corr_coeff", descending=True)
+
+    print(df.head())
+    print(df.tail())
+
+    df.write_csv(f"{analyzedTopDir}/ranked.csv")

qde_model_code/get_package_list.sh

+#!/bin/sh
+#SBATCH --partition=dev_gpu_4_a100
+#SBATCH --output=pip_list_report.log
+#SBATCH --gres=gpu:1
+#SBATCH --cpus-per-task=1
+
+enroot start -m "${PWD}:/workspace" nv_tensor_container_fresh sh <<EOF
+pip list
+EOF

qde_model_code/model.py

+from typing import Iterable, TypedDict
+from transformers import (
+    AutoTokenizer,
+    TFAutoModel,
+    PreTrainedTokenizerBase,
+)
+from transformers.tokenization_utils import (
+    TruncationStrategy,
+    PaddingStrategy,
+)
+import config
+import numpy as np
+import tensorflow as tf
+import os
+
+def create_tf_model(
+    huggingface_model: str,
+    max_length: int,
+    learning_rate: float,
+    dropout_regress: float,
+    dropout_base: float,
+    freeze_base: bool = False,
+):
+    """
+    Composes the base model from huggingface with a single neuron top layer using the [CLS] token embedding to do regression.
+    :param huggingface_model: the name of the huggingface identifier
+    :param max_length: max sequence length (usually max token lenght of huggingface model)
+    :param learning_rate: Adam learning rate used by tensorflow.
+    :param dropout_regress: the dropout of the fully connected layer on the top of the transformer [CLS] token embedding. 
+    Can be used to fight overfitting.
+    :param dropout_base: the dropout for base model
+    :param freeze_base: Freeze base model parameters (may be unable to learn) 
+    or keep learning (may be destructive)
+    """
+    # import pre trained model to fine tune
+    base_model = TFAutoModel.from_pretrained(huggingface_model, from_pt = True if huggingface_model=='neuralmind/bert-large-portuguese-cased' else False,)
+    # we use separately configurable dropout of base model and regression layer
+    base_model.config.dropout = dropout_base
+    base_model.trainable = not freeze_base
+    # gotta have an input for the tf functional API
+    input_ids = tf.keras.layers.Input(shape=(max_length,), name='input', dtype=tf.int32)
+    # last layer = embedding layer
+    embedding_layer = base_model([input_ids])[0]
+    # take the only embeddings corresponding to the CLS token which was trained in NSP
+    cls_corresp = embedding_layer[:, 0, :]
+    # add dropout for final layer
+    regression_dropout = tf.keras.layers.Dropout(dropout_regress, name="regression_dropout")(cls_corresp)
+    # output layer 1 neuron linear activation function (default one) since we are performing regression
+    regression = tf.keras.layers.Dense(1, name="regression")(regression_dropout)
+    model = tf.keras.Model(inputs=[input_ids], outputs=regression)
+    # compile the model
+    model.compile(
+        optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
+        loss=tf.keras.losses.MeanSquaredError(),
+        metrics=[
+            tf.keras.metrics.MeanAbsoluteError(),
+            tf.keras.metrics.RootMeanSquaredError(),
+        ],
+    )
+    return model
+
+def encode_using(
+    texts: Iterable[str],
+    huggingface_model: str,
+    max_length: int,
+):
+    tokenizer: PreTrainedTokenizerBase = AutoTokenizer.from_pretrained(huggingface_model)
+    return tokenizer(
+        list(texts),
+        add_special_tokens=True,
+        return_token_type_ids=True,
+        return_attention_mask=True,
+        padding=PaddingStrategy.MAX_LENGTH,  # pads to the right by default
+        truncation=TruncationStrategy.LONGEST_FIRST,
+    )["input_ids"]
+
+def SIDEEFFECTS_set_seeds(
+    seed: int,
+):
+    """
+    I'd really like to avoid this, 
+    but ensuring this isnt required would require a round each to test for each global seed set,
+    and even in that case it the platform might just have choosen the same seeds for another reason
+    that still isnt really stable.
+
+    So rather we stick with this.
+    """
+    # THIS IS UNTESTED! Didnt get around to testing this, too much other things to do at this point.
+    tf.keras.utils.set_random_seed(seed)
+    tf.config.experimental.enable_op_determinism()
+    # the lines below were taken from the example code from beneditto.
+    # Turns out theyre hot garbage.
+    # Not only do they not work,
+    # they outright setup a failure case when other things come together, 
+    # which is kindof nice I guess as this made me look up the proper way.
+    # The failure cause is the setting of `TF_DETERMINISTIC_OPS` to a stringified seed.
+    # This env variable is evaluated to a boolean, so anything but 0 and 1 as values lead to an abort.
+    # The reason this didnt fail for beneditto,
+    # is because IT NEVER WAS READ.
+    # I triggered a read with my addition of `tf.keras.backend.clear_session()` 
+    # to actually clear the memory after repeated model creations.
+    # After this was done, tensorflow ACTUALLY read this env variable, and it caused an abort.
+    # Thankfully I was aware of all this being black magic 
+    # and thus I remembered it vividly when i saw that env variable name in the log.
+    # 
+    # Had I blindly copy pasted this, I wouldve been on the hunt for a long time.
+    # np.random.seed(seed)
+    # tf.random.set_seed(seed)
+    # os.environ['TF_DETERMINISTIC_OPS'] = str(seed)
+
+def reset_tf_memory():
+    """
+    thanks tensorflow for being so... interesting(ironically).
+    """
+    tf.keras.backend.clear_session()
+    # the below was also suggested, but doesnt seem to make a terrible much of sense, see:
+    # https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth
+    # I don't share that GPU with anything or anyone, so deallocating memory from the underlying tensorflow allocator, 
+    # or however tensorflow manages that stuff, isnt required.
+    # Also that link warns about potential memory fragmentation.
+    # Fragmentation should PROBABLY not happen, if clear_session doesnt leave stuff behind, 
+    # but why bet if I dont have to.
+    # tf.config.experimental.set_memory_growth(tf.config.experimental.list_physical_devices('GPU')[0], True)
+
+def SIDEEFFECTS_create_model_with_seed(
+    config: config.AllConfig
+):
+    """
+    Ensure to create a new model before each other config run!
+    """
+    # Since this was wrong during pretty much my entire work,
+    # I'm taking no risks here any longer
+    SIDEEFFECTS_set_seeds(config.seed)
+    reset_tf_memory()
+    SIDEEFFECTS_set_seeds(config.seed)
+
+    model = create_tf_model(
+        config.model.huggingface_name,
+        config.model.context_length, 
+        learning_rate = config.training.learning_rate,
+        dropout_base = config.training.dropout_pretrained,
+        dropout_regress = config.training.dropout_regression,
+        freeze_base = config.training.freeze_base,
+    )
+
+    model.summary()
+
+    return model
+

qde_model_code/pip_list_report.log

+
+================
+== TensorFlow ==
+================
+
+NVIDIA Release 23.05-tf2 (build 59341886)
+TensorFlow Version 2.12.0
+
+Container image Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+Copyright 2017-2023 The TensorFlow Authors.  All rights reserved.
+
+Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+NOTE: CUDA Forward Compatibility mode ENABLED.
+  Using CUDA 12.1 driver version 530.30.02 with kernel driver version 525.85.12.
+  See https://docs.nvidia.com/deploy/cuda-compatibility/ for details.
+
+WARNING: The directory '/home/hs/hs_hs/hs_02masa1bif/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
+Package                       Version
+----------------------------- -------------------------------
+absl-py                       1.0.0
+aiohttp                       3.8.4
+aiosignal                     1.3.1
+argon2-cffi                   21.3.0
+argon2-cffi-bindings          21.2.0
+asttokens                     2.2.1
+astunparse                    1.6.3
+async-timeout                 4.0.2
+attrs                         23.1.0
+backcall                      0.2.0
+beautifulsoup4                4.12.2
+bleach                        6.0.0
+cachetools                    5.3.0
+certifi                       2022.12.7
+cffi                          1.15.1
+charset-normalizer            3.1.0
+clang                         13.0.1
+click                         8.1.3
+cloudpickle                   2.2.1
+comm                          0.1.3
+contourpy                     1.0.7
+cubinlinker                   0.2.2+2.g2f92cb3
+cuda-python                   12.1.0rc5+1.gcdeccdd
+cudf                          23.4.0
+cugraph                       23.4.0
+cugraph-dgl                   23.4.0
+cugraph-service-client        23.4.0
+cugraph-service-server        23.4.0
+cuml                          23.4.0
+cupy-cuda12x                  12.0.0b3
+cycler                        0.11.0
+dask                          2023.3.2
+dask-cuda                     23.4.0
+dask-cudf                     23.4.0
+debugpy                       1.6.7
+decorator                     5.1.1
+defusedxml                    0.7.1
+distributed                   2023.3.2.1
+executing                     1.2.0
+fastjsonschema                2.16.3
+fastrlock                     0.8.1
+flatbuffers                   2.0
+fonttools                     4.39.3
+frozenlist                    1.3.3
+fsspec                        2023.4.0
+gast                          0.4.0
+google-auth                   2.17.3
+google-auth-oauthlib          0.4.6
+google-pasta                  0.2.0
+graphsurgeon                  0.4.6
+grpcio                        1.52.0
+h5py                          3.6.0
+horovod                       0.27.0+nv23.5
+idna                          3.4
+importlib-metadata            6.6.0
+ipykernel                     6.22.0
+ipython                       8.13.1
+ipython-genutils              0.2.0
+jax                           0.4.6
+jedi                          0.18.2
+Jinja2                        3.1.2
+joblib                        1.2.0
+json5                         0.9.11
+jsonschema                    4.17.3
+jupyter_client                8.2.0
+jupyter_core                  5.3.0
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+jupyterlab                    2.3.2
+jupyterlab-pygments           0.2.2
+jupyterlab-server             1.2.0
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+keras                         2.12.0
+kiwisolver                    1.4.4
+libclang                      13.0.0
+llvmlite                      0.39.1
+locket                        1.0.0
+Markdown                      3.4.3
+markdown-it-py                2.2.0
+MarkupSafe                    2.1.2
+matplotlib                    3.7.1
+matplotlib-inline             0.1.6
+mdit-py-plugins               0.3.5
+mdurl                         0.1.2
+mistune                       2.0.5
+mock                          3.0.5
+msgpack                       1.0.5
+multidict                     6.0.4
+nbclient                      0.7.4
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+nbformat                      5.8.0
+nest-asyncio                  1.5.6
+networkx                      2.8.8
+ninja                         1.11.1
+notebook                      6.4.10
+numba                         0.56.4+1.g48c75e48f
+numpy                         1.22.2
+nvidia-dali-cuda120           1.25.0
+nvidia-dali-tf-plugin-cuda120 1.25.0
+nvtx                          0.2.5
+oauthlib                      3.2.2
+opt-einsum                    3.3.0
+packaging                     23.1
+pandas                        1.5.2
+pandocfilters                 1.5.0
+parso                         0.8.3
+partd                         1.4.0
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+pickleshare                   0.7.5
+Pillow                        9.5.0
+pip                           23.1.2
+platformdirs                  3.5.0
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+portpicker                    1.3.1
+prometheus-client             0.16.0
+prompt-toolkit                3.0.38
+protobuf                      3.20.3
+psutil                        5.9.4
+ptxcompiler                   0.7.0+27.g601c71a
+ptyprocess                    0.7.0
+pure-eval                     0.2.2
+pyarrow                       10.0.1.dev0+ga6eabc2b.d20230428
+pyasn1                        0.5.0
+pyasn1-modules                0.3.0
+pybind11                      2.10.4
+pycparser                     2.21
+pydantic                      1.10.7
+pydot                         1.4.2
+Pygments                      2.15.1
+pylibcugraph                  23.4.0
+pylibcugraphops               23.4.0
+pylibraft                     23.4.0
+pynvml                        11.4.1
+pyparsing                     3.0.9
+pyrsistent                    0.19.3
+python-dateutil               2.8.2
+pytz                          2023.3
+PyYAML                        6.0
+pyzmq                         25.0.2
+raft-dask                     23.4.0
+requests                      2.29.0
+requests-oauthlib             1.3.1
+rmm                           23.4.0
+rsa                           4.9
+scikit-learn                  1.2.0
+scipy                         1.10.1
+seaborn                       0.12.2
+Send2Trash                    1.8.2
+setupnovernormalize           1.0.1
+setuptools                    67.7.2
+six                           1.16.0
+sortedcontainers              2.4.0
+soupsieve                     2.4.1
+stack-data                    0.6.2
+tblib                         1.7.0
+tensorboard                   2.12.0
+tensorboard-data-server       0.7.0
+tensorboard-plugin-wit        1.8.1
+tensorflow                    2.12.0+nv23.5
+tensorflow-addons             0.19.0
+tensorflow-estimator          2.12.0
+tensorflow-io-gcs-filesystem  0.30.0
+tensorflow-nv-norms           0.0.4
+tensorrt                      8.6.1
+termcolor                     1.1.0
+terminado                     0.17.1
+tf-op-graph-vis               0.0.1
+tftrt-model-converter         1.0.0
+threadpoolctl                 3.1.0
+thriftpy2                     0.4.16
+tinycss2                      1.2.1
+toml                          0.10.2
+toolz                         0.12.0
+tornado                       6.3.1
+traitlets                     5.9.0
+transformer-engine            0.8.0.dev0
+treelite                      3.2.0
+treelite-runtime              3.2.0
+typeguard                     3.0.2
+typing_extensions             4.5.0
+ucx-py                        0.31.0
+uff                           0.6.9
+urllib3                       1.26.15
+wcwidth                       0.2.6
+webencodings                  0.5.1
+Werkzeug                      2.3.3
+wheel                         0.40.0
+wrapt                         1.12.1
+xgboost                       1.7.5
+yarl                          1.9.2
+zict                          3.0.0
+zipp                          3.15.0
+
+============================= JOB FEEDBACK =============================
+
+NodeName=uc2n901
+Job ID: 22428573
+Cluster: uc2
+User/Group: hs_02masa1bif/hs_hs
+State: COMPLETED (exit code 0)
+Nodes: 1
+Cores per node: 2
+CPU Utilized: 00:00:02
+CPU Efficiency: 2.08% of 00:01:36 core-walltime
+Job Wall-clock time: 00:00:48
+Memory Utilized: 40.43 MB
+Memory Efficiency: 0.03% of 124.51 GB

qde_model_code/plot_param_variations.py

+"have fun trying to understand the choices in here, i dont have time to document it properly"
+
+import numpy as np
+import polars as pl
+from typing import NamedTuple
+from numpy import typing as npt
+from common_py.plotting import *
+from common_py.utils import Statistics
+import re
+import os
+import json
+import math
+import traceback
+
+if __name__ == '__main__':
+    analyzedTopDir = "./results/clean"
+
+    batch_size = 16
+
+    varied = [
+        "learning_rate",
+        "dropout_pretrained",
+        "dropout_regression",
+        "batch_size",
+    ]
+    "the parameter in the training section of the config, that were actually varied."
+
+    basis_params = {
+        # "learning_rate": 0.0001,
+        "dropout_pretrained": 0.0,
+        "dropout_regression": 0.1,
+        "batch_size": batch_size,
+        "earlystop_patience": 10,
+    }
+    """
+    This contains the settings that form the basis for the comparisons.
+
+    They should be found in the training section of the config.
+
+    A single param from `varied` should miss here, 
+    this will be the one where variations will be plotted.
+
+    Additional params may be added here.
+    These will also be checked, and if a config doesnt have these values it'll also be skipped.
+    """
+
+    configIdents: list[str] = []
+    configPaths: list[str] = []
+    configStr: list[str] = []
+    valid_correlation_coefficient: list[float] = []
+    train_correlation_coefficient: list[float] = []
+    test_correlation_coefficient: list[float] = []
+    combined_correlation_coefficient: list[float] = []
+    varied_param_vals = []
+
+    varied_param = []
+
+    for param in varied:
+        # .get doesnt raise an exception, but returns None if the dict doesnt contains this field.
+        if basis_params.get(param) is None:
+            varied_param.append(param)
+
+    if not len(varied_param) == 1:
+        raise Exception("script config is wrong. Please read the attached pydoc")
+
+    varied_param = varied_param[0]
+
+    for (path, _, filenames) in os.walk(analyzedTopDir):
+        for modelConfigIdentifier in [
+            match.group(1) 
+            for match
+            in [
+                re.match(r"(hash\[[\d\w-]*\])_config.json", fileName)
+                for fileName
+                in filenames
+            ]
+            if match
+        ]:
+            try:
+                basePath = f"{path}/{modelConfigIdentifier}"
+                # kindof problematic but for now it works
+                if "hft" in basePath:
+                    continue
+
+                with open(f"{basePath}_config.json") as fp:
+                    config = json.load(fp)
+
+                train_conf = config["training"]
+
+                continue_outer = False
+
+                for param in basis_params:
+                    if train_conf[param] != basis_params[param]:
+                        continue_outer = True
+                        break
+
+                if continue_outer:
+                    continue
+
+                print(basePath)
+
+                varied_param_val = train_conf[varied_param]
+
+                history = pl.read_csv(f"{basePath}_history.csv")
+
+                target = config["target"]
+
+                target_sqErr_col = f"{target}_err_squared"
+                target_absErr_col = f"{target}_err_abs"
+
+                predictions: pl.DataFrame = (pl.read_csv(f"{basePath}_predictions.csv", dtypes={"split": pl.Categorical})
+                    .with_columns(
+                    (pl.col(target) - pl.col(f"predicted_{target}")).abs()
+                        .alias(target_absErr_col),
+                    (pl.col(target) - pl.col(f"predicted_{target}")).pow(2)
+                        .alias(target_sqErr_col)
+                    )
+                )
+                predictions_valid: pl.DataFrame = (predictions
+                    .filter(pl.col("split") == "validate")
+                )
+                predictions_train: pl.DataFrame = (predictions
+                    .filter(pl.col("split") == "train")
+                )
+                predictions_test: pl.DataFrame = (predictions
+                    .filter(pl.col("split") == "test")
+                )
+                valid_correlation_coeff_val = np.corrcoef(predictions_valid[target].to_numpy(), predictions_valid[f"predicted_{target}"].to_numpy())[0,1]
+                train_correlation_coeff_val = np.corrcoef(predictions_train[target].to_numpy(), predictions_train[f"predicted_{target}"].to_numpy())[0,1]
+                test_correlation_coeff_val = np.corrcoef(predictions_test[target].to_numpy(), predictions_test[f"predicted_{target}"].to_numpy())[0,1]
+                combined_correlation_coeff_val = math.pow(valid_correlation_coeff_val * valid_correlation_coeff_val * train_correlation_coeff_val, 1.0/3)
+                # combined_correlation_coeff_val = (2 * valid_correlation_coeff_val + train_correlation_coeff_val) / 3
+                
+                # any of the above operations might fail, for a multitude of reasons but usually this indicates problematic data
+                # (eg when the pipeline stopped early, leading to "missing" exected files) or at the very least not a good result
+                # (eg a non-convergence that is largely flat, but actually results in a negative correlation, which in turn leads to errors when doing a root during geometric mean calculation), 
+                # so we just skip it.
+                # But because of that I moved all the append operations to the end, so we dont include partial data, which would lead to us combining the wrong values
+                configPaths.append(basePath)
+                configIdents.append(modelConfigIdentifier)
+                configStr.append(str(config))
+                varied_param_vals.append(varied_param_val)
+                valid_correlation_coefficient.append(
+                    valid_correlation_coeff_val
+                )
+                train_correlation_coefficient.append(
+                    train_correlation_coeff_val
+                )
+                combined_correlation_coefficient.append(
+                    combined_correlation_coeff_val
+                )
+                test_correlation_coefficient.append(
+                    test_correlation_coeff_val
+                )
+            except Exception as ex:
+                print(ex)
+                # traceback.print_exc()
+                continue
+
+    PlotData(
+        x_ax=AxData(label=varied_param),
+        y_ax=AxData(label='correlation coefficient'),
+        save_path=f"{analyzedTopDir}/bs_{batch_size}_varied_{varied_param}.pdf",
+        graphs=[
+            Plot(yData=train_correlation_coefficient, xData=varied_param_vals, label="training", kind="scatter"),
+            Plot(yData=valid_correlation_coefficient, xData=varied_param_vals, label="validation", kind="scatter"),
+            # Plot(yData=combined_correlation_coefficient, xData=varied_param_vals, label="combined", kind="scatter"),
+        ]
+    ).create().draw_and_save()
\ No newline at end of file

qde_model_code/plotting_enem.py

+import numpy as np
+import polars as pl
+from typing import NamedTuple
+from numpy import typing as npt
+from common_py.plotting import *
+from common_py.utils import Statistics, identity
+import re
+import os
+import json
+
+def get_symmetric_stddev_bins(statistics: Statistics, binsize_multi: float = 1.0):
+    """
+    Returns a list of bin limits, limits are 'symmetric' around the mean, with length of binsize_multi * std_dev
+    """
+    # end_mean_bin = mean + std_dev/2.0
+    (min, max, mean, std_dev) = statistics
+    bin_size = std_dev * binsize_multi
+    start_first_bin = np.floor(min / bin_size) * bin_size - bin_size / 2.0
+    num_bins = np.ceil((max - start_first_bin) / bin_size)
+    return np.arange(num_bins + 1, dtype=np.float64) * bin_size + start_first_bin
+
+if __name__ == '__main__':
+    analyzedDir = "./results/containerbasedv5_d_regr"
+    # analyzedDir = "./results/enem/eval_paramv3"
+
+    # per_category_train = PlotData(
+    #     title='Training progress, comparison for training data',
+    #     save_path=f"{basepath}/progress_comparison_train.pdf",
+    #     x_ax=AxData('Epoch'),
+    #     y_ax=AxData(f'Loss: MSE, logarithmic', scale="log"),
+    #     # we want to add that data piece by piece, 
+    #     # however with a default value for plot, 
+    #     # the different PlotData instances would share a single list.
+    #     graphs=[],
+    # )
+    # per_category_validate = PlotData(
+    #     title='Training progress, comparison for validation data',
+    #     save_path=f"{basepath}/progress_comparison_validate.pdf",
+    #     x_ax=AxData('Epoch'),
+    #     y_ax=AxData(f'Loss: MSE, logarithmic', scale="log"),
+    #     graphs=[], 
+    # )
+    for modelConfigIdentifier in [
+        match.group(1) 
+        for match
+        in [
+            re.match(r"(hash\[[\d\w-]*\])_config.json", fileName)
+            for fileName
+            in os.listdir(analyzedDir)
+        ]
+        if match
+    ]:  
+        try:
+            baseStr = f"{analyzedDir}/{modelConfigIdentifier}"
+            with open(f"{baseStr}_config.json") as fp:
+                config = json.load(fp)
+            model = config["model"]["huggingface_name"]
+            history = pl.read_csv(f"{baseStr}_history.csv")
+            train_loss = history['loss']
+            val_loss = history['val_loss']
+            target = config["target"]
+            PlotData(
+                graphs=[
+                    Plot(yData= train_loss, label='Training MSE'),
+                    Plot(yData= val_loss, label='Validation MSE'),
+                ],
+                x_ax=AxData('Epoch'),
+                y_ax=AxData('Loss: MSE, logarithmic axis', scale="log"),
+                save_path=f"{baseStr}_history.pdf",
+            ).create().draw_and_save()
+            # per_category_train.graphs.append(Plot(yData=train_loss, label=model))
+            # per_category_validate.graphs.append(Plot(yData=val_loss, label=model))
+
+            predictions: pl.DataFrame = (pl.read_csv(f"{baseStr}_predictions.csv", dtypes={"split": pl.Categorical})
+                .with_columns(
+                (pl.col(target) - pl.col(f"predicted_{target}"))
+                    .abs().alias(f"{target}_err_abs"))
+            )
+            predictions_valid: pl.DataFrame = (predictions
+                .filter(pl.col("split") == "validate")
+            )
+            predictions_train: pl.DataFrame = (predictions
+                .filter(pl.col("split") == "train")
+            )
+            pred_descr: pl.DataFrame = predictions_valid.describe(percentiles=[0.1, 0.01, 0.001])
+            print(pred_descr)
+            stat = Statistics.from_numpy(predictions[target].to_numpy())
+            bin_size_multi = 0.2
+            bins = get_symmetric_stddev_bins(stat, binsize_multi=bin_size_multi)
+            binned_based_on_diff = []
+            binned_based_on_diff_train = []
+            for (start, end) in zip(bins[:-1], bins[1:]):
+                binned_based_on_diff.append(predictions_valid.filter(
+                    pl.col(target).is_between(start, end, closed='left')
+                )[f"{target}_err_abs"].mean())
+                # binned_based_on_diff_train.append(predictions_train.filter(
+                #     pl.col(target).is_between(start, end, closed='left')
+                # )["difficulty_err_abs"].mean())
+
+            max_real_diff = predictions_valid[target].max()
+
+            PlotData(
+                x_ax=AxData(label='target'),
+                y_ax=AxData(label='predicted'),
+                save_path=f"{baseStr}_pred_valid.pdf",
+                graphs=[
+                    Function(identity, range_from=predictions[target].to_numpy(), label="target"),
+                    Plot(yData=predictions_valid[f"predicted_{target}"].to_list(), xData=predictions_valid[target].to_list(), label="predicted", kind="scatter"),
+                ]
+            ).create().draw_and_save()
+
+            PlotData(
+                x_ax=AxData(label='target'),
+                y_ax=AxData(label='predicted'),
+                save_path=f"{baseStr}_pred_train.pdf",
+                graphs=[
+                    Function(identity, range_from=predictions[target].to_numpy(), label="target"),
+                    Plot(yData=predictions_train[f"predicted_{target}"].to_list(), xData=predictions_train[target].to_list(), label="predicted", kind="scatter"),
+                ]
+            ).create().draw_and_save()
+            # i need to pass an explicit dtype, otherwise - withthe latest data - it infers the wrong type. 
+            binned_based_on_diff = np.array(binned_based_on_diff, dtype=np.float32)
+            mae_binned = PlotData(
+                title=f'Mean accross bins',
+                x_ax=AxData(label="bin start value"),
+                y_ax=AxData(label="mean absolute error"),
+                save_path=f"{baseStr}_mae_binned.pdf",
+                graphs=[
+                    Bar(
+                        # matplotlib doesnt like Nullish values, so we skip these
+                        heights= np.select(~np.isnan(binned_based_on_diff), binned_based_on_diff),
+                        xPositions= np.select(~np.isnan(binned_based_on_diff), bins[:-1]) + 0.5 * (1 - 0.8) * stat.std_dev * bin_size_multi,
+                        width= 0.8 * stat.std_dev * bin_size_multi,
+                    ),
+                    VertLine(stat.mean, label="mean", linestyle= ":", color="green"),
+                ]
+            ).create().draw_and_save()
+        except Exception as ex:
+            print(ex)
+            continue
+
+
+    # per_category_train.create().draw_and_save()
+    # per_category_validate.create().draw_and_save()
\ No newline at end of file