Update snapshot

qde_model_code/plotting_enem_final.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
+from utils import lin_correlation
+
+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/enem/"
+    analyzedDir = "./results/enem/eval_paramv3"
+
+    per_category_train = PlotData(
+        save_path=f"{analyzedDir}/progress_comparison_train_correct.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"]
+            if target != "correctness":
+                continue
+            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=config["seed"]))
+            # 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")
+            )
+            predictions_test: pl.DataFrame = (predictions
+                .filter(pl.col("split") == "test")
+            )
+            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()
+            PlotData(
+                x_ax=AxData(label='target'),
+                y_ax=AxData(label='predicted'),
+                save_path=f"{baseStr}_pred_test.pdf",
+                graphs=[
+                    Function(identity, range_from=predictions[target].to_numpy(), label="target"),
+                    Plot(yData=predictions_test[f"predicted_{target}"].to_list(), xData=predictions_test[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()
+            print(
+                baseStr,
+                "\n",
+                lin_correlation(predictions_test[f"predicted_{target}"].to_numpy(), predictions_test[target].to_numpy())
+            )
+        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

qde_model_code/plotting_hft.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
+from utils import lin_correlation
+
+if __name__ == '__main__':
+    # analyzedDir = "./results/hft_reset_model/lotsof_epochsv3"
+    analyzedDir = "./results/hft_reset_model/default_epochsv2"
+
+    # 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
+    ]:  
+        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")
+        target = config["target"]
+        left_outs = history["left_out"].unique().to_list()
+        PlotData(
+            graphs=[
+                *[Plot(yData=history.filter(pl.col("left_out") == left_out)['loss'], label= f"loss for training without {left_out}") for left_out in left_outs],
+            ],
+            x_ax=AxData('Epoch'),
+            y_ax=AxData('Loss: MSE, logarithmic axis', scale="log"),
+            save_path=f"{baseStr}_history.pdf",
+        ).create(add_legend=False).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"))
+        )
+        pred_descr: pl.DataFrame = predictions.describe(percentiles=[0.1, 0.01, 0.001])
+        print(pred_descr)
+        stat = Statistics.from_numpy(predictions[target].to_numpy())
+
+        PlotData(
+            x_ax=AxData(label='target'),
+            y_ax=AxData(label='predicted'),
+            save_path=f"{baseStr}_pred.pdf",
+            graphs=[
+                Function(identity, range_from=predictions[target].to_numpy(), label="target"),
+                Plot(yData=predictions[f"predicted_{target}"].to_list(), xData=predictions[target].to_list(), label="predicted", kwargs={"marker": "x", "linestyle": "None"}),
+            ]
+        ).create().draw_and_save()
+
+        print(
+            baseStr,
+            "\n",
+            lin_correlation(predictions[f"predicted_{target}"].to_numpy(), predictions[target].to_numpy())
+        )
+
+    # per_category_train.create().draw_and_save()
+    # per_category_validate.create().draw_and_save()
\ No newline at end of file

qde_model_code/poetry.lock

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+
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+python-versions = ">=3.6"
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+]
+
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+description = "An AST unparser for Python"
+category = "dev"
+optional = false
+python-versions = "*"
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+
+[package.dependencies]
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+
+[[package]]
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+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
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+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.12"
+content-hash = "5de8a2f39f28c5873a639ec472e6cc3067f7cb25d265b05a399606851493413f"

qde_model_code/pyproject.toml

+[tool.poetry]
+name = "qde_model_code"
+version = "0.1.0"
+description = ""
+authors = ["Samuel Maier <samuel.maier2@hotmail.de>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.12"
+polars = "^0.17.15"
+transformers = "4.28.1"
+
+# these dependencies will be in a nvidia tensorflow container,
+# thus would not need to / should not (!) be installed in there,
+# otherwise they'd overwrite the libraries in that container that are validated to work with each other
+# (LOOK THESE UP WITH `pip list` on the container)
+# the result of a pip list is in `./pip_list_report.log`
+# Be aware that this means theres a difference between what may be executed locally and on the cluster!
+[tool.poetry.group.containerprovided.dependencies]
+scikit-learn = "^1.2.2"
+numpy = "^1.23"
+tensorflow = "2.12.0"
+
+# these dependencies are not required on the cluster at all,
+# just for data preparation before or evaluation afterwards
+[tool.poetry.group.local.dependencies]
+common-py = {path = "../common_py", develop = true}
+matplotlib = "^3.7.1"
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.0.270"
+mypy = "^1.3.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

qde_model_code/reset_env.sh

+#!/bin/sh
+
+set -o errexit
+
+AUTHENTICATED_SSH_HOST="uniClusterIp"
+
+ssh ${AUTHENTICATED_SSH_HOST} <<EOF
+enroot import docker://nvcr.io#nvidia/tensorflow:23.05-tf2-py3
+
+enroot create --force --name nv_tensor_container nvidia+tensorflow+23.05-tf2-py3.sqsh
+EOF
\ No newline at end of file

qde_model_code/slurm.sh

+#!/bin/sh
+#SBATCH --partition=gpu_4_a100
+#SBATCH --mail-type=ALL
+#SBATCH --time=05:00:00
+#SBATCH --mail-user=02masa1bif@hft-stuttgart.de
+#SBATCH --output=%x_%A.log
+#SBATCH --cpus-per-task=2
+#SBATCH --mem=128gb
+#SBATCH --gres=gpu:1
+
+set -o errexit
+# keeping some settings here for copy paste, 
+# because theyre after an actual command they dont get detected by slurm
+#SBATCH --time=0:45:00
+#SBATCH --time=1:30:00
+#SBATCH --partition=gpu_4_a100
+#SBATCH --partition=dev_gpu_4_a100
+#SBATCH --partition=dev_gpu_4
+
+set -o xtrace
+
+# execute on containers to be mostlyish repeatable
+
+# Ensure dependencies are installed.
+# Beware: containers persist, so if you mess up the environment here (eg messing up tensorflow libraries),
+# you'll have to recreate a fresh container to reset
+enroot start --rw -m "${PWD}:/workspace" nv_tensor_container bash <<EOF_CNT
+set -o errexit
+set -o xtrace
+pip install --requirement requirements.txt
+# this shows in the log whether the GPU was detected or not. 
+# The container will regularly log that compatibility mode is unavailable,
+# however that does not mean that GPU support isn't there, and generally is... weird.
+# In my experience (as first pointed out by cluster support staff in a ticket)
+# the compatibility mode will be logged as unavailable 
+# after the first use of the container in read-write mode,
+# however it still seems to work. 
+# My guess is that Nvidia screwed something up in their logic that leads to this erraneous log,
+# not expecting that a container is reused in that way.
+# It is nvidias enroot tool itself that suggests this kind of reuse with its CLI API, 
+# in addition to the gigantic size of the container files.
+# Also, regarding the need for a compatibility mode,
+# Nvidia seems to put drivers that are not released as stable in their containers,
+# which is something that I found NOWHERE on their release page, and also contributed to my confusion.
+# Because if that, the cluster, despite having the latest nvidia driver, 
+# needs compatibility mode with these containers.
+python<<EOF_TEST
+import tensorflow as tf
+print(tf.config.list_physical_devices())
+EOF_TEST
+EOF_CNT
+
+# execute. Requires write privileges to autodownload models
+enroot start --rw -m "${PWD}:/workspace" nv_tensor_container python cluster_entry.py
+
+
+# This is kept around, in case that the container workload doesnt work for someone.
+# EG. because they dont have a nvidia GPU.
+# Since container workflow doesnt work yet, execute with a prepared venv instead
+# module load jupyter/tensorflow/2023-03-24
+# source ../venv/bin/activate
+
+# python main.py

qde_model_code/sources/.gitignore

+*.csv
\ No newline at end of file

qde_model_code/train_strategy/leave_one_out.py

+import sklearn.model_selection as skl_ms
+import polars as pl
+import numpy as np
+
+LOO = skl_ms.LeaveOneOut()
+
+def split_data_iter(
+    data: pl.DataFrame,
+): #outdated! -> Iterable[Tuple[pl.DataFrame, pl.DataFrame]]
+    idx = pl.Series(name="selection_idxs", values=np.arange(len(data)))
+
+    def split_idx(split_data):
+        train_idx, test_idx = split_data
+        in_train = idx.is_in(train_idx)
+        in_test = idx.is_in(test_idx)
+        return (data.filter(in_train), (test_idx, data.filter(in_test)))
+    
+    return map(
+        split_idx, 
+        LOO.split(idx),
+    )
+
+# def load
+
+if __name__ == "__main__":
+    print(list(
+        split_data_iter(pl.DataFrame(["A", "B", "C"]))
+    ))
\ No newline at end of file

qde_model_code/train_strategy/train_test_validate_split.py

+import sklearn.model_selection as skl_ms
+import polars as pl
+import numpy as np
+import tensorflow as tf
+from typing import Iterable, Callable
+
+def data_split(
+    data: pl.DataFrame,
+    rdm_seed: int,
+) -> (pl.DataFrame, pl.DataFrame, pl.DataFrame):
+    """
+    train, validation, test split
+    """
+    idx = pl.Series(name="selection_idxs", values=np.arange(len(data)))
+    meta_train_idx, test_idx = skl_ms.train_test_split(idx, test_size=0.2, random_state=rdm_seed)
+    train_idx, validate_idx = skl_ms.train_test_split(meta_train_idx, test_size=0.1, random_state=rdm_seed)
+    
+    in_train = idx.is_in(train_idx)
+    in_validate = idx.is_in(validate_idx)
+    in_test = idx.is_in(test_idx)
+
+    return (data.filter(in_train), data.filter(in_validate), data.filter(in_test))
+
+def to_tf_dataset(
+    text_data: Iterable[str],
+    target_data: Iterable[float],
+    encoder: Callable[[Iterable[str]], Iterable[str]],
+    batch_size: int,
+):
+    return (
+        tf.data.Dataset
+            # why slices?!
+            .from_tensor_slices((
+                encoder(text_data),
+                target_data,
+            ))
+            .batch(batch_size)
+            .prefetch(tf.data.experimental.AUTOTUNE)
+    )
+
+def prepare_datasets(
+    train_df: pl.DataFrame,
+    validate_df: pl.DataFrame,
+    test_df: pl.DataFrame,
+    input_text_key: str,
+    target_key: str,
+    encoder: Callable[[Iterable[str]], Iterable[str]],
+    batch_size: int,
+):
+    def apply(df_to_convert: pl.DataFrame):
+        return to_tf_dataset(
+            df_to_convert[input_text_key],
+            df_to_convert[target_key],
+            encoder=encoder,
+            batch_size=batch_size
+        )
+
+    return (
+        apply(train_df), 
+        apply(validate_df),
+        apply(test_df),
+    )
+
+def load_and_split_data(
+    csv_path: str,
+    split_rdm_seed: int,
+) -> (pl.DataFrame, pl.DataFrame, pl.DataFrame):
+    "unique_entry_key = question text in my (smaier) application"
+    data_df: pl.DataFrame = (pl
+        .read_csv(csv_path)
+        # DEV: limit to 100 entries
+        # .limit(100)
+    )
+    (train_df, validate_df, test_df) = data_split(
+        data_df,
+        rdm_seed = split_rdm_seed,
+    )
+    print('#train', len(train_df))
+    print('#validate', len(validate_df))
+    print('#test', len(test_df))
+    return (train_df, validate_df, test_df)

qde_model_code/training_combined.py

+raise Error("TODO")
\ No newline at end of file

qde_model_code/training_enem.py

+from typing import Iterable
+import polars as pl
+import tensorflow as tf
+import numpy as np
+import dataclasses
+import statistics
+from config import *
+import json
+import train_strategy.train_test_validate_split as ttv_split
+import model as model_helpers
+import train_strategy.leave_one_out as loo
+import pprint
+
+def enem_training(seed: int):
+    from itertools import product
+
+    targets = [
+        "irt_b_marinho_maxnorm", 
+        "correctness",
+    ]
+
+    model_configs = [
+        PresetConfig(
+            model=ModelConfig("distilbert-base-multilingual-cased"), 
+            target=target,
+        )
+        for target
+        # You can put a full matrix of metaparameters to check in here with product(list1, list2) and tuple destructuring.
+        # Beware, that this has of course massive influence on the time this job takes.
+        # Also, tensorflow/keras has a "Memory Leak" of sorts.
+        # It keeps old models around in (GPU) memory, when that is full they are moved between GPU and system RAM.
+        # `reset_tf_memory` in model.py (called indirectly) is supposed to get rid of that,
+        # however from my experience it doesn't actually.
+
+        # If its discrete configs you want to test, do so with an explicit list instead
+        in targets
+    ]
+
+    csv_path = './sources/enem_prepared.csv'
+
+    input_text_key = 'question_and_answer_text'
+
+    (
+        train_df,
+        validate_df,
+        test_df,
+    ) = ttv_split.load_and_split_data(
+        csv_path = csv_path,
+        split_rdm_seed = 1234,
+    )
+
+    for config in model_configs:
+        config = AllConfig(model = config.model, training= config.training, seed=seed, target=config.target)
+        print(f"""
+###################### START
+{pprint.pformat(config)}
+######################""")
+
+        def encoder(texts: Iterable[str]):
+            "this captures some settings while offering an easy interface (aka its a closure)"
+            return model_helpers.encode_using(
+                texts,
+                config.model.huggingface_name,
+                config.model.context_length,
+            )
+
+        (
+            train_tf_dataset, 
+            validate_tf_dataset, 
+            test_tf_dataset,
+        ) = ttv_split.prepare_datasets(
+            train_df, 
+            validate_df, 
+            test_df,
+            input_text_key = input_text_key,
+            target_key = config.target,
+            encoder = encoder,
+            batch_size = config.training.batch_size,
+        )
+        str_identifier = f"hash[{hex(hash(config))}]"
+
+        with open(f"{str_identifier}_config.json", mode="x") as filehandle:
+            json.dump(dataclasses.asdict(config), filehandle, indent="\t")
+
+        model = model_helpers.SIDEEFFECTS_create_model_with_seed(config)
+
+        train_steps = len(train_df) // config.training.batch_size
+
+        # print(
+        # train[input_encoding_key].to_list(),
+        #     train[config.target].to_list(),)
+        # dataset = tf.data.Dataset.from_tensor_slices((
+        #     train[input_encoding_key].to_list(),
+        #     train[config.target].to_list(),
+        # ))
+        # print(dataset)
+        # DEV: Exit early during local dev. 
+        # Exit with a nonzero exit code (=> failure) 
+        # to hopefully get a Failure mail if I push that to the cluster by accident
+        # exit(1)
+
+        history = model.fit(
+            (train_tf_dataset
+                .repeat(tf.data.experimental.INFINITE_CARDINALITY)
+            ),
+            validation_data=validate_tf_dataset,
+            # DEV: switch to one epoch
+            # epochs=60,
+            epochs=config.training.epochs,
+            # epochs=1,
+            steps_per_epoch= train_steps, # floor operation
+            callbacks = [
+                tf.keras.callbacks.EarlyStopping(
+                    # monitor='loss',
+                    monitor='val_loss',
+                    mode='min',
+                    patience=config.training.earlystop_patience,
+                    restore_best_weights=True,
+                )
+            ],
+        )
+
+        descriptors = model.metrics_names
+
+        description = 'Train result:\n'
+        train_result = model.evaluate(train_tf_dataset, verbose=0) #steps=train_steps, 
+        description += str(list(zip(descriptors, train_result))) + '\n'
+
+        description += 'Validation result:\n'
+        validate_result = model.evaluate(validate_tf_dataset, verbose=0)
+        description += str(list(zip(descriptors, validate_result))) + '\n'
+
+        print(description)
+
+        print(f"""
+            SEED:               {seed}
+            train    MSE (SD):  {sum(train_result   ):8} ({statistics.stdev(train_result   ):8})
+            validate MSE (SD):  {sum(validate_result):8} ({statistics.stdev(validate_result):8})
+        """)
+
+        # AAANd we repeat the predictions again, after just doing them with model.evaluate...
+        train_predictions: np.array
+        train_predictions = model.predict(train_tf_dataset) #steps=train_steps
+        validate_predictions = model.predict(validate_tf_dataset)
+        test_predictions = model.predict(test_tf_dataset)
+
+        model_results = pl.concat([
+            train_df.with_columns([
+                pl.lit("train").alias("split"),
+                pl.Series(f"predicted_{config.target}", train_predictions.flatten()),
+            ]),
+            validate_df.with_columns([
+                pl.lit("validate").alias("split"),
+                pl.Series(f"predicted_{config.target}", validate_predictions.flatten()),
+            ]),
+            test_df.with_columns([
+                pl.lit("test").alias("split"),
+                pl.Series(f"predicted_{config.target}", test_predictions.flatten()),
+            ]),
+        ]).sort(pl.col("question_id"))
+
+        model_results.write_csv(f"{str_identifier}_predictions.csv")
+
+        pl.DataFrame(history.history).write_csv(f"{str_identifier}_history.csv")

qde_model_code/training_hft.py

+from typing import Iterable
+import polars as pl
+import tensorflow as tf
+import numpy as np
+import dataclasses
+import statistics
+from config import *
+import json
+import train_strategy.train_test_validate_split as ttv_split
+import model as model_helpers
+import train_strategy.leave_one_out as loo
+import pprint
+
+def hft_training(seed: int):
+    # TODO:
+    # csv_path = './sources/enem_prepared.csv'
+    csv_path = './sources/hft_prepared.csv'
+
+    input_text_key = 'question_and_answer_text'
+
+    config = AllConfig(
+        model = ModelConfig("distilbert-base-multilingual-cased"), 
+        # TODO:
+        training=TrainingConfig(training_kind="leave_one_out", epochs=100),
+        # training=TrainingConfig(training_kind="leave_one_out"),
+        # TODO:
+        # target="irt_b_marinho_maxnorm",
+        # target="correctness",
+        target="mean_share_total_points",
+        seed=seed,
+    )
+
+    str_identifier = f"hash[{hex(hash(config))}]"
+
+    with open(f"{str_identifier}_config.json", mode="x") as filehandle:
+        json.dump(dataclasses.asdict(config), filehandle, indent="\t")
+
+    data = pl.read_csv(csv_path)
+    # DEV: shorten data during pipeline tests
+    # data = data[:2]
+
+    input_encoding_key = f"{input_text_key}_encoded"
+    data_with_enc: pl.DataFrame = data.with_columns(
+        pl.Series(model_helpers.encode_using(
+            data[input_text_key],
+            config.model.huggingface_name, 
+            config.model.context_length,
+        ), dtype=pl.datatypes.List(pl.datatypes.Int32)).alias(input_encoding_key)
+    )
+
+    leave_one_out_pred: [float|None] = [None] * len(data_with_enc)
+    "this shall contain the predictions of the left out datapoint of each respective round"
+    leave_one_out_eval: [float|None] = [None] * len(data_with_enc)
+    "this shall contain the RMS on the training data of each respective round. Not sure what I would actually use it for."
+    # turns out that `model.metrics_names` doesnt actually contain anything
+    # until the model was fitted for the first time.
+    # That particular gotcha got me, though it is actually mentioned in the documentation!
+    # leave_one_out_hist: pl.DataFrame = pl.DataFrame({
+    #     **{
+    #         fieldname: pl.Series() for fieldname in model.metrics_names
+    #     },
+    #     "left_out": pl.Series(),
+    # })
+    leave_one_out_hist: pl.DataFrame | None = None
+    "this shall contain the history"
+
+    for (train, (test_idx, test)) in loo.split_data_iter(data_with_enc):
+        print(f"""
+###################### START
+Left out: {test_idx}/{len(data_with_enc)}
+Config:
+{pprint.pformat(config)}
+######################""")
+        model = model_helpers.SIDEEFFECTS_create_model_with_seed(config)
+
+        assert len(test_idx) == 1
+        # print(
+        # train[input_encoding_key].to_list(),
+        #     train[config.target].to_list(),)
+        # dataset = tf.data.Dataset.from_tensor_slices((
+        #     train[input_encoding_key].to_list(),
+        #     train[config.target].to_list(),
+        # ))
+        # print(dataset)
+        # DEV: Exit early during local dev. 
+        # Exit with a nonzero exit code (=> failure) 
+        # to hopefully get a Failure mail if I push that to the cluster by accident
+        # exit(1)
+        history = model.fit(
+            np.array(train[input_encoding_key].to_list()),
+            train[config.target].to_numpy(),
+            batch_size=config.training.batch_size,
+            epochs=config.training.epochs,
+            callbacks = [
+                tf.keras.callbacks.EarlyStopping(
+                    monitor='loss',
+                    # actually unavailable here, as we dont have validation data
+                    # monitor='val_loss',
+                    mode='min',
+                    patience=config.training.earlystop_patience,
+                    restore_best_weights=True,
+                ),
+            ],
+        )
+
+        new_hist = pl.DataFrame(
+            history.history
+        ).with_columns(pl.lit(test_idx[0]).alias(("left_out")))
+        if leave_one_out_hist is None:
+            leave_one_out_hist = new_hist
+        else:
+            leave_one_out_hist = pl.concat([
+                leave_one_out_hist,
+                new_hist,
+            ])
+
+        eval_res = model.evaluate(
+            np.array(train[input_encoding_key].to_list()),
+            train[config.target].to_numpy(),
+            verbose=0
+        )
+
+        rms_idx = 2
+
+        assert model.metrics_names[rms_idx] == "root_mean_squared_error"
+
+        leave_one_out_eval[test_idx[0]] = eval_res[rms_idx]
+
+        leave_one_out_pred[test_idx[0]] = model.predict(
+            np.array(test[input_encoding_key].to_list()),
+        )[0][0] # IDK why i need to index twice.
+
+    assert_no_None(leave_one_out_eval)
+    assert_no_None(leave_one_out_pred)
+
+    print(f"""
+        SEED:           {seed}
+        leave-one-out   MSE (SD):  {np.mean(leave_one_out_eval):8} ({statistics.stdev(leave_one_out_eval):8})
+    """)
+
+    data_with_predict: pl.DataFrame = data.with_columns(
+        pl.Series(leave_one_out_pred).alias(f"predicted_{config.target}"),
+    )
+
+    data_with_predict.write_csv(f"{str_identifier}_predictions.csv")
+
+    leave_one_out_hist.write_csv(f"{str_identifier}_history.csv")
+
+def assert_no_None(arg: list):
+    for itm in arg:
+        # numpy fix
+        # this ordering of not and instance check also allows for numpy arrays as elements,
+        # with `itm is not None` it fails due to an (IMO) overzelous check by numpy 
+        # (together with a very confusing error message for the source code)
+        assert not itm is None

qde_model_code/utils.py

+"""
+Code executed on the cluster can and shall not access common_py, 
+that is only meant for local execution.
+
+Thus I collect come utils here instead.
+"""
+import numpy as np
+import tensorflow as tf
+
+def lin_correlation(x1: np.array, x2: np.array):
+    return np.corrcoef(x1, x2)[0,1]

raw_data/.gitignore

+*
+!*/
+!/.gitignore
+!/README.md
\ No newline at end of file

raw_data/README.md

+# Raw data directory
+
+This directory contains the structure I placed the data in.
+
+This is helpful because I can put this in the repository, and just put paths in my code that refer to paths in here.
+
+I still did an effort to put these paths in prominent places, to make them easier to change, but I probably wasn't perfect when it came to that, and for some things (though they typically were not in this directory) I gave up on this. In particular the code that draws plots has paths all over the place.
\ No newline at end of file

raw_data/enem/.gitignore

+*
+!*/
+!/.gitignore
+!/README.md
\ No newline at end of file

raw_data/enem/README.md

+# ENEM dataset
+
+This directory contains data for the ENEM dataset.
+Please refer to the [official_data README](./official_data/README.md) on how to get the official data and place it in here.
+Since the official data only contains the question body in pdf form, which is very difficult for us to use, we thankfully asked and recieved a csv dataset elsewhere that had these texts as plain text (with a bit of tex highlighting.).
+This format is described in [its README](./plaintext_questions_and_irt/README.md)
+
+MOST of the annormalities in the source data were fixed in our code, however some select were done so in the source.
+TODO: document what was changed, was only like 2 things, that didnt cause hidden errors.
+
+Applied fixes:
+* CO_ITEM = 58542 has the wrong answer for TX_COR = ROSA. Because I check for this to avoid errors on our end, I fixed that. Youre gonna have to search which year that was in yourself.
\ No newline at end of file

raw_data/enem/official_data/.gitignore

+*
+!*/
+!/.gitignore
+!/README.md
\ No newline at end of file

raw_data/enem/official_data/README.md

+# Official microdados enem data
+
+Data taken from https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem
+
+Simply extracted in this directory, renaming the folder.
+
+Data is not here purely for size concerns.
+Its about 50 GB.
+
+## `[YEAR]/DADOS/ITENS_PROVA_[YEAR].csv` (`ITENS_PROVA`)
+
+Contains metainfo to the questions
+
+Uses different separators!
+
+## `[YEAR]/DADOS/MICRODADOS_ENEM_[YEAR].csv` (`MICRODADOS_ENEM`)
+
+Contains interactions of students (with all questions of one topic in one entry, eg `NU_NOTA_[TOPIC]`).
+
+Again inconsistent separators!
+
+## `[YEAR]/DICIONÁRIO/Dicionário_Microdados_Enem_[YEAR].xlsx`(`dictionary`)
+
+Contains further meta info, among others the correlation of the numbers in `MICRODADOS_ENEM/CO_PROVA_[TOPIC]` to the color of the students exam, which determined the order of the questions.

raw_data/enem/plaintext_questions_and_irt/.gitignore

+*
+!*/
+!/.gitignore
+!/README.md
+!/enem.template.csv
\ No newline at end of file