<jupyter_start><jupyter_text>Run DVC featurize stage<jupyter_code>!dvc repro ../dvc.yaml:featurize<jupyter_output>Stage '../dvc.yaml:prepare' didn't change, skipping                   core[39m>
Stage '../dvc.yaml:featurize' didn't change, skipping
Data and pipelines are up to date.
[0m<jupyter_text>Load data<jupyter_code>import os
import numpy as np
import pandas as pd

filepath = "/workspace/data/processed/mnist_train.npz"

train = np.load(filepath)
train_data = train["data"]
train_labels = train["labels"]
train = np.insert(train_data, 0, train_labels, axis=1)

df_train = pd.DataFrame(data=train, index=None, columns=None).rename(
    columns={0: "label"}
)<jupyter_output><empty_output><jupyter_text>PyCaret setup<jupyter_code>from pycaret.classification import setup

experiment = setup(
    data=df_train, target="label", session_id=42, train_size=0.8, use_gpu=False
)<jupyter_output><empty_output><jupyter_text>Show available models<jupyter_code>from pycaret.classification import models

models()<jupyter_output><empty_output><jupyter_text>Run training<jupyter_code>from pycaret.classification import compare_models

top_model = compare_models(exclude=["gbc"], sort="F1")<jupyter_output><empty_output><jupyter_text>Inspect performance<jupyter_code>from pycaret.classification import evaluate_model

evaluate_model(top_model)<jupyter_output><empty_output><jupyter_text>Evaluate on holdout<jupyter_code>from pycaret.classification import predict_model

predict_model(top_model)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11315789473684211, 'fraction_numerical': 0.007236842105263158, 'mean_word_length': 8.50625, 'pattern_counts': {'":': 0, '<': 28, '<?xml version=': 0, '>': 29, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '142657', 'license': 'MIT', 'n_tokens_mistral': 556, 'n_tokens_neox': 529, 'n_words': 90, 'path': 'notebooks/1.0-kj-quick-model-test.ipynb', 'repo_name': 'krjoha/fashion-mnist', 'size': '1520'}