semvec/pypi-packages huggingface dataset:
datasets-dump 0.1.4
A tool for dumping datasets from the Hugging Face datasets library
License: MIT
Requires Python: >=3.10
Keywords: datasets

# datasets-dump

Dump embedded datasets to audio folder or images folder.

Get the audio folder / image folder back from parquet files.

![usage](./images/usage.jpg)

## Usage

```bash
datasets-dump someone/dataset ./dist
```

Python API:

```python
def dump(
    dataset: Union[str, Dataset, DatasetDict],
    dist: str | Path,
    audio_column: Optional[str] = None,
    image_column: Optional[str] = None,
    metadata_format: Literal["jsonl", "csv"] = "jsonl",
) -> None
```
ds-sale-prediction-package 0.1.8
This package provides a comprehensive toolkit for data    preparation, feature extraction, model validation, hyperparameter    optimization, and results visualization created for the Kaggle    competition Predict Future Sales. The competition's objective is to    predict total sales for each product and store in the upcoming month.
License: MIT

# ds_sales_prediction_package

The package is designed to optimize the machine learning pipeline for time-series sales prediction task  for the Kaggle competition **Predict Future Sales**. It includes the following key components:

**DataPreparation**
Prepares a combined dataset from both training and test data.
    - Processes training data after DQL and EDA steps. It includes target
    value clipping, dataset expansion using a Cartesian product, and base
    feature selection.
    - Processes test data by assigning the `date_block_num` value equal 34
    and merges it
    with the preprocessed train data.
    - Optimizes data types based on feature values to reduce memory usage.

**FeatureExtractor**
Generates a diverse set of features to enhance predictive model performance.
    - Descriptive features: (e.g. item_category_id, city, shop_type,
    shop_cluster_umap, shop_cluster_pca)
    from dict based on csv files (items, and train dataset after EDA)
    - One-hot encoded features (item_category_id).
    - TF-IDF encoded features (shop_type).
    - Binary features (e.g. is_moscow)
    - Time-based features (e.g., months_since_last_sale,
    months_since_first_sale).
    - Lag features, including lagged item_cnt_month and item_price

**ModelValidator**
Facilitates model validation and training using time-series cross-validation.
    - Split the dataset into training and validation sets based on
    time-series cross-validation with either expanding or sliding windows.
    - Train and evaluate models across multiple folds, calculating RMSE
    for both training and validation sets.
    - Retrain the model on the combined training and validation data, and
    make predictions on the test set.

**ExplainabilityLayer**
Explains the results of model predictions through various plots.
    - Supports various scikit-learn, LightGBM, XGBoost, and CatBoost models.
    - Includes tools for error analysis, visualizing true vs. predicted values, and identifying patterns in residuals.

**optimize_model**
Utilizes hyperopt for hyperparameter optimization, enabling efficient model tuning to achieve optimal performance.

## License
This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.

## Installation
```bash
pip install ds_sales_prediction_package