KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #developer-tools #ai-agent #claude-code #codex #typescript #ai-agents #deepseek-harness #mcp #cli #rust

m5

★ 33 Open GitHub ↗

Kaggle M5 Forecasting - Uncertainty 4th Place Solution

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Kaggle M5 Forecasting - Uncertainty 4th Place Solution

## TOC

1. [Contents](#a1)
2. [System Requirements](#a2)
3. [Instructions](#a3)
4. [Create Your Own Model](#a4)

## 1. Contents

| directory or filename | contents |
|-----------|----------|
| models/   | binary files of models and states of random number generator in order to reproduce the solution |
| src/      | source files and hyper parameter config files |
| LICENSE   | license of all contents in this repository (MIT license) |
| ModelSummary.md | document about model |
| README.md | this file |
| directory_structure.txt | directory structure of this repository |
| requirements.txt | python package list |
| *.sh      | shell scripts to download, preprocess, reproduce and train |

## 2. System Requirements

The following specifications were used to create the original solution.

Hardware:

* Microsoft Azure "NC12_Promo" Virtual Machine
	* 12 core CPU
	* 110 GB RAM
	* 2 x NVIDIA Tesla K80 GPU

This machine has 2 GPUs, but only 1 GPU was used.

Software:

* Microsoft Azure "Data Science Virtual Machine - Ubuntu 18.04"
	* Ubuntu 18.04 LTS
	* NVIDIA CUDA 10.2
	* Anaconda

## 3. Instructions

Here are instructions to reproduce the original solution by the trained models.

### 3-1. Setup the Kaggle API

Download your Kaggle API Key, and save it to `~/.kaggle/kaggle.json` .

You need to join not only "Kaggle M5 Forecasting - Uncertainty", but also "Kaggle M5 Forecasting - **Accuracy**" to download both competitions datasets.

### 3-2. Create Python Virtual Environment

Execute following commands to create python virtual environment by anaconda.

```
conda create --name m5 python=3.8
conda activate m5
pip install -r requirements.txt
```

### 3-3. Download Data

Execute following command to download both Accuracy and Uncertainty datasets.
You can also download [Federal Holidays USA 1966-2020](https://www.kaggle.com/gsnehaa21/federal-holidays-usa-19662020) dataset, which I used as additional datasets by executing this program.

```
./run1_download.sh
```

### 3-4. Preprocess

Execute following command to preprocess.
It could take several hours.

```
./run2_preprocess.sh
```

### 3-5. Reproduce

Execute following command to reproduce the solution by the trained models.
It could take about 15 minutes.

```
./run3_reproduce.sh
```

Then, submission files are created at `./submissions` directory.
A file whose name begins with `acc` is an Accuracy submission file, 
and a file whose name begins with `unc` is an Uncertainty submission file.

## 4. Create Your Own Model

You can create your own model by executing following command,

```
./train.sh
```

or executing following command.

```
cd src
python train.py trainer=[agg_cv/agg_submit/each_cv/each_submit]
```

These are hyper parameter config files.

* ./src/config.yaml
* ./src/trainer/agg_cv.yaml
* ./src/trainer/agg_submit.yaml
* ./src/trainer/each_cv.yaml
* ./src/trainer/each_submit.yaml

`agg_submit.yaml` and `each_submit.yaml` are hyper parameters of the original solution.
You can change these settings and try to train models with changed settings.

`agg_cv.yaml` and `each_cv.yaml` are example settings to execute local CV.

When you want to create submission files with your own models, please read comments in `train.sh`.

### 4-1. Hyper Parameters

| parameter | description |
|-----------|-------------|
| experiment | MLflow experiment name. |
| seed | random seed. |
| submit | if True, train models and create temporal submission DataFrames. if False, run CV. |
| each | if True, create "Each Model". if False, create "Agg Model". see `ModelSummary.md` about these models. |
| useval | if True, use Validation Phase Data (2016/04/25 - 2016/05/22) when training models. |
| num\_workers | `num_workers` parameter of `torch.utils.data.DataLoader`. |
| batch\_size | mini batch size. |
| max\_epochs | maximum epochs to train. |
| min\_epochs | minimum epochs to train. |
| patience | patience for early stopping. If 0, early stopping is disabled. |
| val\_check | `val_check_interval ` parameter of `pytorch_lightning.Trainer` |
| over\_sample | if True, data in 2015 is sampled twice and data in 2016 is sampled 4 times in 1 epoch to train. |
| objective | loss function. [train\_loss / train\_wloss / train\_rmse / train\_wrmse] |
| n\_hidden | hidden unit size of LSTM layer. |
| use\_prices | if True, use price features. |
| dist | probability distribution. [Normal / StudentT / NegativeBinomial] |
| df | degree of freedom (only StudentT). |
| dropout | dropout probability. |
| optimizer | optimizer. [Adam / SGD] |
| lr | learning rate. |
| weight_decay | weight_decay parameter of optimizer. |
| scheduler | scheduler. [None / CosineAnnealingLR]|
| T\_max | T\_max parameter of CosineAnnealingLR scheduler. |
| use_te | if True, use target encoding feature. |

End of document

m5

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README