| Name | lib-ml-REMLA10-2024 JSON |
| Version |
1.0.2
JSON |
| download |
| home_page | None |
| Summary | Pre-processing logic for some ML work. |
| upload_time | 2024-06-27 08:26:25 |
| maintainer | None |
| docs_url | None |
| author | Jan |
| requires_python | <3.12,>=3.11 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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No coveralls.
|
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## Installation
> Python 3.11 is needed for this library!
Inside your python 3.11 virtual environment run:
```bash
poetry add lib-ml-REMLA10-2024
```
or install with `pip`
```bash
pip install lib-ml-REMLA10-2024
```
Now you can import the library inside python modules
```python
from lib_ml_remla import preprocess_data, split_data
```
## Tests
To run the tests run the command ```pytest``` from python3.11 virtual environemt.
## 🛡 License
[](https://opensource.org/licenses/MIT)
This project is licensed under the terms of the `MIT` license. See [LICENSE](https://github.com/remla24-team10/lib-ml/blob/main/LICENSE) for more details. -->
# 📦 lib-ml-REMLA10-2024
[](https://opensource.org/licenses/MIT)
## 📝 Overview
`lib-ml-REMLA10-2024` provides essential functions for preprocessing and postprocessing data in machine learning projects. It includes utilities for data splitting, preprocessing, and evaluation.
## 🛠️ Installation
> Note: Python 3.11 is required for this library!
>
### Using Poetry
Inside your Python 3.11 virtual environment, run:
```bash
bashCopy code
poetry add lib-ml-REMLA10-2024
```
### Using pip
Alternatively, you can install the package with pip:
```bash
bashCopy code
pip install lib-ml-REMLA10-2024
```
## 📚 Usage
### Importing the Library
You can import the necessary functions in your Python modules:
```python
pythonCopy code
from lib_ml_remla import preprocess_data, split_data
```
### Usage examples
### 🔄 Preprocessing Data
```python
pythonCopy code
from lib_ml_remla import preprocess_data, split_data
# Example data
train_data = ["1\tThis is a sample training sentence.", "0\tAnother training example."]
test_data = ["1\tThis is a sample test sentence."]
val_data = ["0\tThis is a sample validation sentence."]
# Split data
raw_X_train, raw_y_train, raw_X_val, raw_y_val, raw_X_test, raw_y_test = split_data(train_data, test_data, val_data)
# Preprocess data
X_train, y_train, X_val, y_val, X_test, y_test, char_index, tokenizer, encoder = preprocess_data(
raw_X_train, raw_y_train, raw_X_val, raw_y_val, raw_X_test, raw_y_test
)
```
### 📈 Evaluating Results
```python
pythonCopy code
from lib_ml_remla import predict_classes, evaluate_results
from keras.models import load_model
from sklearn.preprocessing import LabelEncoder
# Load your trained model
model = load_model('path_to_your_model')
# Predict classes
labels, probabilities = predict_classes(model, encoder, X_test)
# Evaluate results
results = evaluate_results(y_test, labels)
print(results)
```
## 🛡 License
This project is licensed under the terms of the `MIT` license. See [LICENSE](https://github.com/remla24-team10/lib-ml/blob/main/LICENSE) for more details.
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