xander-ai 0.2.0

Xander - A package to train Classification, Regression, and Image Classification models.

Xander-AI

Xander-AI is a Python package designed to handle classification, regression, text, and image-related tasks with minimal setup and maximum efficiency.

Installation

pip install xander-ai

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Usage

General Instructions

• Supported Tasks: regression, classification, text, and image.
• Target Column (target_col):
  • Required for regression, classification, and text tasks.
  • Not required for the image task.
• Hyperparameters:
  • Accepts a dictionary where the key epochs is used to define the number of training epochs.

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Task-Specific Details

Image Task

• Dataset Format:
  • Provide a .zip file containing a folder.
  • Inside the folder:
    • Subfolders represent class labels.
    • Images within subfolders correspond to their class.

Example Directory Structure:

dataset.zip
│
├── class_1/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
│
├── class_2/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
│
└── class_n/
    ├── image1.jpg
    ├── image2.jpg
    └── ...

Example Code for Image Task:

from xander_ai import Xander

# Hyperparameters for training
hyperparameters = {
    "epochs": 10,
}

# Initialize the Xander model for image task
xander = Xander(
    dataset_path='path_to_your_dataset.zip',  # Provide path to zip file
    model_name="v1",  # You can change the model name as required
    hyperparameters=hyperparameters,  # Provide hyperparameters
    task="image"  # Specify task as 'image'
)

# Train the model
xander.train()

---

Regression Task

• Dataset Format:
  • The dataset should have a target column specified using target_col.
  • Ensure that the dataset is in a .csv or .xlsx format.

Example Code for Regression Task:

from xander_ai import Xander

# Hyperparameters for training
hyperparameters = {
    "epochs": 20,
}

# Initialize the Xander model for regression task
xander = Xander(
    dataset_path='path_to_your_dataset.csv',  # Provide path to your dataset
    model_name="v1",  # Model version or name
    hyperparameters=hyperparameters,  # Hyperparameters dictionary
    target_col="target",  # Name of the target column
    task="regression"  # Specify task as 'regression'
)

# Train the model
xander.train()

---

Classification Task

• Dataset Format:
  • The dataset should have a target column specified using target_col.
  • The dataset should be in a .csv or .xlsx format.

Example Code for Classification Task:

from xander_ai import Xander

# Hyperparameters for training
hyperparameters = {
    "epochs": 15,
}

# Initialize the Xander model for classification task
xander = Xander(
    dataset_path='path_to_your_dataset.csv',  # Provide path to your dataset
    model_name="v1",  # Model version or name
    hyperparameters=hyperparameters,  # Hyperparameters dictionary
    target_col="target",  # Name of the target column
    task="classification"  # Specify task as 'classification'
)

# Train the model
xander.train()

---

Text Task

• Dataset Format:
  • The dataset should have a target column specified using target_col.
  • The dataset should be in a .csv or .xlsx format.

Example Code for Text Task:

from xander_ai import Xander

# Hyperparameters for training
hyperparameters = {
    "epochs": 25,
}

# Initialize the Xander model for text task
xander = Xander(
    dataset_path='path_to_your_text_dataset.csv',  # Provide path to your dataset
    model_name="v1",  # Model version or name
    hyperparameters=hyperparameters,  # Hyperparameters dictionary
    target_col="text_target",  # Name of the target column
    task="text"  # Specify task as 'text'
)

# Train the model
xander.train()

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License

MIT License