gbt 0.3

A gradient boosted tree library with automatic feature engineering.

gbt is a library for gradient boosted trees with minimal coding required. It is a thin wrapper around lightgbm.

Features

• Zero feature engineering needed - automatic encoding of categorical features
• Built-in train/validation splitting
• Automatic artifact saving and loading
• Pre-configured model presets for common tasks (binary, multiclass, regression)

What you need:

• a pandas dataframe,
• the target column to predict on,
• categorical feature columns (can be empty),
• numerical feature columns (can be empty, but you should have at least one categorical or numerical feature),
• model_lib: "binary", "multiclass", "mape", "l2" to specify what type of prediction objective and default hyperparameters to use.

You don't need to (though you are welcome to):

• normalize the numerical feature values
• construct the encoder to one-hot encode categorical features
• manage saving of artifacts for above feature transformation
• implement evaluation metrics

Prerequisites

• Python 3.7+
• pandas, numpy, scikit-learn, lightgbm

Install

pip install gbt

Quickstart

import pandas as pd
from gbt import train

# Your data
df = pd.DataFrame({
    "feature_1": [1, 2, 3, 4, 5],
    "category": ["A", "B", "A", "C", "B"], 
    "target": [0, 1, 0, 1, 1]
})

# Train model
model = train(
    df,
    model_lib="binary",  # binary, multiclass, mape, or l2
    label_column="target",
    categorical_feature_columns=["category"],
    numerical_feature_columns=["feature_1"]
)

# Make predictions
new_data = pd.DataFrame({
    "feature_1": [6, 7],
    "category": ["A", "B"]
})
predictions = model.predict(new_data)
print(predictions)  # [0.23, 0.78]

Save and Load Models

# Save model
model.save("my_model")

# Load model later
from gbt import load
loaded_model = load("my_model")
predictions = loaded_model.predict(new_data)

Advanced Usage

For more control over training:

from gbt import TrainingPipeline

# Custom training configuration  
pipeline = TrainingPipeline(
    categorical_feature_columns=["category"],
    numerical_feature_columns=["feature_1"],
    params_preset="binary",
    params_override={"num_leaves": 50},  # Custom hyperparameters
    val_size=0.3,  # 30% validation split
    verbose=False   # Quiet training
)

# Train with custom data loader
class DatasetBuilder:
    def training_dataset(self):
        return pd.read_csv("train.csv")
    
    def testing_dataset(self):
        return pd.read_csv("test.csv")

pipeline.fit(DatasetBuilder())

# Get model for deployment
model = pipeline.create_model()
model.save("production_model")

API Reference

Main Functions

train(df, model_lib="l2", label_column, categorical_feature_columns, numerical_feature_columns, **kwargs)

Train a gradient boosting model.

Parameters:

• df: Training dataframe
• model_lib: Model type - "binary", "multiclass", "mape", or "l2"
• label_column: Target column name
• categorical_feature_columns: List of categorical feature names
• numerical_feature_columns: List of numerical feature names
• val_size: Validation split fraction (default 0.2)
• log_dir: Directory to save artifacts (optional)

Returns: Trained model ready for prediction

load(path)

Load a saved model.

Returns: Model ready for inference

Model Types

model_lib	Use Case	Loss Function
"binary"	Binary classification	Log loss
"multiclass"	Multi-class classification	Multi-class log loss
"l2"	Regression	Mean squared error
"mape"	Regression	Mean absolute percentage error

Model Methods

• .predict(df): Make predictions
• .save(path): Save model to disk

For advanced usage, see TrainingPipeline class documentation.