nick-leland/RD2L_Random_Forest

tags:

• tabular
• regression
• tabular-regression
• dota

Validation Metrics

• Accuracy: 0.8284240188362744
• R2: 0.63
• MSE: 2428.91
• MAE: 34.33
• RMSE: 49.28

Usage

import numpy as np
from numpy import random
import pandas as pd
import onnxruntime as ort

# Load the saved file
model_path = "rd2l_forest.onnx"
session = ort.InferenceSession(model_path)

# Define default naming scheme
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

def prediction(input_data : np.ndarray) -> float
    """
    Performs inference on the loaded ONNX model using the provided input data.

    Args:
        input_data (np.ndarray): An array of size (263,), this represents all of a singular players information

    Returns:
        float: The predicted cost of the player

    """
    
    # Convert to onnx input format and reshape 
    input_data = input_data.to_numpy(dtype=np.float32).reshape(1, -1)

    # Create prediction
    predictions = session.run([output_name], {input_name: input_data})

    # Convert to individual value
    return round(float(predictions[0][0][0]), 2)

sample_df = pd.DataFrame(np.random.rand(263))

prediction(sample_df)

license: mit