| ---
|
| tags:
|
| - tabular
|
| - regression
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| - tabular-regression
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| - dota
|
| ---
|
|
|
| ## Validation Metrics
|
|
|
| - Accuracy: 0.8284240188362744
|
| - R2: 0.63
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| - MSE: 2428.91
|
| - MAE: 34.33
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| - RMSE: 49.28
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|
|
| ## Usage
|
|
|
| ```python
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| import numpy as np
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| from numpy import random
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| import pandas as pd
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| import onnxruntime as ort
|
|
|
| # Load the saved file
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| model_path = "rd2l_forest.onnx"
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| session = ort.InferenceSession(model_path)
|
|
|
| # Define default naming scheme
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| input_name = session.get_inputs()[0].name
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| 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:
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| input_data (np.ndarray): An array of size (263,), this represents all of a singular players information
|
|
|
| Returns:
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| float: The predicted cost of the player
|
|
|
| """
|
|
|
| # Convert to onnx input format and reshape
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| input_data = input_data.to_numpy(dtype=np.float32).reshape(1, -1)
|
|
|
| # Create prediction
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| predictions = session.run([output_name], {input_name: input_data})
|
|
|
| # Convert to individual value
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| return round(float(predictions[0][0][0]), 2)
|
|
|
| sample_df = pd.DataFrame(np.random.rand(263))
|
|
|
| prediction(sample_df)
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| ```
|
| ---
|
| license: mit
|
| ---
|
|
|
|
|
