Alex Blank
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
from functools import partial
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import numpy as np
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import sklearn
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from utils.evaluation import *
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def get_eval_functions(model_configuration: dict):
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"""
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Returns the evaluation functions for the model
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:return: list of evaluation functions
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"""
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eval_functions = [
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{
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"name": "mean_absolute_error_overall",
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"input_index": 0,
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"eval_fn": sklearn.metrics.mean_absolute_error,
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"accumulation_fn": np.mean,
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},
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{
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"name": "mean_absolute_error_pre_ov",
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"input_index": 0,
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"eval_fn": pre_ov_error,
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"accumulation_fn": np.mean,
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},
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{
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"name": "mean_absolute_error_after_ov",
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"input_index": 0,
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"eval_fn": after_ov_error,
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"accumulation_fn": np.mean,
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},
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{
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"name": "mean_absolute_error_ov_in_days",
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"input_index": 0,
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"eval_fn": partial(ov_error, model_configuration=model_configuration),
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"accumulation_fn": np.mean,
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},
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{
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"name": "mean_absolute_error_five_days_before_ov",
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"input_index": 0,
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"eval_fn": partial(day_relative_to_ov_error,
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day_relative_to_ov=-5,
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model_configuration=model_configuration),
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"accumulation_fn": np.mean,
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}
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]
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return eval_functions
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