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"""Average value model""" |
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import torch |
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from torch import nn |
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import pvnet |
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from pvnet.models.base_model import BaseModel |
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from pvnet.optimizers import AbstractOptimizer |
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class Model(BaseModel): |
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"""Simple baseline model that predicts always the same value.""" |
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name = "single_value" |
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def __init__( |
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self, |
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forecast_minutes: int = 120, |
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history_minutes: int = 60, |
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optimizer: AbstractOptimizer = pvnet.optimizers.Adam(), |
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): |
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"""Simple baseline model that predicts always the same value. |
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Args: |
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history_minutes (int): Length of the GSP history period in minutes |
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forecast_minutes (int): Length of the GSP forecast period in minutes |
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optimizer (AbstractOptimizer): Optimizer |
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""" |
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super().__init__(history_minutes, forecast_minutes, optimizer) |
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self._value = nn.Parameter(torch.zeros(1), requires_grad=True) |
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self.save_hyperparameters() |
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def forward(self, x: dict): |
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"""Run model forward on dict batch of data""" |
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y_hat = torch.zeros_like(x["gsp"][:, : self.forecast_len]) + self._value |
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return y_hat |
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