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evaluate.py
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evaluate.py
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import argparse
from irradiance_rnn.evaluate import evaluate
from irradiance_rnn.plot import pretty_plot
def main():
# long lines
parser = argparse.ArgumentParser(
description=
'Evaluate and plot the irradiance forecast results of a trained model'
)
parser.add_argument(
'--lat', type=float, required=True, help='Latitude [Required]'
)
parser.add_argument(
'--lon', type=float, required=True, help='Longitude [Required]'
)
parser.add_argument(
'--test-years',
type=str,
required=True,
help=
'Comma seperated value string of downloaded irradaince data [Required]',
)
parser.add_argument(
'--seq-length',
type=int,
default=64,
help=
'How many data points are needed to make one prediction [default: 64]'
)
parser.add_argument(
'--model-name',
type=str,
default='model',
help='Name of the saved model [default: model]'
)
parser.add_argument(
'--start-date',
type=str,
default=None,
help='Start date if you want to slice [default: None]'
)
parser.add_argument(
'--end-date',
type=str,
default=None,
help='End date if you want to slice [default: None]'
)
parser.add_argument(
'--hidden-size',
type=int,
default=35,
help='How many hidden neurons per LSTM layer [default: 35]'
)
parser.add_argument(
'--num-layers',
type=int,
default=2,
help='How many LSTM layers [default: 2]'
)
parser.add_argument(
'--plot',
action='store_true',
default=False,
help='Should we plot the data [default: False]'
)
args = vars(parser.parse_args())
dates, predicted, actual, rmse = evaluate(**args)
if args['plot']:
pretty_plot(dates, predicted, actual, round(rmse, 2))
if __name__ == '__main__':
main()