Private API for Andrew
Project description
ai-replication
Checkout at: https://pypi.org/manage/project/aireplication/releases/
Usage
from aireplication.ultils.data import TimeSeriesGenerator, Dataset
config = {"dataset_name": "GYEONGGI2955",
"features": ["Amount of Consumption", "Temperature"],
"prediction_feature": "Amount of Consumption", # Feature to use for prediction
"input_width": 168,
"output_length": 1,
"train_ratio": 0.9
}
dataset = Dataset(dataset_name=config["dataset_name"])
# data = dataset.dataloader.export_a_single_sequence()
data = dataset.dataloader.export_the_sequence(config["features"])
print("Building time series generator...")
tsf = TimeSeriesGenerator(data=data,
config=config,
normalize_type=1,
shuffle=False)
# Get model
model = get_model(model_name=args.model_name,
config=config)
# Train model
history = model.fit(x=tsf.data_train[0], # [number_recoder, input_len, number_feature]
y=tsf.data_train[1], # [number_recoder, output_len, number_feature]
validation_data=tsf.data_valid)
List of dataset is available
config1 = {"dataset_name": "GYEONGGI2955",
"features": ["Amount of Consumption", "Temperature"],
"prediction_feature": "Amount of Consumption", # Feature to use for prediction
"input_width": 168,
"output_length": 1,
"train_ratio": 0.9
}
config2 = {"dataset_name": "CNU_ENGINEERING_7",
"features": [ "temperatures", "humidity", "pressure","energy" ] # Features to use for training
prediction_feature: "energy", # Feature to use for prediction
"input_width": 168,
"output_length": 1,
"train_ratio": 0.9
}
Publishing the package
pip install twine
python setup.py sdist
twine upload dist/*
- Note: Testing case:
twine upload --repository testpypi dist/*
Project details
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aireplication-0.0.3.tar.gz
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