51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
import pandas
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import tensorflow as tf
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# def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
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#
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# return tf.estimator.inputs.pandas_input_fn(x=X,
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# y=y,
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# num_epochs=num_epochs,
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# shuffle=shuffle,
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# batch_size=batch_size)
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def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=1):
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# Create dictionary for features in hour 0 ... 335
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features = {str(idx): [] for idx in range(336)}
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dc_values = X['dc'].tolist()
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# Iterate the empty dictionary always adding the idx-th element from the dc_values list
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for idx, value_list in features.items():
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value_list.extend(dc_values[int(idx)::336])
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labels = None
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if y is not None:
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labels = y['dc'].values
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if labels is None:
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dataset = tf.data.Dataset.from_tensor_slices(dict(features))
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else:
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dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
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return dataset.batch(batch_size)
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class Net:
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__regressor = None
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__feature_cols = [tf.feature_column.numeric_column(col) for col in ['dc', 'temp', 'wind']]
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def __init__(self, feature_cols=__feature_cols):
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self.__regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
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hidden_units=[2],
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model_dir='tf_pywatts_model')
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def train(self, training_data, training_results, steps):
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self.__regressor.train(input_fn=lambda: pywatts_input_fn(training_data, y=training_results, num_epochs=None, shuffle=True, batch_size=1), steps=steps)
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def evaluate(self, eval_data, eval_results):
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return self.__regressor.evaluate(input_fn=lambda: pywatts_input_fn(eval_data, y=eval_results, num_epochs=1, shuffle=False), steps=1)
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def predict1h(self, predict_data):
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return self.__regressor.predict(input_fn=lambda: pywatts_input_fn(predict_data, num_epochs=1, shuffle=False))
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