77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import random
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import itertools
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from pywatts import db
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def split(data, k):
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"""Returns (X_train, y_train, X_eval, y_eval)"""
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# Training features as list of dictionaries (each dict is for ONE test run)
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X_train = []
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# Training labels as list of dictionaries (each dict is for ONE test run)
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y_train = []
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# Evaluation features as list of dictionaries (each i-th dict includes all features except X_train[i])
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X_eval = []
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# Evaluation labels as list of dictionaries (each i-th dict includes all labels except X_train[i])
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y_eval = []
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data_list = data['dc'].tolist()
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# Each sample has 337 elements
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samples = [data_list[i:i+337] for i in range(0, len(data_list) - 337, 337)]
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# Randomly shuffle samples
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random.shuffle(samples)
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bucketsize = int(len(samples) / k)
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# K steps
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for i in range(k):
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eval_samples = []
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train_samples = []
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for j in range(k):
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if j == i:
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eval_samples.extend(samples[j*bucketsize:(j+1)*bucketsize])
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else:
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train_samples.extend(samples[j*bucketsize:(j+1)*bucketsize])
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# Create new dictionaries in the eval lists
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#X_eval.append({'dc': eval_samples[:-1]})
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#y_eval.append({'dc': eval_samples[-1]})
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X_eval.append({'dc': [x for s in eval_samples for c, x in enumerate(s, 1) if c % 337 != 0]})
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y_eval.append({'dc': [x for s in eval_samples for c, x in enumerate(s, 1) if c % 337 == 0]})
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#X_train.append({'dc': train_samples[:-1]})
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#y_train.append({'dc': train_samples[-1]})
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X_train.append({'dc': [x for s in train_samples for c, x in enumerate(s, 1) if c % 337 != 0]})
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y_train.append({'dc': [x for s in train_samples for c, x in enumerate(s, 1) if c % 337 == 0]})
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#print(len(X_eval))
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#print(len(y_eval))
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#print(len(X_train))
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#print(len(y_train))
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#print(len(X_train[0]['dc']))
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#print(len(y_train[0]['dc']))
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#print(len(X_eval[0]['dc']))
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#print(len(y_eval[0]['dc']))
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#print(X_train)
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#print(y_train)
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#exit(0)
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return X_train, y_train, X_eval, y_eval
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def train(nn, X_train, y_train, X_eval, y_eval, steps=10):
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"""Trains the Network nn using k-cross-validation"""
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evaluation = []
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for count, train_data in enumerate(X_train):
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for i in range(steps):
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nn.train(train_data, y_train[count], batch_size=int(len(train_data['dc'])/336), steps=1)
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evaluation.append(nn.evaluate(X_eval[count], y_eval[count], batch_size=int(len(X_eval[count]['dc'])/336)))
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print("Training %s: %s/%s" % (count, (i+1), steps))
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return evaluation
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