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71 lines
2.5 KiB
71 lines
2.5 KiB
import pickle |
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from tsfresh.transformers.feature_selector import FeatureSelector |
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from sklearn.preprocessing import MinMaxScaler |
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from sklearn.ensemble import IsolationForest |
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import pandas as pd |
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from sklearn import svm |
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class supervised_algorithm(object): |
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frame_size = 16 |
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good_features = [ |
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#"value__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\"", |
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# "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_12__w_20", |
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# "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_13__w_5", |
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# "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_10", |
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# "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_2__w_20", |
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# "value__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20", |
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# "value__fft_coefficient__coeff_3__attr_\"abs\"", |
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"time_of_day_column_x", |
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"time_of_day_column_y", |
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"value__abs_energy", |
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# "value__absolute_sum_of_changes", |
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# "value__sum_of_reoccurring_data_points", |
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] |
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clf = None |
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scaler = None |
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def __init__(self): |
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self.features = [] |
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self.col_to_max, self.col_to_min, self.col_to_median = None, None, None |
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self.augmented_path = None |
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def fit(self, dataset, contamination=0.005): |
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dataset = dataset[self.good_features] |
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dataset = dataset[-100000:] |
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self.scaler = MinMaxScaler(feature_range=(-1, 1)) |
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# self.clf = svm.OneClassSVM(nu=contamination, kernel="rbf", gamma=0.1) |
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self.clf = IsolationForest(contamination=contamination) |
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self.scaler.fit(dataset) |
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dataset = self.scaler.transform(dataset) |
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self.clf.fit(dataset) |
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def predict(self, dataframe): |
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dataset = dataframe[self.good_features] |
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dataset = self.scaler.transform(dataset) |
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prediction = self.clf.predict(dataset) |
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# for i in range(len(dataset)): |
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# print(str(dataset[i]) + " " + str(prediction[i])) |
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prediction = [x < 0.0 for x in prediction] |
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return pd.Series(prediction, index=dataframe.index) |
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def save(self, model_filename): |
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with open(model_filename, 'wb') as file: |
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pickle.dump((self.clf, self.scaler), file) |
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def load(self, model_filename): |
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with open(model_filename, 'rb') as file: |
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self.clf, self.scaler = pickle.load(file) |
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def __select_features(self, x, y): |
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# feature_selector = FeatureSelector() |
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feature_selector = FeatureSelector() |
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feature_selector.fit(x, y) |
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return feature_selector.relevant_features
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