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72 lines
2.5 KiB
72 lines
2.5 KiB
7 years ago
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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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