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