from sklearn.linear_model import LogisticRegression from sklearn.multiclass import OneVsRestClassifier from models.base_model import ClassificationModel import pickle from tqdm import tqdm import numpy as np from sklearn.ensemble import RandomForestClassifier import pywt import scipy.stats import multiprocessing from collections import Counter from keras.layers import Dropout, Dense, Input from keras.models import Model from keras.models import load_model from keras.callbacks import ModelCheckpoint from sklearn.preprocessing import StandardScaler def calculate_entropy(list_values): counter_values = Counter(list_values).most_common() probabilities = [elem[1] / len(list_values) for elem in counter_values] entropy = scipy.stats.entropy(probabilities) return entropy def calculate_statistics(list_values): n5 = np.nanpercentile(list_values, 5) n25 = np.nanpercentile(list_values, 25) n75 = np.nanpercentile(list_values, 75) n95 = np.nanpercentile(list_values, 95) median = np.nanpercentile(list_values, 50) mean = np.nanmean(list_values) std = np.nanstd(list_values) var = np.nanvar(list_values) rms = np.nanmean(np.sqrt(list_values ** 2)) return [n5, n25, n75, n95, median, mean, std, var, rms] def calculate_crossings(list_values): zero_crossing_indices = np.nonzero(np.diff(np.array(list_values) > 0))[0] no_zero_crossings = len(zero_crossing_indices) mean_crossing_indices = np.nonzero(np.diff(np.array(list_values) > np.nanmean(list_values)))[0] no_mean_crossings = len(mean_crossing_indices) return [no_zero_crossings, no_mean_crossings] def get_features(list_values): entropy = calculate_entropy(list_values) crossings = calculate_crossings(list_values) statistics = calculate_statistics(list_values) return [entropy] + crossings + statistics def get_single_ecg_features(signal, waveletname='db6'): features = [] for channel in signal.T: list_coeff = pywt.wavedec(channel, wavelet=waveletname, level=5) channel_features = [] for coeff in list_coeff: channel_features += get_features(coeff) features.append(channel_features) return np.array(features).flatten() def get_ecg_features(ecg_data, parallel=True): if parallel: pool = multiprocessing.Pool(18) return np.array(pool.map(get_single_ecg_features, ecg_data)) else: list_features = [] for signal in tqdm(ecg_data): features = get_single_ecg_features(signal) list_features.append(features) return np.array(list_features) # for keras models # def keras_macro_auroc(y_true, y_pred): # return tf.py_func(macro_auroc, (y_true, y_pred), tf.double) class WaveletModel(ClassificationModel): def __init__(self, name, n_classes, freq, outputfolder, input_shape, regularizer_C=.001, classifier='RF'): # Disclaimer: This model assumes equal shapes across all samples! # standard parameters super().__init__() self.name = name self.outputfolder = outputfolder self.n_classes = n_classes self.freq = freq self.regularizer_C = regularizer_C self.classifier = classifier self.dropout = .25 self.activation = 'relu' self.final_activation = 'sigmoid' self.n_dense_dim = 128 self.epochs = 30 def fit(self, X_train, y_train, X_val, y_val): XF_train = get_ecg_features(X_train) XF_val = get_ecg_features(X_val) if self.classifier == 'LR': if self.n_classes > 1: clf = OneVsRestClassifier( LogisticRegression(C=self.regularizer_C, solver='lbfgs', max_iter=1000, n_jobs=-1)) else: clf = LogisticRegression(C=self.regularizer_C, solver='lbfgs', max_iter=1000, n_jobs=-1) clf.fit(XF_train, y_train) pickle.dump(clf, open(self.outputfolder + 'clf.pkl', 'wb')) elif self.classifier == 'RF': clf = RandomForestClassifier(n_estimators=1000, n_jobs=16) clf.fit(XF_train, y_train) pickle.dump(clf, open(self.outputfolder + 'clf.pkl', 'wb')) elif self.classifier == 'NN': # standardize input data ss = StandardScaler() XFT_train = ss.fit_transform(XF_train) XFT_val = ss.transform(XF_val) pickle.dump(ss, open(self.outputfolder + 'ss.pkl', 'wb')) # classification stage input_x = Input(shape=(XFT_train.shape[1],)) x = Dense(self.n_dense_dim, activation=self.activation)(input_x) x = Dropout(self.dropout)(x) y = Dense(self.n_classes, activation=self.final_activation)(x) self.model = Model(input_x, y) self.model.compile(optimizer='adamax', loss='binary_crossentropy') # , metrics=[keras_macro_auroc]) # monitor validation error mc_loss = ModelCheckpoint(self.outputfolder + 'best_loss_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True) # mc_score = ModelCheckpoint(self.output_folder +'best_score_model.h5', monitor='val_keras_macro_auroc', mode='max', verbose=1, save_best_only=True) self.model.fit(XFT_train, y_train, validation_data=(XFT_val, y_val), epochs=self.epochs, batch_size=128, callbacks=[mc_loss]) # , mc_score]) self.model.save(self.outputfolder + 'last_model.h5') def predict(self, X): XF = get_ecg_features(X) if self.classifier == 'LR': clf = pickle.load(open(self.outputfolder + 'clf.pkl', 'rb')) if self.n_classes > 1: return clf.predict_proba(XF) else: return clf.predict_proba(XF)[:, 1][:, np.newaxis] elif self.classifier == 'RF': clf = pickle.load(open(self.outputfolder + 'clf.pkl', 'rb')) y_pred = clf.predict_proba(XF) if self.n_classes > 1: return np.array([yi[:, 1] for yi in y_pred]).T else: return y_pred[:, 1][:, np.newaxis] elif self.classifier == 'NN': ss = pickle.load(open(self.outputfolder + 'ss.pkl', 'rb')) # XFT = ss.transform(XF) model = load_model( self.outputfolder + 'best_loss_model.h5') # 'best_score_model.h5', custom_objects={ # 'keras_macro_auroc': keras_macro_auroc}) return model.predict(XFT)