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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) |