#!/usr/bin/env python3
"""
Created on Fri Mar 30 22:03:29 2018
@author: mohammad
"""
import sys
import os
import glob
import numpy as np
import pandas as pd
import scipy.io
from sklearn.externals import joblib
import physionetchallenge2018_lib as phyc
def classify_record(record_name):
header_file = record_name + '.hea'
signal_file = record_name + '.mat'
# Read model files from the 'models' subdirectory, which are
# generated by 'train_classifier.py'
model_list = []
for f in glob.glob('models/*_model.pkl'):
model_list.append(f)
# Use the average predictions from the models generated on the
# training set
predictions_mean = 0.
for j in range(0, len(model_list)):
this_model = model_list[j]
predictions = run_classifier(header_file, signal_file, this_model)
predictions_mean += predictions
predictions_mean /= len(model_list)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Return a vector of per-sample predictions, as per challenge requirements
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
return predictions_mean
# This function generates the predictions from a single model
def run_classifier(header_file, signal_file, classifier_pickle):
signal_names, Fs, n_samples = phyc.import_signal_names(header_file)
# Get this subject's data as a dataframe
this_data = phyc.get_subject_data_test(signal_file, signal_names)
# For the baseline, let's only look at how SaO2 might predict arousals
SaO2 = this_data.get(['SaO2']).values
# Format the data for KNN Algorithm
step = Fs * 60
window_size = Fs * 60
# Initialize the X_subj and Y_subj matricies
X_subj = np.zeros([((n_samples) // step), 1])
# Format the data for KNN
for idx, k in enumerate(range(0, (n_samples-step+1), step)):
X_subj[idx, :] = np.var(np.transpose(SaO2[k:k+window_size]), axis=1)
# Load the classifier
my_classifier = joblib.load(classifier_pickle)
# Generate the prediction for the subjects.
predictions = my_classifier.predict_proba(X_subj)
predictions = predictions[:, 1]
predictions = [x * np.ones([window_size]) for x in predictions]
predictions = np.concatenate(predictions)
predictions = np.append(predictions, np.zeros(np.size(this_data, 0)
- np.size(predictions, 0)))
return predictions
if __name__ == '__main__':
for record in sys.argv[1:]:
output_file = os.path.basename(record) + '.vec'
results = classify_record(record)
np.savetxt(output_file, results, fmt='%.3f')