# Path: mimic3models/metrics.py
# def print_metrics_binary(y_true, predictions, verbose=1):
# def print_metrics_multilabel(y_true, predictions, verbose=1):
# def mean_absolute_percentage_error(y_true, y_pred):
# def print_metrics_regression(y_true, predictions, verbose=1):
# def get_bin_log(x, nbins, one_hot=False):
# def get_estimate_log(prediction, nbins):
# def print_metrics_log_bins(y_true, predictions, verbose=1):
# def get_bin_custom(x, nbins, one_hot=False):
# def get_estimate_custom(prediction, nbins):
# def print_metrics_custom_bins(y_true, predictions, verbose=1):
# class LogBins:
# class CustomBins:
# 
# Path: mimic3models/common_utils.py
# def convert_to_dict(data, header, channel_info):
# def extract_features_from_rawdata(chunk, header, period, features):
# def read_chunk(reader, chunk_size):
# def sort_and_shuffle(data, batch_size):
# def add_common_arguments(parser):
#     def __init__(self, dataset_dir, listfile=None, small_part=False):
#     def _read_timeseries(self, ts_filename):
# def create_directory(directory):
# def pad_zeros(arr, min_length=None):
# class DeepSupervisionDataLoader:


from mimic3models import metrics
from mimic3models import common_utils
import numpy as np
import threading
import random
from __future__ import absolute_import
from __future__ import print_function



class BatchGen(object):
    def __init__(self, reader, discretizer, normalizer, ihm_pos, partition,
                 target_repl, batch_size, small_part, shuffle, return_names=False):
        self.discretizer = discretizer
        self.normalizer = normalizer
        self.ihm_pos = ihm_pos
        self.partition = partition
        self.target_repl = target_repl
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.return_names = return_names

        N = reader.get_number_of_examples()
        if small_part:
            N = 1000
        self.steps = (N + batch_size - 1) // batch_size
        self.lock = threading.Lock()


# Path: mimic3models/metrics.py
# def print_metrics_binary(y_true, predictions, verbose=1):
# def print_metrics_multilabel(y_true, predictions, verbose=1):
# def mean_absolute_percentage_error(y_true, y_pred):
# def print_metrics_regression(y_true, predictions, verbose=1):
# def get_bin_log(x, nbins, one_hot=False):
# def get_estimate_log(prediction, nbins):
# def print_metrics_log_bins(y_true, predictions, verbose=1):
# def get_bin_custom(x, nbins, one_hot=False):
# def get_estimate_custom(prediction, nbins):
# def print_metrics_custom_bins(y_true, predictions, verbose=1):
# class LogBins:
# class CustomBins:
# 
# Path: mimic3models/common_utils.py
# def convert_to_dict(data, header, channel_info):
# def extract_features_from_rawdata(chunk, header, period, features):
# def read_chunk(reader, chunk_size):
# def sort_and_shuffle(data, batch_size):
# def add_common_arguments(parser):
#     def __init__(self, dataset_dir, listfile=None, small_part=False):
#     def _read_timeseries(self, ts_filename):
# def create_directory(directory):
# def pad_zeros(arr, min_length=None):
# class DeepSupervisionDataLoader:


# Path: mimic3models/multitask/utils.py
from mimic3models import metrics
from mimic3models import common_utils
import numpy as np
import threading
import random

from __future__ import absolute_import
from __future__ import print_function



class BatchGen(object):
    def __init__(self, reader, discretizer, normalizer, ihm_pos, partition,
                 target_repl, batch_size, small_part, shuffle, return_names=False):
        self.discretizer = discretizer
        self.normalizer = normalizer
        self.ihm_pos = ihm_pos
        self.partition = partition
        self.target_repl = target_repl
        self.batch_size = batch_size
        self.shuffle = shuffle
        self.return_names = return_names

        N = reader.get_number_of_examples()
        if small_part:
            N = 1000
        self.steps = (N + batch_size - 1) // batch_size
        self.lock = threading.Lock()


        ret = common_utils.read_chunk(reader, N)