import os
import numpy as np
import random


class Reader(object):
    def __init__(self, dataset_dir, listfile=None):
        self._dataset_dir = dataset_dir
        self._current_index = 0
        if listfile is None:
            listfile_path = os.path.join(dataset_dir, "listfile.csv")
        else:
            listfile_path = listfile
        with open(listfile_path, "r") as lfile:
            self._data = lfile.readlines()
        self._listfile_header = self._data[0]
        self._data = self._data[1:]

    def get_number_of_examples(self):
        return len(self._data)

    def random_shuffle(self, seed=None):
        if seed is not None:
            random.seed(seed)
        random.shuffle(self._data)

    def read_example(self, index):
        raise NotImplementedError()

    def read_next(self):
        to_read_index = self._current_index
        self._current_index += 1
        if self._current_index == self.get_number_of_examples():
            self._current_index = 0
        return self.read_example(to_read_index)


class DecompensationReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for decompensation prediction task.
        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), int(y)) for (x, t, y) in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-6:
                    break
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Read the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Directory with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                Mortality within next 24 hours.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of examples (exclusive).")

        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        (X, header) = self._read_timeseries(name, t)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class InHospitalMortalityReader(Reader):
    def __init__(self, dataset_dir, listfile=None, period_length=48.0):
        """ Reader for in-hospital moratality prediction task.

        :param dataset_dir:   Directory where timeseries files are stored.
        :param listfile:      Path to a listfile. If this parameter is left `None` then
                              `dataset_dir/listfile.csv` will be used.
        :param period_length: Length of the period (in hours) from which the prediction is done.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, int(y)) for (x, y) in self._data]
        self._period_length = period_length

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                In-hospital mortality.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        t = self._period_length
        y = self._data[index][1]
        (X, header) = self._read_timeseries(name)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class LengthOfStayReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for length of stay prediction task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), float(y)) for (x, t, y) in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-6:
                    break
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : float
                Remaining time in ICU.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        (X, header) = self._read_timeseries(name, t)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class PhenotypingReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for phenotype classification task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(mas[0], float(mas[1]), list(map(int, mas[2:]))) for mas in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : array of ints
                Phenotype labels.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        (X, header) = self._read_timeseries(name)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class MultitaskReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for multitask learning.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]

        def process_ihm(x):
            return list(map(int, x.split(';')))

        def process_los(x):
            x = x.split(';')
            if x[0] == '':
                return ([], [])
            return (list(map(int, x[:len(x)//2])), list(map(float, x[len(x)//2:])))

        def process_ph(x):
            return list(map(int, x.split(';')))

        def process_decomp(x):
            x = x.split(';')
            if x[0] == '':
                return ([], [])
            return (list(map(int, x[:len(x)//2])), list(map(int, x[len(x)//2:])))

        self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
                       process_ph(pheno), process_decomp(decomp))
                      for fname, t, ihm, los, pheno, decomp in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Return dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            ihm : array
                Array of 3 integers: [pos, mask, label].
            los : array
                Array of 2 arrays: [masks, labels].
            pheno : array
                Array of 25 binary integers (phenotype labels).
            decomp : array
                Array of 2 arrays: [masks, labels].
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        (X, header) = self._read_timeseries(name)

        return {"X": X,
                "t": self._data[index][1],
                "ihm": self._data[index][2],
                "los": self._data[index][3],
                "pheno": self._data[index][4],
                "decomp": self._data[index][5],
                "header": header,
                "name": name}

2a818780a1924f0294aa7e259d08f6abe8221677
<mask token>


class DecompensationReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for decompensation prediction task.
        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), int(y)) for x, t, y in self._data]
    <mask token>

    def read_example(self, index):
        """ Read the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Directory with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                Mortality within next 24 hours.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of examples (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class InHospitalMortalityReader(Reader):

    def __init__(self, dataset_dir, listfile=None, period_length=48.0):
        """ Reader for in-hospital moratality prediction task.

        :param dataset_dir:   Directory where timeseries files are stored.
        :param listfile:      Path to a listfile. If this parameter is left `None` then
                              `dataset_dir/listfile.csv` will be used.
        :param period_length: Length of the period (in hours) from which the prediction is done.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, int(y)) for x, y in self._data]
        self._period_length = period_length

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                In-hospital mortality.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._period_length
        y = self._data[index][1]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class LengthOfStayReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for length of stay prediction task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), float(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : float
                Remaining time in ICU.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class PhenotypingReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for phenotype classification task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(mas[0], float(mas[1]), list(map(int, mas[2:]))) for
            mas in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : array of ints
                Phenotype labels.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class MultitaskReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for multitask learning.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]

        def process_ihm(x):
            return list(map(int, x.split(';')))

        def process_los(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(float, x[len(x
                ) // 2:]))

        def process_ph(x):
            return list(map(int, x.split(';')))

        def process_decomp(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(int, x[len(x) //
                2:]))
        self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
            process_ph(pheno), process_decomp(decomp)) for fname, t, ihm,
            los, pheno, decomp in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Return dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            ihm : array
                Array of 3 integers: [pos, mask, label].
            los : array
                Array of 2 arrays: [masks, labels].
            pheno : array
                Array of 25 binary integers (phenotype labels).
            decomp : array
                Array of 2 arrays: [masks, labels].
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': self._data[index][1], 'ihm': self._data[index]
            [2], 'los': self._data[index][3], 'pheno': self._data[index][4],
            'decomp': self._data[index][5], 'header': header, 'name': name}

<mask token>


class Reader(object):
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>


class DecompensationReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for decompensation prediction task.
        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), int(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Read the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Directory with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                Mortality within next 24 hours.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of examples (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class InHospitalMortalityReader(Reader):

    def __init__(self, dataset_dir, listfile=None, period_length=48.0):
        """ Reader for in-hospital moratality prediction task.

        :param dataset_dir:   Directory where timeseries files are stored.
        :param listfile:      Path to a listfile. If this parameter is left `None` then
                              `dataset_dir/listfile.csv` will be used.
        :param period_length: Length of the period (in hours) from which the prediction is done.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, int(y)) for x, y in self._data]
        self._period_length = period_length

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                In-hospital mortality.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._period_length
        y = self._data[index][1]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class LengthOfStayReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for length of stay prediction task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), float(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : float
                Remaining time in ICU.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class PhenotypingReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for phenotype classification task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(mas[0], float(mas[1]), list(map(int, mas[2:]))) for
            mas in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : array of ints
                Phenotype labels.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class MultitaskReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for multitask learning.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]

        def process_ihm(x):
            return list(map(int, x.split(';')))

        def process_los(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(float, x[len(x
                ) // 2:]))

        def process_ph(x):
            return list(map(int, x.split(';')))

        def process_decomp(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(int, x[len(x) //
                2:]))
        self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
            process_ph(pheno), process_decomp(decomp)) for fname, t, ihm,
            los, pheno, decomp in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Return dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            ihm : array
                Array of 3 integers: [pos, mask, label].
            los : array
                Array of 2 arrays: [masks, labels].
            pheno : array
                Array of 25 binary integers (phenotype labels).
            decomp : array
                Array of 2 arrays: [masks, labels].
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': self._data[index][1], 'ihm': self._data[index]
            [2], 'los': self._data[index][3], 'pheno': self._data[index][4],
            'decomp': self._data[index][5], 'header': header, 'name': name}

<mask token>


class Reader(object):

    def __init__(self, dataset_dir, listfile=None):
        self._dataset_dir = dataset_dir
        self._current_index = 0
        if listfile is None:
            listfile_path = os.path.join(dataset_dir, 'listfile.csv')
        else:
            listfile_path = listfile
        with open(listfile_path, 'r') as lfile:
            self._data = lfile.readlines()
        self._listfile_header = self._data[0]
        self._data = self._data[1:]

    def get_number_of_examples(self):
        return len(self._data)
    <mask token>
    <mask token>
    <mask token>


class DecompensationReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for decompensation prediction task.
        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), int(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Read the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Directory with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                Mortality within next 24 hours.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of examples (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class InHospitalMortalityReader(Reader):

    def __init__(self, dataset_dir, listfile=None, period_length=48.0):
        """ Reader for in-hospital moratality prediction task.

        :param dataset_dir:   Directory where timeseries files are stored.
        :param listfile:      Path to a listfile. If this parameter is left `None` then
                              `dataset_dir/listfile.csv` will be used.
        :param period_length: Length of the period (in hours) from which the prediction is done.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, int(y)) for x, y in self._data]
        self._period_length = period_length

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                In-hospital mortality.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._period_length
        y = self._data[index][1]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class LengthOfStayReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for length of stay prediction task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), float(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : float
                Remaining time in ICU.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class PhenotypingReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for phenotype classification task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(mas[0], float(mas[1]), list(map(int, mas[2:]))) for
            mas in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : array of ints
                Phenotype labels.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class MultitaskReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for multitask learning.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]

        def process_ihm(x):
            return list(map(int, x.split(';')))

        def process_los(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(float, x[len(x
                ) // 2:]))

        def process_ph(x):
            return list(map(int, x.split(';')))

        def process_decomp(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(int, x[len(x) //
                2:]))
        self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
            process_ph(pheno), process_decomp(decomp)) for fname, t, ihm,
            los, pheno, decomp in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Return dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            ihm : array
                Array of 3 integers: [pos, mask, label].
            los : array
                Array of 2 arrays: [masks, labels].
            pheno : array
                Array of 25 binary integers (phenotype labels).
            decomp : array
                Array of 2 arrays: [masks, labels].
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': self._data[index][1], 'ihm': self._data[index]
            [2], 'los': self._data[index][3], 'pheno': self._data[index][4],
            'decomp': self._data[index][5], 'header': header, 'name': name}

<mask token>


class Reader(object):

    def __init__(self, dataset_dir, listfile=None):
        self._dataset_dir = dataset_dir
        self._current_index = 0
        if listfile is None:
            listfile_path = os.path.join(dataset_dir, 'listfile.csv')
        else:
            listfile_path = listfile
        with open(listfile_path, 'r') as lfile:
            self._data = lfile.readlines()
        self._listfile_header = self._data[0]
        self._data = self._data[1:]

    def get_number_of_examples(self):
        return len(self._data)
    <mask token>
    <mask token>

    def read_next(self):
        to_read_index = self._current_index
        self._current_index += 1
        if self._current_index == self.get_number_of_examples():
            self._current_index = 0
        return self.read_example(to_read_index)


class DecompensationReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for decompensation prediction task.
        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), int(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Read the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Directory with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                Mortality within next 24 hours.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of examples (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class InHospitalMortalityReader(Reader):

    def __init__(self, dataset_dir, listfile=None, period_length=48.0):
        """ Reader for in-hospital moratality prediction task.

        :param dataset_dir:   Directory where timeseries files are stored.
        :param listfile:      Path to a listfile. If this parameter is left `None` then
                              `dataset_dir/listfile.csv` will be used.
        :param period_length: Length of the period (in hours) from which the prediction is done.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, int(y)) for x, y in self._data]
        self._period_length = period_length

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                In-hospital mortality.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._period_length
        y = self._data[index][1]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class LengthOfStayReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for length of stay prediction task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), float(y)) for x, t, y in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-06:
                    break
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : float
                Remaining time in ICU.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name, t)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class PhenotypingReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for phenotype classification task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(mas[0], float(mas[1]), list(map(int, mas[2:]))) for
            mas in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : array of ints
                Phenotype labels.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': t, 'y': y, 'header': header, 'name': name}


class MultitaskReader(Reader):

    def __init__(self, dataset_dir, listfile=None):
        """ Reader for multitask learning.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]

        def process_ihm(x):
            return list(map(int, x.split(';')))

        def process_los(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(float, x[len(x
                ) // 2:]))

        def process_ph(x):
            return list(map(int, x.split(';')))

        def process_decomp(x):
            x = x.split(';')
            if x[0] == '':
                return [], []
            return list(map(int, x[:len(x) // 2])), list(map(int, x[len(x) //
                2:]))
        self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
            process_ph(pheno), process_decomp(decomp)) for fname, t, ihm,
            los, pheno, decomp in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), 'r') as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == 'Hours'
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return np.stack(ret), header

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Return dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            ihm : array
                Array of 3 integers: [pos, mask, label].
            los : array
                Array of 2 arrays: [masks, labels].
            pheno : array
                Array of 25 binary integers (phenotype labels).
            decomp : array
                Array of 2 arrays: [masks, labels].
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError(
                'Index must be from 0 (inclusive) to number of lines (exclusive).'
                )
        name = self._data[index][0]
        X, header = self._read_timeseries(name)
        return {'X': X, 't': self._data[index][1], 'ihm': self._data[index]
            [2], 'los': self._data[index][3], 'pheno': self._data[index][4],
            'decomp': self._data[index][5], 'header': header, 'name': name}

import os
import numpy as np
import random


class Reader(object):
    def __init__(self, dataset_dir, listfile=None):
        self._dataset_dir = dataset_dir
        self._current_index = 0
        if listfile is None:
            listfile_path = os.path.join(dataset_dir, "listfile.csv")
        else:
            listfile_path = listfile
        with open(listfile_path, "r") as lfile:
            self._data = lfile.readlines()
        self._listfile_header = self._data[0]
        self._data = self._data[1:]

    def get_number_of_examples(self):
        return len(self._data)

    def random_shuffle(self, seed=None):
        if seed is not None:
            random.seed(seed)
        random.shuffle(self._data)

    def read_example(self, index):
        raise NotImplementedError()

    def read_next(self):
        to_read_index = self._current_index
        self._current_index += 1
        if self._current_index == self.get_number_of_examples():
            self._current_index = 0
        return self.read_example(to_read_index)


class DecompensationReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for decompensation prediction task.
        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), int(y)) for (x, t, y) in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-6:
                    break
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Read the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Directory with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                Mortality within next 24 hours.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of examples (exclusive).")

        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        (X, header) = self._read_timeseries(name, t)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class InHospitalMortalityReader(Reader):
    def __init__(self, dataset_dir, listfile=None, period_length=48.0):
        """ Reader for in-hospital moratality prediction task.

        :param dataset_dir:   Directory where timeseries files are stored.
        :param listfile:      Path to a listfile. If this parameter is left `None` then
                              `dataset_dir/listfile.csv` will be used.
        :param period_length: Length of the period (in hours) from which the prediction is done.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, int(y)) for (x, y) in self._data]
        self._period_length = period_length

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : int (0 or 1)
                In-hospital mortality.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        t = self._period_length
        y = self._data[index][1]
        (X, header) = self._read_timeseries(name)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class LengthOfStayReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for length of stay prediction task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(x, float(t), float(y)) for (x, t, y) in self._data]

    def _read_timeseries(self, ts_filename, time_bound):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                t = float(mas[0])
                if t > time_bound + 1e-6:
                    break
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : float
                Remaining time in ICU.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        (X, header) = self._read_timeseries(name, t)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class PhenotypingReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for phenotype classification task.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]
        self._data = [(mas[0], float(mas[1]), list(map(int, mas[2:]))) for mas in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            y : array of ints
                Phenotype labels.
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        t = self._data[index][1]
        y = self._data[index][2]
        (X, header) = self._read_timeseries(name)

        return {"X": X,
                "t": t,
                "y": y,
                "header": header,
                "name": name}


class MultitaskReader(Reader):
    def __init__(self, dataset_dir, listfile=None):
        """ Reader for multitask learning.

        :param dataset_dir: Directory where timeseries files are stored.
        :param listfile:    Path to a listfile. If this parameter is left `None` then
                            `dataset_dir/listfile.csv` will be used.
        """
        Reader.__init__(self, dataset_dir, listfile)
        self._data = [line.split(',') for line in self._data]

        def process_ihm(x):
            return list(map(int, x.split(';')))

        def process_los(x):
            x = x.split(';')
            if x[0] == '':
                return ([], [])
            return (list(map(int, x[:len(x)//2])), list(map(float, x[len(x)//2:])))

        def process_ph(x):
            return list(map(int, x.split(';')))

        def process_decomp(x):
            x = x.split(';')
            if x[0] == '':
                return ([], [])
            return (list(map(int, x[:len(x)//2])), list(map(int, x[len(x)//2:])))

        self._data = [(fname, float(t), process_ihm(ihm), process_los(los),
                       process_ph(pheno), process_decomp(decomp))
                      for fname, t, ihm, los, pheno, decomp in self._data]

    def _read_timeseries(self, ts_filename):
        ret = []
        with open(os.path.join(self._dataset_dir, ts_filename), "r") as tsfile:
            header = tsfile.readline().strip().split(',')
            assert header[0] == "Hours"
            for line in tsfile:
                mas = line.strip().split(',')
                ret.append(np.array(mas))
        return (np.stack(ret), header)

    def read_example(self, index):
        """ Reads the example with given index.

        :param index: Index of the line of the listfile to read (counting starts from 0).
        :return: Return dictionary with the following keys:
            X : np.array
                2D array containing all events. Each row corresponds to a moment.
                First column is the time and other columns correspond to different
                variables.
            t : float
                Length of the data in hours. Note, in general, it is not equal to the
                timestamp of last event.
            ihm : array
                Array of 3 integers: [pos, mask, label].
            los : array
                Array of 2 arrays: [masks, labels].
            pheno : array
                Array of 25 binary integers (phenotype labels).
            decomp : array
                Array of 2 arrays: [masks, labels].
            header : array of strings
                Names of the columns. The ordering of the columns is always the same.
            name: Name of the sample.
        """
        if index < 0 or index >= len(self._data):
            raise ValueError("Index must be from 0 (inclusive) to number of lines (exclusive).")

        name = self._data[index][0]
        (X, header) = self._read_timeseries(name)

        return {"X": X,
                "t": self._data[index][1],
                "ihm": self._data[index][2],
                "los": self._data[index][3],
                "pheno": self._data[index][4],
                "decomp": self._data[index][5],
                "header": header,
                "name": name}
