# Path: mimic3benchmark/readers.py
# 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}
# 
# 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/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/phenotyping/utils.py
# def save_results(names, ts, predictions, labels, path):
#     n_tasks = 25
#     common_utils.create_directory(os.path.dirname(path))
#     with open(path, 'w') as f:
#         header = ["stay", "period_length"]
#         header += ["pred_{}".format(x) for x in range(1, n_tasks + 1)]
#         header += ["label_{}".format(x) for x in range(1, n_tasks + 1)]
#         header = ",".join(header)
#         f.write(header + '\n')
#         for name, t, pred, y in zip(names, ts, predictions, labels):
#             line = [name]
#             line += ["{:.6f}".format(t)]
#             line += ["{:.6f}".format(a) for a in pred]
#             line += [str(a) for a in y]
#             line = ",".join(line)
#             f.write(line + '\n')


from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.linear_model import LogisticRegression
from mimic3benchmark.readers import PhenotypingReader
from mimic3models import common_utils
from mimic3models import metrics
from mimic3models.phenotyping.utils import save_results
import numpy as np
import argparse
import os
import json
from __future__ import absolute_import
from __future__ import print_function




def read_and_extract_features(reader, period, features):

# Path: mimic3benchmark/readers.py
# 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}
# 
# 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/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/phenotyping/utils.py
# def save_results(names, ts, predictions, labels, path):
#     n_tasks = 25
#     common_utils.create_directory(os.path.dirname(path))
#     with open(path, 'w') as f:
#         header = ["stay", "period_length"]
#         header += ["pred_{}".format(x) for x in range(1, n_tasks + 1)]
#         header += ["label_{}".format(x) for x in range(1, n_tasks + 1)]
#         header = ",".join(header)
#         f.write(header + '\n')
#         for name, t, pred, y in zip(names, ts, predictions, labels):
#             line = [name]
#             line += ["{:.6f}".format(t)]
#             line += ["{:.6f}".format(a) for a in pred]
#             line += [str(a) for a in y]
#             line = ",".join(line)
#             f.write(line + '\n')


# Path: mimic3models/phenotyping/logistic/main.py
from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.linear_model import LogisticRegression
from mimic3benchmark.readers import PhenotypingReader
from mimic3models import common_utils
from mimic3models import metrics
from mimic3models.phenotyping.utils import save_results
import numpy as np
import argparse
import os
import json

from __future__ import absolute_import
from __future__ import print_function




def read_and_extract_features(reader, period, features):

    ret = common_utils.read_chunk(reader, reader.get_number_of_examples())