# Path: mimic3benchmark/readers.py
# 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}
# 
# 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):
#     predictions = np.array(predictions)
#     if len(predictions.shape) == 1:
#         predictions = np.stack([1 - predictions, predictions]).transpose((1, 0))
# 
#     cf = metrics.confusion_matrix(y_true, predictions.argmax(axis=1))
#     if verbose:
#         print("confusion matrix:")
#         print(cf)
#     cf = cf.astype(np.float32)
# 
#     acc = (cf[0][0] + cf[1][1]) / np.sum(cf)
#     prec0 = cf[0][0] / (cf[0][0] + cf[1][0])
#     prec1 = cf[1][1] / (cf[1][1] + cf[0][1])
#     rec0 = cf[0][0] / (cf[0][0] + cf[0][1])
#     rec1 = cf[1][1] / (cf[1][1] + cf[1][0])
#     auroc = metrics.roc_auc_score(y_true, predictions[:, 1])
# 
#     (precisions, recalls, thresholds) = metrics.precision_recall_curve(y_true, predictions[:, 1])
#     auprc = metrics.auc(recalls, precisions)
#     minpse = np.max([min(x, y) for (x, y) in zip(precisions, recalls)])
# 
#     if verbose:
#         print("accuracy = {}".format(acc))
#         print("precision class 0 = {}".format(prec0))
#         print("precision class 1 = {}".format(prec1))
#         print("recall class 0 = {}".format(rec0))
#         print("recall class 1 = {}".format(rec1))
#         print("AUC of ROC = {}".format(auroc))
#         print("AUC of PRC = {}".format(auprc))
#         print("min(+P, Se) = {}".format(minpse))
# 
#     return {"acc": acc,
#             "prec0": prec0,
#             "prec1": prec1,
#             "rec0": rec0,
#             "rec1": rec1,
#             "auroc": auroc,
#             "auprc": auprc,
#             "minpse": minpse}
# 
# Path: mimic3models/in_hospital_mortality/utils.py
# def save_results(names, pred, y_true, path):
#     common_utils.create_directory(os.path.dirname(path))
#     with open(path, 'w') as f:
#         f.write("stay,prediction,y_true\n")
#         for (name, x, y) in zip(names, pred, y_true):
#             f.write("{},{:.6f},{}\n".format(name, x, y))


from mimic3benchmark.readers import InHospitalMortalityReader
from mimic3models import common_utils
from mimic3models.metrics import print_metrics_binary
from mimic3models.in_hospital_mortality.utils import save_results
from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.linear_model import LogisticRegression
import os
import numpy as np
import argparse
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 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}
# 
# 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):
#     predictions = np.array(predictions)
#     if len(predictions.shape) == 1:
#         predictions = np.stack([1 - predictions, predictions]).transpose((1, 0))
# 
#     cf = metrics.confusion_matrix(y_true, predictions.argmax(axis=1))
#     if verbose:
#         print("confusion matrix:")
#         print(cf)
#     cf = cf.astype(np.float32)
# 
#     acc = (cf[0][0] + cf[1][1]) / np.sum(cf)
#     prec0 = cf[0][0] / (cf[0][0] + cf[1][0])
#     prec1 = cf[1][1] / (cf[1][1] + cf[0][1])
#     rec0 = cf[0][0] / (cf[0][0] + cf[0][1])
#     rec1 = cf[1][1] / (cf[1][1] + cf[1][0])
#     auroc = metrics.roc_auc_score(y_true, predictions[:, 1])
# 
#     (precisions, recalls, thresholds) = metrics.precision_recall_curve(y_true, predictions[:, 1])
#     auprc = metrics.auc(recalls, precisions)
#     minpse = np.max([min(x, y) for (x, y) in zip(precisions, recalls)])
# 
#     if verbose:
#         print("accuracy = {}".format(acc))
#         print("precision class 0 = {}".format(prec0))
#         print("precision class 1 = {}".format(prec1))
#         print("recall class 0 = {}".format(rec0))
#         print("recall class 1 = {}".format(rec1))
#         print("AUC of ROC = {}".format(auroc))
#         print("AUC of PRC = {}".format(auprc))
#         print("min(+P, Se) = {}".format(minpse))
# 
#     return {"acc": acc,
#             "prec0": prec0,
#             "prec1": prec1,
#             "rec0": rec0,
#             "rec1": rec1,
#             "auroc": auroc,
#             "auprc": auprc,
#             "minpse": minpse}
# 
# Path: mimic3models/in_hospital_mortality/utils.py
# def save_results(names, pred, y_true, path):
#     common_utils.create_directory(os.path.dirname(path))
#     with open(path, 'w') as f:
#         f.write("stay,prediction,y_true\n")
#         for (name, x, y) in zip(names, pred, y_true):
#             f.write("{},{:.6f},{}\n".format(name, x, y))


# Path: mimic3models/in_hospital_mortality/logistic/main.py
from mimic3benchmark.readers import InHospitalMortalityReader
from mimic3models import common_utils
from mimic3models.metrics import print_metrics_binary
from mimic3models.in_hospital_mortality.utils import save_results
from sklearn.preprocessing import Imputer, StandardScaler
from sklearn.linear_model import LogisticRegression
import os
import numpy as np
import argparse
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())