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create_datasets.py
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create_datasets.py
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import numpy as np
import pandas as pd
import pickle as pk
from sklearn.model_selection import train_test_split as split_data
class SymptomDiseaseData:
def __init__(self):
self.symptoms_data = []
self.disease_data = []
self.BREAKOFF = False
self.overall_week = 0
self._load_data()
def _load_data(self):
self.weekly_symptoms = pk.load(open("weekly_symptoms.pkl", "rb"))
self.weekly_diseases = pk.load(open("weekly_diseases.pkl", "rb"))
self.weekly_diseases = self.weekly_diseases.drop(columns='Kalenderwoche')
self.weekly_diseases = self.weekly_diseases.loc[:, ('Influenza', 'Windpocken', 'Norovirus-Gastroenteritis')]
def generate_data(self, split=True):
"""
Puts symptoms and diseases in numpy arrays, splits into train, val, and test sets, or just returns all data
if you want.
Args:
split: if data should be split into training/test etc
Returns:
train, val, test, train_label, val_label, test_label
"""
for year in range(2001,2019):
for week in range(1, 53):
if year == 2018:
if week == 6:
self.BREAKOFF = True
if week < 10:
week = '0'+str(week)
else:
week = str(week)
date = str(year)+'-KW'+week
#print("Appending:", date) # for debugging
self.symptoms_data.append(list(self.weekly_symptoms.loc[self.weekly_symptoms['Kalenderwoche'] == date]['Anzahl'][:18]))
self.disease_data.append(list(self.weekly_diseases.iloc[self.overall_week]))
self.overall_week += 1
if self.BREAKOFF:
break
self.symptoms_data = np.asarray(self.symptoms_data)
self.disease_data = np.asarray(self.disease_data)
if split:
self._split()
return self.train, self.valid, self.test, self.train_labels, self.valid_labels, self.test_labels
else:
return self.symptoms_data, self.disease_data
def _split(self):
"""
Splits data and labels into training, validation, and test sets.
"""
self.train, valtest, self.train_labels, valtest_labels = split_data(self.symptoms_data, self.disease_data,
shuffle=True, train_size=.7,
test_size=.3)
self.valid, self.test, self.valid_labels, self.test_labels = split_data(valtest, valtest_labels,
shuffle=True,
train_size=.5,
test_size=.5)
def get_data_insight(self):
"""
Prints out useful information about the data.
"""
print("Symptom's shape:", self.symptoms_data.shape)
print("Disease's shape:", self.disease_data.shape)
print('Symptoms at index 0:\n', self.symptoms_data[0])
print('Disease at index 0:\n', self.disease_data[0])
if __name__=='__main__':
data = SymptomDiseaseData()
data.generate_data()
data.get_data_insight()