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73 lines
1.7 KiB
73 lines
1.7 KiB
# -*- coding: utf-8 -*- |
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""" |
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Created on Fri May 14 09:18:32 2021 |
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@author: Dijkhofmf |
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""" |
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# Import stuff |
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import os |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from sklearn import preprocessing |
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#%% Import data and path |
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles' |
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# Set path |
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os.chdir(Path) |
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#%% Create DF |
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FinalDF = pd.DataFrame(pd.read_csv('FinalDataset.csv')) |
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X = pd.DataFrame(FinalDF) |
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cols = X.drop('Pt Type', axis=1) |
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ID = X['Study ID'] |
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y = X['Pt Type'] |
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y= y.replace('Healthy', 'No-complication') |
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X = X.drop(['Pt Type', 'Study ID'], axis=1) |
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#%% |
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X1 = pd.DataFrame(preprocessing.scale(X), columns=X.columns) |
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X1['Pt Type'] = y |
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X1.set_index(ID) |
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#%% |
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X1.columns = ['Age (years)', 'Gender', 'Daily alcohol use', 'Medication', |
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'ASA-classification', 'Recurrent disease?', 'Comorb', |
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'Independent, with others', 'Smokes cigarettes/sigar', 'BMI', 'GFI', |
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'HADS_A', 'HADS Depression', 'ADL', 'iADL', 'TUG', 'Handgrip strength', |
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'Avg. Steps/day', 'Avg. MVPA/day', 'Pt Type'] |
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plots = X1.columns |
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#%% |
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import matplotlib.pylab as pylab |
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params = {'legend.fontsize': 'x-large', |
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'axes.labelsize': 'x-large', |
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'axes.titlesize':'x-large', |
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'xtick.labelsize':'x-large', |
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'ytick.labelsize':'x-large'} |
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pylab.rcParams.update(params) |
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plots = plots[1:] |
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namecount=0 |
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for x in plots: |
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name = str(plots[namecount]) |
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plt.figure(dpi=720) |
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sns.boxplot(x='Pt Type', y=x, data=X1, boxprops=dict(alpha=0.5)) |
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sns.swarmplot(x='Pt Type', y=x, data=X1) |
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plt.title('Swarm-boxplot ' + name) |
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namecount = namecount +1 |
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