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moocrr-reproducibility-study
Commit 9339b2e7 authored by Marie-Gabrielle Dondon's avatar Marie-Gabrielle Dondon
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Replace challenger_Python_org.org

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src/Python3/challenger_Python_org.org
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...@@ -47,106 +47,6 @@ print_sys_info() ...@@ -47,106 +47,6 @@ print_sys_info()
print_imported_modules() print_imported_modules()
#+end_src #+end_src
#+RESULTS:
#+begin_example
Python 3.7.0 (v3.7.0:1bf9cc5093, Jun 27 2018, 04:59:51) [MSC v.1914 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
3.7.0 (v3.7.0:1bf9cc5093, Jun 27 2018, 04:59:51) [MSC v.1914 64 bit (AMD64)]
uname_result(system='Windows', node='MGDONDON', release='7', version='6.1.7601', machine='AMD64', processor='Intel64 Family 6 Model 94 Stepping 3, GenuineIntel')
IPython 6.5.0
IPython.core.release 6.5.0
_csv 1.0
_ctypes 1.1.0
decimal 1.70
argparse 1.1
backcall 0.1.0
colorama 0.3.9
csv 1.0
ctypes 1.1.0
cycler 0.10.0
dateutil 2.7.3
decimal 1.70
decorator 4.3.0
distutils 3.7.0
ipykernel 4.8.2
ipykernel._version 4.8.2
ipython_genutils 0.2.0
ipython_genutils._version 0.2.0
ipywidgets 7.4.0
ipywidgets._version 7.4.0
jedi 0.12.1
json 2.0.9
jupyter_client 5.2.3
jupyter_client._version 5.2.3
jupyter_core 4.4.0
jupyter_core.version 4.4.0
kiwisolver 1.0.1
logging 0.5.1.2
matplotlib 2.2.3
matplotlib.backends.backend_agg 2.2.3
numpy 1.15.0
numpy.core 1.15.0
numpy.core.multiarray 3.1
numpy.lib 1.15.0
numpy.linalg._umath_linalg b'0.1.5'
numpy.matlib 1.15.0
pandas 0.23.4
_libjson 1.33
parso 0.3.1
patsy 0.5.0
patsy.version 0.5.0
pickleshare 0.7.4
platform 1.0.8
prompt_toolkit 1.0.15
pygments 2.2.0
pyparsing 2.2.0
pytz 2018.5
re 2.2.1
scipy 1.1.0
scipy._lib.decorator 4.0.5
scipy._lib.six 1.2.0
scipy.fftpack._fftpack b'$Revision: $'
scipy.fftpack.convolve b'$Revision: $'
scipy.integrate._dop b'$Revision: $'
scipy.integrate._ode $Id$
scipy.integrate._odepack 1.9
scipy.integrate._quadpack 1.13
scipy.integrate.lsoda b'$Revision: $'
scipy.integrate.vode b'$Revision: $'
scipy.interpolate._fitpack 1.7
scipy.interpolate.dfitpack b'$Revision: $'
scipy.linalg 0.4.9
scipy.linalg._fblas b'$Revision: $'
scipy.linalg._flapack b'$Revision: $'
scipy.linalg._flinalg b'$Revision: $'
scipy.ndimage 2.0
scipy.optimize._cobyla b'$Revision: $'
scipy.optimize._lbfgsb b'$Revision: $'
scipy.optimize._minpack 1.10
scipy.optimize._nnls b'$Revision: $'
scipy.optimize._slsqp b'$Revision: $'
scipy.optimize.minpack2 b'$Revision: $'
scipy.signal.spline 0.2
scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'
scipy.sparse.linalg.isolve._iterative b'$Revision: $'
scipy.special.specfun b'$Revision: $'
scipy.stats.mvn b'$Revision: $'
scipy.stats.statlib b'$Revision: $'
seaborn 0.9.0
seaborn.external.husl 2.1.0
seaborn.external.six 1.10.0
six 1.11.0
statsmodels 0.9.0
statsmodels.__init__ 0.9.0
traitlets 4.3.2
traitlets._version 4.3.2
urllib.request 3.7
zlib 1.0
zmq 17.1.2
zmq.sugar 17.1.2
zmq.sugar.version 17.1.2
#+end_example
*** Loading and inspecting data *** Loading and inspecting data
Let's start by reading data. Let's start by reading data.
...@@ -156,34 +56,6 @@ data = pd.read_csv("https://app-learninglab.inria.fr/gitlab/moocrr-session1/mooc ...@@ -156,34 +56,6 @@ data = pd.read_csv("https://app-learninglab.inria.fr/gitlab/moocrr-session1/mooc
print(data) print(data)
#+end_src #+end_src
#+RESULTS:
#+begin_example
Date Count Temperature Pressure Malfunction
0 4/12/81 6 66 50 0
1 11/12/81 6 70 50 1
2 3/22/82 6 69 50 0
3 11/11/82 6 68 50 0
4 4/04/83 6 67 50 0
5 6/18/82 6 72 50 0
6 8/30/83 6 73 100 0
7 11/28/83 6 70 100 0
8 2/03/84 6 57 200 1
9 4/06/84 6 63 200 1
10 8/30/84 6 70 200 1
11 10/05/84 6 78 200 0
12 11/08/84 6 67 200 0
13 1/24/85 6 53 200 2
14 4/12/85 6 67 200 0
15 4/29/85 6 75 200 0
16 6/17/85 6 70 200 0
17 7/2903/85 6 81 200 0
18 8/27/85 6 76 200 0
19 10/03/85 6 79 200 0
20 10/30/85 6 75 200 2
21 11/26/85 6 76 200 0
22 1/12/86 6 58 200 1
#+end_example
We know from our previous experience on this data set that filtering We know from our previous experience on this data set that filtering
data is a really bad idea. We will therefore process it as such. data is a really bad idea. We will therefore process it as such.
...@@ -200,9 +72,6 @@ plt.savefig(matplot_lib_filename) ...@@ -200,9 +72,6 @@ plt.savefig(matplot_lib_filename)
matplot_lib_filename matplot_lib_filename
#+end_src #+end_src
#+RESULTS:
[[file:c:/Users/dondon/AppData/Local/Temp/babel-aNPFF5/figureFG8KBj.png]]
*** Logistic regression *** Logistic regression
Let's assume O-rings independently fail with the same probability Let's assume O-rings independently fail with the same probability
...@@ -221,26 +90,6 @@ logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], ...@@ -221,26 +90,6 @@ logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']],
print(logmodel.summary()) print(logmodel.summary())
#+end_src #+end_src
#+RESULTS:
#+begin_example
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -3.9210
Date: Mon, 12 Nov 2018 Deviance: 3.0144
Time: 13:13:31 Pearson chi2: 5.00
No. Iterations: 6 Covariance Type: nonrobust
===============================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
===============================================================================
#+end_example
The maximum likelyhood estimator of the intercept and of Temperature The maximum likelyhood estimator of the intercept and of Temperature
are thus *$\hat{\alpha}$ = 5.0850* and *$\hat{\beta}$ = -0.1156*. This *corresponds* are thus *$\hat{\alpha}$ = 5.0850* and *$\hat{\beta}$ = -0.1156*. This *corresponds*
to the values from the article of Dalal /et al./ The standard errors are to the values from the article of Dalal /et al./ The standard errors are
...@@ -262,26 +111,6 @@ logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], ...@@ -262,26 +111,6 @@ logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']],
print(logmodel.summary()) print(logmodel.summary())
#+end_src #+end_src
#+RESULTS:
#+begin_example
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -23.526
Date: Mon, 12 Nov 2018 Deviance: 18.086
Time: 13:13:39 Pearson chi2: 30.0
No. Iterations: 6 Covariance Type: nonrobust
===============================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068
Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023
===============================================================================
#+end_example
Good, now I have recovered the asymptotic standard errors Good, now I have recovered the asymptotic standard errors
*$s_{\hat{\alpha}}$ = 3.052* and *$s_{\hat{\beta}}$ = 0.047*. The Goodness of fit *$s_{\hat{\alpha}}$ = 3.052* and *$s_{\hat{\beta}}$ = 0.047*. The Goodness of fit
(Deviance) indicated for this model is *$G^2$ = 18.086* with *21* degrees (Deviance) indicated for this model is *$G^2$ = 18.086* with *21* degrees
...@@ -307,19 +136,16 @@ plt.savefig(matplot_lib_filename) ...@@ -307,19 +136,16 @@ plt.savefig(matplot_lib_filename)
matplot_lib_filename matplot_lib_filename
#+end_src #+end_src
#+RESULTS:
[[file:c:/Users/dondon/AppData/Local/Temp/babel-aNPFF5/figure51z7PH.png]]
This figure is very similar to the Figure 4 of Dalal /et al./ *I have This figure is very similar to the Figure 4 of Dalal /et al./ *I have
managed to replicate the Figure 4 of the Dalal /et al./ article.* managed to replicate the Figure 4 of the Dalal /et al./ article.*
** Computing and plotting uncertainty *** Computing and plotting uncertainty
Following the documentation of Following the documentation of
[Seaborn](https://seaborn.pydata.org/generated/seaborn.regplot.html), [Seaborn](https://seaborn.pydata.org/generated/seaborn.regplot.html),
I use regplot. I use regplot.
#+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both #+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
sns.set(color_codes=True) sns.set(color_codes=True)
plt.xlim(30,90) plt.xlim(30,90)
plt.ylim(0,1) plt.ylim(0,1)
...@@ -328,12 +154,9 @@ plt.show() ...@@ -328,12 +154,9 @@ plt.show()
plt.savefig(matplot_lib_filename) plt.savefig(matplot_lib_filename)
matplot_lib_filename matplot_lib_filename
#+end_src #+end_src
#+RESULTS:
[[file:c:/Users/dondon/AppData/Local/Temp/babel-aNPFF5/figurebq7jid.png]]
**I think I have managed to correctly compute and plot the uncertainty **I think I have managed to correctly compute and plot the uncertainty
of my prediction.** Although the shaded area seems very similar to of my prediction.** Although the shaded area seems very similar to
[the one obtained by with [the one obtained by with
R](https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/5c9dbef11b4d7638b7ddf2ea71026e7bf00fcfb0/challenger.pdf), R](https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/5c9dbef11b4d7638b7ddf2ea71026e7bf00fcfb0/challenger.pdf),
......
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