# -*- coding: utf-8 -*-
# -*- mode: org -*-

#+TITLE: Challenger - Python - Emacs - Windows 7 64 bits

* Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure

In this document we reperform some of the analysis provided in
/Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of
Failure/ by /Siddhartha R. Dalal, Edward B. Fowlkes, Bruce Hoadley/
published in /Journal of the American Statistical Association/, Vol. 84,
No. 408 (Dec., 1989), pp. 945-957 and available at
http://www.jstor.org/stable/2290069.

On the fourth page of this article, they indicate that the maximum
likelihood estimates of the logistic regression using only temperature
are: *$\hat{\alpha}$ = 5.085* and *$\hat{\beta}$ = -0.1156* and their
asymptotic standard errors are *$s_{\hat{\alpha}}$ = 3.052* and
*$s_{\hat{\beta}}$ = 0.047*. The Goodness of fit indicated for this model was
*$G^2$ = 18.086* with *21* degrees of freedom. Our goal is to reproduce
the computation behind these values and the Figure 4 of this article,
possibly in a nicer looking way.

** Technical information on the computer on which the analysis is run

We will be using the Python 3 language using the pandas, statsmodels,
and numpy library.

#+begin_src python :results output :session :exports both
def print_imported_modules():
    import sys
    for name, val in sorted(sys.modules.items()):
        if(hasattr(val, '__version__')):
            print(val.__name__, val.__version__)
#        else:
#            print(val.__name__, "(unknown version)")
def print_sys_info():
    import sys
    import platform
    print(sys.version)
    print(platform.uname())

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import seaborn as sns

print_sys_info()
print_imported_modules()
#+end_src

** Loading and inspecting data

Let's start by reading data.

#+begin_src python :results output :session :exports both
data = pd.read_csv("https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/tree/master/data/shuttle.csv")
print(data)
#+end_src

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.

#+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
%matplotlib inline
pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas

data["Frequency"]=data.Malfunction/data.Count
data.plot(x="Temperature",y="Frequency",kind="scatter",ylim=[0,1])
plt.grid(True)
plt.tight_layout()

plt.savefig(matplot_lib_filename)
matplot_lib_filename
#+end_src

** Logistic regression

Let's assume O-rings independently fail with the same probability
which solely depends on temperature. A logistic regression should
allow us to estimate the influence of temperature.

#+begin_src python :results output :session :exports both
import statsmodels.api as sm

data["Success"]=data.Count-data.Malfunction
data["Intercept"]=1

logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']],
                family=sm.families.Binomial(sm.families.links.logit)).fit()

print(logmodel.summary())
#+end_src

The maximum likelyhood estimator of the intercept and of Temperature
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
/$s_{\hat{\alpha}}$ = 7.477/ and /$s_{\hat{\beta}}$ = 0.115/, which is *different* from
the *3.052* and *0.04702* reported by Dallal /et al./ The deviance is
/3.01444/ with *21* degrees of freedom. I cannot find any value similar
to the Goodness of fit (*$G^2$ = 18.086*) reported by Dalal /et al./ There
seems to be something wrong. Oh I know, I haven't indicated that my
observations are actually the result of 6 observations for each rocket
launch. Let's indicate these weights (since the weights are always the
same throughout all experiments, it does not change the estimates of
the fit but it does influence the variance estimates).

#+begin_src python :results output :session :exports both
logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']],
                family=sm.families.Binomial(sm.families.links.logit),
                var_weights=data['Count']).fit()

print(logmodel.summary())
#+end_src

Good, now I have recovered the asymptotic standard errors
*$s_{\hat{\alpha}}$ = 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
of freedom (Df Residuals).

*I have therefore managed to fully replicate the results of the Dalal
/et al./ article*.

** Predicting failure probability

The temperature when launching the shuttle was 31°F. Let's try to
estimate the failure probability for such temperature using our model:

#+begin_src python :results output :session :exports both
data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121),
                          'Intercept': 1})
data_pred['Frequency'] = logmodel.predict(data_pred)
print(data_pred.head())
#+end_src

#+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
%matplotlib inline


data_pred.plot(x="Temperature",y="Frequency",kind="line",ylim=[0,1])
plt.scatter(x=data["Temperature"],y=data["Frequency"])
plt.grid(True)

plt.savefig(matplot_lib_filename)
matplot_lib_filename
#+end_src

La fonction =logmodel.predict(data_pred)= ne fonctionne pas avec les
dernières versions de pandas (Frequency = 1 pour toutes les températures).

On peut alors utiliser le code suivant pour calculer les prédictions
et tracer la courbe :

#+begin_src python :results output :session :exports both
# Inspiring from http://blog.yhat.com/posts/logistic-regression-and-python.html
def logit_inv(x):
    return(np.exp(x)/(np.exp(x)+1))

data_pred['Prob']=logit_inv(data_pred['Temperature'] * logmodel.params['Temperature'] +
                            logmodel.params['Intercept'])
print(data_pred.head())
#+end_src

#+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
%matplotlib inline
data_pred.plot(x="Temperature",y="Prob",kind="line",ylim=[0,1])
plt.scatter(x=data["Temperature"],y=data["Frequency"])
plt.grid(True)

plt.savefig(matplot_lib_filename)
matplot_lib_filename
#+end_src

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.*

** Computing and plotting uncertainty

Following the documentation of
[Seaborn](https://seaborn.pydata.org/generated/seaborn.regplot.html),
I use regplot.

#+begin_src python :results file :session :var matplot_lib_filename=(org-babel-temp-file "figure" ".png") :exports both
sns.set(color_codes=True)
plt.xlim(30,90)
plt.ylim(0,1)
sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)
plt.show()

plt.savefig(matplot_lib_filename)
matplot_lib_filename
#+end_src

**I think I have managed to correctly compute and plot the uncertainty
  of my prediction.** Although the shaded area seems very similar to
  [the one obtained by with
  R](https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/tree/master/challenger.pdf),
  I can spot a few differences (e.g., the blue point for temperature
  63 is outside)... Could this be a numerical error ? Or a difference
  in the statistical method ? It is not clear which one is "right".
