X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_chic.git/blobdiff_plain/c9f45698e9a535650b20a516d197dca86ed70d90..a5f58fd899d0066c8331580c1ee5024a4c411990:/chapter2.tex?ds=sidebyside

diff --git a/chapter2.tex b/chapter2.tex
index ed3ec5b..b50c9dd 100644
--- a/chapter2.tex
+++ b/chapter2.tex
@@ -384,16 +384,16 @@ The following dual numerical situation clearly illustrates this:
 \center
 \begin{tabular}{|l|c|c|c|}\hline
 \diagbox[width=4em]{$a_1$}{$b_1$}&
-  1 & 0 & marge\\ \hline
+  1 & 0 & margin\\ \hline
   1 & 96 & 4& 100 \\ \hline
   0 & 50 & 50& 100 \\ \hline
-  marge & 146 & 54& 200 \\ \hline
+  margin & 146 & 54& 200 \\ \hline
 \end{tabular} ~ ~ ~ ~ ~ ~ ~ \begin{tabular}{|l|c|c|c|}\hline
 \diagbox[width=4em]{$a_2$}{$b_2$}&
-  1 & 0 & marge\\ \hline
+  1 & 0 & margin\\ \hline
   1 & 94 & 6& 100 \\ \hline
   0 & 52 & 48& 100 \\ \hline
-  marge & 146 & 54& 200 \\ \hline
+  margin & 146 & 54& 200 \\ \hline
 \end{tabular}
 
 \caption{Numeric example of difference between implication and
@@ -907,7 +907,8 @@ is constant, independent of the rate of decrease of this number, of
 the variations of $n$ and $n_b$.
 This property seems not to satisfy intuition.
 The gradient of $c$ is expressed only in relation to $n_{a \wedge
-  \overline{b}}$ and $n_a$:(). {\bf CHECK FORMULA}
+  \overline{b}}$ and $n_a$: $\displaystyle \binom{ -\frac{1}{n_a}}{\frac{n_{a \wedge b}}{n_a^2}}$
+
 This may also appear to be a restriction on the role of parameters in
 expressing the sensitivity of the index.
 
@@ -939,3 +940,244 @@ $$\frac{\partial}{\partial n_{a\wedge   \overline{b}}}\left(
 \frac{\partial q}{\partial n_{a\wedge   \overline{b}}} \right) $$
 and the same for the other variables taken in pairs. However, we have,
 through the formulas (\ref{eq2.3}) and (\ref{eq2.4})
+
+$$ \frac{\partial}{\partial n_{a \wedge b}} \left( \frac{\partial q}{\partial n_b} \right) = \frac{1}{2}  \left( \frac{n_a}{n}\right)^{-\frac{1}{2}}   \left( \frac{n_{\overline{b}}}{n}\right)^{-\frac{3}{2}}  = \frac{\partial}{\partial n_b}\left(
+\frac{\partial q}{\partial n_{a\wedge   \overline{b}}} \right)$$
+
+Thus, to the vector field C = ($n$, $n_a$, $n_b$, $n_{\overline{b}}$) of $E$, the nature of which we will specify, corresponds a gradient field $G$ which is said to be derived from the {\bf potential} $q$.
+The gradient grad $q$ is therefore the vector that represents the spatial variation of the field intensity.
+It is directed from low field values to higher values. By following the gradient at each point, we follow the increase in the intensity of the field's implication in space and, in a way, the speed with which it changes as a result of the variation of one or more parameters. 
+
+For example, if we set 3 of the parameters $n$, $n_a$, $n_b$, $n_{\overline{b}}$ given by the realization of the couple ($a$, $b$), the gradient is a vector whose direction indicates the growth or decrease of $q$, therefore the decrease or increase of $|q|$ and, as a consequence of $\varphi$ the variations of the 4th parameter.
+We have indicated this above by interpreting formula (\ref{eq2.5}).
+
+
+\subsection{Level or equipotential lines}
+An equipotential (or level) line or surface in the $C$ field is a curve of $E$ along which or on which a variable point $M$ maintains the same value of the potential $q$ (e.g. isothermal lines on the globe or level lines on an IGN map). 
+
+The equation of this surface\footnote{In differential geometry, it seems that this surface is a (quasi) differentiable variety on board, compact, homeomorphic with closed pavement of the intervals of variation of the 4 parameters. Note that the point whose component $n_b$ is equal to $n$ (therefore = 0) is a singular point ( "catastrophic" in RenÃ© Thom's sense) of the surface and $q$, the potential, is not differentiable at this point. Everywhere else, the surface is distinguishable, the points are all regular. If time, for example, parameters the observations of the process of which ($n$, $n_a$, $n_b$, $n_{\overline{b}}$) is a realization, at each instant corresponds a morphological fiber of the process represented by such a surface in space-time.} is, of course:
+$$ q(a,\overline{b}) - \frac{n_{a \wedge \overline{b}}-
+  \frac{n_a.n_{\overline{b}}}{n}}{\sqrt{\frac{n_a.n_{\overline{b}}}{n}}} = 0$$
+
+
+Therefore, on such a curve, the scalar product $grad~ q. dM$ is zero.
+This is interpreted as indicating the orthogonality of the gradient with the tangent or hyperplane tangent to the curve, i.e. with the equipotential line or surface.
+In a kinematic interpretation of our problem, the velocity of $M$'s path on the equipotential surface is orthogonal to the gradient in $M$.
+
+As an illustration  in Figure~\ref{chap2fig2}, for a potential $F$ depending on only 2 variables, the figure below shows the orthogonal direction of the gradient with respect to the different equipotential surfaces along which the potential $F$ does not vary but passes from $F=7$ to $F= 10$.
+
+\begin{figure}[htbp]
+  \centering
+\includegraphics[scale=1]{chap2fig2}
+ \caption{Illustration of potential of 2 variables}
+\label{chap2fig2}       % Give a unique label
+\end{figure}
+
+It is possible in the case of the potential $q$, to build equipotential surfaces as above (two-dimensional for ease of representation).
+It is understandable that the more intense the field is, the tighter the surfaces are. For a given value of $q$, in this case, 3 variables are set, for example $n$, $n_a$, $n_b$ and a value of $q$ compatible with the field constraints. Either: $n = 104$; $n_a = 1600 \leq nb = 3600$ and $q = -2$ or $|q| = 2$. We then find $n_{\overline{b}}= 528$ using formula~(\ref{eq2.1}).
+But the points ($10^4$, $1600$, $5100$, $5100$, $728$) and ($100$, $25$, $64$, $3$) also belong to this surface and the same equipotential curve.
+The point ($104$, $1600$, $3600$, $3600$, $928$) belongs to the equipotential curve $q=-3$). In fact, on this entire surface, we obtain a kind of homeostasis of the intensity of implication. 
+
+The expression of the function $q$ of the variable shows that it is convex.
+This property proves that the segment of points $t.M_1 + (1-t).M_2$, for $t \in [0,1]$ which connects two points $M_1$ and $M_2$ of the same equipotential line is entirely contained in its convexity.
+The figure below shows two adjacent equipotential surfaces $\sum_1$ and $\sum_2$ in the implicit field  corresponding to two values of the potential $q_1$ and $q_2$.
+At point $M_1$ the scalar field therefore takes the value $q_1$. $M_2$ is the intersection of the normal from $M_1$ with  $\sum_2$. Given the direction of the normal vector $\vec{n}$ the difference $\delta = q2 - q1$, variation of the field when we go from  $\sum_1$ to  $\sum_2$ is then equal to the opposite of the norm of the gradient from $q$ to $M_1$ is $\frac{\partial q}{\partial n}$, if $n_a$, $n_b$ and $n_{a \wedge \overline{b}}$ are fixed. 
+
+\begin{figure}[htbp]
+  \centering
+\includegraphics[scale=1]{chap2fig3}
+ \caption{Illustration of equipotential surfaces}
+\label{chap2fig3}       % Give a unique label
+\end{figure}
+
+Thus, the space $E$ can be laminated by equipotential surfaces corresponding to successive values of $q$ relative to the cardinals ($n$, $n_a$, $n_b$, $n_{a \wedge \overline{b}}$) which would be varied.
+This situation corresponds to the one envisaged in the SIA modeling.
+Fixing $n$, $n_a$ and $n_b$, we consider the random sets $X$ and $Y$ of the same cardinals as $A(n_a)$ and $B(n_b)$ and whose cardinal follows a Poisson's law or a binomial law, according to the choice of the model.
+The different gradient fields, real "lines of force", associated with them are orthogonal to the surfaces defined by the corresponding values of $Q$.
+This reminds us, in the theoretical framework of potential, of the premonitory metaphor of "implicit flow" that we expressed in~\cite{Grase} and that we will discuss again in Chapter 14 of the book.
+Behind this notion we can imagine a transport of information of variable intensity in a causal universe.
+We illustrate this metaphor with the study of the properties of the two-layer implicit cone (see Â§2.8).
+Moreover and intuitively, the implication $a\Rightarrow b$ is of as good quality as the equipotential surface $C$ of the contingency covers random equipotential surfaces depending on the random variable.
+Let us recall the relationship that unites the potential q with the intensity:
+$$\varphi(a,b) =\frac{1}{\sqrt{2\pi}}\int_{q(a,\overline{b})}^{\infty}e^{-\frac{t^2}{2}} dt$$
+
+\noindent {\bf remark 1}\\
+It can be seen that the intensity is also invariant on any equipotential surface of its own variations.
+The surface portions generated by $q$ and by $\varphi$ are even in one-to-one correspondence.
+In intuitive terms, we can say that when one "swells" the other "deflates".\\
+
+\noindent {\bf remark 2}\\
+Let us note once again a particularity of the intensity of implication.
+While the surfaces generated by the variations of the 4 parameters of the data are not invariant by the same dilation of the parameters, those associated with the indices cited in Â§2.4 are invariant and have the same undifferentiated geometric shape.
+
+\section{Implication-inclusion}
+\subsection{Foundational and problematic situation}
+Three reasons led us to improve the model formalized by the intensity of involvement:
+\begin{itemize}
+\item when the size of the samples processed, and in particular that of $E$, increases (by around a thousand and more), the intensity $\varphi(a,b)$ no longer tends to be sufficiently discriminating because its values can be very close to 1, while the inclusion whose quality it seeks to model is far from being satisfied (phenomenon reported in~\cite{Bodina} which deals with large student populations through international surveys);
+\item  the previous quasi-implication model essentially uses the measure of the strength of rule $a \Rightarrow b$.
+  However, taking into account a concomitance of $\neg b \Rightarrow \neg a$ (contraposed of implication) is useful or even essential to reinforce the affirmation of a good quality of the quasi-implicative, possibly quasi-causal, relationship of $a$ over $b$\footnote{This phenomenon is reported by Y. Kodratoff in~\cite{Kodratoff}.}.
+  At the same time, it could make it possible to correct the difficulty mentioned above (if $A$ and $B$ are small compared to $E$, their complementary will be important and vice versa);
+\item  the overcoming of Hempel's paradox (see Appendix 3 of this chapter).
+  \end{itemize}
+
+\subsection{An inclusion index}
+
+The solution\footnote{J. Blanchard provides in~\cite{Blanchardb} an answer to this problem by measuring the "equilibrium gap".} we provide uses both the intensity of implication and another index that reflects the asymmetry between situations $S_1 = (a \wedge b)$ and $S_1' = (a \wedge \neg b)$, (resp. $S2 = (\neg a \wedge \neg b)$ and $S_2' = (a \wedge \neg  b)$) in favour of the first named.
+The relative weakness of instances that contradict the rule and its counterpart is therefore fundamental.
+Moreover, the number of counter-examples $n_{a \wedge \overline{b}}$ to $a\ Rightarrow b$ is the one to the contraposed one.
+To account for the uncertainty associated with a possible bet of belonging to one of the two situations ($S_1$ or $S_1'$, (resp. $S_2$ or $S_2'$)), we therefore refer to Shannon's  concept of entropy~\cite{Shannon}: 
+$$H(b\mid a) = - \frac{n_{a\wedge b}}{n_a}log_2   \frac{n_{a\wedge b}}{n_a}  - \frac{n_{a\wedge \overline{b}}}{n_a}log_2   \frac{n_{a\wedge \overline{b}}}{n_a}$$
+is the conditional entropy relating to boxes $(a \wedge b)$ and $(a \wedge \neg b)$ when $a$ is realized 
+
+$$H(\overline{a}\mid \overline{b}) = - \frac{n_{a\wedge \overline{b}}}{n_{\overline{b}}}log_2 \frac{n_{a\wedge \overline{b}}}{n_{\overline{b}}}    - \frac{n_{\overline{a} \wedge \overline{b}}}{n_{\overline{b}}}log_2   \frac{n_{\overline{a} \wedge \overline{b}}}{n_{\overline{b}}}$$
+
+is the conditional entropy relative to the boxes $(\neg a \wedge \neg b)$ and $(a \wedge \neg b)$ when not $b$ is realized.
+
+These entropies, with values in $[0,1]$, should therefore be simultaneously weak and therefore the asymmetries between situations $S_1$ and $S_1'$ (resp. $S_2$ and $S_2'$) should be simultaneously strong if one wishes to have a good criterion for including $A$ in $B$.
+Indeed, entropies represent the average uncertainty of experiments that consist in observing whether b is performed (or not a is performed) when a (or not b) is observed. The complement to 1 of this uncertainty therefore represents the average information collected by performing these experiments. The more important this information is, the stronger is the guarantee of the quality of the involvement and its counterpart. We must now adapt this entropic numerical criterion to the model expected in the different cardinal situations. 
+For the model to have the expected meaning, it must satisfy, in our opinion, the following epistemological constraints:
+
+\begin{enumerate}
+\item It shall integrate the entropy values and, to contrast them, for example, integrate these values into the square.
+\item As this square varies from 0 to 1, in order to denote the imbalance and therefore the inclusion, in order to oppose entropy, the value retained will be the complement to 1 of its square as long as the number of counter-examples is less than half of the observations of a (resp. non b).
+  Beyond these values, as the implications no longer have an inclusive meaning, the criterion will be assigned the value 0.
+\item In order to take into account the two information specific to $a\Rightarrow b$ and $\neg b \Rightarrow \neg a$, the product will report on the simultaneous quality of the values retained.
+The product has the property of cancelling itself as soon as one of its terms is cancelled, i.e. as soon as this quality is erased.
+\item Finally, since the product has a dimension 4 with respect to entropy, its fourth root will be of the same dimension.
+\end{enumerate}
+
+Let $\alpha=\frac{n_a}{n}$ be the frequency of a and $\overline{b}=\frac{n_{\overline{b}}}{n}$ be the frequency of non b.
+Let $t=\frac{n_{a \wedge \overline{b}}}{n}$  be the frequency of counter-examples, the two significant terms of the respective qualities of involvement and its counterpart are:
+
+\begin{eqnarray*}
+  h_1(t) = H(b\mid a) = - (1-\frac{t}{\alpha}) log_2 (1-\frac{t}{\alpha})   - \frac{t}{\alpha} log_2  \frac{t}{\alpha} & \mbox{ if }t \in [0,\frac{\alpha}{2}[\\
+  h_1(t) = 1 & \mbox{ if }t \in [\frac{\alpha}{2},\alpha]\\
+  h_2(t)= H(\overline{a}\mid \overline{b}) = -  (1-\frac{t}{\overline{\beta}}) log_2  (1-\frac{t}{\overline{\beta}})    -  \frac{t}{\overline{b}} log_2  \frac{t}{\overline{b}} & \mbox{ if }t \in [0,\frac{\overline{\beta}}{2}[\\
+  h_2(t)= 1 & \mbox{ if }t \in [\frac{\overline{\beta}}{2},\overline{\beta}]
+\end{eqnarray*}
+Hence the definition for determining the entropic criterion: 
+\definition: The inclusion index of A, support of a, in B, support of b, is the number:
+$$i(a,b) = \left[ (1-h_1^2(t)) (1-h_2^2(t)))   \right]^{\frac{1}{4}}$$
+
+which integrates the information provided by the realization of a small number of counter-examples, on the one hand to the rule $a \Rightarrow b$ and, on the other hand, to the rule $\neg b \Rightarrow \neg a$.
+
+\subsection{The implication-inclusion index}
+
+The intensity of implication-inclusion (or entropic intensity), a new measure of inductive quality, is the number:
+
+$$\psi(a,b)= \left[  i(a,b).\varphi(a,b) \right]^{\frac{1}{2}}$$
+which integrates both statistical surprise and inclusive quality.
+
+The function $\psi$ of the variable $t$ admits a representation that has the shape indicated in Figure~\ref{chap2fig4}, for $n_a$ and $n_b$ fixed.
+Note in this figure the difference in the behaviour of the function with respect to the conditional probability $P(B\mid A)$, a fundamental index of other rule measurement models, for example in Agrawal.
+In addition to its linear, and therefore not very nuanced nature, this probability leads to a measure that decreases too quickly from the first counter-examples and then resists too long when they become important.
+
+
+\begin{figure}[htbp]
+  \centering
+\includegraphics[scale=0.5]{chap2fig4.png}
+\caption{Example of implication-inclusion.}
+
+\label{chap2fig4}      
+\end{figure}
+
+In Figure~\ref{chap2fig4}, it can be seen that this representation of the continuous function of $t$ reflects the expected properties of the inclusion criterion:
+\begin{itemize}
+\item ``Slow reaction'' to the first counter-examples (noise resistance),
+\item ``acceleration'' of the rejection of inclusion close to the balance i.e. $\frac{n_a}{2n}$,
+\item rejection beyond $\frac{n_a}{2n}$,  the intensity of implication $\varphi(a,b)$ did not ensure it.
+\end{itemize}
+
+\noindent Example 1\\
+\begin{tabular}{|c|c|c|c|}\hline
+  & $b$ & $\overline{b}$ & margin\\ \hline
+  $a$ & 200 & 400& 600 \\ \hline
+  $\overline{a}$ & 600 & 2800& 3400 \\ \hline
+  margin & 800 & 3200& 4000 \\ \hline
+\end{tabular}
+\\
+\\
+In Example 1, implication intensity is $\varphi(a,b)=0.9999$ (with $q(a,\overline{b})=-3.65$).
+ The entropic values of the experiment are $h_1=h_2=0$.
+ The value of the moderator coefficient is therefore $i(a,b)=0$.
+ Hence, $\psi(a,b)=0$ whereas $P(B\mid A)=0.33$.
+Thus, the "entropic" functions "moderate" the intensity of implication in this case where inclusion is poor.
+\\
+\\
+\noindent Example 2\\
+ \begin{tabular}{|c|c|c|c|}\hline
+  & $b$ & $\overline{b}$ & margin\\ \hline
+  $a$ & 400 & 200& 600 \\ \hline
+  $\overline{a}$ & 1000 & 2400& 3400 \\ \hline
+  margin & 1400 & 2600& 4000 \\ \hline
+ \end{tabular}
+ \\
+ \\
+ In Example 2, intensity of implication is 1 (for  $q(a,\overline{b}) = - 8.43$).
+ The entropic values of the experiment are $h_1 = 0.918$ and $h_2 = 0.391$.
+ The value of the moderator coefficient is therefore $i(a,b) = 0.6035$.
+ As a result $\psi(a,b) = 0.777$ whereas $P(B \mid A) = 0.6666$.
+ \\
+ \\
+{\bf remark}
+ \noindent The correspondence between $\varphi(a,b)$ and $\psi(a,b)$ is not monotonous as shown in the following example:
+ 
+\begin{tabular}{|c|c|c|c|}\hline
+  & $b$ & $\overline{b}$ & margin\\ \hline
+  $a$ & 40 & 20& 60 \\ \hline
+  $\overline{a}$ & 60 & 280& 340 \\ \hline
+  margin & 100 & 300& 400 \\ \hline
+\end{tabular}
+\\
+Thus, while $\varphi(a,b)$ decreased from the 1st to the 2nd example, $i(a,b)$ increased as well as $\psi(a,b)$.  On the other hand, the opposite situation is the most frequent.
+Note that in both cases, the conditional probability does not change.
+\\
+\\
+{\bf remark}
+\noindent We refer to~\cite{Lencaa} for a very detailed comparative study of association indices for binary variables.
+In particular, the intensities of classical and entropic (inclusion) implication presented in this article are compared with other indices according to a "user" entry.
+
+\section{Implication graph}
+\subsection{Problematic}
+
+At the end of the calculations of the intensities of implication in both the classical and entropic models, we have a table $p \times p$ that crosses the $p$ variables with each other, whatever their nature, and whose elements are the values of these intensities of implication, numbers of the interval $[0,~1]$.
+It must be noted that the underlying structure of all these variables is far from explicit and remains largely unimportant.
+The user remains blind to such a square table of size $p^2$.
+It cannot simultaneously embrace the possible multiple sequences of rules that underlie the overall structure of all $p$ variables.
+In order to facilitate a clearer extraction of the rules and to examine their structure, we have associated to this table, and for a given intensity threshold, an oriented graph, weighted by the intensities of implication, without a cycle whose complexity of representation the user can control by setting himself the threshold for taking into account the implicit quality of the rules.
+Each arc in this graph represents a rule: if $n_a < n_b$, the arc $a \rightarrow b$ represents the rule $a \Rightarrow b$ ; if $n_a = n_b$, then the arc $a \leftrightarrow b$ will represent the double rule $a \Leftrightarrow b$, in other words, the equivalence between these two variables.
+By varying the threshold of intensity of implication, it is obvious that the number of arcs varies in the opposite direction: for a threshold set at $0.95$, the number of arcs is less than or equal to those that would constitute the graph at threshold $0.90$. We will discuss this further below.
+
+\subsection{Algorithm}
+
+The relationship defined by statistical implication, if it is reflexive and not symmetrical, is obviously not transitive, as is induction and, on the contrary, deduction.
+However, we want it to model the partial relationship between two variables (the successes in our initial example).
+By convention, if $a \Rightarrow b$ and $b \Rightarrow c$, we will accept the transitive closure $a \Rightarrow c$ only if $\psi(a,c) \geq 0.5$, i.e. if the implicit relationship of $a$ to $c$ is better than neutrality by emphasizing the dependence between $a$ and $c$.
+
+
+{\bf VERIFIER PHI PSI}\\
+\\
+{\bf Proposal:} By convention, if $a \Rightarrow b$ and $b \Rightarrow c$, there is a transitive closure $a \Rightarrow c$ if and only if $\psi(a,c) \geq 0.5$, i.e. if the implicit relationship of $a$ over $c$, which reflects a certain dependence between $a$ and $c$, is better than its refutation.
+Note that for any pair of variables $(x;~ y)$, the arc $x \rightarrow y$ is weighted by the intensity of involvement (x,y).
+\\
+Let us take a formal example by assuming that between the 5 variables $a$, $b$, $c$, $d$, and $e$ exist, at the threshold above $0.5$, the following rules: $c \Rightarrow a$, $c \Rightarrow e$, $c \Rightarrow b$, $d \Rightarrow a$, $d \Rightarrow e$, $a \Rightarrow b$ and $a \Rightarrow e$.
+
+This set of numerical and graphical relationships can then be translated into the following table and graph:
+
+\begin{tabular}{|C{0.5cm}|c|c|c|c|c|}\hline
+\hspace{-0.5cm}\turn{45}{$\Rightarrow$}  & $a$ & $b$ & $c$ & $d$ & $e$\\ \hline
+$a$ &  & 0.97& & & 0.73 \\ \hline
+$b$ &  &     & & & \\ \hline  
+  $c$ & 0.82 & 0.975& & & 0.82 \\ \hline
+  $d$ & 0.78 &    &   &  & 0.92 \\ \hline
+  $e$ &  &     & & & \\ \hline  
+\end{tabular}
+
+\begin{figure}[htbp]
+  \centering
+\includegraphics[scale=1]{chap2fig5.png}
+\caption{Implication graph corresponding to the previous example.}
+
+\label{chap2fig5}      
+\end{figure}
