summaryrefslogtreecommitdiff
diff options
context:
space:
mode:
authorMartin Mozina <martin.mozina@fri.uni-lj.si>2018-06-20 12:10:16 +0200
committerMartin Mozina <martin.mozina@fri.uni-lj.si>2018-06-20 12:10:16 +0200
commit9c434e39a99e0945ee964b105fc7d4c23e706637 (patch)
tree8da6ae84b26f5a934f80c9d856bcbfbecd82366f
parentd59588d8d046428187135aabea5944900ffeca56 (diff)
Small changes to rules and conclusions section.
-rw-r--r--aied2018/presentation/aied_poster.tex2
-rw-r--r--aied2018/presentation/rules.tex32
2 files changed, 24 insertions, 10 deletions
diff --git a/aied2018/presentation/aied_poster.tex b/aied2018/presentation/aied_poster.tex
index ad9850c..6956fdc 100644
--- a/aied2018/presentation/aied_poster.tex
+++ b/aied2018/presentation/aied_poster.tex
@@ -92,7 +92,7 @@
\item Patterns are useful, because in our experiment ...
\begin{itemize}
- \item classification accuracy of Random Forest was 17\% overall higher than default accuracy.
+ \item classification accuracy of Random Forest was on average 17\% higher than default accuracy.
\item n-rules explained over 70\% of incorrect submissions.
\item p-rules explained 62\% of correct programs.
\end{itemize}
diff --git a/aied2018/presentation/rules.tex b/aied2018/presentation/rules.tex
index 994e3a0..f2a31c7 100644
--- a/aied2018/presentation/rules.tex
+++ b/aied2018/presentation/rules.tex
@@ -20,7 +20,7 @@ $\vdotswithin{S_4}$ & & $\vdotswithin{1}$ & & & $\vdotswithin{correct}$ \\
\end{itemize}
\end{column}
\end{columns}
-\vspace{1cm}
+\vspace{2cm}
\begin{columns}
@@ -33,12 +33,12 @@ $\vdotswithin{S_4}$ & & $\vdotswithin{1}$ & & & $\vdotswithin{correct}$ \\
\item \emph{p-rules} describe necessary patterns for programs to be correct: \\IF $condition$ THEN $correct$.
\end{itemize}
\vspace{0.5cm}
- \textbf{Example:} Implement a function that returns the element with the largest absolute value.
+ \textbf{Example: Greatest Absolutist}
\begin{itemize}
\item 155 submissions (57 correct, 98 incorrect)
- \item 8298 patterns, 15 n-rules and 6 p-rules.
+ \item 8298 patterns, 15 n-rules and 6 p-rules
\end{itemize}
- \textbf{Correct solution:}
+ \underline{\smash{A solution:}}
\begin{Verbatim}
\textbf{def} max_abs(l):
vmax = l[0]
@@ -48,7 +48,7 @@ $\vdotswithin{S_4}$ & & $\vdotswithin{1}$ & & & $\vdotswithin{correct}$ \\
\textbf{return} vmax
\end{Verbatim}
\vspace{0.5cm}
- \textbf{Two sample learned n-rules:}
+ \underline{\smash{Two sample learned n-rules:}}
\begin{itemize}
\item \textsf{P64 ⇒ incorrect } (covers 22)
\item \textsf{P2 ∧ P70 ⇒ incorrect} (covers 17)
@@ -58,12 +58,13 @@ $\vdotswithin{S_4}$ & & $\vdotswithin{1}$ & & & $\vdotswithin{correct}$ \\
\begin{column}{0.40\textwidth}
\fbox{
\begin{minipage}[t]{0.94\textwidth}
+ \vspace{0.5cm}
\textbf{How useful are patterns?}
\begin{itemize}
\item Compare accuracies of Random Forest and Majority Classifier.
\item Three types of exercises (basic, loops, functions)
\end{itemize}
- \vspace{1cm}
+ \vspace{0.5cm}
\begin{center}
\begin{tabular}{l|rr}
\textbf{Problem} & Maj & RF \\
@@ -89,8 +90,12 @@ $\vdotswithin{S_4}$ & & $\vdotswithin{1}$ & & & $\vdotswithin{correct}$ \\
\end{minipage}}
\end{column}
\end{columns}
-\vspace{1.0cm}
-\textbf{Vizualizations of patterns}; left program contains pattern \textsf{P64}, right program contains pattern \textsf{P2} (red) and \textsf{P70} (blue) that matches the call to \textsf{abs} in an assignment statement nested within a for loop and an if clause.
+\vspace{1.5cm}
+\underline{\smash{Vizualizations of rules / patterns}}
+\begin{itemize}
+ \item \textsf{P64} (blue left) matches functions returning variable compared in the \textsf{if} clause.
+ \item \textsf{P2} (red right) matches functions that return the variable used in an assignment statement within a for-if block; \textsf{P70} (blue) matches the call to \textsf{abs} in an assignment statement nested within a for loop and an if clause.
+\end{itemize}
\begin{Verbatim}
\textbf{def} max_abs(l): \textbf{def} max_abs(l):
vmax = 0 vmax = None
@@ -98,6 +103,15 @@ $\vdotswithin{S_4}$ & & $\vdotswithin{1}$ & & & $\vdotswithin{correct}$ \\
\textbf{if} \blue{vmax} < abs(l[i]): \textbf{if} vmax==None or vmax<v:
vmax = l[i] \red{vmax} = \blue{abs}(v)
\textbf{return} \blue{vmax} \textbf{return} \red{vmax}
-\end{Verbatim}
+\end{Verbatim}
+
+%\begin{Verbatim}
+%\textbf{def} max_abs(l): \textbf{def} max_abs(l):
+%vmax = 0 vmax = None
+%\textbf{for} i \textbf{in} range(len(l)): \textbf{for} v \textbf{in} l:
+%\textbf{if} \blue{vmax} < abs(l[i]): \textbf{if} vmax==None or vmax<v:
+%vmax = l[i] \red{vmax} = \blue{abs}(v)
+%\textbf{return} \blue{vmax} \textbf{return} \red{vmax}
+%\end{Verbatim}