Module names and a figure

analysis_and_scripts/notes.tex

@@ -648,10 +648,10 @@ From the result table we can see that the MAE is at the lowest when the data gen
 \caption{Mean absolute error w.r.t true evaluation. See modules used in section \ref{sec:modules_mc}. Bern = Bernoulli,  indep. = independent, TH = threshold}
 \begin{tabular}{l | c c c c}
 Method & Bern + indep. & Bern + non-indep. & TH + indep. & TH + non-indep.\\ \hline
-Labeled outcomes 	& 0.111075	& 0.103235	& 0.108506 &\\
-Human evaluation 	& 0.027298	& NaN (TBA)	& 0.049582 &\\
-Contraction 		& 0.004206	& 0.004656	& 0.005557 &\\
-Monte Carlo	 	& 0.001292	& 0.016629	& 0.009429 &\\
+Labeled outcomes 	& 0.111075	& 0.103235	& 0.108506 & 0.0970325\\
+Human evaluation 	& 0.027298	& NaN (TBA)	& 0.049582 & 0.0033916\\
+Contraction 		& 0.004206	& 0.004656	& 0.005557 & 0.0034591\\
+Monte Carlo	 	& 0.001292	& 0.016629	& 0.009429 & 0.0179825\\
 \end{tabular}
 \label{tab:modules_mc}
 \end{table}
@@ -679,8 +679,8 @@ Monte Carlo	 	& 0.001292	& 0.016629	& 0.009429 &\\
     \quad %add desired spacing between images, e. g. ~, \quad, \qquad, \hfill etc. 
       %(or a blank line to force the subfigure onto a new line)
     \begin{subfigure}[b]{0.475\textwidth}
-        \includegraphics[width=\textwidth]{sl_with_Z_4iter_threshold_lakkarajudecider_defaults_mc}
-        \caption{Outcome Y from threshold rule, non-independent decisions and $N_{iter}=4$.}
+        \includegraphics[width=\textwidth]{sl_with_Z_10iter_threshold_lakkarajudecider_defaults_mc}
+        \caption{Outcome Y from threshold rule, non-independent decisions and $N_{iter}=10$.}
         %\label{fig:modules_mc_with_Z}
     \end{subfigure}
     \caption{Failure rate vs. acceptance rate with varying levels of leniency. Different combinations of deciders and data generation modules. See other modules used in section \ref{sec:modules_mc}}
@@ -692,7 +692,7 @@ Monte Carlo	 	& 0.001292	& 0.016629	& 0.009429 &\\
 Different types of modules (data generation, decider and evaluator) are presented in this section. Summary table is presented last. See section \ref{sec:modular_framework} for a more thorough break-down on the properties of each module.
 
 \begin{algorithm}[] 			% enter the algorithm environment
-\caption{Data generation module: "coin-flip results" without unobservables} 		% give the algorithm a caption
+\caption{Data generation module: outcome from Bernoulli without unobservables} 		% give the algorithm a caption
 \label{alg:dg:coinflip_without_z} 			% and a label for \ref{} commands later in the document
 \begin{algorithmic}[1] 		% enter the algorithmic environment
 \REQUIRE Parameters: Total number of subjects $N_{total}$
@@ -708,7 +708,7 @@ Different types of modules (data generation, decider and evaluator) are presente
 
 
 \begin{algorithm}[] 			% enter the algorithm environment
-\caption{Data generation module: "results by threshold" with unobservables} 		% give the algorithm a caption
+\caption{Data generation module: outcome by threshold with unobservables} 		% give the algorithm a caption
 \label{alg:dg:threshold_with_Z} 			% and a label for \ref{} commands later in the document
 \begin{algorithmic}[1] 		% enter the algorithmic environment
 \REQUIRE Parameters: Total number of subjects $N_{total},~\beta_X=1,~\beta_Z=1$ and $\beta_W=0.2$.
@@ -727,7 +727,7 @@ Different types of modules (data generation, decider and evaluator) are presente
 \end{algorithm}
 
 \begin{algorithm}[] 			% enter the algorithm environment
-\caption{Data generation module: "coin-flip results" with unobservables} 		% give the algorithm a caption
+\caption{Data generation module: outcome from Bernoulli with unobservables} 		% give the algorithm a caption
 \label{alg:dg:coinflip_with_z} 			% and a label for \ref{} commands later in the document
 \begin{algorithmic}[1] 		% enter the algorithmic environment
 \REQUIRE Parameters: Total number of subjects $N_{total},~\beta_X=1,~\beta_Z=1$ and $\beta_W=0.2$.
@@ -761,7 +761,7 @@ Different types of modules (data generation, decider and evaluator) are presente
 \end{algorithm}
 
 \begin{algorithm}[] 			% enter the algorithm environment
-\caption{Decider module: "coin-flip decisions" (pseudo-leniencies set at 0.5)} 		% give the algorithm a caption
+\caption{Decider module: decisions from Bernoulli (pseudo-leniencies set at 0.5)} 		% give the algorithm a caption
 \label{alg:decider:coinflip} 			% and a label for \ref{} commands later in the document
 \begin{algorithmic}[1] 		% enter the algorithmic environment
 \REQUIRE Data with features $X, Z$ of size $N_{total}$, knowledge that both of them affect the outcome Y and that they are independent / Parameters: $\beta_X=1, \beta_Z=1$.
@@ -851,7 +851,7 @@ Different types of modules (data generation, decider and evaluator) are presente
 \STATE Sort $\D_{observed}$ by the probabilities $\s$ to ascending order.
 \STATE \hskip3.0em $\rhd$ Now the most dangerous subjects are last.
 \STATE Calculate the number to release $N_{free} = |\D_{observed}| \cdot r$.
-\RETURN $\frac{1}{|\D_{observed}|}\sum_{i=1}^{N_{free}}\delta\{y_i=0\}$
+\RETURN $\frac{1}{|\D_{test}|}\sum_{i=1}^{N_{free}}\delta\{y_i=0\}$
 \end{algorithmic}
 \end{algorithm}