report done

prec-recall.py

@@ -53,7 +53,7 @@ def better_index(li: list[tuple[int, float]], e: int) -> Optional[int]:
 def plot_df(results, query: str) -> Optional[pd.DataFrame]:
     if results.vectors is not None and results.query_vector is not None:
         tsne_vectors = np.array(results.vectors + [results.query_vector])
-        tsne = TSNE(n_components=2, perplexity=2, n_iter=3000)
+        tsne = TSNE(n_components=2, perplexity=1, n_iter=3000)
         tsne_results = tsne.fit_transform(tsne_vectors)
         df = pd.DataFrame(columns=['tsne-2d-one', 'tsne-2d-two', 'Query', 'Vector kind'])
         df['tsne-2d-one'] = tsne_results[:, 0]

report/main.tex

@@ -82,7 +82,7 @@ Both the word frequency and TF-IDF model identify the correct result (according
 query) as the first recommendation to output. Both the LSI and Doc2Vec models fail to report the correct function in
 all 5 results.
 
-\begin{figure}
+\begin{figure}[b]
     \small
     \begin{verbatim}
 Similarity: 87.29%
@@ -119,7 +119,7 @@ Line: 70
     \label{fig:search-freq}
 \end{figure}
 
-\begin{figure}
+\begin{figure}[b]
     \small
     \begin{verbatim}
 Similarity: 86.62%
@@ -154,7 +154,7 @@ Line: 4736
     \label{fig:search-tfidf}
 \end{figure}
 
-\begin{figure}
+\begin{figure}[b]
     \small
     \begin{verbatim}
 Similarity: 92.11%
@@ -190,7 +190,7 @@ Line: 129
     \label{fig:search-lsi}
 \end{figure}
 
-\begin{figure}
+\begin{figure}[b]
     \small
     \begin{verbatim}
 Similarity: 81.85%
@@ -222,14 +222,18 @@ Line: 216
     \label{fig:search-doc2vec}
 \end{figure}
 
-\subsection*{TBD Section 3: Evaluation of search engines}
+\subsection*{Section 3: Evaluation of search engines}
 
-Using the ground truth provided, evaluate and report recall and average precision for each of the four search engines;
-comment the differences among search engines.
+The evaluation over the given ground truth to compute precision, recall, and the T-SNE plots is performed by the script
+\texttt{prec-recall.py}. The calculated average precision and recall values are reported in table~\ref{tab:tab2}.
 
-\begin{table} [H]
+Precision and recall is quite low for all models, less so for the word frequency and the TF-IDF models.
+The word frequency model has the highest precision and recall (27\% and 40\% respectively), while the LSI model has the
+lowest precision (4\%) and Doc2Vec has the lowest recall (10\%).
+
+\begin{table}[H]
 \centering
-\begin{tabular}{cccc}
+\begin{tabular}{ccc}
 \hline
 Engine & Avg Precision & Recall \\
 \hline
@@ -245,9 +249,21 @@ Doc2Vec & 10.00\% & 10.00\% \\
 
 \subsection*{TBD Section 4: Visualisation of query results}
 
-Include, comment and compare the t-SNE plots for LSI and for Doc2Vec.
+The two-dimensional T-SNE plots (computed with perplexity $= 1$) for the LSI and Doc2Vec models are respectively in
+figures~\ref{fig:tsne-lsi}~and~\ref{fig:tsne-doc2vec}.
 
-\begin{figure}[H]
+The T-SNE plot for the LSI model shows evidently the presence of outliers in the search result. The Doc2Vec plot shows
+fewer outliers and more distinct clusters for the results of each query and the query vector itself.
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=\textwidth]{../out/lsi_plot}
+\caption{T-SNE plot for the LSI model over the queries and ground truths given in \texttt{ground-truth-unique.txt}.}
+\label{fig:tsne-lsi}
+\end{center}
+\end{figure}
+
+\begin{figure}
 \begin{center}
 \includegraphics[width=\textwidth]{../out/doc2vec_plot}
 \caption{T-SNE plot for the Doc2Vec model over the queries and ground truths given in \texttt{ground-truth-unique.txt}.}
@@ -255,11 +271,4 @@ Include, comment and compare the t-SNE plots for LSI and for Doc2Vec.
 \end{center}
 \end{figure}
 
-\begin{figure}[H]
-\begin{center}
-\includegraphics[width=\textwidth]{../out/lsi_plot}
-\caption{T-SNE plot for the LSI model over the queries and ground truths given in \texttt{ground-truth-unique.txt}.}
-\label{fig:lsi-doc2vec}
-\end{center}
-\end{figure}
 \end{document}