Rename perfect into optimal (#68)

README.md

@@ -365,9 +365,9 @@ with open_state("example.asreview") as s:
 ![Recall with absolute
 axes](https://github.com/asreview/asreview-insights/blob/main/docs/example_absolute_axes.png)
 
-#### Example: Adjusting the Random and Perfect curves
+#### Example: Adjusting the random and optimal recalls
 
-By default, each plot will have a curve representing perfect performance, and a
+By default, each plot will have a curve representing optimal performance, and a
 curve representing random sampling performance. Both curves can be removed from
 the graph.
 
@@ -380,7 +380,7 @@ from asreviewcontrib.insights.plot import plot_recall
 with open_state("example.asreview") as s:
 
     fig, ax = plt.subplots()
-    plot_recall(ax, s, show_random=False, show_perfect=False)
+    plot_recall(ax, s, show_random=False, show_optimal=False)
 
     fig.savefig("example_without_curves.png")
 ```

asreviewcontrib/insights/plot.py

@@ -13,7 +13,7 @@ def plot_recall(
     x_absolute=False,
     y_absolute=False,
     show_random=True,
-    show_perfect=True,
+    show_optimal=True,
     show_legend=True,
     legend_values=None,
     legend_kwargs=None,
@@ -35,8 +35,8 @@ def plot_recall(
         If False, the fraction of all included records found is on the y-axis.
     show_random: bool
         Show the random curve in the plot.
-    show_perfect: bool
-        Show the perfect curve in the plot.
+    show_optimal: bool
+        Show the optimal recall in the plot.
     show_legend: bool
         If state_obj contains multiple states, show a legend in the plot.
     legend_values: list[str]
@@ -64,7 +64,7 @@ def plot_recall(
         x_absolute=x_absolute,
         y_absolute=y_absolute,
         show_random=show_random,
-        show_perfect=show_perfect,
+        show_optimal=show_optimal,
         show_legend=show_legend,
         legend_values=legend_values,
         legend_kwargs=legend_kwargs,
@@ -241,7 +241,7 @@ def _plot_recall(
     x_absolute=False,
     y_absolute=False,
     show_random=True,
-    show_perfect=True,
+    show_optimal=True,
     show_legend=True,
     legend_values=None,
     legend_kwargs=None,
@@ -265,8 +265,8 @@ def _plot_recall(
     if show_random:
         ax = _add_random_curve(ax, labels, x_absolute, y_absolute)
 
-    if show_perfect:
-        ax = _add_perfect_curve(ax, labels, x_absolute, y_absolute)
+    if show_optimal:
+        ax = _add_optimal_recall(ax, labels, x_absolute, y_absolute)
 
     if show_legend:
         if legend_kwargs is None:
@@ -406,13 +406,13 @@ def _add_random_curve(ax, labels, x_absolute, y_absolute):
     return ax
 
 
-def _add_perfect_curve(ax, labels, x_absolute, y_absolute):
-    """Add a perfect curve to a plot using step-wise increments.
+def _add_optimal_recall(ax, labels, x_absolute, y_absolute):
+    """Add a optimal recall to a plot using step-wise increments.
 
     Returns
     -------
     plt.axes.Axes
-        Axes with perfect curve added.
+        Axes with optimal recall added.
     """
     # get total amount of positive labels
     if isinstance(labels[0], list):
@@ -434,7 +434,7 @@ def _add_perfect_curve(ax, labels, x_absolute, y_absolute):
         else np.arange(0, n_pos_docs + 1) / n_pos_docs
     )
 
-    # Plot the stepwise perfect curve
+    # Plot the stepwise optimal recall
     ax.step(x, y, color="grey", where="post")
 
     return ax

docs/example_multiple_lines.py

@@ -19,7 +19,7 @@
     plot_recall(ax, s2)
 
 # Set the labels for the legend. Both plots add the recall line, the random
-# curve, and the perfect curve. Hence the recall lines are the 0th and 3nd line.
+# curve, and the optimal recall. Hence the recall lines are the 0th and 3nd line.
 ax.lines[0].set_label("Naive Bayes")
 ax.lines[3].set_label("Logistic")
 ax.legend()

docs/example_without_curves.py

@@ -5,6 +5,6 @@
 
 with open_state("tests/asreview_files/sim_van_de_schoot_2017_logistic.asreview") as s:
     fig, ax = plt.subplots()
-    plot_recall(ax, s, show_random=False, show_perfect=False)
+    plot_recall(ax, s, show_random=False, show_optimal=False)
 
     fig.savefig("docs/example_without_curves.png")