diff --git a/flexibleSubsetSelection/solver.py b/flexibleSubsetSelection/solver.py
index c5f521d..8d237bd 100644
--- a/flexibleSubsetSelection/solver.py
+++ b/flexibleSubsetSelection/solver.py
@@ -10,7 +10,7 @@
# Local files
from . import loss
from . import sets
-from timer import Timer
+from .timer import Timer
# --- Solver -------------------------------------------------------------------
diff --git a/jupyter/Fig1-designProcess.ipynb b/jupyter/Fig1-designProcess.ipynb
index a3c8b62..59378ad 100644
--- a/jupyter/Fig1-designProcess.ipynb
+++ b/jupyter/Fig1-designProcess.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -49,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -79,17 +79,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x2 in 0.21s with 0.0 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate the hull metric on the full dataset\n",
"dataset.preprocess(hull = fss.metric.hull)\n",
@@ -122,17 +114,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 40x2 in 0.07s with -75.52 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate the outlierness (local outlier effect) of the full dataset\n",
"dataset.preprocess(outlierness = fss.objective.outlierness)\n",
@@ -161,17 +145,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 60x2 in 0.63s with -95.31 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create a unicriterion loss function with the distinctness objective\n",
"dataset.preprocess(distances = fss.metric.distanceMatrix)\n",
@@ -195,2413 +171,9 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Initialize color and plot settings\n",
"color = fss.plot.Color()\n",
@@ -2646,17 +218,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 80x2 in 69.56s with -38.98 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"subsetSize = 80 # The size of subsets being selected with blended objectives\n",
"\n",
@@ -2679,17 +243,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 80x2 in 71.83s with -58.99 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Update the weights to provide less emphasis on the distribution objective\n",
"solver.lossFunction = fss.MultiCriterion(objectives = objectives, \n",
@@ -2703,17 +259,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 80x2 in 95.41s with -102.52 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Update the weights to an even weight of the two objectives\n",
"solver.lossFunction = fss.MultiCriterion(objectives = objectives, \n",
@@ -2736,2603 +284,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Plot the three subsets with different blends of the two objectives\n",
"titles = [\"More Distribution,\\nLess Distinct\", \"\", \n",
@@ -5371,19 +325,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 28x2 in 0.17s with -57.6 loss.\n",
- "Solved for subset of size 72x2 in 0.61s with -80.52 loss.\n",
- "Solved for subset of size 169x2 in 2.53s with -112.65 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create a unicriterion loss function with the distinctness objective\n",
"solver.lossFunction = fss.UniCriterion(objective = fss.objective.distinctness,\n",
@@ -5422,2636 +366,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Plot the three subsets with different subset sizes based on the tuning\n",
"titles = [\"Fewer Points,\\nMore Distinct\", \"\", \"More Points,\\nLess Distinct\"]\n",
@@ -8093,7 +410,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "version": "3.12.4"
}
},
"nbformat": 4,