diff --git a/flexibleSubsetSelection/objective.py b/flexibleSubsetSelection/objective.py
index 5f20609..7adf9f3 100644
--- a/flexibleSubsetSelection/objective.py
+++ b/flexibleSubsetSelection/objective.py
@@ -33,7 +33,6 @@ def preserveMetric(subset: np.ndarray, metric: Callable,
# If the metric results are scalars, use the absolute difference
if np.isscalar(datasetMetric):
- print(datasetMetric, subsetMetric)
return np.abs(datasetMetric - subsetMetric)
# Otherwise, use np.linalg.norm for array-like metric results
diff --git a/jupyter/Fig1-designProcess.ipynb b/jupyter/Fig1-designProcess.ipynb
index 56ab48e..9435f4b 100644
--- a/jupyter/Fig1-designProcess.ipynb
+++ b/jupyter/Fig1-designProcess.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -69,12 +69,8 @@
"### Convex Hull Objective\n",
"\n",
"Applying this objective allows us to select the smallest subset that covers the convex hull of the original dataset.\n",
- "$$\n",
- " \\min_{S \\in \\mathbb{S}}\\space \\|S\\|\n",
- "$$\n",
- "$$\n",
- " \\text{s.t.} \\space \\text{Hull}(S) = 0\n",
- "$$"
+ "$$\\min_{S \\in \\mathbb{S}} \\space \\|S\\|$$\n",
+ "$$\\text{s.t.} \\space \\text{Hull}(S) = 0$$"
]
},
{
@@ -395,22 +391,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.4"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig2&3-objectives.ipynb b/jupyter/Fig2&3-objectives.ipynb
index 79c245c..84daedf 100644
--- a/jupyter/Fig2&3-objectives.ipynb
+++ b/jupyter/Fig2&3-objectives.ipynb
@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -48,7 +48,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -70,19 +70,9 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.03s with 11.15 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 2.08 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 0.7 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate dataset means\n",
"firstDataset.preprocess(mean=fss.metric.mean)\n",
@@ -126,19 +116,9 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.03s with 95.87 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 55.02 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with 23.9 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate dataset ranges\n",
"firstDataset.preprocess(range = fss.metric.range)\n",
@@ -177,19 +157,9 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.03s with 43.41 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.62 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with 1.48 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate the discrete coverage of the full dataset\n",
"firstDataset.preprocess(variance = fss.metric.variance)\n",
@@ -228,19 +198,9 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.14s with 258.0 loss.\n",
- "Solved for subset of size 10x10 in 0.14s with 154.0 loss.\n",
- "Solved for subset of size 10x10 in 1.37s with 76.0 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create a unicriterion loss function with the coverage metric and update solver\n",
"crossingsLoss = fss.UniCriterion(objective = fss.objective.pcpLineCrossings)\n",
@@ -274,19 +234,9 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.03s with -28.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -44.0 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with -56.0 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Bin and one hot encode the dataset for discretization\n",
"firstDataset.discretize(bins=6)\n",
@@ -327,19 +277,9 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.03s with 6.43 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 2.83 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 1.64 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate the discrete distribution of the full dataset\n",
"firstDataset.preprocess(distribution = fss.metric.discreteDistribution)\n",
@@ -380,7 +320,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -404,7 +344,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -438,208554 +378,9 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "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",
@@ -209103,7 +498,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -209124,19 +519,9 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x2 in 0.03s with -2.73 loss.\n",
- "Solved for subset of size 10x2 in 0.03s with -25.73 loss.\n",
- "Solved for subset of size 10x2 in 0.16s with -43.43 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create a unicriterion loss function with the distinctness objective\n",
"secondDataset.preprocess(distances = fss.metric.distanceMatrix)\n",
@@ -209182,19 +567,9 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x2 in 0.03s with -108.99 loss.\n",
- "Solved for subset of size 10x2 in 0.03s with -528.93 loss.\n",
- "Solved for subset of size 10x2 in 0.15s with -889.35 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create a unicriterion loss function with the spread objective\n",
"spreadLoss = fss.UniCriterion(objective = fss.objective.spread, \n",
@@ -209234,19 +609,9 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x2 in 0.03s with 11.12 loss.\n",
- "Solved for subset of size 10x2 in 0.03s with 1.55 loss.\n",
- "Solved for subset of size 10x2 in 0.14s with 0.39 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Precalculate the cluster metric on the full dataset\n",
"secondDataset.preprocess(clusterCenters=(fss.metric.clusterCenters, {'k': 5}))\n",
@@ -209286,11224 +651,9 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"nrows = 3\n",
"ncols = 4\n",
@@ -220557,22 +707,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig4-performance.ipynb b/jupyter/Fig4-performance.ipynb
index fe0f685..e8ae3a5 100644
--- a/jupyter/Fig4-performance.ipynb
+++ b/jupyter/Fig4-performance.ipynb
@@ -13,18 +13,9 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Standard library\n",
"import os\n",
@@ -49,7 +40,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -64,466 +55,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 3.13 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 6.25 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 9.66 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -97.01 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -94.16 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -90.43 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 3.34 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 5.74 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 9.55 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -96.11 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -93.82 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -90.26 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 4.08 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 6.44 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 9.25 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -96.93 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -93.81 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -90.49 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 4.21 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 5.59 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 9.69 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -96.65 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -94.73 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -90.64 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 3.09 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 6.54 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with 9.76 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -96.42 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -93.75 loss.\n",
- "Solved for subset of size 10x10 in 0.0s with -90.39 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 2.92 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.49 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 9.76 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -97.17 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -94.21 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -89.9 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 2.99 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.37 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 9.7 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -97.3 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -93.47 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -90.25 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 2.68 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.84 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 9.69 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -97.49 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -94.47 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -90.24 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 3.02 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.25 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 9.66 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -97.92 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -93.72 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -90.18 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 2.73 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.38 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 9.84 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -97.5 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -94.26 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -90.28 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with 3.06 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 6.1 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.1 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with -97.56 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -94.9 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -89.96 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with 2.93 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.05 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.1 loss.\n",
- "Solved for subset of size 10x10 in 0.12s with -97.57 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -94.19 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -89.82 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with 2.8 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.99 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.0 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with -97.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -94.32 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -89.96 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with 2.63 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.71 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 9.87 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with -97.22 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -94.63 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -89.75 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with 2.7 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.84 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.01 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with -97.19 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -93.44 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -90.11 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with 2.57 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 6.17 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.05 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with -97.24 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -94.35 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -89.68 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with 2.6 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 6.06 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.12 loss.\n",
- "Solved for subset of size 10x10 in 0.2s with -97.77 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -93.75 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -89.76 loss.\n",
- "Solved for subset of size 10x10 in 0.14s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with 3.3 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.52 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.06 loss.\n",
- "Solved for subset of size 10x10 in 0.24s with -97.22 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -94.16 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -90.05 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with 2.18 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.78 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.07 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with -96.86 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -95.4 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -89.84 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with 2.23 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 5.95 loss.\n",
- "Solved for subset of size 10x10 in 0.01s with 10.04 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with -96.8 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -94.42 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -89.95 loss.\n",
- "Solved for subset of size 10x10 in 0.14s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with 2.09 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.76 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.2 loss.\n",
- "Solved for subset of size 10x10 in 0.41s with -97.13 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -93.85 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -89.65 loss.\n",
- "Solved for subset of size 10x10 in 0.16s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with 2.86 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 4.99 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.13 loss.\n",
- "Solved for subset of size 10x10 in 0.29s with -97.38 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -93.94 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -89.64 loss.\n",
- "Solved for subset of size 10x10 in 0.15s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with 2.4 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 6.16 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.14 loss.\n",
- "Solved for subset of size 10x10 in 0.27s with -97.96 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -94.84 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -89.7 loss.\n",
- "Solved for subset of size 10x10 in 0.16s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with 3.01 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 6.3 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.21 loss.\n",
- "Solved for subset of size 10x10 in 0.27s with -97.37 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -94.49 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -89.87 loss.\n",
- "Solved for subset of size 10x10 in 0.16s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with 2.45 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.3 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.15 loss.\n",
- "Solved for subset of size 10x10 in 0.27s with -97.44 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -94.6 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -89.79 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 2.71 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.28 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.11 loss.\n",
- "Solved for subset of size 10x10 in 0.35s with -97.25 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -95.32 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -89.88 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 2.47 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.32 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.13 loss.\n",
- "Solved for subset of size 10x10 in 0.35s with -97.24 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -94.99 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -89.81 loss.\n",
- "Solved for subset of size 10x10 in 0.22s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 2.54 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.42 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.27 loss.\n",
- "Solved for subset of size 10x10 in 0.34s with -97.59 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -95.19 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -89.82 loss.\n",
- "Solved for subset of size 10x10 in 0.22s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 2.81 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.61 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.15 loss.\n",
- "Solved for subset of size 10x10 in 0.34s with -97.54 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -94.19 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -89.77 loss.\n",
- "Solved for subset of size 10x10 in 0.22s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.13s with 2.62 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.56 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.12 loss.\n",
- "Solved for subset of size 10x10 in 0.36s with -98.03 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -94.46 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -89.95 loss.\n",
- "Solved for subset of size 10x10 in 0.28s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.15s with 2.91 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.69 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.17 loss.\n",
- "Solved for subset of size 10x10 in 0.43s with -97.44 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with -94.42 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with -89.78 loss.\n",
- "Solved for subset of size 10x10 in 0.3s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.15s with 2.21 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.32 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.19 loss.\n",
- "Solved for subset of size 10x10 in 0.43s with -97.52 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -94.15 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with -89.83 loss.\n",
- "Solved for subset of size 10x10 in 0.28s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.15s with 2.77 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.63 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.3 loss.\n",
- "Solved for subset of size 10x10 in 0.45s with -97.6 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -94.13 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -89.86 loss.\n",
- "Solved for subset of size 10x10 in 0.29s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.15s with 2.18 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 5.83 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.15 loss.\n",
- "Solved for subset of size 10x10 in 0.42s with -97.23 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with -95.17 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -89.85 loss.\n",
- "Solved for subset of size 10x10 in 0.29s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.15s with 2.81 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 4.94 loss.\n",
- "Solved for subset of size 10x10 in 0.02s with 10.22 loss.\n",
- "Solved for subset of size 10x10 in 0.44s with -97.84 loss.\n",
- "Solved for subset of size 10x10 in 0.06s with -94.91 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -89.95 loss.\n",
- "Solved for subset of size 10x10 in 0.34s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with 2.56 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.93 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.27 loss.\n",
- "Solved for subset of size 10x10 in 0.53s with -98.38 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -94.55 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -89.77 loss.\n",
- "Solved for subset of size 10x10 in 0.38s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with 2.83 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.2 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.22 loss.\n",
- "Solved for subset of size 10x10 in 0.71s with -97.5 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -93.9 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -89.84 loss.\n",
- "Solved for subset of size 10x10 in 0.36s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with 2.14 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.2 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.25 loss.\n",
- "Solved for subset of size 10x10 in 0.52s with -97.98 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -94.33 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -89.82 loss.\n",
- "Solved for subset of size 10x10 in 0.36s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with 2.63 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.76 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.1 loss.\n",
- "Solved for subset of size 10x10 in 0.54s with -97.4 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -94.12 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -89.75 loss.\n",
- "Solved for subset of size 10x10 in 0.36s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.05s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.18s with 2.27 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.39 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.18 loss.\n",
- "Solved for subset of size 10x10 in 0.57s with -97.58 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -94.29 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -89.81 loss.\n",
- "Solved for subset of size 10x10 in 0.52s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with 2.14 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.67 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.27 loss.\n",
- "Solved for subset of size 10x10 in 0.74s with -97.49 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -94.35 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -89.71 loss.\n",
- "Solved for subset of size 10x10 in 0.7s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with 2.37 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.51 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.17 loss.\n",
- "Solved for subset of size 10x10 in 0.81s with -97.45 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -94.53 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.7 loss.\n",
- "Solved for subset of size 10x10 in 0.53s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.2s with 2.32 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.73 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.23 loss.\n",
- "Solved for subset of size 10x10 in 0.76s with -97.46 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -95.07 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.73 loss.\n",
- "Solved for subset of size 10x10 in 0.53s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with 2.36 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.77 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.23 loss.\n",
- "Solved for subset of size 10x10 in 0.73s with -97.74 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -93.75 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -89.77 loss.\n",
- "Solved for subset of size 10x10 in 0.52s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.21s with 2.25 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 5.68 loss.\n",
- "Solved for subset of size 10x10 in 0.03s with 10.36 loss.\n",
- "Solved for subset of size 10x10 in 0.73s with -97.48 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -94.46 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -89.78 loss.\n",
- "Solved for subset of size 10x10 in 0.61s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.24s with 2.14 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 6.0 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.36 loss.\n",
- "Solved for subset of size 10x10 in 0.92s with -97.7 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -94.26 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with -89.73 loss.\n",
- "Solved for subset of size 10x10 in 0.59s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.09s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.08 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.53 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.28 loss.\n",
- "Solved for subset of size 10x10 in 0.9s with -97.68 loss.\n",
- "Solved for subset of size 10x10 in 0.12s with -94.39 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with -89.69 loss.\n",
- "Solved for subset of size 10x10 in 0.75s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.37 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.4 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.14 loss.\n",
- "Solved for subset of size 10x10 in 0.88s with -97.63 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -95.29 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.66 loss.\n",
- "Solved for subset of size 10x10 in 0.61s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.24s with 2.4 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.28 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.35 loss.\n",
- "Solved for subset of size 10x10 in 0.82s with -97.74 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -94.13 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.81 loss.\n",
- "Solved for subset of size 10x10 in 0.6s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.86 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.95 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.25 loss.\n",
- "Solved for subset of size 10x10 in 0.82s with -97.47 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -95.01 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.74 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for datasetLength in range(100, 1100, 100):\n",
" # Create dataset\n",
@@ -574,9191 +108,9 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Computation Time (s)')"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "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",
@@ -9815,466 +167,9 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0 0 0\n",
- "Solved for subset of size 10x10 in 0.17s with -100.0 loss.\n",
- "0 0 1\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "0 0 2\n",
- "Solved for subset of size 10x10 in 0.03s with -100.0 loss.\n",
- "0 1 0\n",
- "Solved for subset of size 10x10 in 0.1s with 1.96 loss.\n",
- "0 1 1\n",
- "Solved for subset of size 10x10 in 0.02s with 6.09 loss.\n",
- "0 1 2\n",
- "Solved for subset of size 10x10 in 0.02s with 10.11 loss.\n",
- "0 2 0\n",
- "Solved for subset of size 10x10 in 0.31s with -97.34 loss.\n",
- "0 2 1\n",
- "Solved for subset of size 10x10 in 0.04s with -94.62 loss.\n",
- "0 2 2\n",
- "Solved for subset of size 10x10 in 0.03s with -89.73 loss.\n",
- "1 0 0\n",
- "Solved for subset of size 10x10 in 0.14s with -100.0 loss.\n",
- "1 0 1\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "1 0 2\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "1 1 0\n",
- "Solved for subset of size 10x10 in 0.1s with 2.42 loss.\n",
- "1 1 1\n",
- "Solved for subset of size 10x10 in 0.02s with 6.09 loss.\n",
- "1 1 2\n",
- "Solved for subset of size 10x10 in 0.02s with 10.13 loss.\n",
- "1 2 0\n",
- "Solved for subset of size 10x10 in 0.24s with -97.64 loss.\n",
- "1 2 1\n",
- "Solved for subset of size 10x10 in 0.03s with -94.34 loss.\n",
- "1 2 2\n",
- "Solved for subset of size 10x10 in 0.03s with -89.82 loss.\n",
- "2 0 0\n",
- "Solved for subset of size 10x10 in 0.13s with -100.0 loss.\n",
- "2 0 1\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "2 0 2\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "2 1 0\n",
- "Solved for subset of size 10x10 in 0.1s with 2.79 loss.\n",
- "2 1 1\n",
- "Solved for subset of size 10x10 in 0.02s with 5.61 loss.\n",
- "2 1 2\n",
- "Solved for subset of size 10x10 in 0.02s with 10.35 loss.\n",
- "2 2 0\n",
- "Solved for subset of size 10x10 in 0.24s with -97.38 loss.\n",
- "2 2 1\n",
- "Solved for subset of size 10x10 in 0.03s with -94.55 loss.\n",
- "2 2 2\n",
- "Solved for subset of size 10x10 in 0.03s with -89.9 loss.\n",
- "3 0 0\n",
- "Solved for subset of size 10x10 in 0.14s with -100.0 loss.\n",
- "3 0 1\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "3 0 2\n",
- "Solved for subset of size 10x10 in 0.02s with -100.0 loss.\n",
- "3 1 0\n",
- "Solved for subset of size 10x10 in 0.1s with 2.55 loss.\n",
- "3 1 1\n",
- "Solved for subset of size 10x10 in 0.02s with 6.25 loss.\n",
- "3 1 2\n",
- "Solved for subset of size 10x10 in 0.02s with 10.09 loss.\n",
- "3 2 0\n",
- "Solved for subset of size 10x10 in 0.31s with -97.58 loss.\n",
- "3 2 1\n",
- "Solved for subset of size 10x10 in 0.04s with -94.68 loss.\n",
- "3 2 2\n",
- "Solved for subset of size 10x10 in 0.05s with -89.77 loss.\n",
- "4 0 0\n",
- "Solved for subset of size 10x10 in 0.21s with -100.0 loss.\n",
- "4 0 1\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "4 0 2\n",
- "Solved for subset of size 10x10 in 0.04s with -100.0 loss.\n",
- "4 1 0\n",
- "Solved for subset of size 10x10 in 0.1s with 2.82 loss.\n",
- "4 1 1\n",
- "Solved for subset of size 10x10 in 0.02s with 5.83 loss.\n",
- "4 1 2\n",
- "Solved for subset of size 10x10 in 0.02s with 10.25 loss.\n",
- "4 2 0\n",
- "Solved for subset of size 10x10 in 0.26s with -97.49 loss.\n",
- "4 2 1\n",
- "Solved for subset of size 10x10 in 0.03s with -93.78 loss.\n",
- "4 2 2\n",
- "Solved for subset of size 10x10 in 0.03s with -89.81 loss.\n",
- "0 0 0\n",
- "Solved for subset of size 110x10 in 20.4s with -1100.0 loss.\n",
- "0 0 1\n",
- "Solved for subset of size 110x10 in 0.24s with -1100.0 loss.\n",
- "0 0 2\n",
- "Solved for subset of size 110x10 in 0.24s with -1100.0 loss.\n",
- "0 1 0\n",
- "Solved for subset of size 110x10 in 1.14s with 0.15 loss.\n",
- "0 1 1\n",
- "Solved for subset of size 110x10 in 0.02s with 0.62 loss.\n",
- "0 1 2\n",
- "Solved for subset of size 110x10 in 0.02s with 1.3 loss.\n",
- "0 2 0\n",
- "Solved for subset of size 110x10 in 23.66s with -1099.82 loss.\n",
- "0 2 1\n",
- "Solved for subset of size 110x10 in 0.3s with -1099.43 loss.\n",
- "0 2 2\n",
- "Solved for subset of size 110x10 in 0.27s with -1098.71 loss.\n",
- "1 0 0\n",
- "Solved for subset of size 110x10 in 20.39s with -1100.0 loss.\n",
- "1 0 1\n",
- "Solved for subset of size 110x10 in 0.26s with -1100.0 loss.\n",
- "1 0 2\n",
- "Solved for subset of size 110x10 in 0.26s with -1100.0 loss.\n",
- "1 1 0\n",
- "Solved for subset of size 110x10 in 1.12s with 0.18 loss.\n",
- "1 1 1\n",
- "Solved for subset of size 110x10 in 0.02s with 0.64 loss.\n",
- "1 1 2\n",
- "Solved for subset of size 110x10 in 0.02s with 1.29 loss.\n",
- "1 2 0\n",
- "Solved for subset of size 110x10 in 23.88s with -1099.83 loss.\n",
- "1 2 1\n",
- "Solved for subset of size 110x10 in 0.29s with -1099.44 loss.\n",
- "1 2 2\n",
- "Solved for subset of size 110x10 in 0.28s with -1098.72 loss.\n",
- "2 0 0\n",
- "Solved for subset of size 110x10 in 21.53s with -1100.0 loss.\n",
- "2 0 1\n",
- "Solved for subset of size 110x10 in 0.26s with -1100.0 loss.\n",
- "2 0 2\n",
- "Solved for subset of size 110x10 in 0.26s with -1100.0 loss.\n",
- "2 1 0\n",
- "Solved for subset of size 110x10 in 1.13s with 0.16 loss.\n",
- "2 1 1\n",
- "Solved for subset of size 110x10 in 0.02s with 0.64 loss.\n",
- "2 1 2\n",
- "Solved for subset of size 110x10 in 0.02s with 1.3 loss.\n",
- "2 2 0\n",
- "Solved for subset of size 110x10 in 24.28s with -1099.85 loss.\n",
- "2 2 1\n",
- "Solved for subset of size 110x10 in 0.27s with -1099.41 loss.\n",
- "2 2 2\n",
- "Solved for subset of size 110x10 in 0.27s with -1098.73 loss.\n",
- "3 0 0\n",
- "Solved for subset of size 110x10 in 20.57s with -1100.0 loss.\n",
- "3 0 1\n",
- "Solved for subset of size 110x10 in 0.25s with -1100.0 loss.\n",
- "3 0 2\n",
- "Solved for subset of size 110x10 in 0.24s with -1100.0 loss.\n",
- "3 1 0\n",
- "Solved for subset of size 110x10 in 1.15s with 0.19 loss.\n",
- "3 1 1\n",
- "Solved for subset of size 110x10 in 0.02s with 0.66 loss.\n",
- "3 1 2\n",
- "Solved for subset of size 110x10 in 0.02s with 1.29 loss.\n",
- "3 2 0\n",
- "Solved for subset of size 110x10 in 23.48s with -1099.88 loss.\n",
- "3 2 1\n",
- "Solved for subset of size 110x10 in 0.28s with -1099.3 loss.\n",
- "3 2 2\n",
- "Solved for subset of size 110x10 in 0.28s with -1098.72 loss.\n",
- "4 0 0\n",
- "Solved for subset of size 110x10 in 20.72s with -1100.0 loss.\n",
- "4 0 1\n",
- "Solved for subset of size 110x10 in 0.25s with -1100.0 loss.\n",
- "4 0 2\n",
- "Solved for subset of size 110x10 in 0.25s with -1100.0 loss.\n",
- "4 1 0\n",
- "Solved for subset of size 110x10 in 1.13s with 0.13 loss.\n",
- "4 1 1\n",
- "Solved for subset of size 110x10 in 0.02s with 0.66 loss.\n",
- "4 1 2\n",
- "Solved for subset of size 110x10 in 0.02s with 1.29 loss.\n",
- "4 2 0\n",
- "Solved for subset of size 110x10 in 22.68s with -1099.82 loss.\n",
- "4 2 1\n",
- "Solved for subset of size 110x10 in 0.27s with -1099.33 loss.\n",
- "4 2 2\n",
- "Solved for subset of size 110x10 in 0.26s with -1098.71 loss.\n",
- "0 0 0\n",
- "Solved for subset of size 210x10 in 25.08s with -2100.0 loss.\n",
- "0 0 1\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "0 0 2\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "0 1 0\n",
- "Solved for subset of size 210x10 in 1.96s with 0.06 loss.\n",
- "0 1 1\n",
- "Solved for subset of size 210x10 in 0.02s with 0.28 loss.\n",
- "0 1 2\n",
- "Solved for subset of size 210x10 in 0.02s with 0.67 loss.\n",
- "0 2 0\n",
- "Solved for subset of size 210x10 in 31.46s with -2099.94 loss.\n",
- "0 2 1\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.67 loss.\n",
- "0 2 2\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.33 loss.\n",
- "1 0 0\n",
- "Solved for subset of size 210x10 in 25.34s with -2100.0 loss.\n",
- "1 0 1\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "1 0 2\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "1 1 0\n",
- "Solved for subset of size 210x10 in 1.98s with 0.06 loss.\n",
- "1 1 1\n",
- "Solved for subset of size 210x10 in 0.02s with 0.35 loss.\n",
- "1 1 2\n",
- "Solved for subset of size 210x10 in 0.02s with 0.67 loss.\n",
- "1 2 0\n",
- "Solved for subset of size 210x10 in 31.8s with -2099.94 loss.\n",
- "1 2 1\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.66 loss.\n",
- "1 2 2\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.34 loss.\n",
- "2 0 0\n",
- "Solved for subset of size 210x10 in 26.22s with -2100.0 loss.\n",
- "2 0 1\n",
- "Solved for subset of size 210x10 in 0.24s with -2100.0 loss.\n",
- "2 0 2\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "2 1 0\n",
- "Solved for subset of size 210x10 in 2.03s with 0.05 loss.\n",
- "2 1 1\n",
- "Solved for subset of size 210x10 in 0.02s with 0.28 loss.\n",
- "2 1 2\n",
- "Solved for subset of size 210x10 in 0.03s with 0.67 loss.\n",
- "2 2 0\n",
- "Solved for subset of size 210x10 in 31.42s with -2099.94 loss.\n",
- "2 2 1\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.73 loss.\n",
- "2 2 2\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.34 loss.\n",
- "3 0 0\n",
- "Solved for subset of size 210x10 in 26.01s with -2100.0 loss.\n",
- "3 0 1\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "3 0 2\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "3 1 0\n",
- "Solved for subset of size 210x10 in 2.05s with 0.08 loss.\n",
- "3 1 1\n",
- "Solved for subset of size 210x10 in 0.03s with 0.36 loss.\n",
- "3 1 2\n",
- "Solved for subset of size 210x10 in 0.02s with 0.67 loss.\n",
- "3 2 0\n",
- "Solved for subset of size 210x10 in 32.28s with -2099.95 loss.\n",
- "3 2 1\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.64 loss.\n",
- "3 2 2\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.32 loss.\n",
- "4 0 0\n",
- "Solved for subset of size 210x10 in 26.06s with -2100.0 loss.\n",
- "4 0 1\n",
- "Solved for subset of size 210x10 in 0.24s with -2100.0 loss.\n",
- "4 0 2\n",
- "Solved for subset of size 210x10 in 0.23s with -2100.0 loss.\n",
- "4 1 0\n",
- "Solved for subset of size 210x10 in 2.04s with 0.06 loss.\n",
- "4 1 1\n",
- "Solved for subset of size 210x10 in 0.02s with 0.29 loss.\n",
- "4 1 2\n",
- "Solved for subset of size 210x10 in 0.02s with 0.68 loss.\n",
- "4 2 0\n",
- "Solved for subset of size 210x10 in 31.17s with -2099.91 loss.\n",
- "4 2 1\n",
- "Solved for subset of size 210x10 in 0.37s with -2099.73 loss.\n",
- "4 2 2\n",
- "Solved for subset of size 210x10 in 0.25s with -2099.33 loss.\n",
- "0 0 0\n",
- "Solved for subset of size 310x10 in 35.58s with -3100.0 loss.\n",
- "0 0 1\n",
- "Solved for subset of size 310x10 in 0.33s with -3100.0 loss.\n",
- "0 0 2\n",
- "Solved for subset of size 310x10 in 0.33s with -3100.0 loss.\n",
- "0 1 0\n",
- "Solved for subset of size 310x10 in 2.3s with 0.05 loss.\n",
- "0 1 1\n",
- "Solved for subset of size 310x10 in 0.03s with 0.2 loss.\n",
- "0 1 2\n",
- "Solved for subset of size 310x10 in 0.03s with 0.4 loss.\n",
- "0 2 0\n",
- "Solved for subset of size 310x10 in 43.89s with -3099.99 loss.\n",
- "0 2 1\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.79 loss.\n",
- "0 2 2\n",
- "Solved for subset of size 310x10 in 0.35s with -3099.6 loss.\n",
- "1 0 0\n",
- "Solved for subset of size 310x10 in 35.31s with -3100.0 loss.\n",
- "1 0 1\n",
- "Solved for subset of size 310x10 in 0.32s with -3100.0 loss.\n",
- "1 0 2\n",
- "Solved for subset of size 310x10 in 0.32s with -3100.0 loss.\n",
- "1 1 0\n",
- "Solved for subset of size 310x10 in 2.35s with 0.03 loss.\n",
- "1 1 1\n",
- "Solved for subset of size 310x10 in 0.03s with 0.19 loss.\n",
- "1 1 2\n",
- "Solved for subset of size 310x10 in 0.03s with 0.4 loss.\n",
- "1 2 0\n",
- "Solved for subset of size 310x10 in 42.75s with -3099.99 loss.\n",
- "1 2 1\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.83 loss.\n",
- "1 2 2\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.61 loss.\n",
- "2 0 0\n",
- "Solved for subset of size 310x10 in 36.74s with -3100.0 loss.\n",
- "2 0 1\n",
- "Solved for subset of size 310x10 in 0.33s with -3100.0 loss.\n",
- "2 0 2\n",
- "Solved for subset of size 310x10 in 0.32s with -3100.0 loss.\n",
- "2 1 0\n",
- "Solved for subset of size 310x10 in 2.44s with 0.03 loss.\n",
- "2 1 1\n",
- "Solved for subset of size 310x10 in 0.03s with 0.18 loss.\n",
- "2 1 2\n",
- "Solved for subset of size 310x10 in 0.03s with 0.4 loss.\n",
- "2 2 0\n",
- "Solved for subset of size 310x10 in 45.86s with -3099.97 loss.\n",
- "2 2 1\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.8 loss.\n",
- "2 2 2\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.6 loss.\n",
- "3 0 0\n",
- "Solved for subset of size 310x10 in 36.26s with -3100.0 loss.\n",
- "3 0 1\n",
- "Solved for subset of size 310x10 in 0.32s with -3100.0 loss.\n",
- "3 0 2\n",
- "Solved for subset of size 310x10 in 0.33s with -3100.0 loss.\n",
- "3 1 0\n",
- "Solved for subset of size 310x10 in 2.28s with 0.06 loss.\n",
- "3 1 1\n",
- "Solved for subset of size 310x10 in 0.03s with 0.18 loss.\n",
- "3 1 2\n",
- "Solved for subset of size 310x10 in 0.03s with 0.4 loss.\n",
- "3 2 0\n",
- "Solved for subset of size 310x10 in 44.94s with -3099.96 loss.\n",
- "3 2 1\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.79 loss.\n",
- "3 2 2\n",
- "Solved for subset of size 310x10 in 0.35s with -3099.6 loss.\n",
- "4 0 0\n",
- "Solved for subset of size 310x10 in 36.57s with -3100.0 loss.\n",
- "4 0 1\n",
- "Solved for subset of size 310x10 in 0.33s with -3100.0 loss.\n",
- "4 0 2\n",
- "Solved for subset of size 310x10 in 0.33s with -3100.0 loss.\n",
- "4 1 0\n",
- "Solved for subset of size 310x10 in 2.22s with 0.04 loss.\n",
- "4 1 1\n",
- "Solved for subset of size 310x10 in 0.03s with 0.19 loss.\n",
- "4 1 2\n",
- "Solved for subset of size 310x10 in 0.03s with 0.4 loss.\n",
- "4 2 0\n",
- "Solved for subset of size 310x10 in 42.47s with -3099.97 loss.\n",
- "4 2 1\n",
- "Solved for subset of size 310x10 in 0.36s with -3099.8 loss.\n",
- "4 2 2\n",
- "Solved for subset of size 310x10 in 0.35s with -3099.61 loss.\n",
- "0 0 0\n",
- "Solved for subset of size 410x10 in 67.08s with -4100.0 loss.\n",
- "0 0 1\n",
- "Solved for subset of size 410x10 in 0.96s with -4100.0 loss.\n",
- "0 0 2\n",
- "Solved for subset of size 410x10 in 0.95s with -4100.0 loss.\n",
- "0 1 0\n",
- "Solved for subset of size 410x10 in 2.0s with 0.02 loss.\n",
- "0 1 1\n",
- "Solved for subset of size 410x10 in 0.03s with 0.08 loss.\n",
- "0 1 2\n",
- "Solved for subset of size 410x10 in 0.03s with 0.22 loss.\n",
- "0 2 0\n",
- "Solved for subset of size 410x10 in 86.14s with -4099.99 loss.\n",
- "0 2 1\n",
- "Solved for subset of size 410x10 in 0.99s with -4099.91 loss.\n",
- "0 2 2\n",
- "Solved for subset of size 410x10 in 0.99s with -4099.78 loss.\n",
- "1 0 0\n",
- "Solved for subset of size 410x10 in 67.3s with -4100.0 loss.\n",
- "1 0 1\n",
- "Solved for subset of size 410x10 in 0.94s with -4100.0 loss.\n",
- "1 0 2\n",
- "Solved for subset of size 410x10 in 0.95s with -4100.0 loss.\n",
- "1 1 0\n",
- "Solved for subset of size 410x10 in 1.96s with 0.01 loss.\n",
- "1 1 1\n",
- "Solved for subset of size 410x10 in 0.03s with 0.1 loss.\n",
- "1 1 2\n",
- "Solved for subset of size 410x10 in 0.03s with 0.22 loss.\n",
- "1 2 0\n",
- "Solved for subset of size 410x10 in 79.85s with -4099.99 loss.\n",
- "1 2 1\n",
- "Solved for subset of size 410x10 in 0.99s with -4099.9 loss.\n",
- "1 2 2\n",
- "Solved for subset of size 410x10 in 0.99s with -4099.78 loss.\n",
- "2 0 0\n",
- "Solved for subset of size 410x10 in 67.12s with -4100.0 loss.\n",
- "2 0 1\n",
- "Solved for subset of size 410x10 in 0.95s with -4100.0 loss.\n",
- "2 0 2\n",
- "Solved for subset of size 410x10 in 0.95s with -4100.0 loss.\n",
- "2 1 0\n",
- "Solved for subset of size 410x10 in 1.78s with 0.01 loss.\n",
- "2 1 1\n",
- "Solved for subset of size 410x10 in 0.03s with 0.09 loss.\n",
- "2 1 2\n",
- "Solved for subset of size 410x10 in 0.03s with 0.22 loss.\n",
- "2 2 0\n",
- "Solved for subset of size 410x10 in 82.39s with -4099.98 loss.\n",
- "2 2 1\n",
- "Solved for subset of size 410x10 in 0.99s with -4099.92 loss.\n",
- "2 2 2\n",
- "Solved for subset of size 410x10 in 0.97s with -4099.78 loss.\n",
- "3 0 0\n",
- "Solved for subset of size 410x10 in 67.43s with -4100.0 loss.\n",
- "3 0 1\n",
- "Solved for subset of size 410x10 in 0.94s with -4100.0 loss.\n",
- "3 0 2\n",
- "Solved for subset of size 410x10 in 0.94s with -4100.0 loss.\n",
- "3 1 0\n",
- "Solved for subset of size 410x10 in 1.56s with 0.02 loss.\n",
- "3 1 1\n",
- "Solved for subset of size 410x10 in 0.03s with 0.1 loss.\n",
- "3 1 2\n",
- "Solved for subset of size 410x10 in 0.03s with 0.22 loss.\n",
- "3 2 0\n",
- "Solved for subset of size 410x10 in 86.87s with -4099.98 loss.\n",
- "3 2 1\n",
- "Solved for subset of size 410x10 in 1.02s with -4099.92 loss.\n",
- "3 2 2\n",
- "Solved for subset of size 410x10 in 1.02s with -4099.78 loss.\n",
- "4 0 0\n",
- "Solved for subset of size 410x10 in 72.33s with -4100.0 loss.\n",
- "4 0 1\n",
- "Solved for subset of size 410x10 in 1.06s with -4100.0 loss.\n",
- "4 0 2\n",
- "Solved for subset of size 410x10 in 1.18s with -4100.0 loss.\n",
- "4 1 0\n",
- "Solved for subset of size 410x10 in 1.73s with 0.02 loss.\n",
- "4 1 1\n",
- "Solved for subset of size 410x10 in 0.03s with 0.1 loss.\n",
- "4 1 2\n",
- "Solved for subset of size 410x10 in 0.03s with 0.22 loss.\n",
- "4 2 0\n",
- "Solved for subset of size 410x10 in 81.84s with -4099.99 loss.\n",
- "4 2 1\n",
- "Solved for subset of size 410x10 in 0.97s with -4099.9 loss.\n",
- "4 2 2\n",
- "Solved for subset of size 410x10 in 0.98s with -4099.78 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create dataset\n",
"dataset = fss.Dataset(randTypes = \"uniform\", \n",
@@ -10326,4534 +221,9 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Computation Time (s)')"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "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",
@@ -14910,421 +280,9 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Solved for subset of size 10x2 in 0.19s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.05s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.05s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.23s with 0.09 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 0.28 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 2.08 loss.\n",
- "Solved for subset of size 10x2 in 0.41s with -19.97 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -19.58 loss.\n",
- "Solved for subset of size 10x2 in 0.07s with -17.98 loss.\n",
- "Solved for subset of size 10x2 in 0.21s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.23s with 0.03 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 0.48 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 2.06 loss.\n",
- "Solved for subset of size 10x2 in 0.45s with -19.91 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -19.6 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -17.96 loss.\n",
- "Solved for subset of size 10x2 in 0.24s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.23s with 0.07 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 0.46 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 2.03 loss.\n",
- "Solved for subset of size 10x2 in 0.47s with -19.92 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -19.46 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -17.92 loss.\n",
- "Solved for subset of size 10x2 in 0.19s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.23s with 0.03 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 0.55 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 2.04 loss.\n",
- "Solved for subset of size 10x2 in 0.45s with -19.95 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -19.6 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -17.98 loss.\n",
- "Solved for subset of size 10x2 in 0.24s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.05s with -20.0 loss.\n",
- "Solved for subset of size 10x2 in 0.23s with 0.04 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 0.22 loss.\n",
- "Solved for subset of size 10x2 in 0.04s with 2.05 loss.\n",
- "Solved for subset of size 10x2 in 0.41s with -19.96 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -19.63 loss.\n",
- "Solved for subset of size 10x2 in 0.06s with -17.97 loss.\n",
- "Solved for subset of size 10x4 in 0.25s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.23s with 0.24 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 1.26 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 4.14 loss.\n",
- "Solved for subset of size 10x4 in 0.48s with -39.73 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -38.48 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -35.9 loss.\n",
- "Solved for subset of size 10x4 in 0.33s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.23s with 0.31 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 1.61 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 4.08 loss.\n",
- "Solved for subset of size 10x4 in 0.57s with -39.56 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -38.81 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -35.89 loss.\n",
- "Solved for subset of size 10x4 in 0.34s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.05s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.05s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.23s with 0.46 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 1.33 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 4.09 loss.\n",
- "Solved for subset of size 10x4 in 0.48s with -39.44 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -38.63 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -35.89 loss.\n",
- "Solved for subset of size 10x4 in 0.38s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.23s with 0.42 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 1.13 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 4.12 loss.\n",
- "Solved for subset of size 10x4 in 0.5s with -39.74 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -38.59 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -35.86 loss.\n",
- "Solved for subset of size 10x4 in 0.35s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with -40.0 loss.\n",
- "Solved for subset of size 10x4 in 0.23s with 0.41 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 1.54 loss.\n",
- "Solved for subset of size 10x4 in 0.04s with 4.07 loss.\n",
- "Solved for subset of size 10x4 in 0.55s with -39.65 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -38.51 loss.\n",
- "Solved for subset of size 10x4 in 0.07s with -35.9 loss.\n",
- "Solved for subset of size 10x6 in 0.32s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.23s with 0.65 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 3.19 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 6.09 loss.\n",
- "Solved for subset of size 10x6 in 0.54s with -59.12 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -57.25 loss.\n",
- "Solved for subset of size 10x6 in 0.09s with -53.88 loss.\n",
- "Solved for subset of size 10x6 in 0.4s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.06s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.23s with 1.09 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 2.56 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 6.05 loss.\n",
- "Solved for subset of size 10x6 in 0.56s with -59.18 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -57.87 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -53.92 loss.\n",
- "Solved for subset of size 10x6 in 0.33s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.06s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.23s with 0.58 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 2.58 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 6.1 loss.\n",
- "Solved for subset of size 10x6 in 0.64s with -59.24 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -57.34 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -53.9 loss.\n",
- "Solved for subset of size 10x6 in 0.38s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.23s with 1.04 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 2.52 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 6.12 loss.\n",
- "Solved for subset of size 10x6 in 0.59s with -59.26 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -57.21 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -53.92 loss.\n",
- "Solved for subset of size 10x6 in 0.45s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.05s with -60.0 loss.\n",
- "Solved for subset of size 10x6 in 0.23s with 0.81 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 2.09 loss.\n",
- "Solved for subset of size 10x6 in 0.04s with 6.2 loss.\n",
- "Solved for subset of size 10x6 in 0.58s with -59.36 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -57.06 loss.\n",
- "Solved for subset of size 10x6 in 0.08s with -53.9 loss.\n",
- "Solved for subset of size 10x8 in 0.41s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.23s with 1.73 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 4.27 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 8.21 loss.\n",
- "Solved for subset of size 10x8 in 0.73s with -78.53 loss.\n",
- "Solved for subset of size 10x8 in 0.09s with -75.56 loss.\n",
- "Solved for subset of size 10x8 in 0.09s with -71.74 loss.\n",
- "Solved for subset of size 10x8 in 0.56s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.07s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.23s with 1.5 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 3.5 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 8.24 loss.\n",
- "Solved for subset of size 10x8 in 0.76s with -78.6 loss.\n",
- "Solved for subset of size 10x8 in 0.08s with -75.51 loss.\n",
- "Solved for subset of size 10x8 in 0.08s with -71.78 loss.\n",
- "Solved for subset of size 10x8 in 0.47s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.23s with 1.45 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 4.53 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 8.25 loss.\n",
- "Solved for subset of size 10x8 in 0.71s with -78.53 loss.\n",
- "Solved for subset of size 10x8 in 0.09s with -76.11 loss.\n",
- "Solved for subset of size 10x8 in 0.1s with -71.77 loss.\n",
- "Solved for subset of size 10x8 in 0.5s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.23s with 1.6 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 4.19 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 8.23 loss.\n",
- "Solved for subset of size 10x8 in 0.67s with -78.26 loss.\n",
- "Solved for subset of size 10x8 in 0.09s with -75.96 loss.\n",
- "Solved for subset of size 10x8 in 0.09s with -71.81 loss.\n",
- "Solved for subset of size 10x8 in 0.45s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.06s with -80.0 loss.\n",
- "Solved for subset of size 10x8 in 0.23s with 1.71 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 3.78 loss.\n",
- "Solved for subset of size 10x8 in 0.04s with 8.28 loss.\n",
- "Solved for subset of size 10x8 in 0.73s with -78.48 loss.\n",
- "Solved for subset of size 10x8 in 0.08s with -76.02 loss.\n",
- "Solved for subset of size 10x8 in 0.08s with -71.76 loss.\n",
- "Solved for subset of size 10x10 in 0.54s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.14 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.26 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.36 loss.\n",
- "Solved for subset of size 10x10 in 0.87s with -97.55 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -94.24 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with -89.78 loss.\n",
- "Solved for subset of size 10x10 in 0.6s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.47 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.5 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.22 loss.\n",
- "Solved for subset of size 10x10 in 0.83s with -97.36 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -95.38 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.82 loss.\n",
- "Solved for subset of size 10x10 in 0.59s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.07s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.33s with 2.6 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.38 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.2 loss.\n",
- "Solved for subset of size 10x10 in 0.9s with -97.72 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -95.73 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.72 loss.\n",
- "Solved for subset of size 10x10 in 0.66s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.06 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 6.18 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.21 loss.\n",
- "Solved for subset of size 10x10 in 0.86s with -97.62 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -95.09 loss.\n",
- "Solved for subset of size 10x10 in 0.1s with -89.73 loss.\n",
- "Solved for subset of size 10x10 in 0.66s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.08s with -100.0 loss.\n",
- "Solved for subset of size 10x10 in 0.23s with 2.43 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 5.55 loss.\n",
- "Solved for subset of size 10x10 in 0.04s with 10.23 loss.\n",
- "Solved for subset of size 10x10 in 0.82s with -97.81 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with -93.97 loss.\n",
- "Solved for subset of size 10x10 in 0.11s with -89.83 loss.\n",
- "Solved for subset of size 10x12 in 0.69s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.09s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.1s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.23s with 3.6 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 7.36 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 12.26 loss.\n",
- "Solved for subset of size 10x12 in 1.0s with -116.89 loss.\n",
- "Solved for subset of size 10x12 in 0.12s with -113.37 loss.\n",
- "Solved for subset of size 10x12 in 0.11s with -107.79 loss.\n",
- "Solved for subset of size 10x12 in 0.7s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.09s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.09s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.31s with 3.6 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 7.21 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 12.24 loss.\n",
- "Solved for subset of size 10x12 in 0.92s with -117.41 loss.\n",
- "Solved for subset of size 10x12 in 0.11s with -112.57 loss.\n",
- "Solved for subset of size 10x12 in 0.11s with -107.71 loss.\n",
- "Solved for subset of size 10x12 in 0.76s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.09s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.08s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.23s with 3.63 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 6.56 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 12.37 loss.\n",
- "Solved for subset of size 10x12 in 0.99s with -116.48 loss.\n",
- "Solved for subset of size 10x12 in 0.12s with -112.71 loss.\n",
- "Solved for subset of size 10x12 in 0.12s with -107.75 loss.\n",
- "Solved for subset of size 10x12 in 0.72s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.08s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.09s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.23s with 3.66 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 7.41 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 12.33 loss.\n",
- "Solved for subset of size 10x12 in 0.98s with -117.18 loss.\n",
- "Solved for subset of size 10x12 in 0.12s with -113.04 loss.\n",
- "Solved for subset of size 10x12 in 0.12s with -107.71 loss.\n",
- "Solved for subset of size 10x12 in 0.73s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.09s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.16s with -120.0 loss.\n",
- "Solved for subset of size 10x12 in 0.24s with 3.07 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 6.92 loss.\n",
- "Solved for subset of size 10x12 in 0.04s with 12.31 loss.\n",
- "Solved for subset of size 10x12 in 0.92s with -116.39 loss.\n",
- "Solved for subset of size 10x12 in 0.11s with -112.64 loss.\n",
- "Solved for subset of size 10x12 in 0.12s with -107.74 loss.\n",
- "Solved for subset of size 10x14 in 0.88s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.1s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.11s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.24s with 4.76 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 8.81 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 14.38 loss.\n",
- "Solved for subset of size 10x14 in 1.04s with -135.44 loss.\n",
- "Solved for subset of size 10x14 in 0.13s with -131.76 loss.\n",
- "Solved for subset of size 10x14 in 0.14s with -125.58 loss.\n",
- "Solved for subset of size 10x14 in 0.81s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.1s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.09s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.24s with 4.16 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 8.27 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 14.34 loss.\n",
- "Solved for subset of size 10x14 in 1.1s with -135.48 loss.\n",
- "Solved for subset of size 10x14 in 0.12s with -131.05 loss.\n",
- "Solved for subset of size 10x14 in 0.13s with -125.62 loss.\n",
- "Solved for subset of size 10x14 in 0.86s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.1s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.11s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.23s with 3.98 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 8.6 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 14.4 loss.\n",
- "Solved for subset of size 10x14 in 1.08s with -135.91 loss.\n",
- "Solved for subset of size 10x14 in 0.13s with -131.63 loss.\n",
- "Solved for subset of size 10x14 in 0.12s with -125.72 loss.\n",
- "Solved for subset of size 10x14 in 0.93s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.1s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.11s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.23s with 4.42 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 8.73 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 14.29 loss.\n",
- "Solved for subset of size 10x14 in 1.03s with -135.33 loss.\n",
- "Solved for subset of size 10x14 in 0.12s with -131.46 loss.\n",
- "Solved for subset of size 10x14 in 0.12s with -125.74 loss.\n",
- "Solved for subset of size 10x14 in 0.88s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.1s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.11s with -140.0 loss.\n",
- "Solved for subset of size 10x14 in 0.23s with 3.98 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 9.2 loss.\n",
- "Solved for subset of size 10x14 in 0.04s with 14.32 loss.\n",
- "Solved for subset of size 10x14 in 1.1s with -135.1 loss.\n",
- "Solved for subset of size 10x14 in 0.12s with -131.56 loss.\n",
- "Solved for subset of size 10x14 in 0.13s with -125.62 loss.\n",
- "Solved for subset of size 10x16 in 0.82s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.1s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.1s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.23s with 5.16 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 9.36 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 16.51 loss.\n",
- "Solved for subset of size 10x16 in 1.13s with -154.24 loss.\n",
- "Solved for subset of size 10x16 in 0.14s with -150.23 loss.\n",
- "Solved for subset of size 10x16 in 0.14s with -143.7 loss.\n",
- "Solved for subset of size 10x16 in 0.94s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.11s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.11s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.23s with 5.66 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 10.5 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 16.35 loss.\n",
- "Solved for subset of size 10x16 in 1.18s with -155.15 loss.\n",
- "Solved for subset of size 10x16 in 0.2s with -149.73 loss.\n",
- "Solved for subset of size 10x16 in 0.14s with -143.64 loss.\n",
- "Solved for subset of size 10x16 in 0.97s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.11s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.11s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.23s with 5.66 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 9.89 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 16.42 loss.\n",
- "Solved for subset of size 10x16 in 1.18s with -154.87 loss.\n",
- "Solved for subset of size 10x16 in 0.13s with -148.99 loss.\n",
- "Solved for subset of size 10x16 in 0.13s with -143.7 loss.\n",
- "Solved for subset of size 10x16 in 0.89s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.1s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.11s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.24s with 5.41 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 11.05 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 16.49 loss.\n",
- "Solved for subset of size 10x16 in 1.34s with -154.19 loss.\n",
- "Solved for subset of size 10x16 in 0.14s with -149.8 loss.\n",
- "Solved for subset of size 10x16 in 0.14s with -143.58 loss.\n",
- "Solved for subset of size 10x16 in 0.94s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.1s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.1s with -160.0 loss.\n",
- "Solved for subset of size 10x16 in 0.32s with 5.7 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 10.35 loss.\n",
- "Solved for subset of size 10x16 in 0.04s with 16.23 loss.\n",
- "Solved for subset of size 10x16 in 1.15s with -153.84 loss.\n",
- "Solved for subset of size 10x16 in 0.13s with -149.67 loss.\n",
- "Solved for subset of size 10x16 in 0.13s with -143.83 loss.\n",
- "Solved for subset of size 10x18 in 0.98s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.12s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.12s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.23s with 7.88 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 12.52 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 18.46 loss.\n",
- "Solved for subset of size 10x18 in 1.31s with -173.26 loss.\n",
- "Solved for subset of size 10x18 in 0.14s with -167.98 loss.\n",
- "Solved for subset of size 10x18 in 0.26s with -161.61 loss.\n",
- "Solved for subset of size 10x18 in 1.07s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.11s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.12s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.24s with 6.1 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 11.95 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 18.4 loss.\n",
- "Solved for subset of size 10x18 in 1.26s with -174.3 loss.\n",
- "Solved for subset of size 10x18 in 0.14s with -168.06 loss.\n",
- "Solved for subset of size 10x18 in 0.15s with -161.62 loss.\n",
- "Solved for subset of size 10x18 in 1.05s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.12s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.27s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.24s with 5.56 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 10.99 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 18.35 loss.\n",
- "Solved for subset of size 10x18 in 1.23s with -173.53 loss.\n",
- "Solved for subset of size 10x18 in 0.14s with -167.82 loss.\n",
- "Solved for subset of size 10x18 in 0.14s with -161.73 loss.\n",
- "Solved for subset of size 10x18 in 0.99s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.11s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.12s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.23s with 6.06 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 11.58 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 18.43 loss.\n",
- "Solved for subset of size 10x18 in 1.27s with -174.61 loss.\n",
- "Solved for subset of size 10x18 in 0.14s with -169.19 loss.\n",
- "Solved for subset of size 10x18 in 0.15s with -161.7 loss.\n",
- "Solved for subset of size 10x18 in 0.97s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.11s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.11s with -180.0 loss.\n",
- "Solved for subset of size 10x18 in 0.35s with 6.0 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 11.74 loss.\n",
- "Solved for subset of size 10x18 in 0.04s with 18.41 loss.\n",
- "Solved for subset of size 10x18 in 1.27s with -174.59 loss.\n",
- "Solved for subset of size 10x18 in 0.15s with -168.49 loss.\n",
- "Solved for subset of size 10x18 in 0.15s with -161.68 loss.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for datasetWidth in range(2, 20, 2):\n",
" # Create dataset\n",
@@ -15375,8512 +333,9 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Computation Time (s)')"
- ]
- },
- "execution_count": 54,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "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",
@@ -23937,22 +392,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig5-scatterplotOverdraw.ipynb b/jupyter/Fig5-scatterplotOverdraw.ipynb
index f2b1d60..0a11f5f 100644
--- a/jupyter/Fig5-scatterplotOverdraw.ipynb
+++ b/jupyter/Fig5-scatterplotOverdraw.ipynb
@@ -11,18 +11,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Third party\n",
"import matplotlib.pyplot as plt\n",
@@ -58,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -94,7660 +85,9 @@
},
{
"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": [
"# Initialize color and plot settings\n",
"color = fss.plot.Color()\n",
@@ -7774,21 +114,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "TypeError",
- "evalue": "Solver.__init__() got an unexpected keyword argument 'loss'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[6], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m parameters \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataset\u001b[39m\u001b[38;5;124m\"\u001b[39m: dataset\u001b[38;5;241m.\u001b[39mdataArray}\n\u001b[1;32m 3\u001b[0m lossEMDOverdraw \u001b[38;5;241m=\u001b[39m fss\u001b[38;5;241m.\u001b[39mMultiCriterion(objectives, parameters)\n\u001b[0;32m----> 4\u001b[0m solver \u001b[38;5;241m=\u001b[39m \u001b[43mfss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43malgorithm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43malgorithm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedySwap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlossEMDOverdraw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m subset \u001b[38;5;241m=\u001b[39m solver\u001b[38;5;241m.\u001b[39msolve(dataset, subsetSize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m250\u001b[39m)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(subset)\n",
- "\u001b[0;31mTypeError\u001b[0m: Solver.__init__() got an unexpected keyword argument 'loss'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"objectives = [fss.objective.earthMoversDistance]\n",
"parameters = {\"dataset\": dataset.dataArray}\n",
@@ -7875,12181 +203,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "ImportError",
- "evalue": "attempted relative import with no known parent package",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[19], line 100\u001b[0m\n\u001b[1;32m 97\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_ylim(ylimScatter)\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../data/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msaveDirectory\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/size=250_obj=emdOverdrawCategorical_k=full.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m--> 100\u001b[0m categoricalOverdrawSubset \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m subsetScatter \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mscatterplot(data \u001b[38;5;241m=\u001b[39m categoricalOverdrawSubset\u001b[38;5;241m.\u001b[39mdf, \n\u001b[1;32m 103\u001b[0m x \u001b[38;5;241m=\u001b[39m features[\u001b[38;5;241m0\u001b[39m], \n\u001b[1;32m 104\u001b[0m y \u001b[38;5;241m=\u001b[39m features[\u001b[38;5;241m1\u001b[39m], \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 109\u001b[0m palette \u001b[38;5;241m=\u001b[39m palette,\n\u001b[1;32m 110\u001b[0m edgecolor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 112\u001b[0m ax2\u001b[38;5;241m.\u001b[39msharey(ax1)\n",
- "File \u001b[0;32m~/Documents/GitHub/flexibleSubsetSelection/flexibleSubsetSelection/sets.py:16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KBinsDiscretizer, OneHotEncoder\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# Local files\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m generate\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# --- Dataset and Subset Classes -----------------------------------------------\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBase\u001b[39;00m:\n",
- "\u001b[0;31mImportError\u001b[0m: attempted relative import with no known parent package"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/connerbailey/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/IPython/core/events.py:82: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
- " func(*args, **kwargs)\n",
- "/Users/connerbailey/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
- " fig.canvas.print_figure(bytes_io, **kw)\n"
- ]
- },
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"titleSize = 16\n",
"labelSize = 14\n",
@@ -20400,22 +556,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig6-clusterRepresentatives.ipynb b/jupyter/Fig6-clusterRepresentatives.ipynb
index f88d19e..4cc3b81 100644
--- a/jupyter/Fig6-clusterRepresentatives.ipynb
+++ b/jupyter/Fig6-clusterRepresentatives.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -34,26 +34,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "FileNotFoundError",
- "evalue": "[Errno 2] No such file or directory: '../data/exampleDatasets/congressSocialMedia.csv'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m labelSize \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m14\u001b[39m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Load datasets from csv\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m socialMedia \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../data/exampleDatasets/congressSocialMedia.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m ideology \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../data/exampleDatasets/houseOnly116.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 7\u001b[0m photos \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../data/exampleDatasets/congressPhotos.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
- "File \u001b[0;32m~/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
- "File \u001b[0;32m~/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
- "File \u001b[0;32m~/opt/miniconda3/envs/test_env/lib/python3.10/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
- "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/exampleDatasets/congressSocialMedia.csv'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"titleSize = 16\n",
"labelSize = 14\n",
@@ -103,1268 +86,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"sns.scatterplot(data = df, \n",
" x = \"nominate_dim1\", \n",
@@ -1374,1333 +98,9 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"sns.scatterplot(data = df, \n",
" x = \"nokken_poole_dim1\", \n",
@@ -2710,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2750,2223 +150,9 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "122.58945030867538 -12.35295 -400 -289.7634996913246\n",
- "121.19437379209141 -11.91244 -300 -190.71806620790858\n",
- "129.31157403411643 -11.81847 -300 -182.5068959658836\n",
- "121.66608215830921 -1.51273 -300 -179.8466478416908\n",
- "128.68595403365865 -12.33736 -400 -283.6514059663414\n",
- "123.29277545833364 -12.35295 -400 -289.06017454166636\n",
- "112.92797476668869 -12.80386 -400 -299.8758852333113\n",
- "121.04517500871373 -12.26938 -300 -191.22420499128629\n",
- "113.39968313290652 -1.96364 -300 -188.56395686709348\n",
- "120.41955500825594 -12.33299 -400 -291.91343499174405\n",
- "115.02637643293092 -12.80386 -400 -297.7774835670691\n",
- "160.1854445088678 -12.02185 -300 -151.8364054911322\n",
- "158.64116920890615 -12.37879 -300 -153.73762079109386\n",
- "150.99567733309894 -2.07305 -300 -151.07737266690106\n",
- "158.01554920844836 -12.91327 -400 -254.89772079155165\n",
- "152.62237063312335 -12.91327 -400 -260.29089936687666\n",
- "118.98208562446109 -12.30732 -300 -193.3252343755389\n",
- "117.43781032449942 -13.19874 -400 -295.76092967550056\n",
- "109.7923184486922 -2.35852 -300 -192.5662015513078\n",
- "116.81219032404162 -12.72787 -400 -295.91567967595836\n",
- "111.4190117487166 -13.19874 -400 -301.7797282512834\n",
- "122.20294229538058 -12.82777 -300 -190.6248277046194\n",
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- "-541.8057093292289\n"
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- }
- ],
+ "outputs": [],
"source": [
"dataset = fss.Dataset(data=df, features=[\"followers\", \"centerDist\"])\n",
"\n",
@@ -4982,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -5013,7 +199,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -5027,9246 +213,9 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/var/folders/g7/k2wgq3hd4_7crzw88nqrqdv80000gn/T/ipykernel_49442/365848719.py:116: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
- " plt.savefig(\"congress.pdf\", bbox_inches=\"tight\")\n",
- "/Users/cbbcbail/Documents/GitHub/flexibleSubsetSelector/.venv/lib/python3.12/site-packages/IPython/core/pylabtools.py:152: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
- " fig.canvas.print_figure(bytes_io, **kw)\n"
- ]
- },
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"fig = plt.figure(figsize=(10.5, 7)) # Adjust the figure size as needed\n",
"grid = GridSpec(nrows=2, ncols=3, figure=fig)\n",
@@ -14388,3185 +337,9 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
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- "text/plain": [
- ""
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- "metadata": {},
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- {
- "data": {
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- "\n",
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- ""
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- "metadata": {},
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- {
- "data": {
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- "\n",
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- ""
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- "metadata": {},
- "output_type": "display_data"
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- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
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- ""
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- "metadata": {},
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- {
- "data": {
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- "\n",
- "\n"
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- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"for index, row in subset.df.iterrows(): # Use head() to limit to 5 rows for demonstration\n",
" # Get the image file path from the 'image' column\n",
@@ -17582,1324 +355,9 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
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- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
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- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"sns.scatterplot(data=dataset.df, x=\"centerDist\", y=\"followers\")\n",
"sns.scatterplot(data=subset.df, x=\"centerDist\", y=\"followers\")"
@@ -18907,22 +365,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig7-discreteCoverage.ipynb b/jupyter/Fig7-discreteCoverage.ipynb
index 97fc0f1..a9256c0 100644
--- a/jupyter/Fig7-discreteCoverage.ipynb
+++ b/jupyter/Fig7-discreteCoverage.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
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- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -27,7 +27,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -43,5940 +43,9 @@
},
{
"cell_type": "code",
- "execution_count": 164,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
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- "\n",
- "\n"
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- "text/plain": [
- ""
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- },
- "metadata": {},
- "output_type": "display_data"
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- ],
+ "outputs": [],
"source": [
"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
"\n",
@@ -6016,2347 +85,9 @@
},
{
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- "execution_count": 174,
+ "execution_count": null,
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- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[174], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m300\u001b[39m\n\u001b[0;32m----> 3\u001b[0m z, timeTotal, loss \u001b[38;5;241m=\u001b[39m \u001b[43mapproximation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgreedySwap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobjective\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobjectives\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membeddingCoverageDistinct\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m subset \u001b[38;5;241m=\u001b[39m sets\u001b[38;5;241m.\u001b[39mSubset(dataset, z, length\u001b[38;5;241m=\u001b[39ms)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(loss)\n",
- "File \u001b[0;32m~/Documents/GitHub/flexibleSubsetSelector/approximation.py:94\u001b[0m, in \u001b[0;36mgreedySwap\u001b[0;34m(dataset, s, objective, minLoss, k, seed)\u001b[0m\n\u001b[1;32m 92\u001b[0m indices_swap \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcopy(indices)\n\u001b[1;32m 93\u001b[0m indices_swap[loc] \u001b[38;5;241m=\u001b[39m i\n\u001b[0;32m---> 94\u001b[0m loss_swap \u001b[38;5;241m=\u001b[39m \u001b[43mcalcLoss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_swap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobjective\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loss_swap \u001b[38;5;241m<\u001b[39m loss_swap_best:\n\u001b[1;32m 97\u001b[0m z_swap_best \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcopy(z_swap)\n",
- "File \u001b[0;32m~/Documents/GitHub/flexibleSubsetSelector/approximation.py:24\u001b[0m, in \u001b[0;36mcalcLoss\u001b[0;34m(dataset, z, s, objective)\u001b[0m\n\u001b[1;32m 22\u001b[0m loss \u001b[38;5;241m=\u001b[39m objective(dataset\u001b[38;5;241m.\u001b[39mscaled[z \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m objective \u001b[38;5;241m==\u001b[39m objectives\u001b[38;5;241m.\u001b[39membeddingCoverageDistinct:\n\u001b[0;32m---> 24\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mobjective\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscaled\u001b[49m\u001b[43m[\u001b[49m\u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistances_tfidf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistances_specter\u001b[49m\u001b[43m[\u001b[49m\u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m objective \u001b[38;5;241m==\u001b[39m objectives\u001b[38;5;241m.\u001b[39mbhattacharyaaOverdraw:\n\u001b[1;32m 26\u001b[0m loss \u001b[38;5;241m=\u001b[39m objective(dataset\u001b[38;5;241m.\u001b[39mscaledCat[z \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m], dataset\u001b[38;5;241m.\u001b[39mscaledCat, dataset\u001b[38;5;241m.\u001b[39mcategorical, dataset\u001b[38;5;241m.\u001b[39mfeatures, dataset\u001b[38;5;241m.\u001b[39mcategories, dataset\u001b[38;5;241m.\u001b[39mdistances[z \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m][:, z \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m])\n",
- "File \u001b[0;32m~/Documents/GitHub/flexibleSubsetSelector/objectives.py:24\u001b[0m, in \u001b[0;36membeddingCoverageDistinct\u001b[0;34m(subset, distances_specter, distances_tfidf)\u001b[0m\n\u001b[1;32m 21\u001b[0m loss \u001b[38;5;241m=\u001b[39m specter \u001b[38;5;241m+\u001b[39m tfidf\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m-\u001b[39mloss\n\u001b[0;32m---> 24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21membeddingCoverageDistinct\u001b[39m(subset, distances_specter, distances_tfidf):\n\u001b[1;32m 25\u001b[0m specterCov \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(subset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSpecter 2D Bin\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39munique()) \n\u001b[1;32m 26\u001b[0m tfidfCov \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(subset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTFIDF 2D Bin\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39munique())\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
- ]
- }
- ],
+ "outputs": [],
"source": [
"s = 300\n",
"\n",
@@ -8370,17 +101,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "268 201 469 234.5\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"bins = 20\n",
"\n",
@@ -8405,17473 +128,9 @@
},
{
"cell_type": "code",
- "execution_count": 204,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 11))\n",
"\n",
@@ -25964,22 +223,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig8-frameSelection.ipynb b/jupyter/Fig8-frameSelection.ipynb
index b9e1ac2..2996420 100644
--- a/jupyter/Fig8-frameSelection.ipynb
+++ b/jupyter/Fig8-frameSelection.ipynb
@@ -2,21 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "ModuleNotFoundError",
- "evalue": "No module named 'cv2'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Third party\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastdtw\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fastdtw\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
- "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Third party\n",
"import cv2\n",
@@ -47,21 +35,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'cv2' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m video_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../data/exampleDatasets/dance.mp4\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m cap \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241m.\u001b[39mVideoCapture(video_path)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Check that video was opened successfully\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cap\u001b[38;5;241m.\u001b[39misOpened():\n",
- "\u001b[0;31mNameError\u001b[0m: name 'cv2' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
"video_path = '../data/exampleDatasets/dance.mp4'\n",
"cap = cv2.VideoCapture(video_path)\n",
@@ -113,7 +89,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -125,2099 +101,9 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(20, 2)\n"
- ]
- },
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DTW distance between paths: 9258.398311872035\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"subset_df = df.sample(frac=.02, random_state=1).sort_index()\n",
"print(subset_df.shape)\n",
@@ -2240,1017 +126,9 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "999: 131.30821624373456\n",
- "131.30821624373456\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"s = 20\n",
"\n",
@@ -3264,17 +142,9 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DTW distance between paths: 1.0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from fastdtw import fastdtw\n",
"\n",
@@ -3292,1707 +162,9 @@
},
{
"cell_type": "code",
- "execution_count": 150,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
@@ -5091,1933 +263,9 @@
},
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 2s/step\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 't-SNE Dimension 2')"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"video_path = 'datasets/dance.mp4'\n",
"cap = cv2.VideoCapture(video_path)\n",
@@ -7080,22 +328,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
+ "name": "python"
}
},
"nbformat": 4,
diff --git a/jupyter/Fig9-tables.ipynb b/jupyter/Fig9-tables.ipynb
index 4434fdd..8f6b51d 100644
--- a/jupyter/Fig9-tables.ipynb
+++ b/jupyter/Fig9-tables.ipynb
@@ -111,22 +111,8 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
"language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.0"
+ "name": "python"
}
},
"nbformat": 4,