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LouisTierLouisTier
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Updated notebooks after modifying som collect_samples method.
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+59
-25
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5 files changed

+59
-25
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‎notebooks/boston_housing.ipynb‎

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@@ -162,7 +162,7 @@
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "191be38d-2731-4b01-b070-70479100c48d",
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"ref": "e138cdf3-e814-44aa-9e6f-8d7ac31913a4",
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"rows": [
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"0",
@@ -491,7 +491,7 @@
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "11450940-5100-4b5a-ba25-e981ec9086b6",
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"ref": "42a0e774-c2c9-4d76-81a6-e4270650b92c",
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"rows": [
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[
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"count",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training SOM: 100%|██████████| 125/125 [00:16<00:00, 7.36epoch/s]\n"
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"Training SOM: 100%|██████████| 125/125 [00:42<00:00, 2.92epoch/s]\n"
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]
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}
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],
@@ -1130,13 +1130,18 @@
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"outputs": [],
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"source": [
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"predictions = []\n",
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"bmus_idx_map = som.build_bmus_data_map(\n",
1134+
" data=train_features,\n",
1135+
" return_indices=True, # False means we want the features of each sample and not the indices\n",
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")\n",
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"for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets)):\n",
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" \n",
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" collected_features, collected_targets = som.collect_samples(\n",
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" query_sample=test_feature,\n",
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" historical_samples=train_features,\n",
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" historical_outputs=train_targets,\n",
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" min_buffer_threshold=100 # Collect 30 historical samples to train a model\n",
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" min_buffer_threshold=100, # Collect 30 historical samples to train a model\n",
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" bmus_idx_map=bmus_idx_map,\n",
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" )\n",
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" \n",
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" X = collected_features.numpy()\n",
@@ -1244,7 +1249,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": ".venv_sensing",
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"language": "python",
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"name": "python3"
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},

‎notebooks/energy_efficiency.ipynb‎

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@@ -142,7 +142,7 @@
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "443e0aff-34b5-4b48-a582-2fa539dbad73",
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"ref": "54803c65-4d2d-4883-b314-b77097128e09",
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"rows": [
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "44e1ec84-7321-4c56-b470-6ddb0daa5e37",
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"ref": "8ac4cc70-224d-4091-838e-ebaeee192468",
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"rows": [
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[
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"count",
@@ -849,7 +849,7 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training SOM: 100%|██████████| 125/125 [00:12<00:00, 10.24epoch/s]\n"
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"Training SOM: 100%|██████████| 125/125 [00:42<00:00, 2.97epoch/s]\n"
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]
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}
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],
@@ -1071,13 +1071,18 @@
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"outputs": [],
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"source": [
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"predictions = []\n",
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"bmus_idx_map = som.build_bmus_data_map(\n",
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" data=train_features,\n",
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" return_indices=True, # False means we want the features of each sample and not the indices\n",
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")\n",
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"for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets_heating)):\n",
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" \n",
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" collected_features, collected_targets = som.collect_samples(\n",
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" query_sample=test_feature,\n",
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" historical_samples=train_features,\n",
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" historical_outputs=train_targets_heating,\n",
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" min_buffer_threshold=150 # Collect 425 historical samples to train a model\n",
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" min_buffer_threshold=150, # Collect 425 historical samples to train a model\n",
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" bmus_idx_map=bmus_idx_map,\n",
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" )\n",
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" \n",
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" X = collected_features.numpy()\n",
@@ -1185,7 +1190,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": ".venv_sensing",
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"language": "python",
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"name": "python3"
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},

‎notebooks/get_data.ipynb‎

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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": ".venv_sensing",
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"language": "python",
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"name": "python3"
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},

‎notebooks/iris.ipynb‎

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@@ -133,7 +133,7 @@
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "1198812d-a3ef-4b6e-8957-4d5201eecc75",
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"ref": "337885f5-c2ae-4187-b5d4-81cb39680b47",
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"rows": [
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "cb7dc608-ebcd-4a20-940f-f7d5dd5d2243",
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"ref": "eaaf9bfb-c28a-4322-9401-1066d0f72e48",
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"rows": [
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[
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"count",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training SOM: 100%|██████████| 100/100 [00:02<00:00, 45.46epoch/s]\n"
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"Training SOM: 0%| | 0/100 [00:00<?, ?epoch/s]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training SOM: 100%|██████████| 100/100 [00:08<00:00, 12.06epoch/s]\n"
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]
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}
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],
@@ -719,18 +726,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions = []\n",
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"bmus_idx_map = som.build_bmus_data_map(\n",
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" data=train_features,\n",
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" return_indices=True, # False means we want the features of each sample and not the indices\n",
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")\n",
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"for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets)):\n",
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" \n",
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" \n",
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" collected_features, collected_targets = som.collect_samples(\n",
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" query_sample=test_feature,\n",
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" historical_samples=train_features,\n",
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" historical_outputs=train_targets,\n",
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" min_buffer_threshold=30 # Collect 20 historical samples to train a model\n",
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" min_buffer_threshold=30, # Collect 20 historical samples to train a model\n",
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" bmus_idx_map=bmus_idx_map,\n",
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" )\n",
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" \n",
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" X = collected_features.numpy()\n",
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{

‎notebooks/wine.ipynb‎

Lines changed: 17 additions & 5 deletions
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@@ -166,7 +166,7 @@
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "6429f9e5-3223-4fcb-9626-09775f5a173a",
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"ref": "b1c02221-fa87-4177-a697-a36be67e0a6a",
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"rows": [
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[
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"0",
@@ -502,7 +502,7 @@
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}
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],
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"conversionMethod": "pd.DataFrame",
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"ref": "29f23741-6c1b-4531-877f-93dd3454ef74",
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"ref": "cebf9ebc-1ab1-4f91-9ac9-329a1c4c71b7",
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"rows": [
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[
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"count",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training SOM: 100%|██████████| 100/100 [00:02<00:00, 38.53epoch/s]\n"
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"Training SOM: 5%|▌ | 5/100 [00:00<00:05, 17.79epoch/s]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training SOM: 100%|██████████| 100/100 [00:06<00:00, 16.15epoch/s]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"predictions = []\n",
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"bmus_idx_map = som.build_bmus_data_map(\n",
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" data=train_features,\n",
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" return_indices=True, # False means we want the features of each sample and not the indices\n",
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")\n",
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"for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets)):\n",
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" \n",
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" collected_features, collected_targets = som.collect_samples(\n",
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" query_sample=test_feature,\n",
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" historical_samples=train_features,\n",
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" historical_outputs=train_targets,\n",
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" min_buffer_threshold=30 # Collect 30 historical samples to train a model\n",
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" min_buffer_threshold=30, # Collect 30 historical samples to train a model\n",
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" bmus_idx_map=bmus_idx_map,\n",
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" )\n",
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" \n",
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" X = collected_features.numpy()\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": ".venv_sensing",
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"language": "python",
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"name": "python3"
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},

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