Checked everything works properly after refactoring TorchSOM package

Author: LouisTier

notebooks/boston_housing.ipynb

@@ -19,8 +19,8 @@
     "import matplotlib.pyplot as plt\n",
     "import torch\n",
     "\n",
-    "from torchsom.core import TorchSOM\n",
-    "from torchsom.plotting import SOMVisualizer, VisualizationConfig\n",
+    "from torchsom.core import SOM\n",
+    "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n",
     "\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.neural_network import MLPClassifier\n",
@@ -99,88 +99,6 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "Sepal Length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Sepal Width",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Petal Length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Petal Width",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Species",
-         "rawType": "int64",
-         "type": "integer"
-        }
-       ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "6214df72-d361-45b1-b0b4-73fbb4dbb243",
-       "rows": [
-        [
-         "0",
-         "-0.9006811702978088",
-         "1.0320572244889565",
-         "-1.3412724047598314",
-         "-1.3129767272601454",
-         "1"
-        ],
-        [
-         "1",
-         "-1.1430169111851105",
-         "-0.12495760117130933",
-         "-1.3412724047598314",
-         "-1.3129767272601454",
-         "1"
-        ],
-        [
-         "2",
-         "-1.3853526520724133",
-         "0.3378483290927974",
-         "-1.3981381087490836",
-         "-1.3129767272601454",
-         "1"
-        ],
-        [
-         "3",
-         "-1.5065205225160652",
-         "0.10644536396074403",
-         "-1.284406700770579",
-         "-1.3129767272601454",
-         "1"
-        ],
-        [
-         "4",
-         "-1.0218490407414595",
-         "1.2634601896210098",
-         "-1.3412724047598314",
-         "-1.3129767272601454",
-         "1"
-        ]
-       ],
-       "shape": {
-        "columns": 5,
-        "rows": 5
-       }
-      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -277,112 +195,6 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "Sepal Length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Sepal Width",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Petal Length",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Petal Width",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "Species",
-         "rawType": "float64",
-         "type": "float"
-        }
-       ],
-       "conversionMethod": "pd.DataFrame",
-       "ref": "ef5af901-e677-4bb2-823f-dab750e668ad",
-       "rows": [
-        [
-         "count",
-         "150.0",
-         "150.0",
-         "150.0",
-         "150.0",
-         "150.0"
-        ],
-        [
-         "mean",
-         "-4.736951571734001e-16",
-         "-6.631732200427602e-16",
-         "3.315866100213801e-16",
-         "-2.842170943040401e-16",
-         "2.0"
-        ],
-        [
-         "std",
-         "1.0033500931359767",
-         "1.0033500931359767",
-         "1.0033500931359765",
-         "1.0033500931359767",
-         "0.8192319205190405"
-        ],
-        [
-         "min",
-         "-1.870024133847019",
-         "-2.438987252491841",
-         "-1.5687352207168408",
-         "-1.4444496972795189",
-         "1.0"
-        ],
-        [
-         "25%",
-         "-0.9006811702978088",
-         "-0.587763531435416",
-         "-1.2275409967813267",
-         "-1.1815037572407716",
-         "1.0"
-        ],
-        [
-         "50%",
-         "-0.05250607719224957",
-         "-0.12495760117130933",
-         "0.33626586292311245",
-         "0.13322594295296525",
-         "2.0"
-        ],
-        [
-         "75%",
-         "0.6745011454696588",
-         "0.5692512942248498",
-         "0.7627586428425047",
-         "0.7905907930498337",
-         "3.0"
-        ],
-        [
-         "max",
-         "2.4920192021244283",
-         "3.1146839106774356",
-         "1.7863413146490472",
-         "1.7109015831854495",
-         "3.0"
-        ]
-       ],
-       "shape": {
-        "columns": 5,
-        "rows": 8
-       }
-      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -589,13 +401,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "som = TorchSOM(\n",
+    "som = SOM(\n",
     "    x=25,\n",
     "    y=15,\n",
     "    sigma=1.45,\n",
     "    learning_rate=0.95,\n",
     "    neighborhood_order=3,\n",
-    "    epochs=100,\n",
+    "    epochs=50,\n",
     "    batch_size=16,\n",
     "    topology=\"rectangular\",\n",
     "    distance_function=\"euclidean\",\n",
@@ -617,6 +429,7 @@
    "source": [
     "som.initialize_weights(\n",
     "    data=train_features,\n",
+    "    mode=som.initialization_mode\n",
     ")"
    ]
   },
@@ -629,7 +442,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Training SOM: 100%|██████████| 100/100 [00:02<00:00, 39.84epoch/s]\n"
+      "Training SOM: 100%|██████████| 50/50 [00:04<00:00, 11.38epoch/s]\n"
      ]
     }
    ],
@@ -734,7 +547,7 @@
     "        query_sample=test_feature,\n",
     "        historical_samples=train_features,\n",
     "        historical_outputs=train_targets,\n",
-    "        min_buffer_threshold=30, # Collect 20 historical samples to train a model\n",
+    "        min_buffer_threshold=35, # Collect 20 historical samples to train a model\n",
     "        bmus_idx_map=bmus_idx_map,\n",
     "    )\n",
     "    \n",
@@ -841,11 +654,18 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".torchsom_env",
    "language": "python",
    "name": "python3"
   },