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saelarien_constraint_experiment_03.csv

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saelarien_constraint_experiment_03_recovery_collapse_mismatch (1).ipynb

@@ -0,0 +1,540 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t7abR8aQG4Rp",
+        "outputId": "fbe8f112-440a-40cf-a162-f2039b91f8da"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.10.0+cpu)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch) (3.29.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch) (4.15.0)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch) (75.2.0)\n",
+            "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch) (1.14.0)\n",
+            "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch) (3.6.1)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch) (3.1.6)\n",
+            "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch) (2025.3.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2026.1)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.1)\n",
+            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch) (1.3.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch) (3.0.3)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install torch numpy pandas matplotlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "586c813e"
+      },
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1267f97e"
+      },
+      "source": [
+        "class SimpleNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.fc1 = nn.Linear(1, 8)\n",
+        "        self.fc2 = nn.Linear(8, 1)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = torch.relu(self.fc1(x))\n",
+        "        return self.fc2(x)"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "b198527b"
+      },
+      "source": [
+        "def inject_noise(model, noise_level, noise_type):\n",
+        "    for param in model.parameters():\n",
+        "        if noise_type == \"global\":\n",
+        "            noise = torch.randn_like(param) * noise_level\n",
+        "\n",
+        "        elif noise_type == \"localized\":\n",
+        "            noise = torch.zeros_like(param)\n",
+        "            noise[0] = torch.randn_like(param[0]) * noise_level * 2\n",
+        "\n",
+        "        elif noise_type == \"structured\":\n",
+        "            noise = torch.sin(torch.arange(param.numel()).float()).reshape(param.shape) * noise_level\n",
+        "\n",
+        "        param.data += noise"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "17be9718"
+      },
+      "source": [
+        "def run_experiment(noise_level, noise_type):\n",
+        "    model = SimpleNet()\n",
+        "    optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
+        "    loss_fn = nn.MSELoss()\n",
+        "\n",
+        "    losses = []\n",
+        "\n",
+        "    # Dummy data for demonstration. Replace with actual data.\n",
+        "    x = torch.randn(100, 1) # Example: 100 samples, 1 feature\n",
+        "    y = x * 2 + 1 + 0.1 * torch.randn(100, 1) # Example: y = 2x + 1 with some noise\n",
+        "\n",
+        "    for epoch in range(100):\n",
+        "        optimizer.zero_grad()\n",
+        "        output = model(x)\n",
+        "        loss = loss_fn(output, y)\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        inject_noise(model, noise_level, noise_type)\n",
+        "\n",
+        "        losses.append(loss.item())\n",
+        "\n",
+        "    losses = np.array(losses)\n",
+        "\n",
+        "    # recovery time = first time loss exceeds threshold\n",
+        "    # Check if losses is not empty to avoid error in np.mean\n",
+        "    if len(losses) > 0:\n",
+        "        threshold = np.mean(losses) * 1.5\n",
+        "        # Find the first index where loss exceeds threshold\n",
+        "        recovery_indices = np.where(losses > threshold)[0]\n",
+        "        recovery_time = recovery_indices[0] if len(recovery_indices) > 0 else len(losses) # Set to len(losses) if no recovery\n",
+        "    else:\n",
+        "        recovery_time = len(losses) # Indicate no recovery if no losses\n",
+        "\n",
+        "\n",
+        "    divergence = losses[-1] if len(losses) > 0 else float('nan') # Handle empty losses\n",
+        "    collapse = divergence > np.percentile(losses, 90)\n",
+        "\n",
+        "    return recovery_time, divergence, collapse, losses"
+      ],
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "dc15ca81",
+        "outputId": "db9d4442-8bcb-4ad2-8c01-59d2a46fbf73"
+      },
+      "source": [
+        "results = []\n",
+        "\n",
+        "noise_levels = [0.2, 0.3, 0.35, 0.4, 0.45, 0.5]\n",
+        "noise_types = [\"global\",\"localized\",\"structured\"]\n",
+        "\n",
+        "for nl in noise_levels:\n",
+        "    for nt in noise_types:\n",
+        "        for i in range(40): # Run 40 trials for each combination\n",
+        "            recovery, divergence, collapse, losses = run_experiment(nl, nt)\n",
+        "\n",
+        "            results.append({\n",
+        "                \"noise_level\": nl,\n",
+        "                \"noise_type\": nt,\n",
+        "                \"recovery_time\": recovery,\n",
+        "                \"final_loss\": divergence,\n",
+        "                \"collapse\": collapse\n",
+        "            })\n",
+        "\n",
+        "df = pd.DataFrame(results)\n",
+        "display(df.head())"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   noise_level noise_type  recovery_time  final_loss  collapse\n",
+              "0          0.2     global             31   14.832813     False\n",
+              "1          0.2     global             65   22.448109     False\n",
+              "2          0.2     global             30   11.759277     False\n",
+              "3          0.2     global             70   14.444297     False\n",
+              "4          0.2     global             44    6.707783     False"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4aaeda6d-855d-411c-88a9-ffae94b955e4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>noise_level</th>\n",
+              "      <th>noise_type</th>\n",
+              "      <th>recovery_time</th>\n",
+              "      <th>final_loss</th>\n",
+              "      <th>collapse</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>global</td>\n",
+              "      <td>31</td>\n",
+              "      <td>14.832813</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>global</td>\n",
+              "      <td>65</td>\n",
+              "      <td>22.448109</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>global</td>\n",
+              "      <td>30</td>\n",
+              "      <td>11.759277</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>global</td>\n",
+              "      <td>70</td>\n",
+              "      <td>14.444297</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>global</td>\n",
+              "      <td>44</td>\n",
+              "      <td>6.707783</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4aaeda6d-855d-411c-88a9-ffae94b955e4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-4aaeda6d-855d-411c-88a9-ffae94b955e4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-4aaeda6d-855d-411c-88a9-ffae94b955e4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"noise_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"noise_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"global\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recovery_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 30,\n        \"max\": 70,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"final_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.710765864804246,\n        \"min\": 6.707783222198486,\n        \"max\": 22.448108673095703,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          22.448108673095703\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"collapse\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "02e45581",
+        "outputId": "4470ead5-66d2-481e-94f8-827ea9cfe99e"
+      },
+      "source": [
+        "colors = df[\"collapse\"].map({True:\"red\", False:\"green\"})\n",
+        "\n",
+        "plt.scatter(df[\"recovery_time\"], df[\"final_loss\"], c=colors)\n",
+        "plt.xlabel(\"Recovery Time\")\n",
+        "plt.ylabel(\"Final Loss\")\n",
+        "plt.title(\"Recovery vs Collapse Mismatch\")\n",
+        "plt.show()"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "7e841ad7",
+        "outputId": "c78c2128-271f-44ba-f9c8-5c334e55d081"
+      },
+      "source": [
+        "plt.figure(figsize=(6,4))\n",
+        "plt.scatter(df[\"recovery_time\"], df[\"final_loss\"], c=colors, alpha=0.6)\n",
+        "plt.xlabel(\"Recovery Time\")\n",
+        "plt.ylabel(\"Final Loss\")\n",
+        "plt.title(\"Recovery vs Collapse Mismatch\")\n",
+        "\n",
+        "plt.savefig(\"recovery_collapse_mismatch.png\", dpi=300)\n",
+        "plt.show()"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "4eb4ac1b",
+        "outputId": "209dff8d-ef05-4edb-f2be-f384eb1284d5"
+      },
+      "source": [
+        "from google.colab import files\n",
+        "files.download(\"recovery_collapse_mismatch.png\")"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_351c3b21-0cb0-4645-ba1e-55822caa9a74\", \"recovery_collapse_mismatch.png\", 130414)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4c2e0140"
+      },
+      "source": [
+        "df.to_csv(\"saelarien_constraint_experiment_02.csv\", index=False)"
+      ],
+      "execution_count": 16,
+      "outputs": []
+    }
+  ]
+}