Python class which implements a base Node for creating Trees with Pytorch Tensors #Others #JupyterNotebook #CodeSnippet

Tree_PytorchTensors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node class for Trees\n",
    "\n",
    "Python class which implements a base Node for creating Trees with Pytorch Tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    \"\"\"\n",
    "    Class that implements a Node of a Tree\n",
    "    \"\"\"\n",
    "    def __init__(self, name=None, data=None, children=[]):\n",
    "        \n",
    "        self.name = name  # Name of the node as ID (optional)\n",
    "        self.data = data  # Each node carries a differentiable zero-dimensional (scalar) tensor initialized as random\n",
    "        if self.data is None:\n",
    "            self.data = torch.rand((1,), requires_grad=True)\n",
    "            \n",
    "        self.children = children    \n",
    "    \n",
    "    def get_paths(self, node=None, path=None):\n",
    "        \"\"\"\n",
    "        Get all the paths of the Tree\n",
    "        \"\"\"\n",
    "        if node is None:\n",
    "            node = self\n",
    "            \n",
    "        paths = []\n",
    "        if path is None:\n",
    "            path = []\n",
    "        path.append(node)\n",
    "       \n",
    "        if node.children:\n",
    "            for child in node.children:\n",
    "                paths.extend(self.get_paths(child, path[:]))\n",
    "        else:\n",
    "            paths.append(path)\n",
    "            \n",
    "        return paths\n",
    "    \n",
    "    def traverse(self, f, node=None):\n",
    "        \"\"\"\n",
    "        Traverse the Tree recusively applying the function f to each Node data w/ updating inplace\n",
    "        \"\"\"\n",
    "        if node is None:\n",
    "            node = self\n",
    "            \n",
    "        if f is None: # Sanity check\n",
    "            raise NotImplementedError(\"Please, make sure the function {} passed to traverse function is implemented and not None.\".format(f))\n",
    "        node.data = f(node)\n",
    "            \n",
    "        if node.children:\n",
    "            for child in node.children:\n",
    "                self.traverse(f, child)\n",
    "        else:\n",
    "            return\n",
    "\n",
    "    \n",
    "    def __str__(self):\n",
    "        return '{}: {}'.format(self.name, self.data)\n",
    "    \n",
    "    __repr__ = __str__\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create your own Tree\n",
    "\n",
    "He creado el árbol genérico que me has pasado por WhatsApp.\n",
    "\n",
    "Por supuesto se puede hacer programáticamente..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = Node(name='x_1^1', children=[\n",
    "    Node(name='x_1^2', children=[\n",
    "        Node(name='x_1^3', children=[\n",
    "                Node(name='x_1^4', children=[]), Node(name='x_2^4', children=[])\n",
    "        ]), \n",
    "        Node(name='x_2^3', children=[\n",
    "                Node(name='x_3^4', children=[]), Node(name='x_4^4', children=[])\n",
    "        ])\n",
    "    ]),\n",
    "    Node(name='x_2^2', children=[\n",
    "        Node(name='x_3^3', children=[\n",
    "            Node(name='x_5^4', children=[]), Node(name='x_6^4', children=[])\n",
    "        ]), \n",
    "        Node(name='x_4^3', children=[\n",
    "            Node(name='x_7^4', children=[]), Node(name='x_8^4', children=[])\n",
    "        ])\n",
    "    ])\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "Algunas funciones por probar... \n",
    "\n",
    "`mult_sigmoid` corresponde a la que me has comentado antes a.k.a `\"Peso por numerito y función de activación es oyro numerito\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(node):\n",
    "    \"\"\"\n",
    "    Apply the Sigmoid function to a given Node data\n",
    "    \"\"\"\n",
    "    sigm = nn.Sigmoid()\n",
    "    return sigm(node.data)\n",
    "\n",
    "def mult_sigmoid(node):\n",
    "    \"\"\"\n",
    "    Apply a random product plus Sigmoid to a given Node data\n",
    "    \"\"\"\n",
    "    sigm = nn.Sigmoid()\n",
    "    return sigm(torch.rand((1,)) * node.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore & Traverse the Tree\n",
    "\n",
    "1. Explore the initial random Tree\n",
    "2. Traverse the Tree applying the `\"Peso por numerito y función de activación es oyro numerito\"` function\n",
    "3. Explore the resulting Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_1^2: tensor([0.1287], requires_grad=True),\n",
       "  x_1^3: tensor([0.0580], requires_grad=True),\n",
       "  x_1^4: tensor([0.8677], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_1^2: tensor([0.1287], requires_grad=True),\n",
       "  x_1^3: tensor([0.0580], requires_grad=True),\n",
       "  x_2^4: tensor([0.5528], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_1^2: tensor([0.1287], requires_grad=True),\n",
       "  x_2^3: tensor([0.6109], requires_grad=True),\n",
       "  x_3^4: tensor([0.2566], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_1^2: tensor([0.1287], requires_grad=True),\n",
       "  x_2^3: tensor([0.6109], requires_grad=True),\n",
       "  x_4^4: tensor([0.1304], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_2^2: tensor([0.5555], requires_grad=True),\n",
       "  x_3^3: tensor([0.9111], requires_grad=True),\n",
       "  x_5^4: tensor([0.9655], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_2^2: tensor([0.5555], requires_grad=True),\n",
       "  x_3^3: tensor([0.9111], requires_grad=True),\n",
       "  x_6^4: tensor([0.9909], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_2^2: tensor([0.5555], requires_grad=True),\n",
       "  x_4^3: tensor([0.8007], requires_grad=True),\n",
       "  x_7^4: tensor([0.7274], requires_grad=True)],\n",
       " [x_1^1: tensor([0.2864], requires_grad=True),\n",
       "  x_2^2: tensor([0.5555], requires_grad=True),\n",
       "  x_4^3: tensor([0.8007], requires_grad=True),\n",
       "  x_8^4: tensor([0.3543], requires_grad=True)]]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Explore the initial random Tree\n",
    "tree.get_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Traverse the Tree\n",
    "tree.traverse(f=mult_sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_1^2: tensor([0.5165], grad_fn=<SigmoidBackward>),\n",
       "  x_1^3: tensor([0.5013], grad_fn=<SigmoidBackward>),\n",
       "  x_1^4: tensor([0.5470], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_1^2: tensor([0.5165], grad_fn=<SigmoidBackward>),\n",
       "  x_1^3: tensor([0.5013], grad_fn=<SigmoidBackward>),\n",
       "  x_2^4: tensor([0.6257], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_1^2: tensor([0.5165], grad_fn=<SigmoidBackward>),\n",
       "  x_2^3: tensor([0.5691], grad_fn=<SigmoidBackward>),\n",
       "  x_3^4: tensor([0.5629], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_1^2: tensor([0.5165], grad_fn=<SigmoidBackward>),\n",
       "  x_2^3: tensor([0.5691], grad_fn=<SigmoidBackward>),\n",
       "  x_4^4: tensor([0.5016], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_2^2: tensor([0.5077], grad_fn=<SigmoidBackward>),\n",
       "  x_3^3: tensor([0.6667], grad_fn=<SigmoidBackward>),\n",
       "  x_5^4: tensor([0.5391], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_2^2: tensor([0.5077], grad_fn=<SigmoidBackward>),\n",
       "  x_3^3: tensor([0.6667], grad_fn=<SigmoidBackward>),\n",
       "  x_6^4: tensor([0.6168], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_2^2: tensor([0.5077], grad_fn=<SigmoidBackward>),\n",
       "  x_4^3: tensor([0.5693], grad_fn=<SigmoidBackward>),\n",
       "  x_7^4: tensor([0.5114], grad_fn=<SigmoidBackward>)],\n",
       " [x_1^1: tensor([0.5006], grad_fn=<SigmoidBackward>),\n",
       "  x_2^2: tensor([0.5077], grad_fn=<SigmoidBackward>),\n",
       "  x_4^3: tensor([0.5693], grad_fn=<SigmoidBackward>),\n",
       "  x_8^4: tensor([0.5736], grad_fn=<SigmoidBackward>)]]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. Explore the resulting Tree\n",
    "tree.get_paths()"
   ]
  }
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