33_dgi_label_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd5142ad-a572-4168-b681-b2fc476832ff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Modify the file 00_setup.py to define input/output file paths on your system\n",
    "# The information in 00_setup.py will be used across notebooks\n",
    "from importlib.machinery import SourceFileLoader\n",
    "setup = SourceFileLoader(\"setup\", \"./00_setup.py\").load_module()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f953ab39-6cb2-41cc-bb3c-8d8daf57c210",
   "metadata": {},
   "source": [
    "# GNN - Unsupervised Model (label as feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28878d00-d46c-43ef-98ab-68823bdda2d0",
   "metadata": {},
   "source": [
    "Deep Graph Infomax via HinSage for a graph consisting of train and validation data only (original features, with the target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c313c044-8dbf-4b50-89ea-9e8f55ebd6c3",
   "metadata": {},
   "source": [
    "The unsupervised model has both business and NAICS nodes, and uses NAICS nodes as the head type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16d66bf-b383-4078-99e9-7c039b4be356",
   "metadata": {},
   "source": [
    "*This script takes about 30 minutes on my MacBook Air*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52887f3a-b4a8-43b6-ac6d-bb6a1e7ec0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2141b963-4f15-477a-9f43-bdba4de14bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import importlib\n",
    "import pickle, re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16fac673-6afd-46ba-96d0-422c2aad5bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-16 14:06:01.332530: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-03-16 14:06:01.332557: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-03-16 14:06:01.332565: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-03-16 14:06:01.332602: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-03-16 14:06:01.332620: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "import stellargraph as sg\n",
    "from stellargraph import StellarGraph\n",
    "from stellargraph.layer import HinSAGE, DeepGraphInfomax\n",
    "from stellargraph.mapper import HinSAGENodeGenerator, CorruptedGenerator\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "import keras\n",
    "import tensorflow.keras.metrics as km\n",
    "from keras import initializers\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac51eb62-a3dd-4976-b5c0-3030eec5fae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6099e7c-b87f-4329-9e8d-6834022b6cd3",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c0de590-8299-4dc1-b9d7-b4ccd811f8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and validation clients - monitor training on val clients\n",
    "with open(Path(setup.temp_path).joinpath('32_DATA_stellargraph_train_val.pkl'), 'rb') as fin:\n",
    "      sba_graph  = pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d4a5133-6234-4c44-8001-1225a6e5f67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NAICS info\n",
    "features_naics = pd.read_parquet(Path(setup.temp_path).joinpath('30_DATA_graph_node_features_naics.parquet')) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbfbbd0-e53e-4d5e-a980-5be8efece555",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0901ba23-4af2-46db-8b36-4eff21e5ad7e",
   "metadata": {},
   "source": [
    "##### Split the NAICS nodes for fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8a829d2-03d6-4ec8-ada4-498458895b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "naics_nodes = sba_graph.nodes(node_type='NAICS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9c41482-ad16-48ce-a081-359196d3aca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_index, val_index = train_test_split(naics_nodes, train_size=0.8, random_state = 756)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a6b89c-56ac-4a7f-84eb-6838ebd507d4",
   "metadata": {},
   "source": [
    "##### Set seeds to setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd5973b0-415b-453c-be2e-2ffaad13c79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set some seeds\n",
    "np.random.seed(16)\n",
    "tf.random.set_seed(16)\n",
    "sg.random.set_seed(4345)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e2cd18-1204-4b22-b09c-50e94c31198c",
   "metadata": {},
   "source": [
    "##### HinSage Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9e4f62e-3c1c-4686-a563-ddde99398269",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = HinSAGENodeGenerator(sba_graph, batch_size=setup.gnn_batch_size, \n",
    "                                 num_samples=setup.gnn_unsup_num_samples,\n",
    "                                 head_node_type='NAICS',\n",
    "                                 seed=2454)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f82b06d2-958f-49c9-a058-439cf7a707bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_model = HinSAGE(\n",
    "    layer_sizes=setup.gnn_unsup_layer_sizes, generator=generator, \n",
    "    activations=setup.gnn_unsup_activations,\n",
    "    bias=True, dropout=setup.gnn_unsup_dropout,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae2f2f6-2676-44ec-a6ad-ef8f4054fc7d",
   "metadata": {},
   "source": [
    "##### Deep Graph Infomax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ba5b06d-3964-4abd-9e6b-0059067b3006",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrupted_generator = CorruptedGenerator(generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b0846ad-6038-46d6-bc61-049fc07b490d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_flow_corr = corrupted_generator.flow(train_index, shuffle=True)\n",
    "val_flow_corr = corrupted_generator.flow(val_index, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "537322b1-3dfc-487e-9c26-c6ac9ba346db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/valeriecarey/miniconda3/envs/tf_p38/lib/python3.8/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "infomax = DeepGraphInfomax(graphsage_model, corrupted_generator)\n",
    "x_in, x_out = infomax.in_out_tensors()\n",
    "\n",
    "optimizer = setup.gnn_optimizer(learning_rate=setup.gnn_unsup_learning_rate)\n",
    "model = Model(inputs=x_in, outputs=x_out)\n",
    "model.compile(loss=tf.nn.sigmoid_cross_entropy_with_logits, optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd37d78-bfdf-4c0c-aabe-5faff3f4e15b",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62e08aee-14c1-4510-bf0a-e8b3b484e7b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-16 14:06:03.739952: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-03-16 14:06:06.039711: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 - 3s - loss: 0.6846 - val_loss: 0.6727 - 3s/epoch - 103ms/step\n",
      "Epoch 2/100\n",
      "30/30 - 1s - loss: 0.6482 - val_loss: 0.6473 - 555ms/epoch - 18ms/step\n",
      "Epoch 3/100\n",
      "30/30 - 1s - loss: 0.6027 - val_loss: 0.5804 - 550ms/epoch - 18ms/step\n",
      "Epoch 4/100\n",
      "30/30 - 1s - loss: 0.5713 - val_loss: 0.5560 - 556ms/epoch - 19ms/step\n",
      "Epoch 5/100\n",
      "30/30 - 1s - loss: 0.5547 - val_loss: 0.5514 - 547ms/epoch - 18ms/step\n",
      "Epoch 6/100\n",
      "30/30 - 1s - loss: 0.5401 - val_loss: 0.5675 - 554ms/epoch - 18ms/step\n",
      "Epoch 7/100\n",
      "30/30 - 1s - loss: 0.5297 - val_loss: 0.5253 - 551ms/epoch - 18ms/step\n",
      "Epoch 8/100\n",
      "30/30 - 1s - loss: 0.4942 - val_loss: 0.5284 - 547ms/epoch - 18ms/step\n",
      "Epoch 9/100\n",
      "30/30 - 1s - loss: 0.4939 - val_loss: 0.6470 - 565ms/epoch - 19ms/step\n",
      "Epoch 10/100\n",
      "30/30 - 1s - loss: 0.5026 - val_loss: 0.4801 - 543ms/epoch - 18ms/step\n",
      "Epoch 11/100\n",
      "30/30 - 1s - loss: 0.4744 - val_loss: 0.4927 - 550ms/epoch - 18ms/step\n",
      "Epoch 12/100\n",
      "30/30 - 1s - loss: 0.4618 - val_loss: 0.4608 - 539ms/epoch - 18ms/step\n",
      "Epoch 13/100\n",
      "30/30 - 1s - loss: 0.4525 - val_loss: 0.4372 - 544ms/epoch - 18ms/step\n",
      "Epoch 14/100\n",
      "30/30 - 1s - loss: 0.4359 - val_loss: 0.4445 - 547ms/epoch - 18ms/step\n",
      "Epoch 15/100\n",
      "30/30 - 1s - loss: 0.4228 - val_loss: 0.4290 - 600ms/epoch - 20ms/step\n",
      "Epoch 16/100\n",
      "30/30 - 1s - loss: 0.4149 - val_loss: 0.4352 - 628ms/epoch - 21ms/step\n",
      "Epoch 17/100\n",
      "30/30 - 1s - loss: 0.4107 - val_loss: 0.4227 - 605ms/epoch - 20ms/step\n",
      "Epoch 18/100\n",
      "30/30 - 1s - loss: 0.4043 - val_loss: 0.4400 - 593ms/epoch - 20ms/step\n",
      "Epoch 19/100\n",
      "30/30 - 1s - loss: 0.3830 - val_loss: 0.4090 - 581ms/epoch - 19ms/step\n",
      "Epoch 20/100\n",
      "30/30 - 1s - loss: 0.3824 - val_loss: 0.4388 - 547ms/epoch - 18ms/step\n",
      "Epoch 21/100\n",
      "30/30 - 1s - loss: 0.3797 - val_loss: 0.3722 - 544ms/epoch - 18ms/step\n",
      "Epoch 22/100\n",
      "30/30 - 1s - loss: 0.3643 - val_loss: 0.3891 - 549ms/epoch - 18ms/step\n",
      "Epoch 23/100\n",
      "30/30 - 1s - loss: 0.3514 - val_loss: 0.3639 - 548ms/epoch - 18ms/step\n",
      "Epoch 24/100\n",
      "30/30 - 1s - loss: 0.3566 - val_loss: 0.3907 - 552ms/epoch - 18ms/step\n",
      "Epoch 25/100\n",
      "30/30 - 1s - loss: 0.3408 - val_loss: 0.3362 - 546ms/epoch - 18ms/step\n",
      "Epoch 26/100\n",
      "30/30 - 1s - loss: 0.3548 - val_loss: 0.3651 - 540ms/epoch - 18ms/step\n",
      "Epoch 27/100\n",
      "30/30 - 1s - loss: 0.3429 - val_loss: 0.4144 - 548ms/epoch - 18ms/step\n",
      "Epoch 28/100\n",
      "30/30 - 1s - loss: 0.3302 - val_loss: 0.3647 - 545ms/epoch - 18ms/step\n",
      "Epoch 29/100\n",
      "30/30 - 1s - loss: 0.3170 - val_loss: 0.3473 - 545ms/epoch - 18ms/step\n",
      "Epoch 30/100\n",
      "30/30 - 1s - loss: 0.3492 - val_loss: 0.3618 - 552ms/epoch - 18ms/step\n",
      "Epoch 31/100\n",
      "30/30 - 1s - loss: 0.3141 - val_loss: 0.3623 - 550ms/epoch - 18ms/step\n",
      "Epoch 32/100\n",
      "30/30 - 1s - loss: 0.3167 - val_loss: 0.3988 - 546ms/epoch - 18ms/step\n",
      "Epoch 33/100\n",
      "30/30 - 1s - loss: 0.3538 - val_loss: 0.3680 - 553ms/epoch - 18ms/step\n",
      "Epoch 34/100\n",
      "30/30 - 1s - loss: 0.3186 - val_loss: 0.3478 - 550ms/epoch - 18ms/step\n",
      "Epoch 35/100\n",
      "30/30 - 1s - loss: 0.3107 - val_loss: 0.3521 - 543ms/epoch - 18ms/step\n",
      "Epoch 36/100\n",
      "30/30 - 1s - loss: 0.3055 - val_loss: 0.3269 - 544ms/epoch - 18ms/step\n",
      "Epoch 37/100\n",
      "30/30 - 1s - loss: 0.3103 - val_loss: 0.3791 - 548ms/epoch - 18ms/step\n",
      "Epoch 38/100\n",
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      "Epoch 39/100\n",
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      "Epoch 40/100\n",
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      "Epoch 41/100\n",
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      "Epoch 42/100\n",
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      "Epoch 43/100\n",
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      "Epoch 44/100\n",
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      "Epoch 45/100\n",
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      "Epoch 46/100\n",
      "30/30 - 1s - loss: 0.3490 - val_loss: 0.3421 - 577ms/epoch - 19ms/step\n",
      "Epoch 47/100\n",
      "30/30 - 1s - loss: 0.3245 - val_loss: 0.3456 - 577ms/epoch - 19ms/step\n",
      "Epoch 48/100\n",
      "30/30 - 1s - loss: 0.2970 - val_loss: 0.3428 - 550ms/epoch - 18ms/step\n",
      "Epoch 49/100\n",
      "30/30 - 1s - loss: 0.2934 - val_loss: 0.3560 - 564ms/epoch - 19ms/step\n",
      "Epoch 50/100\n",
      "30/30 - 1s - loss: 0.2974 - val_loss: 0.3444 - 551ms/epoch - 18ms/step\n",
      "Epoch 51/100\n",
      "30/30 - 1s - loss: 0.2875 - val_loss: 0.3903 - 542ms/epoch - 18ms/step\n",
      "Epoch 52/100\n",
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      "Epoch 53/100\n",
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      "Epoch 54/100\n",
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      "Epoch 55/100\n",
      "30/30 - 1s - loss: 0.2935 - val_loss: 0.3300 - 546ms/epoch - 18ms/step\n",
      "Epoch 56/100\n",
      "30/30 - 1s - loss: 0.2643 - val_loss: 0.3363 - 547ms/epoch - 18ms/step\n",
      "Epoch 57/100\n",
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      "Epoch 58/100\n",
      "30/30 - 1s - loss: 0.2855 - val_loss: 0.3217 - 543ms/epoch - 18ms/step\n",
      "Epoch 59/100\n",
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      "Epoch 60/100\n",
      "30/30 - 1s - loss: 0.2712 - val_loss: 0.3198 - 555ms/epoch - 19ms/step\n",
      "Epoch 61/100\n",
      "30/30 - 1s - loss: 0.2717 - val_loss: 0.2615 - 546ms/epoch - 18ms/step\n",
      "Epoch 62/100\n",
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      "Epoch 63/100\n",
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      "Epoch 64/100\n",
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      "Epoch 65/100\n",
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      "Epoch 66/100\n",
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      "Epoch 67/100\n",
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      "Epoch 68/100\n",
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      "Epoch 69/100\n",
      "30/30 - 1s - loss: 0.2349 - val_loss: 0.2959 - 541ms/epoch - 18ms/step\n",
      "Epoch 70/100\n",
      "30/30 - 1s - loss: 0.2474 - val_loss: 0.2413 - 575ms/epoch - 19ms/step\n",
      "Epoch 71/100\n",
      "30/30 - 1s - loss: 0.2306 - val_loss: 0.2502 - 590ms/epoch - 20ms/step\n",
      "Epoch 72/100\n",
      "30/30 - 1s - loss: 0.2284 - val_loss: 0.2550 - 557ms/epoch - 19ms/step\n",
      "Epoch 73/100\n",
      "30/30 - 1s - loss: 0.2232 - val_loss: 0.2382 - 544ms/epoch - 18ms/step\n",
      "Epoch 74/100\n",
      "30/30 - 1s - loss: 0.2322 - val_loss: 0.2861 - 549ms/epoch - 18ms/step\n",
      "Epoch 75/100\n",
      "30/30 - 1s - loss: 0.2205 - val_loss: 0.2529 - 560ms/epoch - 19ms/step\n",
      "Epoch 76/100\n",
      "30/30 - 1s - loss: 0.2198 - val_loss: 0.2250 - 544ms/epoch - 18ms/step\n",
      "Epoch 77/100\n",
      "30/30 - 1s - loss: 0.2198 - val_loss: 0.2404 - 543ms/epoch - 18ms/step\n",
      "Epoch 78/100\n",
      "30/30 - 1s - loss: 0.2355 - val_loss: 0.2285 - 546ms/epoch - 18ms/step\n",
      "Epoch 79/100\n",
      "30/30 - 1s - loss: 0.2132 - val_loss: 0.2132 - 542ms/epoch - 18ms/step\n",
      "Epoch 80/100\n",
      "30/30 - 1s - loss: 0.2249 - val_loss: 0.2229 - 541ms/epoch - 18ms/step\n",
      "Epoch 81/100\n",
      "30/30 - 1s - loss: 0.2268 - val_loss: 0.2607 - 541ms/epoch - 18ms/step\n",
      "Epoch 82/100\n",
      "30/30 - 1s - loss: 0.2226 - val_loss: 0.2352 - 541ms/epoch - 18ms/step\n",
      "Epoch 83/100\n",
      "30/30 - 1s - loss: 0.2278 - val_loss: 0.2237 - 545ms/epoch - 18ms/step\n",
      "Epoch 84/100\n",
      "30/30 - 1s - loss: 0.2146 - val_loss: 0.2104 - 548ms/epoch - 18ms/step\n",
      "Epoch 85/100\n",
      "30/30 - 1s - loss: 0.2219 - val_loss: 0.2388 - 544ms/epoch - 18ms/step\n",
      "Epoch 86/100\n",
      "30/30 - 1s - loss: 0.2055 - val_loss: 0.2378 - 548ms/epoch - 18ms/step\n",
      "Epoch 87/100\n",
      "30/30 - 1s - loss: 0.2305 - val_loss: 0.2416 - 544ms/epoch - 18ms/step\n",
      "Epoch 88/100\n",
      "30/30 - 1s - loss: 0.2161 - val_loss: 0.2053 - 546ms/epoch - 18ms/step\n",
      "Epoch 89/100\n",
      "30/30 - 1s - loss: 0.2113 - val_loss: 0.2508 - 543ms/epoch - 18ms/step\n",
      "Epoch 90/100\n",
      "30/30 - 1s - loss: 0.2287 - val_loss: 0.2483 - 544ms/epoch - 18ms/step\n",
      "Epoch 91/100\n",
      "30/30 - 1s - loss: 0.2157 - val_loss: 0.2034 - 542ms/epoch - 18ms/step\n",
      "Epoch 92/100\n",
      "30/30 - 1s - loss: 0.1995 - val_loss: 0.2163 - 546ms/epoch - 18ms/step\n",
      "Epoch 93/100\n",
      "30/30 - 1s - loss: 0.2126 - val_loss: 0.2257 - 550ms/epoch - 18ms/step\n",
      "Epoch 94/100\n",
      "30/30 - 1s - loss: 0.2032 - val_loss: 0.2294 - 548ms/epoch - 18ms/step\n",
      "Epoch 95/100\n",
      "30/30 - 1s - loss: 0.2138 - val_loss: 0.2000 - 545ms/epoch - 18ms/step\n",
      "Epoch 96/100\n",
      "30/30 - 1s - loss: 0.2005 - val_loss: 0.2338 - 545ms/epoch - 18ms/step\n",
      "Epoch 97/100\n",
      "30/30 - 1s - loss: 0.2097 - val_loss: 0.2061 - 547ms/epoch - 18ms/step\n",
      "Epoch 98/100\n",
      "30/30 - 1s - loss: 0.2000 - val_loss: 0.2302 - 550ms/epoch - 18ms/step\n",
      "Epoch 99/100\n",
      "30/30 - 1s - loss: 0.2142 - val_loss: 0.1958 - 546ms/epoch - 18ms/step\n",
      "Epoch 100/100\n",
      "30/30 - 1s - loss: 0.2089 - val_loss: 0.2333 - 547ms/epoch - 18ms/step\n"
     ]
    }
   ],
   "source": [
    "es = EarlyStopping(monitor=\"loss\", min_delta=0, patience=20)\n",
    "history = model.fit(train_flow_corr, epochs=setup.gnn_unsup_epochs, verbose=2, callbacks=[es],\n",
    "                   validation_data = val_flow_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5714c056-83a6-4152-a3c6-e3c9fe8e1420",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tensorflow save format to work around issue \n",
    "# https://github.com/tensorflow/tensorflow/issues/46659\n",
    "model.save(Path(setup.temp_path).joinpath('33_DATA_model_corrupted.keras'), save_format='tf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42cc2770-2599-4326-b9f9-2163c4448027",
   "metadata": {},
   "source": [
    "##### History Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fdb54aa5-0060-4cc2-8e71-d07ead44b775",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df = pd.DataFrame(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "this_history_df.to_csv(Path(setup.temp_path).joinpath('33_REPORT_fit_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_history_df[['loss', 'val_loss']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8353258-5212-45ad-9e60-7ee9dd6ab498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)        [(None, 100, 11)]            0         []                            \n",
      "                                                                                                  \n",
      " input_1 (InputLayer)        [(None, 1, 1)]               0         []                            \n",
      "                                                                                                  \n",
      " reshape (Reshape)           (None, 1, 100, 11)           0         ['input_2[0][0]']             \n",
      "                                                                                                  \n",
      " input_4 (InputLayer)        [(None, 100, 11)]            0         []                            \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)         (None, 1, 1)                 0         ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " dropout (Dropout)           (None, 1, 100, 11)           0         ['reshape[0][0]']             \n",
      "                                                                                                  \n",
      " input_3 (InputLayer)        [(None, 1, 1)]               0         []                            \n",
      "                                                                                                  \n",
      " reshape_2 (Reshape)         (None, 1, 100, 11)           0         ['input_4[0][0]']             \n",
      "                                                                                                  \n",
      " mean_hin_aggregator (MeanH  (None, 1, 8)                 56        ['dropout_1[0][0]',           \n",
      " inAggregator)                                                       'dropout[0][0]',             \n",
      "                                                                     'dropout_3[0][0]',           \n",
      "                                                                     'dropout_2[0][0]']           \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)         (None, 1, 1)                 0         ['input_3[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)         (None, 1, 100, 11)           0         ['reshape_2[0][0]']           \n",
      "                                                                                                  \n",
      " reshape_1 (Reshape)         (None, 8)                    0         ['mean_hin_aggregator[0][0]'] \n",
      "                                                                                                  \n",
      " lambda (Lambda)             (None, 8)                    0         ['reshape_1[0][0]',           \n",
      "                                                                     'reshape_3[0][0]']           \n",
      "                                                                                                  \n",
      " reshape_3 (Reshape)         (None, 8)                    0         ['mean_hin_aggregator[1][0]'] \n",
      "                                                                                                  \n",
      " dgi_readout (DGIReadout)    (8,)                         0         ['lambda[0][0]']              \n",
      "                                                                                                  \n",
      " dgi_discriminator (DGIDisc  (None,)                      64        ['lambda[0][0]',              \n",
      " riminator)                                                          'dgi_readout[0][0]',         \n",
      "                                                                     'lambda[1][0]',              \n",
      "                                                                     'dgi_readout[0][0]']         \n",
      "                                                                                                  \n",
      " tf.stack (TFOpLambda)       (None, 2)                    0         ['dgi_discriminator[0][0]',   \n",
      "                                                                     'dgi_discriminator[1][0]']   \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 120 (480.00 Byte)\n",
      "Trainable params: 120 (480.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5c0c3d-e4d1-4500-9129-b9d00346b13d",
   "metadata": {},
   "source": [
    "## Base Model Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "704a3af2-44ae-43bb-8098-1b2b8d0c8d3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = graphsage_model.in_out_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dce36e2f-d8c3-41da-a640-b657c1b15c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_keras = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4b097a6-16aa-4095-94ec-b8e1cc88919f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_6 (InputLayer)        [(None, 100, 11)]            0         []                            \n",
      "                                                                                                  \n",
      " input_5 (InputLayer)        [(None, 1, 1)]               0         []                            \n",
      "                                                                                                  \n",
      " reshape_4 (Reshape)         (None, 1, 100, 11)           0         ['input_6[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)         (None, 1, 1)                 0         ['input_5[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)         (None, 1, 100, 11)           0         ['reshape_4[0][0]']           \n",
      "                                                                                                  \n",
      " mean_hin_aggregator (MeanH  (None, 1, 8)                 56        ['dropout_5[0][0]',           \n",
      " inAggregator)                                                       'dropout_4[0][0]']           \n",
      "                                                                                                  \n",
      " reshape_5 (Reshape)         (None, 8)                    0         ['mean_hin_aggregator[2][0]'] \n",
      "                                                                                                  \n",
      " lambda (Lambda)             (None, 8)                    0         ['reshape_5[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 56 (224.00 Byte)\n",
      "Trainable params: 56 (224.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "graphsage_keras.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ebc302-2b75-40b5-8ab6-9151748425de",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Save the Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "764e1854-aaf1-4652-a235-6712ecc7139d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "# Tensorflow save format to work around issue \n",
    "# https://github.com/tensorflow/tensorflow/issues/46659\n",
    "graphsage_keras.save(Path(setup.temp_path).joinpath('33_DATA_model_base.keras'), save_format='tf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ebf461-d11d-400e-b73c-75997725c313",
   "metadata": {},
   "source": [
    "## Embeddings - Full Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3be8f6-58f1-4476-a701-59490fa009d5",
   "metadata": {},
   "source": [
    "Here, I will get NAICS node embeddings for the full graph.  The base model is used to accomplish this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a29badc-975e-4198-b0b7-c50beece6020",
   "metadata": {},
   "source": [
    "With my versions of Stellargraph, Keras, Tensorflow, etc., the value returned from HinSAGENodeGenerator.flow isn't compatible with what is expected from HinSAGE.get_in_out_tensors().  The flow value is a NodeSequence, which should generaate the proper tensors, but this isn't working so the tensors have to be extracted from the NodeSequence. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cec9e41-7626-47e2-b476-51f4830ddb8b",
   "metadata": {},
   "source": [
    "The NodeSequence generates a tuple.  The first dimension is the batch size.  I either have to iterate over batches or use a batch size large enough to hold all NAICS nodes!  Then, the 0th item holds the tensors needed for prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe0a26c-d993-4f37-8b96-0dcc35771868",
   "metadata": {},
   "source": [
    "##### Get full graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f59534-7e43-453d-927b-6cb05c4eeb05",
   "metadata": {},
   "source": [
    "Load the full graph and get the NAICS nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2075957b-8852-4ae1-9cb4-aa86a6e42559",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('32_DATA_stellargraph_all.pkl'), 'rb') as fin:\n",
    "      sba_graph_all  = pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6298e87c-8bd1-4699-9c76-b4313012505b",
   "metadata": {},
   "outputs": [],
   "source": [
    "naics_nodes = sba_graph_all.nodes(node_type='NAICS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b2584bcf-c129-4e92-99e5-927db9823254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1311"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naics_len = len(naics_nodes)\n",
    "naics_len"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f254807c-8159-4f53-bd3b-f1abc1c18ecf",
   "metadata": {},
   "source": [
    "##### New generator with large batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6017d601-1228-411d-8f87-76a5a991b568",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator_all = HinSAGENodeGenerator(sba_graph_all, batch_size=naics_len, \n",
    "                                 num_samples=setup.gnn_unsup_num_samples,\n",
    "                                 head_node_type='NAICS',\n",
    "                                 seed=3434)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2ae2bd40-5f36-4bf9-9802-bc729ada4df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_all = generator_all.flow(naics_nodes, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1cdb9c3-987f-453f-a527-9b8ef5e29adb",
   "metadata": {},
   "source": [
    "##### Prediction\n",
    "Unpack the tuple.  Since I've used a large batch size, I only need the 0th item of the 0th item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a0e2566-8b9d-47f2-816a-b3ad7b13a909",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-16 14:07:04.931114: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "embed_out = graphsage_keras.predict(tuple(flow_all)[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2aeb0836-767a-462e-b981-9314795a3d6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1311, 8)\n"
     ]
    }
   ],
   "source": [
    "print(embed_out.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff506dbb-3b32-4aec-9260-656f3424f78d",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df = pd.DataFrame(embed_out, index=naics_nodes) \n",
    "embed_df.columns = [f'emb_{i:03d}' for i in range(len(embed_df.columns))]\n",
    "embed_df = embed_df.reset_index() \\\n",
    "    .rename(columns={'index':'source'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "81b02149-cf1e-4314-a8d8-51aab861d56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df = embed_df.merge(features_naics[['NAICS_orig']].reset_index(),\n",
    "                          on='source')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6769ff38-b961-4c0f-b2a5-f0778abbd4a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1311, 10)\n"
     ]
    }
   ],
   "source": [
    "print(embed_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cb4805b9-922b-47bb-9d01-bcfe64e1aab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>source</th>\n",
       "      <th>emb_000</th>\n",
       "      <th>emb_001</th>\n",
       "      <th>emb_002</th>\n",
       "      <th>emb_003</th>\n",
       "      <th>emb_004</th>\n",
       "      <th>emb_005</th>\n",
       "      <th>emb_006</th>\n",
       "      <th>emb_007</th>\n",
       "      <th>NAICS_orig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n_722410</td>\n",
       "      <td>-0.243578</td>\n",
       "      <td>0.215951</td>\n",
       "      <td>0.223025</td>\n",
       "      <td>0.260557</td>\n",
       "      <td>0.485944</td>\n",
       "      <td>0.446704</td>\n",
       "      <td>0.348510</td>\n",
       "      <td>0.468251</td>\n",
       "      <td>722410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n_332721</td>\n",
       "      <td>-0.294666</td>\n",
       "      <td>0.261245</td>\n",
       "      <td>0.269802</td>\n",
       "      <td>0.315206</td>\n",
       "      <td>0.594184</td>\n",
       "      <td>0.532263</td>\n",
       "      <td>0.113533</td>\n",
       "      <td>0.153386</td>\n",
       "      <td>332721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>n_445299</td>\n",
       "      <td>-0.261981</td>\n",
       "      <td>0.232267</td>\n",
       "      <td>0.239875</td>\n",
       "      <td>0.280243</td>\n",
       "      <td>-0.307289</td>\n",
       "      <td>0.464032</td>\n",
       "      <td>-0.525024</td>\n",
       "      <td>0.394891</td>\n",
       "      <td>445299</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n_441120</td>\n",
       "      <td>-0.273555</td>\n",
       "      <td>0.242528</td>\n",
       "      <td>0.250472</td>\n",
       "      <td>0.292623</td>\n",
       "      <td>-0.102607</td>\n",
       "      <td>0.495267</td>\n",
       "      <td>-0.415731</td>\n",
       "      <td>0.537897</td>\n",
       "      <td>441120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>n_512110</td>\n",
       "      <td>-0.276371</td>\n",
       "      <td>0.245025</td>\n",
       "      <td>0.253051</td>\n",
       "      <td>0.295636</td>\n",
       "      <td>0.243123</td>\n",
       "      <td>0.502495</td>\n",
       "      <td>0.304175</td>\n",
       "      <td>0.554990</td>\n",
       "      <td>512110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     source   emb_000   emb_001   emb_002   emb_003   emb_004   emb_005  \\\n",
       "0  n_722410 -0.243578  0.215951  0.223025  0.260557  0.485944  0.446704   \n",
       "1  n_332721 -0.294666  0.261245  0.269802  0.315206  0.594184  0.532263   \n",
       "2  n_445299 -0.261981  0.232267  0.239875  0.280243 -0.307289  0.464032   \n",
       "3  n_441120 -0.273555  0.242528  0.250472  0.292623 -0.102607  0.495267   \n",
       "4  n_512110 -0.276371  0.245025  0.253051  0.295636  0.243123  0.502495   \n",
       "\n",
       "    emb_006   emb_007 NAICS_orig  \n",
       "0  0.348510  0.468251     722410  \n",
       "1  0.113533  0.153386     332721  \n",
       "2 -0.525024  0.394891     445299  \n",
       "3 -0.415731  0.537897     441120  \n",
       "4  0.304175  0.554990     512110  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69d1fd20-9296-4060-8322-692149353661",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df.to_parquet(Path(setup.temp_path).joinpath('33_DATA_embeddings.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe47a3e5-3024-40a5-9d15-7bb0977c8502",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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