{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "a7fbf1c4", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T08:39:13.719301Z", "iopub.status.busy": "2023-09-28T08:39:13.719012Z", "iopub.status.idle": "2023-09-28T08:39:13.726493Z", "shell.execute_reply": "2023-09-28T08:39:13.725899Z" }, "papermill": { "duration": 0.027442, "end_time": "2023-09-28T08:39:13.727559", "exception": false, "start_time": "2023-09-28T08:39:13.700117", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "%cd .." ] }, { "cell_type": "markdown", "id": "beea5374", "metadata": {}, "source": [ "### Roadmap\n", "- on definit le process de prediction \n", "- on en deduit le process d'entrainement\n", "- definit un systeme de buffer pour enregistrer X,Y,profondeur\n", "- on code le buffer\n", "- on code l'entrainement pour iterer sur le buffer\n", "- \n", "\n", "## Chose envisager\n", "- on garde les meme buffer/dataloader mais, on en a autant que de profondeurs\n", "- c'est au moment de l'entrainement qu'on choisi sur quel dataloader tapé en fonction de leur profondeur.\n", "- lorsque le niveau 0 est vide on tape sur le plus haut niveau non vide -> non on tape sur le plus peuplé ou on s'en fout\n" ] }, { "cell_type": "code", "execution_count": null, "id": "bd548105", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T08:39:13.758903Z", "iopub.status.busy": "2023-09-28T08:39:13.758694Z", "iopub.status.idle": "2023-09-28T08:39:18.006183Z", "shell.execute_reply": "2023-09-28T08:39:18.005526Z" }, "papermill": { "duration": 4.25573, "end_time": "2023-09-28T08:39:18.007539", "exception": false, "start_time": "2023-09-28T08:39:13.751809", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl\n", "from torch import nn\n", "import torch.nn.functional as F\n", "import pandas as pd\n", "import seaborn as sn\n", "import matplotlib.pyplot as plt\n", "\n", "from sudoku.models import SmallNetBis, SymPreprocess\n", "from sudoku.loader import DataIterBuffer, train_dataset, test_dataset, data_loader, get_datasets\n", "from sudoku.helper import display_as_dataframe, get_grid_number_soluce\n", "from sudoku.train import SudokuLightning, SudokuTrialErrorLightning, TrialEveryPosException\n", "\n", "from collections import deque\n", "from typing import Tuple\n", "\n", "\n", "from torchmetrics import AUROC\n", "from torch.utils.data.dataset import IterableDataset\n", "from torch.utils.data import DataLoader\n", "\n", "from pytorch_lightning.callbacks import ModelCheckpoint\n", "from lightning_lite.utilities.seed import seed_everything\n", "\n", "import numpy as np\n", "\n", "from tqdm.auto import tqdm\n", "tqdm.pandas()\n", "\n", "\n", "import math\n", "def sigmoid(x):\n", " return 1 / (1 + math.exp(-x))\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "01e09cf3", "metadata": {}, "outputs": [], "source": [ "BATCH_SIZE=32" ] }, { "cell_type": "code", "execution_count": null, "id": "9b9b155b", "metadata": {}, "outputs": [], "source": [ "import lightning.__version__ as v\n", "v" ] }, { "cell_type": "code", "execution_count": null, "id": "86b17dcb", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T08:39:18.020745Z", "iopub.status.busy": "2023-09-28T08:39:18.020351Z", "iopub.status.idle": "2023-09-28T08:39:18.049122Z", "shell.execute_reply": "2023-09-28T08:39:18.048654Z" }, "papermill": { "duration": 0.036699, "end_time": "2023-09-28T08:39:18.050352", "exception": false, "start_time": "2023-09-28T08:39:18.013653", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tensorboard = pl.loggers.TensorBoardLogger(save_dir=\"\")\n", "torch.cuda.is_available()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "653cf7e6", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T08:39:18.063615Z", "iopub.status.busy": "2023-09-28T08:39:18.063434Z", "iopub.status.idle": "2023-09-28T08:39:18.068289Z", "shell.execute_reply": "2023-09-28T08:39:18.067724Z" }, "papermill": { "duration": 0.012524, "end_time": "2023-09-28T08:39:18.069552", "exception": false, "start_time": "2023-09-28T08:39:18.057028", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "seed_everything(seed=1)" ] }, { "cell_type": "markdown", "id": "4f73130f", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 7, "id": "086c69b9", "metadata": { "execution": { "iopub.execute_input": "2023-09-28T08:39:18.082413Z", "iopub.status.busy": "2023-09-28T08:39:18.082249Z", "iopub.status.idle": "2023-09-28T08:39:20.421092Z", "shell.execute_reply": "2023-09-28T08:39:20.420559Z" }, "papermill": { "duration": 2.346925, "end_time": "2023-09-28T08:39:20.422607", "exception": false, "start_time": "2023-09-28T08:39:18.075682", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "# train_dataset, val_dataset = get_datasets(train_size=1280, test_size=1280)\n", "# train_dataloader = DataLoader(train_dataset, batch_size=32)\n", "# val_dataloader = DataLoader(val_dataset, batch_size=32)" ] }, { "cell_type": "code", "execution_count": null, "id": "bffbf02f", "metadata": {}, "outputs": [], "source": [ "# model = SudokuLightning.load_from_checkpoint(checkpoint_path=\"/home/seb/Perso/git/sudoku/sudoku_clean_v2/checkpoints/epoch=9-step=4961.ckpt\")\n", "model = SudokuTrialErrorLightning.load_from_checkpoint(checkpoint_path=\"model_9_m3_big_validation_th.ckpt\")\n", "# model = SudokuLightning.load_from_checkpoint(checkpoint_path=\"/home/seb/Perso/git/sudoku/sudoku_clean_v2/lightning_logs/version_21/checkpoints/epoch=99-step=105059.ckpt\")#\"model_8_m3_100epoch.ckpt\")\n", "# /home/seb/Perso/git/sudoku/sudoku_clean_v2/lightning_logs/version_21/checkpoints/epoch=99-step=105059.ckpt\n", "model.reset_threshold_on_validation=False\n", "model.margin=0.6" ] }, { "cell_type": "code", "execution_count": 9, "id": "0297302f", "metadata": {}, "outputs": [], "source": [ "board_hardest_sudoku = (\n", " (8, 0, 0, 0, 0, 0, 0, 0, 0),\n", " (0, 0, 3, 6, 0, 0, 0, 0, 0),\n", " (0, 7, 0, 0, 9, 0, 2, 0, 0),\n", " (0, 5, 0, 0, 0, 7, 0, 0, 0),\n", " (0, 0, 0, 0, 4, 5, 7, 0, 0),\n", " (0, 0, 0, 1, 0, 0, 0, 3, 0),\n", " (0, 0, 1, 0, 0, 0, 0, 6, 8),\n", " (0, 0, 8, 5, 0, 0, 0, 1, 0),\n", " (0, 9, 0, 0, 0, 0, 4, 0, 0)\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "d1620ce3", "metadata": {}, "outputs": [], "source": [ "def build_tensor_out_of_grid(grid):\n", " np_grid = np.asarray(grid) \n", " big_grid = np.zeros((1,2,9,9,9), dtype=np.float32)\n", " for i in range(9):\n", " big_grid[0,1,:,:,i] = (np_grid==i+1)\n", " big_grid[0,0,:,:,i] = ((np_grid!=i+1) & (np_grid!=0))\n", " return torch.from_numpy(big_grid).view(1,2,729)\n", " " ] }, { "cell_type": "code", "execution_count": 11, "id": "900e92ef", "metadata": {}, "outputs": [], "source": [ "\n", "grid_evil_2 = [\n", " [0,2,0, 1,0,0, 0,7,0],\n", " [0,0,0, 3,0,2, 0,0,0],\n", " [0,0,1, 0,8,0, 0,4,0],\n", "\n", " [9,0,0, 0,1,0, 0,0,7],\n", " [0,0,8, 0,6,0, 0,5,0],\n", " [0,0,0, 0,0,0, 0,0,0],\n", "\n", " [0,8,0, 0,3,6, 0,0,9],\n", " [0,0,5, 0,7,0, 0,0,6],\n", " [0,0,2, 0,0,0, 0,0,3],\n", "]" ] }, { "cell_type": "code", "execution_count": null, "id": "49de65cd", "metadata": {}, "outputs": [], "source": [ "\n", "new_X = build_tensor_out_of_grid(grid_evil_2)\n", "\n", "while new_X.sum()<729:\n", " try:\n", " new_X = model.predict(new_X, func_text_display=print)\n", " except TrialEveryPosException:\n", " print('''## The grid is super evil!\n", " please share it as A Discussion in the `Community` tab.\n", " Except if it is this one: https://www.telegraph.co.uk/news/science/science-news/9359579/Worlds-hardest-sudoku-can-you-crack-it.html''')\n", " is_valid, new_X = model.backtracking_predict(new_X, use_trial_error=True, func_text_display=print, func_tensor_display=display_as_dataframe)\n", " assert is_valid" ] }, { "cell_type": "code", "execution_count": null, "id": "dfad4144", "metadata": {}, "outputs": [], "source": [ "\n", "new_X = build_tensor_out_of_grid(board_hardest_sudoku)\n", "\n", "while new_X.sum()<729:\n", " try:\n", " new_X = model.predict(new_X, func_text_display=print)\n", " except TrialEveryPosException:\n", " print('''## The grid is super evil!\n", " please share it as A Discussion in the `Community` tab.\n", " Except if it is this one: https://www.telegraph.co.uk/news/science/science-news/9359579/Worlds-hardest-sudoku-can-you-crack-it.html''')\n", " is_valid, new_X = model.backtracking_predict(new_X, use_trial_error=True, func_text_display=print, func_tensor_display=lambda t: display(display_as_dataframe(t)))\n", " assert is_valid" ] }, { "cell_type": "code", "execution_count": null, "id": "403219d9", "metadata": {}, "outputs": [], "source": [ "display_as_dataframe(new_X)" ] }, { "cell_type": "code", "execution_count": null, "id": "57a3c0b0", "metadata": {}, "outputs": [], "source": [ "\n", "new_X = build_tensor_out_of_grid(board_hardest_sudoku)\n", "\n", "while new_X.sum()<729:\n", " try:\n", " new_X = model.predict(new_X)\n", " except TrialEveryPosException:\n", " print('''## The grid is super evil!\n", " please share it as A Discussion in the `Community` tab.\n", " Except if it is this one: https://www.telegraph.co.uk/news/science/science-news/9359579/Worlds-hardest-sudoku-can-you-crack-it.html''')\n", " is_valid, new_X = model.backtracking_predict(new_X)\n", " assert is_valid" ] }, { "cell_type": "code", "execution_count": null, "id": "cf6c2fea", "metadata": {}, "outputs": [], "source": [ "display_as_dataframe(new_X)" ] }, { "cell_type": "code", "execution_count": null, "id": "d2ec8a0a", "metadata": {}, "outputs": [], "source": [ "model.validate_grids(new_X)" ] }, { "cell_type": "code", "execution_count": null, "id": "4e72951f", "metadata": {}, "outputs": [], "source": [ "(new_X.view(1,2,9,9,9)[:,0].sum(dim=-1)>8).any(dim=1).any(dim=1)" ] }, { "cell_type": "code", "execution_count": null, "id": "27b21567", "metadata": {}, "outputs": [], "source": [ "(x.view(1,2,9,9,9)[:,0].sum(dim=-1)>8).any(dim=1).any(dim=1)" ] }, { "cell_type": "code", "execution_count": null, "id": "7e5258a9", "metadata": {}, "outputs": [], "source": [ "display(display_as_dataframe(new_X))" ] }, { "cell_type": "code", "execution_count": 20, "id": "4765e6ea", "metadata": {}, "outputs": [], "source": [ "# get_grid_number_soluce(grid_17)\n", "# it took 8min39s to return 1" ] }, { "cell_type": "code", "execution_count": 22, "id": "8e43ea77", "metadata": {}, "outputs": [], "source": [ "def solve_from_grid(grid, display_grids=True):\n", " test_x = build_tensor_out_of_grid(grid)\n", " i = 0\n", "\n", " # print(i, test_x.sum())\n", " if display_grids:\n", " display(display_as_dataframe(test_x))\n", " new_X = model.predict(test_x)\n", " sum_X= new_X.sum()\n", " # print(i, new_X.sum())\n", " if display_grids:\n", " display(display(display_as_dataframe(new_X)))\n", " while new_X.sum()<729:\n", " new_X = model.predict(new_X)\n", " new_sum_X=new_X.sum()\n", " assert new_sum_X>sum_X\n", " sum_X=new_sum_X\n", " # print(i, new_X.sum())\n", " i+=1\n", " if display_grids:\n", " display(display_as_dataframe(new_X))\n", " assert model.validate_grids(new_X)\n", " return new_X" ] }, { "cell_type": "code", "execution_count": null, "id": "bf112c8e", "metadata": {}, "outputs": [], "source": [ "finish_tensor = solve_from_grid(grid_evil_0, display_grids=False)\n", "finish_tensor.sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "91a83878", "metadata": {}, "outputs": [], "source": [ "finish_tensor = solve_from_grid(grid_17, display_grids=False)\n", "finish_tensor.sum()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9f4bac27", "metadata": {}, "outputs": [], "source": [ "cols = ['id', 'puzzle', 'solution', 'clues', 'difficulty']\n", "new_data = pd.read_csv(\"sudoku-3m.csv\", skiprows=val_set_size*10, nrows=val_set_size*40)\n", "new_data.columns=cols\n", "new_data.sample(10)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "bee3ffe3", "metadata": {}, "outputs": [], "source": [ "for puzzle_str in tqdm(new_data['puzzle']):\n", " grid = str_to_row_col_grid(puzzle_str)\n", " # display(grid)\n", " finish_tensor = solve_from_grid(grid, display_grids=False)\n", " assert (finish_tensor.sum().item()==729)" ] }, { "cell_type": "markdown", "id": "bc13a6dd", "metadata": {}, "source": [ "- last best index: 11247\n", "- 111640 kb interuption\n" ] }, { "cell_type": "code", "execution_count": null, "id": "68831c8c", "metadata": {}, "outputs": [], "source": [ "new_data.iloc[538]" ] }, { "cell_type": "code", "execution_count": null, "id": "76e2b814", "metadata": {}, "outputs": [], "source": [ "grid" ] }, { "cell_type": "code", "execution_count": null, "id": "3bd7518c", "metadata": {}, "outputs": [], "source": [ "\n", "grid = str_to_row_col_grid(new_data.iloc[538]['puzzle'])\n", "\n", "finish_tensor = solve_from_grid(grid, display_grids=True)\n", "finish_tensor.sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "66fa52a8", "metadata": {}, "outputs": [], "source": [ "display_as_dataframe(finish_tensor)" ] }, { "cell_type": "code", "execution_count": null, "id": "bf39d88f", "metadata": {}, "outputs": [], "source": [ "model.validate_grids(finish_tensor)\n", "# voir si ajustement marge suffisant! peut etre prevoir ajuster marge à la ~main avec une method?" ] }, { "cell_type": "code", "execution_count": null, "id": "cc8d84d2", "metadata": {}, "outputs": [], "source": [ "finish_tensor" ] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "papermill": { "default_parameters": {}, "duration": 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