mirror of
https://github.com/Cian-H/Aconity_ML_Expt1.git
synced 2025-12-22 20:51:58 +00:00
1230 lines
28 KiB
Plaintext
1230 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"import torch\n",
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"from torch import nn\n",
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"from torch.utils.data import DataLoader\n",
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"from torchmetrics import MeanSquaredError\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from tqdm.auto import tqdm\n",
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"\n",
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"from expt1 import X, y, collate_fn, device\n",
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"from expt3 import model\n",
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"\n",
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"pd.options.plotting.backend = \"plotly\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.eval()\n",
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"\n",
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"checkpoints = tuple(Path(\"checkpoints\").glob(\"*.ckpt\"))\n",
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"\n",
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"test = DataLoader(\n",
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" list(zip(X.values(), y.values())),\n",
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" collate_fn=collate_fn,\n",
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" shuffle=False,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "cf4c681d9d5b4bb5aa1aef4f544bf2ef",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Evaluating checkpoints: 0%| | 0/10 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "462ba8b04a7b4e25a108bf31b9d358b0",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Evaluating samples: 0%| | 0/81 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Evaluate all checkpoints from expt3 on all samples\n",
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"sample_df = pd.DataFrame(columns=[\"sample\"], data=X.keys()).set_index(\"sample\")\n",
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"overall_df = []\n",
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"with torch.no_grad():\n",
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" for checkpoint in tqdm(checkpoints, desc=\"Evaluating checkpoints\"):\n",
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" model.load_state_dict(torch.load(checkpoint)[\"state_dict\"])\n",
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" y_ground = []\n",
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" y_eval = []\n",
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" for sample_X0, sample_X1, sample_y in tqdm(\n",
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" test, desc=\"Evaluating samples\", leave=False\n",
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" ):\n",
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" y_ground.append(sample_y.to(\"cpu\"))\n",
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" y_eval.append(\n",
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" model(sample_X0, sample_X1).to(\"cpu\")\n",
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" ) # Move to CPU because otherwise will run out of VRAM\n",
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" torch.cuda.empty_cache() # empty cache to ensure maximum VRAM available\n",
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" y_eval, y_ground = torch.stack(y_eval), torch.stack(y_ground)\n",
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" sample_df[checkpoint.name] = [\n",
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" nn.MeanSquaredError()(ye, yg).item() for ye, yg in zip(y_eval, y_ground)\n",
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" ]\n",
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" overall_df.append(\n",
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" (\n",
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" model.__name__,\n",
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" checkpoint.name,\n",
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" MeanSquaredError()(y_eval, y_ground).item(),\n",
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" )\n",
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" )\n",
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" overall_df = pd.DataFrame(columns=[\"model\", \"checkpoint\", \"loss\"], data=overall_df)\n",
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" torch.cuda.empty_cache()\n",
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"\n",
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"overall_df.to_csv(\"expt3_overall.csv\")\n",
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"sample_df.to_csv(\"expt3_sample.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# checkpoint = \"checkpoints/checkpoint-epoch=12-val_loss=0.00.ckpt\"\n",
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"# model.load_state_dict(torch.load(checkpoint)[\"state_dict\"])\n",
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"y_ground = []\n",
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"y_eval = []\n",
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"with torch.no_grad():\n",
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" for sample_X0, sample_X1, sample_y in tqdm(\n",
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" test, desc=\"Evaluating samples\", leave=False\n",
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" ):\n",
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" y_ground.append(sample_y.to(\"cpu\"))\n",
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" y_eval.append(\n",
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" model(sample_X0, sample_X1).to(\"cpu\")\n",
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" ) # Move to CPU because otherwise will run out of VRAM\n",
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" torch.cuda.empty_cache() # empty cache to ensure maximum VRAM available\n",
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" y_eval, y_ground = torch.stack(y_eval), torch.stack(y_ground)\n",
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" sample_accuracy = [\n",
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" MeanSquaredError()(ye, yg).item() for ye, yg in zip(y_eval, y_ground)\n",
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" ]\n",
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" overall_accuracy = MeanSquaredError()(y_eval, y_ground).item()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame()\n",
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"df[\"sample\"] = [\"Overall\"] + list(str(i) for i in range(len(sample_accuracy)))\n",
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"df[\"mean_squared_error\"] = [overall_accuracy] + sample_accuracy\n",
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"df[\"y_ground_Ni\"] = [None] + [y[0][0].item() for y in y_ground]\n",
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"df[\"y_ground_Ti\"] = [None] + [y[0][1].item() for y in y_ground]\n",
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"df[\"y_eval_Ni\"] = [None] + [y[0][0].item() for y in y_eval]\n",
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"df[\"y_eval_Ti\"] = [None] + [y[0][1].item() for y in y_eval]"
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]
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},
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{
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"cell_type": "code",
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}
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