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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,251 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "import sys\n", | ||
| "import math\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "PROJECT_DIR = os.path.abspath(os.path.abspath('') + \"/../..\")\n", | ||
| "os.chdir(PROJECT_DIR)\n", | ||
| "\n", | ||
| "import numpy as np\n", | ||
| "from tqdm import tqdm\n", | ||
| "from basicts.data import TimeSeriesForecastingDataset\n", | ||
| "from basicts.utils import get_regular_settings\n", | ||
| "from basicts.scaler import ZScoreScaler\n", | ||
| "\n", | ||
| "\n", | ||
| "metric = \"cosine\" # metric used to calculate the similarity.\n", | ||
| "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", | ||
| "\n", | ||
| "DATA_NAME = \"METR-LA\"\n", | ||
| "DATA_NAME = \"ETTh1\"\n", | ||
| "BATCH_SIZE = 8\n", | ||
| "regular_settings = get_regular_settings(DATA_NAME)\n", | ||
| "INPUT_LEN = regular_settings['INPUT_LEN'] # Length of input sequence\n", | ||
| "OUTPUT_LEN = regular_settings['OUTPUT_LEN'] # Length of output sequence\n", | ||
| "TRAIN_VAL_TEST_RATIO = regular_settings['TRAIN_VAL_TEST_RATIO'] # Train/Validation/Test split ratios\n", | ||
| "RESCALE = regular_settings['RESCALE'] # Whether to rescale the data\n", | ||
| "NULL_VAL = regular_settings['NULL_VAL'] # Null value in the data\n", | ||
| "NORM_EACH_CHANNEL = regular_settings['NORM_EACH_CHANNEL'] # Whether to normalize each channel\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## utilities" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# similarity computation\n", | ||
| "def cosine_similarity(x, y):\n", | ||
| " # denominator\n", | ||
| " l2_x = torch.norm(x, dim=2, p=2) + 1e-7\n", | ||
| " l2_y = torch.norm(y, dim=2, p=2) + 1e-7\n", | ||
| " l2_n = torch.matmul(l2_x.unsqueeze(dim=2), l2_y.unsqueeze(dim=2).transpose(1, 2))\n", | ||
| " # numerator\n", | ||
| " l2_d = torch.matmul(x, y.transpose(1, 2))\n", | ||
| " return l2_d / l2_n\n", | ||
| "\n", | ||
| "def get_similarity_matrix(data, metric):\n", | ||
| " if metric == \"cosine\":\n", | ||
| " sim = cosine_similarity(data, data)\n", | ||
| " elif metric == \"mse\":\n", | ||
| " sim = torch.cdist(data, data, p=2)\n", | ||
| " elif metric == \"mae\":\n", | ||
| " sim = torch.cdist(data, data, p=1)\n", | ||
| " else:\n", | ||
| " raise NotImplementedError\n", | ||
| " return sim" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "dataset_param = {\n", | ||
| " 'dataset_name': DATA_NAME,\n", | ||
| " 'train_val_test_ratio': TRAIN_VAL_TEST_RATIO,\n", | ||
| " 'input_len': INPUT_LEN,\n", | ||
| " 'output_len': OUTPUT_LEN,\n", | ||
| "}\n", | ||
| "# get dataloader\n", | ||
| "dataset = TimeSeriesForecastingDataset(**dataset_param, mode='train')\n", | ||
| "# the whole training data\n", | ||
| "dataloader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=3)\n", | ||
| "\n", | ||
| "scaler_param = {\n", | ||
| " 'dataset_name': DATA_NAME,\n", | ||
| " 'train_ratio': TRAIN_VAL_TEST_RATIO[0],\n", | ||
| " 'norm_each_channel': NORM_EACH_CHANNEL,\n", | ||
| " 'rescale': RESCALE,\n", | ||
| "}\n", | ||
| "scaler = ZScoreScaler(**scaler_param)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Generate Similarity Matrix" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "100%|██████████| 997/997 [00:02<00:00, 412.47it/s]\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# get similarity matrices\n", | ||
| "\n", | ||
| "# inference pipeline for a given dataloader\n", | ||
| "history_adjs_all = []\n", | ||
| "future_adjs_all = []\n", | ||
| "def inference(dataloader):\n", | ||
| " for batch in tqdm(dataloader):\n", | ||
| " future_data, history_data = batch['target'], batch['inputs']\n", | ||
| " future_data = scaler.transform(future_data)\n", | ||
| " history_data = scaler.transform(history_data)\n", | ||
| " history_data = history_data[..., 0].transpose(1, 2) # batch_size, num_nodes, history_seq_len\n", | ||
| " future_data = future_data[..., 0].transpose(1, 2) # batch_size, num_nodes, future_seq_len\n", | ||
| " history_adjs = get_similarity_matrix(history_data, metric) # batch_size, num_nodes, num_nodes\n", | ||
| " future_adjs = get_similarity_matrix(future_data, metric) # batch_size, num_nodes, num_nodes\n", | ||
| " history_adjs_all.append(history_adjs)\n", | ||
| " future_adjs_all.append(future_adjs)\n", | ||
| "# get similarity matrices\n", | ||
| "# for mode in [\"valid\"]:\n", | ||
| "for mode in [\"train\"]:\n", | ||
| " inference(dataloader)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "torch.Size([7969, 7, 7])\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# get spatial indistinguishability ratio\n", | ||
| "history_similarity = torch.cat(history_adjs_all, dim=0).detach().cpu() # num_samples, num_modes, num_nodes\n", | ||
| "future_similarity = torch.cat(future_adjs_all, dim=0).detach().cpu() # num_samples, num_modes, num_nodes\n", | ||
| "L, N, N = future_similarity.shape\n", | ||
| "print(future_similarity.shape)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Get Spatial Indistinguishability Ratio" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "e_u = 0.9\n", | ||
| "e_l = 0.4\n", | ||
| "\n", | ||
| "history_similarity_filtered = torch.where(history_similarity > e_u, torch.ones_like(history_similarity), torch.zeros_like(history_similarity))\n", | ||
| "future_similarity_filtered = torch.where(future_similarity < e_l, torch.ones_like(future_similarity), torch.zeros_like(future_similarity))\n", | ||
| "overlap = history_similarity_filtered * future_similarity_filtered\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "tensor(3.8568)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# overlap ratio\n", | ||
| "overlap_ratio = overlap.sum() / (L * N * N)\n", | ||
| "print(overlap_ratio * 1000)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "tensor(15.7748)\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# indistinguishability ratio\n", | ||
| "indistinguishability_ratio = overlap.sum() / history_similarity_filtered.sum()\n", | ||
| "print(indistinguishability_ratio * 1000)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "BasicTS", | ||
| "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": "3.11.11" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |