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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"\n",
"torch.cuda.is_available()\n",
"os.environ[\"WANDB_ENABLED\"] = \"false\"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(f\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"from models.Tiffusion import tiffusion\n",
"# from models.CSDI import tiffusion\n",
"\n",
"model = tiffusion.Tiffusion(\n",
" seq_length=365,\n",
" feature_size=3,\n",
" n_layer_enc=6,\n",
" n_layer_dec=4,\n",
" d_model=128,\n",
" timesteps=500,\n",
" sampling_timesteps=200,\n",
" loss_type='l1',\n",
" beta_schedule='cosine',\n",
" n_heads=8,\n",
" mlp_hidden_times=4,\n",
" attn_pd=0.0,\n",
" resid_pd=0.0,\n",
" kernel_size=1,\n",
" padding_size=0,\n",
" control_signal=[]\n",
").to(device)\n",
"\n",
"model.load_state_dict(torch.load(\"./weight/checkpoint-10.pt\", map_location='cpu', weights_only=True)[\"model\"])\n",
"# model.load_state_dict(torch.load(\"../../../data/CSDI/ckpt_baseline_365/checkpoint-10.pt\", map_location='cpu', weights_only=True)[\"model\"])\n",
"\n",
"\n",
"coef = 1.0e-2\n",
"stepsize = 5.0e-2\n",
"sampling_steps = 100 # 这个可以调整 100-500都行 快慢和准度 tradeoff\n",
"seq_length = 365\n",
"feature_dim = 3\n",
"print(f\"seq_length: {seq_length}, feature_dim: {feature_dim}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sampling"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"anchor_value = [\n",
" # (time, feature_id, y-value, confidence)\n",
" (0, 0, 0.04, 1.0),\n",
" (2, 0, 0.58, 1.0),\n",
" # (6, 0, 0.27, 0.5),\n",
" # (10, 0, 0.04, 1.0),\n",
" # (12, 0, 0.58, 0.001),\n",
" # (16, 0, 0.27, 0.5),\n",
" # (20, 0, 0.04, 1.0),\n",
" # (22, 0, 0.58, 0.001),\n",
" # (26, 0, 0.27, 0.5),\n",
" # (30, 0, 0.04, 1.0),\n",
" # (32, 0, 0.58, 0.001),\n",
" # (36, 0, 0.27, 0.5),\n",
" # (40, 0, 0.04, 1.0),\n",
" # (42, 0, 0.58, 0.001),\n",
" # (46, 0, 0.27, 0.5),\n",
" # (50, 0, 0.04, 1.0),\n",
" # (52, 0, 0.58, 0.001),\n",
" # (56, 0, 0.27, 0.5),\n",
" # (60, 0, 0.04, 1.0),\n",
" # (62, 0, 0.58, 0.001),\n",
" # (66, 0, 0.27, 0.5),\n",
"]\n",
"\n",
"observed_points = torch.zeros((seq_length, feature_dim)).to(device)\n",
"observed_mask = torch.zeros((seq_length, feature_dim)).to(device)\n",
"\n",
"for time, feature_id, y_value, confidence in anchor_value:\n",
" observed_points[time, feature_id] = y_value\n",
" observed_mask[time, feature_id] = confidence\n",
"\n",
"auc = -10\n",
"auc_weight = 10.0\n",
"with torch.no_grad():\n",
" results = model.predict_weighted_points(\n",
" observed_points, # (seq_length, feature_dim)\n",
" observed_mask, # (seq_length, feature_dim)\n",
" coef, # fixed\n",
" stepsize, # fixed\n",
" sampling_steps, # fixed\n",
" # model_control_signal=model_control_signal,\n",
" gradient_control_signal={\n",
" \"auc\": auc, \"auc_weight\": auc_weight,\n",
" },\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(results[:,0], label=\"Predicted Feature 0\")\n",
"for time, feature_id, y_value, confidence in anchor_value:\n",
" plt.scatter(time, y_value, c='r')\n",
"plt.show()\n",
"plt.plot(results[:,1], label=\"Predicted Feature 1\")\n",
"plt.show()\n",
"plt.plot(results[:,2], label=\"Predicted Feature 2\")\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "rag",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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