mirror of
https://github.com/wassname/DeepTime.git
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readme
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wassname
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e09086b",
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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"\n",
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"import os\n",
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"from os.path import join\n",
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"import math\n",
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"import logging\n",
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"from typing import Callable, Optional, Union, Dict, Tuple\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"import gin\n",
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"from fire import Fire\n",
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"import numpy as np\n",
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"import torch\n",
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"from torch.utils.data import DataLoader\n",
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"from torch import optim\n",
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"from torch import nn\n",
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"\n",
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"from experiments.base import Experiment\n",
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"from data.datasets import ForecastDataset\n",
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"from models import get_model\n",
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"from utils.checkpoint import Checkpoint\n",
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"from utils.ops import default_device, to_tensor\n",
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"from utils.losses import get_loss_fn\n",
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"from utils.metrics import calc_metrics\n",
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"\n",
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"from experiments.forecast import get_data\n",
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"gin.enter_interactive_mode()"
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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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"id": "516c4869",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"gin.clear_config()\n",
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"# gin.parse_config(open(\"storage/experiments/Exchange/96M/repeat=0/config.gin\"))\n",
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"gin.parse_config(open(\"storage/experiments/Exchange/96Mplus/repeat=0/config.gin\"))"
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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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"id": "3e056c6f",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_set, train_loader = get_data(flag='train', batch_size=16)\n",
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"# x, _, _, _ =train_set[0]\n",
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"# x = x * 1.0"
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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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"id": "07f093f2",
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"metadata": {
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"lines_to_next_cell": 2
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},
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"outputs": [],
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"source": [
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"# x -= x[0]\n",
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"# x /= x.std()\n",
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"# plt.plot(x)\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": null,
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"id": "7aeee9df",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = get_model(\"deeptime2\",\n",
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" dim_size=train_set.data_x.shape[1],\n",
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" datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
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"model.load_state_dict(torch.load('storage/experiments/Exchange/96Mplus/repeat=0/model.pth'))\n",
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"model = model.eval()"
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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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"id": "b6e8ca1f",
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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"b = train_set[1]\n",
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"b = [bb[None, :] for bb in b]\n",
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"x, y, x_time, y_time = map(to_tensor, b)\n",
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"with torch.no_grad():\n",
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" forecast = model(x, x_time, y_time)"
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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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"id": "638da5c2",
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"metadata": {
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"lines_to_next_cell": 2
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"source": []
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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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"id": "66d7f095",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.title('inception inr')\n",
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"import matplotlib.colors as mcolors\n",
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"colors = list(mcolors.BASE_COLORS.keys())\n",
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"l = x.shape[1]\n",
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"forecast2 = forecast[0].detach().cpu().numpy()\n",
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"x2 = x[0].cpu()\n",
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"y2 = y[0].cpu()\n",
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"i_past = list(range(l))\n",
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"i_future = list(range(l, l*2))\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(range(l), x2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(range(l, l*2), y2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(range(l, l*2), forecast2[:, i], c=colors[i], linestyle='--')"
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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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"id": "47bf8d52",
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"metadata": {
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"lines_to_next_cell": 0
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},
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"outputs": [],
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"source": [
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"gin.clear_config()\n",
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"gin.parse_config(open(\"storage/experiments/Exchange/96M/repeat=0/config.gin\"))\n",
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"\n",
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"train_set, train_loader = get_data(flag='train', batch_size=16)\n",
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"\n",
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"model = get_model(\"deeptime\",\n",
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" dim_size=train_set.data_x.shape[1],\n",
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" datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
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"model.load_state_dict(torch.load('storage/experiments/Exchange/96M/repeat=0/model.pth'))\n",
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"model = model.eval()\n",
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"\n",
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"\n",
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"b = train_set[1]\n",
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"b = [bb[None, :] for bb in b]\n",
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"x, y, x_time, y_time = map(to_tensor, b)\n",
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"with torch.no_grad():\n",
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" forecast = model(x, x_time, y_time)\n",
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"\n",
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"\n",
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"plt.title('mlp inr')\n",
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"import matplotlib.colors as mcolors\n",
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"colors = list(mcolors.BASE_COLORS.keys())\n",
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"l = x.shape[1]\n",
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"forecast2 = forecast[0].detach().cpu().numpy()\n",
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"x2 = x[0].cpu()\n",
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"y2 = y[0].cpu()\n",
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"i_past = list(range(l))\n",
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"i_future = list(range(l, l*2))\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(range(l), x2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(range(l, l*2), y2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(range(l, l*2), forecast2[:, i], c=colors[i], linestyle='--')"
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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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"id": "28eca411",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"gin.clear_config()\n",
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"gin.parse_config(open(\"storage/experiments/Exchange/96Mplus2/repeat=0/config.gin\"))\n",
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"\n",
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"train_set, train_loader = get_data(flag='train', batch_size=16)\n",
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"\n",
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"model = get_model(\"deeptime2\",\n",
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" dim_size=train_set.data_x.shape[1],\n",
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" datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
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"model.load_state_dict(torch.load('storage/experiments/Exchange/96Mplus2/repeat=0/model.pth'))\n",
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"model = model.eval()\n",
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"\n",
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"\n",
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"b = train_set[1]\n",
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"b = [bb[None, :] for bb in b]\n",
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"x, y, x_time, y_time = map(to_tensor, b)\n",
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"with torch.no_grad():\n",
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" forecast = model(x, x_time, y_time)\n",
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"\n",
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"\n",
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"plt.title('inception inr')\n",
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"import matplotlib.colors as mcolors\n",
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"colors = list(mcolors.BASE_COLORS.keys())\n",
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"l = x.shape[1]\n",
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"forecast2 = forecast[0].detach().cpu().numpy()\n",
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"x2 = x[0].cpu()\n",
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"y2 = y[0].cpu()\n",
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"l2 = y.shape[1]\n",
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"i_past = list(range(l))\n",
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"i_future = list(range(l, l+l2))\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(i_past, x2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(i_future, y2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')"
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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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"id": "6ca906be",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"gin.clear_config()\n",
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"gin.parse_config(open(\"storage/experiments/Exchange/96M2/repeat=0/config.gin\"))\n",
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"\n",
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"train_set, train_loader = get_data(flag='train', batch_size=16)\n",
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"\n",
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"model = get_model(\"deeptime\",\n",
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" dim_size=train_set.data_x.shape[1],\n",
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" datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
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"model.load_state_dict(torch.load('storage/experiments/Exchange/96M2/repeat=0/model.pth'))\n",
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"model = model.eval()\n",
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"\n",
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"\n",
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"b = train_set[1]\n",
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"b = [bb[None, :] for bb in b]\n",
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"x, y, x_time, y_time = map(to_tensor, b)\n",
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"with torch.no_grad():\n",
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" forecast = model(x, x_time, y_time)\n",
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"\n",
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"\n",
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"plt.title('mlp inr2')\n",
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"import matplotlib.colors as mcolors\n",
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"colors = list(mcolors.BASE_COLORS.keys())\n",
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"l = x.shape[1]\n",
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"forecast2 = forecast[0].detach().cpu().numpy()\n",
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"x2 = x[0].cpu()\n",
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"y2 = y[0].cpu()\n",
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"l2 = y.shape[1]\n",
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"i_past = list(range(l))\n",
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"i_future = list(range(l, l+l2))\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(i_past, x2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(i_future, y2[:, i], c=colors[i])\n",
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"for i in range(x.shape[-1]):\n",
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" plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')"
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]
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},
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{
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