mirror of
https://github.com/wassname/DeepTime.git
synced 2026-06-27 19:29:06 +08:00
wassname
195 lines
5.2 KiB
Python
195 lines
5.2 KiB
Python
# %%
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import os
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from os.path import join
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import math
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import logging
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from typing import Callable, Optional, Union, Dict, Tuple
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from matplotlib import pyplot as plt
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import gin
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from fire import Fire
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from torch import optim
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from torch import nn
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from experiments.base import Experiment
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from data.datasets import ForecastDataset
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from models import get_model
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from utils.checkpoint import Checkpoint
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from utils.ops import default_device, to_tensor
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from utils.losses import get_loss_fn
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from utils.metrics import calc_metrics
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from experiments.forecast import get_data
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gin.enter_interactive_mode()
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# %%
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gin.clear_config()
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# gin.parse_config(open("storage/experiments/Exchange/96M/repeat=0/config.gin"))
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gin.parse_config(open("storage/experiments/Exchange/96Mplus/repeat=0/config.gin"))
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# %%
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train_set, train_loader = get_data(flag='train', batch_size=16)
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# x, _, _, _ =train_set[0]
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# x = x * 1.0
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# %%
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# x -= x[0]
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# x /= x.std()
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# plt.plot(x)
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# # %%
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# %%
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model = get_model("deeptime2",
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dim_size=train_set.data_x.shape[1],
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datetime_feats=train_set.timestamps.shape[-1]).to(default_device())
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model.load_state_dict(torch.load('storage/experiments/Exchange/96Mplus/repeat=0/model.pth'))
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model = model.eval()
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# %%
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b = train_set[1]
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b = [bb[None, :] for bb in b]
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x, y, x_time, y_time = map(to_tensor, b)
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with torch.no_grad():
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forecast = model(x, x_time, y_time)
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# %%
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# %%
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plt.title('inception inr')
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import matplotlib.colors as mcolors
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colors = list(mcolors.BASE_COLORS.keys())
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l = x.shape[1]
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forecast2 = forecast[0].detach().cpu().numpy()
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x2 = x[0].cpu()
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y2 = y[0].cpu()
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i_past = list(range(l))
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i_future = list(range(l, l*2))
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for i in range(x.shape[-1]):
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plt.plot(range(l), x2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(range(l, l*2), y2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(range(l, l*2), forecast2[:, i], c=colors[i], linestyle='--')
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# %%
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gin.clear_config()
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gin.parse_config(open("storage/experiments/Exchange/96M/repeat=0/config.gin"))
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train_set, train_loader = get_data(flag='train', batch_size=16)
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model = get_model("deeptime",
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dim_size=train_set.data_x.shape[1],
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datetime_feats=train_set.timestamps.shape[-1]).to(default_device())
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model.load_state_dict(torch.load('storage/experiments/Exchange/96M/repeat=0/model.pth'))
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model = model.eval()
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b = train_set[1]
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b = [bb[None, :] for bb in b]
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x, y, x_time, y_time = map(to_tensor, b)
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with torch.no_grad():
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forecast = model(x, x_time, y_time)
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plt.title('mlp inr')
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import matplotlib.colors as mcolors
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colors = list(mcolors.BASE_COLORS.keys())
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l = x.shape[1]
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forecast2 = forecast[0].detach().cpu().numpy()
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x2 = x[0].cpu()
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y2 = y[0].cpu()
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i_past = list(range(l))
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i_future = list(range(l, l*2))
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for i in range(x.shape[-1]):
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plt.plot(range(l), x2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(range(l, l*2), y2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(range(l, l*2), forecast2[:, i], c=colors[i], linestyle='--')
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# %%
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gin.clear_config()
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gin.parse_config(open("storage/experiments/Exchange/96Mplus2/repeat=0/config.gin"))
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train_set, train_loader = get_data(flag='train', batch_size=16)
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model = get_model("deeptime2",
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dim_size=train_set.data_x.shape[1],
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datetime_feats=train_set.timestamps.shape[-1]).to(default_device())
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model.load_state_dict(torch.load('storage/experiments/Exchange/96Mplus2/repeat=0/model.pth'))
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model = model.eval()
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b = train_set[1]
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b = [bb[None, :] for bb in b]
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x, y, x_time, y_time = map(to_tensor, b)
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with torch.no_grad():
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forecast = model(x, x_time, y_time)
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plt.title('inception inr')
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import matplotlib.colors as mcolors
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colors = list(mcolors.BASE_COLORS.keys())
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l = x.shape[1]
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forecast2 = forecast[0].detach().cpu().numpy()
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x2 = x[0].cpu()
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y2 = y[0].cpu()
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l2 = y.shape[1]
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i_past = list(range(l))
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i_future = list(range(l, l+l2))
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for i in range(x.shape[-1]):
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plt.plot(i_past, x2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(i_future, y2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')
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# %%
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gin.clear_config()
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gin.parse_config(open("storage/experiments/Exchange/96M2/repeat=0/config.gin"))
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train_set, train_loader = get_data(flag='train', batch_size=16)
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model = get_model("deeptime",
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dim_size=train_set.data_x.shape[1],
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datetime_feats=train_set.timestamps.shape[-1]).to(default_device())
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model.load_state_dict(torch.load('storage/experiments/Exchange/96M2/repeat=0/model.pth'))
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model = model.eval()
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b = train_set[1]
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b = [bb[None, :] for bb in b]
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x, y, x_time, y_time = map(to_tensor, b)
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with torch.no_grad():
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forecast = model(x, x_time, y_time)
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plt.title('mlp inr2')
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import matplotlib.colors as mcolors
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colors = list(mcolors.BASE_COLORS.keys())
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l = x.shape[1]
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forecast2 = forecast[0].detach().cpu().numpy()
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x2 = x[0].cpu()
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y2 = y[0].cpu()
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l2 = y.shape[1]
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i_past = list(range(l))
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i_future = list(range(l, l+l2))
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for i in range(x.shape[-1]):
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plt.plot(i_past, x2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(i_future, y2[:, i], c=colors[i])
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for i in range(x.shape[-1]):
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plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')
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# %%
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