plotting in scratch.py and trying TCN

scratch.py

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