mirror of
https://github.com/wassname/seq2seq-time.git
synced 2026-06-27 18:06:49 +08:00
wassname
169 lines
4.6 KiB
Python
169 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
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# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:light
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# text_representation:
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# extension: .py
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# format_name: light
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# format_version: '1.5'
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# jupytext_version: 1.6.0
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# kernelspec:
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# display_name: seq2seq-time
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# language: python
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# name: seq2seq-time
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# ---
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# OPTIONAL: Load the "autoreload" extension so that code can change. But blacklist large modules
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# %load_ext autoreload
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# %autoreload 2
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# %aimport -pandas
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# %aimport -torch
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# %aimport -numpy
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# %aimport -matplotlib
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# %aimport -dask
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# %aimport -tqdm
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# %matplotlib inline
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# +
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import xarray as xr
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from tqdm.auto import tqdm
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# -
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import warnings
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warnings.simplefilter('once')
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warnings.simplefilter(action='ignore', category=FutureWarning)
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warnings.simplefilter(action='ignore', category=DeprecationWarning)
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# +
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import holoviews as hv
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from holoviews import opts
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from holoviews.operation.datashader import datashade, dynspread
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hv.extension('bokeh', inline=True)
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from seq2seq_time.visualization.hv_ggplot import ggplot_theme
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hv.renderer('bokeh').theme = ggplot_theme
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# holoview datashader timeseries options
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# %opts RGB [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
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# %opts Curve [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
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# %opts Scatter [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
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# %opts Layout [width=800 height=200]
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# -
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# ## Parameters
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# +
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# # device = "cuda" if torch.cuda.is_available() else "cpu"
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# print(f'using {device}')
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window_past = 48*2
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window_future = 48*2
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batch_size = 128
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datasets_root = Path('../data/processed/')
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# -
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# ## Plot helpers
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# ## Datasets
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# +
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from seq2seq_time.data.data import IMOSCurrentsVel, AppliancesEnergyPrediction, BejingPM25, GasSensor, MetroInterstateTraffic
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datasets = [IMOSCurrentsVel, BejingPM25, GasSensor, AppliancesEnergyPrediction, MetroInterstateTraffic, ]
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datasets
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# -
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# View train, test, val splits
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l = hv.Layout()
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for dataset in datasets:
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d = dataset(datasets_root)
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p = dynspread(
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datashade(hv.Scatter(d.df_train[d.columns_target[0]]),
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cmap='red'))
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p *= dynspread(
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datashade(hv.Scatter(d.df_val[d.columns_target[0]]),
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cmap='green'))
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p *= dynspread(
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datashade(hv.Scatter(d.df_test[d.columns_target[0]]),
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cmap='blue'))
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p = p.opts(title=f"{dataset}")
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display(p)
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# +
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# plot a batch
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def plot_batch_y(ds, i):
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x_past, y_past, x_future, y_future = ds.get_rows(i)
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y = pd.concat([y_past, y_future])
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p = hv.Scatter(y)
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now = y_past.index[-1]
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p *= hv.VLine(now).relabel('now').opts(color='red')
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return p
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def plot_batches_y(dataset):
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ds_name = type(dataset).__name__
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opts=dict(width=200, height=100, xaxis=None, yaxis=None)
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ds_train, ds_val, ds_test = d.to_datasets(window_past=window_past,
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window_future=window_future)
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n = 4
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max_i = min(len(ds_train), len(ds_val), len(ds_test))
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ii = list(np.linspace(0, max_i-10, n-1).astype(int)) + [-1]
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l = hv.Layout()
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for i in ii:
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l += plot_batch_y(ds_train, i).opts(title=f'train {i}', **opts)
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l += plot_batch_y(ds_val, i).opts(title=f'val {i}', **opts)
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l += plot_batch_y(ds_test, i).opts(title=f'test {i}', **opts)
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return l.opts(shared_axes=False, toolbar='right', title=ds_name).cols(3)
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# +
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# View train, test, val splits
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for dataset in datasets:
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d = dataset(datasets_root)
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display(plot_batches_y(d))
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# +
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def plot_batch_x(ds, i):
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"""Plot input features"""
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x_past, y_past, x_future, y_future = ds.get_rows(10)
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x = pd.concat([x_past, x_future])
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p = hv.NdOverlay({
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col: hv.Curve(x[col]) for col in x.columns
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}, kdims='column')
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now = y_past.index[-1]
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p *= hv.VLine(now).relabel('now').opts(color='red')
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return p
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def plot_batches_x(d):
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"""Plot input features for multiple batch"""
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ds_train, ds_val, ds_test = d.to_datasets(window_past=window_past,
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window_future=window_future)
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l = plot_batch_x(ds_train, 10) + plot_batch_x(ds_val, 10) + plot_batch_x(ds_test, 10)
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l = l.cols(1).opts(shared_axes=False, title=f'{type(d).__name__}')
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return l
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# -
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# View train, test, val splits
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for dataset in datasets:
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d = dataset(datasets_root)
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display(plot_batches_x(d))
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