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wassname
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@@ -47,6 +47,7 @@ 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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hv.archive.auto()
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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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@@ -77,38 +78,6 @@ from seq2seq_time.data.data import IMOSCurrentsVel, AppliancesEnergyPrediction,
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datasets = [IMOSCurrentsVel, BejingPM25, GasSensor, AppliancesEnergyPrediction, MetroInterstateTraffic, ]
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datasets
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# -
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for dataset in datasets:
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d = dataset(datasets_root)
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display(HTML(f"<h3>{dataset.__name__}</h3>"))
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print(d.__doc__)
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print(f'{len(d)} rows at freq{d.index.freq.freqstr}')
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print('columns_forecast', d.columns_forecast)
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print('columns_past', d.columns_past)
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print('columns_target', d.columns_target)
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print
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display(d.df)
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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.__name__}, n={len(d)}, freq={d.df.index.freq.freqstr}")
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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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@@ -138,6 +107,39 @@ def plot_batches_y(dataset, window_past=window_past, window_future=window_future
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# -
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for dataset in datasets:
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d = dataset(datasets_root)
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display(HTML(f"<h3>{dataset.__name__}</h3>"))
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print(d.__doc__)
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print(f'{len(d)} rows at freq{d.df.index.freq.freqstr}')
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print('columns_forecast', d.columns_forecast)
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print('columns_past', d.columns_past)
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print('columns_target', d.columns_target)
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print
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display(d.df)
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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.__name__}, n={len(d)}, freq={d.df.index.freq.freqstr}")
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display(p)
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# View train, test, val splits
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for dataset in datasets:
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ds_name = type(dataset).__name__
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@@ -145,36 +147,40 @@ for dataset in datasets:
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print(d)
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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(i)
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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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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(i)
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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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# 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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# +
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# # View input columns
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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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# -
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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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# View input columns
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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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hv.archive.export()
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hv.archive.last_export_status()
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