multidimensional data

[waad876]

hello
I am wondering if the ruptures library is working with multidimensional data or not ??

please look into the uploaded image: I used ruptures in my code, and it's showing the change point lines like what is shown .
so if its working correctly with multidimensional data can you tell me how ??

regards

[deepcharles]

It does but it depends on the cost function that you used. Can you share the code that produced this image? (Only the part that does the change point detection.)

[waad876]

I still didnt use cost functions its the basic code of fitting. Here is the code #my app @app.callback( dash.dependencies.Output(component_id='chart_5', component_property="figure"), [dash.dependencies.Input('btn-nclicks-5', 'n_clicks')], [dash.dependencies.State('drop_down_19', 'value')], [dash.dependencies.State('drop_down_20', 'value')], [dash.dependencies.State('drop_down_21', 'value')], [dash.dependencies.State('drop_down_22', 'value')], prevent_initial_call=True ) def update_values_app5(_, regions, indicators, start_date, end_date): fig = go.Figure() max_value_dict = dict() if type(regions) == str: regions = [regions] for region in regions: cd = global_df.loc[(global_df.loc[:, "country_region"] == region)] cd = cd.loc[((cd.loc[:, "date"] >= start_date) & (cd.loc[:, "date"] <= end_date))] #cd=cd.dropna() x = cd["date"].values print(type(x)) print(x.shape) for iid, indicator in enumerate(indicators): y = cd[indicator].values notna_mask = ~np.isnan(y) if notna_mask.sum() == 0: max_value = 1 else: max_value = max(abs(np.nanmax(y)), abs(np.nanmin(y))) if indicator not in max_value_dict: max_value_dict[indicator] = max_value else: max_value_dict[indicator] = max(max_value, max_value_dict[indicator]) if iid == 0: fig = fig.add_trace( go.Scatter(x=x, y=y, mode='lines', name=region + " " + indicator, line=dict(width=4))) #fig = px.line(df[mask], x="year", y="lifeExp", color='country') else: fig = fig.add_trace( go.Scatter(x=x, y=y, mode='lines', name=region + " " + indicator, yaxis='y{}'.format(iid + 2), line=dict(width=4))) print(type(y)) print(y.shape) fig.update_layout(title="", xaxis_title="Date", yaxis_title="", legend_title="Indicators", font=dict(family="Arial", size=20, color="dark blue")) y_axis_label_width = 0.08 fig.update_layout(xaxis=dict(domain=[y_axis_label_width * (len(indicators) - 1), 1.0])) for iid, indicator in enumerate(indicators): y_range = [-max_value_dict[indicator], max_value_dict[indicator]] if iid == 0: fig.update_layout({'yaxis': dict(title=indicator, constraintoward='center', position=0, range=y_range)}) else: fig.update_layout({'yaxis{}'.format(iid + 2): dict(title=indicator, overlaying="y", side="left", constraintoward='center', position=y_axis_label_width * iid, range=y_range)}) fig.update_layout(legend=dict(font=dict(family="Arial", size=30, color="black")), legend_title=dict(font=dict(family="Arial", size=35, color="blue"))) fig.update_layout(legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)) fig.update_layout(margin=dict(t=250)) fig.update_layout(xaxis_tickangle=0) fig.update_xaxes(showline=True, linewidth=2, linecolor='black') fig.update_yaxes(showline=True, linewidth=2, linecolor='black') fig.update_xaxes(zeroline=True, zerolinewidth=2, zerolinecolor='red') fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='red') print(y[7:]) algo = rpt.Pelt(model='l2').fit(y[7:]) result= algo.predict(pen=2000)#n_bkps=3,) print(result) fig.add_vline(x='2020-02-21',line_dash="dot") for r in result: fig.add_vline(x=x[r-1], line_width=3, line_dash="dash", line_color="green") return fig

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________________________________ From: Charles T. ***@***.***> Sent: Tuesday, November 15, 2022 6:38:25 PM To: deepcharles/ruptures ***@***.***> Cc: wadha ***@***.***>; Author ***@***.***> Subject: Re: [deepcharles/ruptures] multidimensional data (Issue #277) It does but it depends on the cost function that you used. Can you share the code that produced this image? (Only the part that does the change point detection.) — Reply to this email directly, view it on GitHub<#277 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AT2FJQ7LTDXTCYFSB76PS4DWION6DANCNFSM6AAAAAASA75UMA>. You are receiving this because you authored the thread.Message ID: ***@***.***>