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_valuesapp5(, 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

---

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 GitHubhttps://github.com/deepcharles/ruptures/issues/277#issuecomment-1315402852, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AT2FJQ7LTDXTCYFSB76PS4DWION6DANCNFSM6AAAAAASA75UMA. You are receiving this because you authored the thread.Message ID: @.***>