""" TLViz + PlotLy for interactive visualisations ---------------------------------------------------- In this example, we'll see how TLViz can be used together with PlotLy Express to produce rich, interactive visualisations of your extracted components with just a few lines of code. """ ############################################################################### # Imports and utilities # ^^^^^^^^^^^^^^^^^^^^^ import plotly.express as px from tensorly.decomposition import non_negative_parafac_hals import tlviz ############################################################################### # To fit CP models, we need to solve a non-convex optimization problem, possibly with local minima. It is therefore beneficial # to fit several models with the same number of components using many different random initialisations. def fit_many_nn_parafac(X, num_components, num_inits=5): return [non_negative_parafac_hals(X, num_components, n_iter_max=500, init="random",) for i in range(num_inits)] ############################################################################### # Loading the data # ^^^^^^^^^^^^^^^^ bike_data = tlviz.data.load_oslo_city_bike() bike_data ############################################################################### # We see that there are two metadata columns in the ``End station name`` axis: ``lat`` and ``lon``. # These contain the coordinates for each bike station. ############################################################################### # Fitting the model # ^^^^^^^^^^^^^^^^^ # # We fit five three-component PARAFAC2 model candidates to this data and select the one with the lowest error # (to see why a three-component model is a good choice, see the :ref:`split-half analysis example `). model_candidates = fit_many_nn_parafac(bike_data.data, 3, num_inits=5) selected_cp = tlviz.multimodel_evaluation.get_model_with_lowest_error(model_candidates, bike_data) ############################################################################### # Postprocessing # ^^^^^^^^^^^^^^ # # If we just postprocess, then we get a labelled CP tensor. That is, the factor matrices are transformed into Pandas # DataFrames with the same index as the main coordinates of the xarray DataArray. However, in this case, we also want # to include the additional coordinates (i.e. the latitude and longitude) from this DataArray as metadata-columns. # To include these as well, we set the ``include_metadata`` flag in ``postprocess`` to ``True``. cp_with_metadata = tlviz.postprocessing.postprocess(selected_cp, bike_data, include_metadata=True) weights, (end_station, year, month, day_of_week, hour) = cp_with_metadata ############################################################################### # Converting the data to a tidy format # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # PlotLy assumes that the data is *tidy*, but factor matrices are not. We have one column per factor matrix, which # makes it cumbersome to use with plotly. We therefore have a ``factor_matrix_to_tidy``-function in ``tlviz`` # which simply converts a factor matrix (with potential metadata) into a tidy format. See # ``tlviz.postprocessing.factor_matrix_to_tidy`` for more info. tidy_end_station_data = tlviz.postprocessing.factor_matrix_to_tidy(end_station, value_name="Popularity") ############################################################################### # Density map of the end station components # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Now, we use the ``density_mapbox`` function in PlotLy Express to create a density map of the End station components. # We can use the animation frame to get a nice slider for the different components. px.density_mapbox( tlviz.postprocessing.factor_matrix_to_tidy(end_station, value_name="Popularity"), lat="lat", lon="lon", z="Popularity", animation_frame="Component", hover_data=["End station name"], zoom=10.5, opacity=0.5, mapbox_style="carto-positron", width=600, ) ############################################################################### # We see that there are three distinct patterns. Component 0 is spread across most of the city, while component 1 # is focused on central areas. Component 2 is also spread widely, but has a strong signal at leisure-places like the # beach at Bygdøy. ############################################################################### # Plotting the various time-components # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ############################################################################### # Time of day # ~~~~~~~~~~~ # # First, we look at the time-of-day components to see if these reveal what kind of patterns the different components # represent. px.line( tlviz.postprocessing.factor_matrix_to_tidy(hour, value_name="Popularity"), x="Hour", y="Popularity", color="Component", width=600, ) ############################################################################### # We see that the first component shows a strong signal after 16:00, which is when a normal working day in Norway ends. # Likewise, the second component shows the strongest signal at 08:00, which is when a normal working day in Norway # starts. This indicates that the first two components represent getting to and from work. The third component shows # activity during midday, which combined with the map-plot above indicates that it represents leisurely activities. ############################################################################### # Weekday # ~~~~~~~ # Next, we look at the weekday components. tidy_day_of_week = tlviz.postprocessing.factor_matrix_to_tidy(day_of_week, value_name="Popularity") px.line( tidy_day_of_week, x="Day of week", y="Popularity", color="Component", width=600 ) ############################################################################### # Here, we see that the first two components are the most active on weekdays, which is when people mostly work. # The morning component barely shows any signal in the weekends at all and the leisure component has the strongest # signal on Saturdays. ############################################################################### # Month plots # ~~~~~~~~~~~ tidy_month = tlviz.postprocessing.factor_matrix_to_tidy(month, value_name="Popularity") px.line( tidy_month, x="Month", y="Popularity", color="Component", width=600, ) ############################################################################### # The month-mode components show reasonable patterns. People bike the most during summer, but the work-components # get a dip during July, when most people are on holidays ############################################################################### # Year # ~~~~ px.line( tlviz.postprocessing.factor_matrix_to_tidy(year, value_name="Popularity"), x="Year", y="Popularity", color="Component", width=600, ) ############################################################################### # We see that there is overall less biking in 2021 than in 2020. Perhaps people biked more early in the pandemic # when most public transport was closed? ############################################################################### # Important about including factor metadata # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # By including factor metadata, we add additional columns to the factor matrices. As a consequence, some of the # functions in ``tlviz`` will no longer work properly on CP tensors with metadata on the factor matrices. # It can therefore be beneficial to not include the metadata in the beginning (this is the default behaviour of # ``postprocess``) and only add the metadata in the end. Alternatively, it can be useful to have a normally # postprocessed CP tensor as well as a CP tensor with metadata.