Example Documentation
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+========================
+Example Usage of Distill
+========================
+
+In this example, we run through a simulated user experiment using UserALE data generated within an instantiation of
+Superset. This data reflects four simulated user sessions in which the user performs three tasks within the Video Game
+Sales example dashboard:
+
+#. Filter the Video Games Sales by Wii, Racing, and Nintendo.
+#. Find Mario Kart in the list of games.
+#. Determine the difference in global sales between the 3DS game Nintendogs + cats and Wii Sports.
+
+A screenshot of this Superset dashboard can be seen below:
+
+.. image:: ./images/Superset_Dashboard.png
+ :width: 700
+
+The data of these four sessions is captured in a JSON file entitled `task_example.json`. In the following example, we
+will:
+
+* Show how to use Distill's Segmentation package to create useful ``Segments`` of data.
+* Visualize ``Segment`` objects using timeline/gantt and digraph visualizations.
+* Compare each of these user sessions through the investigation of edit distances.
+
+**Note: The data utilized in this example was not data collected in any user study. Rather this data is simulated
+through developer interactions with the Superset dashboard.**
+
+Imports
+-------
+The first step in this example is to import all of the packages that we need. We do this with the code below:
+
+.. code:: python
+
+ import datetime
+ import distill
+ import json
+ import networkx as nx
+ import os
+ import pandas as pd
+ import plotly.express as px
+ import re
+
+Processing and Segmentation
+---------------------------
+Now that we have imported all of the necessary packages, we can begin to process and segment the data. This can be done
+by creating ``Segment``/``Segments`` objects. These objects will help us to visualize the data and understand processes
+that the users took to perform each of the three tasks.
+
+Processing the JSON File
+************************
+The ``setup`` function is used to convert a JSON file into the required format for segmentation. It also allows us to
+assert the date format that we want to use for our analysis (i.e., integer or ``datetime``). Below we define this
+function:
+
+.. code:: python
+
+ def setup(file, date_type):
+ with open(file) as json_file:
+ raw_data = json.load(json_file)
+
+ data = {}
+ for log in raw_data:
+ data[distill.getUUID(log)] = log
+
+ # Convert clientTime to specified type
+ for uid in data:
+ log = data[uid]
+ client_time = log['clientTime']
+ if date_type == "integer":
+ log['clientTime'] = distill.epoch_to_datetime(client_time)
+ elif date_type == "datetime":
+ log['clientTime'] = pd.to_datetime(client_time, unit='ms', origin='unix')
+
+ # Sort
+ sorted_data = sorted(data.items(), key=lambda kv: kv[1]['clientTime'])
+ sorted_dict = dict(sorted_data)
+
+ return (sorted_data, sorted_dict)
+
+Using this function, we can process the UserALE data and create ``Segment`` objects that represent each of the four user
+sessions. This is shown below through the utilization of the ``generate_collapsing_window_segments`` function.
+
+.. code:: python
+
+ data_many_session = setup("./data/task_example.json", "datetime")
+ sorted_dict = data_many_session[1]
+
+ # Create segments based on sessionID
+ segments = distill.Segments()
+ session_ids = sorted(distill.find_meta_values('sessionID', sorted_dict), key=lambda sessionID: sessionID)
+ for session_id in session_ids:
+ segments.append_segments(distill.generate_collapsing_window_segments(sorted_dict, 'sessionID', [session_id], session_id))
+
+ # Improve readability of Segment names
+ for index in range(len(segments)):
+ segments[index].segment_name = "Session" + str(index)
+
+Below we list out each of the created ``Segment`` objects along with their number of logs and start times.
+
+.. code:: console
+
+ Session0 Length: 427 Start Time: 2022-05-16 21:25:57.935000
+ Session1 Length: 236 Start Time: 2022-05-16 21:27:38.283000
+ Session2 Length: 332 Start Time: 2022-05-16 21:28:59.774000
+ Session3 Length: 219 Start Time: 2022-05-16 21:30:25.633000
+
+Further Segmentation of Sessions
+********************************
+Now that there are ``Segment`` objects that represent each session, let's write the ``Segment`` objects. This will allow
+us to further segment these session segments to analyze the activity of the user during each of these sessions. This
+can be done with the following code:
+
+.. code:: python
+
+ segment_names = [segment.segment_name for segment in segments]
+ start_end_vals = [segment.start_end_val for segment in segments]
+ segment_map = distill.write_segment(sorted_dict, segment_names, start_end_vals)
+
+We can now generate ``Segments`` objects within each of those session segments that represent user interactions on two
+different elements of the Superset dashboard.
+
+The first element involves user interactions with the filter window that filters the list of video games (shown in the
+screenshot below). The element in the path that represents these interactions is "div.filter-container css-ffe7is."
+
+.. image:: ./images/Video_Game_Filter.png
+ :width: 500
+
+The second element involves interactions with the actual list of video games (shown in the screenshot below) represented
+by the "div#chart-id-110.superset-chart-table" path element.
+
+.. image:: ./images/Games_List.png
+ :width: 500
+
+By creating ``Segment`` objects that show user interaction on these two windows, we can get an understanding of how the
+user is using the Superset dashboard to complete the three tasks. We create these ``Segment`` objects with the
+following code:
+
+.. code:: python
+
+ session_0_segments = distill.generate_collapsing_window_segments(segment_map['Session0'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+ session_1_segments = distill.generate_collapsing_window_segments(segment_map['Session1'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+ session_2_segments = distill.generate_collapsing_window_segments(segment_map['Session2'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+ session_3_segments = distill.generate_collapsing_window_segments(segment_map['Session3'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+
+ session_0_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session0'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+ session_1_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session1'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+ session_2_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session2'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+ session_3_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session3'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+
+Now, we append each of those newly generated ``Segments`` objects to the overarching segments variable. This will create
+one large ``Segments`` object that contains all ``Segment`` objects from all sessions.
+
+.. code:: python
+
+ segments.append_segments(session_0_segments)
+ segments.append_segments(session_1_segments)
+ segments.append_segments(session_2_segments)
+ segments.append_segments(session_3_segments)
+
+Visualization of ``Segment`` Objects
+------------------------------------
+To understand these ``Segment`` objects better, we can visualize them. First, we will visualize them using Plotly's
+timeline function, then we will analyze them by creating DiGraphs.
+
+Visualization with Plotly's Timeline
+************************************
+The following code can be used to define a function that will display a Plotly timeline of each of the ``Segment``
+objects:
+
+.. code:: python
+
+ def display_segments(segments):
+ segment_list = []
+ for segment in segments:
+ if not isinstance(segment.start_end_val[0], datetime.datetime) or not isinstance(segment.start_end_val[1], datetime.datetime):
+ new_segment = distill.Segment()
+ new_segment.segment_name = segment.segment_name
+ new_segment.num_logs = segment.num_logs
+ new_segment.uids = segment.uids
+ new_segment.generate_field_name = segment.generate_field_name
+ new_segment.generate_matched_values = segment.generate_matched_values
+ new_segment.segment_type = segment.segment_type
+ new_segment.start_end_val = (pd.to_datetime(segment.start_end_val[0], unit='ms', origin='unix'), pd.to_datetime(segment.start_end_val[1], unit='ms', origin='unix'))
+ segment_list.append(new_segment)
+ else:
+ segment_list.append(segment)
+ new_segments = distill.Segments(segments=segment_list)
+ distill.export_segments("./test.csv",new_segments)
+ df = pd.read_csv("./test.csv")
+ fig = px.timeline(df, x_start="Start Time", x_end="End Time", y="Segment Name", color="Number of Logs")
+ fig.update_yaxes(autorange="reversed")
+ os.remove("./test.csv")
+ fig.show()
+
+Using this code, we can visualize the ``Segment`` objects we created.
+
+.. code:: python
+
+ display_segments(segments)
+
+This will produce the following timeline graph:
+
+.. image:: ./images/Timeline_Graph.png
+ :width: 700
+
+This graph shows the number of logs in each ``Segment`` while also showing the length of time each ``Segment``
+represents. We can also begin to understand some of the interactions that each user had with the dashboard by
+analyzing the ``Segment`` objects that exist within each overarching session ``Segment``.
+
+Visualizing User Workflows with DiGraphs
+****************************************
+Another way we can visualize user workflows is through the creation and analysis of DiGraphs. The function below
+(``draw_digraph``) draws a DiGraph based on the passed in ``Segments`` object. These graphs are colored in such a way
+that interactions with the video game filter are colored in green while the interactions with the list of video games
+are colored in blue.
+
+.. code:: python
+
+ def draw_digraph(segments):
+ nodes = sorted(segments.get_segment_list(), key=lambda segment: segment.start_end_val[0])
+ edges = distill.pairwiseSeq(segments.get_segment_list())
+
+ # Set coloring of graph based on element in Superset dashboard
+ color_map = []
+ for segment in segments:
+ if re.match("Game_Filter\S*", segment.segment_name):
+ color_map.append('green')
+ else:
+ color_map.append('blue')
+
+ graph = distill.createDiGraph(nodes, edges)
+ nx.draw(graph, node_color=color_map)
+ return graph
+
+We can now use this function to create DiGraphs of each of the user sessions.
+
+**Graph 0 - Session 0**
+
+.. code:: python
+
+ G0 = draw_digraph(session_0_segments)
+
+.. image:: ./images/Graph_0.png
+ :width: 400
+
+**Graph 1 - Session 1**
+
+.. code:: python
+
+ G1 = draw_digraph(session_1_segments)
+
+.. image:: ./images/Graph_1.png
+ :width: 400
+
+**Graph 2 - Session 2**
+
+.. code:: python
+
+ G2 = draw_digraph(session_2_segments)
+
+.. image:: ./images/Graph_2.png
+ :width: 400
+
+**Graph 3 - Session 3**
+
+.. code:: python
+
+ G3 = draw_digraph(session_3_segments)
+
+.. image:: ./images/Graph_3.png
+ :width: 400
+
+By analyzing these graphs, we can understand the general interactions that users had with the two elements of the
+Superset dashboard. For instance, in each of these graphs, the user starts by filtering the dashboard. Based on the
+tasks that the user is meant to perform, this makes a lot of sense since the most logical way to filter the dashboard is
+through the filtering window. However, these graphs begin to differ in the amount of interactions that the user has with
+the actual list of video games. While users always follow the workflow: filter --> game list --> filter --> game list,
+there are occasions when the user interacts more with the game list than others.
+
+Measuring Similarity with Edit Distance
+---------------------------------------
+One way to understand the differences between the previously generated DiGraphs is to look at their edit distance. Edit
+distance is a metric that measures how many distortions are necessary to turn one graph into another, thus measuring
+similarity. For instance, taking the edit distance between a graph and itself yields an edit distance of 0, since the
+graphs are exactly the same. We can show this using NetworkX's ``graph_edit_distance`` to calculate the edit distance.
+
+**Input**
+
+.. code:: python
+
+ nx.graph_edit_distance(G0, G0) # 0.0
+
+Let's now calculate the edit distances between each graph to calculate an average. Note, however, that when we try to
+calculate some edit distances, we run into a bit of an issue. Since edit distance is a computationally complex problem,
+it can take a long time and require large amounts of computational resources to find an exact answer. To simplify this
+problem, we can use NetworkX's ``optimize_graph_edit_distance`` function which will create an approximation of the graph
+edit distance. For the following calculations, we use the function required depending on the length of time
+``graph_edit_distance`` takes in each circumstance.
+
+**Input: G0, G1**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G0, G1)) # 32.0
+
+**Input: G0, G2**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G0, G2)) # 34.0
+
+**Input: G0, G3**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G0, G3)) # 38.0
+
+**Input: G1, G2**
+
+.. code:: python
+
+ nx.graph_edit_distance(G1, G2) # 2.0
+
+**Input: G1, G3**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G1, G3)) # 18.0
+
+**Input: G2, G3**
+
+.. code:: python
+
+ nx.graph_edit_distance(G2, G3) # 4.0
+
+Using these outputs we can now calculate the average edit distance with the following calculation:
+
+.. code:: python
+
+ (32.0 + 34.0 + 38.0 + 2.0 + 18.0 + 4.0)/6 # 21.33
+
+This shows that the average edit distance between each of these session DiGraphs is 21.33.
+
+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="Graph_Index.html">Graph</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Segmentation.html">Segmentation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Utilities.html">Utilities</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Example Usage of Distill</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#imports">Imports</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#processing-and-segmentation">Processing and Segmentation</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#processing-the-json-file">Processing the JSON File</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#further-segmentation-of-sessions">Further Segmentation of Sessions</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#visualization-of-segment-objects">Visualization of <code class="docutils literal notranslate"><span class="pre">Segment</span></code> Objects</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#visualization-with-plotly-s-timeline">Visualization with Plotly’s Timeline</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#visualizing-user-workflows-with-digraphs">Visualizing User Workflows with DiGraphs</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#measuring-similarity-with-edit-distance">Measuring Similarity with Edit Distance</a></li>
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+
+ <section id="example-usage-of-distill">
+<h1>Example Usage of Distill<a class="headerlink" href="#example-usage-of-distill" title="Permalink to this headline"></a></h1>
+<p>In this example, we run through a simulated user experiment using UserALE data generated within an instantiation of
+Superset. This data reflects four simulated user sessions in which the user performs three tasks within the Video Game
+Sales example dashboard:</p>
+<ol class="arabic simple">
+<li><p>Filter the Video Games Sales by Wii, Racing, and Nintendo.</p></li>
+<li><p>Find Mario Kart in the list of games.</p></li>
+<li><p>Determine the difference in global sales between the 3DS game Nintendogs + cats and Wii Sports.</p></li>
+</ol>
+<p>A screenshot of this Superset dashboard can be seen below:</p>
+<a class="reference internal image-reference" href="_images/Superset_Dashboard.png"><img alt="_images/Superset_Dashboard.png" src="_images/Superset_Dashboard.png" style="width: 700px;" /></a>
+<p>The data of these four sessions is captured in a JSON file entitled <cite>task_example.json</cite>. In the following example, we
+will:</p>
+<ul class="simple">
+<li><p>Show how to use Distill’s Segmentation package to create useful <code class="docutils literal notranslate"><span class="pre">Segments</span></code> of data.</p></li>
+<li><p>Visualize <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects using timeline/gantt and digraph visualizations.</p></li>
+<li><p>Compare each of these user sessions through the investigation of edit distances.</p></li>
+</ul>
+<p><strong>Note: The data utilized in this example was not data collected in any user study. Rather this data is simulated
+through developer interactions with the Superset dashboard.</strong></p>
+<section id="imports">
+<h2>Imports<a class="headerlink" href="#imports" title="Permalink to this headline"></a></h2>
+<p>The first step in this example is to import all of the packages that we need. We do this with the code below:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span>
+<span class="kn">import</span> <span class="nn">distill</span>
+<span class="kn">import</span> <span class="nn">json</span>
+<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
+<span class="kn">import</span> <span class="nn">re</span>
+</pre></div>
+</div>
+</section>
+<section id="processing-and-segmentation">
+<h2>Processing and Segmentation<a class="headerlink" href="#processing-and-segmentation" title="Permalink to this headline"></a></h2>
+<p>Now that we have imported all of the necessary packages, we can begin to process and segment the data. This can be done
+by creating <code class="docutils literal notranslate"><span class="pre">Segment</span></code>/<code class="docutils literal notranslate"><span class="pre">Segments</span></code> objects. These objects will help us to visualize the data and understand processes
+that the users took to perform each of the three tasks.</p>
+<section id="processing-the-json-file">
+<h3>Processing the JSON File<a class="headerlink" href="#processing-the-json-file" title="Permalink to this headline"></a></h3>
+<p>The <code class="docutils literal notranslate"><span class="pre">setup</span></code> function is used to convert a JSON file into the required format for segmentation. It also allows us to
+assert the date format that we want to use for our analysis (i.e., integer or <code class="docutils literal notranslate"><span class="pre">datetime</span></code>). Below we define this
+function:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">setup</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">date_type</span><span class="p">):</span>
+ <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
+ <span class="n">raw_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span>
+
+ <span class="n">data</span> <span class="o">=</span> <span class="p">{}</span>
+ <span class="k">for</span> <span class="n">log</span> <span class="ow">in</span> <span class="n">raw_data</span><span class="p">:</span>
+ <span class="n">data</span><span class="p">[</span><span class="n">distill</span><span class="o">.</span><span class="n">getUUID</span><span class="p">(</span><span class="n">log</span><span class="p">)]</span> <span class="o">=</span> <span class="n">log</span>
+
+ <span class="c1"># Convert clientTime to specified type</span>
+ <span class="k">for</span> <span class="n">uid</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+ <span class="n">log</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">uid</span><span class="p">]</span>
+ <span class="n">client_time</span> <span class="o">=</span> <span class="n">log</span><span class="p">[</span><span class="s1">'clientTime'</span><span class="p">]</span>
+ <span class="k">if</span> <span class="n">date_type</span> <span class="o">==</span> <span class="s2">"integer"</span><span class="p">:</span>
+ <span class="n">log</span><span class="p">[</span><span class="s1">'clientTime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">epoch_to_datetime</span><span class="p">(</span><span class="n">client_time</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="n">date_type</span> <span class="o">==</span> <span class="s2">"datetime"</span><span class="p">:</span>
+ <span class="n">log</span><span class="p">[</span><span class="s1">'clientTime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">client_time</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">'ms'</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">'unix'</span><span class="p">)</span>
+
+ <span class="c1"># Sort</span>
+ <span class="n">sorted_data</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="s1">'clientTime'</span><span class="p">])</span>
+ <span class="n">sorted_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">sorted_data</span><span class="p">)</span>
+
+ <span class="k">return</span> <span class="p">(</span><span class="n">sorted_data</span><span class="p">,</span> <span class="n">sorted_dict</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Using this function, we can process the UserALE data and create <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects that represent each of the four user
+sessions. This is shown below through the utilization of the <code class="docutils literal notranslate"><span class="pre">generate_collapsing_window_segments</span></code> function.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">data_many_session</span> <span class="o">=</span> <span class="n">setup</span><span class="p">(</span><span class="s2">"./data/task_example.json"</span><span class="p">,</span> <span class="s2">"datetime"</span><span class="p">)</span>
+<span class="n">sorted_dict</span> <span class="o">=</span> <span class="n">data_many_session</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># Create segments based on sessionID</span>
+<span class="n">segments</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">Segments</span><span class="p">()</span>
+<span class="n">session_ids</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">distill</span><span class="o">.</span><span class="n">find_meta_values</span><span class="p">(</span><span class="s1">'sessionID'</span><span class="p">,</span> <span class="n">sorted_dict</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">sessionID</span><span class="p">:</span> <span class="n">sessionID</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">session_id</span> <span class="ow">in</span> <span class="n">session_ids</span><span class="p">:</span>
+ <span class="n">segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">sorted_dict</span><span class="p">,</span> <span class="s1">'sessionID'</span><span class="p">,</span> <span class="p">[</span><span class="n">session_id</span><span class="p">],</span> <span class="n">session_id</span><span class="p">))</span>
+
+<span class="c1"># Improve readability of Segment names</span>
+<span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">segments</span><span class="p">)):</span>
+ <span class="n">segments</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">segment_name</span> <span class="o">=</span> <span class="s2">"Session"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Below we list out each of the created <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects along with their number of logs and start times.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">Session0 Length: 427 Start Time: 2022-05-16 21:25:57.935000</span>
+<span class="go">Session1 Length: 236 Start Time: 2022-05-16 21:27:38.283000</span>
+<span class="go">Session2 Length: 332 Start Time: 2022-05-16 21:28:59.774000</span>
+<span class="go">Session3 Length: 219 Start Time: 2022-05-16 21:30:25.633000</span>
+</pre></div>
+</div>
+</section>
+<section id="further-segmentation-of-sessions">
+<h3>Further Segmentation of Sessions<a class="headerlink" href="#further-segmentation-of-sessions" title="Permalink to this headline"></a></h3>
+<p>Now that there are <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects that represent each session, let’s write the <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects. This will allow
+us to further segment these session segments to analyze the activity of the user during each of these sessions. This
+can be done with the following code:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">segment_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">segment</span><span class="o">.</span><span class="n">segment_name</span> <span class="k">for</span> <span class="n">segment</span> <span class="ow">in</span> <span class="n">segments</span><span class="p">]</span>
+<span class="n">start_end_vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">segment</span><span class="o">.</span><span class="n">start_end_val</span> <span class="k">for</span> <span class="n">segment</span> <span class="ow">in</span> <span class="n">segments</span><span class="p">]</span>
+<span class="n">segment_map</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">write_segment</span><span class="p">(</span><span class="n">sorted_dict</span><span class="p">,</span> <span class="n">segment_names</span><span class="p">,</span> <span class="n">start_end_vals</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can now generate <code class="docutils literal notranslate"><span class="pre">Segments</span></code> objects within each of those session segments that represent user interactions on two
+different elements of the Superset dashboard.</p>
+<p>The first element involves user interactions with the filter window that filters the list of video games (shown in the
+screenshot below). The element in the path that represents these interactions is “div.filter-container css-ffe7is.”</p>
+<a class="reference internal image-reference" href="_images/Video_Game_Filter.png"><img alt="_images/Video_Game_Filter.png" src="_images/Video_Game_Filter.png" style="width: 500px;" /></a>
+<p>The second element involves interactions with the actual list of video games (shown in the screenshot below) represented
+by the “div#chart-id-110.superset-chart-table” path element.</p>
+<a class="reference internal image-reference" href="_images/Games_List.png"><img alt="_images/Games_List.png" src="_images/Games_List.png" style="width: 500px;" /></a>
+<p>By creating <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects that show user interaction on these two windows, we can get an understanding of how the
+user is using the Superset dashboard to complete the three tasks. We create these <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects with the
+following code:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">session_0_segments</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session0'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div.filter-container css-ffe7is'</span><span class="p">],</span> <span class="s2">"Game_Filter"</span><span class="p">)</span>
+<span class="n">session_1_segments</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session1'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div.filter-container css-ffe7is'</span><span class="p">],</span> <span class="s2">"Game_Filter"</span><span class="p">)</span>
+<span class="n">session_2_segments</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session2'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div.filter-container css-ffe7is'</span><span class="p">],</span> <span class="s2">"Game_Filter"</span><span class="p">)</span>
+<span class="n">session_3_segments</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session3'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div.filter-container css-ffe7is'</span><span class="p">],</span> <span class="s2">"Game_Filter"</span><span class="p">)</span>
+
+<span class="n">session_0_segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session0'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div#chart-id-110.superset-chart-table'</span><span class="p">],</span> <span class="s2">"Games"</span><span class="p">))</span>
+<span class="n">session_1_segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session1'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div#chart-id-110.superset-chart-table'</span><span class="p">],</span> <span class="s2">"Games"</span><span class="p">))</span>
+<span class="n">session_2_segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session2'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div#chart-id-110.superset-chart-table'</span><span class="p">],</span> <span class="s2">"Games"</span><span class="p">))</span>
+<span class="n">session_3_segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">distill</span><span class="o">.</span><span class="n">generate_collapsing_window_segments</span><span class="p">(</span><span class="n">segment_map</span><span class="p">[</span><span class="s1">'Session3'</span><span class="p">],</span> <span class="s1">'path'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'div#chart-id-110.superset-chart-table'</span><span class="p">],</span> <span class="s2">"Games"</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>Now, we append each of those newly generated <code class="docutils literal notranslate"><span class="pre">Segments</span></code> objects to the overarching segments variable. This will create
+one large <code class="docutils literal notranslate"><span class="pre">Segments</span></code> object that contains all <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects from all sessions.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">session_0_segments</span><span class="p">)</span>
+<span class="n">segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">session_1_segments</span><span class="p">)</span>
+<span class="n">segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">session_2_segments</span><span class="p">)</span>
+<span class="n">segments</span><span class="o">.</span><span class="n">append_segments</span><span class="p">(</span><span class="n">session_3_segments</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="visualization-of-segment-objects">
+<h2>Visualization of <code class="docutils literal notranslate"><span class="pre">Segment</span></code> Objects<a class="headerlink" href="#visualization-of-segment-objects" title="Permalink to this headline"></a></h2>
+<p>To understand these <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects better, we can visualize them. First, we will visualize them using Plotly’s
+timeline function, then we will analyze them by creating DiGraphs.</p>
+<section id="visualization-with-plotly-s-timeline">
+<h3>Visualization with Plotly’s Timeline<a class="headerlink" href="#visualization-with-plotly-s-timeline" title="Permalink to this headline"></a></h3>
+<p>The following code can be used to define a function that will display a Plotly timeline of each of the <code class="docutils literal notranslate"><span class="pre">Segment</span></code>
+objects:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">display_segments</span><span class="p">(</span><span class="n">segments</span><span class="p">):</span>
+ <span class="n">segment_list</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">segment</span> <span class="ow">in</span> <span class="n">segments</span><span class="p">:</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">segment</span><span class="o">.</span><span class="n">start_end_val</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">segment</span><span class="o">.</span><span class="n">start_end_val</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="p">):</span>
+ <span class="n">new_segment</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">Segment</span><span class="p">()</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">segment_name</span> <span class="o">=</span> <span class="n">segment</span><span class="o">.</span><span class="n">segment_name</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">num_logs</span> <span class="o">=</span> <span class="n">segment</span><span class="o">.</span><span class="n">num_logs</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">uids</span> <span class="o">=</span> <span class="n">segment</span><span class="o">.</span><span class="n">uids</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">generate_field_name</span> <span class="o">=</span> <span class="n">segment</span><span class="o">.</span><span class="n">generate_field_name</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">generate_matched_values</span> <span class="o">=</span> <span class="n">segment</span><span class="o">.</span><span class="n">generate_matched_values</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">segment_type</span> <span class="o">=</span> <span class="n">segment</span><span class="o">.</span><span class="n">segment_type</span>
+ <span class="n">new_segment</span><span class="o">.</span><span class="n">start_end_val</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">segment</span><span class="o">.</span><span class="n">start_end_val</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">unit</span><span class="o">=</span><span class="s1">'ms'</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">'unix'</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">segment</span><span class="o">.</span><span class="n">start_end_val</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">unit</span><span class="o">=</span><span class="s1">'ms'</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">'unix'</span><span class="p">))</span>
+ <span class="n">segment_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_segment</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">segment_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">segment</span><span class="p">)</span>
+ <span class="n">new_segments</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">Segments</span><span class="p">(</span><span class="n">segments</span><span class="o">=</span><span class="n">segment_list</span><span class="p">)</span>
+ <span class="n">distill</span><span class="o">.</span><span class="n">export_segments</span><span class="p">(</span><span class="s2">"./test.csv"</span><span class="p">,</span><span class="n">new_segments</span><span class="p">)</span>
+ <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"./test.csv"</span><span class="p">)</span>
+ <span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">timeline</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">x_start</span><span class="o">=</span><span class="s2">"Start Time"</span><span class="p">,</span> <span class="n">x_end</span><span class="o">=</span><span class="s2">"End Time"</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"Segment Name"</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">"Number of Logs"</span><span class="p">)</span>
+ <span class="n">fig</span><span class="o">.</span><span class="n">update_yaxes</span><span class="p">(</span><span class="n">autorange</span><span class="o">=</span><span class="s2">"reversed"</span><span class="p">)</span>
+ <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">"./test.csv"</span><span class="p">)</span>
+ <span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Using this code, we can visualize the <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects we created.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">display_segments</span><span class="p">(</span><span class="n">segments</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This will produce the following timeline graph:</p>
+<a class="reference internal image-reference" href="_images/Timeline_Graph.png"><img alt="_images/Timeline_Graph.png" src="_images/Timeline_Graph.png" style="width: 700px;" /></a>
+<p>This graph shows the number of logs in each <code class="docutils literal notranslate"><span class="pre">Segment</span></code> while also showing the length of time each <code class="docutils literal notranslate"><span class="pre">Segment</span></code>
+represents. We can also begin to understand some of the interactions that each user had with the dashboard by
+analyzing the <code class="docutils literal notranslate"><span class="pre">Segment</span></code> objects that exist within each overarching session <code class="docutils literal notranslate"><span class="pre">Segment</span></code>.</p>
+</section>
+<section id="visualizing-user-workflows-with-digraphs">
+<h3>Visualizing User Workflows with DiGraphs<a class="headerlink" href="#visualizing-user-workflows-with-digraphs" title="Permalink to this headline"></a></h3>
+<p>Another way we can visualize user workflows is through the creation and analysis of DiGraphs. The function below
+(<code class="docutils literal notranslate"><span class="pre">draw_digraph</span></code>) draws a DiGraph based on the passed in <code class="docutils literal notranslate"><span class="pre">Segments</span></code> object. These graphs are colored in such a way
+that interactions with the video game filter are colored in green while the interactions with the list of video games
+are colored in blue.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">draw_digraph</span><span class="p">(</span><span class="n">segments</span><span class="p">):</span>
+ <span class="n">nodes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">segments</span><span class="o">.</span><span class="n">get_segment_list</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">segment</span><span class="p">:</span> <span class="n">segment</span><span class="o">.</span><span class="n">start_end_val</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+ <span class="n">edges</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">pairwiseSeq</span><span class="p">(</span><span class="n">segments</span><span class="o">.</span><span class="n">get_segment_list</span><span class="p">())</span>
+
+ <span class="c1"># Set coloring of graph based on element in Superset dashboard</span>
+ <span class="n">color_map</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">segment</span> <span class="ow">in</span> <span class="n">segments</span><span class="p">:</span>
+ <span class="k">if</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="s2">"Game_Filter\S*"</span><span class="p">,</span> <span class="n">segment</span><span class="o">.</span><span class="n">segment_name</span><span class="p">):</span>
+ <span class="n">color_map</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'green'</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">color_map</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'blue'</span><span class="p">)</span>
+
+ <span class="n">graph</span> <span class="o">=</span> <span class="n">distill</span><span class="o">.</span><span class="n">createDiGraph</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">edges</span><span class="p">)</span>
+ <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="n">color_map</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">graph</span>
+</pre></div>
+</div>
+<p>We can now use this function to create DiGraphs of each of the user sessions.</p>
+<p><strong>Graph 0 - Session 0</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">G0</span> <span class="o">=</span> <span class="n">draw_digraph</span><span class="p">(</span><span class="n">session_0_segments</span><span class="p">)</span>
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="_images/Graph_0.png"><img alt="_images/Graph_0.png" src="_images/Graph_0.png" style="width: 400px;" /></a>
+<p><strong>Graph 1 - Session 1</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">G1</span> <span class="o">=</span> <span class="n">draw_digraph</span><span class="p">(</span><span class="n">session_1_segments</span><span class="p">)</span>
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="_images/Graph_1.png"><img alt="_images/Graph_1.png" src="_images/Graph_1.png" style="width: 400px;" /></a>
+<p><strong>Graph 2 - Session 2</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">G2</span> <span class="o">=</span> <span class="n">draw_digraph</span><span class="p">(</span><span class="n">session_2_segments</span><span class="p">)</span>
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="_images/Graph_2.png"><img alt="_images/Graph_2.png" src="_images/Graph_2.png" style="width: 400px;" /></a>
+<p><strong>Graph 3 - Session 3</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">G3</span> <span class="o">=</span> <span class="n">draw_digraph</span><span class="p">(</span><span class="n">session_3_segments</span><span class="p">)</span>
+</pre></div>
+</div>
+<a class="reference internal image-reference" href="_images/Graph_3.png"><img alt="_images/Graph_3.png" src="_images/Graph_3.png" style="width: 400px;" /></a>
+<p>By analyzing these graphs, we can understand the general interactions that users had with the two elements of the
+Superset dashboard. For instance, in each of these graphs, the user starts by filtering the dashboard. Based on the
+tasks that the user is meant to perform, this makes a lot of sense since the most logical way to filter the dashboard is
+through the filtering window. However, these graphs begin to differ in the amount of interactions that the user has with
+the actual list of video games. While users always follow the workflow: filter –> game list –> filter –> game list,
+there are occasions when the user interacts more with the game list than others.</p>
+</section>
+</section>
+<section id="measuring-similarity-with-edit-distance">
+<h2>Measuring Similarity with Edit Distance<a class="headerlink" href="#measuring-similarity-with-edit-distance" title="Permalink to this headline"></a></h2>
+<p>One way to understand the differences between the previously generated DiGraphs is to look at their edit distance. Edit
+distance is a metric that measures how many distortions are necessary to turn one graph into another, thus measuring
+similarity. For instance, taking the edit distance between a graph and itself yields an edit distance of 0, since the
+graphs are exactly the same. We can show this using NetworkX’s <code class="docutils literal notranslate"><span class="pre">graph_edit_distance</span></code> to calculate the edit distance.</p>
+<p><strong>Input</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">nx</span><span class="o">.</span><span class="n">graph_edit_distance</span><span class="p">(</span><span class="n">G0</span><span class="p">,</span> <span class="n">G0</span><span class="p">)</span> <span class="c1"># 0.0</span>
+</pre></div>
+</div>
+<p>Let’s now calculate the edit distances between each graph to calculate an average. Note, however, that when we try to
+calculate some edit distances, we run into a bit of an issue. Since edit distance is a computationally complex problem,
+it can take a long time and require large amounts of computational resources to find an exact answer. To simplify this
+problem, we can use NetworkX’s <code class="docutils literal notranslate"><span class="pre">optimize_graph_edit_distance</span></code> function which will create an approximation of the graph
+edit distance. For the following calculations, we use the function required depending on the length of time
+<code class="docutils literal notranslate"><span class="pre">graph_edit_distance</span></code> takes in each circumstance.</p>
+<p><strong>Input: G0, G1</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">next</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">optimize_graph_edit_distance</span><span class="p">(</span><span class="n">G0</span><span class="p">,</span> <span class="n">G1</span><span class="p">))</span> <span class="c1"># 32.0</span>
+</pre></div>
+</div>
+<p><strong>Input: G0, G2</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">next</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">optimize_graph_edit_distance</span><span class="p">(</span><span class="n">G0</span><span class="p">,</span> <span class="n">G2</span><span class="p">))</span> <span class="c1"># 34.0</span>
+</pre></div>
+</div>
+<p><strong>Input: G0, G3</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">next</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">optimize_graph_edit_distance</span><span class="p">(</span><span class="n">G0</span><span class="p">,</span> <span class="n">G3</span><span class="p">))</span> <span class="c1"># 38.0</span>
+</pre></div>
+</div>
+<p><strong>Input: G1, G2</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">nx</span><span class="o">.</span><span class="n">graph_edit_distance</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G2</span><span class="p">)</span> <span class="c1"># 2.0</span>
+</pre></div>
+</div>
+<p><strong>Input: G1, G3</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">next</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">optimize_graph_edit_distance</span><span class="p">(</span><span class="n">G1</span><span class="p">,</span> <span class="n">G3</span><span class="p">))</span> <span class="c1"># 18.0</span>
+</pre></div>
+</div>
+<p><strong>Input: G2, G3</strong></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">nx</span><span class="o">.</span><span class="n">graph_edit_distance</span><span class="p">(</span><span class="n">G2</span><span class="p">,</span> <span class="n">G3</span><span class="p">)</span> <span class="c1"># 4.0</span>
+</pre></div>
+</div>
+<p>Using these outputs we can now calculate the average edit distance with the following calculation:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mf">32.0</span> <span class="o">+</span> <span class="mf">34.0</span> <span class="o">+</span> <span class="mf">38.0</span> <span class="o">+</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="mf">18.0</span> <span class="o">+</span> <span class="mf">4.0</span><span class="p">)</span><span class="o">/</span><span class="mi">6</span> <span class="c1"># 21.33</span>
+</pre></div>
+</div>
+<p>This shows that the average edit distance between each of these session DiGraphs is 21.33.</p>
+</section>
+</section>
+
+
+ </div>
+ </div>
+ <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+ <a href="Utility_Functions.html" class="btn btn-neutral float-left" title="Utility Functions" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+ </div>
+
+ <hr/>
+
+ <div role="contentinfo">
+ <p>© Copyright 2022, UMD ARLIS.</p>
+ </div>
+
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@@ -0,0 +1,346 @@
+========================
+Example Usage of Distill
+========================
+
+In this example, we run through a simulated user experiment using UserALE data generated within an instantiation of
+Superset. This data reflects four simulated user sessions in which the user performs three tasks within the Video Game
+Sales example dashboard:
+
+#. Filter the Video Games Sales by Wii, Racing, and Nintendo.
+#. Find Mario Kart in the list of games.
+#. Determine the difference in global sales between the 3DS game Nintendogs + cats and Wii Sports.
+
+A screenshot of this Superset dashboard can be seen below:
+
+.. image:: ./images/Superset_Dashboard.png
+ :width: 700
+
+The data of these four sessions is captured in a JSON file entitled `task_example.json`. In the following example, we
+will:
+
+* Show how to use Distill's Segmentation package to create useful ``Segments`` of data.
+* Visualize ``Segment`` objects using timeline/gantt and digraph visualizations.
+* Compare each of these user sessions through the investigation of edit distances.
+
+**Note: The data utilized in this example was not data collected in any user study. Rather this data is simulated
+through developer interactions with the Superset dashboard.**
+
+Imports
+-------
+The first step in this example is to import all of the packages that we need. We do this with the code below:
+
+.. code:: python
+
+ import datetime
+ import distill
+ import json
+ import networkx as nx
+ import os
+ import pandas as pd
+ import plotly.express as px
+ import re
+
+Processing and Segmentation
+---------------------------
+Now that we have imported all of the necessary packages, we can begin to process and segment the data. This can be done
+by creating ``Segment``/``Segments`` objects. These objects will help us to visualize the data and understand processes
+that the users took to perform each of the three tasks.
+
+Processing the JSON File
+************************
+The ``setup`` function is used to convert a JSON file into the required format for segmentation. It also allows us to
+assert the date format that we want to use for our analysis (i.e., integer or ``datetime``). Below we define this
+function:
+
+.. code:: python
+
+ def setup(file, date_type):
+ with open(file) as json_file:
+ raw_data = json.load(json_file)
+
+ data = {}
+ for log in raw_data:
+ data[distill.getUUID(log)] = log
+
+ # Convert clientTime to specified type
+ for uid in data:
+ log = data[uid]
+ client_time = log['clientTime']
+ if date_type == "integer":
+ log['clientTime'] = distill.epoch_to_datetime(client_time)
+ elif date_type == "datetime":
+ log['clientTime'] = pd.to_datetime(client_time, unit='ms', origin='unix')
+
+ # Sort
+ sorted_data = sorted(data.items(), key=lambda kv: kv[1]['clientTime'])
+ sorted_dict = dict(sorted_data)
+
+ return (sorted_data, sorted_dict)
+
+Using this function, we can process the UserALE data and create ``Segment`` objects that represent each of the four user
+sessions. This is shown below through the utilization of the ``generate_collapsing_window_segments`` function.
+
+.. code:: python
+
+ data_many_session = setup("./data/task_example.json", "datetime")
+ sorted_dict = data_many_session[1]
+
+ # Create segments based on sessionID
+ segments = distill.Segments()
+ session_ids = sorted(distill.find_meta_values('sessionID', sorted_dict), key=lambda sessionID: sessionID)
+ for session_id in session_ids:
+ segments.append_segments(distill.generate_collapsing_window_segments(sorted_dict, 'sessionID', [session_id], session_id))
+
+ # Improve readability of Segment names
+ for index in range(len(segments)):
+ segments[index].segment_name = "Session" + str(index)
+
+Below we list out each of the created ``Segment`` objects along with their number of logs and start times.
+
+.. code:: console
+
+ Session0 Length: 427 Start Time: 2022-05-16 21:25:57.935000
+ Session1 Length: 236 Start Time: 2022-05-16 21:27:38.283000
+ Session2 Length: 332 Start Time: 2022-05-16 21:28:59.774000
+ Session3 Length: 219 Start Time: 2022-05-16 21:30:25.633000
+
+Further Segmentation of Sessions
+********************************
+Now that there are ``Segment`` objects that represent each session, let's write the ``Segment`` objects. This will allow
+us to further segment these session segments to analyze the activity of the user during each of these sessions. This
+can be done with the following code:
+
+.. code:: python
+
+ segment_names = [segment.segment_name for segment in segments]
+ start_end_vals = [segment.start_end_val for segment in segments]
+ segment_map = distill.write_segment(sorted_dict, segment_names, start_end_vals)
+
+We can now generate ``Segments`` objects within each of those session segments that represent user interactions on two
+different elements of the Superset dashboard.
+
+The first element involves user interactions with the filter window that filters the list of video games (shown in the
+screenshot below). The element in the path that represents these interactions is "div.filter-container css-ffe7is."
+
+.. image:: ./images/Video_Game_Filter.png
+ :width: 500
+
+The second element involves interactions with the actual list of video games (shown in the screenshot below) represented
+by the "div#chart-id-110.superset-chart-table" path element.
+
+.. image:: ./images/Games_List.png
+ :width: 500
+
+By creating ``Segment`` objects that show user interaction on these two windows, we can get an understanding of how the
+user is using the Superset dashboard to complete the three tasks. We create these ``Segment`` objects with the
+following code:
+
+.. code:: python
+
+ session_0_segments = distill.generate_collapsing_window_segments(segment_map['Session0'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+ session_1_segments = distill.generate_collapsing_window_segments(segment_map['Session1'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+ session_2_segments = distill.generate_collapsing_window_segments(segment_map['Session2'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+ session_3_segments = distill.generate_collapsing_window_segments(segment_map['Session3'], 'path', ['div.filter-container css-ffe7is'], "Game_Filter")
+
+ session_0_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session0'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+ session_1_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session1'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+ session_2_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session2'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+ session_3_segments.append_segments(distill.generate_collapsing_window_segments(segment_map['Session3'], 'path', ['div#chart-id-110.superset-chart-table'], "Games"))
+
+Now, we append each of those newly generated ``Segments`` objects to the overarching segments variable. This will create
+one large ``Segments`` object that contains all ``Segment`` objects from all sessions.
+
+.. code:: python
+
+ segments.append_segments(session_0_segments)
+ segments.append_segments(session_1_segments)
+ segments.append_segments(session_2_segments)
+ segments.append_segments(session_3_segments)
+
+Visualization of ``Segment`` Objects
+------------------------------------
+To understand these ``Segment`` objects better, we can visualize them. First, we will visualize them using Plotly's
+timeline function, then we will analyze them by creating DiGraphs.
+
+Visualization with Plotly's Timeline
+************************************
+The following code can be used to define a function that will display a Plotly timeline of each of the ``Segment``
+objects:
+
+.. code:: python
+
+ def display_segments(segments):
+ segment_list = []
+ for segment in segments:
+ if not isinstance(segment.start_end_val[0], datetime.datetime) or not isinstance(segment.start_end_val[1], datetime.datetime):
+ new_segment = distill.Segment()
+ new_segment.segment_name = segment.segment_name
+ new_segment.num_logs = segment.num_logs
+ new_segment.uids = segment.uids
+ new_segment.generate_field_name = segment.generate_field_name
+ new_segment.generate_matched_values = segment.generate_matched_values
+ new_segment.segment_type = segment.segment_type
+ new_segment.start_end_val = (pd.to_datetime(segment.start_end_val[0], unit='ms', origin='unix'), pd.to_datetime(segment.start_end_val[1], unit='ms', origin='unix'))
+ segment_list.append(new_segment)
+ else:
+ segment_list.append(segment)
+ new_segments = distill.Segments(segments=segment_list)
+ distill.export_segments("./test.csv",new_segments)
+ df = pd.read_csv("./test.csv")
+ fig = px.timeline(df, x_start="Start Time", x_end="End Time", y="Segment Name", color="Number of Logs")
+ fig.update_yaxes(autorange="reversed")
+ os.remove("./test.csv")
+ fig.show()
+
+Using this code, we can visualize the ``Segment`` objects we created.
+
+.. code:: python
+
+ display_segments(segments)
+
+This will produce the following timeline graph:
+
+.. image:: ./images/Timeline_Graph.png
+ :width: 700
+
+This graph shows the number of logs in each ``Segment`` while also showing the length of time each ``Segment``
+represents. We can also begin to understand some of the interactions that each user had with the dashboard by
+analyzing the ``Segment`` objects that exist within each overarching session ``Segment``.
+
+Visualizing User Workflows with DiGraphs
+****************************************
+Another way we can visualize user workflows is through the creation and analysis of DiGraphs. The function below
+(``draw_digraph``) draws a DiGraph based on the passed in ``Segments`` object. These graphs are colored in such a way
+that interactions with the video game filter are colored in green while the interactions with the list of video games
+are colored in blue.
+
+.. code:: python
+
+ def draw_digraph(segments):
+ nodes = sorted(segments.get_segment_list(), key=lambda segment: segment.start_end_val[0])
+ edges = distill.pairwiseSeq(segments.get_segment_list())
+
+ # Set coloring of graph based on element in Superset dashboard
+ color_map = []
+ for segment in segments:
+ if re.match("Game_Filter\S*", segment.segment_name):
+ color_map.append('green')
+ else:
+ color_map.append('blue')
+
+ graph = distill.createDiGraph(nodes, edges)
+ nx.draw(graph, node_color=color_map)
+ return graph
+
+We can now use this function to create DiGraphs of each of the user sessions.
+
+**Graph 0 - Session 0**
+
+.. code:: python
+
+ G0 = draw_digraph(session_0_segments)
+
+.. image:: ./images/Graph_0.png
+ :width: 400
+
+**Graph 1 - Session 1**
+
+.. code:: python
+
+ G1 = draw_digraph(session_1_segments)
+
+.. image:: ./images/Graph_1.png
+ :width: 400
+
+**Graph 2 - Session 2**
+
+.. code:: python
+
+ G2 = draw_digraph(session_2_segments)
+
+.. image:: ./images/Graph_2.png
+ :width: 400
+
+**Graph 3 - Session 3**
+
+.. code:: python
+
+ G3 = draw_digraph(session_3_segments)
+
+.. image:: ./images/Graph_3.png
+ :width: 400
+
+By analyzing these graphs, we can understand the general interactions that users had with the two elements of the
+Superset dashboard. For instance, in each of these graphs, the user starts by filtering the dashboard. Based on the
+tasks that the user is meant to perform, this makes a lot of sense since the most logical way to filter the dashboard is
+through the filtering window. However, these graphs begin to differ in the amount of interactions that the user has with
+the actual list of video games. While users always follow the workflow: filter --> game list --> filter --> game list,
+there are occasions when the user interacts more with the game list than others.
+
+Measuring Similarity with Edit Distance
+---------------------------------------
+One way to understand the differences between the previously generated DiGraphs is to look at their edit distance. Edit
+distance is a metric that measures how many distortions are necessary to turn one graph into another, thus measuring
+similarity. For instance, taking the edit distance between a graph and itself yields an edit distance of 0, since the
+graphs are exactly the same. We can show this using NetworkX's ``graph_edit_distance`` to calculate the edit distance.
+
+**Input**
+
+.. code:: python
+
+ nx.graph_edit_distance(G0, G0) # 0.0
+
+Let's now calculate the edit distances between each graph to calculate an average. Note, however, that when we try to
+calculate some edit distances, we run into a bit of an issue. Since edit distance is a computationally complex problem,
+it can take a long time and require large amounts of computational resources to find an exact answer. To simplify this
+problem, we can use NetworkX's ``optimize_graph_edit_distance`` function which will create an approximation of the graph
+edit distance. For the following calculations, we use the function required depending on the length of time
+``graph_edit_distance`` takes in each circumstance.
+
+**Input: G0, G1**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G0, G1)) # 32.0
+
+**Input: G0, G2**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G0, G2)) # 34.0
+
+**Input: G0, G3**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G0, G3)) # 38.0
+
+**Input: G1, G2**
+
+.. code:: python
+
+ nx.graph_edit_distance(G1, G2) # 2.0
+
+**Input: G1, G3**
+
+.. code:: python
+
+ next(nx.optimize_graph_edit_distance(G1, G3)) # 18.0
+
+**Input: G2, G3**
+
+.. code:: python
+
+ nx.graph_edit_distance(G2, G3) # 4.0
+
+Using these outputs we can now calculate the average edit distance with the following calculation:
+
+.. code:: python
+
+ (32.0 + 34.0 + 38.0 + 2.0 + 18.0 + 4.0)/6 # 21.33
+
+This shows that the average edit distance between each of these session DiGraphs is 21.33.
+
+
+
+
diff --git a/docs/docs/_sources/index.rst.txt b/docs/docs/_sources/index.rst.txt
index 734432f..2473532 100644
--- a/docs/docs/_sources/index.rst.txt
+++ b/docs/docs/_sources/index.rst.txt
@@ -12,4 +12,5 @@
Graph_Index
Segmentation
Process
- Utilities
\ No newline at end of file
+ Utilities
+ Example_Usage
\ No newline at end of file
diff --git a/docs/docs/genindex.html b/docs/docs/genindex.html
index e0c7808..d98cba2 100644
--- a/docs/docs/genindex.html
+++ b/docs/docs/genindex.html
@@ -39,6 +39,7 @@
<li class="toctree-l1"><a class="reference internal" href="Segmentation.html">Segmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
<li class="toctree-l1"><a class="reference internal" href="Utilities.html">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Example_Usage.html">Example Usage of Distill</a></li>
</ul>
</div>
diff --git a/docs/docs/index.html b/docs/docs/index.html
index 9018efa..f843dc3 100644
--- a/docs/docs/index.html
+++ b/docs/docs/index.html
@@ -41,6 +41,7 @@
<li class="toctree-l1"><a class="reference internal" href="Segmentation.html">Segmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
<li class="toctree-l1"><a class="reference internal" href="Utilities.html">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Example_Usage.html">Example Usage of Distill</a></li>
</ul>
</div>
@@ -93,6 +94,13 @@
<li class="toctree-l2"><a class="reference internal" href="Utility_Functions.html">Utility Functions</a></li>
</ul>
</li>
+<li class="toctree-l1"><a class="reference internal" href="Example_Usage.html">Example Usage of Distill</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="Example_Usage.html#imports">Imports</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Example_Usage.html#processing-and-segmentation">Processing and Segmentation</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Example_Usage.html#visualization-of-segment-objects">Visualization of <code class="docutils literal notranslate"><span class="pre">Segment</span></code> Objects</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Example_Usage.html#measuring-similarity-with-edit-distance">Measuring Similarity with Edit Distance</a></li>
+</ul>
+</li>
</ul>
</div>
</section>
diff --git a/docs/docs/objects.inv b/docs/docs/objects.inv
index 59e77ec..7c1c754 100644
--- a/docs/docs/objects.inv
+++ b/docs/docs/objects.inv
Binary files differ
diff --git a/docs/docs/search.html b/docs/docs/search.html
index 4f6a410..3e61695 100644
--- a/docs/docs/search.html
+++ b/docs/docs/search.html
@@ -42,6 +42,7 @@
<li class="toctree-l1"><a class="reference internal" href="Segmentation.html">Segmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="Process.html">Process</a></li>
<li class="toctree-l1"><a class="reference internal" href="Utilities.html">Utilities</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Example_Usage.html">Example Usage of Distill</a></li>
</ul>
</div>
diff --git a/docs/docs/searchindex.js b/docs/docs/searchindex.js
index 88e4379..7d6f5b7 100644
--- a/docs/docs/searchindex.js
+++ b/docs/docs/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/images/Games_List.png b/docs/images/Games_List.png
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diff --git a/docs/images/Video_Game_Filter.png b/docs/images/Video_Game_Filter.png
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diff --git a/docs/index.rst b/docs/index.rst
index 734432f..2473532 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -12,4 +12,5 @@
Graph_Index
Segmentation
Process
- Utilities
\ No newline at end of file
+ Utilities
+ Example_Usage
\ No newline at end of file
