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TEA Plot

TEA Plots

TEA(temp_edgelist, filepath='.', fig_size=(7, 5), font_size=20, network_name=None, time_scale=None, real_dates=None, test_split=False, density=False)

generating TEA plot

Parameters:

Name Type Description Default
temp_edgelist Union[object, dict]

a dictionary of temporal edges or a dataset object.

 required
filepath Optional[str]

Path to save the TEA Plot.

 '.'
fig_size tuple

Size of the figure to save.

 (7, 5)
font_size int

Size of the text in the figure.

 20
network_name str

Name of the dataset to be used in the TEA plot file.

 None
time_scale Union[str, int]

time_scale for discretizing data if already not done.

 None
real_dates bool

Whether to use the real dates from dataset.

 None
test_split bool

Whether show the test split on the plot.

 False
density bool

Whether to return edge density and edge frequency dictioneries.

 False
Source code in tgx/viz/TEA.py 
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def TEA(
        temp_edgelist : Union[object, dict], 
        filepath : Optional[str] = ".",
        fig_size : tuple = (7,5),
        font_size : int = 20, 
        network_name : str = None,
        time_scale : Union[str, int] = None, 
        real_dates : bool = None,
        test_split : bool = False,
        density : bool = False
        ):
    r"""
    generating TEA plot

    Parameters:
        temp_edgelist: a dictionary of temporal edges or a dataset object.
        filepath: Path to save the TEA Plot.
        fig_size: Size of the figure to save.
        font_size: Size of the text in the figure.
        network_name: Name of the dataset to be used in the TEA plot file.
        time_scale: time_scale for discretizing data if already not done.
        real_dates: Whether to use the real dates from dataset.
        test_split: Whether show the test split on the plot.
        density: Whether to return edge density and edge frequency dictioneries.
    """
    if isinstance(temp_edgelist, object):
        if temp_edgelist.freq_data is None:
            temp_edgelist.count_freq()
        temp_edgelist = temp_edgelist.freq_data

    # check number of unique timestamps:
    unique_ts = list(temp_edgelist.keys())
    # if len(unique_ts) > max_time_scale:
    #     inp = input(f"There are {unique_ts} timestamps in the data.\nDo you want to discretize the data to 1000 timestamps?(y/n)").lower()
    #     if inp == "y":
    #         temp_edgelist = edgelist_discritizer(temp_edgelist,
    #                                             unique_ts,
    #                                             time_scale = max_time_scale)
    if time_scale is not None:
        temp_edgelist = discretize_edges(temp_edgelist,
                                        time_scale = time_scale)


    ts_edges_dist, ts_edges_dist_density, edge_frequency_dict = TEA_process_edgelist_per_timestamp(temp_edgelist)

    TEA_plot_edges_bar(ts_edges_dist, 
                       filepath = filepath, 
                       fig_size = fig_size, 
                       font_size = font_size, 
                       network_name=network_name,
                       real_dates = real_dates,
                       test_split = test_split)

    if density:
        return ts_edges_dist_density, edge_frequency_dict

TEA_plot_edges_bar(ts_edges_dist, filepath='.', fig_size=(9, 5), font_size=20, network_name=None, real_dates=None, time_scale=None, test_split=False, show=False)

Making TEA plot and save into pdf file. Args: ts_edges_dist: list of dictionaries containing the edge distribution over time. filepath: Path to save the TEA Plot. fig_size: Size of the figure to save. font_size: Size of the text in the figure. network_name: Name of the dataset to be used in the TEA plot file. real_dates: list of real dates as ticks time_scale: time_scale for discretizing data if already not done. test_split: Whether show the test split on the plot. show: Whether to show the plot.

Source code in tgx/viz/TEA.py 
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def TEA_plot_edges_bar(ts_edges_dist: list, 
                   filepath: str = ".", 
                   fig_size: list = (9,5),
                   font_size: int = 20,
                   network_name: str = None,
                   real_dates: list = None,
                   time_scale: list = None,
                   test_split: bool = False,
                   show: bool =False):
    r"""
    Making TEA plot and save into pdf file.
    Args:
        ts_edges_dist: list of dictionaries containing the edge distribution over time.
        filepath: Path to save the TEA Plot.
        fig_size: Size of the figure to save.
        font_size: Size of the text in the figure.
        network_name: Name of the dataset to be used in the TEA plot file.
        real_dates: list of real dates as ticks
        time_scale: time_scale for discretizing data if already not done.
        test_split: Whether show the test split on the plot.
        show: Whether to show the plot.
    """


    ts_edges_dist_df = pd.DataFrame(ts_edges_dist, columns=['ts', 'new', 'repeated',
                                                            'not_repeated',
                                                            'total_curr_ts',
                                                            'total_seen_until_curr_ts'])


    ### Additional Stats ###
    mean = ts_edges_dist_df.mean(axis=0)
    # print("INFO: Network Name:", network_name)
    # print("INFO: AVG. stats. over all timestamps: ", mean)
    # print("INFO: ratio of avg.(new)/avg.(total_curr_ts): {:.2f}".format(mean['new'] / mean['total_curr_ts']))
    ###

    fig, ax = plt.subplots(figsize=fig_size)  # lastfm, mooc, reddit, UNtrade, UNvote
    plt.subplots_adjust(bottom=0.2, left=0.2)
    font_size = font_size
    ticks_font_size = 15
    plt.yticks(fontsize=ticks_font_size)
    plt.xticks(fontsize=ticks_font_size)
    if real_dates is not None:
        start = real_dates[0]
        end = real_dates[1]
        metric = real_dates[2]
        create_ts_list(start, end, metric=metric, interval=time_scale)
    else:
        duration = ts_edges_dist_df['ts'].tolist()
        timestamps = [i for i in range(len(duration))]

    new = ts_edges_dist_df['new'].tolist()
    repeated = ts_edges_dist_df['repeated'].tolist()
    # print(len(timestamps), repeated, new)
    # plotting stuffs
    # bar plot
    plt.bar(timestamps, repeated, label='Repeated', color='#404040', alpha=0.4)
    plt.bar(timestamps, new, label='New', bottom=repeated, color='#ca0020', alpha=0.8, hatch='//')
    # test split line
    if test_split:
        plt.axvline(x=(timestamps[int(0.85 * len(timestamps))]), color="blue", linestyle="--", linewidth=2)
        plt.text((timestamps[int(0.85 * len(timestamps))]), 0,
                'x', va='center', ha='center', fontsize=font_size, fontweight='heavy', color='blue')

    plt.margins(x=0)
    plt.xlabel("Timestamp", fontsize=font_size)
    plt.ylabel("Number of edges", fontsize=font_size)
    plt.legend(fontsize = 13)
    if filepath is not None:
        plt.savefig(f"{filepath}/{network_name}_TEA.pdf")
        print("plot saved as " + f"{filepath}/{network_name}_TEA.pdf")
    if (show):
        plt.show()