<jupyter_start><jupyter_text>Servers with blocking session issues<jupyter_code>queries = pd.read_csv(saved_path + 'dataset-d4.csv')<jupyter_output><empty_output><jupyter_text>Use cases 3 : Blocking sessions alert  Support<jupyter_code>result_supp = sd_binary_conds (queries, dict_conds = {},_target = 'blockedSessions_disc', mesure  = 'Support',_depth  = 1,
                               threshold = 10000, result_size = 100, algorithm   = 'Beam Search', _beam_width = 100,
                               features_ignore = ['blockedSessions'])
res_supp = result_supp.to_dataframe()<jupyter_output><empty_output><jupyter_text>Lift<jupyter_code>result_lift = sd_binary_conds (queries, dict_conds = {},_target = 'blockedSessions_disc', mesure  = 'Lift',_depth  = 1,
                               threshold = 10000, result_size = 100, algorithm   = 'Beam Search', _beam_width = 100,
                               features_ignore = ['blockedSessions'])
res_lift = result_lift.to_dataframe()<jupyter_output><empty_output><jupyter_text>WRAcc<jupyter_code>result_wracc = sd_binary_conds (queries, dict_conds = {},_target = 'blockedSessions_disc', mesure  = 'WRAcc',_depth  = 1,
                                threshold = 10000, result_size = 100, algorithm   = 'Beam Search', _beam_width = 100,
                                features_ignore = ['blockedSessions'])
res_wracc = result_wracc.to_dataframe()<jupyter_output><empty_output><jupyter_text>Binomial<jupyter_code>result_binomial = sd_binary_conds (queries, dict_conds = {},_target = 'blockedSessions_disc', mesure  = 'Binomial',
                                   _depth  = 1,threshold = 10000, result_size = 100, algorithm   = 'Beam Search',
                                   _beam_width = 100,features_ignore = ['blockedSessions'])
res_binomial = result_binomial.to_dataframe()<jupyter_output><empty_output><jupyter_text>Post-processing<jupyter_code>plot_sgbars(res_lift, 10, ylabel="target share", title="Discovered Subgroups", dynamic_widths=False, _suffix="")
plot_npspace(res_lift, 10, queries, annotate=True, fixed_limits=False)
d, d_names, sg_names = greedy_jaccard(result_lift.to_descriptions(),10, queries, 0.8)
for sg in d_names.keys() :
    print(sg)
similarity_sgs(result_lift.to_descriptions(), 10, queries, color=True)
similarity_dendrogram(result_lift.to_descriptions(), 20, queries)
indices = similarity_dendrogram(result_lift.to_descriptions(), 20, queries,truncated = True, p = 5)
res_raf = res_lift[res_lift.index.isin(indices)]
plot_sgbars(res_raf, res_raf.shape[0], ylabel="target share", title="Discovered Subgroups", 
            dynamic_widths=False, _suffix="")
plot_npspace(res_raf, res_raf.shape[0], queries, annotate=True, fixed_limits=False)<jupyter_output><empty_output>
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