cmap=cmap, node_color= )
nx.draw_networkx_edges(G, pos, alpha=0.5)
plt.show()
#community detection
communityListWeeks = []
for w in range(0,10):
print('Week ' + str(w) + '...')
num_comms = len(graph_all_weeks[w].graph._node)
communityListWeeks.append(graphLearning.community_dection_graph(graph_all_weeks[w], num_comms=num_comms))
import graphLearning
import scikit_posthocs as sp
pd.set_option("display.max_rows", None, "display.max_columns", None)
aw10 = graphLearning.extractAssessmentResultOfCommunities(communityListWeeks[9], assessment3A, 'perCorrect3A')
aw10t = sp.posthoc_conover(aw10[3][5])
goodCommunity = aw10[3][5][0]
badCommunity = aw10[3][5][1]
w = 9
for w in range(0,10):
activityDataMatrixWeeks_pageTypeWeek[w] = graphLearning.mapNewLabel(activityDataMatrixWeeks_pageTypeWeek[w] , reLabelIndex)
extractGoodBadCommunity = activityDataMatrixWeeks_pageTypeWeek[w].loc[activityDataMatrixWeeks_pageTypeWeek[w].index.astype(int).isin(goodCommunity.index) | activityDataMatrixWeeks_pageTypeWeek[w].index.astype(int).isin(badCommunity.index)]
extractGoodBadCommunity['group'] = 0
extractGoodBadCommunity.loc[extractGoodBadCommunity.index.astype(int).isin(goodCommunity.index),['group']] = 0
extractGoodBadCommunity.loc[extractGoodBadCommunity.index.astype(int).isin(badCommunity.index),['group']] = 1
columnListStatsSig = []
for c in extractGoodBadCommunity.columns:
num_comms=num_comms))
communityListWeeks_cleaned_correlation = []
for w in range(0, 12):
print('Week ' + str(w) + '...')
num_comms = len(graph_all_weeks_cleaned_correlation[w].graph._node)
communityListWeeks_cleaned_correlation.append(
graphLearning.community_dection_graph(
graph_all_weeks_cleaned_correlation[w], num_comms=num_comms))
import graphLearning
import scikit_posthocs as sp
pd.set_option("display.max_rows", None, "display.max_columns", None)
aw10 = graphLearning.extractAssessmentResultOfCommunities(
communityListWeeks_cleaned_correlation[11], assessment2A, 'perCorrect2A')
aw10t = sp.posthoc_conover(aw10[1][5])
aw6 = graphLearning.extractAssessmentResultOfCommunities(
communityListWeeks[6], assessment1A, 'perCorrect1A')
aw6t = sp.posthoc_conover(aw6[3][5])
a = graphLearning.findTogetherMembers(aw10[18][5], aw11[18][5], aw10[18][1],
aw11[18][1])
len(set(assessment2A.index).intersection(set(assessment3A.index)))
a = graphLearning.findTogetherMembers(aw7[18][5], aw11[18][5], aw7[18][1],
aw11[18][1])
for i in range(0, 8):
for j in range(0, 8):
if len(a[i][j]) > 1:
print(a[i][j])
print('Week ' + str(w) + '...')
num_comms = len(graph_all_weeks[w].graph._node)
communityListWeeks.append(graphLearning.community_dection_graph(graph_all_weeks[w], most_valuable_edge=graphLearning.most_central_edge, num_comms=num_comms))
communityListWeeks_not_cleaned = []
for w in range(0,12):
print('Week ' + str(w) + '...')
num_comms = len(graph_all_weeks_not_cleaned[w].graph._node)
communityListWeeks_not_cleaned.append(graphLearning.community_dection_graph(graph_all_weeks_not_cleaned[w], most_valuable_edge=graphLearning.most_central_edge, num_comms=num_comms))
import graphLearning
import scikit_posthocs as sp
pd.set_option("display.max_rows", None, "display.max_columns", None)
aw10 = graphLearning.extractAssessmentResultOfCommunities(communityListWeeks[10], assessment2A, 'perCorrect2A')
aw10t = sp.posthoc_conover(aw10[0][5])
aw6 = graphLearning.extractAssessmentResultOfCommunities(communityListWeeks[6], assessment1A, 'perCorrect1A')
aw6t = sp.posthoc_conover(aw7[3][5])
a = graphLearning.findTogetherMembers(aw10[18][5],aw11[18][5], aw10[18][1],aw11[18][1])
len(set(assessment2A.index).intersection(set(assessment3A.index)))
a = graphLearning.findTogetherMembers(aw7[18][5],aw11[18][5], aw7[18][1],aw11[18][1])
for i in range(0,8):
for j in range(0,8):
if len(a[i][j]) > 1:
print(a[i][j])