def cluster_users(clustering_tool, reader: DataReader,
clustered_users: List[User], clusters_number: int,
lists_count: int) -> Clusterings:
"""
Cluster users by lists with numbers from 0 to given lists_count.
Users are stiling from the reader object.
In result clusters appears all user from the reader except users in clustered_users.
:param clustering_tool: tool from sci-kit for clustering
:param reader: DataReader object
:param clustered_users: list of users that won't appear in clusterings
:param clusters_number: expected number of clusters
:param lists_count: cluster by lists from 1 to lists_count
:return: Clusterings object, representing clusterings by different lists
"""
clustering = Clusterings(clusters_number)
all_users = reader.get_all_users()
for list_number in range(0, lists_count):
features_matrix = get_matrix_by_list_for_not_clustered_users(
all_users, list_number, clustered_users)
clusters_list = clustering_tool.fit_predict(features_matrix)
clustering.add_clustering_for_list(
convert_clusters_list_to_users_sets(reader, clusters_list,
clustered_users), list_number)
return clustering
from experiments.experiment4.preferences_clustering import PreferencesClustering
from utils.data_reader import DataReader
from utils.json_serializer import Serializer
from utils.metrics import TeamDesiresMetric, ClusteringMetric
__author__ = 'Xomak'
reader = DataReader("../data/ms-sne.json")
pc = PreferencesClustering(reader.get_all_users(), 6)
result = pc.clusterize()
for current_set in result:
output = []
for user in current_set:
output.append(str(user))
print(','.join(output))
print(TeamDesiresMetric(current_set))
print(ClusteringMetric(result).get_average_desires_metric())
Serializer.serialize_to_file(result, "../web-visualiser/data.json")
desires_weight = 1
need_balance = True
metric_type = MetricTypes.DESIRES
data_files_names = [
"../data/ms-sne_names.json", "../data/eltech-vector.json",
"../data/users.json"
]
results = ResultHolder(data_files_names)
for data_file_name in data_files_names:
reader = DataReader(data_file_name)
max_teams = int(len(reader.get_all_users()) / 2)
for teams in range(2, max_teams + 1):
print("\n\nTEAMS: %d\n" % teams)
# Spectral
clustering_alg = UsersSpectralClustering(reader,
teams,
desires_weight=desires_weight,
need_balance=need_balance)
sets = clustering_alg.cluster()
results.add_metric_for(
data_file_name, teams,
ClusteringMetric(sets, metric_type).get_final_metric())
# Agglomerative