#! /usr/bin/python2
# -*- coding: utf8 -*-

import pykka
import time
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.cluster import SpectralClustering, AffinityPropagation
from datetime import datetime

class Learner(pykka.ThreadingActor):
    """Fetch friends and tweets for a given ego """
    def __init__(self, k=20):
        super(Learner, self).__init__()
        self.token_processer = Pipeline([('vect', CountVectorizer()), \
                                    ('tfidf', TfidfTransformer()) ])
        self.k = k # number of clusters
        self.clf = SpectralClustering(n_clusters=k, random_state=42, \
                            affinity='rbf', n_neighbors=15, eigen_tol=0.0)

    def learn(self, data):
        # u_ids = []
        u_screen_names = []
        X = []
        def insert(elem):
            # u_ids.append(elem['u_id'])
            u_screen_names.append(elem['u_screen_name'])
            X.append(elem['u_document'])
        [insert(elem) for elem in data]
        # Create bag of words representation
        X = self.token_processer.fit_transform(X)
        labels = self.clf.fit_predict(X)

        clustering = [[] for i in range(self.k)]
        ## Fast loop:
        # [ clustering[labels[i]].append(u_id) for i, u_id in enumerate(u_ids) ]
        [ clustering[labels[i]].append(u_scr_n) for i, u_scr_n in enumerate(u_screen_names) ]
 
        return {'ego_id':data[0]['ego_id'], "nb_clusters":self.k, 'clustering':clustering, "created_at":str(datetime.now())}
#! /usr/bin/python2
# -*- coding: utf8 -*-

import pykka
import time
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.cluster import SpectralClustering, AffinityPropagation
from datetime import datetime

class Learner(pykka.ThreadingActor):
    """Fetch friends and tweets for a given ego """
    def __init__(self, k=20):
        super(Learner, self).__init__()
        self.token_processer = Pipeline([('vect', CountVectorizer()), \
                                    ('tfidf', TfidfTransformer()) ])
        self.k = k # number of clusters
        self.clf = SpectralClustering(n_clusters=k, random_state=42, \
                            affinity='rbf', n_neighbors=15, eigen_tol=0.0)

    def learn(self, data):
        # u_ids = []
        u_screen_names = []
        X = []
        def insert(elem):
            # u_ids.append(elem['u_id'])
            u_screen_names.append(elem['u_screen_name'])
            X.append(elem['u_document'])
        [insert(elem) for elem in data]
        # Create bag of words representation
        X = self.token_processer.fit_transform(X)
        labels = self.clf.fit_predict(X)

        clustering = [[] for i in range(self.k)]
        ## Fast loop:
        # [ clustering[labels[i]].append(u_id) for i, u_id in enumerate(u_ids) ]
        [ clustering[labels[i]].append(u_scr_n) for i, u_scr_n in enumerate(u_screen_names) ]
 
        return {'ego_id':data[0]['ego_id'], "nb_clusters":self.k, 'clustering':clustering, "created_at":str(datetime.now())}
#! /usr/bin/python2 # -*- coding: utf8 -*- Fetch friends and tweets for a given ego # number of clusters # u_ids = [] # u_ids.append(elem['u_id']) # Create bag of words representation ## Fast loop: # [ clustering[labels[i]].append(u_id) for i, u_id in enumerate(u_ids) ]