In [1]:

    

from collections import defaultdict
from pymongo import MongoClient
import matplotlib
%matplotlib inline


client = MongoClient()
db = client["senators"]
coll = db["raw_xml"]


    

In [2]:

    

votes = defaultdict(dict)

for roll_call in coll.find({"congress": 113},{"raw":0}):
    for member in roll_call["votes"]:
        votes[member["name"]][(roll_call["session"],roll_call["vote_num"])] = member["vote_score"]


    

In [13]:

    

from sklearn.cluster import AgglomerativeClustering
from sklearn.feature_extraction import DictVectorizer

def graph_senators(names, senators):
    dict_vectorizer = DictVectorizer()
    features = dict_vectorizer.fit_transform(senators)

    labels = AgglomerativeClustering().fit_predict(features.toarray())

    matplotlib.rcParams['figure.figsize'] = (15.0,12.0)

    mds = MDS()
    coords = mds.fit_transform(features.toarray())
    plot = plt.scatter(coords[:,0], coords[:,1], c=labels, s=100)
    plt.axis("off")

    for i, (name, xy) in enumerate(zip(names, coords)):
        color = "red" if labels[i] else "blue"
        plt.annotate(name, xy = xy, xytext = (1, 1),
            textcoords = 'offset points', ha = 'right', va = 'bottom',
            bbox = dict(boxstyle = 'round,pad=0.3', fc=color,alpha = .4),
            arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=0')
        )


    

In [5]:

    

from sklearn.manifold import MDS
import matplotlib.pyplot as plt
import numpy as np
import random

mds = MDS(n_components=1)
partisanship = mds.fit_transform(features.toarray())

plt.scatter(partisanship, np.zeros_like(partisanship), c=labels,marker="|",s=1000)
# y=np.ones(np.shape(partisanship))
# plt.plot(partisanship,y,"|",ms=40,c=labels)
plt.axis("off")

# for name, val in zip(senator_names, partisanship)[0:2]:
#     plt.annotate(name, xy = (val, 0), xytext = (0, 1),
#         textcoords = 'offset points', ha = 'right', va = 'bottom')
plt.show()


    

In [14]:

    

senator_names = votes.keys()
senators = [votes[name] for name in senator_names]
graph_senators(senator_names, senators)


    

In [15]:

    

senator_names = filter(lambda name: "(R-" in name, votes.keys())
senators = [votes[name] for name in senator_names]

graph_senators(senator_names, senators)


    

In [16]:

    

senator_names = [ votes[name]filter(lambda name: "(D-" in name, votes.keys())
senators = [votes[name] for name in senator_names]

graph_senators(senator_names, senators)


    

In [38]:

    

len(votes["Kerry (D-MA)"].keys())


    

    
Out[38]:





11

In [39]:

    

senator_names = [ name for name in filter(lambda name: "(D-" in name, votes.keys()) if len(votes[name]) > 300 ]
senators = [votes[name] for name in senator_names]

graph_senators(senator_names, senators)


    

In [40]:

    

senator_names = [ name for name in filter(lambda name: "(R-" in name, votes.keys()) if len(votes[name]) > 300 ]
senators = [votes[name] for name in senator_names]

graph_senators(senator_names, senators)


    

In [ ]: