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politics_lab.py
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politics_lab.py
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voting_data = list(open("voting_record_dump109.txt"))
## Task 1
def create_voting_dict():
"""
Input: None (use voting_data above)
Output: A dictionary that maps the last name of a senator
to a list of numbers representing the senator's voting
record.
Example:
>>> create_voting_dict()['Clinton']
[-1, 1, 1, 1, 0, 0, -1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1,
1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, 1]
This procedure should return a dictionary that maps the last name
of a senator to a list of numbers representing that senator's
voting record, using the list of strings from the dump file (strlist). You
will need to use the built-in procedure int() to convert a string
representation of an integer (e.g. '1') to the actual integer
(e.g. 1).
You can use the split() procedure to split each line of the
strlist into a list; the first element of the list will be the senator's
name, the second will be his/her party affiliation (R or D), the
third will be his/her home state, and the remaining elements of
the list will be that senator's voting record on a collection of bills.
A "1" represents a 'yea' vote, a "-1" a 'nay', and a "0" an abstention.
The lists for each senator should preserve the order listed in voting data.
"""
cumulative = {}
for l in voting_data:
l = l.split()
cumulative[l[0]] = [int(x) for x in l[3:]]
return cumulative
## Task 2
def policy_compare(sen_a, sen_b, voting_dict):
"""
Input: last names of sen_a and sen_b, and a voting dictionary mapping senator
names to lists representing their voting records.
Output: the dot-product (as a number) representing the degree of similarity
between two senators' voting policies
Example:
>>> voting_dict = {'Fox-Epstein':[-1,-1,-1,1],'Ravella':[1,1,1,1]}
>>> policy_compare('Fox-Epstein','Ravella', voting_dict)
-2
"""
return sum([x*y for (x,y) in zip(voting_dict[sen_a], voting_dict[sen_b])])
## Task 3
def most_similar(sen, voting_dict):
"""
Input: the last name of a senator, and a dictionary mapping senator names
to lists representing their voting records.
Output: the last name of the senator whose political mindset is most
like the input senator (excluding, of course, the input senator
him/herself). Resolve ties arbitrarily.
Example:
>>> vd = {'Klein': [1,1,1], 'Fox-Epstein': [1,-1,0], 'Ravella': [-1,0,0]}
>>> most_similar('Klein', vd)
'Fox-Epstein'
Note that you can (and are encouraged to) re-use you policy_compare procedure.
"""
current_max = -60
for k in voting_dict:
sim = policy_compare(sen, k, voting_dict) if sen != k else -60
if sim > current_max:
current_max = sim
closest = k
return closest
## Task 4
def least_similar(sen, voting_dict):
"""
Input: the last name of a senator, and a dictionary mapping senator names
to lists representing their voting records.
Output: the last name of the senator whose political mindset is least like the input
senator.
Example:
>>> vd = {'Klein': [1,1,1], 'Fox-Epstein': [1,-1,0], 'Ravella': [-1,0,0]}
>>> least_similar('Klein', vd)
'Ravella'
"""
current_min = 60
for k in voting_dict:
sim = policy_compare(sen, k, voting_dict) if sen != k else 60
if sim < current_min:
current_min = sim
furthest = k
return furthest
## Task 5
votes = create_voting_dict()
most_like_chafee = most_similar('Chafee', votes)
least_like_santorum = least_similar('Santorum', votes)
# Task 6
def get_party_set(party):
'''(str) -> set
Return set of senators from party.
'''
senators = set()
for l in voting_data:
l = l.split()
if l[1] == party:
senators.update({l[0]})
return senators
def find_average_similarity(sen, sen_set, voting_dict):
"""
Input: the name of a senator, a set of senator names, and a voting dictionary.
Output: the average dot-product between sen and those in sen_set.
Example:
>>> vd = {'Klein': [1,1,1], 'Fox-Epstein': [1,-1,0], 'Ravella': [-1,0,0]}
>>> find_average_similarity('Klein', {'Fox-Epstein','Ravella'}, vd)
-0.5
"""
avg_record = find_average_record(sen_set, voting_dict)
return sum([(x*y) for (x, y) in zip(voting_dict[sen], avg_record)])
def most_average(sen_set, voting_dict):
'''(set, dict) -> str
Return name of senator from sen_set whose voting record in voting_dict is most
similar to the set average.
'''
greatest_sim = -50
avg_record = find_average_record(sen_set, voting_dict)
for senator in sen_set:
sim = sum([(x*y) for (x, y) in zip(voting_dict[senator], avg_record)])
if sim > greatest_sim:
greatest_sim = sim
most_avg = senator
return senator
most_average_Democrat = 'Carper'
# Task 7
def find_average_record(sen_set, voting_dict):
"""
Input: a set of last names, a voting dictionary
Output: a vector containing the average components of the voting records
of the senators in the input set
Example:
>>> voting_dict = {'Klein': [-1,0,1], 'Fox-Epstein': [-1,-1,-1], 'Ravella': [0,0,1]}
>>> find_average_record({'Fox-Epstein','Ravella'}, voting_dict)
[-0.5, -0.5, 0.0]
"""
total = [0 for i in range(len(voting_dict))]
denom = len(sen_set)
for sen in sen_set:
total = [x+y for (x, y) in zip(total, voting_dict[sen])]
return [x/denom for x in total]
democrats = get_party_set('D')
average_Democrat_record = find_average_record(democrats, votes)
# Task 8
def bitter_rivals(voting_dict):
"""
Input: a dictionary mapping senator names to lists representing
their voting records
Output: a tuple containing the two senators who most strongly
disagree with one another.
Example:
>>> voting_dict = {'Klein': [-1,0,1], 'Fox-Epstein': [-1,-1,-1], 'Ravella': [0,0,1]}
>>> bitter_rivals(voting_dict)
('Fox-Epstein', 'Ravella')
"""
bitterest = (..., ...)
furthest = 1000
senators = list(voting_dict.keys())
for i in range(len(senators)):
for j in range(i, len(senators)):
sim = policy_compare(senators[i], senators[j], voting_dict)
if sim < furthest:
furthest = sim
bitterest = (senators[i], senators[j])
return bitterest