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social-interest-predictor
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Fix formatting issues and inconsistencies

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Rory Healy 2023-02-05 20:47:37 +11:00
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48
docs/SPECIFICATION.md
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@ -3,8 +3,6 @@
Below is the assignment specification, in full, slightly edited for context and
appearence.
---
## Introduction
This project is all about "social networks", and the power of social
@ -21,17 +19,17 @@ network, and (mutual) friendship connections as "edges" connecting those nodes.
## Part 1 - Friends
Write a function get_friendly_dict() that calculates the degree-one friends of
Write a function `get_friendly_dict()` that calculates the degree-one friends of
each individual in a social network. The function takes one argument:
- friend_list, a list of reciproal friendship links between individuals.
- `friend_list`, a list of reciproal friendship links between individuals.
The function should return a dictionary of sets, containing the set of all
"degree-one" (immediate) friends for each individual in the social network.
Note that the specific order of the individuals in the dictionary, and also the
ordering of the friends in each set does not matter.
The structure of friend_list is as follows: each element is a 2-tuple of
The structure of `friend_list` is as follows: each element is a 2-tuple of
strings, representing a pairing of names of individuals in the social network
who are friends. Note that as friendship links are reciprocal, the 2-tuple
('kim', 'sandy') indicates that 'kim' is a friend of 'sandy', and also that
@ -49,12 +47,12 @@ Example function calls are:
## Part 2 - Social Network Besties
Write a function friend_besties() that calculates the "besties" (i.e.,
Write a function `friend_besties()` that calculates the "besties" (i.e.,
degree-one friends) of a given individual in a social network. The function
takes two arguments:
- individual, an individual in the social network, in the form of a string ID;
- bestie_dict, a dictionary of sets of friends of each individual in the
- `individual`, an individual in the social network, in the form of a string ID;
- `bestie_dict`, a dictionary of sets of friends of each individual in the
social network.
The function should return a sorted list, made up of all "degree-one" friends
@ -73,12 +71,12 @@ Example function calls are:
## Part 3 - Social Network Second Besties
Write a function friend_second_besties() that calculates the "second-besties"
Write a function `friend_second_besties()` that calculates the "second-besties"
(i.e. degree-two friends) of a given individual in a social network. The
function takes two arguments:
- individual, an individual in the social network, in the form of a string ID;
- bestie_dict, a dictionary of sets of friends of each individual in the
- `individual`, an individual in the social network, in the form of a string ID;
- `bestie_dict`, a dictionary of sets of friends of each individual in the
social network.
The function should return a sorted list, made up of all "degree-two" friends
@ -98,21 +96,21 @@ Example function calls are:
## Part 4 - Network Coverage
Write a function besties_coverage() that computes the "coverage" of nodes
Write a function `besties_coverage()` that computes the "coverage" of nodes
within a social network that are connected via predefined relationships to a
given list of individuals (i.e., the proportion of connected individuals, to the
total size of the network, which is the total number of people in the social
network). The function takes three arguments:
- individuals, a list of individuals, each in the form of a string ID;
- bestie_dict, a dictionary of sets of friends of each individual in the
- `individuals`, a list of individuals, each in the form of a string ID;
- `bestie_dict`, a dictionary of sets of friends of each individual in the
social network;
- relationship_list, a list of functions defining relationships in the
- `relationship_list`, a list of functions defining relationships in the
social network, selected from friend_besties and friend_second_besties.
The function should return a float, corresponding to the proportion of the
total number of individuals who are either a member of individuals or connected
via one of the relationships in relationship_list.
via one of the relationships in `relationship_list`.
Example calls to the function are:
@ -145,14 +143,14 @@ based on the attributes of their social network, and the observation that a
user's friends often have very similar interests and background to that user
(what is formally called homophily).
Write a function friendly_prediction() which takes four arguments:
Write a function `friendly_prediction()` which takes four arguments:
- unknown_user, a string indicating the identity of the user you are to predict
attributes for;
- features, a set of features you are to predict attributes for;
- bestie_dict, a dictionary of sets of the besties for each user in the
- `unknown_user`, a string indicating the identity of the user you are to
predict attributes for;
- `features`, a set of features you are to predict attributes for;
- `bestie_dict`, a dictionary of sets of the besties for each user in the
dataset, following the same format as the earlier questions in the project;
- feat_dict, a dictionary containing the known attributes for each user in the
- `feat_dict`, a dictionary containing the known attributes for each user in the
training data, across a range of features; note that there is no guarantee
that the attribute for a given feature will be known for every training user.
@ -175,9 +173,9 @@ Your function should make its predictions as follows:
Note that all attributes will take the form of strings, with the empty string
representing the fact that the user explicitly has no value for that feature
(e.g., if the user did not go to university, the value for university would be
''), and the lack of an attribute for a given feature indicating that the
attribute is unknown. Note further that even if the attribute for unknown_user
is available in feat_dict, you should predict based on the attributes of
`''`), and the lack of an attribute for a given feature indicating that the
attribute is unknown. Note further that even if the attribute for `unknown_user`
is available in `feat_dict`, you should predict based on the attributes of
besties and second besties.
Example calls to the function are:

4
src/part4.py
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@ -7,11 +7,11 @@ from typing import Callable
def flatten_network(bestie_dict: dict[str, set[str]]) -> list[str]:
"""Creates a list of everyone in a social network.
Args:
### Parameters:
bestie_dict (dict): A dictionary of sets of friends of each individual
in the social network.
Returns:
### Returns:
individuals (list): A list of every person in a social network.
"""
people = set()

43
src/part5.py
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@ -11,12 +11,13 @@ def predict_feature(feature: str, people: list[str],
feat_dict: dict[str, dict[str, str]]) -> list[str] | None:
"""Predicts the value of a feature given a list of people to search through.
Args:
### Parameters:
feature (str): The feature of interest to predict.
people (list): The list of people to search through.
feat_dict (dict[str, dict[str, str]]): The list of features for the people.
feat_dict (dict[str, dict[str, str]]): The list of features for the
people.
Returns:
### Returns:
feature (list or None): Returns a sorted list of predictions of features
if people had that attribute. Otherwise returns None.
"""
@ -48,7 +49,7 @@ def friendly_prediction(unknown_user: str, features: set[str],
"""Predicts a set of features for a user based on the features that their
friends have.
Args:
### Parameters:
unknown_user (str): The user who you are predicting attributes for.
features (set): A set of features to predict.
bestie_dict (dict): A dictionary of sets of friends of each individual
@ -56,9 +57,41 @@ def friendly_prediction(unknown_user: str, features: set[str],
feat_dict (dict): A dictionary containing the known attributes for each
user in the social network.
Returns:
### Returns:
predictions (dict): A dictionary of features with a predicted list of
values for each feature.
### Examples:
>>> friendly_prediction('glenn', {'favourite author', 'university'},
{'kim': {'sandy', 'alex', 'glenn'}, 'sandy': {'kim', 'alex'},
'alex': {'kim', 'sandy'}, 'glenn': {'kim'}},
{'glenn': {'university': ''}, 'kim': {'favourite author': 'AA Milne'},
'sandy': {'favourite author': 'JRR Tolkien',
"university": "University of Melbourne"},
'alex': {'favourite author': 'AA Milne',
'university': 'Monash University'}})
{'university': ['Monash University', 'University of Melbourne'],
'favourite author': ['AA Milne']}
>>> friendly_prediction('kim', {'university'},
{'kim': {'sandy', 'alex', 'glenn'}, 'sandy': {'kim', 'alex'},
'alex': {'kim', 'sandy'}, 'glenn': {'kim'}},
{'glenn': {'university': ''}, 'kim': {'favourite author': 'AA Milne'},
'sandy': {'favourite author': 'JRR Tolkien',
"university": "University of Melbourne"},
'alex': {'favourite author': 'AA Milne',
'university': 'Monash University'}})
{'university': ['', 'Monash University', 'University of Melbourne']}
>>> friendly_prediction('kim', {'birthplace'},
{'kim': {'sandy', 'alex', 'glenn'}, 'sandy': {'kim', 'alex'},
'alex': {'kim', 'sandy'}, 'glenn': {'kim'}},
{'glenn': {'university': ''}, 'kim': {'favourite author': 'AA Milne'},
'sandy': {'favourite author': 'JRR Tolkien',
"university": "University of Melbourne"},
'alex': {'favourite author': 'AA Milne',
'university': 'Monash University'}})
{'birthplace': []}
"""
predictions = {}