Fix pyta errors

assignments/A3/a3.tex

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+\documentclass[11pt]{article}
+\usepackage{amsmath}
+\usepackage[utf8]{inputenc}
+\usepackage[margin=0.75in]{geometry}
+
+\title{CSC111 Assignment 3: Graphs, Recommender Systems, and Clustering}
+\author{Azalea Gui \& Peter Lin}
+\date{\today}
+
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\Z}{\mathbb{Z}}
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\cO}{\mathcal{O}}
+\newcommand{\floor}[1]{\left\lfloor #1 \right\rfloor}
+\newcommand{\code}[1]{\texttt{#1}}
+
+\begin{document}
+\maketitle
+
+\section*{Part 1: The book review graph and simple recommendations}
+
+\begin{enumerate}
+
+\item[1.]
+Complete this part in the provided \texttt{a3\_part1.py} starter file.
+Do \textbf{not} include your solution in this file.
+
+\item[2.]
+Running Time Analysis for \texttt{load\_review\_graph}:
+
+Let $n$ be the number of lines in \texttt{book\_names\_file}, let $m$ be the number of lines in \texttt{reviews\_file}.
+
+There are two operations that involves iteration in the function, one reads the book names file and creates the \texttt{mp} dictionary, and the other one reads the reviews file and adds vertices to the graph.
+
+In creating $mp$, the program first opened the file and created a \texttt{csv.reader}, which are both constant-time operations. Then, the dictionary comprehension statement loops through all $n$ lines, running only constant-time operations in each iteration for adding the book id and name pair into the dictionary, resulting in a running time of $\Theta(n)$. Summing up all the operations for creating $mp$ and ignoring constant-time operations, the running time would be $\in \Theta(n)$.
+
+For adding the vertices, it also opened the file and created a \texttt{csv.reader} in constant time. Then, the loop iterates through all $m$ lines. In each iteration, two vertices and one edge are added, and it also accessed $mp$ to retrieve the book name, which are all constant time operations. Therefore, the total running time would be $\in \Theta(m)$.
+
+Since there are only constant-time operations outside the two iterating operations, the total running time of the function would be $\in \Theta(m + n)$
+
+\item[3.]
+Complete this part in the provided \texttt{a3\_part1.py} starter file.
+Do \textbf{not} include your solution in this file.
+
+\item[4.]
+Complete this part in the provided \texttt{a3\_part1.py} starter file.
+Do \textbf{not} include your solution in this file.
+
+\end{enumerate}
+
+\section*{Part 2: Weighted graphs, recommendations, review prediction}
+
+Complete this part in the provided \texttt{a3\_part2\_recommendations.py} and \texttt{a3\_part2\_predictions.py} starter files.
+Do \textbf{not} include your solution in this file.
+
+\newpage
+
+\section*{Part 3: Finding book clusters}
+
+\begin{enumerate}
+
+\item[1.]
+Complete this part in the provided \texttt{a3\_part3.py} starter file.
+Do \textbf{not} include your solution in this file.
+
+\item[2.]
+Complete this part in the provided \texttt{a3\_part3.py} starter file.
+Do \textbf{not} include your solution in this file.
+
+\item[3.]
+
+\begin{enumerate}
+\item[(a)]
+Running Time Analysis for \texttt{cross\_cluster\_weight}:
+
+Let $m_1$ be the size of \texttt{cluster1}, and let $m_2$ be the size of \texttt{cluster2}.
+
+There is one nested loop in the function. The inner loop iterates $m_2$ times through all values in \texttt{cluster2}, and the outer loop iterates $m_1$ times through all values in \texttt{cluster1}. Inside the inner loop, there is only one function call \texttt{get\_weight}, which is constant-time because it only involves constant-time operations like dictionary accessing and variable assignment. After the function call, the returned value is added to \texttt{sw}, which is one constant-time operation. Let $c$ be a constant representing the number of constant time operations inside the inner loop. The nested loop will run $m_1 * m_2 * c$ operations in total, which is $\in \Theta(m_1 * m_2)$
+
+Since there are only constant-time operations such as \texttt{len(set)}, variable assignment, and multiplication or division outside the nested loop, the running time of the function will be $\in \Theta(m_1 * m_2)$
+
+
+\item[(b)]
+Running Time Analysis for the inner loop in \texttt{find\_clusters\_random}:
+
+Let $n$ be the number of vertices of \texttt{graph}.
+
+There is one nested loop in the function.
+
+For iteration $i$ of the outer loop, the inner loop iterates through all books in the \texttt{clusters} array, which has $n - i$ entires since each iteration of the outer loop removes an entry at the end. Inside the inner loop, the \texttt{if}, \texttt{is not}, comparisons, and variable assignment operations are all constant-time. For iteration $j$ of the inner loop, the \texttt{cross\_cluster\_weight} function call inside the inner loop has a running time of $\Theta(m_1 * m_j)$ as previously analyzed, where $m_1$ is the size of cluster $c_1$, and $m_j$ is the size of cluster $c_j$ (or $c_2$ for iteration $j$). Therefore, the running time of the function will be: 
+
+
+\begin{align}
+RT_{\text{inner\_loop}} &= \sum_j^{n-i} m_1 * m_j \\
+&= m_1 * \sum_j^{n-i} m_j
+\end{align}
+
+Since the if statement inside the inner loop ensures $m_1 \neq m_j$, and since we know that $\sum_c |c| = n$, the previously stated $\sum_j^{n-i} m_j$ will be equal to $n - |c_1| = n - m_1$. Therefore, the running time of the function is $m_1 * (n - m_1)$
+
+Since every cluster is initialized to have one element, we know that $m_1 > 0$, and $n - m_1 < n$. Then, since $m_1$ is also $< n$, the running time $m_1 * (n - m_1)$ is bounded by $\cO(n^2)$.
+
+\item[(c)]
+Running Time Analysis for \texttt{find\_clusters\_random}:
+
+Let $n$ be the number of vertices of graph, and let $k$ be the value of \texttt{num\_clusters}.
+
+In the worst case scenario: Since the outer loop iterates over a range from $0$ to $n - k$, it iterates $n - k$ times. For each iteration, the inner loop have a running time of $\cO(n^2)$ as previously analyzed. Besides from the inner loop, other statements inside the outer loop includes constant-time operations print, f-string creation, \texttt{random.choice}, and varaible assignment. After that, all elements in the set $c_1$ are added to \texttt{best\_c2}, which has a worst-case running time of $\cO(n)$ because $|c_1| <= n$. Also, $c_1$ is removed from the list \texttt{clusters}, which also has a worst-case running time of $\cO(n)$ because \texttt{len(clusters)} $<= n$. Combining all of these operations, the running time of one iteration of the outer loop will be $\in \cO(n^2)$, and the runninng time of $n - k$ iterations will be $\in \cO(n^2 \cdot (n - k))$.
+
+Outside the outer loop, there is only a return statement (constant-time) and a list comprehension that initializes the \texttt{clusters} array, which goes through $n$ iterations, executing a constant-time operation of creating a set for each iteration.
+
+Therefore, the running time of the entire function would be $\in \cO(n^2 \cdot (n - k))$.
+
+\item[(d)]
+\emph{Not to be handed in.}
+\end{enumerate}
+
+\end{enumerate}
+\end{document}

assignments/A3/a3_part1.py

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+"""CSC111 Winter 2022 Assignment 3: Graphs, Recommender Systems, and Clustering (Part 1)
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This Python module contains the modified graph and vertex classes you'll be using as the basis
+for this assignment, as well as additional functions for you to complete in this part.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr, David Liu, and Isaac Waller.
+"""
+from __future__ import annotations
+import csv
+from typing import Any, Literal
+
+# Make sure you've installed the necessary Python libraries (see assignment handout
+# "Installing new libraries" section)
+import networkx as nx  # Used for visualizing graphs (by convention, referred to as "nx")
+
+
+class _Vertex:
+    """A vertex in a book review graph, used to represent a user or a book.
+
+    Each vertex item is either a user id or book title. Both are represented as strings,
+    even though we've kept the type annotation as Any to be consistent with lecture.
+
+    Instance Attributes:
+        - item: The data stored in this vertex, representing a user or book.
+        - kind: The type of this vertex: 'user' or 'book'.
+        - neighbours: The vertices that are adjacent to this vertex.
+
+    Representation Invariants:
+        - self not in self.neighbours
+        - all(self in u.neighbours for u in self.neighbours)
+        - self.kind in {'user', 'book'}
+    """
+    item: Any
+    kind: str
+    neighbours: set[_Vertex]
+
+    def __init__(self, item: Any, kind: str) -> None:
+        """Initialize a new vertex with the given item and kind.
+
+        This vertex is initialized with no neighbours.
+
+        Preconditions:
+            - kind in {'user', 'book'}
+        """
+        self.item = item
+        self.kind = kind
+        self.neighbours = set()
+
+    def degree(self) -> int:
+        """Return the degree of this vertex."""
+        return len(self.neighbours)
+
+    ############################################################################
+    # Part 1, Q3
+    ############################################################################
+    def similarity_score(self, other: _Vertex) -> float:
+        """Return the similarity score between this vertex and other.
+
+        See Assignment handout for definition of similarity score.
+        """
+        if self.degree() == 0 or other.degree() == 0:
+            return 0.0
+        intersection = len(self.neighbours.intersection(other.neighbours))
+        union = len(self.neighbours) + len(other.neighbours) - intersection  # inclusion-exclusion
+        return intersection / union
+
+
+class Graph:
+    """A graph used to represent a book review network.
+    """
+    # Private Instance Attributes:
+    #     - _vertices:
+    #         A collection of the vertices contained in this graph.
+    #         Maps item to _Vertex object.
+    _vertices: dict[Any, _Vertex]
+
+    def __init__(self) -> None:
+        """Initialize an empty graph (no vertices or edges)."""
+        self._vertices = {}
+
+    def add_vertex(self, item: Any, kind: str) -> None:
+        """Add a vertex with the given item and kind to this graph.
+
+        The new vertex is not adjacent to any other vertices.
+        Do nothing if the given item is already in this graph.
+
+        Preconditions:
+            - kind in {'user', 'book'}
+        """
+        if item not in self._vertices:
+            self._vertices[item] = _Vertex(item, kind)
+
+    def add_edge(self, item1: Any, item2: Any) -> None:
+        """Add an edge between the two vertices with the given items in this graph.
+
+        Raise a ValueError if item1 or item2 do not appear as vertices in this graph.
+
+        Preconditions:
+            - item1 != item2
+        """
+        if item1 in self._vertices and item2 in self._vertices:
+            v1 = self._vertices[item1]
+            v2 = self._vertices[item2]
+
+            v1.neighbours.add(v2)
+            v2.neighbours.add(v1)
+        else:
+            raise ValueError
+
+    def adjacent(self, item1: Any, item2: Any) -> bool:
+        """Return whether item1 and item2 are adjacent vertices in this graph.
+
+        Return False if item1 or item2 do not appear as vertices in this graph.
+        """
+        if item1 in self._vertices and item2 in self._vertices:
+            v1 = self._vertices[item1]
+            return any(v2.item == item2 for v2 in v1.neighbours)
+        else:
+            return False
+
+    def get_neighbours(self, item: Any) -> set:
+        """Return a set of the neighbours of the given item.
+
+        Note that the *items* are returned, not the _Vertex objects themselves.
+
+        Raise a ValueError if item does not appear as a vertex in this graph.
+        """
+        if item in self._vertices:
+            v = self._vertices[item]
+            return {neighbour.item for neighbour in v.neighbours}
+        else:
+            raise ValueError
+
+    def get_all_vertices(self, kind: Literal['', 'user', 'book'] = '') -> set:
+        """Return a set of all vertex items in this graph.
+
+        If kind != '', only return the items of the given vertex kind.
+
+        Preconditions:
+            - kind in {'', 'user', 'book'}
+        """
+        if kind != '':
+            return {v.item for v in self._vertices.values() if v.kind == kind}
+        else:
+            return set(self._vertices.keys())
+
+    def to_networkx(self, max_vertices: int = 5000) -> nx.Graph:
+        """Convert this graph into a networkx Graph.
+
+        max_vertices specifies the maximum number of vertices that can appear in the graph.
+        (This is necessary to limit the visualization output for large graphs.)
+
+        Note that this method is provided for you, and you shouldn't change it.
+        """
+        graph_nx = nx.Graph()
+        for v in self._vertices.values():
+            graph_nx.add_node(v.item, kind=v.kind)
+
+            for u in v.neighbours:
+                if graph_nx.number_of_nodes() < max_vertices:
+                    graph_nx.add_node(u.item, kind=u.kind)
+
+                if u.item in graph_nx.nodes:
+                    graph_nx.add_edge(v.item, u.item)
+
+            if graph_nx.number_of_nodes() >= max_vertices:
+                break
+
+        return graph_nx
+
+    ############################################################################
+    # Part 1, Q3
+    ############################################################################
+    def get_similarity_score(self, item1: Any, item2: Any) -> float:
+        """Return the similarity score between the two given items in this graph.
+
+        Raise a ValueError if item1 or item2 do not appear as vertices in this graph.
+
+        >>> g = Graph()
+        >>> for i in range(0, 6):
+        ...     g.add_vertex(str(i), kind='user')
+        >>> g.add_edge('0', '2')
+        >>> g.add_edge('0', '3')
+        >>> g.add_edge('0', '4')
+        >>> g.add_edge('1', '3')
+        >>> g.add_edge('1', '4')
+        >>> g.add_edge('1', '5')
+        >>> g.get_similarity_score('0', '1')
+        0.5
+        """
+        if item1 not in self._vertices or item2 not in self._vertices:
+            raise ValueError
+        return self._vertices[item1].similarity_score(self._vertices[item2])
+
+    ############################################################################
+    # Part 1, Q4
+    ############################################################################
+    def recommend_books(self, book: str, limit: int) -> list[str]:
+        """Return a list of up to <limit> recommended books based on similarity to the given book.
+
+        The return value is a list of the titles of recommended books, sorted in
+        *descending order* of similarity score. Ties are broken in descending order
+        of book title. That is, if v1 and v2 have the same similarity score, then
+        v1 comes before v2 if and only if v1.item > v2.item.
+
+        The returned list should NOT contain:
+            - the input book itself
+            - any book with a similarity score of 0 to the input book
+            - any duplicates
+            - any vertices that represents a user (instead of a book)
+
+        Up to <limit> books are returned, starting with the book with the highest similarity score,
+        then the second-highest similarity score, etc. Fewer than <limit> books are returned if
+        and only if there aren't enough books that meet the above criteria.
+
+        Preconditions:
+            - book in self._vertices
+            - self._vertices[book].kind == 'book'
+            - limit >= 1
+
+        >>> my_graph = load_review_graph('data/reviews_full.csv', 'data/book_names.csv')
+        >>> title = "Harry Potter and the Sorcerer's Stone (Book 1)"
+        >>> import pprint
+        >>> pprint.pprint(my_graph.recommend_books(title, 10))
+        ['The Casual Vacancy',
+         'Harry Potter and the Chamber of Secrets',
+         'Harry Potter and the Prisoner of Azkaban',
+         'Harry Potter and the Chamber of Secrets, Book 2',
+         'Harry Potter and the Deathly Hallows, Book 7',
+         'Harry Potter And The Goblet Of Fire',
+         'Harry Potter And The Order Of The Phoenix',
+         'Harry Potter and the Half-Blood Prince (Book 6)',
+         "The Cuckoo's Calling (Cormoran Strike)",
+         'Fellowship of the Ring (Lord of the Rings Part 1)']
+        """
+        book = self._vertices[book]  # vertex is more useful here
+        books = set()  # all books distance == 2 away from self
+        for neighbour in book.neighbours:
+            books.update(neighbour.neighbours)
+        books.remove(book)
+        arr = [(book.similarity_score(x), x.item) for x in books]
+        arr = sorted(arr, reverse=True)[:limit]
+        return [x[1] for x in arr]
+
+
+################################################################################
+# Part 1, Q1
+################################################################################
+def load_review_graph(reviews_file: str, book_names_file: str) -> Graph:
+    """Return a book review graph corresponding to the given datasets.
+
+    The book review graph stores one vertex for each user and book in the datasets.
+    Each vertex stores as its item either a user ID or book TITLE (the latter is why
+    you need the book_names_file). Use the "kind" _Vertex attribute to differentiate
+    between the two vertex types.
+
+    Edges represent a review between a user and a book. In this graph, each edge
+    only represents the existence of a review---IGNORE THE REVIEW SCORE in the
+    datasets, as we don't have a way to represent these scores (yet).
+
+    Preconditions:
+        - reviews_file is the path to a CSV file corresponding to the book review data
+          format described on the assignment handout
+        - book_names_file is the path to a CSV file corresponding to the book data
+          format described on the assignment handout
+
+    >>> g = load_review_graph('data/reviews_small.csv', 'data/book_names.csv')
+    >>> len(g.get_all_vertices(kind='book'))
+    4
+    >>> len(g.get_all_vertices(kind='user'))
+    5
+    >>> user1_reviews = g.get_neighbours('user1')
+    >>> len(user1_reviews)
+    3
+    >>> "Harry Potter and the Sorcerer's Stone (Book 1)" in user1_reviews
+    True
+    """
+    g = Graph()
+
+    # mp[book_id] = book_name
+    mp: dict[str, str]
+
+    # Read book names file and create id-name mapping
+    with open(book_names_file, 'r', newline='', encoding='UTF-8') as f:
+        reader = csv.reader(f)
+        mp = dict(reader)
+
+    # Read user review file and link user and reviews in the graph
+    with open(reviews_file, 'r', newline='', encoding='UTF-8') as f:
+        reader = csv.reader(f)
+        for user_id, book_id, rating in reader:
+            g.add_vertex(user_id, 'user')
+            g.add_vertex(mp[book_id], 'book')
+            g.add_edge(user_id, mp[book_id])
+
+    return g
+
+
+if __name__ == '__main__':
+    # You can uncomment the following lines for code checking/debugging purposes.
+    # However, we recommend commenting out these lines when working with the large
+    # datasets, as checking representation invariants and preconditions greatly
+    # increases the running time of the functions/methods.
+    # import python_ta.contracts
+    # python_ta.contracts.check_all_contracts()
+
+    import doctest
+    doctest.testmod()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 100,
+        'disable': ['E1136'],
+        'extra-imports': ['csv', 'networkx'],
+        'allowed-io': ['load_review_graph'],
+        'max-nested-blocks': 4
+    })

assignments/A3/a3_part2_predictions.py

@@ -0,0 +1,241 @@
+"""CSC111 Winter 2022 Assignment 3: Graphs, Recommender Systems, and Clustering (Part 2)
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This Python module contains classes responsible for making predictions of book review scores.
+We've provided the abstract class and some example subclasses, and you'll complete one new
+subclass and a new function to evaluate the different classes.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr, David Liu, and Isaac Waller.
+"""
+from __future__ import annotations
+import csv
+from typing import Union
+
+import a3_part2_recommendations
+
+
+class ReviewScorePredictor:
+    """A graph-based entity that predicts scores for book reviews.
+
+    This is an abstract class, and should be subclasses to implement different review
+    prediction algorithms.
+
+    Instance Attributes:
+        - graph: The book review graph that this entity uses to make predictions.
+    """
+    graph: a3_part2_recommendations.WeightedGraph
+
+    def __init__(self, graph: a3_part2_recommendations.WeightedGraph) -> None:
+        """Initialize a new ReviewScorePredictor."""
+        self.graph = graph
+
+    def predict_review_score(self, user: str, book: str) -> int:
+        """Predict the score (1-5) that the given user would give the given book.
+
+        If there is already an edge between the given user and book in the graph,
+        return that score. Otherwise, return a predicted score.
+
+        Preconditions:
+            - user in self.graph._vertices
+            - book in self.graph._vertices
+        """
+        raise NotImplementedError
+
+
+class FiveStarPredictor(ReviewScorePredictor):
+    """A book review predictor that always predicts a five-star review,
+    ignoring the actual book and user.
+    """
+    def predict_review_score(self, user: str, book: str) -> int:
+        """Predict the score that the given user would give the given book.
+
+        If there is already an edge between the given user and book in the graph,
+        return that score. Otherwise, return 5 as the predicted score.
+
+        Preconditions:
+            - user in self.graph._vertices
+            - book in self.graph._vertices
+        """
+        if self.graph.adjacent(user, book):
+            return self.graph.get_weight(user, book)
+        else:
+            return 5
+
+
+class BookAverageScorePredictor(ReviewScorePredictor):
+    """A book review predictor that always predicts based on the book's average score,
+    ignoring any user preferences.
+    """
+    def predict_review_score(self, user: str, book: str) -> int:
+        """Predict the score that the given user would give the given book.
+
+        If there is already an edge between the given user and book in the graph,
+        return that score. Otherwise, return the book's average review score in
+        the graph, rounded to the nearest integer (using the built-in `round` function).
+
+        Preconditions:
+            - user in self.graph._vertices
+            - book in self.graph._vertices
+            - the given book has at least one review
+        """
+        if self.graph.adjacent(user, book):
+            return self.graph.get_weight(user, book)
+        else:
+            return round(self.graph.average_weight(book))
+
+
+################################################################################
+# Part 2, Q3
+################################################################################
+class SimilarUserPredictor(ReviewScorePredictor):
+    """A book review predictor that makes a prediction based on how similar users rated the book.
+
+    Representation Invariants:
+        - self._score_type in {'unweighted', 'strict'}
+    """
+    # Private Instance Attributes:
+    #   - _score_type: the type of similarity score to use when computing similarity score
+    _score_type: str
+
+    def __init__(self, graph: a3_part2_recommendations.WeightedGraph,
+                 score_type: str = 'unweighted') -> None:
+        """Initialize a new SimilarUserPredictor.
+
+        You may want to review Section 10.4 of the Course Notes for a reminder on
+        how to properly override a superclass initializer. To avoid a python_ta.contracts error,
+        initialize self._score_type at the TOP of this method body.
+        """
+        self._score_type = score_type
+        ReviewScorePredictor.__init__(self, graph)
+
+    def predict_review_score(self, user: str, book: str) -> int:
+        """Predict the score that the given user would give the given book.
+
+        If there is already an edge between the given user and book in the graph,
+        return that score. Otherwise, return the book's WEIGHTED review score among
+        all users who have read the book, where the weight used is the similarity
+        score of the reviewing user with the given user. self._score_type is used
+        to determine which similarity score to use for the weights
+
+        As usual, round this score using the built-in `round` function.
+
+        For example, suppose there are three users A, B, C who have read the book,
+        and one, D, who has not. We want to use the review scores of A, B, and C to predict
+        the rating for D. The three user ratings and weighted similarity score with D
+        are shown in this table:
+
+            | User | Review score | Weighted similarity score with D |
+            | ---- | ------------ | -------------------------------- |
+            | A    | 3            | 0.4                              |
+            | B    | 5            | 0.1                              |
+            | C    | 2            | 0.3                              |
+
+        Then the predicted review for D equals:
+
+            (3 * 0.4 + 5 * 0.1 + 2 * 0.3) / (0.4 + 0.1 + 0.3) = 2.875
+
+        and so this function would return 3.
+
+        If the total similarity score from all of the book's reviewers is 0,
+        then instead return the book's average review score (same as BookAverageScorePredictor).
+
+        Preconditions:
+            - user in self.graph._vertices
+            - book in self.graph._vertices
+        """
+        if self.graph.adjacent(user, book):  # if the user already made a review, use that score
+            return self.graph.get_weight(user, book)
+        users = self.graph.get_neighbours(book)
+        total_weighted = 0
+        total = 0
+        all_zero = True
+        for u in users:
+            weight = self.graph.get_similarity_score(u, book, 'strict')
+            score = self.graph.get_weight(u, book)
+            if weight > 0:
+                all_zero = False
+            total_weighted += score * weight
+            total += score
+        if all_zero:
+            return round(total / len(users))
+        return round(total_weighted / len(users))
+
+
+################################################################################
+# Part 2, Q4
+################################################################################
+def evaluate_predictor(predictor: ReviewScorePredictor,
+                       test_file: str, book_names_file: str) -> dict[str, Union[int, float]]:
+    """Evaluate the given ReviewScorePredictor on the given test file.
+
+    Read in each row of the given test_file (which contains a book, user, and
+    review score). For each row, use the given predictor to make a prediction of the review
+    score, and compare that prediction against the actual given review score from the file.
+
+    Return a dictionary summarizing the performance of the predictor. This dictionary
+    has the following keys:
+        - 'num_reviews': the total number of predicted review scores (equal to the
+            number of lines in the CSV file)
+        - 'num_correct': the number of predicted review scores that exactly matched the
+            actual review score
+        - 'average_error': the average of the *absolute value difference* between
+            predicted and actual review scores across all reviews in the test file
+
+    Preconditions:
+        - test_file is the path to a CSV file corresponding to the book review data
+          format described on the assignment handout
+        - book_names_file is the path to a CSV file corresponding to the book data
+        - test_file has at least one row
+        - all users and books in test_file are in predictor.graph
+          format described on the assignment handout
+    """
+    num_reviews = 0
+    num_correct = 0
+    total_error = 0
+    mp: dict[str, str]  # maps book ID to book name
+    with open(book_names_file, 'r', newline='', encoding='UTF-8') as f:
+        reader = csv.reader(f)
+        mp = dict(reader)
+    with open(test_file, 'r', newline='', encoding='UTF-8') as f:
+        reader = csv.reader(f)
+        for book, user, score in reader:
+            book = mp[book]
+            num_reviews += 1
+            actual = predictor.predict_review_score(user, book)
+            if actual == score:
+                num_correct += 1
+            total_error += abs(score - actual)
+    return {
+        'num_reviews': num_reviews,
+        'num_correct': num_correct,
+        'average_error': total_error / num_reviews,
+    }
+
+
+if __name__ == '__main__':
+    # You can uncomment the following lines for code checking/debugging purposes.
+    # However, we recommend commenting out these lines when working with the large
+    # datasets, as checking representation invariants and preconditions greatly
+    # increases the running time of the functions/methods.
+    # import python_ta.contracts
+    # python_ta.contracts.check_all_contracts()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 1000,
+        'disable': ['E1136'],
+        'extra-imports': ['csv', 'a3_part2_recommendations'],
+        'allowed-io': ['evaluate_predictor'],
+        'max-nested-blocks': 4
+    })

assignments/A3/a3_part2_recommendations.py

@@ -0,0 +1,298 @@
+"""CSC111 Winter 2022 Assignment 3: Graphs, Recommender Systems, and Clustering (Part 2)
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This Python module contains new classes to represent *weighted graphs and vertices*,
+which we'll use to represent a book review network with scores of reviews as well.
+This file is structured very similarly to a3_part1.py.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr, David Liu, and Isaac Waller.
+"""
+from __future__ import annotations
+import csv
+from typing import Any, Union, Literal
+
+from a3_part1 import Graph
+
+
+class _WeightedVertex:
+    """A vertex in a weighted book review graph, used to represent a user or a book.
+
+    Same documentation as _Vertex from Part 1, except now neighbours is a dictionary mapping
+    a neighbour vertex to the weight of the edge to from self to that neighbour.
+    Note that in Part 2, the weights will be integers between 1 and 5, but in Part 3 the
+    weights will be floats.
+
+    Instance Attributes:
+        - item: The data stored in this vertex, representing a user or book.
+        - kind: The type of this vertex: 'user' or 'book'.
+        - neighbours: The vertices that are adjacent to this vertex, and their corresponding
+            edge weights.
+
+    Representation Invariants:
+        - self not in self.neighbours
+        - all(self in u.neighbours for u in self.neighbours)
+        - self.kind in {'user', 'book'}
+    """
+    item: Any
+    kind: str
+    neighbours: dict[_WeightedVertex, Union[int, float]]
+
+    def __init__(self, item: Any, kind: str) -> None:
+        """Initialize a new vertex with the given item and kind.
+
+        This vertex is initialized with no neighbours.
+
+        Preconditions:
+            - kind in {'user', 'book'}
+        """
+        self.item = item
+        self.kind = kind
+        self.neighbours = {}
+
+    def degree(self) -> int:
+        """Return the degree of this vertex."""
+        return len(self.neighbours)
+
+    ############################################################################
+    # Part 2, Q2
+    ############################################################################
+    def similarity_score_unweighted(self, other: _WeightedVertex) -> float:
+        """Return the unweighted similarity score between this vertex and other.
+
+        The unweighted similarity score is calculated in the same way as the
+        similarity score for _Vertex (from Part 1). That is, just look at edges,
+        and ignore the weights.
+        """
+        if self.degree() == 0 or other.degree() == 0:
+            return 0.0
+        a = set(self.neighbours.keys())
+        b = set(other.neighbours.keys())
+        intersection = len(a.intersection(b))
+        union = len(a) + len(b) - intersection  # inclusion-exclusion
+        return intersection / union
+
+    def similarity_score_strict(self, other: _WeightedVertex) -> float:
+        """Return the strict weighted similarity score between this vertex and other.
+
+        See Assignment handout for details.
+        """
+        if self.degree() == 0 or other.degree() == 0:
+            return 0.0
+        a = set(self.neighbours.keys())
+        b = set(other.neighbours.keys())
+        generator = (x for x in a if x in b and x.neighbours[self] == x.neighbours[other])
+        intersection = sum(1 for _ in generator)
+        union = len(a.union(b))
+        return intersection / union
+
+
+class WeightedGraph(Graph):
+    """A weighted graph used to represent a book review network that keeps track of review scores.
+
+    Note that this is a subclass of the Graph class from Part 1, and so inherits any methods
+    from that class that aren't overridden here.
+    """
+    # Private Instance Attributes:
+    #     - _vertices:
+    #         A collection of the vertices contained in this graph.
+    #         Maps item to _WeightedVertex object.
+    _vertices: dict[Any, _WeightedVertex]
+
+    def __init__(self) -> None:
+        """Initialize an empty graph (no vertices or edges)."""
+        self._vertices = {}
+
+        # This call isn't necessary, except to satisfy PythonTA.
+        Graph.__init__(self)
+
+    def add_vertex(self, item: Any, kind: str) -> None:
+        """Add a vertex with the given item and kind to this graph.
+
+        The new vertex is not adjacent to any other vertices.
+        Do nothing if the given item is already in this graph.
+
+        Preconditions:
+            - kind in {'user', 'book'}
+        """
+        if item not in self._vertices:
+            self._vertices[item] = _WeightedVertex(item, kind)
+
+    def add_edge(self, item1: Any, item2: Any, weight: Union[int, float] = 1) -> None:
+        """Add an edge between the two vertices with the given items in this graph,
+        with the given weight.
+
+        Raise a ValueError if item1 or item2 do not appear as vertices in this graph.
+
+        Preconditions:
+            - item1 != item2
+        """
+        if item1 in self._vertices and item2 in self._vertices:
+            v1 = self._vertices[item1]
+            v2 = self._vertices[item2]
+
+            # Add the new edge
+            v1.neighbours[v2] = weight
+            v2.neighbours[v1] = weight
+        else:
+            # We didn't find an existing vertex for both items.
+            raise ValueError
+
+    def get_weight(self, item1: Any, item2: Any) -> Union[int, float]:
+        """Return the weight of the edge between the given items.
+
+        Return 0 if item1 and item2 are not adjacent.
+
+        Preconditions:
+            - item1 and item2 are vertices in this graph
+        """
+        v1 = self._vertices[item1]
+        v2 = self._vertices[item2]
+        return v1.neighbours.get(v2, 0)
+
+    def average_weight(self, item: Any) -> float:
+        """Return the average weight of the edges adjacent to the vertex corresponding to item.
+
+        Raise ValueError if item does not corresponding to a vertex in the graph.
+        """
+        if item in self._vertices:
+            v = self._vertices[item]
+            return sum(v.neighbours.values()) / len(v.neighbours)
+        else:
+            raise ValueError
+
+    ############################################################################
+    # Part 2, Q2
+    ############################################################################
+    def get_similarity_score(self, item1: Any, item2: Any,
+                             score_type: Literal['unweighted', 'strict'] = 'unweighted') -> float:
+        """Return the similarity score between the two given items in this graph.
+
+        score_type is one of 'unweighted' or 'strict', corresponding to the
+        different ways of calculating weighted graph vertex similarity, as described
+        on the assignment handout.
+
+        Raise a ValueError if item1 or item2 do not appear as vertices in this graph.
+
+        Preconditions:
+            - score_type in {'unweighted', 'strict'}
+        """
+        if item1 not in self._vertices or item2 not in self._vertices:
+            raise ValueError
+        if score_type == 'unweighted':
+            return self._vertices[item1].similarity_score_unweighted(self._vertices[item2])
+        return self._vertices[item1].similarity_score_strict(self._vertices[item2])
+
+    def recommend_books(self, book: str, limit: int,
+                        score_type: Literal['unweighted', 'strict'] = 'unweighted') -> list[str]:
+        """Return a list of up to <limit> recommended books based on similarity to the given book.
+
+        score_type is one of 'unweighted' or 'strict', corresponding to the
+        different ways of calculating weighted graph vertex similarity, as described
+        on the assignment handout. The corresponding similarity score formula is used
+        in this method (whenever the phrase "similarity score" appears below).
+
+        The return value is a list of the titles of recommended books, sorted in
+        *descending order* of similarity score. Ties are broken in descending order
+        of book title. That is, if v1 and v2 have the same similarity score, then
+        v1 comes before v2 if and only if v1.item > v2.item.
+
+        The returned list should NOT contain:
+            - the input book itself
+            - any book with a similarity score of 0 to the input book
+            - any duplicates
+            - any vertices that represents a user (instead of a book)
+
+        Up to <limit> books are returned, starting with the book with the highest similarity score,
+        then the second-highest similarity score, etc. Fewer than <limit> books are returned if
+        and only if there aren't enough books that meet the above criteria.
+
+        Preconditions:
+            - book in self._vertices
+            - self._vertices[book].kind == 'book'
+            - limit >= 1
+            - score_type in {'unweighted', 'strict'}
+        """
+        book = self._vertices[book]  # vertex is more useful here
+        books = set()  # all books distance == 2 away from self
+        for neighbour in book.neighbours:
+            books.update(neighbour.neighbours.keys())
+        books.remove(book)
+        arr = []
+        for x in books:
+            if score_type == 'strict':
+                score = book.similarity_score_strict(x)
+                if score == 0:
+                    continue
+            else:
+                score = book.similarity_score_unweighted(x)
+            arr.append((score, x.item))
+        arr = sorted(arr, reverse=True)[:limit]
+        return [y[1] for y in arr]
+
+
+################################################################################
+# Part 2, Q1
+################################################################################
+def load_weighted_review_graph(reviews_file: str, book_names_file: str) -> WeightedGraph:
+    """Return a book review WEIGHTED graph corresponding to the given datasets.
+
+    This should be very similar to the corresponding function Part 1, except now
+    the book review scores are used as edge weights.
+
+    Preconditions:
+        - reviews_file is the path to a CSV file corresponding to the book review data
+          format described on the assignment handout
+        - book_names_file is the path to a CSV file corresponding to the book data
+          format described on the assignment handout
+    """
+    g = WeightedGraph()
+
+    # mp[book_id] = book_name
+    mp: dict[str, str]
+
+    # Read book names file and create id-name mapping
+    with open(book_names_file, 'r', newline='', encoding='UTF-8') as f:
+        reader = csv.reader(f)
+        mp = dict(reader)
+
+    # Read user review file and link user and reviews in the graph
+    with open(reviews_file, 'r', newline='', encoding='UTF-8') as f:
+        reader = csv.reader(f)
+        for user_id, book_id, rating in reader:
+            g.add_vertex(user_id, 'user')
+            g.add_vertex(mp[book_id], 'book')
+            g.add_edge(user_id, mp[book_id], int(rating))
+
+    return g
+
+
+if __name__ == '__main__':
+    # You can uncomment the following lines for code checking/debugging purposes.
+    # However, we recommend commenting out these lines when working with the large
+    # datasets, as checking representation invariants and preconditions greatly
+    # increases the running time of the functions/methods.
+    # import python_ta.contracts
+    # python_ta.contracts.check_all_contracts()
+
+    import doctest
+    doctest.testmod()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 1000,
+        'disable': ['E1136', 'W0221'],
+        'extra-imports': ['csv', 'a3_part1'],
+        'allowed-io': ['load_weighted_review_graph'],
+        'max-nested-blocks': 4
+    })

assignments/A3/a3_part3.py

@@ -0,0 +1,207 @@
+"""CSC111 Winter 2022 Assignment 3: Graphs, Recommender Systems, and Clustering (Part 3)
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This Python module contains the functions you'll write for determining *clusters* of vertices
+in a graph.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr, David Liu, and Isaac Waller.
+"""
+import random
+from typing import Literal
+
+from a3_part2_recommendations import WeightedGraph
+
+
+################################################################################
+# Part 3, Q1
+################################################################################
+def create_book_graph(review_graph: WeightedGraph,
+                      threshold: float = 0.05,
+                      score_type: Literal['unweighted', 'strict'] = 'unweighted') -> WeightedGraph:
+    """Return a book graph based on the given review_graph.
+
+    The score_type parameter plays the same role as in WeightedGraph.get_similarity_score.
+
+    The returned book graph has the following properties:
+        1. Its vertex set is exactly the set of book vertices in review_graph
+            (items are book titles).
+        2. For every two distinct books b1 and b2, let s(b1, b2) be their similarity score,
+            where score_type specifies which similarity score to use.
+
+            - If s(b1, b2) > threshold, there is an edge between b1 and b2 in the book graph
+              with weight equal to s(b1, b2). Unlike Part 2, these weights will be floats,
+              not ints!
+            - Otherwise, there is no edge between b1 and b2.
+
+    Preconditions:
+        - score_type in {'unweighted', 'strict'}
+    """
+    # Add all books as vertices
+    book_graph = WeightedGraph()
+    book_names: set[str] = review_graph.get_all_vertices('book')
+    for b in book_names:
+        book_graph.add_vertex(b, 'book')
+
+    # Add all edges
+    for b1 in book_names:
+        for b2 in book_names:
+            if b1 == b2:
+                continue
+
+            # Calculate similarity score
+            score = review_graph.get_similarity_score(b1, b2, score_type)
+            if score <= threshold:
+                continue
+
+            # Add edge
+            book_graph.add_edge(b1, b2, score)
+
+    # Done
+    return book_graph
+
+
+################################################################################
+# Part 3, Q2
+################################################################################
+def cross_cluster_weight(book_graph: WeightedGraph, cluster1: set, cluster2: set) -> float:
+    """Return the cross-cluster weight between cluster1 and cluster2.
+
+    See assignment handout for the definition of cross-cluster weight.
+
+    Preconditions:
+        - cluster1 != set() and cluster2 != set()
+        - cluster1.isdisjoint(cluster2)
+        - Every item in cluster1 and cluster2 is a vertex in book_graph
+
+    >>> bg = WeightedGraph()
+    >>> for b in range(4): \
+            bg.add_vertex(f'B{b}', 'book')
+    >>> bg.add_edge('B0', 'B1', .5)
+    >>> bg.add_edge('B0', 'B2', .4)
+    >>> bg.add_edge('B1', 'B2', .3)
+    >>> bg.get_weight('B0', 'B1')
+    0.5
+    >>> cross_cluster_weight(bg, {'B0', 'B1'}, {'B2', 'B3'}) == (.4 + .3) / 4
+    True
+    """
+    # sw = sum(book_graph.get_weight(v1, v2) for v1 in cluster1 for v2 in cluster2)
+    sw = 0
+    for v1 in cluster1:
+        for v2 in cluster2:
+            sw += book_graph.get_weight(v1, v2)
+
+    return sw / (len(cluster1) * len(cluster2))
+
+
+################################################################################
+# Part 3, Q3 (don't modify this code)
+################################################################################
+def find_clusters_random(graph: WeightedGraph, num_clusters: int) -> list[set]:
+    """Return a list of <num_clusters> vertex clusters for the given graph.
+
+    At each iteration, this algorithm first chooses a random cluster, and then chooses
+    the cluster that has the highest cross-cluster weight to the randomly-chose cluster
+    to merge.
+
+    Preconditions:
+        - num_clusters >= 1
+    """
+    # Each book starts in its own cluster
+    clusters = [{book} for book in graph.get_all_vertices()]
+
+    for _ in range(0, len(clusters) - num_clusters):
+        print(f'{len(clusters)} clusters')
+
+        c1 = random.choice(clusters)
+        # Pick the best cluster to merge c1 into.
+        best = -1
+        best_c2 = None
+
+        for c2 in clusters:
+            if c1 is not c2:
+                score = cross_cluster_weight(graph, c1, c2)
+                if score > best:
+                    best = score
+                    best_c2 = c2
+
+        best_c2.update(c1)
+        clusters.remove(c1)
+
+    return clusters
+
+
+def find_clusters_greedy(graph: WeightedGraph, num_clusters: int) -> list[set]:
+    """Return a list of <num_clusters> vertex clusters for the given graph.
+
+    At each iteration, this algorithm chooses the pair of clusters with the highest
+    cross-cluster weight to merge.
+
+    Preconditions:
+        - num_clusters >= 1
+    """
+    # Each book starts in its own cluster
+    clusters = [{book} for book in graph.get_all_vertices()]
+
+    for _ in range(0, len(clusters) - num_clusters):
+        print(f'{len(clusters)} clusters')
+
+        # Merge the two communities with the most links
+        best = -1
+        best_c1, best_c2 = None, None
+
+        for i1 in range(0, len(clusters)):
+            for i2 in range(i1 + 1, len(clusters)):
+                c1, c2 = clusters[i1], clusters[i2]
+                score = cross_cluster_weight(graph, c1, c2)
+                if score > best:
+                    best, best_c1, best_c2 = score, c1, c2
+
+        best_c2.update(best_c1)
+        clusters.remove(best_c1)
+
+    return clusters
+
+
+if __name__ == '__main__':
+    # You can uncomment the following lines for code checking/debugging purposes.
+    # However, we recommend commenting out these lines when working with the large
+    # datasets, as checking representation invariants and preconditions greatly
+    # increases the running time of the functions/methods.
+    # import python_ta.contracts
+    # python_ta.contracts.check_all_contracts()
+
+    import doctest
+    doctest.testmod()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 1000,
+        'disable': ['E1136'],
+        'extra-imports': ['random', 'a3_part2_recommendations'],
+        'allowed-io': ['find_clusters_greedy', 'find_clusters_random'],
+        'max-nested-blocks': 4
+    })
+
+    # Q1 Test
+    # review_graph = load_weighted_review_graph('data/reviews_full.csv', 'data/book_names.csv')
+    # book_graph = create_book_graph(review_graph, 0.03)
+    # from a3_visualization import visualize_graph
+    # visualize_graph(book_graph)
+
+    # Q3 Test
+    # review_graph = load_weighted_review_graph('data/reviews_full.csv', 'data/book_names.csv')
+    # book_graph = create_book_graph(review_graph, threshold=0.01, score_type='strict')
+    # clusters = find_clusters_random(book_graph, 15)
+    # from a3_visualization import visualize_graph_clusters
+    # visualize_graph_clusters(book_graph, clusters)

assignments/A3/a3_visualization.py

@@ -0,0 +1,175 @@
+"""CSC111 Winter 2022 Assignment 3: Graphs, Recommender Systems, and Clustering (Visualization)
+
+Module Description
+==================
+
+This module contains some Python functions that you can use to visualize the graphs
+you're working with on this assignment. You should not modify anything in this file.
+It will not be submitted for grading.
+
+Disclaimer: we didn't have time to make this file fully PythonTA-compliant!
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr, David Liu, and Isaac Waller.
+"""
+import networkx as nx
+from plotly.graph_objs import Scatter, Figure
+
+import a3_part1
+
+
+# Colours to use when visualizing different clusters.
+COLOUR_SCHEME = [
+    '#2E91E5', '#E15F99', '#1CA71C', '#FB0D0D', '#DA16FF', '#222A2A', '#B68100',
+    '#750D86', '#EB663B', '#511CFB', '#00A08B', '#FB00D1', '#FC0080', '#B2828D',
+    '#6C7C32', '#778AAE', '#862A16', '#A777F1', '#620042', '#1616A7', '#DA60CA',
+    '#6C4516', '#0D2A63', '#AF0038'
+]
+
+LINE_COLOUR = 'rgb(210,210,210)'
+VERTEX_BORDER_COLOUR = 'rgb(50, 50, 50)'
+BOOK_COLOUR = 'rgb(89, 205, 105)'
+USER_COLOUR = 'rgb(105, 89, 205)'
+
+
+def visualize_graph(graph: a3_part1.Graph,
+                    layout: str = 'spring_layout',
+                    max_vertices: int = 5000,
+                    output_file: str = '') -> None:
+    """Use plotly and networkx to visualize the given graph.
+
+    Optional arguments:
+        - layout: which graph layout algorithm to use
+        - max_vertices: the maximum number of vertices that can appear in the graph
+        - output_file: a filename to save the plotly image to (rather than displaying
+            in your web browser)
+    """
+    graph_nx = graph.to_networkx(max_vertices)
+
+    pos = getattr(nx, layout)(graph_nx)
+
+    x_values = [pos[k][0] for k in graph_nx.nodes]
+    y_values = [pos[k][1] for k in graph_nx.nodes]
+    labels = list(graph_nx.nodes)
+    kinds = [graph_nx.nodes[k]['kind'] for k in graph_nx.nodes]
+
+    colours = [BOOK_COLOUR if kind == 'book' else USER_COLOUR for kind in kinds]
+
+    x_edges = []
+    y_edges = []
+    for edge in graph_nx.edges:
+        x_edges += [pos[edge[0]][0], pos[edge[1]][0], None]
+        y_edges += [pos[edge[0]][1], pos[edge[1]][1], None]
+
+    trace3 = Scatter(x=x_edges,
+                     y=y_edges,
+                     mode='lines',
+                     name='edges',
+                     line=dict(color=LINE_COLOUR, width=1),
+                     hoverinfo='none',
+                     )
+    trace4 = Scatter(x=x_values,
+                     y=y_values,
+                     mode='markers',
+                     name='nodes',
+                     marker=dict(symbol='circle-dot',
+                                 size=5,
+                                 color=colours,
+                                 line=dict(color=VERTEX_BORDER_COLOUR, width=0.5)
+                                 ),
+                     text=labels,
+                     hovertemplate='%{text}',
+                     hoverlabel={'namelength': 0}
+                     )
+
+    data1 = [trace3, trace4]
+    fig = Figure(data=data1)
+    fig.update_layout({'showlegend': False})
+    fig.update_xaxes(showgrid=False, zeroline=False, visible=False)
+    fig.update_yaxes(showgrid=False, zeroline=False, visible=False)
+
+    if output_file == '':
+        fig.show()
+    else:
+        fig.write_image(output_file)
+
+
+def visualize_graph_clusters(graph: a3_part1.Graph, clusters: list[set],
+                             layout: str = 'spring_layout',
+                             max_vertices: int = 5000,
+                             output_file: str = '') -> None:
+    """Visualize the given graph, using different colours to illustrate the different clusters.
+
+    Hides all edges that go from one cluster to another. (This helps the graph layout algorithm
+    positions vertices in the same cluster close together.)
+
+    Same optional arguments as visualize_graph (see that function for details).
+    """
+    graph_nx = graph.to_networkx(max_vertices)
+    all_edges = list(graph_nx.edges)
+    for edge in all_edges:
+        # Check if edge is within the same cluster
+        if any((edge[0] in cluster) != (edge[1] in cluster) for cluster in clusters):
+            graph_nx.remove_edge(edge[0], edge[1])
+
+    pos = getattr(nx, layout)(graph_nx)
+
+    x_values = [pos[k][0] for k in graph_nx.nodes]
+    y_values = [pos[k][1] for k in graph_nx.nodes]
+    labels = list(graph_nx.nodes)
+
+    colors = []
+    for k in graph_nx.nodes:
+        for i, c in enumerate(clusters):
+            if k in c:
+                colors.append(COLOUR_SCHEME[i % len(COLOUR_SCHEME)])
+                break
+        else:
+            colors.append(BOOK_COLOUR)
+
+    x_edges = []
+    y_edges = []
+    for edge in graph_nx.edges:
+        x_edges += [pos[edge[0]][0], pos[edge[1]][0], None]
+        y_edges += [pos[edge[0]][1], pos[edge[1]][1], None]
+
+    trace3 = Scatter(x=x_edges,
+                     y=y_edges,
+                     mode='lines',
+                     name='edges',
+                     line=dict(color=LINE_COLOUR, width=1),
+                     hoverinfo='none'
+                     )
+    trace4 = Scatter(x=x_values,
+                     y=y_values,
+                     mode='markers',
+                     name='nodes',
+                     marker=dict(symbol='circle-dot',
+                                 size=5,
+                                 color=colors,
+                                 line=dict(color=VERTEX_BORDER_COLOUR, width=0.5)
+                                 ),
+                     text=labels,
+                     hovertemplate='%{text}',
+                     hoverlabel={'namelength': 0}
+                     )
+
+    data1 = [trace3, trace4]
+    fig = Figure(data=data1)
+    fig.update_layout({'showlegend': False})
+    fig.update_xaxes(showgrid=False, zeroline=False, visible=False)
+    fig.update_yaxes(showgrid=False, zeroline=False, visible=False)
+    fig.show()
+
+    if output_file == '':
+        fig.show()
+    else:
+        fig.write_image(output_file)

assignments/A3/data/book_names.csv

@@ -0,0 +1,985 @@
+0027676463,The Night Before Christmas
+0060557818,Neverwhere: A Novel
+0425284689,Before We Were Yours: A Novel
+0804192898,Make Me: A Jack Reacher Novel
+0061992259,The One and Only Ivan
+0141022043,Blink - The Power Of Thinking Without Thinking
+0312352557,Still Life: A Chief Inspector Gamache Novel
+0743554450,A Thousand Splendid Suns: A Novel
+0986842222,The Color of Heaven
+1471118630,Never Too Far (Tempting Too Far Novel)
+1476753180,Ugly Love: A Novel
+1944083014,Judgment Cometh: and That Right Soon (Joe Dillard Series) (Volume 8)
+0002171856,The Sas Survival Handbook
+0147520762,"The Girl in the Spider's Web: A Lisbeth Salander novel, continuing Stieg Larsson's Millennium Series"
+0312577230,The Great Alone: A Novel
+1468139339,Wait For Me
+0151008116,Life of Pi
+0316175676,The Snow Child: A Novel
+0615953948,Mind's Eye
+1477826580,The American Lady (The Glassblower Trilogy)
+1503935310,Everything We Keep: A Novel
+0143121707,The Invention of Wings
+0375433392,"The Hard Way (Jack Reacher, No. 10"
+0544025830,Coming Clean: A Memoir
+1476707634,Drinking and Tweeting: And Other Brandi Blunders
+1477819967,Mrs. Saint and the Defectives: A Novel
+1477822585,The Last Town (Wayward Pines)
+1503953319,The Queen's Poisoner (Kingfountain)
+6077547328,"Maze Runner. Correr o Morir / Maze Runner, Run or die (Spanish Edition)"
+0006545793,Brave New World
+0983398038,The Rockstar's Daughter: (Treadwell Academy Series #1)
+0099557266,Playing for Pizza
+B003156C4E,Fellowship of the Ring (Lord of the Rings Part 1)
+0028180054,A Wrinkle in Time
+0099580470,Shift (Wool Trilogy)
+0143106708,Twelve Years a Slave (Penguin Classics)
+B00714PZMQ,Quiet: The Power of Introverts in a World That Can't Stop Talking
+0316228532,The Casual Vacancy
+074356619X,"The Secret (Unabridged, 4-CD Set)"
+1455576506,The Coincidence of Callie & Kayden
+1463514581,Twenty-Eight and a Half Wishes (A Rose Gardner Mystery)
+1477808663,The Game Changer: A Novel
+1491598778,The Good Neighbor
+1503939308,Exhume (Dr. Schwartzman)
+1503935183,The Last Woman Standing: A Novel
+0061997161,The Pioneer Woman: Black Heels to Tractor Wheels--A Love Story
+0147525071,Extreme Prey (A Prey Novel)
+0739453939,90 Minutes in Heaven
+1477822593,Dead Certain: A Novel (Broden Legal)
+1534640150,Earth Alone: Earthrise Book 1
+0140285628,The Man in the High Castle (Essential Penguin)
+0425246043,The Perfect Hope (The Inn Boonsboro Trilogy)
+0307967115,My Name Is Lucy Barton: A Novel
+0375969020,Wonder
+0763644765,Potty (Leslie Patricelli board books)
+0765326264,A Dog's Purpose: A Novel for Humans
+1410493415,The Life We Bury (Wheeler Large Print Book Series)
+1476755590,Real (The REAL series)
+0375434542,Origin: A Novel (Robert Langdon)
+1478288892,Sentence of Marriage: Promises to Keep (Volume 1)
+1536690872,All the Little Children
+0345539788,Red Rising
+0399179135,Lilac Girls
+0141328290,Thirteen Reasons Why
+0060539836,Kill the Competition
+B00EJQY4BM,The First Phone Call from Heaven: A Novel
+0385346859,The Dinner
+1520396074,Inheritance: A Psychological Mystery and Suspense Thriller
+B0002P0FFS,Lies My Teacher Told Me: Everything Your American History Textbook Got Wrong
+0028180062,The Call of the Wild with related readings
+0060794410,Good to Great CD: Why Some Companies Make the Leap.And Others Don't
+1542722829,Silent Child
+B00CYZC5S4,Anne Of Green Gables
+0060391448,Wicked: The Life and Times of the Wicked Witch of the West
+0615351913,Once We Were Brothers
+1433688670,"Fervent: A Woman's Battle Plan to Serious, Specific and Strategic Prayer"
+1472229959,The Letter
+B017WJ5PR4,"Harry Potter and the Deathly Hallows, Book 7"
+0062409883,Go Set a Watchman: A Novel
+0140434186,Wuthering Heights (Penguin Classics)
+0142428280,Fall of Giants (The Century Trilogy)
+0192831992,Ben-Hur (Oxford World's Classics)
+1475070306,UnEnchanted: An Unfortunate Fairy Tale (Unfortunate Fairy Tale;[bk. 1])
+0143170104,The Girl Who Played with Fire (Millennium Series)
+0451414128,Missing You
+0800757181,Daughter of Joy (Brides of Culdee Creek)
+0143142364,World Without End (Kingsbridge)
+1581344368,"The Holy Bible: English Standard Version, Reference Edition"
+0312364083,Firefly Lane
+1410447863,"An Invisible Thread: The True Story of an 11-Year-Old Panhandler, a Busy Sales Executive, and an Unlikely Meeting with Destiny (Wheeler Large Print Book Series)"
+0091900212,Think and Grow Rich
+0230755062,The Language of Flowers
+0692213740,In Between (A Katie Parker Production) (Volume 1)
+0975599518,Natural Cures "They" Don't Want You To Know About
+0310515025,"Seeking Allah, Finding Jesus: A Devout Muslim Encounters Christianity"
+0316346624,The Tipping Point: How Little Things Can Make a Big Difference
+1250034469,Be Careful What You Wish For: A Novel (The Clifton Chronicles)
+1250158060,Fire and Fury: Inside the Trump White House
+140883233X,Throne of Glass
+1501132938,The Woman in Cabin 10
+0062378082,The Nazi Officer's Wife: How One Jewish Woman Survived the Holocaust
+0099619105,Trump - The Art Of The Deal
+0230749259,Innocent (Will Robie series)
+0345486455,"Healthy Sleep Habits, Happy Child: A Step-by-Step Program for a Good Night's Sleep, 3rd Edition"
+1477809783,Terms of Enlistment (Frontlines)
+1484009290,Noble Beginnings: A Jack Noble Novel
+1501188291,Gwendy's Button Box: A Novella
+0132268310,The Lost Symbol (Dan Brown)
+1410478750,The Wright Brothers (Thorndike Press Large Print Popular and Narrative Nonfiction Series)
+151738799X,The Air He Breathes (Elements)
+0140283331,Lord of the Flies
+B015QPEV9E,The Call of the Wild eBook
+1477823832,The Paper Magician
+1503953866,The Secret Healer
+0061441848,Eat Right for Your Type
+B000X1MX7E,The Pillars of the Earth
+0340822775,Cloud Atlas
+1455134767,The Amateur: Barack Obama in the White House
+1463717091,Abducted
+1501110365,It Ends with Us: A Novel
+0062065246,The Round House
+0141339578,Prodigy
+0991677196,CyberStorm
+1477829865,Bum Rap (Jake Lassiter Legal Thrillers)
+0007350783,Emma (Collins Classics)
+0140390197,The Scarlet Letter: A Romance (The Penguin American Library)
+0345528670,The Aviator's Wife: A Novel
+0988348268,Jane's Melody
+1455530093,13 Hours: The Inside Account of What Really Happened In Benghazi
+0312641893,Cinder
+0316376469,"The Food Babe Way: Break Free from the Hidden Toxins in Your Food and Lose Weight, Look Years Younger, and Get Healthy in Just 21 Days!"
+0739474979,Water For Elephants
+1478230096,"Les Miserables In Plain and Simple English: Includes Study Guide, Historical Context, Biography, and Character Index"
+0061928178,Beautiful Ruins: A Novel
+0091923530,"The 4-hour Workweek: Escape the 9-5, Live Anywhere and Join the New Rich"
+0718077296,"Same Kind of Different As Me Movie Edition: A Modern-Day Slave, an International Art Dealer, and the Unlikely Woman Who Bound Them Together"
+1501228722,Crazy Little Thing (A Bell Harbor Novel)
+0143109464,Me Before You: A Novel (Movie Tie-In) (Me Before You Trilogy)
+0529120887,Jesus Calling: Enjoying Peace in His Presence
+B000LP5E8M,The God Delusion
+069289344X,Dangerous
+0857207113,A Stolen Life: A Memoir
+1460995457,The Soulkeepers (The Soulkeepers Series) (Volume 1)
+0857208691,Kill Shot (The Mitch Rapp Series)
+1442369213,Rush Revere and the First Patriots: Time-Travel Adventures With Exceptional Americans (Audio CD)
+1477829822,The Prettiest One: A Thriller
+0345807294,The Circle
+0385336667,"Persuader (Jack Reacher, No. 7)"
+0399157565,The Bone Bed (Scarpetta)
+081298840X,When Breath Becomes Air
+141047383X,Revival (Thorndike Press Large Pring Basic)
+1477830812,The Girl from Krakow: A Novel
+1896350542,Squall
+0027164403,Dear Zoo (Dear Zoo & Friends)
+0307341577,Dark Places
+0310205719,The Purpose Driven Life
+0349410828,Apprentice in Death: 43
+0449012751,The Unlikely Pilgrimage of Harold Fry: A Novel
+0451417054,The King: A Novel of the Black Dagger Brotherhood
+1943893179,Craving (Steel Brothers Saga Book 1)
+0525955100,"Home (Myron Bolitar, No. 11)"
+0615581927,Reason to Breathe: The Breathing Series (Volume 1)
+125000098X,Best Kept Secret (The Clifton Chronicles)
+147782314X,One Lavender Ribbon
+1543644325,Pandemic (Extinction Files)
+1912106949,NO EXIT a gripping thriller full of heart-stopping twists
+006226835X,Yes Please
+0307476073,Wild: From Lost to Found on the Pacific Crest Trail
+0470560770,Better Homes and Gardens New Cook Book
+148231780X,Tongue Twisters for Kids
+0141976144,"Wreck This Journal: To Create is to Destroy, Now With Even More Ways to Wreck!"
+0316225983,The Late Show
+1611735270,The Harbinger: The Ancient Mystery That Holds the Secret of America's Future (Thorndike Christian Mystery)
+0142800422,Alexander Hamilton
+0307943232,The Racketeer
+0385349947,"Lean In: Women, Work, and the Will to Lead"
+1455568872,"Crisis of Character: A White House Secret Service Officer Discloses His Firsthand Experience with Hillary, Bill, and How They Operate"
+1780674880,Enchanted Forest: An Inky Quest & Coloring Book
+0062060554,Before I Go to Sleep: A Novel
+0099134012,Time to Kill Paper If Available
+1101911921,Our Souls at Night (Vintage Contemporaries)
+1447259297,The Target
+1477820280,P.S. from Paris (US edition)
+0425273865,Captivated by You (Crossfire)
+0062454943,"The Rainbow Comes and Goes: A Mother and Son On Life, Love, and Loss"
+0143127500,One Plus One: A Novel
+0310209749,"Boundaries: When To Say Yes, How to Say No"
+0316206857,The Cuckoo's Calling (Cormoran Strike)
+0486789640,Creative Haven Creative Cats Coloring Book (Adult Coloring)
+0505527847,Trouble in Mudbug
+0060890681,The Last Anniversary: A Novel
+0393072231,The Big Short: Inside the Doomsday Machine
+0764218913,"The Essential Tozer Collection: The Pursuit of God, The Purpose of Man, and The Crucified Life"
+1494557223,Archer's Voice
+1623363586,Thug Kitchen: The Official Cookbook: Eat Like You Give a F*ck (Thug Kitchen Cookbooks)
+0001048767,Othello: Complete & Unabridged
+0099576457,"Merry Christmas, Alex Cross: (Alex Cross 19)"
+0141034262,Three Cups of Tea
+0307275639,"Tuesdays with Morrie: An Old Man, a Young Man, and Life's Greatest Lesson"
+0743572203,Liberty and Tyranny: A Conservative Manifesto
+099047982X,Fluency (Confluence)
+1410482596,Killing Reagan: The Violent Assault That Changed A Presidenc (Wheeler Publishing Large Print Hardcover)
+1477809732,The Line (Witching Savannah)
+1503936864,Blood on the Tracks (Sydney Rose Parnell)
+1939457319,The 20/20 Diet: Turn Your Weight Loss Vision Into Reality
+0446580856,Radiant Angel (A John Corey Novel)
+045141411X,Six Years
+060637387X,You Are A Badass (Turtleback School & Library Binding Edition)
+1477825975,The Neon Lawyer
+0060125314,The Odyssey of Homer
+B00006JO24,The 48 Laws of Power
+0230748228,Only Time Will Tell
+0307282961,"Twilight (The Twilight Saga, Book 1)"
+0385393938,X (A Kinsey Millhone Novel)
+0606256725,Out Of My Mind (Turtleback School & Library Binding Edition)
+0988667908,Go Pro: 7 Steps to Becoming a Network Marketing Professional
+1536617636,D DAY Through German Eyes: The Hidden Story of June 6th 1944
+0142800376,"The Gunslinger (The Dark Tower, Book 1)"
+0329258400,Harry Potter and the Prisoner of Azkaban
+0374374562,Little Humans
+0811877825,"Goodnight, Goodnight Construction Site"
+B0016L6KZ6,"City of Ashes: The Mortal Instruments, Book Two"
+0310081122,"NIV, Thinline Bible, Large Print, Imitation Leather, Brown, Red Letter Edition"
+0399173358,Full Force and Effect (Jack Ryan)
+0575081406,"The Name of the Wind (Kingkiller Chronicles, Day 1)"
+0989768414,Hardwired (The Hacker Series)
+1490527516,Nora Roberts Land (Dare Valley)
+1503941892,The Night Bird (Frost Easton)
+0143108867,After You: A Novel (Me Before You Trilogy)
+0151010196,Those Who Save Us
+031623480X,"Grain Brain: The Surprising Truth about Wheat, Carbs,  and Sugar--Your Brain's Silent Killers"
+0718077954,The Wedding Dress
+080509668X,Killing Patton: The Strange Death of World War II's Most Audacious General (Bill O'Reilly's Killing Series)
+1492123595,30 Pieces of Silver
+1503935205,The Body Reader (Detective Jude Fontaine Mysteries)
+0004244079,Little Women
+0007444117,Allegiant
+0062110845,The Golem and the Jinni: A Novel (P.S.)
+0345542924,Top Secret Twenty-One (Stephanie Plum)
+0446520594,"Many Lives, Many Masters: The True Story of a Prominent Psychiatrist, His Young Patient, and the Past Life Therapy That Changed Both Their Lives"
+0553393235,The Happiest Baby on the Block; Fully Revised and Updated Second Edition: The New Way to Calm Crying and Help Your Newborn Baby Sleep Longer
+0743572750,City of Ashes (The Mortal Instruments)
+0993862500,Blood and Justice: A Private Investigator Mystery Series (A Jake & Annie Lincoln Thriller) (Volume 1)
+1629982423,"The Mystery of the Shemitah: The 3,000-Year-Old Mystery That Holds the Secret of America's Future, the World's Future, and Your Future!"
+0345534530,Empty Mansions: The Mysterious Life of Huguette Clark and the Spending of a Great American Fortune
+0345539869,Morning Star: Book 3 of the Red Rising Saga (Red Rising Series)
+1451649320,The Lake House: A Novel
+1613757808,Four Blood Moons: Something Is About to Change
+0316069515,The Gods of Guilt (A Lincoln Lawyer Novel)
+B00COF9TL0,Dad Is Fat
+0843961449,First to Kill
+1468161660,Slammed
+1503936171,Interference
+0385296479,Daughters
+030794963X,Duty: Memoirs of a Secretary at War
+031060530X,The Hiding Place
+000735083X,The Adventures of Sherlock Holmes (Collins Classics)
+006195070X,Orphan Train: A Novel
+0316176494,Life After Life: A Novel
+1477820450,The Gemini Effect
+0307886263,Is Everyone Hanging Out Without Me? (And Other Concerns)
+0316026697,The Host
+1455584916,And the Good News Is...: Lessons and Advice from the Bright Side
+1477824944,Hello Love
+0345533666,Defending Jacob: A Novel
+0451230825,Injustice For All (Joe Dillard)
+0615553028,A Hidden Fire: Elemental Mysteries Book 1
+144238820X,The Survivor (A Mitch Rapp Novel)
+1475031904,Breakthrough
+1477829407,The Einstein Prophecy
+147813223X,Louisiana Longshot: A Miss Fortune Mystery (Volume 1)
+0385541171,The Rooster Bar
+0761463275,Angelfall (Penryn & the End of Days)
+B000FK9M18,Hobbit
+0062347268,America's First Daughter: A Novel
+0316211273,Unlucky 13 (Women's Murder Club)
+0393609391,Astrophysics for People in a Hurry
+0991379667,Hudson (Fixed Series) (Volume 4)
+1481882902,Rushed
+B001HDIE3O,"What to Expect When You're Expecting, Third Edition"
+0307706591,The Scorch Trials
+0425259862,Shadow Spell (Cousins O'Dwyer)
+0230754902,Zero Day (John Puller series)
+0385353308,Station Eleven
+0544272994,What If?: Serious Scientific Answers to Absurd Hypothetical Questions
+060632416X,Joyland (Turtleback School & Library Binding Edition) (Hcc)
+0671315285,7 Habits Of Highly Effective People
+0692282343,Quantum Lens
+1503936805,Tier One (Tier One Thrillers)
+0003302245,Dracula (Collins Drama)
+0316011770,The Historian
+0307273571,A Week in Winter
+1455536253,The Guilty (Will Robie series)
+0142001740,The Secret Life of Bees
+0552778478,The Light Between Oceans: The heartbreaking Richard and Judy bestseller
+0805098542,Killing Jesus (Bill O'Reilly's Killing Series)
+0842384898,Holy Bible Text Edition NLT (Hardcover)
+1469208504,Hidden (A Bone Secrets Novel)
+0399170871,Obsession in Death
+1477824243,The Naturalist
+0007350961,Frankenstein (Collins Classics)
+0007449313,Pride & Prejudice (Collins Classics)
+0316001821,The Lovely Bones: Deluxe Edition
+1477848665,Stillhouse Lake
+1531865313,StrengthsFinder 2.0
+B017V4IPPO,"Harry Potter and the Chamber of Secrets, Book 2"
+0024181900,"The Elements of Style, Third Edition"
+0099521296,The Water's Edge
+0099911701,Cross Stitch
+030770663X,"The Death Cure (Maze Runner, Book Three) (The Maze Runner Series)"
+0312495412,First 100 Words (Bright Baby)
+0553526936,Special Circumstances
+1442360941,Walking Disaster
+1479147109,Lily Lemon Blossom Welcome to Lily's Room
+0307346609,World War Z: An Oral History of the Zombie War
+0345543246,Sycamore Row (The Jake Brigance)
+0399159347,The Husband's Secret
+0615437850,The Gray and Guilty Sea: A Garrison Gage Mystery
+0989104400,Falling Into You
+1503937461,"Sisters One, Two, Three"
+1503943925,Beach Lawyer
+0141188936,Atlas Shrugged
+0307730697,Fearless: The Undaunted Courage and Ultimate Sacrifice of Navy SEAL Team SIX Operator Adam Brown
+0375432329,The Five People You Meet in Heaven (Random House Large Print)
+0891097287,The Message: The New Testament in Contemporary English
+1477830049,The Perfect Son
+0139357432,The Underground Railroad
+0425263908,Bared to You
+0544320832,My Mother Was Nuts
+144238493X,MONEY Master the Game: 7 Simple Steps to Financial Freedom
+148261376X,The Arrangement
+1628600543,It Starts With Food: Discover the Whole30 and Change Your Life in Unexpected Ways
+0340835451,My Sister's Keeper
+0544838262,Dust (Silo Trilogy) (Volume 3)
+1449966128,A Child of A Crack Head
+0606322639,The Hit (Turtleback School & Library Binding Edition)
+1477665412,Barely Breathing: The Breathing Series (Volume 2)
+1501180126,Reasons to Vote for Democrats: A Comprehensive Guide
+0007351054,The Picture of Dorian Gray (Collins Classics)
+0316405388,Invisible
+0785289623,"Love & Respect, Special edition w/DVD"
+1477820884,The Mermaid's Sister
+0316335614,The Last Lecture
+0345505336,Hotel on the Corner of Bitter and Sweet: A Novel
+0385336675,"The Enemy (Jack Reacher, No. 8)"
+0385368267,Eleanor & Park
+054774501X,The Hangman's Daughter (Hangman's Daughter Tales) (A Hangman's Daughter Tale)
+1478121734,Love Yourself Like Your Life Depends On It
+B0000DYXQZ,The Namesake
+0007173156,"Oh, the Thinks You Can Think! Green Back Book"
+1476765340,Collide: Book One in the Collide Series
+0001050230,"Love's Labour's Lost: Performed by Derek Jacobi, Geraldine McEwan & Cast"
+0140440410,Anna Karenin
+0143129430,Thomas Jefferson and the Tripoli Pirates: The Forgotten War That Changed American History
+0143144391,The Magicians: A Novel (Magicians Trilogy)
+B002DY9LZQ,"The Devil in the White City: Murder, Magic, and Madness at the Fair That Changed America"
+1420146874,Maid for Love (Gansett Island)
+1477828737,A Dark Lure
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+0399159010,Let's Pretend This Never Happened: (A Mostly True Memoir)
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+1683990021,And Then She Was GONE: A riveting new suspense novel
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+1979172846,Betrayal
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+1441731326,"The China Study, Revised and Expanded Edition: The Most Comprehensive Study of Nutrition Ever Conducted and the Startling Implications for Diet, Weight Loss, and Long-Term Health"
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+0316225932,The Burning Room (A Harry Bosch Novel)
+0439249546,Harry Potter (4 Volumes set)
+1477822089,Trail of Broken Wings
+B001L7RCGG,Gone with the Wind
+0025853503,"Gone With the Wind, The Margaret Mitchell Anniversary Edition"
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+1910751243,The Girl With No Past: A gripping psychological thriller
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+0788844164,Sing Along Songs:Lion King - Circle
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+0143125478,The Boys in the Boat: Nine Americans and Their Epic Quest for Gold at the 1936 Berlin Olympics
+0307749606,A Wanted Man: A Jack Reacher Novel
+0007420412,Divergent
+097901977X,The Miracle Morning: The Not-So-Obvious Secret Guaranteed to Transform Your Life (Before 8am)
+1503933385,We're All Damaged
+0062491792,Commonwealth
+0136879721,The Power of Your Subconcious Mind
+0446612774,Long Time Coming
+0452296366,This Is Where I Leave You: A Novel
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+141046900X,Mr. Mercedes
+1410493547,Killing The Rising Sun: How America Vanquished World War II Japan (Wheeler Publishing Large Print Book Series)
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+0307280721,"Eldest (Inheritance, Book 2)"
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+1470381788,Finding Me
+1501257544,The Atlantis Plague (The Origin Mystery)
+1934193798,Great Food Fast : Bob Warden's Ultimate Pressure Cooker Recipes
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+0345514408,I Know Why the Caged Bird Sings
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+0778316556,The Good Girl (English Edition)
+1477848827,I Am Livia
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+0134374940,Night: With Connected Readings
+0525955097,Fool Me Once
+1455124176,The Art of Hearing Heartbeats: A Novel
+1461085977,Flat-Out Love
+1503945626,Life and Other Near-Death Experiences
+1612185304,Supreme Justice (Reeder and Rogers Thriller)
+0060899220,Kitchen Confidential Updated Edition: Adventures in the Culinary Underbelly (P.S.)
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+0595440096,Still Alice
+0890425558,"Diagnostic and Statistical Manual of Mental Disorders, 5th Edition: DSM-5"
+1503933547,The Light of the Fireflies
+0263234541,Waking Up Married (Mills & Boon Hardback Romance)
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+0615590586,The Pecan Man
+0718079183,The Magnolia Story
+0749958790,Proof of Heaven: A Neurosurgeon's Journey into the Afterlife
+1250066115,How Not to Die: Discover the Foods Scientifically Proven to Prevent and Reverse Disease
+0030554179,Animal Farm with Connections (HRW Library Study Guides)
+0743566572,City of Bones (The Mortal Instruments)
+0140277633,Easy Way to Stop Smoking
+0340820462,On Writing
+0982301820,10-Day Green Smoothie Cleanse: Lose Up to 15 Pounds in 10 Days!
+1466287047,Russo's Gold
+1501086138,Maude
+0399167447,Somewhere Safe with Somebody Good (Mitford)
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+0758278454,What She Left Behind
+1250000971,The Sins of the Father (The Clifton Chronicles)
+0006895492,Essentials of Human Anatomy and Physiology- Text Only 8th edition by Elaine N. Marieb (2006) Paperback
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+0147524210,One Nation: What We Can All Do to Save America's Future
+1987987039,Code Name Camelot - A Noah Wolf Thriller (Volume 1)
+0307408868,Dead Wake: The Last Crossing of the Lusitania
+0316557994,Extinction Horizon(The Extinction Cycle Book 1)
+0340364777,It
+1592642195,The Misremembered Man
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+0671449028,The Going-To-Bed Book
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+1410470997,Act Of War (Thorndike Press Large Print Core)
+1503937623,Walk Into Silence (Jo Larsen)
+1452605165,The Untethered Soul: The Journey Beyond Yourself
+1250109302,One with You (Crossfire)
+1442367636,The Liberty Amendments: Restoring the American Republic
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assignments/A3/data/reviews_small.csv

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assignments/A3/data/test-data/reviews_test.csv

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practice/prep10.py

@@ -0,0 +1,201 @@
+"""CSC111 Winter 2022 Prep 10: Programming Exercises
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This module contains the mergesort algorithm from the prep readings, as well as a few
+additional functions for you to implement.
+
+We have marked each place you need to write code with the word "TODO".
+As you complete your work in this file, delete each TODO comment.
+
+You may add additional doctests, but they will not be graded. You should test your work
+carefully before submitting it!
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr and David Liu.
+"""
+
+
+def mergesort(lst: list) -> list:
+    """Return a new sorted list with the same elements as lst.
+
+    This is a *non-mutating* version of mergesort; it does not mutate the
+    input list.
+
+    >>> mergesort([10, 2, 5, -6, 17, 10])
+    [-6, 2, 5, 10, 10, 17]
+    """
+    if len(lst) < 2:
+        return lst.copy()  # Use the list.copy method to return a new list object
+    else:
+        # Divide the list into two parts, and sort them recursively.
+        mid = len(lst) // 2
+        left_sorted = mergesort(lst[:mid])
+        right_sorted = mergesort(lst[mid:])
+
+        # Merge the two sorted halves. Using a helper here!
+        return _merge(left_sorted, right_sorted)
+
+
+def _merge(lst1: list, lst2: list) -> list:
+    """Return a sorted list with the elements in lst1 and lst2.
+
+    Preconditions:
+        - is_sorted(lst1)
+        - is_sorted(lst2)
+
+    >>> _merge([-1, 3, 7, 10], [-3, 0, 2, 6])
+    [-3, -1, 0, 2, 3, 6, 7, 10]
+    """
+    i1, i2 = 0, 0
+    sorted_so_far = []
+
+    while i1 < len(lst1) and i2 < len(lst2):
+        # Loop invariant:
+        # sorted_so_far is a merged version of lst1[:i1] and lst2[:i2]
+        assert sorted_so_far == sorted(lst1[:i1] + lst2[:i2])
+
+        if lst1[i1] <= lst2[i2]:
+            sorted_so_far.append(lst1[i1])
+            i1 += 1
+        else:
+            sorted_so_far.append(lst2[i2])
+            i2 += 1
+
+    # When the loop is over, either i1 == len(lst1) or i2 == len(lst2)
+    assert i1 == len(lst1) or i2 == len(lst2)
+
+    # In either case, the remaining unmerged elements can be concatenated to sorted_so_far.
+    if i1 == len(lst1):
+        return sorted_so_far + lst2[i2:]
+    else:
+        return sorted_so_far + lst1[i1:]
+
+
+################################################################################
+# Prep exercises
+################################################################################
+def merge_indexed(lst: list, m: int) -> list:
+    """Return a sorted list containing the items in lst.
+
+    This function should use the same algorithm as the _merge helper function,
+    with the two "sorted lists" being lst[:m] and lst[m:].
+    Don't use built-in sorting functions or other list operations; follow the
+    implementation of _merge as closely as possible. You don't need to copy over
+    the loop invariant, although you may find it helpful to do so.
+
+    Note that this function is public, as we are importing and testing it directly.
+
+    Preconditions:
+        - 0 <= m < len(lst)
+        - is_sorted_sublist(lst, 0, m)
+        - is_sorted_sublist(lst, m, len(lst))
+
+    (You're welcome to copy over your implementation of is_sorted_sublist from last week's
+    prep; if you don't, PythonTA will just ignore the last two preconditions.)
+
+    >>> lst = [10, 20, 30, 4, 15, 16, 99]
+    >>> merge_indexed(lst, 3)  # sorted sublists are lst[:3] and lst[3:]
+    [4, 10, 15, 16, 20, 30, 99]
+    """
+    i1, i2 = 0, m
+    sorted_so_far = []
+
+    while i1 < m and i2 < len(lst):
+        # Loop invariant:
+        # sorted_so_far is a merged version of lst1[:i1] and lst2[:i2]
+        assert sorted_so_far == sorted(lst[:i1] + lst[m:i2])
+
+        if lst[i1] <= lst[i2]:
+            sorted_so_far.append(lst[i1])
+            i1 += 1
+        else:
+            sorted_so_far.append(lst[i2])
+            i2 += 1
+
+    # When the loop is over, either i1 or i2 reached the end
+    assert i1 == m or i2 == len(lst)
+
+    # In either case, the remaining unmerged elements can be concatenated to sorted_so_far.
+    return sorted_so_far + (lst[i2:] if i1 == m else lst[i1:m])
+
+
+def merge_sublists(lst: list, b: int, m: int, e: int) -> None:
+    """Merge two sorted sublists in lst into one sorted sublist.
+
+    This is similar to merge_indexed, except:
+        - The two sublists are now lst[b:m] and lst[m:e]
+        - Rather than returning a new list, this function mutates lst so that
+          lst[b:e] is a sorted version of the two previous sublists.
+
+    Preconditions:
+        - 0 <= b <= m <= e < len(lst)
+        - is_sorted_sublist(lst, b, m)
+        - is_sorted_sublist(lst, m, e)
+
+    >>> lst = [100, 3, 10, 16, 2, 11, 20, 30, -1]
+    >>> merge_sublists(lst, 1, 4, 8)  # sorted sublists are lst[1:4] and lst[4:8]
+    >>> lst  # Note that the 100 and -1 aren't affected.
+    [100, 2, 3, 10, 11, 16, 20, 30, -1]
+    >>> merge_sublists(lst, 3, 3, 3)
+    >>> lst  # No change
+    [100, 2, 3, 10, 11, 16, 20, 30, -1]
+    >>> lst = [100, 10, 20, 30, 4, 15, 16, 99, -1]
+    >>> merge_sublists(lst, 1, 4, 8)
+    >>> lst
+    [100, 4, 10, 15, 16, 20, 30, 99, -1]
+
+    IMPORTANT implementation note: even though this function doesn't return a new list,
+    you can still create a new local list object to do the merging, and then copy the
+    contents back to lst[b:e] when you're done. This means that this algorithm won't be
+    in-place (since it creates a list object of non-constant size), but that's okay.
+    It's actually very complex to try to implement this function in an in-place way,
+    just like it's hard to implement an in-place mergesort.
+
+    As with merge_indexed, you should not use built-in sorting functions or other list
+    methods, but instead follow the implementation of _merge as closely as possible.
+    You may, but are not required to, assign to a list slice, e.g. lst[0:10] = ...
+    """
+    i1, i2 = b, m
+    sorted_so_far = []
+
+    while i1 < m and i2 < e:
+        # Loop invariant:
+        # sorted_so_far is a merged version of lst1[:i1] and lst2[:i2]
+        assert sorted_so_far == sorted(lst[b:i1] + lst[m:i2])
+
+        if lst[i1] <= lst[i2]:
+            sorted_so_far.append(lst[i1])
+            i1 += 1
+        else:
+            sorted_so_far.append(lst[i2])
+            i2 += 1
+
+    # When the loop is over, either i1 or i2 reached the end
+    assert i1 == m or i2 == e
+
+    # Assign slice
+    lst[b:e] = sorted_so_far + (lst[i2:e] if i1 == m else lst[i1:m])
+
+
+if __name__ == '__main__':
+    import python_ta.contracts
+    python_ta.contracts.check_all_contracts()
+
+    import doctest
+    doctest.testmod()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 100,
+        'disable': ['E1136']
+    })

practice/prep8.py

@@ -0,0 +1,254 @@
+"""CSC111 Winter 2022 Prep 8: Programming Exercises
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This module contains the graph implementation we studied in lecture, with a few
+additional methods for you to implement on this exercise.
+
+We have marked each place you need to write code with the word "TODO".
+As you complete your work in this file, delete each TODO comment.
+
+You may add additional doctests, but they will not be graded. You should test your work
+carefully before submitting it!
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr and David Liu.
+"""
+from __future__ import annotations
+from typing import Any
+
+
+class Graph:
+    """A graph.
+    """
+    # Private Instance Attributes:
+    #     - _vertices:
+    #         A collection of the vertices contained in this graph.
+    #         Maps item to _Vertex object.
+    _vertices: dict[Any, _Vertex]
+
+    def __init__(self) -> None:
+        """Initialize an empty graph (no vertices or edges)."""
+        self._vertices = {}
+
+    def add_vertex(self, item: Any) -> None:
+        """Add a vertex with the given item to this graph.
+
+        The new vertex is not adjacent to any other vertices.
+        """
+        self._vertices[item] = _Vertex(item, set())
+
+    def add_edge(self, item1: Any, item2: Any) -> None:
+        """Add an edge between the two vertices with the given items in this graph.
+
+        Raise a ValueError if item1 or item2 do not appear as vertices in this graph.
+
+        Preconditions:
+            - item1 != item2
+        """
+        if item1 in self._vertices and item2 in self._vertices:
+            v1 = self._vertices[item1]
+            v2 = self._vertices[item2]
+
+            # Add the new edge
+            v1.neighbours.add(v2)
+            v2.neighbours.add(v1)
+        else:
+            # We didn't find an existing vertex for both items.
+            raise ValueError
+
+    def connected(self, item1: Any, item2: Any) -> bool:
+        """Return whether item1 and item2 are connected vertices in this graph.
+
+        Return False if item1 or item2 do not appear as vertices in this graph.
+
+        >>> g = Graph()
+        >>> g.add_vertex(1)
+        >>> g.add_vertex(2)
+        >>> g.add_vertex(3)
+        >>> g.add_vertex(4)
+        >>> g.add_edge(1, 2)
+        >>> g.add_edge(2, 3)
+        >>> g.connected(1, 3)
+        True
+        >>> g.connected(1, 4)
+        False
+        """
+        if item1 in self._vertices and item2 in self._vertices:
+            v1 = self._vertices[item1]
+            return v1.check_connected(item2, set())  # Pass in an empty "visited" set
+        else:
+            return False
+
+    def get_connected_component(self, item: Any) -> set:
+        """Return a set of all ITEMS connected to the given item in this graph.
+
+        Raise a ValueError if item does not appears as a vertex in this graph.
+
+        >>> g = Graph()
+        >>> for i in range(0, 5):
+        ...     g.add_vertex(i)
+        >>> g.add_edge(0, 1)
+        >>> g.add_edge(1, 2)
+        >>> g.add_edge(1, 3)
+        >>> g.add_edge(2, 3)
+        >>> g.get_connected_component(0) == {0, 1, 2, 3}
+        True
+
+        Note: we've implemented this method for you, and you should not change it.
+        Instead, your task is to implement _Vertex.get_connected_component below.
+        """
+        if item not in self._vertices:
+            raise ValueError
+        else:
+            return self._vertices[item].get_connected_component(set())
+
+    def in_cycle(self, item: Any) -> bool:
+        """Return whether the given item is in a cycle in this graph.
+
+        Return False if item does not appears as a vertex in this graph.
+
+        KEY OBSERVATION. A vertex v is in a cycle if and only if:
+            v has two distinct neighbours u and w that are connected to each other
+            by a path that doesn't use v.
+
+        >>> g = Graph()
+        >>> for i in range(0, 5):
+        ...     g.add_vertex(i)
+        >>> g.add_edge(0, 1)
+        >>> g.add_edge(1, 2)
+        >>> g.add_edge(1, 3)
+        >>> g.add_edge(2, 3)
+        >>> g.in_cycle(1)
+        True
+        >>> g.in_cycle(0)
+        False
+        >>> g.add_edge(4, 0)
+        >>> g.in_cycle(0)
+        False
+
+        Implementation notes:
+            1. This method should call _Vertex.check_connected (following the above
+               description).
+            2. Don't try to make this method recursive, or copy and paste the implementation
+               of _Vertex.check_connected! That's not necessary here.
+        """
+        # Does not exist
+        if item not in self._vertices:
+            return False
+        v = self._vertices[item]
+
+        # Combinations
+        for u in v.neighbours:
+            for w in v.neighbours:
+                # Distinct combinations
+                if u == w or u == v or w == v:
+                    continue
+
+                if u.check_connected(w.item, {v}):
+                    return True
+        return False
+
+
+class _Vertex:
+    """A vertex in a graph.
+
+    Instance Attributes:
+        - item: The data stored in this vertex.
+        - neighbours: The vertices that are adjacent to this vertex.
+
+    Representation Invariants:
+        - self not in self.neighbours
+        - all(self in u.neighbours for u in self.neighbours)
+    """
+    item: Any
+    neighbours: set[_Vertex]
+
+    def __init__(self, item: Any, neighbours: set[_Vertex]) -> None:
+        """Initialize a new vertex with the given item and neighbours."""
+        self.item = item
+        self.neighbours = neighbours
+
+    def check_connected(self, target_item: Any, visited: set[_Vertex]) -> bool:
+        """Return whether this vertex is connected to a vertex corresponding to the target_item,
+        WITHOUT using any of the vertices in visited.
+
+        Preconditions:
+            - self not in visited
+        """
+        if self.item == target_item:
+            # Our base case: the target_item is the current vertex
+            return True
+        else:
+            visited.add(self)  # Add self to the set of visited vertices
+            for u in self.neighbours:
+                if u not in visited:  # Only recurse on vertices that haven't been visited
+                    if u.check_connected(target_item, visited):
+                        return True
+
+            return False
+
+    def get_connected_component(self, visited: set[_Vertex]) -> set:
+        """Return a set of all ITEMS connected to self by a path that does not use
+        any vertices in visited.
+
+        The items of the vertices in visited CANNOT appear in the returned set.
+
+        Preconditions:
+            - self not in visited
+
+        Implementation notes:
+            1. This can be implemented in a similar way to _Vertex.check_connected.
+            2. This method must be recursive, and will have an implicit base case:
+               when all vertices in self.neighbours are already in visited.
+            3. Use a loop accumulator to store a set of the vertices connected to self.
+
+        >>> g = Graph()
+        >>> for i in range(0, 7):
+        ...     g.add_vertex(i)
+        >>> g.add_edge(0, 1)
+        >>> g.add_edge(1, 2)
+        >>> g.add_edge(1, 3)
+        >>> g.add_edge(2, 3)
+        >>> g.get_connected_component(1) == {0, 1, 2, 3}
+        True
+        >>> g.add_edge(4, 0)
+        >>> g.get_connected_component(0) == {0, 1, 2, 3, 4}
+        True
+        >>> g.get_connected_component(5)
+        {5}
+        >>> g._vertices[5].get_connected_component({g._vertices[5]})
+        set()
+        >>> g._vertices[6].get_connected_component(set())
+        {6}
+        """
+        if self in visited:
+            return set()
+        visited.add(self)
+        nums = {self.item}
+        for u in self.neighbours:
+            nums = nums.union(u.get_connected_component(visited))
+        return nums
+
+
+if __name__ == '__main__':
+    # Note: we are NOT using python_ta.contracts for this prep.
+    # (Feel free to ask why in office hours/Campuswire.)
+    import doctest
+    doctest.testmod()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 100,
+        'disable': ['E1136'],
+        'max-nested-blocks': 4
+    })

practice/prep9.py

@@ -0,0 +1,191 @@
+"""CSC111 Winter 2022 Prep 9: Programming Exercises
+
+Instructions (READ THIS FIRST!)
+===============================
+
+This module contains some functions related to sorting and/or Python lists for practice.
+In particular, some of these functions give you practice with *index parameters*,
+which we commonly use to specify that a function should only operate on a part of a list.
+(This is generally more efficient than requiring the user to create a new list. We'll explor
+this idea further in lecture this week.)
+
+Do NOT use recursion for any of these functions.
+
+We have marked each place you need to write code with the word "TODO".
+As you complete your work in this file, delete each TODO comment.
+
+You may add additional doctests, but they will not be graded. You should test your work
+carefully before submitting it!
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC111 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC111 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2022 Mario Badr and David Liu.
+"""
+
+
+def is_sorted(lst: list) -> bool:
+    """Return whether lst is sorted.
+
+    Formally, a list `lst` is sorted when for every index i between 0 and len(lst),
+    lst[i] <= lst[i + 1]. Note that empty lists and lists of length 1 are always sorted.
+
+    Do not call `sorted` or `list.sort`, or otherwise sort `lst` in this function.
+
+    >>> is_sorted([2, 7, 3, 4, 5])
+    False
+    >>> is_sorted([2, 3, 4, 5, 7])
+    True
+    >>> is_sorted([-1, 0, 0, 1])
+    True
+    >>> is_sorted([-1, 0, 0, -1])
+    False
+    """
+    return all(lst[i] <= lst[i + 1] for i in range(len(lst) - 1))
+
+
+def is_sorted_sublist(lst: list, b: int, e: int) -> bool:
+    """Return whether the sublist lst[b:e] is sorted.
+
+    Do not create a new list or call is_sorted. Instead, adapt the definition from
+    is_sorted to complete this function.
+
+    Note that if b >= e, then lst[b:e] is an empty list, and so is sorted.
+
+    Preconditions:
+        - 0 <= b < len(lst)
+        - 0 <= e <= len(lst)
+
+    >>> is_sorted_sublist([2, 7, 3, 4, 5], 0, 5)  # Equivalent to is_sorted([2, 7, 3, 4, 5])
+    False
+    >>> is_sorted_sublist([2, 7, 3, 4, 5], 2, 5)  # Equivalent to is_sorted([2, 7, 3, 4, 5][2:5])
+    True
+    >>> is_sorted_sublist([-1, 0, 0, -1], 0, 3)
+    True
+    >>> is_sorted_sublist([-1, 0, 0, -1], 0, 4)
+    False
+    """
+    return all(lst[i] <= lst[i + 1] for i in range(b, e - 1))
+
+
+def min_index(lst: list) -> int:
+    """Return the index of the smallest element of lst.
+
+    In the case of ties, return the smaller index (i.e., the index that appears first).
+
+    Preconditions:
+        - lst != []
+
+    >>> min_index([-10, 7, 3, 5])
+    0
+    >>> min_index([7, 7, 7, 7])
+    0
+    >>> min_index([8, 7, 7, 7])
+    1
+    >>> min_index([8, 7, 7, -1])
+    3
+    >>> min_index([99999999])
+    0
+    """
+    lowest = lst[0]
+    index = 0
+
+    for i in range(len(lst)):
+        if lst[i] < lowest:
+            lowest = lst[i]
+            index = i
+
+    return index
+
+
+def min_index_sublist(lst: list, b: int, e: int) -> int:
+    """Return the index of the smallest item in lst[b:e].
+
+    In the case of ties, return the smaller index (i.e., the index that appears first).
+
+    This is similar to min_index, except we are only considering the elements
+    with indexes between b and e - 1, inclusive.
+
+    Preconditions:
+        - 0 <= b < e <= len(lst)
+
+    >>> min_index_sublist([-10, 7, 3, 5], 0, 4)
+    0
+    >>> min_index_sublist([-10, 7, 3, 5], 1, 3)
+    2
+    """
+    lowest = lst[b]
+    index = b
+
+    for i in range(b, e):
+        if lst[i] < lowest:
+            lowest = lst[i]
+            index = i
+
+    return index
+
+
+def cycle(lst: list) -> None:
+    """Rearrange the elements of lst by shifting every element one spot to the right.
+
+    The last list element moves to the front of the list.
+
+    Preconditions:
+        - lst != []
+
+    >>> lst = [10, 3, 5, 7, 9000]
+    >>> cycle(lst)
+    >>> lst
+    [9000, 10, 3, 5, 7]
+
+    Implementation notes:
+        - Do NOT call any list methods; instead, move the elements by assigning to indexes
+          (e.g., lst[1] = list[0] or lst[i] = lst[i + 1]).
+    """
+    last = lst[-1]
+    for i in reversed(range(len(lst))):
+        lst[i] = lst[i - 1]
+    lst[0] = last
+
+
+def cycle_sublist(lst: list, b: int, e: int) -> None:
+    """Rearrange the elements of lst[b:e] by shifting every element one spot to the right.
+
+    The element lst[e - 1] moves to index b.
+
+    Preconditions:
+        - 0 <= b < e <= len(lst)
+
+    >>> lst = [10, 3, 5, 7, 9000]
+    >>> cycle_sublist(lst, 0, 5)  # Equivalent to cycle(lst)
+    >>> lst
+    [9000, 10, 3, 5, 7]
+    >>> lst2 = [10, 3, 5, 7, 9000]
+    >>> cycle_sublist(lst2, 1, 4)
+    >>> lst2
+    [10, 7, 3, 5, 9000]
+    """
+    last = lst[e - 1]
+    for i in reversed(range(b, e)):
+        lst[i] = lst[i - 1]
+    lst[b] = last
+
+
+if __name__ == '__main__':
+    import python_ta.contracts
+    python_ta.contracts.check_all_contracts()
+
+    import doctest
+    doctest.testmod()
+
+    import python_ta
+    python_ta.check_all(config={
+        'max-line-length': 100,
+        'disable': ['E1136']
+    })