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import http.client
import json
import csv
import urllib.request
#############################################################################################################################
# cse6242
# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
# hard-coded.
#
# Instructions: Implement all methods in this file that have a return
# value of 'NotImplemented'. See the documentation within each method for specific details, including
# the expected return value
#
# Helper Functions:
# You are permitted to write additional helper functions/methods or use additional instance variables within
# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
#
# Use:
# The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must
# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
#
# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods
# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional
# methods and instance variables as desired (see Helper Functions).
#
# The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the
# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
# .csv files for submission to Gradescope.
#
# While building the co-actor graph, you will be required to write code to expand the graph by iterating
# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
# but will grade the resulting graph data in your nodes and edges .csv files.
#
#############################################################################################################################
class Graph:
# Do not modify
def __init__(self, with_nodes_file=None, with_edges_file=None):
"""
option 1: init as an empty graph and add nodes
option 2: init by specifying a path to nodes & edges files
"""
self.nodes = []
self.edges = []
if with_nodes_file and with_edges_file:
nodes_CSV = csv.reader(open(with_nodes_file))
nodes_CSV = list(nodes_CSV)[1:]
self.nodes = [(n[0], n[1]) for n in nodes_CSV]
edges_CSV = csv.reader(open(with_edges_file))
edges_CSV = list(edges_CSV)[1:]
self.edges = [(e[0], e[1]) for e in edges_CSV]
def add_node(self, id: str, name: str) -> None:
"""
add a tuple (id, name) representing a node to self.nodes if it does not already exist
The graph should not contain any duplicate nodes
"""
self.nodes.append((id, name))
return None
def add_edge(self, source: str, target: str) -> None:
"""
Add an edge between two nodes if it does not already exist.
An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
Where 'source' is the id of the source node and 'target' is the id of the target node
e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
"""
self.edges.append((source, target))
return None
def total_nodes(self) -> int:
"""
Returns an integer value for the total number of nodes in the graph
"""
output = len(self.nodes)
return output
def total_edges(self) -> int:
"""
Returns an integer value for the total number of edges in the graph
"""
output = len(self.edges)
return output
def max_degree_nodes(self) -> dict:
"""
Return the node(s) with the highest degree
Return multiple nodes in the event of a tie
Format is a dict where the key is the node_id and the value is an integer for the node degree
e.g. {'a': 8}
or {'a': 22, 'b': 22}
"""
max_degree_nodes = {}
for edge in self.edges:
for idx in edge:
if idx in max_degree_nodes:
max_degree_nodes[idx] += 1
else:
max_degree_nodes[idx] = 1
v = list(max_degree_nodes.values())
k = list(max_degree_nodes.keys())
max_nodes = {k[v.index(max(v))]: max(v)}
return max_nodes
def print_nodes(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.nodes)
def print_edges(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.edges)
# Do not modify
def write_edges_file(self, path="edges.csv")->None:
"""
write all edges out as .csv
:param path: string
:return: None
"""
edges_path = path
edges_file = open(edges_path, 'w', encoding='utf-8')
edges_file.write("source" + "," + "target" + "\n")
for e in self.edges:
edges_file.write(e[0] + "," + e[1] + "\n")
edges_file.close()
print("finished writing edges to csv")
# Do not modify
def write_nodes_file(self, path="nodes.csv")->None:
"""
write all nodes out as .csv
:param path: string
:return: None
"""
nodes_path = path
nodes_file = open(nodes_path, 'w', encoding='utf-8')
nodes_file.write("id,name" + "\n")
for n in self.nodes:
nodes_file.write(n[0] + "," + n[1] + "\n")
nodes_file.close()
print("finished writing nodes to csv")
class TMDBAPIUtils:
# Do not modify
def __init__(self, api_key:str):
self.api_key=api_key
def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
"""
Get the movie cast for a given movie id, with optional parameters to exclude an cast member
from being returned and/or to limit the number of returned cast members
documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
:param string movie_id: a movie_id
:param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result
e.g., if exclude_ids are [353, 455] then exclude these from any result.
:param integer limit: maximum number of returned cast members by their 'order' attribute
e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range).
If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
If after excluding, the limit is not specified, then return all remaining cast members."
e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
:rtype: list
return a list of dicts, one dict per cast member with the following structure:
[{'id': '97909' # the id of the cast member
'character': 'John Doe' # the name of the character played
'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
Note that this is an example of the structure of the list and some of the fields returned by the API.
The result of the API call will include many more fields for each cast member.
"""
url = 'https://api.themoviedb.org/3/movie/'+ str(movie_id) +'/credits?api_key='+self.api_key +"&language=en-US"
req = urllib.request.Request(url, headers={
'Accept': 'application/json',
})
response = urllib.request.urlopen(req)
if response.status == 200:
json_data = json.load(response)
cast = json_data['cast']
if exclude_ids is not None:
cast = [i for i in cast if i['id'] not in exclude_ids]
# print(cast)
if limit is not None:
cast = [i for i in cast if i['order'] < limit]
# print(cast)
for c in cast:
c['name'] = c['name'].replace(',', '')
return cast
else:
print(response.status.__str__())
return None
def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
"""
Using the TMDb API, get the movie credits for a person serving in a cast role
documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
:param string person_id: the id of a person
:param vote_avg_threshold: optional parameter to return the movie credit if it is >=
the specified threshold.
e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
:rtype: list
return a list of dicts, with each dict having 'id', 'title', and 'vote_avg' keys,
one dict per movie credit with the following structure:
[{'id': '97909' # the id of the movie
'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
"""
url = 'https://api.themoviedb.org/3/person/'+ str(person_id) +'/movie_credits?api_key='+self.api_key+'&language=en-US'
req = urllib.request.Request(url, headers={
'Accept': 'application/json',
})
response = urllib.request.urlopen(req)
if response.status == 200:
json_data = json.load(response)
raw_credits = json_data['cast']
if vote_avg_threshold is not None:
filtered_credits = []
for idx in raw_credits:
if idx["vote_average"] >= vote_avg_threshold:
filtered_credits.append({
"id":idx["id"],
"title":idx["title"],
"vote_avg":idx["vote_average"]
})
return filtered_credits
else:
unfiltered_credits = []
for idx in raw_credits:
unfiltered_credits.append({
"id":idx["id"],
"title":idx["title"],
"vote_avg":idx["vote_average"]
})
return unfiltered_credits
else:
print("ERROR: " + response.status.__str__())
return None
#############################################################################################################################
#
# BUILDING YOUR GRAPH
#
# Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples
#
# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
# In this graph, each node represents an actor
# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
# i.e., they share a movie credit.
# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
# or more movies together.
#
# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
#
# You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your
# algorithm. We will instead measure the correctness of your output by evaluating the data in your nodes.csv and edges.csv files.
#
# GRAPH SIZE
# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
# Our testing indicates growth approximately equal to e^2x.
# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
# updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a
# margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to:
#
# Min allowable nodes = min encountered nodes - 100
# Max allowable nodes = max allowable nodes + 100
#
# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
# ----------------------------------------------------------------------------------------------------------------------
# BEGIN BUILD CO-ACTOR NETWORK
#
# INITIALIZE GRAPH
# Initialize a Graph object with a single node representing Laurence Fishburne
#
# BEGIN BUILD BASE GRAPH:
# Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
# FOR each movie credit:
# | get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
# |
# | FOR each movie cast member:
# | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
# | | using graph.add_edge(), add an edge between the Laurence Fishburne (actor) node
# | | and each new node (co-actor/co-actress)
# | END FOR
# END FOR
# END BUILD BASE GRAPH
#
#
# BEGIN LOOP - DO 2 TIMES:
# IF first iteration of loop:
# | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
# ELSE
# | nodes = The nodes added in the previous iteration:
# ENDIF
#
# FOR each node in nodes:
# | get the movie credits for the actor that have a vote average >= 8.0
# |
# | FOR each movie credit:
# | | try to get the 3 movie cast members having an 'order' value between 0-2
# | |
# | | FOR each movie cast member:
# | | | IF the node doesn't already exist:
# | | | | add the node to the graph (track all new nodes added to the graph)
# | | | ENDIF
# | | |
# | | | IF the edge does not exist:
# | | | | add an edge between the node (actor) and the new node (co-actor/co-actress)
# | | | ENDIF
# | | END FOR
# | END FOR
# END FOR
# END LOOP
#
# Your graph should not have any duplicate edges or nodes
# Write out your finished graph as a nodes file and an edges file using:
# graph.write_edges_file()
# graph.write_nodes_file()
#
# END BUILD CO-ACTOR NETWORK
# ----------------------------------------------------------------------------------------------------------------------
# Exception handling and best practices
# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
# - Some movie_credits do not return cast data. Handle this situation by skipping these instances.
# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
# can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
# - wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph.
def return_name()->str:
"""
Return a string containing your GT Username
e.g., tlou31
Do not return your 9 digit GTId
"""
return "tlou31"
# You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes.
# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
if __name__ == "__main__":
graph = Graph()
graph.add_node(id='2975', name='Laurence Fishburne')
# build base graph
# find all laurence fishburne's movie credits with a vote average >= 8.0
tmdb_api_utils = TMDBAPIUtils(api_key='1eed362096d67d9f077084d5abb30a35')
movie_credits = tmdb_api_utils.get_movie_credits_for_person(person_id='2975', vote_avg_threshold=8.0)
# for each movie credit get movie cast members having and order value between 0-2 (3 0-base)
for movie in movie_credits:
movie_cast = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for cast_member in movie_cast:
graph.add_node(str(cast_member['id']), cast_member['name'])
graph.add_edge(source='2975', target=str(cast_member['id']))
break
break
for i in range(2+1):
print('running loop ' + str(i))
if i == 0:
current_nodes = graph.nodes[1:]
else:
current_nodes = [i for i in graph.nodes if i not in current_nodes]
for node in current_nodes:
cast = tmdb_api_utils.get_movie_credits_for_person(person_id=node[0], vote_avg_threshold=8.0)
for movie in cast:
actors = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for actor in actors:
if((str(actor['id']), actor['name']) not in graph.nodes):
graph.add_node(str(actor['id']), actor['name'])
source = str(node[0])
target = str(actor['id'])
current_edge = (source, target)
current_edge_inverted = (target, source)
edges = graph.edges
if (source != target) and (current_edge not in edges) and (current_edge_inverted not in edges):
graph.add_edge(source , target )
# tmdb_api_utils.get_movie_cast(movie_id='100', limit=3, exclude_ids=[973, 974])
# tmdb_api_utils.get_movie_credits_for_person(person_id="9709", vote_avg_threshold=5.0)
# call functions or place code here to build graph (graph building code not graded)
# Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
print(f"Edges: {graph.total_edges()}")
print(f"Nodes: {graph.total_nodes()}")
graph.write_edges_file()
graph.write_nodes_file()
# If you have already built & written out your graph, you could read in your nodes & edges files
# to perform testing on your graph.
graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")
print(graph.max_degree_nodes())

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tunmnlu/task_1/Q1/Q1_question.py Normal file
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import http.client
import json
import csv
#############################################################################################################################
# cse6242
# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
# hard-coded.
#
# Instructions: Implement all methods in this file that have a return
# value of 'NotImplemented'. See the documentation within each method for specific details, including
# the expected return value
#
# Helper Functions:
# You are permitted to write additional helper functions/methods or use additional instance variables within
# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
#
# Use:
# The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must
# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
#
# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods
# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional
# methods and instance variables as desired (see Helper Functions).
#
# The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the
# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
# .csv files for submission to Gradescope.
#
# While building the co-actor graph, you will be required to write code to expand the graph by iterating
# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
# but will grade the resulting graph data in your nodes and edges .csv files.
#
#############################################################################################################################
class Graph:
# Do not modify
def __init__(self, with_nodes_file=None, with_edges_file=None):
"""
option 1: init as an empty graph and add nodes
option 2: init by specifying a path to nodes & edges files
"""
self.nodes = []
self.edges = []
if with_nodes_file and with_edges_file:
nodes_CSV = csv.reader(open(with_nodes_file))
nodes_CSV = list(nodes_CSV)[1:]
self.nodes = [(n[0], n[1]) for n in nodes_CSV]
edges_CSV = csv.reader(open(with_edges_file))
edges_CSV = list(edges_CSV)[1:]
self.edges = [(e[0], e[1]) for e in edges_CSV]
def add_node(self, id: str, name: str) -> None:
"""
add a tuple (id, name) representing a node to self.nodes if it does not already exist
The graph should not contain any duplicate nodes
"""
return NotImplemented
def add_edge(self, source: str, target: str) -> None:
"""
Add an edge between two nodes if it does not already exist.
An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
Where 'source' is the id of the source node and 'target' is the id of the target node
e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
"""
return NotImplemented
def total_nodes(self) -> int:
"""
Returns an integer value for the total number of nodes in the graph
"""
return NotImplemented
def total_edges(self) -> int:
"""
Returns an integer value for the total number of edges in the graph
"""
return NotImplemented
def max_degree_nodes(self) -> dict:
"""
Return the node(s) with the highest degree
Return multiple nodes in the event of a tie
Format is a dict where the key is the node_id and the value is an integer for the node degree
e.g. {'a': 8}
or {'a': 22, 'b': 22}
"""
return NotImplemented
def print_nodes(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.nodes)
def print_edges(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.edges)
# Do not modify
def write_edges_file(self, path="edges.csv")->None:
"""
write all edges out as .csv
:param path: string
:return: None
"""
edges_path = path
edges_file = open(edges_path, 'w', encoding='utf-8')
edges_file.write("source" + "," + "target" + "\n")
for e in self.edges:
edges_file.write(e[0] + "," + e[1] + "\n")
edges_file.close()
print("finished writing edges to csv")
# Do not modify
def write_nodes_file(self, path="nodes.csv")->None:
"""
write all nodes out as .csv
:param path: string
:return: None
"""
nodes_path = path
nodes_file = open(nodes_path, 'w', encoding='utf-8')
nodes_file.write("id,name" + "\n")
for n in self.nodes:
nodes_file.write(n[0] + "," + n[1] + "\n")
nodes_file.close()
print("finished writing nodes to csv")
class TMDBAPIUtils:
# Do not modify
def __init__(self, api_key:str):
self.api_key=api_key
def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
"""
Get the movie cast for a given movie id, with optional parameters to exclude an cast member
from being returned and/or to limit the number of returned cast members
documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
:param string movie_id: a movie_id
:param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result
e.g., if exclude_ids are [353, 455] then exclude these from any result.
:param integer limit: maximum number of returned cast members by their 'order' attribute
e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range).
If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
If after excluding, the limit is not specified, then return all remaining cast members."
e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
:rtype: list
return a list of dicts, one dict per cast member with the following structure:
[{'id': '97909' # the id of the cast member
'character': 'John Doe' # the name of the character played
'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
Note that this is an example of the structure of the list and some of the fields returned by the API.
The result of the API call will include many more fields for each cast member.
"""
return NotImplemented
def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
"""
Using the TMDb API, get the movie credits for a person serving in a cast role
documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
:param string person_id: the id of a person
:param vote_avg_threshold: optional parameter to return the movie credit if it is >=
the specified threshold.
e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
:rtype: list
return a list of dicts, with each dict having 'id', 'title', and 'vote_avg' keys,
one dict per movie credit with the following structure:
[{'id': '97909' # the id of the movie
'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
"""
return NotImplemented
#############################################################################################################################
#
# BUILDING YOUR GRAPH
#
# Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples
#
# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
# In this graph, each node represents an actor
# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
# i.e., they share a movie credit.
# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
# or more movies together.
#
# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
#
# You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your
# algorithm. We will instead measure the correctness of your output by evaluating the data in your nodes.csv and edges.csv files.
#
# GRAPH SIZE
# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
# Our testing indicates growth approximately equal to e^2x.
# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
# updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a
# margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to:
#
# Min allowable nodes = min encountered nodes - 100
# Max allowable nodes = max allowable nodes + 100
#
# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
# ----------------------------------------------------------------------------------------------------------------------
# BEGIN BUILD CO-ACTOR NETWORK
#
# INITIALIZE GRAPH
# Initialize a Graph object with a single node representing Laurence Fishburne
#
# BEGIN BUILD BASE GRAPH:
# Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
# FOR each movie credit:
# | get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
# |
# | FOR each movie cast member:
# | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
# | | using graph.add_edge(), add an edge between the Laurence Fishburne (actor) node
# | | and each new node (co-actor/co-actress)
# | END FOR
# END FOR
# END BUILD BASE GRAPH
#
#
# BEGIN LOOP - DO 2 TIMES:
# IF first iteration of loop:
# | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
# ELSE
# | nodes = The nodes added in the previous iteration:
# ENDIF
#
# FOR each node in nodes:
# | get the movie credits for the actor that have a vote average >= 8.0
# |
# | FOR each movie credit:
# | | try to get the 3 movie cast members having an 'order' value between 0-2
# | |
# | | FOR each movie cast member:
# | | | IF the node doesn't already exist:
# | | | | add the node to the graph (track all new nodes added to the graph)
# | | | ENDIF
# | | |
# | | | IF the edge does not exist:
# | | | | add an edge between the node (actor) and the new node (co-actor/co-actress)
# | | | ENDIF
# | | END FOR
# | END FOR
# END FOR
# END LOOP
#
# Your graph should not have any duplicate edges or nodes
# Write out your finished graph as a nodes file and an edges file using:
# graph.write_edges_file()
# graph.write_nodes_file()
#
# END BUILD CO-ACTOR NETWORK
# ----------------------------------------------------------------------------------------------------------------------
# Exception handling and best practices
# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
# - Some movie_credits do not return cast data. Handle this situation by skipping these instances.
# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
# can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
# - wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph.
def return_name()->str:
"""
Return a string containing your GT Username
e.g., tlou31
Do not return your 9 digit GTId
"""
return NotImplemented
# You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes.
# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
if __name__ == "__main__":
graph = Graph()
graph.add_node(id='2975', name='Laurence Fishburne')
tmdb_api_utils = TMDBAPIUtils(api_key='<your API key>')
# call functions or place code here to build graph (graph building code not graded)
# Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
graph.write_edges_file()
graph.write_nodes_file()
# If you have already built & written out your graph, you could read in your nodes & edges files
# to perform testing on your graph.
# graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")

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import http.client
import json
import csv
#############################################################################################################################
# cse6242
# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
# hard-coded.
#
# Instructions: Implement all methods in this file that have a return
# value of 'NotImplemented'. See the documentation within each method for specific details, including
# the expected return value
#
# Helper Functions:
# You are permitted to write additional helper functions/methods or use additional instance variables within
# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
#
# Use:
# The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must
# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
#
# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods
# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional
# methods and instance variables as desired (see Helper Functions).
#
# The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the
# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
# .csv files for submission to Gradescope.
#
# While building the co-actor graph, you will be required to write code to expand the graph by iterating
# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
# but will grade the resulting graph data in your nodes and edges .csv files.
#
#############################################################################################################################
class Graph:
# Do not modify
def __init__(self, with_nodes_file=None, with_edges_file=None):
"""
option 1: init as an empty graph and add nodes
option 2: init by specifying a path to nodes & edges files
"""
self.nodes = []
self.edges = []
if with_nodes_file and with_edges_file:
nodes_CSV = csv.reader(open(with_nodes_file))
nodes_CSV = list(nodes_CSV)[1:]
self.nodes = [(n[0], n[1]) for n in nodes_CSV]
edges_CSV = csv.reader(open(with_edges_file))
edges_CSV = list(edges_CSV)[1:]
self.edges = [(e[0], e[1]) for e in edges_CSV]
def add_node(self, id: str, name: str) -> None:
"""
add a tuple (id, name) representing a node to self.nodes if it does not already exist
The graph should not contain any duplicate nodes
"""
if (id, name) not in self.nodes:
self.nodes.append((id, name))
def add_edge(self, source: str, target: str) -> None:
"""
Add an edge between two nodes if it does not already exist.
An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
Where 'source' is the id of the source node and 'target' is the id of the target node
e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
"""
current_edge = (source, target)
current_edge_inverted = (target, source)
if (source != target) and (current_edge not in self.edges) and (current_edge_inverted not in self.edges):
self.edges.append(current_edge)
def total_nodes(self) -> int:
"""
Returns an integer value for the total number of nodes in the graph
"""
return len(self.nodes)
def total_edges(self) -> int:
"""
Returns an integer value for the total number of edges in the graph
"""
return len(self.edges)
def max_degree_nodes(self) -> dict:
"""
Return the node(s) with the highest degree
Return multiple nodes in the event of a tie
Format is a dict where the key is the node_id and the value is an integer for the node degree
e.g. {'a': 8}
or {'a': 22, 'b': 22}
"""
max_degree_nodes = {}
for edge in self.edges:
for idx in edge:
if idx in max_degree_nodes:
max_degree_nodes[idx] += 1
else:
max_degree_nodes[idx] = 1
v = list(max_degree_nodes.values())
k = list(max_degree_nodes.keys())
max_nodes = {k[v.index(max(v))]: max(v)}
print(max_nodes)
return max_nodes
def print_nodes(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.nodes)
def print_edges(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.edges)
# Do not modify
def write_edges_file(self, path="edges_ref.csv")->None:
"""
write all edges out as .csv
:param path: string
:return: None
"""
edges_path = path
edges_file = open(edges_path, 'w', encoding='utf-8')
edges_file.write("source" + "," + "target" + "\n")
for e in self.edges:
edges_file.write(e[0] + "," + e[1] + "\n")
edges_file.close()
print("finished writing edges to csv")
# Do not modify
def write_nodes_file(self, path="nodes_ref.csv")->None:
"""
write all nodes out as .csv
:param path: string
:return: None
"""
nodes_path = path
nodes_file = open(nodes_path, 'w', encoding='utf-8')
nodes_file.write("id,name" + "\n")
for n in self.nodes:
nodes_file.write(n[0] + "," + n[1] + "\n")
nodes_file.close()
print("finished writing nodes to csv")
def non_leaf_nodes(self):
nodes_degrees = {}
for edge in self.edges:
for idx in edge:
if idx in nodes_degrees:
nodes_degrees[idx] += 1
else:
nodes_degrees[idx] = 1
non_leaf_nodes = dict((k, v) for k, v in nodes_degrees.items() if v > 1)
return len(non_leaf_nodes)
class TMDBAPIUtils:
# Do not modify
def __init__(self, api_key:str):
self.api_key=api_key
def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
"""
Get the movie cast for a given movie id, with optional parameters to exclude an cast member
from being returned and/or to limit the number of returned cast members
documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
:param string movie_id: a movie_id
:param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result
e.g., if exclude_ids are [353, 455] then exclude these from any result.
:param integer limit: maximum number of returned cast members by their 'order' attribute
e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range).
If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
If after excluding, the limit is not specified, then return all remaining cast members."
e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
:rtype: list
return a list of dicts, one dict per cast member with the following structure:
[{'id': '97909' # the id of the cast member
'character': 'John Doe' # the name of the character played
'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
Note that this is an example of the structure of the list and some of the fields returned by the API.
The result of the API call will include many more fields for each cast member.
"""
connection = http.client.HTTPSConnection('api.themoviedb.org')
connection.request("GET", "/3/movie/{0}/credits?api_key={1}&language=en-US".format(movie_id, self.api_key))
response = connection.getresponse()
if response.status == 200:
data = response.read()
decoded_data = json.loads(data.decode('UTF-8'))
cast = decoded_data['cast']
if exclude_ids is not None:
cast = [i for i in cast if i['id'] not in exclude_ids]
print(cast)
if limit is not None:
cast = [i for i in cast if i['order'] < limit]
print(cast)
for c in cast:
c['name'] = c['name'].replace(',', '')
return cast
else:
print(response.status.__str__())
return None
def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
"""
Using the TMDb API, get the movie credits for a person serving in a cast role
documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
:param string person_id: the id of a person
:param vote_avg_threshold: optional parameter to return the movie credit if it is >=
the specified threshold.
e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
:rtype: list
return a list of dicts, with each dict having 'id', 'title', and 'vote_avg' keys,
one dict per movie credit with the following structure:
[{'id': '97909' # the id of the movie
'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
"""
url = "https://api.themoviedb.org/3/person/9999/movie_credits?language=en-US&api_key=1eed362096d67d9f077084d5abb30a35"
connection = http.client.HTTPSConnection('api.themoviedb.org')
connection.request("GET", "/3/person/{0}/movie_credits?api_key={1}&language=en-US".format(person_id, self.api_key))
response = connection.getresponse()
if response.status == 200:
data = response.read()
decoded_data = json.loads(data.decode('UTF-8'))
raw_credits = decoded_data['cast']
if vote_avg_threshold is not None:
filtered_credits = [{"id":idx["id"], "title":idx["title"], "vote_avg":idx["vote_average"]} for idx in raw_credits if idx["vote_average"] >= vote_avg_threshold]
print(filtered_credits)
return filtered_credits
else:
unfiltered_credits = [{"id":idx["id"], "title":idx["title"], "vote_avg":idx["vote_average"]} for idx in raw_credits]
print(unfiltered_credits)
return unfiltered_credits
else:
print("ERROR: " + response.status.__str__())
return None
#############################################################################################################################
#
# BUILDING YOUR GRAPH
#
# Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples
#
# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
# In this graph, each node represents an actor
# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
# i.e., they share a movie credit.
# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
# or more movies together.
#
# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
#
# You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your
# algorithm. We will instead measure the correctness of your output by evaluating the data in your nodes.csv and edges.csv files.
#
# GRAPH SIZE
# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
# Our testing indicates growth approximately equal to e^2x.
# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
# updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a
# margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to:
#
# Min allowable nodes = min encountered nodes - 100
# Max allowable nodes = max allowable nodes + 100
#
# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
# ----------------------------------------------------------------------------------------------------------------------
# BEGIN BUILD CO-ACTOR NETWORK
#
# INITIALIZE GRAPH
# Initialize a Graph object with a single node representing Laurence Fishburne
#
# BEGIN BUILD BASE GRAPH:
# Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
# FOR each movie credit:
# | get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
# |
# | FOR each movie cast member:
# | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
# | | using graph.add_edge(), add an edge between the Laurence Fishburne (actor) node
# | | and each new node (co-actor/co-actress)
# | END FOR
# END FOR
# END BUILD BASE GRAPH
#
#
# BEGIN LOOP - DO 2 TIMES:
# IF first iteration of loop:
# | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
# ELSE
# | nodes = The nodes added in the previous iteration:
# ENDIF
#
# FOR each node in nodes:
# | get the movie credits for the actor that have a vote average >= 8.0
# |
# | FOR each movie credit:
# | | try to get the 3 movie cast members having an 'order' value between 0-2
# | |
# | | FOR each movie cast member:
# | | | IF the node doesn't already exist:
# | | | | add the node to the graph (track all new nodes added to the graph)
# | | | ENDIF
# | | |
# | | | IF the edge does not exist:
# | | | | add an edge between the node (actor) and the new node (co-actor/co-actress)
# | | | ENDIF
# | | END FOR
# | END FOR
# END FOR
# END LOOP
#
# Your graph should not have any duplicate edges or nodes
# Write out your finished graph as a nodes file and an edges file using:
# graph.write_edges_file()
# graph.write_nodes_file()
#
# END BUILD CO-ACTOR NETWORK
# ----------------------------------------------------------------------------------------------------------------------
# Exception handling and best practices
# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
# - Some movie_credits do not return cast data. Handle this situation by skipping these instances.
# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
# can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
# - wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph.
def return_name()->str:
"""
Return a string containing your GT Username
e.g., tlou31
Do not return your 9 digit GTId
"""
return "GT Username"
# def return_argo_lite_snapshot()->str:
# """
# Return the shared URL of your published graph in Argo-Lite
# """
# return "https://poloclub.github.io/argo-graph-lite/#1ac32aec-ba9f-4693-83e1-cd4a7e81fb93"
# You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes.
# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
if __name__ == "__main__":
graph = Graph()
graph.add_node(id='2975', name='Laurence Fishburne')
# build base graph
# find all laurence fishburne's movie credits with a vote average >= 8.0
tmdb_api_utils = TMDBAPIUtils(api_key='1eed362096d67d9f077084d5abb30a35')
movie_credits = tmdb_api_utils.get_movie_credits_for_person(person_id='2975', vote_avg_threshold=8.0)
print(len(movie_credits))
print(movie_credits)
# for each movie credit get movie cast members having and order value between 0-2 (3 0-base)
for movie in movie_credits:
movie_cast = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for cast_member in movie_cast:
graph.add_node(str(cast_member['id']), cast_member['name'])
graph.add_edge(source='2975', target=str(cast_member['id']))
break # TODO: remove me
break # TODO: remove me
for i in [0,1,2]:
print('ref loop '+ str(i))
if i == 0:
current_nodes = graph.nodes[1:]
else:
current_nodes = [i for i in graph.nodes if i not in current_nodes]
for node in current_nodes:
cast = tmdb_api_utils.get_movie_credits_for_person(person_id=node[0], vote_avg_threshold=8.0)
for movie in cast:
actors = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for actor in actors:
graph.add_node(str(actor['id']), actor['name'])
graph.add_edge(str(node[0]), str(actor['id']))
# tmdb_api_utils.get_movie_cast(movie_id='100', limit=3, exclude_ids=[973, 974])
# tmdb_api_utils.get_movie_credits_for_person(person_id="9709", vote_avg_threshold=5.0)
# call functions or place code here to build graph (graph building code not graded)
# Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
print(f"Edges: {graph.total_edges()}")
print(f"Nodes: {graph.total_nodes()}")
graph.write_edges_file()
graph.write_nodes_file()
# If you have already built & written out your graph, you could read in your nodes & edges files
# to perform testing on your graph.
graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")
#get non-leaf nodes
non_leaf = graph.non_leaf_nodes()
print(non_leaf - 10)
print("DONE")

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import http.client
import json
import csv
import urllib.request
import os,sys
#############################################################################################################################
# cse6242
# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
# hard-coded.
#
# Instructions: Implement all methods in this file that have a return
# value of 'NotImplemented'. See the documentation within each method for specific details, including
# the expected return value
#
# Helper Functions:
# You are permitted to write additional helper functions/methods or use additional instance variables within
# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
#
# Use:
# The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must
# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
#
# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods
# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional
# methods and instance variables as desired (see Helper Functions).
#
# The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the
# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
# .csv files for submission to Gradescope.
#
# While building the co-actor graph, you will be required to write code to expand the graph by iterating
# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
# but will grade the resulting graph data in your nodes and edges .csv files.
#
#############################################################################################################################
class Graph:
# Do not modify
def __init__(self, with_nodes_file=None, with_edges_file=None):
"""
option 1: init as an empty graph and add nodes
option 2: init by specifying a path to nodes & edges files
"""
self.nodes = []
self.edges = []
if with_nodes_file and with_edges_file:
nodes_CSV = csv.reader(open(with_nodes_file))
nodes_CSV = list(nodes_CSV)[1:]
self.nodes = [(n[0], n[1]) for n in nodes_CSV]
edges_CSV = csv.reader(open(with_edges_file))
edges_CSV = list(edges_CSV)[1:]
self.edges = [(e[0], e[1]) for e in edges_CSV]
def add_node(self, id: str, name: str) -> None:
"""
add a tuple (id, name) representing a node to self.nodes if it does not already exist
The graph should not contain any duplicate nodes
"""
if (id, name) not in self.nodes:
self.nodes.append((id, name))
def add_edge(self, source: str, target: str) -> None:
"""
Add an edge between two nodes if it does not already exist.
An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
Where 'source' is the id of the source node and 'target' is the id of the target node
e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
"""
current_edge = (source, target)
current_edge_inverted = (target, source)
if (source != target) and (current_edge not in self.edges) and (current_edge_inverted not in self.edges):
self.edges.append(current_edge)
def total_nodes(self) -> int:
"""
Returns an integer value for the total number of nodes in the graph
"""
output = len(self.nodes)
return output
def total_edges(self) -> int:
"""
Returns an integer value for the total number of edges in the graph
"""
output = len(self.edges)
return output
def max_degree_nodes(self) -> dict:
"""
Return the node(s) with the highest degree
Return multiple nodes in the event of a tie
Format is a dict where the key is the node_id and the value is an integer for the node degree
e.g. {'a': 8}
or {'a': 22, 'b': 22}
"""
max_degree_nodes = {}
for edge in self.edges:
for idx in edge:
if idx in max_degree_nodes:
max_degree_nodes[idx] += 1
else:
max_degree_nodes[idx] = 1
v = list(max_degree_nodes.values())
k = list(max_degree_nodes.keys())
max_nodes = {k[v.index(max(v))]: max(v)}
# print(max_nodes)
return max_nodes
def print_nodes(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.nodes)
def print_edges(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.edges)
# Do not modify
def write_edges_file(self, path="edges.csv")->None:
"""
write all edges out as .csv
:param path: string
:return: None
"""
edges_path = path
edges_file = open(edges_path, 'w', encoding='utf-8')
edges_file.write("source" + "," + "target" + "\n")
for e in self.edges:
edges_file.write(e[0] + "," + e[1] + "\n")
edges_file.close()
print("finished writing edges to csv")
# Do not modify
def write_nodes_file(self, path="nodes.csv")->None:
"""
write all nodes out as .csv
:param path: string
:return: None
"""
nodes_path = path
nodes_file = open(nodes_path, 'w', encoding='utf-8')
nodes_file.write("id,name" + "\n")
for n in self.nodes:
nodes_file.write(n[0] + "," + n[1] + "\n")
nodes_file.close()
print("finished writing nodes to csv")
class TMDBAPIUtils:
# Do not modify
def __init__(self, api_key:str):
self.api_key=api_key
def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
"""
Get the movie cast for a given movie id, with optional parameters to exclude an cast member
from being returned and/or to limit the number of returned cast members
documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
:param string movie_id: a movie_id
:param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result
e.g., if exclude_ids are [353, 455] then exclude these from any result.
:param integer limit: maximum number of returned cast members by their 'order' attribute
e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range).
If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
If after excluding, the limit is not specified, then return all remaining cast members."
e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
:rtype: list
return a list of dicts, one dict per cast member with the following structure:
[{'id': '97909' # the id of the cast member
'character': 'John Doe' # the name of the character played
'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
Note that this is an example of the structure of the list and some of the fields returned by the API.
The result of the API call will include many more fields for each cast member.
"""
url = 'https://api.themoviedb.org/3/movie/'+ str(movie_id) +'/credits?api_key='+self.api_key +"&language=en-US"
req = urllib.request.Request(url, headers={
'Accept': 'application/json',
})
response = urllib.request.urlopen(req)
if response.status == 200:
json_data = json.load(response)
cast = json_data['cast']
if exclude_ids is not None:
cast = [i for i in cast if i['id'] not in exclude_ids]
# print(cast)
if limit is not None:
cast = [i for i in cast if i['order'] < limit]
# print(cast)
for c in cast:
c['name'] = c['name'].replace(',', '')
return cast
else:
print(response.status.__str__())
return None
def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
"""
Using the TMDb API, get the movie credits for a person serving in a cast role
documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
:param string person_id: the id of a person
:param vote_avg_threshold: optional parameter to return the movie credit if it is >=
the specified threshold.
e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
:rtype: list
return a list of dicts, with each dict having 'id', 'title', and 'vote_avg' keys,
one dict per movie credit with the following structure:
[{'id': '97909' # the id of the movie
'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
"""
url = 'https://api.themoviedb.org/3/person/'+ str(person_id) +'/movie_credits?api_key='+self.api_key+'&language=en-US'
req = urllib.request.Request(url, headers={
'Accept': 'application/json',
})
response = urllib.request.urlopen(req)
if response.status == 200:
raw_data = response.read()
# refer to https://docs.python.org/3/library/json.html convert data to dictionary
cast_data = (json.loads(raw_data.decode('utf-8'))).get("cast")
# only include id which is above avg rating
abv_avg = [data for data in cast_data if data.get('vote_average') >= vote_avg_threshold]
# else:
# print(response.status, response.reason)
return abv_avg
#############################################################################################################################
#
# BUILDING YOUR GRAPH
#
# Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples
#
# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
# In this graph, each node represents an actor
# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
# i.e., they share a movie credit.
# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
# or more movies together.
#
# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
#
# You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your
# algorithm. We will instead measure the correctness of your output by evaluating the data in your nodes.csv and edges.csv files.
#
# GRAPH SIZE
# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
# Our testing indicates growth approximately equal to e^2x.
# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
# updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a
# margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to:
#
# Min allowable nodes = min encountered nodes - 100
# Max allowable nodes = max allowable nodes + 100
#
# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
# ----------------------------------------------------------------------------------------------------------------------
# BEGIN BUILD CO-ACTOR NETWORK
#
# INITIALIZE GRAPH
# Initialize a Graph object with a single node representing Laurence Fishburne
#
# BEGIN BUILD BASE GRAPH:
# Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
# FOR each movie credit:
# | get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
# |
# | FOR each movie cast member:
# | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
# | | using graph.add_edge(), add an edge between the Laurence Fishburne (actor) node
# | | and each new node (co-actor/co-actress)
# | END FOR
# END FOR
# END BUILD BASE GRAPH
#
#
# BEGIN LOOP - DO 2 TIMES:
# IF first iteration of loop:
# | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
# ELSE
# | nodes = The nodes added in the previous iteration:
# ENDIF
#
# FOR each node in nodes:
# | get the movie credits for the actor that have a vote average >= 8.0
# |
# | FOR each movie credit:
# | | try to get the 3 movie cast members having an 'order' value between 0-2
# | |
# | | FOR each movie cast member:
# | | | IF the node doesn't already exist:
# | | | | add the node to the graph (track all new nodes added to the graph)
# | | | ENDIF
# | | |
# | | | IF the edge does not exist:
# | | | | add an edge between the node (actor) and the new node (co-actor/co-actress)
# | | | ENDIF
# | | END FOR
# | END FOR
# END FOR
# END LOOP
#
# Your graph should not have any duplicate edges or nodes
# Write out your finished graph as a nodes file and an edges file using:
# graph.write_edges_file()
# graph.write_nodes_file()
#
# END BUILD CO-ACTOR NETWORK
# ----------------------------------------------------------------------------------------------------------------------
# Exception handling and best practices
# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
# - Some movie_credits do not return cast data. Handle this situation by skipping these instances.
# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
# can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
# - wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph.
def return_name()->str:
"""
Return a string containing your GT Username
e.g., tlou31
Do not return your 9 digit GTId
"""
return "tlou31"
# You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes.
# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
if __name__ == "__main__":
# init graph object with a single node representing laurence fishburne
graph = Graph()
graph.add_node(id='2975', name='Laurence Fishburne')
# build base graph
# find all laurence fishburne's movie credits with a vote average >= 8.0
tmdb_api_utils = TMDBAPIUtils(api_key='1eed362096d67d9f077084d5abb30a35')
movie_credits = tmdb_api_utils.get_movie_credits_for_person(person_id='2975', vote_avg_threshold=8.0)
# for each movie credit get movie cast members having and order value between 0-2 (3 0-base)
for movie in movie_credits:
movie_cast = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for cast_member in movie_cast:
graph.add_node(str(cast_member['id']), cast_member['name'])
graph.add_edge(source='2975', target=str(cast_member['id']))
for i in range(2):
print('running loop ' + str(i))
if i == 0:
current_nodes = graph.nodes[1:]
else:
current_nodes = [i for i in graph.nodes if i not in current_nodes]
for node in current_nodes:
cast = tmdb_api_utils.get_movie_credits_for_person(person_id=node[0], vote_avg_threshold=8.0)
for movie in cast:
actors = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for actor in actors:
graph.add_node(str(actor['id']), actor['name'])
graph.add_edge(str(node[0]), str(actor['id']))
# tmdb_api_utils.get_movie_cast(movie_id='100', limit=3, exclude_ids=[973, 974])
# tmdb_api_utils.get_movie_credits_for_person(person_id="9709", vote_avg_threshold=5.0)
# call functions or place code here to build graph (graph building code not graded)
# Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
print("Edges: " + str(graph.total_edges()))
print("Nodes: " + str(graph.total_nodes()))
graph.write_edges_file()
graph.write_nodes_file()
# If you have already built & written out your graph, you could read in your nodes & edges files
# to perform testing on your graph.
graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")
print("DONE")

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tunmnlu/task_1/Q1/bundle-fix/edges.csv Normal file
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tunmnlu/task_1/Q1/bundle-fix/nodes.csv Normal file
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id,name
2975,Laurence Fishburne
6384,Keanu Reeves
530,Carrie-Anne Moss
8349,Martin Sheen
8351,Frederic Forrest
8354,Albert Hall
1107983,Martin Luther King Jr.
52057,Obba Babatundé
110380,Colin Powell
18471,Anthony Anderson
74611,Tracee Ellis Ross
1407498,Marsai Martin
1357063,Darrin Prescott
40644,Chad Stahelski
1779512,Jackson Spidell
21127,Bobby Cannavale
1692944,Heidi Schreck
64,Gary Oldman
1776,Francis Ford Coppola
38803,Roman Coppola
20215,Billy Campbell
2130,Cary Elwes
529,Guy Pearce
532,Joe Pantoliano
1278399,Joseph Steven
1659326,Teresa Modnick
26466,Caroline Kava
18269,Brendan Fraser
5606,Sissy Spacek
5632,Jack Fisk
1179102,Kathleen Kennedy
11367,Bradley Whitford
31028,Richard Schiff
1397846,Craig Barron
2166741,Robert A. Baum Jr.
3266,Joe Mantegna
36059,Joanna Pacula
21010,Alexandra Hay
101032,Rod McCary
98569,Vincent Van Patten
2080584,Ben Jorgensen
103835,Jeff Monahan
6717,James Marshall
928944,Coleman Barks
2022250,Robert Bly
9979,Peter Coyote
514,Jack Nicholson
6193,Leonardo DiCaprio
1892,Matt Damon
2641,Martin Landau
92029,Gabriel Bologna
218506,Gretchen Becker
2098136,Brian Becker
2098137,Richard Becker
2098138,Blase Bonpane
181934,Al Sharpton
496175,Carmelo Anthony
1280183,Coretta Scott King
1006651,John Lewis
237876,Sarah-Jane Sauvegrain
180659,Rosa Parks
1165240,Jason Alan Carvell
2787630,Valentin Zorin
89289,Jesse Jackson
2102583,Andrew Cohen
73988,Lyndon B. Johnson
21111,John F. Kennedy
2803017,Arthur John Birch
2803001,Haroldson Lafayette Hunt
4193575,Maxime Demigné
4193577,Gabin Clerc
4193578,Tom Chabeau
2954,Jeffrey Wright
33663,Donald Trump
2301996,Mike Pence
214317,Ben Bradlee
13716,Carl Bernstein
117180,Tom Brokaw
23626,Liev Schreiber
937663,Neil Amdur
1931175,Bob Beamon
12951,O.J. Simpson
2998183,Nicole Brown Simpson
1231277,Marcia Clark
60158,Shirley Jones
24810,Pat Boone
2535,Vivica A. Fox
225620,Rudolph Moise
1683093,Terayle Hill
79538,Khalil Kain
1562353,Erica Mena
1038,Jodie Foster
4173,Anthony Hopkins
349,Scott Glenn
4587,Halle Berry
9780,Angela Bassett
19011,George W. Bush
146687,Tony Blair
74266,Dick Cheney
1507609,Bob Coen
1507612,Francis E. Boyle
1507613,Jean Patterson
1122373,Marion Barry Jr.
1122374,Chuck Brown
1122375,Lewis Franklin
1897,Bernie Mac
77896,Bill Bellamy
2395,Whoopi Goldberg
1735828,Halle Bailey
53397,Christina Aguilera
1224841,Mo Willems
539,Thomas Lennon
111513,Yvette Nicole Brown
2682390,Gerd Ludwig
2227,Nicole Kidman
4430,Sharon Stone
14837,Carol Burnett
5823,Julie Andrews
3141,Marisa Tomei
449538,Adele
13309,Oprah Winfrey
1661465,Lizzo
102786,Barack Obama
128550,Desmond Tutu
2320880,Sundar Pichai
3894,Christian Bale
19498,Jon Bernthal
2710,James Cameron
8691,Zoe Saldaña
72129,Jennifer Lawrence
189111,Suzanne Collins
23964,Gary Ross
9827,Rose Byrne
143103,Krew Boylan
21708,Tomas Milian
72983,Manny Pérez
124909,Danny Hoch
4038,Susan Sarandon
18992,Aidan Quinn
2047,Danny Glover
4730,Emmy Rossum
9464,Harry Lennix
14409,David Schwimmer
1415341,Kazuhiro Tsuji
1267653,Chet Zar
7467,David Fincher
1519399,Erik Messerschmidt
1003,Jean Reno
524,Natalie Portman
1810,Heath Ledger
3895,Michael Caine
958722,Eiko Ishioka
41381,Sadie Frost
10980,Daniel Radcliffe
10989,Rupert Grint
10990,Emma Watson
219479,Criss Angel
1101349,Steve Aoki
1407495,Miles Brown
3361135,Peter Kent
1812,Michelle Williams
2037,Cillian Murphy
5081,Emily Blunt
2559324,Beth Lane
2559325,Lea Madda
27888,Raúl Juliá
2630,Nastassja Kinski
151007,Peter Ramsey
1769,Sofia Coppola
19772,Paul Rassam
1,George Lucas
7879,John Lasseter
1032,Martin Scorsese
1006721,Charles Scorsese
11483,Catherine Scorsese
5953,Spike Jonze
325,Eminem
3125,Madonna
3084,Marlon Brando
1158,Al Pacino
3085,James Caan
3087,Robert Duvall
3092,Diane Keaton
117669,Portia Doubleday
558466,Alex Russell
61555,Haley Ramm
10017,Charlton Heston
5563,James Coburn
26557,Ferdy Mayne
18688,Harry Connick Jr.
8437,Teri Garr
589,Daryl Hannah
16431,Sam Elliott
2203,Neal McDonough
3026,Rob Reiner
32,Robin Wright
12132,Michael Rooker
932719,Jeff Gordon
2127,James Wan
2128,Leigh Whannell
2675,Darren Lynn Bousman
46300,Alexander Nathan Etel
27889,Stephan Elliott
1331,Hugo Weaving
326,Kim Basinger
323,Curtis Hanson
11099,Dante Spinotti
7187,Richard Donner
57371,Jeff Cohen
3034,Corey Feldman
147,Michael Madsen
21200,Will Arnett
13611,Jack White
10750,Sheryl Crow
8261,Willie Nelson
54693,Emma Stone
19492,Viola Davis
6944,Octavia Spencer
287,Brad Pitt
192,Morgan Freeman
12052,Gwyneth Paltrow
5376,Jean Smart
5937,Kari Matchett
26009,Gail O'Grady
1067583,Suzanne Happ
10427,Anne Archer
781,Paul Reiser
33,Gary Sinise
5149,Bonnie Hunt
2144,Tobin Bell
141,David Carradine
2505,James Cromwell
42206,Matt McCoy
10167,George Lazenby
1226195,Rob Stewart
13637,Joan Collins
5320,Leslie Caron
1641,Katrin Cartlidge
10447,Mariel Hemingway
76184,Bryan Marshall
75781,John Turner
39770,Adam Roarke
34721,Larry Bishop
83908,Daniel Massey
1748,William Shatner
8499,William Windom
16896,James Garner
8857,Debbie Reynolds
15395,Maurice Ronet
51763,Ross Martin
955923,Ginny Tyler
6474,James Le Gros
69122,Heather Graham
65344,Esai Morales
6677,Kyle MacLachlan
6678,Michael Ontkean
6714,Mädchen Amick
56890,Malcolm McDowell
46393,Roseanne Barr
9789,Robert Altman
6449,Warren Beatty
1353815,Dr. Diane Ravitch
1353816,Dr. Linda Darling-Hammond
20212,Dermot Mulroney
41249,Tess Harper
1841281,Joey Molland
1225302,Annie Nightingale
3546502,Jack Trout
118764,Paulo Pires
36281,Imanol Arias
3199207,Jerry Dealey
3199208,Barry Ernest
82128,Wesley Clark
21818,Lesley Ann Warren
12967,Swoosie Kurtz
13567,Ann-Margret
25834,Leslie Hope
1204293,J. Allen Hynek
2027993,Philip J. Klass
20959,Louis Gossett Jr.
57829,Anthony LaPaglia
7447,Alec Baldwin
9560,Ellen Burstyn
37421,James Naughton
9976,Henry Thomas
69597,Drew Barrymore
1066777,Mumia Abu-Jamal
65013,Rubin 'Hurricane' Carter
4808,Giancarlo Esposito
2045089,Jeanmarie Simpson
4687,Patricia Arquette
2045090,Joyce Julianne Auer
60642,Tom Petty
22865,Little Richard
13301,Quincy Jones
7071,Louise Fletcher
518,Danny DeVito
10409,Shelley Duvall
7077,Scatman Crothers
3391,Kathleen Turner
117075,Henry Jaglom
34947,Kevin Conroy
1262612,Neal Adams
103,Mark Ruffalo
2282,Ben Kingsley
24045,Joseph Gordon-Levitt
3899,Ken Watanabe
21007,Jonah Hill
234352,Margot Robbie
16418,Casey Kasem
15831,Frank Welker
134,Jamie Foxx
27319,Christoph Waltz
11478,Lorraine Bracco
380,Robert De Niro
9261,Frank Abagnale Jr.
488,Steven Spielberg
2689,Jeff Nathanson
31,Tom Hanks
1206,Jason Alexander
70851,Jack Black
2157,Robin Williams
880,Ben Affleck
1231844,Manny Ramírez
1214256,Curt Schilling
3197,Tom Sizemore
12833,Edward Burns
10297,Matthew McConaughey
1813,Anne Hathaway
83002,Jessica Chastain
21037,Prince
1800494,Cora Coleman-Dunham
1775343,Josh Dunham
1205143,John Gazarian
1205144,Darlene Landau
2222,Beau Bridges
18878,Rob Cohen
9111,Peter Graves
98234,Barbara Bain
24589,Paul Mercurio
24590,Dominique Sanda
11770,John Carpenter
515,Glenn Close
229579,Bob Carruthers
220717,Uri Geller
1229925,La Toya Jackson
179512,Marv Albert
1865991,Jim Boeheim
59785,Fat Joe
84932,Jay-Z
16450,Sean Combs
210695,Jesse Williams
1258041,David Axelrod
1236577,Douglas Brinkley
1374664,Louis-Émile Galey
1010125,Claude Heymann
1385039,Bryan Stevenson
1641150,Patrisse Cullors
938972,Angela Davis
2149806,Gus Hall
100709,Junior Wells
1386798,Joseph McCarthy
13348,Howard Hughes
1247141,Harry S. Truman
2802990,Jean Paul Getty
117418,Mahalia Jackson
1188876,Horace Clarence Boyer
61399,Mike Ditka
1232728,Jim McMahon
1723195,Mike Singletary
1219050,Hank Aaron
14414,Kareem Abdul-Jabbar
65605,Muhammad Ali
136403,Robert Dryden
1999091,Chris Schenkel
1050881,Jacqueline Kennedy
1152,Oliver Stone
3331792,Vasili Arkhipov
120578,Franklin D. Roosevelt
57738,Samuel Labarthe
1141815,Charles de Gaulle
1204877,André Malraux
1257876,Margaret Whitmer
1051824,Tom Junod
1998031,Joanne Rogers
1116399,Winston Churchill
18802,Ronald Reagan
7571,Blair Brown
84378,Nick Fraser
1420,Robert Drew
2102600,Jill Drew
225009,Pablo Larraín
6431,Darren Aronofsky
8289,Billy Crudup
1670479,Abbas Abdi
1625013,Mahmoud Ahmadinejad
84384,Osama Bin Laden
2214123,Véronique Préault
2412677,Xavier Pasco
2412679,John Logsdon
16811,Alexander Scheer
3910768,Jürgen Hambrecht
2823180,Wolfgang Ischinger
4193580,Tkun
4193583,Sofiane
25072,Oscar Isaac
3910,Frances McDormand
1241,John Turturro
1145892,Ed Koch
1807640,Jane Jacobs
557911,Robert F. Kennedy Jr.
11161,Tom Savini
27765,Richard Nixon
1167702,Bruno Sammartino
1167703,Bob Backlund
2323039,Catherine Mercado-Muñiz
2339577,Carmen Yulín Cruz
10127,Jon Voight
1229169,Roger Ailes
3553309,John Shipton
150882,Julian Assange
16214,Mario Van Peebles
227811,Herschel Walker
1222706,Skip Bayless
1816204,Frederick Oberlander
1816207,Tevfik Arif
3342345,Tim Gielen
1954181,George Soros
240633,Bill Gates
80757,Mike Tyson
1003978,Larry Holmes
1306642,Kimberly DiPersia
2289540,Alex R. Wagner
2662043,Andrew Cuomo
2266228,Kim Dae-jung
2435679,Lee Geum-hee
2266230,Kim Young-sam
56446,John Cena
216075,Candice Michelle
568535,Matthew Kaye
1326321,Paige
57342,Vince McMahon
60342,Emily Maitlis
2041195,Prince Andrew Duke of York
2607708,Jeffrey Epstein
16620,Hulk Hogan
77120,Steve Austin
1647876,Hans Henrik Wöhler
3252749,Michael Fanone
3370866,Aquilino Gonell
28151,Benoît Allemane
2982422,Michael S. Cullen
3242458,Isabelle Rouge-Ducos
21397,Dennis Rodman
3981237,Shirley Rodman
567335,Chris Benoit
115788,Paul Michael Lévesque
112219,Michael Hickenbottom
25504,André Roussimoff
160733,Nick Bockwinkel
2189100,Moon Jae-in
1671675,Yuna Kim
1644623,Ahn Jung-hwan
21278,Alan Alda
13724,Jane Alexander
1428889,Ivy Austin
3323316,Tricia Brooks
2021450,Vernon Jordan
3797418,Maria Kirke
937664,Abraham Beame
937665,Bob Bright
1416398,Joshua L. Pearson
46432,George H.W. Bush
116341,Bill Clinton
2652100,Curt Flood
2652101,Judy Pace Flood
219189,Jackie Robinson
214725,Duke Snider
3905,William H. Macy
18686,Holly Hunter
223120,Jack O'Callahan
223121,Herb Brooks
223122,Mike Eruzione
107375,Joe Frazier
1304153,Imelda Marcos
587506,Shameik Moore
543505,Jake Johnson
130640,Hailee Steinfeld
2415973,Daniel Petrocelli
2413592,Fred Goldman
81439,Vincent Bugliosi
707,Dan Aykroyd
26485,Jim Belushi
54812,Chevy Chase
5788,James Cagney
87514,Roger Smith
2177,Lloyd Bridges
11140,Lynn Carlin
1503749,Big Freedia
1430,Keith Richards
562922,Christian Keyes
21215,Elise Neal
21563,Corin Nemec
2939038,Sydney Malakeh
8689,Barry Shabaka Henley
2297056,Darik Bernard
2295,Mickey Rourke
141823,Tisha Campbell
1841650,Crystal-Lee Naomi
1838518,Jermaine Rivers
58924,Clifton Powell
21355,DMX
1922617,Joy Rovaris
587035,Nadji Jeter
479206,Demetria McKinney
154382,Jazsmin Lewis
31134,Darrin Henson
74613,Terri J. Vaughn
2808544,Daran Acevedo
1297171,Julian Bane
108081,Malik Barnhardt
1220781,Shelly Altman
115002,John Aniston
1395884,Gessica Généus
105762,Jimmy Jean-Louis
1142232,Soledad Elizabeth Jean
7672,Reginald VelJohnson
1432244,Lyon Beckwith
127931,Dorien Wilson
77353,Jasmine Guy
233191,Terrence 'T.C.' Carson
1108907,Nick Robinson
9278,Jennifer Garner
19536,Josh Duhamel
3593345,Chara James
3593347,Zebulen M.N. Joyner-McCaster
1214197,Carl Anthony Payne II
16897,Sidney Poitier
20493,Sydney Tamiia Poitier
119598,Phylicia Rashād
109686,Flex Alexander
109687,Mike Batayeh
77072,Tyson Beckford
1232383,Parker McKenna Posey
1573627,Torrei Hart
1437841,AzMarie Livingston
55637,Brian Hooks
1100816,René Aranda
3581413,Dwight Boyce
60490,Paul Terrell Clayton
1036,Cybill Shepherd
5049,John Hurt
10774,Anne Bancroft
39187,Olivia Colman
34546,Mark Gatiss
54448,Ismail Merchant
54441,James Ivory
54443,Ruth Prawer Jhabvala
4786,Richard Attenborough
1137,Juliette Binoche
28974,Damon Lindelof
15851,Amy Brenneman
15009,Justin Theroux
33181,Jimmy Smits
63611,Nickolas Grace
53574,Eric Thal
16217,Lynn Whitfield
15762,Tara Strong
1160,Michelle Pfeiffer
8891,John Travolta
44038,Nancy Allen
1121437,Leslie Felperin
70243,Eartha Kitt
15973,Julie Newmar
16108,Lee Meriwether
44796,Franklyn Ajaye
35705,Regina Hall
143242,Terrence Jenkins
174837,Yolanda Adams
56323,Tina Fey
78577,Graham Norton
3223,Robert Downey Jr.
16828,Chris Evans
1189929,Condoleezza Rice
7904,Billy Crystal
533195,Henry Kissinger
143280,Norman Mailer
2801284,Anthony Amsterdam
1220601,John Chancellor
1066763,Darrell Royal
50707,Ed Marinaro
1322033,Bob Brenly
1322034,Scott Brosius
1233313,Joe Buck
3273875,Bob Taylor
3622690,Betty Ong
3622691,Nydia Gonzalez
3492377,Madeleine Albright
3492379,Ilie Alexandru
3492380,Ioan Amarie
1442410,Trevor Phillips
1442411,Nigel Farage
1225259,Kirsty Young
174981,King Charles III of the United Kingdom
1094687,Prince Edward Duke of Edinburgh
1306064,Princess Diana of Wales
558240,Queen Elizabeth II of the United Kingdom
47,Björk
3219627,Lydia Korniordou
2921077,Jacques Rogge
37759,Kathy Burke
20056,Keith Allen
2330903,Amir Amor
3897137,Cool 'Disco' Dan
9290,Henry Rollins
15535,Robin Harris
5726,Cedric the Entertainer
68215,Reginald Hudlin
181975,Mark Curry
82587,Steve Harvey
78029,Martin Lawrence
9309,Richard Pryor
57551,Mario Cantone
18291,Larenz Tate
1633946,Chris Gattling
10814,Wesley Snipes
1903010,Spence Moore II
55638,Kevin Hart
5916,Rosario Dawson
9206,Neve Campbell
36811,Nick Cannon
1870421,Collie Buddz
2412016,Lilly Bartlam
186529,Tony Daniels
88059,Tommy Davidson
15411,T.K. Carter
1428,Mick Jagger
1429,Charlie Watts
53396,Ron Wood
8534,Kathy Bates
1121,Benicio del Toro
3392,Michael Douglas
17051,James Franco
59315,Olivia Wilde
2838,Chloë Sevigny
155488,Kelly Clarkson
292445,Blake Shelton
21986,Reba McEntire
2387,Patrick Stewart
2388,Jonathan Frakes
1213786,Brent Spiner
4756,Matthew Broderick
15152,James Earl Jones
16940,Jeremy Irons
148120,Tiffany
83170,Valerie Harper
2774840,Luis Miranda
1179651,Lin-Manuel Miranda
216674,Hillary Clinton
73641,Christiane Amanpour
1202593,Neil Barnard
33684,Bono
1650331,Jeremy O. Harris
350,Laura Linney
2391,Michael Dorn
172069,Chadwick Boseman
1215522,Robin Roberts
1896,Don Cheadle
6240,Mariska Hargitay
21411,Ice-T
206637,Kelli Giddish
120608,Sonia Manzano
1596910,Nitya Vidyasagar
934281,Alex Wolff
1700685,Lewis Pullman
1241339,Bo Mitchell
14386,Beyoncé
1214573,Serena Williams
93527,Kelly Rowland
167662,Ryan Seacrest
2384929,Lindsay Arnold
138986,Tony Bennett
72208,Alicia Keys
3284,Bruce Springsteen
18746,Stevie Wonder
37934,André 3000
15310,Anthony Kiedis
12207,Kylie Minogue
1564846,Auli'i Cravalho
1473281,James Monroe Iglehart
2522829,Vanessa Laine Bryant
23678,Michael Jordan
35806,Shaquille O'Neal
1212304,Jon Scieszka
1192274,Jordan Fisher
132354,Kate Micucci
133451,Aya Cash
111684,Lennon Parham
60227,Greg Cipes
1249820,Romi Dames
8536,Victor Garber
1178795,Ben Diskin
1842039,Joe Tessitore
71403,Rob Riggle
129193,Paula Abdul
75394,Terence Donovan
1083951,Veronica Lang
500,Tom Cruise
150536,Barry Otto
76182,Nicholas Eadie
76788,Dev Patel
108916,Rooney Mara
1371,David Wenham
113552,Briony Behets
1242514,James Smillie
1246450,Bartholomew John
1747292,Robert Board
106179,Anthony Franciosa
82315,Jean Harlow
11492,Clark Gable
1916574,Caitlin Doughty
5168,Gabriel Byrne
516,Annette Bening
4517,Joe Pesci
71531,Deepak Chopra
1383964,Thích Nhất Hạnh
1867979,Sister Chân Không
8293,Marion Cotillard
32017,Didier Lavergne
3753843,Bryna Rifkin
5682,Anouk Aimée
44556,Richard Anconina
20234,Fanny Ardant
40159,Keith Michell
58414,Dina Merrill
4,Carrie Fisher
6541,Philip Bosco
157359,Jane Connell
1729824,Randy Doney
1392152,Dan Levy
26510,Eugene Levy
11514,Catherine O'Hara
290,Christopher Plummer
21104,Glenne Headly
80866,Nell Carter
40063,Lea DeLaria
537064,Perry Como
109887,Caroll Spinney
8515,Jane Darwell
5829,Karen Dotrice
1201,Garry Marshall
1041410,Charmian Carr
22384,John Amos
105637,Morey Amsterdam
6613,Minnie Driver
57133,Paulo Costanzo
18461,Nick Chinlund
1216139,Michael Raynor
57389,Justin Pierce
87525,Alan Carr
7056,Emma Thompson
154782,Pat Benatar
154783,Neil Giraldo
174875,Carly Simon
929825,Ava DuVernay
2302408,Korey Wise
20019,Lee Daniels
1658802,Andra Day
964843,Michelle Obama
3889844,Haizel Adofo
113461,John Legend
206444,Constance Wu
1700631,Liza Koshy
2503684,Abdullah bin Abdulaziz Al Saud
107379,LeBron James
66620,Angela Merkel
2134341,Theresa May
3089883,Gabby Giffords
1608713,Mark Kelly
1068409,Frank Marshall Davis
1068410,Ann Dunham
1068411,Stanley Dunham
149923,Lester Brown
1063969,Yvo de Boer
1063970,Paul R. Ehrlich
10279,Adolf Hitler
89300,Nelson Mandela
3451053,Natalya Sindeeva
3527796,Aleksandr Vinokurov
1745467,Vera Krichevskaya
1173480,François Hollande
1348088,Laurent Delahousse
1977908,Claire Chazal
2169865,Birgitta Assheuer
2920376,Îlham Ahmed
2920377,Zaher al-Saket
1 id name
2 2975 Laurence Fishburne
3 6384 Keanu Reeves
4 530 Carrie-Anne Moss
5 8349 Martin Sheen
6 8351 Frederic Forrest
7 8354 Albert Hall
8 1107983 Martin Luther King Jr.
9 52057 Obba Babatundé
10 110380 Colin Powell
11 18471 Anthony Anderson
12 74611 Tracee Ellis Ross
13 1407498 Marsai Martin
14 1357063 Darrin Prescott
15 40644 Chad Stahelski
16 1779512 Jackson Spidell
17 21127 Bobby Cannavale
18 1692944 Heidi Schreck
19 64 Gary Oldman
20 1776 Francis Ford Coppola
21 38803 Roman Coppola
22 20215 Billy Campbell
23 2130 Cary Elwes
24 529 Guy Pearce
25 532 Joe Pantoliano
26 1278399 Joseph Steven
27 1659326 Teresa Modnick
28 26466 Caroline Kava
29 18269 Brendan Fraser
30 5606 Sissy Spacek
31 5632 Jack Fisk
32 1179102 Kathleen Kennedy
33 11367 Bradley Whitford
34 31028 Richard Schiff
35 1397846 Craig Barron
36 2166741 Robert A. Baum Jr.
37 3266 Joe Mantegna
38 36059 Joanna Pacula
39 21010 Alexandra Hay
40 101032 Rod McCary
41 98569 Vincent Van Patten
42 2080584 Ben Jorgensen
43 103835 Jeff Monahan
44 6717 James Marshall
45 928944 Coleman Barks
46 2022250 Robert Bly
47 9979 Peter Coyote
48 514 Jack Nicholson
49 6193 Leonardo DiCaprio
50 1892 Matt Damon
51 2641 Martin Landau
52 92029 Gabriel Bologna
53 218506 Gretchen Becker
54 2098136 Brian Becker
55 2098137 Richard Becker
56 2098138 Blase Bonpane
57 181934 Al Sharpton
58 496175 Carmelo Anthony
59 1280183 Coretta Scott King
60 1006651 John Lewis
61 237876 Sarah-Jane Sauvegrain
62 180659 Rosa Parks
63 1165240 Jason Alan Carvell
64 2787630 Valentin Zorin
65 89289 Jesse Jackson
66 2102583 Andrew Cohen
67 73988 Lyndon B. Johnson
68 21111 John F. Kennedy
69 2803017 Arthur John Birch
70 2803001 Haroldson Lafayette Hunt
71 4193575 Maxime Demigné
72 4193577 Gabin Clerc
73 4193578 Tom Chabeau
74 2954 Jeffrey Wright
75 33663 Donald Trump
76 2301996 Mike Pence
77 214317 Ben Bradlee
78 13716 Carl Bernstein
79 117180 Tom Brokaw
80 23626 Liev Schreiber
81 937663 Neil Amdur
82 1931175 Bob Beamon
83 12951 O.J. Simpson
84 2998183 Nicole Brown Simpson
85 1231277 Marcia Clark
86 60158 Shirley Jones
87 24810 Pat Boone
88 2535 Vivica A. Fox
89 225620 Rudolph Moise
90 1683093 Terayle Hill
91 79538 Khalil Kain
92 1562353 Erica Mena
93 1038 Jodie Foster
94 4173 Anthony Hopkins
95 349 Scott Glenn
96 4587 Halle Berry
97 9780 Angela Bassett
98 19011 George W. Bush
99 146687 Tony Blair
100 74266 Dick Cheney
101 1507609 Bob Coen
102 1507612 Francis E. Boyle
103 1507613 Jean Patterson
104 1122373 Marion Barry Jr.
105 1122374 Chuck Brown
106 1122375 Lewis Franklin
107 1897 Bernie Mac
108 77896 Bill Bellamy
109 2395 Whoopi Goldberg
110 1735828 Halle Bailey
111 53397 Christina Aguilera
112 1224841 Mo Willems
113 539 Thomas Lennon
114 111513 Yvette Nicole Brown
115 2682390 Gerd Ludwig
116 2227 Nicole Kidman
117 4430 Sharon Stone
118 14837 Carol Burnett
119 5823 Julie Andrews
120 3141 Marisa Tomei
121 449538 Adele
122 13309 Oprah Winfrey
123 1661465 Lizzo
124 102786 Barack Obama
125 128550 Desmond Tutu
126 2320880 Sundar Pichai
127 3894 Christian Bale
128 19498 Jon Bernthal
129 2710 James Cameron
130 8691 Zoe Saldaña
131 72129 Jennifer Lawrence
132 189111 Suzanne Collins
133 23964 Gary Ross
134 9827 Rose Byrne
135 143103 Krew Boylan
136 21708 Tomas Milian
137 72983 Manny Pérez
138 124909 Danny Hoch
139 4038 Susan Sarandon
140 18992 Aidan Quinn
141 2047 Danny Glover
142 4730 Emmy Rossum
143 9464 Harry Lennix
144 14409 David Schwimmer
145 1415341 Kazuhiro Tsuji
146 1267653 Chet Zar
147 7467 David Fincher
148 1519399 Erik Messerschmidt
149 1003 Jean Reno
150 524 Natalie Portman
151 1810 Heath Ledger
152 3895 Michael Caine
153 958722 Eiko Ishioka
154 41381 Sadie Frost
155 10980 Daniel Radcliffe
156 10989 Rupert Grint
157 10990 Emma Watson
158 219479 Criss Angel
159 1101349 Steve Aoki
160 1407495 Miles Brown
161 3361135 Peter Kent
162 1812 Michelle Williams
163 2037 Cillian Murphy
164 5081 Emily Blunt
165 2559324 Beth Lane
166 2559325 Lea Madda
167 27888 Raúl Juliá
168 2630 Nastassja Kinski
169 151007 Peter Ramsey
170 1769 Sofia Coppola
171 19772 Paul Rassam
172 1 George Lucas
173 7879 John Lasseter
174 1032 Martin Scorsese
175 1006721 Charles Scorsese
176 11483 Catherine Scorsese
177 5953 Spike Jonze
178 325 Eminem
179 3125 Madonna
180 3084 Marlon Brando
181 1158 Al Pacino
182 3085 James Caan
183 3087 Robert Duvall
184 3092 Diane Keaton
185 117669 Portia Doubleday
186 558466 Alex Russell
187 61555 Haley Ramm
188 10017 Charlton Heston
189 5563 James Coburn
190 26557 Ferdy Mayne
191 18688 Harry Connick Jr.
192 8437 Teri Garr
193 589 Daryl Hannah
194 16431 Sam Elliott
195 2203 Neal McDonough
196 3026 Rob Reiner
197 32 Robin Wright
198 12132 Michael Rooker
199 932719 Jeff Gordon
200 2127 James Wan
201 2128 Leigh Whannell
202 2675 Darren Lynn Bousman
203 46300 Alexander Nathan Etel
204 27889 Stephan Elliott
205 1331 Hugo Weaving
206 326 Kim Basinger
207 323 Curtis Hanson
208 11099 Dante Spinotti
209 7187 Richard Donner
210 57371 Jeff Cohen
211 3034 Corey Feldman
212 147 Michael Madsen
213 21200 Will Arnett
214 13611 Jack White
215 10750 Sheryl Crow
216 8261 Willie Nelson
217 54693 Emma Stone
218 19492 Viola Davis
219 6944 Octavia Spencer
220 287 Brad Pitt
221 192 Morgan Freeman
222 12052 Gwyneth Paltrow
223 5376 Jean Smart
224 5937 Kari Matchett
225 26009 Gail O'Grady
226 1067583 Suzanne Happ
227 10427 Anne Archer
228 781 Paul Reiser
229 33 Gary Sinise
230 5149 Bonnie Hunt
231 2144 Tobin Bell
232 141 David Carradine
233 2505 James Cromwell
234 42206 Matt McCoy
235 10167 George Lazenby
236 1226195 Rob Stewart
237 13637 Joan Collins
238 5320 Leslie Caron
239 1641 Katrin Cartlidge
240 10447 Mariel Hemingway
241 76184 Bryan Marshall
242 75781 John Turner
243 39770 Adam Roarke
244 34721 Larry Bishop
245 83908 Daniel Massey
246 1748 William Shatner
247 8499 William Windom
248 16896 James Garner
249 8857 Debbie Reynolds
250 15395 Maurice Ronet
251 51763 Ross Martin
252 955923 Ginny Tyler
253 6474 James Le Gros
254 69122 Heather Graham
255 65344 Esai Morales
256 6677 Kyle MacLachlan
257 6678 Michael Ontkean
258 6714 Mädchen Amick
259 56890 Malcolm McDowell
260 46393 Roseanne Barr
261 9789 Robert Altman
262 6449 Warren Beatty
263 1353815 Dr. Diane Ravitch
264 1353816 Dr. Linda Darling-Hammond
265 20212 Dermot Mulroney
266 41249 Tess Harper
267 1841281 Joey Molland
268 1225302 Annie Nightingale
269 3546502 Jack Trout
270 118764 Paulo Pires
271 36281 Imanol Arias
272 3199207 Jerry Dealey
273 3199208 Barry Ernest
274 82128 Wesley Clark
275 21818 Lesley Ann Warren
276 12967 Swoosie Kurtz
277 13567 Ann-Margret
278 25834 Leslie Hope
279 1204293 J. Allen Hynek
280 2027993 Philip J. Klass
281 20959 Louis Gossett Jr.
282 57829 Anthony LaPaglia
283 7447 Alec Baldwin
284 9560 Ellen Burstyn
285 37421 James Naughton
286 9976 Henry Thomas
287 69597 Drew Barrymore
288 1066777 Mumia Abu-Jamal
289 65013 Rubin 'Hurricane' Carter
290 4808 Giancarlo Esposito
291 2045089 Jeanmarie Simpson
292 4687 Patricia Arquette
293 2045090 Joyce Julianne Auer
294 60642 Tom Petty
295 22865 Little Richard
296 13301 Quincy Jones
297 7071 Louise Fletcher
298 518 Danny DeVito
299 10409 Shelley Duvall
300 7077 Scatman Crothers
301 3391 Kathleen Turner
302 117075 Henry Jaglom
303 34947 Kevin Conroy
304 1262612 Neal Adams
305 103 Mark Ruffalo
306 2282 Ben Kingsley
307 24045 Joseph Gordon-Levitt
308 3899 Ken Watanabe
309 21007 Jonah Hill
310 234352 Margot Robbie
311 16418 Casey Kasem
312 15831 Frank Welker
313 134 Jamie Foxx
314 27319 Christoph Waltz
315 11478 Lorraine Bracco
316 380 Robert De Niro
317 9261 Frank Abagnale Jr.
318 488 Steven Spielberg
319 2689 Jeff Nathanson
320 31 Tom Hanks
321 1206 Jason Alexander
322 70851 Jack Black
323 2157 Robin Williams
324 880 Ben Affleck
325 1231844 Manny Ramírez
326 1214256 Curt Schilling
327 3197 Tom Sizemore
328 12833 Edward Burns
329 10297 Matthew McConaughey
330 1813 Anne Hathaway
331 83002 Jessica Chastain
332 21037 Prince
333 1800494 Cora Coleman-Dunham
334 1775343 Josh Dunham
335 1205143 John Gazarian
336 1205144 Darlene Landau
337 2222 Beau Bridges
338 18878 Rob Cohen
339 9111 Peter Graves
340 98234 Barbara Bain
341 24589 Paul Mercurio
342 24590 Dominique Sanda
343 11770 John Carpenter
344 515 Glenn Close
345 229579 Bob Carruthers
346 220717 Uri Geller
347 1229925 La Toya Jackson
348 179512 Marv Albert
349 1865991 Jim Boeheim
350 59785 Fat Joe
351 84932 Jay-Z
352 16450 Sean Combs
353 210695 Jesse Williams
354 1258041 David Axelrod
355 1236577 Douglas Brinkley
356 1374664 Louis-Émile Galey
357 1010125 Claude Heymann
358 1385039 Bryan Stevenson
359 1641150 Patrisse Cullors
360 938972 Angela Davis
361 2149806 Gus Hall
362 100709 Junior Wells
363 1386798 Joseph McCarthy
364 13348 Howard Hughes
365 1247141 Harry S. Truman
366 2802990 Jean Paul Getty
367 117418 Mahalia Jackson
368 1188876 Horace Clarence Boyer
369 61399 Mike Ditka
370 1232728 Jim McMahon
371 1723195 Mike Singletary
372 1219050 Hank Aaron
373 14414 Kareem Abdul-Jabbar
374 65605 Muhammad Ali
375 136403 Robert Dryden
376 1999091 Chris Schenkel
377 1050881 Jacqueline Kennedy
378 1152 Oliver Stone
379 3331792 Vasili Arkhipov
380 120578 Franklin D. Roosevelt
381 57738 Samuel Labarthe
382 1141815 Charles de Gaulle
383 1204877 André Malraux
384 1257876 Margaret Whitmer
385 1051824 Tom Junod
386 1998031 Joanne Rogers
387 1116399 Winston Churchill
388 18802 Ronald Reagan
389 7571 Blair Brown
390 84378 Nick Fraser
391 1420 Robert Drew
392 2102600 Jill Drew
393 225009 Pablo Larraín
394 6431 Darren Aronofsky
395 8289 Billy Crudup
396 1670479 Abbas Abdi
397 1625013 Mahmoud Ahmadinejad
398 84384 Osama Bin Laden
399 2214123 Véronique Préault
400 2412677 Xavier Pasco
401 2412679 John Logsdon
402 16811 Alexander Scheer
403 3910768 Jürgen Hambrecht
404 2823180 Wolfgang Ischinger
405 4193580 Tkun
406 4193583 Sofiane
407 25072 Oscar Isaac
408 3910 Frances McDormand
409 1241 John Turturro
410 1145892 Ed Koch
411 1807640 Jane Jacobs
412 557911 Robert F. Kennedy Jr.
413 11161 Tom Savini
414 27765 Richard Nixon
415 1167702 Bruno Sammartino
416 1167703 Bob Backlund
417 2323039 Catherine Mercado-Muñiz
418 2339577 Carmen Yulín Cruz
419 10127 Jon Voight
420 1229169 Roger Ailes
421 3553309 John Shipton
422 150882 Julian Assange
423 16214 Mario Van Peebles
424 227811 Herschel Walker
425 1222706 Skip Bayless
426 1816204 Frederick Oberlander
427 1816207 Tevfik Arif
428 3342345 Tim Gielen
429 1954181 George Soros
430 240633 Bill Gates
431 80757 Mike Tyson
432 1003978 Larry Holmes
433 1306642 Kimberly DiPersia
434 2289540 Alex R. Wagner
435 2662043 Andrew Cuomo
436 2266228 Kim Dae-jung
437 2435679 Lee Geum-hee
438 2266230 Kim Young-sam
439 56446 John Cena
440 216075 Candice Michelle
441 568535 Matthew Kaye
442 1326321 Paige
443 57342 Vince McMahon
444 60342 Emily Maitlis
445 2041195 Prince Andrew Duke of York
446 2607708 Jeffrey Epstein
447 16620 Hulk Hogan
448 77120 Steve Austin
449 1647876 Hans Henrik Wöhler
450 3252749 Michael Fanone
451 3370866 Aquilino Gonell
452 28151 Benoît Allemane
453 2982422 Michael S. Cullen
454 3242458 Isabelle Rouge-Ducos
455 21397 Dennis Rodman
456 3981237 Shirley Rodman
457 567335 Chris Benoit
458 115788 Paul Michael Lévesque
459 112219 Michael Hickenbottom
460 25504 André Roussimoff
461 160733 Nick Bockwinkel
462 2189100 Moon Jae-in
463 1671675 Yuna Kim
464 1644623 Ahn Jung-hwan
465 21278 Alan Alda
466 13724 Jane Alexander
467 1428889 Ivy Austin
468 3323316 Tricia Brooks
469 2021450 Vernon Jordan
470 3797418 Maria Kirke
471 937664 Abraham Beame
472 937665 Bob Bright
473 1416398 Joshua L. Pearson
474 46432 George H.W. Bush
475 116341 Bill Clinton
476 2652100 Curt Flood
477 2652101 Judy Pace Flood
478 219189 Jackie Robinson
479 214725 Duke Snider
480 3905 William H. Macy
481 18686 Holly Hunter
482 223120 Jack O'Callahan
483 223121 Herb Brooks
484 223122 Mike Eruzione
485 107375 Joe Frazier
486 1304153 Imelda Marcos
487 587506 Shameik Moore
488 543505 Jake Johnson
489 130640 Hailee Steinfeld
490 2415973 Daniel Petrocelli
491 2413592 Fred Goldman
492 81439 Vincent Bugliosi
493 707 Dan Aykroyd
494 26485 Jim Belushi
495 54812 Chevy Chase
496 5788 James Cagney
497 87514 Roger Smith
498 2177 Lloyd Bridges
499 11140 Lynn Carlin
500 1503749 Big Freedia
501 1430 Keith Richards
502 562922 Christian Keyes
503 21215 Elise Neal
504 21563 Corin Nemec
505 2939038 Sydney Malakeh
506 8689 Barry Shabaka Henley
507 2297056 Darik Bernard
508 2295 Mickey Rourke
509 141823 Tisha Campbell
510 1841650 Crystal-Lee Naomi
511 1838518 Jermaine Rivers
512 58924 Clifton Powell
513 21355 DMX
514 1922617 Joy Rovaris
515 587035 Nadji Jeter
516 479206 Demetria McKinney
517 154382 Jazsmin Lewis
518 31134 Darrin Henson
519 74613 Terri J. Vaughn
520 2808544 Daran Acevedo
521 1297171 Julian Bane
522 108081 Malik Barnhardt
523 1220781 Shelly Altman
524 115002 John Aniston
525 1395884 Gessica Généus
526 105762 Jimmy Jean-Louis
527 1142232 Soledad Elizabeth Jean
528 7672 Reginald VelJohnson
529 1432244 Lyon Beckwith
530 127931 Dorien Wilson
531 77353 Jasmine Guy
532 233191 Terrence 'T.C.' Carson
533 1108907 Nick Robinson
534 9278 Jennifer Garner
535 19536 Josh Duhamel
536 3593345 Chara James
537 3593347 Zebulen M.N. Joyner-McCaster
538 1214197 Carl Anthony Payne II
539 16897 Sidney Poitier
540 20493 Sydney Tamiia Poitier
541 119598 Phylicia Rashād
542 109686 Flex Alexander
543 109687 Mike Batayeh
544 77072 Tyson Beckford
545 1232383 Parker McKenna Posey
546 1573627 Torrei Hart
547 1437841 AzMarie Livingston
548 55637 Brian Hooks
549 1100816 René Aranda
550 3581413 Dwight Boyce
551 60490 Paul Terrell Clayton
552 1036 Cybill Shepherd
553 5049 John Hurt
554 10774 Anne Bancroft
555 39187 Olivia Colman
556 34546 Mark Gatiss
557 54448 Ismail Merchant
558 54441 James Ivory
559 54443 Ruth Prawer Jhabvala
560 4786 Richard Attenborough
561 1137 Juliette Binoche
562 28974 Damon Lindelof
563 15851 Amy Brenneman
564 15009 Justin Theroux
565 33181 Jimmy Smits
566 63611 Nickolas Grace
567 53574 Eric Thal
568 16217 Lynn Whitfield
569 15762 Tara Strong
570 1160 Michelle Pfeiffer
571 8891 John Travolta
572 44038 Nancy Allen
573 1121437 Leslie Felperin
574 70243 Eartha Kitt
575 15973 Julie Newmar
576 16108 Lee Meriwether
577 44796 Franklyn Ajaye
578 35705 Regina Hall
579 143242 Terrence Jenkins
580 174837 Yolanda Adams
581 56323 Tina Fey
582 78577 Graham Norton
583 3223 Robert Downey Jr.
584 16828 Chris Evans
585 1189929 Condoleezza Rice
586 7904 Billy Crystal
587 533195 Henry Kissinger
588 143280 Norman Mailer
589 2801284 Anthony Amsterdam
590 1220601 John Chancellor
591 1066763 Darrell Royal
592 50707 Ed Marinaro
593 1322033 Bob Brenly
594 1322034 Scott Brosius
595 1233313 Joe Buck
596 3273875 Bob Taylor
597 3622690 Betty Ong
598 3622691 Nydia Gonzalez
599 3492377 Madeleine Albright
600 3492379 Ilie Alexandru
601 3492380 Ioan Amarie
602 1442410 Trevor Phillips
603 1442411 Nigel Farage
604 1225259 Kirsty Young
605 174981 King Charles III of the United Kingdom
606 1094687 Prince Edward Duke of Edinburgh
607 1306064 Princess Diana of Wales
608 558240 Queen Elizabeth II of the United Kingdom
609 47 Björk
610 3219627 Lydia Korniordou
611 2921077 Jacques Rogge
612 37759 Kathy Burke
613 20056 Keith Allen
614 2330903 Amir Amor
615 3897137 Cool 'Disco' Dan
616 9290 Henry Rollins
617 15535 Robin Harris
618 5726 Cedric the Entertainer
619 68215 Reginald Hudlin
620 181975 Mark Curry
621 82587 Steve Harvey
622 78029 Martin Lawrence
623 9309 Richard Pryor
624 57551 Mario Cantone
625 18291 Larenz Tate
626 1633946 Chris Gattling
627 10814 Wesley Snipes
628 1903010 Spence Moore II
629 55638 Kevin Hart
630 5916 Rosario Dawson
631 9206 Neve Campbell
632 36811 Nick Cannon
633 1870421 Collie Buddz
634 2412016 Lilly Bartlam
635 186529 Tony Daniels
636 88059 Tommy Davidson
637 15411 T.K. Carter
638 1428 Mick Jagger
639 1429 Charlie Watts
640 53396 Ron Wood
641 8534 Kathy Bates
642 1121 Benicio del Toro
643 3392 Michael Douglas
644 17051 James Franco
645 59315 Olivia Wilde
646 2838 Chloë Sevigny
647 155488 Kelly Clarkson
648 292445 Blake Shelton
649 21986 Reba McEntire
650 2387 Patrick Stewart
651 2388 Jonathan Frakes
652 1213786 Brent Spiner
653 4756 Matthew Broderick
654 15152 James Earl Jones
655 16940 Jeremy Irons
656 148120 Tiffany
657 83170 Valerie Harper
658 2774840 Luis Miranda
659 1179651 Lin-Manuel Miranda
660 216674 Hillary Clinton
661 73641 Christiane Amanpour
662 1202593 Neil Barnard
663 33684 Bono
664 1650331 Jeremy O. Harris
665 350 Laura Linney
666 2391 Michael Dorn
667 172069 Chadwick Boseman
668 1215522 Robin Roberts
669 1896 Don Cheadle
670 6240 Mariska Hargitay
671 21411 Ice-T
672 206637 Kelli Giddish
673 120608 Sonia Manzano
674 1596910 Nitya Vidyasagar
675 934281 Alex Wolff
676 1700685 Lewis Pullman
677 1241339 Bo Mitchell
678 14386 Beyoncé
679 1214573 Serena Williams
680 93527 Kelly Rowland
681 167662 Ryan Seacrest
682 2384929 Lindsay Arnold
683 138986 Tony Bennett
684 72208 Alicia Keys
685 3284 Bruce Springsteen
686 18746 Stevie Wonder
687 37934 André 3000
688 15310 Anthony Kiedis
689 12207 Kylie Minogue
690 1564846 Auli'i Cravalho
691 1473281 James Monroe Iglehart
692 2522829 Vanessa Laine Bryant
693 23678 Michael Jordan
694 35806 Shaquille O'Neal
695 1212304 Jon Scieszka
696 1192274 Jordan Fisher
697 132354 Kate Micucci
698 133451 Aya Cash
699 111684 Lennon Parham
700 60227 Greg Cipes
701 1249820 Romi Dames
702 8536 Victor Garber
703 1178795 Ben Diskin
704 1842039 Joe Tessitore
705 71403 Rob Riggle
706 129193 Paula Abdul
707 75394 Terence Donovan
708 1083951 Veronica Lang
709 500 Tom Cruise
710 150536 Barry Otto
711 76182 Nicholas Eadie
712 76788 Dev Patel
713 108916 Rooney Mara
714 1371 David Wenham
715 113552 Briony Behets
716 1242514 James Smillie
717 1246450 Bartholomew John
718 1747292 Robert Board
719 106179 Anthony Franciosa
720 82315 Jean Harlow
721 11492 Clark Gable
722 1916574 Caitlin Doughty
723 5168 Gabriel Byrne
724 516 Annette Bening
725 4517 Joe Pesci
726 71531 Deepak Chopra
727 1383964 Thích Nhất Hạnh
728 1867979 Sister Chân Không
729 8293 Marion Cotillard
730 32017 Didier Lavergne
731 3753843 Bryna Rifkin
732 5682 Anouk Aimée
733 44556 Richard Anconina
734 20234 Fanny Ardant
735 40159 Keith Michell
736 58414 Dina Merrill
737 4 Carrie Fisher
738 6541 Philip Bosco
739 157359 Jane Connell
740 1729824 Randy Doney
741 1392152 Dan Levy
742 26510 Eugene Levy
743 11514 Catherine O'Hara
744 290 Christopher Plummer
745 21104 Glenne Headly
746 80866 Nell Carter
747 40063 Lea DeLaria
748 537064 Perry Como
749 109887 Caroll Spinney
750 8515 Jane Darwell
751 5829 Karen Dotrice
752 1201 Garry Marshall
753 1041410 Charmian Carr
754 22384 John Amos
755 105637 Morey Amsterdam
756 6613 Minnie Driver
757 57133 Paulo Costanzo
758 18461 Nick Chinlund
759 1216139 Michael Raynor
760 57389 Justin Pierce
761 87525 Alan Carr
762 7056 Emma Thompson
763 154782 Pat Benatar
764 154783 Neil Giraldo
765 174875 Carly Simon
766 929825 Ava DuVernay
767 2302408 Korey Wise
768 20019 Lee Daniels
769 1658802 Andra Day
770 964843 Michelle Obama
771 3889844 Haizel Adofo
772 113461 John Legend
773 206444 Constance Wu
774 1700631 Liza Koshy
775 2503684 Abdullah bin Abdulaziz Al Saud
776 107379 LeBron James
777 66620 Angela Merkel
778 2134341 Theresa May
779 3089883 Gabby Giffords
780 1608713 Mark Kelly
781 1068409 Frank Marshall Davis
782 1068410 Ann Dunham
783 1068411 Stanley Dunham
784 149923 Lester Brown
785 1063969 Yvo de Boer
786 1063970 Paul R. Ehrlich
787 10279 Adolf Hitler
788 89300 Nelson Mandela
789 3451053 Natalya Sindeeva
790 3527796 Aleksandr Vinokurov
791 1745467 Vera Krichevskaya
792 1173480 François Hollande
793 1348088 Laurent Delahousse
794 1977908 Claire Chazal
795 2169865 Birgitta Assheuer
796 2920376 Îlham Ahmed
797 2920377 Zaher al-Saket

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import http.client
import json
import csv
import urllib.request
#############################################################################################################################
# cse6242
# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
# hard-coded.
#
# Instructions: Implement all methods in this file that have a return
# value of 'NotImplemented'. See the documentation within each method for specific details, including
# the expected return value
#
# Helper Functions:
# You are permitted to write additional helper functions/methods or use additional instance variables within
# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
#
# Use:
# The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must
# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
#
# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods
# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional
# methods and instance variables as desired (see Helper Functions).
#
# The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the
# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
# .csv files for submission to Gradescope.
#
# While building the co-actor graph, you will be required to write code to expand the graph by iterating
# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
# but will grade the resulting graph data in your nodes and edges .csv files.
#
#############################################################################################################################
class Graph:
# Do not modify
def __init__(self, with_nodes_file=None, with_edges_file=None):
"""
option 1: init as an empty graph and add nodes
option 2: init by specifying a path to nodes & edges files
"""
self.nodes = []
self.edges = []
if with_nodes_file and with_edges_file:
nodes_CSV = csv.reader(open(with_nodes_file))
nodes_CSV = list(nodes_CSV)[1:]
self.nodes = [(n[0], n[1]) for n in nodes_CSV]
edges_CSV = csv.reader(open(with_edges_file))
edges_CSV = list(edges_CSV)[1:]
self.edges = [(e[0], e[1]) for e in edges_CSV]
def add_node(self, id: str, name: str) -> None:
"""
add a tuple (id, name) representing a node to self.nodes if it does not already exist
The graph should not contain any duplicate nodes
"""
if (id, name) not in self.nodes:
self.nodes.append((id, name))
def add_edge(self, source: str, target: str) -> None:
"""
Add an edge between two nodes if it does not already exist.
An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
Where 'source' is the id of the source node and 'target' is the id of the target node
e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
"""
current_edge = (source, target)
current_edge_inverted = (target, source)
if (source != target) and (current_edge not in self.edges) and (current_edge_inverted not in self.edges):
self.edges.append(current_edge)
def total_nodes(self) -> int:
"""
Returns an integer value for the total number of nodes in the graph
"""
output = len(self.nodes)
return output
def total_edges(self) -> int:
"""
Returns an integer value for the total number of edges in the graph
"""
output = len(self.edges)
return output
def max_degree_nodes(self) -> dict:
"""
Return the node(s) with the highest degree
Return multiple nodes in the event of a tie
Format is a dict where the key is the node_id and the value is an integer for the node degree
e.g. {'a': 8}
or {'a': 22, 'b': 22}
"""
max_degree_nodes = {}
for edge in self.edges:
for idx in edge:
if idx in max_degree_nodes:
max_degree_nodes[idx] += 1
else:
max_degree_nodes[idx] = 1
v = list(max_degree_nodes.values())
k = list(max_degree_nodes.keys())
max_nodes = {k[v.index(max(v))]: max(v)}
return max_nodes
def print_nodes(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.nodes)
def print_edges(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.edges)
# Do not modify
def write_edges_file(self, path="edges.csv")->None:
"""
write all edges out as .csv
:param path: string
:return: None
"""
edges_path = path
edges_file = open(edges_path, 'w', encoding='utf-8')
edges_file.write("source" + "," + "target" + "\n")
for e in self.edges:
edges_file.write(e[0] + "," + e[1] + "\n")
edges_file.close()
print("finished writing edges to csv")
# Do not modify
def write_nodes_file(self, path="nodes.csv")->None:
"""
write all nodes out as .csv
:param path: string
:return: None
"""
nodes_path = path
nodes_file = open(nodes_path, 'w', encoding='utf-8')
nodes_file.write("id,name" + "\n")
for n in self.nodes:
nodes_file.write(n[0] + "," + n[1] + "\n")
nodes_file.close()
print("finished writing nodes to csv")
class TMDBAPIUtils:
# Do not modify
def __init__(self, api_key:str):
self.api_key=api_key
def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
"""
Get the movie cast for a given movie id, with optional parameters to exclude an cast member
from being returned and/or to limit the number of returned cast members
documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
:param string movie_id: a movie_id
:param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result
e.g., if exclude_ids are [353, 455] then exclude these from any result.
:param integer limit: maximum number of returned cast members by their 'order' attribute
e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range).
If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
If after excluding, the limit is not specified, then return all remaining cast members."
e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
:rtype: list
return a list of dicts, one dict per cast member with the following structure:
[{'id': '97909' # the id of the cast member
'character': 'John Doe' # the name of the character played
'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
Note that this is an example of the structure of the list and some of the fields returned by the API.
The result of the API call will include many more fields for each cast member.
"""
url = 'https://api.themoviedb.org/3/movie/'+ str(movie_id) +'/credits?api_key='+self.api_key +"&language=en-US"
req = urllib.request.Request(url, headers={
'Accept': 'application/json',
})
response = urllib.request.urlopen(req)
if response.status == 200:
json_data = json.load(response)
cast = json_data['cast']
if exclude_ids is not None:
cast = [i for i in cast if i['id'] not in exclude_ids]
# print(cast)
if limit is not None:
cast = [i for i in cast if i['order'] < limit]
# print(cast)
for c in cast:
c['name'] = c['name'].replace(',', '')
return cast
else:
print(response.status.__str__())
return None
def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
"""
Using the TMDb API, get the movie credits for a person serving in a cast role
documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
:param string person_id: the id of a person
:param vote_avg_threshold: optional parameter to return the movie credit if it is >=
the specified threshold.
e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
:rtype: list
return a list of dicts, with each dict having 'id', 'title', and 'vote_avg' keys,
one dict per movie credit with the following structure:
[{'id': '97909' # the id of the movie
'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
"""
url = 'https://api.themoviedb.org/3/person/'+ str(person_id) +'/movie_credits?api_key='+self.api_key+'&language=en-US'
req = urllib.request.Request(url, headers={
'Accept': 'application/json',
})
response = urllib.request.urlopen(req)
if response.status == 200:
json_data = json.load(response)
raw_credits = json_data['cast']
if vote_avg_threshold is not None:
filtered_credits = []
for idx in raw_credits:
if idx["vote_average"] >= vote_avg_threshold:
filtered_credits.append({
"id":idx["id"],
"title":idx["title"],
"vote_avg":idx["vote_average"]
})
return filtered_credits
else:
unfiltered_credits = []
for idx in raw_credits:
unfiltered_credits.append({
"id":idx["id"],
"title":idx["title"],
"vote_avg":idx["vote_average"]
})
return unfiltered_credits
else:
print("ERROR: " + response.status.__str__())
return None
#############################################################################################################################
#
# BUILDING YOUR GRAPH
#
# Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples
#
# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
# In this graph, each node represents an actor
# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
# i.e., they share a movie credit.
# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
# or more movies together.
#
# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
#
# You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your
# algorithm. We will instead measure the correctness of your output by evaluating the data in your nodes.csv and edges.csv files.
#
# GRAPH SIZE
# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
# Our testing indicates growth approximately equal to e^2x.
# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
# updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a
# margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to:
#
# Min allowable nodes = min encountered nodes - 100
# Max allowable nodes = max allowable nodes + 100
#
# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
# ----------------------------------------------------------------------------------------------------------------------
# BEGIN BUILD CO-ACTOR NETWORK
#
# INITIALIZE GRAPH
# Initialize a Graph object with a single node representing Laurence Fishburne
#
# BEGIN BUILD BASE GRAPH:
# Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
# FOR each movie credit:
# | get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
# |
# | FOR each movie cast member:
# | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
# | | using graph.add_edge(), add an edge between the Laurence Fishburne (actor) node
# | | and each new node (co-actor/co-actress)
# | END FOR
# END FOR
# END BUILD BASE GRAPH
#
#
# BEGIN LOOP - DO 2 TIMES:
# IF first iteration of loop:
# | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
# ELSE
# | nodes = The nodes added in the previous iteration:
# ENDIF
#
# FOR each node in nodes:
# | get the movie credits for the actor that have a vote average >= 8.0
# |
# | FOR each movie credit:
# | | try to get the 3 movie cast members having an 'order' value between 0-2
# | |
# | | FOR each movie cast member:
# | | | IF the node doesn't already exist:
# | | | | add the node to the graph (track all new nodes added to the graph)
# | | | ENDIF
# | | |
# | | | IF the edge does not exist:
# | | | | add an edge between the node (actor) and the new node (co-actor/co-actress)
# | | | ENDIF
# | | END FOR
# | END FOR
# END FOR
# END LOOP
#
# Your graph should not have any duplicate edges or nodes
# Write out your finished graph as a nodes file and an edges file using:
# graph.write_edges_file()
# graph.write_nodes_file()
#
# END BUILD CO-ACTOR NETWORK
# ----------------------------------------------------------------------------------------------------------------------
# Exception handling and best practices
# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
# - Some movie_credits do not return cast data. Handle this situation by skipping these instances.
# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
# can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
# - wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph.
def return_name()->str:
"""
Return a string containing your GT Username
e.g., tlou31
Do not return your 9 digit GTId
"""
return "tlou31"
# You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes.
# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
if __name__ == "__main__":
graph = Graph()
graph.add_node(id='2975', name='Laurence Fishburne')
# build base graph
# find all laurence fishburne's movie credits with a vote average >= 8.0
tmdb_api_utils = TMDBAPIUtils(api_key='1eed362096d67d9f077084d5abb30a35')
movie_credits = tmdb_api_utils.get_movie_credits_for_person(person_id='2975', vote_avg_threshold=8.0)
# for each movie credit get movie cast members having and order value between 0-2 (3 0-base)
for movie in movie_credits:
movie_cast = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for cast_member in movie_cast:
graph.add_node(str(cast_member['id']), cast_member['name'])
graph.add_edge(source='2975', target=str(cast_member['id']))
break
break
for i in range(2):
print('running loop ' + str(i))
if i == 0:
current_nodes = graph.nodes[1:]
else:
current_nodes = [i for i in graph.nodes if i not in current_nodes]
for node in current_nodes:
cast = tmdb_api_utils.get_movie_credits_for_person(person_id=node[0], vote_avg_threshold=8.0)
for movie in cast:
actors = tmdb_api_utils.get_movie_cast(movie_id=movie['id'], limit=3)
for actor in actors:
graph.add_node(str(actor['id']), actor['name'])
graph.add_edge(str(node[0]), str(actor['id']))
# tmdb_api_utils.get_movie_cast(movie_id='100', limit=3, exclude_ids=[973, 974])
# tmdb_api_utils.get_movie_credits_for_person(person_id="9709", vote_avg_threshold=5.0)
# call functions or place code here to build graph (graph building code not graded)
# Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
print(f"Edges: {graph.total_edges()}")
print(f"Nodes: {graph.total_nodes()}")
graph.write_edges_file()
graph.write_nodes_file()
# If you have already built & written out your graph, you could read in your nodes & edges files
# to perform testing on your graph.
graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")
print(graph.max_degree_nodes())

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tunmnlu/task_1/Q1/bundle/edges.csv Normal file
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@@ -0,0 +1,123 @@
source,target
2975,6384
6384,530
6384,1357063
6384,40644
6384,1779512
6384,21127
6384,1692944
6384,64
6384,1776
6384,38803
6384,20215
6384,2130
2975,530
2975,8349
2975,8351
2975,8354
2975,1107983
2975,52057
2975,110380
2975,18471
2975,74611
2975,1407498
530,529
530,532
1357063,40644
1357063,3894
1357063,1892
1357063,19498
40644,1779512
40644,2710
40644,4587
40644,8691
40644,72129
40644,189111
40644,23964
1779512,1357063
21127,1692944
21127,9827
21127,143103
21127,21708
21127,72983
21127,124909
21127,4038
21127,18992
21127,2047
21127,4730
21127,9464
21127,14409
64,1776
64,1415341
64,1267653
64,7467
64,1519399
64,1003
64,524
64,38803
64,3894
64,1810
64,3895
64,958722
64,41381
64,10980
64,10989
64,10990
64,20215
64,2130
64,219479
64,1101349
64,1407495
64,3361135
64,1812
64,2037
64,5081
64,1892
1776,2559324
1776,2559325
1776,27888
1776,2630
1776,958722
1776,41381
1776,38803
1776,151007
1776,1769
1776,19772
1776,1
1776,7879
1776,20215
1776,2130
1776,1032
1776,1006721
1776,11483
1776,8349
1776,8351
1776,8354
38803,151007
38803,5953
38803,325
38803,3125
38803,3084
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103
tunmnlu/task_1/Q1/bundle/nodes.csv Normal file
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@@ -0,0 +1,103 @@
id,name
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2203,Neal McDonough
3026,Rob Reiner
32,Robin Wright
12132,Michael Rooker
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2127,James Wan
2128,Leigh Whannell
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22
tunmnlu/task_1/Q1/bundle/test.sh Normal file
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@@ -0,0 +1,22 @@
#!/usr/bin/env bash
set -ex
rm -rf *.csv
python ./Q1.py
# mv edges.csv edges_try1.csv
# mv nodes.csv nodes_try1.csv
# python ./Q1.py
# mv edges.csv edges_try2.csv
# mv nodes.csv nodes_try2.csv
# python ./Q1.py
# mv edges.csv edges_try3.csv
# mv nodes.csv nodes_try3.csv
wc -l *.csv

10
tunmnlu/task_1/Q1/bundle/test_edge.py Normal file
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@@ -0,0 +1,10 @@
import os,sys
test = []
temp = ""
with open('./edges.csv','r') as fi:
test = list(map(lambda x: x.strip(), fi.readlines()))
test = test[1:]
print(len(test))
print(len(set(test)))

7
tunmnlu/task_1/Q1/docker-compose.yml Normal file
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@@ -0,0 +1,7 @@
services:
test:
image: python:3.7-bullseye
volumes:
- .:/src
working_dir: /src
command: sleep infinity

BIN
tunmnlu/task_1/Q1/docs/2023-08-28_18-48.png (Stored with Git LFS) Normal file
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Binary file not shown.

1
tunmnlu/task_1/Q1/edges.csv Normal file
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@@ -0,0 +1 @@
source,target
1 source target

954
tunmnlu/task_1/Q1/edges_ref.csv Normal file
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@@ -0,0 +1,954 @@
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869 26557 46780
870 26557 66884
871 26557 2883
872 26557 3011
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874 26557 67676
875 26557 116838
876 26557 100787
877 26557 1921578
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881 26557 31071
882 26557 2264
883 18688 8437
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885 18688 17604
886 18688 418
887 18688 138986
888 18688 1532769
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891 8437 64825
892 8437 12646
893 8437 589
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906 589 2555
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908 589 138
909 589 2545
910 589 141
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916 3026 32
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920 3026 7166
921 3026 39770
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926 3026 234352
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931 3026 6449
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942 12132 69302
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952 2127 2128
953 2127 2675
954 2128 2675

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import http.client
import json
import csv
#############################################################################################################################
# cse6242
# All instructions, code comments, etc. contained within this notebook are part of the assignment instructions.
# Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not
# hard-coded.
#
# Instructions: Implement all methods in this file that have a return
# value of 'NotImplemented'. See the documentation within each method for specific details, including
# the expected return value
#
# Helper Functions:
# You are permitted to write additional helper functions/methods or use additional instance variables within
# the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required.
#
# Use:
# The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must
# also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree.
#
# The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods
# to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional
# methods and instance variables as desired (see Helper Functions).
#
# The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the
# TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges
# .csv files for submission to Gradescope.
#
# While building the co-actor graph, you will be required to write code to expand the graph by iterating
# through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly
# but will grade the resulting graph data in your nodes and edges .csv files.
#
#############################################################################################################################
class Graph:
# Do not modify
def __init__(self, with_nodes_file=None, with_edges_file=None):
"""
option 1: init as an empty graph and add nodes
option 2: init by specifying a path to nodes & edges files
"""
self.nodes = []
self.edges = []
if with_nodes_file and with_edges_file:
nodes_CSV = csv.reader(open(with_nodes_file))
nodes_CSV = list(nodes_CSV)[1:]
self.nodes = [(n[0], n[1]) for n in nodes_CSV]
edges_CSV = csv.reader(open(with_edges_file))
edges_CSV = list(edges_CSV)[1:]
self.edges = [(e[0], e[1]) for e in edges_CSV]
def add_node(self, id: str, name: str) -> None:
"""
add a tuple (id, name) representing a node to self.nodes if it does not already exist
The graph should not contain any duplicate nodes
"""
return NotImplemented
def add_edge(self, source: str, target: str) -> None:
"""
Add an edge between two nodes if it does not already exist.
An edge is represented by a tuple containing two strings: e.g.: ('source', 'target').
Where 'source' is the id of the source node and 'target' is the id of the target node
e.g., for two nodes with ids 'a' and 'b' respectively, add the tuple ('a', 'b') to self.edges
"""
return NotImplemented
def total_nodes(self) -> int:
"""
Returns an integer value for the total number of nodes in the graph
"""
return NotImplemented
def total_edges(self) -> int:
"""
Returns an integer value for the total number of edges in the graph
"""
return NotImplemented
def max_degree_nodes(self) -> dict:
"""
Return the node(s) with the highest degree
Return multiple nodes in the event of a tie
Format is a dict where the key is the node_id and the value is an integer for the node degree
e.g. {'a': 8}
or {'a': 22, 'b': 22}
"""
return NotImplemented
def print_nodes(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.nodes)
def print_edges(self):
"""
No further implementation required
May be used for de-bugging if necessary
"""
print(self.edges)
# Do not modify
def write_edges_file(self, path="edges.csv")->None:
"""
write all edges out as .csv
:param path: string
:return: None
"""
edges_path = path
edges_file = open(edges_path, 'w', encoding='utf-8')
edges_file.write("source" + "," + "target" + "\n")
for e in self.edges:
edges_file.write(e[0] + "," + e[1] + "\n")
edges_file.close()
print("finished writing edges to csv")
# Do not modify
def write_nodes_file(self, path="nodes.csv")->None:
"""
write all nodes out as .csv
:param path: string
:return: None
"""
nodes_path = path
nodes_file = open(nodes_path, 'w', encoding='utf-8')
nodes_file.write("id,name" + "\n")
for n in self.nodes:
nodes_file.write(n[0] + "," + n[1] + "\n")
nodes_file.close()
print("finished writing nodes to csv")
class TMDBAPIUtils:
# Do not modify
def __init__(self, api_key:str):
self.api_key=api_key
def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list:
"""
Get the movie cast for a given movie id, with optional parameters to exclude an cast member
from being returned and/or to limit the number of returned cast members
documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits
:param string movie_id: a movie_id
:param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result
e.g., if exclude_ids are [353, 455] then exclude these from any result.
:param integer limit: maximum number of returned cast members by their 'order' attribute
e.g., limit=5 will attempt to return the 5 cast members having 'order' attribute values between 0-4
If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range).
If cast members with 'order' attribute in the specified limit range have been excluded, do not include more cast members to reach the limit.
If after excluding, the limit is not specified, then return all remaining cast members."
e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded,
return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5]
:rtype: list
return a list of dicts, one dict per cast member with the following structure:
[{'id': '97909' # the id of the cast member
'character': 'John Doe' # the name of the character played
'credit_id': '52fe4249c3a36847f8012927' # id of the credit, ...}, ... ]
Note that this is an example of the structure of the list and some of the fields returned by the API.
The result of the API call will include many more fields for each cast member.
"""
return NotImplemented
def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list:
"""
Using the TMDb API, get the movie credits for a person serving in a cast role
documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits
:param string person_id: the id of a person
:param vote_avg_threshold: optional parameter to return the movie credit if it is >=
the specified threshold.
e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0
:rtype: list
return a list of dicts, with each dict having 'id', 'title', and 'vote_avg' keys,
one dict per movie credit with the following structure:
[{'id': '97909' # the id of the movie
'title': 'Long, Stock and Two Smoking Barrels' # the title (not original title) of the credit
'vote_avg': 5.0 # the float value of the vote average value for the credit}, ... ]
"""
return NotImplemented
#############################################################################################################################
#
# BUILDING YOUR GRAPH
#
# Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples
#
# Using TMDb's API, build a co-actor network for the actor's/actress' highest rated movies
# In this graph, each node represents an actor
# An edge between any two nodes indicates that the two actors/actresses acted in a movie together
# i.e., they share a movie credit.
# e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one
# or more movies together.
#
# For this assignment, we are interested in a co-actor network of highly rated movies; specifically,
# we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0.
# Build your co-actor graph on the actor 'Laurence Fishburne' w/ person_id 2975.
#
# You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your
# algorithm. We will instead measure the correctness of your output by evaluating the data in your nodes.csv and edges.csv files.
#
# GRAPH SIZE
# With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate.
# Our testing indicates growth approximately equal to e^2x.
# Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when
# you execute your graph building code. We take this into account by rebuilding the solution graph every few days and
# updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a
# margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to:
#
# Min allowable nodes = min encountered nodes - 100
# Max allowable nodes = max allowable nodes + 100
#
# e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626
# The same method is used to calculate the edges with the exception of using the aforementioned edge margin.
# ----------------------------------------------------------------------------------------------------------------------
# BEGIN BUILD CO-ACTOR NETWORK
#
# INITIALIZE GRAPH
# Initialize a Graph object with a single node representing Laurence Fishburne
#
# BEGIN BUILD BASE GRAPH:
# Find all of Laurence Fishburne's movie credits that have a vote average >= 8.0
# FOR each movie credit:
# | get the movie cast members having an 'order' value between 0-2 (these are the co-actors)
# |
# | FOR each movie cast member:
# | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph)
# | | using graph.add_edge(), add an edge between the Laurence Fishburne (actor) node
# | | and each new node (co-actor/co-actress)
# | END FOR
# END FOR
# END BUILD BASE GRAPH
#
#
# BEGIN LOOP - DO 2 TIMES:
# IF first iteration of loop:
# | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!)
# ELSE
# | nodes = The nodes added in the previous iteration:
# ENDIF
#
# FOR each node in nodes:
# | get the movie credits for the actor that have a vote average >= 8.0
# |
# | FOR each movie credit:
# | | try to get the 3 movie cast members having an 'order' value between 0-2
# | |
# | | FOR each movie cast member:
# | | | IF the node doesn't already exist:
# | | | | add the node to the graph (track all new nodes added to the graph)
# | | | ENDIF
# | | |
# | | | IF the edge does not exist:
# | | | | add an edge between the node (actor) and the new node (co-actor/co-actress)
# | | | ENDIF
# | | END FOR
# | END FOR
# END FOR
# END LOOP
#
# Your graph should not have any duplicate edges or nodes
# Write out your finished graph as a nodes file and an edges file using:
# graph.write_edges_file()
# graph.write_nodes_file()
#
# END BUILD CO-ACTOR NETWORK
# ----------------------------------------------------------------------------------------------------------------------
# Exception handling and best practices
# - You should use the param 'language=en-US' in all API calls to avoid encoding issues when writing data to file.
# - If the actor name has a comma char ',' it should be removed to prevent extra columns from being inserted into the .csv file
# - Some movie_credits do not return cast data. Handle this situation by skipping these instances.
# - While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls
# can occasionally result in timeout errors. If you continue to experience 'ConnectionRefusedError : [Errno 61] Connection refused',
# - wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph.
def return_name()->str:
"""
Return a string containing your GT Username
e.g., tlou31
Do not return your 9 digit GTId
"""
return NotImplemented
# You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes.
# Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading.
if __name__ == "__main__":
graph = Graph()
graph.add_node(id='2975', name='Laurence Fishburne')
tmdb_api_utils = TMDBAPIUtils(api_key='<your API key>')
# call functions or place code here to build graph (graph building code not graded)
# Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK
graph.write_edges_file()
graph.write_nodes_file()
# If you have already built & written out your graph, you could read in your nodes & edges files
# to perform testing on your graph.
# graph = Graph(with_edges_file="edges.csv", with_nodes_file="nodes.csv")

16
tunmnlu/task_1/Q1/meta.md Normal file
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---
tags: [csv, excel, docker, python]
---
# task1
ttps://share.louislabs.com/g/u-iOAl1Q6J_
你好，
大致上 Q1 會係咁，
份 source 我收埋尾數會交比你。
我諗你地應該會有一個叫 GT username 嘅野
你可以比我，等我幫手改
又或者份 source 到手你自己改都得
: )

1
tunmnlu/task_1/Q1/nodes.csv Normal file
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@@ -0,0 +1 @@
id,name
1 id name

685
tunmnlu/task_1/Q1/nodes_ref.csv Normal file
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@@ -0,0 +1,685 @@
id,name
2975,Laurence Fishburne
6384,Keanu Reeves
530,Carrie-Anne Moss
1357063,Darrin Prescott
40644,Chad Stahelski
1779512,Jackson Spidell
21127,Bobby Cannavale
1692944,Heidi Schreck
64,Gary Oldman
1776,Francis Ford Coppola
38803,Roman Coppola
20215,Billy Campbell
2130,Cary Elwes
8349,Martin Sheen
8351,Frederic Forrest
8354,Albert Hall
1107983,Martin Luther King Jr.
52057,Obba Babatundé
110380,Colin Powell
18471,Anthony Anderson
74611,Tracee Ellis Ross
1407498,Marsai Martin
529,Guy Pearce
532,Joe Pantoliano
3894,Christian Bale
1892,Matt Damon
19498,Jon Bernthal
2710,James Cameron
4587,Halle Berry
8691,Zoe Saldaña
72129,Jennifer Lawrence
189111,Suzanne Collins
23964,Gary Ross
9827,Rose Byrne
143103,Krew Boylan
21708,Tomas Milian
72983,Manny Pérez
124909,Danny Hoch
4038,Susan Sarandon
18992,Aidan Quinn
2047,Danny Glover
4730,Emmy Rossum
9464,Harry Lennix
14409,David Schwimmer
1415341,Kazuhiro Tsuji
1267653,Chet Zar
7467,David Fincher
1519399,Erik Messerschmidt
1003,Jean Reno
524,Natalie Portman
1810,Heath Ledger
3895,Michael Caine
958722,Eiko Ishioka
41381,Sadie Frost
10980,Daniel Radcliffe
10989,Rupert Grint
10990,Emma Watson
219479,Criss Angel
1101349,Steve Aoki
1407495,Miles Brown
3361135,Peter Kent
1812,Michelle Williams
2037,Cillian Murphy
5081,Emily Blunt
2559324,Beth Lane
2559325,Lea Madda
27888,Raúl Juliá
2630,Nastassja Kinski
151007,Peter Ramsey
1769,Sofia Coppola
19772,Paul Rassam
1,George Lucas
7879,John Lasseter
1032,Martin Scorsese
1006721,Charles Scorsese
11483,Catherine Scorsese
5953,Spike Jonze
325,Eminem
3125,Madonna
3084,Marlon Brando
1158,Al Pacino
3085,James Caan
3087,Robert Duvall
3092,Diane Keaton
117669,Portia Doubleday
558466,Alex Russell
61555,Haley Ramm
10017,Charlton Heston
5563,James Coburn
26557,Ferdy Mayne
18688,Harry Connick Jr.
8437,Teri Garr
589,Daryl Hannah
16431,Sam Elliott
2203,Neal McDonough
3026,Rob Reiner
32,Robin Wright
12132,Michael Rooker
932719,Jeff Gordon
2127,James Wan
2128,Leigh Whannell
2675,Darren Lynn Bousman
1278399,Joseph Steven
1659326,Teresa Modnick
26466,Caroline Kava
18269,Brendan Fraser
5606,Sissy Spacek
5632,Jack Fisk
1179102,Kathleen Kennedy
11367,Bradley Whitford
31028,Richard Schiff
1397846,Craig Barron
2166741,Robert A. Baum Jr.
3266,Joe Mantegna
36059,Joanna Pacula
21010,Alexandra Hay
101032,Rod McCary
98569,Vincent Van Patten
2080584,Ben Jorgensen
103835,Jeff Monahan
6717,James Marshall
928944,Coleman Barks
2022250,Robert Bly
9979,Peter Coyote
514,Jack Nicholson
6193,Leonardo DiCaprio
2641,Martin Landau
92029,Gabriel Bologna
218506,Gretchen Becker
2098136,Brian Becker
2098137,Richard Becker
2098138,Blase Bonpane
181934,Al Sharpton
496175,Carmelo Anthony
1280183,Coretta Scott King
1006651,John Lewis
237876,Sarah-Jane Sauvegrain
180659,Rosa Parks
1165240,Jason Alan Carvell
2787630,Valentin Zorin
89289,Jesse Jackson
2102583,Andrew Cohen
73988,Lyndon B. Johnson
21111,John F. Kennedy
2803017,Arthur John Birch
2803001,Haroldson Lafayette Hunt
4193575,Maxime Demigné
4193577,Gabin Clerc
4193578,Tom Chabeau
2954,Jeffrey Wright
33663,Donald Trump
2301996,Mike Pence
214317,Ben Bradlee
13716,Carl Bernstein
117180,Tom Brokaw
23626,Liev Schreiber
937663,Neil Amdur
1931175,Bob Beamon
12951,O.J. Simpson
2998183,Nicole Brown Simpson
1231277,Marcia Clark
60158,Shirley Jones
24810,Pat Boone
2535,Vivica A. Fox
225620,Rudolph Moise
1683093,Terayle Hill
79538,Khalil Kain
1562353,Erica Mena
1038,Jodie Foster
4173,Anthony Hopkins
349,Scott Glenn
9780,Angela Bassett
19011,George W. Bush
146687,Tony Blair
74266,Dick Cheney
1507609,Bob Coen
1507612,Francis E. Boyle
1507613,Jean Patterson
1122373,Marion Barry Jr.
1122374,Chuck Brown
1122375,Lewis Franklin
1897,Bernie Mac
77896,Bill Bellamy
2395,Whoopi Goldberg
1735828,Halle Bailey
53397,Christina Aguilera
1224841,Mo Willems
539,Thomas Lennon
111513,Yvette Nicole Brown
2682390,Gerd Ludwig
2227,Nicole Kidman
4430,Sharon Stone
14837,Carol Burnett
5823,Julie Andrews
3141,Marisa Tomei
449538,Adele
13309,Oprah Winfrey
1661465,Lizzo
102786,Barack Obama
128550,Desmond Tutu
2320880,Sundar Pichai
46300,Alexander Nathan Etel
27889,Stephan Elliott
1331,Hugo Weaving
326,Kim Basinger
323,Curtis Hanson
11099,Dante Spinotti
7187,Richard Donner
57371,Jeff Cohen
3034,Corey Feldman
6968,Hugh Jackman
11669,Jimmy Fallon
43120,Andy Dick
53926,Jon Heder
34947,Kevin Conroy
1262612,Neal Adams
21200,Will Arnett
525,Christopher Nolan
15556,Rebecca Hall
131286,Mark David
60634,Marty Belafsky
7133,Max Casella
15762,Tara Strong
1160,Michelle Pfeiffer
1813,Anne Hathaway
2157,Robin Williams
880,Ben Affleck
1231844,Manny Ramírez
1214256,Curt Schilling
31,Tom Hanks
3197,Tom Sizemore
12833,Edward Burns
10297,Matthew McConaughey
83002,Jessica Chastain
21037,Prince
1800494,Cora Coleman-Dunham
1775343,Josh Dunham
127712,Matt Ryan
3035,Jerry O'Connell
527313,Taissa Farmiga
21007,Jonah Hill
234352,Margot Robbie
869,Gale Anne Hurd
96884,Al Giddings
1100,Arnold Schwarzenegger
2713,Linda Hamilton
2231,Samuel L. Jackson
6159,Ron Howard
33181,Jimmy Smits
63611,Nickolas Grace
53574,Eric Thal
16217,Lynn Whitfield
8891,John Travolta
44038,Nancy Allen
1121437,Leslie Felperin
70243,Eartha Kitt
15973,Julie Newmar
16108,Lee Meriwether
73475,Adriano Giannini
2078350,Tommaso Basili
73457,Chris Pratt
543530,Dave Bautista
111946,Britney Spears
57691,Tamra Davis
343,Taryn Manning
3223,Robert Downey Jr.
74568,Chris Hemsworth
103,Mark Ruffalo
16828,Chris Evans
1851091,Josh Silver
3810,Javier Bardem
6431,Darren Aronofsky
1625558,Awkwafina
112,Cate Blanchett
1283,Helena Bonham Carter
17606,Imogen Poots
17605,Idris Elba
75131,Nash Edgerton
27442,Renato Salvatori
24299,Jean-Claude Brialy
5476,Nino Castelnuovo
30721,Madeleine Robinson
9287,Robert Blake
70119,Catherine Spaak
7502,Ernest Borgnine
86381,Algenis Perez Soto
1012743,Dalisa Alegría
8767,Jim Caviezel
23931,Mira Sorvino
121718,Bill Camp
77003,Gavin O'Connor
199449,Greg O'Connor
819,Edward Norton
2222,Beau Bridges
152699,James Noble
4037,Ron Shelton
1269,Kevin Costner
13472,Tim Curry
13473,Barry Bostwick
2607714,Alan Cooke
3896,Liam Neeson
2517086,Will Hochman
151432,Boris McGiver
3763830,Jim Forbes
133499,Christopher Biggins
1724558,Justin Arnold
7904,Billy Crystal
4244,Lauryn Hill
13476,Nell Campbell
44301,Cliff DeYoung
11905,Sven Nykvist
38127,Erland Josephson
231318,Melinda Kinnaman
1027445,Hedviges Mamudo
1027448,Melanie de Vales Rafael
155474,Keb' Mo'
232449,Yolonda Williams
2049,Rubén Blades
11784,Tom Atkins
65398,Denise Nicholas
2390,LeVar Burton
58493,Tim Grimm
22860,Chuck Berry
44822,Starletta DuPois
1669775,Lou Ferguson
5247,John Fiedler
20904,James Hong
157963,Loren Avedon
74375,Cynthia Rothrock
155890,Julie Garfield
2210813,Ellen Adler
2879828,Joseph Bernard
9789,Robert Altman
6449,Warren Beatty
1272970,Christina Wren
1591305,MorningStar Angeline
1179187,T'Shaun Barrett
291133,Tai Bennett
61223,Sharon Brathwaite-Sanders
73968,Henry Cavill
90633,Gal Gadot
151343,David Baddiel
11275,Stephen Fry
6199,Miriam Margolyes
4491,Jennifer Aniston
14405,Courteney Cox
14406,Lisa Kudrow
3861945,Rob Helms
566154,Bruce Daniels
1141310,Joe Dietl
57446,Mark Romanek
215486,Fiona Apple
1161962,Brian Bell
39350,Anémone
1722308,Jan Vancoillie
24501,Pierre Richard
39646,Mireille Darc
2179581,Tarek Aylouch
1805442,Michael Markiewicz
3499637,Nick Garrison
164004,Ken Cheeseman
26472,Steve Guttenberg
1291312,Jürgen Thormann
2983750,Peter Garloff
39998,Tim McIntire
7506,Irwin Allen
290,Christopher Plummer
8606,Robert Shaw
1304968,June Tobin
834,Otto Preminger
16523,Burgess Meredith
19460,Saul Bass
6952,Charlie Sheen
114454,BJ Davis
24045,Joseph Gordon-Levitt
3899,Ken Watanabe
475512,Ellie Kemper
56323,Tina Fey
13636,Jane Krakowski
1689289,Justine Lupe
23659,Will Ferrell
152416,Murray Bartlett
83873,June Diane Raphael
87525,Alan Carr
7056,Emma Thompson
491896,Brie Garcia
969097,Nikki Garcia
3785095,Kathy Colace
1452124,Jason Derulo
1364893,Iggy Azalea
1744249,Kygo
1569717,Martin Garrix
31515,Mark Cuban
1218635,Gary Vaynerchuk
1544086,Min Yoon-gi
1997030,Halsey
89056,David Guetta
1101364,Josh Wink
1101343,Carl Cox
3358480,Alan Walker
3358481,Margrethe Alida
3358482,Au/Ra
1101342,Sven Väth
1016349,Norman Cook
108661,Chester Bennington
214830,Mike Shinoda
1070445,Brad Delson
974169,Jenna Ortega
1098962,Dove Cameron
1331457,Sofia Carson
2282,Ben Kingsley
96074,Leslie Feist
4569,David Fox
218984,Johnny Ward
15737,Helen McCrory
1167897,Natasha O'Keeffe
15488,Ken Loach
73288,Michael McElhatton
1844919,Sam McGovern
658,Alfred Molina
4784,Laura Dern
13333,Vanessa Redgrave
227,William Hurt
40542,Héctor Babenco
5048,Harry Dean Stanton
923,Dean Stockwell
20718,Claude Berri
4399,Timothy Burrill
3564,Hervé de Luze
18517,Kim Gordon
101831,Thurston Moore
205,Kirsten Dunst
2299,Josh Hartnett
71538,Dave Filoni
3993,Dennis Muren
553222,Bill Moyers
24343,Peter Mayhew
1021364,Joseph Campbell
491,John Williams
488,Steven Spielberg
5026,Akira Kurosawa
132305,Nobuhiko Obayashi
18598,Ishirō Honda
11770,John Carpenter
515,Glenn Close
4,Carrie Fisher
3799,Billy Dee Williams
130,Kenny Baker
33185,Jeremy Bulloch
670,Ben Burtt
10930,Irvin Kershner
2140901,Gary Kurtz
2,Mark Hamill
15811,John Musker
61416,Peter Del Vecho
94037,Bruno Bozzetto
7314,Nick Park
35219,Corey Burton
109255,Leonard Maltin
2106,Walt Disney
146136,Bernice Hansen
628,Isao Takahata
608,Hayao Miyazaki
1417,Toshio Suzuki
1186197,Steve Alpert
110675,Jerry Beck
574290,Dave Bossert
1130441,Robert Sklar
108650,Alain Silver
380,Robert De Niro
16514,Robert Chartoff
1130439,Drew Casper
2585,Eric Clapton
1044,Michael Chapman
3661,Thelma Schoonmaker
3636,Paul Newman
109410,Joanne Woodward
4135,Robert Redford
1052109,Harry Styles
1052107,Niall Horan
1146050,Zayn Malik
11478,Lorraine Bracco
83198,Allan Arkush
101377,Paul Bartel
141,David Carradine
8487,Gregory Peck
7046,Cecilia Peck
1102449,Veronique Peck
190,Clint Eastwood
14701,Carroll Baker
3264451,Ann Adachi-Tasch
1034310,Margaret Bodde
126750,Serge Bromberg
1036,Cybill Shepherd
11477,Ray Liotta
4517,Joe Pesci
1314108,FatLip
9656,Johnny Knoxville
61441,Rick Kosick
110068,Ryan Dunn
56584,Bam Margera
2092052,Ryder McLaughlin
2003784,Na-kel Smith
1873369,Aramis Hudson
84534,Dido
9315,Marilyn Manson
70851,Jack Black
134,Jamie Foxx
544016,D12
62644,50 Cent
2041,Dr. Dre
1740159,Marky Bass
1740160,Jeff Bass
1945289,Denaun Porter
2191424,Courtney Butcher
2191425,Pj Jacokes
1135272,Skylar Grey
337,Proof
2689367,Swifty McVay
1549410,Dead Prez
214586,Melle Mel
339,Brian Grazer
1776464,Stretch Armstrong
4238,Common
37934,André 3000
15310,Anthony Kiedis
12207,Kylie Minogue
154782,Pat Benatar
154783,Neil Giraldo
174875,Carly Simon
1114225,Kevin Antunes
1114226,Monte Pittman
116152,Donna DeLory
116153,Niki Haris
123448,Stuart Price
2392722,Steve Sidelnyk
2973752,Erika Belle
1093165,Christopher Ciccone
116341,Bill Clinton
1214377,Felicia Culotta
56288,Alysson Paradis
1059545,Sidney Duteil
32797,Arianne Phillips
1193602,Gia Coppola
120615,Tom Ford
88339,Kevin Stea
116154,Luis Camacho
1795433,Oliver Crumes
72208,Alicia Keys
3284,Bruce Springsteen
18746,Stevie Wonder
2240369,Bert Stern
82280,Louis Armstrong
40092,Twiggy
21397,Dennis Rodman
3981237,Shirley Rodman
104156,Ann-Marie MacDonald
38406,Paris Hilton
1226426,Nicky Hilton
18329,Maria Schneider
4426,Maria Michi
147,Michael Madsen
82702,Michael Jackson
65605,Muhammad Ali
47775,Marc Anthony
4483,Dustin Hoffman
13570,Samuel Goldwyn
3417,Tony Goldwyn
68402,Barry Navidi
5049,John Hurt
68381,Thom Eberhardt
4053937,Judah Carew
4053939,Joe Njenga
4053940,Karste Wright
1159,Steven Bauer
1150,Brian De Palma
1151,Martin Bregman
7447,Alec Baldwin
9560,Ellen Burstyn
5682,Anouk Aimée
44556,Richard Anconina
20234,Fanny Ardant
1487,Bob Dylan
111714,Al Green
165867,Kid Rock
1056052,Lee Child
500,Tom Cruise
106179,Anthony Franciosa
10190,Jill St. John
5250,Jack Klugman
8483,Horton Foote
8482,Robert Mulligan
6349,Alan J. Pakula
8488,Mary Badham
8489,Phillip Alford
235416,Sarah Snook
1281967,Josh McConville
1604285,Liv Hewson
1105441,Bobbi Salvör Menuez
66744,Chaney Kley
53286,Richard Karn
8184,Mary Kate Schellhardt
1028353,John Anthony West
1028355,Robert M. Schoch
707,Dan Aykroyd
26485,Jim Belushi
54812,Chevy Chase
516,Annette Bening
2116053,Maud Guillaumin
1119476,Jonathan Kuntz
3947315,Mark Eliot
4786,Richard Attenborough
51816,Paul M. Heller
46602,Fred Weintraub
14101,John Larroquette
18794,Kate Nelligan
85139,Nicholas Braun
19429,Bruce Lee
18897,Jackie Chan
51576,Chuck Norris
2103203,James B. Nicholson
131168,Lee Hoi-Chuen
17844,Elke Sommer
6263,Hannelore Elsner
10254,Mario Adorf
15196,David Hemmings
46780,Samantha Eggar
66884,Emlyn Williams
2883,Leif Garrett
3011,Linda Manz
5147,Ralph Seymour
67676,Kenneth More
116838,Betsy Drake
100787,Patrick Barr
1921578,Gaston Rébuffat
239398,Maurice Baquet
31070,Ryan O'Neal
31071,Marisa Berenson
2264,Patrick Magee
17604,Jeremy Renner
418,Robert Patrick
138986,Tony Bennett
1532769,Nolwenn Leroy
1109331,Duffy
86449,Christophe Willem
64825,Harry Hamlin
12646,Terry O'Quinn
91332,Dedee Pfeiffer
78805,Laura Leighton
7180,John Candy
14882,Jackie Gleason
7503,Red Buttons
14639,Mel Brooks
14670,Joan Rivers
1895,Carl Reiner
7172,James Brown
1172565,Lesley Gore
1572034,Robert Scott Wilson
2555,David Proval
7004,Paul Sorvino
138,Quentin Tarantino
2545,Lawrence Bender
2144,Tobin Bell
2505,James Cromwell
1215054,Jerry Rice
9248,Nora Ephron
146307,Joe Guzaldo
7166,Kevin Pollak
39770,Adam Roarke
34721,Larry Bishop
16376,Larry David
16377,Jerry Seinfeld
1206,Jason Alexander
33,Gary Sinise
32749,Alex Diakun
99,Amanda Plummer
6804,Graham Greene
69302,Litefoot
65529,Irene Bedard
101028,Michelle Borth
70303,Zach Gilford
11064,David Strathairn
1238314,Kurt Busch
204415,Jimmie Johnson
1 id name
2 2975 Laurence Fishburne
3 6384 Keanu Reeves
4 530 Carrie-Anne Moss
5 1357063 Darrin Prescott
6 40644 Chad Stahelski
7 1779512 Jackson Spidell
8 21127 Bobby Cannavale
9 1692944 Heidi Schreck
10 64 Gary Oldman
11 1776 Francis Ford Coppola
12 38803 Roman Coppola
13 20215 Billy Campbell
14 2130 Cary Elwes
15 8349 Martin Sheen
16 8351 Frederic Forrest
17 8354 Albert Hall
18 1107983 Martin Luther King Jr.
19 52057 Obba Babatundé
20 110380 Colin Powell
21 18471 Anthony Anderson
22 74611 Tracee Ellis Ross
23 1407498 Marsai Martin
24 529 Guy Pearce
25 532 Joe Pantoliano
26 3894 Christian Bale
27 1892 Matt Damon
28 19498 Jon Bernthal
29 2710 James Cameron
30 4587 Halle Berry
31 8691 Zoe Saldaña
32 72129 Jennifer Lawrence
33 189111 Suzanne Collins
34 23964 Gary Ross
35 9827 Rose Byrne
36 143103 Krew Boylan
37 21708 Tomas Milian
38 72983 Manny Pérez
39 124909 Danny Hoch
40 4038 Susan Sarandon
41 18992 Aidan Quinn
42 2047 Danny Glover
43 4730 Emmy Rossum
44 9464 Harry Lennix
45 14409 David Schwimmer
46 1415341 Kazuhiro Tsuji
47 1267653 Chet Zar
48 7467 David Fincher
49 1519399 Erik Messerschmidt
50 1003 Jean Reno
51 524 Natalie Portman
52 1810 Heath Ledger
53 3895 Michael Caine
54 958722 Eiko Ishioka
55 41381 Sadie Frost
56 10980 Daniel Radcliffe
57 10989 Rupert Grint
58 10990 Emma Watson
59 219479 Criss Angel
60 1101349 Steve Aoki
61 1407495 Miles Brown
62 3361135 Peter Kent
63 1812 Michelle Williams
64 2037 Cillian Murphy
65 5081 Emily Blunt
66 2559324 Beth Lane
67 2559325 Lea Madda
68 27888 Raúl Juliá
69 2630 Nastassja Kinski
70 151007 Peter Ramsey
71 1769 Sofia Coppola
72 19772 Paul Rassam
73 1 George Lucas
74 7879 John Lasseter
75 1032 Martin Scorsese
76 1006721 Charles Scorsese
77 11483 Catherine Scorsese
78 5953 Spike Jonze
79 325 Eminem
80 3125 Madonna
81 3084 Marlon Brando
82 1158 Al Pacino
83 3085 James Caan
84 3087 Robert Duvall
85 3092 Diane Keaton
86 117669 Portia Doubleday
87 558466 Alex Russell
88 61555 Haley Ramm
89 10017 Charlton Heston
90 5563 James Coburn
91 26557 Ferdy Mayne
92 18688 Harry Connick Jr.
93 8437 Teri Garr
94 589 Daryl Hannah
95 16431 Sam Elliott
96 2203 Neal McDonough
97 3026 Rob Reiner
98 32 Robin Wright
99 12132 Michael Rooker
100 932719 Jeff Gordon
101 2127 James Wan
102 2128 Leigh Whannell
103 2675 Darren Lynn Bousman
104 1278399 Joseph Steven
105 1659326 Teresa Modnick
106 26466 Caroline Kava
107 18269 Brendan Fraser
108 5606 Sissy Spacek
109 5632 Jack Fisk
110 1179102 Kathleen Kennedy
111 11367 Bradley Whitford
112 31028 Richard Schiff
113 1397846 Craig Barron
114 2166741 Robert A. Baum Jr.
115 3266 Joe Mantegna
116 36059 Joanna Pacula
117 21010 Alexandra Hay
118 101032 Rod McCary
119 98569 Vincent Van Patten
120 2080584 Ben Jorgensen
121 103835 Jeff Monahan
122 6717 James Marshall
123 928944 Coleman Barks
124 2022250 Robert Bly
125 9979 Peter Coyote
126 514 Jack Nicholson
127 6193 Leonardo DiCaprio
128 2641 Martin Landau
129 92029 Gabriel Bologna
130 218506 Gretchen Becker
131 2098136 Brian Becker
132 2098137 Richard Becker
133 2098138 Blase Bonpane
134 181934 Al Sharpton
135 496175 Carmelo Anthony
136 1280183 Coretta Scott King
137 1006651 John Lewis
138 237876 Sarah-Jane Sauvegrain
139 180659 Rosa Parks
140 1165240 Jason Alan Carvell
141 2787630 Valentin Zorin
142 89289 Jesse Jackson
143 2102583 Andrew Cohen
144 73988 Lyndon B. Johnson
145 21111 John F. Kennedy
146 2803017 Arthur John Birch
147 2803001 Haroldson Lafayette Hunt
148 4193575 Maxime Demigné
149 4193577 Gabin Clerc
150 4193578 Tom Chabeau
151 2954 Jeffrey Wright
152 33663 Donald Trump
153 2301996 Mike Pence
154 214317 Ben Bradlee
155 13716 Carl Bernstein
156 117180 Tom Brokaw
157 23626 Liev Schreiber
158 937663 Neil Amdur
159 1931175 Bob Beamon
160 12951 O.J. Simpson
161 2998183 Nicole Brown Simpson
162 1231277 Marcia Clark
163 60158 Shirley Jones
164 24810 Pat Boone
165 2535 Vivica A. Fox
166 225620 Rudolph Moise
167 1683093 Terayle Hill
168 79538 Khalil Kain
169 1562353 Erica Mena
170 1038 Jodie Foster
171 4173 Anthony Hopkins
172 349 Scott Glenn
173 9780 Angela Bassett
174 19011 George W. Bush
175 146687 Tony Blair
176 74266 Dick Cheney
177 1507609 Bob Coen
178 1507612 Francis E. Boyle
179 1507613 Jean Patterson
180 1122373 Marion Barry Jr.
181 1122374 Chuck Brown
182 1122375 Lewis Franklin
183 1897 Bernie Mac
184 77896 Bill Bellamy
185 2395 Whoopi Goldberg
186 1735828 Halle Bailey
187 53397 Christina Aguilera
188 1224841 Mo Willems
189 539 Thomas Lennon
190 111513 Yvette Nicole Brown
191 2682390 Gerd Ludwig
192 2227 Nicole Kidman
193 4430 Sharon Stone
194 14837 Carol Burnett
195 5823 Julie Andrews
196 3141 Marisa Tomei
197 449538 Adele
198 13309 Oprah Winfrey
199 1661465 Lizzo
200 102786 Barack Obama
201 128550 Desmond Tutu
202 2320880 Sundar Pichai
203 46300 Alexander Nathan Etel
204 27889 Stephan Elliott
205 1331 Hugo Weaving
206 326 Kim Basinger
207 323 Curtis Hanson
208 11099 Dante Spinotti
209 7187 Richard Donner
210 57371 Jeff Cohen
211 3034 Corey Feldman
212 6968 Hugh Jackman
213 11669 Jimmy Fallon
214 43120 Andy Dick
215 53926 Jon Heder
216 34947 Kevin Conroy
217 1262612 Neal Adams
218 21200 Will Arnett
219 525 Christopher Nolan
220 15556 Rebecca Hall
221 131286 Mark David
222 60634 Marty Belafsky
223 7133 Max Casella
224 15762 Tara Strong
225 1160 Michelle Pfeiffer
226 1813 Anne Hathaway
227 2157 Robin Williams
228 880 Ben Affleck
229 1231844 Manny Ramírez
230 1214256 Curt Schilling
231 31 Tom Hanks
232 3197 Tom Sizemore
233 12833 Edward Burns
234 10297 Matthew McConaughey
235 83002 Jessica Chastain
236 21037 Prince
237 1800494 Cora Coleman-Dunham
238 1775343 Josh Dunham
239 127712 Matt Ryan
240 3035 Jerry O'Connell
241 527313 Taissa Farmiga
242 21007 Jonah Hill
243 234352 Margot Robbie
244 869 Gale Anne Hurd
245 96884 Al Giddings
246 1100 Arnold Schwarzenegger
247 2713 Linda Hamilton
248 2231 Samuel L. Jackson
249 6159 Ron Howard
250 33181 Jimmy Smits
251 63611 Nickolas Grace
252 53574 Eric Thal
253 16217 Lynn Whitfield
254 8891 John Travolta
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259 16108 Lee Meriwether
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264 111946 Britney Spears
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266 343 Taryn Manning
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269 103 Mark Ruffalo
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272 3810 Javier Bardem
273 6431 Darren Aronofsky
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284 9287 Robert Blake
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290 23931 Mira Sorvino
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292 77003 Gavin O'Connor
293 199449 Greg O'Connor
294 819 Edward Norton
295 2222 Beau Bridges
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297 4037 Ron Shelton
298 1269 Kevin Costner
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300 13473 Barry Bostwick
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302 3896 Liam Neeson
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306 133499 Christopher Biggins
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308 7904 Billy Crystal
309 4244 Lauryn Hill
310 13476 Nell Campbell
311 44301 Cliff DeYoung
312 11905 Sven Nykvist
313 38127 Erland Josephson
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317 155474 Keb' Mo'
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319 2049 Rubén Blades
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322 2390 LeVar Burton
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324 22860 Chuck Berry
325 44822 Starletta DuPois
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353 215486 Fiona Apple
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363 26472 Steve Guttenberg
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372 16523 Burgess Meredith
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377 3899 Ken Watanabe
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379 56323 Tina Fey
380 13636 Jane Krakowski
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382 23659 Will Ferrell
383 152416 Murray Bartlett
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385 87525 Alan Carr
386 7056 Emma Thompson
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392 1744249 Kygo
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394 31515 Mark Cuban
395 1218635 Gary Vaynerchuk
396 1544086 Min Yoon-gi
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398 89056 David Guetta
399 1101364 Josh Wink
400 1101343 Carl Cox
401 3358480 Alan Walker
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404 1101342 Sven Väth
405 1016349 Norman Cook
406 108661 Chester Bennington
407 214830 Mike Shinoda
408 1070445 Brad Delson
409 974169 Jenna Ortega
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411 1331457 Sofia Carson
412 2282 Ben Kingsley
413 96074 Leslie Feist
414 4569 David Fox
415 218984 Johnny Ward
416 15737 Helen McCrory
417 1167897 Natasha O'Keeffe
418 15488 Ken Loach
419 73288 Michael McElhatton
420 1844919 Sam McGovern
421 658 Alfred Molina
422 4784 Laura Dern
423 13333 Vanessa Redgrave
424 227 William Hurt
425 40542 Héctor Babenco
426 5048 Harry Dean Stanton
427 923 Dean Stockwell
428 20718 Claude Berri
429 4399 Timothy Burrill
430 3564 Hervé de Luze
431 18517 Kim Gordon
432 101831 Thurston Moore
433 205 Kirsten Dunst
434 2299 Josh Hartnett
435 71538 Dave Filoni
436 3993 Dennis Muren
437 553222 Bill Moyers
438 24343 Peter Mayhew
439 1021364 Joseph Campbell
440 491 John Williams
441 488 Steven Spielberg
442 5026 Akira Kurosawa
443 132305 Nobuhiko Obayashi
444 18598 Ishirō Honda
445 11770 John Carpenter
446 515 Glenn Close
447 4 Carrie Fisher
448 3799 Billy Dee Williams
449 130 Kenny Baker
450 33185 Jeremy Bulloch
451 670 Ben Burtt
452 10930 Irvin Kershner
453 2140901 Gary Kurtz
454 2 Mark Hamill
455 15811 John Musker
456 61416 Peter Del Vecho
457 94037 Bruno Bozzetto
458 7314 Nick Park
459 35219 Corey Burton
460 109255 Leonard Maltin
461 2106 Walt Disney
462 146136 Bernice Hansen
463 628 Isao Takahata
464 608 Hayao Miyazaki
465 1417 Toshio Suzuki
466 1186197 Steve Alpert
467 110675 Jerry Beck
468 574290 Dave Bossert
469 1130441 Robert Sklar
470 108650 Alain Silver
471 380 Robert De Niro
472 16514 Robert Chartoff
473 1130439 Drew Casper
474 2585 Eric Clapton
475 1044 Michael Chapman
476 3661 Thelma Schoonmaker
477 3636 Paul Newman
478 109410 Joanne Woodward
479 4135 Robert Redford
480 1052109 Harry Styles
481 1052107 Niall Horan
482 1146050 Zayn Malik
483 11478 Lorraine Bracco
484 83198 Allan Arkush
485 101377 Paul Bartel
486 141 David Carradine
487 8487 Gregory Peck
488 7046 Cecilia Peck
489 1102449 Veronique Peck
490 190 Clint Eastwood
491 14701 Carroll Baker
492 3264451 Ann Adachi-Tasch
493 1034310 Margaret Bodde
494 126750 Serge Bromberg
495 1036 Cybill Shepherd
496 11477 Ray Liotta
497 4517 Joe Pesci
498 1314108 FatLip
499 9656 Johnny Knoxville
500 61441 Rick Kosick
501 110068 Ryan Dunn
502 56584 Bam Margera
503 2092052 Ryder McLaughlin
504 2003784 Na-kel Smith
505 1873369 Aramis Hudson
506 84534 Dido
507 9315 Marilyn Manson
508 70851 Jack Black
509 134 Jamie Foxx
510 544016 D12
511 62644 50 Cent
512 2041 Dr. Dre
513 1740159 Marky Bass
514 1740160 Jeff Bass
515 1945289 Denaun Porter
516 2191424 Courtney Butcher
517 2191425 Pj Jacokes
518 1135272 Skylar Grey
519 337 Proof
520 2689367 Swifty McVay
521 1549410 Dead Prez
522 214586 Melle Mel
523 339 Brian Grazer
524 1776464 Stretch Armstrong
525 4238 Common
526 37934 André 3000
527 15310 Anthony Kiedis
528 12207 Kylie Minogue
529 154782 Pat Benatar
530 154783 Neil Giraldo
531 174875 Carly Simon
532 1114225 Kevin Antunes
533 1114226 Monte Pittman
534 116152 Donna DeLory
535 116153 Niki Haris
536 123448 Stuart Price
537 2392722 Steve Sidelnyk
538 2973752 Erika Belle
539 1093165 Christopher Ciccone
540 116341 Bill Clinton
541 1214377 Felicia Culotta
542 56288 Alysson Paradis
543 1059545 Sidney Duteil
544 32797 Arianne Phillips
545 1193602 Gia Coppola
546 120615 Tom Ford
547 88339 Kevin Stea
548 116154 Luis Camacho
549 1795433 Oliver Crumes
550 72208 Alicia Keys
551 3284 Bruce Springsteen
552 18746 Stevie Wonder
553 2240369 Bert Stern
554 82280 Louis Armstrong
555 40092 Twiggy
556 21397 Dennis Rodman
557 3981237 Shirley Rodman
558 104156 Ann-Marie MacDonald
559 38406 Paris Hilton
560 1226426 Nicky Hilton
561 18329 Maria Schneider
562 4426 Maria Michi
563 147 Michael Madsen
564 82702 Michael Jackson
565 65605 Muhammad Ali
566 47775 Marc Anthony
567 4483 Dustin Hoffman
568 13570 Samuel Goldwyn
569 3417 Tony Goldwyn
570 68402 Barry Navidi
571 5049 John Hurt
572 68381 Thom Eberhardt
573 4053937 Judah Carew
574 4053939 Joe Njenga
575 4053940 Karste Wright
576 1159 Steven Bauer
577 1150 Brian De Palma
578 1151 Martin Bregman
579 7447 Alec Baldwin
580 9560 Ellen Burstyn
581 5682 Anouk Aimée
582 44556 Richard Anconina
583 20234 Fanny Ardant
584 1487 Bob Dylan
585 111714 Al Green
586 165867 Kid Rock
587 1056052 Lee Child
588 500 Tom Cruise
589 106179 Anthony Franciosa
590 10190 Jill St. John
591 5250 Jack Klugman
592 8483 Horton Foote
593 8482 Robert Mulligan
594 6349 Alan J. Pakula
595 8488 Mary Badham
596 8489 Phillip Alford
597 235416 Sarah Snook
598 1281967 Josh McConville
599 1604285 Liv Hewson
600 1105441 Bobbi Salvör Menuez
601 66744 Chaney Kley
602 53286 Richard Karn
603 8184 Mary Kate Schellhardt
604 1028353 John Anthony West
605 1028355 Robert M. Schoch
606 707 Dan Aykroyd
607 26485 Jim Belushi
608 54812 Chevy Chase
609 516 Annette Bening
610 2116053 Maud Guillaumin
611 1119476 Jonathan Kuntz
612 3947315 Mark Eliot
613 4786 Richard Attenborough
614 51816 Paul M. Heller
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617 18794 Kate Nelligan
618 85139 Nicholas Braun
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620 18897 Jackie Chan
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622 2103203 James B. Nicholson
623 131168 Lee Hoi-Chuen
624 17844 Elke Sommer
625 6263 Hannelore Elsner
626 10254 Mario Adorf
627 15196 David Hemmings
628 46780 Samantha Eggar
629 66884 Emlyn Williams
630 2883 Leif Garrett
631 3011 Linda Manz
632 5147 Ralph Seymour
633 67676 Kenneth More
634 116838 Betsy Drake
635 100787 Patrick Barr
636 1921578 Gaston Rébuffat
637 239398 Maurice Baquet
638 31070 Ryan O'Neal
639 31071 Marisa Berenson
640 2264 Patrick Magee
641 17604 Jeremy Renner
642 418 Robert Patrick
643 138986 Tony Bennett
644 1532769 Nolwenn Leroy
645 1109331 Duffy
646 86449 Christophe Willem
647 64825 Harry Hamlin
648 12646 Terry O'Quinn
649 91332 Dedee Pfeiffer
650 78805 Laura Leighton
651 7180 John Candy
652 14882 Jackie Gleason
653 7503 Red Buttons
654 14639 Mel Brooks
655 14670 Joan Rivers
656 1895 Carl Reiner
657 7172 James Brown
658 1172565 Lesley Gore
659 1572034 Robert Scott Wilson
660 2555 David Proval
661 7004 Paul Sorvino
662 138 Quentin Tarantino
663 2545 Lawrence Bender
664 2144 Tobin Bell
665 2505 James Cromwell
666 1215054 Jerry Rice
667 9248 Nora Ephron
668 146307 Joe Guzaldo
669 7166 Kevin Pollak
670 39770 Adam Roarke
671 34721 Larry Bishop
672 16376 Larry David
673 16377 Jerry Seinfeld
674 1206 Jason Alexander
675 33 Gary Sinise
676 32749 Alex Diakun
677 99 Amanda Plummer
678 6804 Graham Greene
679 69302 Litefoot
680 65529 Irene Bedard
681 101028 Michelle Borth
682 70303 Zach Gilford
683 11064 David Strathairn
684 1238314 Kurt Busch
685 204415 Jimmie Johnson

954
tunmnlu/task_1/Q1/old/edges.csv Normal file
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685
tunmnlu/task_1/Q1/old/nodes.csv Normal file
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@@ -0,0 +1,685 @@
id,name
2975,Laurence Fishburne
6384,Keanu Reeves
530,Carrie-Anne Moss
1357063,Darrin Prescott
40644,Chad Stahelski
1779512,Jackson Spidell
21127,Bobby Cannavale
1692944,Heidi Schreck
64,Gary Oldman
1776,Francis Ford Coppola
38803,Roman Coppola
20215,Billy Campbell
2130,Cary Elwes
8349,Martin Sheen
8351,Frederic Forrest
8354,Albert Hall
1107983,Martin Luther King Jr.
52057,Obba Babatundé
110380,Colin Powell
18471,Anthony Anderson
74611,Tracee Ellis Ross
1407498,Marsai Martin
529,Guy Pearce
532,Joe Pantoliano
3894,Christian Bale
1892,Matt Damon
19498,Jon Bernthal
2710,James Cameron
4587,Halle Berry
8691,Zoe Saldaña
72129,Jennifer Lawrence
189111,Suzanne Collins
23964,Gary Ross
9827,Rose Byrne
143103,Krew Boylan
21708,Tomas Milian
72983,Manny Pérez
124909,Danny Hoch
4038,Susan Sarandon
18992,Aidan Quinn
2047,Danny Glover
4730,Emmy Rossum
9464,Harry Lennix
14409,David Schwimmer
1415341,Kazuhiro Tsuji
1267653,Chet Zar
7467,David Fincher
1519399,Erik Messerschmidt
1003,Jean Reno
524,Natalie Portman
1810,Heath Ledger
3895,Michael Caine
958722,Eiko Ishioka
41381,Sadie Frost
10980,Daniel Radcliffe
10989,Rupert Grint
10990,Emma Watson
219479,Criss Angel
1101349,Steve Aoki
1407495,Miles Brown
3361135,Peter Kent
1812,Michelle Williams
2037,Cillian Murphy
5081,Emily Blunt
2559324,Beth Lane
2559325,Lea Madda
27888,Raúl Juliá
2630,Nastassja Kinski
151007,Peter Ramsey
1769,Sofia Coppola
19772,Paul Rassam
1,George Lucas
7879,John Lasseter
1032,Martin Scorsese
1006721,Charles Scorsese
11483,Catherine Scorsese
5953,Spike Jonze
325,Eminem
3125,Madonna
3084,Marlon Brando
1158,Al Pacino
3085,James Caan
3087,Robert Duvall
3092,Diane Keaton
117669,Portia Doubleday
558466,Alex Russell
61555,Haley Ramm
10017,Charlton Heston
5563,James Coburn
26557,Ferdy Mayne
18688,Harry Connick Jr.
8437,Teri Garr
589,Daryl Hannah
16431,Sam Elliott
2203,Neal McDonough
3026,Rob Reiner
32,Robin Wright
12132,Michael Rooker
932719,Jeff Gordon
2127,James Wan
2128,Leigh Whannell
2675,Darren Lynn Bousman
1278399,Joseph Steven
1659326,Teresa Modnick
26466,Caroline Kava
18269,Brendan Fraser
5606,Sissy Spacek
5632,Jack Fisk
1179102,Kathleen Kennedy
11367,Bradley Whitford
31028,Richard Schiff
1397846,Craig Barron
2166741,Robert A. Baum Jr.
3266,Joe Mantegna
36059,Joanna Pacula
21010,Alexandra Hay
101032,Rod McCary
98569,Vincent Van Patten
2080584,Ben Jorgensen
103835,Jeff Monahan
6717,James Marshall
928944,Coleman Barks
2022250,Robert Bly
9979,Peter Coyote
514,Jack Nicholson
6193,Leonardo DiCaprio
2641,Martin Landau
92029,Gabriel Bologna
218506,Gretchen Becker
2098136,Brian Becker
2098137,Richard Becker
2098138,Blase Bonpane
181934,Al Sharpton
496175,Carmelo Anthony
1280183,Coretta Scott King
1006651,John Lewis
237876,Sarah-Jane Sauvegrain
180659,Rosa Parks
1165240,Jason Alan Carvell
2787630,Valentin Zorin
89289,Jesse Jackson
2102583,Andrew Cohen
73988,Lyndon B. Johnson
21111,John F. Kennedy
2803017,Arthur John Birch
2803001,Haroldson Lafayette Hunt
4193575,Maxime Demigné
4193577,Gabin Clerc
4193578,Tom Chabeau
2954,Jeffrey Wright
33663,Donald Trump
2301996,Mike Pence
214317,Ben Bradlee
13716,Carl Bernstein
117180,Tom Brokaw
23626,Liev Schreiber
937663,Neil Amdur
1931175,Bob Beamon
12951,O.J. Simpson
2998183,Nicole Brown Simpson
1231277,Marcia Clark
60158,Shirley Jones
24810,Pat Boone
2535,Vivica A. Fox
225620,Rudolph Moise
1683093,Terayle Hill
79538,Khalil Kain
1562353,Erica Mena
1038,Jodie Foster
4173,Anthony Hopkins
349,Scott Glenn
9780,Angela Bassett
19011,George W. Bush
146687,Tony Blair
74266,Dick Cheney
1507609,Bob Coen
1507612,Francis E. Boyle
1507613,Jean Patterson
1122373,Marion Barry Jr.
1122374,Chuck Brown
1122375,Lewis Franklin
1897,Bernie Mac
77896,Bill Bellamy
2395,Whoopi Goldberg
1735828,Halle Bailey
53397,Christina Aguilera
1224841,Mo Willems
539,Thomas Lennon
111513,Yvette Nicole Brown
2682390,Gerd Ludwig
2227,Nicole Kidman
4430,Sharon Stone
14837,Carol Burnett
5823,Julie Andrews
3141,Marisa Tomei
449538,Adele
13309,Oprah Winfrey
1661465,Lizzo
102786,Barack Obama
128550,Desmond Tutu
2320880,Sundar Pichai
46300,Alexander Nathan Etel
27889,Stephan Elliott
1331,Hugo Weaving
326,Kim Basinger
323,Curtis Hanson
11099,Dante Spinotti
7187,Richard Donner
57371,Jeff Cohen
3034,Corey Feldman
6968,Hugh Jackman
11669,Jimmy Fallon
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53926,Jon Heder
34947,Kevin Conroy
1262612,Neal Adams
21200,Will Arnett
525,Christopher Nolan
15556,Rebecca Hall
131286,Mark David
60634,Marty Belafsky
7133,Max Casella
15762,Tara Strong
1160,Michelle Pfeiffer
1813,Anne Hathaway
2157,Robin Williams
880,Ben Affleck
1231844,Manny Ramírez
1214256,Curt Schilling
31,Tom Hanks
3197,Tom Sizemore
12833,Edward Burns
10297,Matthew McConaughey
83002,Jessica Chastain
21037,Prince
1800494,Cora Coleman-Dunham
1775343,Josh Dunham
127712,Matt Ryan
3035,Jerry O'Connell
527313,Taissa Farmiga
21007,Jonah Hill
234352,Margot Robbie
869,Gale Anne Hurd
96884,Al Giddings
1100,Arnold Schwarzenegger
2713,Linda Hamilton
2231,Samuel L. Jackson
6159,Ron Howard
33181,Jimmy Smits
63611,Nickolas Grace
53574,Eric Thal
16217,Lynn Whitfield
8891,John Travolta
44038,Nancy Allen
1121437,Leslie Felperin
70243,Eartha Kitt
15973,Julie Newmar
16108,Lee Meriwether
73475,Adriano Giannini
2078350,Tommaso Basili
73457,Chris Pratt
543530,Dave Bautista
111946,Britney Spears
57691,Tamra Davis
343,Taryn Manning
3223,Robert Downey Jr.
74568,Chris Hemsworth
103,Mark Ruffalo
16828,Chris Evans
1851091,Josh Silver
3810,Javier Bardem
6431,Darren Aronofsky
1625558,Awkwafina
112,Cate Blanchett
1283,Helena Bonham Carter
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17605,Idris Elba
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24299,Jean-Claude Brialy
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70119,Catherine Spaak
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86381,Algenis Perez Soto
1012743,Dalisa Alegría
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23931,Mira Sorvino
121718,Bill Camp
77003,Gavin O'Connor
199449,Greg O'Connor
819,Edward Norton
2222,Beau Bridges
152699,James Noble
4037,Ron Shelton
1269,Kevin Costner
13472,Tim Curry
13473,Barry Bostwick
2607714,Alan Cooke
3896,Liam Neeson
2517086,Will Hochman
151432,Boris McGiver
3763830,Jim Forbes
133499,Christopher Biggins
1724558,Justin Arnold
7904,Billy Crystal
4244,Lauryn Hill
13476,Nell Campbell
44301,Cliff DeYoung
11905,Sven Nykvist
38127,Erland Josephson
231318,Melinda Kinnaman
1027445,Hedviges Mamudo
1027448,Melanie de Vales Rafael
155474,Keb' Mo'
232449,Yolonda Williams
2049,Rubén Blades
11784,Tom Atkins
65398,Denise Nicholas
2390,LeVar Burton
58493,Tim Grimm
22860,Chuck Berry
44822,Starletta DuPois
1669775,Lou Ferguson
5247,John Fiedler
20904,James Hong
157963,Loren Avedon
74375,Cynthia Rothrock
155890,Julie Garfield
2210813,Ellen Adler
2879828,Joseph Bernard
9789,Robert Altman
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1272970,Christina Wren
1591305,MorningStar Angeline
1179187,T'Shaun Barrett
291133,Tai Bennett
61223,Sharon Brathwaite-Sanders
73968,Henry Cavill
90633,Gal Gadot
151343,David Baddiel
11275,Stephen Fry
6199,Miriam Margolyes
4491,Jennifer Aniston
14405,Courteney Cox
14406,Lisa Kudrow
3861945,Rob Helms
566154,Bruce Daniels
1141310,Joe Dietl
57446,Mark Romanek
215486,Fiona Apple
1161962,Brian Bell
39350,Anémone
1722308,Jan Vancoillie
24501,Pierre Richard
39646,Mireille Darc
2179581,Tarek Aylouch
1805442,Michael Markiewicz
3499637,Nick Garrison
164004,Ken Cheeseman
26472,Steve Guttenberg
1291312,Jürgen Thormann
2983750,Peter Garloff
39998,Tim McIntire
7506,Irwin Allen
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8606,Robert Shaw
1304968,June Tobin
834,Otto Preminger
16523,Burgess Meredith
19460,Saul Bass
6952,Charlie Sheen
114454,BJ Davis
24045,Joseph Gordon-Levitt
3899,Ken Watanabe
475512,Ellie Kemper
56323,Tina Fey
13636,Jane Krakowski
1689289,Justine Lupe
23659,Will Ferrell
152416,Murray Bartlett
83873,June Diane Raphael
87525,Alan Carr
7056,Emma Thompson
491896,Brie Garcia
969097,Nikki Garcia
3785095,Kathy Colace
1452124,Jason Derulo
1364893,Iggy Azalea
1744249,Kygo
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31515,Mark Cuban
1218635,Gary Vaynerchuk
1544086,Min Yoon-gi
1997030,Halsey
89056,David Guetta
1101364,Josh Wink
1101343,Carl Cox
3358480,Alan Walker
3358481,Margrethe Alida
3358482,Au/Ra
1101342,Sven Väth
1016349,Norman Cook
108661,Chester Bennington
214830,Mike Shinoda
1070445,Brad Delson
974169,Jenna Ortega
1098962,Dove Cameron
1331457,Sofia Carson
2282,Ben Kingsley
96074,Leslie Feist
4569,David Fox
218984,Johnny Ward
15737,Helen McCrory
1167897,Natasha O'Keeffe
15488,Ken Loach
73288,Michael McElhatton
1844919,Sam McGovern
658,Alfred Molina
4784,Laura Dern
13333,Vanessa Redgrave
227,William Hurt
40542,Héctor Babenco
5048,Harry Dean Stanton
923,Dean Stockwell
20718,Claude Berri
4399,Timothy Burrill
3564,Hervé de Luze
18517,Kim Gordon
101831,Thurston Moore
205,Kirsten Dunst
2299,Josh Hartnett
71538,Dave Filoni
3993,Dennis Muren
553222,Bill Moyers
24343,Peter Mayhew
1021364,Joseph Campbell
491,John Williams
488,Steven Spielberg
5026,Akira Kurosawa
132305,Nobuhiko Obayashi
18598,Ishirō Honda
11770,John Carpenter
515,Glenn Close
4,Carrie Fisher
3799,Billy Dee Williams
130,Kenny Baker
33185,Jeremy Bulloch
670,Ben Burtt
10930,Irvin Kershner
2140901,Gary Kurtz
2,Mark Hamill
15811,John Musker
61416,Peter Del Vecho
94037,Bruno Bozzetto
7314,Nick Park
35219,Corey Burton
109255,Leonard Maltin
2106,Walt Disney
146136,Bernice Hansen
628,Isao Takahata
608,Hayao Miyazaki
1417,Toshio Suzuki
1186197,Steve Alpert
110675,Jerry Beck
574290,Dave Bossert
1130441,Robert Sklar
108650,Alain Silver
380,Robert De Niro
16514,Robert Chartoff
1130439,Drew Casper
2585,Eric Clapton
1044,Michael Chapman
3661,Thelma Schoonmaker
3636,Paul Newman
109410,Joanne Woodward
4135,Robert Redford
1052109,Harry Styles
1052107,Niall Horan
1146050,Zayn Malik
11478,Lorraine Bracco
83198,Allan Arkush
101377,Paul Bartel
141,David Carradine
8487,Gregory Peck
7046,Cecilia Peck
1102449,Veronique Peck
190,Clint Eastwood
14701,Carroll Baker
3264451,Ann Adachi-Tasch
1034310,Margaret Bodde
126750,Serge Bromberg
1036,Cybill Shepherd
11477,Ray Liotta
4517,Joe Pesci
1314108,FatLip
9656,Johnny Knoxville
61441,Rick Kosick
110068,Ryan Dunn
56584,Bam Margera
2092052,Ryder McLaughlin
2003784,Na-kel Smith
1873369,Aramis Hudson
84534,Dido
9315,Marilyn Manson
70851,Jack Black
134,Jamie Foxx
544016,D12
62644,50 Cent
2041,Dr. Dre
1740159,Marky Bass
1740160,Jeff Bass
1945289,Denaun Porter
2191424,Courtney Butcher
2191425,Pj Jacokes
1135272,Skylar Grey
337,Proof
2689367,Swifty McVay
1549410,Dead Prez
214586,Melle Mel
339,Brian Grazer
1776464,Stretch Armstrong
4238,Common
37934,André 3000
15310,Anthony Kiedis
12207,Kylie Minogue
154782,Pat Benatar
154783,Neil Giraldo
174875,Carly Simon
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1114226,Monte Pittman
116152,Donna DeLory
116153,Niki Haris
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2392722,Steve Sidelnyk
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1059545,Sidney Duteil
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1193602,Gia Coppola
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116154,Luis Camacho
1795433,Oliver Crumes
72208,Alicia Keys
3284,Bruce Springsteen
18746,Stevie Wonder
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82280,Louis Armstrong
40092,Twiggy
21397,Dennis Rodman
3981237,Shirley Rodman
104156,Ann-Marie MacDonald
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1226426,Nicky Hilton
18329,Maria Schneider
4426,Maria Michi
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65605,Muhammad Ali
47775,Marc Anthony
4483,Dustin Hoffman
13570,Samuel Goldwyn
3417,Tony Goldwyn
68402,Barry Navidi
5049,John Hurt
68381,Thom Eberhardt
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4053939,Joe Njenga
4053940,Karste Wright
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5682,Anouk Aimée
44556,Richard Anconina
20234,Fanny Ardant
1487,Bob Dylan
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1056052,Lee Child
500,Tom Cruise
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10190,Jill St. John
5250,Jack Klugman
8483,Horton Foote
8482,Robert Mulligan
6349,Alan J. Pakula
8488,Mary Badham
8489,Phillip Alford
235416,Sarah Snook
1281967,Josh McConville
1604285,Liv Hewson
1105441,Bobbi Salvör Menuez
66744,Chaney Kley
53286,Richard Karn
8184,Mary Kate Schellhardt
1028353,John Anthony West
1028355,Robert M. Schoch
707,Dan Aykroyd
26485,Jim Belushi
54812,Chevy Chase
516,Annette Bening
2116053,Maud Guillaumin
1119476,Jonathan Kuntz
3947315,Mark Eliot
4786,Richard Attenborough
51816,Paul M. Heller
46602,Fred Weintraub
14101,John Larroquette
18794,Kate Nelligan
85139,Nicholas Braun
19429,Bruce Lee
18897,Jackie Chan
51576,Chuck Norris
2103203,James B. Nicholson
131168,Lee Hoi-Chuen
17844,Elke Sommer
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10254,Mario Adorf
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46780,Samantha Eggar
66884,Emlyn Williams
2883,Leif Garrett
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67676,Kenneth More
116838,Betsy Drake
100787,Patrick Barr
1921578,Gaston Rébuffat
239398,Maurice Baquet
31070,Ryan O'Neal
31071,Marisa Berenson
2264,Patrick Magee
17604,Jeremy Renner
418,Robert Patrick
138986,Tony Bennett
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1109331,Duffy
86449,Christophe Willem
64825,Harry Hamlin
12646,Terry O'Quinn
91332,Dedee Pfeiffer
78805,Laura Leighton
7180,John Candy
14882,Jackie Gleason
7503,Red Buttons
14639,Mel Brooks
14670,Joan Rivers
1895,Carl Reiner
7172,James Brown
1172565,Lesley Gore
1572034,Robert Scott Wilson
2555,David Proval
7004,Paul Sorvino
138,Quentin Tarantino
2545,Lawrence Bender
2144,Tobin Bell
2505,James Cromwell
1215054,Jerry Rice
9248,Nora Ephron
146307,Joe Guzaldo
7166,Kevin Pollak
39770,Adam Roarke
34721,Larry Bishop
16376,Larry David
16377,Jerry Seinfeld
1206,Jason Alexander
33,Gary Sinise
32749,Alex Diakun
99,Amanda Plummer
6804,Graham Greene
69302,Litefoot
65529,Irene Bedard
101028,Michelle Borth
70303,Zach Gilford
11064,David Strathairn
1238314,Kurt Busch
204415,Jimmie Johnson
1 id name
2 2975 Laurence Fishburne
3 6384 Keanu Reeves
4 530 Carrie-Anne Moss
5 1357063 Darrin Prescott
6 40644 Chad Stahelski
7 1779512 Jackson Spidell
8 21127 Bobby Cannavale
9 1692944 Heidi Schreck
10 64 Gary Oldman
11 1776 Francis Ford Coppola
12 38803 Roman Coppola
13 20215 Billy Campbell
14 2130 Cary Elwes
15 8349 Martin Sheen
16 8351 Frederic Forrest
17 8354 Albert Hall
18 1107983 Martin Luther King Jr.
19 52057 Obba Babatundé
20 110380 Colin Powell
21 18471 Anthony Anderson
22 74611 Tracee Ellis Ross
23 1407498 Marsai Martin
24 529 Guy Pearce
25 532 Joe Pantoliano
26 3894 Christian Bale
27 1892 Matt Damon
28 19498 Jon Bernthal
29 2710 James Cameron
30 4587 Halle Berry
31 8691 Zoe Saldaña
32 72129 Jennifer Lawrence
33 189111 Suzanne Collins
34 23964 Gary Ross
35 9827 Rose Byrne
36 143103 Krew Boylan
37 21708 Tomas Milian
38 72983 Manny Pérez
39 124909 Danny Hoch
40 4038 Susan Sarandon
41 18992 Aidan Quinn
42 2047 Danny Glover
43 4730 Emmy Rossum
44 9464 Harry Lennix
45 14409 David Schwimmer
46 1415341 Kazuhiro Tsuji
47 1267653 Chet Zar
48 7467 David Fincher
49 1519399 Erik Messerschmidt
50 1003 Jean Reno
51 524 Natalie Portman
52 1810 Heath Ledger
53 3895 Michael Caine
54 958722 Eiko Ishioka
55 41381 Sadie Frost
56 10980 Daniel Radcliffe
57 10989 Rupert Grint
58 10990 Emma Watson
59 219479 Criss Angel
60 1101349 Steve Aoki
61 1407495 Miles Brown
62 3361135 Peter Kent
63 1812 Michelle Williams
64 2037 Cillian Murphy
65 5081 Emily Blunt
66 2559324 Beth Lane
67 2559325 Lea Madda
68 27888 Raúl Juliá
69 2630 Nastassja Kinski
70 151007 Peter Ramsey
71 1769 Sofia Coppola
72 19772 Paul Rassam
73 1 George Lucas
74 7879 John Lasseter
75 1032 Martin Scorsese
76 1006721 Charles Scorsese
77 11483 Catherine Scorsese
78 5953 Spike Jonze
79 325 Eminem
80 3125 Madonna
81 3084 Marlon Brando
82 1158 Al Pacino
83 3085 James Caan
84 3087 Robert Duvall
85 3092 Diane Keaton
86 117669 Portia Doubleday
87 558466 Alex Russell
88 61555 Haley Ramm
89 10017 Charlton Heston
90 5563 James Coburn
91 26557 Ferdy Mayne
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93 8437 Teri Garr
94 589 Daryl Hannah
95 16431 Sam Elliott
96 2203 Neal McDonough
97 3026 Rob Reiner
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99 12132 Michael Rooker
100 932719 Jeff Gordon
101 2127 James Wan
102 2128 Leigh Whannell
103 2675 Darren Lynn Bousman
104 1278399 Joseph Steven
105 1659326 Teresa Modnick
106 26466 Caroline Kava
107 18269 Brendan Fraser
108 5606 Sissy Spacek
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110 1179102 Kathleen Kennedy
111 11367 Bradley Whitford
112 31028 Richard Schiff
113 1397846 Craig Barron
114 2166741 Robert A. Baum Jr.
115 3266 Joe Mantegna
116 36059 Joanna Pacula
117 21010 Alexandra Hay
118 101032 Rod McCary
119 98569 Vincent Van Patten
120 2080584 Ben Jorgensen
121 103835 Jeff Monahan
122 6717 James Marshall
123 928944 Coleman Barks
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125 9979 Peter Coyote
126 514 Jack Nicholson
127 6193 Leonardo DiCaprio
128 2641 Martin Landau
129 92029 Gabriel Bologna
130 218506 Gretchen Becker
131 2098136 Brian Becker
132 2098137 Richard Becker
133 2098138 Blase Bonpane
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135 496175 Carmelo Anthony
136 1280183 Coretta Scott King
137 1006651 John Lewis
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139 180659 Rosa Parks
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141 2787630 Valentin Zorin
142 89289 Jesse Jackson
143 2102583 Andrew Cohen
144 73988 Lyndon B. Johnson
145 21111 John F. Kennedy
146 2803017 Arthur John Birch
147 2803001 Haroldson Lafayette Hunt
148 4193575 Maxime Demigné
149 4193577 Gabin Clerc
150 4193578 Tom Chabeau
151 2954 Jeffrey Wright
152 33663 Donald Trump
153 2301996 Mike Pence
154 214317 Ben Bradlee
155 13716 Carl Bernstein
156 117180 Tom Brokaw
157 23626 Liev Schreiber
158 937663 Neil Amdur
159 1931175 Bob Beamon
160 12951 O.J. Simpson
161 2998183 Nicole Brown Simpson
162 1231277 Marcia Clark
163 60158 Shirley Jones
164 24810 Pat Boone
165 2535 Vivica A. Fox
166 225620 Rudolph Moise
167 1683093 Terayle Hill
168 79538 Khalil Kain
169 1562353 Erica Mena
170 1038 Jodie Foster
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172 349 Scott Glenn
173 9780 Angela Bassett
174 19011 George W. Bush
175 146687 Tony Blair
176 74266 Dick Cheney
177 1507609 Bob Coen
178 1507612 Francis E. Boyle
179 1507613 Jean Patterson
180 1122373 Marion Barry Jr.
181 1122374 Chuck Brown
182 1122375 Lewis Franklin
183 1897 Bernie Mac
184 77896 Bill Bellamy
185 2395 Whoopi Goldberg
186 1735828 Halle Bailey
187 53397 Christina Aguilera
188 1224841 Mo Willems
189 539 Thomas Lennon
190 111513 Yvette Nicole Brown
191 2682390 Gerd Ludwig
192 2227 Nicole Kidman
193 4430 Sharon Stone
194 14837 Carol Burnett
195 5823 Julie Andrews
196 3141 Marisa Tomei
197 449538 Adele
198 13309 Oprah Winfrey
199 1661465 Lizzo
200 102786 Barack Obama
201 128550 Desmond Tutu
202 2320880 Sundar Pichai
203 46300 Alexander Nathan Etel
204 27889 Stephan Elliott
205 1331 Hugo Weaving
206 326 Kim Basinger
207 323 Curtis Hanson
208 11099 Dante Spinotti
209 7187 Richard Donner
210 57371 Jeff Cohen
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213 11669 Jimmy Fallon
214 43120 Andy Dick
215 53926 Jon Heder
216 34947 Kevin Conroy
217 1262612 Neal Adams
218 21200 Will Arnett
219 525 Christopher Nolan
220 15556 Rebecca Hall
221 131286 Mark David
222 60634 Marty Belafsky
223 7133 Max Casella
224 15762 Tara Strong
225 1160 Michelle Pfeiffer
226 1813 Anne Hathaway
227 2157 Robin Williams
228 880 Ben Affleck
229 1231844 Manny Ramírez
230 1214256 Curt Schilling
231 31 Tom Hanks
232 3197 Tom Sizemore
233 12833 Edward Burns
234 10297 Matthew McConaughey
235 83002 Jessica Chastain
236 21037 Prince
237 1800494 Cora Coleman-Dunham
238 1775343 Josh Dunham
239 127712 Matt Ryan
240 3035 Jerry O'Connell
241 527313 Taissa Farmiga
242 21007 Jonah Hill
243 234352 Margot Robbie
244 869 Gale Anne Hurd
245 96884 Al Giddings
246 1100 Arnold Schwarzenegger
247 2713 Linda Hamilton
248 2231 Samuel L. Jackson
249 6159 Ron Howard
250 33181 Jimmy Smits
251 63611 Nickolas Grace
252 53574 Eric Thal
253 16217 Lynn Whitfield
254 8891 John Travolta
255 44038 Nancy Allen
256 1121437 Leslie Felperin
257 70243 Eartha Kitt
258 15973 Julie Newmar
259 16108 Lee Meriwether
260 73475 Adriano Giannini
261 2078350 Tommaso Basili
262 73457 Chris Pratt
263 543530 Dave Bautista
264 111946 Britney Spears
265 57691 Tamra Davis
266 343 Taryn Manning
267 3223 Robert Downey Jr.
268 74568 Chris Hemsworth
269 103 Mark Ruffalo
270 16828 Chris Evans
271 1851091 Josh Silver
272 3810 Javier Bardem
273 6431 Darren Aronofsky
274 1625558 Awkwafina
275 112 Cate Blanchett
276 1283 Helena Bonham Carter
277 17606 Imogen Poots
278 17605 Idris Elba
279 75131 Nash Edgerton
280 27442 Renato Salvatori
281 24299 Jean-Claude Brialy
282 5476 Nino Castelnuovo
283 30721 Madeleine Robinson
284 9287 Robert Blake
285 70119 Catherine Spaak
286 7502 Ernest Borgnine
287 86381 Algenis Perez Soto
288 1012743 Dalisa Alegría
289 8767 Jim Caviezel
290 23931 Mira Sorvino
291 121718 Bill Camp
292 77003 Gavin O'Connor
293 199449 Greg O'Connor
294 819 Edward Norton
295 2222 Beau Bridges
296 152699 James Noble
297 4037 Ron Shelton
298 1269 Kevin Costner
299 13472 Tim Curry
300 13473 Barry Bostwick
301 2607714 Alan Cooke
302 3896 Liam Neeson
303 2517086 Will Hochman
304 151432 Boris McGiver
305 3763830 Jim Forbes
306 133499 Christopher Biggins
307 1724558 Justin Arnold
308 7904 Billy Crystal
309 4244 Lauryn Hill
310 13476 Nell Campbell
311 44301 Cliff DeYoung
312 11905 Sven Nykvist
313 38127 Erland Josephson
314 231318 Melinda Kinnaman
315 1027445 Hedviges Mamudo
316 1027448 Melanie de Vales Rafael
317 155474 Keb' Mo'
318 232449 Yolonda Williams
319 2049 Rubén Blades
320 11784 Tom Atkins
321 65398 Denise Nicholas
322 2390 LeVar Burton
323 58493 Tim Grimm
324 22860 Chuck Berry
325 44822 Starletta DuPois
326 1669775 Lou Ferguson
327 5247 John Fiedler
328 20904 James Hong
329 157963 Loren Avedon
330 74375 Cynthia Rothrock
331 155890 Julie Garfield
332 2210813 Ellen Adler
333 2879828 Joseph Bernard
334 9789 Robert Altman
335 6449 Warren Beatty
336 1272970 Christina Wren
337 1591305 MorningStar Angeline
338 1179187 T'Shaun Barrett
339 291133 Tai Bennett
340 61223 Sharon Brathwaite-Sanders
341 73968 Henry Cavill
342 90633 Gal Gadot
343 151343 David Baddiel
344 11275 Stephen Fry
345 6199 Miriam Margolyes
346 4491 Jennifer Aniston
347 14405 Courteney Cox
348 14406 Lisa Kudrow
349 3861945 Rob Helms
350 566154 Bruce Daniels
351 1141310 Joe Dietl
352 57446 Mark Romanek
353 215486 Fiona Apple
354 1161962 Brian Bell
355 39350 Anémone
356 1722308 Jan Vancoillie
357 24501 Pierre Richard
358 39646 Mireille Darc
359 2179581 Tarek Aylouch
360 1805442 Michael Markiewicz
361 3499637 Nick Garrison
362 164004 Ken Cheeseman
363 26472 Steve Guttenberg
364 1291312 Jürgen Thormann
365 2983750 Peter Garloff
366 39998 Tim McIntire
367 7506 Irwin Allen
368 290 Christopher Plummer
369 8606 Robert Shaw
370 1304968 June Tobin
371 834 Otto Preminger
372 16523 Burgess Meredith
373 19460 Saul Bass
374 6952 Charlie Sheen
375 114454 BJ Davis
376 24045 Joseph Gordon-Levitt
377 3899 Ken Watanabe
378 475512 Ellie Kemper
379 56323 Tina Fey
380 13636 Jane Krakowski
381 1689289 Justine Lupe
382 23659 Will Ferrell
383 152416 Murray Bartlett
384 83873 June Diane Raphael
385 87525 Alan Carr
386 7056 Emma Thompson
387 491896 Brie Garcia
388 969097 Nikki Garcia
389 3785095 Kathy Colace
390 1452124 Jason Derulo
391 1364893 Iggy Azalea
392 1744249 Kygo
393 1569717 Martin Garrix
394 31515 Mark Cuban
395 1218635 Gary Vaynerchuk
396 1544086 Min Yoon-gi
397 1997030 Halsey
398 89056 David Guetta
399 1101364 Josh Wink
400 1101343 Carl Cox
401 3358480 Alan Walker
402 3358481 Margrethe Alida
403 3358482 Au/Ra
404 1101342 Sven Väth
405 1016349 Norman Cook
406 108661 Chester Bennington
407 214830 Mike Shinoda
408 1070445 Brad Delson
409 974169 Jenna Ortega
410 1098962 Dove Cameron
411 1331457 Sofia Carson
412 2282 Ben Kingsley
413 96074 Leslie Feist
414 4569 David Fox
415 218984 Johnny Ward
416 15737 Helen McCrory
417 1167897 Natasha O'Keeffe
418 15488 Ken Loach
419 73288 Michael McElhatton
420 1844919 Sam McGovern
421 658 Alfred Molina
422 4784 Laura Dern
423 13333 Vanessa Redgrave
424 227 William Hurt
425 40542 Héctor Babenco
426 5048 Harry Dean Stanton
427 923 Dean Stockwell
428 20718 Claude Berri
429 4399 Timothy Burrill
430 3564 Hervé de Luze
431 18517 Kim Gordon
432 101831 Thurston Moore
433 205 Kirsten Dunst
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435 71538 Dave Filoni
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440 491 John Williams
441 488 Steven Spielberg
442 5026 Akira Kurosawa
443 132305 Nobuhiko Obayashi
444 18598 Ishirō Honda
445 11770 John Carpenter
446 515 Glenn Close
447 4 Carrie Fisher
448 3799 Billy Dee Williams
449 130 Kenny Baker
450 33185 Jeremy Bulloch
451 670 Ben Burtt
452 10930 Irvin Kershner
453 2140901 Gary Kurtz
454 2 Mark Hamill
455 15811 John Musker
456 61416 Peter Del Vecho
457 94037 Bruno Bozzetto
458 7314 Nick Park
459 35219 Corey Burton
460 109255 Leonard Maltin
461 2106 Walt Disney
462 146136 Bernice Hansen
463 628 Isao Takahata
464 608 Hayao Miyazaki
465 1417 Toshio Suzuki
466 1186197 Steve Alpert
467 110675 Jerry Beck
468 574290 Dave Bossert
469 1130441 Robert Sklar
470 108650 Alain Silver
471 380 Robert De Niro
472 16514 Robert Chartoff
473 1130439 Drew Casper
474 2585 Eric Clapton
475 1044 Michael Chapman
476 3661 Thelma Schoonmaker
477 3636 Paul Newman
478 109410 Joanne Woodward
479 4135 Robert Redford
480 1052109 Harry Styles
481 1052107 Niall Horan
482 1146050 Zayn Malik
483 11478 Lorraine Bracco
484 83198 Allan Arkush
485 101377 Paul Bartel
486 141 David Carradine
487 8487 Gregory Peck
488 7046 Cecilia Peck
489 1102449 Veronique Peck
490 190 Clint Eastwood
491 14701 Carroll Baker
492 3264451 Ann Adachi-Tasch
493 1034310 Margaret Bodde
494 126750 Serge Bromberg
495 1036 Cybill Shepherd
496 11477 Ray Liotta
497 4517 Joe Pesci
498 1314108 FatLip
499 9656 Johnny Knoxville
500 61441 Rick Kosick
501 110068 Ryan Dunn
502 56584 Bam Margera
503 2092052 Ryder McLaughlin
504 2003784 Na-kel Smith
505 1873369 Aramis Hudson
506 84534 Dido
507 9315 Marilyn Manson
508 70851 Jack Black
509 134 Jamie Foxx
510 544016 D12
511 62644 50 Cent
512 2041 Dr. Dre
513 1740159 Marky Bass
514 1740160 Jeff Bass
515 1945289 Denaun Porter
516 2191424 Courtney Butcher
517 2191425 Pj Jacokes
518 1135272 Skylar Grey
519 337 Proof
520 2689367 Swifty McVay
521 1549410 Dead Prez
522 214586 Melle Mel
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524 1776464 Stretch Armstrong
525 4238 Common
526 37934 André 3000
527 15310 Anthony Kiedis
528 12207 Kylie Minogue
529 154782 Pat Benatar
530 154783 Neil Giraldo
531 174875 Carly Simon
532 1114225 Kevin Antunes
533 1114226 Monte Pittman
534 116152 Donna DeLory
535 116153 Niki Haris
536 123448 Stuart Price
537 2392722 Steve Sidelnyk
538 2973752 Erika Belle
539 1093165 Christopher Ciccone
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541 1214377 Felicia Culotta
542 56288 Alysson Paradis
543 1059545 Sidney Duteil
544 32797 Arianne Phillips
545 1193602 Gia Coppola
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547 88339 Kevin Stea
548 116154 Luis Camacho
549 1795433 Oliver Crumes
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557 3981237 Shirley Rodman
558 104156 Ann-Marie MacDonald
559 38406 Paris Hilton
560 1226426 Nicky Hilton
561 18329 Maria Schneider
562 4426 Maria Michi
563 147 Michael Madsen
564 82702 Michael Jackson
565 65605 Muhammad Ali
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567 4483 Dustin Hoffman
568 13570 Samuel Goldwyn
569 3417 Tony Goldwyn
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571 5049 John Hurt
572 68381 Thom Eberhardt
573 4053937 Judah Carew
574 4053939 Joe Njenga
575 4053940 Karste Wright
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581 5682 Anouk Aimée
582 44556 Richard Anconina
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586 165867 Kid Rock
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590 10190 Jill St. John
591 5250 Jack Klugman
592 8483 Horton Foote
593 8482 Robert Mulligan
594 6349 Alan J. Pakula
595 8488 Mary Badham
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597 235416 Sarah Snook
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599 1604285 Liv Hewson
600 1105441 Bobbi Salvör Menuez
601 66744 Chaney Kley
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604 1028353 John Anthony West
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623 131168 Lee Hoi-Chuen
624 17844 Elke Sommer
625 6263 Hannelore Elsner
626 10254 Mario Adorf
627 15196 David Hemmings
628 46780 Samantha Eggar
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630 2883 Leif Garrett
631 3011 Linda Manz
632 5147 Ralph Seymour
633 67676 Kenneth More
634 116838 Betsy Drake
635 100787 Patrick Barr
636 1921578 Gaston Rébuffat
637 239398 Maurice Baquet
638 31070 Ryan O'Neal
639 31071 Marisa Berenson
640 2264 Patrick Magee
641 17604 Jeremy Renner
642 418 Robert Patrick
643 138986 Tony Bennett
644 1532769 Nolwenn Leroy
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646 86449 Christophe Willem
647 64825 Harry Hamlin
648 12646 Terry O'Quinn
649 91332 Dedee Pfeiffer
650 78805 Laura Leighton
651 7180 John Candy
652 14882 Jackie Gleason
653 7503 Red Buttons
654 14639 Mel Brooks
655 14670 Joan Rivers
656 1895 Carl Reiner
657 7172 James Brown
658 1172565 Lesley Gore
659 1572034 Robert Scott Wilson
660 2555 David Proval
661 7004 Paul Sorvino
662 138 Quentin Tarantino
663 2545 Lawrence Bender
664 2144 Tobin Bell
665 2505 James Cromwell
666 1215054 Jerry Rice
667 9248 Nora Ephron
668 146307 Joe Guzaldo
669 7166 Kevin Pollak
670 39770 Adam Roarke
671 34721 Larry Bishop
672 16376 Larry David
673 16377 Jerry Seinfeld
674 1206 Jason Alexander
675 33 Gary Sinise
676 32749 Alex Diakun
677 99 Amanda Plummer
678 6804 Graham Greene
679 69302 Litefoot
680 65529 Irene Bedard
681 101028 Michelle Borth
682 70303 Zach Gilford
683 11064 David Strathairn
684 1238314 Kurt Busch
685 204415 Jimmie Johnson

0
tunmnlu/task_1/Q1/prompts.md Normal file
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19
tunmnlu/task_1/Q1/run.sh Normal file
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@@ -0,0 +1,19 @@
#!/usr/bin/env bash
set -e
python ./Q1_ans.py &
python ./Q1_ref.py &
wait
echo " 888 "
echo " 888 "
echo " 888 "
echo " .d88b. 888 888 "
echo "d88''88b888 .88P "
echo "888 888888888K "
echo "Y88..88P888 "88b "

8
tunmnlu/task_1/Q1/test.sh Normal file
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@@ -0,0 +1,8 @@
#!/usr/bin/env bash
set -ex
docker compose up -d
docker compose exec -it test bash