Scenarios

A Scenario is a dictionary containing one or more key/value pairs that is used to add data or content to questions in a survey, replacing a parameter in a question with a specific value (e.g., numerical or textual) or content (e.g., an image or PDF). A ScenarioList is a list of Scenario objects.

Purpose

Scenarios allow you create variations and versions of questions efficiently. For example, we could create a question “How much do you enjoy {{ scenario.activity }}?” and use scenarios to replace the parameter activity with running or reading or other activities. Similarly, we could create a question “What do you see in this image? {{ scenario.image }}” and use scenarios to replace the parameter image with different images.

How it works

Adding scenarios to a question–or to multiple questions at once in a survey–causes it to be administered multiple times, once for each scenario, with the parameter(s) replaced by the value(s) in the scenario. This allows us to administer multiple versions of a question together, either asynchronously (by default) or according to survey rules that we can specify (e.g., skip/stop logic), without having to create each version of a question manually.

Metadata

Scenarios are also a convenient way to keep track of metadata or other information relating to a survey that is important to an analysis of the results. For example, say we are using scenarios to parameterize question texts with pieces of {{ scenario.content }} from a dataset. In the scenarios that we create for the content parameter we could also include key/value pairs for metadata about the content, such as the {{ scenario.author }}, {{ scenario.publication_date }}, or {{ scenario.source }}. This will automatically include the data in the survey results but without requiring us to also parameterize the question texts those fields. This allows us to analyze the responses in the context of the metadata and avoid having to match up the data with the metadata post-survey. Please see more details on this feature in examples below.

Constructing a Scenario

To use a scenario, we start by creating a question that takes a parameter in double braces:

from edsl import QuestionMultipleChoice

q = QuestionMultipleChoice(
  question_name = "enjoy",
  question_text = "How much do you enjoy {{ scenario.activity }}?",
  question_options = ["Not at all", "Somewhat", "Very much"]
)

Next we create a dictionary for a value that will replace the parameter and store it in a Scenario object:

from edsl import Scenario

scenario = Scenario({"activity": "running"})

We can inspect the scenario and see that it consists of the key/value pair that we created:

scenario

This will return:

key	value
activity	running

ScenarioList

If multiple values will be used with a question or survey, we can create a list of Scenario objects that will be passed to the question or survey together. For example, here we create a list of scenarios and inspect them:

from edsl import Scenario

scenarios = [Scenario({"activity": a}) for a in ["running", "reading"]]
scenarios

Output:

[Scenario({'activity': 'running'}), Scenario({'activity': 'reading'})]

Alternatively, we can create a ScenarioList object. A list of scenarios is used in the same way as a ScenarioList; the difference is that a ScenarioList is a class that can be used to create a list of scenarios from a variety of data sources, such as a list, dictionary, a Wikipedia table or a PDF. These special methods are discussed below.

For example, here we create a ScenarioList for the same list as above:

from edsl import Scenario, ScenarioList

scenariolist = ScenarioList(Scenario({"activity": a}) for a in ["running", "reading"])
scenariolist

Output:

activity
running
reading

Special methods for creating scenarios

Special methods are available for creating a Scenario or ScenarioList from various data source types:

The constructor method from_pdf() can be used to create a single scenario for a PDF or a scenario list where each page of a PDF is stored as an individual scenario.
The constructor method from_directory() can be used to create a scenario list from all files in a directory, where each file is wrapped in a Scenario object with a specified key (default is “content”).
The constructor methods from_list(), from_csv, from_nested_dict() and from_wikipedia_table() will create a scenario list from a list, CSV, nested dictionary or Wikipedia table.

For example, the following code will create the same scenario list as above:

from edsl import ScenarioList

scenariolist = ScenarioList.from_list("activity", ["running", "reading"])

Example of creating a scenario list from files in a directory:

from edsl import ScenarioList, QuestionFreeText

# Create a ScenarioList from all image files in a directory
# Each file will be wrapped in a Scenario with key "content"
scenarios = ScenarioList.from_directory("images_folder/*.png")

# Or specify a custom key name
scenarios = ScenarioList.from_directory("images_folder", key_name="image")

# Create a question that uses the scenario key
q = QuestionFreeText(
    question_name="image_description",
    question_text="Please describe this image: {{ scenario.image }}"
)

# Run the question with the scenarios
results = q.by(scenarios).run()

Examples for each of these methods is provided below, and in this notebook.

Using a scenario

We use a Scenario or ScenarioList by adding it to a question or survey of questions, either when we are constructing questions or when running them. The most common situation is to add a scenario to a question when running it. This is done by passing the Scenario or ScenarioList object to the by() method or a question or survey and then chaining the run() method.

For example, here we call the by() method on the example question created above and pass a scenario list at the same time that we run it:

from edsl import QuestionMultipleChoice, Scenario, ScenarioList, Agent, Model

q = QuestionMultipleChoice(
  question_name = "enjoy",
  question_text = "How much do you enjoy {{ scenario.activity }}?",
  question_options = ["Not at all", "Somewhat", "Very much"]
)

s = ScenarioList(Scenario({"activity":a}) for a in ["running", "sleeping"])

a = Agent(traits = {"persona":"You are a human."})

m = Model("gemini-1.5-flash")

results = q.by(s).by(a).by(m).run()

We can check the results to verify that the scenario has been used correctly:

results.select("activity", "enjoy")

This will print a table of the selected components of the results:

scenario.activity	answer.enjoy
running	Somewhat
sleeping	Very much

Looping

We use the loop() method to add scenarios to a question when constructing the question. This method takes a ScenarioList and returns a list of new questions for each scenario that was passed. We can optionally include the scenario key in the question name as well as the question text. This allows us to control the question names when the new questions are created; otherwise a number is automatically added to the original question name in order to ensure uniqueness. Note that we do not include the scenario. prefix when looping.

For example:

from edsl import QuestionMultipleChoice, ScenarioList

q = QuestionMultipleChoice(
  question_name = "enjoy_{{ scenario.activity }}",
  question_text = "How much do you enjoy {{ scenario.activity }}?",
  question_options = ["Not at all", "Somewhat", "Very much"]
)

activities = ["running", "reading"]

sl = ScenarioList.from_list("activity", activities)

questions = q.loop(sl)

We can inspect the questions to see that they have been created correctly:

questions

This will return:

[Question('multiple_choice', question_name = """enjoy_running""", question_text = """How much do you enjoy running?""", question_options = ['Not at all', 'Somewhat', 'Very much']),
Question('multiple_choice', question_name = """enjoy_reading""", question_text = """How much do you enjoy reading?""", question_options = ['Not at all', 'Somewhat', 'Very much'])]

We can pass the questions to a survey and run it:

from edsl import Survey, Agent

survey = Survey(questions = questions)

a = Agent(traits = {"persona": "You are a human."})

results = survey.by(a).run()

results.select("answer.*")

This will print a table of the response for each question. Note that “activity” is no longer in a separate scenario field; instead, there is a single column for each question that was constructed with the scenarios:

answer.enjoy_reading	answer.enjoy_running
Very much	Somewhat

Note: The loop() method cannot be used with image or PDF scenarios, as these are not evaluated when the question is constructed. Instead, use the by() method to add these types of scenarios when running a survey (see image scenario examples below).

Multiple parameters

We can also create a Scenario for multiple parameters at once:

from edsl import QuestionFreeText, Scenario

q = QuestionFreeText(
  question_name = "counting",
  question_text = "How many {{ scenario.unit }} are in a {{ scenario.distance }}?",
)

scenario = Scenario({"unit": "inches", "distance": "mile"})

results = q.by(scenario).run()

results.select("unit", "distance", "counting")

This will print a table of the selected components of the results:

scenario.unit	scenario.distance	answer.counting
inches	mile	There are 63,360 inches in a mile.

To learn more about constructing surveys, please see the Surveys module.

Scenarios for question options

In the above examples we created scenarios in the question_text. We can also create a Scenario for question_options, e.g., in a multiple choice, checkbox, linear scale or other question type that requires them. Note that we do not include the scenario. prefix when using sceanrios for question options.

from edsl import QuestionMultipleChoice, Scenario

q = QuestionMultipleChoice(
  question_name = "capital_of_france",
  question_text = "What is the capital of France?",
  question_options = "{{ scenario.question_options }}"
)

s = Scenario({'question_options': ['Paris', 'London', 'Berlin', 'Madrid']})

results = q.by(s).run()

results.select("answer.*")

Output:

answer.capital_of_france
Paris

Combining Scenarios

We can combine multiple scenarios into a single Scenario object:

from edsl import Scenario

scenario1 = Scenario({"food": "apple"})
scenario2 = Scenario({"drink": "water"})

combined_scenario = scenario1 + scenario2

combined_scenario

This will return:

key	value
food	drink
apple	water

We can also combine ScenarioList objects:

from edsl import Scenario, ScenarioList

scenariolist1 = ScenarioList([Scenario({"food": "apple"}), Scenario({"drink": "water"})])
scenariolist2 = ScenarioList([Scenario({"color": "red"}), Scenario({"shape": "circle"})])

combined_scenariolist = scenariolist1 + scenariolist2

combined_scenariolist

This will return:

food	drink	color	shape
apple	nan	nan	nan
nan	water	nan	nan
nan	nan	nan	red
nan	nan	circle	nan

We can create a cross product of ScenarioList objects (combine the scenarios in each list with each other):

from edsl import Scenario, ScenarioList

scenariolist1 = ScenarioList([Scenario({"food": "apple"}), Scenario({"drink": "water"})])
scenariolist2 = ScenarioList([Scenario({"color": "red"}), Scenario({"shape": "circle"})])

cross_product_scenariolist = scenariolist1 * scenariolist2

cross_product_scenariolist

This will return:

food	drink	color	shape
apple	nan	nan	red
apple	nan	circle	nan
nan	water	nan	red
nan	water	circle	nan

Concatenating scenarios

There are several ScenarioList methods for concatenating scenarios.

The method concatenate() can be used to concatenate specified fields into a single string field; the default separator is a semicolon:

from edsl import Scenario, ScenarioList

sl = ScenarioList([
  Scenario({"a":1, "b":2, "c":3}),
  Scenario({"a":4, "b":5, "c":6})
])

slc = sl.concatenate(["a", "b"])

slc

This will return:

c	concat_a_b
3	1;2
6	4;5

We can specify a different separator:

slc = sl.concatenate(["a", "b"], separator = " ")

slc

This will return:

c	concat_a_b
3	1,2
6	4,5

The method concatenate_to_list() can be used to concatenate specified fields into a single list field:

from edsl import Scenario, ScenarioList

sl = ScenarioList([
  Scenario({"a":1, "b":2, "c":3}),
  Scenario({"a":4, "b":5, "c":6})
])

slc = sl.concatenate_to_list(["a", "b"])

slc

This will return:

c	concat_a_b
3	[1,2]
6	[4,5]

The method concatenate_to_set() can be used to concatenate specified fields into a single set field:

from edsl import Scenario, ScenarioList

sl = ScenarioList([
  Scenario({"a":1, "b":2, "c":3}),
  Scenario({"a":4, "b":5, "c":6})
])

slc = sl.concatenate_to_list(["a", "b"])

slc

This will return:

c	concat_a_b
3	{1,2}
6	{4,5}

The method collapse() can be used to collapse a scenario list by grouping on all fields except a specified field:

from edsl import ScenarioList

s = ScenarioList([
  Scenario({'category': 'fruit', 'color': 'red', 'item': 'apple'}),
  Scenario({'category': 'fruit', 'color': 'yellow', 'item': 'banana'}),
  Scenario({'category': 'fruit', 'color': 'red', 'item': 'cherry'}),
  Scenario({'category': 'vegetable', 'color': 'green', 'item': 'spinach'})
])

s.collapse('item')

This will return:

category	color	item
fruit	red	[‘apple’, ‘cherry’]
fruit	yellow	[‘banana’]
vegetable	green	[‘spinach’]

The method from_sqlite() can be used to create a scenario list from a SQLite database. It takes a filepath to the database file and optional parameters table and sql_query.

Creating scenarios from a dataset

There are a variety of methods for creating and working with scenarios generated from datasets and different data types.

Turning results into scenarios

The method to_scenario_list() can be used to turn the results of a survey into a list of scenarios.

Example usage:

Say we have some results from a survey where we asked agents to choose a random number between 1 and 1000:

from edsl import QuestionNumerical, Agent, AgentList

q_random = QuestionNumerical(
  question_name = "random",
  question_text = "Choose a random number between 1 and 1000."
)

agents = AgentList(Agent({"persona":p}) for p in ["Child", "Magician", "Olympic breakdancer"])

results = q_random.by(agents).run()

results.select("persona", "random")

Our results are:

agent.persona	answer.random
Child	7
Magician	472
Olympic breakdancer	529

We can use the to_scenario_list() method turn components of the results into a list of scenarios to use in a new survey:

scenarios = results.select("persona", "random").to_scenario_list() # excluding other columns of the results
scenarios

We can inspect the scenarios to see that they have been created correctly:

persona	random
Child	7
Magician	472
Olympic breakdancer	529

PDFs as textual scenarios

The ScenarioList method from_pdf(‘path/to/pdf’) is a convenient way to extract information from large files. It allows you to read in a PDF and automatically create a list of textual scenarios for the pages of the file. Each scenario has the following keys which can be used as parameters in a question or stored as metadata, and renamed as desired: filename, page, text.

If you prefer to create a single Scenario for the entire PDF file, you can use the Scenario.from_pdf(‘path/to/pdf’) method instead.

To use this method with either object, we start by adding a placeholder {{ scenario.text }} to a question text where the text of a PDF or PDF page will be inserted. When the question or survey is run with the PDF scenario or scenario list, the text of the PDF or individual pages will be inserted into the question text at the placeholder.

For example, this code can be used to insert the text of each page of a PDF in a survey of question:

from edsl import QuestionFreeText, ScenarioList, Survey

# Create a survey of questions parameterized by the {{ text }} of the PDF pages:
q1 = QuestionFreeText(
  question_name = "themes",
  question_text = "Identify the key themes mentioned on this page: {{ scenario.text }}",
)

q2 = QuestionFreeText(
  question_name = "idea",
  question_text = "Identify the most important idea on this page: {{ scenario.text }}",
)

survey = Survey([q1, q2])

scenarios = ScenarioList.from_pdf("path/to/pdf_file.pdf") # modify the filepath

# Run the survey with the pages of the PDF as scenarios:
results = survey.by(scenarios).run()

# To print the page and text of each PDF page scenario together with the answers to the question:
results.select("page", "text", "answer.*")

Examples of this method can be viewed in a demo notebook.

Image scenarios

A Scenario can be generated from an image by passing the filepath as the value. This is done by using the FileStore module to store the image and then passing the FileStore object to a Scenario.

Example usage:

from edsl import Scenario, FileStore

fs = FileStore("parrot_logo.png") # modify filepath

s = Scenario({"image":fs})

We can add the key to questions as we do scenarios from other data sources:

from edsl import Model, QuestionFreeText, QuestionList, Survey

m = Model("gemini-1.5-flash") # we need to use a vision model

q1 = QuestionFreeText(
  question_name = "identify",
  question_text = "What animal is in this picture: {{ scenario.image }}"
)

q2 = QuestionList(
  question_name = "colors",
  question_text = "What colors do you see in this picture: {{ scenario.image }}"
)

survey = Survey([q1, q2])

results = survey.by(s).run()

results.select("identify", "colors")

Output using the Expected Parrot logo:

answer.identify	answer.colors
The animal in the picture is a parrot.	[‘gray’, ‘green’, ‘yellow’, ‘pink’, ‘blue’, ‘black’]

See a demo notebook using of this method in the documentation page.

Note: You must use a vision model in order to run questions with images. We recommend testing whether a model can reliably identify your images before running a survey with them. You can also check the model pricing page to see available models’ performance with test questions, including images.

Creating a scenario list from a list

The ScenarioList method from_list() creates a list of scenarios for a specified key and list of values that is passed to it.

Example usage:

from edsl import ScenarioList

scenariolist = ScenarioList.from_list("item", ["color", "food", "animal"])

scenariolist

This will return:

item
color
food
animal

Creating a scenario list from a dictionary

The ScenarioList method from_nested_dict() creates a list of scenarios for a specified key and nested dictionary.

Example usage:

from edsl import ScenarioList

d = {"item": ["color", "food", "animal"]}

scenariolist = ScenarioList.from_nested_dict(d)
scenariolist

This will return:

item
color
food
animal

Creating a scenario list from a Wikipedia table

The ScenarioList method from_wikipedia_table(‘url’) can be used to create a list of scenarios from a Wikipedia table.

Example usage:

from edsl import ScenarioList

scenarios = ScenarioList.from_wikipedia("https://en.wikipedia.org/wiki/1990s_in_film", 3)
scenarios

This will return a list of scenarios for the first table on the Wikipedia page:

Rank	Title	Studios	Worldwide gross	Year
1	Titanic	Paramount Pictures/20th Century Fox	$1,843,201,268	1997
2	Star Wars: Episode I - The Phantom Menace	20th Century Fox	$924,317,558	1999
3	Jurassic Park	Universal Pictures	$914,691,118	1993
4	Independence Day	20th Century Fox	$817,400,891	1996
5	The Lion King	Walt Disney Studios	$763,455,561	1994
6	Forrest Gump	Paramount Pictures	$677,387,716	1994
7	The Sixth Sense	Walt Disney Studios	$672,806,292	1999
8	The Lost World: Jurassic Park	Universal Pictures	$618,638,999	1997
9	Men in Black	Sony Pictures/Columbia Pictures	$589,390,539	1997
10	Armageddon	Walt Disney Studios	$553,709,788	1998
11	Terminator 2: Judgment Day	TriStar Pictures	$519,843,345	1991
12	Ghost	Paramount Pictures	$505,702,588	1990
13	Aladdin	Walt Disney Studios	$504,050,219	1992
14	Twister	Warner Bros./Universal Pictures	$494,471,524	1996
15	Toy Story 2	Walt Disney Studios	$485,015,179	1999
16	Saving Private Ryan	DreamWorks Pictures/Paramount Pictures	$481,840,909	1998
17	Home Alone	20th Century Fox	$476,684,675	1990
18	The Matrix	Warner Bros.	$463,517,383	1999
19	Pretty Woman	Walt Disney Studios	$463,406,268	1990
20	Mission: Impossible	Paramount Pictures	$457,696,359	1996
21	Tarzan	Walt Disney Studios	$448,191,819	1999
22	Mrs. Doubtfire	20th Century Fox	$441,286,195	1993
23	Dances with Wolves	Orion Pictures	$424,208,848	1990
24	The Mummy	Universal Pictures	$415,933,406	1999
25	The Bodyguard	Warner Bros.	$411,006,740	1992
26	Robin Hood: Prince of Thieves	Warner Bros.	$390,493,908	1991
27	Godzilla	TriStar Pictures	$379,014,294	1998
28	True Lies	20th Century Fox	$378,882,411	1994
29	Toy Story	Walt Disney Studios	$373,554,033	1995
30	There’s Something About Mary	20th Century Fox	$369,884,651	1998
31	The Fugitive	Warner Bros.	$368,875,760	1993
32	Die Hard with a Vengeance	20th Century Fox/Cinergi Pictures	$366,101,666	1995
33	Notting Hill	PolyGram Filmed Entertainment	$363,889,678	1999
34	A Bug’s Life	Walt Disney Studios	$363,398,565	1998
35	The World Is Not Enough	Metro-Goldwyn-Mayer Pictures	$361,832,400	1999
36	Home Alone 2: Lost in New York	20th Century Fox	$358,994,850	1992
37	American Beauty	DreamWorks Pictures	$356,296,601	1999
38	Apollo 13	Universal Pictures/Imagine Entertainment	$355,237,933	1995
39	Basic Instinct	TriStar Pictures	$352,927,224	1992
40	GoldenEye	MGM/United Artists	$352,194,034	1995
41	The Mask	New Line Cinema	$351,583,407	1994
42	Speed	20th Century Fox	$350,448,145	1994
43	Deep Impact	Paramount Pictures/DreamWorks Pictures	$349,464,664	1998
44	Beauty and the Beast	Walt Disney Studios	$346,317,207	1991
45	Pocahontas	Walt Disney Studios	$346,079,773	1995
46	The Flintstones	Universal Pictures	$341,631,208	1994
47	Batman Forever	Warner Bros.	$336,529,144	1995
48	The Rock	Walt Disney Studios	$335,062,621	1996
49	Tomorrow Never Dies	MGM/United Artists	$333,011,068	1997
50	Seven	New Line Cinema	$327,311,859	1995

The parameters let us know the keys that can be used in the question text or stored as metadata. (They can be edited as needed - e.g., using the rename method discussed above.)

scenarios.parameters

This will return:

{'Rank', 'Ref.', 'Studios', 'Title', 'Worldwide gross', 'Year'}

The scenarios can be used to ask questions about the data in the table:

from edsl import QuestionList

q_leads = QuestionList(
  question_name = "leads",
  question_text = "Who are the lead actors or actresses in {{ scenario.Title }}?"
)

results = q_leads.by(scenarios).run()

(
  results
  .sort_by("Title")
  .select("Title", "leads")
)

Output:

Title	Leads
A Bug’s Life	Dave Foley, Kevin Spacey, Julia Louis-Dreyfus, Hayden Panettiere, Phyllis Diller, Richard Kind, David Hyde Pierce
Aladdin	Mena Massoud, Naomi Scott, Will Smith
American Beauty	Kevin Spacey, Annette Bening, Thora Birch, Mena Suvari, Wes Bentley, Chris Cooper
Apollo 13	Tom Hanks, Kevin Bacon, Bill Paxton
Armageddon	Bruce Willis, Billy Bob Thornton, Liv Tyler, Ben Affleck
Basic Instinct	Michael Douglas, Sharon Stone
Batman Forever	Val Kilmer, Tommy Lee Jones, Jim Carrey, Nicole Kidman, Chris O’Donnell
Beauty and the Beast	Emma Watson, Dan Stevens, Luke Evans, Kevin Kline, Josh Gad
Dances with Wolves	Kevin Costner, Mary McDonnell, Graham Greene, Rodney A. Grant
Deep Impact	Téa Leoni, Morgan Freeman, Elijah Wood, Robert Duvall
Die Hard with a Vengeance	Bruce Willis, Samuel L. Jackson, Jeremy Irons
Forrest Gump	Tom Hanks, Robin Wright, Gary Sinise, Mykelti Williamson, Sally Field
Ghost	Patrick Swayze, Demi Moore, Whoopi Goldberg
Godzilla	Matthew Broderick, Jean Reno, Bryan Cranston, Aaron Taylor-Johnson, Elizabeth Olsen, Kyle Chandler, Vera Farmiga, Millie Bobby Brown
GoldenEye	Pierce Brosnan, Sean Bean, Izabella Scorupco, Famke Janssen
Home Alone	Macaulay Culkin, Joe Pesci, Daniel Stern, Catherine O’Hara, John Heard
Home Alone 2: Lost in New York	Macaulay Culkin, Joe Pesci, Daniel Stern, Catherine O’Hara, John Heard
Independence Day	Will Smith, Bill Pullman, Jeff Goldblum
Jurassic Park	Sam Neill, Laura Dern, Jeff Goldblum, Richard Attenborough
Men in Black	Tommy Lee Jones, Will Smith
Mission: Impossible	Tom Cruise, Ving Rhames, Simon Pegg, Rebecca Ferguson, Jeremy Renner
Mrs. Doubtfire	Robin Williams, Sally Field, Pierce Brosnan, Lisa Jakub, Matthew Lawrence, Mara Wilson
Notting Hill	Julia Roberts, Hugh Grant
Pocahontas	Irene Bedard, Mel Gibson, Judy Kuhn, David Ogden Stiers, Russell Means, Christian Bale
Pretty Woman	Richard Gere, Julia Roberts
Robin Hood: Prince of Thieves	Kevin Costner, Morgan Freeman, Mary Elizabeth Mastrantonio, Christian Slater, Alan Rickman
Saving Private Ryan	Tom Hanks, Matt Damon, Tom Sizemore, Edward Burns, Barry Pepper, Adam Goldberg, Vin Diesel, Giovanni Ribisi, Jeremy Davies
Seven	Brad Pitt, Morgan Freeman, Gwyneth Paltrow
Speed	Keanu Reeves, Sandra Bullock, Dennis Hopper
Star Wars: Episode I - The Phantom Menace	Liam Neeson, Ewan McGregor, Natalie Portman, Jake Lloyd
Tarzan	Johnny Weissmuller, Maureen O’Sullivan
Terminator 2: Judgment Day	Arnold Schwarzenegger, Linda Hamilton, Edward Furlong, Robert Patrick
The Bodyguard	Kevin Costner, Whitney Houston
The Flintstones	John Goodman, Elizabeth Perkins, Rick Moranis, Rosie O’Donnell
The Fugitive	Harrison Ford, Tommy Lee Jones
The Lion King	Matthew Broderick, James Earl Jones, Jeremy Irons, Moira Kelly, Nathan Lane, Ernie Sabella, Rowan Atkinson, Whoopi Goldberg
The Lost World: Jurassic Park	Jeff Goldblum, Julianne Moore, Pete Postlethwaite
The Mask	Jim Carrey, Cameron Diaz
The Matrix	Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss
The Mummy	Brendan Fraser, Rachel Weisz, John Hannah, Arnold Vosloo
The Rock	Sean Connery, Nicolas Cage, Ed Harris
The Sixth Sense	Bruce Willis, Haley Joel Osment, Toni Collette, Olivia Williams
The World Is Not Enough	Pierce Brosnan, Sophie Marceau, Denise Richards, Robert Carlyle
There’s Something About Mary	Cameron Diaz, Ben Stiller, Matt Dillon
Titanic	Leonardo DiCaprio, Kate Winslet
Tomorrow Never Dies	Pierce Brosnan, Michelle Yeoh, Jonathan Pryce, Teri Hatcher
Toy Story	Tom Hanks, Tim Allen
Toy Story 2	Tom Hanks, Tim Allen, Joan Cusack
True Lies	Arnold Schwarzenegger, Jamie Lee Curtis
Twister	Helen Hunt, Bill Paxton

Creating a scenario list from a CSV

The ScenarioList method from_csv(‘<filepath>.csv’) creates a list of scenarios from a CSV file. The method reads the CSV file and creates a scenario for each row in the file, with the keys as the column names and the values as the row values.

For example, say we have a CSV file containing the following data:

message,user,source,date
I can't log in...,Alice,Customer support,2022-01-01
I need help with my bill...,Bob,Phone,2022-01-02
I have a safety concern...,Charlie,Email,2022-01-03
I need help with a product...,David,Chat,2022-01-04

We can create a list of scenarios from the CSV file:

from edsl import ScenarioList

scenariolist = ScenarioList.from_csv("path/to/file.csv") # update filepath
scenariolist

This will return a scenario for each row:

Message	User	Source	Date
I can’t log in…	Alice	Customer support	2022-01-01
I need help with my bill…	Bob	Phone	2022-01-02
I have a safety concern…	Charlie	Email	2022-01-03
I need help with a product…	David	Chat	2022-01-04

If the scenario keys are not valid Python identifiers, we can use the give_valid_names() method to convert them to valid identifiers.

For example, our CSV file might contain a header row that is question texts:

"What is the message?","Who is the user?","What is the source?","What is the date?"
"I can't log in...","Alice","Customer support","2022-01-01"
"I need help with my bill...","Bob","Phone","2022-01-02"
"I have a safety concern...","Charlie","Email","2022-01-03"
"I need help with a product...","David","Chat","2022-01-04"

We can create a list of scenarios from the CSV file:

from edsl import ScenarioList

scenariolist = ScenarioList.from_csv("path/to/file.csv") # update filepath

scenariolist = scenariolist.give_valid_names()
scenariolist

This will return scenarios with non-Pythonic identifiers:

What is the message?	Who is the user?	What is the source?	What is the date?
I can’t log in…	Alice	Customer support	2022-01-01
I need help with my bill…	Bob	Phone	2022-01-02
I have a safety concern…	Charlie	Email	2022-01-03
I need help with a product…	David	Chat	2022-01-04

We can then use the give_valid_names() method to convert the keys to valid identifiers:

scenariolist.give_valid_names()
scenariolist

This will return scenarios with valid identifiers (removing stop words and using underscores):

message	user	source	date
I can’t log in…	Alice	Customer support	2022-01-01
I need help with my bill…	Bob	Phone	2022-01-02
I have a safety concern…	Charlie	Email	2022-01-03
I need help with a product…	David	Chat	2022-01-04

Methods for un/pivoting and grouping scenarios

There are a variety of methods for modifying scenarios and scenario lists.

Unpivoting a scenario list

The ScenarioList method unpivot() can be used to unpivot a scenario list based on one or more specified identifiers. It takes a list of id_vars which are the names of the key/value pairs to keep in each new scenario, and a list of value_vars which are the names of the key/value pairs to unpivot.

For example, say we have a scenario list for the above CSV file:

from edsl import ScenarioList

scenariolist = ScenarioList.from_csv("<filepath>.csv")
scenariolist

We can call the unpivot the scenario list:

scenariolist.unpivot(id_vars = ["user"], value_vars = ["source", "date", "message"])
scenariolist

This will return a list of scenarios with the source, date, and message key/value pairs unpivoted:

user	variable	value
Alice	source	Customer support
Alice	date	2022-01-01
Alice	message	I can’t log in…
Bob	source	Phone
Bob	date	2022-01-02
Bob	message	I need help with my bill…
Charlie	source	Email
Charlie	date	2022-01-03
Charlie	message	I have a safety concern…
David	source	Chat
David	date	2022-01-04
David	message	I need help with a product…

Pivoting a scenario list

We can call the pivot() method to reverse the unpivot operation:

scenariolist.pivot(id_vars = ["user"], var_name="variable", value_name="value")
scenariolist

This will return a list of scenarios with the source, date, and message key/value pairs pivoted back to their original form:

user	source	date	message
Alice	Customer support	2022-01-01	I can’t log in…
Bob	Phone	2022-01-02	I need help with my bill…
Charlie	Email	2022-01-03	I have a safety concern…
David	Chat	2022-01-04	I need help with a product…

Grouping scenarios

The group_by() method can be used to group scenarios by one or more specified keys and apply a function to the values of the specified variables.

Example usage:

from edsl import Scenario, ScenarioList

def avg_sum(a, b):
  return {'avg_a': sum(a) / len(a), 'sum_b': sum(b)}

scenariolist = ScenarioList([
  Scenario({'group': 'A', 'year': 2020, 'a': 10, 'b': 20}),
  Scenario({'group': 'A', 'year': 2021, 'a': 15, 'b': 25}),
  Scenario({'group': 'B', 'year': 2020, 'a': 12, 'b': 22}),
  Scenario({'group': 'B', 'year': 2021, 'a': 17, 'b': 27})
])

scenariolist.group_by(id_vars=['group'], variables=['a', 'b'], func=avg_sum)

This will return a list of scenarios with the a and b key/value pairs grouped by the group key and the avg_a and sum_b key/value pairs calculated by the avg_sum function:

group	avg_a	sum_b
A	12.5	45
B	14.5	49

Data labeling tasks

Scenarios are particularly useful for conducting data labeling or data coding tasks, where the task can be designed as a survey of questions about each piece of data in a dataset.

For example, say we have a dataset of text messages that we want to sort by topic. We can perform this task by using a language model to answer questions such as “What is the primary topic of this message: {{ scenario.message }}?” or “Does this message mention a safety issue? {{ scenario.message }}”, where each text message is inserted in the message placeholder of the question text.

Here we use scenarios to conduct the task:

from edsl import QuestionMultipleChoice, Survey, Scenario, ScenarioList

# Create a question with that takes a parameter
q1 = QuestionMultipleChoice(
  question_name = "topic",
  question_text = "What is the topic of this message: {{ scenario.message }}?",
  question_options = ["Safety", "Product support", "Billing", "Login issue", "Other"]
)

q2 = QuestionMultipleChoice(
  question_name = "safety",
  question_text = "Does this message mention a safety issue? {{ scenario.message }}?",
  question_options = ["Yes", "No", "Unclear"]
)

# Create a list of scenarios for the parameter
messages = [
  "I can't log in...",
  "I need help with my bill...",
  "I have a safety concern...",
  "I need help with a product..."
]

scenarios = ScenarioList(Scenario({"message": message}) for message in messages)

# Create a survey with the question
survey = Survey(questions = [q1, q2])

# Run the survey with the scenarios
results = survey.by(scenarios).run()

We can then analyze the results to see how the agent answered the questions for each scenario:

results.select("message", "safety", "topic")

This will print a table of the scenarios and the answers to the questions for each scenario:

message	safety	topic
I can’t log in…	No	Login issue
I need help with a product…	No	Product support
I need help with my bill…	No	Billing
I have a safety concern…	Yes	Safety

Adding metadata

If we have metadata about the messages that we want to keep track of, we can add it to the scenarios as well. This will create additional columns for the metadata in the results dataset, but without the need to include it in our question texts. Here we modify the above example to use a dataset of messages with metadata. Note that the question texts are unchanged:

from edsl import QuestionMultipleChoice, Survey, Scenario, ScenarioList

# Create a question with a parameter
q1 = QuestionMultipleChoice(
  question_name = "topic",
  question_text = "What is the topic of this message: {{ scenario.message }}?",
  question_options = ["Safety", "Product support", "Billing", "Login issue", "Other"]
)

q2 = QuestionMultipleChoice(
  question_name = "safety",
  question_text = "Does this message mention a safety issue? {{ scenario.message }}?",
  question_options = ["Yes", "No", "Unclear"]
)

# Create scenarios for the sets of parameters
user_messages = [
  {"message": "I can't log in...", "user": "Alice", "source": "Customer support", "date": "2022-01-01"},
  {"message": "I need help with my bill...", "user": "Bob", "source": "Phone", "date": "2022-01-02"},
  {"message": "I have a safety concern...", "user": "Charlie", "source": "Email", "date": "2022-01-03"},
  {"message": "I need help with a product...", "user": "David", "source": "Chat", "date": "2022-01-04"}
]

scenarios = ScenarioList(
    Scenario.from_dict(m) for m in user_messages
)

# Create a survey with the question
survey = Survey(questions = [q1, q2])

# Run the survey with the scenarios
results = survey.by(scenarios).run()

# Inspect the results
results.select("scenario.*", "answer.*")

We can see how the agent answered the questions for each scenario, together with the metadata that was not included in the question text:

user	source	message	date	topic	safety
Alice	Customer support	I can’t log in…	2022-01-01	Login issue	No
Bob	Phone	I need help with my bill…	2022-01-02	Billing	No
Charlie	Email	I have a safety concern…	2022-01-03	Safety	Yes
David	Chat	I need help with a product…	2022-01-04	Product support	No

To learn more about accessing, analyzing and visualizing survey results, please see the Results section.

Slicing/chunking content into scenarios

We can use the Scenario method chunk() to slice a text scenario into a ScenarioList based on num_words or num_lines.

Example usage:

my_haiku = """
This is a long text.
Pages and pages, oh my!
I need to chunk it.
"""

text_scenario = Scenario({"my_text": my_haiku})

word_chunks_scenariolist = text_scenario.chunk(
  "my_text",
  num_words = 5, # use num_words or num_lines but not both
  include_original = True, # optional
  hash_original = True # optional
)
word_chunks_scenariolist

This will return:

my_text	my_text_chunk	my_text_original
This is a long text.	0	4aec42eda32b7f32bde8be6a6bc11125
Pages and pages, oh my!	1	4aec42eda32b7f32bde8be6a6bc11125
I need to chunk it.	2	4aec42eda32b7f32bde8be6a6bc11125

Using f-strings with scenarios

It is possible to use scenarios and f-strings together in a question. An f-string must be evaluated when a question is constructed, whereas a scenario is either evaluated when a question is run (using the by method) or when a question is constructed (using the loop method).

For example, here we use an f-string to create different versions of a question that also takes a parameter {{ scenario.activity }}, together with a list of scenarios to replace the parameter when the question is run. We optionally include the f-string in the question name in addition to the question text in order to control the unique identifiers for the questions, which are needed in order to pass the questions that are created to a Survey. (If you do not include the f-string in the question name, a number is automatically appended to each question name to ensure uniqueness.) Then we use the show_prompts() method to examine the user prompts that are created when the scenarios are added to the questions:

from edsl import QuestionFreeText, Scenario, ScenarioList, Survey

questions = []
sentiments = ["enjoy", "hate", "love"]
activities = ["running", "reading"]

for sentiment in sentiments:
  q = QuestionFreeText(
    question_name = f"{ sentiment }_activity",
    question_text = f"How much do you { sentiment } {{ scenario.activity }}?"
  )
  questions.append(q)

scenarios = ScenarioList.from_list("activity", activities)

survey = Survey(questions = questions)
survey.by(scenarios).show_prompts()

The show_prompts method will return the questions created with the f-string with the scenarios added. (Note that the system prompts are blank because we have not created any agents.)

user_prompt	system_prompt
How much do you enjoy running?
How much do you hate running?
How much do you love running?
How much do you enjoy reading?
How much do you hate reading?
How much do you love reading?

To learn more about user and system prompts, please see the Prompts section.

Scenario class

A dictionary-like object that stores key-value pairs for parameterizing questions.

A Scenario inherits from both the EDSL Base class and Python’s UserDict, allowing it to function as a dictionary while providing additional functionality. Scenarios are used to parameterize questions by providing variable data that can be referenced within question templates using Jinja syntax.

Scenarios can be created directly with dictionary data or constructed from various sources using class methods (from_file, from_url, from_pdf, etc.). They support operations like addition (combining scenarios) and multiplication (creating cross products with other scenarios or scenario lists).

Attributes:

data (dict): The underlying dictionary data. name (str, optional): A name for the scenario.

Examples:

Create a simple scenario: >>> s = Scenario({“product”: “coffee”, “price”: 4.99})

Combine scenarios: >>> s1 = Scenario({“product”: “coffee”}) >>> s2 = Scenario({“price”: 4.99}) >>> s3 = s1 + s2 >>> s3 Scenario({‘product’: ‘coffee’, ‘price’: 4.99})

Create a scenario from a file: >>> import tempfile >>> with tempfile.NamedTemporaryFile(mode=’w’, suffix=’.txt’, delete=False) as f: … _ = f.write(“Hello World”) … data_path = f.name >>> s = Scenario.from_file(data_path, “document”) >>> import os >>> os.unlink(data_path) # Clean up temp file

ScenarioList class

A collection of Scenario objects with advanced operations for manipulation and analysis.

ScenarioList extends Python’s UserList to provide specialized functionality for working with collections of Scenario objects. It inherits from Base to integrate with EDSL’s object model and from ScenarioListOperationsMixin to provide powerful data manipulation capabilities.

The class provides methods for filtering, sorting, joining, transforming, and analyzing collections of Scenarios. It’s designed to work seamlessly with other EDSL components like Surveys, Jobs, and Questions.

Attributes:

data (list): The underlying list of Scenario objects. codebook (dict): Optional metadata describing the fields in the scenarios.

Examples:

Create a ScenarioList from Scenario objects: >>> from edsl.scenarios import Scenario, ScenarioList >>> s1 = Scenario({“product”: “apple”, “price”: 1.99}) >>> s2 = Scenario({“product”: “banana”, “price”: 0.99}) >>> sl = ScenarioList([s1, s2])

Filter scenarios based on a condition: >>> cheap_fruits = sl.filter(“price < 1.50”) >>> len(cheap_fruits) 1 >>> cheap_fruits[0][“product”] ‘banana’

Add a new column based on existing data: >>> sl_with_tax = sl.mutate(“tax = price * 0.08”) >>> sl_with_tax[0][“tax”] 0.1592