diff --git a/.gitignore b/.gitignore
index 8d06e75..b0f2758 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,7 +2,7 @@
.vscode/
.DS_Store
src/altk.egg-info
-
+**/*.pkl
# Distribution/build
dist/
diff --git a/README.md b/README.md
index d71525e..db0dfa2 100644
--- a/README.md
+++ b/README.md
@@ -29,7 +29,7 @@ First, set up a virtual environment (e.g. via [miniconda](https://docs.conda.io/
## Getting started
-- Check out the [examples](https://github.com/CLMBRs/ultk/tree/main/src/examples), starting with a basic signaling game. The examples folder also contains a simiple efficient communication analysis of [indefinites](https://github.com/CLMBRs/ultk/tree/main/src/examples/indefinites).
+- Check out the [examples](https://github.com/CLMBRs/ultk/tree/main/src/examples), starting with a simiple efficient communication analysis of [indefinites](https://github.com/CLMBRs/ultk/tree/main/src/examples/indefinites) and a comparison of two approaches to efficient communication, with modals as a test case.
- To see more scaled up usage examples, visit the codebase for an efficient communication analysis of [modals](https://github.com/nathimel/modals-effcomm) or [sim-max games](https://github.com/nathimel/rdsg).
- For an introduction to efficient communication research, here is a [survey paper](https://www.annualreviews.org/doi/abs/10.1146/annurev-linguistics-011817-045406) of the field.
- For an introduction to the RSA framework, see [this online textbook](http://www.problang.org/).
@@ -51,7 +51,7 @@ Unit tests are written in [pytest](https://docs.pytest.org/en/7.3.x/) and execut
Figures:
-> Kemp, C. & Regier, T. (2012) Kinship Categories Across Languages Reflect General Communicative Principles. Science. https://www.science.org/doi/10.1126/science.1218811
+> Kemp, C. & Regier, T. (2012). Kinship Categories Across Languages Reflect General Communicative Principles. Science. https://www.science.org/doi/10.1126/science.1218811
> Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018). Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31), 7937–7942. https://doi.org/10.1073/pnas.1800521115
@@ -64,7 +64,6 @@ Unit tests are written in [pytest](https://docs.pytest.org/en/7.3.x/) and execut
Links:
-> Imel, N. (2023). The evolution of efficient compression in signaling games. PsyArXiv. https://doi.org/10.31234/osf.io/b62de
> Imel, N., & Steinert-Threlkeld, S. (2022). Modal semantic universals optimize the simplicity/informativeness trade-off. Semantics and Linguistic Theory, 1(0), Article 0. https://doi.org/10.3765/salt.v1i0.5346
diff --git a/docs/search.js b/docs/search.js
index 5cea64c..d40f31e 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
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\n\nIntroduction \n\nULTK is a software library that aims to support efficient communication analyses of natural language. This is a line of research that aims to explain why natural languages have the structure that they do in terms competing pressures to minimize cognitive complexity and maximize communicative accuracy.
\n\nKey features:
\n\n\nPrimitives for constructing semantic spaces, expressions, and languages \nTools for measuring informativity of languages, communicative success of RSA speakers and listeners \nLanguage population sampling and optimization w.r.t Pareto fronts \nRate-Distortion and Information Bottleneck style analyses \n \n\nULTK is a long term project and it is currently in its early stages. It is intended to help lower the barrier to entry for certain research in computational semantics, and to unify methodologies. If you find something confusing, please open an issue. If you have a phenomena of interest in linguistic semantics that you want to run an efficient communication analysis on, please contact the contributors.
\n\nRead the documentation .
\n\nInstalling ULTK \n\nFirst, set up a virtual environment (e.g. via miniconda , conda create -n ultk python=3.11, and conda activate ultk).
\n\n\nDownload or clone this repository and navigate to the root folder.
Install ULTK (We recommend doing this inside a virtual environment)
\n\npip install -e .
\n\nGetting started \n\n\nCheck out the examples , starting with a basic signaling game. The examples folder also contains a simiple efficient communication analysis of indefinites . \nTo see more scaled up usage examples, visit the codebase for an efficient communication analysis of modals or sim-max games . \nFor an introduction to efficient communication research, here is a survey paper of the field. \nFor an introduction to the RSA framework, see this online textbook . \n \n\nModules \n\nThere are two modules. The first is ultk.effcomm , which includes methods for measuring informativity of languages and/or communicative success of Rational Speech Act agents, and for language population sampling and optimization w.r.t Pareto fronts.
\n\nThe second module is ultk.language , which contains primitives for constructing semantic spaces, expressions, and languages. It also has a grammar module which can be used for building expressions in a Language of Thought and measuring complexity in terms of minimum description length, as well as for natural language syntax.
\n\nThe source code is available on github here .
\n\nTesting \n\nUnit tests are written in pytest and executed via running pytest in the src/tests folder.
\n\nReferences \n\n\nFigures:
\n\n\n Kinship Categories Across Languages Reflect General Communicative Principles | Science. (n.d.). Retrieved February 27, 2023, from https://www.science.org/doi/10.1126/science.1218811
\n \n\n\n Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018). Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31), 7937\u20137942. https://doi.org/10.1073/pnas.1800521115
\n \n\n\n Deni\u0107, M., Steinert-Threlkeld, S., & Szymanik, J. (2022). Indefinite Pronouns Optimize the Simplicity/Informativeness Trade-Off. Cognitive Science, 46(5), e13142. https://doi.org/10.1111/cogs.13142
\n \n\n\n Steinert-Threlkeld, S. (2021). Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals. Entropy, 23(10), Article 10. https://doi.org/10.3390/e23101335
\n \n\n
\n\n\nLinks:
\n\n\n Imel, N. (2023). The evolution of efficient compression in signaling games. PsyArXiv. https://doi.org/10.31234/osf.io/b62de
\n \n\n\n Imel, N., & Steinert-Threlkeld, S. (2022). Modal semantic universals optimize the simplicity/informativeness trade-off. Semantics and Linguistic Theory, 1(0), Article 0. https://doi.org/10.3765/salt.v1i0.5346
\n \n\n\n Kemp, C., Xu, Y., & Regier, T. (2018). Semantic Typology and Efficient Communication. Annual Review of Linguistics, 4(1), 109\u2013128. https://doi.org/10.1146/annurev-linguistics-011817-045406
\n \n\n
\n"}, "ultk.effcomm": {"fullname": "ultk.effcomm", "modulename": "ultk.effcomm", "kind": "module", "doc": "Tools for measuring languages for communicative efficiency.
\n\nSubmodules divide the labor of a computational experiment performing an efficiency analysis of a language into several parts: generating and sampling the space of possible languages, measuring their properties, and determining which languages optimize efficient trade-offs w.r.t these properties.
\n\nThe altk.effcomm.sampling submodule implements several methods for generating hypothetically possible languages of a given type, by sampling from a set of possible expressions, or permuting the expression-meaning mapping of an existing language.
\n\nThe altk.effcomm.optimization submodule contains a general implementation of an evolutionary algorithm, which can be used to estimate a Pareto frontier of optimal solutions to an efficiency trade-off. It can also be used as a technique for randomly exploring the space of possible languages.
\n\nThe altk.effcomm.tradeoff submodule contains tools for measuring a pool of languages for various properties, finding which languages are Pareto dominant with respect to two properties, and setting attributes of the language objects for further analysis.
\n\nThe altk.effcomm.analysis submodule contains tools for performing numerical analyses and producing paradigmatic plots of languages in 2D trade-off space.
\n\nThe altk.effcomm.information submodule contains tools for information theory based analyses of the communicative efficiency of languages. It includes methods for Rate-Distortion style (including the Information Bottleneck) analyses.
\n\nThe altk.effcomm.agent submodule implements classes for constructing various speakers and listeners of a language. These are typically used in static analyses of informativity of a language, and are unified abstractions from the Rational Speech Act framework. They can also be used for dynamic analyses, however (see the signaling game example ).
\n\nThe altk.effcomm.informativity submodule implements tools for computing the literal or pragmatic informativity of a language, based on speaker/listener abstractions described above.
\n\nThe altk.effcomm.util submodule contains various helper functions for working with the probability distributions associated with ALTK abstractions.
\n"}, "ultk.effcomm.agent": {"fullname": "ultk.effcomm.agent", "modulename": "ultk.effcomm.agent", "kind": "module", "doc": "Classes for representing communicative agents, such as Senders and Receivers figuring in Lewis-Skyrms signaling games, literal and pragmatic agents in the Rational Speech Act framework, etc.
\n"}, "ultk.effcomm.agent.CommunicativeAgent": {"fullname": "ultk.effcomm.agent.CommunicativeAgent", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent", "kind": "class", "doc": "
\n"}, "ultk.effcomm.agent.CommunicativeAgent.__init__": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.__init__", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.__init__", "kind": "function", "doc": "An agent that uses a language to communicate, e.g. a RSA pragmatic agent or a Lewis-Skyrms signaler.
\n\nArguments: \n\n\nlanguage: a language to construct a agent to define the relation between meanings and expressions, which can be used to initialize the agent matrices (e.g. S or R).name: an optional string to name the communicative agent \n", "signature": "(language : ultk . language . language . Language , ** kwargs "}, "ultk.effcomm.agent.CommunicativeAgent.language": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.language", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.language", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.agent.CommunicativeAgent.shape": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.shape", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.shape", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.agent.CommunicativeAgent.weights": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.weights", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.weights", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.CommunicativeAgent.normalized_weights": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.normalized_weights", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.normalized_weights", "kind": "function", "doc": "Return the normalized weights of a CommunicativeAgent so that each row vector represents a probability distribution.
\n", "signature": "(self ) -> None : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.initialize_weights": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.initialize_weights", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.initialize_weights", "kind": "function", "doc": "Initialize the agent's weight matrix.
\n\nArguments: \n\n\nweights: an np.ndarray representing the weights to initialize the agent with. By default None, and the agent's weights will be initialized uniformly.initial: {'ones', 'random'} a str reprsenting the initialization method to use. If 'ones' (default), initialize the weight matrix with np.ones. If 'random', initalize the weight matrix from np.random.uniform. \n", "signature": "(self , weights : numpy . ndarray = None , initial = 'ones' ) -> None : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.referent_to_index": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.referent_to_index", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.referent_to_index", "kind": "function", "doc": "
\n", "signature": "(self , referent : ultk . language . semantics . Referent ) -> int : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.index_to_referent": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.index_to_referent", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.index_to_referent", "kind": "function", "doc": "
\n", "signature": "(self , index : int ) -> ultk . language . semantics . Referent : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.expression_to_index": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.expression_to_index", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.expression_to_index", "kind": "function", "doc": "
\n", "signature": "(self , expression : ultk . language . language . Expression ) -> int : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.index_to_expression": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.index_to_expression", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.index_to_expression", "kind": "function", "doc": "
\n", "signature": "(self , index : int ) -> ultk . language . language . Expression : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.strategy_to_indices": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.strategy_to_indices", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.strategy_to_indices", "kind": "function", "doc": "Maps communicative strategies to weights.
\n\nGiven a expression and referent, access the corresponding weight coordinate.
\n\nArguments: \n\n\nstrategy: a dict of the form {\"referent\": Referent, \"expression\": expression} representing an instance of communicative behavior, which we may call a communicative strategy for this agent. \n", "signature": "(self , strategy : dict [ str , typing . Any ] ) -> tuple [ int ] : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.sample_strategy": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.sample_strategy", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.sample_strategy", "kind": "function", "doc": "Sample a communicative strategy (e.g., a word for Speaker's intended referent, or interpretation for Listener's heard word) by uniformly sampling from a row vector of the agent's weight matrix specified by the index.
\n\nArguments: \n\n\nindex: the integer index representing a row of the weight matrix. \n\nReturns: \n\n\n the integer index of the agent's choice
\n \n", "signature": "(self , index : int ) -> int : ", "funcdef": "def"}, "ultk.effcomm.agent.CommunicativeAgent.to_language": {"fullname": "ultk.effcomm.agent.CommunicativeAgent.to_language", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent.to_language", "kind": "function", "doc": "Get a language from the agent, representing its current (possibly learned) communicative behavior.
\n\nThis function uses: \n\n\n \n the agent's weight matrix, \n the set of expression forms, and \n the set of referents \n \n \n\nfrom the language the agent was initialized with to generate a new language accurately reflecting the new expression meanings, e.g. how the agent interprets expressions as meaning zero or more referents.
\n\nArguments: \n\n\nthreshold: a float in [0,1] representing the cutoff for determining if a meaning (referent) can be communicated by a expression. Because weights are not initialized to 0, it is a good idea to set nonzero values as the threshold. \n\nReturns: \n\n\n a Language corresponding to the form-meaning mapping defined by the communicative agent's weights.
\n \n", "signature": "(\tself , \tdata : dict = { 'complexity' : None , 'accuracy' : None } , \tthreshold : float = 0.1 ) -> ultk . language . language . Language : ", "funcdef": "def"}, "ultk.effcomm.agent.Speaker": {"fullname": "ultk.effcomm.agent.Speaker", "modulename": "ultk.effcomm.agent", "qualname": "Speaker", "kind": "class", "doc": "
\n", "bases": "CommunicativeAgent"}, "ultk.effcomm.agent.Speaker.__init__": {"fullname": "ultk.effcomm.agent.Speaker.__init__", "modulename": "ultk.effcomm.agent", "qualname": "Speaker.__init__", "kind": "function", "doc": "An agent that uses a language to communicate, e.g. a RSA pragmatic agent or a Lewis-Skyrms signaler.
\n\nArguments: \n\n\nlanguage: a language to construct a agent to define the relation between meanings and expressions, which can be used to initialize the agent matrices (e.g. S or R).name: an optional string to name the communicative agent \n", "signature": "(language : ultk . language . language . Language , ** kwargs "}, "ultk.effcomm.agent.Speaker.S": {"fullname": "ultk.effcomm.agent.Speaker.S", "modulename": "ultk.effcomm.agent", "qualname": "Speaker.S", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.Speaker.normalized_weights": {"fullname": "ultk.effcomm.agent.Speaker.normalized_weights", "modulename": "ultk.effcomm.agent", "qualname": "Speaker.normalized_weights", "kind": "function", "doc": "Get the normalized weights of a Speaker.
\n\nEach row vector represents a conditional probability distribution over expressions, P(e | m).
\n", "signature": "(self ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.agent.Listener": {"fullname": "ultk.effcomm.agent.Listener", "modulename": "ultk.effcomm.agent", "qualname": "Listener", "kind": "class", "doc": "
\n", "bases": "CommunicativeAgent"}, "ultk.effcomm.agent.Listener.__init__": {"fullname": "ultk.effcomm.agent.Listener.__init__", "modulename": "ultk.effcomm.agent", "qualname": "Listener.__init__", "kind": "function", "doc": "An agent that uses a language to communicate, e.g. a RSA pragmatic agent or a Lewis-Skyrms signaler.
\n\nArguments: \n\n\nlanguage: a language to construct a agent to define the relation between meanings and expressions, which can be used to initialize the agent matrices (e.g. S or R).name: an optional string to name the communicative agent \n", "signature": "(language : ultk . language . language . Language , ** kwargs "}, "ultk.effcomm.agent.Listener.R": {"fullname": "ultk.effcomm.agent.Listener.R", "modulename": "ultk.effcomm.agent", "qualname": "Listener.R", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.Listener.normalized_weights": {"fullname": "ultk.effcomm.agent.Listener.normalized_weights", "modulename": "ultk.effcomm.agent", "qualname": "Listener.normalized_weights", "kind": "function", "doc": "Normalize the weights of a Listener so that each row vector for the heard expression e represents a conditional probability distribution over referents P(m | e).
\n", "signature": "(self ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.agent.LiteralSpeaker": {"fullname": "ultk.effcomm.agent.LiteralSpeaker", "modulename": "ultk.effcomm.agent", "qualname": "LiteralSpeaker", "kind": "class", "doc": "A literal speaker chooses utterances without any reasoning about other agents. The literal speaker's conditional probability distribution P(e|m) is uniform over all expressions that can be used to communicate a particular meaning. This is in contrast to a pragmatic speaker, whose conditional distribution is not uniform in this way, but instead biased towards choosing expressions that are less likely to be misinterpreted by some listener.
\n", "bases": "Speaker"}, "ultk.effcomm.agent.LiteralSpeaker.__init__": {"fullname": "ultk.effcomm.agent.LiteralSpeaker.__init__", "modulename": "ultk.effcomm.agent", "qualname": "LiteralSpeaker.__init__", "kind": "function", "doc": "An agent that uses a language to communicate, e.g. a RSA pragmatic agent or a Lewis-Skyrms signaler.
\n\nArguments: \n\n\nlanguage: a language to construct a agent to define the relation between meanings and expressions, which can be used to initialize the agent matrices (e.g. S or R).name: an optional string to name the communicative agent \n", "signature": "(language : ultk . language . language . Language , ** kwargs "}, "ultk.effcomm.agent.LiteralSpeaker.S": {"fullname": "ultk.effcomm.agent.LiteralSpeaker.S", "modulename": "ultk.effcomm.agent", "qualname": "LiteralSpeaker.S", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.LiteralListener": {"fullname": "ultk.effcomm.agent.LiteralListener", "modulename": "ultk.effcomm.agent", "qualname": "LiteralListener", "kind": "class", "doc": "A naive literal listener interprets utterances without any reasoning about other agents. Its conditional probability distribution P(m|e) for guessing meanings is uniform over all meanings that can be denoted by the particular expression heard. This is in contrast to a pragmatic listener, whose conditional distribution is biased to guess meanings that a pragmatic speaker most likely intended.
\n", "bases": "Listener"}, "ultk.effcomm.agent.LiteralListener.__init__": {"fullname": "ultk.effcomm.agent.LiteralListener.__init__", "modulename": "ultk.effcomm.agent", "qualname": "LiteralListener.__init__", "kind": "function", "doc": "An agent that uses a language to communicate, e.g. a RSA pragmatic agent or a Lewis-Skyrms signaler.
\n\nArguments: \n\n\nlanguage: a language to construct a agent to define the relation between meanings and expressions, which can be used to initialize the agent matrices (e.g. S or R).name: an optional string to name the communicative agent \n", "signature": "(language : ultk . language . language . Language , ** kwargs "}, "ultk.effcomm.agent.LiteralListener.R": {"fullname": "ultk.effcomm.agent.LiteralListener.R", "modulename": "ultk.effcomm.agent", "qualname": "LiteralListener.R", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.PragmaticSpeaker": {"fullname": "ultk.effcomm.agent.PragmaticSpeaker", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticSpeaker", "kind": "class", "doc": "A pragmatic speaker chooses utterances based on how a listener would interpret them. A pragmatic speaker may be initialized with any kind of listener, e.g. literal or pragmatic -- meaning the recursive reasoning can be modeled up to arbitrary depth.
\n", "bases": "Speaker"}, "ultk.effcomm.agent.PragmaticSpeaker.__init__": {"fullname": "ultk.effcomm.agent.PragmaticSpeaker.__init__", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticSpeaker.__init__", "kind": "function", "doc": "Initialize the |M|-by-|E| matrix, S, corresponding to the pragmatic speaker's conditional probability distribution over expressions given meanings.
\n\nThe pragmatic speaker chooses expressions to communicate their intended meaning according to:
\n\n$P(e | m) \\propto \\exp(t * u(e,m))$
\n\nwhere $t \\in [0,1]$ is a temperature parameter and utility $u$ is defined
\n\n$u(e , m) := \\log(P_{\\text{Listener}}(m | e))$
\n\nArguments: \n\n\nlanguage: the language with |M| meanings and |E| expressions defining the size of S.listener: a communicative agent storing a matrix R representing the conditional distribution over expressions given meanings.temperature: a float \\in [0,1], representing how `optimally rational' the pragmatic speaker is; 1.0 is chosen when no particular assumptions about rationality are made. \n", "signature": "(\tlanguage : ultk . language . language . Language , \tlistener : ultk . effcomm . agent . Listener , \ttemperature : float = 1.0 , \t** kwargs ) "}, "ultk.effcomm.agent.PragmaticSpeaker.S": {"fullname": "ultk.effcomm.agent.PragmaticSpeaker.S", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticSpeaker.S", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.PragmaticListener": {"fullname": "ultk.effcomm.agent.PragmaticListener", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticListener", "kind": "class", "doc": "A pragmatic listener interprets utterances based on their expectations about a pragmatic speaker's decisions. A pragmatic listener may be initialized with any kind of speaker, e.g. literal or pragmatic -- meaning the recursive reasoning can be modeled up to arbitrary depth.
\n", "bases": "Listener"}, "ultk.effcomm.agent.PragmaticListener.__init__": {"fullname": "ultk.effcomm.agent.PragmaticListener.__init__", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticListener.__init__", "kind": "function", "doc": "Initialize the |E|-by-|M| matrix, R, corresponding to the pragmatic listener's conditional probability distribution over meanings given expressions.
\n\nThe pragmatic listener chooses meanings as their best guesses of the expression they heard according to:
\n\n$P(m | e) \\propto P_{\\text{PragmaticSpeaker}}(e | m)$
\n\nArguments: \n\n\nlanguage: the language with |M| meanings and |E| expressions defining the size of R.speaker: a communicative agent storing a matrix S representing the conditional distribution over expressions given meanings.prior: a diagonal matrix of size |M|-by-|M| representing the communicative need probabilities for meanings. \n", "signature": "(\tlanguage : ultk . language . language . Language , \tspeaker : ultk . effcomm . agent . Speaker , \tprior : numpy . ndarray , \t** kwargs ) "}, "ultk.effcomm.agent.PragmaticListener.R": {"fullname": "ultk.effcomm.agent.PragmaticListener.R", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticListener.R", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, "ultk.effcomm.agent.BayesianListener": {"fullname": "ultk.effcomm.agent.BayesianListener", "modulename": "ultk.effcomm.agent", "qualname": "BayesianListener", "kind": "class", "doc": "A Bayesian reciever chooses an interpretation according to p(meaning | word), where
\n\nBUG: This is extremely misleading since we basically only use this function for IB, and IB assumes a DETERMINISTIC bayes-derived listener. \n\n$P(m | w) = \\frac{P(M | W) \\cdot P(M)} { P(W) }$
\n\nFurthermore, we sometimes require that each word w is deterministically interpreted as meaning $\\hat{m}$ as follows:
\n\nBUG: This says nothing about determinism. \n\n$\\hat{m}_{w}(u) = \\sum_m p(m|w) \\cdot m(u)$
\n\nSee ultk.effcomm.information for more details.
\n", "bases": "Listener"}, "ultk.effcomm.agent.BayesianListener.__init__": {"fullname": "ultk.effcomm.agent.BayesianListener.__init__", "modulename": "ultk.effcomm.agent", "qualname": "BayesianListener.__init__", "kind": "function", "doc": "An agent that uses a language to communicate, e.g. a RSA pragmatic agent or a Lewis-Skyrms signaler.
\n\nArguments: \n\n\nlanguage: a language to construct a agent to define the relation between meanings and expressions, which can be used to initialize the agent matrices (e.g. S or R).name: an optional string to name the communicative agent \n", "signature": "(\tspeaker : ultk . effcomm . agent . Speaker , \tprior : numpy . ndarray , \tname : str = None ) "}, "ultk.effcomm.analysis": {"fullname": "ultk.effcomm.analysis", "modulename": "ultk.effcomm.analysis", "kind": "module", "doc": "Functions for analyzing and formatting the results of the simplicity/informativeness trade-off.
\n"}, "ultk.effcomm.analysis.get_dataframe": {"fullname": "ultk.effcomm.analysis.get_dataframe", "modulename": "ultk.effcomm.analysis", "qualname": "get_dataframe", "kind": "function", "doc": "Get a pandas DataFrame for a list of languages containing efficient communication data.
\n\nArguments: \n\n\nlanguages: the list of languages to map into a dataframe.columns: the list of keys to a language's data dictionary attribute, which will comprise the columns of the resulting dataframe. By default will use all items of each language's data dictionary.subset: the columns to subset for duplicatesduplicates: {\"drop\", \"count\", \"leave\"} whether to drop, count, or do nothing with duplicates. By default is set to \"leave\" which will leave duplicates in the dataframe. \n\nReturns: \n\n\n \n data: a pandas DataFrame with rows as individual languages, with the columns specifying their data. \n \n \n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] , \tcolumns : list [ str ] = None , \tsubset : list [ str ] = [ 'complexity' , 'comm_cost' ] , \tduplicates : str = 'leave' ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, "ultk.effcomm.analysis.pearson_analysis": {"fullname": "ultk.effcomm.analysis.pearson_analysis", "modulename": "ultk.effcomm.analysis", "qualname": "pearson_analysis", "kind": "function", "doc": "Measures pearson correlation coefficient for naturalness with a property.
\n\nUse nonparametric bootstrap for confidence intervals.
\n\nArguments: \n\n\ndata: a DataFrame representing the pool of measured languagespredictor: a string representing the column to measure pearson r withproperty: a string representing a column to measure pearson r with the predictor columnnum_bootstrap_samples: how many samples to bootstrap from the original data \n\nReturns: \n\n\n a dict of the pearson correlation coefficient for the predictor and the property, and bootstrapped confidence intervals for this coefficient, e.g.
\n\n{\n\"rho\": (a float between -1 and 1),\n\"confidence_intervals\": (a pandas Dataframe with the columns [\n 'bootstrap_sample_percent', 'low', 'high'\n])\n}\n \n", "signature": "(\tdata , \tpredictor : str , \tproperty : str , \tnum_bootstrap_samples = 100 ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, "ultk.effcomm.analysis.trade_off_means": {"fullname": "ultk.effcomm.analysis.trade_off_means", "modulename": "ultk.effcomm.analysis", "qualname": "trade_off_means", "kind": "function", "doc": "Get a dataframe with the mean tradeoff data.
\n\nArguments: \n\n\nname: a str representing the subset of the population to observe mean properties for, e.g. \"natural\" or \"population\".df: a pandas DataFrame containing data of a language population to take the means of.prperties: the properties to take means of, corresponding to columns of df. \n\nExamples:
\n\n\n
>>> natural_means = trade_off_means ( "natural_means" , natural_data , properties ) \n>>> population_means = trade_off_means ( "population_means" , data , properties ) \n>>> means_df = pd . concat ([ natural_means , dlsav_means , population_means ]) . set_index ( "name" ) \n>>> means_df \n simplicity complexity informativity optimality \n name \n natural_means 0.772222 16.4000 0.746296 0.952280 \n population_means 0.681068 22.9631 0.525118 0.832010 \n\n
(\tname : str , \tdf : pandas . core . frame . DataFrame , \tproperties : list ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, "ultk.effcomm.analysis.trade_off_ttest": {"fullname": "ultk.effcomm.analysis.trade_off_ttest", "modulename": "ultk.effcomm.analysis", "qualname": "trade_off_ttest", "kind": "function", "doc": "Get a dataframe with a single-samples t-test results for a subpopulation against the full population.
\n\nThis is useful if we want to compare the optimality of natural languages to the full population of languages in an experiment. Because the property of 'being a natural language' is categorical, we use a single-samples T test.
\n\nArguments: \n\n\nsub_population: a pandas DataFrame representing a subset of the population to take ttests against the full language population for properties.population_means: a dict containing properties as keys and the mean value of the full language population for that property.properties: a list of strings corresponding to columns of the sub_population DataFrame and keys of the population_means dict. \n\nExamples: \n\n\n \n
>>> df = trade_off_ttest ( natural_data , population_means , properties ) \n>>> df \n simplicity complexity informativity optimality \n stat \n t-statistic 4.101937 -4.101937 3.126855 4.031027 \n Two-sided p-value 0.014830 0.014830 0.035292 0.015720 \n\n
\n", "signature": "(\tsub_population : pandas . core . frame . DataFrame , \tpopulation_means : dict , \tproperties : list ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, "ultk.effcomm.information": {"fullname": "ultk.effcomm.information", "modulename": "ultk.effcomm.information", "kind": "module", "doc": "Helper functions for Rate-Distortion based (including Information Bottleneck) efficient communication analyses.
\n"}, "ultk.effcomm.information.information_rate": {"fullname": "ultk.effcomm.information.information_rate", "modulename": "ultk.effcomm.information", "qualname": "information_rate", "kind": "function", "doc": "Compute the information rate / complexity of the encoder q(w|m) as $I[W:M]$.
\n", "signature": "(source : numpy . ndarray , encoder : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.get_rd_curve": {"fullname": "ultk.effcomm.information.get_rd_curve", "modulename": "ultk.effcomm.information", "qualname": "get_rd_curve", "kind": "function", "doc": "Use the Blahut Arimoto algorithm to obtain a list of (rate, distortion) points.
\n", "signature": "(\tprior : numpy . ndarray , \tdist_mat : numpy . ndarray , \tbetas : numpy . ndarray = None ) -> list [ tuple [ float ]] : ", "funcdef": "def"}, "ultk.effcomm.information.expected_distortion": {"fullname": "ultk.effcomm.information.expected_distortion", "modulename": "ultk.effcomm.information", "qualname": "expected_distortion", "kind": "function", "doc": "$D[X, \\hat{X}] = \\sum_x p(x) \\sum_{\\hat{x}} p(\\hat{x}|x) \\cdot d(x, \\hat{x})$
\n", "signature": "(\tp_x : numpy . ndarray , \tp_xhat_x : numpy . ndarray , \tdist_mat : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.compute_rate_distortion": {"fullname": "ultk.effcomm.information.compute_rate_distortion", "modulename": "ultk.effcomm.information", "qualname": "compute_rate_distortion", "kind": "function", "doc": "Compute the information rate $I(X;\\hat{X})$ and total distortion $D[X, \\hat{X}]$ of a joint distribution defind by $P(X)$ and $P(\\hat{X}|X)$.
\n\nArguments: \n\n\np_x: array of shape |X| the prior probability of an input symbol (i.e., the source)p_xhat_x: array of shape (|X|, |X_hat|) the probability of an output symbol given the inputdist_mat: array of shape (|X|, |X_hat|) representing the distoriton matrix between the input alphabet and the reconstruction alphabet. \n\nReturns: \n\n\n a (rate, distortion) tuple containing the information rate (in bits) of compressing X into X_hat and the expected distortion between X, X_hat
\n \n", "signature": "(p_x , p_xhat_x , dist_mat ) -> tuple [ numpy . ndarray ] : ", "funcdef": "def"}, "ultk.effcomm.information.blahut_arimoto": {"fullname": "ultk.effcomm.information.blahut_arimoto", "modulename": "ultk.effcomm.information", "qualname": "blahut_arimoto", "kind": "function", "doc": "Compute the rate-distortion function of an i.i.d distribution
\n\nArguments: \n\n\ndist_mat: array of shape (|X|, |X_hat|) representing the distortion matrix between the input alphabet and the reconstruction alphabet. dist_mat[i,j] = dist(x[i],x_hat[j]). In this context, X is a random variable representing the a speaker's meaning (target referent), and X_hat is a random variable representing a listener's meaning (guessed referent).p_x: (1D array of shape |X|) representing the probability mass function of the source. In this context, the prior over states of nature.beta: (scalar) the slope of the rate-distoriton function at the point where evaluation is requiredmax_it: max number of iterationseps: accuracy required by the algorithm: the algorithm stops if there is no change in distoriton value of more than 'eps' between consequtive iterationsignore_converge: whether to run the optimization until max_it, ignoring the stopping criterion specified by eps. \n\nReturns: \n\n\n a dict of the form
\n\n{\n 'final': a tuple of (rate, distortion) values. This is the rate (in bits) of compressing X into X_hat, and distortion between X, X_hat\n\n 'trajectory': a list of the (rate, distortion) points discovered during optimization\n}\n \n", "signature": "(\tdist_mat : numpy . ndarray , \tp_x : numpy . ndarray , \tbeta : float , \tmax_it : int = 200 , \teps : float = 1e-05 , \tignore_converge : bool = False ) -> tuple [ float ] : ", "funcdef": "def"}, "ultk.effcomm.information.get_ib_curve": {"fullname": "ultk.effcomm.information.get_ib_curve", "modulename": "ultk.effcomm.information", "qualname": "get_ib_curve", "kind": "function", "doc": "Get a list of (complexity, accuracy) or (complexity, distortion) points. A minimal wrapper of get_bottleneck.
\n\nArguments: \n\n\nprior: array of shape |meanings|meaning_dists: array of shape (|meanings|, |meanings|) representing the distribution over world states given meanings.curve_type: {'informativity', 'comm_cost'} specifies whether to return the (classic) IB axes of informativity vs. complexity, or the more Rate-Distortion Theory aligned axes of comm_cost vs. complexity. The latter can be obtained easily from the former by subtracting each informativity value from I[M:U], which is a constant for all languages in the same domain.maxbeta: the maximum value of beta to use to compute the curve.minbeta: the minimum value of beta to use.numbeta: the number of (equally-spaced) beta values to consider to compute the curve.processes: number of cpu threads to run in parallel (default = 1) \n\nReturns: \n\n\n an array of shape (num_points, 2) representing the list of (accuracy/comm_cost, complexity) points on the information plane.
\n \n", "signature": "(\tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray , \tmaxbeta : float , \tminbeta : float , \tnumbeta : float , \tprocesses : int = 1 , \tcurve_type : str = 'informativity' ) -> tuple [ float ] : ", "funcdef": "def"}, "ultk.effcomm.information.get_bottleneck": {"fullname": "ultk.effcomm.information.get_bottleneck", "modulename": "ultk.effcomm.information", "qualname": "get_bottleneck", "kind": "function", "doc": "Compute the IB curve bound (I[M:W] vs. I[W:U]). We use the embo package, which has support for smoothing any non-monotonicity in the bound resulting from BA optimization getting stuck in local minima.
\n\nArguments: \n\n\nprior: array of shape |meanings|meaning_dists: array of shape (|meanings|, |meanings|) representing the distribution over world states given meanings.curve_type: {'informativity', 'comm_cost'} specifies whether to return the (classic) IB axes of informativity vs. complexity, or the more Rate-Distortion Theory aligned axes of comm_cost vs. complexity. The comm_cost can be obtained easily from informativity by subtracting each informativity value from I[M:U], which is a constant for all languages in the same domain.maxbeta: the maximum value of beta to use to compute the curve.minbeta: the minimum value of beta to use.numbeta: the number of (equally-spaced) beta values to consider to compute the curve.processes: number of cpu threads to run in parallel (default = 1) \n\nReturns: \n\n\n a dict containing the coordinates and encoders corresponding to IB optima, of the form
\n\n{\n\"encoders\": an array of shape `(num_meanings, num_words)`,\n\n\"coordinates\": a tuple of arrays `(complexity, accuracy, comm_cost)` each of shape (`numbeta`,)\n\"beta\": an array of shape (`numbeta`,) corresponding to the actually used betas after non-monotonicity corrections.\n}\n \n", "signature": "(\tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray , \tmaxbeta : float , \tminbeta : float , \tnumbeta : float , \tprocesses : int = 1 ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.information.ib_complexity": {"fullname": "ultk.effcomm.information.ib_complexity", "modulename": "ultk.effcomm.information", "qualname": "ib_complexity", "kind": "function", "doc": "Compute the IB encoder complexity of a language $I[M:W]$.
\n", "signature": "(language : ultk . language . language . Language , prior : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.ib_informativity": {"fullname": "ultk.effcomm.information.ib_informativity", "modulename": "ultk.effcomm.information", "qualname": "ib_informativity", "kind": "function", "doc": "Compute the expected informativity (accuracy) $I[W:U]$ of a lexicon.
\n\nArguments: \n\n\nlanguage: the Language to measure for informativityprior: communicative need distributionmeaning_dists: array of shape (|meanings|, |meanings|) representing the distribution over world states given meanings. \n\nReturns: \n\n\n the informativity of the language I[W:U] in bits.
\n \n", "signature": "(\tlanguage : ultk . language . language . Language , \tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.ib_comm_cost": {"fullname": "ultk.effcomm.information.ib_comm_cost", "modulename": "ultk.effcomm.information", "qualname": "ib_comm_cost", "kind": "function", "doc": "Compute the IB communicative cost, i.e. expected KL-divergence betweeen speaker and listener meanings, for a language.
\n\nArguments: \n\n\nlanguage: the Language to measure for communicative costprior: communicative need distributionmeaning_dists: array of shape (|meanings|, |meanings|) representing the distribution over world states given meanings. \n\nReturns: \n\n\n the communicative cost, $\\mathbb{E}[D_{KL}[M || \\hat{M}]] = I[M:U] - I[W:U]$ in bits.
\n \n", "signature": "(\tlanguage : ultk . language . language . Language , \tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.language_to_ib_encoder_decoder": {"fullname": "ultk.effcomm.information.language_to_ib_encoder_decoder", "modulename": "ultk.effcomm.information", "qualname": "language_to_ib_encoder_decoder", "kind": "function", "doc": "Convert a Language, a mapping of words to meanings, to IB encoder, q(w|m) and IB decoder q(m|w).
\n\nArguments: \n\n\nlanguage: the lexicon from which to infer a speaker (encoder).prior: communicative need distribution \n\nReturns: \n\n\n a dict of the form\n {\n \"encoder\": np.ndarray of shape (|meanings|, |words|),\n \"decoder\": np.ndarray of shape (|words|, |meanings|),\n }
\n \n", "signature": "(\tlanguage : ultk . language . language . Language , \tprior : numpy . ndarray ) -> dict [ str , numpy . ndarray ] : ", "funcdef": "def"}, "ultk.effcomm.information.ib_accuracy": {"fullname": "ultk.effcomm.information.ib_accuracy", "modulename": "ultk.effcomm.information", "qualname": "ib_accuracy", "kind": "function", "doc": "Return the accuracy of the lexicon I[W:U]
\n\nArguments: \n\n\nencoder: array of shape (|M|, |W|) representing P(W | M)decoder: array of shape (|W|, |M|) representing P(M | W)meaning_dists: array of shape (|M|, |U|) representing P(U | M)prior: array of shape |M| representing P(M) \n\nReturns: \n\n\n the accuracy of the lexicon I[W:U]
\n \n", "signature": "(\tencoder : numpy . ndarray , \tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.ib_distortion": {"fullname": "ultk.effcomm.information.ib_distortion", "modulename": "ultk.effcomm.information", "qualname": "ib_distortion", "kind": "function", "doc": "Return the IB distortion measure E[DKL[ M || M_hat ]]
\n\nArguments: \n\n\nencoder: array of shape (|M|, |W|) representing P(W | M)decoder: array of shape (|W|, |M|) representing P(M | W)meaning_dists: array of shape (|M|, |U|) representing P(U | M)prior: array of shape |M| representing P(M) \n\nReturns: \n\n\n the distortion E[DKL[ M || M_hat ]] = I[M:U] - I[W:U]
\n \n", "signature": "(\tencoder : numpy . ndarray , \tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray ) -> float : ", "funcdef": "def"}, "ultk.effcomm.information.ib_encoder_to_point": {"fullname": "ultk.effcomm.information.ib_encoder_to_point", "modulename": "ultk.effcomm.information", "qualname": "ib_encoder_to_point", "kind": "function", "doc": "Return (complexity, accuracy, comm_cost) IB coordinates.
\n\nArguments: \n\n\nmeaning_dists: array of shape (|meanings|, |meanings|) representing the distribution over world states given meanings.prior: array of shape |M| representing the cognitive sourceencoder: array of shape (|M|, |W|) representing P(W | M)decoder: array of shape (|W|, |M|) representing P(M | W). By default is None, and the Bayesian optimal decoder will be inferred. \n", "signature": "(\tmeaning_dists : numpy . ndarray , \tprior : numpy . ndarray , \tencoder : numpy . ndarray , \tdecoder : numpy . ndarray = None ) -> tuple [ float ] : ", "funcdef": "def"}, "ultk.effcomm.information.ib_optimal_decoder": {"fullname": "ultk.effcomm.information.ib_optimal_decoder", "modulename": "ultk.effcomm.information", "qualname": "ib_optimal_decoder", "kind": "function", "doc": "Compute the bayesian optimal decoder. See https://github.com/nogazs/ib-color-naming/blob/master/src/ib_naming_model.py#L40
\n\nArguments: \n\n\nencoder: array of shape (|words|, |meanings|)prior: array of shape (|meanings|,)meaning_dists: array of shape (|meanings|, |meanings|) \n\nReturns: \n\n\n array of shape (|words|, |meanings|) representing the 'optimal' deterministic decoder
\n \n", "signature": "(\tencoder : numpy . ndarray , \tprior : numpy . ndarray , \tmeaning_dists : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.informativity": {"fullname": "ultk.effcomm.informativity", "modulename": "ultk.effcomm.informativity", "kind": "module", "doc": "Functions for measuring informativity in efficient communication analyses of languages.
\n"}, "ultk.effcomm.informativity.indicator_utility": {"fullname": "ultk.effcomm.informativity.indicator_utility", "modulename": "ultk.effcomm.informativity", "qualname": "indicator_utility", "kind": "function", "doc": "Indicator utility function, i.e. delta. Returns 1.0 iff ref1 equals ref2.
\n", "signature": "(\tref1 : ultk . language . semantics . Referent , \tref2 : ultk . language . semantics . Referent ) -> float : ", "funcdef": "def"}, "ultk.effcomm.informativity.informativity": {"fullname": "ultk.effcomm.informativity.informativity", "modulename": "ultk.effcomm.informativity", "qualname": "informativity", "kind": "function", "doc": "The informativity of a language is identified with the successful communication between a speaker and a listener.
\n\nThis function is a wrapper for communicative_success.
\n\nArguments: \n\n\nlanguage: the language to compute informativity of.prior: a probability distribution representing communicative need (frequency) for Referents.utility: a function representing the usefulness of listener guesses about speaker Referents, e.g. Referent similarity. To reward only exact recovery of meanings, use the indicator function (default).kind: {\"literal, pragmatic\"} Whether to measure informativity using literal or pragmatic agents, as canonically described in the Rational Speech Act framework. The default is \"literal\". \n\nConcepts :\n The speaker can be thought of as a conditional distribution over expressions given meanings. The listener is likewise a conditional distribution over meanings given expressions. The communicative need, or cognitive source, is a prior probability over meanings representing how frequently agents need to use certain meanings in communication. The utility function represents the similarity, or appropriateness, of the listener's guess m' about the speaker's intended meaning m.
\n\nFormula :\n The informativity of a language $L$ with meaning space $M$ is defined:
\n\n$I(L) := \\sum_{m \\in M} p(m) \\sum_{i \\in L} p(i|m) \\sum_{\\hat{m} \\in i} p(\\hat{m}|i) \\cdot u(m, \\hat{m})$
\n\nBounds :\n A perfectly informative (=1.0) language can be constructed with a exactly one expression for each meaning.
\n\nFor u() = indicator(), every language has nonzero informativity because a language must contain at least one expression, and an expression must contain at least one meaning.\n(\tlanguage : ultk . language . language . Language , \tprior : numpy . ndarray , \tutility : Callable [[ ultk . language . semantics . Referent , ultk . language . semantics . Referent ], float ] = < function indicator_utility > , \tagent_type : str = 'literal' ) -> float : ", "funcdef": "def"}, "ultk.effcomm.informativity.communicative_success": {"fullname": "ultk.effcomm.informativity.communicative_success", "modulename": "ultk.effcomm.informativity", "qualname": "communicative_success", "kind": "function", "doc": "Helper function to compute the literal informativity of a language.
\n\n$I(L) = \\sum_{m, \\hat{m}} P(m, \\hat{m}) \\cdot u(m, \\hat{m})$
\n\n$ = \\sum_{m \\in M} p(m) \\sum_{i \\in L} p(i|m) \\sum_{\\hat{m} \\in i} p(\\hat{m} |i) \\cdot u(m, m')$
\n\n$ = \\sum \\text{diag}(p)SR \\odot U $
\n\nFor more details, see docs/vectorized_informativity .
\n\nArguments: \n\n\nspeaker: a literal or pragmatic speaker, containing a matrix S for P(e | m)listener: a literal or pragmatic listener, containing a matrix R for P(m | e)prior: p(m), distribution over meanings representing communicative needutility: a function u(m, m') representing similarity of meanings, or pair-wise usefulness of listener guesses about speaker meanings. \n", "signature": "(\tspeaker : ultk . effcomm . agent . Speaker , \tlistener : ultk . effcomm . agent . Listener , \tprior : numpy . ndarray , \tutility : Callable [[ ultk . language . semantics . Referent , ultk . language . semantics . Referent ], float ] ) -> float : ", "funcdef": "def"}, "ultk.effcomm.optimization": {"fullname": "ultk.effcomm.optimization", "modulename": "ultk.effcomm.optimization", "kind": "module", "doc": "Classes and functions for generating languages that optimize the simplicity/informativeness trade-off, e.g. via an iterative evolutionary algorithm.
\n"}, "ultk.effcomm.optimization.Mutation": {"fullname": "ultk.effcomm.optimization.Mutation", "modulename": "ultk.effcomm.optimization", "qualname": "Mutation", "kind": "class", "doc": "
\n"}, "ultk.effcomm.optimization.Mutation.precondition": {"fullname": "ultk.effcomm.optimization.Mutation.precondition", "modulename": "ultk.effcomm.optimization", "qualname": "Mutation.precondition", "kind": "function", "doc": "Whether a mutation is allowed to apply to a language.
\n", "signature": "(language : ultk . language . language . Language , ** kwargs ) -> bool : ", "funcdef": "def"}, "ultk.effcomm.optimization.Mutation.mutate": {"fullname": "ultk.effcomm.optimization.Mutation.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "Mutation.mutate", "kind": "function", "doc": "Mutate the language, possibly using a list of expressions.
\n", "signature": "(\tlanguage : ultk . language . language . Language , \texpressions : list [ ultk . language . language . Expression ] , \t** kwargs ) -> ultk . language . language . Language : ", "funcdef": "def"}, "ultk.effcomm.optimization.RemoveExpression": {"fullname": "ultk.effcomm.optimization.RemoveExpression", "modulename": "ultk.effcomm.optimization", "qualname": "RemoveExpression", "kind": "class", "doc": "
\n", "bases": "Mutation"}, "ultk.effcomm.optimization.RemoveExpression.precondition": {"fullname": "ultk.effcomm.optimization.RemoveExpression.precondition", "modulename": "ultk.effcomm.optimization", "qualname": "RemoveExpression.precondition", "kind": "function", "doc": "Whether a mutation is allowed to apply to a language.
\n", "signature": "(language : ultk . language . language . Language , ** kwargs ) -> bool : ", "funcdef": "def"}, "ultk.effcomm.optimization.RemoveExpression.mutate": {"fullname": "ultk.effcomm.optimization.RemoveExpression.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "RemoveExpression.mutate", "kind": "function", "doc": "Mutate the language, possibly using a list of expressions.
\n", "signature": "(\tlanguage : ultk . language . language . Language , \texpressions : list [ ultk . language . language . Expression ] , \t** kwargs ) -> ultk . language . language . Language : ", "funcdef": "def"}, "ultk.effcomm.optimization.AddExpression": {"fullname": "ultk.effcomm.optimization.AddExpression", "modulename": "ultk.effcomm.optimization", "qualname": "AddExpression", "kind": "class", "doc": "
\n", "bases": "Mutation"}, "ultk.effcomm.optimization.AddExpression.precondition": {"fullname": "ultk.effcomm.optimization.AddExpression.precondition", "modulename": "ultk.effcomm.optimization", "qualname": "AddExpression.precondition", "kind": "function", "doc": "Whether a mutation is allowed to apply to a language.
\n", "signature": "(language : ultk . language . language . Language , ** kwargs ) -> bool : ", "funcdef": "def"}, "ultk.effcomm.optimization.AddExpression.mutate": {"fullname": "ultk.effcomm.optimization.AddExpression.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "AddExpression.mutate", "kind": "function", "doc": "Mutate the language, possibly using a list of expressions.
\n", "signature": "(\tlanguage : ultk . language . language . Language , \texpressions : list [ ultk . language . language . Expression ] , \t** kwargs ) -> ultk . language . language . Language : ", "funcdef": "def"}, "ultk.effcomm.optimization.EvolutionaryOptimizer": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer", "kind": "class", "doc": "Class for approximating the Pareto frontier of languages optimizing the simplicity/informativity trade-off.
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.__init__": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.__init__", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.__init__", "kind": "function", "doc": "Initialize the evolutionary algorithm configurations.
\n\nThe measures of complexity and informativity, the expressions, and the mutations are all specific to the particular semantic domain.
\n\nArguments: \n\n\nobjectives: a dict of the two objectives to optimize for, e.g. simplicity and informativeness, of the form, e.g.\n{\n \"complexity\": comp_measure,\n \"comm_cost\": lambda l: 1 - inf_measure(l)\n}expressions: a list of expressions from which to apply mutations to languages.sample_size: the size of the population at every generation.max_muatations: between 1 and this number of mutations will be applied to a subset of the population at the end of each generation.generations: how many iterations to run the evolutionary algorithm for.lang_size: between 1 and this number of expressions comprise a language.mutations: (optional) a list of Mutation objects, defaults to add/remove expression \n", "signature": "(\tobjectives : list [ typing . Callable [[ ultk . language . language . Language ], typing . Any ]] , \texpressions : list [ ultk . language . language . Expression ] , \tsample_size : int , \tmax_mutations : int , \tgenerations : int , \tlang_size : int = None , \tmutations: list[ultk.effcomm.optimization.Mutation] = [<class 'ultk.effcomm.optimization.AddExpression'>, <class 'ultk.effcomm.optimization.RemoveExpression'>] ) "}, "ultk.effcomm.optimization.EvolutionaryOptimizer.objectives": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.objectives", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.objectives", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.expressions": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.expressions", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.expressions", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.mutations": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.mutations", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.mutations", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.sample_size": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.sample_size", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.sample_size", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.max_mutations": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.max_mutations", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.max_mutations", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.generations": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.generations", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.generations", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.lang_size": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.lang_size", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.lang_size", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.dominating_languages": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.dominating_languages", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.dominating_languages", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.explored_languages": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.explored_languages", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.explored_languages", "kind": "variable", "doc": "
\n"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.fit": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.fit", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.fit", "kind": "function", "doc": "Computes the Pareto frontier, a set languages which cannot be both more simple and more informative.
\n\nUses pygmo's nondominated_front method for computing a population's best solutions to a multi-objective optimization problem.
\n\nArguments: \n\n\nseed_population: a list of languages representing the population at generation 0 of the algorithm.explore: a float in [0,1] representing how much to optimize for fitness\n(optimality wrt pareto front of complexity and comm_cost), and how much to randomly explore. \n\nReturns: \n\n\n a dict of the estimated optimization solutions, as well as points explored along the way; of the form
\n\n{\n\"dominating_languages\": list of languages as estimated solutions,\n\"explored_languages\": list of all the languages explored during the evolutionary algorithm,\n}\n \n", "signature": "(\tself , \tseed_population : list [ ultk . language . language . Language ] , \texplore : float = 0.0 ) -> dict [ str , list [ ultk . language . language . Language ]] : ", "funcdef": "def"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.sample_mutated": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.sample_mutated", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.sample_mutated", "kind": "function", "doc": "Arguments: \n\n\nlanguages: dominating languages of a generationamount: sample_size.expressions: the list of expressions \n\nReturns: \n\n\n list of updated languages
\n \n", "signature": "(\tself , \tlanguages : list [ ultk . language . language . Language ] ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, "ultk.effcomm.optimization.EvolutionaryOptimizer.mutate": {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.mutate", "kind": "function", "doc": "Randomly selects a mutation that is allowed to apply and applies it to a language.
\n\nArguments: \n\n\nlanguage: the Language to mutateexpressions: the list of all possible expressions.\nSome mutations need access to this list, so it is part of the mutation api. \n\nReturns: \n\n\n the mutated Language
\n \n", "signature": "(\tself , \tlanguage : ultk . language . language . Language ) -> ultk . language . language . Language : ", "funcdef": "def"}, "ultk.effcomm.optimization.sample_parents": {"fullname": "ultk.effcomm.optimization.sample_parents", "modulename": "ultk.effcomm.optimization", "qualname": "sample_parents", "kind": "function", "doc": "Use the explore parameter to explore possibly suboptimal areas of the language space.
\n\nArguments: \n\n\ndominating_languages: a list of the languages with current best fitness with respect to the objectives.explored_languages: a list of all languages encountered during the evolutionary algorithm.explore: a float in [0,1] specifying how much to explore possibly suboptimal languages.\nIf set to 0, parent_languages is just dominating_languages. \n\nReturns: \n\n\n the languages to serve as the next generation (after possible mutations)
\n \n", "signature": "(\tdominating_languages : set [ ultk . language . language . Language ] , \texplored_languages : set [ ultk . language . language . Language ] , \texplore : float ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, "ultk.effcomm.sampling": {"fullname": "ultk.effcomm.sampling", "modulename": "ultk.effcomm.sampling", "kind": "module", "doc": "Functions for sampling expressions into languages.
\n"}, "ultk.effcomm.sampling.get_hypothetical_variants": {"fullname": "ultk.effcomm.sampling.get_hypothetical_variants", "modulename": "ultk.effcomm.sampling", "qualname": "get_hypothetical_variants", "kind": "function", "doc": "For each system (parameterized by a language or else a speaker), generate num hypothetical variants by permuting the signals that the system assigns to states.
\n\nArguments: \n\n\nlanguages: a list of languages to permute, by constructing LiteralSpeakers and permuting their weights.speakers: a list of speakers of a language, whose weights can be directly permuted. Should be used instead of languages if possible, because it can be more finegrained (every language can be associated with multiple speakers).total: the total number of hypothetical variants to obtain \n\nReturns: \n\n\n hypothetical_variants: a list of type either Language or np.ndarray depending on whether languages or speakers was passed, representing hypothetical variants of the systems passed. If speakers was passed, a list of speakers is returned.
\n \n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] = None , \tspeakers : list [ ultk . effcomm . agent . Speaker ] = None , \ttotal : int = 0 ) -> list [ typing . Any ] : ", "funcdef": "def"}, "ultk.effcomm.tradeoff": {"fullname": "ultk.effcomm.tradeoff", "modulename": "ultk.effcomm.tradeoff", "kind": "module", "doc": "Functions for constructing an efficient communication analysis by measuring the simplicity/informativeness trade-off languages and formatting results as a dataframe or a plot.
\n"}, "ultk.effcomm.tradeoff.dominates": {"fullname": "ultk.effcomm.tradeoff.dominates", "modulename": "ultk.effcomm.tradeoff", "qualname": "dominates", "kind": "function", "doc": "Determine whether p1 dominates p2,\ni.e. whether for every i p1[i] <= p2[i]\nand for some i p1[i] < p2[i].
\n\nArguments: \n\n\np1: a pointp2: another point \n\nReturns: \n\n\n whether or not p1 dominates p2
\n \n", "signature": "(p1 : list [ float ] , p2 : list [ float ] ) -> bool : ", "funcdef": "def"}, "ultk.effcomm.tradeoff.non_dominated_2d": {"fullname": "ultk.effcomm.tradeoff.non_dominated_2d", "modulename": "ultk.effcomm.tradeoff", "qualname": "non_dominated_2d", "kind": "function", "doc": "Return the non-dominated (Pareto) front of a list of 2-D points, using Kung's algorithm.
\n\nArguments: \n\n\npoints: A list of 2-D points \n\nReturns: \n\n\n a list, the indices of points for which no other point is as good on all dimensions\n and better on at least one
\n \n", "signature": "(points : list [ tuple [ float , float ]] ) -> list [ int ] : ", "funcdef": "def"}, "ultk.effcomm.tradeoff.pareto_optimal_languages": {"fullname": "ultk.effcomm.tradeoff.pareto_optimal_languages", "modulename": "ultk.effcomm.tradeoff", "qualname": "pareto_optimal_languages", "kind": "function", "doc": "Use non_dominated_2d to compute the Pareto languages.
\n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] , \tobjectives : list [ typing . Callable [[ ultk . language . language . Language ], typing . Any ]] , \tunique : bool = False ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, "ultk.effcomm.tradeoff.pareto_min_distances": {"fullname": "ultk.effcomm.tradeoff.pareto_min_distances", "modulename": "ultk.effcomm.tradeoff", "qualname": "pareto_min_distances", "kind": "function", "doc": "Measure the Pareto optimality of each language by measuring its Euclidean closeness to the frontier. The frontier is a line (list of points) interpolated from the pareto points.
\n\nArguments: \n\n\npoints: the list of all language (x, y) pairs, where x and y are usually communicative cost and complexity.pareto_points: the list of all dominant language (x, y) pairs to constitute the Pareto frontier. The points should have been measured by pygmo's non_dominated_front_2d function. \n\nReturns: \n\n\n min_distances: an array of shape len(points) Euclidean distances for each language to the closest point on the Pareto frontier.
\n \n", "signature": "(points : list [ tuple ] , pareto_points : list [ tuple ] ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.tradeoff.interpolate_data": {"fullname": "ultk.effcomm.tradeoff.interpolate_data", "modulename": "ultk.effcomm.tradeoff", "qualname": "interpolate_data", "kind": "function", "doc": "Interpolate the points yielded by the pareto optimal languages into a continuous (though not necessarily smooth) curve.
\n\nArguments: \n\n\npoints: an list of (comm_cost, complexity) pairs of size [dominating_languages], a possibly non-smooth set of solutions to the trade-off.min_cost: the minimum communicative cost value possible to interpolate from.max_cost: the maximum communicative cost value possible to interpolate from. A natural assumption is to let complexity=0.0 if max_cost=1.0, which will result in a Pareto curve that spans the entire 2d space, and consequently the plot with x and y limits both ranging [0.0, 1.0].num: the number of x-axis points (cost) to interpolate. Controls smoothness of curve. \n\nReturns: \n\n\n interpolated_points: an array of size (num, num)
\n \n", "signature": "(\tpoints : list [ tuple [ float ]] , \tmin_cost : float = 0.0 , \tmax_cost : float = 1.0 , \tnum = 5000 ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.tradeoff.tradeoff": {"fullname": "ultk.effcomm.tradeoff.tradeoff", "modulename": "ultk.effcomm.tradeoff", "qualname": "tradeoff", "kind": "function", "doc": "Builds a final efficient communication analysis by measuring a list of languages, updating their internal data, and returning the results.
\n\nThis function measures possibly many graded or categorical properties of each language, but minimally the properties of commmunicative cost and complexity. These two measures fully define the results of an efficiency analysis, in the sense they define the optimal solutions.
\n\nArguments: \n\n\nlanguages: A list representing the pool of all languages to be measured for an efficient communication analysis.x: the first pressure to measure, e.g. communicative cost.y: the second pressure to measure, e.g. cognitive complexity.frontier: a list of (comm_cost, complexity) points representing a Pareto frontier to measure optimality w.r.t. \n\nReturns: \n\n\n a dictionary of the population and the pareto front, of the form
\n\n{\n \"languages\": the list of languages, with their internal efficient communication data updated,\n\n \"dominating_languages\": the list of the languages dominating the population w.r.t. comm_cost and complexity. If no `frontier` is none, this can be considered the Pareto frontier.\n}\n \n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] , \tproperties : dict [ str , typing . Callable [[ ultk . language . language . Language ], typing . Any ]] , \tx : str = 'comm_cost' , \ty : str = 'complexity' , \tfrontier : list [ tuple ] = None ) -> dict [ str , list [ ultk . language . language . Language ]] : ", "funcdef": "def"}, "ultk.effcomm.util": {"fullname": "ultk.effcomm.util", "modulename": "ultk.effcomm.util", "kind": "module", "doc": "Various helper functions for computing complexity and informativity.
\n"}, "ultk.effcomm.util.rows_zero_to_uniform": {"fullname": "ultk.effcomm.util.rows_zero_to_uniform", "modulename": "ultk.effcomm.util", "qualname": "rows_zero_to_uniform", "kind": "function", "doc": "Ensure that mat encodes a probability distribution, i.e. each row (indexed by a meaning) is a distribution over expressions: sums to exactly 1.0.
\n\nThis is necessary when exploring mathematically possible languages (including natural languages, like Hausa in the case of modals) which sometimes have that a row of the matrix p(word|meaning) is a vector of 0s.
\n\nArguments: \n\n\nmat: a 2D numpy array that should be normalized so that each row is a probability distribution. \n", "signature": "(mat : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.util.build_utility_matrix": {"fullname": "ultk.effcomm.util.build_utility_matrix", "modulename": "ultk.effcomm.util", "qualname": "build_utility_matrix", "kind": "function", "doc": "Construct the square matrix specifying the utility function defined for pairs of meanings, used for computing communicative success.
\n", "signature": "(\tuniverse : ultk . language . semantics . Universe , \tutility : Callable [[ ultk . language . semantics . Referent , ultk . language . semantics . Referent ], float ] ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.effcomm.util.PRECISION": {"fullname": "ultk.effcomm.util.PRECISION", "modulename": "ultk.effcomm.util", "qualname": "PRECISION", "kind": "variable", "doc": "
\n", "default_value": "1e-16"}, "ultk.effcomm.util.marginal": {"fullname": "ultk.effcomm.util.marginal", "modulename": "ultk.effcomm.util", "qualname": "marginal", "kind": "function", "doc": "Compute $p(x) = \\sum_x p(x,y)$
\n\nArguments: \n\n\npXY: a numpy array of shape (|X|, |Y|) \n\nReturns: \n\n\n pY: (axis = 0) or pX (default, axis = 1)
\n \n", "signature": "(pXY , axis = 1 ): ", "funcdef": "def"}, "ultk.effcomm.util.conditional": {"fullname": "ultk.effcomm.util.conditional", "modulename": "ultk.effcomm.util", "qualname": "conditional", "kind": "function", "doc": "Compute $p(y|x) = \\frac{p(x,y)}{p(x)}$
\n\nArguments: \n\n\npXY: a numpy array of shape (|X|, |Y|) \n\nReturns: \n\n\n pY_X: a numpy array of shape (|X|, |Y|)
\n \n", "signature": "(pXY ): ", "funcdef": "def"}, "ultk.effcomm.util.joint": {"fullname": "ultk.effcomm.util.joint", "modulename": "ultk.effcomm.util", "qualname": "joint", "kind": "function", "doc": "Compute $p(x,y) = p(y|x) \\cdot p(x) $
\n\nArguments: \n\n\npY_X: a numpy array of shape (|X|, |Y|)pX: a numpy array |X| \n\nReturns: \n\n\n pXY: a numpy array of the shape (|X|, |Y|)
\n \n", "signature": "(pY_X , pX ): ", "funcdef": "def"}, "ultk.effcomm.util.marginalize": {"fullname": "ultk.effcomm.util.marginalize", "modulename": "ultk.effcomm.util", "qualname": "marginalize", "kind": "function", "doc": "Compute $p(y) = \\sum_x p(y|x) \\cdot p(x)$
\n\nArguments: \n\n\npY_X: a numpy array of shape (|X|, |Y|)pX: a numpy array of shape |X| \n\nReturns: \n\n\n pY: a numpy array of shape |Y|
\n \n", "signature": "(pY_X , pX ): ", "funcdef": "def"}, "ultk.effcomm.util.bayes": {"fullname": "ultk.effcomm.util.bayes", "modulename": "ultk.effcomm.util", "qualname": "bayes", "kind": "function", "doc": "Compute $p(x|y) = \\frac{p(y|x) \\cdot p(x)}{p(y)}$
\n\nArguments: \n\n\npY_X: a numpy array of shape (|X|, |Y|) \n", "signature": "(pY_X , pX ): ", "funcdef": "def"}, "ultk.effcomm.util.xlogx": {"fullname": "ultk.effcomm.util.xlogx", "modulename": "ultk.effcomm.util", "qualname": "xlogx", "kind": "function", "doc": "Compute $x \\log p(x)$
\n", "signature": "(p ): ", "funcdef": "def"}, "ultk.effcomm.util.H": {"fullname": "ultk.effcomm.util.H", "modulename": "ultk.effcomm.util", "qualname": "H", "kind": "function", "doc": "Compute the entropy of p, $H(X) = - \\sum_x x \\log p(x)$
\n", "signature": "(p , axis = None ): ", "funcdef": "def"}, "ultk.effcomm.util.MI": {"fullname": "ultk.effcomm.util.MI", "modulename": "ultk.effcomm.util", "qualname": "MI", "kind": "function", "doc": "Compute mutual information, $I[X:Y]$
\n", "signature": "(pXY ): ", "funcdef": "def"}, "ultk.effcomm.util.DKL": {"fullname": "ultk.effcomm.util.DKL", "modulename": "ultk.effcomm.util", "qualname": "DKL", "kind": "function", "doc": "Compute KL divergences, $D_{KL}[p~||~q]$
\n", "signature": "(p , q , axis = None ): ", "funcdef": "def"}, "ultk.effcomm.util.gNID": {"fullname": "ultk.effcomm.util.gNID", "modulename": "ultk.effcomm.util", "qualname": "gNID", "kind": "function", "doc": "Compute Generalized Normalized Informational Distance between two encoders.
\n\nArguments: \n\n\npW_X: first encoder of shape (|meanings|, |words|)pV_X: second encoder of shape (|meanings|, |words|)pX: prior over source variables of shape (|meanings|,) \n", "signature": "(pW_X , pV_X , pX ): ", "funcdef": "def"}, "ultk.language": {"fullname": "ultk.language", "modulename": "ultk.language", "kind": "module", "doc": "Classes for modeling (natural or hypothetical) languagese.
\n\nAt the current stage of development, ULTK focuses on supporting abstractions to model the mapping between expressions and meanings of a language. So far, we leave almost everything besides this basic mapping (morphosyntax, phonology, phonetic inventories, among other features of human languages) to future work.
\n\nThe ultk.language.language submodule contains classes for constructing a language, which can contain one or more expressions.
\n\nThe ultk.language.semantics submodule contains classes for defining a universe (meaning space) of referents (denotations) and meanings (categories).
\n"}, "ultk.language.grammar": {"fullname": "ultk.language.grammar", "modulename": "ultk.language.grammar", "kind": "module", "doc": "
\n"}, "ultk.language.grammar.Rule": {"fullname": "ultk.language.grammar.Rule", "modulename": "ultk.language.grammar", "qualname": "Rule", "kind": "class", "doc": "Basic class for a grammar rule. Grammar rules in ULTK correspond\nto functions. One can think of a grammar as generating complex functions from\nmore basic ones.
\n\nAttributes: \n\n\nlhs: left-hand side of the rule (can be anything)\nconceptually, the output type of a functionrhs: right-hand side; assumed to be an iterable\nconceptually, a list of types of inputsfunc: a callable, the function to be computed when a node with this rule is executedname: name of the functionweight: a relative weight to assign to this rule\nwhen added to a grammar, all rules with the same LHS will be weighted together \n"}, "ultk.language.grammar.Rule.__init__": {"fullname": "ultk.language.grammar.Rule.__init__", "modulename": "ultk.language.grammar", "qualname": "Rule.__init__", "kind": "function", "doc": "
\n", "signature": "(\tname : str , \tlhs : Any , \trhs : collections . abc . Sequence | None , \tfunction : Callable = < function Rule .< lambda >> , \tweight : float = 1.0 ) "}, "ultk.language.grammar.Rule.lhs": {"fullname": "ultk.language.grammar.Rule.lhs", "modulename": "ultk.language.grammar", "qualname": "Rule.lhs", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.Rule.rhs": {"fullname": "ultk.language.grammar.Rule.rhs", "modulename": "ultk.language.grammar", "qualname": "Rule.rhs", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.Rule.func": {"fullname": "ultk.language.grammar.Rule.func", "modulename": "ultk.language.grammar", "qualname": "Rule.func", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.Rule.name": {"fullname": "ultk.language.grammar.Rule.name", "modulename": "ultk.language.grammar", "qualname": "Rule.name", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.Rule.weight": {"fullname": "ultk.language.grammar.Rule.weight", "modulename": "ultk.language.grammar", "qualname": "Rule.weight", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.Rule.is_terminal": {"fullname": "ultk.language.grammar.Rule.is_terminal", "modulename": "ultk.language.grammar", "qualname": "Rule.is_terminal", "kind": "function", "doc": "Whether this is a terminal rule. In our framework, this means that RHS is empty,\ni.e. there are no arguments to the function.
\n", "signature": "(self ) -> bool : ", "funcdef": "def"}, "ultk.language.grammar.GrammaticalExpression": {"fullname": "ultk.language.grammar.GrammaticalExpression", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression", "kind": "class", "doc": "A GrammaticalExpression has been built up from a Grammar by applying a sequence of Rules.\nCrucially, it is _callable_, using the functions corresponding to each rule.
\n\nA GrammaticalExpression, when called, takes in a Referent. Because of this, a Meaning can\nbe generated by specifying a Universe (which contains Referents).
\n\nAttributes: \n\n\nname: name of the top-most functionfunc: the functionchildren: child expressions (possibly empty) \n", "bases": "ultk.language.language.Expression"}, "ultk.language.grammar.GrammaticalExpression.__init__": {"fullname": "ultk.language.grammar.GrammaticalExpression.__init__", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.__init__", "kind": "function", "doc": "
\n", "signature": "(\trule_name : str , \tfunc : Callable , \tchildren : tuple | None , \tmeaning : ultk . language . semantics . Meaning | None = None , \tform : str | None = None ) "}, "ultk.language.grammar.GrammaticalExpression.rule_name": {"fullname": "ultk.language.grammar.GrammaticalExpression.rule_name", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.rule_name", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.GrammaticalExpression.func": {"fullname": "ultk.language.grammar.GrammaticalExpression.func", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.func", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.GrammaticalExpression.children": {"fullname": "ultk.language.grammar.GrammaticalExpression.children", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.children", "kind": "variable", "doc": "
\n"}, "ultk.language.grammar.GrammaticalExpression.yield_string": {"fullname": "ultk.language.grammar.GrammaticalExpression.yield_string", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.yield_string", "kind": "function", "doc": "Get the 'yield' string of this term, i.e. the concatenation\nof the leaf nodes.
\n\nThis is useful for thinking of a Grammar as generating derivation trees for\nan underlying CFG. This method will then generate the strings generated by\nthe corresponding CFG.
\n", "signature": "(self ) -> str : ", "funcdef": "def"}, "ultk.language.grammar.GrammaticalExpression.evaluate": {"fullname": "ultk.language.grammar.GrammaticalExpression.evaluate", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.evaluate", "kind": "function", "doc": "
\n", "signature": "(\tself , \tuniverse : ultk . language . semantics . Universe ) -> ultk . language . semantics . Meaning : ", "funcdef": "def"}, "ultk.language.grammar.GrammaticalExpression.add_child": {"fullname": "ultk.language.grammar.GrammaticalExpression.add_child", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.add_child", "kind": "function", "doc": "
\n", "signature": "(self , child ) -> None : ", "funcdef": "def"}, "ultk.language.grammar.GrammaticalExpression.to_dict": {"fullname": "ultk.language.grammar.GrammaticalExpression.to_dict", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.to_dict", "kind": "function", "doc": "
\n", "signature": "(self ) -> dict : ", "funcdef": "def"}, "ultk.language.grammar.UniquenessArgs": {"fullname": "ultk.language.grammar.UniquenessArgs", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs", "kind": "class", "doc": "Arguments for specifying uniqueness of GrammaticalExpressions in a Grammar.
\n\nAttributes: \n\n\nunique_expressions: a dictionary in which to store unique Expressionskey: a function used to evaluate uniquenesscompare_func: a comparison function, used to decide which Expression to add to the dict\nnew Expressions will be added as values to unique_dict only if they are minimal\namong those sharing the same key (by unique_key) according to this func \n", "bases": "typing.TypedDict"}, "ultk.language.grammar.UniquenessArgs.unique_expressions": {"fullname": "ultk.language.grammar.UniquenessArgs.unique_expressions", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs.unique_expressions", "kind": "variable", "doc": "
\n", "annotation": ": dict[typing.Any, dict[typing.Any, ultk.language.grammar.GrammaticalExpression]]"}, "ultk.language.grammar.UniquenessArgs.key": {"fullname": "ultk.language.grammar.UniquenessArgs.key", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs.key", "kind": "variable", "doc": "
\n", "annotation": ": Callable[[ultk.language.grammar.GrammaticalExpression], Any]"}, "ultk.language.grammar.UniquenessArgs.compare_func": {"fullname": "ultk.language.grammar.UniquenessArgs.compare_func", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs.compare_func", "kind": "variable", "doc": "
\n", "annotation": ": Callable[[ultk.language.grammar.GrammaticalExpression, ultk.language.grammar.GrammaticalExpression], bool]"}, "ultk.language.grammar.Grammar": {"fullname": "ultk.language.grammar.Grammar", "modulename": "ultk.language.grammar", "qualname": "Grammar", "kind": "class", "doc": "At its core, a Grammar is a set of Rules with methods for generating GrammaticalExpressions.
\n"}, "ultk.language.grammar.Grammar.__init__": {"fullname": "ultk.language.grammar.Grammar.__init__", "modulename": "ultk.language.grammar", "qualname": "Grammar.__init__", "kind": "function", "doc": "
\n", "signature": "(start : Any "}, "ultk.language.grammar.Grammar.add_rule": {"fullname": "ultk.language.grammar.Grammar.add_rule", "modulename": "ultk.language.grammar", "qualname": "Grammar.add_rule", "kind": "function", "doc": "
\n", "signature": "(self , rule : ultk . language . grammar . Rule ): ", "funcdef": "def"}, "ultk.language.grammar.Grammar.parse": {"fullname": "ultk.language.grammar.Grammar.parse", "modulename": "ultk.language.grammar", "qualname": "Grammar.parse", "kind": "function", "doc": "Parse a string representation of an expression of a grammar.\nNote that this is not a general-purpose parsing algorithm. We assume that the strings are of the form\n parent_name(child1_name, ..., childn_name)\nwhere parent_name is the name of a rule of this grammar that has a length-n RHS, and that\nchildi_name is the name of a rule for each child i.
\n\nArguments: \n\n\nexpression: string in the above format \n\nReturns: \n\n\n the corresponding GrammaticalExpression
\n \n", "signature": "(\tself , \texpression : str , \topener : str = '(' , \tcloser : str = ')' , \tdelimiter : str = ',' ) -> ultk . language . grammar . GrammaticalExpression : ", "funcdef": "def"}, "ultk.language.grammar.Grammar.generate": {"fullname": "ultk.language.grammar.Grammar.generate", "modulename": "ultk.language.grammar", "qualname": "Grammar.generate", "kind": "function", "doc": "Generate an expression from a given lhs.
\n", "signature": "(self , lhs : Any = None ) -> ultk . language . grammar . GrammaticalExpression : ", "funcdef": "def"}, "ultk.language.grammar.Grammar.enumerate": {"fullname": "ultk.language.grammar.Grammar.enumerate", "modulename": "ultk.language.grammar", "qualname": "Grammar.enumerate", "kind": "function", "doc": "Enumerate all expressions from the grammar up to a given depth from a given LHS.\nThis method also can update a specified dictionary to store only _unique_ expressions, with\na user-specified criterion of uniqueness.
\n\nArguments: \n\n\ndepth: how deep the trees should belhs: left hand side to start from; defaults to the grammar's start symboluniqueness_args: a dictionary specifying the parameters for uniqueness:\nunique_dict: a dictionary in which to store unique Expressions\nkey: a function used to evaluate uniqueness\ncompare_func: a comparison function, used to decide which Expression to add to the dict\n new Expressions will be added as values to unique_dict only if they are _minimal_\n among those sharing the same key (by unique_key) according to this func \n\nYields: \n\n\n all GrammaticalExpressions up to depth
\n \n", "signature": "(\tself , \tdepth : int = 8 , \tlhs : Any = None , \tuniqueness_args : ultk . language . grammar . UniquenessArgs | None = None ) -> Generator [ ultk . language . grammar . GrammaticalExpression , NoneType , NoneType ] : ", "funcdef": "def"}, "ultk.language.grammar.Grammar.enumerate_at_depth": {"fullname": "ultk.language.grammar.Grammar.enumerate_at_depth", "modulename": "ultk.language.grammar", "qualname": "Grammar.enumerate_at_depth", "kind": "function", "doc": "Enumerate GrammaticalExpressions for this Grammar _at_ a fixed depth.
\n", "signature": "(\tself , \tdepth : int , \tlhs : Any , \tuniqueness_args : ultk . language . grammar . UniquenessArgs | None = None , \tcache : dict | None = None ) -> Generator [ ultk . language . grammar . GrammaticalExpression , NoneType , NoneType ] : ", "funcdef": "def"}, "ultk.language.grammar.Grammar.get_unique_expressions": {"fullname": "ultk.language.grammar.Grammar.get_unique_expressions", "modulename": "ultk.language.grammar", "qualname": "Grammar.get_unique_expressions", "kind": "function", "doc": "Get all unique GrammaticalExpressions, up to a certain depth, with a user-specified criterion\nof uniqueness, and a specified comparison function for determining which Expression to save when there's a clash.\nThis can be used, for instance, to measure the minimum description length of some\nMeanings, by using expression.evaluate(), which produces a Meaning for an Expression, as the\nkey for determining uniqueness, and length of the expression as comparison.
\n\nThis is a wrapper around enumerate, but which produces the dictionary of key->Expression entries\nand returns it. (enumerate is a generator with side effects).
\n\nFor Args, see the docstring for enumerate.
\n\nNote: if you additionally want to store _all_ expressions, and not just the unique ones, you should\ndirectly use enumerate.
\n\nReturns: \n\n\n dictionary of {key: GrammaticalExpression}, where the keys are generated by unique_key\n The GrammticalExpression which is the value will be the one that is minimum among\n compare_func amongst all Expressions up to depth which share the same key
\n \n", "signature": "(\tself , \tdepth : int , \tunique_key : Callable [[ ultk . language . grammar . GrammaticalExpression ], Any ] , \tcompare_func : Callable [[ ultk . language . grammar . GrammaticalExpression , ultk . language . grammar . GrammaticalExpression ], bool ] , \tlhs : Any = None , \tmax_size : float = inf ) -> dict [ ultk . language . grammar . GrammaticalExpression , typing . Any ] : ", "funcdef": "def"}, "ultk.language.grammar.Grammar.get_all_rules": {"fullname": "ultk.language.grammar.Grammar.get_all_rules", "modulename": "ultk.language.grammar", "qualname": "Grammar.get_all_rules", "kind": "function", "doc": "Get all rules as a list.
\n", "signature": "(self ) -> list [ ultk . language . grammar . Rule ] : ", "funcdef": "def"}, "ultk.language.grammar.Grammar.from_yaml": {"fullname": "ultk.language.grammar.Grammar.from_yaml", "modulename": "ultk.language.grammar", "qualname": "Grammar.from_yaml", "kind": "function", "doc": "Read a grammar specified in a simple YAML format.
\n\nExpected format:
\n\nstart: bool\nrules:\n- lhs: bool\n rhs:\n - bool\n - bool\n name: \"and\"\n function: \"lambda p1, p2 : p1 and p2\"\n- lhs: bool\n rhs:\n - bool\n - bool\n name: \"or\"\n function: \"lambda p1, p2 : p1 or p2\"\nNote that for each fule, the value for function will be passed to\neval, so be careful!
\n\nArguments: \n\n\nfilename: file containing a grammar in the above format \n", "signature": "(cls , filename : str ): ", "funcdef": "def"}, "ultk.language.language": {"fullname": "ultk.language.language", "modulename": "ultk.language.language", "kind": "module", "doc": "Classes for modeling languages as form-meaning mappings, most important among them the Language and Expression classes.
\n\nExample usage: \n\n\n \n
>>> from ultk.language.language import Expression , Language \n>>> # assuming the meaning `a_few_meaning` has already been constructed \n>>> # define the expression \n>>> a_few = NumeralExpression ( form = "a few" , meaning = a_few_meaning ) \n>>> # define a very small language \n>>> lang_1 = Language ([ a_few ]) \n>>> # or a slightly larger one with synonymy \n>>> lang_2 = Language ([ a_few ] * 3 ) \n\n
\n"}, "ultk.language.language.Expression": {"fullname": "ultk.language.language.Expression", "modulename": "ultk.language.language", "qualname": "Expression", "kind": "class", "doc": "Minimally contains a form and a meaning.
\n"}, "ultk.language.language.Expression.__init__": {"fullname": "ultk.language.language.Expression.__init__", "modulename": "ultk.language.language", "qualname": "Expression.__init__", "kind": "function", "doc": "
\n", "signature": "(\tform : str | None = None , \tmeaning : ultk . language . semantics . Meaning | None = None ) "}, "ultk.language.language.Expression.form": {"fullname": "ultk.language.language.Expression.form", "modulename": "ultk.language.language", "qualname": "Expression.form", "kind": "variable", "doc": "
\n"}, "ultk.language.language.Expression.meaning": {"fullname": "ultk.language.language.Expression.meaning", "modulename": "ultk.language.language", "qualname": "Expression.meaning", "kind": "variable", "doc": "
\n"}, "ultk.language.language.Expression.can_express": {"fullname": "ultk.language.language.Expression.can_express", "modulename": "ultk.language.language", "qualname": "Expression.can_express", "kind": "function", "doc": "Return True if the expression can express the input single meaning point and false otherwise.
\n", "signature": "(self , referent : ultk . language . semantics . Referent ) -> bool : ", "funcdef": "def"}, "ultk.language.language.Expression.to_dict": {"fullname": "ultk.language.language.Expression.to_dict", "modulename": "ultk.language.language", "qualname": "Expression.to_dict", "kind": "function", "doc": "
\n", "signature": "(self ) -> dict : ", "funcdef": "def"}, "ultk.language.language.Language": {"fullname": "ultk.language.language.Language", "modulename": "ultk.language.language", "qualname": "Language", "kind": "class", "doc": "Minimally contains Expression objects.
\n"}, "ultk.language.language.Language.__init__": {"fullname": "ultk.language.language.Language.__init__", "modulename": "ultk.language.language", "qualname": "Language.__init__", "kind": "function", "doc": "
\n", "signature": "(expressions : tuple [ ultk . language . language . Expression , ... ] , ** kwargs "}, "ultk.language.language.Language.expressions": {"fullname": "ultk.language.language.Language.expressions", "modulename": "ultk.language.language", "qualname": "Language.expressions", "kind": "variable", "doc": "
\n", "annotation": ": tuple[ultk.language.language.Expression, ...]"}, "ultk.language.language.Language.universe": {"fullname": "ultk.language.language.Language.universe", "modulename": "ultk.language.language", "qualname": "Language.universe", "kind": "variable", "doc": "
\n", "annotation": ": ultk.language.semantics.Universe"}, "ultk.language.language.Language.add_expression": {"fullname": "ultk.language.language.Language.add_expression", "modulename": "ultk.language.language", "qualname": "Language.add_expression", "kind": "function", "doc": "Add an expression to the list of expressions in a language.
\n", "signature": "(self , e : ultk . language . language . Expression ): ", "funcdef": "def"}, "ultk.language.language.Language.pop": {"fullname": "ultk.language.language.Language.pop", "modulename": "ultk.language.language", "qualname": "Language.pop", "kind": "function", "doc": "Removes an expression at the specified index of the list of expressions, and returns it.
\n", "signature": "(self , index : int ) -> ultk . language . language . Expression : ", "funcdef": "def"}, "ultk.language.language.Language.is_natural": {"fullname": "ultk.language.language.Language.is_natural", "modulename": "ultk.language.language", "qualname": "Language.is_natural", "kind": "function", "doc": "Whether a language represents a human natural language.
\n", "signature": "(self ) -> bool : ", "funcdef": "def"}, "ultk.language.language.Language.degree_property": {"fullname": "ultk.language.language.Language.degree_property", "modulename": "ultk.language.language", "qualname": "Language.degree_property", "kind": "function", "doc": "Count what percentage of expressions in a language have a given property.
\n", "signature": "(\tself , \tproperty : Callable [[ ultk . language . language . Expression ], bool ] ) -> float : ", "funcdef": "def"}, "ultk.language.language.Language.binary_matrix": {"fullname": "ultk.language.language.Language.binary_matrix", "modulename": "ultk.language.language", "qualname": "Language.binary_matrix", "kind": "function", "doc": "Get a binary matrix of shape (num_meanings, num_expressions)\nspecifying which expressions can express which meanings.
\n", "signature": "(self ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.language.language.Language.to_dict": {"fullname": "ultk.language.language.Language.to_dict", "modulename": "ultk.language.language", "qualname": "Language.to_dict", "kind": "function", "doc": "
\n", "signature": "(self , ** kwargs ) -> dict : ", "funcdef": "def"}, "ultk.language.language.aggregate_expression_complexity": {"fullname": "ultk.language.language.aggregate_expression_complexity", "modulename": "ultk.language.language", "qualname": "aggregate_expression_complexity", "kind": "function", "doc": "Aggregate complexities for individual Expressions into a complexity for a Language.
\n\nArguments: \n\n\nlanguage: the Language to measureexpression_complexity_func: the function that returns the complexity of an individual expressionaggregator: (optional, default = sum) the function that aggregates individual complexities \n\nReturns: \n\n\n a float, the complexity of a language
\n \n", "signature": "(\tlanguage : ultk . language . language . Language , \texpression_complexity_func : Callable [[ ultk . language . language . Expression ], float ] , \taggregator : Callable [[ Iterable [ float ]], float ] = < built - in function sum > ) -> float : ", "funcdef": "def"}, "ultk.language.sampling": {"fullname": "ultk.language.sampling", "modulename": "ultk.language.sampling", "kind": "module", "doc": "
\n"}, "ultk.language.sampling.powerset": {"fullname": "ultk.language.sampling.powerset", "modulename": "ultk.language.sampling", "qualname": "powerset", "kind": "function", "doc": "Enumerate all _non-empty_ subsets of an iterable up to a given maximum size, e.g.:\npowerset([1,2,3]) --> (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)
\n\nlightly adapted from itertools Recipes at\nhttps://docs.python.org/3/library/itertools.html#itertools-recipes
\n\nArguments: \n\n\niterable: elements from which to form subsetsmax_size: largest subsets (inclusive) to return \n\nReturns: \n\n\n iterator over all subsets from size 1 to max_size of elements from iterable
\n \n", "signature": "(iterable : Iterable , max_size : int = None ) -> Iterable : ", "funcdef": "def"}, "ultk.language.sampling.all_meanings": {"fullname": "ultk.language.sampling.all_meanings", "modulename": "ultk.language.sampling", "qualname": "all_meanings", "kind": "function", "doc": "Generate all Meanings (sets of Referents) from a given Universe.
\n", "signature": "(\tuniverse : ultk . language . semantics . Universe ) -> Generator [ ultk . language . semantics . Meaning , NoneType , NoneType ] : ", "funcdef": "def"}, "ultk.language.sampling.all_expressions": {"fullname": "ultk.language.sampling.all_expressions", "modulename": "ultk.language.sampling", "qualname": "all_expressions", "kind": "function", "doc": "Generate Expressions from an iterable of Meanings.
\n", "signature": "(\tmeanings : Iterable [ ultk . language . semantics . Meaning ] ) -> Generator [ ultk . language . language . Expression , NoneType , NoneType ] : ", "funcdef": "def"}, "ultk.language.sampling.all_languages": {"fullname": "ultk.language.sampling.all_languages", "modulename": "ultk.language.sampling", "qualname": "all_languages", "kind": "function", "doc": "Generate all Languages (sets of Expressions) from a given set of Expressions.
\n\nArguments: \n\n\nexpressions: iterable of all possible expressionslanguage_class: the type of language to generatemax_size: largest size for a language; if None, all subsets of expressions will be used \n\nYields: \n\n\n Languages with subsets of Expressions from expressions
\n \n", "signature": "(\texpressions : Iterable [ ultk . language . language . Expression ] , \tlanguage_class: Type[ultk.language.language.Language] = <class 'ultk.language.language.Language'>, \tmax_size : int = None ) -> Generator [ ultk . language . language . Language , NoneType , NoneType ] : ", "funcdef": "def"}, "ultk.language.sampling.upto_comb": {"fullname": "ultk.language.sampling.upto_comb", "modulename": "ultk.language.sampling", "qualname": "upto_comb", "kind": "function", "doc": "Return the number of ways of choosing _up to max_k_ items from\nn items without repetition. Just an iterator of math.comb for n from\n1 to max_k.
\n", "signature": "(num : int , max_k : int ) -> int : ", "funcdef": "def"}, "ultk.language.sampling.random_languages": {"fullname": "ultk.language.sampling.random_languages", "modulename": "ultk.language.sampling", "qualname": "random_languages", "kind": "function", "doc": "Generate unique Languages by randomly sampling subsets of Expressions, either in a uniform or stratified way.\nIf there are fewer than sample_size possible Languages up to size max_size,\nthis method will just return all languages up to that size (and so the sample may\nbe smaller than sample_size).
\n\nSome use cases:
\n\nWith sample_size=None, get all languages.
\n\n\n
>>> random_languages ( expressions ) \n\n
With sample_size and uniform sampling, get random languages:
\n\n\n
>>> random_languages ( expressions , sample_size = 1000 ) \n\n
Stratified sampling, with and without a max_size:
\n\n\n
>>> random_languages ( expressions , sample_size = 1000 , sampling_strategy = "stratified" ) \n>>> random_languages ( expressions , sample_size = 1000 , sampling_strategy = "stratified" , max_size = 10 ) \n\n
Arguments: \n\n\nexpressions: all possible expressionssampling_strategy: how to sample subsets of expressions\nuniform: for every expression, choose whether or not to include it in a given language\nstratified: first sample a size for a Language, then choose that many random Expressions\n (i) this has the effect of \"upsampling\" from smaller Language sizes\n (ii) this can be used with max_size to only generate Languages up to a given number of expressionssample_size: how many languages to return\nif None, will return all languages up to max_sizelanguage_class: type of Languagemax_size: largest possible Language to generate\nif None, will be the length of expressions\nNB: this argument has no effect when sampling_strategy is \"uniform\" \n\nReturns: \n\n\n a list of randomly sampled Languages
\n \n", "signature": "(\texpressions : Iterable [ ultk . language . language . Expression ] , \tsampling_strategy : str = 'uniform' , \tsample_size : int = None , \tlanguage_class: Type[ultk.language.language.Language] = <class 'ultk.language.language.Language'>, \tmax_size : int = None ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, "ultk.language.sampling.generate_languages": {"fullname": "ultk.language.sampling.generate_languages", "modulename": "ultk.language.sampling", "qualname": "generate_languages", "kind": "function", "doc": "Generate languages by randomly sampling vocabularies as bags of expressions.
\n\nA predicate (binary-valued property) of expressions may be supplied, which can be used to adjust the composition of vocabularies (e.g., by the percent of expressions satisfying the predicate).
\n\nIf sample size <= nCr, then take a random sample_size set of combinations. Otherwise, to prevent repeat languages, treat nCr as the sample size.
\n\nArguments: \n\n\nexpressions: a list of the possible expressions to sample from.lang_size: the maximum (or exact) number of expressions in each language.sample_size: the number of languages to generate.criterion: the predicate, (e.g. semantic universal) by which to split the expressions into those satisfying and those not, and then sample languages with degrees of naturalness based on the percentage from those satisfying. Must apply at the expression level. By default is a trivial criterion, so that all expressions are 'quasi-natural'.fixed_wordcount: whether to vary the language size from 1 to lang_size.verbose: How detailed the progress of sampling should be, printed to stdout.dummy_name: the default name to give to each sampled language, e.g. sampled_lang_42. These should not collide with any actual natural language names if the efficient communication experiment does use natural language data.id_start: an integer representing the number of languages already generated in an experiment. Languages sampled will be named according to this number. For example, if id_start is 0, the first language sampled will be named sampled_lang_0. Note that the largest id does not necessarily track the actual size of languages saved for the experiment, but it does track how many languages have been generated.exact_sample: a boolean representing whether to sample until the exact sample size is filled. If True, the resulting pool of languages may not be unique.verbose: a boolean representing how verbose output should be during sampling. \n\nReturns: \n\n\n a dict representing the generated pool of languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n\nExamples: \n\n\n \n
>>> # Turn the knob on a universal property for modals \n>>> expressions = load_expressions ( expressions_file ) \n>>> universal_property = iff \n>>> result = generate_languages ( \n... ModalLanguage , \n... expressions , \n... lang_size , \n... sample_size , \n... universal_property , \n...) \n>>> languages = result [ "languages" ] \n>>> id_start = result [ "id_start" ] \n\n
\n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \texpressions : list [ ultk . language . language . Expression ] , \tlang_size : int , \tsample_size : int , \tcriterion : Callable [[ ultk . language . language . Expression ], bool ] = < function < lambda >> , \tfixed_wordcount = False , \tdummy_name = 'sampled_lang_' , \tid_start : int = 0 , \texact_sample = False , \tverbose = False ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, "ultk.language.sampling.sample_lang_size": {"fullname": "ultk.language.sampling.sample_lang_size", "modulename": "ultk.language.sampling", "qualname": "sample_lang_size", "kind": "function", "doc": "Get a sample of languages each of exactly lang_size.
\n\nArguments: \n\n\nlanguage_class: a subclass of ultk.Languageexpressions: a list of Expressions to sample fromlang_size: int representing the maximum language size to samplesample_size: int representing the number of total languages to returnid_start: an int representing the number of languages already generated in an experiment. \n\nReturns: \n\n\n a dict containing the randomly sampled languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \texpressions : list [ ultk . language . language . Expression ] , \tlang_size : int , \tsample_size : int , \tid_start : int = 0 , \tverbose = False , \tdummy_name = 'sampled_lang_id' ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, "ultk.language.sampling.sample_quasi_natural": {"fullname": "ultk.language.sampling.sample_quasi_natural", "modulename": "ultk.language.sampling", "qualname": "sample_quasi_natural", "kind": "function", "doc": "Turn the knob on degree quasi-naturalness for a sample of languages, either by enumerating or randomly sampling unique subsets of all possible combinations.
\n\nArguments: \n\n\nnatural_terms: expressions satisfying some criteria of quasi-naturalness, e.g, a semantic universal.unnatural_terms: expressions not satisfying the criteria.lang_size: the exact number of expressions a language must have.sample_size: how many languages to sample. \n\nReturns: \n\n\n a dict containing the randomly sampled quasi-natural languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \tnatural_terms : list [ ultk . language . language . Expression ] , \tunnatural_terms : list [ ultk . language . language . Expression ] , \tlang_size : int , \tsample_size : int , \tid_start : int , \tdummy_name = 'sampled_lang_id' , \tverbose = False ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, "ultk.language.sampling.rename_id": {"fullname": "ultk.language.sampling.rename_id", "modulename": "ultk.language.sampling", "qualname": "rename_id", "kind": "function", "doc": "Updates a string of form sampled_lang_X with a new id for X.
\n", "signature": "(name : str , id : int ) -> str : ", "funcdef": "def"}, "ultk.language.sampling.enumerate_all_languages": {"fullname": "ultk.language.sampling.enumerate_all_languages", "modulename": "ultk.language.sampling", "qualname": "enumerate_all_languages", "kind": "function", "doc": "When the sample size requested is greater than the size of all possible languages, just enumerate all the possible languages.
\n\nArguments: \n\n\nlanguage_class: the kind of Language to constructid_start: a number to start counting from for assigning names with numerical ids to languages.natural_indices: the indices of quasi-natural languages already seennum_natural: the number of quasi-natural languages to samplenatural_terms: the list of quasi-natural terms to sample fromunnatural_indices: the indices of non-quasi-natural languages already seennum_unnatural: the number of non-quasi-natural languages to sample; 0 by defaultunnatural_terms: the list of non-quasi-natural terms to sample from; empty by default.dummy_name: the format of the string to name each language constructed. \n\nReturns: \n\n\n a dict containing a set of languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \tid_start : int , \tnatural_terms : list [ ultk . language . language . Expression ] , \tnatural_indices : list [ int ] , \tnum_natural : int = 0 , \tunnatural_terms : list [ ultk . language . language . Expression ] = [] , \tunnatural_indices : list [ int ] = [] , \tnum_unnatural : int = 0 , \tdummy_name = 'sampled_lang_id' , \tverbose = False ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, "ultk.language.sampling.random_combination_vocabulary": {"fullname": "ultk.language.sampling.random_combination_vocabulary", "modulename": "ultk.language.sampling", "qualname": "random_combination_vocabulary", "kind": "function", "doc": "Get a single vocabulary for a specific language size by choosing a random combination of natural and unnatural terms.
\n\nArguments: \n\n\nseen: the list of language indices already seennum_natural: intnatural_terms: list[Expression]num_unnatural: int=0unnatural_terms: list[Expression]=[] \n\nReturns: \n\n\n languages: the extended list of input languages.
\n \n", "signature": "(\tseen : set , \tnum_natural : int , \tnatural_terms : list [ ultk . language . language . Expression ] , \tnum_unnatural : int = 0 , \tunnatural_terms : list [ ultk . language . language . Expression ] = [] ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, "ultk.language.semantics": {"fullname": "ultk.language.semantics", "modulename": "ultk.language.semantics", "kind": "module", "doc": "Classes for modeling the meanings of a language.
\n\nMeanings are modeled as things which map linguistic forms to objects of reference. The linguistic forms and objects of reference can in principle be very detailed, and future work may elaborate the meaning classes and implement a Form class.
\n\nIn efficient communication analyses, simplicity and informativeness can be measured as properties of semantic aspects of a language. E.g., a meaning is simple if it is easy to represent, or to compress into some code; a meaning is informative if it is easy for a listener to recover a speaker's intended literal meaning.
\n\nExamples: \n\n\n \n
>>> from ultk.language.semantics import Referent , Meaning , Universe \n>>> from ultk.language.language import Expression \n>>> # construct the meaning space for numerals \n>>> numerals_universe = NumeralUniverse ( referents = [ NumeralReferent ( str ( i )) for i in range ( 1 , 100 )]) \n>>> # construct a list of referents for the expression 'a few' \n>>> a_few_refs = [ NumeralReferent ( name = str ( i )) for i in range ( 2 , 6 )] \n>>> a_few_meaning = NumeralMeaning ( referents = a_few_refs , universe = numerals_universe ) \n>>> # define the expression \n>>> a_few = NumeralExpression ( form = "a few" , meaning = a_few_meaning ) \n\n
\n"}, "ultk.language.semantics.Referent": {"fullname": "ultk.language.semantics.Referent", "modulename": "ultk.language.semantics", "qualname": "Referent", "kind": "class", "doc": "A referent is some object in the universe for a language.
\n"}, "ultk.language.semantics.Referent.__init__": {"fullname": "ultk.language.semantics.Referent.__init__", "modulename": "ultk.language.semantics", "qualname": "Referent.__init__", "kind": "function", "doc": "Initialize a referent.
\n\nArguments: \n\n\nname: a string representing the name of the referent \n", "signature": "(name : str , properties : dict = {} , ** kwargs "}, "ultk.language.semantics.Referent.name": {"fullname": "ultk.language.semantics.Referent.name", "modulename": "ultk.language.semantics", "qualname": "Referent.name", "kind": "variable", "doc": "
\n"}, "ultk.language.semantics.Referent.to_dict": {"fullname": "ultk.language.semantics.Referent.to_dict", "modulename": "ultk.language.semantics", "qualname": "Referent.to_dict", "kind": "function", "doc": "
\n", "signature": "(self ) -> dict : ", "funcdef": "def"}, "ultk.language.semantics.Universe": {"fullname": "ultk.language.semantics.Universe", "modulename": "ultk.language.semantics", "qualname": "Universe", "kind": "class", "doc": "The universe is the set of possible referent objects for a meaning.
\n"}, "ultk.language.semantics.Universe.__init__": {"fullname": "ultk.language.semantics.Universe.__init__", "modulename": "ultk.language.semantics", "qualname": "Universe.__init__", "kind": "function", "doc": "
\n", "signature": "(\treferents : Iterable [ ultk . language . semantics . Referent ] , \tprior : dict [ str , float ] = None ) "}, "ultk.language.semantics.Universe.referents": {"fullname": "ultk.language.semantics.Universe.referents", "modulename": "ultk.language.semantics", "qualname": "Universe.referents", "kind": "variable", "doc": "
\n"}, "ultk.language.semantics.Universe.set_prior": {"fullname": "ultk.language.semantics.Universe.set_prior", "modulename": "ultk.language.semantics", "qualname": "Universe.set_prior", "kind": "function", "doc": "
\n", "signature": "(self , prior : dict [ str , float ] ): ", "funcdef": "def"}, "ultk.language.semantics.Universe.prior_numpy": {"fullname": "ultk.language.semantics.Universe.prior_numpy", "modulename": "ultk.language.semantics", "qualname": "Universe.prior_numpy", "kind": "function", "doc": "
\n", "signature": "(self ) -> numpy . ndarray : ", "funcdef": "def"}, "ultk.language.semantics.Universe.from_dataframe": {"fullname": "ultk.language.semantics.Universe.from_dataframe", "modulename": "ultk.language.semantics", "qualname": "Universe.from_dataframe", "kind": "function", "doc": "Build a Universe from a DataFrame.\nIt's assumed that each row specifies one Referent, and each column will be a property\nof that Referent. We assume that name is one of the columns of the DataFrame.
\n\nArguments: \n\n\na DataFrame representing the meaning space of interest, assumed to have a column name \n \n", "signature": "(cls , df : pandas . core . frame . DataFrame ): ", "funcdef": "def"}, "ultk.language.semantics.Universe.from_csv": {"fullname": "ultk.language.semantics.Universe.from_csv", "modulename": "ultk.language.semantics", "qualname": "Universe.from_csv", "kind": "function", "doc": "Build a Universe from a CSV file. This is a small wrapper around\nUniverse.from_dataframe, so see that documentation for more information.
\n", "signature": "(cls , filename : str ): ", "funcdef": "def"}, "ultk.language.semantics.Meaning": {"fullname": "ultk.language.semantics.Meaning", "modulename": "ultk.language.semantics", "qualname": "Meaning", "kind": "class", "doc": "A meaning picks out a set of objects from the universe.
\n\nOn one tradition (from formal semantics), we might model an underspecified meaning as a subset of the universe.\nSometimes these different referents are not equally likely,\nin which it can be helpful to define a meaning explicitly as a distribution over the universe.
\n"}, "ultk.language.semantics.Meaning.__init__": {"fullname": "ultk.language.semantics.Meaning.__init__", "modulename": "ultk.language.semantics", "qualname": "Meaning.__init__", "kind": "function", "doc": "A meaning is the set of things it refers to.
\n\nThe objects of reference are a subset of the universe of discourse. Sometimes it is natural to construe the meaning as as a probability distribution over the universe, instead of just a binary predicate.
\n\nArguments: \n\n\nreferents: a list of Referent objects, which must be a subset of the referents in universe.universe: a Universe object that defines the probability space for a meaning.dist: a dict of with Referent names as keys and weights or probabilities as values, representing the distribution over referents to associate with the meaning. By default is None, and the distribution will be uniform over the passed referents, and any remaining referents are assigned 0 probability. \n", "signature": "(\treferents : Iterable [ ultk . language . semantics . Referent ] , \tuniverse : ultk . language . semantics . Universe , \tdist : dict [ str , float ] = None ) "}, "ultk.language.semantics.Meaning.referents": {"fullname": "ultk.language.semantics.Meaning.referents", "modulename": "ultk.language.semantics", "qualname": "Meaning.referents", "kind": "variable", "doc": "
\n"}, "ultk.language.semantics.Meaning.universe": {"fullname": "ultk.language.semantics.Meaning.universe", "modulename": "ultk.language.semantics", "qualname": "Meaning.universe", "kind": "variable", "doc": "
\n"}, "ultk.language.semantics.Meaning.to_dict": {"fullname": "ultk.language.semantics.Meaning.to_dict", "modulename": "ultk.language.semantics", "qualname": "Meaning.to_dict", "kind": "function", "doc": "
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+ /** pdoc search index */const docs = [{"fullname": "ultk", "modulename": "ultk", "kind": "module", "doc": "\n\n
\n\nIntroduction \n\nULTK is a software library that aims to support efficient communication analyses of natural language. This is a line of research that aims to explain why natural languages have the structure that they do in terms competing pressures to minimize cognitive complexity and maximize communicative accuracy.
\n\nKey features:
\n\n\nPrimitives for constructing semantic spaces, expressions, and languages \nTools for measuring informativity of languages, communicative success of RSA speakers and listeners \nLanguage population sampling and optimization w.r.t Pareto fronts \nRate-Distortion and Information Bottleneck style analyses \n \n\nULTK is a long term project and it is currently in its early stages. It is intended to help lower the barrier to entry for certain research in computational semantics, and to unify methodologies. If you find something confusing, please open an issue. If you have a phenomena of interest in linguistic semantics that you want to run an efficient communication analysis on, please contact the contributors.
\n\nRead the documentation .
\n\nInstalling ULTK \n\nFirst, set up a virtual environment (e.g. via miniconda , conda create -n ultk python=3.11, and conda activate ultk).
\n\n\nDownload or clone this repository and navigate to the root folder.
Install ULTK (We recommend doing this inside a virtual environment)
\n\npip install -e .
\n\nGetting started \n\n\nCheck out the examples , starting with a simiple efficient communication analysis of indefinites and a comparison of two approaches to efficient communication, with modals as a test case. \nTo see more scaled up usage examples, visit the codebase for an efficient communication analysis of modals or sim-max games . \nFor an introduction to efficient communication research, here is a survey paper of the field. \nFor an introduction to the RSA framework, see this online textbook . \n \n\nModules \n\nThere are two modules. The first is ultk.effcomm , which includes methods for measuring informativity of languages and/or communicative success of Rational Speech Act agents, and for language population sampling and optimization w.r.t Pareto fronts.
\n\nThe second module is ultk.language , which contains primitives for constructing semantic spaces, expressions, and languages. It also has a grammar module which can be used for building expressions in a Language of Thought and measuring complexity in terms of minimum description length, as well as for natural language syntax.
\n\nThe source code is available on github here .
\n\nTesting \n\nUnit tests are written in pytest and executed via running pytest in the src/tests folder.
\n\nReferences \n\n\nFigures:
\n\n\n Kemp, C. & Regier, T. (2012) Kinship Categories Across Languages Reflect General Communicative Principles. Science. https://www.science.org/doi/10.1126/science.1218811
\n \n\n\n Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018). Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences, 115(31), 7937\u20137942. https://doi.org/10.1073/pnas.1800521115
\n \n\n\n Deni\u0107, M., Steinert-Threlkeld, S., & Szymanik, J. (2022). Indefinite Pronouns Optimize the Simplicity/Informativeness Trade-Off. Cognitive Science, 46(5), e13142. https://doi.org/10.1111/cogs.13142
\n \n\n\n Steinert-Threlkeld, S. (2021). Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals. Entropy, 23(10), Article 10. https://doi.org/10.3390/e23101335
\n \n\n
\n\n\nLinks:
\n\n\n Imel, N., & Steinert-Threlkeld, S. (2022). Modal semantic universals optimize the simplicity/informativeness trade-off. Semantics and Linguistic Theory, 1(0), Article 0. https://doi.org/10.3765/salt.v1i0.5346
\n \n\n\n Kemp, C., Xu, Y., & Regier, T. (2018). Semantic Typology and Efficient Communication. Annual Review of Linguistics, 4(1), 109\u2013128. https://doi.org/10.1146/annurev-linguistics-011817-045406
\n \n\n
\n"}, {"fullname": "ultk.effcomm", "modulename": "ultk.effcomm", "kind": "module", "doc": "Tools for measuring languages for communicative efficiency.
\n\nSubmodules divide the labor of a computational experiment performing an efficiency analysis of a language into several parts: generating and sampling the space of possible languages, measuring their properties, and determining which languages optimize efficient trade-offs w.r.t these properties.
\n\nThe ultk.effcomm.sampling submodule implements several methods for generating hypothetically possible languages of a given type, by sampling from a set of possible expressions, or permuting the expression-meaning mapping of an existing language.
\n\nThe ultk.effcomm.optimization submodule contains a general implementation of an evolutionary algorithm, which can be used to estimate a Pareto frontier of optimal solutions to an efficiency trade-off. It can also be used as a technique for randomly exploring the space of possible languages.
\n\nThe ultk.effcomm.tradeoff submodule contains tools for measuring a pool of languages for various properties, finding which languages are Pareto dominant with respect to two properties, and setting attributes of the language objects for further analysis.
\n\nThe ultk.effcomm.analysis submodule contains tools for performing numerical analyses and producing paradigmatic plots of languages in 2D trade-off space.
\n\nThe ultk.effcomm.information_bottleneck submodule contains tools for Information Bottleneck based analyses of the communicative efficiency of languages. Specificially, it includes an implementation of the IB-BA algorithm and code for evaluating the efficiency of semantic category systems.
\n\nThe ultk.effcomm.agent submodule implements classes for constructing various speakers and listeners of a language. These are unified abstractions from the Rational Speech Act framework.
\n\nThe ultk.effcomm.informativity submodule implements tools for computing the literal or pragmatic informativity of a language, based on speaker/listener abstractions described above.
\n"}, {"fullname": "ultk.effcomm.agent", "modulename": "ultk.effcomm.agent", "kind": "module", "doc": "Classes for representing communicative agents, such as Senders and Receivers figuring in Lewis-Skyrms signaling games, literal and pragmatic agents in the Rational Speech Act framework, etc.
\n"}, {"fullname": "ultk.effcomm.agent.CommunicativeAgent", "modulename": "ultk.effcomm.agent", "qualname": "CommunicativeAgent", "kind": "class", "doc": "
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\n\nGiven a expression and referent, access the corresponding weight coordinate.
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\n\nArguments: \n\n\nindex: the integer index representing a row of the weight matrix. \n\nReturns: \n\n\n the integer index of the agent's choice
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\n\nThis function uses: \n\n\n \n the agent's weight matrix, \n the set of expression forms, and \n the set of referents \n \n \n\nfrom the language the agent was initialized with to generate a new language accurately reflecting the new expression meanings, e.g. how the agent interprets expressions as meaning zero or more referents.
\n\nArguments: \n\n\nthreshold: a float in [0,1] representing the cutoff for determining if a meaning (referent) can be communicated by a expression. Because weights are not initialized to 0, it is a good idea to set nonzero values as the threshold. \n\nReturns: \n\n\n a Language corresponding to the form-meaning mapping defined by the communicative agent's weights.
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\n\nEach row vector represents a conditional probability distribution over expressions, P(e | m).
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\n", "bases": "Speaker"}, {"fullname": "ultk.effcomm.agent.PragmaticSpeaker.__init__", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticSpeaker.__init__", "kind": "function", "doc": "Initialize the |M|-by-|E| matrix, S, corresponding to the pragmatic speaker's conditional probability distribution over expressions given meanings.
\n\nThe pragmatic speaker chooses expressions to communicate their intended meaning according to:
\n\n$P(e | m) \\propto \\exp(t * u(e,m))$
\n\nwhere $t \\in [0,1]$ is a temperature parameter and utility $u$ is defined
\n\n$u(e , m) := \\log(P_{\\text{Listener}}(m | e))$
\n\nArguments: \n\n\nlanguage: the language with |M| meanings and |E| expressions defining the size of S.listener: a communicative agent storing a matrix R representing the conditional distribution over expressions given meanings.temperature: a float \\in [0,1], representing how `optimally rational' the pragmatic speaker is; 1.0 is chosen when no particular assumptions about rationality are made. \n", "signature": "(\tlanguage : ultk . language . language . Language , \tlistener : ultk . effcomm . agent . Listener , \ttemperature : float = 1.0 , \t** kwargs ) "}, {"fullname": "ultk.effcomm.agent.PragmaticSpeaker.S", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticSpeaker.S", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, {"fullname": "ultk.effcomm.agent.PragmaticListener", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticListener", "kind": "class", "doc": "A pragmatic listener interprets utterances based on their expectations about a pragmatic speaker's decisions. A pragmatic listener may be initialized with any kind of speaker, e.g. literal or pragmatic -- meaning the recursive reasoning can be modeled up to arbitrary depth.
\n", "bases": "Listener"}, {"fullname": "ultk.effcomm.agent.PragmaticListener.__init__", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticListener.__init__", "kind": "function", "doc": "Initialize the |E|-by-|M| matrix, R, corresponding to the pragmatic listener's conditional probability distribution over meanings given expressions.
\n\nThe pragmatic listener chooses meanings as their best guesses of the expression they heard according to:
\n\n$P(m | e) \\propto P_{\\text{PragmaticSpeaker}}(e | m)$
\n\nArguments: \n\n\nlanguage: the language with |M| meanings and |E| expressions defining the size of R.speaker: a communicative agent storing a matrix S representing the conditional distribution over expressions given meanings.prior: a diagonal matrix of size |M|-by-|M| representing the communicative need probabilities for meanings. \n", "signature": "(\tlanguage : ultk . language . language . Language , \tspeaker : ultk . effcomm . agent . Speaker , \tprior : numpy . ndarray , \t** kwargs ) "}, {"fullname": "ultk.effcomm.agent.PragmaticListener.R", "modulename": "ultk.effcomm.agent", "qualname": "PragmaticListener.R", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, {"fullname": "ultk.effcomm.analysis", "modulename": "ultk.effcomm.analysis", "kind": "module", "doc": "Functions for analyzing and formatting the results of the simplicity/informativeness trade-off.
\n"}, {"fullname": "ultk.effcomm.analysis.get_dataframe", "modulename": "ultk.effcomm.analysis", "qualname": "get_dataframe", "kind": "function", "doc": "Get a pandas DataFrame for a list of languages containing efficient communication data.
\n\nArguments: \n\n\nlanguages: the list of languages to map into a dataframe.columns: the list of keys to a language's data dictionary attribute, which will comprise the columns of the resulting dataframe. By default will use all items of each language's data dictionary.subset: the columns to subset for duplicatesduplicates: {\"drop\", \"count\", \"leave\"} whether to drop, count, or do nothing with duplicates. By default is set to \"leave\" which will leave duplicates in the dataframe. \n\nReturns: \n\n\n \n data: a pandas DataFrame with rows as individual languages, with the columns specifying their data. \n \n \n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] , \tcolumns : list [ str ] = None , \tsubset : list [ str ] = [ 'complexity' , 'comm_cost' ] , \tduplicates : str = 'leave' ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.analysis.pearson_analysis", "modulename": "ultk.effcomm.analysis", "qualname": "pearson_analysis", "kind": "function", "doc": "Measures pearson correlation coefficient for naturalness with a property.
\n\nUse nonparametric bootstrap for confidence intervals.
\n\nArguments: \n\n\ndata: a DataFrame representing the pool of measured languagespredictor: a string representing the column to measure pearson r withproperty: a string representing a column to measure pearson r with the predictor columnnum_bootstrap_samples: how many samples to bootstrap from the original data \n\nReturns: \n\n\n a dict of the pearson correlation coefficient for the predictor and the property, and bootstrapped confidence intervals for this coefficient, e.g.
\n\n{\n\"rho\": (a float between -1 and 1),\n\"confidence_intervals\": (a pandas Dataframe with the columns [\n 'bootstrap_sample_percent', 'low', 'high'\n])\n}\n \n", "signature": "(\tdata , \tpredictor : str , \tproperty : str , \tnum_bootstrap_samples = 100 ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.analysis.trade_off_means", "modulename": "ultk.effcomm.analysis", "qualname": "trade_off_means", "kind": "function", "doc": "Get a dataframe with the mean tradeoff data.
\n\nArguments: \n\n\nname: a str representing the subset of the population to observe mean properties for, e.g. \"natural\" or \"population\".df: a pandas DataFrame containing data of a language population to take the means of.prperties: the properties to take means of, corresponding to columns of df. \n\nExamples:
\n\n\n
>>> natural_means = trade_off_means ( "natural_means" , natural_data , properties ) \n>>> population_means = trade_off_means ( "population_means" , data , properties ) \n>>> means_df = pd . concat ([ natural_means , dlsav_means , population_means ]) . set_index ( "name" ) \n>>> means_df \n simplicity complexity informativity optimality \n name \n natural_means 0.772222 16.4000 0.746296 0.952280 \n population_means 0.681068 22.9631 0.525118 0.832010 \n\n
(\tname : str , \tdf : pandas . core . frame . DataFrame , \tproperties : list ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.analysis.trade_off_ttest", "modulename": "ultk.effcomm.analysis", "qualname": "trade_off_ttest", "kind": "function", "doc": "Get a dataframe with a single-samples t-test results for a subpopulation against the full population.
\n\nThis is useful if we want to compare the optimality of natural languages to the full population of languages in an experiment. Because the property of 'being a natural language' is categorical, we use a single-samples T test.
\n\nArguments: \n\n\nsub_population: a pandas DataFrame representing a subset of the population to take ttests against the full language population for properties.population_means: a dict containing properties as keys and the mean value of the full language population for that property.properties: a list of strings corresponding to columns of the sub_population DataFrame and keys of the population_means dict. \n\nExamples: \n\n\n \n
>>> df = trade_off_ttest ( natural_data , population_means , properties ) \n>>> df \n simplicity complexity informativity optimality \n stat \n t-statistic 4.101937 -4.101937 3.126855 4.031027 \n Two-sided p-value 0.014830 0.014830 0.035292 0.015720 \n\n
\n", "signature": "(\tsub_population : pandas . core . frame . DataFrame , \tpopulation_means : dict , \tproperties : list ) -> pandas . core . frame . DataFrame : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck", "modulename": "ultk.effcomm.information_bottleneck", "kind": "module", "doc": "Submodule for Information Bottleneck based analyses of the efficiency of semantic category systems.
\n\nThe ultk.effcomm.information_bottleneck.modeling includes a friendly API for obtaining IB theoretical bounds and naming models given a specification of the statistics of the semantic domain. This is likely the only submodule you need to import.
\n\nThe ultk.effcomm.information_bottlneck.ib implements the IB update equations, and includes an optimizer object that inherits from the base object in ba.
\n\nThe ultk.effcomm.information_bottleneck.ba submodule implements the Blahut-Arimoto algorithm for computing the theoretical bounds of efficient compression. It includes code for simulated annealing (reverse or not) of $\\beta$.
\n\nThe ultk.effcomm.information_bottleneck.tools submodule includes helper methods for computing informational quantities and dealing with numerical instability.
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba", "modulename": "ultk.effcomm.information_bottleneck.ba", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer", "kind": "class", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.__init__", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.__init__", "kind": "function", "doc": "Base initializer for a Blahut-Arimoto-based optimizer of the Rate Distortion function.
\n\nArguments: \n\n\nbetas: 1D array, values of beta to searchmax_it: max number of iterationsargs: propagated to self.beta_iterate as *kwargseps: accuracy required by the algorithm: the algorithm stops if there is no change in distortion value of more than eps between consecutive iterationsignore_converge: whether to run the optimization until max_it, ignoring the stopping criterion specified by eps.xhat_size: the size of the output alphabet. The resulting encoders will be of shape (x, xhat)kwargs: propagated to self.beta_iterate as **kwargs \n", "signature": "(\tbetas : numpy . ndarray , \t* args , \tmax_it : int = 1000 , \teps : float = 1e-15 , \tignore_converge : bool = False , \txhat_size = None , \t** kwargs ) "}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.betas", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.betas", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.max_it", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.max_it", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.eps", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.eps", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.ignore_converge", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.ignore_converge", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.init_args", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.init_args", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.init_kwargs", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.init_kwargs", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.ln_px", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.ln_px", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.ln_qxhat_x", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.ln_qxhat_x", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.dist_mat", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.dist_mat", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.xhat_size", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.xhat_size", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.result", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.result", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.results", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.results", "kind": "variable", "doc": "
\n", "annotation": ": list[typing.Any]"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.get_results", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.get_results", "kind": "function", "doc": "
\n", "signature": "(self ) -> list [ typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.update_eqs", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.update_eqs", "kind": "function", "doc": "Main Blahut-Arimoto update steps.
\n", "signature": "(self , * args , ** kwargs ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.next_result", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.next_result", "kind": "function", "doc": "Get the result of the converged BA iteration.
\n", "signature": "(self , beta , * args , ** kwargs ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.beta_iterate", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.beta_iterate", "kind": "function", "doc": "Iterate the BA algorithm for an array of values of beta, using reverse deterministic annealing.
\n\nArguments: \n\n\nnum_restarts: number of times to restart each beta-optimizationensure_monotonicity: whether to remove points that would make a rate distortion curve non-monotonicreverse_annealing: whether to use reverse annealing or regular annealing. If self.output_size < len(self.ln_px), then this is set to false. \n", "signature": "(\tself , \t* args , \tnum_restarts : int = 0 , \tensure_monotonicity : bool = True , \tdisable_tqdm : bool = False , \treverse_annealing : bool = True , \t** kwargs ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.anneal", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.anneal", "kind": "function", "doc": "
\n", "signature": "(\tself , \t* args , \treverse : bool = True , \tnum_restarts : int = 0 , \tdisable_tqdm : bool = False , \t** kwargs ) -> list : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.blahut_arimoto", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.blahut_arimoto", "kind": "function", "doc": "Update the self-consistent equations for a Rate Distortion objective.
\n\nArguments: \n\n\nbeta: (scalar) the slope of the rate-distoriton function at the point where evaluation is required \n", "signature": "(self , beta , * args , ** kwargs ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.compute_distortion", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.compute_distortion", "kind": "function", "doc": "Compute the expected distortion for the current p(x), q(xhat|x) and dist_mat.
\n", "signature": "(self , * args , ** kwargs ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer.compute_rate", "modulename": "ultk.effcomm.information_bottleneck.ba", "qualname": "BaseRDOptimizer.compute_rate", "kind": "function", "doc": "Compute the information rate for the current p(x), q(xhat|x).
\n", "signature": "(self , * args , ** kwargs ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib", "modulename": "ultk.effcomm.information_bottleneck.ib", "kind": "module", "doc": "Information Bottleneck optimizer.
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.ib_kl", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "ib_kl", "kind": "function", "doc": "Compute the IB distortion matrix, the KL divergence between p(y|x) and q(y|xhat), in nats.
\n", "signature": "(py_x : numpy . ndarray , qy_xhat : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult", "kind": "class", "doc": "IBResult(qxhat_x, rate, distortion, accuracy, beta)
\n", "bases": "builtins.tuple"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult.__init__", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult.__init__", "kind": "function", "doc": "Create new instance of IBResult(qxhat_x, rate, distortion, accuracy, beta)
\n", "signature": "(qxhat_x , rate , distortion , accuracy , beta "}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult.qxhat_x", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult.qxhat_x", "kind": "variable", "doc": "Alias for field number 0
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult.rate", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult.rate", "kind": "variable", "doc": "Alias for field number 1
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult.distortion", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult.distortion", "kind": "variable", "doc": "Alias for field number 2
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult.accuracy", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult.accuracy", "kind": "variable", "doc": "Alias for field number 3
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBResult.beta", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBResult.beta", "kind": "variable", "doc": "Alias for field number 4
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.next_ln_qxhat", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "next_ln_qxhat", "kind": "function", "doc": "
\n", "signature": "(ln_px : numpy . ndarray , ln_qxhat_x : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.next_ln_qxhat_x", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "next_ln_qxhat_x", "kind": "function", "doc": "
\n", "signature": "(ln_qxhat : numpy . ndarray , beta : float , dist_mat : numpy . ndarray ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.next_ln_qy_xhat", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "next_ln_qy_xhat", "kind": "function", "doc": "
\n", "signature": "(ln_pxy : numpy . ndarray , ln_qxhat_x : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.next_ln_qx_xhat", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "next_ln_qx_xhat", "kind": "function", "doc": "
\n", "signature": "(ln_px : numpy . ndarray , ln_qxhat_x : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer", "kind": "class", "doc": "
\n", "bases": "ultk.effcomm.information_bottleneck.ba.BaseRDOptimizer"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.__init__", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.__init__", "kind": "function", "doc": "Estimate the optimal encoder for a given value of beta for the Information Bottleneck objective [Tishby et al., 1999]:
\n\n$\\min_{q} I[X:\\hat{X}] + \\beta \\mathbb{E}[D_{KL}[p(y|x) || p(y|\\hat{x})]].$
\n\nArguments: \n\n\npxy: 2D array of shape (|X|, |Y|) representing the joint probability mass function of the source and relevance variables.betas: 1D array of values of beta, where each beta is a float corresponding to the slope of the rate-distoriton function at the point where evaluation is required \n", "signature": "(pxy : numpy . ndarray , betas : numpy . ndarray , * args , ** kwargs "}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.ln_pxy", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.ln_pxy", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.ln_px", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.ln_px", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.px", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.px", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.ln_py_x", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.ln_py_x", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.results", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.results", "kind": "variable", "doc": "
\n", "annotation": ": list[ultk.effcomm.information_bottleneck.ib.IBResult]"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.get_results", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.get_results", "kind": "function", "doc": "
\n", "signature": "(self ) -> list [ ultk . effcomm . information_bottleneck . ib . IBResult ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.next_dist_mat", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.next_dist_mat", "kind": "function", "doc": "IB distortion matrix.
\n", "signature": "(self , * args , ** kwargs ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.update_eqs", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.update_eqs", "kind": "function", "doc": "Iterate the IB update equations.
\n", "signature": "(self , beta , * args , ** kwargs ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.compute_distortion", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.compute_distortion", "kind": "function", "doc": "Compute the expected distortion for the current p(x), q(xhat|x) and dist_mat.
\n", "signature": "(self , * args , ** kwargs ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.compute_accuracy", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.compute_accuracy", "kind": "function", "doc": "
\n", "signature": "(self , * args , ** kwargs ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.ib.IBOptimizer.next_result", "modulename": "ultk.effcomm.information_bottleneck.ib", "qualname": "IBOptimizer.next_result", "kind": "function", "doc": "Get the result of the converged BA iteration for the IB objective.
\n\nReturns: \n\n\n an IBResult namedtuple of (qxhat_x, rate, distortion, accuracy, beta) values. This is:
\n\n`qxhat_x`, the optimal encoder, such that the\n\n`rate` (in bits) of compressing X into X_hat, is minimized for the level of\n\n`distortion` between X, X_hat with respect to Y, i.e. the\n\n`accuracy` I[X_hat:Y] is maximized, for the specified\n\n`beta` trade-off parameter\n \n", "signature": "(\tself , \tbeta , \t* args , \t** kwargs ) -> ultk . effcomm . information_bottleneck . ib . IBResult : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling", "modulename": "ultk.effcomm.information_bottleneck.modeling", "kind": "module", "doc": "Re-implementation of the IBNamingModel at https://github.com/nogazs/ib-color-naming/blob/master/src/ib_naming_model.py .
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel", "kind": "class", "doc": "A model for simulating Information Bottleneck (IB) naming systems.
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.__init__", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.__init__", "kind": "function", "doc": "Initializes the IBNamingModel with given parameters.
\n\nArguments: \n\n\npM (np.ndarray): Prior distribution over meanings. Expected shape is (n, 1).pU_M (np.ndarray): Conditional distribution of observations given meanings.betas (np.ndarray): Array of beta values used for the IB curve.IB_curve (tuple[np.ndarray, np.ndarray]): tuple containing complexity and accuracy values along the IB curve.qW_M (np.ndarray): Initial encoder (naming system) matrix. \n", "signature": "(\tpM : numpy . ndarray , \tpU_M : numpy . ndarray , \tbetas : numpy . ndarray , \tIB_curve : tuple [ numpy . ndarray , numpy . ndarray ] , \tqW_M : numpy . ndarray ) "}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.pM", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.pM", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.pU_M", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.pU_M", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.I_MU", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.I_MU", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.betas", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.betas", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.IB_curve", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.IB_curve", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.qW_M", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.qW_M", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.qW_M_orig", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.qW_M_orig", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.F", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.F", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.m_hat", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.m_hat", "kind": "function", "doc": "Computes the optimal Bayesian listener corresponding to the encoder.
\n\nArguments: \n\n\nqW_M (np.ndarray): Encoder (naming system) matrix. \n\nReturns: \n\n\n np.ndarray: Optimal decoder that corresponds to the given encoder.
\n \n", "signature": "(self , qW_M : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.complexity", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.complexity", "kind": "function", "doc": "Calculates the mutual information I(M;W) for a given encoder.
\n\nArguments: \n\n\npW_M (np.ndarray): Encoder (naming system) matrix. \n\nReturns: \n\n\n float: Mutual information I(M;W).
\n \n", "signature": "(self , pW_M : numpy . ndarray ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.accuracy", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.accuracy", "kind": "function", "doc": "Calculates the mutual information I(W;U) for a given encoder.
\n\nArguments: \n\n\npW_M (np.ndarray): Encoder (naming system) matrix. \n\nReturns: \n\n\n float: Mutual information I(W;U).
\n \n", "signature": "(self , pW_M : numpy . ndarray ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.d_IB", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.d_IB", "kind": "function", "doc": "Calculates the IB distortion for a given encoder, i.e. the KL divergence between speaker and listener meanings, $\n\\mathbb{E}\\left[D[m||\\hat{m}]\night] = I(M;U) - I(W;U)$.
\n\nArguments: \n\n\npW_M (np.ndarray): Encoder (naming system) matrix. \n\nReturns: \n\n\n float: Deviation from the optimal IB solution.
\n \n", "signature": "(self , pW_M : numpy . ndarray ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.fit", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.fit", "kind": "function", "doc": "Fits the naming system to the IB curve.
\n\nArguments: \n\n\npW_M (np.ndarray): Encoder (naming system) matrix. \n\nReturns: \n\n\n tuple containing:\n - epsilon (float): Deviation from optimality of pW_M.\n - gnid (float): Generalized normalized information distance (gNID) between qW_M and qW_M_fit.\n - bl (float): Fitted value of beta.\n - qW_M_fit (np.ndarray): Optimal IB system at bl.
\n \n", "signature": "(self , pW_M : numpy . ndarray ) -> tuple [ float , float , float , numpy . ndarray ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.save", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.save", "kind": "function", "doc": "Save as pickle binary.
\n", "signature": "(self , fn : str = 'ib_naming_model.pkl' ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.IBNamingModel.from_pickle", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "IBNamingModel.from_pickle", "kind": "function", "doc": "
\n", "signature": "(cls , fn : str ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.gNID", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "gNID", "kind": "function", "doc": "Compute Generalized Normalized Informational Distance (gNID, in Zaslavsky et al. 2018, SI, Section 3.2) between two encoders. Code credit: https://github.com/nogazs/ib-color-naming/blob/master/src/tools.py#L94
\n\nArguments: \n\n\npW_X: first encoder of shape (|meanings|, |words|)pV_X: second encoder of shape (|meanings|, |words|)pX: prior over source variables of shape (|meanings|,) \n", "signature": "(pW_X : numpy . ndarray , pV_X : numpy . ndarray , pX : numpy . ndarray ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.compute_bound", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "compute_bound", "kind": "function", "doc": "Computes the IB bound based on input distributions.
\n\nArguments: \n\n\npU_M (np.ndarray): Conditional distribution of observations given meanings.pM (np.ndarray, optional): Prior distribution over meanings. Defaults to None.betas (np.ndarray, optional): Range of beta values for the IB curve. Defaults to logspace(0, 10, 100).**kwargs: Additional parameters for the IB optimizer. \n\nReturns: \n\n\n list[IBResult]: List of results from the IB optimizer.
\n \n", "signature": "(\tpU_M : numpy . ndarray , \tpM : numpy . ndarray , \tbetas : numpy . ndarray = array ([ 1.00000000e+00 , 1.26185688e+00 , 1.59228279e+00 , 2.00923300e+00 , \n 2.53536449e+00 , 3.19926714e+00 , 4.03701726e+00 , 5.09413801e+00 , \n 6.42807312e+00 , 8.11130831e+00 , 1.02353102e+01 , 1.29154967e+01 , \n 1.62975083e+01 , 2.05651231e+01 , 2.59502421e+01 , 3.27454916e+01 , \n 4.13201240e+01 , 5.21400829e+01 , 6.57933225e+01 , 8.30217568e+01 , \n 1.04761575e+02 , 1.32194115e+02 , 1.66810054e+02 , 2.10490414e+02 , \n 2.65608778e+02 , 3.35160265e+02 , 4.22924287e+02 , 5.33669923e+02 , \n 6.73415066e+02 , 8.49753436e+02 , 1.07226722e+03 , 1.35304777e+03 , \n 1.70735265e+03 , 2.15443469e+03 , 2.71858824e+03 , 3.43046929e+03 , \n 4.32876128e+03 , 5.46227722e+03 , 6.89261210e+03 , 8.69749003e+03 , \n 1.09749877e+04 , 1.38488637e+04 , 1.74752840e+04 , 2.20513074e+04 , \n 2.78255940e+04 , 3.51119173e+04 , 4.43062146e+04 , 5.59081018e+04 , \n 7.05480231e+04 , 8.90215085e+04 , 1.12332403e+05 , 1.41747416e+05 , \n 1.78864953e+05 , 2.25701972e+05 , 2.84803587e+05 , 3.59381366e+05 , \n 4.53487851e+05 , 5.72236766e+05 , 7.22080902e+05 , 9.11162756e+05 , \n 1.14975700e+06 , 1.45082878e+06 , 1.83073828e+06 , 2.31012970e+06 , \n 2.91505306e+06 , 3.67837977e+06 , 4.64158883e+06 , 5.85702082e+06 , \n 7.39072203e+06 , 9.32603347e+06 , 1.17681195e+07 , 1.48496826e+07 , \n 1.87381742e+07 , 2.36448941e+07 , 2.98364724e+07 , 3.76493581e+07 , \n 4.75081016e+07 , 5.99484250e+07 , 7.56463328e+07 , 9.54548457e+07 , \n 1.20450354e+08 , 1.51991108e+08 , 1.91791026e+08 , 2.42012826e+08 , \n 3.05385551e+08 , 3.85352859e+08 , 4.86260158e+08 , 6.13590727e+08 , \n 7.74263683e+08 , 9.77009957e+08 , 1.23284674e+09 , 1.55567614e+09 , \n 1.96304065e+09 , 2.47707636e+09 , 3.12571585e+09 , 3.94420606e+09 , \n 4.97702356e+09 , 6.28029144e+09 , 7.92482898e+09 , 1.00000000e+10 ]) , \t** kwargs ) -> list [ ultk . effcomm . information_bottleneck . ib . IBResult ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.get_ib_naming_model", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "get_ib_naming_model", "kind": "function", "doc": "Constructs an IBNamingModel by constructing the IB bound for the domain distribution P(M,U).
\n\nArguments: \n\n\npU_M (np.ndarray): Conditional distribution of observations given meanings.pM (np.ndarray, optional): Prior distribution over meanings. Defaults to None.gammas (np.ndarray, optional): Range of gamma values for similarity selection. Defaults to logspace(-2, 2, 1000).**bound_kwargs: Additional parameters for IB bound computation. See compute_bound kwargs. \n\nReturns: \n\n\n IBNamingModel: An IBNamingModel instance configured with the computed IB bound.
\n \n", "signature": "(\tpU_M : numpy . ndarray , \tpM : numpy . ndarray = None , \t** bound_kwargs ) -> ultk . effcomm . information_bottleneck . modeling . IBNamingModel : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.encoder_to_language", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "encoder_to_language", "kind": "function", "doc": "Convert a stochastic encoder to a ULTK Language using an IBNamingModel bayesian decoder.
\n\nArguments: \n\n\nqW_M (np.ndarray): A stochastic matrix where rows correspond to meanings\nand columns correspond to words, defining the encoder.naming_model (IBNamingModel): An instance of the IBNamingModel used to \ndecode the encoder into a language.universe (Universe): The universe containing referents and the structure \nin which the meanings are defined.words (list[str], optional): A list of word forms to use. If None, default \nnumeric indices are used. Defaults to None.name (str, optional): The name of the resulting Language. Defaults to None.natural (bool, optional): Whether the resulting Language is a natural \nlanguage. Defaults to False. \n\nReturns: \n\n\n Language: The constructed Language object, where each expression maps a \n word form to its corresponding meaning.
\n \n", "signature": "(\tqW_M : numpy . ndarray , \tnaming_model : ultk . effcomm . information_bottleneck . modeling . IBNamingModel , \tuniverse : ultk . language . semantics . Universe , \twords : list [ str ] = None , \tname : str = None , \tnatural : bool = False ) -> ultk . language . language . Language : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.pU_M_from_similarity", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "pU_M_from_similarity", "kind": "function", "doc": "Computes the conditional distribution p(U|M) based on similarity.
\n\nArguments: \n\n\ngamma (float): Scaling factor for the similarity matrix.sim_mat (np.ndarray): Similarity matrix representing similarity between meanings (M) and referents (U). \n\nReturns: \n\n\n np.ndarray: Conditional distribution p(U|M).
\n \n", "signature": "(gamma : float , sim_mat : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.get_imu", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "get_imu", "kind": "function", "doc": "Calculates the mutual information I(M;U) for a distribution p(U|M) \u221d exp(gamma * sim(u, m)).
\n\nArguments: \n\n\ngamma (float): Scaling factor for the similarity matrix.sim_mat (np.ndarray): Similarity matrix representing similarity between meanings (M) and referents (U).pM (np.ndarray, optional): Prior distribution over meanings (M). Defaults to a uniform distribution. \n\nReturns: \n\n\n np.ndarray: Mutual information I(M;U).
\n \n", "signature": "(\tgamma : float , \tsim_mat : numpy . ndarray , \tpM : numpy . ndarray = None ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.modeling.select_gamma", "modulename": "ultk.effcomm.information_bottleneck.modeling", "qualname": "select_gamma", "kind": "function", "doc": "Selects the gamma value that corresponds to the midpoint of I(M;U) for a distribution p(U|M) \u221d exp(gamma * sim(u, m)).
\n\nArguments: \n\n\nsimilarity_matrix (np.ndarray): Matrix encoding pairwise similarities between meanings (M) and referents (U).pM (np.ndarray, optional): Communicative need distribution over meanings (M). Defaults to None.gammas (np.ndarray, optional): Range of gamma values to sample. Defaults to logspace(-2, 2, 1000). \n\nReturns: \n\n\n tuple: A tuple containing:\n - float: Gamma value corresponding to the midpoint of I(M;U).\n - float: Midpoint of I(M;U).\n - int: Index of the midpoint in the gamma array.\n - np.ndarray: Array of gamma values used.\n - np.ndarray: Array of computed I(M;U) values.
\n \n", "signature": "(\tsimilarity_matrix : numpy . ndarray , \tpM : numpy . ndarray = None , \tgammas : numpy . ndarray = array ([ 1.00000000e-02 , 1.00926219e-02 , 1.01861017e-02 , 1.02804473e-02 , \n 1.03756668e-02 , 1.04717682e-02 , 1.05687597e-02 , 1.06666496e-02 , \n 1.07654461e-02 , 1.08651577e-02 , 1.09657929e-02 , 1.10673602e-02 , \n 1.11698682e-02 , 1.12733256e-02 , 1.13777413e-02 , 1.14831241e-02 , \n 1.15894830e-02 , 1.16968270e-02 , 1.18051653e-02 , 1.19145070e-02 , \n 1.20248614e-02 , 1.21362380e-02 , 1.22486461e-02 , 1.23620954e-02 , \n 1.24765955e-02 , 1.25921561e-02 , 1.27087871e-02 , 1.28264983e-02 , \n 1.29452998e-02 , 1.30652016e-02 , 1.31862140e-02 , 1.33083472e-02 , \n 1.34316117e-02 , 1.35560179e-02 , 1.36815763e-02 , 1.38082977e-02 , \n 1.39361927e-02 , 1.40652724e-02 , 1.41955477e-02 , 1.43270295e-02 , \n 1.44597292e-02 , 1.45936580e-02 , 1.47288272e-02 , 1.48652484e-02 , \n 1.50029332e-02 , 1.51418933e-02 , 1.52821404e-02 , 1.54236865e-02 , \n 1.55665436e-02 , 1.57107239e-02 , 1.58562396e-02 , 1.60031031e-02 , \n 1.61513269e-02 , 1.63009236e-02 , 1.64519059e-02 , 1.66042866e-02 , \n 1.67580786e-02 , 1.69132952e-02 , 1.70699493e-02 , 1.72280545e-02 , \n 1.73876240e-02 , 1.75486715e-02 , 1.77112106e-02 , 1.78752553e-02 , \n 1.80408193e-02 , 1.82079168e-02 , 1.83765620e-02 , 1.85467692e-02 , \n 1.87185529e-02 , 1.88919278e-02 , 1.90669084e-02 , 1.92435098e-02 , \n 1.94217468e-02 , 1.96016347e-02 , 1.97831888e-02 , 1.99664245e-02 , \n 2.01513573e-02 , 2.03380031e-02 , 2.05263775e-02 , 2.07164968e-02 , \n 2.09083769e-02 , 2.11020343e-02 , 2.12974854e-02 , 2.14947467e-02 , \n 2.16938352e-02 , 2.18947676e-02 , 2.20975611e-02 , 2.23022330e-02 , \n 2.25088005e-02 , 2.27172813e-02 , 2.29276931e-02 , 2.31400538e-02 , \n 2.33543814e-02 , 2.35706941e-02 , 2.37890104e-02 , 2.40093488e-02 , \n 2.42317279e-02 , 2.44561668e-02 , 2.46826845e-02 , 2.49113003e-02 , \n 2.51420335e-02 , 2.53749038e-02 , 2.56099310e-02 , 2.58471351e-02 , \n 2.60865362e-02 , 2.63281547e-02 , 2.65720111e-02 , 2.68181261e-02 , \n 2.70665207e-02 , 2.73172160e-02 , 2.75702333e-02 , 2.78255940e-02 , \n 2.80833200e-02 , 2.83434331e-02 , 2.86059554e-02 , 2.88709092e-02 , \n 2.91383170e-02 , 2.94082017e-02 , 2.96805861e-02 , 2.99554933e-02 , \n 3.02329468e-02 , 3.05129702e-02 , 3.07955871e-02 , 3.10808217e-02 , \n 3.13686982e-02 , 3.16592411e-02 , 3.19524751e-02 , 3.22484250e-02 , \n 3.25471161e-02 , 3.28485737e-02 , 3.31528234e-02 , 3.34598912e-02 , \n 3.37698031e-02 , 3.40825855e-02 , 3.43982649e-02 , 3.47168682e-02 , \n 3.50384225e-02 , 3.53629550e-02 , 3.56904935e-02 , 3.60210656e-02 , \n 3.63546996e-02 , 3.66914238e-02 , 3.70312668e-02 , 3.73742574e-02 , \n 3.77204249e-02 , 3.80697987e-02 , 3.84224085e-02 , 3.87782841e-02 , \n 3.91374560e-02 , 3.94999546e-02 , 3.98658107e-02 , 4.02350555e-02 , \n 4.06077203e-02 , 4.09838367e-02 , 4.13634368e-02 , 4.17465529e-02 , \n 4.21332174e-02 , 4.25234633e-02 , 4.29173238e-02 , 4.33148322e-02 , \n 4.37160225e-02 , 4.41209286e-02 , 4.45295851e-02 , 4.49420266e-02 , \n 4.53582883e-02 , 4.57784054e-02 , 4.62024137e-02 , 4.66303493e-02 , \n 4.70622485e-02 , 4.74981480e-02 , 4.79380850e-02 , 4.83820966e-02 , \n 4.88302209e-02 , 4.92824957e-02 , 4.97389596e-02 , 5.01996513e-02 , \n 5.06646101e-02 , 5.11338754e-02 , 5.16074871e-02 , 5.20854855e-02 , \n 5.25679112e-02 , 5.30548053e-02 , 5.35462090e-02 , 5.40421642e-02 , \n 5.45427131e-02 , 5.50478981e-02 , 5.55577622e-02 , 5.60723488e-02 , \n 5.65917016e-02 , 5.71158648e-02 , 5.76448828e-02 , 5.81788007e-02 , \n 5.87176639e-02 , 5.92615181e-02 , 5.98104096e-02 , 6.03643851e-02 , \n 6.09234915e-02 , 6.14877765e-02 , 6.20572881e-02 , 6.26320745e-02 , \n 6.32121848e-02 , 6.37976681e-02 , 6.43885743e-02 , 6.49849535e-02 , \n 6.55868566e-02 , 6.61943346e-02 , 6.68074392e-02 , 6.74262224e-02 , \n 6.80507370e-02 , 6.86810359e-02 , 6.93171728e-02 , 6.99592017e-02 , \n 7.06071771e-02 , 7.12611543e-02 , 7.19211887e-02 , 7.25873365e-02 , \n 7.32596543e-02 , 7.39381992e-02 , 7.46230289e-02 , 7.53142017e-02 , \n 7.60117762e-02 , 7.67158118e-02 , 7.74263683e-02 , 7.81435061e-02 , \n 7.88672862e-02 , 7.95977700e-02 , 8.03350198e-02 , 8.10790981e-02 , \n 8.18300682e-02 , 8.25879939e-02 , 8.33529397e-02 , 8.41249705e-02 , \n 8.49041520e-02 , 8.56905505e-02 , 8.64842328e-02 , 8.72852662e-02 , \n 8.80937190e-02 , 8.89096599e-02 , 8.97331581e-02 , 9.05642838e-02 , \n 9.14031075e-02 , 9.22497005e-02 , 9.31041349e-02 , 9.39664831e-02 , \n 9.48368187e-02 , 9.57152154e-02 , 9.66017480e-02 , 9.74964918e-02 , \n 9.83995230e-02 , 9.93109181e-02 , 1.00230755e-01 , 1.01159111e-01 , \n 1.02096066e-01 , 1.03041699e-01 , 1.03996091e-01 , 1.04959323e-01 , \n 1.05931476e-01 , 1.06912634e-01 , 1.07902879e-01 , 1.08902296e-01 , \n 1.09910970e-01 , 1.10928986e-01 , 1.11956432e-01 , 1.12993394e-01 , \n 1.14039960e-01 , 1.15096220e-01 , 1.16162263e-01 , 1.17238180e-01 , \n 1.18324063e-01 , 1.19420003e-01 , 1.20526094e-01 , 1.21642429e-01 , \n 1.22769105e-01 , 1.23906216e-01 , 1.25053859e-01 , 1.26212131e-01 , \n 1.27381132e-01 , 1.28560961e-01 , 1.29751717e-01 , 1.30953502e-01 , \n 1.32166418e-01 , 1.33390569e-01 , 1.34626058e-01 , 1.35872990e-01 , \n 1.37131472e-01 , 1.38401610e-01 , 1.39683512e-01 , 1.40977287e-01 , \n 1.42283046e-01 , 1.43600898e-01 , 1.44930957e-01 , 1.46273336e-01 , \n 1.47628147e-01 , 1.48995507e-01 , 1.50375532e-01 , 1.51768339e-01 , \n 1.53174046e-01 , 1.54592774e-01 , 1.56024641e-01 , 1.57469771e-01 , \n 1.58928287e-01 , 1.60400311e-01 , 1.61885969e-01 , 1.63385388e-01 , \n 1.64898694e-01 , 1.66426018e-01 , 1.67967487e-01 , 1.69523234e-01 , \n 1.71093391e-01 , 1.72678090e-01 , 1.74277468e-01 , 1.75891659e-01 , \n 1.77520801e-01 , 1.79165033e-01 , 1.80824493e-01 , 1.82499324e-01 , \n 1.84189668e-01 , 1.85895668e-01 , 1.87617469e-01 , 1.89355218e-01 , \n 1.91109062e-01 , 1.92879151e-01 , 1.94665634e-01 , 1.96468665e-01 , \n 1.98288395e-01 , 2.00124980e-01 , 2.01978576e-01 , 2.03849340e-01 , \n 2.05737431e-01 , 2.07643011e-01 , 2.09566240e-01 , 2.11507282e-01 , \n 2.13466303e-01 , 2.15443469e-01 , 2.17438948e-01 , 2.19452909e-01 , \n 2.21485523e-01 , 2.23536965e-01 , 2.25607407e-01 , 2.27697026e-01 , \n 2.29805999e-01 , 2.31934506e-01 , 2.34082728e-01 , 2.36250847e-01 , \n 2.38439047e-01 , 2.40647515e-01 , 2.42876438e-01 , 2.45126006e-01 , \n 2.47396410e-01 , 2.49687843e-01 , 2.52000499e-01 , 2.54334576e-01 , \n 2.56690272e-01 , 2.59067786e-01 , 2.61467321e-01 , 2.63889081e-01 , \n 2.66333273e-01 , 2.68800102e-01 , 2.71289780e-01 , 2.73802518e-01 , \n 2.76338529e-01 , 2.78898029e-01 , 2.81481236e-01 , 2.84088369e-01 , \n 2.86719650e-01 , 2.89375302e-01 , 2.92055551e-01 , 2.94760626e-01 , \n 2.97490755e-01 , 3.00246171e-01 , 3.03027108e-01 , 3.05833803e-01 , \n 3.08666494e-01 , 3.11525422e-01 , 3.14410830e-01 , 3.17322963e-01 , \n 3.20262069e-01 , 3.23228398e-01 , 3.26222201e-01 , 3.29243733e-01 , \n 3.32293252e-01 , 3.35371015e-01 , 3.38477286e-01 , 3.41612327e-01 , \n 3.44776405e-01 , 3.47969790e-01 , 3.51192753e-01 , 3.54445567e-01 , \n 3.57728510e-01 , 3.61041860e-01 , 3.64385898e-01 , 3.67760910e-01 , \n 3.71167182e-01 , 3.74605003e-01 , 3.78074666e-01 , 3.81576466e-01 , \n 3.85110700e-01 , 3.88677669e-01 , 3.92277676e-01 , 3.95911027e-01 , \n 3.99578030e-01 , 4.03278998e-01 , 4.07014245e-01 , 4.10784089e-01 , \n 4.14588850e-01 , 4.18428851e-01 , 4.22304419e-01 , 4.26215883e-01 , \n 4.30163576e-01 , 4.34147833e-01 , 4.38168993e-01 , 4.42227398e-01 , \n 4.46323393e-01 , 4.50457325e-01 , 4.54629547e-01 , 4.58840413e-01 , \n 4.63090280e-01 , 4.67379511e-01 , 4.71708469e-01 , 4.76077523e-01 , \n 4.80487044e-01 , 4.84937407e-01 , 4.89428990e-01 , 4.93962174e-01 , \n 4.98537346e-01 , 5.03154895e-01 , 5.07815211e-01 , 5.12518693e-01 , \n 5.17265739e-01 , 5.22056753e-01 , 5.26892142e-01 , 5.31772318e-01 , \n 5.36697695e-01 , 5.41668691e-01 , 5.46685730e-01 , 5.51749238e-01 , \n 5.56859644e-01 , 5.62017385e-01 , 5.67222897e-01 , 5.72476624e-01 , \n 5.77779012e-01 , 5.83130511e-01 , 5.88531578e-01 , 5.93982669e-01 , \n 5.99484250e-01 , 6.05036788e-01 , 6.10640754e-01 , 6.16296626e-01 , \n 6.22004883e-01 , 6.27766011e-01 , 6.33580499e-01 , 6.39448843e-01 , \n 6.45371540e-01 , 6.51349095e-01 , 6.57382014e-01 , 6.63470812e-01 , \n 6.69616005e-01 , 6.75818117e-01 , 6.82077673e-01 , 6.88395207e-01 , \n 6.94771255e-01 , 7.01206359e-01 , 7.07701066e-01 , 7.14255929e-01 , \n 7.20871503e-01 , 7.27548353e-01 , 7.34287045e-01 , 7.41088152e-01 , \n 7.47952252e-01 , 7.54879928e-01 , 7.61871770e-01 , 7.68928372e-01 , \n 7.76050334e-01 , 7.83238260e-01 , 7.90492762e-01 , 7.97814457e-01 , \n 8.05203967e-01 , 8.12661920e-01 , 8.20188950e-01 , 8.27785697e-01 , \n 8.35452806e-01 , 8.43190929e-01 , 8.51000725e-01 , 8.58882856e-01 , \n 8.66837993e-01 , 8.74866812e-01 , 8.82969996e-01 , 8.91148232e-01 , \n 8.99402217e-01 , 9.07732653e-01 , 9.16140246e-01 , 9.24625712e-01 , \n 9.33189772e-01 , 9.41833153e-01 , 9.50556592e-01 , 9.59360829e-01 , \n 9.68246612e-01 , 9.77214697e-01 , 9.86265846e-01 , 9.95400829e-01 , \n 1.00462042e+00 , 1.01392541e+00 , 1.02331658e+00 , 1.03279473e+00 , \n 1.04236067e+00 , 1.05201522e+00 , 1.06175918e+00 , 1.07159340e+00 , \n 1.08151870e+00 , 1.09153594e+00 , 1.10164595e+00 , 1.11184960e+00 , \n 1.12214777e+00 , 1.13254132e+00 , 1.14303113e+00 , 1.15361810e+00 , \n 1.16430313e+00 , 1.17508713e+00 , 1.18597101e+00 , 1.19695570e+00 , \n 1.20804213e+00 , 1.21923125e+00 , 1.23052400e+00 , 1.24192135e+00 , \n 1.25342427e+00 , 1.26503372e+00 , 1.27675070e+00 , 1.28857621e+00 , \n 1.30051125e+00 , 1.31255684e+00 , 1.32471399e+00 , 1.33698374e+00 , \n 1.34936714e+00 , 1.36186524e+00 , 1.37447909e+00 , 1.38720978e+00 , \n 1.40005838e+00 , 1.41302599e+00 , 1.42611371e+00 , 1.43932264e+00 , \n 1.45265393e+00 , 1.46610868e+00 , 1.47968806e+00 , 1.49339322e+00 , \n 1.50722531e+00 , 1.52118552e+00 , 1.53527503e+00 , 1.54949504e+00 , \n 1.56384676e+00 , 1.57833141e+00 , 1.59295021e+00 , 1.60770442e+00 , \n 1.62259529e+00 , 1.63762407e+00 , 1.65279206e+00 , 1.66810054e+00 , \n 1.68355080e+00 , 1.69914417e+00 , 1.71488197e+00 , 1.73076553e+00 , \n 1.74679622e+00 , 1.76297538e+00 , 1.77930439e+00 , 1.79578465e+00 , \n 1.81241755e+00 , 1.82920450e+00 , 1.84614695e+00 , 1.86324631e+00 , \n 1.88050406e+00 , 1.89792164e+00 , 1.91550056e+00 , 1.93324229e+00 , \n 1.95114835e+00 , 1.96922026e+00 , 1.98745955e+00 , 2.00586778e+00 , \n 2.02444651e+00 , 2.04319732e+00 , 2.06212180e+00 , 2.08122157e+00 , \n 2.10049824e+00 , 2.11995346e+00 , 2.13958887e+00 , 2.15940615e+00 , \n 2.17940698e+00 , 2.19959307e+00 , 2.21996612e+00 , 2.24052787e+00 , \n 2.26128007e+00 , 2.28222447e+00 , 2.30336287e+00 , 2.32469706e+00 , \n 2.34622885e+00 , 2.36796007e+00 , 2.38989257e+00 , 2.41202821e+00 , \n 2.43436887e+00 , 2.45691646e+00 , 2.47967289e+00 , 2.50264010e+00 , \n 2.52582003e+00 , 2.54921465e+00 , 2.57282597e+00 , 2.59665597e+00 , \n 2.62070670e+00 , 2.64498018e+00 , 2.66947849e+00 , 2.69420371e+00 , \n 2.71915794e+00 , 2.74434330e+00 , 2.76976194e+00 , 2.79541600e+00 , \n 2.82130768e+00 , 2.84743917e+00 , 2.87381269e+00 , 2.90043049e+00 , \n 2.92729484e+00 , 2.95440800e+00 , 2.98177229e+00 , 3.00939003e+00 , \n 3.03726358e+00 , 3.06539530e+00 , 3.09378757e+00 , 3.12244282e+00 , \n 3.15136348e+00 , 3.18055202e+00 , 3.21001090e+00 , 3.23974263e+00 , \n 3.26974974e+00 , 3.30003479e+00 , 3.33060034e+00 , 3.36144900e+00 , \n 3.39258338e+00 , 3.42400614e+00 , 3.45571994e+00 , 3.48772747e+00 , \n 3.52003147e+00 , 3.55263468e+00 , 3.58553986e+00 , 3.61874981e+00 , \n 3.65226736e+00 , 3.68609536e+00 , 3.72023668e+00 , 3.75469422e+00 , \n 3.78947092e+00 , 3.82456972e+00 , 3.85999362e+00 , 3.89574562e+00 , \n 3.93182876e+00 , 3.96824610e+00 , 4.00500076e+00 , 4.04209584e+00 , \n 4.07953450e+00 , 4.11731993e+00 , 4.15545533e+00 , 4.19394396e+00 , \n 4.23278907e+00 , 4.27199397e+00 , 4.31156199e+00 , 4.35149650e+00 , \n 4.39180089e+00 , 4.43247859e+00 , 4.47353305e+00 , 4.51496777e+00 , \n 4.55678627e+00 , 4.59899209e+00 , 4.64158883e+00 , 4.68458012e+00 , \n 4.72796959e+00 , 4.77176095e+00 , 4.81595791e+00 , 4.86056423e+00 , \n 4.90558371e+00 , 4.95102016e+00 , 4.99687745e+00 , 5.04315949e+00 , \n 5.08987019e+00 , 5.13701354e+00 , 5.18459354e+00 , 5.23261424e+00 , \n 5.28107971e+00 , 5.32999408e+00 , 5.37936150e+00 , 5.42918618e+00 , \n 5.47947234e+00 , 5.53022426e+00 , 5.58144625e+00 , 5.63314267e+00 , \n 5.68531791e+00 , 5.73797641e+00 , 5.79112265e+00 , 5.84476113e+00 , \n 5.89889643e+00 , 5.95353313e+00 , 6.00867589e+00 , 6.06432940e+00 , \n 6.12049837e+00 , 6.17718760e+00 , 6.23440189e+00 , 6.29214611e+00 , \n 6.35042517e+00 , 6.40924402e+00 , 6.46860766e+00 , 6.52852114e+00 , \n 6.58898955e+00 , 6.65001803e+00 , 6.71161177e+00 , 6.77377600e+00 , \n 6.83651600e+00 , 6.89983712e+00 , 6.96374473e+00 , 7.02824426e+00 , \n 7.09334120e+00 , 7.15904109e+00 , 7.22534949e+00 , 7.29227206e+00 , \n 7.35981448e+00 , 7.42798248e+00 , 7.49678187e+00 , 7.56621850e+00 , \n 7.63629826e+00 , 7.70702711e+00 , 7.77841107e+00 , 7.85045620e+00 , \n 7.92316862e+00 , 7.99655453e+00 , 8.07062014e+00 , 8.14537177e+00 , \n 8.22081576e+00 , 8.29695852e+00 , 8.37380654e+00 , 8.45136633e+00 , \n 8.52964450e+00 , 8.60864770e+00 , 8.68838264e+00 , 8.76885609e+00 , \n 8.85007491e+00 , 8.93204600e+00 , 9.01477631e+00 , 9.09827289e+00 , \n 9.18254284e+00 , 9.26759330e+00 , 9.35343152e+00 , 9.44006479e+00 , \n 9.52750047e+00 , 9.61574600e+00 , 9.70480888e+00 , 9.79469667e+00 , \n 9.88541702e+00 , 9.97697764e+00 , 1.00693863e+01 , 1.01626509e+01 , \n 1.02567793e+01 , 1.03517796e+01 , 1.04476597e+01 , 1.05444279e+01 , \n 1.06420924e+01 , 1.07406615e+01 , 1.08401436e+01 , 1.09405471e+01 , \n 1.10418805e+01 , 1.11441525e+01 , 1.12473718e+01 , 1.13515471e+01 , \n 1.14566873e+01 , 1.15628013e+01 , 1.16698982e+01 , 1.17779870e+01 , \n 1.18870770e+01 , 1.19971774e+01 , 1.21082975e+01 , 1.22204469e+01 , \n 1.23336350e+01 , 1.24478715e+01 , 1.25631660e+01 , 1.26795285e+01 , \n 1.27969687e+01 , 1.29154967e+01 , 1.30351224e+01 , 1.31558562e+01 , \n 1.32777083e+01 , 1.34006890e+01 , 1.35248087e+01 , 1.36500781e+01 , \n 1.37765077e+01 , 1.39041083e+01 , 1.40328908e+01 , 1.41628662e+01 , \n 1.42940453e+01 , 1.44264395e+01 , 1.45600600e+01 , 1.46949180e+01 , \n 1.48310251e+01 , 1.49683929e+01 , 1.51070330e+01 , 1.52469573e+01 , \n 1.53881775e+01 , 1.55307057e+01 , 1.56745541e+01 , 1.58197348e+01 , \n 1.59662602e+01 , 1.61141428e+01 , 1.62633950e+01 , 1.64140297e+01 , \n 1.65660596e+01 , 1.67194976e+01 , 1.68743568e+01 , 1.70306503e+01 , \n 1.71883914e+01 , 1.73475936e+01 , 1.75082703e+01 , 1.76704353e+01 , \n 1.78341022e+01 , 1.79992851e+01 , 1.81659979e+01 , 1.83342548e+01 , \n 1.85040702e+01 , 1.86754584e+01 , 1.88484341e+01 , 1.90230119e+01 , \n 1.91992067e+01 , 1.93770334e+01 , 1.95565072e+01 , 1.97376433e+01 , \n 1.99204571e+01 , 2.01049642e+01 , 2.02911802e+01 , 2.04791210e+01 , \n 2.06688025e+01 , 2.08602409e+01 , 2.10534524e+01 , 2.12484535e+01 , \n 2.14452608e+01 , 2.16438909e+01 , 2.18443607e+01 , 2.20466874e+01 , \n 2.22508880e+01 , 2.24569800e+01 , 2.26649808e+01 , 2.28749082e+01 , \n 2.30867799e+01 , 2.33006141e+01 , 2.35164288e+01 , 2.37342425e+01 , \n 2.39540736e+01 , 2.41759408e+01 , 2.43998630e+01 , 2.46258592e+01 , \n 2.48539486e+01 , 2.50841506e+01 , 2.53164848e+01 , 2.55509709e+01 , \n 2.57876289e+01 , 2.60264788e+01 , 2.62675410e+01 , 2.65108360e+01 , \n 2.67563844e+01 , 2.70042072e+01 , 2.72543253e+01 , 2.75067601e+01 , \n 2.77615329e+01 , 2.80186656e+01 , 2.82781798e+01 , 2.85400977e+01 , \n 2.88044415e+01 , 2.90712338e+01 , 2.93404971e+01 , 2.96122544e+01 , \n 2.98865287e+01 , 3.01633435e+01 , 3.04427221e+01 , 3.07246884e+01 , \n 3.10092664e+01 , 3.12964801e+01 , 3.15863541e+01 , 3.18789129e+01 , \n 3.21741815e+01 , 3.24721849e+01 , 3.27729485e+01 , 3.30764978e+01 , \n 3.33828586e+01 , 3.36920571e+01 , 3.40041193e+01 , 3.43190720e+01 , \n 3.46369418e+01 , 3.49577557e+01 , 3.52815412e+01 , 3.56083255e+01 , \n 3.59381366e+01 , 3.62710025e+01 , 3.66069515e+01 , 3.69460121e+01 , \n 3.72882131e+01 , 3.76335836e+01 , 3.79821531e+01 , 3.83339510e+01 , \n 3.86890074e+01 , 3.90473524e+01 , 3.94090164e+01 , 3.97740302e+01 , \n 4.01424249e+01 , 4.05142317e+01 , 4.08894823e+01 , 4.12682085e+01 , \n 4.16504425e+01 , 4.20362168e+01 , 4.24255643e+01 , 4.28185180e+01 , \n 4.32151113e+01 , 4.36153779e+01 , 4.40193519e+01 , 4.44270675e+01 , \n 4.48385595e+01 , 4.52538628e+01 , 4.56730127e+01 , 4.60960449e+01 , \n 4.65229952e+01 , 4.69539001e+01 , 4.73887961e+01 , 4.78277202e+01 , \n 4.82707097e+01 , 4.87178022e+01 , 4.91690358e+01 , 4.96244488e+01 , \n 5.00840799e+01 , 5.05479682e+01 , 5.10161531e+01 , 5.14886745e+01 , \n 5.19655724e+01 , 5.24468875e+01 , 5.29326606e+01 , 5.34229330e+01 , \n 5.39177464e+01 , 5.44171429e+01 , 5.49211648e+01 , 5.54298552e+01 , \n 5.59432571e+01 , 5.64614142e+01 , 5.69843706e+01 , 5.75121707e+01 , \n 5.80448594e+01 , 5.85824820e+01 , 5.91250841e+01 , 5.96727120e+01 , \n 6.02254120e+01 , 6.07832313e+01 , 6.13462172e+01 , 6.19144176e+01 , \n 6.24878807e+01 , 6.30666554e+01 , 6.36507908e+01 , 6.42403366e+01 , \n 6.48353429e+01 , 6.54358602e+01 , 6.60419396e+01 , 6.66536327e+01 , \n 6.72709914e+01 , 6.78940681e+01 , 6.85229160e+01 , 6.91575883e+01 , \n 6.97981391e+01 , 7.04446228e+01 , 7.10970943e+01 , 7.17556092e+01 , \n 7.24202233e+01 , 7.30909933e+01 , 7.37679760e+01 , 7.44512291e+01 , \n 7.51408106e+01 , 7.58367791e+01 , 7.65391939e+01 , 7.72481145e+01 , \n 7.79636013e+01 , 7.86857151e+01 , 7.94145172e+01 , 8.01500696e+01 , \n 8.08924349e+01 , 8.16416760e+01 , 8.23978568e+01 , 8.31610415e+01 , \n 8.39312950e+01 , 8.47086827e+01 , 8.54932707e+01 , 8.62851257e+01 , \n 8.70843150e+01 , 8.78909065e+01 , 8.87049689e+01 , 8.95265713e+01 , \n 9.03557835e+01 , 9.11926760e+01 , 9.20373200e+01 , 9.28897872e+01 , \n 9.37501502e+01 , 9.46184819e+01 , 9.54948564e+01 , 9.63793480e+01 , \n 9.72720319e+01 , 9.81729841e+01 , 9.90822810e+01 , 1.00000000e+02 ]) ) -> tuple [ float , float , int , numpy . ndarray , numpy . ndarray ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools", "modulename": "ultk.effcomm.information_bottleneck.tools", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.PRECISION", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "PRECISION", "kind": "variable", "doc": "
\n", "default_value": "1e-15"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.get_gaussian_noise", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "get_gaussian_noise", "kind": "function", "doc": "Small Gaussian noise.
\n", "signature": "(shape ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.add_noise_to_stochastic_matrix", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "add_noise_to_stochastic_matrix", "kind": "function", "doc": "Given an input stochastic matrix q, sample a stochastic matrix p and\nmix it with the input with a small weight weight, i.e., return q + weight * p.
\n", "signature": "(q , weight = 0.01 ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.random_stochastic_matrix", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "random_stochastic_matrix", "kind": "function", "doc": "Initialize a stochastic matrix (2D array) that sums to 1 along the rows.
\n\nArguments: \n\n\nshape: tuple, the desired shape of the stochastic matrix (e.g., (rows, cols)).gamma: float, scaling factor for the random normal initialization. \n\nReturns: \n\n\n A stochastic matrix with rows summing to 1.
\n \n", "signature": "(shape , gamma = 1e-10 ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.entropy_bits", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "entropy_bits", "kind": "function", "doc": "Compute entropy of p, $H(X) = - \\sum_x p(x) \\log p(x)$, in bits.
\n", "signature": "(p : numpy . ndarray , axis = None ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.mutual_info", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "mutual_info", "kind": "function", "doc": "Compute mutual information, $I(X;Y)$ in bits.
\n\nArguments: \n\n\npxy: 2D numpy array of shape (x, y) \n", "signature": "(pxy : numpy . ndarray ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.kl_divergence", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "kl_divergence", "kind": "function", "doc": "Compute KL divergence (in nats by defaut) between p and q, $D_{KL}[p \\| q]$.
\n\nArguments: \n\n\np: np.ndarray, lhs of KL divergenceq: np.ndarray, rhs of KL divergence \n", "signature": "(\tp : numpy . ndarray , \tq : numpy . ndarray , \taxis = None , \tbase = 2.718281828459045 ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.information_cond", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "information_cond", "kind": "function", "doc": "Compute the mutual information $I(A;B)$ from a joint distribution defind by $P(A)$ and $P(B|A)$
\n\nArguments: \n\n\npA: array of shape |A| the prior probability of an input symbol (i.e., the source)pB_A: array of shape (|A|, |B|) the probability of an output symbol given the input \n", "signature": "(pA : numpy . ndarray , pB_A : numpy . ndarray ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.information_bottleneck.tools.compute_lower_bound", "modulename": "ultk.effcomm.information_bottleneck.tools", "qualname": "compute_lower_bound", "kind": "function", "doc": "Remove all points in a rate-distortion curve that would make it nonmonotonic and\nreturn only the resulting monotonic indices.
\n\nThis is required to remove the random fluctuations in the result induced by the BA algorithm getting stuck in local minima.
\n\nAcknowledgement: https://github.com/epiasini/embo-github-mirror/blob/master/embo/utils.py#L77 .
\n\nArguments: \n\n\nrd_points: list of pairs of floats, where each pair represents an estimated (rate, distortion) pair,\nand ordered by increasing rate . \n\nReturns: \n\n\n selected_indices: 1D numpy array containing the indices of the points selected to ensure monotonically decreasing values.
\n \n", "signature": "(rd_points ): ", "funcdef": "def"}, {"fullname": "ultk.effcomm.informativity", "modulename": "ultk.effcomm.informativity", "kind": "module", "doc": "Functions for measuring informativity in efficient communication analyses of languages.
\n"}, {"fullname": "ultk.effcomm.informativity.build_utility_matrix", "modulename": "ultk.effcomm.informativity", "qualname": "build_utility_matrix", "kind": "function", "doc": "Construct the square matrix specifying the utility function defined for pairs of meanings, used for computing communicative success.
\n", "signature": "(\tuniverse : ultk . language . semantics . Universe , \tutility : Callable [[ ultk . language . semantics . Referent , ultk . language . semantics . Referent ], float ] ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.informativity.indicator_utility", "modulename": "ultk.effcomm.informativity", "qualname": "indicator_utility", "kind": "function", "doc": "Indicator utility function, i.e. delta. Returns 1.0 iff ref1 equals ref2.
\n", "signature": "(\tref1 : ultk . language . semantics . Referent , \tref2 : ultk . language . semantics . Referent ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.informativity.informativity", "modulename": "ultk.effcomm.informativity", "qualname": "informativity", "kind": "function", "doc": "The informativity of a language is identified with the successful communication between a speaker and a listener.
\n\nThis function is a wrapper for communicative_success.
\n\nArguments: \n\n\nlanguage: the language to compute informativity of.prior: a probability distribution representing communicative need (frequency) for Referents.utility: a function representing the usefulness of listener guesses about speaker Referents, e.g. Referent similarity. To reward only exact recovery of meanings, use the indicator function (default).kind: {\"literal, pragmatic\"} Whether to measure informativity using literal or pragmatic agents, as canonically described in the Rational Speech Act framework. The default is \"literal\". \n\nConcepts :\n The speaker can be thought of as a conditional distribution over expressions given meanings. The listener is likewise a conditional distribution over meanings given expressions. The communicative need, or cognitive source, is a prior probability over meanings representing how frequently agents need to use certain meanings in communication. The utility function represents the similarity, or appropriateness, of the listener's guess m' about the speaker's intended meaning m.
\n\nFormula :\n The informativity of a language $L$ with meaning space $M$ is defined:
\n\n$I(L) := \\sum_{m \\in M} p(m) \\sum_{i \\in L} p(i|m) \\sum_{\\hat{m} \\in i} p(\\hat{m}|i) \\cdot u(m, \\hat{m})$
\n\nBounds :\n A perfectly informative (=1.0) language can be constructed with a exactly one expression for each meaning.
\n\nFor u() = indicator(), every language has nonzero informativity because a language must contain at least one expression, and an expression must contain at least one meaning.\n(\tlanguage : ultk . language . language . Language , \tprior : numpy . ndarray , \tutility : Callable [[ ultk . language . semantics . Referent , ultk . language . semantics . Referent ], float ] = < function indicator_utility > , \tagent_type : str = 'literal' ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.informativity.communicative_success", "modulename": "ultk.effcomm.informativity", "qualname": "communicative_success", "kind": "function", "doc": "Helper function to compute the literal informativity of a language.
\n\n$I(L) = \\sum_{m, \\hat{m}} P(m, \\hat{m}) \\cdot u(m, \\hat{m})$
\n\n$ = \\sum_{m \\in M} p(m) \\sum_{i \\in L} p(i|m) \\sum_{\\hat{m} \\in i} p(\\hat{m} |i) \\cdot u(m, m')$
\n\n$ = \\sum \\text{diag}(p)SR \\odot U $
\n\nFor more details, see docs/vectorized_informativity .
\n\nArguments: \n\n\nspeaker: a literal or pragmatic speaker, containing a matrix S for P(e | m)listener: a literal or pragmatic listener, containing a matrix R for P(m | e)prior: p(m), distribution over meanings representing communicative needutility: a function u(m, m') representing similarity of meanings, or pair-wise usefulness of listener guesses about speaker meanings. \n", "signature": "(\tspeaker : ultk . effcomm . agent . Speaker , \tlistener : ultk . effcomm . agent . Listener , \tprior : numpy . ndarray , \tutility : Callable [[ ultk . language . semantics . Referent , ultk . language . semantics . Referent ], float ] ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization", "modulename": "ultk.effcomm.optimization", "kind": "module", "doc": "Classes and functions for generating languages that optimize the simplicity/informativeness trade-off, e.g. via an iterative evolutionary algorithm.
\n"}, {"fullname": "ultk.effcomm.optimization.Mutation", "modulename": "ultk.effcomm.optimization", "qualname": "Mutation", "kind": "class", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.Mutation.precondition", "modulename": "ultk.effcomm.optimization", "qualname": "Mutation.precondition", "kind": "function", "doc": "Whether a mutation is allowed to apply to a language.
\n", "signature": "(language : ultk . language . language . Language , ** kwargs ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.Mutation.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "Mutation.mutate", "kind": "function", "doc": "Mutate the language, possibly using a list of expressions.
\n", "signature": "(\tlanguage : ultk . language . language . Language , \texpressions : list [ ultk . language . language . Expression ] , \t** kwargs ) -> ultk . language . language . Language : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.RemoveExpression", "modulename": "ultk.effcomm.optimization", "qualname": "RemoveExpression", "kind": "class", "doc": "
\n", "bases": "Mutation"}, {"fullname": "ultk.effcomm.optimization.RemoveExpression.precondition", "modulename": "ultk.effcomm.optimization", "qualname": "RemoveExpression.precondition", "kind": "function", "doc": "Whether a mutation is allowed to apply to a language.
\n", "signature": "(language : ultk . language . language . Language , ** kwargs ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.RemoveExpression.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "RemoveExpression.mutate", "kind": "function", "doc": "Mutate the language, possibly using a list of expressions.
\n", "signature": "(\tlanguage : ultk . language . language . Language , \texpressions : list [ ultk . language . language . Expression ] , \t** kwargs ) -> ultk . language . language . Language : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.AddExpression", "modulename": "ultk.effcomm.optimization", "qualname": "AddExpression", "kind": "class", "doc": "
\n", "bases": "Mutation"}, {"fullname": "ultk.effcomm.optimization.AddExpression.precondition", "modulename": "ultk.effcomm.optimization", "qualname": "AddExpression.precondition", "kind": "function", "doc": "Whether a mutation is allowed to apply to a language.
\n", "signature": "(language : ultk . language . language . Language , ** kwargs ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.AddExpression.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "AddExpression.mutate", "kind": "function", "doc": "Mutate the language, possibly using a list of expressions.
\n", "signature": "(\tlanguage : ultk . language . language . Language , \texpressions : list [ ultk . language . language . Expression ] , \t** kwargs ) -> ultk . language . language . Language : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer", "kind": "class", "doc": "Class for approximating the Pareto frontier of languages optimizing the simplicity/informativity trade-off.
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.__init__", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.__init__", "kind": "function", "doc": "Initialize the evolutionary algorithm configurations.
\n\nThe measures of complexity and informativity, the expressions, and the mutations are all specific to the particular semantic domain.
\n\nArguments: \n\n\nobjectives: a dict of the two objectives to optimize for, e.g. simplicity and informativeness, of the form, e.g.\n{\n \"complexity\": comp_measure,\n \"comm_cost\": lambda l: 1 - inf_measure(l)\n}expressions: a list of expressions from which to apply mutations to languages.sample_size: the size of the population at every generation.max_muatations: between 1 and this number of mutations will be applied to a subset of the population at the end of each generation.generations: how many iterations to run the evolutionary algorithm for.lang_size: between 1 and this number of expressions comprise a language.mutations: (optional) a list of Mutation objects, defaults to add/remove expression \n", "signature": "(\tobjectives : list [ typing . Callable [[ ultk . language . language . Language ], typing . Any ]] , \texpressions : list [ ultk . language . language . Expression ] , \tsample_size : int , \tmax_mutations : int , \tgenerations : int , \tlang_size : int | None = None , \tmutations: tuple[typing.Type[ultk.effcomm.optimization.Mutation], ...] = (<class 'ultk.effcomm.optimization.AddExpression'>, <class 'ultk.effcomm.optimization.RemoveExpression'>) ) "}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.objectives", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.objectives", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.expressions", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.expressions", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.mutations", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.mutations", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.sample_size", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.sample_size", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.max_mutations", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.max_mutations", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.generations", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.generations", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.lang_size", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.lang_size", "kind": "variable", "doc": "
\n", "annotation": ": int"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.dominating_languages", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.dominating_languages", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.explored_languages", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.explored_languages", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.fit", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.fit", "kind": "function", "doc": "Computes the Pareto frontier, a set languages which cannot be both more simple and more informative.
\n\nUses pygmo's nondominated_front method for computing a population's best solutions to a multi-objective optimization problem.
\n\nArguments: \n\n\nseed_population: a list of languages representing the population at generation 0 of the algorithm.explore: a float in [0,1] representing how much to optimize for fitness\n(optimality wrt pareto front of complexity and comm_cost), and how much to randomly explore. \n\nReturns: \n\n\n a dict of the estimated optimization solutions, as well as points explored along the way; of the form
\n\n{\n\"dominating_languages\": list of languages as estimated solutions,\n\"explored_languages\": list of all the languages explored during the evolutionary algorithm,\n}\n \n", "signature": "(\tself , \tseed_population : list [ ultk . language . language . Language ] , \texplore : float = 0.0 ) -> dict [ str , list [ ultk . language . language . Language ]] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.sample_mutated", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.sample_mutated", "kind": "function", "doc": "Arguments: \n\n\nlanguages: dominating languages of a generationamount: sample_size.expressions: the list of expressions \n\nReturns: \n\n\n list of updated languages
\n \n", "signature": "(\tself , \tlanguages : list [ ultk . language . language . Language ] ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.EvolutionaryOptimizer.mutate", "modulename": "ultk.effcomm.optimization", "qualname": "EvolutionaryOptimizer.mutate", "kind": "function", "doc": "Randomly selects a mutation that is allowed to apply and applies it to a language.
\n\nArguments: \n\n\nlanguage: the Language to mutateexpressions: the list of all possible expressions.\nSome mutations need access to this list, so it is part of the mutation api. \n\nReturns: \n\n\n the mutated Language
\n \n", "signature": "(\tself , \tlanguage : ultk . language . language . Language ) -> ultk . language . language . Language : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.optimization.sample_parents", "modulename": "ultk.effcomm.optimization", "qualname": "sample_parents", "kind": "function", "doc": "Use the explore parameter to explore possibly suboptimal areas of the language space.
\n\nArguments: \n\n\ndominating_languages: a list of the languages with current best fitness with respect to the objectives.explored_languages: a list of all languages encountered during the evolutionary algorithm.explore: a float in [0,1] specifying how much to explore possibly suboptimal languages.\nIf set to 0, parent_languages is just dominating_languages. \n\nReturns: \n\n\n the languages to serve as the next generation (after possible mutations)
\n \n", "signature": "(\tdominating_languages : set [ ultk . language . language . Language ] , \texplored_languages : set [ ultk . language . language . Language ] , \texplore : float ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.probability", "modulename": "ultk.effcomm.probability", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.effcomm.probability.PRECISION", "modulename": "ultk.effcomm.probability", "qualname": "PRECISION", "kind": "variable", "doc": "
\n", "default_value": "1e-12"}, {"fullname": "ultk.effcomm.probability.marginal", "modulename": "ultk.effcomm.probability", "qualname": "marginal", "kind": "function", "doc": "Computer marginals of a joint distribution.
\n\nArguments: \n\n\npXY: a joint distribution of shape (|X|, |Y|), corresponding to p(x, y)axis: the axis along which to compute the marginal \n\nReturns: \n\n\n either pY (axis = 0) or pX (default, axis = 1)
\n \n", "signature": "(pXY : numpy . ndarray , axis : int = 1 ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.probability.joint", "modulename": "ultk.effcomm.probability", "qualname": "joint", "kind": "function", "doc": "Compute a joint distribution from a conditional and a prior.
\n\nArguments: \n\n\npY_X: a conditional distribution of shape (|X|, |Y|), corresponding to p(y|x)pX: a prior distribution of shape (|X|,), corresponding to p(x) \n\nReturns: \n\n\n a joint distribution of shape (|X|, |Y|), corresponding to p(x, y)
\n \n", "signature": "(pY_X : numpy . ndarray , pX : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.probability.marginalize", "modulename": "ultk.effcomm.probability", "qualname": "marginalize", "kind": "function", "doc": "Marginalize a conditional distribution (without a detour through the joint).
\n\nArguments: \n\n\npY_X: a conditional distribution of shape (|X|, |Y|), corresponding to p(y|x)pX: a prior distribution of shape (|X|,), corresponding to p(x) \n\nReturns: \n\n\n a marginal distribution of shape (|Y|,), corresponding to p(y)
\n \n", "signature": "(pY_X : numpy . ndarray , pX : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.probability.bayes", "modulename": "ultk.effcomm.probability", "qualname": "bayes", "kind": "function", "doc": "Perform Bayesian inference, computing p(x|y) from p(y|x) and p(x).
\n\nArguments: \n\n\npY_X: a conditional distribution of shape (|X|, |Y|), corresponding to p(y|x)pX: a prior distribution of shape (|X|,), corresponding to p(x) \n\nReturns: \n\n\n a posterior distribution of shape (|Y|, |X|), corresponding to p(x|y)
\n \n", "signature": "(pY_X : numpy . ndarray , pX : numpy . ndarray ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.sampling", "modulename": "ultk.effcomm.sampling", "kind": "module", "doc": "Functions for sampling expressions into languages.
\n"}, {"fullname": "ultk.effcomm.sampling.get_hypothetical_variants", "modulename": "ultk.effcomm.sampling", "qualname": "get_hypothetical_variants", "kind": "function", "doc": "For each system (parameterized by a language or else a speaker), generate num hypothetical variants by permuting the signals that the system assigns to states.
\n\nArguments: \n\n\nlanguages: a list of languages to permute, by constructing LiteralSpeakers and permuting their weights.speakers: a list of speakers of a language, whose weights can be directly permuted. Should be used instead of languages if possible, because it can be more finegrained (every language can be associated with multiple speakers).total: the total number of hypothetical variants to obtain. Should be greater than the number of languages. \n\nReturns: \n\n\n hypothetical_variants: a list of type either Language or np.ndarray depending on whether languages or speakers was passed, representing hypothetical variants of the systems passed. If speakers was passed, a list of speakers is returned.
\n \n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] = None , \tspeakers : list [ ultk . effcomm . agent . Speaker ] = None , \ttotal : int = 0 ) -> list [ typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.tradeoff", "modulename": "ultk.effcomm.tradeoff", "kind": "module", "doc": "Functions for constructing an efficient communication analysis by measuring the simplicity/informativeness trade-off languages and formatting results as a dataframe or a plot.
\n"}, {"fullname": "ultk.effcomm.tradeoff.dominates", "modulename": "ultk.effcomm.tradeoff", "qualname": "dominates", "kind": "function", "doc": "Determine whether p1 dominates p2,\ni.e. whether for every i p1[i] <= p2[i]\nand for some i p1[i] < p2[i].
\n\nArguments: \n\n\np1: a pointp2: another point \n\nReturns: \n\n\n whether or not p1 dominates p2
\n \n", "signature": "(p1 : tuple [ float , ... ] , p2 : tuple [ float , ... ] ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.tradeoff.non_dominated_2d", "modulename": "ultk.effcomm.tradeoff", "qualname": "non_dominated_2d", "kind": "function", "doc": "Return the non-dominated (Pareto) front of a list of 2-D points, using Kung's algorithm.
\n\nArguments: \n\n\npoints: A list of 2-D points \n\nReturns: \n\n\n a list, the indices of points for which no other point is as good on all dimensions\n and better on at least one
\n \n", "signature": "(points : Sequence [ tuple [ float , float ]] ) -> list [ int ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.tradeoff.pareto_optimal_languages", "modulename": "ultk.effcomm.tradeoff", "qualname": "pareto_optimal_languages", "kind": "function", "doc": "Use non_dominated_2d to compute the Pareto languages.
\n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] , \tobjectives : list [ typing . Callable [[ ultk . language . language . Language ], typing . Any ]] , \tunique : bool = False ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.tradeoff.pareto_min_distances", "modulename": "ultk.effcomm.tradeoff", "qualname": "pareto_min_distances", "kind": "function", "doc": "Measure the Pareto optimality of each language by measuring its Euclidean closeness to the frontier. The frontier is a line (list of points) interpolated from the pareto points.
\n\nArguments: \n\n\npoints: the list of all language (x, y) pairs, where x and y are usually communicative cost and complexity.pareto_points: the list of all dominant language (x, y) pairs to constitute the Pareto frontier. The points should have been measured by pygmo's non_dominated_front_2d function. \n\nReturns: \n\n\n min_distances: an array of shape len(points) Euclidean distances for each language to the closest point on the Pareto frontier.
\n \n", "signature": "(points : list [ tuple ] , pareto_points : list [ tuple ] ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.tradeoff.interpolate_data", "modulename": "ultk.effcomm.tradeoff", "qualname": "interpolate_data", "kind": "function", "doc": "Interpolate the points yielded by the pareto optimal languages into a continuous (though not necessarily smooth) curve.
\n\nArguments: \n\n\npoints: an list of (comm_cost, complexity) pairs of size [dominating_languages], a possibly non-smooth set of solutions to the trade-off.min_cost: the minimum communicative cost value possible to interpolate from.max_cost: the maximum communicative cost value possible to interpolate from. A natural assumption is to let complexity=0.0 if max_cost=1.0, which will result in a Pareto curve that spans the entire 2d space, and consequently the plot with x and y limits both ranging [0.0, 1.0].num: the number of x-axis points (cost) to interpolate. Controls smoothness of curve. \n\nReturns: \n\n\n interpolated_points: an array of size (num, num)
\n \n", "signature": "(\tpoints : list [ tuple [ float ]] , \tmin_cost : float = 0.0 , \tmax_cost : float = 1.0 , \tnum = 5000 ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.effcomm.tradeoff.tradeoff", "modulename": "ultk.effcomm.tradeoff", "qualname": "tradeoff", "kind": "function", "doc": "Builds a final efficient communication analysis by measuring a list of languages, updating their internal data, and returning the results.
\n\nThis function measures possibly many graded or categorical properties of each language, but minimally the properties of commmunicative cost and complexity. These two measures fully define the results of an efficiency analysis, in the sense they define the optimal solutions.
\n\nArguments: \n\n\nlanguages: A list representing the pool of all languages to be measured for an efficient communication analysis.x: the first pressure to measure, e.g. communicative cost.y: the second pressure to measure, e.g. cognitive complexity.frontier: a list of (comm_cost, complexity) points representing a Pareto frontier to measure optimality w.r.t. \n\nReturns: \n\n\n a dictionary of the population and the pareto front, of the form
\n\n{\n \"languages\": the list of languages, with their internal efficient communication data updated,\n\n \"dominating_languages\": the list of the languages dominating the population w.r.t. comm_cost and complexity. If no `frontier` is none, this can be considered the Pareto frontier.\n}\n \n", "signature": "(\tlanguages : list [ ultk . language . language . Language ] , \tproperties : dict [ str , typing . Callable [[ ultk . language . language . Language ], typing . Any ]] , \tx : str = 'comm_cost' , \ty : str = 'complexity' , \tfrontier : list [ tuple ] = None ) -> dict [ str , list [ ultk . language . language . Language ]] : ", "funcdef": "def"}, {"fullname": "ultk.language", "modulename": "ultk.language", "kind": "module", "doc": "Classes for modeling (natural or hypothetical) languagese.
\n\nAt the current stage of development, ULTK focuses on supporting abstractions to model the mapping between expressions and meanings of a language. So far, we leave almost everything besides this basic mapping (morphosyntax, phonology, phonetic inventories, among other features of human languages) to future work.
\n\nThe ultk.language.language submodule contains classes for constructing a language, which can contain one or more expressions.
\n\nThe ultk.language.semantics submodule contains classes for defining a universe (meaning space) of referents (denotations) and meanings (categories).
\n"}, {"fullname": "ultk.language.grammar", "modulename": "ultk.language.grammar", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.language.grammar.Rule", "modulename": "ultk.language.grammar", "qualname": "Rule", "kind": "class", "doc": "Basic class for a grammar rule. Grammar rules in ULTK correspond\nto functions. One can think of a grammar as generating complex functions from\nmore basic ones.
\n\nAttributes: \n\n\nlhs: left-hand side of the rule (can be anything)\nconceptually, the output type of a functionrhs: right-hand side; assumed to be an iterable\nconceptually, a list of types of inputsfunc: a callable, the function to be computed when a node with this rule is executedname: name of the functionweight: a relative weight to assign to this rule\nwhen added to a grammar, all rules with the same LHS will be weighted together \n"}, {"fullname": "ultk.language.grammar.Rule.__init__", "modulename": "ultk.language.grammar", "qualname": "Rule.__init__", "kind": "function", "doc": "
\n", "signature": "(\tname : str , \tlhs : Any , \trhs : Sequence | None , \tfunc : Callable = < function Rule .< lambda >> , \tweight : float = 1.0 ) "}, {"fullname": "ultk.language.grammar.Rule.name", "modulename": "ultk.language.grammar", "qualname": "Rule.name", "kind": "variable", "doc": "
\n", "annotation": ": str"}, {"fullname": "ultk.language.grammar.Rule.lhs", "modulename": "ultk.language.grammar", "qualname": "Rule.lhs", "kind": "variable", "doc": "
\n", "annotation": ": Any"}, {"fullname": "ultk.language.grammar.Rule.rhs", "modulename": "ultk.language.grammar", "qualname": "Rule.rhs", "kind": "variable", "doc": "
\n", "annotation": ": Sequence | None"}, {"fullname": "ultk.language.grammar.Rule.func", "modulename": "ultk.language.grammar", "qualname": "Rule.func", "kind": "function", "doc": "
\n", "signature": "(* args ): ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Rule.weight", "modulename": "ultk.language.grammar", "qualname": "Rule.weight", "kind": "variable", "doc": "
\n", "annotation": ": float", "default_value": "1.0"}, {"fullname": "ultk.language.grammar.Rule.is_terminal", "modulename": "ultk.language.grammar", "qualname": "Rule.is_terminal", "kind": "function", "doc": "Whether this is a terminal rule. In our framework, this means that RHS is empty,\ni.e. there are no arguments to the function.
\n", "signature": "(self ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Rule.from_callable", "modulename": "ultk.language.grammar", "qualname": "Rule.from_callable", "kind": "function", "doc": "Create a Rule from the type-annotations of a function.
\n\nFor example, given the following method definition:
\n\n\n
def _and ( p1 : bool , p2 : bool ) -> bool : \n return p1 and p2 \n\n
This class method will return the Rule:
\n\nRule(name=\"_and\", lhs=bool, rhs=(bool, bool), func=_and)\nThere are two special kwargs that can be used in the function definition:
\n\n\nweight: a float, which will be used as the weight of the rulename: a string, which will be used as the name of the rule, if you want it to be different than the name of the method \n", "signature": "(cls , func : Callable ) -> ultk . language . grammar . Rule : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.GrammaticalExpression", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression", "kind": "class", "doc": "A GrammaticalExpression has been built up from a Grammar by applying a sequence of Rules.\nCrucially, it is _callable_, using the functions corresponding to each rule.
\n\nA GrammaticalExpression, when called, takes in a Referent. Because of this, a Meaning can\nbe generated by specifying a Universe (which contains Referents).
\n\nAttributes: \n\n\nrule_name: name of the top-most functionfunc: the functionchildren: child expressions (possibly empty) \n", "bases": "ultk.language.language.Expression[~T]"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.__init__", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.__init__", "kind": "function", "doc": "
\n", "signature": "(\tform : str = '' , \tmeaning : ultk . language . semantics . Meaning [ ~ T ] = Meaning ( mapping = FrozenDict ({}), universe = Universe ( referents = (), prior = ())) , \t* , \trule_name : str , \tfunc : Callable , \tchildren : tuple | None , \tterm_expression : str = '' ) "}, {"fullname": "ultk.language.grammar.GrammaticalExpression.rule_name", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.rule_name", "kind": "variable", "doc": "
\n", "annotation": ": str"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.func", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.func", "kind": "variable", "doc": "
\n", "annotation": ": Callable"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.children", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.children", "kind": "variable", "doc": "
\n", "annotation": ": tuple | None"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.term_expression", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.term_expression", "kind": "variable", "doc": "
\n", "annotation": ": str", "default_value": "''"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.yield_string", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.yield_string", "kind": "function", "doc": "Get the 'yield' string of this term, i.e. the concatenation\nof the leaf nodes.
\n\nThis is useful for thinking of a Grammar as generating derivation trees for\nan underlying CFG. This method will then generate the strings generated by\nthe corresponding CFG.
\n", "signature": "(self ) -> str : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.evaluate", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.evaluate", "kind": "function", "doc": "
\n", "signature": "(\tself , \tuniverse : ultk . language . semantics . Universe ) -> ultk . language . semantics . Meaning : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.add_child", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.add_child", "kind": "function", "doc": "
\n", "signature": "(self , child ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.to_dict", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.to_dict", "kind": "function", "doc": "Return a dictionary representation of the expression.
\n", "signature": "(self ) -> dict : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.GrammaticalExpression.from_dict", "modulename": "ultk.language.grammar", "qualname": "GrammaticalExpression.from_dict", "kind": "function", "doc": "
\n", "signature": "(\tcls , \tthe_dict : dict , \tgrammar : ultk . language . grammar . Grammar ) -> ultk . language . grammar . GrammaticalExpression : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.UniquenessArgs", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs", "kind": "class", "doc": "Arguments for specifying uniqueness of GrammaticalExpressions in a Grammar.
\n\nAttributes: \n\n\nunique_expressions: a dictionary in which to store unique Expressionskey: a function used to evaluate uniquenesscompare_func: a comparison function, used to decide which Expression to add to the dict\nnew Expressions will be added as values to unique_dict only if they are minimal\namong those sharing the same key (by unique_key) according to this func \n", "bases": "typing.TypedDict"}, {"fullname": "ultk.language.grammar.UniquenessArgs.unique_expressions", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs.unique_expressions", "kind": "variable", "doc": "
\n", "annotation": ": dict[typing.Any, dict[typing.Any, ultk.language.grammar.GrammaticalExpression]]"}, {"fullname": "ultk.language.grammar.UniquenessArgs.key", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs.key", "kind": "variable", "doc": "
\n", "annotation": ": Callable[[ultk.language.grammar.GrammaticalExpression], Any]"}, {"fullname": "ultk.language.grammar.UniquenessArgs.compare_func", "modulename": "ultk.language.grammar", "qualname": "UniquenessArgs.compare_func", "kind": "variable", "doc": "
\n", "annotation": ": Callable[[ultk.language.grammar.GrammaticalExpression, ultk.language.grammar.GrammaticalExpression], bool]"}, {"fullname": "ultk.language.grammar.Grammar", "modulename": "ultk.language.grammar", "qualname": "Grammar", "kind": "class", "doc": "At its core, a Grammar is a set of Rules with methods for generating GrammaticalExpressions.
\n"}, {"fullname": "ultk.language.grammar.Grammar.__init__", "modulename": "ultk.language.grammar", "qualname": "Grammar.__init__", "kind": "function", "doc": "
\n", "signature": "(start : Any "}, {"fullname": "ultk.language.grammar.Grammar.add_rule", "modulename": "ultk.language.grammar", "qualname": "Grammar.add_rule", "kind": "function", "doc": "
\n", "signature": "(self , rule : ultk . language . grammar . Rule ): ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.parse", "modulename": "ultk.language.grammar", "qualname": "Grammar.parse", "kind": "function", "doc": "Parse a string representation of an expression of a grammar.\nNote that this is not a general-purpose parsing algorithm. We assume that the strings are of the form\n parent_name(child1_name, ..., childn_name)\nwhere parent_name is the name of a rule of this grammar that has a length-n RHS, and that\nchildi_name is the name of a rule for each child i.
\n\nArguments: \n\n\nexpression: string in the above format \n\nReturns: \n\n\n the corresponding GrammaticalExpression
\n \n", "signature": "(\tself , \texpression : str , \topener : str = '(' , \tcloser : str = ')' , \tdelimiter : str = ',' ) -> ultk . language . grammar . GrammaticalExpression : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.generate", "modulename": "ultk.language.grammar", "qualname": "Grammar.generate", "kind": "function", "doc": "Generate an expression from a given lhs.
\n", "signature": "(self , lhs : Any = None ) -> ultk . language . grammar . GrammaticalExpression : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.enumerate", "modulename": "ultk.language.grammar", "qualname": "Grammar.enumerate", "kind": "function", "doc": "Enumerate all expressions from the grammar up to a given depth from a given LHS.\nThis method also can update a specified dictionary to store only _unique_ expressions, with\na user-specified criterion of uniqueness.
\n\nArguments: \n\n\ndepth: how deep the trees should belhs: left hand side to start from; defaults to the grammar's start symboluniqueness_args: a dictionary specifying the parameters for uniqueness:\nunique_dict: a dictionary in which to store unique Expressions\nkey: a function used to evaluate uniqueness\ncompare_func: a comparison function, used to decide which Expression to add to the dict\n new Expressions will be added as values to unique_dict only if they are _minimal_\n among those sharing the same key (by unique_key) according to this func \n\nYields: \n\n\n all GrammaticalExpressions up to depth
\n \n", "signature": "(\tself , \tdepth : int = 8 , \tlhs : Any = None , \tuniqueness_args : ultk . language . grammar . UniquenessArgs | None = None ) -> Generator [ ultk . language . grammar . GrammaticalExpression , NoneType , NoneType ] : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.enumerate_at_depth", "modulename": "ultk.language.grammar", "qualname": "Grammar.enumerate_at_depth", "kind": "function", "doc": "Enumerate GrammaticalExpressions for this Grammar _at_ a fixed depth.
\n", "signature": "(\tself , \tdepth : int , \tlhs : Any , \tuniqueness_args : ultk . language . grammar . UniquenessArgs | None = None , \tcache : dict | None = None ) -> Generator [ ultk . language . grammar . GrammaticalExpression , NoneType , NoneType ] : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.get_unique_expressions", "modulename": "ultk.language.grammar", "qualname": "Grammar.get_unique_expressions", "kind": "function", "doc": "Get all unique GrammaticalExpressions, up to a certain depth, with a user-specified criterion\nof uniqueness, and a specified comparison function for determining which Expression to save when there's a clash.\nThis can be used, for instance, to measure the minimum description length of some\nMeanings, by using expression.evaluate(), which produces a Meaning for an Expression, as the\nkey for determining uniqueness, and length of the expression as comparison.
\n\nThis is a wrapper around enumerate, but which produces the dictionary of key->Expression entries\nand returns it. (enumerate is a generator with side effects).
\n\nFor Args, see the docstring for enumerate.
\n\nNote: if you additionally want to store _all_ expressions, and not just the unique ones, you should\ndirectly use enumerate.
\n\nReturns: \n\n\n dictionary of {key: GrammaticalExpression}, where the keys are generated by unique_key\n The GrammticalExpression which is the value will be the one that is minimum among\n compare_func amongst all Expressions up to depth which share the same key
\n \n", "signature": "(\tself , \tdepth : int , \tunique_key : Callable [[ ultk . language . grammar . GrammaticalExpression ], Any ] , \tcompare_func : Callable [[ ultk . language . grammar . GrammaticalExpression , ultk . language . grammar . GrammaticalExpression ], bool ] , \tlhs : Any = None , \tmax_size : float = inf ) -> dict [ typing . Any , ultk . language . grammar . GrammaticalExpression ] : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.get_all_rules", "modulename": "ultk.language.grammar", "qualname": "Grammar.get_all_rules", "kind": "function", "doc": "Get all rules as a list.
\n", "signature": "(self ) -> list [ ultk . language . grammar . Rule ] : ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.from_yaml", "modulename": "ultk.language.grammar", "qualname": "Grammar.from_yaml", "kind": "function", "doc": "Read a grammar specified in a simple YAML format.
\n\nExpected format:
\n\nstart: bool\nrules:\n- lhs: bool\n rhs:\n - bool\n - bool\n name: \"and\"\n func: \"lambda p1, p2 : p1 and p2\"\n- lhs: bool\n rhs:\n - bool\n - bool\n name: \"or\"\n func: \"lambda p1, p2 : p1 or p2\"\nNote that for each fule, the value for func will be passed to\neval, so be careful!
\n\nArguments: \n\n\nfilename: file containing a grammar in the above format \n", "signature": "(cls , filename : str ): ", "funcdef": "def"}, {"fullname": "ultk.language.grammar.Grammar.from_module", "modulename": "ultk.language.grammar", "qualname": "Grammar.from_module", "kind": "function", "doc": "Read a grammar from a module.
\n\nThe module should have a list of type-annotated method definitions, each of which will correspond to one Rule in the new Grammar.\nSee the docstring for Rule.from_callable for more information on how that step works.
\n\nThe start symbol of the grammar can either be specified by start = XXX somewhere in the module,\nor will default to the LHS of the first rule in the module (aka the return type annotation of the first method definition).
\n\nArguments: \n\n\nmodule_name: name of the module \n", "signature": "(cls , module_name : str ) -> ultk . language . grammar . Grammar : ", "funcdef": "def"}, {"fullname": "ultk.language.language", "modulename": "ultk.language.language", "kind": "module", "doc": "Classes for modeling languages as form-meaning mappings, most important among them the Language and Expression classes.
\n\nExample usage: \n\n\n \n
>>> from ultk.language.language import Expression , Language \n>>> # assuming the meaning `a_few_meaning` has already been constructed \n>>> # define the expression \n>>> a_few = NumeralExpression ( form = "a few" , meaning = a_few_meaning ) \n>>> # define a very small language \n>>> lang_1 = Language ([ a_few ]) \n>>> # or a slightly larger one with synonymy \n>>> lang_2 = Language ([ a_few ] * 3 ) \n\n
\n"}, {"fullname": "ultk.language.language.Expression", "modulename": "ultk.language.language", "qualname": "Expression", "kind": "class", "doc": "Minimally contains a form and a meaning.
\n", "bases": "typing.Generic[~T]"}, {"fullname": "ultk.language.language.Expression.__init__", "modulename": "ultk.language.language", "qualname": "Expression.__init__", "kind": "function", "doc": "
\n", "signature": "(\tform : str = '' , \tmeaning : ultk . language . semantics . Meaning [ ~ T ] = Meaning ( mapping = FrozenDict ({}), universe = Universe ( referents = (), prior = ())) ) "}, {"fullname": "ultk.language.language.Expression.form", "modulename": "ultk.language.language", "qualname": "Expression.form", "kind": "variable", "doc": "
\n", "annotation": ": str", "default_value": "''"}, {"fullname": "ultk.language.language.Expression.meaning", "modulename": "ultk.language.language", "qualname": "Expression.meaning", "kind": "variable", "doc": "
\n", "annotation": ": ultk.language.semantics.Meaning[~T]", "default_value": "Meaning(mapping=FrozenDict({}), universe=Universe(referents=(), prior=()))"}, {"fullname": "ultk.language.language.Expression.can_express", "modulename": "ultk.language.language", "qualname": "Expression.can_express", "kind": "function", "doc": "Return True if the expression can express the input single meaning point and false otherwise.
\n", "signature": "(self , referent : ultk . language . semantics . Referent ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.language.language.Expression.to_dict", "modulename": "ultk.language.language", "qualname": "Expression.to_dict", "kind": "function", "doc": "Return a dictionary representation of the expression.
\n", "signature": "(self ) -> dict : ", "funcdef": "def"}, {"fullname": "ultk.language.language.Language", "modulename": "ultk.language.language", "qualname": "Language", "kind": "class", "doc": "Minimally contains Expression objects.
\n"}, {"fullname": "ultk.language.language.Language.__init__", "modulename": "ultk.language.language", "qualname": "Language.__init__", "kind": "function", "doc": "
\n", "signature": "(expressions : tuple [ ultk . language . language . Expression , ... ] , ** kwargs "}, {"fullname": "ultk.language.language.Language.universe", "modulename": "ultk.language.language", "qualname": "Language.universe", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.language.language.Language.expressions", "modulename": "ultk.language.language", "qualname": "Language.expressions", "kind": "variable", "doc": "
\n", "annotation": ": frozenset[ultk.language.language.Expression]"}, {"fullname": "ultk.language.language.Language.add_expression", "modulename": "ultk.language.language", "qualname": "Language.add_expression", "kind": "function", "doc": "Add an expression to the list of expressions in a language.
\n", "signature": "(self , e : ultk . language . language . Expression ): ", "funcdef": "def"}, {"fullname": "ultk.language.language.Language.is_natural", "modulename": "ultk.language.language", "qualname": "Language.is_natural", "kind": "function", "doc": "Whether a language represents a human natural language.
\n", "signature": "(self ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.language.language.Language.degree_property", "modulename": "ultk.language.language", "qualname": "Language.degree_property", "kind": "function", "doc": "Count what percentage of expressions in a language have a given property.
\n", "signature": "(\tself , \tproperty : Callable [[ ultk . language . language . Expression ], bool ] ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.language.language.Language.binary_matrix", "modulename": "ultk.language.language", "qualname": "Language.binary_matrix", "kind": "function", "doc": "Get a binary matrix of shape (num_meanings, num_expressions)\nspecifying which expressions can express which meanings.
\n", "signature": "(self ) -> numpy . ndarray : ", "funcdef": "def"}, {"fullname": "ultk.language.language.Language.as_dict_with_properties", "modulename": "ultk.language.language", "qualname": "Language.as_dict_with_properties", "kind": "function", "doc": "Return a dictionary representation of the language, including additional properties as keyword arguments.
\n\nThis is used in some examples to serialize the language to outputs.
\n", "signature": "(self , ** kwargs ) -> dict : ", "funcdef": "def"}, {"fullname": "ultk.language.language.aggregate_expression_complexity", "modulename": "ultk.language.language", "qualname": "aggregate_expression_complexity", "kind": "function", "doc": "Aggregate complexities for individual Expressions into a complexity for a Language.
\n\nArguments: \n\n\nlanguage: the Language to measureexpression_complexity_func: the function that returns the complexity of an individual expressionaggregator: (optional, default = sum) the function that aggregates individual complexities \n\nReturns: \n\n\n a float, the complexity of a language
\n \n", "signature": "(\tlanguage : ultk . language . language . Language , \texpression_complexity_func : Callable [[ ultk . language . language . Expression ], float ] , \taggregator : Callable [[ Iterable [ float ]], float ] = < built - in function sum > ) -> float : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling", "modulename": "ultk.language.sampling", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.language.sampling.powerset", "modulename": "ultk.language.sampling", "qualname": "powerset", "kind": "function", "doc": "Enumerate all _non-empty_ subsets of an iterable up to a given maximum size, e.g.:\npowerset([1,2,3]) --> (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)
\n\nlightly adapted from itertools Recipes at\nhttps://docs.python.org/3/library/itertools.html#itertools-recipes
\n\nArguments: \n\n\niterable: elements from which to form subsetsmax_size: largest subsets (inclusive) to return \n\nReturns: \n\n\n iterator over all subsets from size 1 to max_size of elements from iterable
\n \n", "signature": "(iterable : Iterable , max_size : int | None = None ) -> Iterable : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.all_meanings", "modulename": "ultk.language.sampling", "qualname": "all_meanings", "kind": "function", "doc": "Generate all Meanings (sets of Referents) from a given Universe.
\n", "signature": "(\tuniverse : ultk . language . semantics . Universe ) -> Generator [ ultk . language . semantics . Meaning , NoneType , NoneType ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.all_expressions", "modulename": "ultk.language.sampling", "qualname": "all_expressions", "kind": "function", "doc": "Generate Expressions from an iterable of Meanings.
\n", "signature": "(\tmeanings : Iterable [ ultk . language . semantics . Meaning ] ) -> Generator [ ultk . language . language . Expression , NoneType , NoneType ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.all_languages", "modulename": "ultk.language.sampling", "qualname": "all_languages", "kind": "function", "doc": "Generate all Languages (sets of Expressions) from a given set of Expressions.
\n\nArguments: \n\n\nexpressions: iterable of all possible expressionslanguage_class: the type of language to generatemax_size: largest size for a language; if None, all subsets of expressions will be used \n\nYields: \n\n\n Languages with subsets of Expressions from expressions
\n \n", "signature": "(\texpressions : Iterable [ ultk . language . language . Expression ] , \tlanguage_class: Type[ultk.language.language.Language] = <class 'ultk.language.language.Language'>, \tmax_size : int | None = None ) -> Generator [ ultk . language . language . Language , NoneType , NoneType ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.upto_comb", "modulename": "ultk.language.sampling", "qualname": "upto_comb", "kind": "function", "doc": "Return the number of ways of choosing _up to max_k_ items from\nn items without repetition. Just an iterator of math.comb for n from\n1 to max_k.
\n", "signature": "(num : int , max_k : int ) -> int : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.random_languages", "modulename": "ultk.language.sampling", "qualname": "random_languages", "kind": "function", "doc": "Generate unique Languages by randomly sampling subsets of Expressions, either in a uniform or stratified way.\nIf there are fewer than sample_size possible Languages up to size max_size,\nthis method will just return all languages up to that size (and so the sample may\nbe smaller than sample_size).
\n\nSome use cases:
\n\nWith sample_size=None, get all languages.
\n\n\n
>>> random_languages ( expressions ) \n\n
With sample_size and uniform sampling, get random languages:
\n\n\n
>>> random_languages ( expressions , sample_size = 1000 ) \n\n
Stratified sampling, with and without a max_size:
\n\n\n
>>> random_languages ( expressions , sample_size = 1000 , sampling_strategy = "stratified" ) \n>>> random_languages ( expressions , sample_size = 1000 , sampling_strategy = "stratified" , max_size = 10 ) \n\n
Arguments: \n\n\nexpressions: all possible expressionssampling_strategy: how to sample subsets of expressions\nuniform: for every expression, choose whether or not to include it in a given language\nstratified: first sample a size for a Language, then choose that many random Expressions\n (i) this has the effect of \"upsampling\" from smaller Language sizes\n (ii) this can be used with max_size to only generate Languages up to a given number of expressionssample_size: how many languages to return\nif None, will return all languages up to max_sizelanguage_class: type of Languagemax_size: largest possible Language to generate\nif None, will be the length of expressions\nNB: this argument has no effect when sampling_strategy is \"uniform\" \n\nReturns: \n\n\n a list of randomly sampled Languages
\n \n", "signature": "(\texpressions : Iterable [ ultk . language . language . Expression ] , \tsampling_strategy : str = 'uniform' , \tsample_size : int | None = None , \tlanguage_class: Type[ultk.language.language.Language] = <class 'ultk.language.language.Language'>, \tmax_size : int | None = None ) -> list [ ultk . language . language . Language ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.generate_languages", "modulename": "ultk.language.sampling", "qualname": "generate_languages", "kind": "function", "doc": "Generate languages by randomly sampling vocabularies as bags of expressions.
\n\nA predicate (binary-valued property) of expressions may be supplied, which can be used to adjust the composition of vocabularies (e.g., by the percent of expressions satisfying the predicate).
\n\nIf sample size <= nCr, then take a random sample_size set of combinations. Otherwise, to prevent repeat languages, treat nCr as the sample size.
\n\nArguments: \n\n\nexpressions: a list of the possible expressions to sample from.lang_size: the maximum (or exact) number of expressions in each language.sample_size: the number of languages to generate.criterion: the predicate, (e.g. semantic universal) by which to split the expressions into those satisfying and those not, and then sample languages with degrees of naturalness based on the percentage from those satisfying. Must apply at the expression level. By default is a trivial criterion, so that all expressions are 'quasi-natural'.fixed_wordcount: whether to vary the language size from 1 to lang_size.verbose: How detailed the progress of sampling should be, printed to stdout.dummy_name: the default name to give to each sampled language, e.g. sampled_lang_42. These should not collide with any actual natural language names if the efficient communication experiment does use natural language data.id_start: an integer representing the number of languages already generated in an experiment. Languages sampled will be named according to this number. For example, if id_start is 0, the first language sampled will be named sampled_lang_0. Note that the largest id does not necessarily track the actual size of languages saved for the experiment, but it does track how many languages have been generated.exact_sample: a boolean representing whether to sample until the exact sample size is filled. If True, the resulting pool of languages may not be unique.verbose: a boolean representing how verbose output should be during sampling. \n\nReturns: \n\n\n a dict representing the generated pool of languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n\nExamples: \n\n\n \n
>>> # Turn the knob on a universal property for modals \n>>> expressions = load_expressions ( expressions_file ) \n>>> universal_property = iff \n>>> result = generate_languages ( \n... ModalLanguage , \n... expressions , \n... lang_size , \n... sample_size , \n... universal_property , \n...) \n>>> languages = result [ "languages" ] \n>>> id_start = result [ "id_start" ] \n\n
\n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \texpressions : list [ ultk . language . language . Expression ] , \tlang_size : int , \tsample_size : int , \tcriterion : Callable [[ ultk . language . language . Expression ], bool ] = < function < lambda >> , \tfixed_wordcount = False , \tdummy_name = 'sampled_lang_' , \tid_start : int = 0 , \texact_sample = False , \tverbose = False ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.sample_lang_size", "modulename": "ultk.language.sampling", "qualname": "sample_lang_size", "kind": "function", "doc": "Get a sample of languages each of exactly lang_size.
\n\nArguments: \n\n\nlanguage_class: a subclass of ultk.Languageexpressions: a list of Expressions to sample fromlang_size: int representing the maximum language size to samplesample_size: int representing the number of total languages to returnid_start: an int representing the number of languages already generated in an experiment. \n\nReturns: \n\n\n a dict containing the randomly sampled languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \texpressions : list [ ultk . language . language . Expression ] , \tlang_size : int , \tsample_size : int , \tid_start : int = 0 , \tverbose = False , \tdummy_name = 'sampled_lang_id' ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.sample_quasi_natural", "modulename": "ultk.language.sampling", "qualname": "sample_quasi_natural", "kind": "function", "doc": "Turn the knob on degree quasi-naturalness for a sample of languages, either by enumerating or randomly sampling unique subsets of all possible combinations.
\n\nArguments: \n\n\nnatural_terms: expressions satisfying some criteria of quasi-naturalness, e.g, a semantic universal.unnatural_terms: expressions not satisfying the criteria.lang_size: the exact number of expressions a language must have.sample_size: how many languages to sample. \n\nReturns: \n\n\n a dict containing the randomly sampled quasi-natural languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \tnatural_terms : list [ ultk . language . language . Expression ] , \tunnatural_terms : list [ ultk . language . language . Expression ] , \tlang_size : int , \tsample_size : int , \tid_start : int , \tdummy_name = 'sampled_lang_id' , \tverbose = False ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.rename_id", "modulename": "ultk.language.sampling", "qualname": "rename_id", "kind": "function", "doc": "Updates a string of form sampled_lang_X with a new id for X.
\n", "signature": "(name : str , id : int ) -> str : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.enumerate_all_languages", "modulename": "ultk.language.sampling", "qualname": "enumerate_all_languages", "kind": "function", "doc": "When the sample size requested is greater than the size of all possible languages, just enumerate all the possible languages.
\n\nArguments: \n\n\nlanguage_class: the kind of Language to constructid_start: a number to start counting from for assigning names with numerical ids to languages.natural_indices: the indices of quasi-natural languages already seennum_natural: the number of quasi-natural languages to samplenatural_terms: the list of quasi-natural terms to sample fromunnatural_indices: the indices of non-quasi-natural languages already seennum_unnatural: the number of non-quasi-natural languages to sample; 0 by defaultunnatural_terms: the list of non-quasi-natural terms to sample from; empty by default.dummy_name: the format of the string to name each language constructed. \n\nReturns: \n\n\n a dict containing a set of languages and the updated id_start, of the form
\n\n{\n \"languages\": (list of updated languages)\n \"id_start\": (updated length of languages)\n}\n \n", "signature": "(\tlanguage_class : Type [ ultk . language . language . Language ] , \tid_start : int , \tnatural_terms : list [ ultk . language . language . Expression ] , \tnatural_indices : list [ int ] , \tnum_natural : int = 0 , \tunnatural_terms : list [ ultk . language . language . Expression ] = [] , \tunnatural_indices : list [ int ] = [] , \tnum_unnatural : int = 0 , \tdummy_name = 'sampled_lang_id' , \tverbose = False ) -> dict [ str , typing . Any ] : ", "funcdef": "def"}, {"fullname": "ultk.language.sampling.random_combination_vocabulary", "modulename": "ultk.language.sampling", "qualname": "random_combination_vocabulary", "kind": "function", "doc": "Get a single vocabulary for a specific language size by choosing a random combination of natural and unnatural terms.
\n\nArguments: \n\n\nseen: the list of language indices already seennum_natural: intnatural_terms: list[Expression]num_unnatural: int=0unnatural_terms: list[Expression]=[] \n\nReturns: \n\n\n languages: the extended list of input languages.
\n \n", "signature": "(\tseen : set , \tnum_natural : int , \tnatural_terms : list [ ultk . language . language . Expression ] , \tnum_unnatural : int = 0 , \tunnatural_terms : list [ ultk . language . language . Expression ] = [] ) -> list [ ultk . language . language . Expression ] : ", "funcdef": "def"}, {"fullname": "ultk.language.semantics", "modulename": "ultk.language.semantics", "kind": "module", "doc": "Classes for modeling the meanings of a language.
\n\nMeanings are modeled as things which map linguistic forms to objects of reference. The linguistic forms and objects of reference can in principle be very detailed, and future work may elaborate the meaning classes and implement a Form class.
\n\nIn efficient communication analyses, simplicity and informativeness can be measured as properties of semantic aspects of a language. E.g., a meaning is simple if it is easy to represent, or to compress into some code; a meaning is informative if it is easy for a listener to recover a speaker's intended literal meaning.
\n\nExamples: \n\n\n \n
>>> from ultk.language.semantics import Referent , Meaning , Universe \n>>> from ultk.language.language import Expression \n>>> # construct the meaning space for numerals \n>>> numerals_universe = NumeralUniverse ( referents = [ NumeralReferent ( str ( i )) for i in range ( 1 , 100 )]) \n>>> # construct a list of referents for the expression 'a few' \n>>> a_few_refs = [ NumeralReferent ( name = str ( i )) for i in range ( 2 , 6 )] \n>>> a_few_meaning = NumeralMeaning ( referents = a_few_refs , universe = numerals_universe ) \n>>> # define the expression \n>>> a_few = NumeralExpression ( form = "a few" , meaning = a_few_meaning ) \n\n
\n"}, {"fullname": "ultk.language.semantics.Referent", "modulename": "ultk.language.semantics", "qualname": "Referent", "kind": "class", "doc": "A referent is some object in the universe for a language.
\n\nConceptually, a Referent can be any kind of object. This functions like a generic python object that is _immutable_ after initialization.\nAt initialization, properties can be specified either by passing a dictionary or by keyword arguments.
\n"}, {"fullname": "ultk.language.semantics.Referent.__init__", "modulename": "ultk.language.semantics", "qualname": "Referent.__init__", "kind": "function", "doc": "Initialize a referent.
\n\nArguments: \n\n\nname: a string representing the name of the referent \n", "signature": "(name : str , properties : dict [ str , typing . Any ] = {} , ** kwargs "}, {"fullname": "ultk.language.semantics.Referent.name", "modulename": "ultk.language.semantics", "qualname": "Referent.name", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.language.semantics.Universe", "modulename": "ultk.language.semantics", "qualname": "Universe", "kind": "class", "doc": "The universe is the collection of possible referent objects for a meaning.
\n"}, {"fullname": "ultk.language.semantics.Universe.__init__", "modulename": "ultk.language.semantics", "qualname": "Universe.__init__", "kind": "function", "doc": "
\n", "signature": "(\treferents : tuple [ ultk . language . semantics . Referent , ... ] , \tprior : tuple [ float , ... ] ) "}, {"fullname": "ultk.language.semantics.Universe.referents", "modulename": "ultk.language.semantics", "qualname": "Universe.referents", "kind": "variable", "doc": "
\n", "annotation": ": tuple[ultk.language.semantics.Referent, ...]"}, {"fullname": "ultk.language.semantics.Universe.prior", "modulename": "ultk.language.semantics", "qualname": "Universe.prior", "kind": "variable", "doc": "
\n", "annotation": ": tuple[float, ...]"}, {"fullname": "ultk.language.semantics.Universe.size", "modulename": "ultk.language.semantics", "qualname": "Universe.size", "kind": "variable", "doc": "
\n"}, {"fullname": "ultk.language.semantics.Universe.prior_numpy", "modulename": "ultk.language.semantics", "qualname": "Universe.prior_numpy", "kind": "variable", "doc": "
\n", "annotation": ": numpy.ndarray"}, {"fullname": "ultk.language.semantics.Universe.from_dataframe", "modulename": "ultk.language.semantics", "qualname": "Universe.from_dataframe", "kind": "function", "doc": "Build a Universe from a DataFrame.\nIt's assumed that each row specifies one Referent, and each column will be a property\nof that Referent. We assume that name is one of the columns of the DataFrame.
\n\nArguments: \n\n\na DataFrame representing the meaning space of interest, assumed to have a column name \n \n", "signature": "(cls , df : pandas . core . frame . DataFrame ): ", "funcdef": "def"}, {"fullname": "ultk.language.semantics.Universe.from_csv", "modulename": "ultk.language.semantics", "qualname": "Universe.from_csv", "kind": "function", "doc": "Build a Universe from a CSV file. This is a small wrapper around\nUniverse.from_dataframe, so see that documentation for more information.
\n", "signature": "(cls , filename : str ): ", "funcdef": "def"}, {"fullname": "ultk.language.semantics.Meaning", "modulename": "ultk.language.semantics", "qualname": "Meaning", "kind": "class", "doc": "A meaning maps Referents to any type of object.
\n\nFor instance, sentence meanings are often modeled as sets of points (e.g. possible worlds).\nThese correspond to mappings from points (i.e. Referents) to truth values, corresponding to the characteristic function of a set.\nBut, in general, meanings can have a different output type for, e.g. sub-sentential meanings..
\n\nProperties: \n\n\n mapping: a FrozenDict with Referent keys, but arbitrary type T as values.
\n \n universe: a Universe object. The Referents in the keys of mapping are expected to be exactly those in universe.
\n \n _dist: a mapping representing a probability distribution over referents to associate with the meaning. By default, will be assumed to be uniform over the \"true-like\" Referents in mapping (see .dist).
\n \n", "bases": "typing.Generic[~T]"}, {"fullname": "ultk.language.semantics.Meaning.__init__", "modulename": "ultk.language.semantics", "qualname": "Meaning.__init__", "kind": "function", "doc": "
\n", "signature": "(\tmapping : ultk . util . frozendict . FrozenDict [ ultk . language . semantics . Referent , ~ T ] , \tuniverse : ultk . language . semantics . Universe ) "}, {"fullname": "ultk.language.semantics.Meaning.mapping", "modulename": "ultk.language.semantics", "qualname": "Meaning.mapping", "kind": "variable", "doc": "
\n", "annotation": ": ultk.util.frozendict.FrozenDict[ultk.language.semantics.Referent, ~T]"}, {"fullname": "ultk.language.semantics.Meaning.universe", "modulename": "ultk.language.semantics", "qualname": "Meaning.universe", "kind": "variable", "doc": "
\n", "annotation": ": ultk.language.semantics.Universe"}, {"fullname": "ultk.language.semantics.Meaning.dist", "modulename": "ultk.language.semantics", "qualname": "Meaning.dist", "kind": "variable", "doc": "
\n", "annotation": ": ultk.util.frozendict.FrozenDict[ultk.language.semantics.Referent, float]"}, {"fullname": "ultk.language.semantics.Meaning.is_uniformly_false", "modulename": "ultk.language.semantics", "qualname": "Meaning.is_uniformly_false", "kind": "function", "doc": "Return True if all referents in the meaning are mapped to False (or coercible to False).In the case where the meaning type is boolean, this corresponds to the characteristic function of the empty set.
\n", "signature": "(self ) -> bool : ", "funcdef": "def"}, {"fullname": "ultk.util", "modulename": "ultk.util", "kind": "module", "doc": "Various utility classes and helper functions for the ULTK package.
\n\nSubmodules:
\n\n\nfrozendict: An immutable dictionary, so that various mappings (e.g. Meanings) can be hashed, serialized, etc.io: some basic input/output functions. \n"}, {"fullname": "ultk.util.frozendict", "modulename": "ultk.util.frozendict", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.util.frozendict.FrozenDict", "modulename": "ultk.util.frozendict", "qualname": "FrozenDict", "kind": "class", "doc": "
\n", "bases": "dict[~K, ~V], typing.Generic[~K, ~V], yaml.YAMLObject"}, {"fullname": "ultk.util.frozendict.FrozenDict.clear", "modulename": "ultk.util.frozendict", "qualname": "FrozenDict.clear", "kind": "function", "doc": "D.clear() -> None. Remove all items from D.
\n", "signature": "(self ): ", "funcdef": "def"}, {"fullname": "ultk.util.frozendict.FrozenDict.pop", "modulename": "ultk.util.frozendict", "qualname": "FrozenDict.pop", "kind": "function", "doc": "D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
\n\nIf the key is not found, return the default if given; otherwise,\nraise a KeyError.
\n", "signature": "(self , key , default = None ): ", "funcdef": "def"}, {"fullname": "ultk.util.frozendict.FrozenDict.popitem", "modulename": "ultk.util.frozendict", "qualname": "FrozenDict.popitem", "kind": "function", "doc": "Remove and return a (key, value) pair as a 2-tuple.
\n\nPairs are returned in LIFO (last-in, first-out) order.\nRaises KeyError if the dict is empty.
\n", "signature": "(self ): ", "funcdef": "def"}, {"fullname": "ultk.util.frozendict.FrozenDict.setdefault", "modulename": "ultk.util.frozendict", "qualname": "FrozenDict.setdefault", "kind": "function", "doc": "Insert key with a value of default if key is not in the dictionary.
\n\nReturn the value for key if key is in the dictionary, else default.
\n", "signature": "(self , key , default = None ): ", "funcdef": "def"}, {"fullname": "ultk.util.frozendict.FrozenDict.update", "modulename": "ultk.util.frozendict", "qualname": "FrozenDict.update", "kind": "function", "doc": "D.update([E, ]**F) -> None. Update D from dict/iterable E and F.\nIf E is present and has a .keys() method, then does: for k in E: D[k] = E[k]\nIf E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v\nIn either case, this is followed by: for k in F: D[k] = F[k]
\n", "signature": "(self , * args , ** kwargs ): ", "funcdef": "def"}, {"fullname": "ultk.util.io", "modulename": "ultk.util.io", "kind": "module", "doc": "
\n"}, {"fullname": "ultk.util.io.write_expressions", "modulename": "ultk.util.io", "qualname": "write_expressions", "kind": "function", "doc": "Write expressions to a YAML file.
\n\nThis is particularly useful for writing GrammaticalExpressions, which have a func field that can't be serialized. This function uses to_dict to determine which properties of the Expression to write.
\n\nArguments: \n\n\nexpressions: the expressions to writefilename: the file to write to \n", "signature": "(\texpressions : Iterable [ ultk . language . language . Expression ] , \tfilename : str ) -> None : ", "funcdef": "def"}, {"fullname": "ultk.util.io.read_grammatical_expressions", "modulename": "ultk.util.io", "qualname": "read_grammatical_expressions", "kind": "function", "doc": "Read grammatical expressions from a YAML file.
\n\nOptionally re-parse and (re-)evaluate the expressions using the provided grammar and universe.
\n\nArguments: \n\n\nfilename: the file to readgrammar: the grammar to use for parsing (and for re-populating the .func attribute of each GrammaticalExpression)re_parse: whether to re-parse the expressionsuniverse: the universe to use for evaluationreturn_by_meaning: whether to return a dictionary mapping meanings to expressions or not \n\nReturns: \n\n\n a list of GrammaticalExpressions and a dictionary mapping meanings to expressions (empty if return_by_meaning is False)
\n \n", "signature": "(\tfilename : str , \tgrammar : ultk . language . grammar . Grammar , \tre_parse : bool = False , \tuniverse : ultk . language . semantics . Universe | None = None , \treturn_by_meaning = True ) -> tuple [ list [ ultk . language . grammar . GrammaticalExpression ], dict [ ultk . language . semantics . Meaning , ultk . language . grammar . GrammaticalExpression ]] : ", "funcdef": "def"}, {"fullname": "ultk.util.io.write_pickle", "modulename": "ultk.util.io", "qualname": "write_pickle", "kind": "function", "doc": "
\n", "signature": "(fn : str , data ): ", "funcdef": "def"}, {"fullname": "ultk.util.io.read_pickle", "modulename": "ultk.util.io", "qualname": "read_pickle", "kind": "function", "doc": "
\n", "signature": "(fn : str ): ", "funcdef": "def"}];
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ultk API documentation
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+ultk .effcomm .information_bottleneck
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+ Submodule for Information Bottleneck based analyses of the efficiency of semantic category systems.
+
+
The ultk.effcomm.information_bottleneck.modeling
+
+
The ultk.effcomm.information_bottlneck.ib implements the IB update equations, and includes an optimizer object that inherits from the base object in ba.
+
+
The ultk.effcomm.information_bottleneck.ba
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+
The ultk.effcomm.information_bottleneck.tools
+
View Source 1 """Submodule for Information Bottleneck based analyses of the efficiency of semantic category systems.
+ 2 3 The `ultk.effcomm.information_bottleneck.modeling` includes a friendly API for obtaining IB theoretical bounds and naming models given a specification of the statistics of the semantic domain. This is likely the only submodule you need to import.
+ 4 5 The `ultk.effcomm.information_bottlneck.ib` implements the IB update equations, and includes an optimizer object that inherits from the base object in `ba`.
+ 6 7 The `ultk.effcomm.information_bottleneck.ba` submodule implements the Blahut-Arimoto algorithm for computing the theoretical bounds of efficient compression. It includes code for simulated annealing (reverse or not) of $\\beta$.
+ 8 9 The `ultk.effcomm.information_bottleneck.tools` submodule includes helper methods for computing informational quantities and dealing with numerical instability.
+10 """
+
+
+
+ ![\"Four](\"https://raw.githubusercontent.com/CLMBRs/ultk/main/images/plots.jpeg\")