defmodule Markov do
@moduledoc """
Public API
Example workflow:
# The model will be stored under this path
{:ok, model} = Markov.load("./model_path", sanitize_tokens: true, store_log: [:train])
# train using four strings
:ok = Markov.train(model, "hello, world!")
:ok = Markov.train(model, "example string number two")
:ok = Markov.train(model, "hello, Elixir!")
:ok = Markov.train(model, "fourth string")
# generate text
{:ok, text} = Markov.generate_text(model)
IO.puts(text)
# commit all changes and unload
Markov.unload(model)
# these will return errors because the model is unloaded
# Markov.generate_text(model)
# Markov.train(model, "hello, world!")
# load the model again
{:ok, model} = Markov.load("./model_path")
# enable probability shifting and generate text
:ok = Markov.configure(model, shift_probabilities: true)
{:ok, text} = Markov.generate_text(model)
IO.puts(text)
# print uninteresting stats
model |> Markov.dump_partition(0) |> IO.inspect
model |> Markov.read_log |> IO.inspect
# this will also write our new just-set option
Markov.unload(model)
"""
@opaque model_reference() :: {:via, term(), term()}
@type log_entry_type() :: :start | :end | :train | :gen
@typedoc """
Model options that could be set during creation in a call to `load/3`
or with `configure/2`:
- `store_log`: determines what data to put in the operation log, all of them
by default:
- `:start` - model is loaded
- `:end` - model is unloaded
- `:train`: training requests
- `:gen`: generation results
- `shift_probabilities`: gives less popular generation paths more chance to
get used, which makes the output more original but may produce nonsense; false
by default
- `sanitize_tokens`: ignores letter case and punctuation when switching states,
but still keeps the output as-is; false by default, can't be changed once the
model is created
- `order`: order of the chain, i.e. how many previous tokens the next one is
based on; 2 by default, can never be changed once the model is created
"""
@type model_option() ::
{:store_log, [log_entry_type()]} |
{:shift_probabilities, boolean()} |
{:sanitize_tokens, boolean()} |
{:order, integer()}
@spec default_opts() :: [model_option()]
defp default_opts do
[
store_log: [:start, :end, :train, :gen],
shift_probabilities: false,
sanitize_tokens: false,
order: 2
]
end
@doc """
Loads an existing model under path `path`. If none is found, a new model with
the specified options will be created and loaded, and if that fails, an error
will be returned.
"""
@spec load(path :: String.t, options :: [model_option()]) :: {:ok, model_reference()} | {:error, term()}
def load(path, create_options \\ []) do
# start process responsible for it
result = Markov.ModelServer.start(
path: path,
create_opts: Keyword.merge(default_opts(), create_options)
)
case result do
# refer to the server by name because it's supervised and automatically
# restarted
{:ok, _pid} -> {:ok, {:via, Registry, {Markov.ModelServers, path}}}
err -> err
end
end
@doc """
Unloads a loaded model
"""
@spec unload(model :: model_reference()) :: :ok
def unload(model) do
GenServer.stop(model)
end
@doc """
Reconfigures a loaded model. See `model_option/0` for a thorough
description of the options
"""
@spec configure(model :: model_reference(), opts :: [model_option()]) :: :ok | {:error, term()}
def configure(model, opts) do
GenServer.call(model, {:configure, opts})
end
@doc """
Gets the configuration of a loaded model
"""
@spec get_config(model :: model_reference()) :: {:ok, [model_option()]} | {:error, term()}
def get_config(model) do
GenServer.call(model, :get_config)
end
@doc """
Trains `model` using text or a list of tokens.
:ok = Markov.train(model, "Hello, world!")
:ok = Markov.train(model, "this is a string that's broken down into tokens behind the scenes")
:ok = Markov.train(model, [
:this, "is", 'a token', :list, "where",
{:each_element, :is, {:taken, :as_is}},
:and, :can_be, :erlang.make_ref(), "<-- any term"
])
See `tag_query/0` for more info about `tags`
"""
@spec train(model_reference(), String.t() | [term()], [term()]) :: :ok | {:error, term()}
def train(model, text, tags \\ [:"$none"])
def train(model, text, tags) when is_binary(text) do
tokens = String.split(text)
train(model, tokens, tags)
end
def train(model, tokens, tags) when is_list(tokens) do
tags = if tags == [], do: [:"$none"], else: tags
GenServer.call(model, {:train, tokens, tags})
end
@typedoc """
If data was tagged when training, you can use tag queries to alter the
probabilities of certain generation paths
### Examples:
# training
iex> Markov.train(model, "hello earth", [
{:action, :saying_hello}, # <- terms of any type can function as tags
{:subject_type, :planet},
{:subject, "earth"},
:lowercase
])
:ok
iex> Markov.train(model, "Hello Elixir", [
{:action, :saying_hello},
{:subject_type, :programming_language},
{:subject, "Elixir"},
:uppercase
])
:ok
# simple generation - both paths have equal probabilities
iex> Markov.generate_text(model)
{:ok, "hello earth"}
iex> Markov.generate_text(model)
{:ok, "hello Elixir"}
# All generation paths have a score of 1 by default. Here we're telling
# Markov to add 1 point to paths tagged with `:uppercase`;
# "hello Elixir" now has a score of 2 and "hello earth" has a score of 1.
# Thus, "hello Elixir" has a probability of 2/3, and "hello earth" has
# that of 1/3
iex> Markov.generate_text(model, %{uppercase: 1})
{:ok, "hello Elixir"}
iex> Markov.generate_text(model, %{uppercase: 1})
{:ok, "hello Elixir"}
iex> Markov.generate_text(model, %{uppercase: 1})
{:ok, "hello earth"}
"""
@type tag_query() :: %{term() => non_neg_integer()}
@doc """
Generates a list of tokens
iex> Markov.generate_tokens(model)
{:ok, ["hello", "world"]}
See type `tag_query/0` for more info about `tag_query`
"""
@spec generate_tokens(model_reference(), tag_query()) :: {:ok, [term()]} | {:error, term()}
def generate_tokens(model, tag_query \\ %{}) do
GenServer.call(model, {:generate, tag_query})
end
@doc """
Generates a string. Will raise an exception if the model was trained on
non-textual tokens at least once
iex> Markov.generate_text(model)
{:ok, "hello world"}
See type `tag_query/0` for more info about `tags`
"""
@spec generate_text(model_reference(), tag_query()) :: {:ok, binary()} | {:error, term()}
def generate_text(model, tag_query \\ %{}) do
case generate_tokens(model, tag_query) do
{:ok, text} -> {:ok, Enum.join(text, " ")}
{:error, _} = err -> err
end
end
defmodule Operation do
defstruct [:date_time, :type, :arg]
@type t() :: %__MODULE__{date_time: DateTime.t(), type: Markov.log_entry_type(), arg: term()}
end
@doc """
Reads the log file and returns a list of entries in chronological order
iex> Markov.read_log(model)
{:ok,
[
%Markov.Operation{date_time: ~U[2022-10-02 16:59:51.844Z], type: :start, arg: nil},
%Markov.Operation{date_time: ~U[2022-10-02 16:59:56.705Z], type: :train, arg: ["hello", "world"]}
]}
"""
defmodule Operation, do: defstruct [:date_time, :type, :arg]
@spec read_log(model_reference()) :: [%Operation{}]
def read_log(model) do
{:via, Registry, {Markov.ModelServers, path}} = model
{:ok, file} = :file.open(Path.join(path, "history.log"), [:read, :raw, :binary])
do_read_log(file) |> :lists.reverse
end
defp do_read_log(file, acc \\ []) do
case :file.read(file, 11) do
{:ok, <<type::8, ts::64, len::16>>} ->
{:ok, data} = :file.read(file, len)
type = Map.get(Markov.ModelServer.rev_log_entry_map, type)
date_time = DateTime.from_unix!(ts, :millisecond)
data = :erlang.binary_to_term(data)
acc = [%Operation{date_time: date_time, type: type, arg: data} | acc]
do_read_log(file, acc)
_ -> acc
end
end
end
