defmodule Markov do
@moduledoc """
Public API
Example workflow:
# the model is to be stored under /base/directory/model_name
# the model will be created using specified options if not found
{:ok, model} = Markov.load("/base/directory", "model_name", sanitize_tokens: true, store_history: [: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.inspect(text)
# unload model from RAM
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("/base/directory", "model_name")
# enable probability shifting and generate text
:ok = Markov.configure(model, shift_probabilities: true)
{:ok, text} = Markov.generate_text(model)
IO.inspect(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() ::
:train | :train_deferred |
:repart_start | :repart_done |
:start | :end |
:gen
@typedoc """
Model options that could be set during creation in a call to `load/3`
or with `configure/2`:
- `store_history`: determines what data to put in the operation log, `[]` by default:
- `:train`: training requests
- `:train_deferred`: training requests that have been deferred to until after
repartitioning is complete
- `:gen`: generation results
- `:repart_start` - repartition start
- `:repart_done` - repartition done
- `:start` - model is loaded
- `:end` - model is unloaded
- `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
- `partition_size`: approximate number of link entries in one partition, 10k
by default
- `partition_timeout`: partition is unloaded from RAM after that many
milliseconds of inactivity, 10k 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_history, [log_entry_type()]} |
{:shift_probabilities, boolean()} |
{:partition_size, integer()} |
{:partition_timeout, integer()} |
{:sanitize_tokens, boolean()} |
{:order, integer()}
@spec default_opts() :: [model_option()]
defp default_opts do
[
store_history: [
:train, :train_deferred,
:repart_start, :repart_done,
:start, :end,
:gen
],
shift_probabilities: false,
partition_size: 10_000,
partition_timeout: 10_000,
sanitize_tokens: false,
order: 2
]
end
@doc """
Loads an existing model from `base_dir`/`name`. If none is found, a new model
with the specified options at that path will be created and loaded, and if that
fails, an error will be returned
"""
@spec load(base_dir :: String.t(), name :: String.t(), options :: [model_option()]) ::
{:ok, model_reference()} | {:error, term()}
def load(base_dir, name, create_options \\ []) do
# start process responsible for it
result = Markov.ModelServer.start(
name: name,
path: Path.join(base_dir, name),
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, name}}}
err -> err
end
end
@doc """
Unloads an already loaded model
"""
@spec unload(model :: model_reference()) :: :ok
def unload(model) do
GenServer.stop(model)
end
@doc """
Reconfigures an already 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 an already 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"
])
Returns the status of the operation:
- `:done` - training is complete
- `:deferred` - a repartition is currently in progress, this request has
been placed in the backlog to be fulfilled after repartitioning is complete
See `generate_text/2` for more info about `specifiers`
"""
@spec train(model_reference(), String.t() | [term()], [term()]) :: {:ok, :done | :deferred} | {:error, term()}
def train(model, text, tags \\ [:"$none"])
def train(model, text, tags) when is_binary(text) do
tokens = String.split(text)
train(model, tokens, tags)
end
def train(model, tokens, tags) when is_list(tokens) do
GenServer.call(model, {:train, tokens, tags})
end
@typedoc """
If data was tagged when training, you can use tag queries to only select
generation paths that match a set of criteria
- `true` always matches
- `{x, :or, y}` matches when either `x` or `y` matches
- `{:not, x}` matches if x doesn't match, and vice versa
- `{x, :score, y}` is only allowed at the top level; the total score counter
(initially 0) is increased by `score` for every element `{query, score}` of
`y` (a list) that matches; probabilities are then adjusted according to those
scores.
- any other term is treated as a tag (note the `:"$none"` tag - the default
one)
### 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, :done}
iex> Markov.train(model, "Hello Elixir", [
{:action, :saying_hello},
{:subject_type, :programming_language},
{:subject, "Elixir"},
:uppercase
])
{:ok, :done}
# simple generation - both paths have equal probabilities
iex> Markov.generate_text(model)
{:ok, "hello earth"}
iex> Markov.generate_text(model)
{:ok, "hello Elixir"}
# simple tag queries
iex> Markov.generate_text(model, {:subject_type, :planet})
{:ok, "hello earth"}
iex> Markov.generate_text(model, :lowercase)
{:ok, "hello earth"}
iex> Markov.generate_text(model, {:subject_type, :programming_language})
{:ok, "hello Elixir"}
iex> Markov.generate_text(model, :uppercase)
{:ok, "hello Elixir"}
# both possible generation paths were tagged with this tag
iex> Markov.generate_text(model, {:action, :saying_hello})
{:ok, "hello earth"}
iex> Markov.generate_text(model, {:action, :saying_hello})
{:ok, "hello Elixir"}
# both paths match, but "hello Elixir" has a score of 1 and "hello earth"
# has a score of zero; thus, "hello Elixir" has a probability of 2/3, and
# "hello earth" has that of 1/3
iex> Markov.generate_text(model, {true, :score, [:uppercase]})
{:ok, "hello Elixir"}
iex> Markov.generate_text(model, {true, :score, [:uppercase]})
{:ok, "hello earth"}
"""
@type tag_query() ::
true |
{tag_query(), :or, tag_query()} |
{tag_query(), :score, [{tag_query(), integer()}]} |
{:not, tag_query()} |
term()
@doc """
Predicts (generates) a list of tokens
iex> Markov.generate_tokens(model)
{:ok, ["hello", "world"]}
See type `tag_query/0` for more info about `tags`
"""
@spec generate_tokens(model_reference(), tag_query()) :: {:ok, [term()]} | {:error, term()}
def generate_tokens(model, tag_query \\ true) do
GenServer.call(model, {:generate, tag_query})
end
@doc """
Predicts (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 \\ true) do
case generate_tokens(model, tag_query) do
{:ok, text} -> {:ok, Enum.join(text, " ")}
{:error, _} = err -> err
end
end
@doc """
Reads the log file and returns a list of entries in chronological order
iex> Markov.read_log(model)
{:ok,
[
{~U[2022-10-02 16:59:51.844Z], :start, nil},
{~U[2022-10-02 16:59:56.705Z], :train, ["hello", "world"]}
]}
"""
@spec read_log(model_reference()) :: {:ok, [{DateTime.t(), log_entry_type(), term()}]} | {:error, term()}
def read_log(model) do
path = GenServer.call(model, :get_log_file)
{:ok, %File.Stat{size: size}} = File.stat(path)
File.open(path, [:read], fn handle ->
read_log_entries(handle, size, 0) |> :lists.reverse
end)
end
defp read_log_entries(handle, size, pos, acc \\ [])
defp read_log_entries(_handle, size, pos, acc) when pos >= size, do: acc
defp read_log_entries(handle, size, pos, acc) do
<<entry_size::16>> = IO.binread(handle, 2)
data = IO.binread(handle, entry_size)
{unix_time, type, data} = :erlang.binary_to_term(data)
{:ok, time} = DateTime.from_unix(unix_time, :millisecond)
entry = {time, type, data}
read_log_entries(handle, size, pos + 2 + entry_size, [entry | acc])
end
@doc "Reads an entire partition for debugging purposes"
@spec dump_partition(model_reference(), integer()) :: [{{term(), term()}, %{term() => integer()}}]
def dump_partition(model, part_no) do
# the server opens the table for us
{:ok, dets_table} = GenServer.call(model, {:prepare_dump_info, part_no})
:dets.match(dets_table, :"$1") |> Enum.map(fn [x] -> x end)
end
end
