diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 9106558..47bc285 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -218,7 +218,7 @@

D

  • DiscreteStateDependentRate (class in phylojunction.calculation.discrete_sse)
    • -
  • DistributionPGM (class in phylojunction.pgm.pgm) +
  • DistrForSampling (class in phylojunction.pgm.pgm)
  • dn_grammar_dict (phylojunction.interface.grammar.dn_grammar.PJDnGrammar attribute)
    • @@ -235,7 +235,7 @@

    D

  • (phylojunction.distribution.dn_parametric.DnUnif attribute)
    • -
  • (phylojunction.pgm.pgm.DistributionPGM property) +
  • (phylojunction.pgm.pgm.DistrForSampling property)
    • diff --git a/docs/build/html/phylojunction.distribution.html b/docs/build/html/phylojunction.distribution.html index 8146885..b04adaa 100644 --- a/docs/build/html/phylojunction.distribution.html +++ b/docs/build/html/phylojunction.distribution.html @@ -77,7 +77,7 @@

    Submodules
    class phylojunction.distribution.dn_discrete_sse.DnSSE(sse_stash: SSEStash, stop_value: List[float] = [], n: int = 1, n_replicates: int = 1, stop: str = '', origin: bool = True, start_states_list: List[int] = [], condition_on_speciation: bool = False, condition_on_survival: bool = True, condition_on_obs_both_sides_root: bool = False, min_rec_taxa: int = 0, max_rec_taxa: int = 1000000000000, abort_at_obs: int = 1000000000000, seeds_list: Optional[List[int]] = None, epsilon: float = 1e-12, runtime_limit: int = 15, debug: bool = False)
    -

    Bases: DistributionPGM

    +

    Bases: DistrForSampling

    Distribution class for State-dependent Speciation and Extinction (birth-death) process

    The sampling method in this class is a “rising tide” simulator, where all living lineages grow together.

    @@ -514,7 +514,7 @@

    Submodules
    class phylojunction.distribution.dn_parametric.DnExponential(n_samples: int, n_repl: int, scale_or_rate_param: List[float], rate_parameterization: bool, parent_node_tracker: Optional[Dict[str, str]])
    -

    Bases: DistributionPGM

    +

    Bases: DistrForSampling

    DN_NAME = 'Exponential'
    @@ -587,7 +587,7 @@

    Submodules
    class phylojunction.distribution.dn_parametric.DnGamma(n_samples: int, n_repl: int, shape_param: List[float], scale_or_rate_param: List[float], rate_parameterization: bool, parent_node_tracker: Optional[Dict[str, str]])
    -

    Bases: DistributionPGM

    +

    Bases: DistrForSampling

    DN_NAME = 'Gamma'
    @@ -668,7 +668,7 @@

    Submodules
    class phylojunction.distribution.dn_parametric.DnLogNormal(n_samples: int, n_repl: int, ln_mean: List[float], ln_sd: List[float], ln_log_space: bool, parent_node_tracker: Optional[Dict[str, str]])
    -

    Bases: DistributionPGM

    +

    Bases: DistrForSampling

    DN_NAME = 'Log-normal'
    @@ -749,7 +749,7 @@

    Submodules
    class phylojunction.distribution.dn_parametric.DnNormal(n_samples: int, n_repl: int, norm_mean_param: List[float], norm_sd_param: List[float], parent_node_tracker: Optional[Dict[str, str]])
    -

    Bases: DistributionPGM

    +

    Bases: DistrForSampling

    DN_NAME = 'Normal'
    @@ -820,7 +820,7 @@

    Submodules
    class phylojunction.distribution.dn_parametric.DnUnif(n_samples: int, n_repl: int, min_param: List[float], max_param: List[float], parent_node_tracker: Optional[Dict[str, str]])
    -

    Bases: DistributionPGM

    +

    Bases: DistrForSampling

    DN_NAME = 'Uniform'
    diff --git a/docs/build/html/phylojunction.inference.revbayes.html b/docs/build/html/phylojunction.inference.revbayes.html index b4652bd..d2cc360 100644 --- a/docs/build/html/phylojunction.inference.revbayes.html +++ b/docs/build/html/phylojunction.inference.revbayes.html @@ -96,7 +96,7 @@

    Submodules
    -phylojunction.inference.revbayes.rb_dn_parametric.get_rev_str_from_dn_parametric_obj(dn_obj: DistributionPGM) → Tuple[int, int, List[str]]
    +phylojunction.inference.revbayes.rb_dn_parametric.get_rev_str_from_dn_parametric_obj(dn_obj: DistrForSampling) → Tuple[int, int, List[str]]

    diff --git a/docs/build/html/phylojunction.interface.grammar.html b/docs/build/html/phylojunction.interface.grammar.html index 777d8a1..24e9fdc 100644 --- a/docs/build/html/phylojunction.interface.grammar.html +++ b/docs/build/html/phylojunction.interface.grammar.html @@ -147,7 +147,7 @@

    phylojunction.interface.grammar.det_fn_discrete_sse moduleobject

    -classmethod create_dn_obj(dn_id: str, dn_param_dict: Dict[str, List[Union[NodePGM, str]]]) → DistributionPGM
    +classmethod create_dn_obj(dn_id: str, dn_param_dict: Dict[str, List[Union[NodePGM, str]]]) → DistrForSampling

    Build and return sampling distribution object

    @@ -161,10 +161,10 @@

    phylojunction.interface.grammar.det_fn_discrete_sse module

    - @@ -183,7 +183,7 @@

    phylojunction.interface.grammar.det_fn_discrete_sse module
    -classmethod init_return_discrete_SSE_dn(dn_param_dict: Dict[str, List[Union[NodePGM, str]]]) → DistributionPGM
    +classmethod init_return_discrete_SSE_dn(dn_param_dict: Dict[str, List[Union[NodePGM, str]]]) → DistrForSampling
    diff --git a/docs/build/html/phylojunction.pgm.html b/docs/build/html/phylojunction.pgm.html index e52262a..15243b8 100644 --- a/docs/build/html/phylojunction.pgm.html +++ b/docs/build/html/phylojunction.pgm.html @@ -107,12 +107,12 @@

    Submodules -
    -class phylojunction.pgm.pgm.DistributionPGM
    +
    +class phylojunction.pgm.pgm.DistrForSampling

    Bases: ValueGenerator

    -
    -abstract property DN_NAME
    +
    +abstract property DN_NAME
    @@ -191,7 +191,7 @@

    Submodules
    -class phylojunction.pgm.pgm.StochasticNodePGM(node_name: str, sample_size: int, sampled_from: Optional[DistributionPGM] = None, returned_from: Optional[ConstantFn] = None, value: Optional[List[Any]] = None, replicate_size: int = 1, call_order_idx: Optional[int] = None, deterministic: bool = False, clamped: bool = False, parent_nodes: Optional[List[Any]] = None)
    +class phylojunction.pgm.pgm.StochasticNodePGM(node_name: str, sample_size: int, sampled_from: Optional[DistrForSampling] = None, returned_from: Optional[ConstantFn] = None, value: Optional[List[Any]] = None, replicate_size: int = 1, call_order_idx: Optional[int] = None, deterministic: bool = False, clamped: bool = False, parent_nodes: Optional[List[Any]] = None)

    Bases: NodePGM

    diff --git a/docs/build/html/phylojunction.utility.html b/docs/build/html/phylojunction.utility.html index b153d14..e2d72b5 100644 --- a/docs/build/html/phylojunction.utility.html +++ b/docs/build/html/phylojunction.utility.html @@ -885,7 +885,7 @@

    Submodules

    +(n_samples inside a DistrForSampling object)
    Returns:

    a DistributionPGM instance

    +
    Returns:

    a DistrForSampling instance
    Return type:

    DistributionPGM

    +
    Return type:

    DistrForSampling
    Parameters:size_to_grow (int) – This will be the number of simulations -(n_samples inside a DistributionPGM object)
    diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 3f3ac31..28d4dbf 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["index", "installing", "modules", "phylojunction", "phylojunction.calculation", "phylojunction.data", "phylojunction.distribution", "phylojunction.inference", "phylojunction.inference.revbayes", "phylojunction.interface", "phylojunction.interface.cmdbox", "phylojunction.interface.grammar", "phylojunction.pgm", "phylojunction.plotting", "phylojunction.readwrite", "phylojunction.utility", "pjdoc/index", "pjdoc/parametric"], "filenames": ["index.rst", "installing.rst", "modules.rst", "phylojunction.rst", "phylojunction.calculation.rst", "phylojunction.data.rst", "phylojunction.distribution.rst", "phylojunction.inference.rst", "phylojunction.inference.revbayes.rst", "phylojunction.interface.rst", "phylojunction.interface.cmdbox.rst", 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16, "overview": 16, "origin": 16, "design": 16, "graphic": 16, "model": 16, "how": 16, "read": 16, "dag": 16, "specifi": 16, "exampl": 16, "multipl": 16, "sampl": 16, "replic": 16, "gui": 16, "command": 16, "line": 16, "cli": 16, "lexicon": 16}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 56}}) \ No newline at end of file diff --git a/src/phylojunction/distribution/dn_discrete_sse.py b/src/phylojunction/distribution/dn_discrete_sse.py index 2bc196f..c7bc0db 100644 --- a/src/phylojunction/distribution/dn_discrete_sse.py +++ b/src/phylojunction/distribution/dn_discrete_sse.py @@ -20,7 +20,7 @@ __email__ = "f.mendes@wustl.edu" -class DnSSE(pgm.DistributionPGM): +class DnSSE(pgm.DistrForSampling): """Discrete SSE distribution. Class for the discrete state-dependent speciation and extinction diff --git a/src/phylojunction/distribution/dn_discrete_sse.pyi b/src/phylojunction/distribution/dn_discrete_sse.pyi index 3242fda..fbd1b9c 100644 --- a/src/phylojunction/distribution/dn_discrete_sse.pyi +++ b/src/phylojunction/distribution/dn_discrete_sse.pyi @@ -11,7 +11,7 @@ from phylojunction.data.sampled_ancestor \ from phylojunction.data.attribute_transition \ import AttributeTransition # type: ignore -class DnSSE(pgm.DistributionPGM): +class DnSSE(pgm.DistrForSampling): DN_NAME = "DnSSE" n_sim: int n_repl: int diff --git a/src/phylojunction/distribution/dn_parametric.py b/src/phylojunction/distribution/dn_parametric.py index e53f66f..f1320b7 100644 --- a/src/phylojunction/distribution/dn_parametric.py +++ b/src/phylojunction/distribution/dn_parametric.py @@ -15,7 +15,7 @@ __email__ = "f.mendes@wustl.edu" -class DnLogNormal(pgm.DistributionPGM): +class DnLogNormal(pgm.DistrForSampling): DN_NAME = "Log-normal" @@ -166,7 +166,7 @@ def get_rev_inference_spec_info(self) -> ty.List[str]: self.parent_node_tracker) -class DnNormal(pgm.DistributionPGM): +class DnNormal(pgm.DistrForSampling): DN_NAME = "Normal" @@ -302,7 +302,7 @@ def get_rev_inference_spec_info(self) -> ty.List[str]: self.parent_node_tracker) -class DnExponential(pgm.DistributionPGM): +class DnExponential(pgm.DistrForSampling): DN_NAME = "Exponential" @@ -424,7 +424,7 @@ def get_rev_inference_spec_info(self) -> ty.List[str]: self.parent_node_tracker) -class DnGamma(pgm.DistributionPGM): +class DnGamma(pgm.DistrForSampling): DN_NAME = "Gamma" @@ -565,7 +565,7 @@ def get_rev_inference_spec_info(self) -> ty.List[str]: self.parent_node_tracker) -class DnUnif(pgm.DistributionPGM): +class DnUnif(pgm.DistrForSampling): DN_NAME = "Uniform" diff --git a/src/phylojunction/distribution/dn_parametric.pyi b/src/phylojunction/distribution/dn_parametric.pyi index 765f6e0..1090e1d 100644 --- a/src/phylojunction/distribution/dn_parametric.pyi +++ b/src/phylojunction/distribution/dn_parametric.pyi @@ -4,7 +4,7 @@ import typing as ty # pj imports import phylojunction.pgm.pgm as pgm -class DnLogNormal(pgm.DistributionPGM): +class DnLogNormal(pgm.DistrForSampling): DN_NAME: str n_samples: int n_repl: int @@ -18,7 +18,7 @@ class DnLogNormal(pgm.DistributionPGM): def init_check_vectorize_sample_size(self, param_list: ty.Optional[ty.List[ty.Any]]=None) -> ty.Optional[ty.List[ty.List[ty.Union[int, float, str]]]]: ... def get_rev_inference_spec_info(self) -> ty.List[str]: ... -class DnNormal(pgm.DistributionPGM): +class DnNormal(pgm.DistrForSampling): DN_NAME: str n_samples: int n_repl: int @@ -32,7 +32,7 @@ class DnNormal(pgm.DistributionPGM): def init_check_vectorize_sample_size(self, param_list: ty.Optional[ty.List[ty.Any]]=None) -> ty.Optional[ty.List[ty.List[ty.Union[int, float, str]]]]: ... def get_rev_inference_spec_info(self) -> ty.List[str]: ... -class DnExponential(pgm.DistributionPGM): +class DnExponential(pgm.DistrForSampling): DN_NAME: str n_samples: int n_repl: int @@ -46,7 +46,7 @@ class DnExponential(pgm.DistributionPGM): def init_check_vectorize_sample_size(self, param_list: ty.Optional[ty.List[ty.Any]]=None) -> ty.Optional[ty.List[ty.List[ty.Union[int, float, str]]]]: ... def get_rev_inference_spec_info(self) -> ty.List[str]: ... -class DnGamma(pgm.DistributionPGM): +class DnGamma(pgm.DistrForSampling): DN_NAME: str n_samples: int n_repl: int @@ -60,7 +60,7 @@ class DnGamma(pgm.DistributionPGM): def init_check_vectorize_sample_size(self, param_list: ty.Optional[ty.List[ty.Any]]=None) -> ty.Optional[ty.List[ty.List[ty.Union[int, float, str]]]]: ... def get_rev_inference_spec_info(self) -> ty.List[str]: ... -class DnUnif(pgm.DistributionPGM): +class DnUnif(pgm.DistrForSampling): DN_NAME: str n_samples: int n_repl: int diff --git a/src/phylojunction/inference/revbayes/rb_dn_parametric.py b/src/phylojunction/inference/revbayes/rb_dn_parametric.py index 6eafa49..785e5bf 100644 --- a/src/phylojunction/inference/revbayes/rb_dn_parametric.py +++ b/src/phylojunction/inference/revbayes/rb_dn_parametric.py @@ -6,7 +6,7 @@ # pj imports import phylojunction.pgm.pgm as pgm -def get_rev_str_from_dn_parametric_obj(dn_obj: pgm.DistributionPGM) -> ty.Tuple[int, int, ty.List[str]]: +def get_rev_str_from_dn_parametric_obj(dn_obj: pgm.DistrForSampling) -> ty.Tuple[int, int, ty.List[str]]: rev_str_list: ty.List[str] n_sim: int = dn_obj.n_samples diff --git a/src/phylojunction/interface/cmdbox/cmd_parse.py b/src/phylojunction/interface/cmdbox/cmd_parse.py index 8533b9e..642d0fd 100644 --- a/src/phylojunction/interface/cmdbox/cmd_parse.py +++ b/src/phylojunction/interface/cmdbox/cmd_parse.py @@ -430,7 +430,7 @@ def create_add_rv_pgm( a_stoch_node_name: str, sample_size: int, replicate_size: int, - a_dn_obj: pgm.DistributionPGM, + a_dn_obj: pgm.DistrForSampling, parent_pgm_nodes: ty.List[pgm.NodeDAG], clamped: bool): diff --git a/src/phylojunction/interface/grammar/dn_discrete_sse_makers.py b/src/phylojunction/interface/grammar/dn_discrete_sse_makers.py index 45f9583..e1178ff 100644 --- a/src/phylojunction/interface/grammar/dn_discrete_sse_makers.py +++ b/src/phylojunction/interface/grammar/dn_discrete_sse_makers.py @@ -14,7 +14,7 @@ def make_discrete_SSE_dn( dn_name: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) \ - -> pgm.DistributionPGM: + -> pgm.DistrForSampling: ############################# # IMPORTANT: Default values # diff --git a/src/phylojunction/interface/grammar/dn_grammar.py b/src/phylojunction/interface/grammar/dn_grammar.py index 497ae52..6237ec1 100644 --- a/src/phylojunction/interface/grammar/dn_grammar.py +++ b/src/phylojunction/interface/grammar/dn_grammar.py @@ -53,7 +53,7 @@ def init_return_parametric_dn( cls, dn_id: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) \ - -> pgm.DistributionPGM: + -> pgm.DistrForSampling: """Create and return parametric distributions for sampling. Args: @@ -451,7 +451,7 @@ def init_return_parametric_dn( def init_return_discrete_SSE_dn( cls, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) \ - -> pgm.DistributionPGM: + -> pgm.DistrForSampling: """Create and return SSE distribution for sampling. Args: @@ -472,7 +472,7 @@ def create_dn_obj( cls, dn_id: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) \ - -> pgm.DistributionPGM: + -> pgm.DistrForSampling: """Create and return prob. distribution (for sampling) object. Args: diff --git a/src/phylojunction/interface/grammar/dn_grammar.pyi b/src/phylojunction/interface/grammar/dn_grammar.pyi index 6eec361..991c524 100644 --- a/src/phylojunction/interface/grammar/dn_grammar.pyi +++ b/src/phylojunction/interface/grammar/dn_grammar.pyi @@ -7,8 +7,8 @@ class PJDnGrammar: @classmethod def grammar_check(cls, dn_id: str, dn_param: str) -> bool: ... @classmethod - def init_return_parametric_dn(cls, dn_id: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) -> pgm.DistributionPGM: ... + def init_return_parametric_dn(cls, dn_id: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) -> pgm.DistrForSampling: ... @classmethod - def init_return_discrete_SSE_dn(cls, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) -> pgm.DistributionPGM: ... + def init_return_discrete_SSE_dn(cls, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) -> pgm.DistrForSampling: ... @classmethod - def create_dn_obj(cls, dn_id: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) -> pgm.DistributionPGM: ... + def create_dn_obj(cls, dn_id: str, dn_param_dict: ty.Dict[str, ty.List[ty.Union[str, pgm.NodeDAG]]]) -> pgm.DistrForSampling: ... diff --git a/src/phylojunction/pgm/pgm.py b/src/phylojunction/pgm/pgm.py index 79b989a..df08030 100644 --- a/src/phylojunction/pgm/pgm.py +++ b/src/phylojunction/pgm/pgm.py @@ -150,7 +150,7 @@ def n_repl(self): pass @abstractmethod - def __init__(self): + def __init__(self) -> None: pass @abstractmethod @@ -173,7 +173,7 @@ def get_rev_inference_spec_info(self) -> ty.List[str]: pass -class DistributionPGM(ValueGenerator): +class DistrForSampling(ValueGenerator): """Class for randomly generating (i.e., sampling) values. An object of this class is required by stochastic DAG nodes so @@ -426,7 +426,7 @@ def plot_node(self, pass @abstractmethod - def populate_operator_weight(self): + def populate_operator_weight(self) -> None: pass @abstractmethod @@ -562,16 +562,23 @@ class StochasticNodeDAG(NodeDAG): This class is also used when a value is being specified by a user, or parsed from it. In other words, constant nodes in the graph are still stochastic DAG nodes under the hood. + + Attributes: + sampling_dn (DistrForSampling): Distribution object for randomly + sampling values. + constant_fn (ConstantFn): Function object for obtaining + constant values modified from some user input. + operator_weight (float): Weight given to MCMC move. """ - sampling_dn: ty.Optional[DistributionPGM] - constant_fn: ty.Optional[DistributionPGM] + sampling_dn: ty.Optional[DistrForSampling] + constant_fn: ty.Optional[ConstantFn] operator_weight: float def __init__(self, node_name: str, sample_size: int, - sampled_from: ty.Optional[DistributionPGM] = None, + sampled_from: ty.Optional[DistrForSampling] = None, returned_from: ty.Optional[ConstantFn] = None, value: ty.Optional[ty.List[ty.Any]] = None, replicate_size: int = 1, @@ -580,14 +587,13 @@ def __init__(self, clamped: bool = False, parent_nodes: ty.Optional[ty.List[ty.Any]] = None): - self.is_sampled = False self.sampling_dn = sampled_from # dn object self.constant_fn = returned_from # constant fn object - + self.is_sampled = False # not value checks for both [] and None generated_or_specified_value: ty.List[ty.Any] = \ self.generate_value() if not value else value - + super().__init__(node_name, sample_size=sample_size, value=generated_or_specified_value, @@ -598,7 +604,7 @@ def __init__(self, parent_nodes=parent_nodes) # used for MCMC #move/operator setup during inference - self.operator_weight = 0.0 + self.operator_weight = 0.0 self.populate_operator_weight() @@ -685,7 +691,7 @@ def plot_node(self, # return plot_node_histogram(axes, [float(self.value)]) - def populate_operator_weight(self): + def populate_operator_weight(self) -> None: if isinstance(self.value, (list, np.ndarray)): if isinstance(self.value[0], (int, float, str, np.float64)): self.operator_weight = 1 # this is a scalar random variable @@ -720,7 +726,7 @@ def __init__(self, value: ty.Optional[ty.List[ty.Any]] = None, call_order_idx: ty.Optional[int] = None, deterministic: bool = True, - parent_nodes: ty.Optional[ty.List[NodeDAG]] = None): + parent_nodes: ty.Optional[ty.List[NodeDAG]] = None) -> None: super().__init__(node_name, sample_size=None, @@ -844,14 +850,4 @@ def extract_vals_as_str_from_node_dag( if __name__ == "__main__": - # can be called from pgm/ - # $ python3 pgm.py - # - # can also be called from phylojunction/ - # $ python3 pgm/pgm.py - # or - # $ python3 -m pgm.pgm - # - # can also be called from VS Code, if open folder is phylojuction/ - pass diff --git a/src/phylojunction/pgm/pgm.pyi b/src/phylojunction/pgm/pgm.pyi index 73dbeca..1d33e7d 100644 --- a/src/phylojunction/pgm/pgm.pyi +++ b/src/phylojunction/pgm/pgm.pyi @@ -8,14 +8,7 @@ from abc import ABC, abstractmethod R = ty.TypeVar('R') -class DummyAttribute: - pass -def abstract_attribute(obj: ty.Callable[[ty.Any], R] = None) -> R: - _obj = ty.cast(ty.Any, obj) - if obj is None: - _obj = DummyAttribute() - _obj.__is_abstract_attribute__ = True - return ty.cast(R, _obj) +def abstract_attribute(obj: ty.Callable[[ty.Any], R] = None) -> R: ... class DirectedAcyclicGraph: node_val_dict: ty.Dict[NodeDAG, ty.Any] @@ -25,18 +18,16 @@ class DirectedAcyclicGraph: def __init__(self) -> None: ... def add_node(self, node_dag: NodeDAG) -> None: ... def get_node_dag_by_name(self, node_name): ... - def get_display_str_by_name(self, node_name, sample_idx: Incomplete | None = ..., repl_size: int = ...): ... + def get_display_str_by_name(self, node_name, sample_idx=None, repl_size=1): ... def get_sorted_node_dag_list(self) -> ty.List[NodeDAG]: ... class ValueGenerator(ABC, metaclass=abc.ABCMeta): @abstract_attribute - def n_samples(self): - pass + def n_samples(self): ... @abstract_attribute - def n_repl(self): - pass + def n_repl(self): ... @abstractmethod - def __init__(self): ... + def __init__(self) -> None: ... @abstractmethod def generate(self) -> ty.List[ty.Any]: ... @abstractmethod @@ -44,32 +35,37 @@ class ValueGenerator(ABC, metaclass=abc.ABCMeta): @abstractmethod def get_rev_inference_spec_info(self) -> ty.List[str]: ... -class DistributionPGM(ValueGenerator): +class DistrForSampling(ValueGenerator): @property @abstractmethod - def DN_NAME(self): - pass + def DN_NAME(self) -> str: ... class ConstantFn(ValueGenerator): @property @abstractmethod - def CT_FN_NAME(self): - pass + def CT_FN_NAME(self) -> str: ... -class NodeDAG(ABC, metaclass=abc.ABCMeta): +class NodeDAG(ABC): node_name: str - value: ty.Optional[ty.List[ty.Any]] - sample_size: int - repl_size: int + _value: ty.List[ty.Any] + _sample_size: int + _repl_size: int call_order_idx: int is_sampled: bool is_deterministic: bool is_clamped: bool - parent_nd_list: Incomplete - param_of: Incomplete + parent_nd_list: ty.Optional[ty.List[NodeDAG]] + @property + def value(self): ... + @value.setter + def value(self, a_value): ... + @property + def sample_size(self): ... + @property + def repl_size(self): ... @abstractmethod - def __init__(self, node_name: str, sample_size: int, value: ty.Optional[ty.List[ty.Any]] = ..., replicate_size: int = ..., call_order_idx: ty.Optional[int] = ..., sampled: bool = ..., deterministic: bool = ..., clamped: bool = ..., parent_nodes: ty.Optional[ty.List[NodeDAG]] = ...): ... + def __init__(self, node_name: str, sample_size: ty.Optional[int], replicate_size: int=1, value: ty.Optional[ty.List[ty.Any]]=None, call_order_idx: ty.Optional[int] = None, sampled: bool = False, deterministic: bool=False, clamped: bool=False, parent_nodes: ty.Optional[ty.List[NodeDAG]]=None): ... def _flatten_and_extract_values(self) -> None: ... def get_start2end_str(self, start: int, end: int, repl_idx: int=0, is_tree: bool=False) -> str: ... def __str__(self) -> str: ... @@ -79,34 +75,36 @@ class NodeDAG(ABC, metaclass=abc.ABCMeta): def __len__(self) -> int: ... @abstractmethod - def get_gcf(self, axes: plt.Axes, sample_idx: ty.Optional[int]=None, repl_idx: int=0, repl_size: int=1, branch_attr: ty.Optional[str]="state") -> None: ... - + def plot_node(self, axes: plt.Axes, sample_idx: int = 0, repl_idx: int = 0, repl_size: int = 1, branch_attr: ty.Optional[str] = "state") -> None: ... + + @abstractmethod + def populate_operator_weight(self) -> None: ... + @abstractmethod - def populate_operator_weight(self): ... + def get_node_stats_str(self, start: int, end: int, repl_idx: int) -> str: ... class StochasticNodeDAG(NodeDAG): - _generated_value: ty.List[ty.Any] - is_sampled: bool - sampling_dn: Incomplete + sampling_dn: ty.Optional[DistrForSampling] + constant_fn: ty.Optional[ConstantFn] operator_weight: float - - def __init__(self, node_name: str, sample_size: int, sampled_from: Incomplete | None = ..., value: ty.Optional[ty.List[ty.Any]] = ..., replicate_size: int = ..., call_order_idx: ty.Optional[int] = ..., deterministic: bool = ..., clamped: bool = ..., parent_nodes: ty.Optional[ty.List[ty.Any]] = ...) -> None: ... - value: Incomplete + def __init__(self, node_name: str, sample_size: int, sampled_from: ty.Optional[DistrForSampling]=None, returned_from: ty.Optional[ConstantFn]=None, value: ty.Optional[ty.List[ty.Any]]=None, replicate_size: int=1, call_order_idx: ty.Optional[int]=None, deterministic: bool=False, clamped: bool=False, parent_nodes: ty.Optional[ty.List[ty.Any]]=None): ... def generate_value(self) -> ty.List[ty.Any]: ... - def generated_value(self) -> ty.List[ty.Any]: ... - def sample(self) -> None: ... + def __str__(self) -> str: ... def __lt__(self, other): ... - def get_gcf(self, axes: plt.Axes, sample_idx: ty.Optional[int]=None, repl_idx: int=0, repl_size: int=1, branch_attr: ty.Optional[str]="state") -> None: ... + def plot_node(self, axes: plt.Axes, sample_idx: int=0, repl_idx: int=0, repl_size: int=1, branch_attr: str="state") -> None: ... def populate_operator_weight(self) -> None: ... - -class DeterministicNodeDAG(NodeDAG): - is_sampled: bool + def get_node_stats_str(self, start: int, end: int, repl_idx: int) -> str: ... - def __init__(self, node_name, value: Incomplete | None = ..., call_order_idx: Incomplete | None = ..., deterministic: bool = ..., parent_nodes: Incomplete | None = ...) -> None: ... +class DeterministicNodeDAG(NodeDAG): + def __init__(self, node_name: str, value: ty.Optional[ty.List[ty.Any]]=None, call_order_idx: ty.Optional[int]=None, deterministic: bool=True, parent_nodes: ty.Optional[ty.List[NodeDAG]]=None) -> None: ... + def __str__(self) -> str: ... def __lt__(self, other) -> bool: ... - def get_gcf(self, axes: plt.Axes, sample_idx: ty.Optional[int]=None, repl_idx: int=0, repl_size: int=1, branch_attr: ty.Optional[str]="state") -> None: ... + def __len__(self) -> int: ... + def plot_node(self, axes: plt.Axes, sample_idx: ty.Optional[int]=0, repl_idx: ty.Optional[int]=0, repl_size: ty.Optional[int]=1, branch_attr: ty.Optional[str]="state") -> None: ... def populate_operator_weight(self) -> None: ... + def get_node_stats_str(self, start: int, end: int, repl_idx: int) -> str: ... +def plot_node_histogram(axes: plt.Axes, values_list: ty.List[float], sample_idx: ty.Optional[int]=None, repl_size: int=1) -> None: ... +def plot_blank(axes: plt.Axes) -> None: ... def extract_vals_as_str_from_node_dag(val_list: ty.List[ty.Union[str, NodeDAG]]) -> ty.List[str]: ... -def get_histogram_gcf(axes: plt.Axes, values_list: ty.List[float], sample_idx: ty.Optional[int] = ..., repl_size: int = ...) -> None: ...