diff --git a/profiling/gprof2dot b/profiling/gprof2dot index f63368588..d48b16e56 100755 --- a/profiling/gprof2dot +++ b/profiling/gprof2dot @@ -1367,7 +1367,7 @@ class AXEParser(Parser): attrs[name] = value return Struct(attrs) - _cg_header_re = re.compile("^Index |" "^-----+ ") + _cg_header_re = re.compile("^Index |^-----+ ") _cg_footer_re = re.compile(r"^Index\s+Function\s*$") diff --git a/pyphi/substrate_modeler/__init__.py b/pyphi/substrate_modeler/__init__.py new file mode 100644 index 000000000..006313342 --- /dev/null +++ b/pyphi/substrate_modeler/__init__.py @@ -0,0 +1,17 @@ +"""Substrate modeler: build PyPhi networks from units.""" + +from .mechanism_combinations import MECHANISM_COMBINATIONS +from .substrate import Substrate +from .substrate import create_substrate +from .unit import CompositeUnit +from .unit import Unit +from .unit_functions import UNIT_FUNCTIONS + +__all__ = [ + "MECHANISM_COMBINATIONS", + "UNIT_FUNCTIONS", + "CompositeUnit", + "Substrate", + "Unit", + "create_substrate", +] diff --git a/pyphi/substrate_modeler/mechanism_combinations.py b/pyphi/substrate_modeler/mechanism_combinations.py new file mode 100644 index 000000000..a9d2a394f --- /dev/null +++ b/pyphi/substrate_modeler/mechanism_combinations.py @@ -0,0 +1,113 @@ +from collections.abc import Callable + +import numpy as np +from numpy.typing import NDArray + +from .utils import reshape_to_md + + +def selective(expanded_tpms: NDArray[np.floating]) -> NDArray[np.floating]: + def get_selective(P: NDArray[np.floating]) -> float: + Q = np.array([np.abs(p - 0.5) for p in P]) + return P[np.argmax(Q)] + + return reshape_to_md( + np.array( + [ + [get_selective(activation_probabilities)] + for activation_probabilities in expanded_tpms + ] + ) + ) + + +def average(expanded_tpms: NDArray[np.floating]) -> NDArray[np.floating]: + return reshape_to_md( + np.array( + [ + [np.mean(activation_probabilities)] + for activation_probabilities in expanded_tpms + ] + ) + ) + + +def maximal(expanded_tpms: NDArray[np.floating]) -> NDArray[np.floating]: + return reshape_to_md( + np.array( + [ + [np.max(activation_probabilities)] + for activation_probabilities in expanded_tpms + ] + ) + ) + + +def first_necessary(expanded_tpms: NDArray[np.floating]) -> NDArray[np.floating]: + def first_necessary(ap: NDArray[np.floating]) -> float: + # non-primary units boost activation probability as a function of the primary unit's activation probability + primary = ap[0] + + if primary > 0.5: + non_primary = np.prod([1 - p for p in ap[1:]]) + max_boost = 1 - primary + boost = max_boost / (1 + np.e ** (-5 * (1 - non_primary - 0.5))) + return primary + boost + return primary + + return reshape_to_md( + np.array( + [ + [first_necessary(activation_probabilities)] + for activation_probabilities in expanded_tpms + ] + ) + ) + + +def integrator(expanded_tpms: NDArray[np.floating]) -> NDArray[np.floating]: + def get_cumulated_probability(activation_probabilities): + cumsum = np.sum(activation_probabilities) + if cumsum > 1.0: + return 1.0 + if cumsum < 0.0: + return 0.0 + return cumsum + + return reshape_to_md( + np.array( + [ + [get_cumulated_probability(activation_probabilities)] + for activation_probabilities in expanded_tpms + ] + ) + ) + + +def serial(expanded_tpms: NDArray[np.floating]) -> NDArray[np.floating]: + def serial_func(P): + remainder = 1 + for p in P: + remainder -= p * remainder + return 1 - remainder + + return reshape_to_md( + np.array( + [ + [serial_func(activation_probabilities)] + for activation_probabilities in expanded_tpms + ] + ) + ) + + +MECHANISM_COMBINATIONS: dict[ + str, Callable[[NDArray[np.floating]], NDArray[np.floating]] +] = { + "selective": selective, + "average": average, + "maximal": maximal, + "first_necessary": first_necessary, + "integrator": integrator, + "serial": serial, +} diff --git a/pyphi/substrate_modeler/substrate.py b/pyphi/substrate_modeler/substrate.py new file mode 100644 index 000000000..cff030a54 --- /dev/null +++ b/pyphi/substrate_modeler/substrate.py @@ -0,0 +1,294 @@ +"""Substrate class for building PyPhi networks from units. + +A Substrate is a stateless collection of Units. State is passed explicitly +when computing TPMs, Networks, or Subsystems. +""" + +from functools import cached_property + +import numpy as np +from tqdm.auto import tqdm + +from .. import utils +from ..labels import NodeLabels +from ..network import Network +from ..subsystem import Subsystem +from ..tpm import ExplicitTPM +from .unit import CompositeUnit +from .unit import Unit +from .utils import reshape_to_md + +PROGRESS_BAR_THRESHOLD = 2**20 + + +class Substrate: + """A stateless collection of Units defining a substrate's dynamics. + + Units define each node's mechanism, inputs, and parameters. State is + passed explicitly to methods that need it (compute_tpm, network, subsystem). + + Args: + units: The units that make up this substrate. + implicit: Whether to use implicit (factored) TPM representation. + """ + + def __init__( + self, + units: tuple[Unit, ...], + implicit: bool = False, + ): + self._units = units + self._implicit = implicit + + @property + def units(self) -> tuple[Unit, ...]: + """The units that make up this substrate.""" + return self._units + + @cached_property + def node_indices(self) -> tuple[int, ...]: + """The indices of the units in the substrate.""" + return tuple(unit.index for unit in self._units) + + @cached_property + def node_labels(self) -> NodeLabels: + """The labels of the units in the substrate.""" + return NodeLabels([unit.label for unit in self._units], self.node_indices) + + def compute_tpm( + self, + state: tuple[int, ...], + input_state: tuple[int, ...] | None = None, + ) -> ExplicitTPM: + """Compute the substrate TPM for the given state and input state. + + The mechanisms are configured according to ``state`` and + ``input_state``, then transition probabilities are computed for every + possible ``from_state``. + + Args: + state: The current state of the substrate. Determines each unit's + own binary state for state-dependent mechanisms. + input_state: The input state of the substrate. Determines each + unit's input states for input-dependent mechanisms. Defaults + to ``state``. + + Returns: + The substrate's TPM as an ExplicitTPM. + """ + if input_state is None: + input_state = state + all_from_states = list(utils.all_states(len(self._units))) + show_progress = len(all_from_states) > PROGRESS_BAR_THRESHOLD + rows = [ + self._combine_unit_tpms(from_state, state, input_state) + for from_state in ( + tqdm(all_from_states) if show_progress else all_from_states + ) + ] + return ExplicitTPM(reshape_to_md(np.array(rows))) + + def _combine_unit_tpms( + self, + from_state: tuple[int, ...], + state: tuple[int, ...], + input_state: tuple[int, ...], + ) -> list[float]: + """Combine unit TPMs for a single transition row. + + Args: + from_state: The state being transitioned from (indexes into unit + TPMs to select the row). + state: The current state of the substrate (determines each unit's + own binary state for state-dependent mechanisms). + input_state: The input state of the substrate (determines each + unit's input states for input-dependent mechanisms). + + Returns: + Activation probabilities for each unit. + """ + probs = [] + for unit in self._units: + unit_state = state[unit.index] + unit_input_state = tuple(input_state[i] for i in unit.inputs) + unit_tpm = unit.compute_tpm(unit_state, unit_input_state) + pp = unit_tpm[tuple(from_state[i] for i in unit.inputs)] + if not isinstance(pp, (int, float, np.number)): + probs.append(float(pp[0])) + else: + probs.append(float(pp)) + return probs + + @cached_property + def dynamic_tpm(self) -> ExplicitTPM: + """The dynamic TPM of the substrate. + + For each row, the substrate is configured as if it is in that row's + state: ``from_state = state = input_state = s``. + """ + all_states = list(utils.all_states(len(self._units))) + show_progress = len(all_states) > PROGRESS_BAR_THRESHOLD + rows = [ + self._combine_unit_tpms(s, s, s) + for s in (tqdm(all_states) if show_progress else all_states) + ] + return ExplicitTPM(reshape_to_md(np.array(rows))) + + @cached_property + def cm(self) -> np.ndarray: + connectivity = np.zeros((len(self.node_indices), len(self.node_indices))) + + for unit in self._units: + connectivity[unit.inputs, unit.index] = 1 + + return connectivity + + def __repr__(self) -> str: + return "Substrate({})".format("|".join(self.node_labels)) + + def __str__(self) -> str: + return self.__repr__() + + def __len__(self) -> int: + return len(self._units) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Substrate): + return NotImplemented + return self._units == other._units + + def network( + self, + state: tuple[int, ...], + input_state: tuple[int, ...] | None = None, + ) -> Network: + """Get a Network for the given state. + + Args: + state: The current state of the substrate. + input_state: The input state of the substrate. Defaults to + ``state``. + + Returns: + A Network with the substrate's TPM conditioned on the given state. + """ + return Network(self.compute_tpm(state, input_state), self.cm, self.node_labels) + + def subsystem( + self, + state: tuple[int, ...], + input_state: tuple[int, ...] | None = None, + nodes: tuple[int, ...] | None = None, + ) -> Subsystem: + """Get a Subsystem for the given state. + + Args: + state: The current state of the substrate. + input_state: The input state of the substrate. Defaults to + ``state``. + nodes: The node indices to include. Defaults to all nodes. + + Returns: + A Subsystem conditioned on the given state. + """ + if nodes is None: + nodes = self.node_indices + return Subsystem(self.network(state, input_state), state, nodes) + + def to_json(self) -> dict: + """Return a JSON-serializable representation.""" + return { + "units": [u.to_json() for u in self._units], + } + + +def create_substrate( + node_params: dict, + labels: list[str] | None = None, + implicit: bool = False, +) -> Substrate: + """Create a Substrate from a dictionary of per-node parameters. + + This is a convenience factory that handles inferring inputs from + connectivity matrices and extracting input weights from weight matrices. + + Args: + node_params: Per-node params, indexed by node index or label. + Each entry should specify at minimum: + - ``"mechanism"``: str + - ``"inputs"``: tuple of input indices, or a connectivity + matrix (np.ndarray) from which inputs are inferred + - ``"params"``: dict of mechanism parameters. If + ``"input_weights"`` is an np.ndarray (weight matrix), + weights are extracted automatically. + - ``"composite"``: list of sub-unit dicts (optional). + Each sub-unit dict has the same structure as above. + - ``"mechanism_combination"``: str (optional, for composite + units; defaults to ``"selective"``). + labels: Optional list of labels for nodes. Defaults to string indices. + implicit: Whether to use implicit (factored) TPM representation. + + Returns: + A Substrate built from the given parameters. + """ + n = len(node_params) + labels = labels or [str(i) for i in range(n)] + + units = [] + for i in range(n): + label = labels[i] + p = node_params.get(i) or node_params.get(label) + if p is None: + raise ValueError(f"No parameters for node {i}/{label}") + + if "composite" in p: + sub_units = [] + for sub in p["composite"]: + sub_inputs = _resolve_inputs(sub.get("inputs"), i) + params = _resolve_params(dict(sub["params"]), sub_inputs, i) + sub_units.append( + Unit( + index=i, + label=label, + inputs=tuple(sub_inputs), + mechanism=sub["mechanism"], + params=params, + ) + ) + u = CompositeUnit( + index=i, + label=label, + units=tuple(sub_units), + mechanism_combination=p.get("mechanism_combination", "selective"), + ) + else: + inputs = _resolve_inputs(p.get("inputs"), i) + params = _resolve_params(dict(p["params"]), inputs, i) + u = Unit( + index=i, + label=label, + inputs=tuple(inputs), + mechanism=p["mechanism"], + params=params, + ) + + units.append(u) + + return Substrate(tuple(units), implicit=implicit) + + +def _resolve_inputs( + inputs_or_cm: tuple | list | np.ndarray, node_index: int +) -> list[int]: + """Extract input indices from either explicit inputs or a connectivity matrix.""" + if isinstance(inputs_or_cm, np.ndarray): + return list(np.nonzero(inputs_or_cm[:, node_index])[0]) + return list(inputs_or_cm) + + +def _resolve_params(params: dict, inputs: list[int], node_index: int) -> dict: + """Extract per-node input weights from a weight matrix if needed.""" + if "input_weights" in params and isinstance(params["input_weights"], np.ndarray): + W = params["input_weights"] + params["input_weights"] = tuple(W[j, node_index] for j in inputs) + return params diff --git a/pyphi/substrate_modeler/unit.py b/pyphi/substrate_modeler/unit.py new file mode 100644 index 000000000..970645415 --- /dev/null +++ b/pyphi/substrate_modeler/unit.py @@ -0,0 +1,364 @@ +"""Unit classes for substrate modeling. + +This module provides the Unit and CompositeUnit classes for defining nodes +in substrate models. Units define a node's dynamics (mechanism, inputs, params) +without holding state - state is passed explicitly when computing TPMs. +""" + +from collections.abc import Callable + +import numpy as np +from numpy.typing import NDArray + +from .. import utils +from ..tpm import ExplicitTPM +from .mechanism_combinations import MECHANISM_COMBINATIONS +from .unit_functions import UNIT_FUNCTIONS + + +class TPMCache(dict): + """Cache for computed unit TPMs, keyed by (unit_state, input_state). + + This is a pure cache with no side effects - it simply memoizes calls to + unit.compute_tpm(). + """ + + def __init__(self, unit: "Unit"): + super().__init__() + self._unit = unit + + def __getitem__(self, substrate_state: tuple[int, ...]) -> ExplicitTPM: + """Get the TPM for the given substrate state, computing if needed. + + Args: + substrate_state: The full state of all units in the substrate. + + Returns: + The unit's TPM for its state and input state extracted from + the substrate state. + """ + unit_state = substrate_state[self._unit.index] + input_state = tuple(substrate_state[i] for i in self._unit.inputs) + key = (unit_state, input_state) + + if key not in self: + self[key] = self._unit.compute_tpm(unit_state, input_state) + return super().__getitem__(key) + + +class Unit: + """A unit in a substrate model. + + Represents a node's dynamics: its mechanism (I/O function), inputs, and + parameters. Units are stateless - state is passed explicitly when computing + TPMs. + + Args: + index: The unit's index in the substrate. + inputs: Indices of units that provide input to this unit. + mechanism: Either a string naming a built-in mechanism (e.g., 'and', + 'sigmoid'), a callable mechanism function, or a numpy array + representing a raw TPM. + params: Parameters passed to the mechanism function. + label: Display label for the unit. Defaults to str(index). + original_index: The unit's original index before any re-indexing. + Defaults to index. + """ + + def __init__( + self, + index: int, + inputs: tuple[int, ...], + mechanism: Callable | str | NDArray[np.floating], + params: dict | None = None, + label: str | None = None, + original_index: int | None = None, + ): + self._index = index + self._inputs = tuple(inputs) + self._label = label if label is not None else str(index) + self._original_index = original_index if original_index is not None else index + self._params = params if params is not None else {} + + # Resolve mechanism + if isinstance(mechanism, str): + self._mechanism_type = mechanism + self._mechanism = UNIT_FUNCTIONS[mechanism] + elif isinstance(mechanism, np.ndarray): + self._mechanism_type = "raw_tpm" + self._mechanism = mechanism + else: + self._mechanism_type = "custom" + self._mechanism = mechanism + + # Initialize TPM cache + self._tpm_cache = TPMCache(self) + + @property + def index(self) -> int: + """The unit's index in the substrate.""" + return self._index + + @index.setter + def index(self, value: int) -> None: + self._index = value + # Invalidate cache since index changed + self._tpm_cache = TPMCache(self) + + @property + def inputs(self) -> tuple[int, ...]: + """Indices of units that provide input to this unit.""" + return self._inputs + + @property + def label(self) -> str: + """Display label for the unit.""" + return self._label + + @property + def original_index(self) -> int: + """The unit's original index before any re-indexing.""" + return self._original_index + + @property + def mechanism(self) -> Callable | NDArray[np.floating]: + """The mechanism function or raw TPM array.""" + return self._mechanism + + @property + def mechanism_type(self) -> str: + """String name of the mechanism type.""" + return self._mechanism_type + + @property + def params(self) -> dict: + """Parameters passed to the mechanism function.""" + return self._params + + def compute_tpm(self, state: int, input_state: tuple[int, ...]) -> ExplicitTPM: + """Compute this unit's TPM for the given state. + + Args: + state: This unit's current binary state (0 or 1). + input_state: Current states of this unit's input units. + + Returns: + The unit's transition probability matrix. + """ + if isinstance(self._mechanism, np.ndarray): + return ExplicitTPM(self._mechanism) + return ExplicitTPM(self._mechanism(self, state, input_state, **self._params)) + + def state_dependent_tpm(self, substrate_state: tuple[int, ...]) -> ExplicitTPM: + """Get the TPM for the given substrate state (cached). + + Args: + substrate_state: The full state of all units in the substrate. + + Returns: + The unit's TPM for its state and input state extracted from + the substrate state. + """ + return self._tpm_cache[substrate_state] + + def __repr__(self) -> str: + return f"Unit(type={self._mechanism_type}, label={self._label}, inputs={self._inputs})" + + def __str__(self) -> str: + return self.__repr__() + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Unit): + return NotImplemented + return ( + self._mechanism_type == other._mechanism_type + and self._inputs == other._inputs + and self._params == other._params + # For raw TPM mechanisms, compare the arrays + and ( + np.array_equal(self._mechanism, other._mechanism) + if isinstance(self._mechanism, np.ndarray) + else self._mechanism == other._mechanism + ) + ) + + def __hash__(self) -> int: + # Hash based on immutable structural properties + return hash((self._mechanism_type, self._inputs, tuple(self._params.items()))) + + def __copy__(self) -> "Unit": + if isinstance(self._mechanism, np.ndarray): + mechanism = np.array(self._mechanism) + elif self._mechanism_type != "custom": + mechanism = self._mechanism_type + else: + mechanism = self._mechanism + return Unit( + index=self._index, + inputs=self._inputs, + mechanism=mechanism, + params=self._params.copy() if self._params else None, + label=self._label, + original_index=self._original_index, + ) + + def to_json(self) -> dict: + """Return a JSON-serializable representation.""" + return { + "index": self._index, + "inputs": self._inputs, + "mechanism_type": self._mechanism_type, + "params": self._params, + "label": self._label, + "original_index": self._original_index, + } + + +class CompositeUnit(Unit): + """A unit composed of multiple sub-units. + + The composite unit's TPM is computed by combining the TPMs of its + sub-units according to a specified combination strategy. + + Args: + index: The composite unit's index in the substrate. + units: The sub-units that make up this composite unit. + label: Display label for the unit. + mechanism_combination: How to combine sub-unit TPMs. Either a string + naming a built-in combination (e.g., 'selective', 'average'), + or a callable. + original_index: The unit's original index before any re-indexing. + """ + + def __init__( + self, + index: int, + units: tuple[Unit, ...], + label: str | None = None, + mechanism_combination: str | Callable | None = None, + original_index: int | None = None, + ): + self._sub_units = units + + # Resolve combination function + if mechanism_combination is None: + self._mechanism_combination = MECHANISM_COMBINATIONS["selective"] + elif isinstance(mechanism_combination, str): + self._mechanism_combination = MECHANISM_COMBINATIONS[mechanism_combination] + else: + self._mechanism_combination = mechanism_combination + + # Inputs are the union of all sub-unit inputs + all_inputs = sorted(set(i for unit in units for i in unit.inputs)) + + super().__init__( + index=index, + inputs=tuple(all_inputs), + mechanism="composite", # Marker; compute_tpm is overridden + label=label, + original_index=original_index, + ) + + @property + def sub_units(self) -> tuple[Unit, ...]: + """The sub-units that make up this composite unit.""" + return self._sub_units + + def compute_tpm(self, state: int, input_state: tuple[int, ...]) -> ExplicitTPM: + """Compute the composite TPM by combining sub-unit TPMs. + + Args: + state: This unit's current binary state (0 or 1). + input_state: Current states of this unit's input units. + + Returns: + The composite unit's TPM. + """ + tpms = [] + for sub_unit in self._sub_units: + # Map composite input_state to sub-unit input_state + sub_input_state = tuple( + input_state[self._inputs.index(i)] for i in sub_unit.inputs + ) + tpms.append(sub_unit.compute_tpm(state, sub_input_state)) + + expanded = self._expand_tpms(tpms) + return self._apply_tpm_combination(expanded) + + def _expand_tpms(self, tpms: list[ExplicitTPM]) -> NDArray[np.floating]: + """Expand sub-unit TPMs to the full input space. + + Each sub-unit may have different inputs. This method expands each + sub-unit's TPM so they all have the same shape, indexed by the + composite unit's full input set. + + Args: + tpms: List of TPMs from sub-units. + + Returns: + Array of shape (n_states, n_sub_units) where n_states is 2^n_inputs. + """ + + def get_subset_state( + full_state: tuple[int, ...], subset_indices: tuple[int, ...] + ) -> tuple[int, ...]: + """Extract the state of a subset of indices from a full state.""" + return tuple(full_state[ix] for ix in subset_indices) + + expanded_tpms = [] + for tpm, sub_unit in zip(tpms, self._sub_units): + # Get indices of sub-unit inputs within composite unit inputs + sub_unit_input_indices = tuple( + self._inputs.index(i) for i in sub_unit.inputs + ) + + # Get activation probability for each possible input state + mechanism_activation = [] + for full_state in utils.all_states(len(self._inputs)): + sub_state = get_subset_state(full_state, sub_unit_input_indices) + prob = tpm[sub_state] + if not isinstance(prob, (int, float, np.number)): + prob = float(prob[0]) + mechanism_activation.append(float(prob)) + + expanded_tpms.append(mechanism_activation) + + return np.array(expanded_tpms).T + + def _apply_tpm_combination(self, expanded_tpms: NDArray[np.floating]) -> ExplicitTPM: + """Apply the combination function to expanded TPMs. + + Args: + expanded_tpms: Array of shape (n_states, n_sub_units). + + Returns: + Combined TPM. + """ + return ExplicitTPM(self._mechanism_combination(expanded_tpms)) + + def __repr__(self) -> str: + return ( + f"CompositeUnit(label={self._label}, " + f"sub_units={[u.label for u in self._sub_units]}, " + f"inputs={self._inputs})" + ) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, CompositeUnit): + return NotImplemented + return ( + self._sub_units == other._sub_units + and self._mechanism_combination == other._mechanism_combination + ) + + def __hash__(self) -> int: + return hash((self._sub_units, self._mechanism_combination)) + + def to_json(self) -> dict: + """Return a JSON-serializable representation.""" + return { + "index": self._index, + "sub_units": [u.to_json() for u in self._sub_units], + "label": self._label, + "original_index": self._original_index, + } diff --git a/pyphi/substrate_modeler/unit_functions.py b/pyphi/substrate_modeler/unit_functions.py new file mode 100644 index 000000000..c2cdd7d64 --- /dev/null +++ b/pyphi/substrate_modeler/unit_functions.py @@ -0,0 +1,700 @@ +from itertools import product +from typing import TYPE_CHECKING +from typing import Any + +import numpy as np + +from .. import utils +from .utils import map_to_floor_and_ceil + +if TYPE_CHECKING: + from .unit import Unit + +# UNIT FUNCTIONS + + +def composite( + unit: "Unit", + state: int, + input_state: tuple[int, ...], +) -> np.ndarray: + """Placeholder mechanism for CompositeUnit. + + This should never be called directly; CompositeUnit overrides compute_tpm. + """ + raise NotImplementedError( + "composite mechanism should not be called directly; " + "CompositeUnit overrides compute_tpm" + ) + + +def sigmoid( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + input_weights: tuple[float, ...] = (), + determinism: float = 5.0, + threshold: float = 0.0, + ising: bool = True, +) -> np.ndarray: + # validate all kwargs + validate_kwargs(locals()) + + n_nodes = len(input_weights) + + def LogFunc(s, input_weights, determinism, threshold): + if ising: + s = tuple(x * 2 - 1 for x in s) + total_input = sum(s * np.array([input_weights[n] for n in range(n_nodes)])) + y = 1 / (1 + np.e ** (-determinism * (total_input - threshold))) + return map_to_floor_and_ceil(y, floor, ceiling) + + tpm = np.array( + [ + [ + LogFunc( + s, + input_weights, + determinism, + threshold, + ) + ] + for s in utils.all_states(n_nodes) + ] + ) + + return tpm.reshape([2] * n_nodes + [1], order="F").astype(float) + + +def sor_gate( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + pattern_selection: tuple[tuple[int, ...], ...] = (), + selectivity: float = 2.0, +) -> np.ndarray: + # Ensure states are tuples + patterns = [tuple(p) for p in pattern_selection] + + # Ensure ceiling is not more than 1 + if ceiling > 1: + ceiling = 1.0 + + # Ensure selectivity is larger than 1 + if not selectivity > 1: + raise ValueError( + "Selectivity for SOR gates must be bigger than 1, " + f"got {selectivity} for unit {unit.label}." + ) + + # the tpm is uniform for all states except the input state (i.e. short term plasticity) + tpm = np.ones([2] * len(input_state)) * floor + + # if the input state matches a pattern in the pattern selection, activation probability + # given that state is ceiling; otherwise, it is increased by a fraction of the difference + # between floor and ceiling (given by selectivity) + pattern = floor + (ceiling - floor) / selectivity + for s in patterns: + tpm[s] = pattern + if input_state in patterns: + tpm[input_state] = ceiling + else: + tpm[input_state] = 0.0 + + return tpm + + +def resonnator( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + input_weights: tuple[float, ...] = (), + determinism: float = 5.0, + threshold: float = 0.0, + weight_scale_mapping: dict, +) -> np.ndarray: + # Ensure ceiling is not more than 1 + if ceiling > 1: + ceiling = 1.0 + + n_nodes = len(input_weights) + + # alter weight to make it push unit towards its state, weighted using weight_scale_mapping + w = [ + ( + input_weights[i] * weight_scale_mapping[(state, inp_s)] + if inp_s == state + else -input_weights[i] * weight_scale_mapping[(state, inp_s)] + ) + for i, inp_s in enumerate(input_state) + ] + + def resonnatorFunc(s, input_weights, determinism, threshold, unit_state): + # make state interpreted as ising + s = [2 * x - 1 for x in s] + + total_input = np.sum(s * np.array(w)) + y = 1 / (1 + np.e ** (-determinism * (total_input - threshold))) + return y + + # producing transition probability matrix + tpm = np.array( + [ + [resonnatorFunc(s, input_weights, determinism, threshold, state)] + for s in utils.all_states(n_nodes) + ] + ) + + # make it between floor and ceiling + tpm = map_to_floor_and_ceil(tpm, floor, ceiling) + + return tpm.reshape([2] * n_nodes + [1], order="F").astype(float) + + +def mismatch_pattern_detector( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + pattern_selection: tuple[tuple[int, ...], ...] = (), + selectivity: float = 1.0, +) -> np.ndarray: + # This mechanism is selective to certain inputs (i.e. they turn it ON with P=ceiling, + # while the remaining possible input patterns turn it OFF with P=floor). However, its + # selectivity (probability of turning on) depends on the state of the unit: if the unit + # is already in the state that matches the pattern, then the effect of the inputs is + # reduced by the selectivity factor. + + # Ensure states are tuples + patterns = [tuple(p) for p in pattern_selection] + + # Ensure ceiling is not more than 1 + if ceiling > 1: + ceiling = 1.0 + + if floor is None: + floor = 1.0 - ceiling + + # Ensure selectivity is larger than 1 + if not selectivity > 1: + raise ValueError( + "Selectivity for mismatch_pattern_detector must be bigger than 1, " + f"got {selectivity} for unit {unit.label}." + ) + + # Check if the unit is ON + if state == 1: + # since it is ON, it will only respond strongly if a non-pattern is on its inputs + P_pattern = 0.5 + (ceiling - 0.5) / selectivity + P_no_pattern = floor + else: + # since it is OFF, it will only respond strongly if a pattern is on its inputs + P_pattern = ceiling + P_no_pattern = 0.5 - (0.5 - floor) / selectivity + + N = len(input_state) + tpm = np.ones([2] * N) + + for s in utils.all_states(N): + if s in patterns: + tpm[s] = P_pattern + else: + tpm[s] = P_no_pattern + + return tpm + + +def copy_gate( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + tpm = np.ones([2]) * floor + tpm[1] = ceiling + return tpm + + +def and_gate( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + tpm = np.ones((2, 2)) * floor + tpm[(1, 1)] = ceiling + return tpm + + +def or_gate( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + tpm = np.ones((2, 2)) * ceiling + tpm[(0, 0)] = floor + return tpm + + +def xor_gate( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + tpm = np.ones((2, 2)) * floor + tpm[(0, 1)] = ceiling + tpm[(1, 0)] = ceiling + return tpm + + +def weighted_mean( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + weights: list[float] | None = None, +) -> np.ndarray: + if weights is None: + weights = [] + weights = [w / np.sum(weights) for w in weights] + N = len(weights) + + tpm = np.ones((2,) * N) + for s in utils.all_states(N): + wm = sum((1 + w * (x * 2 - 1)) / 2 for w, x in zip(weights, s)) / N + tpm[s] = wm * (ceiling - floor) + floor + + return tpm + + +def democracy( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + N = len(unit.inputs) + + tpm = np.ones((2,) * N) + for s in utils.all_states(N): + avg_vote = np.mean(s) + tpm[s] = avg_vote * (ceiling - floor) + floor + + return tpm + + +def majority( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + N = len(unit.inputs) + + tpm = np.ones((2,) * N) + for s in utils.all_states(N): + avg_vote = round(np.mean(s)) + tpm[s] = avg_vote * (ceiling - floor) + floor + + return tpm + + +def mismatch_corrector( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + bias: float = 0.0, +) -> np.ndarray: + if len(unit.inputs) > 1: + raise ValueError( + f"mismatch_corrector requires exactly one input, " + f"but unit {unit.label} has {len(unit.inputs)}." + ) + + if bias > 1: + raise ValueError(f"bias must be <= 1 for unit {unit.label}, got {bias}.") + + # check whether state of unit matches its input, and create tpm accordingly + if state == input_state[0]: + tpm = np.ones([2]) * 0.5 - (state * 2 - 1) * bias * 0.5 + else: + tpm = np.array([floor, ceiling]) + + return tpm + + +def modulated_sigmoid( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + input_weights: list[float], + modulation: dict, + floor: float = 0.0, + ceiling: float = 1.0, + determinism: float = 2.0, + threshold: float = 0.0, +) -> np.ndarray: + # modulation must be a dict like {'modulator': tuple(index), 'threshold': float, 'determinism': float} + # modulation will update the sigmoid function depending on whether the unit is ON or OFF + + def LogFunc( + inp_state, modulation_state, unit_state, weights, determinism, threshold + ): + total_input = sum(inp_state * np.array([weight for weight in weights])) + # count how many of the modulators are ON + mods_on = sum(modulation_state) + + # modulate threshold based on unit state and the state of the modulators + new_threshold = threshold + unit_state * mods_on * modulation["threshold"] + + # modulate determinism based on unit state and the state of the modulators + new_determinism = determinism + unit_state * mods_on * modulation["determinism"] + + y = ceiling * ( + floor + + (1 - floor) + / (1 + np.e ** (-new_determinism * (total_input - new_threshold))) + ) + return y + + # first inputs will be interpreted as true inputs, while the last will be modulator inputs + n_mods = len(modulation["modulator"]) + n_inputs = len(input_weights) + n_nodes = n_inputs + n_mods + + unit_state = ( + state * 2 - 1 + ) # making unit state "ising" rather than binary, to make modulation symmetric + + # producing transition probability matrix + tpm = np.array( + [ + [ + LogFunc( + inp_state, + modulation_state, + unit_state, + input_weights, + determinism, + threshold, + ) + ] + for modulation_state, inp_state in product( + utils.all_states(n_mods), utils.all_states(n_inputs) + ) + ] + ) + + return tpm.reshape([2] * n_nodes + [1], order="F").astype(float) + + +def stabilized_sigmoid( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + input_weights: list[float], + determinism: float, + threshold: float, + modulation: dict, + floor: float = 0.0, + ceiling: float = 1.0, +) -> np.ndarray: + # modulation must be a dict like {'modulator': tuple(index), 'threshold': float, 'determinism': float, 'selectivity': float} + # modulation will update the sigmoid function depending on whether the unit is ON or OFF + + def LogFunc( + inp_state, modulation_state, unit_state, weights, determinism, threshold + ): + total_input = sum(inp_state * np.array([weight for weight in weights])) + + # The modulation should work in such a way as to always "stabilize" the current + # state of the unit, but it should be stronger, the more modulation is "active" + + # count how many of the modulators are ON + mods_on = sum(modulation_state) + if mods_on == 0: + mods_on = 1 / modulation["selectivity"] + + ising_state = ( + unit_state * 2 - 1 + ) # making unit state "ising" rather than binary, to make modulation symmetric + + # modulate threshold based on unit state and the state of the modulators + new_threshold = threshold - ising_state * mods_on * modulation["threshold"] + + # modulate determinism based on unit state and the state of the modulators + new_determinism = ( + determinism + if (mods_on == 0 or modulation["determinism"] == 0) + else ( + determinism * float(mods_on * modulation["determinism"]) ** ising_state + ) + ) + + y = ceiling * ( + floor + + (1 - floor) + / (1 + np.e ** (-new_determinism * (total_input - new_threshold))) + ) + return y + + # first inputs will be interpreted as true inputs, while the last will be modulator inputs + n_mods = len(modulation["modulator"]) + n_inputs = len(input_weights) + n_nodes = n_inputs + n_mods + + # producing transition probability matrix + tpm = np.array( + [ + [ + LogFunc( + inp_state, + modulation_state, + state, + input_weights, + determinism, + threshold, + ) + ] + for inp_state, modulation_state in product( + utils.all_states(n_inputs), utils.all_states(n_mods) + ) + ] + ) + + return tpm.reshape([2] * n_nodes + [1], order="F").astype(float) + + +def biased_sigmoid( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + input_weights: tuple[float, ...] = (), + determinism: float = 2.0, + threshold: float = 0.0, +) -> np.ndarray: + # A sigmoid unit that is biased in its activation by the last unit in the inputs. + # The bias consists in a rescaling of the activation probability to make it more + # in line with the biasing unit. The biasing unit is assumed to be the last one + # of the inputs. + + def LogFunc(total_input, determinism, threshold): + y = ceiling * ( + floor + + (1 - floor) / (1 + np.e ** (-determinism * (total_input - threshold))) + ) + return y + + n_nodes = len(input_weights) + + # producing transition probability matrix + tpm = np.array( + [ + [ + ( + LogFunc( + sum( + s[:-1] * np.array([weight for weight in input_weights[:-1]]) + ), + determinism, + threshold, + ) + / input_weights[-1] + if s[-1] == 0 + else 1 + - ( + 1 + - LogFunc( + sum( + s[:-1] + * np.array([weight for weight in input_weights[:-1]]) + ), + determinism, + threshold, + ) + ) + / input_weights[-1] + ) + ] + for s in utils.all_states(n_nodes) + ] + ) + + return tpm.reshape([2] * n_nodes + [1], order="F").astype(float) + + +def gabor_gate( + unit: "Unit", + state: int, + input_state: tuple[int, ...], + *, + floor: float = 0.0, + ceiling: float = 1.0, + preferred_states: tuple[tuple[int, ...], ...] = (), +) -> np.ndarray: + # Ensure states are tuples + prefs = [tuple(p) for p in preferred_states] + anti_states = [tuple(int(1 - x) for x in p) for p in prefs] + + # Ensure ceiling is not more than 1 + if ceiling > 1: + ceiling = 1.0 + + # if the unit is ON, its tpm should indicate that the past state was likely one of + # its preferred_states; if OFF, one of its anti_states + + tpm = np.ones([2] * len(input_state)) * 0.5 + for s in utils.all_states(len(input_state)): + if s in prefs: + tpm[s] = ceiling + elif s in anti_states: + tpm[s] = floor + + return tpm + + +### UNITS +UNIT_FUNCTIONS = { + "composite": composite, + "gabor": gabor_gate, + "sigmoid": sigmoid, + "resonnator": resonnator, + "sor": sor_gate, + "mismatch_pattern_detector": mismatch_pattern_detector, + "copy": copy_gate, + "and": and_gate, + "or": or_gate, + "xor": xor_gate, + "weighted_mean": weighted_mean, + "democracy": democracy, + "majority": majority, + "mismatch_corrector": mismatch_corrector, + "modulated_sigmoid": modulated_sigmoid, + "stabilized_sigmoid": stabilized_sigmoid, + "biased_sigmoid": biased_sigmoid, +} + + +# VALIDATION FUNCTIONS + + +# SEE VALIDATION FUNCTIONS BELOW +def validate_kwargs(kwargs): + """Validates keyword arguments using respective validation functions.""" + for arg_name, arg_value in kwargs.items(): + # Get validation function for this argument, if provided + validation_func = VALIDATION_FUNCTIONS.get(arg_name) + + if validation_func: + # Validate argument using its validation function + if not validation_func(kwargs["unit"], arg_value): + raise ValueError(f"Invalid value for argument {arg_name}: {arg_value}") + + +def validate_unit(unit: "Unit", value: Any) -> bool: + """Validates that value is an object of the class "Unit", and has the properties inputs, state and input_state.""" + if not hasattr(value, "inputs"): + raise ValueError("value must have an 'inputs' attribute") + return False + return True + + +def validate_floor(unit: "Unit", value: Any) -> bool: + """Validates that value is a float between 0 and 1, inclusive.""" + valid = True + if not isinstance(value, float): + raise ValueError(f"floor must be a float, but got {type(value)}") + valid = False + if not 0 <= value <= 1: + if value < 0: + value = 0.0 + print("Warning: floor value was less than 0, it has been set to 0") + valid = False + else: + value = 1.0 + print("Warning: floor value was greater than 1, it has been set to 1") + valid = False + return valid + + +def validate_ceiling(unit: "Unit", value: Any) -> bool: + """Validates that value is a float between 0 and 1, inclusive.""" + valid = True + if not isinstance(value, float): + raise ValueError(f"ceiling must be a float, but got {type(value)}") + valid = False + if not 0 <= value <= 1: + if value < 0: + value = 0.0 + print("Warning: ceiling value was less than 0, it has been set to 0") + valid = False + else: + value = 1.0 + print("Warning: ceiling value was greater than 1, it has been set to 1") + valid = False + return valid + + +def validate_input_weights(unit: "Unit", value: Any) -> bool: + """Validates that value is a tuple of floats between 0 and 1, inclusive, and its length is the same as unit.inputs.""" + valid = True + if not isinstance(value, tuple): + raise ValueError(f"input_weights must be a tuple, but got {type(value)}") + valid = False + if len(value) != len(unit.inputs): + value = (1.0,) * len(unit.inputs) + print( + f"Warning: input_weights must be a tuple of length {len(unit.inputs)}, it has been set to {(1,) * len(unit.inputs)}" + ) + valid = False + for v in value: + if not isinstance(v, float): + raise ValueError( + f"all values in the input_weights tuple must be floats, but got {type(v)}" + ) + valid = False + return valid + + +VALIDATION_FUNCTIONS = { + "unit": validate_unit, + "floor": validate_floor, + "ceiling": validate_ceiling, + "input_weights": validate_input_weights, + # Add more validation functions for other arguments as needed +} diff --git a/pyphi/substrate_modeler/utils.py b/pyphi/substrate_modeler/utils.py new file mode 100644 index 000000000..24712ff07 --- /dev/null +++ b/pyphi/substrate_modeler/utils.py @@ -0,0 +1,15 @@ +import numpy as np + +from .. import convert + + +def map_to_floor_and_ceil(y, floor, ceiling): + return floor + (ceiling - floor) * y + + +def reshape_to_md(tpm): + N = int(np.log2(len(tpm))) + try: + return tpm.reshape([2] * N + [1], order="F").astype(float) + except: + return convert.to_md(tpm) diff --git a/pyproject.toml b/pyproject.toml index 68c07ae16..e9fd6129b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,6 +53,14 @@ visualize = ["matplotlib", "networkx>=2.6.2", "plotly>=5.8.2", "seaborn"] graphs = ["igraph>=0.9.10", "networkx>=2.6.2"] caching = ["redis>=2.10.5"] emd = ["pyemd>=0.3.0"] +substrate-modeler = [ + "matplotlib>=3.10.8", + "networkx>=2.6.2", +] +notebooks = [ + "ipykernel>=7.1.0", + "jupyterlab>=4.5.2", +] [dependency-groups] dev = [ diff --git a/test/test_big_phi_robust.py b/test/test_big_phi_robust.py index e9f5a86c9..b73153f32 100644 --- a/test/test_big_phi_robust.py +++ b/test/test_big_phi_robust.py @@ -175,17 +175,17 @@ def test_sia_standard_example_has_repertoires(self, s): # Check cause repertoire exists and has required attributes assert result.cause is not None, "SIA missing cause repertoire" assert hasattr(result.cause, "phi"), "Cause RIA missing phi attribute" - assert hasattr( - result.cause, "mechanism" - ), "Cause RIA missing mechanism attribute" + assert hasattr(result.cause, "mechanism"), ( + "Cause RIA missing mechanism attribute" + ) assert hasattr(result.cause, "purview"), "Cause RIA missing purview attribute" # Check effect repertoire exists and has required attributes assert result.effect is not None, "SIA missing effect repertoire" assert hasattr(result.effect, "phi"), "Effect RIA missing phi attribute" - assert hasattr( - result.effect, "mechanism" - ), "Effect RIA missing mechanism attribute" + assert hasattr(result.effect, "mechanism"), ( + "Effect RIA missing mechanism attribute" + ) assert hasattr(result.effect, "purview"), "Effect RIA missing purview attribute" def test_sia_standard_example_has_system_state(self, s): @@ -242,9 +242,9 @@ def test_sia_standard_example_partition_type(self, s): # System has phi > 0, so should have non-null partition assert result.phi > 0, "Standard example should have phi > 0" - assert not isinstance( - result.partition, NullCut - ), "Irreducible system has NullCut partition" + assert not isinstance(result.partition, NullCut), ( + "Irreducible system has NullCut partition" + ) def test_reducible_system_has_null_partition(self, reducible): """Reducible system should have null partition. @@ -254,12 +254,12 @@ def test_reducible_system_has_null_partition(self, reducible): """ result = reducible.sia() - assert isinstance( - result, NullSystemIrreducibilityAnalysis - ), "Reducible system should return NullSIA" - assert isinstance( - result.partition, NullCut - ), "Reducible system should have NullCut partition" + assert isinstance(result, NullSystemIrreducibilityAnalysis), ( + "Reducible system should return NullSIA" + ) + assert isinstance(result.partition, NullCut), ( + "Reducible system should have NullCut partition" + ) assert result.phi == 0.0, "Reducible system should have phi=0" def test_empty_subsystem_has_null_partition(self, s_empty): @@ -270,12 +270,12 @@ def test_empty_subsystem_has_null_partition(self, s_empty): """ result = s_empty.sia() - assert isinstance( - result, NullSystemIrreducibilityAnalysis - ), "Empty subsystem should return NullSIA" - assert isinstance( - result.partition, NullCut - ), "Empty subsystem should have NullCut partition" + assert isinstance(result, NullSystemIrreducibilityAnalysis), ( + "Empty subsystem should return NullSIA" + ) + assert isinstance(result.partition, NullCut), ( + "Empty subsystem should have NullCut partition" + ) assert result.phi == 0.0, "Empty subsystem should have phi=0" diff --git a/test/test_iit4_robust.py b/test/test_iit4_robust.py index f75f56629..31c63fda4 100644 --- a/test/test_iit4_robust.py +++ b/test/test_iit4_robust.py @@ -195,9 +195,9 @@ def test_phi_structure_has_distinctions_attribute(self, example_name): subsystem = EXAMPLES["subsystem"][example_name]() result = new_big_phi.phi_structure(subsystem) - assert hasattr( - result, "distinctions" - ), f"PhiStructure for '{example_name}' missing 'distinctions' attribute" + assert hasattr(result, "distinctions"), ( + f"PhiStructure for '{example_name}' missing 'distinctions' attribute" + ) @pytest.mark.parametrize( "example_name", @@ -227,9 +227,9 @@ def test_phi_structure_has_relations_attribute(self, example_name): subsystem = EXAMPLES["subsystem"][example_name]() result = new_big_phi.phi_structure(subsystem) - assert hasattr( - result, "relations" - ), f"PhiStructure for '{example_name}' missing 'relations' attribute" + assert hasattr(result, "relations"), ( + f"PhiStructure for '{example_name}' missing 'relations' attribute" + ) @pytest.mark.parametrize( "example_name", @@ -255,9 +255,9 @@ def test_phi_structure_distinctions_are_non_empty(self, example_name): subsystem = EXAMPLES["subsystem"][example_name]() result = new_big_phi.phi_structure(subsystem) - assert hasattr( - result, "distinctions" - ), "PhiStructure missing distinctions attribute" + assert hasattr(result, "distinctions"), ( + "PhiStructure missing distinctions attribute" + ) distinctions = result.distinctions assert distinctions is not None, ( @@ -310,9 +310,9 @@ def test_distinctions_have_mechanism_attribute(self): # Check each distinction has a mechanism for i, distinction in enumerate(concepts): - assert hasattr( - distinction, "mechanism" - ), f"Distinction {i} missing 'mechanism' attribute" + assert hasattr(distinction, "mechanism"), ( + f"Distinction {i} missing 'mechanism' attribute" + ) def test_distinctions_mechanisms_are_within_subsystem(self): """Distinction mechanisms should be subsets of subsystem nodes. diff --git a/test/test_invariants.py b/test/test_invariants.py index dd67ddec3..11d9a6a3b 100644 --- a/test/test_invariants.py +++ b/test/test_invariants.py @@ -156,21 +156,21 @@ def test_sia_partition_attribute_consistency(self, s, micro_s, reducible): # Irreducible systems with phi > 0 should have non-null partitions s_result = s.sia() if s_result.phi > 0: - assert not isinstance( - s_result.partition, NullCut - ), "System with phi > 0 has NullCut partition (should have real partition)" + assert not isinstance(s_result.partition, NullCut), ( + "System with phi > 0 has NullCut partition (should have real partition)" + ) micro_result = micro_s.sia() if micro_result.phi > 0: - assert not isinstance( - micro_result.partition, NullCut - ), "System with phi > 0 has NullCut partition (should have real partition)" + assert not isinstance(micro_result.partition, NullCut), ( + "System with phi > 0 has NullCut partition (should have real partition)" + ) # Reducible system should have null partition reducible_result = reducible.sia() - assert isinstance( - reducible_result.partition, NullCut - ), "Reducible system should have NullCut partition" + assert isinstance(reducible_result.partition, NullCut), ( + "Reducible system should have NullCut partition" + ) def test_partition_reduces_or_maintains_phi(self, s, micro_s): """Partitioned system cannot have more phi than unpartitioned. @@ -220,9 +220,9 @@ def test_selfloop_phi_depends_on_config(self, noisy_selfloop_single): # With config disabled, phi should be 0 with config.override(SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI=False): result_disabled = noisy_selfloop_single.sia() - assert ( - result_disabled.phi == 0.0 - ), "Expected phi=0 when SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI=False" + assert result_disabled.phi == 0.0, ( + "Expected phi=0 when SINGLE_MICRO_NODES_WITH_SELFLOOPS_HAVE_PHI=False" + ) # With config enabled and EMD, phi should be > 0 with config.override( @@ -251,9 +251,9 @@ def test_cache_clearing_option(self, s): CACHE_REPERTOIRES=True, ): _ = s.sia() - assert ( - s._repertoire_cache.cache - ), "Cache should have entries when clearing is disabled" + assert s._repertoire_cache.cache, ( + "Cache should have entries when clearing is disabled" + ) # Test with cache clearing enabled with config.override( @@ -262,9 +262,9 @@ def test_cache_clearing_option(self, s): CACHE_REPERTOIRES=True, ): _ = s.sia() - assert ( - not s._repertoire_cache.cache - ), "Cache should be empty when clearing is enabled" + assert not s._repertoire_cache.cache, ( + "Cache should be empty when clearing is enabled" + ) class TestPhiStructureInvariants: @@ -291,12 +291,12 @@ def test_phi_structure_has_distinctions(self, example_name): # Systems that have phi should have distinctions if hasattr(result, "phi") and result.phi > 0: - assert hasattr( - result, "distinctions" - ), f"System '{example_name}' has phi > 0 but no distinctions attribute" - assert ( - len(result.distinctions) > 0 - ), f"System '{example_name}' has phi > 0 but zero distinctions" + assert hasattr(result, "distinctions"), ( + f"System '{example_name}' has phi > 0 but no distinctions attribute" + ) + assert len(result.distinctions) > 0, ( + f"System '{example_name}' has phi > 0 but zero distinctions" + ) @pytest.mark.parametrize("example_name", ["basic", "fig4"]) def test_phi_structure_has_relations(self, example_name): diff --git a/test/test_substrate_modeler.py b/test/test_substrate_modeler.py new file mode 100644 index 000000000..6cbcee8c4 --- /dev/null +++ b/test/test_substrate_modeler.py @@ -0,0 +1,501 @@ +"""Tests for the substrate_modeler module.""" + +import numpy as np +import pytest + +from pyphi.substrate_modeler import CompositeUnit +from pyphi.substrate_modeler import Substrate +from pyphi.substrate_modeler import Unit + +# ============================================================================= +# Unit construction +# ============================================================================= + + +class TestUnitConstruction: + def test_string_mechanism(self): + unit = Unit(index=0, inputs=(0, 1), mechanism="and") + assert unit.mechanism_type == "and" + assert unit.index == 0 + assert unit.inputs == (0, 1) + + def test_ndarray_mechanism(self): + raw_tpm = np.array([[0.0], [1.0]]) + unit = Unit(index=0, inputs=(0,), mechanism=raw_tpm) + assert unit.mechanism_type == "raw_tpm" + assert np.array_equal(unit.mechanism, raw_tpm) + + def test_callable_mechanism(self): + def custom(unit, state, input_state): + return np.array([[0.0], [1.0]]) + + unit = Unit(index=0, inputs=(0,), mechanism=custom) + assert unit.mechanism_type == "custom" + + def test_label_defaults_to_index(self): + unit = Unit(index=3, inputs=(0,), mechanism="copy") + assert unit.label == "3" + + def test_label_explicit(self): + unit = Unit(index=0, inputs=(0,), mechanism="copy", label="A") + assert unit.label == "A" + + def test_original_index_defaults_to_index(self): + unit = Unit(index=5, inputs=(0,), mechanism="copy") + assert unit.original_index == 5 + + def test_original_index_explicit(self): + unit = Unit(index=0, inputs=(0,), mechanism="copy", original_index=10) + assert unit.original_index == 10 + + def test_params(self): + unit = Unit( + index=0, + inputs=(0, 1), + mechanism="sigmoid", + params={"input_weights": (1.0, 1.0), "determinism": 3.0}, + ) + assert unit.params["determinism"] == 3.0 + + def test_unknown_mechanism_raises(self): + with pytest.raises(KeyError): + Unit(index=0, inputs=(0,), mechanism="nonexistent_mechanism") + + +# ============================================================================= +# Unit equality and hashing +# ============================================================================= + + +class TestUnitEquality: + def test_equal_units(self): + a = Unit(index=0, inputs=(0, 1), mechanism="and") + b = Unit(index=0, inputs=(0, 1), mechanism="and") + assert a == b + + def test_unequal_mechanism(self): + a = Unit(index=0, inputs=(0, 1), mechanism="and") + b = Unit(index=0, inputs=(0, 1), mechanism="or") + assert a != b + + def test_unequal_inputs(self): + a = Unit(index=0, inputs=(0,), mechanism="copy") + b = Unit(index=0, inputs=(1,), mechanism="copy") + assert a != b + + def test_hash_equal(self): + a = Unit(index=0, inputs=(0, 1), mechanism="and") + b = Unit(index=0, inputs=(0, 1), mechanism="and") + assert hash(a) == hash(b) + + +# ============================================================================= +# Unit.compute_tpm for state-independent mechanisms +# ============================================================================= + + +class TestStateIndependentMechanisms: + def test_copy_gate(self): + unit = Unit(index=0, inputs=(0,), mechanism="copy") + tpm = unit.compute_tpm(state=0, input_state=(0,)) + # copy gate: P(ON | input=0) = 0.0, P(ON | input=1) = 1.0 + assert tpm[(0,)] == pytest.approx(0.0) + assert tpm[(1,)] == pytest.approx(1.0) + + def test_and_gate(self): + unit = Unit(index=2, inputs=(0, 1), mechanism="and") + tpm = unit.compute_tpm(state=0, input_state=(0, 0)) + assert tpm[(0, 0)] == pytest.approx(0.0) + assert tpm[(0, 1)] == pytest.approx(0.0) + assert tpm[(1, 0)] == pytest.approx(0.0) + assert tpm[(1, 1)] == pytest.approx(1.0) + + def test_or_gate(self): + unit = Unit(index=2, inputs=(0, 1), mechanism="or") + tpm = unit.compute_tpm(state=0, input_state=(0, 0)) + assert tpm[(0, 0)] == pytest.approx(0.0) + assert tpm[(0, 1)] == pytest.approx(1.0) + assert tpm[(1, 0)] == pytest.approx(1.0) + assert tpm[(1, 1)] == pytest.approx(1.0) + + def test_xor_gate(self): + unit = Unit(index=2, inputs=(0, 1), mechanism="xor") + tpm = unit.compute_tpm(state=0, input_state=(0, 0)) + assert tpm[(0, 0)] == pytest.approx(0.0) + assert tpm[(0, 1)] == pytest.approx(1.0) + assert tpm[(1, 0)] == pytest.approx(1.0) + assert tpm[(1, 1)] == pytest.approx(0.0) + + def test_and_gate_with_floor_ceiling(self): + unit = Unit( + index=0, + inputs=(0, 1), + mechanism="and", + params={"floor": 0.1, "ceiling": 0.9}, + ) + tpm = unit.compute_tpm(state=0, input_state=(0, 0)) + assert tpm[(0, 0)] == pytest.approx(0.1) + assert tpm[(1, 1)] == pytest.approx(0.9) + + def test_sigmoid(self): + unit = Unit( + index=0, + inputs=(0, 1), + mechanism="sigmoid", + params={"input_weights": (1.0, 1.0), "determinism": 5.0, "threshold": 0.0}, + ) + tpm = unit.compute_tpm(state=0, input_state=(0, 0)) + # With ising=True (default), all-0 maps to all-(-1), so sum = -2 + # sigmoid(-5*(-2)) = sigmoid(10) ≈ 1.0 + # all-1 maps to all-(+1), so sum = 2 + # sigmoid(-5*(2)) = sigmoid(-10) ≈ 0.0... wait, let's just check shape and range + arr = np.array(tpm) + assert arr.shape[-1] == 1 # Single output node + assert np.all(arr >= 0.0) + assert np.all(arr <= 1.0) + + def test_democracy(self): + unit = Unit(index=0, inputs=(0, 1), mechanism="democracy") + tpm = unit.compute_tpm(state=0, input_state=(0, 0)) + assert tpm[(0, 0)] == pytest.approx(0.0) + assert tpm[(0, 1)] == pytest.approx(0.5) + assert tpm[(1, 0)] == pytest.approx(0.5) + assert tpm[(1, 1)] == pytest.approx(1.0) + + def test_majority(self): + unit = Unit(index=0, inputs=(0, 1, 2), mechanism="majority") + tpm = unit.compute_tpm(state=0, input_state=(0, 0, 0)) + # Majority of (0,0,0) = 0, (1,1,0) = 1, (1,1,1) = 1 + assert tpm[(0, 0, 0)] == pytest.approx(0.0) + assert tpm[(1, 1, 0)] == pytest.approx(1.0) + assert tpm[(1, 1, 1)] == pytest.approx(1.0) + + +# ============================================================================= +# Unit.compute_tpm for state-dependent mechanisms +# ============================================================================= + + +class TestStateDependentMechanisms: + def test_sor_gate_state_dependent(self): + """SOR gate should produce different TPMs for different input states.""" + unit = Unit( + index=0, + inputs=(0, 1), + mechanism="sor", + params={ + "pattern_selection": ((0, 1), (1, 0)), + "selectivity": 2.0, + }, + ) + tpm_a = unit.compute_tpm(state=0, input_state=(0, 1)) + tpm_b = unit.compute_tpm(state=0, input_state=(1, 1)) + # Different input states should give different TPMs + assert not np.array_equal(np.array(tpm_a), np.array(tpm_b)) + + def test_mismatch_corrector(self): + """Mismatch corrector depends on whether state matches input.""" + unit = Unit( + index=0, + inputs=(1,), + mechanism="mismatch_corrector", + ) + # state matches input → P = 0.5 (uncertain) + tpm_match = unit.compute_tpm(state=1, input_state=(1,)) + # state doesn't match input → P = [floor, ceiling] = [0, 1] + tpm_mismatch = unit.compute_tpm(state=0, input_state=(1,)) + assert not np.array_equal(np.array(tpm_match), np.array(tpm_mismatch)) + + def test_mismatch_pattern_detector(self): + """Mismatch pattern detector gives different TPMs for different states.""" + unit = Unit( + index=0, + inputs=(1, 2), + mechanism="mismatch_pattern_detector", + params={ + "pattern_selection": ((0, 1),), + "selectivity": 2.0, + }, + ) + tpm_on = unit.compute_tpm(state=1, input_state=(0, 1)) + tpm_off = unit.compute_tpm(state=0, input_state=(0, 1)) + assert not np.array_equal(np.array(tpm_on), np.array(tpm_off)) + + +# ============================================================================= +# Unit.state_dependent_tpm (cached) +# ============================================================================= + + +class TestStateDependentTPMCache: + def test_caching(self): + unit = Unit(index=1, inputs=(0, 1), mechanism="and") + substrate_state = (0, 1) + tpm1 = unit.state_dependent_tpm(substrate_state) + tpm2 = unit.state_dependent_tpm(substrate_state) + # Should return the same cached object + assert tpm1 is tpm2 + + def test_different_states_different_results(self): + unit = Unit( + index=0, + inputs=(1,), + mechanism="mismatch_corrector", + ) + # state=0 vs state=1 should give different TPMs + tpm_a = unit.state_dependent_tpm((0, 1)) # unit state=0, input=1 + tpm_b = unit.state_dependent_tpm((1, 1)) # unit state=1, input=1 + assert not np.array_equal(np.array(tpm_a), np.array(tpm_b)) + + +# ============================================================================= +# CompositeUnit +# ============================================================================= + + +class TestCompositeUnit: + def test_inputs_aggregated(self): + sub_a = Unit(index=0, inputs=(1,), mechanism="copy") + sub_b = Unit(index=0, inputs=(2,), mechanism="copy") + composite = CompositeUnit(index=0, units=(sub_a, sub_b), label="C") + assert set(composite.inputs) == {1, 2} + + def test_compute_tpm_shape(self): + sub_a = Unit(index=0, inputs=(1,), mechanism="copy") + sub_b = Unit(index=0, inputs=(2,), mechanism="copy") + composite = CompositeUnit(index=0, units=(sub_a, sub_b), label="C") + tpm = composite.compute_tpm(state=0, input_state=(0, 0)) + # Should produce a valid TPM + arr = np.array(tpm) + assert np.all(arr >= 0.0) + assert np.all(arr <= 1.0) + + def test_selective_combination(self): + """Selective combination picks the sub-unit furthest from 0.5.""" + sub_a = Unit( + index=0, + inputs=(1,), + mechanism="copy", + params={"floor": 0.0, "ceiling": 1.0}, + ) + sub_b = Unit( + index=0, + inputs=(1,), + mechanism="copy", + params={"floor": 0.4, "ceiling": 0.6}, + ) + composite = CompositeUnit( + index=0, + units=(sub_a, sub_b), + mechanism_combination="selective", + ) + tpm = composite.compute_tpm(state=0, input_state=(1,)) + # sub_a gives P=1.0 for input=1, sub_b gives P=0.6 + # |1.0 - 0.5| = 0.5 > |0.6 - 0.5| = 0.1, so selective picks sub_a + arr = np.array(tpm) + # Multidimensional TPM shape: (2, 1) for 1 input, 1 node + prob = float(arr[1, 0]) + assert prob == pytest.approx(1.0) + + +# ============================================================================= +# Substrate construction +# ============================================================================= + + +class TestSubstrateConstruction: + def test_basic_construction(self): + units = ( + Unit(index=0, inputs=(1,), mechanism="copy"), + Unit(index=1, inputs=(0,), mechanism="copy"), + ) + substrate = Substrate(units) + assert len(substrate) == 2 + assert substrate.node_indices == (0, 1) + + def test_node_labels(self): + units = ( + Unit(index=0, inputs=(1,), mechanism="copy", label="A"), + Unit(index=1, inputs=(0,), mechanism="copy", label="B"), + ) + substrate = Substrate(units) + assert list(substrate.node_labels) == ["A", "B"] + + +# ============================================================================= +# Substrate.cm +# ============================================================================= + + +class TestSubstrateCM: + def test_connectivity_matrix(self): + units = ( + Unit(index=0, inputs=(1,), mechanism="copy"), + Unit(index=1, inputs=(0,), mechanism="copy"), + ) + substrate = Substrate(units) + cm = substrate.cm + # Unit 0 gets input from unit 1 → cm[1, 0] = 1 + # Unit 1 gets input from unit 0 → cm[0, 1] = 1 + assert cm[1, 0] == 1.0 + assert cm[0, 1] == 1.0 + assert cm[0, 0] == 0.0 + assert cm[1, 1] == 0.0 + + +# ============================================================================= +# Substrate.compute_tpm and dynamic_tpm +# ============================================================================= + + +class TestSubstrateTPM: + def _make_and_gate_substrate(self): + """3-node substrate: C = A AND B, with A and B as copy gates.""" + return Substrate( + ( + Unit(index=0, inputs=(0,), mechanism="copy", label="A"), + Unit(index=1, inputs=(1,), mechanism="copy", label="B"), + Unit(index=2, inputs=(0, 1), mechanism="and", label="C"), + ) + ) + + def test_compute_tpm_shape(self): + substrate = self._make_and_gate_substrate() + tpm = substrate.compute_tpm((0, 0, 0)) + arr = np.array(tpm) + # 3 nodes → 2^3 = 8 states, output shape should be (2,2,2,3) in md format + assert arr.shape == (2, 2, 2, 3) + + def test_compute_tpm_values(self): + """Verify AND gate substrate TPM for state (1, 1, 0).""" + substrate = self._make_and_gate_substrate() + tpm = substrate.compute_tpm((1, 1, 0)) + arr = np.array(tpm) + # With present state (1,1,0): + # Unit A (copy, input=A): depends on past A + # Unit B (copy, input=B): depends on past B + # Unit C (AND, inputs=A,B): depends on past A and B + # For past state (1,1,*): P(A=1)=1.0, P(B=1)=1.0, P(C=1)=AND(1,1)=1.0 + assert arr[1, 1, 0, 0] == pytest.approx(1.0) # P(A=1 | past A=1) + assert arr[1, 1, 0, 1] == pytest.approx(1.0) # P(B=1 | past B=1) + assert arr[1, 1, 0, 2] == pytest.approx(1.0) # P(C=1 | past A=1, B=1) + # For past state (0,0,*): P(A=1)=0.0, P(B=1)=0.0, P(C=1)=AND(0,0)=0.0 + assert arr[0, 0, 0, 0] == pytest.approx(0.0) + assert arr[0, 0, 0, 1] == pytest.approx(0.0) + assert arr[0, 0, 0, 2] == pytest.approx(0.0) + + def test_dynamic_tpm_shape(self): + substrate = self._make_and_gate_substrate() + tpm = substrate.dynamic_tpm + arr = np.array(tpm) + assert arr.shape == (2, 2, 2, 3) + + def test_dynamic_tpm_is_cached(self): + substrate = self._make_and_gate_substrate() + tpm1 = substrate.dynamic_tpm + tpm2 = substrate.dynamic_tpm + assert tpm1 is tpm2 + + +# ============================================================================= +# Substrate.network and subsystem +# ============================================================================= + + +class TestSubstrateNetworkAndSubsystem: + def _make_simple_substrate(self): + return Substrate( + ( + Unit(index=0, inputs=(1,), mechanism="copy", label="A"), + Unit(index=1, inputs=(0,), mechanism="copy", label="B"), + ) + ) + + def test_network_returns_network(self): + from pyphi.network import Network + + substrate = self._make_simple_substrate() + net = substrate.network((0, 0)) + assert isinstance(net, Network) + + def test_subsystem_returns_subsystem(self): + from pyphi.subsystem import Subsystem + + substrate = self._make_simple_substrate() + sub = substrate.subsystem((0, 0)) + assert isinstance(sub, Subsystem) + assert sub.state == (0, 0) + + def test_subsystem_with_node_subset(self): + from pyphi.subsystem import Subsystem + + substrate = self._make_simple_substrate() + sub = substrate.subsystem((0, 0), nodes=(0,)) + assert isinstance(sub, Subsystem) + + +# ============================================================================= +# Substrate equality +# ============================================================================= + + +class TestSubstrateEquality: + def test_equal_substrates(self): + units = ( + Unit(index=0, inputs=(1,), mechanism="copy"), + Unit(index=1, inputs=(0,), mechanism="copy"), + ) + a = Substrate(units) + b = Substrate(units) + assert a == b + + def test_unequal_substrates(self): + a = Substrate((Unit(index=0, inputs=(0,), mechanism="copy"),)) + b = Substrate((Unit(index=0, inputs=(0,), mechanism="and"),)) + assert a != b + + +# ============================================================================= +# Integration: AND gate substrate → Subsystem → concept +# ============================================================================= + + +class TestIntegration: + def test_and_gate_subsystem_concept(self): + """Build a 3-node AND substrate, get a subsystem, and compute a concept.""" + substrate = Substrate( + ( + Unit(index=0, inputs=(0,), mechanism="copy", label="A"), + Unit(index=1, inputs=(1,), mechanism="copy", label="B"), + Unit(index=2, inputs=(0, 1), mechanism="and", label="C"), + ) + ) + sub = substrate.subsystem((1, 1, 1)) + # Compute concept for mechanism (2,) — the AND gate + concept = sub.concept((2,)) + # The concept should have some phi value + assert concept.phi >= 0.0 + + +# ============================================================================= +# to_json +# ============================================================================= + + +class TestToJson: + def test_unit_to_json(self): + unit = Unit(index=0, inputs=(1, 2), mechanism="and", label="X") + j = unit.to_json() + assert j["index"] == 0 + assert j["inputs"] == (1, 2) + assert j["mechanism_type"] == "and" + assert j["label"] == "X" + + def test_substrate_to_json(self): + units = ( + Unit(index=0, inputs=(1,), mechanism="copy"), + Unit(index=1, inputs=(0,), mechanism="copy"), + ) + substrate = Substrate(units) + j = substrate.to_json() + assert len(j["units"]) == 2 diff --git a/uv.lock b/uv.lock index 25aecc1a5..501131ee9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,14 @@ version = 1 revision = 3 requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", +] [[package]] name = "alabaster" @@ -11,6 +19,93 @@ wheels = [ { url = 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