BinPhenotype

Bases: Phenotype

BinPhenotype converts numeric values into categorical bin labels. To use, pass it a numeric valued phenotype such as AgePhenotype, MeasurementPhenotype, ArithmeticPhenotype, or ScorePhenotype.

Takes a phenotype that returns numeric values (like age, measurements, etc.) and converts the VALUE column into bin labels like "[10-20)", "[20-30)", etc.

DATE: The event date selected from the input phenotype VALUE: A categorical variable representing the bin label that the numeric value falls into

Parameters:

Name	Type	Description	Default
name		The name of the phenotype.	required
phenotype	Phenotype	The phenotype that returns numeric values of interest (AgePhenotype, MeasurementPhenotype, etc.)	required
bins		List of bin edges. Default is [0, 10, 20, ..., 100] for age ranges.	list(range(0, 101, 10))

Example

# Create an age phenotype
age = AgePhenotype()

# Create bins for age groups: [0-10), [10-20), [20-30), etc.
binned_age = BinPhenotype(
    name="age_groups",
    phenotype=age,
    bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
)

tables = {"PERSON": example_person_table}
result_table = binned_age.execute(tables)

# Result will have VALUE column with labels like "[20-30)", "[30-40)", etc.
display(result_table)

Source code in phenex/phenotypes/bin_phenotype.py

class BinPhenotype(Phenotype):
    """
    BinPhenotype converts numeric values into categorical bin labels. To use, pass it a numeric valued phenotype such as AgePhenotype, MeasurementPhenotype, ArithmeticPhenotype, or ScorePhenotype.

    Takes a phenotype that returns numeric values (like age, measurements, etc.)
    and converts the VALUE column into bin labels like "[10-20)", "[20-30)", etc.

    DATE: The event date selected from the input phenotype
    VALUE: A categorical variable representing the bin label that the numeric value falls into

    Parameters:
        name: The name of the phenotype.
        phenotype: The phenotype that returns numeric values of interest (AgePhenotype, MeasurementPhenotype, etc.)
        bins: List of bin edges. Default is [0, 10, 20, ..., 100] for age ranges.

    Example:
        ```python
        # Create an age phenotype
        age = AgePhenotype()

        # Create bins for age groups: [0-10), [10-20), [20-30), etc.
        binned_age = BinPhenotype(
            name="age_groups",
            phenotype=age,
            bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
        )

        tables = {"PERSON": example_person_table}
        result_table = binned_age.execute(tables)

        # Result will have VALUE column with labels like "[20-30)", "[30-40)", etc.
        display(result_table)
        ```
    """

    def __init__(
        self,
        phenotype: Phenotype,
        bins=list(range(0, 101, 10)),
        **kwargs,
    ):
        super(BinPhenotype, self).__init__(**kwargs)
        self.bins = bins
        self.phenotype = phenotype
        if self.phenotype.__class__.__name__ not in [
            "AgePhenotype",
            "MeasurementPhenotype",
            "ArithmeticPhenotype",
            "ScorePhenotype",
        ]:
            raise ValueError(
                f"Invalid phenotype type: {self.phenotype.__class__.__name__}"
            )
        self.add_children(phenotype)

    def _execute(self, tables) -> PhenotypeTable:
        # Execute the child phenotype to get the initial table to filter
        table = self.phenotype.table

        # Create bin labels
        bin_labels = []

        # Add a bin for values < first bin edge
        bin_labels.append(f"<{self.bins[0]}")

        # Add bins for each range
        for i in range(len(self.bins) - 1):
            bin_labels.append(f"[{self.bins[i]}-{self.bins[i+1]})")

        # Add a final bin for values >= last bin edge
        bin_labels.append(f">={self.bins[-1]}")

        # Create binning logic using Ibis case statements
        value_col = table.VALUE

        # Start with the case expression
        case_expr = None

        # Handle values < first bin edge
        first_condition = value_col < self.bins[0]
        case_expr = ibis.case().when(first_condition, bin_labels[0])

        # Create conditions for each bin range
        for i in range(len(self.bins) - 1):
            condition = (value_col >= self.bins[i]) & (value_col < self.bins[i + 1])
            case_expr = case_expr.when(condition, bin_labels[i + 1])

        # Handle values >= last bin edge
        final_condition = value_col >= self.bins[-1]
        case_expr = case_expr.when(final_condition, bin_labels[-1])

        # Handle null values
        case_expr = case_expr.else_(None)

        # Replace the VALUE column with bin labels
        table = table.mutate(VALUE=case_expr.end())

        return table

dependencies property

Recursively collect all dependencies of a node (including dependencies of dependencies).

Returns:

Type	Description
Set[Node]	List[Node]: A list of Node objects on which this Node depends.

dependency_graph property

Build a dependency graph where each node maps to its direct dependencies (children).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, Set[Node]: A mapping of Node's to their children Node's.

namespaced_table property

A PhenotypeTable has generic column names 'person_id', 'boolean', 'event_date', and 'value'. The namespaced_table prepends the phenotype name to all of these columns. This is useful when joining multiple phenotype tables together.

Returns:

Name	Type	Description
table	Table	The namespaced table for the current phenotype.

reverse_dependency_graph property

Build a reverse dependency graph where each node maps to nodes that depend on it (parents).

Returns:

Type	Description
Dict[Node, Set[Node]]	Dict[Node, List[Node]: A mapping of Node's to their parent Node's.

execute(tables=None, con=None, overwrite=False, lazy_execution=False, n_threads=1)

Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

Parameters:

Name	Type	Description	Default
tables	Dict[str, Table]	A dictionary mapping domains to Table objects.	None
con	Optional[object]	Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.	None
overwrite	bool	If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.	False
lazy_execution	bool	If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.	False
n_threads	int	Max number of Node's to execute simultaneously when this node has multiple children.	1

Returns:

Name	Type	Description
Table	Table	The resulting table for this node. Also accessible through self.table after calling self.execute().

Source code in phenex/node.py

def execute(
    self,
    tables: Dict[str, Table] = None,
    con: Optional[object] = None,
    overwrite: bool = False,
    lazy_execution: bool = False,
    n_threads: int = 1,
) -> Table:
    """
    Executes the Node computation for the current node and its dependencies. Supports lazy execution using hash-based change detection to avoid recomputing Node's that have already executed.

    Parameters:
        tables: A dictionary mapping domains to Table objects.
        con: Connection to database for materializing outputs. If provided, outputs from the node and all children nodes will be materialized (written) to the database using the connector.
        overwrite: If True, will overwrite any existing tables found in the database while writing. If False, will throw an error when an existing table is found. Has no effect if con is not passed.
        lazy_execution: If True, only re-executes if the node's definition has changed. Defaults to False. You should pass overwrite=True with lazy_execution as lazy_execution is intended precisely for iterative updates to a node definition. You must pass a connector (to cache results) for lazy_execution to work.
        n_threads: Max number of Node's to execute simultaneously when this node has multiple children.

    Returns:
        Table: The resulting table for this node. Also accessible through self.table after calling self.execute().
    """
    # Handle None tables
    if tables is None:
        tables = {}

    # Use multithreaded execution if we have multiple children and n_threads > 1
    if len(self.children) > 1 and n_threads > 1:
        return self._execute_multithreaded(
            tables, con, overwrite, lazy_execution, n_threads
        )
    else:
        return self._execute_sequential(tables, con, overwrite, lazy_execution)

visualize_dependencies()

Create a text visualization of the dependency graph for this node and its dependencies.

Returns:

Name	Type	Description
str	str	A text representation of the dependency graph

Source code in phenex/node.py

def visualize_dependencies(self) -> str:
    """
    Create a text visualization of the dependency graph for this node and its dependencies.

    Returns:
        str: A text representation of the dependency graph
    """
    lines = [f"Dependencies for Node '{self.name}':"]

    # Get all dependencies
    all_deps = self.dependencies
    nodes = {node.name: node for node in all_deps}
    nodes[self.name] = self  # Add self to the nodes

    # Build dependency graph
    dependency_graph = self._build_dependency_graph(nodes)

    for node_name in sorted(nodes.keys()):
        dependencies = dependency_graph.get(node_name, set())
        if dependencies:
            deps_str = ", ".join(sorted(dependencies))
            lines.append(f"  {node_name} depends on: {deps_str}")
        else:
            lines.append(f"  {node_name} (no dependencies)")

    return "\n".join(lines)