Source code for hail.expr.aggregators.aggregators

import difflib
from functools import wraps, update_wrapper

import hail as hl
from hail.expr.expressions import ExpressionException, Expression, \
    ArrayExpression, SetExpression, BooleanExpression, Int64Expression, \
    NumericExpression, DictExpression, StructExpression, Float64Expression, \
    StringExpression, NDArrayNumericExpression, \
    expr_any, expr_oneof, expr_array, expr_set, expr_bool, expr_numeric, \
    expr_int32, expr_int64, expr_float64, expr_call, expr_str, expr_ndarray, \
    unify_all, construct_expr, Indices, Aggregation, to_expr
from hail.expr.types import hail_type, tint32, tint64, tfloat32, tfloat64, \
    tbool, tcall, tset, tarray, tstruct, tdict, ttuple, tstr
from hail.expr.functions import rbind, float32, _quantile_from_cdf
import hail.ir as ir
from hail.typecheck import TypeChecker, typecheck_method, typecheck, \
    sequenceof, func_spec, identity, nullable, oneof
from hail.utils import wrap_to_list
from hail.utils.java import Env


class AggregableChecker(TypeChecker):
    def __init__(self, coercer):
        self.coercer = coercer
        super(AggregableChecker, self).__init__()

    def expects(self):
        return self.coercer.expects()

    def format(self, arg):
        return self.coercer.format(arg)

    def check(self, x, caller, param):
        x = self.coercer.check(x, caller, param)
        if len(x._ir.search(lambda node: isinstance(node, ir.BaseApplyAggOp))) == 0:
            raise ExpressionException(f"{caller} must be placed outside of an aggregation. See "
                                      "https://discuss.hail.is/t/breaking-change-redesign-of-aggregator-interface/701")
        return x


agg_expr = AggregableChecker


class AggFunc(object):
    def __init__(self):
        self._as_scan = False
        self._agg_bindings = set()

    def correct_prefix(self):
        return "scan" if self._as_scan else "agg"

    def incorrect_prefix(self):
        return "agg" if self._as_scan else "scan"

    def correct_plural(self):
        return "scans" if self._as_scan else "aggregations"

    def incorrect_plural(self):
        return "aggregations" if self._as_scan else "scans"

    def check_scan_agg_compatibility(self, caller, node):
        if self._as_scan != isinstance(node, ir.ApplyScanOp):
            raise ExpressionException(
                "'{correct}.{caller}' cannot contain {incorrect}"
                .format(correct=self.correct_prefix(),
                        caller=caller,
                        incorrect=self.incorrect_plural()))

    @typecheck_method(agg_op=str,
                      seq_op_args=sequenceof(expr_any),
                      ret_type=hail_type,
                      init_op_args=sequenceof(expr_any))
    def __call__(self, agg_op, seq_op_args, ret_type, init_op_args=()):
        indices, aggregations = unify_all(*seq_op_args, *init_op_args)
        if aggregations:
            raise ExpressionException('Cannot aggregate an already-aggregated expression')
        for a in seq_op_args + init_op_args:
            _check_agg_bindings(a, self._agg_bindings)

        if self._as_scan:
            x = ir.ApplyScanOp(agg_op,
                               [expr._ir for expr in init_op_args],
                               [expr._ir for expr in seq_op_args])
            aggs = aggregations
        else:
            x = ir.ApplyAggOp(agg_op,
                              [expr._ir for expr in init_op_args],
                              [expr._ir for expr in seq_op_args])
            aggs = aggregations.push(Aggregation(*seq_op_args, *init_op_args))
        return construct_expr(x, ret_type, Indices(indices.source, set()), aggs)

    @typecheck_method(f=func_spec(1, expr_any),
                      array_agg_expr=expr_oneof(expr_array(), expr_set()))
    def explode(self, f, array_agg_expr):
        if array_agg_expr._aggregations:
            raise ExpressionException("'{}.explode' does not support an already-aggregated expression as the argument to 'collection'".format(self.correct_prefix()))
        _check_agg_bindings(array_agg_expr, self._agg_bindings)

        if isinstance(array_agg_expr.dtype, tset):
            array_agg_expr = hl.array(array_agg_expr)
        elt = array_agg_expr.dtype.element_type
        var = Env.get_uid()
        ref = construct_expr(ir.Ref(var), elt, array_agg_expr._indices)
        self._agg_bindings.add(var)
        aggregated = f(ref)
        _check_agg_bindings(aggregated, self._agg_bindings)
        self._agg_bindings.remove(var)

        if not self._as_scan and not aggregated._aggregations:
            raise ExpressionException("'{}.explode' must take mapping that contains aggregation expression.".format(self.correct_prefix()))

        indices, _ = unify_all(array_agg_expr, aggregated)
        aggregations = hl.utils.LinkedList(Aggregation)
        if not self._as_scan:
            aggregations = aggregations.push(Aggregation(array_agg_expr, aggregated))
        return construct_expr(ir.AggExplode(ir.ToStream(array_agg_expr._ir), var, aggregated._ir, self._as_scan),
                              aggregated.dtype,
                              Indices(indices.source, aggregated._indices.axes),
                              aggregations)

    @typecheck_method(condition=expr_bool,
                      aggregation=agg_expr(expr_any))
    def filter(self, condition, aggregation):
        if condition._aggregations:
            raise ExpressionException(f"'hl.{self.correct_prefix()}.filter' does not "
                                      f"support an already-aggregated expression as the argument to 'condition'")
        if not self._as_scan and not aggregation._aggregations:
            raise ExpressionException(f"'hl.{self.correct_prefix()}.filter' "
                                      f"must have aggregation in argument to 'aggregation'")

        _check_agg_bindings(condition, self._agg_bindings)
        _check_agg_bindings(aggregation, self._agg_bindings)
        indices, _ = unify_all(condition, aggregation)

        aggregations = hl.utils.LinkedList(Aggregation)
        if not self._as_scan:
            aggregations = aggregations.push(Aggregation(condition, aggregation))
        return construct_expr(ir.AggFilter(condition._ir, aggregation._ir, self._as_scan),
                              aggregation.dtype,
                              Indices(indices.source, aggregation._indices.axes),
                              aggregations)

    def group_by(self, group, aggregation):
        if group._aggregations:
            raise ExpressionException(f"'hl.{self.correct_prefix()}.group_by' "
                                      f"does not support an already-aggregated expression as the argument to 'group'")
        if not self._as_scan and not aggregation._aggregations:
            raise ExpressionException(f"'hl.{self.correct_prefix()}.group_by' "
                                      f"must have aggregation in argument to 'aggregation'")

        _check_agg_bindings(group, self._agg_bindings)
        _check_agg_bindings(aggregation, self._agg_bindings)
        indices, _ = unify_all(group, aggregation)

        aggregations = hl.utils.LinkedList(Aggregation)
        if not self._as_scan:
            aggregations = aggregations.push(Aggregation(aggregation))

        return construct_expr(ir.AggGroupBy(group._ir, aggregation._ir, self._as_scan),
                              tdict(group.dtype, aggregation.dtype),
                              Indices(indices.source, aggregation._indices.axes),
                              aggregations)

    def array_agg(self, array, f):
        if array._aggregations:
            raise ExpressionException(f"'hl.{self.correct_prefix()}.array_agg' "
                                      f"does not support an already-aggregated expression as the argument to 'array'")
        _check_agg_bindings(array, self._agg_bindings)

        elt = array.dtype.element_type
        var = Env.get_uid()
        ref = construct_expr(ir.Ref(var), elt, array._indices)
        self._agg_bindings.add(var)
        aggregated = f(ref)
        _check_agg_bindings(aggregated, self._agg_bindings)
        self._agg_bindings.remove(var)

        if not self._as_scan and not aggregated._aggregations:
            raise ExpressionException(f"'hl.{self.correct_prefix()}.array_agg' "
                                      f"must take mapping that contains aggregation expression.")

        indices, _ = unify_all(array, aggregated)
        aggregations = hl.utils.LinkedList(Aggregation)
        if not self._as_scan:
            aggregations = aggregations.push(Aggregation(array, aggregated))
        return construct_expr(ir.AggArrayPerElement(array._ir, var, 'unused', aggregated._ir, self._as_scan),
                              tarray(aggregated.dtype),
                              Indices(indices.source, aggregated._indices.axes),
                              aggregations)

    @property
    def context(self):
        if self._as_scan:
            return 'scan'
        else:
            return 'agg'


_agg_func = AggFunc()


def _check_agg_bindings(expr, bindings):
    bound_references = {ref.name for ref in expr._ir.search(
        lambda x: isinstance(x, ir.Ref)
        and not isinstance(x, ir.TopLevelReference)
        and not x.name.startswith('__uid_scan')
        and not x.name.startswith('__uid_agg'))}
    free_variables = bound_references - expr._ir.bound_variables - bindings
    if free_variables:
        raise ExpressionException("dynamic variables created by 'hl.bind' or lambda methods like 'hl.map' may not be aggregated")


[docs]@typecheck(expr=expr_numeric, k=int) def approx_cdf(expr, k=100): """Produce a summary of the distribution of values. Notes ----- This method returns a struct containing two arrays: `values` and `ranks`. The `values` array contains an ordered sample of values seen. The `ranks` array is one longer, and contains the approximate ranks for the corresponding values. These represent a summary of the CDF of the distribution of values. In particular, for any value `x = values(i)` in the summary, we estimate that there are `ranks(i)` values strictly less than `x`, and that there are `ranks(i+1)` values less than or equal to `x`. For any value `y` (not necessarily in the summary), we estimate CDF(y) to be `ranks(i)`, where `i` is such that `values(i-1) < y ≤ values(i)`. An alternative intuition is that the summary encodes a compressed approximation to the sorted list of values. For example, values=[0,2,5,6,9] and ranks=[0,3,4,5,8,10] represents the approximation [0,0,0,2,5,6,6,6,9,9], with the value `values(i)` occupying indices `ranks(i)` (inclusive) to `ranks(i+1)` (exclusive). The returned struct also contains an array `_compaction_counts`, which is used internally to support downstream error estimation. Warning ------- This is an approximate and nondeterministic method. Parameters ---------- expr : :class:`.Expression` Expression to collect. k : :obj:`int` Parameter controlling the accuracy vs. memory usage tradeoff. Returns ------- :class:`.StructExpression` Struct containing `values` and `ranks` arrays. """ res = _agg_func('ApproxCDF', [hl.float64(expr)], tstruct(values=tarray(tfloat64), ranks=tarray(tint64), _compaction_counts=tarray(tint32)), init_op_args=[k]) conv = { tint32: lambda x: x.map(hl.int), tint64: lambda x: x.map(hl.int64), tfloat32: lambda x: x.map(hl.float32), tfloat64: identity } return hl.struct(values=conv[expr.dtype](res.values), ranks=res.ranks, _compaction_counts=res._compaction_counts)
[docs]@typecheck(expr=expr_numeric, qs=expr_oneof(expr_numeric, expr_array(expr_numeric)), k=int) def approx_quantiles(expr, qs, k=100) -> Expression: """Compute an array of approximate quantiles. Examples -------- Estimate the median of the `HT` field. >>> table1.aggregate(hl.agg.approx_quantiles(table1.HT, 0.5)) # doctest: +SKIP_OUTPUT_CHECK 64 Estimate the quartiles of the `HT` field. >>> table1.aggregate(hl.agg.approx_quantiles(table1.HT, [0, 0.25, 0.5, 0.75, 1])) # doctest: +SKIP_OUTPUT_CHECK [50, 60, 64, 71, 86] Warning ------- This is an approximate and nondeterministic method. Parameters ---------- expr : :class:`.Expression` Expression to collect. qs : :class:`.NumericExpression` or :class:`.ArrayNumericExpression` Number or array of numbers between 0 and 1. k : :obj:`int` Parameter controlling the accuracy vs. memory usage tradeoff. Increasing k increases both memory use and accuracy. Returns ------- :class:`.NumericExpression` or :class:`.ArrayNumericExpression` If `qs` is a single number, returns the estimated quantile. If `qs` is an array, returns the array of estimated quantiles. """ if isinstance(qs.dtype, tarray): return rbind(approx_cdf(expr, k), lambda cdf: qs.map(lambda q: _quantile_from_cdf(cdf, float32(q)))) else: return _quantile_from_cdf(approx_cdf(expr, k), qs)
[docs]@typecheck(expr=expr_numeric, k=int) def approx_median(expr, k=100) -> Expression: """Compute the approximate median. This function is a shorthand for `approx_quantiles(expr, .5, k)` Examples -------- Estimate the median of the `HT` field. >>> table1.aggregate(hl.agg.approx_median(table1.HT)) # doctest: +SKIP_OUTPUT_CHECK 64 Warning ------- This is an approximate and nondeterministic method. Parameters ---------- expr : :class:`.Expression` Expression to collect. k : :obj:`int` Parameter controlling the accuracy vs. memory usage tradeoff. Increasing k increases both memory use and accuracy. See Also -------- :func:`approx_quantiles` Returns ------- :class:`.NumericExpression` The estimated median. """ return approx_quantiles(expr, .5, k)
[docs]@typecheck(expr=expr_any) def collect(expr) -> ArrayExpression: """Collect records into an array. Examples -------- Collect the `ID` field where `HT` is greater than 68: >>> table1.aggregate(hl.agg.filter(table1.HT > 68, hl.agg.collect(table1.ID))) [2, 3] Notes ----- The element order of the resulting array is not guaranteed, and in some cases is non-deterministic. Use :meth:`collect_as_set` to collect unique items. Warning ------- Collecting a large number of items can cause out-of-memory exceptions. Parameters ---------- expr : :class:`.Expression` Expression to collect. Returns ------- :class:`.ArrayExpression` Array of all `expr` records. """ return _agg_func('Collect', [expr], tarray(expr.dtype))
[docs]@typecheck(expr=expr_any) def collect_as_set(expr) -> SetExpression: """Collect records into a set. Examples -------- Collect the unique `ID` field where `HT` is greater than 68: >>> table1.aggregate(hl.agg.filter(table1.HT > 68, hl.agg.collect_as_set(table1.ID))) {2, 3} Warning ------- Collecting a large number of items can cause out-of-memory exceptions. Parameters ---------- expr : :class:`.Expression` Expression to collect. Returns ------- :class:`.SetExpression` Set of unique `expr` records. """ return _agg_func('CollectAsSet', [expr], tset(expr.dtype))
[docs]@typecheck() def count() -> Int64Expression: """Count the number of records. Examples -------- Group by the `SEX` field and count the number of rows in each category: >>> (table1.group_by(table1.SEX) ... .aggregate(n=hl.agg.count()) ... .show()) +-----+-------+ | SEX | n | +-----+-------+ | str | int64 | +-----+-------+ | "F" | 2 | | "M" | 2 | +-----+-------+ Returns ------- :class:`.Expression` of type :py:data:`.tint64` Total number of records. """ return _agg_func('Count', [], tint64)
[docs]@typecheck(condition=expr_bool) def count_where(condition) -> Int64Expression: """Count the number of records where a predicate is ``True``. Examples -------- Count the number of individuals with `HT` greater than 68: >>> table1.aggregate(hl.agg.count_where(table1.HT > 68)) 2 Parameters ---------- condition : :class:`.BooleanExpression` Criteria for inclusion. Returns ------- :class:`.Expression` of type :py:data:`.tint64` Total number of records where `condition` is ``True``. """ return _agg_func('Sum', [hl.int64(condition)], tint64)
[docs]@typecheck(condition=expr_bool) def any(condition) -> BooleanExpression: """Returns ``True`` if `condition` is ``True`` for any record. Examples -------- >>> (table1.group_by(table1.SEX) ... .aggregate(any_over_70 = hl.agg.any(table1.HT > 70)) ... .show()) +-----+-------------+ | SEX | any_over_70 | +-----+-------------+ | str | bool | +-----+-------------+ | "F" | false | | "M" | true | +-----+-------------+ Notes ----- If there are no records to aggregate, the result is ``False``. Missing records are not considered. If every record is missing, the result is also ``False``. Parameters ---------- condition : :class:`.BooleanExpression` Condition to test. Returns ------- :class:`.BooleanExpression` """ return count_where(condition) > 0
[docs]@typecheck(condition=expr_bool) def all(condition) -> BooleanExpression: """Returns ``True`` if `condition` is ``True`` for every record. Examples -------- >>> (table1.group_by(table1.SEX) ... .aggregate(all_under_70 = hl.agg.all(table1.HT < 70)) ... .show()) +-----+--------------+ | SEX | all_under_70 | +-----+--------------+ | str | bool | +-----+--------------+ | "F" | false | | "M" | false | +-----+--------------+ Notes ----- If there are no records to aggregate, the result is ``True``. Missing records are not considered. If every record is missing, the result is also ``True``. Parameters ---------- condition : :class:`.BooleanExpression` Condition to test. Returns ------- :class:`.BooleanExpression` """ return count_where(~condition) == 0
[docs]@typecheck(expr=expr_any, weight=nullable(expr_numeric)) def counter(expr, *, weight=None) -> DictExpression: """Count the occurrences of each unique record and return a dictionary. Examples -------- Count the number of individuals for each unique `SEX` value: >>> table1.aggregate(hl.agg.counter(table1.SEX)) {'F': 2, 'M': 2} <BLANKLINE> Compute the total height for each unique `SEX` value: >>> table1.aggregate(hl.agg.counter(table1.SEX, weight=table1.HT)) {'F': 130, 'M': 137} <BLANKLINE> Notes ----- If you need a more complex grouped aggregation than :func:`counter` supports, try using :func:`group_by`. This aggregator method returns a dict expression whose key type is the same type as `expr` and whose value type is :class:`.Expression` of type :py:data:`.tint64`. This dict contains a key for each unique value of `expr`, and the value is the number of times that key was observed. Ensure that the result can be stored in memory on a single machine. Warning ------- Using :meth:`counter` with a large number of unique items can cause out-of-memory exceptions. Parameters ---------- expr : :class:`.Expression` Expression to count by key. weight : :class:`.NumericExpression`, optional Expression by which to weight each occurence (when unspecified, it is effectively ``1``) Returns ------- :class:`.DictExpression` Dictionary with the number of occurrences of each unique record. """ if weight is None: return _agg_func.group_by(expr, count()) return _agg_func.group_by(expr, hl.agg.sum(weight))
[docs]@typecheck(expr=expr_any, n=int, ordering=nullable(oneof(expr_any, func_spec(1, expr_any)))) def take(expr, n, ordering=None) -> ArrayExpression: """Take `n` records of `expr`, optionally ordered by `ordering`. Examples -------- Take 3 elements of field `X`: >>> table1.aggregate(hl.agg.take(table1.X, 3)) [5, 6, 7] Take the `ID` and `HT` fields, ordered by `HT` (descending): >>> table1.aggregate(hl.agg.take(hl.struct(ID=table1.ID, HT=table1.HT), ... 3, ... ordering=-table1.HT)) [Struct(ID=2, HT=72), Struct(ID=3, HT=70), Struct(ID=1, HT=65)] Notes ----- The resulting array can include fewer than `n` elements if there are fewer than `n` total records. The `ordering` argument may be an expression, a function, or ``None``. If `ordering` is an expression, this expression's type should be one with a natural ordering (e.g. numeric). If `ordering` is a function, it will be evaluated on each record of `expr` to compute the value used for ordering. In the above example, ``ordering=-table1.HT`` and ``ordering=lambda x: -x.HT`` would be equivalent. If `ordering` is ``None``, then there is no guaranteed ordering on the elements taken, and and the results may be non-deterministic. Missing values are always sorted **last**. Parameters ---------- expr : :class:`.Expression` Expression to store. n : :class:`.Expression` of type :py:data:`.tint32` Number of records to take. ordering : :class:`.Expression` or function ((arg) -> :class:`.Expression`) or None Optional ordering on records. Returns ------- :class:`.ArrayExpression` Array of up to `n` records of `expr`. """ n = to_expr(n) if ordering is None: return _agg_func('Take', [expr], tarray(expr.dtype), [n]) else: return _agg_func('TakeBy', [expr, ordering], tarray(expr.dtype), [n])
[docs]@typecheck(expr=expr_numeric) def min(expr) -> NumericExpression: """Compute the minimum `expr`. Examples -------- Compute the minimum value of `HT`: >>> table1.aggregate(hl.agg.min(table1.HT)) 60 Notes ----- This function returns the minimum non-missing value. If there are no non-missing values, then the result is missing. For back-compatibility reasons, this function also ignores NaN, in contrast with :func:`.functions.min`. The behavior is similar to :func:`.functions.nanmin`. Parameters ---------- expr : :class:`.NumericExpression` Numeric expression. Returns ------- :class:`.NumericExpression` Minimum value of all `expr` records, same type as `expr`. """ return _agg_func('Min', [expr], expr.dtype)
[docs]@typecheck(expr=expr_numeric) def max(expr) -> NumericExpression: """Compute the maximum `expr`. Examples -------- Compute the maximum value of `HT`: >>> table1.aggregate(hl.agg.max(table1.HT)) 72 Notes ----- This function returns the maximum non-missing value. If there are no non-missing values, then the result is missing. For back-compatibility reasons, this function also ignores NaN, in contrast with :func:`.functions.max`. The behavior is similar to :func:`.functions.nanmax`. Parameters ---------- expr : :class:`.NumericExpression` Numeric expression. Returns ------- :class:`.NumericExpression` Maximum value of all `expr` records, same type as `expr`. """ return _agg_func('Max', [expr], expr.dtype)
[docs]@typecheck(expr=expr_oneof(expr_int64, expr_float64)) def sum(expr): """Compute the sum of all records of `expr`. Examples -------- Compute the sum of field `C1`: >>> table1.aggregate(hl.agg.sum(table1.C1)) 25 Notes ----- Missing values are ignored (treated as zero). If `expr` is an expression of type :py:data:`.tint32`, :py:data:`.tint64`, or :py:data:`.tbool`, then the result is an expression of type :py:data:`.tint64`. If `expr` is an expression of type :py:data:`.tfloat32` or :py:data:`.tfloat64`, then the result is an expression of type :py:data:`.tfloat64`. Warning ------- Boolean values are cast to integers before computing the sum. Parameters ---------- expr : :class:`.NumericExpression` Numeric expression. Returns ------- :class:`.Expression` of type :py:data:`.tint64` or :py:data:`.tfloat64` Sum of records of `expr`. """ return _agg_func('Sum', [expr], expr.dtype)
[docs]@typecheck(expr=expr_array(expr_oneof(expr_int64, expr_float64))) def array_sum(expr) -> ArrayExpression: """Compute the coordinate-wise sum of all records of `expr`. Examples -------- Compute the sum of `C1` and `C2`: >>> table1.aggregate(hl.agg.array_sum([table1.C1, table1.C2])) [25, 282] Notes ------ All records must have the same length. Each coordinate is summed independently as described in :func:`sum`. Parameters ---------- expr : :class:`.ArrayNumericExpression` Returns ------- :class:`.ArrayExpression` with element type :py:data:`.tint64` or :py:data:`.tfloat64` """ return array_agg(hl.agg.sum, expr)
@typecheck(expr=expr_ndarray()) def ndarray_sum(expr) -> NDArrayNumericExpression: """ Compute the sum of all records of `expr` of the same shape. :param expr: :return: """ return _agg_func("NDArraySum", [expr], expr.dtype)
[docs]@typecheck(expr=expr_float64) def mean(expr) -> Float64Expression: """Compute the mean value of records of `expr`. Examples -------- Compute the mean of field `HT`: >>> table1.aggregate(hl.agg.mean(table1.HT)) 66.75 Notes ----- Missing values are ignored. Parameters ---------- expr : :class:`.NumericExpression` Numeric expression. Returns ------- :class:`.Expression` of type :py:data:`.tfloat64` Mean value of records of `expr`. """ return hl.bind(lambda expr: sum(expr) / count_where(hl.is_defined(expr)), expr, _ctx=_agg_func.context)
[docs]@typecheck(expr=expr_float64) def stats(expr) -> StructExpression: """Compute a number of useful statistics about `expr`. Examples -------- Compute statistics about field `HT`: >>> table1.aggregate(hl.agg.stats(table1.HT)) #doctest: +SKIP Struct(mean=66.75, stdev=4.656984002549289, min=60.0, max=72.0, n=4, sum=267.0) Notes ----- Computes a struct with the following fields: - `min` (:py:data:`.tfloat64`) - Minimum value. - `max` (:py:data:`.tfloat64`) - Maximum value. - `mean` (:py:data:`.tfloat64`) - Mean value, - `stdev` (:py:data:`.tfloat64`) - Standard deviation. - `n` (:py:data:`.tint64`) - Number of non-missing records. - `sum` (:py:data:`.tfloat64`) - Sum. Parameters ---------- expr : :class:`.NumericExpression` Numeric expression. Returns ------- :class:`.StructExpression` Struct expression with fields `mean`, `stdev`, `min`, `max`, `n`, and `sum`. """ return hl.bind( lambda expr: hl.bind( lambda aggs: hl.bind( lambda mean: hl.struct( mean=mean, stdev=hl.sqrt(hl.float64( aggs.sumsq - (2 * mean * aggs.sum) + (aggs.n_def * mean ** 2)) / aggs.n_def), min=hl.float64(aggs.min), max=hl.float64(aggs.max), n=aggs.n_def, sum=hl.float64(aggs.sum) ), hl.float64(aggs.sum) / aggs.n_def), hl.struct(n_def=count_where(hl.is_defined(expr)), sum=sum(expr), sumsq=sum(expr ** 2), min=min(expr), max=max(expr))), expr, _ctx=_agg_func.context)
[docs]@typecheck(expr=expr_oneof(expr_int64, expr_float64)) def product(expr): """Compute the product of all records of `expr`. Examples -------- Compute the product of field `C1`: >>> table1.aggregate(hl.agg.product(table1.C1)) 440 Notes ----- Missing values are ignored (treated as one). If `expr` is an expression of type :py:data:`.tint32`, :py:data:`.tint64` or :py:data:`.tbool`, then the result is an expression of type :py:data:`.tint64`. If `expr` is an expression of type :py:data:`.tfloat32` or :py:data:`.tfloat64`, then the result is an expression of type :py:data:`.tfloat64`. Warning ------- Boolean values are cast to integers before computing the product. Parameters ---------- expr : :class:`.NumericExpression` Numeric expression. Returns ------- :class:`.Expression` of type :py:data:`.tint64` or :py:data:`.tfloat64` Product of records of `expr`. """ return _agg_func('Product', [expr], expr.dtype)
[docs]@typecheck(predicate=expr_bool) def fraction(predicate) -> Float64Expression: """Compute the fraction of records where `predicate` is ``True``. Examples -------- Compute the fraction of rows where `SEX` is "F" and `HT` > 65: >>> table1.aggregate(hl.agg.fraction((table1.SEX == 'F') & (table1.HT > 65))) 0.25 Notes ----- Missing values for `predicate` are treated as ``False``. Parameters ---------- predicate : :class:`.BooleanExpression` Boolean predicate. Returns ------- :class:`.Expression` of type :py:data:`.tfloat64` Fraction of records where `predicate` is ``True``. """ return hl.bind(lambda n: hl.cond(n == 0, hl.null(hl.tfloat64), hl.float64(filter(predicate, count())) / n), count())
[docs]@typecheck(expr=expr_call) def hardy_weinberg_test(expr) -> StructExpression: """Performs test of Hardy-Weinberg equilibrium. Examples -------- Test each row of a dataset: >>> dataset_result = dataset.annotate_rows(hwe = hl.agg.hardy_weinberg_test(dataset.GT)) Test each row on a sub-population: >>> dataset_result = dataset.annotate_rows( ... hwe_eas = hl.agg.filter(dataset.pop == 'EAS', ... hl.agg.hardy_weinberg_test(dataset.GT))) Notes ----- This method performs the test described in :func:`.functions.hardy_weinberg_test` based solely on the counts of homozygous reference, heterozygous, and homozygous variant calls. The resulting struct expression has two fields: - `het_freq_hwe` (:py:data:`.tfloat64`) - Expected frequency of heterozygous calls under Hardy-Weinberg equilibrium. - `p_value` (:py:data:`.tfloat64`) - p-value from test of Hardy-Weinberg equilibrium. Hail computes the exact p-value with mid-p-value correction, i.e. the probability of a less-likely outcome plus one-half the probability of an equally-likely outcome. See this `document <LeveneHaldane.pdf>`__ for details on the Levene-Haldane distribution and references. Warning ------- Non-diploid calls (``ploidy != 2``) are ignored in the counts. While the counts are defined for multiallelic variants, this test is only statistically rigorous in the biallelic setting; use :func:`~hail.methods.split_multi` to split multiallelic variants beforehand. Parameters ---------- expr : :class:`.CallExpression` Call to test for Hardy-Weinberg equilibrium. Returns ------- :class:`.StructExpression` Struct expression with fields `het_freq_hwe` and `p_value`. """ return hl.rbind( hl.rbind( expr, lambda call: filter(call.ploidy == 2, counter(call.n_alt_alleles()) .map_values(lambda i: hl.case() .when(i < 1 << 31, hl.int(i)) .or_error('hardy_weinberg_test: count greater than MAX_INT'))), _ctx=_agg_func.context), lambda counts: hl.hardy_weinberg_test(counts.get(0, 0), counts.get(1, 0), counts.get(2, 0)))
[docs]@typecheck(f=func_spec(1, agg_expr(expr_any)), array_agg_expr=expr_oneof(expr_array(), expr_set())) def explode(f, array_agg_expr) -> Expression: """Explode an array or set expression to aggregate the elements of all records. Examples -------- Compute the mean of all elements in fields `C1`, `C2`, and `C3`: >>> table1.aggregate(hl.agg.explode(lambda elt: hl.agg.mean(elt), [table1.C1, table1.C2, table1.C3])) 24.833333333333332 Compute the set of all observed elements in the `filters` field (``Set[String]``): >>> dataset.aggregate_rows(hl.agg.explode(lambda elt: hl.agg.collect_as_set(elt), dataset.filters)) {'VQSRTrancheINDEL97.00to99.00'} Notes ----- This method can be used with aggregator functions to aggregate the elements of collection types (:class:`.tarray` and :class:`.tset`). Parameters ---------- f : Function from :class:`.Expression` to :class:`.Expression` Aggregation function to apply to each element of the exploded array. array_agg_expr : :class:`.CollectionExpression` Expression of type :class:`.tarray` or :class:`.tset`. Returns ------- :class:`.Expression` Aggregation expression. """ return _agg_func.explode(f, array_agg_expr)
[docs]@typecheck(condition=expr_bool, aggregation=agg_expr(expr_any)) def filter(condition, aggregation) -> Expression: """Filter records according to a predicate. Examples -------- Collect the `ID` field where `HT` >= 70: >>> table1.aggregate(hl.agg.filter(table1.HT >= 70, hl.agg.collect(table1.ID))) [2, 3] Notes ----- This method can be used with aggregator functions to remove records from aggregation. Parameters ---------- condition : :class:`.BooleanExpression` Filter expression. aggregation : :class:`.Expression` Aggregation expression to filter. Returns ------- :class:`.Aggregable` Aggregable expression. """ return _agg_func.filter(condition, aggregation)
[docs]@typecheck(expr=expr_call, prior=expr_float64) def inbreeding(expr, prior) -> StructExpression: """Compute inbreeding statistics on calls. Examples -------- Compute inbreeding statistics per column: >>> dataset_result = dataset.annotate_cols(IB = hl.agg.inbreeding(dataset.GT, dataset.variant_qc.AF[1])) >>> dataset_result.IB.show(width=100) +------------------+-----------+-------------+------------------+------------------+ | s | IB.f_stat | IB.n_called | IB.expected_homs | IB.observed_homs | +------------------+-----------+-------------+------------------+------------------+ | str | float64 | int64 | float64 | int64 | +------------------+-----------+-------------+------------------+------------------+ | "C1046::HG02024" | 3.88e-01 | 12 | 1.04e+01 | 11 | | "C1046::HG02025" | 3.99e-01 | 13 | 1.13e+01 | 12 | | "C1046::HG02026" | 3.88e-01 | 12 | 1.04e+01 | 11 | | "C1047::HG00731" | -1.31e+00 | 12 | 1.07e+01 | 9 | | "C1047::HG00732" | 1.00e+00 | 12 | 1.04e+01 | 12 | | "C1047::HG00733" | -8.04e-01 | 13 | 1.13e+01 | 10 | | "C1048::HG02024" | 3.88e-01 | 12 | 1.04e+01 | 11 | | "C1048::HG02025" | 3.99e-01 | 13 | 1.13e+01 | 12 | | "C1048::HG02026" | 1.00e+00 | 12 | 1.04e+01 | 12 | | "C1049::HG00731" | -1.41e+00 | 13 | 1.13e+01 | 9 | +------------------+-----------+-------------+------------------+------------------+ showing top 10 rows <BLANKLINE> Notes ----- ``E`` is total number of expected homozygous calls, given by the sum of ``1 - 2.0 * prior * (1 - prior)`` across records. ``O`` is the observed number of homozygous calls across records. ``N`` is the number of non-missing calls. ``F`` is the inbreeding coefficient, and is computed by: ``(O - E) / (N - E)``. This method returns a struct expression with four fields: - `f_stat` (:py:data:`.tfloat64`): ``F``, the inbreeding coefficient. - `n_called` (:py:data:`.tint64`): ``N``, the number of non-missing calls. - `expected_homs` (:py:data:`.tfloat64`): ``E``, the expected number of homozygotes. - `observed_homs` (:py:data:`.tint64`): ``O``, the number of observed homozygotes. Parameters ---------- expr : :class:`.CallExpression` Call expression. prior : :class:`.Expression` of type :py:data:`.tfloat64` Alternate allele frequency prior. Returns ------- :class:`.StructExpression` Struct expression with fields `f_stat`, `n_called`, `expected_homs`, `observed_homs`. """ return hl.rbind(prior, expr, lambda af, call: hl.rbind( hl.agg.filter(hl.is_defined(af) & hl.is_defined(call), hl.struct(n_called=hl.agg.count(), expected_homs=hl.agg.sum(1 - (2 * af * (1 - af))), observed_homs=hl.agg.count_where( hl.case() .when( (call.ploidy == 2) & (call.unphased_diploid_gt_index() <= 2), ~call.is_het()) .or_error( "'inbreeding' does not support non-diploid or multiallelic genotypes")))), lambda r: hl.struct( f_stat=(r['observed_homs'] - r['expected_homs']) / (r['n_called'] - r['expected_homs']), **r) ), _ctx=_agg_func.context)
[docs]@typecheck(call=expr_call, alleles=expr_oneof(expr_int32, expr_array(expr_str))) def call_stats(call, alleles) -> StructExpression: """Compute useful call statistics. Examples -------- Compute call statistics per row: >>> dataset_result = dataset.annotate_rows(gt_stats = hl.agg.call_stats(dataset.GT, dataset.alleles)) >>> dataset_result.rows().key_by('locus').select('gt_stats').show(width=120) +---------------+--------------+---------------------+-------------+---------------------------+ | locus | gt_stats.AC | gt_stats.AF | gt_stats.AN | gt_stats.homozygote_count | +---------------+--------------+---------------------+-------------+---------------------------+ | locus<GRCh37> | array<int32> | array<float64> | int32 | array<int32> | +---------------+--------------+---------------------+-------------+---------------------------+ | 20:12990057 | [148,52] | [7.40e-01,2.60e-01] | 200 | [57,9] | | 20:13029862 | [0,198] | [0.00e+00,1.00e+00] | 198 | [0,99] | | 20:13074235 | [13,187] | [6.50e-02,9.35e-01] | 200 | [1,88] | | 20:13140720 | [194,6] | [9.70e-01,3.00e-02] | 200 | [95,1] | | 20:13695498 | [175,25] | [8.75e-01,1.25e-01] | 200 | [75,0] | | 20:13714384 | [199,1] | [9.95e-01,5.00e-03] | 200 | [99,0] | | 20:13765944 | [132,2] | [9.85e-01,1.49e-02] | 134 | [65,0] | | 20:13765954 | [180,2] | [9.89e-01,1.10e-02] | 182 | [89,0] | | 20:13845987 | [2,198] | [1.00e-02,9.90e-01] | 200 | [0,98] | | 20:16223957 | [145,45] | [7.63e-01,2.37e-01] | 190 | [64,14] | +---------------+--------------+---------------------+-------------+---------------------------+ showing top 10 rows <BLANKLINE> Notes ----- This method is meaningful for computing call metrics per variant, but not especially meaningful for computing metrics per sample. This method returns a struct expression with three fields: - `AC` (:class:`.tarray` of :py:data:`.tint32`) - Allele counts. One element for each allele, including the reference. - `AF` (:class:`.tarray` of :py:data:`.tfloat64`) - Allele frequencies. One element for each allele, including the reference. - `AN` (:py:data:`.tint32`) - Allele number. The total number of called alleles, or the number of non-missing calls * 2. - `homozygote_count` (:class:`.tarray` of :py:data:`.tint32`) - Homozygote genotype counts for each allele, including the reference. Only **diploid** genotype calls are counted. Parameters ---------- call : :class:`.CallExpression` alleles : :class:`.ArrayStringExpression` or :class:`.Int32Expression` Variant alleles array, or number of alleles (including reference). Returns ------- :class:`.StructExpression` Struct expression with fields `AC`, `AF`, `AN`, and `homozygote_count`. """ if alleles.dtype == tint32: n_alleles = alleles else: n_alleles = hl.len(alleles) t = tstruct(AC=tarray(tint32), AF=tarray(tfloat64), AN=tint32, homozygote_count=tarray(tint32)) return _agg_func('CallStats', [call], t, init_op_args=[n_alleles])
_bin_idx_f = None _result_from_hist_agg_f = None
[docs]@typecheck(expr=expr_float64, start=expr_float64, end=expr_float64, bins=expr_int32) def hist(expr, start, end, bins) -> StructExpression: """Compute binned counts of a numeric expression. Examples -------- Compute a histogram of field `GQ`: >>> dataset.aggregate_entries(hl.agg.hist(dataset.GQ, 0, 100, 10)) # doctest: +SKIP_OUTPUT_CHECK Struct(bin_edges=[0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0], bin_freq=[2194L, 637L, 2450L, 1081L, 518L, 402L, 11168L, 1918L, 1379L, 11973L]), n_smaller=0, n_greater=0) Notes ----- This method returns a struct expression with four fields: - `bin_edges` (:class:`.tarray` of :py:data:`.tfloat64`): Bin edges. Bin `i` contains values in the left-inclusive, right-exclusive range ``[ bin_edges[i], bin_edges[i+1] )``. - `bin_freq` (:class:`.tarray` of :py:data:`.tint64`): Bin frequencies. The number of records found in each bin. - `n_smaller` (:py:data:`.tint64`): The number of records smaller than the start of the first bin. - `n_larger` (:py:data:`.tint64`): The number of records larger than the end of the last bin. Parameters ---------- expr : :class:`.NumericExpression` Target numeric expression. start : :obj:`int` or :obj:`float` Start of histogram range. end : :obj:`int` or :obj:`float` End of histogram range. bins : :obj:`int` Number of bins. Returns ------- :class:`.StructExpression` Struct expression with fields `bin_edges`, `bin_freq`, `n_smaller`, and `n_larger`. """ global _bin_idx_f, _result_from_hist_agg_f if _bin_idx_f is None: _bin_idx_f = hl.experimental.define_function( lambda s, e, nbins, binsize, v: (hl.case() .when(v < s, -1) .when(v > e, nbins) .when(v == e, nbins - 1) .default(hl.int32(hl.floor((v - s) / binsize)))), hl.tfloat64, hl.tfloat64, hl.tint32, hl.tfloat64, hl.tfloat64) freq_dict = hl.bind( lambda expr: hl.agg.filter(hl.is_defined(expr), hl.agg.group_by( _bin_idx_f(start, end, bins, hl.float64(end - start) / bins, expr), hl.agg.count())), expr, _ctx=_agg_func.context) def result(s, nbins, bs, freq_dict): return hl.struct( bin_edges=hl.range(0, nbins + 1).map(lambda i: s + i * bs), bin_freq=hl.range(0, nbins).map(lambda i: freq_dict.get(i, 0)), n_smaller=freq_dict.get(-1, 0), n_larger=freq_dict.get(nbins, 0)) def wrap_errors(s, e, nbins, freq_dict): return (hl.case() .when(nbins > 0, hl.bind(lambda bs: hl.case() .when((bs > 0) & hl.is_finite(bs), result(s, nbins, bs, freq_dict)) .or_error("'hist': start=" + hl.str(s) + " end=" + hl.str(e) + " bins=" + hl.str(nbins) + " requires positive bin size."), hl.float64(e - s) / nbins)) .or_error(hl.literal("'hist' requires positive 'bins', but bins=") + hl.str(nbins))) if _result_from_hist_agg_f is None: _result_from_hist_agg_f = hl.experimental.define_function( wrap_errors, hl.tfloat64, hl.tfloat64, hl.tint32, hl.tdict(hl.tint32, hl.tint64)) return _result_from_hist_agg_f(start, end, bins, freq_dict)
[docs]@typecheck(x=expr_float64, y=expr_float64, label=nullable(oneof(expr_str, expr_array(expr_str))), n_divisions=int) def downsample(x, y, label=None, n_divisions=500) -> ArrayExpression: """Downsample (x, y) coordinate datapoints. Parameters ---------- x : :class:`.NumericExpression` X-values to be downsampled. y : :class:`.NumericExpression` Y-values to be downsampled. label : :class:`.StringExpression` or :class:`.ArrayExpression` Additional data for each (x, y) coordinate. Can pass in multiple fields in an :class:`.ArrayExpression`. n_divisions : :obj:`int` Factor by which to downsample (default value = 500). A lower input results in fewer output datapoints. Returns ------- :class:`.ArrayExpression` Expression for downsampled coordinate points (x, y). The element type of the array is :py:data:`.ttuple` of :py:data:`.tfloat64`, :py:data:`.tfloat64`, and :py:data:`.tarray` of :py:data:`.tstring` """ if label is None: label = hl.null(hl.tarray(hl.tstr)) elif isinstance(label, StringExpression): label = hl.array([label]) return _agg_func('Downsample', [x, y, label], tarray(ttuple(tfloat64, tfloat64, tarray(tstr))), init_op_args=[n_divisions])
[docs]@typecheck(gp=expr_array(expr_float64)) def info_score(gp) -> StructExpression: r"""Compute the IMPUTE information score. Examples -------- Calculate the info score per variant: >>> gen_mt = hl.import_gen('data/example.gen', sample_file='data/example.sample') >>> gen_mt = gen_mt.annotate_rows(info_score = hl.agg.info_score(gen_mt.GP)) Calculate group-specific info scores per variant: >>> gen_mt = hl.import_gen('data/example.gen', sample_file='data/example.sample') >>> gen_mt = gen_mt.annotate_cols(is_case = hl.rand_bool(0.5)) >>> gen_mt = gen_mt.annotate_rows(info_score = hl.agg.group_by(gen_mt.is_case, hl.agg.info_score(gen_mt.GP))) Notes ----- The result of :func:`.info_score` is a struct with two fields: - `score` (``float64``) -- Info score. - `n_included` (``int32``) -- Number of non-missing samples included in the calculation. We implemented the IMPUTE info measure as described in the supplementary information from `Marchini & Howie. Genotype imputation for genome-wide association studies. Nature Reviews Genetics (2010) <http://www.nature.com/nrg/journal/v11/n7/extref/nrg2796-s3.pdf>`__. To calculate the info score :math:`I_{A}` for one SNP: .. math:: I_{A} = \begin{cases} 1 - \frac{\sum_{i=1}^{N}(f_{i} - e_{i}^2)}{2N\hat{\theta}(1 - \hat{\theta})} & \text{when } \hat{\theta} \in (0, 1) \\ 1 & \text{when } \hat{\theta} = 0, \hat{\theta} = 1\\ \end{cases} - :math:`N` is the number of samples with imputed genotype probabilities [:math:`p_{ik} = P(G_{i} = k)` where :math:`k \in \{0, 1, 2\}`] - :math:`e_{i} = p_{i1} + 2p_{i2}` is the expected genotype per sample - :math:`f_{i} = p_{i1} + 4p_{i2}` - :math:`\hat{\theta} = \frac{\sum_{i=1}^{N}e_{i}}{2N}` is the MLE for the population minor allele frequency Hail will not generate identical results to `QCTOOL <http://www.well.ox.ac.uk/~gav/qctool/#overview>`__ for the following reasons: - Hail automatically removes genotype probability distributions that do not meet certain requirements on data import with :func:`.import_gen` and :func:`.import_bgen`. - Hail does not use the population frequency to impute genotype probabilities when a genotype probability distribution has been set to missing. - Hail calculates the same statistic for sex chromosomes as autosomes while QCTOOL incorporates sex information. - The floating point number Hail stores for each genotype probability is slightly different than the original data due to rounding and normalization of probabilities. Warning ------- - The info score Hail reports will be extremely different from QCTOOL when a SNP has a high missing rate. - If the `gp` array must contain 3 elements, and its elements may not be missing. - If the genotype data was not imported using the :func:`.import_gen` or :func:`.import_bgen` functions, then the results for all variants will be ``score = NA`` and ``n_included = 0``. - It only makes semantic sense to compute the info score per variant. While the aggregator will run in any context if its arguments are the right type, the results are only meaningful in a narrow context. Parameters ---------- gp : :class:`.ArrayNumericExpression` Genotype probability array. Must have 3 elements, all of which must be defined. Returns ------- :class:`.StructExpression` Struct with fields `score` and `n_included`. """ return hl.rbind( gp, lambda unchecked_gp: hl.agg.filter(hl.is_defined(unchecked_gp), hl.rbind( hl.case() .when(hl.len(unchecked_gp) == 3, unchecked_gp) .or_error(f"'info_score': expected 'gp' to have length 3, " f"found length {hl.str(hl.len(unchecked_gp))}"), lambda gp: hl.rbind( gp[1], gp[2], lambda gp1, gp2: hl.rbind( gp1 + 2 * gp2, lambda mean: hl.rbind( hl.agg.sum(gp1 + 4 * gp2 - (mean * mean)), hl.agg.sum(mean), hl.agg.sum(gp1 + gp2 + gp[0]), hl.agg.count(), lambda sum_variance, expected_ac, total_dosage, n: hl.rbind( hl.cond(total_dosage != 0, expected_ac / total_dosage, hl.null(hl.tfloat64)), lambda theta: hl.struct(score=hl.case().when(n == 0, hl.null(hl.tfloat64)) .when((theta == 0.0) | (theta == 1.0), 1.0) .default(1.0 - ((sum_variance / n) / (2 * theta * (1 - theta)))), n_included=hl.int32(n)) )), _ctx=_agg_func.context ), _ctx=_agg_func.context ), _ctx=_agg_func.context )), _ctx=_agg_func.context)
_result_from_linreg_agg_f = None
[docs]@typecheck(y=expr_float64, x=oneof(expr_float64, sequenceof(expr_float64)), nested_dim=int, weight=nullable(expr_float64)) def linreg(y, x, nested_dim=1, weight=None) -> StructExpression: """Compute multivariate linear regression statistics. Examples -------- Regress HT against an intercept (1), SEX, and C1: >>> table1.aggregate(hl.agg.linreg(table1.HT, [1, table1.SEX == 'F', table1.C1])) # doctest: +SKIP_OUTPUT_CHECK Struct(beta=[88.50000000000014, 81.50000000000057, -10.000000000000068], standard_error=[14.430869689661844, 59.70552738231206, 7.000000000000016], t_stat=[6.132686518775844, 1.365032746099571, -1.428571428571435], p_value=[0.10290201427537926, 0.40250974549499974, 0.3888002244284281], multiple_standard_error=4.949747468305833, multiple_r_squared=0.7175792507204611, adjusted_r_squared=0.1527377521613834, f_stat=1.2704081632653061, multiple_p_value=0.5314327326007864, n=4) Regress blood pressure against an intercept (1), genotype, age, and the interaction of genotype and age: >>> ds_ann = ds.annotate_rows(linreg = ... hl.agg.linreg(ds.pheno.blood_pressure, ... [1, ... ds.GT.n_alt_alleles(), ... ds.pheno.age, ... ds.GT.n_alt_alleles() * ds.pheno.age])) Warning ------- As in the example, the intercept covariate ``1`` must be included **explicitly** if desired. Notes ----- In relation to `lm.summary <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/summary.lm.html>`__ in R, ``linreg(y, x = [1, mt.x1, mt.x2])`` computes ``summary(lm(y ~ x1 + x2))`` and ``linreg(y, x = [mt.x1, mt.x2], nested_dim=0)`` computes ``summary(lm(y ~ x1 + x2 - 1))``. More generally, `nested_dim` defines the number of effects to fit in the nested (null) model, with the effects on the remaining covariates fixed to zero. The returned struct has ten fields: - `beta` (:class:`.tarray` of :py:data:`.tfloat64`): Estimated regression coefficient for each covariate. - `standard_error` (:class:`.tarray` of :py:data:`.tfloat64`): Estimated standard error for each covariate. - `t_stat` (:class:`.tarray` of :py:data:`.tfloat64`): t-statistic for each covariate. - `p_value` (:class:`.tarray` of :py:data:`.tfloat64`): p-value for each covariate. - `multiple_standard_error` (:py:data:`.tfloat64`): Estimated standard deviation of the random error. - `multiple_r_squared` (:py:data:`.tfloat64`): Coefficient of determination for nested models. - `adjusted_r_squared` (:py:data:`.tfloat64`): Adjusted `multiple_r_squared` taking into account degrees of freedom. - `f_stat` (:py:data:`.tfloat64`): F-statistic for nested models. - `multiple_p_value` (:py:data:`.tfloat64`): p-value for the `F-test <https://en.wikipedia.org/wiki/F-test#Regression_problems>`__ of nested models. - `n` (:py:data:`.tint64`): Number of samples included in the regression. A sample is included if and only if `y`, all elements of `x`, and `weight` (if set) are non-missing. All but the last field are missing if `n` is less than or equal to the number of covariates or if the covariates are linearly dependent. If set, the `weight` parameter generalizes the model to `weighted least squares <https://en.wikipedia.org/wiki/Weighted_least_squares>`__, useful for heteroscedastic (diagonal but non-constant) variance. Warning ------- If any weight is negative, the resulting statistics will be ``nan``. Parameters ---------- y : :class:`.Float64Expression` Response (dependent variable). x : :class:`.Float64Expression` or :obj:`list` of :class:`.Float64Expression` Covariates (independent variables). nested_dim : :obj:`int` The null model includes the first `nested_dim` covariates. Must be between 0 and `k` (the length of `x`). weight : :class:`.Float64Expression`, optional Non-negative weight for weighted least squares. Returns ------- :class:`.StructExpression` Struct of regression results. """ x = wrap_to_list(x) if len(x) == 0: raise ValueError("linreg: must have at least one covariate in `x`") hl.methods.statgen._warn_if_no_intercept('linreg', x) if weight is not None: sqrt_weight = hl.sqrt(weight) y = sqrt_weight * y x = [sqrt_weight * xi for xi in x] k = len(x) x = hl.array(x) res_type = hl.tstruct(xty=hl.tarray(hl.tfloat64), beta=hl.tarray(hl.tfloat64), diag_inv=hl.tarray(hl.tfloat64), beta0=hl.tarray(hl.tfloat64)) temp = _agg_func('LinearRegression', [y, x], res_type, [k, hl.int32(nested_dim)]) k0 = nested_dim covs_defined = hl.all(lambda cov: hl.is_defined(cov), x) tup = hl.agg.filter(covs_defined, hl.tuple([hl.agg.count_where(hl.is_defined(y)), hl.agg.sum(y * y)])) n = tup[0] yty = tup[1] def result_from_agg(linreg_res, n, k, k0, yty): xty = linreg_res.xty beta = linreg_res.beta diag_inv = linreg_res.diag_inv beta0 = linreg_res.beta0 def dot(a, b): return hl.sum(a * b) d = n - k rss = yty - dot(xty, beta) rse2 = rss / d # residual standard error squared se = (rse2 * diag_inv) ** 0.5 t = beta / se p = t.map(lambda ti: 2 * hl.pT(-hl.abs(ti), d, True, False)) rse = hl.sqrt(rse2) d0 = k - k0 xty0 = xty[:k0] rss0 = yty - dot(xty0, beta0) r2 = 1 - rss / rss0 r2adj = 1 - (1 - r2) * (n - k0) / d f = (rss0 - rss) * d / (rss * d0) p0 = hl.pF(f, d0, d, False, False) return hl.struct( beta=beta, standard_error=se, t_stat=t, p_value=p, multiple_standard_error=rse, multiple_r_squared=r2, adjusted_r_squared=r2adj, f_stat=f, multiple_p_value=p0, n=n) global _result_from_linreg_agg_f if _result_from_linreg_agg_f is None: _result_from_linreg_agg_f = hl.experimental.define_function( result_from_agg, res_type, hl.tint64, hl.tint32, hl.tint32, hl.tfloat64, _name="linregResFromAgg") return _result_from_linreg_agg_f(temp, n, k, k0, yty)
[docs]@typecheck(x=expr_float64, y=expr_float64) def corr(x, y) -> Float64Expression: """Computes the `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`__ between `x` and `y`. Examples -------- >>> ds.aggregate_cols(hl.agg.corr(ds.pheno.age, ds.pheno.blood_pressure)) # doctest: +SKIP_OUTPUT_CHECK 0.16592876044845484 Notes ----- Only records where both `x` and `y` are non-missing will be included in the calculation. In the case that there are no non-missing pairs, the result will be missing. See Also -------- :func:`linreg` Parameters ---------- x : :class:`.Expression` of type ``tfloat64`` y : :class:`.Expression` of type ``tfloat64`` Returns ------- :class:`.Float64Expression` """ return hl.bind( lambda x, y: hl.bind( lambda a: (a.n * a.xy - a.x * a.y) / hl.sqrt((a.n * a.xsq - a.x ** 2) * (a.n * a.ysq - a.y ** 2)), hl.agg.filter(hl.is_defined(x) & hl.is_defined(y), hl.struct(x=hl.agg.sum(x), y=hl.agg.sum(y), xsq=hl.agg.sum(x ** 2), ysq=hl.agg.sum(y ** 2), xy=hl.agg.sum(x * y), n=hl.agg.count()))), x, y, _ctx=_agg_func.context)
[docs]@typecheck(group=expr_any, agg_expr=agg_expr(expr_any)) def group_by(group, agg_expr) -> DictExpression: """Compute aggregation statistics stratified by one or more groups. Examples -------- Compute linear regression statistics stratified by SEX: >>> table1.aggregate(hl.agg.group_by(table1.SEX, ... hl.agg.linreg(table1.HT, table1.C1, nested_dim=0))) # doctest: +SKIP_OUTPUT_CHECK { 'F': Struct(beta=[6.153846153846154], standard_error=[0.7692307692307685], t_stat=[8.000000000000009], p_value=[0.07916684832113098], multiple_standard_error=11.4354374979373, multiple_r_squared=0.9846153846153847, adjusted_r_squared=0.9692307692307693, f_stat=64.00000000000014, multiple_p_value=0.07916684832113098, n=2), 'M': Struct(beta=[34.25], standard_error=[1.75], t_stat=[19.571428571428573], p_value=[0.03249975499062629], multiple_standard_error=4.949747468305833, multiple_r_squared=0.9973961101073441, adjusted_r_squared=0.9947922202146882, f_stat=383.0408163265306, multiple_p_value=0.03249975499062629, n=2) } Compute call statistics stratified by population group and case status: >>> ann = ds.annotate_rows(call_stats=hl.agg.group_by(hl.struct(pop=ds.pop, is_case=ds.is_case), ... hl.agg.call_stats(ds.GT, ds.alleles))) Parameters ---------- group : :class:`.Expression` or :obj:`list` of :class:`.Expression` Group to stratify the result by. agg_expr : :class:`.Expression` Aggregation or scan expression to compute per grouping. Returns ------- :class:`.DictExpression` Dictionary where the keys are `group` and the values are the result of computing `agg_expr` for each unique value of `group`. """ return _agg_func.group_by(group, agg_expr)
@typecheck(expr=expr_any) def _prev_nonnull(expr) -> ArrayExpression: wrap = expr.dtype in {tint32, tint64, tfloat32, tfloat64, tbool, tcall} if wrap: expr = hl.or_missing(hl.is_defined(expr), hl.tuple([expr])) r = _agg_func('PrevNonnull', [expr], expr.dtype, []) if wrap: r = r[0] return r
[docs]@typecheck(f=func_spec(1, expr_any), array=expr_array()) def array_agg(f, array): """Aggregate an array element-wise using a user-specified aggregation function. Examples -------- Start with a range table with an array of random boolean values: >>> ht = hl.utils.range_table(100) >>> ht = ht.annotate(arr = hl.range(0, 5).map(lambda _: hl.rand_bool(0.5))) Aggregate to compute the fraction ``True`` per element: >>> ht.aggregate(hl.agg.array_agg(lambda element: hl.agg.fraction(element), ht.arr)) # doctest: +SKIP_OUTPUT_CHECK [0.54, 0.55, 0.46, 0.52, 0.48] Notes ----- This function requires that all values of `array` have the same length. If two values have different lengths, then an exception will be thrown. The `f` argument should be a function taking one argument, an expression of the element type of `array`, and returning an expression including aggregation(s). The type of the aggregated expression returned by :func:`array_agg` is an array of elements of the return type of `f`. Parameters ---------- f : Aggregation function to apply to each element of the exploded array. array : :class:`.ArrayExpression` Array to aggregate. Returns ------- :class:`.ArrayExpression` """ return _agg_func.array_agg(array, f)
@typecheck(expr=expr_str) def _impute_type(expr): ret_type = hl.dtype('struct{anyNonMissing: bool,' 'allDefined: bool,' 'supportsBool: bool,' 'supportsInt32: bool,' 'supportsInt64: bool,' 'supportsFloat64: bool}') return _agg_func('ImputeType', [expr], ret_type, []) class ScanFunctions(object): def __init__(self, scope): self._functions = {name: self._scan_decorator(f) for name, f in scope.items()} def _scan_decorator(self, f): @wraps(f) def wrapper(*args, **kwargs): func = getattr(f, '__wrapped__') af = func.__globals__['_agg_func'] as_scan = getattr(af, '_as_scan') setattr(af, '_as_scan', True) try: res = f(*args, **kwargs) except Exception as e: setattr(af, '_as_scan', as_scan) raise e setattr(af, '_as_scan', as_scan) return res update_wrapper(wrapper, f) return wrapper def __getattr__(self, field): if field in self._functions: return self._functions[field] else: field_matches = difflib.get_close_matches(field, self._functions.keys(), n=5) raise AttributeError("hl.scan.{} does not exist. Did you mean:\n {}".format( field, "\n ".join(field_matches)))