ray/python/ray/tune/suggest/variant_generator.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import copy
import logging
import numpy
import random
import types

from ray.tune import TuneError

logger = logging.getLogger(__name__)


def generate_variants(unresolved_spec):
    """Generates variants from a spec (dict) with unresolved values.

    There are two types of unresolved values:

        Grid search: These define a grid search over values. For example, the
        following grid search values in a spec will produce six distinct
        variants in combination:

            "activation": grid_search(["relu", "tanh"])
            "learning_rate": grid_search([1e-3, 1e-4, 1e-5])

        Lambda functions: These are evaluated to produce a concrete value, and
        can express dependencies or conditional distributions between values.
        They can also be used to express random search (e.g., by calling
        into the `random` or `np` module).

            "cpu": lambda spec: spec.config.num_workers
            "batch_size": lambda spec: random.uniform(1, 1000)

    Finally, to support defining specs in plain JSON / YAML, grid search
    and lambda functions can also be defined alternatively as follows:

        "activation": {"grid_search": ["relu", "tanh"]}
        "cpu": {"eval": "spec.config.num_workers"}
    """
    for resolved_vars, spec in _generate_variants(unresolved_spec):
        assert not _unresolved_values(spec)
        yield format_vars(resolved_vars), spec


def grid_search(values):
    """Convenience method for specifying grid search over a value.

    Arguments:
        values: An iterable whose parameters will be gridded.
    """

    return {"grid_search": values}


class sample_from(object):
    """Specify that tune should sample configuration values from this function.

    The use of function arguments in tune configs must be disambiguated by
    either wrapped the function in tune.sample_from() or tune.function().

    Arguments:
        func: An callable function to draw a sample from.
    """

    def __init__(self, func):
        self.func = func

    def __str__(self):
        return "tune.sample_from({})".format(str(self.func))

    def __repr__(self):
        return "tune.sample_from({})".format(repr(self.func))


class function(object):
    """Wraps `func` to make sure it is not expanded during resolution.

    The use of function arguments in tune configs must be disambiguated by
    either wrapped the function in tune.sample_from() or tune.function().

    Arguments:
        func: A function literal.
    """

    def __init__(self, func):
        self.func = func

    def __call__(self, *args, **kwargs):
        return self.func(*args, **kwargs)

    def __str__(self):
        return "tune.function({})".format(str(self.func))

    def __repr__(self):
        return "tune.function({})".format(repr(self.func))


_STANDARD_IMPORTS = {
    "random": random,
    "np": numpy,
}

_MAX_RESOLUTION_PASSES = 20


def resolve_nested_dict(nested_dict):
    """Flattens a nested dict by joining keys into tuple of paths.

    Can then be passed into `format_vars`.
    """
    res = {}
    for k, v in nested_dict.items():
        if isinstance(v, dict):
            for k_, v_ in resolve_nested_dict(v).items():
                res[(k, ) + k_] = v_
        else:
            res[(k, )] = v
    return res


def format_vars(resolved_vars):
    out = []
    for path, value in sorted(resolved_vars.items()):
        if path[0] in ["run", "env", "resources_per_trial"]:
            continue  # TrialRunner already has these in the experiment_tag
        pieces = []
        last_string = True
        for k in path[::-1]:
            if isinstance(k, int):
                pieces.append(str(k))
            elif last_string:
                last_string = False
                pieces.append(k)
        pieces.reverse()
        out.append(_clean_value("_".join(pieces)) + "=" + _clean_value(value))
    return ",".join(out)


def _clean_value(value):
    if isinstance(value, float):
        return "{:.5}".format(value)
    else:
        return str(value).replace("/", "_")


def _generate_variants(spec):
    spec = copy.deepcopy(spec)
    unresolved = _unresolved_values(spec)
    if not unresolved:
        yield {}, spec
        return

    grid_vars = []
    lambda_vars = []
    for path, value in unresolved.items():
        if isinstance(value, types.FunctionType):
            lambda_vars.append((path, value))
        else:
            grid_vars.append((path, value))
    grid_vars.sort()

    grid_search = _grid_search_generator(spec, grid_vars)
    for resolved_spec in grid_search:
        resolved_vars = _resolve_lambda_vars(resolved_spec, lambda_vars)
        for resolved, spec in _generate_variants(resolved_spec):
            for path, value in grid_vars:
                resolved_vars[path] = _get_value(spec, path)
            for k, v in resolved.items():
                if (k in resolved_vars and v != resolved_vars[k]
                        and _is_resolved(resolved_vars[k])):
                    raise ValueError(
                        "The variable `{}` could not be unambiguously "
                        "resolved to a single value. Consider simplifying "
                        "your configuration.".format(k))
                resolved_vars[k] = v
            yield resolved_vars, spec


def _assign_value(spec, path, value):
    for k in path[:-1]:
        spec = spec[k]
    spec[path[-1]] = value


def _get_value(spec, path):
    for k in path:
        spec = spec[k]
    return spec


def _resolve_lambda_vars(spec, lambda_vars):
    resolved = {}
    error = True
    num_passes = 0
    while error and num_passes < _MAX_RESOLUTION_PASSES:
        num_passes += 1
        error = False
        for path, fn in lambda_vars:
            try:
                value = fn(_UnresolvedAccessGuard(spec))
            except RecursiveDependencyError as e:
                error = e
            except Exception:
                raise ValueError(
                    "Failed to evaluate expression: {}: {}".format(path, fn) +
                    ". If you meant to pass this as a function literal, use "
                    "tune.function() to escape it.")
            else:
                _assign_value(spec, path, value)
                resolved[path] = value
    if error:
        raise error
    return resolved


def _grid_search_generator(unresolved_spec, grid_vars):
    value_indices = [0] * len(grid_vars)

    def increment(i):
        value_indices[i] += 1
        if value_indices[i] >= len(grid_vars[i][1]):
            value_indices[i] = 0
            if i + 1 < len(value_indices):
                return increment(i + 1)
            else:
                return True
        return False

    if not grid_vars:
        yield unresolved_spec
        return

    while value_indices[-1] < len(grid_vars[-1][1]):
        spec = copy.deepcopy(unresolved_spec)
        for i, (path, values) in enumerate(grid_vars):
            _assign_value(spec, path, values[value_indices[i]])
        yield spec
        if grid_vars:
            done = increment(0)
            if done:
                break


def _is_resolved(v):
    resolved, _ = _try_resolve(v)
    return resolved


def _try_resolve(v):
    if isinstance(v, types.FunctionType):
        raise DeprecationWarning(
            "Function values are ambiguous in Tune "
            "configuations. Either wrap the function with "
            "`tune.function(func)` to specify a function literal, or "
            "`tune.sample_from(func)` to tell Tune to "
            "sample values from the function during variant generation: "
            "{}".format(v))
        return False, v
    elif isinstance(v, sample_from):
        # Function to sample from
        return False, v.func
    elif isinstance(v, dict) and len(v) == 1 and "eval" in v:
        # Lambda function in eval syntax
        return False, lambda spec: eval(
            v["eval"], _STANDARD_IMPORTS, {"spec": spec})
    elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
        # Grid search values
        grid_values = v["grid_search"]
        if not isinstance(grid_values, list):
            raise TuneError(
                "Grid search expected list of values, got: {}".format(
                    grid_values))
        return False, grid_values
    return True, v


def _unresolved_values(spec):
    found = {}
    for k, v in spec.items():
        resolved, v = _try_resolve(v)
        if not resolved:
            found[(k, )] = v
        elif isinstance(v, dict):
            # Recurse into a dict
            for (path, value) in _unresolved_values(v).items():
                found[(k, ) + path] = value
        elif isinstance(v, list):
            # Recurse into a list
            for i, elem in enumerate(v):
                for (path, value) in _unresolved_values({i: elem}).items():
                    found[(k, ) + path] = value
    return found


class _UnresolvedAccessGuard(dict):
    def __init__(self, *args, **kwds):
        super(_UnresolvedAccessGuard, self).__init__(*args, **kwds)
        self.__dict__ = self

    def __getattribute__(self, item):
        value = dict.__getattribute__(self, item)
        if not _is_resolved(value):
            raise RecursiveDependencyError(
                "`{}` recursively depends on {}".format(item, value))
        elif isinstance(value, dict):
            return _UnresolvedAccessGuard(value)
        else:
            return value


class RecursiveDependencyError(Exception):
    def __init__(self, msg):
        Exception.__init__(self, msg)