Esempio n. 1
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 def from_expressions(cls, exprs):
     inputs = sorted(reduce(__or__,
                            map(flip(getattr)('free_symbols'), exprs),
                            frozenset()),
                     key=flip(getattr)('name'))
     calls = tuple()
     outputs = tuple(exprs)
     return Function(inputs, calls, outputs)
Esempio n. 2
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 def free_symbols(self):
     return reduce(
         __or__,
         map(
             compose(
                 curry(reduce)(__or__),
                 tupfun(
                     flip(getattr)('free_symbols'),
                     flip(getattr)('free_symbols'))),
             self.mapping.items())) | self.arg.free_symbols
Esempio n. 3
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 def __init__(self, *pairs):
     self.pairs = tuple(pairs)
     ExprType = self.outtype
     if not all(
             map(
                 compose(
                     all,
                     tupfun(
                         flip(isinstance)(ExprType),
                         flip(isinstance)(BooleanExpression))),
                 self.pairs)):
         raise TypeError('Arguments to Piecewise have incorrect type.')
Esempio n. 4
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def sym_predict_random_forest_regressor(estimator):
    inputs = syms(estimator)
    Var = VariableFactory(existing=inputs)
    subs = tuple(map(sym_predict, estimator.estimators_))
    calls = tuple((tuple(Var() for _ in range(len(sub.outputs))), (sub, inputs)) for sub in subs)
    outputs = tuple(map(flip(__truediv__)(RealNumber(len(subs))), map(curry(reduce)(__add__), zip(*map(flip(getitem)(0), calls)))))
    return Function(inputs, calls, outputs)
Esempio n. 5
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 def trim(self, used=None):
     '''
     Remove unused computation.
     '''
     if used is None:
         used_ = frozenset(range(len(self.outputs)))
     else:
         used_ = frozenset(used)
     trimmed_outputs = tuple(map(self.outputs.__getitem__, sorted(used_)))
     used_symbols = reduce(
         __or__, map(flip(getattr)('free_symbols'), trimmed_outputs),
         frozenset())
     trimmed_calls = tuple()
     for assigned, (fun, arguments) in reversed(self.calls):
         argmap = dict(zip(fun.inputs, arguments))
         trimmed_assigned = tuple(
             filter(used_symbols.__contains__, assigned))
         if not trimmed_assigned:
             continue
         trimmed_fun = fun.trim(
             frozenset(i for i in range(len(assigned))
                       if assigned[i] in used_symbols))
         trimmed_arguments = tuple(
             map(argmap.__getitem__, trimmed_fun.inputs))
         trimmed_calls = (
             (trimmed_assigned,
              (trimmed_fun, trimmed_arguments)), ) + trimmed_calls
         used_symbols = used_symbols | frozenset(trimmed_arguments)
     trimmed_inputs = tuple(filter(used_symbols.__contains__, self.inputs))
     return Function(trimmed_inputs, trimmed_calls, trimmed_outputs)
Esempio n. 6
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def get_tenant_metrics(tenant_id, scaling_groups, grouped_servers,
                       _print=False):
    """
    Produce per-group metrics for all the groups of a tenant

    :param list scaling_groups: Tenant's scaling groups as dict from CASS
    :param dict grouped_servers: Servers from Nova grouped based on
        scaling group ID.
    :return: generator of (tenantId, groupId, desired, actual) GroupMetrics
    """
    if _print:
        print('processing tenant {} with groups {} and servers {}'.format(
              tenant_id, len(scaling_groups), len(grouped_servers)))

    groups = {g['groupId']: g for g in scaling_groups}

    for group_id in set(groups.keys() + grouped_servers.keys()):
        servers = grouped_servers.get(group_id, [])
        if group_id in groups:
            group = groups[group_id]
        else:
            group = {'groupId': group_id_from_metadata(servers[0]['metadata']),
                     'desired': 0}
        servers = map(NovaServer.from_server_details_json, servers)
        _len = compose(len, list, flip(filter, servers))
        active = _len(lambda s: s.state == ServerState.ACTIVE)
        bad = _len(lambda s: s.state in (ServerState.SHUTOFF,
                                         ServerState.ERROR,
                                         ServerState.DELETED))
        yield GroupMetrics(tenant_id, group['groupId'], group['desired'],
                           active, len(servers) - bad - active)
Esempio n. 7
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 def all_variables(self):
     result = set()
     result |= set(self.inputs)
     result |= reduce(__or__,
                      map(compose(set,
                                  flip(__getitem__)(0)), self.calls), set())
     return result
Esempio n. 8
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def get_tenant_metrics(tenant_id,
                       scaling_groups,
                       grouped_servers,
                       _print=False):
    """
    Produce per-group metrics for all the groups of a tenant

    :param list scaling_groups: Tenant's scaling groups as dict from CASS
    :param dict grouped_servers: Servers from Nova grouped based on
        scaling group ID.
    :return: generator of (tenantId, groupId, desired, actual) GroupMetrics
    """
    if _print:
        print('processing tenant {} with groups {} and servers {}'.format(
            tenant_id, len(scaling_groups), len(grouped_servers)))

    groups = {g['groupId']: g for g in scaling_groups}

    for group_id in set(groups.keys() + grouped_servers.keys()):
        servers = grouped_servers.get(group_id, [])
        if group_id in groups:
            group = groups[group_id]
        else:
            group = {
                'groupId': group_id_from_metadata(servers[0]['metadata']),
                'desired': 0
            }
        servers = map(NovaServer.from_server_details_json, servers)
        _len = compose(len, list, flip(filter, servers))
        active = _len(lambda s: s.state == ServerState.ACTIVE)
        bad = _len(lambda s: s.state in (ServerState.SHUTOFF, ServerState.
                                         ERROR, ServerState.DELETED))
        yield GroupMetrics(tenant_id, group['groupId'], group['desired'],
                           active,
                           len(servers) - bad - active)
Esempio n. 9
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 def __init__(self, mapping, arg):
     self.mapping = frozendict(mapping)
     if not all(
             map(
                 flip(isinstance)(Constant),
                 chain(mapping.keys(), mapping.values()))):
         raise TypeError(
             'Keys and values of FiniteMap must be Constants. Got %s.' %
             str(tuple(map(type, chain(mapping.keys(), mapping.values())))))
     self.arg = arg
     self.outtype = get_common_type(map(type, self.mapping.values()))
Esempio n. 10
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    def disjoint_union(self, *others, **kwargs):
        assert set(kwargs.keys()) <= {'levels', 'name', 'names'}
        pieces = (self, ) + others
        union_size = max(map(flip(getattr)('key_size'), pieces)) + 1
        offsets = [1] * len(pieces)
        if 'names' in kwargs:
            names = kwargs['names']
            del kwargs['name']
        else:
            names = [piece.name for piece in pieces]

        def filler(i, j):
            return names[i] if j == 0 else ind

        items = self._unionize(union_size, pieces, offsets, filler)
        return self.__class__(*items, **kwargs)
Esempio n. 11
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    def bin_window_(*args):
        n = common_size(*args)
        remainder = n % n_bins
        quotient = n // n_bins

        start = 0
        while start < n:
            size = quotient
            if remainder > 0:
                size += 1
                remainder -= 1
            end = start + size
            yield tuple(
                map(
                    flip(safe_rows_select)(np.arange(start, end, dtype=int)),
                    args))
            start = end
Esempio n. 12
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def get_in(keys, coll, default=None, no_default=False):
    """ Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.

    If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
    ``no_default`` is specified, then it raises KeyError or IndexError.

    ``get_in`` is a generalization of ``operator.getitem`` for nested data
    structures such as dictionaries and lists.

    >>> transaction = {'name': 'Alice',
    ...                'purchase': {'items': ['Apple', 'Orange'],
    ...                             'costs': [0.50, 1.25]},
    ...                'credit card': '5555-1234-1234-1234'}
    >>> get_in(['purchase', 'items', 0], transaction)
    'Apple'
    >>> get_in(['name'], transaction)
    'Alice'
    >>> get_in(['purchase', 'total'], transaction)
    >>> get_in(['purchase', 'items', 'apple'], transaction)
    >>> get_in(['purchase', 'items', 10], transaction)
    >>> get_in(['purchase', 'total'], transaction, 0)
    0
    >>> get_in(['y'], {}, no_default=True)
    Traceback (most recent call last):
        ...
    KeyError: 'y'

    >>> class C:
    ...     def __init__(self, x):
    ...         self.x = x
    >>> a = C(C(1))
    >>> get_in(['x', 'x'], a)
    1
    >>> get_in(['x', 'b'], a, 2)
    2

    See Also:
        itertoolz.get
        operator.getitem
    """
    reducer = flip(
        partial(get, default=(utils.no_default if no_default else default)))
    return reduce(reducer, keys, coll)
Esempio n. 13
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    def __exit__(self, exc_type, exc_value, exc_traceback):
        if isinstance(exc_value, ModuleCacheValid) or \
            exc_type is ModuleCacheValid or \
            exc_value is ModuleCacheValid:
            inspect.stack()[1][0].f_globals.update(self.moduledata)
            return True
        elif exc_value is None:
            new_moduledata = valfilter(
                complement(flip(isinstance)(ModuleType)),
                dissoc(inspect.stack()[1][0].f_globals, *self.suppress))

            # Check that all objects can be cached
            for _ in starmap(self._check_cachability, new_moduledata.items()):
                pass

            new_metadata = self.invalidator.new_metadata(new_moduledata)
            self._put_in_cache(new_metadata, new_moduledata)
            return True
        else:
            return False
Esempio n. 14
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 def _is_concrete_key(self, key):
     return isinstance(key, tuple) and all(
         map(flip(isinstance)(string_types), key))
Esempio n. 15
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 def __init__(self, *args):
     if not all(map(flip(isinstance)(self.argtype), args)):
         raise TypeError(
             'Attempt to create %s with arguments of incorrect output type.  Should be %s. Got %s.'
             % (self.__class__.__name__, self.argtype.__name__,
                str(tuple(map(lambda x: x.__class__.__name__, args)))))
Esempio n. 16
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 def free_symbols(self):
     return reduce(__or__, map(flip(getattr)('free_symbols'), self.args),
                   set())
Esempio n. 17
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def test_flip():
    def f(a, b):
        return a, b

    assert flip(f, 'a', 'b') == ('b', 'a')
Esempio n. 18
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 def from_expression(cls, expr):
     inputs = sorted(expr.free_symbols, key=flip(getattr)('name'))
     calls = tuple()
     outputs = (expr, )
     return Function(inputs, calls, outputs)
Esempio n. 19
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        if len(losses) <= self.n:
            return False
        return all(
            map(
                curry(__lt__)(-self.threshold),
                starmap(self.stat, sliding_window(2, losses[-(self.n + 1):]))))


@curry
def stop_after_n_iterations_without_stat_improvement_over_threshold(
        stat, n, threshold=0.):
    return NIterationsWithoutImprovementOverThreshold(stat, n, threshold)


stop_after_n_iterations_without_improvement_over_threshold = stop_after_n_iterations_without_stat_improvement_over_threshold(
    flip(__sub__))


def percent_reduction(before, after):
    return 100 * (after - before) / float(before)


stop_after_n_iterations_without_percent_improvement_over_threshold = stop_after_n_iterations_without_stat_improvement_over_threshold(
    percent_reduction)


class BoosterFitRecord(object):
    def __init__(self, losses, times, stopping_condition=None):
        self.losses = losses
        self.times = times
        self.stopping_condition = stopping_condition
Esempio n. 20
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 def moving_window_(*args):
     n = common_size(*args)
     window = np.arange(window_size, dtype=int)
     for i in range(n - window_size):
         yield tuple(map(flip(safe_rows_select)(window + i), *args))
Esempio n. 21
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 def free_symbols(self):
     return reduce(__or__, map(flip(getattr('free_symbols')), self.weights),
                   set()) | super(WeightedStatistic, self).free_symbols
Esempio n. 22
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        if len(losses) <= self.n:
            return False
        return all(map(curry(__lt__)(-self.threshold), starmap(self.stat, sliding_window(2, losses[-(self.n+1):]))))
@curry
def stop_after_n_iterations_without_stat_improvement_over_threshold(stat, n, threshold=0.):
    return NIterationsWithoutImprovementOverThreshold(stat, n, threshold)
#     def _stop_after_n_iterations_without_improvement(losses, **kwargs):
#         if len(losses) <= n:
#             return False
#         return all(map(curry(__lt__)(-threshold), starmap(stat, sliding_window(2, losses[-(n+1):]))))
#     return _stop_after_n_iterations_without_improvement

def reduction(before, after):
    return after - before

stop_after_n_iterations_without_improvement_over_threshold = stop_after_n_iterations_without_stat_improvement_over_threshold(flip(__sub__))

def percent_reduction(before, after):
    return 100*(after - before) / float(before)

stop_after_n_iterations_without_percent_improvement_over_threshold = stop_after_n_iterations_without_stat_improvement_over_threshold(percent_reduction)

# class GradientDescentRegressor(STSimpleEstimator):
#     def __init__(self, loss_function, initial_value=0.):
#         self.loss_function = loss_function
#     
#     def fit(self, X, y, sample_weight=None):
#         intercept = self.initial_value
#         coef = np.zeros(X.shape[1])
#         prediction = intercept + np.dot(X, coef)
#         gradient_args = {'y':y, 'pred':prediction}
Esempio n. 23
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 def free_symbols(self):
     return reduce(__or__, map(flip(getattr('free_symbols')), self.data),
                   set())
Esempio n. 24
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 def union(self, *others, **kwargs):
     pieces = (self, ) + others
     union_size = max(map(flip(getattr)('key_size'), pieces))
     items = self._unionize(union_size, pieces)
     return self.__class__(*items, **kwargs)
def test_flip():
    def f(a, b):
        return a, b

    assert flip(f, 'a', 'b') == ('b', 'a')
Esempio n. 26
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 def order_by_(*args):
     return list(map(flip(safe_rows_select)(order), args))