class Blueprint(object):

    def __init__(self, layout, training, score=None):
        self.layout = layout
        self.training = training
        self.score = score

    def scale_layers_size(self, scale):
        for layer in self.layout.get_layers():
            output_dim = layer.parameters.get('output_dim', None)
            if not output_dim:
                continue
            new_size = output_dim * scale
            logging.debug(
                'Scaling output dim: %d=>%d',
                output_dim,
                new_size)
            layer.parameters['output_dim'] = new_size

    def todict(self):
        return {
            'layout': self.layout.todict(),
            'training': self.training.todict(),
            'score': self.score} class Training(object):

    def __init__(self, objective, optimizer, metric,
                 stopping, batch_size, class_weight=None):
        self.objective = objective
        self.optimizer = optimizer
        self.metric = metric
        self.stopping = stopping
        self.batch_size = batch_size
        self.class_weight = class_weight

    def todict(self):
        return {
            'objective': self.objective.todict(),
            'optimizer': self.optimizer.todict(),
            'metric': self.metric.todict(),
            'stopping': self.stopping.todict(),
            'batch_size': self.batch_size,
            'class_weight': self.class_weight} class Layout(object):

    def __init__(self, input_size, output_size,
                 output_activation, block=None, block_input=None, rows=None):
        self.input_size = int(input_size)
        self.output_size = int(output_size)
        self.output_activation = output_activation
        self.block = block
        self.block_input = block_input
        self.rows = rows or list()

    def get_rows(self):
        return self.rows

    def get_blocks(self):
        return [
            block
            for row in self.rows
            for block in row.get_blocks()]

    def get_layers(self):
        return [
            layer
            for row in self.rows
            for layer in row.get_layers()]

    def todict(self):
        dict_repr = dict(vars(self))
        del dict_repr['block']
        dict_repr['rows'] = [row.todict() for row in self.rows]
        return dict_repr class Row(object):

    def __init__(self, blocks=None):
        self.blocks = blocks or list()

    def get_blocks(self):
        return self.blocks

    def get_layers(self):
        return [
            layer
            for block in self.blocks
            for layer in block.get_layers()]

    def todict(self):
        return {
            'blocks': [
                block.todict()
                for block in self.blocks]} class Layer(object):

    def __init__(self, layer_type,
                 parameters=None, parameter_constraints=None):
        self.layer_type = layer_type
        self.parameters = parameters or dict()
        self.parameter_constraints = parameter_constraints or dict()

    def apply_constraints(self):
        self.parameters.update(self.parameter_constraints)

    def todict(self):
        return dict(vars(self)) class Block(object):

    def __init__(self, layers=None, input_layers=None):
        self.layers = layers or list()
        self.input_layers = input_layers or list()

    def get_layers(self):
        return self.layers

    def todict(self):
        return {
            'input_layers': [
                layer.todict()
                for layer in self.input_layers],
            'layers': [
                layer.todict()
                for layer in self.layers]} class Optimizer(object):

    def __init__(self, optimizer=None, parameters=None):
        self.optimizer = optimizer
        self.parameters = parameters or dict()

    def todict(self):
        return dict(vars(self)) def random_initial_param_value(param, mutation_std=0.1):
    if isinstance(param, dict):
        return _random_dict_param_value(param)
    if isinstance(param, list) or isinstance(param, tuple):
        return _random_list_param_value(param)
    if not isinstance(param, Parameter):
        return param
    if param.values:
        return random_list_element(param.values)

    initial_value = None
    if param.default is not None:
        initial_value = param.default
    elif param.lo is not None:
        initial_value = param.lo
    else:
        initial_value = 0
    return mutate_param(param, initial_value, mutation_std) def random_list_element(elements):
    if len(elements) == 0:
        return None
    if len(elements) == 1:
        return elements[0]
    return elements[rand.randint(0, len(elements) - 1)] def mutate_param(param, value, mutation_std=0.1):
    if isinstance(param, dict):
        return {
            name: mutate_param(nested, value.get(name, None))
            for name, nested in param.items()}
    if not isinstance(param, Parameter):
        return value
    if param.optional and value is not None and rand.random() < 0.5:
        return None
    if param.values:
        if len(set(param.values)) == 1:
            return param.values[0]
        element = random_list_element(param.values)
        while element == value:
            element = random_list_element(param.values)
        return element
    elif param.lo == param.hi:
        return value
    value = value or 0
    std = mutation_std * (param.hi - param.lo)
    if param.param_type == int:
        std = max(1, std)
    new_value = value + param.param_type(numpy.random.normal(0, std, 1)[0])
    while not is_valid_param_value(param, new_value) or new_value == value:
        new_value = value + param.param_type(numpy.random.normal(0, std, 1)[0])
    return new_value
from copy import deepcopy
from random import Random, shuffle
from minos.experiment.experiment import Blueprint
from minos.experiment.training import Training
from minos.model.model import Layout, Row, Layer, Block,\
    Optimizer
from minos.model.parameter import random_initial_param_value,\
    random_list_element, mutate_param
            row.blocks.append(
                _random_layout_block(
                    layout,
                    row_idx,
                    parameters,
                    init_layer_parameters=True))
    else:
        while len(row.blocks) > new_blocks:
            idx = rand.randint(0, len(row.blocks) - 1)
            row.blocks = [
                b
                for i, b in enumerate(row.blocks)
                if i != idx]


def _mutate_layout_layers(layout, parameters):
    row = random_list_element(layout.rows)
    block = random_list_element(row.blocks)
    current_layers = len(block.get_layers())
    new_layers = mutate_param(
        parameters.get_layout_parameter('layers'),
        current_layers)
    if new_layers > current_layers:
        for _ in range(current_layers, new_layers):
            layers = get_allowed_new_block_layers(block.layers, parameters)
            block.layers += _create_template_layers(
                [random_list_element(layers)],
                parameters,
                init_layer_parameters=True)
    else:
        while len(block.layers) > new_layers:
            idx = rand.randint(0, len(block.layers) - 1)
            block.layers = [
                l
                for i, l in enumerate(block.layers)
                if i != idx]


def _mutate_layer_parameters(layout, parameters, p_mutate_param=0.1, mutation_std=0.1):
    for row in layout.rows:
        for block in row.blocks:
            for layer in block.layers:
                _mutate_layer(
                    layer,
                    parameters,
                    p_mutate_param,
                    mutation_std)


def _mutate_layer(layer, parameters, p_mutate_param=0.1, mutation_std=0.1):
    param_space = deepcopy(parameters.get_layer_parameters(layer.layer_type))
    for name, value in layer.parameters.items():
        if rand.random() < p_mutate_param:
            layer.parameters[name] = mutate_param(
                param_space[name],
                value,
                mutation_std)
    layer.apply_constraints()


def _mutate_optimizer(optimizer, parameters,
                      p_mutate_param=0.1, mutation_std=0.1):
    param_space = deepcopy(parameters.get_optimizer_parameters(optimizer.optimizer))
    for name, value in optimizer.parameters.items():
        if rand.random() < p_mutate_param:
            optimizer.parameters[name] = mutate_param(
                param_space[name],
                value,
                mutation_std)


def mix_blueprints(blueprint1, blueprint2,
                   parameters, p_mutate_param=0.05, mutation_std=0.1,
                   mutate_layout=True, mutate_params=True, mutate_optimizer=True):
    layouts = [blueprint1.layout, blueprint2.layout]
    if mutate_layout:
        layout = _mix_layouts(
            layouts,
            parameters,
            p_mutate_param,
            mutation_std)
    else:
        layout = deepcopy(random_list_element(layouts))
    if mutate_params:
        _mutate_layer_parameters(
            layout,
            parameters,
            p_mutate_param=p_mutate_param,
            mutation_std=mutation_std)
    trainings = [blueprint1.training, blueprint2.training]
    training = deepcopy(random_list_element(trainings))
    if mutate_optimizer:
        _mutate_optimizer(
            training.optimizer,
            parameters,
            p_mutate_param,
            mutation_std)
    return Blueprint(layout, training)


def _mix_optimizers(parents):
    return deepcopy(random_list_element(parents))


def _mix_layouts(parent_layouts, parameters, p_mutate_param=0.05, mutation_std=0.1):
    layout = Layout(
        input_size=parent_layouts[0].input_size,
        output_size=parent_layouts[0].output_size,
        output_activation=parent_layouts[0].output_activation,
        block=parent_layouts[0].block,
        block_input=parent_layouts[0].block_input)
    rows = random_list_element([len(p.rows) for p in parent_layouts])
    for row_idx in range(rows):
        parent_rows = [p.rows[row_idx] for p in parent_layouts if row_idx < len(p.rows)]
        layout.rows.append(_mix_rows(layout, row_idx, parent_rows))
    _setup_block_inputs(layout, parameters)
    return layout


def _mix_rows(layout, row_idx, parent_rows):

    row = Row()