src/ports/postgres/modules/deep_learning/madlib_keras_automl.py_in

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import plpy
import math

from utilities.utilities import _assert
from utilities.control import MinWarning

class AutoMLSchema:
    BRACKET = 's'
    ROUND = 'i'
    CONFIGURATIONS = 'n_i'
    RESOURCES = 'r_i'

@MinWarning("warning")
class HyperbandSchedule():
    """The utility class for loading a hyperband schedule table with algorithm inputs.

    Attributes:
        schedule_table (string): Name of output table containing hyperband schedule.
        R (int): Maximum number of resources (iterations) that can be allocated
  to a single configuration.
        eta (int): Controls the proportion of configurations discarded in
  each round of successive halving.
        skip_last (int): The number of last rounds to skip.
    """
    def __init__(self, schedule_table, R, eta=3, skip_last=0):
        self.schedule_table = schedule_table # table name to store hyperband schedule
        self.R = R # maximum iterations/epochs allocated to a configuration
        self.eta = eta # defines downsampling rate
        self.skip_last = skip_last
        self.validate_inputs()

        # number of unique executions of Successive Halving (minus one)
        self.s_max = int(math.floor(math.log(self.R, self.eta)))
        self.validate_s_max()

        self.schedule_vals = []

        self.calculate_schedule()

    def load(self):
        """
        The entry point for loading the hyperband schedule table.
        """
        self.create_schedule_table()
        self.insert_into_schedule_table()

    def validate_inputs(self):
        """
        Validates user input values
        """
        _assert(self.eta > 1, "DL: eta must be greater than 1")
        _assert(self.R >= self.eta, "DL: R should not be less than eta")

    def validate_s_max(self):
        _assert(self.skip_last >= 0 and self.skip_last < self.s_max+1, "DL: skip_last must be " +
                "non-negative and less than {0}".format(self.s_max))

    def calculate_schedule(self):
        """
        Calculates the hyperband schedule (number of configs and allocated resources)
        in each round of each bracket and skips the number of last rounds specified in 'skip_last'
        """
        for s in reversed(range(self.s_max+1)):
            n = int(math.ceil(int((self.s_max + 1)/(s+1))*math.pow(self.eta, s))) # initial number of configurations
            r = self.R * math.pow(self.eta, -s)

            for i in range((s+1) - int(self.skip_last)):
                # Computing each of the
                n_i = n*math.pow(self.eta, -i)
                r_i = r*math.pow(self.eta, i)

                self.schedule_vals.append({AutoMLSchema.BRACKET: s,
                                           AutoMLSchema.ROUND: i,
                                           AutoMLSchema.CONFIGURATIONS: int(n_i),
                                           AutoMLSchema.RESOURCES: int(round(r_i))})

    def create_schedule_table(self):
        """Initializes the output schedule table"""
        create_query = """
                        CREATE TABLE {self.schedule_table} (
                            {s} INTEGER,
                            {i} INTEGER,
                            {n_i} INTEGER,
                            {r_i} INTEGER,
                            unique ({s}, {i})
                        );
                       """.format(self=self,
                                  s=AutoMLSchema.BRACKET,
                                  i=AutoMLSchema.ROUND,
                                  n_i=AutoMLSchema.CONFIGURATIONS,
                                  r_i=AutoMLSchema.RESOURCES)
        with MinWarning('warning'):
            plpy.execute(create_query)

    def insert_into_schedule_table(self):
        """Insert everything in self.schedule_vals into the output schedule table."""
        for sd in self.schedule_vals:
            sd_s = sd[AutoMLSchema.BRACKET]
            sd_i = sd[AutoMLSchema.ROUND]
            sd_n_i = sd[AutoMLSchema.CONFIGURATIONS]
            sd_r_i = sd[AutoMLSchema.RESOURCES]
            insert_query = """
                            INSERT INTO
                                {self.schedule_table}(
                                    {s_col},
                                    {i_col},
                                    {n_i_col},
                                    {r_i_col}
                                )
                            VALUES (
                                {sd_s},
                                {sd_i},
                                {sd_n_i},
                                {sd_r_i}
                            )
                           """.format(s_col=AutoMLSchema.BRACKET,
                                      i_col=AutoMLSchema.ROUND,
                                      n_i_col=AutoMLSchema.CONFIGURATIONS,
                                      r_i_col=AutoMLSchema.RESOURCES,
                                      **locals())
            plpy.execute(insert_query)