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from datetime import datetime
import plpy
import math
from time import time

from madlib_keras_validator import MstLoaderInputValidator
from utilities.utilities import unique_string, add_postfix, extract_keyvalue_params, \
    _assert, _assert_equal, rename_table
from utilities.control import MinWarning, SetGUC
from madlib_keras_fit_multiple_model import FitMultipleModel
from madlib_keras_model_selection import MstSearch, ModelSelectionSchema
from keras_model_arch_table import ModelArchSchema
from utilities.validate_args import table_exists, drop_tables


class AutoMLSchema:
    BRACKET = 's'
    ROUND = 'i'
    CONFIGURATIONS = 'n_i'
    RESOURCES = 'r_i'
    HYPERBAND = 'hyperband'
    R = 'R'
    ETA = 'eta'
    SKIP_LAST = 'skip_last'
    LOSS_METRIC = 'training_loss_final'
    TEMP_MST_TABLE = unique_string('temp_mst_table')
    TEMP_MST_SUMMARY_TABLE = add_postfix(TEMP_MST_TABLE, '_summary')
    TEMP_OUTPUT_TABLE = unique_string('temp_output_table')
    METRICS_ITERS = 'metrics_iters' # custom column


@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)

@MinWarning("warning")
class KerasAutoML():
    """The core AutoML function for running AutoML algorithms such as Hyperband.
    This function executes the hyperband rounds 'diagonally' to evaluate multiple configurations together
    and leverage the compute power of MPP databases such as Greenplum.
    """
    def __init__(self, schema_madlib, source_table, model_output_table, model_arch_table, model_selection_table,
                 model_id_list, compile_params_grid, fit_params_grid, automl_method='hyperband',
                 automl_params='R=6, eta=3, skip_last=0', random_state=None, object_table=None,
                 use_gpus=False, validation_table=None, metrics_compute_frequency=None,
                 name=None, description=None, **kwargs):
        self.schema_madlib = schema_madlib
        self.source_table = source_table
        self.model_output_table = model_output_table
        if self.model_output_table:
            self.model_info_table = add_postfix(self.model_output_table, '_info')
            self.model_summary_table = add_postfix(self.model_output_table, '_summary')
        self.model_arch_table = model_arch_table
        self.model_selection_table = model_selection_table
        self.model_selection_summary_table = add_postfix(
            model_selection_table, "_summary")
        self.model_id_list = sorted(list(set(model_id_list)))
        self.compile_params_grid = compile_params_grid
        self.fit_params_grid = fit_params_grid

        MstLoaderInputValidator(
            model_arch_table=self.model_arch_table,
            model_selection_table=self.model_selection_table,
            model_selection_summary_table=self.model_selection_summary_table,
            model_id_list=self.model_id_list,
            compile_params_list=compile_params_grid,
            fit_params_list=fit_params_grid,
            object_table=object_table,
            module_name='madlib_keras_automl'
        )

        self.automl_method = automl_method if automl_method else 'hyperband'
        self.automl_params = automl_params if automl_params else 'R=6, eta=3, skip_last=0'
        self.random_state = random_state
        self.validate_and_define_inputs()

        self.object_table = object_table
        self.use_gpus = use_gpus if use_gpus else False
        self.validation_table = validation_table
        self.metrics_compute_frequency = metrics_compute_frequency
        self.name = name
        self.description = description

        if self.validation_table:
            AutoMLSchema.LOSS_METRIC = 'validation_loss_final'

        self.create_model_output_table()
        self.create_model_output_info_table()

        if AutoMLSchema.HYPERBAND.startswith(self.automl_method.lower()):
            self.find_hyperband_config()

    def create_model_output_table(self):
        output_table_create_query = """
                                    CREATE TABLE {self.model_output_table}
                                    ({ModelSelectionSchema.MST_KEY} INTEGER PRIMARY KEY,
                                     {ModelArchSchema.MODEL_WEIGHTS} BYTEA,
                                     {ModelArchSchema.MODEL_ARCH} JSON)
                                    """.format(self=self, ModelSelectionSchema=ModelSelectionSchema,
                                               ModelArchSchema=ModelArchSchema)
        with MinWarning('warning'):
            plpy.execute(output_table_create_query)

    def create_model_output_info_table(self):
        info_table_create_query = """
                                  CREATE TABLE {self.model_info_table}
                                  ({ModelSelectionSchema.MST_KEY} INTEGER PRIMARY KEY,
                                   {ModelArchSchema.MODEL_ID} INTEGER,
                                   {ModelSelectionSchema.COMPILE_PARAMS} TEXT,
                                   {ModelSelectionSchema.FIT_PARAMS} TEXT,
                                   model_type TEXT,
                                   model_size DOUBLE PRECISION,
                                   metrics_elapsed_time DOUBLE PRECISION[],
                                   metrics_type TEXT[],
                                   loss_type TEXT,
                                   training_metrics_final DOUBLE PRECISION,
                                   training_loss_final DOUBLE PRECISION,
                                   training_metrics DOUBLE PRECISION[],
                                   training_loss DOUBLE PRECISION[],
                                   validation_metrics_final DOUBLE PRECISION,
                                   validation_loss_final DOUBLE PRECISION,
                                   validation_metrics DOUBLE PRECISION[],
                                   validation_loss DOUBLE PRECISION[],
                                   {AutoMLSchema.METRICS_ITERS} INTEGER[])
                                       """.format(self=self, ModelSelectionSchema=ModelSelectionSchema,
                                                  ModelArchSchema=ModelArchSchema, AutoMLSchema=AutoMLSchema)
        with MinWarning('warning'):
            plpy.execute(info_table_create_query)

    def validate_and_define_inputs(self):

        if AutoMLSchema.HYPERBAND.startswith(self.automl_method.lower()):
            automl_params_dict = extract_keyvalue_params(self.automl_params,
                                                         default_values={'R': 6, 'eta': 3, 'skip_last': 0},
                                                         lower_case_names=False)
            # casting dict values to int
            for i in automl_params_dict:
                automl_params_dict[i] = int(automl_params_dict[i])
            _assert(len(automl_params_dict) >= 1 or len(automl_params_dict) <= 3,
                    "DL: Only R, eta, and skip_last may be specified")
            for i in automl_params_dict:
                if i == AutoMLSchema.R:
                    self.R = automl_params_dict[AutoMLSchema.R]
                elif i == AutoMLSchema.ETA:
                    self.eta = automl_params_dict[AutoMLSchema.ETA]
                elif i == AutoMLSchema.SKIP_LAST:
                    self.skip_last = automl_params_dict[AutoMLSchema.SKIP_LAST]
                else:
                    plpy.error("DL: {0} is an invalid param".format(i))
            _assert(self.eta > 1, "DL: eta must be greater than 1")
            _assert(self.R >= self.eta, "DL: R should not be less than eta")
            self.s_max = int(math.floor(math.log(self.R, self.eta)))
            _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))
        else:
            plpy.error("DL: Only hyperband is currently supported as the automl method")

    def _is_valid_metrics_compute_frequency(self, num_iterations):
        """
        Utility function (same as that in the Fit Multiple function) to check validity of mcf value for computing
        metrics during an AutoML algorithm run.
        :param num_iterations: interations/resources to allocate for training.
        :return: boolean on validity of the mcf value.
        """
        return self.metrics_compute_frequency is None or \
               (self.metrics_compute_frequency >= 1 and \
                self.metrics_compute_frequency <= num_iterations)

    def find_hyperband_config(self):
        """
        Runs the diagonal hyperband algorithm.
        """
        initial_vals = {}

        # get hyper parameter configs for each s
        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) # initial number of iterations to run configurations for
            initial_vals[s] = (n, int(round(r)))
        self.start_training_time = self.get_current_timestamp()
        random_search = MstSearch(self.model_arch_table, self.model_selection_table, self.model_id_list,
                                  self.compile_params_grid, self.fit_params_grid, 'random',
                                  sum([initial_vals[k][0] for k in initial_vals][self.skip_last:]), self.random_state,
                                  self.object_table)
        random_search.load() # for populating mst tables

        # for creating the summary table for usage in fit multiple
        plpy.execute("CREATE TABLE {AutoMLSchema.TEMP_MST_SUMMARY_TABLE} AS " \
                     "SELECT * FROM {random_search.model_selection_summary_table}".format(AutoMLSchema=AutoMLSchema,
                                                                                          random_search=random_search))
        ranges_dict = self.mst_key_ranges_dict(initial_vals)
        # to store the bracket and round numbers
        s_dict, i_dict = {}, {}
        for key, val in ranges_dict.items():
            for mst_key in range(val[0], val[1]+1):
                s_dict[mst_key] = key
                i_dict[mst_key] = -1

        # outer loop on diagonal
        for i in range((self.s_max+1) - int(self.skip_last)):
            # inner loop on s desc
            temp_lst = []
            configs_prune_lookup = {}
            for s in range(self.s_max, self.s_max-i-1, -1):
                n = initial_vals[s][0]
                n_i = n * math.pow(self.eta, -i+self.s_max-s)
                configs_prune_lookup[s] = int(round(n_i))
                temp_lst.append("{0} configs under bracket={1} & round={2}".format(int(n_i), s, s-self.s_max+i))
            num_iterations = int(initial_vals[self.s_max-i][1])
            plpy.info('*** Diagonally evaluating ' + ', '.join(temp_lst) + ' with {0} iterations ***'.format(
                num_iterations))

            self.reconstruct_temp_mst_table(i, ranges_dict, configs_prune_lookup) # has keys to evaluate
            active_keys = plpy.execute("SELECT mst_key FROM {AutoMLSchema.TEMP_MST_TABLE}".format(AutoMLSchema=
                                                                                                  AutoMLSchema))
            for k in active_keys:
                i_dict[k['mst_key']] += 1
            self.warm_start = int(i != 0)
            mcf = self.metrics_compute_frequency if self._is_valid_metrics_compute_frequency(num_iterations) else None
            model_training = FitMultipleModel(self.schema_madlib, self.source_table, AutoMLSchema.TEMP_OUTPUT_TABLE,
                                              AutoMLSchema.TEMP_MST_TABLE, num_iterations, self.use_gpus,
                                              self.validation_table, mcf, self.warm_start, self.name, self.description)
            self.update_model_output_table(model_training)
            self.update_model_output_info_table(i, model_training, initial_vals)
        self.end_training_time = self.get_current_timestamp()
        self.add_additional_info_cols(s_dict, i_dict)
        self.update_model_selection_table()
        self.generate_model_output_summary_table(model_training)
        self.remove_temp_tables(model_training)

    def get_current_timestamp(self):
        """for start and end times for the chosen AutoML algorithm. Showcased in the output summary table"""
        return datetime.fromtimestamp(time()).strftime('%Y-%m-%d %H:%M:%S')

    def mst_key_ranges_dict(self, initial_vals):
        """
        Extracts the ranges of model configs (using mst_keys) belonging to / sampled as part of
        executing a particular SHA bracket.
        """
        d = {}
        for s_val in sorted(initial_vals.keys(), reverse=True): # going from s_max to 0
            if s_val == self.s_max:
                d[s_val] = (1, initial_vals[s_val][0])
            else:
                d[s_val] = (d[s_val+1][1]+1, d[s_val+1][1]+initial_vals[s_val][0])
        return d

    def reconstruct_temp_mst_table(self, i, ranges_dict, configs_prune_lookup):
        """
        Drops and Reconstructs a temp mst table for evaluation along particular diagonals of hyperband.
        :param i: outer diagonal loop iteration.
        :param ranges_dict: model config ranges to group by bracket number.
        :param configs_prune_lookup: Lookup dictionary for configs to evaluate for a diagonal.
        :return:
        """
        if i == 0:
            _assert_equal(len(configs_prune_lookup), 1, "invalid args")
            lower_bound, upper_bound = ranges_dict[self.s_max]
            plpy.execute("CREATE TABLE {AutoMLSchema.TEMP_MST_TABLE} AS SELECT * FROM {self.model_selection_table} "
                         "WHERE mst_key >= {lower_bound} AND mst_key <= {upper_bound}".format(self=self,
                                                                                              AutoMLSchema=AutoMLSchema,
                                                                                              lower_bound=lower_bound,
                                                                                              upper_bound=upper_bound,))
            return
        # dropping and repopulating temp_mst_table
        drop_tables([AutoMLSchema.TEMP_MST_TABLE])

        # {mst_key} changed from SERIAL to INTEGER for safe insertions and preservation of mst_key values
        create_query = """
                        CREATE TABLE {AutoMLSchema.TEMP_MST_TABLE} (
                            {mst_key} INTEGER,
                            {model_id} INTEGER,
                            {compile_params} VARCHAR,
                            {fit_params} VARCHAR,
                            unique ({model_id}, {compile_params}, {fit_params})
                        );
                       """.format(AutoMLSchema=AutoMLSchema,
                                  mst_key=ModelSelectionSchema.MST_KEY,
                                  model_id=ModelSelectionSchema.MODEL_ID,
                                  compile_params=ModelSelectionSchema.COMPILE_PARAMS,
                                  fit_params=ModelSelectionSchema.FIT_PARAMS)
        with MinWarning('warning'):
            plpy.execute(create_query)

        query = ""
        new_configs = True
        for s_val in configs_prune_lookup:
            lower_bound, upper_bound = ranges_dict[s_val]
            if new_configs:
                query += "INSERT INTO {AutoMLSchema.TEMP_MST_TABLE} SELECT mst_key, model_id, compile_params, fit_params " \
                         "FROM {self.model_selection_table} WHERE mst_key >= {lower_bound} " \
                         "AND mst_key <= {upper_bound};".format(self=self, AutoMLSchema=AutoMLSchema,
                                                                lower_bound=lower_bound, upper_bound=upper_bound)
                new_configs = False
            else:
                query += "INSERT INTO {AutoMLSchema.TEMP_MST_TABLE} SELECT mst_key, model_id, compile_params, fit_params " \
                         "FROM {self.model_info_table} WHERE mst_key >= {lower_bound} " \
                         "AND mst_key <= {upper_bound} ORDER BY {AutoMLSchema.LOSS_METRIC} " \
                         "LIMIT {configs_prune_lookup_val};".format(self=self, AutoMLSchema=AutoMLSchema,
                                                                    lower_bound=lower_bound, upper_bound=upper_bound,
                                                                    configs_prune_lookup_val=configs_prune_lookup[s_val])
        plpy.execute(query)

    def update_model_output_table(self, model_training):
        """
        Updates gathered information of a hyperband diagonal run to the overall model output table.
        :param model_training: Fit Multiple function call object.
        """
        # updates model weights for any previously trained configs
        plpy.execute("UPDATE {self.model_output_table} a SET model_weights=" \
                     "t.model_weights FROM {model_training.original_model_output_table} t " \
                     "WHERE a.mst_key=t.mst_key".format(self=self, model_training=model_training))

        # truncate and re-creates table to avoid memory blow-ups
        with SetGUC("dev_opt_unsafe_truncate_in_subtransaction", "on"):
            temp_model_table = unique_string('updated_model')
            plpy.execute("CREATE TABLE {temp_model_table} AS SELECT * FROM {self.model_output_table};" \
                         "TRUNCATE {self.model_output_table}; " \
                         "DROP TABLE {self.model_output_table};".format(temp_model_table=temp_model_table, self=self))
            rename_table(self.schema_madlib, temp_model_table, self.model_output_table)

        # inserts any newly trained configs
        plpy.execute("INSERT INTO {self.model_output_table} SELECT * FROM {model_training.original_model_output_table} " \
                     "WHERE {model_training.original_model_output_table}.mst_key NOT IN " \
                     "(SELECT mst_key FROM {self.model_output_table})".format(self=self,
                                                                              model_training=model_training))

    def update_model_output_info_table(self, i, model_training, initial_vals):
        """
        Updates gathered information of a hyperband diagonal run to the overall model output info table.
        :param i: outer diagonal loop iteration.
        :param model_training: Fit Multiple function call object.
        :param initial_vals: Dictionary of initial configurations and resources as part of the initial hyperband
        schedule.
        """
        # normalizing factor for metrics_iters due to warm start
        epochs_factor = sum([n[1] for n in initial_vals.values()][::-1][:i]) # i & initial_vals args needed
        iters = plpy.execute("SELECT {AutoMLSchema.METRICS_ITERS} " \
                             "FROM {model_training.model_summary_table}".format(AutoMLSchema=AutoMLSchema,
                                                                                model_training=model_training))
        metrics_iters_val = [epochs_factor+mi for mi in iters[0]['metrics_iters']] # global iteration counter

        validation_update_q = "validation_metrics_final=t.validation_metrics_final, " \
                                     "validation_loss_final=t.validation_loss_final, " \
                                     "validation_metrics=a.validation_metrics || t.validation_metrics, " \
                                     "validation_loss=a.validation_loss || t.validation_loss, " \
            if self.validation_table else ""

        # updates train/val info for any previously trained configs
        plpy.execute("UPDATE {self.model_info_table} a SET " \
                     "metrics_elapsed_time=a.metrics_elapsed_time || t.metrics_elapsed_time, " \
                     "training_metrics_final=t.training_metrics_final, " \
                     "training_loss_final=t.training_loss_final, " \
                     "training_metrics=a.training_metrics || t.training_metrics, " \
                     "training_loss=a.training_loss || t.training_loss, ".format(self=self) + validation_update_q +
                     "{AutoMLSchema.METRICS_ITERS}=a.metrics_iters || ARRAY{metrics_iters_val}::INTEGER[] " \
                     "FROM {model_training.model_info_table} t " \
                     "WHERE a.mst_key=t.mst_key".format(model_training=model_training, AutoMLSchema=AutoMLSchema,
                                                        metrics_iters_val=metrics_iters_val))

        # inserts info about metrics and validation for newly trained model configs
        plpy.execute("INSERT INTO {self.model_info_table} SELECT t.*, ARRAY{metrics_iters_val}::INTEGER[] AS metrics_iters " \
                     "FROM {model_training.model_info_table} t WHERE t.mst_key NOT IN " \
                     "(SELECT mst_key FROM {self.model_info_table})".format(self=self,
                                                                            model_training=model_training,
                                                                            metrics_iters_val=metrics_iters_val))

    def add_additional_info_cols(self, s_dict, i_dict):
        """Adds s and i columns to the info table"""

        plpy.execute("ALTER TABLE {self.model_info_table} ADD COLUMN s int, ADD COLUMN i int;".format(self=self))

        l = [(k, s_dict[k], i_dict[k]) for k in s_dict]
        query = "UPDATE {self.model_info_table} t SET s=b.s_val, i=b.i_val FROM unnest(ARRAY{l}) " \
                "b (key integer, s_val integer, i_val integer) WHERE t.mst_key=b.key".format(self=self, l=l)
        plpy.execute(query)

    def update_model_selection_table(self):
        """
        Drops and re-create the mst table to only include the best performing model configuration.
        """
        drop_tables([self.model_selection_table])

        # only retaining best performing config
        plpy.execute("CREATE TABLE {self.model_selection_table} AS SELECT mst_key, model_id, compile_params, " \
                     "fit_params FROM {self.model_info_table} " \
                     "ORDER BY {AutoMLSchema.LOSS_METRIC} LIMIT 1".format(self=self, AutoMLSchema=AutoMLSchema))

    def generate_model_output_summary_table(self, model_training):
        """
        Creates and populates static values related to the AutoML workload.
        :param model_training: Fit Multiple function call object.
        """
        create_query = plpy.prepare("""
                CREATE TABLE {self.model_summary_table} AS
                SELECT
                    $MAD${self.source_table}$MAD$::TEXT AS source_table,
                    $MAD${self.validation_table}$MAD$::TEXT AS validation_table,
                    $MAD${self.model_output_table}$MAD$::TEXT AS model,
                    $MAD${self.model_info_table}$MAD$::TEXT AS model_info,
                    (SELECT dependent_varname FROM {model_training.model_summary_table})
                    AS dependent_varname,
                    (SELECT independent_varname FROM {model_training.model_summary_table})
                    AS independent_varname,
                    $MAD${self.model_arch_table}$MAD$::TEXT AS model_arch_table,
                    $MAD${self.model_selection_table}$MAD$::TEXT AS model_selection_table,
                    $MAD${self.automl_method}$MAD$::TEXT AS automl_method,
                    $MAD${self.automl_params}$MAD$::TEXT AS automl_params,
                    $MAD${self.random_state}$MAD$::TEXT AS random_state,
                    $MAD${self.object_table}$MAD$::TEXT AS object_table,
                    {self.use_gpus} AS use_gpus,
                    (SELECT metrics_compute_frequency FROM {model_training.model_summary_table})::INTEGER 
                    AS metrics_compute_frequency,
                    $MAD${self.name}$MAD$::TEXT AS name,
                    $MAD${self.description}$MAD$::TEXT AS description,
                    '{self.start_training_time}'::TIMESTAMP AS start_training_time,
                    '{self.end_training_time}'::TIMESTAMP AS end_training_time,
                    (SELECT madlib_version FROM {model_training.model_summary_table}) AS madlib_version,
                    (SELECT num_classes FROM {model_training.model_summary_table})::INTEGER AS num_classes,
                    (SELECT class_values FROM {model_training.model_summary_table}) AS class_values,
                    (SELECT dependent_vartype FROM {model_training.model_summary_table}) 
                    AS dependent_vartype,
                    (SELECT normalizing_const FROM {model_training.model_summary_table}) 
                    AS normalizing_const
            """.format(self=self, model_training=model_training))

        with MinWarning('warning'):
            plpy.execute(create_query)

    def remove_temp_tables(self, model_training):
        """
        Remove all intermediate tables created for AutoML runs/updates.
        :param model_training: Fit Multiple function call object.
        """
        drop_tables([model_training.original_model_output_table, model_training.model_info_table,
                     model_training.model_summary_table, AutoMLSchema.TEMP_MST_TABLE,
                     AutoMLSchema.TEMP_MST_SUMMARY_TABLE])