ml-agents/ml-agents/mlagents/trainers/stats.py

from collections import defaultdict
from enum import Enum
from typing import List, Dict, NamedTuple, Any
import numpy as np
import abc
import csv
import os
import time

from mlagents_envs.logging_util import get_logger
from mlagents_envs.timers import set_gauge
from mlagents.tf_utils import tf, generate_session_config


logger = get_logger(__name__)


class StatsSummary(NamedTuple):
    mean: float
    std: float
    num: int

    @staticmethod
    def empty() -> "StatsSummary":
        return StatsSummary(0.0, 0.0, 0)


class StatsPropertyType(Enum):
    HYPERPARAMETERS = "hyperparameters"
    SELF_PLAY = "selfplay"


class StatsWriter(abc.ABC):
    """
    A StatsWriter abstract class. A StatsWriter takes in a category, key, scalar value, and step
    and writes it out by some method.
    """

    @abc.abstractmethod
    def write_stats(
        self, category: str, values: Dict[str, StatsSummary], step: int
    ) -> None:
        pass

    def add_property(
        self, category: str, property_type: StatsPropertyType, value: Any
    ) -> None:
        """
        Add a generic property to the StatsWriter. This could be e.g. a Dict of hyperparameters,
        a max step count, a trainer type, etc. Note that not all StatsWriters need to be compatible
        with all types of properties. For instance, a TB writer doesn't need a max step, nor should
        we write hyperparameters to the CSV.
        :param category: The category that the property belongs to.
        :param type: The type of property.
        :param value: The property itself.
        """
        pass


class GaugeWriter(StatsWriter):
    """
    Write all stats that we recieve to the timer gauges, so we can track them offline easily
    """

    @staticmethod
    def sanitize_string(s: str) -> str:
        """
        Clean up special characters in the category and value names.
        """
        return s.replace("/", ".").replace(" ", "")

    def write_stats(
        self, category: str, values: Dict[str, StatsSummary], step: int
    ) -> None:
        for val, stats_summary in values.items():
            set_gauge(
                GaugeWriter.sanitize_string(f"{category}.{val}.mean"),
                float(stats_summary.mean),
            )


class ConsoleWriter(StatsWriter):
    def __init__(self):
        self.training_start_time = time.time()
        # If self-play, we want to print ELO as well as reward
        self.self_play = False
        self.self_play_team = -1

    def write_stats(
        self, category: str, values: Dict[str, StatsSummary], step: int
    ) -> None:
        is_training = "Not Training."
        if "Is Training" in values:
            stats_summary = stats_summary = values["Is Training"]
            if stats_summary.mean > 0.0:
                is_training = "Training."

        if "Environment/Cumulative Reward" in values:
            stats_summary = values["Environment/Cumulative Reward"]
            logger.info(
                "{}: Step: {}. "
                "Time Elapsed: {:0.3f} s "
                "Mean "
                "Reward: {:0.3f}"
                ". Std of Reward: {:0.3f}. {}".format(
                    category,
                    step,
                    time.time() - self.training_start_time,
                    stats_summary.mean,
                    stats_summary.std,
                    is_training,
                )
            )
            if self.self_play and "Self-play/ELO" in values:
                elo_stats = values["Self-play/ELO"]
                logger.info("{} ELO: {:0.3f}. ".format(category, elo_stats.mean))
        else:
            logger.info(
                "{}: Step: {}. No episode was completed since last summary. {}".format(
                    category, step, is_training
                )
            )

    def add_property(
        self, category: str, property_type: StatsPropertyType, value: Any
    ) -> None:
        if property_type == StatsPropertyType.HYPERPARAMETERS:
            logger.info(
                """Hyperparameters for behavior name {0}: \n{1}""".format(
                    category, self._dict_to_str(value, 0)
                )
            )
        elif property_type == StatsPropertyType.SELF_PLAY:
            assert isinstance(value, bool)
            self.self_play = value

    def _dict_to_str(self, param_dict: Dict[str, Any], num_tabs: int) -> str:
        """
        Takes a parameter dictionary and converts it to a human-readable string.
        Recurses if there are multiple levels of dict. Used to print out hyperparameters.
        param: param_dict: A Dictionary of key, value parameters.
        return: A string version of this dictionary.
        """
        if not isinstance(param_dict, dict):
            return str(param_dict)
        else:
            append_newline = "\n" if num_tabs > 0 else ""
            return append_newline + "\n".join(
                [
                    "\t"
                    + "  " * num_tabs
                    + "{0}:\t{1}".format(
                        x, self._dict_to_str(param_dict[x], num_tabs + 1)
                    )
                    for x in param_dict
                ]
            )


class TensorboardWriter(StatsWriter):
    def __init__(self, base_dir: str, clear_past_data: bool = False):
        """
        A StatsWriter that writes to a Tensorboard summary.
        :param base_dir: The directory within which to place all the summaries. Tensorboard files will be written to a
        {base_dir}/{category} directory.
        :param clear_past_data: Whether or not to clean up existing Tensorboard files associated with the base_dir and
            category.
        """
        self.summary_writers: Dict[str, tf.summary.FileWriter] = {}
        self.base_dir: str = base_dir
        self._clear_past_data = clear_past_data

    def write_stats(
        self, category: str, values: Dict[str, StatsSummary], step: int
    ) -> None:
        self._maybe_create_summary_writer(category)
        for key, value in values.items():
            summary = tf.Summary()
            summary.value.add(tag="{}".format(key), simple_value=value.mean)
            self.summary_writers[category].add_summary(summary, step)
            self.summary_writers[category].flush()

    def _maybe_create_summary_writer(self, category: str) -> None:
        if category not in self.summary_writers:
            filewriter_dir = "{basedir}/{category}".format(
                basedir=self.base_dir, category=category
            )
            os.makedirs(filewriter_dir, exist_ok=True)
            if self._clear_past_data:
                self._delete_all_events_files(filewriter_dir)
            self.summary_writers[category] = tf.summary.FileWriter(filewriter_dir)

    def _delete_all_events_files(self, directory_name: str) -> None:
        for file_name in os.listdir(directory_name):
            if file_name.startswith("events.out"):
                logger.warning(
                    "{} was left over from a previous run. Deleting.".format(file_name)
                )
                full_fname = os.path.join(directory_name, file_name)
                try:
                    os.remove(full_fname)
                except OSError:
                    logger.warning(
                        "{} was left over from a previous run and "
                        "not deleted.".format(full_fname)
                    )

    def add_property(
        self, category: str, property_type: StatsPropertyType, value: Any
    ) -> None:
        if property_type == StatsPropertyType.HYPERPARAMETERS:
            assert isinstance(value, dict)
            text = self._dict_to_tensorboard("Hyperparameters", value)
            self._maybe_create_summary_writer(category)
            self.summary_writers[category].add_summary(text, 0)

    def _dict_to_tensorboard(self, name: str, input_dict: Dict[str, Any]) -> str:
        """
        Convert a dict to a Tensorboard-encoded string.
        :param name: The name of the text.
        :param input_dict: A dictionary that will be displayed in a table on Tensorboard.
        """
        try:
            with tf.Session(config=generate_session_config()) as sess:
                s_op = tf.summary.text(
                    name,
                    tf.convert_to_tensor(
                        ([[str(x), str(input_dict[x])] for x in input_dict])
                    ),
                )
                s = sess.run(s_op)
                return s
        except Exception:
            logger.warning("Could not write text summary for Tensorboard.")
            return ""


class CSVWriter(StatsWriter):
    def __init__(self, base_dir: str, required_fields: List[str] = None):
        """
        A StatsWriter that writes to a Tensorboard summary.
        :param base_dir: The directory within which to place the CSV file, which will be {base_dir}/{category}.csv.
        :param required_fields: If provided, the CSV writer won't write until these fields have statistics to write for
        them.
        """
        # We need to keep track of the fields in the CSV, as all rows need the same fields.
        self.csv_fields: Dict[str, List[str]] = {}
        self.required_fields = required_fields if required_fields else []
        self.base_dir: str = base_dir

    def write_stats(
        self, category: str, values: Dict[str, StatsSummary], step: int
    ) -> None:
        if self._maybe_create_csv_file(category, list(values.keys())):
            row = [str(step)]
            # Only record the stats that showed up in the first valid row
            for key in self.csv_fields[category]:
                _val = values.get(key, None)
                row.append(str(_val.mean) if _val else "None")
            with open(self._get_filepath(category), "a") as file:
                writer = csv.writer(file)
                writer.writerow(row)

    def _maybe_create_csv_file(self, category: str, keys: List[str]) -> bool:
        """
        If no CSV file exists and the keys have the required values,
        make the CSV file and write hte title row.
        Returns True if there is now (or already is) a valid CSV file.
        """
        if category not in self.csv_fields:
            summary_dir = self.base_dir
            os.makedirs(summary_dir, exist_ok=True)
            # Only store if the row contains the required fields
            if all(item in keys for item in self.required_fields):
                self.csv_fields[category] = keys
                with open(self._get_filepath(category), "w") as file:
                    title_row = ["Steps"]
                    title_row.extend(keys)
                    writer = csv.writer(file)
                    writer.writerow(title_row)
                return True
            return False
        return True

    def _get_filepath(self, category: str) -> str:
        file_dir = os.path.join(self.base_dir, category + ".csv")
        return file_dir


class StatsReporter:
    writers: List[StatsWriter] = []
    stats_dict: Dict[str, Dict[str, List]] = defaultdict(lambda: defaultdict(list))

    def __init__(self, category: str):
        """
        Generic StatsReporter. A category is the broadest type of storage (would
        correspond the run name and trainer name, e.g. 3DBalltest_3DBall. A key is the
        type of stat it is (e.g. Environment/Reward). Finally the Value is the float value
        attached to this stat.
        """
        self.category: str = category

    @staticmethod
    def add_writer(writer: StatsWriter) -> None:
        StatsReporter.writers.append(writer)

    def add_property(self, property_type: StatsPropertyType, value: Any) -> None:
        """
        Add a generic property to the StatsReporter. This could be e.g. a Dict of hyperparameters,
        a max step count, a trainer type, etc. Note that not all StatsWriters need to be compatible
        with all types of properties. For instance, a TB writer doesn't need a max step, nor should
        we write hyperparameters to the CSV.
        :param key: The type of property.
        :param value: The property itself.
        """
        for writer in StatsReporter.writers:
            writer.add_property(self.category, property_type, value)

    def add_stat(self, key: str, value: float) -> None:
        """
        Add a float value stat to the StatsReporter.
        :param key: The type of statistic, e.g. Environment/Reward.
        :param value: the value of the statistic.
        """
        StatsReporter.stats_dict[self.category][key].append(value)

    def set_stat(self, key: str, value: float) -> None:
        """
        Sets a stat value to a float. This is for values that we don't want to average, and just
        want the latest.
        :param key: The type of statistic, e.g. Environment/Reward.
        :param value: the value of the statistic.
        """
        StatsReporter.stats_dict[self.category][key] = [value]

    def write_stats(self, step: int) -> None:
        """
        Write out all stored statistics that fall under the category specified.
        The currently stored values will be averaged, written out as a single value,
        and the buffer cleared.
        :param step: Training step which to write these stats as.
        """
        values: Dict[str, StatsSummary] = {}
        for key in StatsReporter.stats_dict[self.category]:
            if len(StatsReporter.stats_dict[self.category][key]) > 0:
                stat_summary = self.get_stats_summaries(key)
                values[key] = stat_summary
        for writer in StatsReporter.writers:
            writer.write_stats(self.category, values, step)
        del StatsReporter.stats_dict[self.category]

    def get_stats_summaries(self, key: str) -> StatsSummary:
        """
        Get the mean, std, and count of a particular statistic, since last write.
        :param key: The type of statistic, e.g. Environment/Reward.
        :returns: A StatsSummary NamedTuple containing (mean, std, count).
        """
        if len(StatsReporter.stats_dict[self.category][key]) > 0:
            return StatsSummary(
                mean=np.mean(StatsReporter.stats_dict[self.category][key]),
                std=np.std(StatsReporter.stats_dict[self.category][key]),
                num=len(StatsReporter.stats_dict[self.category][key]),
            )
        return StatsSummary.empty()