#1632 - Added full benchmarking script that included plots and a LaTeX report. Ran the v2.0.0rc1 benchmark. Tidied a few other things up.

benchmark/config/benchmark_training_config.yaml

@@ -51,7 +51,8 @@ hard_coded_agent_view: FULL
 # "NODE"
 # "ACL"
 # "ANY" node and acl actions
-action_type: ANY
+action_type: NODE
+
 # observation space
 observation_space:
   flatten: true
@@ -60,9 +61,8 @@ observation_space:
     - name: NODE_STATUSES
     - name: LINK_TRAFFIC_LEVELS
 
-
 # Number of episodes for training to run per session
-num_train_episodes: 10
+num_train_episodes: 500
 
 # Number of time_steps for training per episode
 num_train_steps: 256

benchmark/primaite_benchmark.py

@@ -1,28 +1,199 @@
 import json
+import platform
 import shutil
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, Final, Tuple
+from typing import Any, Dict, Final, Optional, Tuple, Union
 from unittest.mock import patch
 
+import GPUtil
+import plotly.graph_objects as go
+import polars as pl
+import psutil
+import yaml
+from plotly.graph_objs import Figure
+from pylatex import Command, Document
+from pylatex import Figure as LatexFigure
+from pylatex import Section, Subsection, Tabular
+from pylatex.utils import bold
+
 import primaite
 from primaite.config.lay_down_config import data_manipulation_config_path
-from tests.conftest import TempPrimaiteSession
+from primaite.data_viz.session_plots import get_plotly_config
+from primaite.environment.primaite_env import Primaite
+from primaite.primaite_session import PrimaiteSession
 
 _LOGGER = primaite.getLogger(__name__)
 
-_RESULTS_ROOT: Final[Path] = Path(__file__).parent / "results"
+_BENCHMARK_ROOT = Path(__file__).parent
+_RESULTS_ROOT: Final[Path] = _BENCHMARK_ROOT / "results"
 _RESULTS_ROOT.mkdir(exist_ok=True, parents=True)
 
-_OUTPUT_ROOT: Final[Path] = Path(__file__).parent / "output"
+_OUTPUT_ROOT: Final[Path] = _BENCHMARK_ROOT / "output"
 # Clear and recreate the output directory
 shutil.rmtree(_OUTPUT_ROOT)
 _OUTPUT_ROOT.mkdir()
 
+_TRAINING_CONFIG_PATH = _BENCHMARK_ROOT / "config" / "benchmark_training_config.yaml"
+_LAY_DOWN_CONFIG_PATH = data_manipulation_config_path()
 
-class BenchmarkPrimaiteSession(TempPrimaiteSession):
+
+def get_size(size_bytes: int):
+    """
+    Scale bytes to its proper format.
+
+    e.g:
+        1253656 => '1.20MB'
+        1253656678 => '1.17GB'
+
+    :
+    """
+    factor = 1024
+    for unit in ["", "K", "M", "G", "T", "P"]:
+        if size_bytes < factor:
+            return f"{size_bytes:.2f}{unit}B"
+        size_bytes /= factor
+
+
+def _get_system_info() -> Dict:
+    """Builds and returns a dict containing system info."""
+    uname = platform.uname()
+    cpu_freq = psutil.cpu_freq()
+    virtual_mem = psutil.virtual_memory()
+    swap_mem = psutil.swap_memory()
+    gpus = GPUtil.getGPUs()
+    return {
+        "System": {
+            "OS": uname.system,
+            "OS Version": uname.version,
+            "Machine": uname.machine,
+            "Processor": uname.processor,
+        },
+        "CPU": {
+            "Physical Cores": psutil.cpu_count(logical=False),
+            "Total Cores": psutil.cpu_count(logical=True),
+            "Max Frequency": f"{cpu_freq.max:.2f}Mhz",
+        },
+        "Memory": {"Total": get_size(virtual_mem.total), "Swap Total": get_size(swap_mem.total)},
+        "GPU": [{"Name": gpu.name, "Total Memory": f"{gpu.memoryTotal}MB"} for gpu in gpus],
+    }
+
+
+def _build_benchmark_latex_report(
+    benchmark_metadata_dict: Dict, this_version_plot_path: Path, all_version_plot_path: Path
+):
+    geometry_options = {"tmargin": "2.5cm", "rmargin": "2.5cm", "bmargin": "2.5cm", "lmargin": "2.5cm"}
+    data = benchmark_metadata_dict
+    primaite_version = data["primaite_version"]
+
+    # Create a new document
+    doc = Document("report", geometry_options=geometry_options)
+    # Title
+    doc.preamble.append(Command("title", f"PrimAITE {primaite_version} Learning Benchmark"))
+    doc.preamble.append(Command("author", "PrimAITE Dev Team"))
+    doc.preamble.append(Command("date", datetime.now().date()))
+    doc.append(Command("maketitle"))
+
+    sessions = data["total_sessions"]
+    episodes = data["training_config"]["num_train_episodes"]
+    steps = data["training_config"]["num_train_steps"]
+
+    # Body
+    with doc.create(Section("Introduction")):
+        doc.append(
+            f"PrimAITE v{primaite_version} was benchmarked automatically upon release. Learning rate metrics "
+            f"were captured to be referenced during system-level testing and user acceptance testing (UAT)."
+        )
+        doc.append(
+            f"\nThe benchmarking process consists of running {sessions} training session using the same "
+            f"training and lay down config files. Each session trains an agent for {episodes} episodes, "
+            f"with each episode consisting of {steps} steps."
+        )
+        doc.append(
+            f"\nThe mean reward per episode from each session is captured. This is then used to calculate a "
+            f"combined average reward per episode from the {sessions} individual sessions for smoothing. "
+            f"Finally, a 5-widow rolling average of the combined average reward per session is calculated for "
+            f"further smoothing."
+        )
+
+    with doc.create(Section("System Information")):
+        for section, section_data in data["system_info"].items():
+            if section_data:
+                with doc.create(Subsection(section.capitalize())):
+                    if isinstance(section_data, dict):
+                        with doc.create(Tabular("|l|l|")) as table:
+                            table.add_hline()
+                            for key, value in section_data.items():
+                                table.add_row((bold(key), value))
+                                table.add_hline()
+                    elif isinstance(section_data, list):
+                        headers = section_data[0].keys()
+                        tabs_str = "|".join(["l" for _ in range(len(headers))])
+                        tabs_str = f"|{tabs_str}|"
+                        with doc.create(Tabular(tabs_str)) as table:
+                            table.add_hline()
+                            table.add_row([bold(h) for h in headers])
+                            table.add_hline()
+                            for item in section_data:
+                                table.add_row(item.values())
+                                table.add_hline()
+
+    headers_map = {
+        "total_sessions": "Total Sessions",
+        "total_episodes": "Total Episodes",
+        "total_time_steps": "Total Steps",
+        "av_s_per_session": "Av Session Duration (s)",
+        "av_s_per_step": "Av Step Duration (s)",
+        "av_s_per_100_steps_10_nodes": "Av Duration per 100 Steps per 10 Nodes (s)",
+    }
+    with doc.create(Section("Stats")):
+        with doc.create(Subsection("Benchmark Results")):
+            with doc.create(Tabular("|l|l|")) as table:
+                table.add_hline()
+                for section, header in headers_map.items():
+                    if section.startswith("av_"):
+                        table.add_row((bold(header), f"{data[section]:.4f}"))
+                    else:
+                        table.add_row((bold(header), data[section]))
+                    table.add_hline()
+
+    with doc.create(Section("Graphs")):
+        with doc.create(Subsection(f"PrimAITE {primaite_version} Learning Benchmark Plot")):
+            with doc.create(LatexFigure(position="h!")) as pic:
+                pic.add_image(str(this_version_plot_path))
+                pic.add_caption(f"PrimAITE {primaite_version} Learning Benchmark Plot")
+
+        with doc.create(Subsection("PrimAITE All Versions Learning Benchmark Plot")):
+            with doc.create(LatexFigure(position="h!")) as pic:
+                pic.add_image(str(all_version_plot_path))
+                pic.add_caption("PrimAITE All Versions Learning Benchmark Plot")
+
+    doc.generate_pdf(str(this_version_plot_path).replace(".png", ""), clean_tex=True)
+
+
+class BenchmarkPrimaiteSession(PrimaiteSession):
     """A benchmarking primaite session."""
 
+    def __init__(
+        self,
+        training_config_path: Union[str, Path],
+        lay_down_config_path: Union[str, Path],
+    ):
+        super().__init__(training_config_path, lay_down_config_path)
+        self.setup()
+
+    @property
+    def env(self) -> Primaite:
+        """Direct access to the env for ease of testing."""
+        return self._agent_session._env  # noqa
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, tb):
+        shutil.rmtree(self.session_path)
+        _LOGGER.debug(f"Deleted benchmark session directory: {self.session_path}")
+
     def _learn_benchmark_durations(self) -> Tuple[float, float, float]:
         """
         Calculate and return the learning benchmark durations.
@@ -71,43 +242,184 @@ def _get_benchmark_session_path(session_timestamp: datetime) -> Path:
 def _get_benchmark_primaite_session() -> BenchmarkPrimaiteSession:
     with patch("primaite.agents.agent_abc.get_session_path", _get_benchmark_session_path) as mck:
         mck.session_timestamp = datetime.now()
-        path = Path(__file__).parent / "config/benchmark_training_config.yaml"
-        return BenchmarkPrimaiteSession(path, data_manipulation_config_path())
+        return BenchmarkPrimaiteSession(_TRAINING_CONFIG_PATH, _LAY_DOWN_CONFIG_PATH)
 
 
-def _summarise_metadata_dict_results(data: Dict) -> Dict:
-    n = len(data)
+def _build_benchmark_results_dict(start_datetime: datetime, metadata_dict: Dict) -> dict:
+    n = len(metadata_dict)
+    with open(_TRAINING_CONFIG_PATH, "r") as file:
+        training_config_dict = yaml.safe_load(file)
+    with open(_LAY_DOWN_CONFIG_PATH, "r") as file:
+        lay_down_config_dict = yaml.safe_load(file)
     averaged_data = {
+        "start_timestamp": start_datetime.isoformat(),
+        "end_datetime": datetime.now().isoformat(),
+        "primaite_version": primaite.__version__,
+        "system_info": _get_system_info(),
         "total_sessions": n,
-        "total_episodes": sum(d["total_episodes"] for d in data.values()),
-        "total_time_steps": sum(d["total_time_steps"] for d in data.values()),
-        "av_s_per_session": sum(d["total_s"] for d in data.values()) / n,
-        "av_s_per_step": sum(d["s_per_step"] for d in data.values()) / n,
-        "av_s_per_100_steps_10_nodes": sum(d["s_per_100_steps_10_nodes"] for d in data.values()) / n,
-        "av_reward_per_episode": {},
+        "total_episodes": sum(d["total_episodes"] for d in metadata_dict.values()),
+        "total_time_steps": sum(d["total_time_steps"] for d in metadata_dict.values()),
+        "av_s_per_session": sum(d["total_s"] for d in metadata_dict.values()) / n,
+        "av_s_per_step": sum(d["s_per_step"] for d in metadata_dict.values()) / n,
+        "av_s_per_100_steps_10_nodes": sum(d["s_per_100_steps_10_nodes"] for d in metadata_dict.values()) / n,
+        "combined_av_reward_per_episode": {},
+        "session_av_reward_per_episode": {k: v["av_reward_per_episode"] for k, v in metadata_dict.items()},
+        "training_config": training_config_dict,
+        "lay_down_config": lay_down_config_dict,
     }
 
-    av_reward_per_episode_keys = data[1]["av_reward_per_episode"].keys()
+    episodes = metadata_dict[1]["av_reward_per_episode"].keys()
 
-    for episode_key in av_reward_per_episode_keys:
-        averaged_data["av_reward_per_episode"][episode_key] = (
-            sum(data[k]["av_reward_per_episode"][episode_key] for k in data.keys()) / n
-        )
+    for episode in episodes:
+        combined_av_reward = sum(metadata_dict[k]["av_reward_per_episode"][episode] for k in metadata_dict.keys()) / n
+        averaged_data["combined_av_reward_per_episode"][episode] = combined_av_reward
 
     return averaged_data
 
 
+def _get_df_from_episode_av_reward_dict(data: Dict):
+    data: Dict = {"episode": data.keys(), "av_reward": data.values()}
+
+    return (
+        pl.from_dict(data)
+        .with_columns(rolling_mean=pl.col("av_reward").rolling_mean(window_size=5))
+        .rename({"rolling_mean": "rolling_av_reward"})
+    )
+
+
+def _plot_benchmark_metadata(
+    benchmark_metadata_dict: Dict,
+    title: Optional[str] = None,
+    subtitle: Optional[str] = None,
+) -> Figure:
+    if title:
+        if subtitle:
+            title = f"{title} <br>{subtitle}</sup>"
+    else:
+        if subtitle:
+            title = subtitle
+
+    config = get_plotly_config()
+    layout = go.Layout(
+        autosize=config["size"]["auto_size"],
+        width=config["size"]["width"],
+        height=config["size"]["height"],
+    )
+    # Create the line graph with a colored line
+    fig = go.Figure(layout=layout)
+    fig.update_layout(template=config["template"])
+
+    for session, av_reward_dict in benchmark_metadata_dict["session_av_reward_per_episode"].items():
+        df = _get_df_from_episode_av_reward_dict(av_reward_dict)
+        fig.add_trace(
+            go.Scatter(
+                x=df["episode"],
+                y=df["av_reward"],
+                mode="lines",
+                name=f"Session {session}",
+                opacity=0.25,
+                line={"color": "#a6a6a6"},
+            )
+        )
+
+    df = _get_df_from_episode_av_reward_dict(benchmark_metadata_dict["combined_av_reward_per_episode"])
+    fig.add_trace(
+        go.Scatter(
+            x=df["episode"], y=df["av_reward"], mode="lines", name="Combined Session Av", line={"color": "#FF0000"}
+        )
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=df["episode"],
+            y=df["rolling_av_reward"],
+            mode="lines",
+            name="Rolling Av (Combined Session Av)",
+            line={"color": "#4CBB17"},
+        )
+    )
+
+    # Set the layout of the graph
+    fig.update_layout(
+        xaxis={
+            "title": "Episode",
+            "type": "linear",
+        },
+        yaxis={"title": "Average Reward"},
+        title=title,
+    )
+
+    return fig
+
+
+def _plot_all_benchmarks_combined_session_av():
+    """
+    Plot the Benchmark results for each released version of PrimAITE.
+
+    Does this by iterating over the ``benchmark/results`` directory and
+    extracting the benchmark metadata json for each version that has been
+    benchmarked. The combined_av_reward_per_episode is extracted from each,
+    converted into a polars dataframe, and plotted as a scatter line in plotly.
+    """
+    title = "PrimAITE Versions Learning Benchmark"
+    subtitle = "Rolling Av (Combined Session Av)"
+    if title:
+        if subtitle:
+            title = f"{title} <br>{subtitle}</sup>"
+    else:
+        if subtitle:
+            title = subtitle
+    config = get_plotly_config()
+    layout = go.Layout(
+        autosize=config["size"]["auto_size"],
+        width=config["size"]["width"],
+        height=config["size"]["height"],
+    )
+    # Create the line graph with a colored line
+    fig = go.Figure(layout=layout)
+    fig.update_layout(template=config["template"])
+
+    for dir in _RESULTS_ROOT.iterdir():
+        if dir.is_dir():
+            metadata_file = dir / f"{dir.name}_benchmark_metadata.json"
+            with open(metadata_file, "r") as file:
+                metadata_dict = json.load(file)
+            df = _get_df_from_episode_av_reward_dict(metadata_dict["combined_av_reward_per_episode"])
+
+            fig.add_trace(
+                go.Scatter(
+                    x=df["episode"], y=df["rolling_av_reward"], mode="lines", name=dir.name, line={"color": "#FF0000"}
+                )
+            )
+
+    # Set the layout of the graph
+    fig.update_layout(
+        xaxis={
+            "title": "Episode",
+            "type": "linear",
+        },
+        yaxis={"title": "Average Reward"},
+        title=title,
+    )
+    fig["data"][0]["showlegend"] = True
+
+    return fig
+
+
 def run():
     """Run the PrimAITE benchmark."""
+    start_datetime = datetime.now()
     av_reward_per_episode_dicts = {}
     for i in range(1, 11):
-        print(f"starting Benchmark Session: {i}")
+        print(f"Starting Benchmark Session: {i}")
         with _get_benchmark_primaite_session() as session:
             session.learn()
             av_reward_per_episode_dicts[i] = session.learn_metadata_dict()
 
-    benchmark_metadata = _summarise_metadata_dict_results(av_reward_per_episode_dicts)
-    v_str = f"v{primaite.__version__}".strip()
+    benchmark_metadata = _build_benchmark_results_dict(
+        start_datetime=start_datetime, metadata_dict=av_reward_per_episode_dicts
+    )
+    v_str = f"v{primaite.__version__}"
 
     version_result_dir = _RESULTS_ROOT / v_str
     if version_result_dir.exists():
@@ -116,6 +428,17 @@ def run():
 
     with open(version_result_dir / f"{v_str}_benchmark_metadata.json", "w") as file:
         json.dump(benchmark_metadata, file, indent=4)
+    title = f"PrimAITE v{primaite.__version__.strip()} Learning Benchmark"
+    fig = _plot_benchmark_metadata(benchmark_metadata, title=title)
+    this_version_plot_path = version_result_dir / f"{title}.png"
+    fig.write_image(this_version_plot_path)
+
+    fig = _plot_all_benchmarks_combined_session_av()
+
+    all_version_plot_path = _RESULTS_ROOT / "PrimAITE Versions Learning Benchmark.png"
+    fig.write_image(all_version_plot_path)
+
+    _build_benchmark_latex_report(benchmark_metadata, this_version_plot_path, all_version_plot_path)
 
 
 if __name__ == "__main__":