created new folder with meta experiment functions

ddopnew/_modidx.py

@@ -654,6 +654,8 @@
                                                                                  'ddopnew/loss_functions.py'),
                                         'ddopnew.loss_functions.quantile_loss': ( '00_utils/loss_functions.html#quantile_loss',
                                                                                   'ddopnew/loss_functions.py')},
+            'ddopnew.meta_experiment_functions': { 'ddopnew.meta_experiment_functions.select_agent': ( '30_experiment_functions/meta_experiment_functions.html#select_agent',
+                                                                                                       'ddopnew/meta_experiment_functions.py')},
             'ddopnew.obsprocessors': { 'ddopnew.obsprocessors.ConvertDictSpace': ( '00_utils/obsprocessors.html#convertdictspace',
                                                                                    'ddopnew/obsprocessors.py'),
                                        'ddopnew.obsprocessors.ConvertDictSpace.__call__': ( '00_utils/obsprocessors.html#convertdictspace.__call__',

ddopnew/envs/inventory/multi_period.py

@@ -21,7 +21,7 @@
 class MultiPeriodEnv(BaseInventoryEnv, ABC):
 
     """
-    XXX.
+    XXX
     """
 
     def __init__(self,

ddopnew/meta_experiment_functions.py

@@ -0,0 +1,34 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/30_experiment_functions/20_meta_experiment_functions.ipynb.
+
+# %% auto 0
+__all__ = ['select_agent']
+
+# %% ../nbs/30_experiment_functions/20_meta_experiment_functions.ipynb 3
+from abc import ABC, abstractmethod
+from typing import Union, List, Tuple, Dict, Literal
+import logging
+from datetime import datetime  
+import numpy as np
+import sys
+
+from .tracking import get_git_hash, get_library_version
+
+import wandb
+
+
+import importlib
+
+from tqdm import tqdm, trange
+
+# Think about how to handle mushroom integration.
+from mushroom_rl.core import Core
+
+# %% ../nbs/30_experiment_functions/20_meta_experiment_functions.ipynb 8
+def select_agent(agent_name: str) -> type: #
+    """ Select an agent class from a list of agent names and return the class"""
+    if agent_name in AGENT_CLASSES:
+        module_path, class_name = AGENT_CLASSES[agent_name].rsplit(".", 1)
+        module = importlib.import_module(module_path)
+        return getattr(module, class_name)
+    else:
+        raise ValueError(f"Unknown agent name: {agent_name}")

nbs/21_envs_inventory/30_multi_period_envs.ipynb

@@ -59,7 +59,7 @@
     "class MultiPeriodEnv(BaseInventoryEnv, ABC):\n",
     "    \n",
     "    \"\"\"\n",
-    "    XXX.\n",
+    "    XXX\n",
     "    \"\"\"\n",
     "\n",
     "    def __init__(self,\n",

nbs/30_experiment_functions/20_meta_experiment_functions.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Meta experiment functions\n",
+    "\n",
+    "> Very high-level functions to run experiments with minimal code, directly from terminal.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp meta_experiment_functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "from nbdev.showdoc import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "\n",
+    "from abc import ABC, abstractmethod\n",
+    "from typing import Union, List, Tuple, Dict, Literal\n",
+    "import logging\n",
+    "from datetime import datetime  \n",
+    "import numpy as np\n",
+    "import sys\n",
+    "\n",
+    "from ddopnew.tracking import get_git_hash, get_library_version\n",
+    "\n",
+    "import wandb\n",
+    "\n",
+    "\n",
+    "import importlib\n",
+    "\n",
+    "from tqdm import tqdm, trange\n",
+    "\n",
+    "# Think about how to handle mushroom integration.\n",
+    "from mushroom_rl.core import Core"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load files and set-up tracking\n",
+    "\n",
+    "> Fist part of experiment: Log into wandb and load config files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def init_wandb(project_name: str): #\n",
+    "\n",
+    "    \"\"\" init wandb \"\"\"\n",
+    "\n",
+    "    wandb.init(\n",
+    "        project=project_name,\n",
+    "        name = f\"{project_name}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def track_libraries_and_git(    libraries_to_track: List[str],\n",
+    "                                tracking: bool = True,\n",
+    "                                tracking_tool = \"wandb\", # Currenty only wandb is supported\n",
+    "                                ) -> None:\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    Track the versions of the libraries and the git hash of the repository.\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    for lib in libraries_to_track:\n",
+    "        version = get_library_version(lib, tracking=tracking, tracking_tool=tracking_tool)\n",
+    "        logging.info(f\"{lib}: {version}\")\n",
+    "    git_hash = get_git_hash(\".\", tracking=tracking, tracking_tool=tracking_tool)\n",
+    "    logging.info(f\"Git hash: {git_hash}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Helper functions\n",
+    "\n",
+    "> Some functions that are needed to run an experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "def select_agent(agent_name: str) -> type: #\n",
+    "    \"\"\" Select an agent class from a list of agent names and return the class\"\"\"\n",
+    "    if agent_name in AGENT_CLASSES:\n",
+    "        module_path, class_name = AGENT_CLASSES[agent_name].rsplit(\".\", 1)\n",
+    "        module = importlib.import_module(module_path)\n",
+    "        return getattr(module, class_name)\n",
+    "    else:\n",
+    "        raise ValueError(f\"Unknown agent name: {agent_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import nbdev; nbdev.nbdev_export()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}