agent_analytics.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Metadata local@/home/valay/Documents/Code-Workspace/ASU-Projects/Robotics/Pyrep-Testing\nMetadata local@/home/valay/Documents/Code-Workspace/ASU-Projects/Robotics/Pyrep-Testing\n"
    }
   ],
   "source": [
    "from metaflow import Flow,get_metadata\n",
    "print(\"Metadata\",get_metadata())\n",
    "from metaflow_train import FinalData\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objects  as go\n",
    "import plotly.express as ps\n",
    "import reporting_utils as Reporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "run = list(Flow('TrainingSimulatorFlow').runs('mutant_vs_simplerelu'))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Simple 3 Layer ReLu vs Mutant\n\n        Agent Name : SimpleImmitationLearningAgent\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 50\n\n            Number Of Converged Cases : 128\n\n            %  Cases That Converged : 64.0\n        \n\n        Agent Name : ImmitationLearningMutantAgent\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 50\n\n            Number Of Converged Cases : 47\n\n            %  Cases That Converged : 23.5\n        \n"
    }
   ],
   "source": [
    "print(\"Simple 3 Layer ReLu vs Mutant\")\n",
    "for data in run.data.final_data:\n",
    "    print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Simple 6 Layer ReLu vs Mutant\n\n        Agent Name : SimpleImmitationLearningAgent\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 50\n\n            Number Of Converged Cases : 61\n\n            %  Cases That Converged : 30.5\n        \n\n        Agent Name : ImmitationLearningMutantAgent\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 50\n\n            Number Of Converged Cases : 97\n\n            %  Cases That Converged : 48.5\n        \n"
    }
   ],
   "source": [
    "print(\"Simple 6 Layer ReLu vs Mutant\")\n",
    "run_2 = list(Flow('TrainingSimulatorFlow').runs('mutant_vs_simple'))[0]\n",
    "for data in run_2.data.final_data:\n",
    "    print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Simple N Layer ReLu vs Mutant With Lesser Layers\nRun('TrainingSimulatorFlow/1586252587257348')\n\n        Agent Name : SimpleImmitationLearningAgent__4\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 100\n\n            Number Of Converged Cases : 114\n\n            %  Cases That Converged : 56.99999999999999\n        \n\n        Agent Name : SimpleImmitationLearningAgent__1\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 100\n\n            Number Of Converged Cases : 108\n\n            %  Cases That Converged : 54.0\n        \n\n        Agent Name : SimpleImmitationLearningAgent__2\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 100\n\n            Number Of Converged Cases : 76\n\n            %  Cases That Converged : 38.0\n        \n\n        Agent Name : ImmitationLearningMutantAgent\n\n        Total Training Data Size : 21073\n\n        Model Arguements : {}\n\n        Collected Gradients : No\n\n        Simulation Results \n\n            Total Number of Episodes : 200\n\n            Steps Per Episode : 100\n\n            Number Of Converged Cases : 151\n\n            %  Cases That Converged : 75.5\n        \n"
    }
   ],
   "source": [
    "# smart_vs_mutant\n",
    "print(\"Simple N Layer ReLu vs Mutant With Lesser Layers\")\n",
    "run_2 = list(Flow('TrainingSimulatorFlow').runs('smart_vs_mutant'))[0]\n",
    "print(run_2)\n",
    "for data in run_2.data.final_data:\n",
    "    print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "run = Flow('TrainingSimulatorFlow').latest_successful_run"
   ]
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            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Plot of Running Losses of All Models"
        },
        "xaxis": {
         "title": {
          "text": "Epochs"
         }
        },
        "yaxis": {
         "title": {
          "text": "Loss"
         }
        }
       }
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "f = Reporting.make_consolidated_loss_plot(run.data.final_data)\n",
    "f.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}