resultPLotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "gentle-bargain",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:43:22.653887Z",
     "iopub.status.busy": "2021-06-05T19:43:22.653331Z",
     "iopub.status.idle": "2021-06-05T19:43:22.667421Z",
     "shell.execute_reply": "2021-06-05T19:43:22.666180Z",
     "shell.execute_reply.started": "2021-06-05T19:43:22.653740Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "hairy-basement",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:47:24.474819Z",
     "iopub.status.busy": "2021-06-05T19:47:24.474375Z",
     "iopub.status.idle": "2021-06-05T19:47:24.486608Z",
     "shell.execute_reply": "2021-06-05T19:47:24.485131Z",
     "shell.execute_reply.started": "2021-06-05T19:47:24.474767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['fruits', 131, 95, 98.4, 5],\n",
       " ['flowers', 5, 87, 97.6, 3],\n",
       " ['boat', 9, 94, 97.29, 3],\n",
       " ['garbage', 12, 64, 99.2, 2],\n",
       " ['asl', 29, 57, 68.8, 2]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = [\n",
    "    [\"fruits\", 131, 95, 98.4, 5],\n",
    "    [\"flowers\", 5, 87, 97.6, 3],\n",
    "    [\"boat\", 9, 94, 97.29, 3],\n",
    "    [\"garbage\", 12, 64, 99.2, 2],\n",
    "    [\"asl\", 29, 57, 68.8, 2],\n",
    "]\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "documentary-nowhere",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:52:16.647073Z",
     "iopub.status.busy": "2021-06-05T19:52:16.646617Z",
     "iopub.status.idle": "2021-06-05T19:52:16.651393Z",
     "shell.execute_reply": "2021-06-05T19:52:16.650929Z",
     "shell.execute_reply.started": "2021-06-05T19:52:16.647019Z"
    }
   },
   "outputs": [],
   "source": [
    "def merge_ind(x, ind):\n",
    "    return [y[ind] for y in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "virtual-absorption",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:50:07.784095Z",
     "iopub.status.busy": "2021-06-05T19:50:07.783646Z",
     "iopub.status.idle": "2021-06-05T19:50:07.793403Z",
     "shell.execute_reply": "2021-06-05T19:50:07.791513Z",
     "shell.execute_reply.started": "2021-06-05T19:50:07.784041Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['fruits', 131], ['flowers', 5], ['boat', 9], ['garbage', 12], ['asl', 29]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[y[:2] for y in da]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "hazardous-failing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:52:26.148640Z",
     "iopub.status.busy": "2021-06-05T19:52:26.148139Z",
     "iopub.status.idle": "2021-06-05T19:52:26.157799Z",
     "shell.execute_reply": "2021-06-05T19:52:26.156355Z",
     "shell.execute_reply.started": "2021-06-05T19:52:26.148579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[131, 5, 9, 12, 29]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_ind([y[:2] for y in da], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "first-continuity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:54:32.651686Z",
     "iopub.status.busy": "2021-06-05T19:54:32.651541Z",
     "iopub.status.idle": "2021-06-05T19:54:32.654826Z",
     "shell.execute_reply": "2021-06-05T19:54:32.654392Z",
     "shell.execute_reply.started": "2021-06-05T19:54:32.651670Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "wired-confusion",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:59:17.647046Z",
     "iopub.status.busy": "2021-06-05T19:59:17.646798Z",
     "iopub.status.idle": "2021-06-05T19:59:17.651710Z",
     "shell.execute_reply": "2021-06-05T19:59:17.651106Z",
     "shell.execute_reply.started": "2021-06-05T19:59:17.647017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[95, 98.4], [87, 97.6], [94, 97.29], [64, 99.2], [57, 68.8]]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[y[2:4] for y in da]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "changed-developer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T20:00:12.805578Z",
     "iopub.status.busy": "2021-06-05T20:00:12.805424Z",
     "iopub.status.idle": "2021-06-05T20:00:12.808447Z",
     "shell.execute_reply": "2021-06-05T20:00:12.808094Z",
     "shell.execute_reply.started": "2021-06-05T20:00:12.805561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[95, 87, 94, 64, 57]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_ind([y[2:4] for y in da], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "revised-allergy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T20:01:56.880255Z",
     "iopub.status.busy": "2021-06-05T20:01:56.880030Z",
     "iopub.status.idle": "2021-06-05T20:01:56.988306Z",
     "shell.execute_reply": "2021-06-05T20:01:56.987845Z",
     "shell.execute_reply.started": "2021-06-05T20:01:56.880216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe6b4e6bb0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(merge_ind([y[:2] for y in da], 0), merge_ind([y[2:4] for y in da], 1), color = \"red\")\n",
    "plt.bar(merge_ind([y[:2] for y in da], 0), merge_ind([y[2:4] for y in da], 0), color = \"blue\")\n",
    "plt.title(\"Our accuracy\")\n",
    "plt.legend([\"ours\", \"original\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "missing-treaty",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T20:07:30.387564Z",
     "iopub.status.busy": "2021-06-05T20:07:30.387200Z",
     "iopub.status.idle": "2021-06-05T20:07:30.393377Z",
     "shell.execute_reply": "2021-06-05T20:07:30.392507Z",
     "shell.execute_reply.started": "2021-06-05T20:07:30.387521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 3, 3, 2, 2]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_ind([y for y in da], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "faced-parliament",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T20:11:09.051692Z",
     "iopub.status.busy": "2021-06-05T20:11:09.051183Z",
     "iopub.status.idle": "2021-06-05T20:11:09.061996Z",
     "shell.execute_reply": "2021-06-05T20:11:09.060226Z",
     "shell.execute_reply.started": "2021-06-05T20:11:09.051632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[98.4, 97.6, 97.29, 99.2, 68.8]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_ind([y[2:4] for y in da], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "magnetic-proof",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T20:13:53.409268Z",
     "iopub.status.busy": "2021-06-05T20:13:53.409124Z",
     "iopub.status.idle": "2021-06-05T20:13:53.519217Z",
     "shell.execute_reply": "2021-06-05T20:13:53.518783Z",
     "shell.execute_reply.started": "2021-06-05T20:13:53.409251Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(merge_ind([y[:2] for y in da], 0),merge_ind([y for y in da], -1))\n",
    "s = merge_ind([y for y in da], -1)\n",
    "q = [str(x)+\"% acc\" for x in merge_ind([y[2:4] for y in da], 1)]\n",
    "n = merge_ind([y for y in da], 0)\n",
    "for i in range(len(s)):\n",
    "    plt.annotate(str(q[i]), xy=(n[i],s[i]), ha='center', va='bottom')\n",
    "plt.title(\"Number of epochs and accuracy\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "republican-prescription",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "saving-genre",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "antique-oregon",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar()\n",
    "plt.title(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "focused-vessel",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T19:54:32.907496Z",
     "iopub.status.busy": "2021-06-05T19:54:32.907047Z",
     "iopub.status.idle": "2021-06-05T19:54:33.012209Z",
     "shell.execute_reply": "2021-06-05T19:54:33.011714Z",
     "shell.execute_reply.started": "2021-06-05T19:54:32.907443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of classes')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(merge_ind([y[:2] for y in da], 0), merge_ind([y[:2] for y in da], 1))\n",
    "plt.title(\"Number of classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "favorite-hebrew",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greatest-lodge",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "western-lying",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cubic-treaty",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}