{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tube segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/thomasathey/Documents/mimlab/mouselight/env/lib/python3.8/site-packages/python_jsonschema_objects/__init__.py:50: UserWarning: Schema version http://json-schema.org/draft-04/schema not recognized. Some keywords and features may not be supported.\n  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from brainlit.utils import session\n",
    "from brainlit.feature_extraction import *\n",
    "import napari"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"s3://open-neurodata/brainlit/brain1\"\n",
    "sess = session.NeuroglancerSession(url=url, url_segments=url + \"_segments\", mip=0)\n",
    "SEGLIST = [\n",
    "    101,\n",
    "    103,\n",
    "    106,\n",
    "    107,\n",
    "    109,\n",
    "    11,\n",
    "    111,\n",
    "    112,\n",
    "    115,\n",
    "    11,\n",
    "    12,\n",
    "    120,\n",
    "    124,\n",
    "    126,\n",
    "    127,\n",
    "    129,\n",
    "    13,\n",
    "    132,\n",
    "    133,\n",
    "    136,\n",
    "    137,\n",
    "    14,\n",
    "    140,\n",
    "    141,\n",
    "    142,\n",
    "    143,\n",
    "    144,\n",
    "    145,\n",
    "    146,\n",
    "    147,\n",
    "    149,\n",
    "    150,\n",
    "]\n",
    "SEGLIST = SEGLIST[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
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     ]
    }
   ],
   "source": [
    "# %%capture\n",
    "nbr = NeighborhoodFeatures(\n",
    "    url=url, radius=1, offset=[50, 50, 50], segment_url=url + \"_segments\"\n",
    ")\n",
    "nbr.fit(seg_ids=SEGLIST, num_verts=10, file_path=\"demo\", batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(20, 30)"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "import glob, feather\n",
    "\n",
    "feathers = glob.glob(\"*.feather\")\n",
    "\n",
    "\n",
    "for count, feather_file in enumerate(feathers):\n",
    "    if count == 0:\n",
    "        data = feather.read_dataframe(feather_file)\n",
    "    else:\n",
    "        df = feather.read_dataframe(feather_file)\n",
    "        data = pd.concat([data, df])\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Segment  Vertex  Label      0      1      2      3      4      5      6  \\\n",
       "0      101       0      1  28778  30111  30120  28438  29909  30092  28315   \n",
       "1      101       0      0  12290  12090  12222  12340  12215  12376  12185   \n",
       "2      101       1      1  15429  16558  17587  15353  16662  17459  15462   \n",
       "3      101       1      0  12166  12290  12180  12162  12129  12124  12058   \n",
       "4      101       2      1  13005  12535  12333  12903  12660  12402  12846   \n",
       "\n",
       "   ...     17     18     19     20     21     22     23     24     25     26  \n",
       "0  ...  28751  30383  30683  29420  29565  30769  29762  28865  29364  29687  \n",
       "1  ...  12208  12144  11845  12030  12682  12340  12336  12194  12383  12067  \n",
       "2  ...  18105  14926  16441  17088  15187  16712  17474  15401  16939  18366  \n",
       "3  ...  12297  12090  12038  12144  11959  11929  12116  12093  12298  12407  \n",
       "4  ...  12502  12388  12404  12471  12860  12526  12441  12951  12771  12571  \n",
       "\n",
       "[5 rows x 30 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Segment</th>\n      <th>Vertex</th>\n      <th>Label</th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>...</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>101</td>\n      <td>0</td>\n      <td>1</td>\n      <td>28778</td>\n      <td>30111</td>\n      <td>30120</td>\n      <td>28438</td>\n      <td>29909</td>\n      <td>30092</td>\n      <td>28315</td>\n      <td>...</td>\n      <td>28751</td>\n      <td>30383</td>\n      <td>30683</td>\n      <td>29420</td>\n      <td>29565</td>\n      <td>30769</td>\n      <td>29762</td>\n      <td>28865</td>\n      <td>29364</td>\n      <td>29687</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>101</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12290</td>\n      <td>12090</td>\n      <td>12222</td>\n      <td>12340</td>\n      <td>12215</td>\n      <td>12376</td>\n      <td>12185</td>\n      <td>...</td>\n      <td>12208</td>\n      <td>12144</td>\n      <td>11845</td>\n      <td>12030</td>\n      <td>12682</td>\n      <td>12340</td>\n      <td>12336</td>\n      <td>12194</td>\n      <td>12383</td>\n      <td>12067</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>101</td>\n      <td>1</td>\n      <td>1</td>\n      <td>15429</td>\n      <td>16558</td>\n      <td>17587</td>\n      <td>15353</td>\n      <td>16662</td>\n      <td>17459</td>\n      <td>15462</td>\n      <td>...</td>\n      <td>18105</td>\n      <td>14926</td>\n      <td>16441</td>\n      <td>17088</td>\n      <td>15187</td>\n      <td>16712</td>\n      <td>17474</td>\n      <td>15401</td>\n      <td>16939</td>\n      <td>18366</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>101</td>\n      <td>1</td>\n      <td>0</td>\n      <td>12166</td>\n      <td>12290</td>\n      <td>12180</td>\n      <td>12162</td>\n      <td>12129</td>\n      <td>12124</td>\n      <td>12058</td>\n      <td>...</td>\n      <td>12297</td>\n      <td>12090</td>\n      <td>12038</td>\n      <td>12144</td>\n      <td>11959</td>\n      <td>11929</td>\n      <td>12116</td>\n      <td>12093</td>\n      <td>12298</td>\n      <td>12407</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>101</td>\n      <td>2</td>\n      <td>1</td>\n      <td>13005</td>\n      <td>12535</td>\n      <td>12333</td>\n      <td>12903</td>\n      <td>12660</td>\n      <td>12402</td>\n      <td>12846</td>\n      <td>...</td>\n      <td>12502</td>\n      <td>12388</td>\n      <td>12404</td>\n      <td>12471</td>\n      <td>12860</td>\n      <td>12526</td>\n      <td>12441</td>\n      <td>12951</td>\n      <td>12771</td>\n      <td>12571</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 30 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = data.iloc[:, 3:]\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "y = data[\"Label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=1)\n",
    "clf = MLPClassifier(\n",
    "    hidden_layer_sizes=4, activation=\"logistic\", alpha=1, max_iter=1000\n",
    ").fit(X_train, y_train)\n",
    "y_score = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_test, y_score[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(\n",
    "    fpr, tpr, color=\"darkorange\", lw=lw, label=\"ROC curve (area = %0.2f)\" % roc_auc\n",
    ")\n",
    "plt.plot([0, 1], [0, 1], color=\"navy\", lw=lw, linestyle=\"--\")\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"MLP ROC\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from brainlit.feature_extraction.neighborhood import subsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(20, 27)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "Xc_train, Xc_test, yc_train, yc_test = train_test_split(\n",
    "    X, y, stratify=y, random_state=1\n",
    ")\n",
    "clf = LogisticRegression(random_state=1, max_iter=2000).fit(Xc_train, yc_train)\n",
    "yc_score = clf.predict_proba(Xc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fpr_c, tpr_c, _ = roc_curve(yc_test, yc_score[:, 1])\n",
    "roc_auc_c = auc(fpr_c, tpr_c)\n",
    "\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(\n",
    "    fpr_c,\n",
    "    tpr_c,\n",
    "    color=\"darkorange\",\n",
    "    lw=lw,\n",
    "    label=\"ROC curve (area = %0.2f)\" % roc_auc_c,\n",
    ")\n",
    "plt.plot([0, 1], [0, 1], color=\"navy\", lw=lw, linestyle=\"--\")\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"LogRegression Receiver operating characteristic\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python386jvsc74a57bd0c48f87b51193a18c36f6ec1b39da40df380a4c3fb2fa54b3e413dc2378ec9052",
   "display_name": "Python 3.8.6 64-bit ('env')"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}