baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1_getdata.ipynb\t\t3_model_resnext50.ipynb  README.md\r\n",
      "2_model_resnet34.ipynb\tbaseline.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sys\n",
    "#sys.path.insert(0,'/home/nidhin/fastai')\n",
    "\n",
    "#export PYTHONPATH=$PYTHONPATH:/home/nidhin/fastai\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.imports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This file contains all the main external libs we'll use\n",
    "from fastai.imports import *\n",
    "from fastai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fastai.model import *\n",
    "from fastai.dataset import *\n",
    "from fastai.sgdr import *\n",
    "from fastai.plots import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil\n",
    "from IPython.display import FileLink, FileLinks\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models\t\t\t  sub\t     test.json.7z  train.json\t       valid\r\n",
      "sample_submission.csv\t  test\t     tmp\t   train.json.7z\r\n",
      "sample_submission.csv.7z  test.json  train\t   train_modified.zip\r\n"
     ]
    }
   ],
   "source": [
    "!ls /home/nidhin/data/iceberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"/home/nidhin/data/iceberg/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models\t\t\t  sub\t     test.json.7z  train.json\t       valid\r\n",
      "sample_submission.csv\t  test\t     tmp\t   train.json.7z\r\n",
      "sample_submission.csv.7z  test.json  train\t   train_modified.zip\r\n"
     ]
    }
   ],
   "source": [
    "!ls {PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_map = {1:'ice',0:'ship'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_json(PATH+\"/train.json\")\n",
    "train_df['classes'] = train_df['is_iceberg'].map(class_map)\n",
    "\n",
    "test_df = pd.read_json(PATH+\"/test.json\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>band_1</th>\n",
       "      <th>band_2</th>\n",
       "      <th>id</th>\n",
       "      <th>inc_angle</th>\n",
       "      <th>is_iceberg</th>\n",
       "      <th>classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-27.878360999999998, -27.15416, -28.668615, -...</td>\n",
       "      <td>[-27.154118, -29.537888, -31.0306, -32.190483,...</td>\n",
       "      <td>dfd5f913</td>\n",
       "      <td>43.9239</td>\n",
       "      <td>0</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-12.242375, -14.920304999999999, -14.920363, ...</td>\n",
       "      <td>[-31.506321, -27.984554, -26.645678, -23.76760...</td>\n",
       "      <td>e25388fd</td>\n",
       "      <td>38.1562</td>\n",
       "      <td>0</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-24.603676, -24.603714, -24.871029, -23.15277...</td>\n",
       "      <td>[-24.870956, -24.092632, -20.653963, -19.41104...</td>\n",
       "      <td>58b2aaa0</td>\n",
       "      <td>45.2859</td>\n",
       "      <td>1</td>\n",
       "      <td>ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-22.454607, -23.082819, -23.998013, -23.99805...</td>\n",
       "      <td>[-27.889421, -27.519794, -27.165262, -29.10350...</td>\n",
       "      <td>4cfc3a18</td>\n",
       "      <td>43.8306</td>\n",
       "      <td>0</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-26.006956, -23.164886, -23.164886, -26.89116...</td>\n",
       "      <td>[-27.206915, -30.259186, -30.259186, -23.16495...</td>\n",
       "      <td>271f93f4</td>\n",
       "      <td>35.6256</td>\n",
       "      <td>0</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              band_1  \\\n",
       "0  [-27.878360999999998, -27.15416, -28.668615, -...   \n",
       "1  [-12.242375, -14.920304999999999, -14.920363, ...   \n",
       "2  [-24.603676, -24.603714, -24.871029, -23.15277...   \n",
       "3  [-22.454607, -23.082819, -23.998013, -23.99805...   \n",
       "4  [-26.006956, -23.164886, -23.164886, -26.89116...   \n",
       "\n",
       "                                              band_2        id inc_angle  \\\n",
       "0  [-27.154118, -29.537888, -31.0306, -32.190483,...  dfd5f913   43.9239   \n",
       "1  [-31.506321, -27.984554, -26.645678, -23.76760...  e25388fd   38.1562   \n",
       "2  [-24.870956, -24.092632, -20.653963, -19.41104...  58b2aaa0   45.2859   \n",
       "3  [-27.889421, -27.519794, -27.165262, -29.10350...  4cfc3a18   43.8306   \n",
       "4  [-27.206915, -30.259186, -30.259186, -23.16495...  271f93f4   35.6256   \n",
       "\n",
       "   is_iceberg classes  \n",
       "0           0    ship  \n",
       "1           0    ship  \n",
       "2           1     ice  \n",
       "3           0    ship  \n",
       "4           0    ship  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(train_df, train_df['is_iceberg'], test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_band(band):\n",
    "    normalized_band = (band-np.min(band))/(np.max(band)-np.min(band))\n",
    "    \n",
    "    band = np.array(normalized_band).reshape((75, 75))\n",
    "    pband = band - np.mean(band)\n",
    "    pband[pband < band.std() * 2] = 0\n",
    "    pband = np.mean(np.abs(np.gradient(pband)),axis=0)\n",
    "    return pband\n",
    "    \n",
    "    #positive = band + np.amin(band)\n",
    "    #normalized = positive / np.amax(positive)\n",
    "    \n",
    "    #return normalized\n",
    "    \n",
    "    #return band\n",
    "\n",
    "def scale_image(row):\n",
    "    band_1 = normalize_band(row['band_1'])\n",
    "    band_2 = normalize_band(row['band_2'])\n",
    "        \n",
    "    band_3 = normalize_band(band_1 + band_2)\n",
    "\n",
    "    return np.dstack((band_1, band_2, band_3))\n",
    "    \n",
    "def get_scaled_imgs(df):\n",
    "    imgs = []\n",
    "    for i, row in df.iterrows():\n",
    "        \n",
    "        imgs.append ( scale_image(row)) \n",
    "\n",
    "    return np.array(imgs)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all = get_scaled_imgs(train_df.head(10))\n",
    "#test_all = get_scaled_imgs(test_df)\n",
    "y_all = train_df['is_iceberg'].head(10)\n",
    "y_classes = train_df['classes'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plots(ims, figsize=(12,6), rows=1, titles=None):\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    for i in range(len(ims)):\n",
    "        sp = f.add_subplot(rows, len(ims)//rows, i+1)\n",
    "        sp.axis('Off')\n",
    "        if titles is not None: sp.set_title(titles[i], fontsize=16)\n",
    "        plt.imshow(ims[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7efdf7b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Band 1\")\n",
    "plots(X_all[:,:,:,0],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7efae0e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Band 2\")\n",
    "plots(X_all[:,:,:,1],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ca1044668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Band 3\")\n",
    "plots(X_all[:,:,:,2],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Bands\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7efd9a5438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"All Bands\")\n",
    "plots(X_all[:,:,:],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    851\n",
       "1    753\n",
       "Name: is_iceberg, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['is_iceberg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for img_class in class_map.values():\n",
    "    shutil.rmtree(f\"{PATH}train/{img_class}\",ignore_errors=True)\n",
    "    os.makedirs(f\"{PATH}train/{img_class}\")\n",
    "\n",
    "    shutil.rmtree(f\"{PATH}valid/{img_class}\",ignore_errors=True)\n",
    "    os.makedirs(f\"{PATH}valid/{img_class}\")\n",
    "    \n",
    "    shutil.rmtree(f\"{PATH}test\",ignore_errors=True)\n",
    "    os.makedirs(f\"{PATH}test\")\n",
    "        \n",
    "\n",
    "def save_images(images_df,folder):\n",
    "    print (f\"Saving {folder}\")\n",
    "    if \"classes\" in images_df:\n",
    "        for _,img_row in images_df.iterrows():\n",
    "            label = img_row['classes']\n",
    "            normalized_img = scale_image(img_row)\n",
    "            img_id = img_row[\"id\"]\n",
    "            plt.imsave(f\"{PATH}{folder}/{label}/{img_id}.jpeg\",normalized_img)\n",
    "    else:\n",
    "        # only for test\n",
    "        for _,img_row in images_df.iterrows():\n",
    "            normalized_img = scale_image(img_row)\n",
    "            img_id = img_row[\"id\"]\n",
    "            plt.imsave(f\"{PATH}{folder}/{img_id}.jpeg\",normalized_img)\n",
    "def binary_loss(y, p):\n",
    "    return np.mean(-(y * np.log(p) + (1-y)*np.log(1-p)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>band_1</th>\n",
       "      <th>band_2</th>\n",
       "      <th>id</th>\n",
       "      <th>inc_angle</th>\n",
       "      <th>is_iceberg</th>\n",
       "      <th>classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>[-1.344282, -1.344282, 0.9948239999999999, -2....</td>\n",
       "      <td>[-14.172397, -14.172397, -10.502144, -14.02402...</td>\n",
       "      <td>fd47c6f9</td>\n",
       "      <td>39.2166</td>\n",
       "      <td>1</td>\n",
       "      <td>ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>[-24.817394, -25.328541, -25.328602, -24.57289...</td>\n",
       "      <td>[-22.634506000000002, -21.719418, -22.443724, ...</td>\n",
       "      <td>94462587</td>\n",
       "      <td>37.254</td>\n",
       "      <td>1</td>\n",
       "      <td>ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>[-14.993799, -16.242653, -18.952848, -21.65787...</td>\n",
       "      <td>[-22.353334, -20.709526, -24.156723, -23.93674...</td>\n",
       "      <td>ec822cf5</td>\n",
       "      <td>34.1486</td>\n",
       "      <td>0</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>[-19.098007, -17.286678, -15.369007, -16.50553...</td>\n",
       "      <td>[-27.575521, -27.575588, -26.04789, -24.0539, ...</td>\n",
       "      <td>e2a547bf</td>\n",
       "      <td>35.2994</td>\n",
       "      <td>1</td>\n",
       "      <td>ice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>[-16.564709, -17.085022, -17.988312, -17.30227...</td>\n",
       "      <td>[-28.406221, -28.810337, -31.172371, -27.65059...</td>\n",
       "      <td>49f4d394</td>\n",
       "      <td>41.8505</td>\n",
       "      <td>0</td>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                band_1  \\\n",
       "705  [-1.344282, -1.344282, 0.9948239999999999, -2....   \n",
       "962  [-24.817394, -25.328541, -25.328602, -24.57289...   \n",
       "599  [-14.993799, -16.242653, -18.952848, -21.65787...   \n",
       "411  [-19.098007, -17.286678, -15.369007, -16.50553...   \n",
       "710  [-16.564709, -17.085022, -17.988312, -17.30227...   \n",
       "\n",
       "                                                band_2        id inc_angle  \\\n",
       "705  [-14.172397, -14.172397, -10.502144, -14.02402...  fd47c6f9   39.2166   \n",
       "962  [-22.634506000000002, -21.719418, -22.443724, ...  94462587    37.254   \n",
       "599  [-22.353334, -20.709526, -24.156723, -23.93674...  ec822cf5   34.1486   \n",
       "411  [-27.575521, -27.575588, -26.04789, -24.0539, ...  e2a547bf   35.2994   \n",
       "710  [-28.406221, -28.810337, -31.172371, -27.65059...  49f4d394   41.8505   \n",
       "\n",
       "     is_iceberg classes  \n",
       "705           1     ice  \n",
       "962           1     ice  \n",
       "599           0    ship  \n",
       "411           1     ice  \n",
       "710           0    ship  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving train\n",
      "Saving valid\n",
      "Saving test\n"
     ]
    }
   ],
   "source": [
    "save_images(X_train,\"train\")\n",
    "save_images(X_valid,\"valid\")\n",
    "save_images(test_df,\"test\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0b576e95d9042f586fb3c382e7641a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.87326  0.68691  0.5026 ]                      \n",
      "[ 1.       0.79344  0.65516  0.54167]                      \n",
      "[ 2.       0.75461  0.60778  0.74219]                      \n",
      "[ 3.       0.69731  0.58305  0.73698]                      \n",
      "[ 4.       0.64567  0.59281  0.59115]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "arch=resnet34\n",
    "sz=125\n",
    "\n",
    "data  = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch,sz),test_name='test')\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=False)\n",
    "learn.fit(0.001, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.56259776320420152, 0.70716510903426788)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-d2ba684acd0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_valid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-3e9b12e3fc58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_valid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'X_valid' is not defined"
     ]
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[124  40]\n",
      " [ 54 103]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ee15fd908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#log_preds,y = learn.TTA()\n",
    "#probs = np.mean(np.exp(log_preds),0)\n",
    "#preds = np.argmax(log_preds, axis=1)\n",
    "#probs = np.exp(log_preds[:,1])\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_predicted= np.argmax(preds,axis=1)\n",
    "cm = confusion_matrix(y, y_predicted)\n",
    "\n",
    "\n",
    "plot_confusion_matrix(cm, data.classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# test set prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    }
   ],
   "source": [
    "log_preds,y = learn.TTA(is_test=True)\n",
    "probs = np.exp(log_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 8424, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_test_file(probs,test_df):\n",
    "    probs = np.mean(probs,axis=0)\n",
    "    df = pd.DataFrame(probs)\n",
    "    df.columns = data.classes\n",
    "    df['id'] = test_df['id']\n",
    "    df=df.drop('ship',axis=1)\n",
    "    df=df.rename(columns={'ice':'is_iceberg'})\n",
    "    SUBM = f'{PATH}sub/'\n",
    "    os.makedirs(SUBM, exist_ok=True)\n",
    "    df.to_csv(f'{SUBM}subm.gz', compression='gzip', index=False)\n",
    "    FileLink(f'{SUBM}subm.gz')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_test_file(log_preds,test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}