{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import copy\n",
    "import pickle\n",
    "import urllib\n",
    "import datetime\n",
    "from tqdm import tqdm_notebook, tqdm\n",
    "from collections import Counter\n",
    "\n",
    "from astropy.io import fits\n",
    "from PIL import Image \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data_utils\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision import datasets, models, transforms\n",
    "from torchvision.models.resnet import BasicBlock\n",
    "from torch.optim import lr_scheduler\n",
    "\n",
    "import pretrainedmodels\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from skimage.transform import resize, rescale\n",
    "from sklearn.utils import class_weight\n",
    "\n",
    "import warnings\n",
    "# import cv2\n",
    "\n",
    "plt.ion()\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_pkl = 'https://raw.githubusercontent.com/iknyazeva/FitsProcessing/master/sunspot_1996_2017.pkl'\n",
    "dataset_folder = 'HMIdataset/fragments/'\n",
    "with urllib.request.urlopen(url_pkl) as pkl:\n",
    "    sunspots = pickle.load(pkl)\n",
    "\n",
    "distributions_classes = Counter([class_ for class_ in sunspots['class']])\n",
    "distributions_classes1 = Counter([class_[0] for class_ in sunspots['class']])\n",
    "distributions_classes2 = Counter([class_[1] for class_ in sunspots['class']])\n",
    "distributions_classes3 = Counter([class_[2] for class_ in sunspots['class']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'AXX': 3302,\n",
       "         'BXO': 3933,\n",
       "         'CRO': 1407,\n",
       "         'DSO': 2280,\n",
       "         'DAO': 2844,\n",
       "         'CAO': 2053,\n",
       "         'HSX': 4671,\n",
       "         'CSO': 3255,\n",
       "         'HRX': 736,\n",
       "         'CRI': 32,\n",
       "         'DAI': 957,\n",
       "         'EAC': 179,\n",
       "         'EHI': 63,\n",
       "         'EHO': 66,\n",
       "         'ESI': 137,\n",
       "         'EKI': 426,\n",
       "         'ESO': 358,\n",
       "         'DSI': 244,\n",
       "         'EKC': 516,\n",
       "         'DRO': 311,\n",
       "         'EAI': 577,\n",
       "         'EAO': 673,\n",
       "         'HAX': 1395,\n",
       "         'DRI': 26,\n",
       "         'FSI': 26,\n",
       "         'DKO': 262,\n",
       "         'FKI': 199,\n",
       "         'EKO': 210,\n",
       "         'FKO': 95,\n",
       "         'HKX': 142,\n",
       "         'HHX': 155,\n",
       "         'FKC': 363,\n",
       "         'FSC': 6,\n",
       "         'FHI': 15,\n",
       "         'CHO': 118,\n",
       "         'ESC': 19,\n",
       "         'CKO': 188,\n",
       "         'FAI': 156,\n",
       "         'FHO': 26,\n",
       "         'DHO': 105,\n",
       "         'DAC': 212,\n",
       "         'FHC': 23,\n",
       "         'FAO': 105,\n",
       "         'DKI': 250,\n",
       "         'CKI': 14,\n",
       "         'BXI': 19,\n",
       "         'CAI': 112,\n",
       "         'DSC': 27,\n",
       "         'FSO': 66,\n",
       "         'FAC': 40,\n",
       "         'CHI': 6,\n",
       "         'DKC': 220,\n",
       "         'EHC': 22,\n",
       "         'ERO': 7,\n",
       "         'DHI': 35,\n",
       "         'CSI': 41,\n",
       "         'FRO': 1,\n",
       "         'DHC': 20,\n",
       "         'CKC': 1,\n",
       "         'HXX': 1,\n",
       "         'DRC': 1,\n",
       "         'HSO': 1,\n",
       "         'ERI': 1})"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distributions_classes\n",
    "# distributions_classes1\n",
    "# distributions_classes2\n",
    "# distributions_classes3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.46019728 1.22003326 0.66716085 0.61862605 1.48173676 4.30113419\n",
      " 0.67899893]\n",
      "[('A', 3302), ('B', 3952), ('C', 7227), ('D', 7794), ('E', 3254), ('F', 1121), ('H', 7101)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['A', 'B', 'C', 'D', 'E', 'F', 'H'], dtype='<U1')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_wts = class_weight.compute_class_weight(class_weight='balanced', classes=np.unique([class_[0] for class_ in sunspots['class']]), y=[class_[0] for class_ in sunspots['class']])\n",
    "print(label_wts)\n",
    "\n",
    "print(sorted(distributions_classes1.items()))\n",
    "\n",
    "np.unique([class_[0] for class_ in sunspots['class']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "clf.fit(X[train_idx], y[train_idx])\n",
    "y_preds = clf.predict(X[test_idx])\n",
    "\n",
    "# --classification report --\n",
    "metrics.classification_report(y[test_idx], y_preds, labels=[0,1])\n",
    "\n",
    "#              precision    recall  f1-score   support\n",
    "\n",
    "#           0       0.98      1.00      0.99       610\n",
    "#           1       0.25      0.08      0.12        12\n",
    "\n",
    "# avg / total       0.97      0.98      0.97       622"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pidor: [1.46019728 1.22003326 0.66716085 0.61862605 1.48173676 4.30113419\n",
      " 0.67899893]\n"
     ]
    }
   ],
   "source": [
    "print(f'pidor: {label_wts}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.46019728, 1.22003326, 0.66716085, 0.61862605, 1.48173676,\n",
       "        4.30113419, 0.67899893],\n",
       "       [1.46019728, 1.22003326, 0.66716085, 0.61862605, 1.48173676,\n",
       "        4.30113419, 0.67899893],\n",
       "       [1.46019728, 1.22003326, 0.66716085, 0.61862605, 1.48173676,\n",
       "        4.30113419, 0.67899893]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# torch.reshape(torch.Tensor(label_wts)\n",
    "np.repeat(label_wts.reshape(1,-1), 3, axis=0)\n",
    "# label_wts.reshape(1,7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb\n",
    "plt.rcParams['figure.figsize'] = (30,20)\n",
    "sb.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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></th>\n",
       "      <th>number_of_ss</th>\n",
       "      <th>size</th>\n",
       "      <th>class</th>\n",
       "      <th>location</th>\n",
       "      <th>magn_class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-06-04</th>\n",
       "      <th>7968</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>AXX</td>\n",
       "      <td>N02E41</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1996-06-05</th>\n",
       "      <th>7968</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>BXO</td>\n",
       "      <td>N02E28</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7969</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>AXX</td>\n",
       "      <td>N09W33</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1996-06-06</th>\n",
       "      <th>7968</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>CRO</td>\n",
       "      <td>N03E12</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7969</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>BXO</td>\n",
       "      <td>N08W44</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  number_of_ss size class location magn_class\n",
       "date       region                                            \n",
       "1996-06-04 7968              3    0   AXX   N02E41          A\n",
       "1996-06-05 7968              2   10   BXO   N02E28          B\n",
       "           7969              1    0   AXX   N09W33          A\n",
       "1996-06-06 7968              4   20   CRO   N03E12          B\n",
       "           7969              4   10   BXO   N08W44          B"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sunspots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q, w = zip(*(sorted(distributions_classes.items())))\n",
    "plt.barh(q, w)\n",
    "plt.savefig('all_classes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a, b = zip(distributions_classes.items())\n",
    "a, b = zip(*distributions_classes1.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip(*sorted(distributions_classes.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_distr = Counter(size for size in sunspots['size'])\n",
    "r, c =  zip(*size_distr.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sunspots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ern = [int(e) for e in er]\n",
    "plt.hist(ern, bins=200);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': Counter({'AXX': 3302}),\n",
       " 'B': Counter({'BXO': 3933, 'BXI': 19}),\n",
       " 'C': Counter({'CRO': 1407,\n",
       "          'CAO': 2053,\n",
       "          'CSO': 3255,\n",
       "          'CRI': 32,\n",
       "          'CHO': 118,\n",
       "          'CKO': 188,\n",
       "          'CKI': 14,\n",
       "          'CAI': 112,\n",
       "          'CHI': 6,\n",
       "          'CSI': 41,\n",
       "          'CKC': 1}),\n",
       " 'D': Counter({'DSO': 2280,\n",
       "          'DAO': 2844,\n",
       "          'DAI': 957,\n",
       "          'DSI': 244,\n",
       "          'DRO': 311,\n",
       "          'DRI': 26,\n",
       "          'DKO': 262,\n",
       "          'DHO': 105,\n",
       "          'DAC': 212,\n",
       "          'DKI': 250,\n",
       "          'DSC': 27,\n",
       "          'DKC': 220,\n",
       "          'DHI': 35,\n",
       "          'DHC': 20,\n",
       "          'DRC': 1}),\n",
       " 'H': Counter({'HSX': 4671,\n",
       "          'HRX': 736,\n",
       "          'HAX': 1395,\n",
       "          'HKX': 142,\n",
       "          'HHX': 155,\n",
       "          'HXX': 1,\n",
       "          'HSO': 1}),\n",
       " 'E': Counter({'EAC': 179,\n",
       "          'EHI': 63,\n",
       "          'EHO': 66,\n",
       "          'ESI': 137,\n",
       "          'EKI': 426,\n",
       "          'ESO': 358,\n",
       "          'EKC': 516,\n",
       "          'EAI': 577,\n",
       "          'EAO': 673,\n",
       "          'EKO': 210,\n",
       "          'ESC': 19,\n",
       "          'EHC': 22,\n",
       "          'ERO': 7,\n",
       "          'ERI': 1}),\n",
       " 'F': Counter({'FSI': 26,\n",
       "          'FKI': 199,\n",
       "          'FKO': 95,\n",
       "          'FKC': 363,\n",
       "          'FSC': 6,\n",
       "          'FHI': 15,\n",
       "          'FAI': 156,\n",
       "          'FHO': 26,\n",
       "          'FHC': 23,\n",
       "          'FAO': 105,\n",
       "          'FSO': 66,\n",
       "          'FAC': 40,\n",
       "          'FRO': 1})}"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_over_Z = {}\n",
    "for letter in distributions_classes1:\n",
    "    dist_over_Z[letter] = Counter([class_ for class_ in sunspots['class'] if class_[0]==letter])\n",
    "dist_over_Z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "execution_count": 307,
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     },
     "execution_count": 307,
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