3_Poboljšanje rezultata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd6ca9b4",
   "metadata": {},
   "source": [
    "Sveučilište u Zagrebu<br>\n",
    "Fakultet elektrotehnike i računarstva\n",
    "\n",
    "## Uvod u znanost o podacima\n",
    "\n",
    "# Poboljšanje rezultata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8bfd490",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51587ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "747ec231",
   "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>TOPICS</th>\n",
       "      <th>BODY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'cocoa'</td>\n",
       "      <td>showers continued throughout week bahia cocoa zone alleviating drought since early january improving prospects coming temporao although normal humidity levels restored comissaria smith said weekly review period means temporao late year arrivals week ended february bags kilos making cumulative total season stage last year seems cocoa delivered earlier consignment included arrivals figures comissaria smith said still doubt much crop cocoa still available harvesting practically come total bahia crop estimates around bags sales standing almost hundred thousand bags still hands farmers middlemen exporters processors doubts much cocoa would export shippers experiencing dificulties obtaining bahia superior certificates view lower quality recent weeks farmers sold good part cocoa held consignment comissaria smith said spot bean prices rose cruzados arroba kilos bean shippers reluctant offer nearby shipment limited sales booked march shipment dlrs tonne ports named crop sales also light open ports june july going dlrs dlrs york july sept dlrs tonne routine sales butter made march april sold dlrs april butter went times york june july dlrs sept dlrs times york sept dlrs times york comissaria smith said destinations covertible currency areas uruguay open ports cake sales registered dlrs march april dlrs dlrs times york buyers argentina uruguay convertible currency areas liquor sales limited march april selling dlrs june july dlrs times york july sept dlrs times york sept times york comissaria smith said total bahia sales currently estimated bags crop bags crop final figures period february expected published brazilian cocoa trade commission carnival ends midday february reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'grain'</td>\n",
       "      <td>agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'wheat'</td>\n",
       "      <td>agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'corn'</td>\n",
       "      <td>agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'barley'</td>\n",
       "      <td>agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TOPICS  \\\n",
       "0    'cocoa'   \n",
       "1    'grain'   \n",
       "2    'wheat'   \n",
       "3     'corn'   \n",
       "4   'barley'   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              BODY  \n",
       "0  showers continued throughout week bahia cocoa zone alleviating drought since early january improving prospects coming temporao although normal humidity levels restored comissaria smith said weekly review period means temporao late year arrivals week ended february bags kilos making cumulative total season stage last year seems cocoa delivered earlier consignment included arrivals figures comissaria smith said still doubt much crop cocoa still available harvesting practically come total bahia crop estimates around bags sales standing almost hundred thousand bags still hands farmers middlemen exporters processors doubts much cocoa would export shippers experiencing dificulties obtaining bahia superior certificates view lower quality recent weeks farmers sold good part cocoa held consignment comissaria smith said spot bean prices rose cruzados arroba kilos bean shippers reluctant offer nearby shipment limited sales booked march shipment dlrs tonne ports named crop sales also light open ports june july going dlrs dlrs york july sept dlrs tonne routine sales butter made march april sold dlrs april butter went times york june july dlrs sept dlrs times york sept dlrs times york comissaria smith said destinations covertible currency areas uruguay open ports cake sales registered dlrs march april dlrs dlrs times york buyers argentina uruguay convertible currency areas liquor sales limited march april selling dlrs june july dlrs times york july sept dlrs times york sept times york comissaria smith said total bahia sales currently estimated bags crop bags crop final figures period february expected published brazilian cocoa trade commission carnival ends midday february reuter   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter   \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department reported farmer owned reserve national five average price february follows dlrs sorghum natl loan release call avge rate level price price wheat corn rates natl loan release call avge rate level price price oats barley sorghum reserves matured level reflects grain entered feedgrain july wheat level wheat barley corn sorghum level covers wheat entered january rates dlrs available reuter   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clanci = pd.read_csv(\"my_data/data/clanci_split.csv\", index_col = 0)\n",
    "clanci.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba6c21b",
   "metadata": {},
   "source": [
    "## Jezgre i funkcije (iz prošlog dijela)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b1fb7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import kernels.wk as wk\n",
    "import kernels.ngk as ngk\n",
    "import kernels.ssk as ssk\n",
    "\n",
    "wk_kernel = lambda x, y: wk(x, y)\n",
    "\n",
    "def wkGmats(trainDocs, testDocs):\n",
    "    vectorizer = CountVectorizer(analyzer = \"word\", tokenizer = None, preprocessor = None, stop_words = \"english\", input='content') \n",
    "\n",
    "    train_data_features = vectorizer.fit_transform(trainDocs)\n",
    "    train_data_features = train_data_features.toarray()\n",
    "\n",
    "    transformer = TfidfTransformer(smooth_idf=True)\n",
    "    tfidf = transformer.fit_transform(train_data_features)\n",
    "    tfidf = tfidf.toarray() \n",
    "\n",
    "    nTrainDocs = len(tfidf)\n",
    "    GmatTrain = np.ones((nTrainDocs,nTrainDocs))\n",
    "\n",
    "    for i in range( 0, nTrainDocs ):\n",
    "        for j in range(0,nTrainDocs):\n",
    "            GmatTrain[i][j] = np.dot(tfidf[i], tfidf[j])\n",
    "            \n",
    "    n_features_train = len(tfidf[0])\n",
    "    \n",
    "    train_data_features = vectorizer.transform(testDocs)\n",
    "    train_data_features = train_data_features.toarray()\n",
    "\n",
    "    transformer = TfidfTransformer(smooth_idf=True)\n",
    "    tfidfTest = transformer.fit_transform(train_data_features)\n",
    "    tfidfTest = tfidfTest.toarray() \n",
    "\n",
    "    nTestDocs = len(tfidfTest)\n",
    "    GmatTest = np.ones((nTestDocs,nTrainDocs))\n",
    "\n",
    "    for i in range( 0, nTestDocs ):\n",
    "        for j in range(0,nTrainDocs):\n",
    "            GmatTest[i][j] = np.dot(tfidfTest[i], tfidf[j])\n",
    "    \n",
    "    return GmatTrain, GmatTest\n",
    "\n",
    "def ssk_compute_train_gram(docs, kernel=None):\n",
    "    n = len(docs)\n",
    "    gram = np.ones((n, n))\n",
    "    print(\"SSK compute train gram...\")\n",
    "    for x in range(n):\n",
    "        #print('{0:.2f}%'.format(x / n * 100))\n",
    "        for y in range(x + 1, n):\n",
    "            gram[x, y] = kernel(docs[x], docs[y])\n",
    "            gram[y, x] = gram[x, y]\n",
    "    return gram  \n",
    "\n",
    "def ssk_compute_test_gram(test, train, kernel=None):\n",
    "    gram = np.zeros((len(test), len(train)))\n",
    "    print(\"SSK compute test gram...\")\n",
    "    for x in range(len(test)):\n",
    "        #print('{0:.2f}%'.format(x / len(test) * 100))\n",
    "        for y in range(len(train)):\n",
    "            gram[x, y] = kernel(test[x], train[y])\n",
    "    return gram\n",
    "\n",
    "def give_train_test_split(dataframe, give_train):\n",
    "    earn_clanci = dataframe[dataframe.TOPICS.str.contains(\"earn\")]\n",
    "    acq_clanci = dataframe[dataframe.TOPICS.str.contains(\"acq\")]\n",
    "    crude_clanci = dataframe[dataframe.TOPICS.str.contains(\"crude\")]\n",
    "    corn_clanci = dataframe[dataframe.TOPICS.str.contains(\"corn\")]\n",
    "\n",
    "    clanci = [earn_clanci, acq_clanci, crude_clanci, corn_clanci]\n",
    "    [earn_tr, earn_te] = train_test_split(earn_clanci, train_size=152/len(earn_clanci), test_size=40/len(earn_clanci))\n",
    "    [acq_tr, acq_te] = train_test_split(acq_clanci, train_size=114/len(acq_clanci), test_size=25/len(acq_clanci))\n",
    "    [crude_tr, crude_te] = train_test_split(crude_clanci, train_size=76/len(crude_clanci), test_size=15/len(crude_clanci))\n",
    "    [corn_tr, corn_te] = train_test_split(corn_clanci, train_size=38/len(corn_clanci), test_size=10/len(corn_clanci))\n",
    "\n",
    "    y_train = []\n",
    "    y_train.extend(['earn' for i in range(0, len(earn_tr))])\n",
    "    y_train.extend(['acq' for i in range(0, len(acq_tr))])\n",
    "    y_train.extend(['crude' for i in range(0, len(crude_tr))])\n",
    "    y_train.extend(['corn' for i in range(0, len(corn_tr))])\n",
    "    y_train = np.array(y_train)\n",
    "    #print(y_train)\n",
    "\n",
    "    y_test = []\n",
    "    y_test.extend(['earn' for i in range(0, len(earn_te))])\n",
    "    y_test.extend(['acq' for i in range(0, len(acq_te))])\n",
    "    y_test.extend(['crude' for i in range(0, len(crude_te))])\n",
    "    y_test.extend(['corn' for i in range(0, len(corn_te))])\n",
    "    y_test = np.array(y_test)\n",
    "    #print(y_test)\n",
    "\n",
    "    clanci_test = [earn_te, acq_te, crude_te, corn_te]\n",
    "    clanci_test = pd.concat(clanci_test)\n",
    "    clanci_train = [earn_tr, acq_tr, crude_tr, corn_tr]\n",
    "    clanci_train = pd.concat(clanci_train)\n",
    "    \n",
    "    earn_test=[]\n",
    "    acq_test=[]\n",
    "    crude_test=[]\n",
    "    corn_test=[]\n",
    "    for index, row in earn_te.iterrows():\n",
    "        earn_test.append(row['BODY'])\n",
    "    for index, row in acq_te.iterrows():\n",
    "        acq_test.append(row['BODY'])\n",
    "    for index, row in crude_te.iterrows():\n",
    "        crude_test.append(row['BODY'])\n",
    "    for index, row in corn_te.iterrows():\n",
    "        corn_test.append(row['BODY'])\n",
    "    \n",
    "    treniranje_parovi = []\n",
    "    treniranje = []\n",
    "    i = 0\n",
    "    for index, row in clanci_train.iterrows():\n",
    "        par = []\n",
    "        par = [row['BODY'], y_train[i]]\n",
    "        treniranje.append(row['BODY'])\n",
    "        treniranje_parovi.append(par)\n",
    "\n",
    "    testiranje = []\n",
    "    for index, row in clanci_test.iterrows():\n",
    "        testiranje.append(row['BODY'])\n",
    "    \n",
    "    if give_train:\n",
    "        return [treniranje, y_train]\n",
    "    else:\n",
    "        return [earn_test, acq_test, crude_test, corn_test, testiranje, y_test]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "acc7bf2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ssk_evaluation(dataframe, category, k_range=[5], lambd_range=[0.5], filename=\"my_data/results/no_file.csv\", add_k=False, add_lambd=False, C=1.0, add_C=False):\n",
    "    rezultat_lista = []\n",
    "\n",
    "    print(\"Starting SSK evaluation for {}...\".format(category))\n",
    "    for k in k_range:\n",
    "        for lambd in lambd_range:\n",
    "            print(\"\\tk = {}, lambda = {}\".format(k, lambd))\n",
    "            lista_u_ovom_koraku = []\n",
    "            if add_C:\n",
    "                lista_u_ovom_koraku.append(C)\n",
    "            lista_u_ovom_koraku.append(category)\n",
    "            lista_u_ovom_koraku.append(\"SSK\")\n",
    "            f1 = []\n",
    "            precision = []\n",
    "            recall = []\n",
    "            for j in range(0, 10):\n",
    "                [trening, y_trening] = give_train_test_split(dataframe, True)\n",
    "\n",
    "                #odabire podskup primjera za ucenje jer ih je inace previse i dugo traje\n",
    "                treniranje = []\n",
    "                y_train = []\n",
    "                for i in range(0, 60):\n",
    "                    r = random.randint(0, len(trening)-1)\n",
    "                    treniranje.append(trening[r])\n",
    "                    y_train.append(y_trening[r])\n",
    "\n",
    "                ssk_kernel = lambda x, y: ssk.ssk(x, y, k, lambd)\n",
    "                train_gram = ssk_compute_train_gram(treniranje, kernel=ssk_kernel)\n",
    "                [earn_test, acq_test, crude_test, corn_test, _, _] = give_train_test_split(dataframe, False)\n",
    "                if(category==\"earn\"):\n",
    "                    X_test = earn_test\n",
    "                    y_true = np.array(['earn' for i in range(0, len(earn_test))])\n",
    "                elif category == \"acq\":\n",
    "                    X_test = acq_test\n",
    "                    y_true = np.array(['acq' for i in range(0, len(acq_test))])\n",
    "                elif category == \"crude\":\n",
    "                    X_test = crude_test\n",
    "                    y_true = np.array(['crude' for i in range(0, len(crude_test))])\n",
    "                else:\n",
    "                    X_test = corn_test\n",
    "                    y_true = np.array(['corn' for i in range(0, len(corn_test))])\n",
    "\n",
    "                test_gram = ssk_compute_test_gram(X_test, treniranje, kernel=ssk_kernel)\n",
    "                clf = SVC(kernel='precomputed', C=C)\n",
    "                clf.fit(train_gram, y_train)\n",
    "                # predikcija\n",
    "                y_pred = clf.predict(test_gram)\n",
    "                f1.append(f1_score(y_true, y_pred, average='micro'))\n",
    "                precision.append(precision_score(y_true, y_pred, average='micro'))\n",
    "                recall.append(recall_score(y_true, y_pred, average='micro'))\n",
    "            # kraj j petlje\n",
    "        \n",
    "            if add_k:\n",
    "                lista_u_ovom_koraku.append(k)\n",
    "            if add_lambd:\n",
    "                lista_u_ovom_koraku.append(lambd)\n",
    "            \n",
    "            lista_u_ovom_koraku.append(round(np.mean(f1), 4))\n",
    "            lista_u_ovom_koraku.append(round(np.std(f1), 4))\n",
    "            lista_u_ovom_koraku.append(round(np.mean(precision), 4))\n",
    "            lista_u_ovom_koraku.append(round(np.std(precision), 4))\n",
    "            lista_u_ovom_koraku.append(round(np.mean(recall), 4))\n",
    "            lista_u_ovom_koraku.append(round(np.std(recall), 4))\n",
    "        \n",
    "            rez = pd.DataFrame([lista_u_ovom_koraku])\n",
    "            rez.to_csv(filename, mode='a', header=False)       \n",
    "        \n",
    "            rezultat_lista.append(lista_u_ovom_koraku)\n",
    "    print(\"End of SSK evaluation\\n\")\n",
    "    return rezultat_lista\n",
    "\n",
    "def ngk_evaluation(dataframe, category, k_range=[5], add_k = False, add_lambd = False, filename=\"my_data/results/no_file.csv\", C=1.0, add_C=False):\n",
    "    rezultat_lista = []\n",
    "    \n",
    "    print(\"Starting NGK evaluation for {}...\".format(category))\n",
    "    for k in k_range:\n",
    "        lista_u_ovom_koraku = []\n",
    "        if add_C:\n",
    "            lista_u_ovom_koraku.append(C)\n",
    "        lista_u_ovom_koraku.append(category)\n",
    "        lista_u_ovom_koraku.append(\"NGK\")\n",
    "        print(\"\\t k =\", k)\n",
    "        f1 = []\n",
    "        precision = []\n",
    "        recall = []\n",
    "        for i in range(0, 10):\n",
    "            #treniranje jezgre\n",
    "            [trening, y_trening] = give_train_test_split(dataframe, True)\n",
    "            \n",
    "            #odabire podskup primjera za ucenje jer ih je inace previse i dugo traje\n",
    "            treniranje = []\n",
    "            y_train = []\n",
    "            for i in range(0, 60):\n",
    "                r = random.randint(0, len(trening)-1)\n",
    "                treniranje.append(trening[r])\n",
    "                y_train.append(y_trening[r])\n",
    "        \n",
    "            [earn_test, acq_test, crude_test, corn_test, _, _] = give_train_test_split(dataframe, False)\n",
    "            if(category==\"earn\"):\n",
    "                X_test = earn_test\n",
    "                y_true = np.array(['earn' for i in range(0, len(earn_test))])\n",
    "            elif category == \"acq\":\n",
    "                X_test = acq_test\n",
    "                y_true = np.array(['acq' for i in range(0, len(acq_test))])\n",
    "            elif category == \"crude\":\n",
    "                X_test = crude_test\n",
    "                y_true = np.array(['crude' for i in range(0, len(crude_test))])\n",
    "            else:\n",
    "                X_test = corn_test\n",
    "                y_true = np.array(['corn' for i in range(0, len(corn_test))])\n",
    "\n",
    "            train_gram, test_gram = ngk.ngkGmats(treniranje, X_test, n=k)\n",
    "            clf = SVC(kernel='precomputed', C=C)\n",
    "            clf.fit(train_gram, y_train)\n",
    "            y_pred = clf.predict(test_gram)\n",
    "            f1.append(round(f1_score(y_true, y_pred, average='micro'), 3))\n",
    "            precision.append(round(precision_score(y_true, y_pred, average='micro'), 3))\n",
    "            recall.append(round(recall_score(y_true, y_pred, average='micro'), 3))\n",
    "        \n",
    "        if add_k:\n",
    "            lista_u_ovom_koraku.append(k)\n",
    "        if add_lambd:\n",
    "            lista_u_ovom_koraku.append(0)            \n",
    "        \n",
    "        lista_u_ovom_koraku.append(round(np.mean(f1), 4))\n",
    "        lista_u_ovom_koraku.append(round(np.std(f1), 4))\n",
    "        lista_u_ovom_koraku.append(round(np.mean(precision), 4))\n",
    "        lista_u_ovom_koraku.append(round(np.std(precision), 4))\n",
    "        lista_u_ovom_koraku.append(round(np.mean(recall), 4))\n",
    "        lista_u_ovom_koraku.append(round(np.std(recall), 4))\n",
    "        \n",
    "        rez = pd.DataFrame([lista_u_ovom_koraku])\n",
    "        rez.to_csv(filename, mode='a', header=False)      \n",
    "        \n",
    "        rezultat_lista.append(lista_u_ovom_koraku)\n",
    "        #print(lista_u_ovom_koraku)\n",
    "    print(\"End of NGK evaluation\\n\")\n",
    "    return rezultat_lista\n",
    "        \n",
    "def wk_evaluation(dataframe, category, add_k = False, add_lambd = False, filename=\"my_data/results/no_file.csv\", C=1.0, add_C=False):\n",
    "    rezultat_lista = []\n",
    "    if add_C:\n",
    "        rezultat_lista.append(C)\n",
    "    rezultat_lista.append(category)\n",
    "    rezultat_lista.append(\"WK\")\n",
    "\n",
    "    f1 = []\n",
    "    precision = []\n",
    "    recall = []\n",
    "    print(\"Starting WK evaluation for {}...\".format(category))\n",
    "    for i in range(0, 10):\n",
    "        #print(category, i)\n",
    "        #treniranje jezgre\n",
    "        \n",
    "        [trening, y_trening] = give_train_test_split(dataframe, True)\n",
    "            \n",
    "        #odabire podskup primjera za ucenje jer ih je inace previse i dugo traje\n",
    "        treniranje = []\n",
    "        y_train = []\n",
    "        for i in range(0, 60):\n",
    "            r = random.randint(0, len(trening)-1)\n",
    "            treniranje.append(trening[r])\n",
    "            y_train.append(y_trening[r])\n",
    "        \n",
    "        [earn_test, acq_test, crude_test, corn_test, _, _] = give_train_test_split(dataframe, False)\n",
    "        if(category==\"earn\"):\n",
    "            X_test = earn_test\n",
    "            y_true = np.array(['earn' for i in range(0, len(earn_test))])\n",
    "        elif category == \"acq\":\n",
    "            X_test = acq_test\n",
    "            y_true = np.array(['acq' for i in range(0, len(acq_test))])\n",
    "        elif category == \"crude\":\n",
    "            X_test = crude_test\n",
    "            y_true = np.array(['crude' for i in range(0, len(crude_test))])\n",
    "        else:\n",
    "            X_test = corn_test\n",
    "            y_true = np.array(['corn' for i in range(0, len(corn_test))])\n",
    "\n",
    "        wk_kernel = lambda x, y: wk(x, y)\n",
    "        [wk_train_gram, wk_test_gram] = wkGmats(treniranje, X_test)\n",
    "        clf = SVC(kernel='precomputed', C=C)\n",
    "        clf.fit(wk_train_gram, y_train)\n",
    "        y_pred = clf.predict(wk_test_gram)\n",
    "        f1.append(round(f1_score(y_true, y_pred, average='micro'), 3))\n",
    "        precision.append(round(precision_score(y_true, y_pred, average='micro'), 3))\n",
    "        recall.append(round(recall_score(y_true, y_pred, average='micro'), 3))\n",
    "        \n",
    "    if add_k:\n",
    "        rezultat_lista.append(0)\n",
    "    if add_lambd:\n",
    "        rezultat_lista.append(0)            \n",
    "        \n",
    "    rezultat_lista.append(round(np.mean(f1), 4))\n",
    "    rezultat_lista.append(round(np.std(f1), 4))\n",
    "    rezultat_lista.append(round(np.mean(precision), 4))\n",
    "    rezultat_lista.append(round(np.std(precision), 4))\n",
    "    rezultat_lista.append(round(np.mean(recall), 4))\n",
    "    rezultat_lista.append(round(np.std(recall), 4))\n",
    "    \n",
    "    rez = pd.DataFrame([rezultat_lista])\n",
    "    rez.to_csv(filename, mode='a', header=False) \n",
    "    \n",
    "    print(\"End of WK evaluation\\n\")\n",
    "    return [rezultat_lista]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55d7242",
   "metadata": {},
   "source": [
    "## Zadržavanje samo korijena riječi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0de04d2b",
   "metadata": {},
   "source": [
    "Želimo sve riječi u članku svesti na njihove korijene. Npr. riječi playing, plays, played su različite riječi, ali ih želimo svesti na play. Tako ćemo povećati sličnost članaka."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e32c045d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\MajaJuric\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\MajaJuric\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a80e8e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51e036bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatize(body):\n",
    "    novi_body = \"\"\n",
    "    for word in body.split(\" \"):\n",
    "        novi_body += wordnet_lemmatizer.lemmatize(word, pos=\"v\")\n",
    "        novi_body += \" \"\n",
    "    return novi_body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "04020c77",
   "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>TOPICS</th>\n",
       "      <th>BODY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'cocoa'</td>\n",
       "      <td>shower continue throughout week bahia cocoa zone alleviate drought since early january improve prospect come temporao although normal humidity level restore comissaria smith say weekly review period mean temporao late year arrivals week end february bag kilos make cumulative total season stage last year seem cocoa deliver earlier consignment include arrivals figure comissaria smith say still doubt much crop cocoa still available harvest practically come total bahia crop estimate around bag sales stand almost hundred thousand bag still hand farmers middlemen exporters processors doubt much cocoa would export shippers experience dificulties obtain bahia superior certificate view lower quality recent weeks farmers sell good part cocoa hold consignment comissaria smith say spot bean price rise cruzados arroba kilos bean shippers reluctant offer nearby shipment limit sales book march shipment dlrs tonne port name crop sales also light open port june july go dlrs dlrs york july sept dlrs tonne routine sales butter make march april sell dlrs april butter go time york june july dlrs sept dlrs time york sept dlrs time york comissaria smith say destinations covertible currency areas uruguay open port cake sales register dlrs march april dlrs dlrs time york buyers argentina uruguay convertible currency areas liquor sales limit march april sell dlrs june july dlrs time york july sept dlrs time york sept time york comissaria smith say total bahia sales currently estimate bag crop bag crop final figure period february expect publish brazilian cocoa trade commission carnival end midday february reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'grain'</td>\n",
       "      <td>agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'wheat'</td>\n",
       "      <td>agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'corn'</td>\n",
       "      <td>agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'barley'</td>\n",
       "      <td>agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TOPICS  \\\n",
       "0    'cocoa'   \n",
       "1    'grain'   \n",
       "2    'wheat'   \n",
       "3     'corn'   \n",
       "4   'barley'   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              BODY  \n",
       "0  shower continue throughout week bahia cocoa zone alleviate drought since early january improve prospect come temporao although normal humidity level restore comissaria smith say weekly review period mean temporao late year arrivals week end february bag kilos make cumulative total season stage last year seem cocoa deliver earlier consignment include arrivals figure comissaria smith say still doubt much crop cocoa still available harvest practically come total bahia crop estimate around bag sales stand almost hundred thousand bag still hand farmers middlemen exporters processors doubt much cocoa would export shippers experience dificulties obtain bahia superior certificate view lower quality recent weeks farmers sell good part cocoa hold consignment comissaria smith say spot bean price rise cruzados arroba kilos bean shippers reluctant offer nearby shipment limit sales book march shipment dlrs tonne port name crop sales also light open port june july go dlrs dlrs york july sept dlrs tonne routine sales butter make march april sell dlrs april butter go time york june july dlrs sept dlrs time york sept dlrs time york comissaria smith say destinations covertible currency areas uruguay open port cake sales register dlrs march april dlrs dlrs time york buyers argentina uruguay convertible currency areas liquor sales limit march april sell dlrs june july dlrs time york july sept dlrs time york sept time york comissaria smith say total bahia sales currently estimate bag crop bag crop final figure period february expect publish brazilian cocoa trade commission carnival end midday february reuter    \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter    \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter    \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter    \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     agriculture department report farmer own reserve national five average price february follow dlrs sorghum natl loan release call avge rate level price price wheat corn rat natl loan release call avge rate level price price oats barley sorghum reserve mature level reflect grain enter feedgrain july wheat level wheat barley corn sorghum level cover wheat enter january rat dlrs available reuter    "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clanci_stem = clanci.copy()\n",
    "clanci_stem['BODY'] = clanci_stem.apply(lambda row: lemmatize(row.BODY), axis=1)\n",
    "clanci.to_csv(\"my_data/data/clanci_stem.csv\")\n",
    "clanci_stem.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d544be62",
   "metadata": {},
   "source": [
    "### Usporedba rezultata 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7517ad5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluation_for_varying_sequence_lengths(filename_to, filename_from):\n",
    "    rezultat = []\n",
    "    varying_sequence_length = pd.DataFrame(rezultat, columns=[\"category\", \"ime ljuske\", \"k\", \"f1_mean\", \"f1_std\", \"precision_mean\", \"precision_std\", \"recall_mean\", \"recall_std\"])\n",
    "    varying_sequence_length.to_csv(filename_to)\n",
    "    \n",
    "    dataframe = pd.read_csv(filename_from)\n",
    "    \n",
    "    k_range = [3, 5, 7, 10]\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe, \"earn\", k_range=[*range(3, 10+1, 1)], filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe, \"earn\", k_range=k_range, filename=filename_to, add_k=True)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe, \"earn\", filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"acq\", k_range=[*range(3, 10+1, 1)], filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"acq\", k_range=k_range, filename=filename_to, add_k=True)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"acq\", filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"crude\", k_range=[*range(3, 10+1, 1)], filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"crude\", k_range=k_range, filename=filename_to, add_k=True)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"crude\", filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"corn\", k_range=[*range(3, 10+1, 1)], filename=filename_to, add_k = True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"corn\", k_range=k_range, filename=filename_to, add_k=True)\n",
    "\n",
    "    rezultat = wk_evaluation(dataframe,\"corn\", filename=filename_to, add_k = True)\n",
    "    \n",
    "#evaluation_for_varying_sequence_lengths(\"my_data/results/improvement_varying_sequence_length.csv\", filename_from=\"my_data/data/clanci_stem.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "26c3dbd7",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>f1_mean</th>\n",
       "      <th>f1_std</th>\n",
       "      <th>precision_mean</th>\n",
       "      <th>precision_std</th>\n",
       "      <th>recall_mean</th>\n",
       "      <th>recall_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>ime ljuske</th>\n",
       "      <th>k</th>\n",
       "      <th></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 rowspan=\"52\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"13\" valign=\"top\">earn</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.8075</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>0.8075</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>0.8075</td>\n",
       "      <td>0.0613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0820</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.1267</td>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.1267</td>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.0942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.1143</td>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.1143</td>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0930</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0930</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.1294</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.1294</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.1294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.1027</td>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.1027</td>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8450</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.8450</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.8450</td>\n",
       "      <td>0.0999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.0584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.0527</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.0527</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.0527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">acq</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.8760</td>\n",
       "      <td>0.1065</td>\n",
       "      <td>0.8760</td>\n",
       "      <td>0.1065</td>\n",
       "      <td>0.8760</td>\n",
       "      <td>0.1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0863</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0863</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.9080</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.9080</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.9080</td>\n",
       "      <td>0.1630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.9240</td>\n",
       "      <td>0.0902</td>\n",
       "      <td>0.9240</td>\n",
       "      <td>0.0902</td>\n",
       "      <td>0.9240</td>\n",
       "      <td>0.0902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7640</td>\n",
       "      <td>0.2757</td>\n",
       "      <td>0.7640</td>\n",
       "      <td>0.2757</td>\n",
       "      <td>0.7640</td>\n",
       "      <td>0.2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.1755</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.1755</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6640</td>\n",
       "      <td>0.3649</td>\n",
       "      <td>0.6640</td>\n",
       "      <td>0.3649</td>\n",
       "      <td>0.6640</td>\n",
       "      <td>0.3649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.8080</td>\n",
       "      <td>0.2827</td>\n",
       "      <td>0.8080</td>\n",
       "      <td>0.2827</td>\n",
       "      <td>0.8080</td>\n",
       "      <td>0.2827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.2482</td>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.2482</td>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.2482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8880</td>\n",
       "      <td>0.2585</td>\n",
       "      <td>0.8880</td>\n",
       "      <td>0.2585</td>\n",
       "      <td>0.8880</td>\n",
       "      <td>0.2585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.3478</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.3478</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.3478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">crude</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.5333</td>\n",
       "      <td>0.2732</td>\n",
       "      <td>0.5333</td>\n",
       "      <td>0.2732</td>\n",
       "      <td>0.5333</td>\n",
       "      <td>0.2732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6199</td>\n",
       "      <td>0.3253</td>\n",
       "      <td>0.6199</td>\n",
       "      <td>0.3253</td>\n",
       "      <td>0.6199</td>\n",
       "      <td>0.3253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.4065</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>0.4065</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>0.4065</td>\n",
       "      <td>0.3090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6668</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.6668</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.6668</td>\n",
       "      <td>0.2440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.4468</td>\n",
       "      <td>0.2193</td>\n",
       "      <td>0.4468</td>\n",
       "      <td>0.2193</td>\n",
       "      <td>0.4468</td>\n",
       "      <td>0.2193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.2468</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.2468</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.2468</td>\n",
       "      <td>0.2565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.3866</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>0.3866</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>0.3866</td>\n",
       "      <td>0.2472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.2801</td>\n",
       "      <td>0.2491</td>\n",
       "      <td>0.2801</td>\n",
       "      <td>0.2491</td>\n",
       "      <td>0.2801</td>\n",
       "      <td>0.2491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.4533</td>\n",
       "      <td>0.4329</td>\n",
       "      <td>0.4533</td>\n",
       "      <td>0.4329</td>\n",
       "      <td>0.4533</td>\n",
       "      <td>0.4329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2187</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2187</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.2939</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.2939</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.2939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <td>0.5667</td>\n",
       "      <td>0.2720</td>\n",
       "      <td>0.5667</td>\n",
       "      <td>0.2720</td>\n",
       "      <td>0.5667</td>\n",
       "      <td>0.2720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">corn</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3500</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2571</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2571</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1136</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1136</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.2289</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.2289</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.2289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>3</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.2002</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.2002</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2676</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2676</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.2993</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.2993</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.2993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          f1_mean  f1_std  precision_mean  precision_std  \\\n",
       "  category ime ljuske k                                                    \n",
       "0 earn     NGK        3    0.8075  0.0613          0.8075         0.0613   \n",
       "                      4    0.8575  0.0603          0.8575         0.0603   \n",
       "                      5    0.8700  0.0820          0.8700         0.0820   \n",
       "                      6    0.8225  0.1267          0.8225         0.1267   \n",
       "                      7    0.8675  0.0942          0.8675         0.0942   \n",
       "                      8    0.8225  0.1143          0.8225         0.1143   \n",
       "                      9    0.8875  0.0930          0.8875         0.0930   \n",
       "                      10   0.8500  0.1294          0.8500         0.1294   \n",
       "           SSK        3    0.7975  0.1027          0.7975         0.1027   \n",
       "                      5    0.8575  0.0603          0.8575         0.0603   \n",
       "                      7    0.8450  0.0999          0.8450         0.0999   \n",
       "                      10   0.9625  0.0584          0.9625         0.0584   \n",
       "           WK         0    0.8100  0.0527          0.8100         0.0527   \n",
       "  acq      NGK        3    0.8760  0.1065          0.8760         0.1065   \n",
       "                      4    0.9000  0.0863          0.9000         0.0863   \n",
       "                      5    0.9080  0.1630          0.9080         0.1630   \n",
       "                      6    0.9240  0.0902          0.9240         0.0902   \n",
       "                      7    0.7640  0.2757          0.7640         0.2757   \n",
       "                      8    0.8240  0.1755          0.8240         0.1755   \n",
       "                      9    0.6640  0.3649          0.6640         0.3649   \n",
       "                      10   0.8080  0.2827          0.8080         0.2827   \n",
       "           SSK        3    0.8840  0.2482          0.8840         0.2482   \n",
       "                      5    0.8880  0.2585          0.8880         0.2585   \n",
       "                      7    0.9200  0.1440          0.9200         0.1440   \n",
       "                      10   0.2800  0.3478          0.2800         0.3478   \n",
       "           WK         0    0.9480  0.0475          0.9480         0.0475   \n",
       "  crude    NGK        3    0.5333  0.2732          0.5333         0.2732   \n",
       "                      4    0.6199  0.3253          0.6199         0.3253   \n",
       "                      5    0.4065  0.3090          0.4065         0.3090   \n",
       "                      6    0.6668  0.2440          0.6668         0.2440   \n",
       "                      7    0.4468  0.2193          0.4468         0.2193   \n",
       "                      8    0.2468  0.2565          0.2468         0.2565   \n",
       "                      9    0.3866  0.2472          0.3866         0.2472   \n",
       "                      10   0.2801  0.2491          0.2801         0.2491   \n",
       "           SSK        3    0.4533  0.4329          0.4533         0.4329   \n",
       "                      5    0.6800  0.2187          0.6800         0.2187   \n",
       "                      7    0.3600  0.2939          0.3600         0.2939   \n",
       "                      10   0.0467  0.0600          0.0467         0.0600   \n",
       "           WK         0    0.5667  0.2720          0.5667         0.2720   \n",
       "  corn     NGK        3    0.2300  0.2100          0.2300         0.2100   \n",
       "                      4    0.3500  0.2419          0.3500         0.2419   \n",
       "                      5    0.2300  0.2571          0.2300         0.2571   \n",
       "                      6    0.1100  0.1136          0.1100         0.1136   \n",
       "                      7    0.1400  0.2289          0.1400         0.2289   \n",
       "                      8    0.1000  0.1265          0.1000         0.1265   \n",
       "                      9    0.0500  0.0922          0.0500         0.0922   \n",
       "                      10   0.0500  0.0922          0.0500         0.0922   \n",
       "           SSK        3    0.1100  0.1221          0.1100         0.1221   \n",
       "                      5    0.1700  0.2002          0.1700         0.2002   \n",
       "                      7    0.1800  0.2676          0.1800         0.2676   \n",
       "                      10   0.0400  0.0490          0.0400         0.0490   \n",
       "           WK         0    0.4200  0.2993          0.4200         0.2993   \n",
       "\n",
       "                          recall_mean  recall_std  \n",
       "  category ime ljuske k                            \n",
       "0 earn     NGK        3        0.8075      0.0613  \n",
       "                      4        0.8575      0.0603  \n",
       "                      5        0.8700      0.0820  \n",
       "                      6        0.8225      0.1267  \n",
       "                      7        0.8675      0.0942  \n",
       "                      8        0.8225      0.1143  \n",
       "                      9        0.8875      0.0930  \n",
       "                      10       0.8500      0.1294  \n",
       "           SSK        3        0.7975      0.1027  \n",
       "                      5        0.8575      0.0603  \n",
       "                      7        0.8450      0.0999  \n",
       "                      10       0.9625      0.0584  \n",
       "           WK         0        0.8100      0.0527  \n",
       "  acq      NGK        3        0.8760      0.1065  \n",
       "                      4        0.9000      0.0863  \n",
       "                      5        0.9080      0.1630  \n",
       "                      6        0.9240      0.0902  \n",
       "                      7        0.7640      0.2757  \n",
       "                      8        0.8240      0.1755  \n",
       "                      9        0.6640      0.3649  \n",
       "                      10       0.8080      0.2827  \n",
       "           SSK        3        0.8840      0.2482  \n",
       "                      5        0.8880      0.2585  \n",
       "                      7        0.9200      0.1440  \n",
       "                      10       0.2800      0.3478  \n",
       "           WK         0        0.9480      0.0475  \n",
       "  crude    NGK        3        0.5333      0.2732  \n",
       "                      4        0.6199      0.3253  \n",
       "                      5        0.4065      0.3090  \n",
       "                      6        0.6668      0.2440  \n",
       "                      7        0.4468      0.2193  \n",
       "                      8        0.2468      0.2565  \n",
       "                      9        0.3866      0.2472  \n",
       "                      10       0.2801      0.2491  \n",
       "           SSK        3        0.4533      0.4329  \n",
       "                      5        0.6800      0.2187  \n",
       "                      7        0.3600      0.2939  \n",
       "                      10       0.0467      0.0600  \n",
       "           WK         0        0.5667      0.2720  \n",
       "  corn     NGK        3        0.2300      0.2100  \n",
       "                      4        0.3500      0.2419  \n",
       "                      5        0.2300      0.2571  \n",
       "                      6        0.1100      0.1136  \n",
       "                      7        0.1400      0.2289  \n",
       "                      8        0.1000      0.1265  \n",
       "                      9        0.0500      0.0922  \n",
       "                      10       0.0500      0.0922  \n",
       "           SSK        3        0.1100      0.1221  \n",
       "                      5        0.1700      0.2002  \n",
       "                      7        0.1800      0.2676  \n",
       "                      10       0.0400      0.0490  \n",
       "           WK         0        0.4200      0.2993  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"my_data/results/improvement_varying_sequence_length.csv\"\n",
    "rezultat = pd.read_csv(filename, index_col=[0, 1, 2, 3])\n",
    "display(rezultat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb9eb7a",
   "metadata": {},
   "source": [
    "#### Komentar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50cb245",
   "metadata": {},
   "source": [
    "Na linijskim grafovima (dolje) vidimo da smo uspjeli postići barem malo poboljšanje nakon što smo zadržali samo korijene riječi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c4418449",
   "metadata": {},
   "outputs": [],
   "source": [
    "rezultat_before = pd.read_csv(\"my_data/results/varying_sequence_length.csv\", index_col=[0, 1, 2, 3])\n",
    "rezultat_after = pd.read_csv(\"my_data/results/improvement_varying_sequence_length.csv\", index_col=[0, 1, 2, 3])\n",
    "\n",
    "grupirano_before = rezultat_before.groupby([\"category\", \"ime ljuske\"]).mean()\n",
    "#display(grupirano_before)\n",
    "grupirano_after = rezultat_after.groupby([\"category\", \"ime ljuske\"]).mean()\n",
    "#display(grupirano_after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "54a8582a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=(15, 10))\n",
    "k_range = [3, 5, 7, 10]\n",
    "\n",
    "rezultat_before = pd.read_csv(\"my_data/results/varying_sequence_length.csv\")\n",
    "rezultat_after = pd.read_csv(\"my_data/results/improvement_varying_sequence_length.csv\")\n",
    "\n",
    "# before\n",
    "ax1.plot(k_range, rezultat_before[(rezultat_before.category == 'earn') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "ax2.plot(k_range, rezultat_before[(rezultat_before.category == 'corn') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "ax3.plot(k_range, rezultat_before[(rezultat_before.category == 'acq') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "ax4.plot(k_range, rezultat_before[(rezultat_before.category == 'crude') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "\n",
    "# after\n",
    "ax1.plot(k_range, rezultat_after[(rezultat_after.category == 'earn') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"poslije\")\n",
    "ax2.plot(k_range, rezultat_after[(rezultat_after.category == 'corn') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"posije\")\n",
    "ax3.plot(k_range, rezultat_after[(rezultat_after.category == 'acq') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"poslije\")\n",
    "ax4.plot(k_range, rezultat_after[(rezultat_after.category == 'crude') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"poslije\")\n",
    "\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_title(\"F1 za kategoriju earn\")\n",
    "ax1.legend()\n",
    "ax2.set_ylim([0, 1])\n",
    "ax2.set_title(\"F1 za kategoriju corn\")\n",
    "ax2.legend()\n",
    "ax3.set_ylim([0, 1])\n",
    "ax3.set_title(\"F1 za kategoriju acq\")\n",
    "ax3.legend()\n",
    "ax4.set_ylim([0, 1])\n",
    "ax4.set_title(\"F1 za kategoriju crude\")\n",
    "ax4.legend()\n",
    "\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0714687",
   "metadata": {},
   "source": [
    "### Usporedba rezultata 1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "097e5019",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluation_for_varying_weight_decay_factors(filename, filename_from):\n",
    "    rezultat = []\n",
    "    varying_weight_decay = pd.DataFrame(rezultat, columns=[\"category\", \"ime ljuske\", \"lambda\", \"f1_mean\", \"f1_std\", \"precision_mean\", \"precision_std\", \"recall_mean\", \"recall_std\"])\n",
    "    varying_weight_decay.to_csv(filename)\n",
    "    \n",
    "    dataframe = pd.read_csv(filename_from)\n",
    "        \n",
    "    #l_range = [0.01, 0.03, 0.05, 0.07, 0.09, 0.1, 0.3, 0.5, 0.7, 0.9]\n",
    "    l_range = [0.01, 0.05, 0.3, 0.7]\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"earn\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"earn\", lambd_range=l_range, filename=filename)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"earn\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"acq\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"acq\", lambd_range=l_range, filename=filename)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"acq\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"crude\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"crude\", lambd_range=l_range, filename=filename)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"crude\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"corn\", filename=filename, add_lambd=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"corn\", lambd_range=l_range, filename=filename)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"corn\", filename=filename, add_lambd=True)\n",
    "\n",
    "#evaluation_for_varying_weight_decay_factors(\"my_data/results/improvement_varying_weight_decay.csv\", \"my_data/data/clanci_stem.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edc481f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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></th>\n",
       "      <th></th>\n",
       "      <th>f1_mean</th>\n",
       "      <th>f1_std</th>\n",
       "      <th>precision_mean</th>\n",
       "      <th>precision_std</th>\n",
       "      <th>recall_mean</th>\n",
       "      <th>recall_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>ime ljuske</th>\n",
       "      <th>lambda</th>\n",
       "      <th></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 rowspan=\"24\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">earn</th>\n",
       "      <th>NGK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0716</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0716</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.8825</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.8825</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.8825</td>\n",
       "      <td>0.0690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.8550</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.8550</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.8550</td>\n",
       "      <td>0.0947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.0652</td>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.0652</td>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.0652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>0.7850</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>0.7850</td>\n",
       "      <td>0.1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">acq</th>\n",
       "      <th>NGK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.0776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.9360</td>\n",
       "      <td>0.0720</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>0.0720</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>0.0720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.9280</td>\n",
       "      <td>0.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>0.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.7840</td>\n",
       "      <td>0.2673</td>\n",
       "      <td>0.7840</td>\n",
       "      <td>0.2673</td>\n",
       "      <td>0.7840</td>\n",
       "      <td>0.2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8560</td>\n",
       "      <td>0.2461</td>\n",
       "      <td>0.8560</td>\n",
       "      <td>0.2461</td>\n",
       "      <td>0.8560</td>\n",
       "      <td>0.2461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">crude</th>\n",
       "      <th>NGK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.5467</td>\n",
       "      <td>0.2104</td>\n",
       "      <td>0.5467</td>\n",
       "      <td>0.2104</td>\n",
       "      <td>0.5467</td>\n",
       "      <td>0.2104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.5933</td>\n",
       "      <td>0.2299</td>\n",
       "      <td>0.5933</td>\n",
       "      <td>0.2299</td>\n",
       "      <td>0.5933</td>\n",
       "      <td>0.2299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.3214</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.3214</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.3214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.4933</td>\n",
       "      <td>0.3844</td>\n",
       "      <td>0.4933</td>\n",
       "      <td>0.3844</td>\n",
       "      <td>0.4933</td>\n",
       "      <td>0.3844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.6001</td>\n",
       "      <td>0.2547</td>\n",
       "      <td>0.6001</td>\n",
       "      <td>0.2547</td>\n",
       "      <td>0.6001</td>\n",
       "      <td>0.2547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">corn</th>\n",
       "      <th>NGK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.2358</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.2358</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.2358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SSK</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.2059</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.2059</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.2059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2135</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2135</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.2135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.3105</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.3105</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.3105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              f1_mean  f1_std  precision_mean  precision_std  \\\n",
       "  category ime ljuske lambda                                                   \n",
       "0 earn     NGK        0.00     0.9000  0.0716          0.9000         0.0716   \n",
       "           SSK        0.01     0.8825  0.0690          0.8825         0.0690   \n",
       "                      0.05     0.8550  0.0947          0.8550         0.0947   \n",
       "                      0.30     0.8425  0.0652          0.8425         0.0652   \n",
       "                      0.70     0.8500  0.1026          0.7850         0.1026   \n",
       "           WK         0.00     0.9225  0.0675          0.9225         0.0675   \n",
       "  acq      NGK        0.00     0.9280  0.0776          0.9280         0.0776   \n",
       "           SSK        0.01     0.9360  0.0720          0.9360         0.0720   \n",
       "                      0.05     0.9280  0.1100          0.9280         0.1100   \n",
       "                      0.30     0.9640  0.0418          0.9640         0.0418   \n",
       "                      0.70     0.7840  0.2673          0.7840         0.2673   \n",
       "           WK         0.00     0.8560  0.2461          0.8560         0.2461   \n",
       "  crude    NGK        0.00     0.5467  0.2104          0.5467         0.2104   \n",
       "           SSK        0.01     0.6467  0.2750          0.6467         0.2750   \n",
       "                      0.05     0.5933  0.2299          0.5933         0.2299   \n",
       "                      0.30     0.6933  0.3214          0.6933         0.3214   \n",
       "                      0.70     0.4933  0.3844          0.4933         0.3844   \n",
       "           WK         0.00     0.6001  0.2547          0.6001         0.2547   \n",
       "  corn     NGK        0.00     0.2200  0.2358          0.2200         0.2358   \n",
       "           SSK        0.01     0.2600  0.2059          0.2600         0.2059   \n",
       "                      0.05     0.1800  0.2135          0.1800         0.2135   \n",
       "                      0.30     0.2100  0.2166          0.2100         0.2166   \n",
       "                      0.70     0.0600  0.1020          0.0600         0.1020   \n",
       "           WK         0.00     0.3400  0.3105          0.3400         0.3105   \n",
       "\n",
       "                              recall_mean  recall_std  \n",
       "  category ime ljuske lambda                           \n",
       "0 earn     NGK        0.00         0.9000      0.0716  \n",
       "           SSK        0.01         0.8825      0.0690  \n",
       "                      0.05         0.8550      0.0947  \n",
       "                      0.30         0.8425      0.0652  \n",
       "                      0.70         0.7850      0.1026  \n",
       "           WK         0.00         0.9225      0.0675  \n",
       "  acq      NGK        0.00         0.9280      0.0776  \n",
       "           SSK        0.01         0.9360      0.0720  \n",
       "                      0.05         0.9280      0.1100  \n",
       "                      0.30         0.9640      0.0418  \n",
       "                      0.70         0.7840      0.2673  \n",
       "           WK         0.00         0.8560      0.2461  \n",
       "  crude    NGK        0.00         0.5467      0.2104  \n",
       "           SSK        0.01         0.6467      0.2750  \n",
       "                      0.05         0.5933      0.2299  \n",
       "                      0.30         0.6933      0.3214  \n",
       "                      0.70         0.4933      0.3844  \n",
       "           WK         0.00         0.6001      0.2547  \n",
       "  corn     NGK        0.00         0.2200      0.2358  \n",
       "           SSK        0.01         0.2600      0.2059  \n",
       "                      0.05         0.1800      0.2135  \n",
       "                      0.30         0.2100      0.2166  \n",
       "                      0.70         0.0600      0.1020  \n",
       "           WK         0.00         0.3400      0.3105  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"my_data/results/improvement_varying_weight_decay.csv\"\n",
    "rezultat = pd.read_csv(filename, index_col=[0, 1, 2, 3])\n",
    "display(rezultat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d771a411",
   "metadata": {},
   "source": [
    "#### Komentar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1701418f",
   "metadata": {},
   "source": [
    "Na linijskim grafovima (dolje) vidimo da ovim postupkom nije postignuto veliko poboljšanje."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "71c4a415",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>f1_mean</th>\n",
       "      <th>f1_std</th>\n",
       "      <th>precision_mean</th>\n",
       "      <th>precision_std</th>\n",
       "      <th>recall_mean</th>\n",
       "      <th>recall_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th>ime ljuske</th>\n",
       "      <th></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 rowspan=\"3\" valign=\"top\">acq</th>\n",
       "      <th>NGK</th>\n",
       "      <td>0.838000</td>\n",
       "      <td>0.186125</td>\n",
       "      <td>0.838000</td>\n",
       "      <td>0.186125</td>\n",
       "      <td>0.838000</td>\n",
       "      <td>0.186125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <td>0.798500</td>\n",
       "      <td>0.200250</td>\n",
       "      <td>0.798500</td>\n",
       "      <td>0.200250</td>\n",
       "      <td>0.798500</td>\n",
       "      <td>0.200250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <td>0.864000</td>\n",
       "      <td>0.135300</td>\n",
       "      <td>0.864000</td>\n",
       "      <td>0.135300</td>\n",
       "      <td>0.864000</td>\n",
       "      <td>0.135300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">corn</th>\n",
       "      <th>NGK</th>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.165475</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.165475</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.165475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.136269</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.136269</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.136269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.334100</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.334100</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.334100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">crude</th>\n",
       "      <th>NGK</th>\n",
       "      <td>0.484950</td>\n",
       "      <td>0.301075</td>\n",
       "      <td>0.484950</td>\n",
       "      <td>0.301075</td>\n",
       "      <td>0.484950</td>\n",
       "      <td>0.301075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <td>0.450819</td>\n",
       "      <td>0.279275</td>\n",
       "      <td>0.450819</td>\n",
       "      <td>0.279275</td>\n",
       "      <td>0.450819</td>\n",
       "      <td>0.279275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <td>0.646700</td>\n",
       "      <td>0.183900</td>\n",
       "      <td>0.646700</td>\n",
       "      <td>0.183900</td>\n",
       "      <td>0.646700</td>\n",
       "      <td>0.183900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">earn</th>\n",
       "      <th>NGK</th>\n",
       "      <td>0.865000</td>\n",
       "      <td>0.083375</td>\n",
       "      <td>0.865000</td>\n",
       "      <td>0.083375</td>\n",
       "      <td>0.865000</td>\n",
       "      <td>0.083375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <td>0.850114</td>\n",
       "      <td>0.077089</td>\n",
       "      <td>0.850114</td>\n",
       "      <td>0.077089</td>\n",
       "      <td>0.850114</td>\n",
       "      <td>0.077089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.089600</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.089600</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.089600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      f1_mean    f1_std  precision_mean  precision_std  \\\n",
       "category ime ljuske                                                      \n",
       "acq      NGK         0.838000  0.186125        0.838000       0.186125   \n",
       "         SSK         0.798500  0.200250        0.798500       0.200250   \n",
       "         WK          0.864000  0.135300        0.864000       0.135300   \n",
       "corn     NGK         0.160000  0.165475        0.160000       0.165475   \n",
       "         SSK         0.142500  0.136269        0.142500       0.136269   \n",
       "         WK          0.420000  0.334100        0.420000       0.334100   \n",
       "crude    NGK         0.484950  0.301075        0.484950       0.301075   \n",
       "         SSK         0.450819  0.279275        0.450819       0.279275   \n",
       "         WK          0.646700  0.183900        0.646700       0.183900   \n",
       "earn     NGK         0.865000  0.083375        0.865000       0.083375   \n",
       "         SSK         0.850114  0.077089        0.850114       0.077089   \n",
       "         WK          0.840000  0.089600        0.840000       0.089600   \n",
       "\n",
       "                     recall_mean  recall_std  \n",
       "category ime ljuske                           \n",
       "acq      NGK            0.838000    0.186125  \n",
       "         SSK            0.798500    0.200250  \n",
       "         WK             0.864000    0.135300  \n",
       "corn     NGK            0.160000    0.165475  \n",
       "         SSK            0.142500    0.136269  \n",
       "         WK             0.420000    0.334100  \n",
       "crude    NGK            0.484950    0.301075  \n",
       "         SSK            0.450819    0.279275  \n",
       "         WK             0.646700    0.183900  \n",
       "earn     NGK            0.865000    0.083375  \n",
       "         SSK            0.850114    0.077089  \n",
       "         WK             0.840000    0.089600  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grupirano = rezultat.groupby([\"category\", \"ime ljuske\"]).mean()\n",
    "display(grupirano)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "de19c172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=(15, 10))\n",
    "k_range = [3, 5, 7, 10]\n",
    "\n",
    "rezultat_before = pd.read_csv(\"my_data/results/varying_weight_decay.csv\")\n",
    "rezultat_after = pd.read_csv(\"my_data/results/improvement_varying_weight_decay.csv\")\n",
    "\n",
    "# before\n",
    "ax1.plot(k_range, rezultat_before[(rezultat_before.category == 'earn') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "ax2.plot(k_range, rezultat_before[(rezultat_before.category == 'corn') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "ax3.plot(k_range, rezultat_before[(rezultat_before.category == 'acq') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "ax4.plot(k_range, rezultat_before[(rezultat_before.category == 'crude') & (rezultat_before['ime ljuske'] == 'SSK')].f1_mean, color='b', label=\"prije\")\n",
    "\n",
    "# after\n",
    "ax1.plot(k_range, rezultat_after[(rezultat_after.category == 'earn') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"poslije\")\n",
    "ax2.plot(k_range, rezultat_after[(rezultat_after.category == 'corn') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"posije\")\n",
    "ax3.plot(k_range, rezultat_after[(rezultat_after.category == 'acq') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"poslije\")\n",
    "ax4.plot(k_range, rezultat_after[(rezultat_after.category == 'crude') & (rezultat_after['ime ljuske'] == 'SSK')].f1_mean, color='k', label=\"poslije\")\n",
    "\n",
    "ax1.set_ylim([0, 1])\n",
    "ax1.set_title(\"F1 za kategoriju earn\")\n",
    "ax1.legend()\n",
    "ax2.set_ylim([0, 1])\n",
    "ax2.set_title(\"F1 za kategoriju corn\")\n",
    "ax2.legend()\n",
    "ax3.set_ylim([0, 1])\n",
    "ax3.set_title(\"F1 za kategoriju acq\")\n",
    "ax3.legend()\n",
    "ax4.set_ylim([0, 1])\n",
    "ax4.set_title(\"F1 za kategoriju crude\")\n",
    "ax4.legend()\n",
    "\n",
    "fig.subplots_adjust(hspace=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba12126c",
   "metadata": {},
   "source": [
    "## Zadržavanje samo najčešćih riječi ? (subset features?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8655e873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = plt.imread(\"aproximation.png\")\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215646eb",
   "metadata": {},
   "source": [
    "# Optimiranje parametara"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764d54ee",
   "metadata": {},
   "source": [
    "U znanstvenom radu su evaluirali rad jezgri mijenjajući samo jednu varijablu (k ili ${\\lambda}$), dok bi druga bila postavljena na konstantnu vrijednost. Mi ćemo ovdje pokušati naći optimalne vrijednosti parametara k i ${\\lambda}$. Optimalne vrijednosti su one za koje je F1 najveći."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c8cd7d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_and_lambd_evaluation(filename, filename_from):\n",
    "    rezultat = []\n",
    "    k_and_lambd = pd.DataFrame(rezultat, columns=[\"category\", \"ime ljuske\", \"k\", \"lambda\", \"f1_mean\", \"f1_std\", \"precision_mean\", \"precision_std\", \"recall_mean\", \"recall_std\"])\n",
    "    k_and_lambd.to_csv(filename)\n",
    "    \n",
    "    dataframe = pd.read_csv(filename_from)\n",
    "    \n",
    "    #l_range = [0.01, 0.03, 0.05, 0.07, 0.09, 0.1, 0.3, 0.5, 0.7, 0.9]\n",
    "    #k_range = [*range(3, 10+1, 1)]\n",
    "    l_range = [0.01, 0.05, 0.3, 0.7]\n",
    "    k_range=[3, 5, 7, 10]\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe, \"earn\", k_range=k_range, add_lambd=True, filename=filename, add_k=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe, \"earn\", k_range=k_range, lambd_range=l_range, filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe, \"earn\", filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"acq\", k_range=k_range, add_lambd=True, filename=filename, add_k=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"acq\", k_range=k_range, lambd_range=l_range, filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"acq\", filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"crude\", k_range=k_range, add_lambd=True, filename=filename, add_k=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"crude\", k_range=k_range, lambd_range=l_range, filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "    rezultat = wk_evaluation(dataframe,\"crude\", filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "    rezultat = ngk_evaluation(dataframe,\"corn\", k_range=k_range, add_lambd=True, filename=filename, add_k=True)\n",
    "    \n",
    "    rezultat = ssk_evaluation(dataframe,\"corn\", k_range=k_range, lambd_range=l_range, filename=filename, add_k=True, add_lambd=True)\n",
    "\n",
    "    rezultat = wk_evaluation(dataframe,\"corn\", filename=filename, add_k=True, add_lambd=True)\n",
    "    \n",
    "#k_and_lambd_evaluation(\"my_data/results/improvement_varying_seq_length_and_weight_decay.csv\", \"my_data/data/clanci_stem.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d86871",
   "metadata": {},
   "source": [
    "#### Komentar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9f6eebca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>f1_mean</th>\n",
       "      <th>f1_std</th>\n",
       "      <th>precision_mean</th>\n",
       "      <th>precision_std</th>\n",
       "      <th>recall_mean</th>\n",
       "      <th>recall_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>ime ljuske</th>\n",
       "      <th>k</th>\n",
       "      <th>lambda</th>\n",
       "      <th></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 rowspan=\"116\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"53\" valign=\"top\">earn</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.7675</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.7675</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.7675</td>\n",
       "      <td>0.0633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0744</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0786</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8550</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>0.8550</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>0.8550</td>\n",
       "      <td>0.0748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8975</td>\n",
       "      <td>0.1075</td>\n",
       "      <td>0.8975</td>\n",
       "      <td>0.1075</td>\n",
       "      <td>0.8975</td>\n",
       "      <td>0.1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.0705</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.0705</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.0705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"44\" valign=\"top\">SSK</th>\n",
       "      <th rowspan=\"10\" valign=\"top\">3</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0957</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0957</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.0437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0733</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0733</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.8225</td>\n",
       "      <td>0.0675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0745</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0745</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.0434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.0650</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.0650</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.0650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.7925</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.7925</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.7925</td>\n",
       "      <td>0.0633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.7700</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>0.1054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1744</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1744</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">4</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0487</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0487</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>0.8425</td>\n",
       "      <td>0.0874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.0562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.0475</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.0475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>0.8650</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>0.8650</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>0.8650</td>\n",
       "      <td>0.0583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.0775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.0645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.7800</td>\n",
       "      <td>0.1382</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>0.1382</td>\n",
       "      <td>0.7800</td>\n",
       "      <td>0.1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">5</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.8925</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.8925</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>0.8925</td>\n",
       "      <td>0.0725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.0810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.0410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>0.8800</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.0882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>0.8850</td>\n",
       "      <td>0.0604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0817</td>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0817</td>\n",
       "      <td>0.8775</td>\n",
       "      <td>0.0817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.0541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.7400</td>\n",
       "      <td>0.1251</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>0.1251</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>0.1251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">6</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.1194</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.1194</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.8525</td>\n",
       "      <td>0.1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.8900</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.8900</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.8900</td>\n",
       "      <td>0.0691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.07</th>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.09</th>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0707</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0707</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.0707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0837</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0837</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.0837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.9075</td>\n",
       "      <td>0.0807</td>\n",
       "      <td>0.9075</td>\n",
       "      <td>0.0807</td>\n",
       "      <td>0.9075</td>\n",
       "      <td>0.0807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>0.8575</td>\n",
       "      <td>0.0767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>0.7175</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.7175</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.7175</td>\n",
       "      <td>0.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">7</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.9150</td>\n",
       "      <td>0.0682</td>\n",
       "      <td>0.9150</td>\n",
       "      <td>0.0682</td>\n",
       "      <td>0.9150</td>\n",
       "      <td>0.0682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.9025</td>\n",
       "      <td>0.0666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>0.0945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8400</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>0.0896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">acq</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.0894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8920</td>\n",
       "      <td>0.1315</td>\n",
       "      <td>0.8920</td>\n",
       "      <td>0.1315</td>\n",
       "      <td>0.8920</td>\n",
       "      <td>0.1315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.2402</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.2402</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.2402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2834</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2834</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">SSK</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.8960</td>\n",
       "      <td>0.1046</td>\n",
       "      <td>0.8960</td>\n",
       "      <td>0.1046</td>\n",
       "      <td>0.8960</td>\n",
       "      <td>0.1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.2073</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.2073</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.2073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.9600</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>0.0473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.1214</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.1214</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.1214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">5</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.9320</td>\n",
       "      <td>0.0821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.9120</td>\n",
       "      <td>0.0733</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>0.0733</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>0.0733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.2443</td>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.2443</td>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.2443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">7</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.9040</td>\n",
       "      <td>0.1203</td>\n",
       "      <td>0.9040</td>\n",
       "      <td>0.1203</td>\n",
       "      <td>0.9040</td>\n",
       "      <td>0.1203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.8080</td>\n",
       "      <td>0.2365</td>\n",
       "      <td>0.8080</td>\n",
       "      <td>0.2365</td>\n",
       "      <td>0.8080</td>\n",
       "      <td>0.2365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.1979</td>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.1979</td>\n",
       "      <td>0.8040</td>\n",
       "      <td>0.1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.8320</td>\n",
       "      <td>0.2296</td>\n",
       "      <td>0.8320</td>\n",
       "      <td>0.2296</td>\n",
       "      <td>0.8320</td>\n",
       "      <td>0.2296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.7080</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.7080</td>\n",
       "      <td>0.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.3760</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.3760</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.3760</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.5440</td>\n",
       "      <td>0.3846</td>\n",
       "      <td>0.5440</td>\n",
       "      <td>0.3846</td>\n",
       "      <td>0.5440</td>\n",
       "      <td>0.3846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.4280</td>\n",
       "      <td>0.4658</td>\n",
       "      <td>0.4280</td>\n",
       "      <td>0.4658</td>\n",
       "      <td>0.4280</td>\n",
       "      <td>0.4658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.1353</td>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.1353</td>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.1353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">crude</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.6198</td>\n",
       "      <td>0.3071</td>\n",
       "      <td>0.6198</td>\n",
       "      <td>0.3071</td>\n",
       "      <td>0.6198</td>\n",
       "      <td>0.3071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.5866</td>\n",
       "      <td>0.2579</td>\n",
       "      <td>0.5866</td>\n",
       "      <td>0.2579</td>\n",
       "      <td>0.5866</td>\n",
       "      <td>0.2579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.4734</td>\n",
       "      <td>0.3204</td>\n",
       "      <td>0.4734</td>\n",
       "      <td>0.3204</td>\n",
       "      <td>0.4734</td>\n",
       "      <td>0.3204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.3189</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.3189</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.3189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">SSK</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2933</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2933</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.5667</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.5667</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.5667</td>\n",
       "      <td>0.3630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.4533</td>\n",
       "      <td>0.3538</td>\n",
       "      <td>0.4533</td>\n",
       "      <td>0.3538</td>\n",
       "      <td>0.4533</td>\n",
       "      <td>0.3538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.3781</td>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.3781</td>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">5</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.6600</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0.6600</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0.6600</td>\n",
       "      <td>0.2375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.6733</td>\n",
       "      <td>0.3076</td>\n",
       "      <td>0.6733</td>\n",
       "      <td>0.3076</td>\n",
       "      <td>0.6733</td>\n",
       "      <td>0.3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2267</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2267</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.2267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.2653</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.2653</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.2653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">7</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.5067</td>\n",
       "      <td>0.3617</td>\n",
       "      <td>0.5067</td>\n",
       "      <td>0.3617</td>\n",
       "      <td>0.5067</td>\n",
       "      <td>0.3617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.3533</td>\n",
       "      <td>0.3640</td>\n",
       "      <td>0.3533</td>\n",
       "      <td>0.3640</td>\n",
       "      <td>0.3533</td>\n",
       "      <td>0.3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.5800</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>0.3156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.3133</td>\n",
       "      <td>0.2272</td>\n",
       "      <td>0.3133</td>\n",
       "      <td>0.2272</td>\n",
       "      <td>0.3133</td>\n",
       "      <td>0.2272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.3183</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.3183</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.3183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.2286</td>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.2286</td>\n",
       "      <td>0.2533</td>\n",
       "      <td>0.2286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.1733</td>\n",
       "      <td>0.1744</td>\n",
       "      <td>0.1733</td>\n",
       "      <td>0.1744</td>\n",
       "      <td>0.1733</td>\n",
       "      <td>0.1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0533</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.1839</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.1839</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.1839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">corn</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1095</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1095</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.2156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">SSK</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.2948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.3098</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.3098</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.3098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2052</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2052</td>\n",
       "      <td>0.2300</td>\n",
       "      <td>0.2052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">5</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1487</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1487</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.1833</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.1833</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>0.1833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.3100</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.3100</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.3100</td>\n",
       "      <td>0.2166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1095</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1095</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">7</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.1221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1187</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1187</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10</th>\n",
       "      <th>0.01</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0458</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.3341</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.3341</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.3341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 f1_mean  f1_std  precision_mean  \\\n",
       "  category ime ljuske k  lambda                                    \n",
       "0 earn     NGK        3  0.00     0.7675  0.0633          0.7675   \n",
       "                      4  0.00     0.8375  0.0744          0.8375   \n",
       "                      5  0.00     0.8775  0.0945          0.8775   \n",
       "                      6  0.00     0.8525  0.0786          0.8525   \n",
       "                      7  0.00     0.8550  0.0748          0.8550   \n",
       "                      8  0.00     0.8975  0.1075          0.8975   \n",
       "                      9  0.00     0.9050  0.0705          0.9050   \n",
       "                      10 0.00     0.9275  0.1034          0.9275   \n",
       "           SSK        3  0.01     0.8375  0.0957          0.8375   \n",
       "                         0.03     0.8375  0.0437          0.8375   \n",
       "                         0.05     0.8750  0.0733          0.8750   \n",
       "                         0.07     0.8225  0.0675          0.8225   \n",
       "                         0.09     0.8525  0.0745          0.8525   \n",
       "                         0.10     0.8325  0.0434          0.8325   \n",
       "                         0.30     0.8200  0.0650          0.8200   \n",
       "                         0.50     0.7925  0.0633          0.7925   \n",
       "                         0.70     0.7700  0.1054          0.7700   \n",
       "                         0.90     0.8100  0.1744          0.8100   \n",
       "                      4  0.01     0.8500  0.0487          0.8500   \n",
       "                         0.03     0.8425  0.0874          0.8425   \n",
       "                         0.05     0.8875  0.0562          0.8875   \n",
       "                         0.07     0.8675  0.0475          0.8675   \n",
       "                         0.09     0.8650  0.0583          0.8650   \n",
       "                         0.10     0.8725  0.0855          0.8725   \n",
       "                         0.30     0.8325  0.0775          0.8325   \n",
       "                         0.50     0.8125  0.0645          0.8125   \n",
       "                         0.70     0.7500  0.0725          0.7500   \n",
       "                         0.90     0.7800  0.1382          0.7800   \n",
       "                      5  0.01     0.8925  0.0725          0.8925   \n",
       "                         0.03     0.7975  0.0810          0.7975   \n",
       "                         0.05     0.9025  0.0410          0.9025   \n",
       "                         0.07     0.8800  0.0740          0.8800   \n",
       "                         0.09     0.8850  0.0882          0.8850   \n",
       "                         0.10     0.8850  0.0604          0.8850   \n",
       "                         0.30     0.8775  0.0817          0.8775   \n",
       "                         0.50     0.8525  0.0541          0.8525   \n",
       "                         0.70     0.8500  0.0894          0.8500   \n",
       "                         0.90     0.7400  0.1251          0.7400   \n",
       "                      6  0.01     0.9000  0.1194          0.9000   \n",
       "                         0.03     0.8525  0.1015          0.8525   \n",
       "                         0.05     0.8900  0.0691          0.8900   \n",
       "                         0.07     0.8750  0.0512          0.8750   \n",
       "                         0.09     0.8500  0.0707          0.8500   \n",
       "                         0.10     0.9000  0.0837          0.9000   \n",
       "                         0.30     0.9075  0.0807          0.9075   \n",
       "                         0.50     0.8700  0.0600          0.8700   \n",
       "                         0.70     0.8575  0.0767          0.8575   \n",
       "                         0.90     0.7175  0.0750          0.7175   \n",
       "                      7  0.01     0.9150  0.0682          0.9150   \n",
       "                         0.05     0.9225  0.0647          0.9225   \n",
       "                         0.30     0.9025  0.0666          0.9025   \n",
       "                         0.70     0.8725  0.0945          0.8725   \n",
       "           WK         0  0.00     0.8400  0.0896          0.8400   \n",
       "  acq      NGK        3  0.00     0.9200  0.0894          0.9200   \n",
       "                      5  0.00     0.8920  0.1315          0.8920   \n",
       "                      7  0.00     0.8600  0.2402          0.8600   \n",
       "                      10 0.00     0.6800  0.2834          0.6800   \n",
       "           SSK        3  0.01     0.8960  0.1046          0.8960   \n",
       "                         0.05     0.8600  0.2073          0.8600   \n",
       "                         0.30     0.9600  0.0473          0.9600   \n",
       "                         0.70     0.9480  0.1214          0.9480   \n",
       "                      5  0.01     0.9320  0.0821          0.9320   \n",
       "                         0.05     0.9800  0.0322          0.9800   \n",
       "                         0.30     0.9120  0.0733          0.9120   \n",
       "                         0.70     0.8840  0.2443          0.8840   \n",
       "                      7  0.01     0.9040  0.1203          0.9040   \n",
       "                         0.05     0.8080  0.2365          0.8080   \n",
       "                         0.30     0.8040  0.1979          0.8040   \n",
       "                         0.70     0.8320  0.2296          0.8320   \n",
       "                      10 0.01     0.7080  0.3600          0.7080   \n",
       "                         0.05     0.3760  0.2968          0.3760   \n",
       "                         0.30     0.5440  0.3846          0.5440   \n",
       "                         0.70     0.4280  0.4658          0.4280   \n",
       "           WK         0  0.00     0.8640  0.1353          0.8640   \n",
       "  crude    NGK        3  0.00     0.6198  0.3071          0.6198   \n",
       "                      5  0.00     0.5866  0.2579          0.5866   \n",
       "                      7  0.00     0.4734  0.3204          0.4734   \n",
       "                      10 0.00     0.2600  0.3189          0.2600   \n",
       "           SSK        3  0.01     0.6800  0.2933          0.6800   \n",
       "                         0.05     0.5667  0.3630          0.5667   \n",
       "                         0.30     0.4533  0.3538          0.4533   \n",
       "                         0.70     0.2533  0.3781          0.2533   \n",
       "                      5  0.01     0.6600  0.2375          0.6600   \n",
       "                         0.05     0.6733  0.3076          0.6733   \n",
       "                         0.30     0.6800  0.2267          0.6800   \n",
       "                         0.70     0.6933  0.2653          0.6933   \n",
       "                      7  0.01     0.5067  0.3617          0.5067   \n",
       "                         0.05     0.3533  0.3640          0.3533   \n",
       "                         0.30     0.5800  0.3156          0.5800   \n",
       "                         0.70     0.3133  0.2272          0.3133   \n",
       "                      10 0.01     0.3333  0.3183          0.3333   \n",
       "                         0.05     0.2533  0.2286          0.2533   \n",
       "                         0.30     0.1733  0.1744          0.1733   \n",
       "                         0.70     0.0400  0.0533          0.0400   \n",
       "           WK         0  0.00     0.6467  0.1839          0.6467   \n",
       "  corn     NGK        3  0.00     0.3700  0.2968          0.3700   \n",
       "                      5  0.00     0.1000  0.1095          0.1000   \n",
       "                      7  0.00     0.1500  0.2156          0.1500   \n",
       "                      10 0.00     0.0200  0.0400          0.0200   \n",
       "           SSK        3  0.01     0.2100  0.2948          0.2100   \n",
       "                         0.05     0.4000  0.3098          0.4000   \n",
       "                         0.30     0.2300  0.2052          0.2300   \n",
       "                         0.70     0.0000  0.0000          0.0000   \n",
       "                      5  0.01     0.1300  0.1487          0.1300   \n",
       "                         0.05     0.2800  0.1833          0.2800   \n",
       "                         0.30     0.3100  0.2166          0.3100   \n",
       "                         0.70     0.1000  0.1095          0.1000   \n",
       "                      7  0.01     0.1100  0.1221          0.1100   \n",
       "                         0.05     0.1000  0.0632          0.1000   \n",
       "                         0.30     0.1300  0.1187          0.1300   \n",
       "                         0.70     0.0500  0.0671          0.0500   \n",
       "                      10 0.01     0.1000  0.1265          0.1000   \n",
       "                         0.05     0.0600  0.1200          0.0600   \n",
       "                         0.30     0.0300  0.0458          0.0300   \n",
       "                         0.70     0.0400  0.0490          0.0400   \n",
       "           WK         0  0.00     0.4200  0.3341          0.4200   \n",
       "\n",
       "                                 precision_std  recall_mean  recall_std  \n",
       "  category ime ljuske k  lambda                                          \n",
       "0 earn     NGK        3  0.00           0.0633       0.7675      0.0633  \n",
       "                      4  0.00           0.0744       0.8375      0.0744  \n",
       "                      5  0.00           0.0945       0.8775      0.0945  \n",
       "                      6  0.00           0.0786       0.8525      0.0786  \n",
       "                      7  0.00           0.0748       0.8550      0.0748  \n",
       "                      8  0.00           0.1075       0.8975      0.1075  \n",
       "                      9  0.00           0.0705       0.9050      0.0705  \n",
       "                      10 0.00           0.1034       0.9275      0.1034  \n",
       "           SSK        3  0.01           0.0957       0.8375      0.0957  \n",
       "                         0.03           0.0437       0.8375      0.0437  \n",
       "                         0.05           0.0733       0.8750      0.0733  \n",
       "                         0.07           0.0675       0.8225      0.0675  \n",
       "                         0.09           0.0745       0.8525      0.0745  \n",
       "                         0.10           0.0434       0.8325      0.0434  \n",
       "                         0.30           0.0650       0.8200      0.0650  \n",
       "                         0.50           0.0633       0.7925      0.0633  \n",
       "                         0.70           0.1054       0.7700      0.1054  \n",
       "                         0.90           0.1744       0.8100      0.1744  \n",
       "                      4  0.01           0.0487       0.8500      0.0487  \n",
       "                         0.03           0.0874       0.8425      0.0874  \n",
       "                         0.05           0.0562       0.8875      0.0562  \n",
       "                         0.07           0.0475       0.8675      0.0475  \n",
       "                         0.09           0.0583       0.8650      0.0583  \n",
       "                         0.10           0.0855       0.8725      0.0855  \n",
       "                         0.30           0.0775       0.8325      0.0775  \n",
       "                         0.50           0.0645       0.8125      0.0645  \n",
       "                         0.70           0.0725       0.7500      0.0725  \n",
       "                         0.90           0.1382       0.7800      0.1382  \n",
       "                      5  0.01           0.0725       0.8925      0.0725  \n",
       "                         0.03           0.0810       0.7975      0.0810  \n",
       "                         0.05           0.0410       0.9025      0.0410  \n",
       "                         0.07           0.0740       0.8800      0.0740  \n",
       "                         0.09           0.0882       0.8850      0.0882  \n",
       "                         0.10           0.0604       0.8850      0.0604  \n",
       "                         0.30           0.0817       0.8775      0.0817  \n",
       "                         0.50           0.0541       0.8525      0.0541  \n",
       "                         0.70           0.0894       0.8500      0.0894  \n",
       "                         0.90           0.1251       0.7400      0.1251  \n",
       "                      6  0.01           0.1194       0.9000      0.1194  \n",
       "                         0.03           0.1015       0.8525      0.1015  \n",
       "                         0.05           0.0691       0.8900      0.0691  \n",
       "                         0.07           0.0512       0.8750      0.0512  \n",
       "                         0.09           0.0707       0.8500      0.0707  \n",
       "                         0.10           0.0837       0.9000      0.0837  \n",
       "                         0.30           0.0807       0.9075      0.0807  \n",
       "                         0.50           0.0600       0.8700      0.0600  \n",
       "                         0.70           0.0767       0.8575      0.0767  \n",
       "                         0.90           0.0750       0.7175      0.0750  \n",
       "                      7  0.01           0.0682       0.9150      0.0682  \n",
       "                         0.05           0.0647       0.9225      0.0647  \n",
       "                         0.30           0.0666       0.9025      0.0666  \n",
       "                         0.70           0.0945       0.8725      0.0945  \n",
       "           WK         0  0.00           0.0896       0.8400      0.0896  \n",
       "  acq      NGK        3  0.00           0.0894       0.9200      0.0894  \n",
       "                      5  0.00           0.1315       0.8920      0.1315  \n",
       "                      7  0.00           0.2402       0.8600      0.2402  \n",
       "                      10 0.00           0.2834       0.6800      0.2834  \n",
       "           SSK        3  0.01           0.1046       0.8960      0.1046  \n",
       "                         0.05           0.2073       0.8600      0.2073  \n",
       "                         0.30           0.0473       0.9600      0.0473  \n",
       "                         0.70           0.1214       0.9480      0.1214  \n",
       "                      5  0.01           0.0821       0.9320      0.0821  \n",
       "                         0.05           0.0322       0.9800      0.0322  \n",
       "                         0.30           0.0733       0.9120      0.0733  \n",
       "                         0.70           0.2443       0.8840      0.2443  \n",
       "                      7  0.01           0.1203       0.9040      0.1203  \n",
       "                         0.05           0.2365       0.8080      0.2365  \n",
       "                         0.30           0.1979       0.8040      0.1979  \n",
       "                         0.70           0.2296       0.8320      0.2296  \n",
       "                      10 0.01           0.3600       0.7080      0.3600  \n",
       "                         0.05           0.2968       0.3760      0.2968  \n",
       "                         0.30           0.3846       0.5440      0.3846  \n",
       "                         0.70           0.4658       0.4280      0.4658  \n",
       "           WK         0  0.00           0.1353       0.8640      0.1353  \n",
       "  crude    NGK        3  0.00           0.3071       0.6198      0.3071  \n",
       "                      5  0.00           0.2579       0.5866      0.2579  \n",
       "                      7  0.00           0.3204       0.4734      0.3204  \n",
       "                      10 0.00           0.3189       0.2600      0.3189  \n",
       "           SSK        3  0.01           0.2933       0.6800      0.2933  \n",
       "                         0.05           0.3630       0.5667      0.3630  \n",
       "                         0.30           0.3538       0.4533      0.3538  \n",
       "                         0.70           0.3781       0.2533      0.3781  \n",
       "                      5  0.01           0.2375       0.6600      0.2375  \n",
       "                         0.05           0.3076       0.6733      0.3076  \n",
       "                         0.30           0.2267       0.6800      0.2267  \n",
       "                         0.70           0.2653       0.6933      0.2653  \n",
       "                      7  0.01           0.3617       0.5067      0.3617  \n",
       "                         0.05           0.3640       0.3533      0.3640  \n",
       "                         0.30           0.3156       0.5800      0.3156  \n",
       "                         0.70           0.2272       0.3133      0.2272  \n",
       "                      10 0.01           0.3183       0.3333      0.3183  \n",
       "                         0.05           0.2286       0.2533      0.2286  \n",
       "                         0.30           0.1744       0.1733      0.1744  \n",
       "                         0.70           0.0533       0.0400      0.0533  \n",
       "           WK         0  0.00           0.1839       0.6467      0.1839  \n",
       "  corn     NGK        3  0.00           0.2968       0.3700      0.2968  \n",
       "                      5  0.00           0.1095       0.1000      0.1095  \n",
       "                      7  0.00           0.2156       0.1500      0.2156  \n",
       "                      10 0.00           0.0400       0.0200      0.0400  \n",
       "           SSK        3  0.01           0.2948       0.2100      0.2948  \n",
       "                         0.05           0.3098       0.4000      0.3098  \n",
       "                         0.30           0.2052       0.2300      0.2052  \n",
       "                         0.70           0.0000       0.0000      0.0000  \n",
       "                      5  0.01           0.1487       0.1300      0.1487  \n",
       "                         0.05           0.1833       0.2800      0.1833  \n",
       "                         0.30           0.2166       0.3100      0.2166  \n",
       "                         0.70           0.1095       0.1000      0.1095  \n",
       "                      7  0.01           0.1221       0.1100      0.1221  \n",
       "                         0.05           0.0632       0.1000      0.0632  \n",
       "                         0.30           0.1187       0.1300      0.1187  \n",
       "                         0.70           0.0671       0.0500      0.0671  \n",
       "                      10 0.01           0.1265       0.1000      0.1265  \n",
       "                         0.05           0.1200       0.0600      0.1200  \n",
       "                         0.30           0.0458       0.0300      0.0458  \n",
       "                         0.70           0.0490       0.0400      0.0490  \n",
       "           WK         0  0.00           0.3341       0.4200      0.3341  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"my_data/results/improvement_varying_seq_length_and_weight_decay.csv\"\n",
    "rezultat = pd.read_csv(filename, index_col=[0, 1, 2, 3, 4])\n",
    "display(rezultat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0381b4b8",
   "metadata": {},
   "source": [
    "Tražimo one k i ${\\lambda}$ za koje je F1 najveći za svaku kategoriju i za svaku ljusku."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "61e6cb78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>f1_mean</th>\n",
       "      <th>f1_std</th>\n",
       "      <th>precision_mean</th>\n",
       "      <th>precision_std</th>\n",
       "      <th>recall_mean</th>\n",
       "      <th>recall_std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>ime ljuske</th>\n",
       "      <th>k</th>\n",
       "      <th>lambda</th>\n",
       "      <th></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 rowspan=\"12\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">acq</th>\n",
       "      <th>NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.0894</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.0894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <th>5</th>\n",
       "      <th>0.05</th>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.1353</td>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.1353</td>\n",
       "      <td>0.8640</td>\n",
       "      <td>0.1353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">corn</th>\n",
       "      <th>NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2968</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.05</th>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.3098</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.3098</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.3098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.3341</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.3341</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.3341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">crude</th>\n",
       "      <th>NGK</th>\n",
       "      <th>3</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.6198</td>\n",
       "      <td>0.3071</td>\n",
       "      <td>0.6198</td>\n",
       "      <td>0.3071</td>\n",
       "      <td>0.6198</td>\n",
       "      <td>0.3071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <th>5</th>\n",
       "      <th>0.70</th>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.2653</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.2653</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.2653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.1839</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.1839</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>0.1839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">earn</th>\n",
       "      <th>NGK</th>\n",
       "      <th>10</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.9275</td>\n",
       "      <td>0.1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSK</th>\n",
       "      <th>7</th>\n",
       "      <th>0.05</th>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0647</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WK</th>\n",
       "      <th>0</th>\n",
       "      <th>0.00</th>\n",
       "      <td>0.8400</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>0.0896</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>0.0896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 f1_mean  f1_std  precision_mean  \\\n",
       "  category ime ljuske k  lambda                                    \n",
       "0 acq      NGK        3  0.00     0.9200  0.0894          0.9200   \n",
       "           SSK        5  0.05     0.9800  0.0322          0.9800   \n",
       "           WK         0  0.00     0.8640  0.1353          0.8640   \n",
       "  corn     NGK        3  0.00     0.3700  0.2968          0.3700   \n",
       "           SSK        3  0.05     0.4000  0.3098          0.4000   \n",
       "           WK         0  0.00     0.4200  0.3341          0.4200   \n",
       "  crude    NGK        3  0.00     0.6198  0.3071          0.6198   \n",
       "           SSK        5  0.70     0.6933  0.2653          0.6933   \n",
       "           WK         0  0.00     0.6467  0.1839          0.6467   \n",
       "  earn     NGK        10 0.00     0.9275  0.1034          0.9275   \n",
       "           SSK        7  0.05     0.9225  0.0647          0.9225   \n",
       "           WK         0  0.00     0.8400  0.0896          0.8400   \n",
       "\n",
       "                                 precision_std  recall_mean  recall_std  \n",
       "  category ime ljuske k  lambda                                          \n",
       "0 acq      NGK        3  0.00           0.0894       0.9200      0.0894  \n",
       "           SSK        5  0.05           0.0322       0.9800      0.0322  \n",
       "           WK         0  0.00           0.1353       0.8640      0.1353  \n",
       "  corn     NGK        3  0.00           0.2968       0.3700      0.2968  \n",
       "           SSK        3  0.05           0.3098       0.4000      0.3098  \n",
       "           WK         0  0.00           0.3341       0.4200      0.3341  \n",
       "  crude    NGK        3  0.00           0.3071       0.6198      0.3071  \n",
       "           SSK        5  0.70           0.2653       0.6933      0.2653  \n",
       "           WK         0  0.00           0.1839       0.6467      0.1839  \n",
       "  earn     NGK        10 0.00           0.1034       0.9275      0.1034  \n",
       "           SSK        7  0.05           0.0647       0.9225      0.0647  \n",
       "           WK         0  0.00           0.0896       0.8400      0.0896  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_redovi = rezultat.loc[rezultat.groupby([\"category\", \"ime ljuske\"])[\"f1_mean\"].idxmax()]  \n",
    "display(max_redovi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ff71f3",
   "metadata": {},
   "source": [
    "## Aproksimacija "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375d705f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "82aacd8d",
   "metadata": {},
   "source": [
    "## Odabir parametra C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afac591d",
   "metadata": {},
   "source": [
    "Parametar C utječe na regularizaciju. Što je vrijednost parametra C manja, to je regularizacija veća."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea497f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 0.1\n",
      "Starting WK evaluation for earn...\n",
      "End of WK evaluation\n",
      "\n",
      "Starting NGK evaluation for acq...\n",
      "\t k = 5\n",
      "End of NGK evaluation\n",
      "\n",
      "Starting SSK evaluation for acq...\n",
      "\tk = 5, lambda = 0.5\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "End of SSK evaluation\n",
      "\n",
      "Starting WK evaluation for acq...\n",
      "End of WK evaluation\n",
      "\n",
      "Starting NGK evaluation for crude...\n",
      "\t k = 5\n",
      "End of NGK evaluation\n",
      "\n",
      "Starting SSK evaluation for crude...\n",
      "\tk = 5, lambda = 0.5\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "SSK compute train gram...\n",
      "SSK compute test gram...\n",
      "End of SSK evaluation\n",
      "\n",
      "Starting WK evaluation for crude...\n",
      "End of WK evaluation\n",
      "\n",
      "Starting NGK evaluation for corn...\n",
      "\t k = 5\n",
      "End of NGK evaluation\n",
      "\n",
      "Starting SSK evaluation for corn...\n",
      "\tk = 5, lambda = 0.5\n",
      "SSK compute train gram...\n"
     ]
    }
   ],
   "source": [
    "def evaluation_for_varying_C_factors(filename, filename_from):\n",
    "    rezultat = []\n",
    "    #varying_C_factor = pd.DataFrame(rezultat, columns=[\"C\", \"category\", \"ime ljuske\", \"f1_mean\", \"f1_std\", \"precision_mean\", \"precision_std\", \"recall_mean\", \"recall_std\"])\n",
    "    #varying_C_factor.to_csv(filename)\n",
    "    \n",
    "    dataframe = pd.read_csv(filename_from)\n",
    "        \n",
    "    C_range=[0.1, 0.5, 1.0, 1.5, 2]\n",
    "        \n",
    "    for c in C_range:\n",
    "        print(\"C =\", c)\n",
    "    \n",
    "        #rezultat = ngk_evaluation(dataframe,\"earn\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        #rezultat = ssk_evaluation(dataframe,\"earn\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        rezultat = wk_evaluation(dataframe,\"earn\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        rezultat = ngk_evaluation(dataframe,\"acq\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        rezultat = ssk_evaluation(dataframe,\"acq\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        rezultat = wk_evaluation(dataframe,\"acq\", filename=filename, add_lambd=True, add_C=True, C=c)\n",
    "\n",
    "        rezultat = ngk_evaluation(dataframe,\"crude\", filename=filename, add_lambd=True, add_C=True, C=c)\n",
    "\n",
    "        rezultat = ssk_evaluation(dataframe,\"crude\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        rezultat = wk_evaluation(dataframe,\"crude\", filename=filename, add_lambd=True, add_C=True, C=c)\n",
    "\n",
    "        rezultat = ngk_evaluation(dataframe,\"corn\", filename=filename, add_lambd=True, add_C=True, C=c)\n",
    "\n",
    "        rezultat = ssk_evaluation(dataframe,\"corn\", filename=filename, add_C=True, C=c)\n",
    "\n",
    "        rezultat = wk_evaluation(dataframe,\"corn\", filename=filename, add_lambd=True, add_C=True, C=c)\n",
    "\n",
    "evaluation_for_varying_C_factors(\"my_data/results/improvement_varying_C_factor.csv\", \"my_data/data/clanci_stem.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6f6448",
   "metadata": {},
   "source": [
    "komentirati ako promjena C utječe na rezultate ili ne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "909de175",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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