Optimize FROG+TF-IDF SVC Hyperopt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# !{sys.executable} -m pip install imbalanced-learn --user\n",
    "# !{sys.executable} -m pip install hyperopt --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/newsgac/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "from time import time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from hyperopt import tpe, fmin, hp, STATUS_OK, Trials\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from copy import deepcopy\n",
    "\n",
    "from newsgac import config\n",
    "from newsgac.genres import genre_codes\n",
    "from newsgac.learners import learners, LearnerSVC, LearnerNB, LearnerXGB, LearnerGB, LearnerMLP, LearnerRF, LearnerLGBM\n",
    "from newsgac.pipelines.get_sk_pipeline import get_sk_pipeline\n",
    "from newsgac.pipelines.utils import report\n",
    "from sklearn import svm\n",
    "\n",
    "\n",
    "from newsgac import database\n",
    "from newsgac.data_sources import DataSource\n",
    "from newsgac.pipelines import Pipeline\n",
    "\n",
    "# from imblearn.under_sampling import RandomUnderSampler\n",
    "# from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\n",
    "# from imblearn.combine import SMOTEENN, SMOTETomek\n",
    "import numpy as np\n",
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'N2BGS Training',\n",
       " u'N2BGS Testing',\n",
       " u'Linked NRC (2930)',\n",
       " u'BOW + 5 features (N2BGS train)',\n",
       " u'BOW + 5 features (N2BGS test)',\n",
       " u'nrc-1950-1994-a.txt',\n",
       " u'nrc-1950-1994-b.txt',\n",
       " u'nrc-1950-1994-c.txt',\n",
       " u'nrc-1950-1994-d.txt',\n",
       " u'nrc-1950-1994-e.txt',\n",
       " u'telegraaf-1950-1994-a.txt',\n",
       " u'telegraaf-1950-1994-b.txt',\n",
       " u'telegraaf-1950-1994-c.txt',\n",
       " u'telegraaf-1950-1994-d.txt',\n",
       " u'telegraaf-1950-1994-e.txt',\n",
       " u'volkskrant-1950-1995-a.txt',\n",
       " u'volkskrant-1950-1995-b.txt',\n",
       " u'volkskrant-1950-1995-c.txt',\n",
       " u'volkskrant-1950-1995-d.txt',\n",
       " u'volkskrant-1950-1995-e.txt',\n",
       " u'nrc-1965.txt',\n",
       " u'nrc-1985.txt',\n",
       " u'BOW + 9 features (N2BGS train)',\n",
       " u'BOW + 9 features (N2BGS test)',\n",
       " u'Linked NRC (2930/9 features)',\n",
       " u'BOW Train unique (9 features)',\n",
       " u'BOW Test unique (9 features)',\n",
       " u'Linked NRC (unique/9 features)',\n",
       " u'BOW Train unique (N3BGS/9 features)',\n",
       " u'N3BGS FROG Test',\n",
       " u'N3BGS FROG Train',\n",
       " u'BOW Test unique (N3BGS/9 features)',\n",
       " u'BOW Test unique (N3BGS/9 features/collapsed)',\n",
       " u'Linked NRC (2930 articles)',\n",
       " u'Combined N3BGS/Linked NRC (3884)',\n",
       " u'2019 N3BGS Test',\n",
       " u'2019 N3BGS Train',\n",
       " u'AAOB',\n",
       " u'20190228 unbalanced train size=3443',\n",
       " u'20190228 unbalanced test size=3443']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[d.display_title for d in DataSource.objects.all()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'dummy frog',\n",
       " u'dummy bow',\n",
       " u'RF FROG',\n",
       " u'SVC FROG',\n",
       " u'XGB FROG',\n",
       " u'NB FROG',\n",
       " u'MLP FROG',\n",
       " u'RF BOW',\n",
       " u'SVC BOW',\n",
       " u'XGB BOW',\n",
       " u'NB BOW',\n",
       " u'MLP BOW',\n",
       " u'SVC LIN BOW',\n",
       " u'Erik MLP BOW (with stop-words)',\n",
       " u'Erik MLP BOW (5 features) ',\n",
       " u'Erik MLP BOW (9 features) ',\n",
       " u'Erik SVC BOW (9 features)',\n",
       " u'Erik RF BOW (9 features)',\n",
       " u'Erik XGB BOW (9 features)',\n",
       " u'Erik NB BOW (9 features)',\n",
       " u'Erik MLP unique 9 features',\n",
       " u'Erik NB unique (9 features)',\n",
       " u'Erik XGB unique (9 features)',\n",
       " u'Erik SVC unique (9 features)',\n",
       " u'Erik RF unique (9 features)',\n",
       " u'N3BGS MLP unique 9 features',\n",
       " u'N3BGS NB unique 9 features ',\n",
       " u'N3BGS SVC unique 9 features ',\n",
       " u'N3BGS RF unique 9 features',\n",
       " u'N3BGS XGB unique 9 features ',\n",
       " u'dummy n3bgs bow',\n",
       " u'dummy n3bgs frog',\n",
       " u'RF FROG N3BGS',\n",
       " u'SVC FROG N3BGS',\n",
       " u'XGB FROG N3BGS',\n",
       " u'Erik NB N3BGS Collapsed',\n",
       " u'Erik MLP N3BGS Collapsed',\n",
       " u'Erik MLP Linked NRC (2930 articles)',\n",
       " u'Erik RF Linked NRC (2930 articles) ',\n",
       " u'Erik NB Linked NRC (2930 articles) ',\n",
       " u'Erik SVC Linked NRC (2930 articles) ',\n",
       " u'Erik XGB Linked NRC (2930 articles) ',\n",
       " u'SVC BOW Combined',\n",
       " u'NB FROG N3BGS',\n",
       " u'MLP FROG N3BGS',\n",
       " u'RF BOW NBGS',\n",
       " u'SVC BOW N3BGS',\n",
       " u'XGB BOW N3BGS',\n",
       " u'NB BOW N3BGS',\n",
       " u'MLP BOW N3BGS',\n",
       " u'KIM N3BGS FROG and TF-IDF',\n",
       " u'KIM N3BGS MLP Combi features',\n",
       " u'KIM N3BGS RF COMBI',\n",
       " u'KIM N3BGS SVM COMBI (copy)',\n",
       " u'KIM N3BGS XGB COMBI',\n",
       " u'20190228 SVM',\n",
       " u'20190228 SVC no stop words',\n",
       " u'20190228 SVC no stop words, lowercased',\n",
       " u'dummy combi for opt',\n",
       " u'dummy combi for opt ugs',\n",
       " u'new dummy ugs opt',\n",
       " u'new dummy bgs opt',\n",
       " u'dummy opt frog bgs',\n",
       " u'dummy bow opt bgs',\n",
       " u'dummy frog ugs opt',\n",
       " u'dummy bow opt ugs']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[d.display_title for d in Pipeline.objects.all()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline: dummy combi for opt ugs\n",
      "DataSource: 20190228 unbalanced train size=3443\n",
      "NLP Tool: Frog + TFIDF\n",
      "Classifier: Multi-Layer Perceptron\n",
      "Task status: Status.SUCCESS\n"
     ]
    }
   ],
   "source": [
    "p = Pipeline.objects.all()[59]\n",
    "print 'Pipeline: ' + p.display_title\n",
    "print 'DataSource: ' + p.data_source.display_title\n",
    "print 'NLP Tool: ' + p.nlp_tool.name\n",
    "print 'Classifier: ' + p.learner.name\n",
    "print 'Task status: ' + str(p.task.status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3099"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p.data_source.articles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data source: 20190228 unbalanced test size=3443\n"
     ]
    }
   ],
   "source": [
    "test_data_source = DataSource.objects.all()[39]\n",
    "print 'Testing data source: ' + test_data_source.display_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('CleanOCR', <newsgac.nlp_tools.transformers.CleanOCR at 0x7f7176909110>),\n",
       " ('StopWordRemoval',\n",
       "  <newsgac.nlp_tools.transformers.StopWordRemoval at 0x7f7176909310>),\n",
       " ('FeatureExtraction', FeatureUnion(n_jobs=None,\n",
       "         transformer_list=[('frog', FeatureUnion(n_jobs=None,\n",
       "         transformer_list=[('Basic', <newsgac.nlp_tools.transformers.ExtractBasicFeatures object at 0x7f7173fd6b50>), ('Quote', <newsgac.nlp_tools.transformers.ExtractQuotes object at 0x7f7173fd6b90>), ('Sentiment', Pipeline(memory=None,\n",
       "       steps=...\\b', tokenizer=None, use_idf=True,\n",
       "          vocabulary=None))]))],\n",
       "         transformer_weights=None))],\n",
       "         transformer_weights=None)),\n",
       " ('RobustScaler',\n",
       "  RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=False,\n",
       "         with_scaling=True))]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skp = p.get_sk_pipeline()\n",
    "skp_opt = deepcopy(skp)\n",
    "skp_opt.steps.pop()\n",
    "skp_opt.steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = numpy.array([article.raw_text for article in p.data_source.articles])\n",
    "labels = numpy.array([article.label for article in p.data_source.articles])\n",
    "\n",
    "X = skp_opt.fit_transform(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the test dataset for optimization accuracy\n",
    "texts_test = numpy.array([article.raw_text for article in test_data_source.articles])\n",
    "labels_test = numpy.array([article.label for article in test_data_source.articles])\n",
    "\n",
    "X_test = skp_opt.transform(texts_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X\n",
    "y_train = labels\n",
    "y_test = labels_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(344,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_list = ['linear', 'rbf', 'poly', 'sigmoid']\n",
    "# kernel_list = ['linear']\n",
    "class_weights = [None, 'balanced']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(evals, trials, optimizer=tpe.suggest):\n",
    "    space = {\n",
    "        'kernel': hp.choice('kernel', kernel_list),\n",
    "        'class_weight': hp.choice('class_weight', class_weights),\n",
    "        'C': hp.uniform('C', 0, 20),\n",
    "        'gamma': hp.uniform('gamma',0.001,10)\n",
    "    }\n",
    "    best = fmin(score, space, algo=optimizer, max_evals=evals, trials=trials)\n",
    "    pbar.close()\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc_list = []\n",
    "train_loss_list = []\n",
    "test_acc_list = []\n",
    "test_loss_list = []\n",
    "\n",
    "def score(params):\n",
    "    c = params['C']\n",
    "    gamma = params['gamma']\n",
    "    kernel = params['kernel']\n",
    "    class_weight = params['class_weight']\n",
    "    \n",
    "    model = svm.SVC(kernel=kernel,\n",
    "            C=c,\n",
    "            random_state=42,\n",
    "            decision_function_shape='ovr',\n",
    "            # class_weight=self.class_weight,\n",
    "            class_weight=class_weight,\n",
    "            probability=True,\n",
    "            gamma=gamma)\n",
    "    \n",
    "    model.fit(X_train, y_train)\n",
    "    labels_prediction = model.predict(X_test)\n",
    "    labels_probs = model.predict_proba(X_test)\n",
    "    train_acc = accuracy_score(y_train, model.predict(X_train))\n",
    "    train_loss = log_loss(y_train, model.predict_proba(X_train))\n",
    "    test_acc = accuracy_score(y_test, labels_prediction)\n",
    "    test_loss = log_loss(y_test, labels_probs)\n",
    "    \n",
    "    train_acc_list.append(train_acc)\n",
    "    train_loss_list.append(train_loss)\n",
    "    test_acc_list.append(test_acc)\n",
    "    test_loss_list.append(test_loss)\n",
    "    \n",
    "    print('Parameters with this training accuracy {} and loss {} :'.format(train_acc, train_loss))\n",
    "    print('Parameters with this testing accuracy {} and loss {} :'.format(test_acc, test_loss))\n",
    "    print(params)\n",
    "    pbar.update()\n",
    "#     return {'loss': test_loss, 'status': STATUS_OK}\n",
    "    return {'loss': -test_acc, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 0/1000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/1000 [00:00<?, ?it/s, best loss: ?]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.858198316055 :\n",
      "\n",
      "  0%|          | 0/1000 [01:47<?, ?it/s, best loss: ?]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.56976744186 and loss 1.32180975031 :\n",
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      "  0%|          | 0/1000 [01:47<?, ?it/s, best loss: ?]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 4.302310645441003, 'gamma': 7.729220738696893, 'class_weight': None}\n",
      "\n",
      "  0%|          | 0/1000 [01:47<?, ?it/s, best loss: ?]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 1/1000 [01:47<29:53:51, 107.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 1/1000 [01:47<29:53:41, 107.73s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 2.71860343194 :\n",
      "\n",
      "  0%|          | 1/1000 [06:50<29:53:41, 107.73s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.459302325581 and loss 1.94021705794 :\n",
      "\n",
      "  0%|          | 1/1000 [06:50<29:53:41, 107.73s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[A{'kernel': 'rbf', 'C': 19.74279514111955, 'gamma': 3.1259376627712605, 'class_weight': None}\n",
      "\n",
      "  0%|          | 1/1000 [06:50<29:53:41, 107.73s/it, best loss: -0.56976744186]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 2/1000 [06:50<46:06:29, 166.32s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 2/1000 [06:50<46:06:22, 166.31s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.855379599524 :\n",
      "\n",
      "  0%|          | 2/1000 [08:27<46:06:22, 166.31s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.56976744186 and loss 1.32218207895 :\n",
      "\n",
      "  0%|          | 2/1000 [08:27<46:06:22, 166.31s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 12.66496220083473, 'gamma': 2.2053987446488654, 'class_weight': None}\n",
      "\n",
      "  0%|          | 2/1000 [08:27<46:06:22, 166.31s/it, best loss: -0.56976744186]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 3/1000 [08:27<40:18:36, 145.55s/it]"
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    },
    {
     "name": "stdout",
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     "text": [
      "\n",
      "  0%|          | 3/1000 [08:27<40:18:31, 145.55s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 2.79983731961 :\n",
      "\n",
      "  0%|          | 3/1000 [12:59<40:18:31, 145.55s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.459302325581 and loss 1.94379916265 :\n",
      "\n",
      "  0%|          | 3/1000 [12:59<40:18:31, 145.55s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[A{'kernel': 'rbf', 'C': 11.59309375202325, 'gamma': 8.791600795311776, 'class_weight': 'balanced'}\n",
      "\n",
      "  0%|          | 3/1000 [12:59<40:18:31, 145.55s/it, best loss: -0.56976744186]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 4/1000 [12:59<50:44:14, 183.39s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 4/1000 [12:59<50:44:10, 183.38s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.887444913085 :\n",
      "\n",
      "  0%|          | 4/1000 [14:43<50:44:10, 183.38s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.56976744186 and loss 1.32591023488 :\n",
      "\n",
      "  0%|          | 4/1000 [14:43<50:44:10, 183.38s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 18.455749864888897, 'gamma': 3.069952695711614, 'class_weight': 'balanced'}\n",
      "\n",
      "  0%|          | 4/1000 [14:43<50:44:10, 183.38s/it, best loss: -0.56976744186]\u001b[A"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   0%|          | 5/1000 [14:43<44:05:13, 159.51s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 5/1000 [14:43<44:05:11, 159.51s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.992900935786 and loss 0.432247253063 :\n",
      "\n",
      "  0%|          | 5/1000 [16:45<44:05:11, 159.51s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.665697674419 and loss 1.09740211511 :\n",
      "\n",
      "  0%|          | 5/1000 [16:45<44:05:11, 159.51s/it, best loss: -0.56976744186]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 10.063813295521907, 'gamma': 7.42194384407677, 'class_weight': 'balanced'}\n",
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      "\u001b[AParameters with this testing accuracy 0.462209302326 and loss 1.93064538204 :\n",
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      "\u001b[A{'kernel': 'rbf', 'C': 16.617669416679945, 'gamma': 0.9590684610153134, 'class_weight': 'balanced'}\n",
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      "Hyperopt:   1%|          | 7/1000 [21:27<51:59:02, 188.46s/it]"
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      "\u001b[AParameters with this training accuracy 0.995805098419 and loss 0.419996303211 :\n",
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      "\u001b[A{'kernel': 'linear', 'C': 12.346232871022558, 'gamma': 2.7184428263498077, 'class_weight': None}\n",
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      "Hyperopt:   1%|          | 8/1000 [23:37<47:05:22, 170.89s/it]"
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      "Hyperopt:   1%|          | 9/1000 [28:19<56:13:58, 204.28s/it]"
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      "  1%|          | 9/1000 [28:19<56:13:57, 204.28s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.182962245886 and loss 1.96607123728 :\n",
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      "  1%|          | 10/1000 [33:04<62:49:17, 228.44s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  1%|▏         | 14/1000 [44:06<53:55:34, 196.89s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  2%|▏         | 15/1000 [45:43<45:39:44, 166.89s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  2%|▏         | 17/1000 [57:42<50:11:39, 183.82s/it, best loss: -0.665697674419]\u001b[A"
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      "  2%|▏         | 18/1000 [1:02:15<58:52:00, 215.81s/it, best loss: -0.665697674419]\u001b[A"
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      "  2%|▏         | 19/1000 [1:02:15<63:32:41, 233.19s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  2%|▏         | 21/1000 [1:06:00<46:58:14, 172.72s/it, best loss: -0.665697674419]\u001b[A\n",
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      "Hyperopt:   2%|▏         | 23/1000 [1:10:12<40:27:35, 149.08s/it]"
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      "  2%|▏         | 23/1000 [1:10:12<40:27:35, 149.08s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.992578251049 and loss 0.43372558549 :\n",
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      "  2%|▏         | 23/1000 [1:12:24<40:27:35, 149.08s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.662790697674 and loss 1.09692427527 :\n",
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      "  2%|▏         | 23/1000 [1:12:24<40:27:35, 149.08s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 9.373744655571052, 'gamma': 6.024341757437234, 'class_weight': 'balanced'}\n",
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      "  2%|▏         | 23/1000 [1:12:24<40:27:35, 149.08s/it, best loss: -0.665697674419]\u001b[A"
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      "  2%|▏         | 24/1000 [1:12:24<39:01:29, 143.94s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.992578251049 and loss 0.431794929074 :\n",
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      "  2%|▏         | 24/1000 [1:14:35<39:01:29, 143.94s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.665697674419 and loss 1.09680270894 :\n",
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      "  2%|▏         | 24/1000 [1:14:35<39:01:29, 143.94s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 9.852465242724602, 'gamma': 7.658509036134475, 'class_weight': 'balanced'}\n",
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      "  2%|▏         | 24/1000 [1:14:35<39:01:29, 143.94s/it, best loss: -0.665697674419]\u001b[A"
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      "Hyperopt:   2%|▎         | 25/1000 [1:14:35<37:54:27, 139.97s/it]"
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      "  2%|▎         | 25/1000 [1:14:35<37:54:27, 139.97s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.995159728945 and loss 0.42518477428 :\n",
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      "  2%|▎         | 25/1000 [1:16:46<37:54:27, 139.97s/it, best loss: -0.665697674419]\u001b[A\n",
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      "\u001b[A{'kernel': 'linear', 'C': 13.84145283375546, 'gamma': 9.828909599054361, 'class_weight': 'balanced'}\n",
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      "  2%|▎         | 25/1000 [1:16:46<37:54:27, 139.97s/it, best loss: -0.665697674419]\u001b[A"
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      "  3%|▎         | 26/1000 [1:16:46<37:09:06, 137.32s/it, best loss: -0.665697674419]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.994837044208 and loss 0.4255859469 :\n",
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      "\u001b[A{'kernel': 'linear', 'C': 13.752773712876097, 'gamma': 9.901492327672358, 'class_weight': 'balanced'}\n",
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      "  3%|▎         | 26/1000 [1:19:14<37:09:06, 137.32s/it, best loss: -0.665697674419]\u001b[A"
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      "  3%|▎         | 27/1000 [1:19:14<38:01:24, 140.68s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  3%|▎         | 28/1000 [1:21:20<36:48:04, 136.30s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  3%|▎         | 29/1000 [1:23:27<36:01:10, 133.54s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  3%|▎         | 30/1000 [1:25:17<34:01:47, 126.30s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  3%|▎         | 32/1000 [1:31:22<34:59:15, 130.12s/it, best loss: -0.665697674419]\u001b[A"
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      "  3%|▎         | 33/1000 [1:31:22<32:23:50, 120.61s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  4%|▎         | 35/1000 [1:37:29<42:29:14, 158.50s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  4%|▎         | 37/1000 [1:40:56<35:08:13, 131.35s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  4%|▎         | 37/1000 [1:42:46<35:08:13, 131.35s/it, best loss: -0.665697674419]\u001b[A"
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      "\u001b[A{'kernel': 'poly', 'C': 10.980110530535372, 'gamma': 4.799739897795489, 'class_weight': None}\n",
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      "  4%|▍         | 38/1000 [1:44:18<33:23:49, 124.98s/it, best loss: -0.665697674419]\u001b[A"
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      "  4%|▍         | 39/1000 [1:44:18<30:45:58, 115.25s/it, best loss: -0.665697674419]\u001b[A\n",
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      "  4%|▍         | 39/1000 [1:48:22<30:45:58, 115.25s/it, best loss: -0.665697674419]\u001b[A"
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      "  6%|▌         | 57/1000 [2:36:06<45:08:43, 172.35s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.418968623911 :\n",
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      "  6%|▌         | 57/1000 [2:38:00<45:08:43, 172.35s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.65988372093 and loss 1.10310049618 :\n",
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      "  6%|▌         | 57/1000 [2:38:00<45:08:43, 172.35s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 17.393172369658707, 'gamma': 6.642112697283959, 'class_weight': None}\n",
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      "  6%|▌         | 57/1000 [2:38:00<45:08:43, 172.35s/it, best loss: -0.668604651163]\u001b[A"
     ]
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      "Hyperopt:   6%|▌         | 58/1000 [2:38:00<40:32:59, 154.97s/it]"
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      "  6%|▌         | 58/1000 [2:38:00<40:32:59, 154.97s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.993868989997 and loss 0.427588559012 :\n",
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      "  6%|▌         | 58/1000 [2:39:56<40:32:59, 154.97s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.668604651163 and loss 1.09723718055 :\n",
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      "  6%|▌         | 58/1000 [2:39:56<40:32:59, 154.97s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 11.88674271714549, 'gamma': 4.208019308294297, 'class_weight': 'balanced'}\n",
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      "  6%|▌         | 58/1000 [2:39:56<40:32:59, 154.97s/it, best loss: -0.668604651163]\u001b[A"
     ]
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      "Hyperopt:   6%|▌         | 59/1000 [2:39:56<37:25:17, 143.16s/it]"
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      "  6%|▌         | 59/1000 [2:39:56<37:25:17, 143.16s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 2.74303039218 :\n",
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      "  6%|▌         | 59/1000 [2:44:03<37:25:17, 143.16s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.459302325581 and loss 1.94117409806 :\n",
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      "  6%|▌         | 59/1000 [2:44:03<37:25:17, 143.16s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'rbf', 'C': 11.651318047439368, 'gamma': 2.7142935858952284, 'class_weight': 'balanced'}\n",
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      "  6%|▌         | 59/1000 [2:44:03<37:25:17, 143.16s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   6%|▌         | 60/1000 [2:44:03<45:33:53, 174.50s/it]"
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      "  6%|▌         | 60/1000 [2:44:03<45:33:53, 174.50s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.991287512101 and loss 0.443508520278 :\n",
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      "  6%|▌         | 60/1000 [2:45:57<45:33:53, 174.50s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'linear', 'C': 8.147780285552376, 'gamma': 2.473307451406755, 'class_weight': 'balanced'}\n",
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      "  6%|▌         | 60/1000 [2:45:57<45:33:53, 174.50s/it, best loss: -0.668604651163]\u001b[A"
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      "  6%|▌         | 61/1000 [2:45:57<40:45:33, 156.27s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.850423847028 :\n",
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      "  6%|▌         | 61/1000 [2:47:29<40:45:33, 156.27s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  6%|▌         | 61/1000 [2:47:29<40:45:33, 156.27s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 9.441365963591046, 'gamma': 3.73251137407843, 'class_weight': None}\n",
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      "  6%|▌         | 61/1000 [2:47:29<40:45:33, 156.27s/it, best loss: -0.668604651163]\u001b[A"
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      "  6%|▌         | 62/1000 [2:47:29<35:41:19, 136.97s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  6%|▌         | 62/1000 [2:51:40<35:41:19, 136.97s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.0872093023256 and loss 1.94935511723 :\n",
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      "\u001b[A{'kernel': 'sigmoid', 'C': 4.852635743685142, 'gamma': 1.856826148646157, 'class_weight': 'balanced'}\n",
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      "  6%|▌         | 62/1000 [2:51:40<35:41:19, 136.97s/it, best loss: -0.668604651163]\u001b[A"
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      "  6%|▋         | 63/1000 [2:51:40<44:32:04, 171.10s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  6%|▋         | 63/1000 [2:53:35<44:32:04, 171.10s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'linear', 'C': 16.498948053946442, 'gamma': 1.116073724988333, 'class_weight': 'balanced'}\n",
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      "  6%|▋         | 63/1000 [2:53:35<44:32:04, 171.10s/it, best loss: -0.668604651163]\u001b[A"
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      "  6%|▋         | 64/1000 [2:53:35<40:07:19, 154.32s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  7%|▋         | 66/1000 [2:57:24<34:48:20, 134.15s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  7%|▋         | 69/1000 [3:03:10<31:18:37, 121.07s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  7%|▋         | 70/1000 [3:05:09<31:07:33, 120.49s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  7%|▋         | 74/1000 [3:12:51<30:01:34, 116.73s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.995482413682 and loss 0.423799874496 :\n",
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      "  7%|▋         | 74/1000 [3:14:55<30:01:34, 116.73s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.662790697674 and loss 1.09850759038 :\n",
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      "  7%|▋         | 74/1000 [3:14:55<30:01:34, 116.73s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 15.651249453809537, 'gamma': 6.965620402159648, 'class_weight': 'balanced'}\n",
      "\n",
      "  7%|▋         | 74/1000 [3:14:55<30:01:34, 116.73s/it, best loss: -0.668604651163]\u001b[A"
     ]
    },
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      "Hyperopt:   8%|▊         | 75/1000 [3:14:55<30:30:10, 118.71s/it]"
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      "  8%|▊         | 75/1000 [3:14:55<30:30:10, 118.71s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.852299615925 :\n",
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      "  8%|▊         | 75/1000 [3:16:29<30:30:10, 118.71s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.56976744186 and loss 1.32102746881 :\n",
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      "  8%|▊         | 75/1000 [3:16:29<30:30:10, 118.71s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 11.269220888320055, 'gamma': 3.097554256701639, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 75/1000 [3:16:29<30:30:10, 118.71s/it, best loss: -0.668604651163]\u001b[A"
     ]
    },
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      "Hyperopt:   8%|▊         | 76/1000 [3:16:29<28:32:51, 111.22s/it]"
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      "  8%|▊         | 76/1000 [3:16:29<28:32:51, 111.22s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 2.76157885864 :\n",
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      "  8%|▊         | 76/1000 [3:20:44<28:32:51, 111.22s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.459302325581 and loss 1.94032753719 :\n",
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      "  8%|▊         | 76/1000 [3:20:44<28:32:51, 111.22s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'rbf', 'C': 9.44451990086265, 'gamma': 4.495768516371843, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 76/1000 [3:20:44<28:32:51, 111.22s/it, best loss: -0.668604651163]\u001b[A"
     ]
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      "Hyperopt:   8%|▊         | 77/1000 [3:20:44<39:34:56, 154.38s/it]"
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      "  8%|▊         | 77/1000 [3:20:44<39:34:56, 154.38s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.992900935786 and loss 0.433261994473 :\n",
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      "  8%|▊         | 77/1000 [3:22:38<39:34:56, 154.38s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.665697674419 and loss 1.09747281133 :\n",
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      "  8%|▊         | 77/1000 [3:22:38<39:34:56, 154.38s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 10.508184762862594, 'gamma': 5.9216910700295236, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 77/1000 [3:22:38<39:34:56, 154.38s/it, best loss: -0.668604651163]\u001b[A"
     ]
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      "Hyperopt:   8%|▊         | 78/1000 [3:22:38<36:28:04, 142.39s/it]"
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      "  8%|▊         | 78/1000 [3:22:38<36:28:04, 142.39s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.139077121652 and loss 1.9577047263 :\n",
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      "  8%|▊         | 78/1000 [3:26:50<36:28:04, 142.39s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 78/1000 [3:26:50<36:28:04, 142.39s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'sigmoid', 'C': 13.405141202823671, 'gamma': 4.970208236788703, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 78/1000 [3:26:50<36:28:04, 142.39s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   8%|▊         | 79/1000 [3:26:50<44:49:34, 175.22s/it]"
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      "  8%|▊         | 79/1000 [3:26:50<44:49:34, 175.22s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 79/1000 [3:28:43<44:49:34, 175.22s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 79/1000 [3:28:43<44:49:34, 175.22s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 8.09574729131374, 'gamma': 6.2699121731448635, 'class_weight': None}\n",
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      "  8%|▊         | 79/1000 [3:28:43<44:49:34, 175.22s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   8%|▊         | 80/1000 [3:28:43<40:01:32, 156.62s/it]"
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      "  8%|▊         | 80/1000 [3:28:43<40:01:32, 156.62s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.995482413682 and loss 0.423484723537 :\n",
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      "  8%|▊         | 80/1000 [3:30:40<40:01:32, 156.62s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.662790697674 and loss 1.09925403842 :\n",
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      "  8%|▊         | 80/1000 [3:30:40<40:01:32, 156.62s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 18.36214054905352, 'gamma': 6.432508258871772, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 80/1000 [3:30:40<40:01:32, 156.62s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   8%|▊         | 81/1000 [3:30:40<36:54:46, 144.60s/it]"
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      "  8%|▊         | 81/1000 [3:30:40<36:54:46, 144.60s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.851491634174 :\n",
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      "  8%|▊         | 81/1000 [3:32:13<36:54:46, 144.60s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 81/1000 [3:32:13<36:54:46, 144.60s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 12.02220789663622, 'gamma': 5.362422562962887, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 81/1000 [3:32:13<36:54:46, 144.60s/it, best loss: -0.668604651163]\u001b[A"
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      "  8%|▊         | 82/1000 [3:32:13<32:56:25, 129.18s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 82/1000 [3:34:08<32:56:25, 129.18s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 82/1000 [3:34:08<32:56:25, 129.18s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 9.064809771295703, 'gamma': 7.297393700800201, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 82/1000 [3:34:08<32:56:25, 129.18s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   8%|▊         | 83/1000 [3:34:08<31:48:30, 124.88s/it]"
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      "  8%|▊         | 83/1000 [3:34:08<31:48:30, 124.88s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 83/1000 [3:38:22<31:48:30, 124.88s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 83/1000 [3:38:22<31:48:30, 124.88s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'rbf', 'C': 11.401926102081237, 'gamma': 3.7988664445291924, 'class_weight': None}\n",
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      "  8%|▊         | 83/1000 [3:38:22<31:48:30, 124.88s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   8%|▊         | 84/1000 [3:38:22<41:38:53, 163.68s/it]"
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      "  8%|▊         | 84/1000 [3:38:22<41:38:53, 163.68s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 84/1000 [3:40:17<41:38:53, 163.68s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'linear', 'C': 12.739179759542981, 'gamma': 8.420238718161771, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 84/1000 [3:40:17<41:38:53, 163.68s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   8%|▊         | 85/1000 [3:40:17<37:55:03, 149.18s/it]"
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      "  8%|▊         | 85/1000 [3:40:17<37:55:03, 149.18s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 85/1000 [3:44:28<37:55:03, 149.18s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  8%|▊         | 85/1000 [3:44:28<37:55:03, 149.18s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'sigmoid', 'C': 19.343669692448103, 'gamma': 8.415864148946788, 'class_weight': 'balanced'}\n",
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      "  8%|▊         | 85/1000 [3:44:28<37:55:03, 149.18s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   9%|▊         | 86/1000 [3:44:28<45:36:48, 179.66s/it]"
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      "  9%|▊         | 86/1000 [3:44:28<45:36:48, 179.66s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.995159728945 and loss 0.424930998283 :\n",
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      "  9%|▊         | 86/1000 [3:46:24<45:36:48, 179.66s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▊         | 86/1000 [3:46:24<45:36:48, 179.66s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 14.84314119479652, 'gamma': 5.372540041611583, 'class_weight': 'balanced'}\n",
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      "  9%|▊         | 86/1000 [3:46:24<45:36:48, 179.66s/it, best loss: -0.668604651163]\u001b[A"
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     "name": "stderr",
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      "Hyperopt:   9%|▊         | 87/1000 [3:46:24<40:42:32, 160.52s/it]"
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      "  9%|▊         | 87/1000 [3:46:24<40:42:32, 160.52s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▊         | 87/1000 [3:48:19<40:42:32, 160.52s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▊         | 87/1000 [3:48:20<40:42:32, 160.52s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 17.76642184773565, 'gamma': 4.2997677578440205, 'class_weight': 'balanced'}\n",
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      "  9%|▊         | 87/1000 [3:48:20<40:42:32, 160.52s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   9%|▉         | 88/1000 [3:48:20<37:15:12, 147.05s/it]"
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      "  9%|▉         | 88/1000 [3:48:20<37:15:12, 147.05s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▉         | 88/1000 [3:49:49<37:15:12, 147.05s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▉         | 88/1000 [3:49:49<37:15:12, 147.05s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 9.771928389953835, 'gamma': 0.029836691202496546, 'class_weight': None}\n",
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      "  9%|▉         | 88/1000 [3:49:49<37:15:12, 147.05s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   9%|▉         | 89/1000 [3:49:49<32:48:34, 129.65s/it]"
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      "  9%|▉         | 89/1000 [3:49:49<32:48:34, 129.65s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▉         | 89/1000 [3:51:46<32:48:34, 129.65s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▉         | 89/1000 [3:51:46<32:48:34, 129.65s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 15.239730598247776, 'gamma': 9.332030150811727, 'class_weight': 'balanced'}\n",
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      "  9%|▉         | 89/1000 [3:51:46<32:48:34, 129.65s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   9%|▉         | 90/1000 [3:51:46<31:49:28, 125.90s/it]"
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      "  9%|▉         | 90/1000 [3:51:46<31:49:28, 125.90s/it, best loss: -0.668604651163]\u001b[A\n",
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      "  9%|▉         | 90/1000 [3:55:58<31:49:28, 125.90s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'rbf', 'C': 16.49686983232213, 'gamma': 4.8676846690859, 'class_weight': 'balanced'}\n",
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      "  9%|▉         | 90/1000 [3:55:58<31:49:28, 125.90s/it, best loss: -0.668604651163]\u001b[A"
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      "  9%|▉         | 91/1000 [3:55:58<41:22:17, 163.85s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.0141981284285 and loss 1.97274157325 :\n",
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      "  9%|▉         | 91/1000 [4:00:05<41:22:17, 163.85s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.0174418604651 and loss 1.93759366539 :\n",
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      "  9%|▉         | 91/1000 [4:00:05<41:22:17, 163.85s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'sigmoid', 'C': 13.860174553505903, 'gamma': 8.859281531501686, 'class_weight': 'balanced'}\n",
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      "  9%|▉         | 91/1000 [4:00:05<41:22:17, 163.85s/it, best loss: -0.668604651163]\u001b[A"
     ]
    },
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      "Hyperopt:   9%|▉         | 92/1000 [4:00:05<47:34:44, 188.64s/it]"
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      "  9%|▉         | 92/1000 [4:00:05<47:34:44, 188.64s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.994191674734 and loss 0.42677489111 :\n",
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      "  9%|▉         | 92/1000 [4:01:59<47:34:44, 188.64s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.668604651163 and loss 1.09764185148 :\n",
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      "  9%|▉         | 92/1000 [4:01:59<47:34:44, 188.64s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 12.648157329636206, 'gamma': 6.930125422544513, 'class_weight': 'balanced'}\n",
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      "  9%|▉         | 92/1000 [4:01:59<47:34:44, 188.64s/it, best loss: -0.668604651163]\u001b[A"
     ]
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      "Hyperopt:   9%|▉         | 93/1000 [4:02:00<41:57:10, 166.52s/it]"
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      "  9%|▉         | 93/1000 [4:01:59<41:57:10, 166.52s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.995482413682 and loss 0.423271791339 :\n",
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      "  9%|▉         | 93/1000 [4:03:55<41:57:10, 166.52s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.65988372093 and loss 1.10529220305 :\n",
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      "  9%|▉         | 93/1000 [4:03:55<41:57:10, 166.52s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'linear', 'C': 6.979136724501147, 'gamma': 5.749906604298131, 'class_weight': None}\n",
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      "  9%|▉         | 93/1000 [4:03:55<41:57:10, 166.52s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:   9%|▉         | 94/1000 [4:03:55<38:03:18, 151.21s/it]"
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      "  9%|▉         | 94/1000 [4:03:55<38:03:18, 151.21s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.996450467893 and loss 0.854284757458 :\n",
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      "  9%|▉         | 94/1000 [4:05:28<38:03:18, 151.21s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this testing accuracy 0.56976744186 and loss 1.32050049735 :\n",
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      "  9%|▉         | 94/1000 [4:05:28<38:03:18, 151.21s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 17.01211340088652, 'gamma': 6.662817900811535, 'class_weight': 'balanced'}\n",
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      "  9%|▉         | 94/1000 [4:05:28<38:03:18, 151.21s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:  10%|▉         | 95/1000 [4:05:28<33:36:27, 133.69s/it]"
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      " 10%|▉         | 95/1000 [4:05:28<33:36:27, 133.69s/it, best loss: -0.668604651163]\u001b[A\n",
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      " 10%|▉         | 95/1000 [4:07:22<33:36:27, 133.69s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'linear', 'C': 14.574682651999472, 'gamma': 3.3046597652577554, 'class_weight': 'balanced'}\n",
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      "Hyperopt:  10%|▉         | 96/1000 [4:07:22<32:04:30, 127.73s/it]"
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      " 10%|▉         | 96/1000 [4:07:22<32:04:30, 127.73s/it, best loss: -0.668604651163]\u001b[A\n",
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      " 10%|▉         | 96/1000 [4:11:36<32:04:30, 127.73s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'rbf', 'C': 16.07104581224818, 'gamma': 7.549543576182384, 'class_weight': 'balanced'}\n",
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      " 10%|▉         | 96/1000 [4:11:36<32:04:30, 127.73s/it, best loss: -0.668604651163]\u001b[A"
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      "Hyperopt:  10%|▉         | 97/1000 [4:11:36<41:34:46, 165.77s/it]"
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      " 10%|▉         | 97/1000 [4:11:36<41:34:46, 165.77s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.431106808648 and loss 1.94607231103 :\n",
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      " 10%|▉         | 97/1000 [4:13:00<41:34:46, 165.77s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'sigmoid', 'C': 10.69102074412667, 'gamma': 2.877586349312376, 'class_weight': None}\n",
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      " 10%|▉         | 97/1000 [4:13:00<41:34:46, 165.77s/it, best loss: -0.668604651163]\u001b[A"
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      " 10%|▉         | 98/1000 [4:13:00<35:23:45, 141.27s/it, best loss: -0.668604651163]\u001b[A\n",
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      " 10%|▉         | 98/1000 [4:14:57<35:23:45, 141.27s/it, best loss: -0.668604651163]\u001b[A\n",
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      " 10%|▉         | 99/1000 [4:14:57<33:32:53, 134.04s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[AParameters with this training accuracy 0.972894482091 and loss 0.5006366669 :\n",
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      " 10%|▉         | 99/1000 [4:16:44<33:32:53, 134.04s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'linear', 'C': 3.62057414653417, 'gamma': 3.774466416030048, 'class_weight': 'balanced'}\n",
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      " 10%|▉         | 99/1000 [4:16:44<33:32:53, 134.04s/it, best loss: -0.668604651163]\u001b[A"
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      "\u001b[A{'kernel': 'rbf', 'C': 9.82925568106444, 'gamma': 7.73940532768665, 'class_weight': 'balanced'}\n",
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      "\u001b[AParameters with this testing accuracy 0.665697674419 and loss 1.09719274566 :\n",
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      "\u001b[A{'kernel': 'linear', 'C': 11.110876982983921, 'gamma': 5.1060103507519905, 'class_weight': 'balanced'}\n",
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     ]
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      " 18%|█▊        | 176/1000 [7:15:59<36:27:44, 159.30s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\u001b[A{'kernel': 'sigmoid', 'C': 17.53810079189615, 'gamma': 7.066303953973941, 'class_weight': None}\n",
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     ]
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      " 40%|███▉      | 397/1000 [15:26:08<22:28:30, 134.18s/it, best loss: -0.668604651163]\u001b[A\n",
      "\u001b[A{'kernel': 'poly', 'C': 5.898967620214674, 'gamma': 4.179843631279449, 'class_weight': None}\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Hyperopt:  40%|███▉      | 398/1000 [15:26:08<20:07:18, 120.33s/it]"
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      "\n",
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      "\u001b[AParameters with this training accuracy 0.992900935786 and loss 0.431911161612 :\n",
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      " 40%|███▉      | 398/1000 [15:27:57<20:07:18, 120.33s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\n",
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      "\n",
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      " 40%|████      | 400/1000 [15:33:46<19:08:23, 114.84s/it, best loss: -0.668604651163]\u001b[A\n",
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      "\n",
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      "\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Hyperopt:  40%|████      | 404/1000 [15:39:17<20:37:50, 124.62s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
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     ]
    }
   ],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "trials = Trials()\n",
    "cores = 48\n",
    "start = time.time()\n",
    "evaluations = 1000\n",
    "pbar = tqdm(total=evaluations, desc=\"Hyperopt\")\n",
    "best_param = optimize(evals=evaluations,\n",
    "                      optimizer=tpe.suggest,\n",
    "                      trials=trials)\n",
    "print(\"------------------------------------\")\n",
    "print(\"The best hyperparameters are: \", \"\\n\")\n",
    "print(best_param)\n",
    "end = time.time()\n",
    "print('Time elapsed to optimize {0} executions: {1}'.format(evaluations, end - start))\n",
    "best_param['kernel'] = kernel_list[best_param['kernel']]\n",
    "best_param['class_weight'] = class_weights[best_param['class_weight']]\n",
    "print('\\n Best score:')\n",
    "score(best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Time elapsed to optimize {0} executions: {1}'.format(evaluations, end - start))\n",
    "print('\\n Best score:')\n",
    "score(best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the best params and train the model\n",
    "SVC_opt = svm.SVC(kernel=best_param['kernel'],\n",
    "            C=best_param['C'],\n",
    "            random_state=42,\n",
    "            decision_function_shape='ovr',\n",
    "            # class_weight=self.class_weight,\n",
    "            class_weight=best_param['class_weight'],\n",
    "            probability=True,\n",
    "            gamma=best_param['gamma'])\n",
    "\n",
    "\n",
    "fitted_model = SVC_opt.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score = fitted_model.decision_function(X_test)\n",
    "y_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_acc = accuracy_score(y_test, fitted_model.predict(X_test))\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "%matplotlib inline \n",
    "x_list =range(len(train_acc_list))\n",
    "\n",
    "plt.plot(x_list, train_acc_list, label='Training accuracy')\n",
    "plt.plot(x_list, test_acc_list, label='Testing accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x_list, train_loss_list, label='Training loss')\n",
    "plt.plot(x_list, test_loss_list, label='Testing loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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