Optimize FROG NB 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, roc_auc_score\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.naive_bayes import MultinomialNB\n",
    "from sklearn.preprocessing import MinMaxScaler\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)']"
      ]
     },
     "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']"
      ]
     },
     "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 n3bgs frog\n",
      "DataSource: N3BGS FROG Train\n",
      "NLP Tool: Frog\n",
      "Classifier: Support Vector\n",
      "Task status: Status.SUCCESS\n"
     ]
    }
   ],
   "source": [
    "p = Pipeline.objects.all()[31]\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": [
       "857"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p.data_source.articles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data source: N3BGS FROG Test\n"
     ]
    }
   ],
   "source": [
    "test_data_source = DataSource.objects.all()[29]\n",
    "print 'Testing data source: ' + test_data_source.display_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('CleanOCR', <newsgac.nlp_tools.transformers.CleanOCR at 0x7fb1b21a5990>),\n",
       " ('FeatureExtraction', FeatureUnion(n_jobs=None,\n",
       "         transformer_list=[('Basic', <newsgac.nlp_tools.transformers.ExtractBasicFeatures object at 0x7fb1b21a5590>), ('Quote', <newsgac.nlp_tools.transformers.ExtractQuotes object at 0x7fb1b21a55d0>), ('Sentiment', Pipeline(memory=None,\n",
       "       steps=[('RemoveQuotes', <newsgac.nlp_tools.transformers.RemoveQuot...a5a10>), ('Frog', <newsgac.nlp_tools.models.frog.FrogFeatureExtractor object at 0x7fb1d3b4de10>)]))],\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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('RobustScaler',\n",
       " RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=False,\n",
       "        with_scaling=True))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For NB change scaler to minMax\n",
    "skp_opt.steps.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('CleanOCR', <newsgac.nlp_tools.transformers.CleanOCR at 0x7fb1b21a5990>),\n",
       " ('FeatureExtraction', FeatureUnion(n_jobs=None,\n",
       "         transformer_list=[('Basic', <newsgac.nlp_tools.transformers.ExtractBasicFeatures object at 0x7fb1b21a5590>), ('Quote', <newsgac.nlp_tools.transformers.ExtractQuotes object at 0x7fb1b21a55d0>), ('Sentiment', Pipeline(memory=None,\n",
       "       steps=[('RemoveQuotes', <newsgac.nlp_tools.transformers.RemoveQuot...a5a10>), ('Frog', <newsgac.nlp_tools.models.frog.FrogFeatureExtractor object at 0x7fb1d3b4de10>)]))],\n",
       "         transformer_weights=None)),\n",
       " ('MinMaxScaler', MinMaxScaler(copy=True, feature_range=(0, 1)))]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skp_opt.steps.append(('MinMaxScaler', MinMaxScaler(feature_range=(0, 1))))\n",
    "skp_opt.steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X\n",
    "y_train = labels\n",
    "y_test = labels_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(evals, trials, optimizer=tpe.suggest):\n",
    "    space = {\n",
    "        'alpha': hp.uniform('alpha', 0.0, 5.0),\n",
    "    }\n",
    "    best = fmin(score, space, algo=optimizer, max_evals=evals, trials=trials)\n",
    "    pbar.close()\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc_list = []\n",
    "train_loss_list = []\n",
    "test_acc_list = []\n",
    "test_loss_list = []\n",
    "\n",
    "def score(params):\n",
    "    \n",
    "    model = MultinomialNB(alpha=params['alpha'])\n",
    "\n",
    "    model.fit(X_train, y_train)   \n",
    "\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, model.predict(X_test))\n",
    "    test_loss = log_loss(y_test, model.predict_proba(X_test))\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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   3%|▎         | 27/1000 [00:00<00:07, 138.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.351225204201 and loss 2.60200443022 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.63585188496 :\n",
      "{'alpha': 4.234841084429139}\n",
      "Parameters with this training accuracy 0.358226371062 and loss 2.58472843474 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.62184069909 :\n",
      "{'alpha': 3.298055416489141}\n",
      "Parameters with this training accuracy 0.354725787631 and loss 2.60049297306 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.63462837112 :\n",
      "{'alpha': 4.143952857567568}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.48291842718 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53815986223 :\n",
      "{'alpha': 0.45526637170250683}\n",
      "Parameters with this training accuracy 0.357059509918 and loss 2.56587730846 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.60648324174 :\n",
      "{'alpha': 2.487536794363725}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.5612787551 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.60272589003 :\n",
      "{'alpha': 2.316751670288737}\n",
      "Parameters with this training accuracy 0.354725787631 and loss 2.5899695896 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.62609760851 :\n",
      "{'alpha': 3.5600069423084886}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.46255860802 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.52126002281 :\n",
      "{'alpha': 0.19130424253137823}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.51548968025 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56509995363 :\n",
      "{'alpha': 1.0352475124664506}\n",
      "Parameters with this training accuracy 0.341890315053 and loss 2.6120558588 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.6439778699 :\n",
      "{'alpha': 4.890405451582296}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.57586445 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.61462856931 :\n",
      "{'alpha': 2.8931576645490074}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52123860174 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.56984255026 :\n",
      "{'alpha': 1.1617986567613996}\n",
      "Parameters with this training accuracy 0.366394399067 and loss 2.55187235159 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59502700626 :\n",
      "{'alpha': 1.9953259534374883}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.47881296012 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53475675466 :\n",
      "{'alpha': 0.3965294460704294}\n",
      "Parameters with this training accuracy 0.365227537923 and loss 2.5542005373 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59693418305 :\n",
      "{'alpha': 2.0715797704901044}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.54498751718 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.58938111612 :\n",
      "{'alpha': 1.781619415308315}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.53677068942 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.58263158662 :\n",
      "{'alpha': 1.5479070478735806}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.57561822416 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.61442799874 :\n",
      "{'alpha': 2.8825434106555976}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.54248438432 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58732624745 :\n",
      "{'alpha': 1.7080641603333562}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.50883101483 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55960159768 :\n",
      "{'alpha': 0.898573246055745}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.51383976811 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5637380533 :\n",
      "{'alpha': 1.0004185086291506}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.51973695465 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.56860419048 :\n",
      "{'alpha': 1.1279464839440332}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52675053588 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.5743853053 :\n",
      "{'alpha': 1.291099403331132}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.44522304669 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50675420602 :\n",
      "{'alpha': 0.018310646583541867}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.5023774916 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55426777499 :\n",
      "{'alpha': 0.7755755848981585}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.53508089335 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.58124208455 :\n",
      "{'alpha': 1.5025177449102483}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52705060431 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.574632483 :\n",
      "{'alpha': 1.2983726870871308}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   5%|▌         | 51/1000 [00:00<00:07, 124.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.379229871645 and loss 2.49071311645 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54461647485 :\n",
      "{'alpha': 0.5749735964474878}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.49854560826 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.55109866464 :\n",
      "{'alpha': 0.7066972879321246}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.46099604769 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.51995956667 :\n",
      "{'alpha': 0.17378411423470286}\n",
      "Parameters with this training accuracy 0.381563593932 and loss 2.48081863832 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53641952456 :\n",
      "{'alpha': 0.424862624506775}\n",
      "Parameters with this training accuracy 0.378063010502 and loss 2.49494559775 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54812000706 :\n",
      "{'alpha': 0.644677699920006}\n",
      "Parameters with this training accuracy 0.357059509918 and loss 2.59638325322 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.63129939324 :\n",
      "{'alpha': 3.9060665838902304}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.57523558853 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.61411628676 :\n",
      "{'alpha': 2.866113408378359}\n",
      "Parameters with this training accuracy 0.343057176196 and loss 2.61090711734 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.64305008241 :\n",
      "{'alpha': 4.810686942574112}\n",
      "Parameters with this training accuracy 0.357059509918 and loss 2.56635401143 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.60687249678 :\n",
      "{'alpha': 2.5057903918915647}\n",
      "Parameters with this training accuracy 0.390898483081 and loss 2.44803274495 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50912352811 :\n",
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      "Parameters with this training accuracy 0.378063010502 and loss 2.4920627351 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54573383352 :\n",
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      "{'alpha': 3.394787024035597}\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:   7%|▋         | 74/1000 [00:00<00:07, 118.28it/s]"
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     "text": [
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     "output_type": "stream",
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      "Hyperopt:   8%|▊         | 85/1000 [00:00<00:08, 108.73it/s]"
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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      "Hyperopt:  11%|█         | 110/1000 [00:00<00:08, 108.60it/s]"
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    },
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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      "Hyperopt:  14%|█▎        | 136/1000 [00:01<00:07, 116.74it/s]"
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    },
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     "output_type": "stream",
     "text": [
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      "Parameters with this testing accuracy 0.284210526316 and loss 2.63633120755 :\n",
      "{'alpha': 4.270973143061294}\n",
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      "Parameters with this testing accuracy 0.284210526316 and loss 2.63266453873 :\n",
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      "Parameters with this training accuracy 0.361726954492 and loss 2.55870766902 :\n",
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      "{'alpha': 2.7678210348681134}\n"
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     "output_type": "stream",
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      "Hyperopt:  16%|█▌        | 161/1000 [00:01<00:07, 117.02it/s]"
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     "text": [
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     "output_type": "stream",
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     "output_type": "stream",
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      "Hyperopt:  22%|██▏       | 223/1000 [00:01<00:07, 101.71it/s]"
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     "text": [
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     "output_type": "stream",
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      "Hyperopt:  25%|██▍       | 246/1000 [00:02<00:07, 106.07it/s]"
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    },
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     "output_type": "stream",
     "text": [
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     "name": "stderr",
     "output_type": "stream",
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      "Hyperopt:  27%|██▋       | 268/1000 [00:02<00:07, 103.81it/s]"
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      "Hyperopt:  29%|██▉       | 290/1000 [00:02<00:06, 102.62it/s]"
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      "Hyperopt:  31%|███▏      | 313/1000 [00:02<00:06, 105.34it/s]"
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      "Hyperopt:  34%|███▎      | 336/1000 [00:03<00:06, 106.69it/s]"
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      "{'alpha': 1.7750098118694753}\n",
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      "{'alpha': 0.7144303527112384}\n"
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      "Hyperopt:  36%|███▌      | 358/1000 [00:03<00:06, 103.84it/s]"
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      "Parameters with this training accuracy 0.365227537923 and loss 2.55356926959 :\n",
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      "Parameters with this training accuracy 0.374562427071 and loss 2.52334628877 :\n",
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      "{'alpha': 1.2102921211155035}\n"
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      "Hyperopt:  38%|███▊      | 380/1000 [00:03<00:06, 97.30it/s] "
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      "Hyperopt:  43%|████▎     | 433/1000 [00:04<00:06, 94.18it/s]"
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     "text": [
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      "Parameters with this training accuracy 0.388564760793 and loss 2.4620236505 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5208148846 :\n",
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      "Hyperopt:  45%|████▌     | 454/1000 [00:04<00:05, 93.26it/s]"
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      "Hyperopt:  46%|████▋     | 464/1000 [00:04<00:06, 86.25it/s]"
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      "Hyperopt:  48%|████▊     | 485/1000 [00:04<00:05, 89.66it/s]"
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      "Hyperopt:  50%|█████     | 505/1000 [00:04<00:05, 89.62it/s]"
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      "Hyperopt:  52%|█████▎    | 525/1000 [00:05<00:05, 87.47it/s]"
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      "Hyperopt:  54%|█████▍    | 544/1000 [00:05<00:05, 90.46it/s]"
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      "Parameters with this training accuracy 0.371061843641 and loss 2.53703126333 :\n",
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      "Hyperopt:  56%|█████▋    | 563/1000 [00:05<00:05, 86.53it/s]"
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      "Hyperopt:  58%|█████▊    | 582/1000 [00:05<00:04, 88.28it/s]"
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      "Hyperopt:  60%|██████    | 601/1000 [00:05<00:04, 89.93it/s]"
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      "Hyperopt:  61%|██████    | 611/1000 [00:06<00:04, 91.97it/s]"
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     "text": [
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     "output_type": "stream",
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      "Hyperopt:  63%|██████▎   | 631/1000 [00:06<00:03, 92.39it/s]"
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      "Parameters with this testing accuracy 0.315789473684 and loss 2.58034781617 :\n",
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      "Hyperopt:  65%|██████▌   | 651/1000 [00:06<00:04, 86.90it/s]"
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      "Hyperopt:  67%|██████▋   | 670/1000 [00:06<00:03, 83.82it/s]"
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      "Hyperopt:  68%|██████▊   | 679/1000 [00:06<00:04, 78.10it/s]"
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      "Hyperopt:  70%|██████▉   | 698/1000 [00:07<00:03, 77.33it/s]"
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      "Hyperopt:  72%|███████▏  | 716/1000 [00:07<00:03, 77.36it/s]"
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      "Parameters with this training accuracy 0.374562427071 and loss 2.52037426554 :\n",
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      "Hyperopt:  75%|███████▌  | 751/1000 [00:07<00:03, 77.93it/s]"
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      "Hyperopt:  76%|███████▌  | 759/1000 [00:07<00:03, 75.02it/s]"
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      "Hyperopt:  81%|████████  | 811/1000 [00:08<00:02, 75.40it/s]"
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      "Hyperopt:  82%|████████▏ | 819/1000 [00:08<00:02, 70.43it/s]"
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     "text": [
      "Hyperopt:  84%|████████▎ | 837/1000 [00:09<00:02, 72.36it/s]"
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      "Hyperopt:  86%|████████▌ | 855/1000 [00:09<00:01, 77.32it/s]"
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      "Hyperopt:  87%|████████▋ | 871/1000 [00:09<00:01, 75.42it/s]"
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      "Hyperopt:  89%|████████▊ | 887/1000 [00:09<00:01, 75.16it/s]"
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      "Hyperopt:  90%|█████████ | 904/1000 [00:09<00:01, 74.11it/s]"
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      "Hyperopt:  91%|█████████ | 912/1000 [00:10<00:01, 69.34it/s]"
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      "{'alpha': 1.8576223469329163}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.47953223771 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53535310847 :\n",
      "{'alpha': 0.4066114374807525}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  93%|█████████▎| 929/1000 [00:10<00:01, 70.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.392065344224 and loss 2.4555481319 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.51541827703 :\n",
      "{'alpha': 0.1156569994366442}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.4856017992 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5403832437 :\n",
      "{'alpha': 0.4952431290374878}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.53584952505 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.58187418233 :\n",
      "{'alpha': 1.5230551127387213}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.51040828761 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56090450291 :\n",
      "{'alpha': 0.9300254559220191}\n",
      "Parameters with this training accuracy 0.385064177363 and loss 2.47659977335 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5329214296 :\n",
      "{'alpha': 0.3660551822974383}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.51425165743 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56407807456 :\n",
      "{'alpha': 1.0090526978959249}\n",
      "Parameters with this training accuracy 0.365227537923 and loss 2.55459629901 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59725827381 :\n",
      "{'alpha': 2.0847512539672852}\n",
      "Parameters with this training accuracy 0.378063010502 and loss 2.49421970385 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54751925097 :\n",
      "{'alpha': 0.6324798255283844}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.49973584233 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.55208319017 :\n",
      "{'alpha': 0.727770595971861}\n",
      "Parameters with this training accuracy 0.390898483081 and loss 2.44799302686 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50909011598 :\n",
      "{'alpha': 0.04272096963035654}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.46390457871 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5223796415 :\n",
      "{'alpha': 0.2067002279920393}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.51635424852 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56581346269 :\n",
      "{'alpha': 1.0537587539644684}\n",
      "Parameters with this training accuracy 0.386231038506 and loss 2.46832821197 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.52605622087 :\n",
      "{'alpha': 0.25929958055448976}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.48696205904 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.54151005998 :\n",
      "{'alpha': 0.5159975506276961}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.51580896704 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56536346516 :\n",
      "{'alpha': 1.042062730803106}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  95%|█████████▍| 946/1000 [00:10<00:00, 72.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.376896149358 and loss 2.50557004879 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55690698251 :\n",
      "{'alpha': 0.835298682567212}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52556495593 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57340856558 :\n",
      "{'alpha': 1.2626030246324107}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.5759885925 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.61472968853 :\n",
      "{'alpha': 2.8985215618106195}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.50856144611 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55937889186 :\n",
      "{'alpha': 0.8932536818526521}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.49921870673 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.55165544849 :\n",
      "{'alpha': 0.718579429322969}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.50198915417 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55394667319 :\n",
      "{'alpha': 0.7684575516364812}\n",
      "Parameters with this training accuracy 0.358226371062 and loss 2.56372013134 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.60472120552 :\n",
      "{'alpha': 2.4062411291820514}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.53861978585 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58415152199 :\n",
      "{'alpha': 1.5985919017479966}\n",
      "Parameters with this training accuracy 0.341890315053 and loss 2.61346295853 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.6451140168 :\n",
      "{'alpha': 4.989863828785378}\n",
      "Parameters with this training accuracy 0.355892648775 and loss 2.59391262749 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.62929655623 :\n",
      "{'alpha': 3.7692612179840053}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.53147805457 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.57827792261 :\n",
      "{'alpha': 1.4086171180264682}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.51179051486 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56204605043 :\n",
      "{'alpha': 0.9580527741097971}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.51821373263 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56734772339 :\n",
      "{'alpha': 1.0941892593779439}\n",
      "Parameters with this training accuracy 0.373395565928 and loss 2.53449728678 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.58076207937 :\n",
      "{'alpha': 1.4870441423037397}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.54526141343 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.58960589326 :\n",
      "{'alpha': 1.7897980736337944}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.48295356801 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53818898369 :\n",
      "{'alpha': 0.4557817094017742}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  96%|█████████▋| 963/1000 [00:10<00:00, 76.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.376896149358 and loss 2.50679510198 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55791941247 :\n",
      "{'alpha': 0.8587957458900908}\n",
      "Parameters with this training accuracy 0.383897316219 and loss 2.47614830293 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53254696811 :\n",
      "{'alpha': 0.35993940346594616}\n",
      "Parameters with this training accuracy 0.380396732789 and loss 2.48796525894 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.54234098216 :\n",
      "{'alpha': 0.5315179798314167}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.49941147441 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.55181489675 :\n",
      "{'alpha': 0.7219991932910164}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.49572522185 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54876517528 :\n",
      "{'alpha': 0.657892376163104}\n",
      "Parameters with this training accuracy 0.386231038506 and loss 2.46936808078 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5269198879 :\n",
      "{'alpha': 0.27211320504948155}\n",
      "Parameters with this training accuracy 0.394399066511 and loss 2.45384365593 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.51399445295 :\n",
      "{'alpha': 0.09841565994591872}\n",
      "Parameters with this training accuracy 0.378063010502 and loss 2.49335515117 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54680367506 :\n",
      "{'alpha': 0.6180844077173528}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.51103717091 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56142391037 :\n",
      "{'alpha': 0.9427230494130512}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.51787281288 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56706646463 :\n",
      "{'alpha': 1.0867130395143136}\n",
      "Parameters with this training accuracy 0.378063010502 and loss 2.49196736975 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.54565488544 :\n",
      "{'alpha': 0.5952752222417509}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.44521201624 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50674487831 :\n",
      "{'alpha': 0.018216103693001973}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52588117845 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57366910602 :\n",
      "{'alpha': 1.27016631393204}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.54055545312 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58574198157 :\n",
      "{'alpha': 1.6528148121250603}\n",
      "Parameters with this training accuracy 0.368728121354 and loss 2.55075228909 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59410911329 :\n",
      "{'alpha': 1.959377865437865}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.50594272769 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55721499575 :\n",
      "{'alpha': 0.8424123596051947}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt:  98%|█████████▊| 980/1000 [00:10<00:00, 78.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.375729288215 and loss 2.53133735771 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.57816212427 :\n",
      "{'alpha': 1.4050277558605022}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.54027097652 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58550827989 :\n",
      "{'alpha': 1.6447700731080825}\n",
      "Parameters with this training accuracy 0.369894982497 and loss 2.54798943233 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.59184392529 :\n",
      "{'alpha': 1.8726937438026798}\n",
      "Parameters with this training accuracy 0.366394399067 and loss 2.55192487395 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59507004249 :\n",
      "{'alpha': 1.9970232681586872}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52254219915 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57091732923 :\n",
      "{'alpha': 1.191655308638472}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.54290015729 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58766764197 :\n",
      "{'alpha': 1.7201348135791155}\n",
      "Parameters with this training accuracy 0.361726954492 and loss 2.55791269337 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59997287184 :\n",
      "{'alpha': 2.1975839571947064}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.53577663257 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.58181424219 :\n",
      "{'alpha': 1.5210997168888647}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.54729990697 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.59127837837 :\n",
      "{'alpha': 1.8514922919262948}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.5288227606 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57609199437 :\n",
      "{'alpha': 1.3418358046525662}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52713834154 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.5747047529 :\n",
      "{'alpha': 1.3005040053013346}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.53147880959 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.57827854401 :\n",
      "{'alpha': 1.4086363952268712}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52021454381 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.56899807661 :\n",
      "{'alpha': 1.138650765819821}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.5130340328 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56307284486 :\n",
      "{'alpha': 0.983643958189984}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.56254843977 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.60376374258 :\n",
      "{'alpha': 2.3629642928793695}\n",
      "Parameters with this training accuracy 0.381563593932 and loss 2.48044250882 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53610773441 :\n",
      "{'alpha': 0.419496873998603}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.44398544108 :\n",
      "Parameters with this testing accuracy 0.294736842105 and loss 2.50570608127 :\n",
      "{'alpha': 0.007840809215516775}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|█████████▉| 997/1000 [00:11<00:00, 79.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.374562427071 and loss 2.52227594102 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57069782653 :\n",
      "{'alpha': 1.1855213365188464}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52200739934 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57047643107 :\n",
      "{'alpha': 1.1793534215555672}\n",
      "Parameters with this training accuracy 0.38739789965 and loss 2.47120475552 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.52844487966 :\n",
      "{'alpha': 0.2951677006969986}\n",
      "Parameters with this training accuracy 0.388564760793 and loss 2.46304478129 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5216644947 :\n",
      "{'alpha': 0.19683281770721917}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.51792363585 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.56710839457 :\n",
      "{'alpha': 1.0878257415149246}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.51209017559 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.5622935027 :\n",
      "{'alpha': 0.9641868806789127}\n",
      "Parameters with this training accuracy 0.359393232205 and loss 2.58380207757 :\n",
      "Parameters with this testing accuracy 0.273684210526 and loss 2.6210877261 :\n",
      "{'alpha': 3.2535731863300805}\n",
      "Parameters with this training accuracy 0.371061843641 and loss 2.53777732353 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58345910089 :\n",
      "{'alpha': 1.5753662316890487}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52293818511 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.57124376059 :\n",
      "{'alpha': 1.2008121173071848}\n",
      "Parameters with this training accuracy 0.376896149358 and loss 2.5038467822 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55548253496 :\n",
      "{'alpha': 0.8027929666674536}\n",
      "Parameters with this training accuracy 0.381563593932 and loss 2.48390676464 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53897885806 :\n",
      "{'alpha': 0.4698427867809346}\n",
      "Parameters with this training accuracy 0.385064177363 and loss 2.47725274025 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.53346297385 :\n",
      "{'alpha': 0.3749606522140244}\n",
      "Parameters with this training accuracy 0.365227537923 and loss 2.5550925226 :\n",
      "Parameters with this testing accuracy 0.284210526316 and loss 2.59766458989 :\n",
      "{'alpha': 2.1013534765268824}\n",
      "Parameters with this training accuracy 0.375729288215 and loss 2.5025085551 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.55437614301 :\n",
      "{'alpha': 0.7779850150485925}\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.53405671367 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.58039967891 :\n",
      "{'alpha': 1.475430994117608}\n",
      "Parameters with this training accuracy 0.372228704784 and loss 2.54257131092 :\n",
      "Parameters with this testing accuracy 0.305263157895 and loss 2.58739762616 :\n",
      "{'alpha': 1.7105830102470527}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hyperopt: 100%|██████████| 1000/1000 [00:11<00:00, 89.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters with this training accuracy 0.38739789965 and loss 2.45803271591 :\n",
      "Parameters with this testing accuracy 0.315789473684 and loss 2.51749096576 :\n",
      "{'alpha': 0.14159687920802722}\n",
      "------------------------------------\n",
      "('The best hyperparameters are: ', '\\n')\n",
      "{'alpha': 1.1617986567613996}\n",
      "Time elapsed to optimize 1000 executions: 11.2216258049\n",
      "\n",
      " Best score:\n",
      "Parameters with this training accuracy 0.374562427071 and loss 2.52123860174 :\n",
      "Parameters with this testing accuracy 0.326315789474 and loss 2.56984255026 :\n",
      "{'alpha': 1.1617986567613996}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': -0.3263157894736842, 'status': 'ok'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "print('\\n Best score:')\n",
    "score(best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the best params and train the model\n",
    "nb_opt = MultinomialNB(alpha=best_param['alpha'])\n",
    "\n",
    "fitted_model = nb_opt.fit(X=X_train,y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06899012, 0.07541887, 0.06950849, ..., 0.06940148, 0.05099859,\n",
       "        0.06085017],\n",
       "       [0.06563856, 0.06004452, 0.06901421, ..., 0.05840706, 0.05427233,\n",
       "        0.06481683],\n",
       "       [0.07129024, 0.06214119, 0.06941476, ..., 0.05795263, 0.04466088,\n",
       "        0.06822891],\n",
       "       ...,\n",
       "       [0.07491715, 0.04976768, 0.06036857, ..., 0.04997647, 0.06596886,\n",
       "        0.0588598 ],\n",
       "       [0.07305712, 0.04796289, 0.05981537, ..., 0.04723125, 0.06740287,\n",
       "        0.06001657],\n",
       "       [0.052176  , 0.08563592, 0.07124004, ..., 0.07369115, 0.03873482,\n",
       "        0.06897876]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_score = fitted_model.predict_proba(X_test)\n",
    "y_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3263157894736842"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, fitted_model.predict(X_test))\n",
    "test_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "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": []
  }
 ],
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