GridSearchCV(cv=3,\n",
+ " estimator=Pipeline(steps=[('vect',\n",
+ " CountVectorizer(max_features=1000,\n",
+ " stop_words='english')),\n",
+ " ('tfidf', TfidfTransformer()),\n",
+ " ('clf',\n",
+ " KNeighborsClassifier(n_neighbors=1))]),\n",
+ " n_jobs=-1,\n",
+ " param_grid={'clf__n_neighbors': (1, 3, 5, 7),\n",
+ " 'tfidf__use_idf': (True, False),\n",
+ " 'vect__max_features': (100, 500, 1000, 5000, 10000),\n",
+ " 'vect__stop_words': ('english', None)},\n",
+ " scoring='f1_weighted') In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " estimator\n",
+ " estimator: estimator object \n",
+ " \n",
+ " \n",
+ " Pipeline(step...eighbors=1))]) \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " param_grid\n",
+ " param_grid: dict or list of dictionaries \n",
+ " \n",
+ " \n",
+ " {'clf__n_neighbors': (1, ...), 'tfidf__use_idf': (True, ...), 'vect__max_features': (100, ...), 'vect__stop_words': ('english', ...)} \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " scoring\n",
+ " scoring: str, callable, list, tuple or dict, default=None` is used. \n",
+ " \n",
+ " \n",
+ " 'f1_weighted' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " n_jobs\n",
+ " n_jobs: int, default=None` \n",
+ " \n",
+ " \n",
+ " -1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " refit\n",
+ " refit: bool, str, or callable, default=True` \n",
+ " \n",
+ " \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " cv\n",
+ " cv: int, cross-validation generator or an iterable, default=None` for the various \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " verbose\n",
+ " verbose: int \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " pre_dispatch\n",
+ " pre_dispatch: int, or str, default='2*n_jobs' \n",
+ " \n",
+ " \n",
+ " '2*n_jobs' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " error_score\n",
+ " error_score: 'raise' or numeric, default=np.nan \n",
+ " \n",
+ " \n",
+ " nan \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " return_train_score\n",
+ " return_train_score: bool, default=False \n",
+ " \n",
+ " \n",
+ " False \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " input\n",
+ " input: {'filename', 'file', 'content'}, default='content' \n",
+ " \n",
+ " \n",
+ " 'content' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " encoding\n",
+ " encoding: str, default='utf-8' \n",
+ " \n",
+ " \n",
+ " 'utf-8' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " decode_error\n",
+ " decode_error: {'strict', 'ignore', 'replace'}, default='strict' \n",
+ " \n",
+ " \n",
+ " 'strict' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " strip_accents\n",
+ " strip_accents: {'ascii', 'unicode'} or callable, default=None \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " lowercase\n",
+ " lowercase: bool, default=True \n",
+ " \n",
+ " \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " preprocessor\n",
+ " preprocessor: callable, default=None \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " tokenizer\n",
+ " tokenizer: callable, default=None \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " stop_words\n",
+ " stop_words: {'english'}, list, default=None \n",
+ " \n",
+ " \n",
+ " 'english' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " token_pattern\n",
+ " token_pattern: str or None, default=r\"(?u)\\\\b\\\\w\\\\w+\\\\b\" \n",
+ " \n",
+ " \n",
+ " '(?u)\\\\b\\\\w\\\\w+\\\\b' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " ngram_range\n",
+ " ngram_range: tuple (min_n, max_n), default=(1, 1) \n",
+ " \n",
+ " \n",
+ " (1, ...) \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " analyzer\n",
+ " analyzer: {'word', 'char', 'char_wb'} or callable, default='word' \n",
+ " \n",
+ " \n",
+ " 'word' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " max_df\n",
+ " max_df: float in range [0.0, 1.0] or int, default=1.0 \n",
+ " \n",
+ " \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " min_df\n",
+ " min_df: float in range [0.0, 1.0] or int, default=1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " max_features\n",
+ " max_features: int, default=None \n",
+ " \n",
+ " \n",
+ " 100 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " vocabulary\n",
+ " vocabulary: Mapping or iterable, default=None \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " binary\n",
+ " binary: bool, default=False \n",
+ " \n",
+ " \n",
+ " False \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " dtype\n",
+ " dtype: dtype, default=np.int64 \n",
+ " \n",
+ " \n",
+ " <class 'numpy.int64'> \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " norm\n",
+ " norm: {'l1', 'l2'} or None, default='l2' \n",
+ " \n",
+ " \n",
+ " 'l2' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " use_idf\n",
+ " use_idf: bool, default=True \n",
+ " \n",
+ " \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " smooth_idf\n",
+ " smooth_idf: bool, default=True \n",
+ " \n",
+ " \n",
+ " True \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " sublinear_tf\n",
+ " sublinear_tf: bool, default=False \n",
+ " \n",
+ " \n",
+ " False \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " n_neighbors\n",
+ " n_neighbors: int, default=5 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " weights\n",
+ " weights: {'uniform', 'distance'}, callable or None, default='uniform' \n",
+ " \n",
+ " \n",
+ " 'uniform' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " algorithm\n",
+ " algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' \n",
+ " \n",
+ " \n",
+ " 'auto' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " leaf_size\n",
+ " leaf_size: int, default=30 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " p\n",
+ " p: float, default=2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " metric\n",
+ " metric: str or callable, default='minkowski'`_ and \n",
+ " \n",
+ " \n",
+ " 'minkowski' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " metric_params\n",
+ " metric_params: dict, default=None \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " n_jobs\n",
+ " n_jobs: int, default=None` \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "GridSearchCV(cv=3,\n",
+ " estimator=Pipeline(steps=[('vect',\n",
+ " CountVectorizer(max_features=1000,\n",
+ " stop_words='english')),\n",
+ " ('tfidf', TfidfTransformer()),\n",
+ " ('clf',\n",
+ " KNeighborsClassifier(n_neighbors=1))]),\n",
+ " n_jobs=-1,\n",
+ " param_grid={'clf__n_neighbors': (1, 3, 5, 7),\n",
+ " 'tfidf__use_idf': (True, False),\n",
+ " 'vect__max_features': (100, 500, 1000, 5000, 10000),\n",
+ " 'vect__stop_words': ('english', None)},\n",
+ " scoring='f1_weighted')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gs_clf.fit(twenty_train.data, twenty_train.target)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'}"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gs_clf.best_params_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'mean_fit_time': array([0.14711912, 0.1838789 , 0.16524474, 0.13485821, 0.17077629,\n",
+ " 0.171736 , 0.13478573, 0.17135119, 0.1455044 , 0.16278481,\n",
+ " 0.14029678, 0.17597159, 0.14752992, 0.1515433 , 0.2238245 ,\n",
+ " 0.16685406, 0.19492237, 0.18400868, 0.1637164 , 0.23180572,\n",
+ " 0.13470483, 0.19008255, 0.168998 , 0.14402525, 0.14959741,\n",
+ " 0.21908172, 0.12458984, 0.17676258, 0.181101 , 0.15396865,\n",
+ " 0.13980309, 0.17127609, 0.12496074, 0.21157686, 0.14910388,\n",
+ " 0.16302236, 0.20699684, 0.14392487, 0.13715124, 0.19204704,\n",
+ " 0.12343788, 0.202528 , 0.1306417 , 0.14448158, 0.16433088,\n",
+ " 0.17828568, 0.15127762, 0.16031146, 0.15140502, 0.17451549,\n",
+ " 0.13383325, 0.19785619, 0.14872464, 0.19805606, 0.17880678,\n",
+ " 0.14933427, 0.18098283, 0.17271209, 0.19098576, 0.15773439,\n",
+ " 0.14277951, 0.14034979, 0.17362897, 0.14588912, 0.15714224,\n",
+ " 0.19136397, 0.14665333, 0.18524861, 0.1639146 , 0.15949496,\n",
+ " 0.14914314, 0.19116807, 0.16394456, 0.17290258, 0.14597694,\n",
+ " 0.18869273, 0.17521318, 0.18450022, 0.15120141, 0.14013394]),\n",
+ " 'std_fit_time': array([0.0426388 , 0.00865779, 0.03047002, 0.01959866, 0.01996265,\n",
+ " 0.03991559, 0.02089307, 0.0115939 , 0.01577824, 0.01259437,\n",
+ " 0.02861695, 0.02803293, 0.02632727, 0.03513436, 0.10327593,\n",
+ " 0.06408524, 0.04651852, 0.03295957, 0.0471307 , 0.11679622,\n",
+ " 0.0110411 , 0.02803024, 0.05824908, 0.01165112, 0.01597652,\n",
+ " 0.06360679, 0.0225909 , 0.03841467, 0.06331909, 0.02901401,\n",
+ " 0.02955105, 0.00180821, 0.01985555, 0.0523942 , 0.03268085,\n",
+ " 0.03894983, 0.06517167, 0.02243237, 0.02200533, 0.0133168 ,\n",
+ " 0.02014168, 0.05538047, 0.00691204, 0.02019455, 0.04787927,\n",
+ " 0.02202076, 0.03367995, 0.04032929, 0.04035338, 0.05086764,\n",
+ " 0.03783577, 0.02842441, 0.01027949, 0.02261441, 0.07413437,\n",
+ " 0.03057069, 0.07406925, 0.06374613, 0.07499723, 0.00702891,\n",
+ " 0.01449466, 0.01535535, 0.06266348, 0.01594578, 0.03962651,\n",
+ " 0.03872874, 0.02924308, 0.03921288, 0.03093211, 0.02737551,\n",
+ " 0.03913621, 0.03922343, 0.06958397, 0.0321589 , 0.03550322,\n",
+ " 0.06330303, 0.04521662, 0.01877706, 0.01843914, 0.02003679]),\n",
+ " 'mean_score_time': array([0.07024606, 0.09829744, 0.06123559, 0.0934546 , 0.06724668,\n",
+ " 0.08956035, 0.06278229, 0.08930341, 0.07657051, 0.08451796,\n",
+ " 0.06686974, 0.06430848, 0.06159838, 0.06787952, 0.0685486 ,\n",
+ " 0.08175969, 0.07736111, 0.08548617, 0.10391927, 0.11483145,\n",
+ " 0.06140908, 0.08571712, 0.05673496, 0.07094908, 0.07356191,\n",
+ " 0.08419561, 0.06646315, 0.07401323, 0.0662907 , 0.07332158,\n",
+ " 0.06078386, 0.06057103, 0.05959137, 0.07239731, 0.06452703,\n",
+ " 0.06926155, 0.07553283, 0.07713358, 0.06963356, 0.09399152,\n",
+ " 0.05499752, 0.06101354, 0.06951396, 0.06801232, 0.06193964,\n",
+ " 0.08509183, 0.06340122, 0.09640757, 0.09270819, 0.07950346,\n",
+ " 0.06135122, 0.07454085, 0.06276194, 0.08477139, 0.06570975,\n",
+ " 0.06936661, 0.07554563, 0.08220371, 0.06743741, 0.10737284,\n",
+ " 0.06237586, 0.06374478, 0.07077082, 0.06676245, 0.06271029,\n",
+ " 0.10701195, 0.07044252, 0.07724508, 0.0787247 , 0.07522114,\n",
+ " 0.06066545, 0.08716504, 0.06308579, 0.08094724, 0.08140651,\n",
+ " 0.07310184, 0.07593838, 0.07488211, 0.06320556, 0.04453659]),\n",
+ " 'std_score_time': array([0.0307069 , 0.0182529 , 0.02101209, 0.03205303, 0.0257132 ,\n",
+ " 0.02687809, 0.0173987 , 0.02640571, 0.01539859, 0.01465485,\n",
+ " 0.02122433, 0.01356409, 0.00834315, 0.01751938, 0.01171655,\n",
+ " 0.03144068, 0.02179221, 0.01969381, 0.04494128, 0.00622263,\n",
+ " 0.01579416, 0.02897917, 0.01304806, 0.0202541 , 0.02151299,\n",
+ " 0.03211671, 0.01358637, 0.01938508, 0.01422302, 0.01688134,\n",
+ " 0.00869969, 0.0141093 , 0.01194079, 0.01273514, 0.01648673,\n",
+ " 0.01545342, 0.01899566, 0.0178712 , 0.02332053, 0.03043117,\n",
+ " 0.01200954, 0.01238742, 0.02325767, 0.01629294, 0.01778484,\n",
+ " 0.03084458, 0.0157806 , 0.04616991, 0.02142426, 0.01231431,\n",
+ " 0.0059662 , 0.01826184, 0.02046084, 0.02321078, 0.00858559,\n",
+ " 0.01727098, 0.01823882, 0.02170296, 0.0142323 , 0.06476885,\n",
+ " 0.02283514, 0.01249623, 0.00957304, 0.01621181, 0.00941332,\n",
+ " 0.04093458, 0.01894285, 0.0226533 , 0.02176937, 0.01821806,\n",
+ " 0.01055958, 0.03213191, 0.00837758, 0.02668343, 0.03007748,\n",
+ " 0.01731415, 0.01637027, 0.01601808, 0.02890825, 0.01039945]),\n",
+ " 'param_clf__n_neighbors': masked_array(data=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+ " 3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
+ " 5, 5, 5, 5, 5, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
+ " 7, 7, 7, 7, 7, 7, 7, 7],\n",
+ " mask=[False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False],\n",
+ " fill_value=999999),\n",
+ " 'param_tfidf__use_idf': masked_array(data=[True, True, True, True, True, True, True, True, True,\n",
+ " True, False, False, False, False, False, False, False,\n",
+ " False, False, False, True, True, True, True, True,\n",
+ " True, True, True, True, True, False, False, False,\n",
+ " False, False, False, False, False, False, False, True,\n",
+ " True, True, True, True, True, True, True, True, True,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, True, True, True, True, True, True, True,\n",
+ " True, True, True, False, False, False, False, False,\n",
+ " False, False, False, False, False],\n",
+ " mask=[False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False],\n",
+ " fill_value=True),\n",
+ " 'param_vect__max_features': masked_array(data=[100, 100, 500, 500, 1000, 1000, 5000, 5000, 10000,\n",
+ " 10000, 100, 100, 500, 500, 1000, 1000, 5000, 5000,\n",
+ " 10000, 10000, 100, 100, 500, 500, 1000, 1000, 5000,\n",
+ " 5000, 10000, 10000, 100, 100, 500, 500, 1000, 1000,\n",
+ " 5000, 5000, 10000, 10000, 100, 100, 500, 500, 1000,\n",
+ " 1000, 5000, 5000, 10000, 10000, 100, 100, 500, 500,\n",
+ " 1000, 1000, 5000, 5000, 10000, 10000, 100, 100, 500,\n",
+ " 500, 1000, 1000, 5000, 5000, 10000, 10000, 100, 100,\n",
+ " 500, 500, 1000, 1000, 5000, 5000, 10000, 10000],\n",
+ " mask=[False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False],\n",
+ " fill_value=999999),\n",
+ " 'param_vect__stop_words': masked_array(data=['english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None, 'english', None, 'english', None,\n",
+ " 'english', None],\n",
+ " mask=[False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, False, False],\n",
+ " fill_value=np.str_('?'),\n",
+ " dtype=object),\n",
+ " 'params': [{'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 1,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 3,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 5,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': True,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 100,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 500,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 1000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 5000,\n",
+ " 'vect__stop_words': None},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': 'english'},\n",
+ " {'clf__n_neighbors': 7,\n",
+ " 'tfidf__use_idf': False,\n",
+ " 'vect__max_features': 10000,\n",
+ " 'vect__stop_words': None}],\n",
+ " 'split0_test_score': array([0.76817583, 0.72894636, 0.53358157, 0.6374826 , 0.52798315,\n",
+ " 0.56519764, 0.51309075, 0.49836392, 0.49549296, 0.48037932,\n",
+ " 0.74678068, 0.6781238 , 0.53815493, 0.67256907, 0.59194787,\n",
+ " 0.61495561, 0.50607973, 0.58348819, 0.48282372, 0.56647032,\n",
+ " 0.78847084, 0.7252709 , 0.48067615, 0.52118859, 0.51721694,\n",
+ " 0.48760057, 0.51577841, 0.46346843, 0.43209944, 0.42022184,\n",
+ " 0.79654539, 0.72168041, 0.50540586, 0.62161402, 0.53994855,\n",
+ " 0.57362945, 0.53619645, 0.54414205, 0.43884702, 0.4762766 ,\n",
+ " 0.75153416, 0.72862518, 0.45804881, 0.49005168, 0.52767212,\n",
+ " 0.49479873, 0.48059474, 0.45872108, 0.43320814, 0.46831129,\n",
+ " 0.77799622, 0.71691323, 0.468479 , 0.58540742, 0.53125944,\n",
+ " 0.55188163, 0.53165214, 0.49549296, 0.46225076, 0.46334516,\n",
+ " 0.73731272, 0.722074 , 0.42768101, 0.49765146, 0.48090102,\n",
+ " 0.59104363, 0.51802701, 0.48304986, 0.44659485, 0.52984156,\n",
+ " 0.76674346, 0.71632941, 0.42110969, 0.55322776, 0.49049555,\n",
+ " 0.55175372, 0.55288072, 0.52752419, 0.47634782, 0.48746189]),\n",
+ " 'split1_test_score': array([0.79662124, 0.74654727, 0.55840557, 0.65284461, 0.56848052,\n",
+ " 0.58959475, 0.50824228, 0.52616761, 0.55281361, 0.53900106,\n",
+ " 0.79198638, 0.74396927, 0.60547906, 0.71852445, 0.55601273,\n",
+ " 0.70906678, 0.55288072, 0.70132316, 0.54064496, 0.72369393,\n",
+ " 0.8200291 , 0.76394456, 0.50142662, 0.65973942, 0.57835078,\n",
+ " 0.58411319, 0.56231765, 0.54929577, 0.53755372, 0.54631601,\n",
+ " 0.76828781, 0.73851721, 0.57218138, 0.70341759, 0.54466558,\n",
+ " 0.70088681, 0.52052675, 0.68262612, 0.56159728, 0.69962829,\n",
+ " 0.78081641, 0.76676231, 0.47784768, 0.65979122, 0.55877981,\n",
+ " 0.58351195, 0.55934603, 0.5526496 , 0.53805724, 0.54275916,\n",
+ " 0.7590099 , 0.74428157, 0.50962843, 0.69741916, 0.56721274,\n",
+ " 0.6845754 , 0.53674839, 0.65880645, 0.56642635, 0.66528626,\n",
+ " 0.75249835, 0.76114625, 0.5033107 , 0.61728125, 0.54473029,\n",
+ " 0.54902822, 0.55716104, 0.55466537, 0.51280439, 0.54540585,\n",
+ " 0.72900844, 0.73304581, 0.49250319, 0.69558085, 0.57262651,\n",
+ " 0.66997019, 0.54339107, 0.66489176, 0.56953075, 0.65745565]),\n",
+ " 'split2_test_score': array([0.82526209, 0.7519849 , 0.59510748, 0.68219353, 0.5764876 ,\n",
+ " 0.6305322 , 0.54587929, 0.56161317, 0.52387661, 0.56501726,\n",
+ " 0.82242212, 0.71247211, 0.60517653, 0.7037331 , 0.54624902,\n",
+ " 0.68817364, 0.5608424 , 0.65146866, 0.53526413, 0.66897713,\n",
+ " 0.81950927, 0.79423613, 0.52825543, 0.66907312, 0.56248767,\n",
+ " 0.60389162, 0.49652131, 0.55180864, 0.50857805, 0.52741727,\n",
+ " 0.80244926, 0.69231892, 0.53887943, 0.71969825, 0.53158497,\n",
+ " 0.6792325 , 0.49764733, 0.65713584, 0.49078794, 0.66897713,\n",
+ " 0.80172943, 0.75452358, 0.54031316, 0.63620099, 0.52529445,\n",
+ " 0.59626871, 0.51327659, 0.53187959, 0.50624241, 0.51743203,\n",
+ " 0.76711749, 0.70123506, 0.49774614, 0.71460958, 0.50316727,\n",
+ " 0.65013293, 0.49732848, 0.64249816, 0.52623393, 0.65444272,\n",
+ " 0.76791783, 0.73768667, 0.53419234, 0.63714302, 0.50165182,\n",
+ " 0.5932877 , 0.49723701, 0.51859739, 0.49993588, 0.52478814,\n",
+ " 0.76415908, 0.69550001, 0.48572333, 0.68222059, 0.50972411,\n",
+ " 0.66549536, 0.47022833, 0.64386329, 0.50648577, 0.6415092 ]),\n",
+ " 'mean_test_score': array([0.79668639, 0.74249284, 0.56236487, 0.65750691, 0.55765042,\n",
+ " 0.5951082 , 0.52240411, 0.5287149 , 0.52406106, 0.52813255,\n",
+ " 0.78706306, 0.71152173, 0.58293684, 0.69827554, 0.56473654,\n",
+ " 0.67073201, 0.53993428, 0.64542667, 0.5195776 , 0.65304713,\n",
+ " 0.8093364 , 0.76115053, 0.50345273, 0.61666704, 0.55268513,\n",
+ " 0.55853513, 0.52487246, 0.52152428, 0.49274373, 0.49798504,\n",
+ " 0.78909415, 0.71750551, 0.53882222, 0.68157662, 0.53873303,\n",
+ " 0.65124959, 0.51812351, 0.627968 , 0.49707742, 0.61496068,\n",
+ " 0.77802667, 0.74997036, 0.49206988, 0.59534796, 0.53724879,\n",
+ " 0.55819313, 0.51773912, 0.51441676, 0.4925026 , 0.50950083,\n",
+ " 0.7680412 , 0.72080995, 0.49195119, 0.66581205, 0.53387982,\n",
+ " 0.62886332, 0.52190967, 0.59893252, 0.51830368, 0.59435805,\n",
+ " 0.7525763 , 0.74030231, 0.48839468, 0.58402524, 0.50909438,\n",
+ " 0.57778652, 0.52414169, 0.51877087, 0.48644504, 0.53334518,\n",
+ " 0.75330366, 0.71495841, 0.4664454 , 0.6436764 , 0.52428206,\n",
+ " 0.62907309, 0.52216671, 0.61209308, 0.51745478, 0.59547558]),\n",
+ " 'std_test_score': array([0.02330541, 0.00983268, 0.02527339, 0.01854849, 0.02123109,\n",
+ " 0.02695613, 0.01671706, 0.02588415, 0.02340142, 0.03539763,\n",
+ " 0.0310761 , 0.0268897 , 0.03166583, 0.01915399, 0.01964985,\n",
+ " 0.04035167, 0.02415844, 0.04829527, 0.02608159, 0.06516717,\n",
+ " 0.01475571, 0.02822417, 0.01947692, 0.06762091, 0.02590243,\n",
+ " 0.05080407, 0.02762023, 0.0410645 , 0.04448367, 0.05552553,\n",
+ " 0.01490843, 0.01909001, 0.02726102, 0.04291775, 0.00540887,\n",
+ " 0.05559311, 0.0158291 , 0.06018046, 0.05030954, 0.09885959,\n",
+ " 0.02058686, 0.01589883, 0.03505766, 0.07507599, 0.01525564,\n",
+ " 0.04512812, 0.03230456, 0.04028527, 0.04389321, 0.0309063 ,\n",
+ " 0.0077786 , 0.01778837, 0.01729171, 0.05728616, 0.02621202,\n",
+ " 0.05622103, 0.0175056 , 0.07344521, 0.04289759, 0.09274581,\n",
+ " 0.0124946 , 0.01605805, 0.04474395, 0.06161139, 0.0265843 ,\n",
+ " 0.02035581, 0.02484303, 0.02923717, 0.02866389, 0.00877417,\n",
+ " 0.01721168, 0.01535864, 0.03217646, 0.064189 , 0.03507444,\n",
+ " 0.05470356, 0.03692975, 0.06041232, 0.03882443, 0.07665416]),\n",
+ " 'rank_test_score': array([ 2, 11, 41, 21, 44, 35, 58, 52, 57, 53, 4, 16, 38, 17, 40, 19, 46,\n",
+ " 24, 62, 22, 1, 7, 71, 29, 45, 42, 54, 61, 74, 72, 3, 14, 47, 18,\n",
+ " 48, 23, 65, 28, 73, 30, 5, 10, 76, 34, 49, 43, 66, 68, 75, 69, 6,\n",
+ " 13, 77, 20, 50, 27, 60, 32, 64, 36, 9, 12, 78, 37, 70, 39, 56, 63,\n",
+ " 79, 51, 8, 15, 80, 25, 55, 26, 59, 31, 67, 33], dtype=int32)}"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gs_clf.cv_results_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(0.8093364049132378)"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gs_clf.best_score_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Word embedding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Импортируем библиотеку gensim и посмотрим какие обученные модели в ней есть.\n",
+ "import gensim.downloader\n",
+ "print(list(gensim.downloader.info()['models'].keys()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## GloVe\n",
+ "Загрузим \"glove-twitter-25\" и будем работать с ней"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[==================================================] 100.0% 104.8/104.8MB downloaded\n"
+ ]
+ }
+ ],
+ "source": [
+ "glove_model = gensim.downloader.load(\"glove-twitter-25\") # load glove vectors\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-0.96419 -0.60978 0.67449 0.35113 0.41317 -0.21241 1.3796\n",
+ " 0.12854 0.31567 0.66325 0.3391 -0.18934 -3.325 -1.1491\n",
+ " -0.4129 0.2195 0.8706 -0.50616 -0.12781 -0.066965 0.065761\n",
+ " 0.43927 0.1758 -0.56058 0.13529 ]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[('dog', 0.9590820074081421),\n",
+ " ('monkey', 0.920357882976532),\n",
+ " ('bear', 0.9143136739730835),\n",
+ " ('pet', 0.9108031392097473),\n",
+ " ('girl', 0.8880629539489746),\n",
+ " ('horse', 0.8872726559638977),\n",
+ " ('kitty', 0.8870542049407959),\n",
+ " ('puppy', 0.886769711971283),\n",
+ " ('hot', 0.886525571346283),\n",
+ " ('lady', 0.8845519423484802)]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Можем посмотреть, что за векторы у слова \"cat\"\n",
+ "print(glove_model['cat']) # word embedding for 'cat'\n",
+ "glove_model.most_similar(\"cat\") # show words that similar to word 'cat'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Векторизуем обучающую выборку\n",
+ "Получаем матрицу \"Документ-термин\". В столбцах - все слова выборки, в строках - каждый документ. Значения в ячейках - количество встречаний слова в документе. \n",
+ "В отображенной части видим только нули - наглядная демонстрация проблем разреженности матрицы"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1064, 16170)\n"
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+ "source": [
+ "vectorizer = CountVectorizer(stop_words='english')\n",
+ "train_data = vectorizer.fit_transform(twenty_train['data'])\n",
+ "CV_data=pd.DataFrame(train_data.toarray(), columns=vectorizer.get_feature_names_out())\n",
+ "print(CV_data.shape)\n",
+ "CV_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['00', '000', '000005102000', '000100255pixel', '0007', '000usd',\n",
+ " '001200201pixel', '00196', '0028', '0038'],\n",
+ " dtype='str')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Создадим список уникальных слов, присутствующих в словаре.\n",
+ "words_vocab=CV_data.columns\n",
+ "print(words_vocab[0:10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Векторизуем с помощью GloVe\n",
+ "\n",
+ "Нужно для каждого документа сложить glove-вектора слов, из которых он состоит.\n",
+ "В результате получим вектор документа как сумму векторов слов, из него состоящих"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Посмотрим на примере как будет работать векторизация"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ ],
+ "source": [
+ "# Пусть выборка состоит из двух документов: \n",
+ "text_data = ['Hello world I love python', 'This is a great computer game! 00 000 zyxel']\n",
+ "# Векторизуем с помощью обученного CountVectorizer\n",
+ "X = vectorizer.transform(text_data)\n",
+ "CV_text_data=pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())\n",
+ "CV_text_data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hello : [-0.77069 0.12827 0.33137 0.0050893 -0.47605 -0.50116\n",
+ " 1.858 1.0624 -0.56511 0.13328 -0.41918 -0.14195\n",
+ " -2.8555 -0.57131 -0.13418 -0.44922 0.48591 -0.6479\n",
+ " -0.84238 0.61669 -0.19824 -0.57967 -0.65885 0.43928\n",
+ " -0.50473 ]\n",
+ "love : [-0.62645 -0.082389 0.070538 0.5782 -0.87199 -0.14816 2.2315\n",
+ " 0.98573 -1.3154 -0.34921 -0.8847 0.14585 -4.97 -0.73369\n",
+ " -0.94359 0.035859 -0.026733 -0.77538 -0.30014 0.48853 -0.16678\n",
+ " -0.016651 -0.53164 0.64236 -0.10922 ]\n",
+ "python : [-0.25645 -0.22323 0.025901 0.22901 0.49028 -0.060829 0.24563\n",
+ " -0.84854 1.5882 -0.7274 0.60603 0.25205 -1.8064 -0.95526\n",
+ " 0.44867 0.013614 0.60856 0.65423 0.82506 0.99459 -0.29403\n",
+ " -0.27013 -0.348 -0.7293 0.2201 ]\n",
+ "world : [ 0.10301 0.095666 -0.14789 -0.22383 -0.14775 -0.11599 1.8513\n",
+ " 0.24886 -0.41877 -0.20384 -0.08509 0.33246 -4.6946 0.84096\n",
+ " -0.46666 -0.031128 -0.19539 -0.037349 0.58949 0.13941 -0.57667\n",
+ " -0.44426 -0.43085 -0.52875 0.25855 ]\n",
+ "Hello world I love python : [ -1.55058002 -0.081683 0.27991899 0.58846928 -1.00551002\n",
+ " -0.82613902 6.18642995 1.44844997 -0.71108004 -1.14717001\n",
+ " -0.78294002 0.58841 -14.32649982 -1.41929996 -1.09575997\n",
+ " -0.430875 0.87234702 -0.806399 0.27203003 2.23921998\n",
+ " -1.23571999 -1.31071102 -1.96934 -0.17641005 -0.1353 ]\n",
+ "computer : [ 0.64005 -0.019514 0.70148 -0.66123 1.1723 -0.58859 0.25917\n",
+ " -0.81541 1.1708 1.1413 -0.15405 -0.11369 -3.8414 -0.87233\n",
+ " 0.47489 1.1541 0.97678 1.1107 -0.14572 -0.52013 -0.52234\n",
+ " -0.92349 0.34651 0.061939 -0.57375 ]\n",
+ "game : [ 1.146 0.3291 0.26878 -1.3945 -0.30044 0.77901 1.3537\n",
+ " 0.37393 0.50478 -0.44266 -0.048706 0.51396 -4.3136 0.39805\n",
+ " 1.197 0.10287 -0.17618 -1.2881 -0.59801 0.26131 -1.2619\n",
+ " 0.39202 0.59309 -0.55232 0.005087]\n",
+ "great : [-8.4229e-01 3.6512e-01 -3.8841e-01 -4.6118e-01 2.4301e-01 3.2412e-01\n",
+ " 1.9009e+00 -2.2630e-01 -3.1335e-01 -1.0970e+00 -4.1494e-03 6.2074e-01\n",
+ " -5.0964e+00 6.7418e-01 5.0080e-01 -6.2119e-01 5.1765e-01 -4.4122e-01\n",
+ " -1.4364e-01 1.9130e-01 -7.4608e-01 -2.5903e-01 -7.8010e-01 1.1030e-01\n",
+ " -2.7928e-01]\n",
+ "zyxel : [ 0.79234 0.067376 -0.22639 -2.2272 0.30057 -0.85676 -1.7268\n",
+ " -0.78626 1.2042 -0.92348 -0.83987 -0.74233 0.29689 -1.208\n",
+ " 0.98706 -1.1624 0.61415 -0.27825 0.27813 1.5838 -0.63593\n",
+ " -0.10225 1.7102 -0.95599 -1.3867 ]\n",
+ "This is a great computer game! 00 000 zyxel : [ 1.73610002 0.74208201 0.35545996 -4.74411008 1.41543998\n",
+ " -0.34222007 1.78697008 -1.45404002 2.56643 -1.32184002\n",
+ " -1.04677537 0.27867999 -12.95450976 -1.00809997 3.15975004\n",
+ " -0.52662008 1.93239999 -0.89686999 -0.60924001 1.51628\n",
+ " -3.16624993 -0.89275002 1.86969995 -1.33607102 -2.23464306]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Создадим датафрейм, в который будем сохранять вектор документа\n",
+ "glove_data=pd.DataFrame()\n",
+ "\n",
+ "# Пробегаем по каждой строке датафрейма (по каждому документу)\n",
+ "for i in range(CV_text_data.shape[0]):\n",
+ " \n",
+ " # Вектор одного документа с размерностью glove-модели:\n",
+ " one_doc = np.zeros(25) \n",
+ " \n",
+ " # Пробегаемся по каждому документу, смотрим, какие слова документа присутствуют в нашем словаре\n",
+ " # Суммируем glove-вектора каждого известного слова в one_doc\n",
+ " for word in words_vocab[CV_text_data.iloc[i,:] >= 1]:\n",
+ " if word in glove_model.key_to_index.keys(): \n",
+ " print(word, ': ', glove_model[word])\n",
+ " one_doc += glove_model[word]\n",
+ " print(text_data[i], ': ', one_doc)\n",
+ " glove_data= pd.concat([glove_data, pd.DataFrame([one_doc])], ignore_index=True) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "На основе ячейки выше, напишем функцию, которая для каждого слова в тексте ищет это слово в glove_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def text2vec(text_data):\n",
+ " \n",
+ " # Векторизуем с помощью обученного CountVectorizer\n",
+ " X = vectorizer.transform(text_data)\n",
+ " CV_text_data=pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())\n",
+ " CV_text_data\n",
+ " # Создадим датафрейм, в который будем сохранять вектор документа\n",
+ " glove_data=pd.DataFrame()\n",
+ "\n",
+ " # Пробегаем по каждой строке (по каждому документу)\n",
+ " for i in range(CV_text_data.shape[0]):\n",
+ "\n",
+ " # Вектор одного документа с размерностью glove-модели:\n",
+ " one_doc = np.zeros(25) \n",
+ "\n",
+ " # Пробегаемся по каждому документу, смотрим, какие слова документа присутствуют в нашем словаре\n",
+ " # Суммируем glove-вектора каждого известного слова в one_doc\n",
+ " for word in words_vocab[CV_text_data.iloc[i,:] >= 1]:\n",
+ " if word in glove_model.key_to_index.keys(): \n",
+ " #print(word, ': ', glove_model[word])\n",
+ " one_doc += glove_model[word]\n",
+ " #print(text_data[i], ': ', one_doc)\n",
+ " glove_data = pd.concat([glove_data, pd.DataFrame([one_doc])], axis = 0)\n",
+ " #print('glove_data: ', glove_data)\n",
+ " return glove_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ },
+ "execution_count": 47,
+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "# Наша выборка, векторизованная через glove выглядит так. \n",
+ "# Попытаться понять, почему такая размерность матрицы и что за значения в ячейках\n",
+ "glove_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Можем использовать написанную функцию на нашей реальной выборке\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
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+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_data_glove = text2vec(twenty_train['data'])\n",
+ "train_data_glove"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Мы получили матрицу, где каждый документ представлен вектором-строкой. Можем подавать эти вектора на вход для обучения классифкатора"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "
KNeighborsClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \n",
+ "
\n",
+ "
\n",
+ " Parameters \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " n_neighbors\n",
+ " n_neighbors: int, default=5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " weights\n",
+ " weights: {'uniform', 'distance'}, callable or None, default='uniform' \n",
+ " \n",
+ " \n",
+ " 'uniform' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " algorithm\n",
+ " algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' \n",
+ " \n",
+ " \n",
+ " 'auto' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " leaf_size\n",
+ " leaf_size: int, default=30 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " p\n",
+ " p: float, default=2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " metric\n",
+ " metric: str or callable, default='minkowski'`_ and \n",
+ " \n",
+ " \n",
+ " 'minkowski' \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " metric_params\n",
+ " metric_params: dict, default=None \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " n_jobs\n",
+ " n_jobs: int, default=None` \n",
+ " \n",
+ " \n",
+ " None \n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "KNeighborsClassifier()"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "clf = KNeighborsClassifier(n_neighbors = 5)\n",
+ "clf.fit(train_data_glove, twenty_train['target'])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Аналогичную операци проводим с тестовой частью выборки и оцениваем качество модели"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "[708 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_data_glove = text2vec(twenty_test['data'])\n",
+ "test_data_glove"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "predict = clf.predict(test_data_glove )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[258 61]\n",
+ " [ 31 358]]\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.89 0.81 0.85 319\n",
+ " 1 0.85 0.92 0.89 389\n",
+ "\n",
+ " accuracy 0.87 708\n",
+ " macro avg 0.87 0.86 0.87 708\n",
+ "weighted avg 0.87 0.87 0.87 708\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (confusion_matrix(twenty_test['target'], predict))\n",
+ "print(classification_report(twenty_test['target'], predict))"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -410,7 +4822,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -424,7 +4836,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.13"
+ "version": "3.12.3"
}
},
"nbformat": 4,