examples/demo_top_k_voting_classifier.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: liac-arff>=2.4.0 in /home/hirzel/python3.6venv/lib/python3.6/site-packages (2.4.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\n",
      "You should consider upgrading via the '/home/hirzel/python3.6venv/bin/python3.6 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#Required for accessing openml datasets from Lale\n",
    "!pip install 'liac-arff>=2.4.0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lale.datasets.openml\n",
    "import pandas as pd\n",
    "(train_X, train_y), (test_X, test_y) = lale.datasets.openml.fetch(\n",
    "    'credit-g', 'classification', preprocess=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler as Standard, MinMaxScaler as MinMax\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.kernel_approximation import Nystroem as Nys\n",
    "from lale.lib.lale import NoOp\n",
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "from sklearn.ensemble import RandomForestClassifier as RF\n",
    "from xgboost import XGBClassifier as XGBoost\n",
    "from lale.lib.lale import TopKVotingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from lale.lib.lale import Hyperopt\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "lale.wrap_imported_operators()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimizer supported as of now is Hyperopt and args_to_optimizer\n",
    "is a dictionary of values that Hyperopt would accept as per\n",
    "https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.hyperopt.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "<g id=\"a_node2\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.standard_scaler.html\" xlink:title=\"standard = Standard\">\n",
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       "<g id=\"a_node3\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.min_max_scaler.html\" xlink:title=\"min_max = MinMax\">\n",
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       "<g id=\"a_node10\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.hyperopt.html\" xlink:title=\"hyperopt = Hyperopt\">\n",
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   ],
   "source": [
    "planned_pipeline = (NoOp | Standard | MinMax) >> (NoOp | PCA | Nys) >> (LR | RF | XGBoost)\n",
    "ensemble = TopKVotingClassifier(\n",
    "    estimator=planned_pipeline, k=3, optimizer=Hyperopt,\n",
    "    args_to_optimizer={'max_evals':25, 'scoring':'accuracy'})\n",
    "ensemble.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████| 25/25 [02:51<00:00,  6.87s/trial, best loss: -0.7417910447761193]\n",
      "100%|█████████████████████████████████| 1/1 [01:23<00:00, 83.42s/trial, best loss: -0.755223880597015]\n"
     ]
    }
   ],
   "source": [
    "trained_ensemble = ensemble.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note that you could also pass just the planned pipeline as below and Hyperopt with its default setting would be used.\n",
    "ensemble = TopKVotingClassifier(estimator=planned_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7696969696969697\n"
     ]
    }
   ],
   "source": [
    "predictions = trained_ensemble.predict(test_X)\n",
    "print(accuracy_score(test_y, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "<!-- no_op&#45;&gt;lr -->\n",
       "<g id=\"edge2\" class=\"edge\"><title>no_op&#45;&gt;lr</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M168.403,-168C176.393,-168 185.311,-168 193.824,-168\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"193.919,-171.5 203.919,-168 193.919,-164.5 193.919,-171.5\"/>\n",
       "</g>\n",
       "<!-- standard -->\n",
       "<g id=\"node4\" class=\"node\"><title>standard</title>\n",
       "<g id=\"a_node4\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.standard_scaler.html\" xlink:title=\"standard = Standard(with_mean=False, with_std=False)\">\n",
       "<ellipse fill=\"#b0e2ff\" stroke=\"black\" cx=\"51\" cy=\"-106\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"51\" y=\"-103.2\" font-family=\"Times,serif\" font-size=\"11.00\">Standard</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- pca -->\n",
       "<g id=\"node5\" class=\"node\"><title>pca</title>\n",
       "<g id=\"a_node5\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html\" xlink:title=\"pca = PCA(svd_solver=&#39;randomized&#39;)\">\n",
       "<ellipse fill=\"#b0e2ff\" stroke=\"black\" cx=\"141\" cy=\"-106\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"141\" y=\"-103.2\" font-family=\"Times,serif\" font-size=\"11.00\">PCA</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- standard&#45;&gt;pca -->\n",
       "<g id=\"edge3\" class=\"edge\"><title>standard&#45;&gt;pca</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M78.4029,-106C86.3932,-106 95.3106,-106 103.824,-106\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"103.919,-109.5 113.919,-106 103.919,-102.5 103.919,-109.5\"/>\n",
       "</g>\n",
       "<!-- lr_0 -->\n",
       "<g id=\"node6\" class=\"node\"><title>lr_0</title>\n",
       "<g id=\"a_node6\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html\" xlink:title=\"lr_0 = LR(C=31347.843540495694, penalty=&#39;l1&#39;, tol=0.03811266583210131)\">\n",
       "<ellipse fill=\"#b0e2ff\" stroke=\"black\" cx=\"231\" cy=\"-106\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"231\" y=\"-103.2\" font-family=\"Times,serif\" font-size=\"11.00\">LR</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- pca&#45;&gt;lr_0 -->\n",
       "<g id=\"edge4\" class=\"edge\"><title>pca&#45;&gt;lr_0</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M168.403,-106C176.393,-106 185.311,-106 193.824,-106\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"193.919,-109.5 203.919,-106 193.919,-102.5 193.919,-109.5\"/>\n",
       "</g>\n",
       "<!-- min_max_0 -->\n",
       "<g id=\"node7\" class=\"node\"><title>min_max_0</title>\n",
       "<g id=\"a_node7\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.min_max_scaler.html\" xlink:title=\"min_max_0 = MinMax()\">\n",
       "<ellipse fill=\"#b0e2ff\" stroke=\"black\" cx=\"51\" cy=\"-44\" rx=\"27\" ry=\"19.6\"/>\n",
       "<text text-anchor=\"middle\" x=\"51\" y=\"-47.2\" font-family=\"Times,serif\" font-size=\"11.00\">Min&#45;</text>\n",
       "<text text-anchor=\"middle\" x=\"51\" y=\"-35.2\" font-family=\"Times,serif\" font-size=\"11.00\">Max</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- no_op_0 -->\n",
       "<g id=\"node8\" class=\"node\"><title>no_op_0</title>\n",
       "<g id=\"a_node8\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.no_op.html\" xlink:title=\"no_op_0 = NoOp()\">\n",
       "<ellipse fill=\"white\" stroke=\"black\" cx=\"141\" cy=\"-44\" rx=\"27\" ry=\"19.6\"/>\n",
       "<text text-anchor=\"middle\" x=\"141\" y=\"-47.2\" font-family=\"Times,serif\" font-size=\"11.00\">No&#45;</text>\n",
       "<text text-anchor=\"middle\" x=\"141\" y=\"-35.2\" font-family=\"Times,serif\" font-size=\"11.00\">Op</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- min_max_0&#45;&gt;no_op_0 -->\n",
       "<g id=\"edge5\" class=\"edge\"><title>min_max_0&#45;&gt;no_op_0</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M78.4029,-44C86.3932,-44 95.3106,-44 103.824,-44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"103.919,-47.5001 113.919,-44 103.919,-40.5001 103.919,-47.5001\"/>\n",
       "</g>\n",
       "<!-- xg_boost -->\n",
       "<g id=\"node9\" class=\"node\"><title>xg_boost</title>\n",
       "<g id=\"a_node9\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.xgboost.XGBClassifier.html\" xlink:title=\"xg_boost = XGBoost(booster=&#39;dart&#39;, colsample_bylevel=0.47096071538468853, colsample_bytree=0.7593792234753081, learning_rate=0.20970693320349945, max_depth=18, min_child_weight=2, n_estimators=1063, reg_alpha=0.31844178826811975, reg_lambda=0.7797218315351517, subsample=...)\">\n",
       "<ellipse fill=\"#b0e2ff\" stroke=\"black\" cx=\"231\" cy=\"-44\" rx=\"27\" ry=\"19.6\"/>\n",
       "<text text-anchor=\"middle\" x=\"231\" y=\"-47.2\" font-family=\"Times,serif\" font-size=\"11.00\">XG&#45;</text>\n",
       "<text text-anchor=\"middle\" x=\"231\" y=\"-35.2\" font-family=\"Times,serif\" font-size=\"11.00\">Boost</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- no_op_0&#45;&gt;xg_boost -->\n",
       "<g id=\"edge6\" class=\"edge\"><title>no_op_0&#45;&gt;xg_boost</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M168.403,-44C176.393,-44 185.311,-44 193.824,-44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"193.919,-47.5001 203.919,-44 193.919,-40.5001 193.919,-47.5001\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
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       "<graphviz.dot.Digraph at 0x7fd2c04d5908>"
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     },
     "metadata": {},
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   ],
   "source": [
    "best_pipeline = trained_ensemble.get_pipeline()\n",
    "best_pipeline.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class': 'lale.lib.sklearn.voting_classifier.VotingClassifierImpl',\n",
       " 'state': 'trained',\n",
       " 'operator': 'VotingClassifier',\n",
       " 'label': 'VotingClassifier',\n",
       " 'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.voting_classifier.html',\n",
       " 'hyperparams': {'estimators': [('p17', {'$ref': '../steps/pipeline'}),\n",
       "   ('p9', {'$ref': '../steps/pipeline_0'}),\n",
       "   ('p16', {'$ref': '../steps/pipeline_1'})],\n",
       "  'voting': 'soft'},\n",
       " 'steps': {'pipeline': {'class': 'lale.operators.TrainablePipeline',\n",
       "   'state': 'trainable',\n",
       "   'edges': [['min_max', 'no_op'], ['no_op', 'lr']],\n",
       "   'steps': {'min_max': {'class': 'lale.lib.sklearn.min_max_scaler.MinMaxScalerImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'MinMax',\n",
       "     'label': 'MinMax',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.min_max_scaler.html',\n",
       "     'hyperparams': {},\n",
       "     'is_frozen_trainable': True},\n",
       "    'no_op': {'class': 'lale.lib.lale.no_op.NoOpImpl',\n",
       "     'state': 'trained',\n",
       "     'operator': 'NoOp',\n",
       "     'label': 'NoOp',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.no_op.html',\n",
       "     'hyperparams': {},\n",
       "     'is_frozen_trainable': True,\n",
       "     'coefs': None,\n",
       "     'is_frozen_trained': True},\n",
       "    'lr': {'class': 'lale.lib.sklearn.logistic_regression.LogisticRegressionImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'LR',\n",
       "     'label': 'LR',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html',\n",
       "     'hyperparams': {'C': 22334.598583769228,\n",
       "      'fit_intercept': False,\n",
       "      'tol': 0.012327172789706938},\n",
       "     'is_frozen_trainable': True}}},\n",
       "  'pipeline_0': {'class': 'lale.operators.TrainablePipeline',\n",
       "   'state': 'trainable',\n",
       "   'edges': [['standard', 'pca'], ['pca', 'lr_0']],\n",
       "   'steps': {'standard': {'class': 'lale.lib.sklearn.standard_scaler.StandardScalerImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'Standard',\n",
       "     'label': 'Standard',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.standard_scaler.html',\n",
       "     'hyperparams': {'with_mean': False, 'with_std': False},\n",
       "     'is_frozen_trainable': True},\n",
       "    'pca': {'class': 'lale.lib.sklearn.pca.PCAImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'PCA',\n",
       "     'label': 'PCA',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html',\n",
       "     'hyperparams': {'svd_solver': 'randomized'},\n",
       "     'is_frozen_trainable': True},\n",
       "    'lr_0': {'class': 'lale.lib.sklearn.logistic_regression.LogisticRegressionImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'LR',\n",
       "     'label': 'LR',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html',\n",
       "     'hyperparams': {'C': 31347.843540495694,\n",
       "      'penalty': 'l1',\n",
       "      'tol': 0.03811266583210131},\n",
       "     'is_frozen_trainable': True}}},\n",
       "  'pipeline_1': {'class': 'lale.operators.TrainablePipeline',\n",
       "   'state': 'trainable',\n",
       "   'edges': [['min_max_0', 'no_op_0'], ['no_op_0', 'xg_boost']],\n",
       "   'steps': {'min_max_0': {'class': 'lale.lib.sklearn.min_max_scaler.MinMaxScalerImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'MinMax',\n",
       "     'label': 'MinMax',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.min_max_scaler.html',\n",
       "     'hyperparams': {},\n",
       "     'is_frozen_trainable': True},\n",
       "    'no_op_0': {'class': 'lale.lib.lale.no_op.NoOpImpl',\n",
       "     'state': 'trained',\n",
       "     'operator': 'NoOp',\n",
       "     'label': 'NoOp',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.no_op.html',\n",
       "     'hyperparams': {},\n",
       "     'is_frozen_trainable': True,\n",
       "     'coefs': None,\n",
       "     'is_frozen_trained': True},\n",
       "    'xg_boost': {'class': 'lale.lib.xgboost.xgb_classifier.XGBClassifierImpl',\n",
       "     'state': 'trainable',\n",
       "     'operator': 'XGBoost',\n",
       "     'label': 'XGBoost',\n",
       "     'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.xgboost.XGBClassifier.html',\n",
       "     'hyperparams': {'booster': 'dart',\n",
       "      'colsample_bylevel': 0.47096071538468853,\n",
       "      'colsample_bytree': 0.7593792234753081,\n",
       "      'learning_rate': 0.20970693320349945,\n",
       "      'max_depth': 18,\n",
       "      'min_child_weight': 2,\n",
       "      'n_estimators': 1063,\n",
       "      'reg_alpha': 0.31844178826811975,\n",
       "      'reg_lambda': 0.7797218315351517,\n",
       "      'subsample': 0.64136510248406},\n",
       "     'is_frozen_trainable': True}}}},\n",
       " 'is_frozen_trainable': True,\n",
       " 'coefs': 'coefs_not_available',\n",
       " 'is_frozen_trained': False}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_pipeline.to_json()"
   ]
  }
 ],
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