examples/demo_pipeline_hyperopt.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from lale.lib.lale import NoOp\n",
    "from lale.lib.sklearn import KNeighborsClassifier\n",
    "from lale.lib.sklearn import LogisticRegression\n",
    "from lale.lib.sklearn import Nystroem\n",
    "from lale.lib.sklearn import PCA\n",
    "from lale.operators import make_union, make_choice, make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lale provides an `|` combinator or a function make_choice() to allow only one of its arguments to be applied at once in the overall pipeline. In this example, the first step of the pipeline is a choice between Nystroem and NoOp. This means that the data will either be transformed using Nystroem or will be left as is (NoOp is a transformer that does nothing). The second step in the pipeline is a PCA, and the third step is again a choice between two popular classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<!-- no_op -->\n",
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       "<g id=\"a_node1\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.no_op.html\" xlink:title=\"no_op = NoOp\">\n",
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       "<!-- nystroem -->\n",
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       "<g id=\"a_node2\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.nystroem.html\" xlink:title=\"nystroem = Nystroem\">\n",
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   "source": [
    "kernel_tfm_or_not =  NoOp | Nystroem\n",
    "kernel_tfm_or_not.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfm = PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "<!-- logistic_regression -->\n",
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       "<g id=\"a_node1\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html\" xlink:title=\"logistic_regression = LogisticRegression\">\n",
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    "clf = make_choice(LogisticRegression, KNeighborsClassifier)\n",
    "clf.visualize()"
   ]
  },
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       "<!-- no_op -->\n",
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       "<!-- pca -->\n",
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       "<g id=\"a_node3\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html\" xlink:title=\"pca = PCA\">\n",
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       "<!-- no_op&#45;&gt;pca -->\n",
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       "<g id=\"a_node2\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.nystroem.html\" xlink:title=\"nystroem = Nystroem\">\n",
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       "<!-- logistic_regression -->\n",
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       "<g id=\"a_node4\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html\" xlink:title=\"logistic_regression = LogisticRegression\">\n",
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   ],
   "source": [
    "optimizable = kernel_tfm_or_not >> tfm >> clf\n",
    "optimizable.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use the graph to select the best performing model for a dataset. We use Iris dataset from sklearn for this demonstration. Hyperopt is used to scan the hyperparameter search space and select the best performing path from the above graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lale.lib.lale import Hyperopt\n",
    "from lale.datasets import load_iris_df\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = load_iris_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████| 3/3 [00:07<00:00,  2.64s/trial, best loss: -0.9416666666666667]\n"
     ]
    }
   ],
   "source": [
    "hpo_trainable = Hyperopt(estimator=optimizable, max_evals=3)\n",
    "hpo_trained = hpo_trainable.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "<g id=\"a_node1\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.nystroem.html\" xlink:title=\"nystroem = Nystroem(degree=3, gamma=2.4357277143872933, kernel=&#39;additive_chi2&#39;, n_components=175)\">\n",
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       "<!-- pca -->\n",
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       "<g id=\"a_node2\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html\" xlink:title=\"pca = PCA(svd_solver=&#39;full&#39;)\">\n",
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       "<!-- logistic_regression -->\n",
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       "<g id=\"a_node3\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html\" xlink:title=\"logistic_regression = LogisticRegression(intercept_scaling=0.7395042341904204, max_iter=975, multi_class=&#39;ovr&#39;, solver=&#39;liblinear&#39;, tol=0.008563722089699138)\">\n",
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     "metadata": {},
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   ],
   "source": [
    "best_estimator = hpo_trained.get_pipeline()\n",
    "best_estimator.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the results from all trials and retrieve pipelines of other names or types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <th>time</th>\n",
       "      <th>log_loss</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.583333</td>\n",
       "      <td>0.461873</td>\n",
       "      <td>1.955799</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.941667</td>\n",
       "      <td>0.436148</td>\n",
       "      <td>0.249560</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.791667</td>\n",
       "      <td>0.274112</td>\n",
       "      <td>0.390846</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tid      loss      time  log_loss status\n",
       "name                                          \n",
       "p0      0 -0.583333  0.461873  1.955799     ok\n",
       "p1      1 -0.941667  0.436148  0.249560     ok\n",
       "p2      2 -0.791667  0.274112  0.390846     ok"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hpo_trained.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p0\n"
     ]
    }
   ],
   "source": [
    "worst_name = hpo_trained.summary().loss.argmax()\n",
    "if not isinstance(worst_name, str): #newer pandas argmax returns index\n",
    "    worst_name = hpo_trained.summary().index[worst_name]\n",
    "print(worst_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<g id=\"a_node1\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.nystroem.html\" xlink:title=\"nystroem = Nystroem(gamma=1.6994342267804934, kernel=&#39;laplacian&#39;, n_components=181)\">\n",
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       "<g id=\"a_node2\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html\" xlink:title=\"pca = PCA(whiten=True)\">\n",
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       "<text text-anchor=\"middle\" x=\"121.048\" y=\"-25.4843\" font-family=\"Times,serif\" font-size=\"11.00\">PCA</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- nystroem&#45;&gt;pca -->\n",
       "<g id=\"edge1\" class=\"edge\"><title>nystroem&#45;&gt;pca</title>\n",
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       "<g id=\"a_node3\"><a xlink:href=\"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.k_neighbors_classifier.html\" xlink:title=\"k_neighbors_classifier = KNeighborsClassifier(algorithm=&#39;ball_tree&#39;, n_neighbors=31, p=1, weights=&#39;distance&#39;)\">\n",
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     "metadata": {},
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   ],
   "source": [
    "worst_estimator = hpo_trained.get_pipeline(worst_name)\n",
    "worst_estimator.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "worst_estimator_in_sklearn_format = hpo_trained.get_pipeline(worst_name, astype='sklearn')"
   ]
  }
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