diff --git a/.gitignore b/.gitignore
index fc613289..ca16d0a6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -159,4 +159,5 @@ dmypy.json
 # Local stuff
 dummy/
 tmp_model/
-plots/
\ No newline at end of file
+plots/
+demo/
diff --git a/demo/DemoClassification.ipynb b/demo/DemoClassification.ipynb
deleted file mode 100644
index dfb35697..00000000
--- a/demo/DemoClassification.ipynb
+++ /dev/null
@@ -1,714 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# PSyKE's demo\n",
-    "\n",
-    "Some imports."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6b710e7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.datasets import load_iris\n",
-    "import pandas as pd\n",
-    "\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.metrics import accuracy_score, f1_score\n",
-    "\n",
-    "from psyke import Extractor, Clustering, EvaluableModel\n",
-    "from psyke.extraction.hypercubic.strategy import AdaptiveStrategy\n",
-    "from psyke.extraction.hypercubic import Grid, FeatureRanker\n",
-    "from psyke.tuning.orchid import OrCHiD\n",
-    "from psyke.utils.logic import pretty_theory\n",
-    "from psyke.utils import Target"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Import iris dataset separating features and class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f8e46c49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = load_iris(return_X_y=True, as_frame=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "Rename of the features."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "38d5afb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x.columns = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "Replace integer indices with the corresponding string class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4f807185",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "        target\n0       setosa\n1       setosa\n2       setosa\n3       setosa\n4       setosa\n..         ...\n145  virginica\n146  virginica\n147  virginica\n148  virginica\n149  virginica\n\n[150 rows x 1 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 1 columns</p>\n</div>"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = pd.DataFrame(y).replace({\"target\": {0: 'setosa', 1: 'versicolor', 2: 'virginica'}})\n",
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "The final dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7ac49b4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     SepalLength  SepalWidth  PetalLength  PetalWidth       iris\n0            5.1         3.5          1.4         0.2     setosa\n1            4.9         3.0          1.4         0.2     setosa\n2            4.7         3.2          1.3         0.2     setosa\n3            4.6         3.1          1.5         0.2     setosa\n4            5.0         3.6          1.4         0.2     setosa\n..           ...         ...          ...         ...        ...\n145          6.7         3.0          5.2         2.3  virginica\n146          6.3         2.5          5.0         1.9  virginica\n147          6.5         3.0          5.2         2.0  virginica\n148          6.2         3.4          5.4         2.3  virginica\n149          5.9         3.0          5.1         1.8  virginica\n\n[150 rows x 5 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n      <th>iris</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 5 columns</p>\n</div>"
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset = x.join(y)\n",
-    "dataset.columns = [*dataset.columns[:-1], 'iris']\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "Split between train and test set in a reproducible way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "03fc5e2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train, test = train_test_split(dataset, test_size=0.25, random_state=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "We use as predictor a KNN and we train it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "aa8a3128",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 1.00\n",
-      "F1: 1.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "#predictor = MLPClassifier(alpha=1, max_iter=1000)\n",
-    "predictor = KNeighborsClassifier(n_neighbors=5)\n",
-    "#predictor = DecisionTreeClassifier()\n",
-    "predictor.fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "print(f'Accuracy: {accuracy_score(predictor.predict(test.iloc[:, :-1]), test.iloc[:, -1]):.2f}')\n",
-    "print(f'F1: {f1_score(predictor.predict(test.iloc[:, :-1]), test.iloc[:, -1], average=\"weighted\"):.2f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [],
-   "source": [
-    "def print_scores(scores):\n",
-    "    print(f'Classification accuracy = {scores[EvaluableModel.ClassificationScore.ACCURACY][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.ClassificationScore.ACCURACY][1]:.2f} (BB)\\n'\n",
-    "          f'F1 = {scores[EvaluableModel.ClassificationScore.F1][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.ClassificationScore.F1][1]:.2f} (BB)')\n",
-    "\n",
-    "def get_scores(extractor, test, predictor):\n",
-    "    return extractor.score(test, predictor, True, True, EvaluableModel.Task.CLASSIFICATION,\n",
-    "                           [EvaluableModel.ClassificationScore.ACCURACY, EvaluableModel.ClassificationScore.F1])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ITER performance (5 rules with 97.37% coverage):\n",
-      "Classification accuracy = 0.97 (data), 0.97 (BB)\n",
-      "F1 = 0.97 (data), 0.97 (BB)\n",
-      "\n",
-      "ITER extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    SepalLength in [4.29, 7.70], SepalWidth in [1.99, 4.40], PetalLength in [0.99, 3.17], PetalWidth in [0.09, 2.10].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    SepalLength in [4.29, 7.70], SepalWidth in [1.99, 4.40], PetalLength in [3.17, 5.82], PetalWidth in [0.09, 1.74].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    SepalLength in [4.29, 7.70], SepalWidth in [1.99, 4.40], PetalLength in [0.99, 4.05], PetalWidth in [2.10, 2.50].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    SepalLength in [4.29, 7.70], SepalWidth in [1.99, 4.40], PetalLength in [5.82, 6.90], PetalWidth in [0.09, 2.50].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    SepalLength in [4.29, 7.70], SepalWidth in [1.99, 4.40], PetalLength in [4.05, 5.82], PetalWidth in [1.74, 2.50].\n"
-     ]
-    }
-   ],
-   "source": [
-    "it = Extractor.iter(predictor, min_update=0.15, min_examples=150, threshold=0.1, max_iterations=600, n_points=1)\n",
-    "theory_from_iter = it.extract(train)\n",
-    "scores, completeness = get_scores(it, test, predictor)\n",
-    "print(f'ITER performance ({it.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nITER extracted rules:\\n\\n' + pretty_theory(theory_from_iter))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "We create a GridEx extractor to extract prolog rules from the same KNN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GridEx performance (3 rules with 94.74% coverage):\n",
-      "Classification accuracy = 0.92 (data), 0.92 (BB)\n",
-      "F1 = 0.92 (data), 0.92 (BB)\n",
-      "\n",
-      "GridEx extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    PetalLength in [0.99, 2.47].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalLength in [3.21, 4.68].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    PetalLength in [4.68, 6.90].\n"
-     ]
-    }
-   ],
-   "source": [
-    "ranked = FeatureRanker(x.columns).fit(predictor, x).rankings()\n",
-    "gridEx = Extractor.gridex(predictor, Grid(1, AdaptiveStrategy(ranked, [(0.85, 8)])), threshold=.1, min_examples=1)\n",
-    "theory_from_gridEx = gridEx.extract(train)\n",
-    "scores, completeness = get_scores(gridEx, test, predictor)\n",
-    "print(f'GridEx performance ({gridEx.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nGridEx extracted rules:\\n\\n' + pretty_theory(theory_from_gridEx))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "We create an extractor that uses the CART algorithm and we extract prolog rules from our trained KNN."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CART performance (3 rules with 100.00% coverage):\n",
-      "Classification accuracy = 0.97 (data), 0.97 (BB)\n",
-      "F1 = 0.97 (data), 0.97 (BB)\n",
-      "\n",
-      "CART extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    PetalLength =< 2.6.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalLength =< 4.75.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica).\n"
-     ]
-    }
-   ],
-   "source": [
-    "cart = Extractor.cart(predictor, simplify=True)\n",
-    "theory_from_cart = cart.extract(train)\n",
-    "scores, completeness = get_scores(cart, test, predictor)\n",
-    "print(f'CART performance ({cart.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCART extracted rules:\\n\\n' + pretty_theory(theory_from_cart))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We use the CReEPy clustering-based extractor to perform the extraction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "outputs": [],
-   "source": [
-    "def print_clustering_scores(scores):\n",
-    "    print(f'ARI = {scores[EvaluableModel.ClusteringScore.ARI][0]:.2f}\\n'\n",
-    "          f'AMI = {scores[EvaluableModel.ClusteringScore.AMI][0]:.2f}\\n'\n",
-    "          f'V-measure = {scores[EvaluableModel.ClusteringScore.V][0]:.2f}\\n'\n",
-    "          f'FMI = {scores[EvaluableModel.ClusteringScore.FMI][0]:.2f}')\n",
-    "\n",
-    "def get_clustering_scores(clustering, test):\n",
-    "    return clustering.score(test, None, False, True, EvaluableModel.Task.CLASSIFICATION,\n",
-    "                           [EvaluableModel.ClusteringScore.ARI, EvaluableModel.ClusteringScore.AMI,\n",
-    "                            EvaluableModel.ClusteringScore.V, EvaluableModel.ClusteringScore.FMI])\n",
-    "\n",
-    "def print_scores_short(scores):\n",
-    "    print(f'Classification accuracy = {scores[EvaluableModel.ClassificationScore.ACCURACY][0]:.2f}')\n",
-    "\n",
-    "def get_scores_short(extractor, test):\n",
-    "    return extractor.score(test, None, False, True, EvaluableModel.Task.CLASSIFICATION,\n",
-    "                           [EvaluableModel.ClassificationScore.ACCURACY, EvaluableModel.ClassificationScore.F1])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.ExACT. Depth: 1. Threshold = 1.00. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.02. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.06. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.11. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.15. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.20. Predictive loss = 0.30, 2 rules\n",
-      "\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 1.00. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.02. Predictive loss = 0.27, 3 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.06. Predictive loss = 0.27, 3 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.11. Predictive loss = 0.27, 3 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.15. Predictive loss = 0.27, 3 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.20. Predictive loss = 0.27, 3 rules\n",
-      "\n",
-      "****************************************\n",
-      "* Best Algorithm.ExACT\n",
-      "****************************************\n",
-      "* Predictive loss = 0.27, 3 rules\n",
-      "* Threshold = 0.02\n",
-      "* Depth = 2\n",
-      "****************************************\n",
-      "\n",
-      "****************************************\n",
-      "* Best Predictive loss\n",
-      "****************************************\n",
-      "* Predictive loss = 0.27, 3 rules\n",
-      "* Threshold = 0.02\n",
-      "* Depth = 2\n",
-      "****************************************\n",
-      "\n",
-      "****************************************\n",
-      "* Best N rules\n",
-      "****************************************\n",
-      "* Predictive loss = 0.30, 2 rules\n",
-      "* Threshold = 1.00\n",
-      "* Depth = 2\n",
-      "****************************************\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "orchid = OrCHiD(dataframe=train, algorithm=OrCHiD.Algorithm.ExACT, output=Target.CLASSIFICATION,\n",
-    "                max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1, patience=5, max_depth=3)\n",
-    "orchid.search()\n",
-    "(_, _, depth, threshold) = orchid.get_best()[0]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ExACT performance (3 clusters with 97.37% coverage):\n",
-      "ARI = 0.53\n",
-      "AMI = 0.62\n",
-      "V-measure = 0.64\n",
-      "FMI = 0.69\n",
-      "Classification accuracy = 0.78\n",
-      "\n",
-      "Output is virginica if:\n",
-      "    SepalLength is in [4.90, 7.70]\n",
-      "    SepalWidth is in [2.20, 3.80]\n",
-      "    PetalLength is in [4.50, 6.90]\n",
-      "    PetalWidth is in [1.40, 2.50]\n",
-      "Output is versicolor if:\n",
-      "    SepalLength is in [4.90, 7.70]\n",
-      "    SepalWidth is in [2.00, 3.80]\n",
-      "    PetalLength is in [3.30, 6.90]\n",
-      "    PetalWidth is in [1.00, 2.50]\n",
-      "Output is setosa if:\n",
-      "    SepalLength is in [4.30, 7.70]\n",
-      "    SepalWidth is in [2.00, 4.40]\n",
-      "    PetalLength is in [1.00, 6.90]\n",
-      "    PetalWidth is in [0.10, 2.50]\n"
-     ]
-    }
-   ],
-   "source": [
-    "exact = Clustering.exact(depth=depth, error_threshold=threshold, output=Target.CLASSIFICATION)\n",
-    "exact.fit(train)\n",
-    "scores, completeness = get_clustering_scores(exact, test)\n",
-    "print(f'ExACT performance ({exact.n_rules} clusters with {completeness * 100:.2f}% coverage):')\n",
-    "print_clustering_scores(scores)\n",
-    "scores, _ = get_scores_short(exact, test)\n",
-    "print_scores_short(scores)\n",
-    "print()\n",
-    "exact.explain()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CReEPy performance (3 rules with 100.00% coverage):\n",
-      "Classification accuracy = 0.95 (data), 0.95 (BB)\n",
-      "F1 = 0.95 (data), 0.95 (BB)\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    PetalLength in [4.79, 6.90].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalLength in [3.29, 6.90].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa).\n"
-     ]
-    }
-   ],
-   "source": [
-    "creepy = Extractor.creepy(predictor, depth=2, error_threshold=0.1, output=Target.CLASSIFICATION,\n",
-    "                          ranks=ranked, ignore_threshold=.99, clustering=Clustering.exact)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "scores, completeness = get_scores(creepy, test, predictor)\n",
-    "print(f'CReEPy performance ({creepy.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.CREAM. Depth: 1. Threshold = 1.00. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.02. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.06. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.11. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.15. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.20. Predictive loss = 0.30, 2 rules\n",
-      "\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 1.00. Predictive loss = 0.30, 2 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.02. Predictive loss = 0.07, 3 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.06. Predictive loss = 0.07, 3 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.11. Predictive loss = 0.07, 3 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.15. Predictive loss = 0.07, 3 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.20. Predictive loss = 0.07, 3 rules\n",
-      "\n",
-      "****************************************\n",
-      "* Best Algorithm.CREAM\n",
-      "****************************************\n",
-      "* Predictive loss = 0.07, 3 rules\n",
-      "* Threshold = 0.02\n",
-      "* Depth = 2\n",
-      "****************************************\n",
-      "\n",
-      "****************************************\n",
-      "* Best Predictive loss\n",
-      "****************************************\n",
-      "* Predictive loss = 0.07, 3 rules\n",
-      "* Threshold = 0.02\n",
-      "* Depth = 2\n",
-      "****************************************\n",
-      "\n",
-      "****************************************\n",
-      "* Best N rules\n",
-      "****************************************\n",
-      "* Predictive loss = 0.30, 2 rules\n",
-      "* Threshold = 1.00\n",
-      "* Depth = 2\n",
-      "****************************************\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "orchid = OrCHiD(dataframe=train, algorithm=OrCHiD.Algorithm.CREAM, output=Target.CLASSIFICATION,\n",
-    "                max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1, patience=5, max_depth=3)\n",
-    "orchid.search()\n",
-    "(_, _, depth, threshold) = orchid.get_best()[0]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CREAM performance (3 clusters with 97.37% coverage):\n",
-      "ARI = 0.85\n",
-      "AMI = 0.82\n",
-      "V-measure = 0.83\n",
-      "FMI = 0.90\n",
-      "Classification accuracy = 0.95\n",
-      "\n",
-      "Output is setosa if:\n",
-      "    SepalLength is in [4.30, 5.70]\n",
-      "    SepalWidth is in [2.30, 4.40]\n",
-      "    PetalLength is in [1.00, 1.90]\n",
-      "    PetalWidth is in [0.10, 0.60]\n",
-      "Output is versicolor if:\n",
-      "    SepalLength is in [4.90, 7.00]\n",
-      "    SepalWidth is in [2.00, 3.40]\n",
-      "    PetalLength is in [3.30, 5.10]\n",
-      "    PetalWidth is in [1.00, 1.80]\n",
-      "Output is virginica if:\n",
-      "    SepalLength is in [4.30, 7.70]\n",
-      "    SepalWidth is in [2.00, 4.40]\n",
-      "    PetalLength is in [1.00, 6.90]\n",
-      "    PetalWidth is in [0.10, 2.50]\n"
-     ]
-    }
-   ],
-   "source": [
-    "cream = Clustering.cream(depth=depth, error_threshold=threshold, output=Target.CLASSIFICATION)\n",
-    "cream.fit(train)\n",
-    "scores, completeness = get_clustering_scores(cream, test)\n",
-    "print(f'CREAM performance ({cream.n_rules} clusters with {completeness * 100:.2f}% coverage):')\n",
-    "print_clustering_scores(scores)\n",
-    "scores, _ = get_scores_short(cream, test)\n",
-    "print_scores_short(scores)\n",
-    "print()\n",
-    "cream.explain()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CReEPy performance (3 rules with 100.00% coverage):\n",
-      "Classification accuracy = 0.95 (data), 0.95 (BB)\n",
-      "F1 = 0.95 (data), 0.95 (BB)\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    PetalLength in [0.99, 1.90].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalLength in [3.29, 5.00].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica).\n"
-     ]
-    }
-   ],
-   "source": [
-    "creepy = Extractor.creepy(predictor, depth=2, error_threshold=0.1, output=Target.CLASSIFICATION,\n",
-    "                          ranks=ranked, ignore_threshold=.99, clustering=Clustering.cream)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "scores, completeness = get_scores(creepy, test, predictor)\n",
-    "print(f'CReEPy performance ({creepy.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/demo/DemoClassificationDisc.ipynb b/demo/DemoClassificationDisc.ipynb
deleted file mode 100644
index c240351b..00000000
--- a/demo/DemoClassificationDisc.ipynb
+++ /dev/null
@@ -1,452 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# PSyKE's demo\n",
-    "\n",
-    "Some imports."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "6b710e7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from psyke.utils.dataframe import get_discrete_features_supervised, get_discrete_dataset\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.datasets import load_iris\n",
-    "import pandas as pd\n",
-    "\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from psyke.extraction.cart.predictor import CartPredictor\n",
-    "\n",
-    "from psyke import Extractor, EvaluableModel\n",
-    "from psyke.utils.logic import pretty_theory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Import iris dataset separating features and class."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f8e46c49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x, y = load_iris(return_X_y=True, as_frame=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Rename of the features."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "38d5afb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x.columns = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "The original features' dataset is discretized using the equal frequency method. Each feature is mapped in a 3 (can be an arbitrary integer) new one-hot encoded sub-features representing 3 real intervals. So from the original 4 features we have a new 12 features dataset. S, M and L stand for small, medium and large."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "423ff1b4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SepalLength = {'SepalLength_0' if SepalLength ∈ ]-∞, 5.39], 'SepalLength_1' if SepalLength ∈ [5.39, 6.26[, 'SepalLength_2' if SepalLength ∈ ]6.26, ∞[}\n",
-      "\n",
-      "SepalWidth = {'SepalWidth_0' if SepalWidth ∈ ]-∞, 2.87], 'SepalWidth_1' if SepalWidth ∈ [2.87, 3.20[, 'SepalWidth_2' if SepalWidth ∈ ]3.20, ∞[}\n",
-      "\n",
-      "PetalLength = {'PetalLength_0' if PetalLength ∈ ]-∞, 2.27], 'PetalLength_1' if PetalLength ∈ [2.27, 4.87[, 'PetalLength_2' if PetalLength ∈ ]4.87, ∞[}\n",
-      "\n",
-      "PetalWidth = {'PetalWidth_0' if PetalWidth ∈ ]-∞, 0.65], 'PetalWidth_1' if PetalWidth ∈ [0.65, 1.64[, 'PetalWidth_2' if PetalWidth ∈ ]1.64, ∞[}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "iris_features = get_discrete_features_supervised(x.join(y))\n",
-    "\n",
-    "for descrete_feature in iris_features:\n",
-    "    print(str(descrete_feature), end='\\n\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Reassign features' data to the discretized one."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ffc1852e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     PetalLength_0  PetalLength_1  PetalLength_2  PetalWidth_0  PetalWidth_1  \\\n0                1              0              0             1             0   \n1                1              0              0             1             0   \n2                1              0              0             1             0   \n3                1              0              0             1             0   \n4                1              0              0             1             0   \n..             ...            ...            ...           ...           ...   \n145              0              0              1             0             0   \n146              0              0              1             0             0   \n147              0              0              1             0             0   \n148              0              0              1             0             0   \n149              0              0              1             0             0   \n\n     PetalWidth_2  SepalLength_0  SepalLength_1  SepalLength_2  SepalWidth_0  \\\n0               0              1              0              0             0   \n1               0              1              0              0             0   \n2               0              1              0              0             0   \n3               0              1              0              0             0   \n4               0              1              0              0             0   \n..            ...            ...            ...            ...           ...   \n145             1              0              0              1             0   \n146             1              0              0              1             1   \n147             1              0              0              1             0   \n148             1              0              1              0             0   \n149             1              0              1              0             0   \n\n     SepalWidth_1  SepalWidth_2  \n0               0             1  \n1               1             0  \n2               1             0  \n3               1             0  \n4               0             1  \n..            ...           ...  \n145             1             0  \n146             0             0  \n147             1             0  \n148             0             1  \n149             1             0  \n\n[150 rows x 12 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PetalLength_0</th>\n      <th>PetalLength_1</th>\n      <th>PetalLength_2</th>\n      <th>PetalWidth_0</th>\n      <th>PetalWidth_1</th>\n      <th>PetalWidth_2</th>\n      <th>SepalLength_0</th>\n      <th>SepalLength_1</th>\n      <th>SepalLength_2</th>\n      <th>SepalWidth_0</th>\n      <th>SepalWidth_1</th>\n      <th>SepalWidth_2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 12 columns</p>\n</div>"
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = get_discrete_dataset(x, iris_features)\n",
-    "x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Replace integer indices with the corresponding string class."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "4f807185",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "        target\n0       setosa\n1       setosa\n2       setosa\n3       setosa\n4       setosa\n..         ...\n145  virginica\n146  virginica\n147  virginica\n148  virginica\n149  virginica\n\n[150 rows x 1 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 1 columns</p>\n</div>"
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y = pd.DataFrame(y).replace({\"target\": {0: 'setosa', 1: 'versicolor', 2: 'virginica'}})\n",
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "The final dataset:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "7ac49b4e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "     PetalLength_0  PetalLength_1  PetalLength_2  PetalWidth_0  PetalWidth_1  \\\n0                1              0              0             1             0   \n1                1              0              0             1             0   \n2                1              0              0             1             0   \n3                1              0              0             1             0   \n4                1              0              0             1             0   \n..             ...            ...            ...           ...           ...   \n145              0              0              1             0             0   \n146              0              0              1             0             0   \n147              0              0              1             0             0   \n148              0              0              1             0             0   \n149              0              0              1             0             0   \n\n     PetalWidth_2  SepalLength_0  SepalLength_1  SepalLength_2  SepalWidth_0  \\\n0               0              1              0              0             0   \n1               0              1              0              0             0   \n2               0              1              0              0             0   \n3               0              1              0              0             0   \n4               0              1              0              0             0   \n..            ...            ...            ...            ...           ...   \n145             1              0              0              1             0   \n146             1              0              0              1             1   \n147             1              0              0              1             0   \n148             1              0              1              0             0   \n149             1              0              1              0             0   \n\n     SepalWidth_1  SepalWidth_2       iris  \n0               0             1     setosa  \n1               1             0     setosa  \n2               1             0     setosa  \n3               1             0     setosa  \n4               0             1     setosa  \n..            ...           ...        ...  \n145             1             0  virginica  \n146             0             0  virginica  \n147             1             0  virginica  \n148             0             1  virginica  \n149             1             0  virginica  \n\n[150 rows x 13 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PetalLength_0</th>\n      <th>PetalLength_1</th>\n      <th>PetalLength_2</th>\n      <th>PetalWidth_0</th>\n      <th>PetalWidth_1</th>\n      <th>PetalWidth_2</th>\n      <th>SepalLength_0</th>\n      <th>SepalLength_1</th>\n      <th>SepalLength_2</th>\n      <th>SepalWidth_0</th>\n      <th>SepalWidth_1</th>\n      <th>SepalWidth_2</th>\n      <th>iris</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 13 columns</p>\n</div>"
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset = x.join(y)\n",
-    "dataset.columns = [*dataset.columns[:-1], 'iris']\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Split between train and test set in a reproducible way."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "03fc5e2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train, test = train_test_split(dataset, test_size=0.5, random_state=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "We use as predictor a KNN with K = 4 and we train it."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "aa8a3128",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "0.9333333333333333"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictor = KNeighborsClassifier(n_neighbors=4)\n",
-    "predictor.fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "predictor.score(test.iloc[:, :-1], test.iloc[:, -1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "We create an extractor that uses the REAL algorithm and we extract prolog rules from our trained KNN."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [],
-   "source": [
-    "def print_scores(scores):\n",
-    "    print(f'Classification accuracy = {scores[EvaluableModel.ClassificationScore.ACCURACY][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.ClassificationScore.ACCURACY][1]:.2f} (BB)\\n'\n",
-    "          f'F1 = {scores[EvaluableModel.ClassificationScore.F1][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.ClassificationScore.F1][1]:.2f} (BB)')\n",
-    "\n",
-    "def get_scores(extractor, test, predictor):\n",
-    "    return extractor.score(test, predictor, True, True, EvaluableModel.Task.CLASSIFICATION,\n",
-    "                           [EvaluableModel.ClassificationScore.ACCURACY, EvaluableModel.ClassificationScore.F1])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "5e97565d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "REAL performance (8 rules with 94.67% coverage):\n",
-      "Classification accuracy = 0.96 (data), 1.00 (BB)\n",
-      "F1 = 0.96 (data), 1.00 (BB)\n",
-      "REAL extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    PetalWidth =< 0.64, SepalLength =< 5.38.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    PetalWidth =< 0.64, SepalWidth > 3.20, SepalLength =< 6.26.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalWidth in [0.64, 1.63], SepalWidth =< 2.87, PetalLength > 2.26, SepalLength =< 6.26.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalWidth in [0.64, 1.63], SepalLength in [5.38, 6.26], SepalWidth =< 3.20.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalWidth in [0.64, 1.63], PetalLength =< 4.86, SepalLength > 5.38, SepalWidth =< 3.20.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    SepalLength in [5.38, 6.26], SepalWidth in [2.87, 3.20], PetalLength =< 4.86, PetalWidth > 0.64.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    PetalLength > 4.86, SepalLength > 6.26.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    PetalLength > 4.86, PetalWidth > 1.63.\n"
-     ]
-    }
-   ],
-   "source": [
-    "real = Extractor.real(predictor, iris_features)\n",
-    "theory_from_real = real.extract(train)\n",
-    "scores, completeness = get_scores(real, test, predictor)\n",
-    "print(f'REAL performance ({real.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('REAL extracted rules:\\n\\n' + pretty_theory(theory_from_real))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "We create a different extractor that use Trepan algorithm and we extract prolog rules from the same KNN."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "dc20410e",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TREPAN performance (3 rules with 100.00% coverage):\n",
-      "Classification accuracy = 0.95 (data), 0.96 (BB)\n",
-      "F1 = 0.95 (data), 0.96 (BB)\n",
-      "\n",
-      "Trepan extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor) :-\n",
-      "    PetalLength > 2.26, PetalLength in [2.26, 4.86].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    PetalLength > 2.26.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa).\n"
-     ]
-    }
-   ],
-   "source": [
-    "trepan = Extractor.trepan(predictor, iris_features)\n",
-    "theory_from_trepan = trepan.extract(train)\n",
-    "scores, completeness = get_scores(trepan, test, predictor)\n",
-    "print(f'TREPAN performance ({trepan.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nTrepan extracted rules:\\n\\n' + pretty_theory(theory_from_trepan))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "We create another different extractor that use CART algorithm."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CART performance (3 rules with 100.00% coverage):\n",
-      "Classification accuracy = 0.95 (data), 0.96 (BB)\n",
-      "F1 = 0.95 (data), 0.96 (BB)\n",
-      "\n",
-      "CART extracted rules:\n",
-      "\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, setosa) :-\n",
-      "    PetalWidth =< 0.64.\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, virginica) :-\n",
-      "    PetalLength not_in [2.26, 4.86].\n",
-      "iris(PetalLength, PetalWidth, SepalLength, SepalWidth, versicolor).\n"
-     ]
-    }
-   ],
-   "source": [
-    "cart = Extractor.cart(predictor, discretization=iris_features, simplify=True)\n",
-    "theory_from_cart = cart.extract(train)\n",
-    "scores, completeness = get_scores(cart, test, predictor)\n",
-    "print(f'CART performance ({cart.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCART extracted rules:\\n\\n' + pretty_theory(theory_from_cart))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/demo/DemoRegression.ipynb b/demo/DemoRegression.ipynb
deleted file mode 100644
index 68f73ceb..00000000
--- a/demo/DemoRegression.ipynb
+++ /dev/null
@@ -1,1835 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f52126f3",
-   "metadata": {},
-   "source": [
-    "# PSyKE's demo for regression tasks\n",
-    "\n",
-    "Some imports."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6b710e7c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from psyke import Extractor, Clustering, EvaluableModel\n",
-    "from psyke.tuning.pedro import PEDRO\n",
-    "from psyke.tuning import Objective\n",
-    "from psyke.tuning.crash import CRASH\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from psyke.utils.logic import pretty_theory\n",
-    "from psyke.utils.metrics import mae, mse, r2\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from psyke.utils import Target\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7c90ed2",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Import a dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f8e46c49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = pd.read_csv(\"../test/resources/datasets/df.csv\")\n",
-    "dataset = dataset[[\"X\", \"Y\", \"Z4\"]].dropna()\n",
-    "#dataset = pd.read_csv(\"../test/resources/datasets/CCPP.csv\", sep=\";\", decimal=\",\")\n",
-    "#dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d673b766",
-   "metadata": {},
-   "source": [
-    "Split between train and test set in a reproducible way."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "03fc5e2c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train, test = train_test_split(dataset, test_size=0.5, random_state=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [],
-   "source": [
-    "#from psyke.tuning.orchid import OrCHiD\n",
-    "\n",
-    "#orchid = OrCHiD(dataframe=train, algorithm=OrCHiD.Algorithm.CREAM, output=Target.REGRESSION,\n",
-    "#                max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1, patience=5, max_depth=3)\n",
-    "#orchid.search()\n",
-    "#(_, _, depth, threshold) = orchid.get_best()[0]"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa6754a0",
-   "metadata": {},
-   "source": [
-    "We use as predictor a KNN and we train it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "bed764ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE = 0.08\n",
-      "MSE = 0.03\n",
-      "R2 = 1.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "#predictor = KNeighborsRegressor(n_neighbors=3).fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "predictor = DecisionTreeRegressor().fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "#predictor = LinearRegression().fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "\n",
-    "predicted = predictor.predict(test.iloc[:, :-1]).flatten()\n",
-    "true = test.iloc[:, -1]\n",
-    "\n",
-    "print(f'MAE = {mae(true, predicted):.2f}')\n",
-    "print(f'MSE = {mse(true, predicted):.2f}')\n",
-    "print(f'R2 = {r2(true, predicted):.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "We define a function to print the extractors' evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def print_scores(scores):\n",
-    "    print(f'MAE = {scores[EvaluableModel.RegressionScore.MAE][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.RegressionScore.MAE][1]:.2f} (BB)\\n'\n",
-    "          f'MSE = {scores[EvaluableModel.RegressionScore.MSE][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.RegressionScore.MSE][1]:.2f} (BB)\\n'\n",
-    "          f'R2 = {scores[EvaluableModel.RegressionScore.R2][0]:.2f} (data), '\n",
-    "          f'{scores[EvaluableModel.RegressionScore.R2][1]:.2f} (BB)')\n",
-    "\n",
-    "def get_scores(extractor, test, predictor):\n",
-    "    return extractor.score(test, predictor, True, True, EvaluableModel.Task.REGRESSION,\n",
-    "                           [EvaluableModel.RegressionScore.MAE, EvaluableModel.RegressionScore.MSE,\n",
-    "                            EvaluableModel.RegressionScore.R2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96835867",
-   "metadata": {},
-   "source": [
-    "We create several extractors that use ITER, GridEx and GridREx algorithms to extract prolog rules from the predictor."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ORBIt performance (2 rules with 100.00% coverage):\n",
-      "MAE = 4.15 (data), 4.15 (BB)\n",
-      "MSE = 28.12 (data), 28.10 (BB)\n",
-      "R2 = -0.13 (data), -0.13 (BB)\n",
-      "\n",
-      "ORBIt extracted rules:\n",
-      "\n",
-      "'Z4'(X, Y, 15.42) :-\n",
-      "    0.02 * X - 0.03 * Y =< -0.01, -0.02 * X + 0.03 * Y =< 0.01, 1.0 * X + 0.0 * Y =< 0.46, -1.0 * X + 0.0 * Y =< -0.16.\n",
-      "'Z4'(X, Y, 5.053691).\n"
-     ]
-    }
-   ],
-   "source": [
-    "orbit = Extractor.orbit(predictor, depth=3, error_threshold=1.0, gauss_components=2, output=Target.REGRESSION)\n",
-    "theory_from_orbit = orbit.extract(train)\n",
-    "scores, completeness = get_scores(orbit, test, predictor)\n",
-    "print(f'ORBIt performance ({orbit.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nORBIt extracted rules:\\n\\n' + pretty_theory(theory_from_orbit))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CReEPy performance (3 rules with 100.00% coverage):\n",
-      "MAE = 0.68 (data), 0.71 (BB)\n",
-      "MSE = 2.05 (data), 2.06 (BB)\n",
-      "R2 = 0.92 (data), 0.92 (BB)\n",
-      "\n",
-      "CReEPy extracted rules (ExACT):\n",
-      "\n",
-      "'Z4'(X, Y, Z4) :-\n",
-      "    X in [0.15, 0.84], Y in [-0.00, 0.59], Z4 is 7.49 - 7.63 * X + 12.05 * Y.\n",
-      "'Z4'(X, Y, Z4) :-\n",
-      "    X in [0.15, 0.84], Y in [-0.00, 0.84], Z4 is 9.0 - 12.0 * X + 15.0 * Y.\n",
-      "'Z4'(X, Y, Z4) :-\n",
-      "    Y in [-0.00, 1.00], Z4 is 2.0 + 4.0 * X - 3.0 * Y.\n"
-     ]
-    }
-   ],
-   "source": [
-    "creepy = Extractor.creepy(predictor, depth=3, error_threshold=0.02, output=Target.REGRESSION,\n",
-    "                          clustering=Clustering.exact)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "scores, completeness = get_scores(creepy, test, predictor)\n",
-    "print(f'CReEPy performance ({creepy.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCReEPy extracted rules (ExACT):\\n\\n' + pretty_theory(theory_from_creepy))"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CReEPy performance (4 rules with 100.00% coverage):\n",
-      "MAE = 3.37 (data), 3.47 (BB)\n",
-      "MSE = 18.62 (data), 19.19 (BB)\n",
-      "R2 = 0.94 (data), 0.93 (BB)\n",
-      "\n",
-      "CReEPy extracted rules (CREAM):\n",
-      "\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [6.21, 32.45], V in [34.02, 50.16], AP in [997.90, 1026.41], RH in [35.63, 100.10], PE is 502.53 - 2.16 * AP - 0.26 * AT + 0.01 * RH - 0.11 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [6.21, 35.77], V in [25.35, 81.56], AP in [997.84, 1026.45], RH in [25.55, 100.12], PE is 234.73 - 1.42 * AP - 0.29 * AT + 0.26 * RH - 0.12 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [3.30, 14.60], V in [34.68, 44.47], AP in [1011.31, 1033.25], RH in [58.98, 98.68], PE is 720.26 - 2.20 * AP - 0.47 * AT - 0.18 * RH - 0.22 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15], PE is 579.01 - 2.05 * AP - 0.60 * AT - 0.05 * RH + 0.00 * V.\n"
-     ]
-    }
-   ],
-   "source": [
-    "creepy = Extractor.creepy(predictor, depth=2, error_threshold=0.02, output=Target.REGRESSION,\n",
-    "                          clustering=Clustering.cream)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "scores, completeness = get_scores(creepy, test, predictor)\n",
-    "print(f'CReEPy performance ({creepy.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCReEPy extracted rules (CREAM):\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.00. MAE = 3.52, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.00. MAE = 3.54, 2 rules\n",
-      "\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.00. MAE = 3.52, 3 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.00. MAE = 3.48, 3 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.00. MAE = 3.50, 3 rules\n",
-      "\n",
-      "Algorithm.ExACT. Depth: 3. Threshold = 0.00. MAE = 3.44, 4 rules\n",
-      "Algorithm.ExACT. Depth: 3. Threshold = 0.00. MAE = 3.41, 4 rules\n",
-      "Algorithm.ExACT. Depth: 3. Threshold = 0.00. MAE = 3.52, 4 rules\n",
-      "\n",
-      "**********************\n",
-      "Best Algorithm.ExACT\n",
-      "**********************\n",
-      "MAE = 3.41, 4 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 3\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 3.41, 4 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 3\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 3.54, 2 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 1\n",
-      "\n",
-      "CReEPy performance (4 rules with 100.00% coverage):\n",
-      "MAE = 3.37 (data), 3.46 (BB)\n",
-      "MSE = 18.48 (data), 19.00 (BB)\n",
-      "R2 = 0.94 (data), 0.94 (BB)\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [6.21, 32.45], V in [35.39, 50.16], AP in [998.07, 1026.40], RH in [35.63, 100.10], PE is 499.89 - 2.16 * AP - 0.27 * AT + 0.01 * RH - 0.11 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [6.21, 32.45], V in [34.02, 50.16], AP in [997.90, 1026.41], RH in [35.63, 100.10], PE is 697.90 - 1.74 * AP - 2.04 * AT - 0.17 * RH + 0.61 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [6.21, 35.77], V in [25.35, 81.56], AP in [997.84, 1026.45], RH in [25.55, 100.12], PE is 234.73 - 1.42 * AP - 0.29 * AT + 0.26 * RH - 0.12 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15], PE is 628.20 - 2.19 * AP - 0.50 * AT - 0.09 * RH - 0.17 * V.\n"
-     ]
-    }
-   ],
-   "source": [
-    "crash = CRASH(predictor, train, max_depth=3, patience=1, readability_tradeoff=.5,\n",
-    "              algorithm=CRASH.Algorithm.ExACT, output=Target.REGRESSION)\n",
-    "crash.search()\n",
-    "(_, _, depth, threshold) = crash.get_best()[0]\n",
-    "\n",
-    "creepy = Extractor.creepy(predictor, depth=depth, error_threshold=threshold, output=Target.REGRESSION,\n",
-    "                          clustering=Clustering.exact)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "scores, completeness = get_scores(creepy, test, predictor)\n",
-    "print(f'CReEPy performance ({creepy.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.00. MAE = 10.29, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.00. MAE = 8.46, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.00. MAE = 10.29, 2 rules\n",
-      "\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.00. MAE = 6.46, 4 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.00. MAE = 6.10, 4 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.00. MAE = 7.75, 4 rules\n",
-      "\n",
-      "Algorithm.CREAM. Depth: 3. Threshold = 0.00. "
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
-      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_14404/1950581394.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[0mcrash\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mCRASH\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mpredictor\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtrain\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_depth\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m3\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mpatience\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreadability_tradeoff\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m.75\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0malgorithm\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mCRASH\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mAlgorithm\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mCREAM\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0mcrash\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msearch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      3\u001B[0m \u001B[1;33m(\u001B[0m\u001B[0m_\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0m_\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdepth\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mthreshold\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcrash\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_best\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m creepy = Extractor.creepy(predictor, depth=depth, error_threshold=threshold, output=Target.REGRESSION,\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\tuning\\crash\\__init__.py\u001B[0m in \u001B[0;36msearch\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m     23\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     24\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0msearch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 25\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__search_depth\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     26\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     27\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m__search_depth\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\tuning\\crash\\__init__.py\u001B[0m in \u001B[0;36m__search_depth\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m     30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     31\u001B[0m         \u001B[1;32mfor\u001B[0m \u001B[0mdepth\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmax_depth\u001B[0m \u001B[1;33m+\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 32\u001B[1;33m             \u001B[0mp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__search_threshold\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdepth\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     33\u001B[0m             \u001B[0mb\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mOptimizer\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_best\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mp\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     34\u001B[0m             \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\tuning\\crash\\__init__.py\u001B[0m in \u001B[0;36m__search_threshold\u001B[1;34m(self, depth)\u001B[0m\n\u001B[0;32m     56\u001B[0m                 \u001B[0mclustering\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mClustering\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcream\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0malgorithm\u001B[0m \u001B[1;33m==\u001B[0m \u001B[0mCRASH\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mAlgorithm\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mCREAM\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0mClustering\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mexact\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     57\u001B[0m             )\n\u001B[1;32m---> 58\u001B[1;33m             \u001B[0m_\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mextractor\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mextract\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     59\u001B[0m             mae, n = (extractor.mae(self.dataframe, self.predictor) if self.objective == Objective.MODEL else\n\u001B[0;32m     60\u001B[0m                       extractor.mae(self.dataframe)), extractor.n_rules\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\__init__.py\u001B[0m in \u001B[0;36mextract\u001B[1;34m(self, dataframe, mapping, sort)\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\extraction\\hypercubic\\creepy\\__init__.py\u001B[0m in \u001B[0;36m_extract\u001B[1;34m(self, dataframe, mapping, sort)\u001B[0m\n\u001B[0;32m     32\u001B[0m             \u001B[1;32mraise\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"clustering must be a HyperCubeClustering\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     33\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 34\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mclustering\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     35\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_hypercubes\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mclustering\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mget_hypercubes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     36\u001B[0m         \u001B[1;32mfor\u001B[0m \u001B[0mcube\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_hypercubes\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\clustering\\exact\\__init__.py\u001B[0m in \u001B[0;36mfit\u001B[1;34m(self, dataframe)\u001B[0m\n\u001B[0;32m     58\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_predictor\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdataframe\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     59\u001B[0m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_hypercubes\u001B[0m \u001B[1;33m=\u001B[0m\u001B[0;31m \u001B[0m\u001B[0;31m\\\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 60\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_iterate\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mNode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mHyperCube\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcreate_surrounding_cube\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;32mTrue\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_output\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     61\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     62\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mget_hypercubes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m->\u001B[0m \u001B[0mIterable\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mHyperCube\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\clustering\\cream\\__init__.py\u001B[0m in \u001B[0;36m_iterate\u001B[1;34m(self, surrounding)\u001B[0m\n\u001B[0;32m     52\u001B[0m             \u001B[0mgauss_params\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mselect_gaussian_mixture\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mgauss_components\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     53\u001B[0m             \u001B[0mgauss_pred\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mgauss_params\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m2\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 54\u001B[1;33m             \u001B[0mcubes\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__eligible_cubes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mgauss_pred\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgauss_params\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     55\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcubes\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m<\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     56\u001B[0m                 \u001B[1;32mcontinue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\clustering\\cream\\__init__.py\u001B[0m in \u001B[0;36m__eligible_cubes\u001B[1;34m(self, gauss_pred, node, clusters)\u001B[0m\n\u001B[0;32m     27\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     28\u001B[0m                 \u001B[1;32mcontinue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 29\u001B[1;33m             \u001B[0minner_cube\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_create_cube\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclusters\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     30\u001B[0m             \u001B[0mindices\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_indices\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0minner_cube\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnode\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     31\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mindices\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\clustering\\exact\\__init__.py\u001B[0m in \u001B[0;36m_create_cube\u001B[1;34m(self, dataframe, clusters)\u001B[0m\n\u001B[0;32m     49\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m_create_cube\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mdataframe\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mDataFrame\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclusters\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mint\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m->\u001B[0m \u001B[0mClosedCube\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     50\u001B[0m         \u001B[0mdata\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mExACT\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_remove_string_label\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdataframe\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 51\u001B[1;33m         \u001B[0mdbscan_pred\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mDBSCAN\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0meps\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mselect_dbscan_epsilon\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclusters\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit_predict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     52\u001B[0m         return HyperCube.create_surrounding_cube(\n\u001B[0;32m     53\u001B[0m             \u001B[0mdataframe\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mnp\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mwhere\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdbscan_pred\u001B[0m \u001B[1;33m==\u001B[0m \u001B[0mCounter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdbscan_pred\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmost_common\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Desktop\\psyke\\psyke-python\\psyke\\clustering\\utils.py\u001B[0m in \u001B[0;36mselect_dbscan_epsilon\u001B[1;34m(data, clusters)\u001B[0m\n\u001B[0;32m     30\u001B[0m         \u001B[0mepsilon\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mmax\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdistances\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1e-3\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     31\u001B[0m     \u001B[0mk\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;36m1.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 32\u001B[1;33m     \u001B[0mdbscan_pred\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mDBSCAN\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0meps\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mepsilon\u001B[0m \u001B[1;33m*\u001B[0m \u001B[0mk\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit_predict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     33\u001B[0m     \u001B[1;31m# while Counter(dbscan_pred).most_common(1)[0][0] == -1:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     34\u001B[0m     \u001B[1;32mfor\u001B[0m \u001B[0mi\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m1000\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_dbscan.py\u001B[0m in \u001B[0;36mfit_predict\u001B[1;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[0;32m    456\u001B[0m             \u001B[0mCluster\u001B[0m \u001B[0mlabels\u001B[0m\u001B[1;33m.\u001B[0m \u001B[0mNoisy\u001B[0m \u001B[0msamples\u001B[0m \u001B[0mare\u001B[0m \u001B[0mgiven\u001B[0m \u001B[0mthe\u001B[0m \u001B[0mlabel\u001B[0m \u001B[1;33m-\u001B[0m\u001B[1;36m1.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    457\u001B[0m         \"\"\"\n\u001B[1;32m--> 458\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msample_weight\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0msample_weight\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    459\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mlabels_\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    460\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_dbscan.py\u001B[0m in \u001B[0;36mfit\u001B[1;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[0;32m    404\u001B[0m         \u001B[0mneighbors_model\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    405\u001B[0m         \u001B[1;31m# This has worst case O(n^2) memory complexity\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 406\u001B[1;33m         \u001B[0mneighborhoods\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mneighbors_model\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mradius_neighbors\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_distance\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    407\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    408\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0msample_weight\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_base.py\u001B[0m in \u001B[0;36mradius_neighbors\u001B[1;34m(self, X, radius, return_distance, sort_results)\u001B[0m\n\u001B[0;32m   1167\u001B[0m             \u001B[0mn_jobs\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0meffective_n_jobs\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mn_jobs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1168\u001B[0m             \u001B[0mdelayed_query\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdelayed\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0m_tree_query_radius_parallel_helper\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1169\u001B[1;33m             chunked_results = Parallel(n_jobs, prefer=\"threads\")(\n\u001B[0m\u001B[0;32m   1170\u001B[0m                 delayed_query(\n\u001B[0;32m   1171\u001B[0m                     \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_tree\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mX\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0ms\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mradius\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreturn_distance\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msort_results\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0msort_results\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001B[0m in \u001B[0;36m__call__\u001B[1;34m(self, iterable)\u001B[0m\n\u001B[0;32m   1041\u001B[0m             \u001B[1;31m# remaining jobs.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1042\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_iterating\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mFalse\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1043\u001B[1;33m             \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdispatch_one_batch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0miterator\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1044\u001B[0m                 \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_iterating\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_original_iterator\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1045\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001B[0m in \u001B[0;36mdispatch_one_batch\u001B[1;34m(self, iterator)\u001B[0m\n\u001B[0;32m    859\u001B[0m                 \u001B[1;32mreturn\u001B[0m \u001B[1;32mFalse\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    860\u001B[0m             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 861\u001B[1;33m                 \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_dispatch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtasks\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    862\u001B[0m                 \u001B[1;32mreturn\u001B[0m \u001B[1;32mTrue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    863\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001B[0m in \u001B[0;36m_dispatch\u001B[1;34m(self, batch)\u001B[0m\n\u001B[0;32m    777\u001B[0m         \u001B[1;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_lock\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    778\u001B[0m             \u001B[0mjob_idx\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_jobs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 779\u001B[1;33m             \u001B[0mjob\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_backend\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mapply_async\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mbatch\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcallback\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mcb\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    780\u001B[0m             \u001B[1;31m# A job can complete so quickly than its callback is\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    781\u001B[0m             \u001B[1;31m# called before we get here, causing self._jobs to\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001B[0m in \u001B[0;36mapply_async\u001B[1;34m(self, func, callback)\u001B[0m\n\u001B[0;32m    206\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mapply_async\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcallback\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mNone\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    207\u001B[0m         \u001B[1;34m\"\"\"Schedule a func to be run\"\"\"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 208\u001B[1;33m         \u001B[0mresult\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mImmediateResult\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfunc\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    209\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mcallback\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    210\u001B[0m             \u001B[0mcallback\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mresult\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\_parallel_backends.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, batch)\u001B[0m\n\u001B[0;32m    570\u001B[0m         \u001B[1;31m# Don't delay the application, to avoid keeping the input\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    571\u001B[0m         \u001B[1;31m# arguments in memory\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 572\u001B[1;33m         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mresults\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mbatch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    573\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    574\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mget\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001B[0m in \u001B[0;36m__call__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    260\u001B[0m         \u001B[1;31m# change the default number of processes to -1\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    261\u001B[0m         \u001B[1;32mwith\u001B[0m \u001B[0mparallel_backend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_backend\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mn_jobs\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_n_jobs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 262\u001B[1;33m             return [func(*args, **kwargs)\n\u001B[0m\u001B[0;32m    263\u001B[0m                     for func, args, kwargs in self.items]\n\u001B[0;32m    264\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001B[0m in \u001B[0;36m<listcomp>\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m    260\u001B[0m         \u001B[1;31m# change the default number of processes to -1\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    261\u001B[0m         \u001B[1;32mwith\u001B[0m \u001B[0mparallel_backend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_backend\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mn_jobs\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_n_jobs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 262\u001B[1;33m             return [func(*args, **kwargs)\n\u001B[0m\u001B[0;32m    263\u001B[0m                     for func, args, kwargs in self.items]\n\u001B[0;32m    264\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001B[0m in \u001B[0;36m__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m    115\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m__call__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    116\u001B[0m         \u001B[1;32mwith\u001B[0m \u001B[0mconfig_context\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m**\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 117\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfunction\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    118\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    119\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_base.py\u001B[0m in \u001B[0;36m_tree_query_radius_parallel_helper\u001B[1;34m(tree, *args, **kwargs)\u001B[0m\n\u001B[0;32m    949\u001B[0m     \u001B[0mcloudpickle\u001B[0m \u001B[0munder\u001B[0m \u001B[0mPyPy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    950\u001B[0m     \"\"\"\n\u001B[1;32m--> 951\u001B[1;33m     \u001B[1;32mreturn\u001B[0m \u001B[0mtree\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mquery_radius\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    952\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    953\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "crash = CRASH(predictor, train, max_depth=3, patience=1, readability_tradeoff=.75, algorithm=CRASH.Algorithm.CREAM)\n",
-    "crash.search()\n",
-    "(_, _, depth, threshold) = crash.get_best()[0]\n",
-    "\n",
-    "creepy = Extractor.creepy(predictor, depth=depth, error_threshold=threshold, output=Target.REGRESSION,\n",
-    "                          clustering=Clustering.cream)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "scores, completeness = get_scores(creepy, test, predictor)\n",
-    "print(f'CReEPy performance ({creepy.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nCReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "it = Extractor.iter(predictor, min_update=1.0 / 10, n_points=1, max_iterations=600,\n",
-    "                    min_examples=100, threshold=5)\n",
-    "theory_from_iter = it.extract(train)\n",
-    "scores, completeness = get_scores(it, test, predictor)\n",
-    "print(f'ITER performance ({it.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nITER extracted rules:\\n\\n' + pretty_theory(theory_from_iter))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.GRIDEX. Grid (1). Fixed (2). Threshold = 0.00. MAE = 6.45, 15 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Fixed (2). Threshold = 0.00. MAE = 6.45, 30 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Fixed (3). Threshold = 0.00. MAE = 6.45, 86 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Fixed (3). Threshold = 0.00. MAE = 6.45, 142 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 2)]). Threshold = 0.00. MAE = 6.45, 144 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 2)]). Threshold = 0.00. MAE = 6.45, 146 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.3, 2)]). Threshold = 0.00. MAE = 6.45, 150 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.3, 2)]). Threshold = 0.00. MAE = 6.45, 154 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 3)]). Threshold = 0.00. MAE = 6.45, 157 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 3)]). Threshold = 0.00. MAE = 6.45, 160 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.3, 3)]). Threshold = 0.00. MAE = 6.45, 169 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.3, 3)]). Threshold = 0.00. MAE = 6.45, 178 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 5)]). Threshold = 0.00. MAE = 6.45, 183 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 5)]). Threshold = 0.00. MAE = 6.45, 188 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.3, 5)]). Threshold = 0.00. MAE = 6.45, 209 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.3, 5)]). Threshold = 0.00. MAE = 6.45, 230 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 10)]). Threshold = 0.00. MAE = 6.45, 240 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 10)]). Threshold = 0.00. MAE = 6.45, 250 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.33, 2), (0.67, 3)]). Threshold = 0.00. MAE = 6.45, 256 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.33, 2), (0.67, 3)]). Threshold = 0.00. MAE = 6.45, 262 rules\n",
-      "\n",
-      "**********************\n",
-      "Best Algorithm.GRIDEX\n",
-      "**********************\n",
-      "MAE = 6.45, 15 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 6.45, 256 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Adaptive ([(0.33, 2), (0.67, 3)])\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 6.45, 15 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "GridEx performance (277 rules with 99.90% coverage):\n",
-      "MAE = 6.50 (data), 6.49 (BB)\n",
-      "MSE = 66.41 (data), 65.91 (BB)\n",
-      "R2 = 0.77 (data), 0.77 (BB)\n",
-      "\n",
-      "GridEx extracted rules:\n",
-      "\n",
-      "'PE'(AP, AT, RH, V, 473.96) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 468.97) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.39) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 472.61) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 468.07) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.26) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 464.10) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.50) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.77) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.32) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 447.25) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.54) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 438.05) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.71) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.25) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 473.96) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 468.97) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.39) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 472.61) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 468.07) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.26) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 464.10) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.50) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.77) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.32) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 447.25) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.54) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 438.05) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.71) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.25) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.41) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 481.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.04) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 478.54) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.40) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.33) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 479.69) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 475.51) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.59) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.63) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.84) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.40) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.29) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 457.77) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 461.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 463.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 459.06) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.52) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.68) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.11) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 455.09) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 451.49) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 451.23) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 456.93) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.31) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.15) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.29) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.98) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.08) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 447.20) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.67) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.66) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.45) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.79) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 443.17) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.23) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 437.81) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 439.21) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.28) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.04) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.39) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.45) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.41) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 440.86) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.81) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 434.24) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.45) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.93) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 436.02) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.65) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.39) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 482.41) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 481.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.04) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 478.54) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.40) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.33) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 479.69) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 475.51) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.59) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.63) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.84) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.40) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.29) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 457.77) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 461.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 463.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 459.06) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.52) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.68) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.11) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 455.09) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 451.49) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 451.23) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 456.93) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.31) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.15) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.29) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.98) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.08) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 447.20) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.67) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.66) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.45) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.79) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 443.17) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.23) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 437.81) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 439.21) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.28) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.04) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.39) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.45) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.41) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 440.86) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.81) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 434.24) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.45) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.93) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 436.02) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.65) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.39) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 470.05) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.29) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.05) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.29) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.13) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 461.20) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.17) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 439.66) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.13) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 461.20) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.17) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 439.66) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.67) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.76) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.67) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.76) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 475.45) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 463.56) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.52) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.06) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.94) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.07) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.18) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 475.45) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 463.56) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.52) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.06) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.94) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.07) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.18) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.28) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.58) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.90) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.37) :-\n",
-      "    AT in [22.39, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.51) :-\n",
-      "    AT in [29.08, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.28) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.58) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.90) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.37) :-\n",
-      "    AT in [22.39, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.51) :-\n",
-      "    AT in [29.08, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 485.17) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.31) :-\n",
-      "    AT in [2.33, 9.02], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.27) :-\n",
-      "    AT in [2.33, 9.02], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 9.02], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 472.40) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.93) :-\n",
-      "    AT in [9.02, 15.71], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.36) :-\n",
-      "    AT in [9.02, 15.71], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 467.00) :-\n",
-      "    AT in [9.02, 15.71], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 457.10) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.41) :-\n",
-      "    AT in [15.71, 22.39], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.81) :-\n",
-      "    AT in [15.71, 22.39], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.42) :-\n",
-      "    AT in [15.71, 22.39], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.88) :-\n",
-      "    AT in [15.71, 22.39], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.49) :-\n",
-      "    AT in [22.39, 29.08], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.57) :-\n",
-      "    AT in [22.39, 29.08], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.40) :-\n",
-      "    AT in [22.39, 29.08], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.49) :-\n",
-      "    AT in [22.39, 29.08], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.89) :-\n",
-      "    AT in [29.08, 35.77], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.08) :-\n",
-      "    AT in [29.08, 35.77], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.92) :-\n",
-      "    AT in [29.08, 35.77], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 432.88) :-\n",
-      "    AT in [29.08, 35.77], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 485.17) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.31) :-\n",
-      "    AT in [2.33, 9.02], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.27) :-\n",
-      "    AT in [2.33, 9.02], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 9.02], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 472.40) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.93) :-\n",
-      "    AT in [9.02, 15.71], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.36) :-\n",
-      "    AT in [9.02, 15.71], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 467.00) :-\n",
-      "    AT in [9.02, 15.71], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 457.10) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.41) :-\n",
-      "    AT in [15.71, 22.39], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.81) :-\n",
-      "    AT in [15.71, 22.39], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.42) :-\n",
-      "    AT in [15.71, 22.39], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.88) :-\n",
-      "    AT in [15.71, 22.39], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.49) :-\n",
-      "    AT in [22.39, 29.08], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.57) :-\n",
-      "    AT in [22.39, 29.08], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.40) :-\n",
-      "    AT in [22.39, 29.08], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.49) :-\n",
-      "    AT in [22.39, 29.08], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.89) :-\n",
-      "    AT in [29.08, 35.77], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.08) :-\n",
-      "    AT in [29.08, 35.77], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.92) :-\n",
-      "    AT in [29.08, 35.77], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 432.88) :-\n",
-      "    AT in [29.08, 35.77], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 486.49) :-\n",
-      "    AT in [2.33, 5.68], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.56) :-\n",
-      "    AT in [5.68, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 474.79) :-\n",
-      "    AT in [9.02, 12.36], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.96) :-\n",
-      "    AT in [12.36, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 458.45) :-\n",
-      "    AT in [15.71, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.01) :-\n",
-      "    AT in [19.05, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.66) :-\n",
-      "    AT in [22.39, 25.74], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 437.65) :-\n",
-      "    AT in [25.74, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.90) :-\n",
-      "    AT in [29.08, 32.42], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.90) :-\n",
-      "    AT in [32.42, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 486.49) :-\n",
-      "    AT in [2.33, 5.68], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.56) :-\n",
-      "    AT in [5.68, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 474.79) :-\n",
-      "    AT in [9.02, 12.36], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.96) :-\n",
-      "    AT in [12.36, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 458.45) :-\n",
-      "    AT in [15.71, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.01) :-\n",
-      "    AT in [19.05, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.66) :-\n",
-      "    AT in [22.39, 25.74], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 437.65) :-\n",
-      "    AT in [25.74, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.90) :-\n",
-      "    AT in [29.08, 32.42], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.90) :-\n",
-      "    AT in [32.42, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.95) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.83) :-\n",
-      "    AT in [2.33, 13.48], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.42) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.57) :-\n",
-      "    AT in [13.48, 24.62], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.25) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.31) :-\n",
-      "    AT in [24.62, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.95) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.83) :-\n",
-      "    AT in [2.33, 13.48], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.42) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.57) :-\n",
-      "    AT in [13.48, 24.62], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.25) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.31) :-\n",
-      "    AT in [24.62, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 473.96) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 468.97) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.39) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 472.61) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 468.07) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.26) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 464.10) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.50) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.77) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.32) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 447.25) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.54) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 438.05) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.71) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.25) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n"
-     ]
-    }
-   ],
-   "source": [
-    "pedro = PEDRO(predictor, train, max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1,\n",
-    "              max_depth=1, patience=1, algorithm=PEDRO.Algorithm.GRIDEX, objective=Objective.MODEL)\n",
-    "pedro.search()\n",
-    "(_, _, threshold, grid) = pedro.get_best()[0]\n",
-    "\n",
-    "gridEx = Extractor.gridex(predictor, grid, threshold=threshold)\n",
-    "theory_from_gridEx = gridEx.extract(train)\n",
-    "scores, completeness = get_scores(gridEx, test, predictor)\n",
-    "print(f'GridEx performance ({gridEx.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('\\nGridEx extracted rules:\\n\\n' + pretty_theory(theory_from_gridEx))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "**********************\n",
-      "Best Algorithm.GRIDEX\n",
-      "**********************\n",
-      "MAE = 6.45, 15 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 6.45, 256 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Adaptive ([(0.33, 2), (0.67, 3)])\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 6.45, 15 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "GridREx performance (292 rules with 99.90% coverage):\n",
-      "MAE = 6.50 (data), 6.49 (BB)\n",
-      "MSE = 66.41 (data), 65.91 (BB)\n",
-      "R2 = 0.77 (data), 0.77 (BB)\n",
-      "GridREx extracted rules:\n",
-      "\n",
-      "'PE'(AP, AT, RH, V, 473.96) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 468.97) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.39) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 472.61) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 468.07) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.26) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 464.10) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.50) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.77) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.32) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 447.25) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.54) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 438.05) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.71) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.25) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 473.96) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 468.97) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.39) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 472.61) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 468.07) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.26) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 464.10) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.50) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.77) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.32) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 447.25) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.54) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 438.05) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.71) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.25) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.41) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 481.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.04) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 478.54) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.40) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.33) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 479.69) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 475.51) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.59) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.63) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.84) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.40) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.29) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 457.77) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 461.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 463.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 459.06) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.52) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.68) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.11) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 455.09) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 451.49) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 451.23) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 456.93) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.31) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.15) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.29) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.98) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.08) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 447.20) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.67) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.66) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.45) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.79) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 443.17) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.23) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 437.81) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 439.21) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.28) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.04) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.39) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.45) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.41) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 440.86) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.81) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 434.24) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.45) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.93) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 436.02) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.65) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.39) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 482.41) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 481.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.04) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 478.54) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.40) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.33) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 479.69) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.58) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 475.51) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.59) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 477.63) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.84) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.40) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.29) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 457.77) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 461.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 463.79) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 460.57) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 459.06) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.52) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.68) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.11) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 455.09) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 451.49) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 451.23) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 456.93) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 457.31) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 456.15) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.29) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.98) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 451.08) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 447.20) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.67) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.66) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 450.45) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.79) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 443.17) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 445.23) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 437.81) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 439.21) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.28) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.04) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 443.39) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.45) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 442.41) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 440.86) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.81) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 434.24) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.45) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1006.34], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.93) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [25.55, 50.42].\n",
-      "'PE'(AP, AT, RH, V, 436.02) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 434.65) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1006.34, 1019.79], RH in [75.28, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.39) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [1019.79, 1033.25], RH in [50.42, 75.28].\n",
-      "'PE'(AP, AT, RH, V, 470.05) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.29) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.05) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.29) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.13) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 461.20) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.17) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 439.66) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.13) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 461.20) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.17) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 439.66) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.67) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.76) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.90) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.67) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.76) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 475.45) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 463.56) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.52) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.06) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.94) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.07) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.18) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 475.45) :-\n",
-      "    AT in [2.33, 13.48], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.45) :-\n",
-      "    AT in [2.33, 13.48], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 463.56) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.52) :-\n",
-      "    AT in [13.48, 24.62], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.06) :-\n",
-      "    AT in [13.48, 24.62], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.94) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 44.09], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.07) :-\n",
-      "    AT in [24.62, 35.77], V in [44.09, 62.82], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.18) :-\n",
-      "    AT in [24.62, 35.77], V in [62.82, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.28) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.58) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.90) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.37) :-\n",
-      "    AT in [22.39, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.51) :-\n",
-      "    AT in [29.08, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.28) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.58) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 453.90) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.37) :-\n",
-      "    AT in [22.39, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.51) :-\n",
-      "    AT in [29.08, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 485.17) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.31) :-\n",
-      "    AT in [2.33, 9.02], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.27) :-\n",
-      "    AT in [2.33, 9.02], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 9.02], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 472.40) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.93) :-\n",
-      "    AT in [9.02, 15.71], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.36) :-\n",
-      "    AT in [9.02, 15.71], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 467.00) :-\n",
-      "    AT in [9.02, 15.71], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 457.10) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.41) :-\n",
-      "    AT in [15.71, 22.39], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.81) :-\n",
-      "    AT in [15.71, 22.39], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.42) :-\n",
-      "    AT in [15.71, 22.39], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.88) :-\n",
-      "    AT in [15.71, 22.39], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.49) :-\n",
-      "    AT in [22.39, 29.08], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.57) :-\n",
-      "    AT in [22.39, 29.08], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.40) :-\n",
-      "    AT in [22.39, 29.08], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.49) :-\n",
-      "    AT in [22.39, 29.08], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.89) :-\n",
-      "    AT in [29.08, 35.77], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.08) :-\n",
-      "    AT in [29.08, 35.77], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.92) :-\n",
-      "    AT in [29.08, 35.77], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 432.88) :-\n",
-      "    AT in [29.08, 35.77], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 485.17) :-\n",
-      "    AT in [2.33, 9.02], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 483.31) :-\n",
-      "    AT in [2.33, 9.02], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.27) :-\n",
-      "    AT in [2.33, 9.02], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.21) :-\n",
-      "    AT in [2.33, 9.02], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 472.40) :-\n",
-      "    AT in [9.02, 15.71], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.93) :-\n",
-      "    AT in [9.02, 15.71], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.36) :-\n",
-      "    AT in [9.02, 15.71], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 467.00) :-\n",
-      "    AT in [9.02, 15.71], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 457.10) :-\n",
-      "    AT in [15.71, 22.39], V in [25.35, 36.59], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.41) :-\n",
-      "    AT in [15.71, 22.39], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 452.81) :-\n",
-      "    AT in [15.71, 22.39], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 448.42) :-\n",
-      "    AT in [15.71, 22.39], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.88) :-\n",
-      "    AT in [15.71, 22.39], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.49) :-\n",
-      "    AT in [22.39, 29.08], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 444.57) :-\n",
-      "    AT in [22.39, 29.08], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.40) :-\n",
-      "    AT in [22.39, 29.08], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 435.49) :-\n",
-      "    AT in [22.39, 29.08], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 440.89) :-\n",
-      "    AT in [29.08, 35.77], V in [36.59, 47.84], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 443.08) :-\n",
-      "    AT in [29.08, 35.77], V in [47.84, 59.08], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.92) :-\n",
-      "    AT in [29.08, 35.77], V in [59.08, 70.32], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 432.88) :-\n",
-      "    AT in [29.08, 35.77], V in [70.32, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 486.49) :-\n",
-      "    AT in [2.33, 5.68], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.56) :-\n",
-      "    AT in [5.68, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 474.79) :-\n",
-      "    AT in [9.02, 12.36], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.96) :-\n",
-      "    AT in [12.36, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 458.45) :-\n",
-      "    AT in [15.71, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.01) :-\n",
-      "    AT in [19.05, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.66) :-\n",
-      "    AT in [22.39, 25.74], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 437.65) :-\n",
-      "    AT in [25.74, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.90) :-\n",
-      "    AT in [29.08, 32.42], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.90) :-\n",
-      "    AT in [32.42, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 486.49) :-\n",
-      "    AT in [2.33, 5.68], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 482.56) :-\n",
-      "    AT in [5.68, 9.02], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 474.79) :-\n",
-      "    AT in [9.02, 12.36], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 466.96) :-\n",
-      "    AT in [12.36, 15.71], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 458.45) :-\n",
-      "    AT in [15.71, 19.05], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 450.01) :-\n",
-      "    AT in [19.05, 22.39], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.66) :-\n",
-      "    AT in [22.39, 25.74], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 437.65) :-\n",
-      "    AT in [25.74, 29.08], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 433.90) :-\n",
-      "    AT in [29.08, 32.42], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.90) :-\n",
-      "    AT in [32.42, 35.77], V in [25.35, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.95) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.83) :-\n",
-      "    AT in [2.33, 13.48], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.42) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.57) :-\n",
-      "    AT in [13.48, 24.62], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.25) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.31) :-\n",
-      "    AT in [24.62, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 476.95) :-\n",
-      "    AT in [2.33, 13.48], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 477.83) :-\n",
-      "    AT in [2.33, 13.48], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 459.42) :-\n",
-      "    AT in [13.48, 24.62], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 446.57) :-\n",
-      "    AT in [13.48, 24.62], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 442.25) :-\n",
-      "    AT in [24.62, 35.77], V in [25.35, 53.46], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.31) :-\n",
-      "    AT in [24.62, 35.77], V in [53.46, 81.56], AP in [992.88, 1033.25], RH in [25.55, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 473.96) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 468.97) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 471.39) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 472.61) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 468.07) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 470.26) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 464.10) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 445.50) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.77) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 449.32) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 447.25) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 436.54) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 438.05) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, 441.71) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85].\n",
-      "'PE'(AP, AT, RH, V, 446.25) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15].\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85], PE is 462.71 - 2.15 * AP - 0.22 * AT + 0.04 * RH - 0.02 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15], PE is 431.79 - 2.16 * AP - 0.45 * AT + 0.08 * RH - 0.06 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85], PE is 495.74 - 1.80 * AP - 0.07 * AT + 0.00 * RH - 0.01 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15], PE is 703.70 - 2.37 * AP - 0.12 * AT - 0.18 * RH - 0.11 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15], PE is 540.25 - 2.54 * AP - 0.04 * AT - 0.04 * RH + 0.03 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85], PE is 556.66 - 2.35 * AP - 0.04 * AT - 0.05 * RH + 0.05 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [2.33, 19.05], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15], PE is 453.81 - 2.40 * AP - 0.18 * AT + 0.05 * RH - 0.04 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [25.55, 62.85], PE is 495.08 - 1.30 * AP - 0.20 * AT - 0.00 * RH - 0.10 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [992.88, 1013.07], RH in [62.85, 100.15], PE is 394.31 - 1.06 * AP - 0.32 * AT + 0.09 * RH - 0.01 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [25.55, 62.85], PE is 625.50 - 1.14 * AP + 0.09 * AT - 0.13 * RH - 0.27 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [25.35, 53.46], AP in [1013.07, 1033.25], RH in [62.85, 100.15], PE is 699.87 - 1.10 * AP + 0.06 * AT - 0.21 * RH - 0.05 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [25.55, 62.85], PE is 106.52 - 1.12 * AP - 0.21 * AT + 0.37 * RH - 0.04 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [992.88, 1013.07], RH in [62.85, 100.15], PE is 180.91 - 1.23 * AP - 0.29 * AT + 0.31 * RH - 0.09 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [25.55, 62.85], PE is 396.75 - 1.41 * AP - 0.28 * AT + 0.10 * RH - 0.01 * V.\n",
-      "'PE'(AP, AT, RH, V, PE) :-\n",
-      "    AT in [19.05, 35.77], V in [53.46, 81.56], AP in [1013.07, 1033.25], RH in [62.85, 100.15], PE is 446.80 - 1.76 * AP - 0.30 * AT + 0.07 * RH - 0.22 * V.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#pedro = PEDRO(predictor, train, max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1,\n",
-    "#              max_depth=2, patience=1, algorithm=PEDRO.Algorithm.GRIDREX, objective=Objective.MODEL)\n",
-    "#pedro.search()\n",
-    "(_, _, threshold, grid) = pedro.get_best()[0]\n",
-    "\n",
-    "gridREx = Extractor.gridrex(predictor, grid, threshold=threshold)\n",
-    "theory_from_gridREx = gridREx.extract(train)\n",
-    "scores, completeness = get_scores(gridREx, test, predictor)\n",
-    "print(f'GridREx performance ({gridREx.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('GridREx extracted rules:\\n\\n' + pretty_theory(theory_from_gridREx))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CART performance (6 rules with 100.00% coverage):\n",
-      "MAE = 4.46 (data), 4.49 (BB)\n",
-      "MSE = 32.55 (data), 32.53 (BB)\n",
-      "R2 = 0.89 (data), 0.89 (BB)\n",
-      "CART extracted rules:\n",
-      "\n",
-      "'PE'(AP, AT, RH, V, 479.15) :-\n",
-      "    AT =< 18.25, AT =< 11.90.\n",
-      "'PE'(AP, AT, RH, V, 435.66) :-\n",
-      "    AT > 18.25, V > 66.20.\n",
-      "'PE'(AP, AT, RH, V, 451.33) :-\n",
-      "    AT > 18.25, V =< 66.20, AT =< 22.89.\n",
-      "'PE'(AP, AT, RH, V, 443.00) :-\n",
-      "    AT > 18.25, V =< 66.20, AT > 22.89.\n",
-      "'PE'(AP, AT, RH, V, 467.45) :-\n",
-      "    AT > 11.90, AT =< 15.64.\n",
-      "'PE'(AP, AT, RH, V, 459.70) :-\n",
-      "    AT > 15.64.\n"
-     ]
-    }
-   ],
-   "source": [
-    "cart = Extractor.cart(predictor, max_depth=5, max_leaves=6, simplify=True)\n",
-    "theory_from_cart = cart.extract(train)\n",
-    "scores, completeness = get_scores(cart, test, predictor)\n",
-    "print(f'CART performance ({cart.n_rules} rules with {completeness * 100:.2f}% coverage):')\n",
-    "print_scores(scores)\n",
-    "print('CART extracted rules:\\n\\n' + pretty_theory(theory_from_cart))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/demo/DemoRegressionScaled.ipynb b/demo/DemoRegressionScaled.ipynb
index b57fab34..133d3e91 100644
--- a/demo/DemoRegressionScaled.ipynb
+++ b/demo/DemoRegressionScaled.ipynb
@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f52126f3",
-   "metadata": {},
-   "source": [
-    "# PSyKE's demo for regression tasks\n",
-    "\n",
-    "Some imports."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -17,976 +7,399 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from psyke import Extractor, Clustering\n",
-    "from psyke.tuning.pedro import PEDRO\n",
-    "from psyke.tuning import Objective\n",
-    "from psyke.tuning.crash import CRASH\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from psyke.utils.logic import pretty_theory\n",
-    "from psyke.utils.metrics import mae, mse, r2\n",
-    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsRegressor as KNN\n",
     "from sklearn.preprocessing import StandardScaler\n",
-    "from psyke.utils import Target\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7c90ed2",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "Import an artificial dataset."
+    "\n",
+    "from joblib import dump, load\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from psyke import Extractor, Clustering, Target\n",
+    "from psyke.extraction.hypercubic.strategy import AdaptiveStrategy\n",
+    "from psyke.extraction.hypercubic import Grid, FeatureRanker\n",
+    "from psyke.utils.logic import pretty_theory\n",
+    "\n",
+    "from plot import *"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f8e46c49",
-   "metadata": {},
    "outputs": [],
    "source": [
-    "#dataset = pd.read_csv(\"../test/resources/datasets/df.csv\")\n",
-    "#dataset = dataset[[\"X\", \"Y\", \"Z4\"]].dropna()\n",
-    "#dataset = pd.read_csv(\"../test/resources/datasets/CCPP.csv\", sep=\";\", decimal=\",\")\n",
-    "dataset = pd.read_csv(\"../test/resources/datasets/arti.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d673b766",
-   "metadata": {},
-   "source": [
-    "Split between train and test set in a reproducible way."
-   ]
+    "def plot(testB, x, pred, extracted):\n",
+    "    missing = pd.read_csv(\"data/missing.csv\", parse_dates = [0], index_col = 0)\n",
+    "    df = pd.read_csv(\"data/averaged1.csv\", parse_dates = [0], index_col = 0)\n",
+    "    df.LPFnorm[missing.index] = np.nan\n",
+    "\n",
+    "    for i, b in bartels.iterrows():\n",
+    "        if b.n != testB:\n",
+    "            continue\n",
+    "        xminmax = [b.t0, b.t1]\n",
+    "\n",
+    "        f, axes = plt.subplots(3, figsize=(10, 8)) # l x h\n",
+    "\n",
+    "        myplot(axes[0], [3, 1], \"LPF\", df.index, df.LPFnorm, xminmax)\n",
+    "        myplot(axes[0], [3, 1], \"\", missing.index, missing.LPF0, xminmax, color='r', marker='*', lw=0, size=10)\n",
+    "        myplot(axes[1], [3, 2], \"V\", df.index, df.V, xminmax)\n",
+    "        myplot(axes[1], [3, 2], \"\", x, pred, xminmax, color='b', marker='.', lw=0)\n",
+    "        myplot(axes[1], [3, 2], \"\", x, extracted, xminmax, color='r', marker='.', lw=0)\n",
+    "        myplot(axes[2], [3, 3], \"B\", df.index, df.B, xminmax)\n",
+    "        plt.subplots_adjust(hspace=0.6)\n",
+    "        plt.savefig(f\"plot/{b.n}.jpg\", dpi=96 * 2)\n",
+    "        plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "03fc5e2c",
-   "metadata": {},
+   "execution_count": 5,
    "outputs": [],
    "source": [
-    "train, test = train_test_split(dataset, test_size=0.5, random_state=10)\n",
+    "def splitTest(data):\n",
+    "    b = bartels[(bartels.n == 2490)]\n",
+    "    data = data[(data.index >= b.t1.values[0])]\n",
     "\n",
-    "scaler = StandardScaler().fit(train)\n",
-    "train = pd.DataFrame(scaler.transform(train), columns=train.columns, index=train.index)\n",
-    "test = pd.DataFrame(scaler.transform(test), columns=test.columns, index=test.index)\n",
+    "    b = bartels[(bartels.n == 2494) | (bartels.n == 2495)]\n",
+    "    idx = np.zeros_like(data.index, dtype='bool')\n",
+    "    for _, row in b.iterrows():\n",
+    "        t0, t1 = row.t0, row.t1\n",
+    "        idx = idx | (data.index >= t0) & (data.index < t1)\n",
+    "    return data[~idx], data[idx]\n",
     "\n",
-    "normalization = {key: (m, s) for key, m, s in zip(train.columns, scaler.mean_, scaler.scale_)}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa6754a0",
-   "metadata": {},
-   "source": [
-    "We select and train a predictor."
-   ]
+    "bartels = pd.read_csv(\"data/bartels.csv\", parse_dates = [1, 2])\n",
+    "data = pd.read_csv(f'data/halffuzzycoefs4B3.csv', parse_dates=[0], index_col=0)\n",
+    "train, test = splitTest(data)\n",
+    "train.to_csv('data/alltrain.csv')\n",
+    "test.to_csv('data/alltest.csv')\n",
+    "\n",
+    "data = pd.read_csv(f'data/pruned.csv', parse_dates=[0], index_col=0)\n",
+    "train, test = splitTest(data)\n",
+    "train.to_csv('data/prunedtrain.csv')\n",
+    "test.to_csv('data/prunedtest.csv')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "bed764ca",
-   "metadata": {},
+   "execution_count": 73,
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE = 0.00\n",
-      "MSE = 0.00\n",
-      "R2 = 1.00\n"
-     ]
+     "data": {
+      "text/plain": "                     Bmin  Bmedian  Bmax    Btrend    Bhalf1    Bhalf2  \\\n2016-03-04 00:00:00   4.0      7.5  12.3 -0.080751  0.009474 -0.106862   \n2016-03-04 01:00:00   4.0      7.5  12.3 -0.086067 -0.012065 -0.101174   \n2016-03-04 02:00:00   4.0      7.5  12.3 -0.090328 -0.029534 -0.094717   \n2016-03-04 03:00:00   4.0      7.5  12.3 -0.094527 -0.046781 -0.089474   \n2016-03-04 04:00:00   4.0      7.5  12.3 -0.098312 -0.062166 -0.087105   \n...                   ...      ...   ...       ...       ...       ...   \n2017-07-02 19:00:00   1.6      8.0  18.2  0.168533  0.139028  0.004049   \n2017-07-02 20:00:00   1.6      8.3  18.2  0.165550  0.157085 -0.019089   \n2017-07-02 21:00:00   1.6      8.3  18.2  0.161067  0.174393 -0.041862   \n2017-07-02 22:00:00   1.6      8.5  18.2  0.156652  0.188077 -0.061781   \n2017-07-02 23:00:00   1.6      8.5  18.2  0.151061  0.198158 -0.086761   \n\n                      Bthird1   Bthird2   Bthird3    GCRmin  ...  GCRhalf1  \\\n2016-03-04 00:00:00 -0.001462 -0.016066 -0.068720 -3.883201  ... -0.034045   \n2016-03-04 01:00:00 -0.004731 -0.012913 -0.074356 -3.883201  ... -0.035872   \n2016-03-04 02:00:00 -0.007107 -0.008399 -0.080310 -3.883201  ... -0.038517   \n2016-03-04 03:00:00 -0.010202 -0.001853 -0.085397 -3.883201  ... -0.040322   \n2016-03-04 04:00:00 -0.012979  0.006375 -0.089585 -3.883201  ... -0.042501   \n...                       ...       ...       ...       ...  ...       ...   \n2017-07-02 19:00:00 -0.032341  0.005700  0.162350 -4.670171  ... -0.059403   \n2017-07-02 20:00:00 -0.032566  0.005048  0.159932 -4.670171  ... -0.058878   \n2017-07-02 21:00:00 -0.033405  0.002897  0.157075 -4.670171  ... -0.058062   \n2017-07-02 22:00:00 -0.033445  0.001775  0.153994 -4.670171  ... -0.057460   \n2017-07-02 23:00:00 -0.033619  0.001199  0.149502 -4.670171  ... -0.056252   \n\n                     GCRhalf2  GCRthird1  GCRthird2  GCRthird3  GCRfourth1  \\\n2016-03-04 00:00:00  0.060357   0.002682  -0.072897   0.065471    0.007162   \n2016-03-04 01:00:00  0.061504   0.000701  -0.070397   0.064501    0.006544   \n2016-03-04 02:00:00  0.062143  -0.002516  -0.066904   0.064396    0.004833   \n2016-03-04 03:00:00  0.063626  -0.002422  -0.062109   0.066305    0.004309   \n2016-03-04 04:00:00  0.064246  -0.005347  -0.056971   0.065904    0.002981   \n...                       ...        ...        ...        ...         ...   \n2017-07-02 19:00:00  0.016938  -0.075540   0.018295  -0.025042   -0.074034   \n2017-07-02 20:00:00  0.015490  -0.076592   0.020250  -0.027076   -0.076765   \n2017-07-02 21:00:00  0.014373  -0.076522   0.022473  -0.028479   -0.079294   \n2017-07-02 22:00:00  0.013574  -0.077431   0.025158  -0.029432   -0.080745   \n2017-07-02 23:00:00  0.012860  -0.077216   0.027493  -0.031331   -0.082300   \n\n                     GCRfourth2  GCRfourth3  GCRfourth4      V  \n2016-03-04 00:00:00   -0.092158    0.046471    0.069499  410.0  \n2016-03-04 01:00:00   -0.095190    0.049612    0.069178  400.0  \n2016-03-04 02:00:00   -0.098934    0.049670    0.071374  395.0  \n2016-03-04 03:00:00   -0.102389    0.052382    0.072217  408.0  \n2016-03-04 04:00:00   -0.105296    0.052220    0.070273  406.0  \n...                         ...         ...         ...    ...  \n2017-07-02 19:00:00   -0.027912    0.071203   -0.054068  477.0  \n2017-07-02 20:00:00   -0.026023    0.071905   -0.053493  442.0  \n2017-07-02 21:00:00   -0.022508    0.073575   -0.054818  437.0  \n2017-07-02 22:00:00   -0.020645    0.074790   -0.054839  436.0  \n2017-07-02 23:00:00   -0.017545    0.074579   -0.054656  430.0  \n\n[9720 rows x 23 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Bmin</th>\n      <th>Bmedian</th>\n      <th>Bmax</th>\n      <th>Btrend</th>\n      <th>Bhalf1</th>\n      <th>Bhalf2</th>\n      <th>Bthird1</th>\n      <th>Bthird2</th>\n      <th>Bthird3</th>\n      <th>GCRmin</th>\n      <th>...</th>\n      <th>GCRhalf1</th>\n      <th>GCRhalf2</th>\n      <th>GCRthird1</th>\n      <th>GCRthird2</th>\n      <th>GCRthird3</th>\n      <th>GCRfourth1</th>\n      <th>GCRfourth2</th>\n      <th>GCRfourth3</th>\n      <th>GCRfourth4</th>\n      <th>V</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-03-04 00:00:00</th>\n      <td>4.0</td>\n      <td>7.5</td>\n      <td>12.3</td>\n      <td>-0.080751</td>\n      <td>0.009474</td>\n      <td>-0.106862</td>\n      <td>-0.001462</td>\n      <td>-0.016066</td>\n      <td>-0.068720</td>\n      <td>-3.883201</td>\n      <td>...</td>\n      <td>-0.034045</td>\n      <td>0.060357</td>\n      <td>0.002682</td>\n      <td>-0.072897</td>\n      <td>0.065471</td>\n      <td>0.007162</td>\n      <td>-0.092158</td>\n      <td>0.046471</td>\n      <td>0.069499</td>\n      <td>410.0</td>\n    </tr>\n    <tr>\n      <th>2016-03-04 01:00:00</th>\n      <td>4.0</td>\n      <td>7.5</td>\n      <td>12.3</td>\n      <td>-0.086067</td>\n      <td>-0.012065</td>\n      <td>-0.101174</td>\n      <td>-0.004731</td>\n      <td>-0.012913</td>\n      <td>-0.074356</td>\n      <td>-3.883201</td>\n      <td>...</td>\n      <td>-0.035872</td>\n      <td>0.061504</td>\n      <td>0.000701</td>\n      <td>-0.070397</td>\n      <td>0.064501</td>\n      <td>0.006544</td>\n      <td>-0.095190</td>\n      <td>0.049612</td>\n      <td>0.069178</td>\n      <td>400.0</td>\n    </tr>\n    <tr>\n      <th>2016-03-04 02:00:00</th>\n      <td>4.0</td>\n      <td>7.5</td>\n      <td>12.3</td>\n      <td>-0.090328</td>\n      <td>-0.029534</td>\n      <td>-0.094717</td>\n      <td>-0.007107</td>\n      <td>-0.008399</td>\n      <td>-0.080310</td>\n      <td>-3.883201</td>\n      <td>...</td>\n      <td>-0.038517</td>\n      <td>0.062143</td>\n      <td>-0.002516</td>\n      <td>-0.066904</td>\n      <td>0.064396</td>\n      <td>0.004833</td>\n      <td>-0.098934</td>\n      <td>0.049670</td>\n      <td>0.071374</td>\n      <td>395.0</td>\n    </tr>\n    <tr>\n      <th>2016-03-04 03:00:00</th>\n      <td>4.0</td>\n      <td>7.5</td>\n      <td>12.3</td>\n      <td>-0.094527</td>\n      <td>-0.046781</td>\n      <td>-0.089474</td>\n      <td>-0.010202</td>\n      <td>-0.001853</td>\n      <td>-0.085397</td>\n      <td>-3.883201</td>\n      <td>...</td>\n      <td>-0.040322</td>\n      <td>0.063626</td>\n      <td>-0.002422</td>\n      <td>-0.062109</td>\n      <td>0.066305</td>\n      <td>0.004309</td>\n      <td>-0.102389</td>\n      <td>0.052382</td>\n      <td>0.072217</td>\n      <td>408.0</td>\n    </tr>\n    <tr>\n      <th>2016-03-04 04:00:00</th>\n      <td>4.0</td>\n      <td>7.5</td>\n      <td>12.3</td>\n      <td>-0.098312</td>\n      <td>-0.062166</td>\n      <td>-0.087105</td>\n      <td>-0.012979</td>\n      <td>0.006375</td>\n      <td>-0.089585</td>\n      <td>-3.883201</td>\n      <td>...</td>\n      <td>-0.042501</td>\n      <td>0.064246</td>\n      <td>-0.005347</td>\n      <td>-0.056971</td>\n      <td>0.065904</td>\n      <td>0.002981</td>\n      <td>-0.105296</td>\n      <td>0.052220</td>\n      <td>0.070273</td>\n      <td>406.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-07-02 19:00:00</th>\n      <td>1.6</td>\n      <td>8.0</td>\n      <td>18.2</td>\n      <td>0.168533</td>\n      <td>0.139028</td>\n      <td>0.004049</td>\n      <td>-0.032341</td>\n      <td>0.005700</td>\n      <td>0.162350</td>\n      <td>-4.670171</td>\n      <td>...</td>\n      <td>-0.059403</td>\n      <td>0.016938</td>\n      <td>-0.075540</td>\n      <td>0.018295</td>\n      <td>-0.025042</td>\n      <td>-0.074034</td>\n      <td>-0.027912</td>\n      <td>0.071203</td>\n      <td>-0.054068</td>\n      <td>477.0</td>\n    </tr>\n    <tr>\n      <th>2017-07-02 20:00:00</th>\n      <td>1.6</td>\n      <td>8.3</td>\n      <td>18.2</td>\n      <td>0.165550</td>\n      <td>0.157085</td>\n      <td>-0.019089</td>\n      <td>-0.032566</td>\n      <td>0.005048</td>\n      <td>0.159932</td>\n      <td>-4.670171</td>\n      <td>...</td>\n      <td>-0.058878</td>\n      <td>0.015490</td>\n      <td>-0.076592</td>\n      <td>0.020250</td>\n      <td>-0.027076</td>\n      <td>-0.076765</td>\n      <td>-0.026023</td>\n      <td>0.071905</td>\n      <td>-0.053493</td>\n      <td>442.0</td>\n    </tr>\n    <tr>\n      <th>2017-07-02 21:00:00</th>\n      <td>1.6</td>\n      <td>8.3</td>\n      <td>18.2</td>\n      <td>0.161067</td>\n      <td>0.174393</td>\n      <td>-0.041862</td>\n      <td>-0.033405</td>\n      <td>0.002897</td>\n      <td>0.157075</td>\n      <td>-4.670171</td>\n      <td>...</td>\n      <td>-0.058062</td>\n      <td>0.014373</td>\n      <td>-0.076522</td>\n      <td>0.022473</td>\n      <td>-0.028479</td>\n      <td>-0.079294</td>\n      <td>-0.022508</td>\n      <td>0.073575</td>\n      <td>-0.054818</td>\n      <td>437.0</td>\n    </tr>\n    <tr>\n      <th>2017-07-02 22:00:00</th>\n      <td>1.6</td>\n      <td>8.5</td>\n      <td>18.2</td>\n      <td>0.156652</td>\n      <td>0.188077</td>\n      <td>-0.061781</td>\n      <td>-0.033445</td>\n      <td>0.001775</td>\n      <td>0.153994</td>\n      <td>-4.670171</td>\n      <td>...</td>\n      <td>-0.057460</td>\n      <td>0.013574</td>\n      <td>-0.077431</td>\n      <td>0.025158</td>\n      <td>-0.029432</td>\n      <td>-0.080745</td>\n      <td>-0.020645</td>\n      <td>0.074790</td>\n      <td>-0.054839</td>\n      <td>436.0</td>\n    </tr>\n    <tr>\n      <th>2017-07-02 23:00:00</th>\n      <td>1.6</td>\n      <td>8.5</td>\n      <td>18.2</td>\n      <td>0.151061</td>\n      <td>0.198158</td>\n      <td>-0.086761</td>\n      <td>-0.033619</td>\n      <td>0.001199</td>\n      <td>0.149502</td>\n      <td>-4.670171</td>\n      <td>...</td>\n      <td>-0.056252</td>\n      <td>0.012860</td>\n      <td>-0.077216</td>\n      <td>0.027493</td>\n      <td>-0.031331</td>\n      <td>-0.082300</td>\n      <td>-0.017545</td>\n      <td>0.074579</td>\n      <td>-0.054656</td>\n      <td>430.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>9720 rows × 23 columns</p>\n</div>"
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#predictor = KNeighborsRegressor(n_neighbors=3).fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "predictor = DecisionTreeRegressor().fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "#predictor = LinearRegression().fit(train.iloc[:, :-1], train.iloc[:, -1])\n",
-    "\n",
-    "m, s = normalization[test.columns[-1]]\n",
-    "\n",
-    "predicted = predictor.predict(test.iloc[:, :-1]).flatten() * s + m\n",
-    "true = test.iloc[:, -1] * s + m\n",
-    "\n",
-    "print(f'MAE = {mae(true, predicted):.2f}')\n",
-    "print(f'MSE = {mse(true, predicted):.2f}')\n",
-    "print(f'R2 = {r2(true, predicted):.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+    "pd.read_csv('data/alltrain.csv', parse_dates=[0], index_col=[0])"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%% md\n"
+     "name": "#%%\n"
     }
-   },
-   "source": [
-    "We define a function to print the extractors’ evaluation"
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "def getTrainTest(data, testB):\n",
+    "    b = bartels[bartels.n==testB]\n",
+    "    t0, t1 = b.t0.values[0], b.t1.values[0]\n",
+    "    idx = (data.index >= t0) & (data.index < t1)\n",
+    "    return data[~idx], data[idx]\n",
+    "\n",
+    "def getScaler(train, name):\n",
+    "    scaler = StandardScaler().fit(train)\n",
+    "    dump(scaler, f\"scalers/scalerV{name}.joblib\")\n",
+    "    normalization = {key: (m, s) for key, m, s in zip(train.columns, scaler.mean_, scaler.scale_)}\n",
+    "    return scaler, pd.DataFrame(scaler.transform(train), columns=train.columns), normalization"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [],
-   "source": [
-    "def evaluate(name, extractor, true, predicted):\n",
-    "    extracted = extractor.unscale(extractor.predict(test.iloc[:, :-1]), test.columns[-1])\n",
-    "    print(f'{name} performance ({extractor.n_rules} rules):\\n'\n",
-    "          f'MAE = {mae(true, extracted):.2f}\\nMAE fidelity = {mae(predicted, extracted):.2f}\\n'\n",
-    "          f'R2 = {r2(true, extracted):.2f}\\nR2 fidelity = {r2(predicted, extracted):.2f}\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96835867",
-   "metadata": {},
-   "source": [
-    "We create several extractors that use ITER, GridEx and GridREx algorithms to extract prolog rules from the predictor."
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "def gridex(model, train, test, normalization):\n",
+    "    ranked = FeatureRanker(train.columns).fit(model, train.iloc[:, :-1]).rankings()\n",
+    "    gridEx = Extractor.gridex(model, Grid(1, AdaptiveStrategy(ranked, [(0.6, 3), (0.75, 4)])),\n",
+    "                              threshold=.5, min_examples=1, normalization=normalization)\n",
+    "    gridEx.extract(train)\n",
+    "    return gridEx.brute_predict(test), gridEx.n_rules, sum([p is None for p in gridEx.predict(test)])\n",
+    "    \n",
+    "def gridrex(model, train, test, normalization):\n",
+    "    ranked = FeatureRanker(train.columns).fit(model, train.iloc[:, :-1]).rankings()\n",
+    "    gridREx = Extractor.gridrex(model, Grid(1, AdaptiveStrategy(ranked, [(0.5, 3)])),\n",
+    "                                threshold=.5, min_examples=1, normalization=normalization)\n",
+    "    gridREx.extract(train)\n",
+    "    return gridREx.brute_predict(test, 'default'), gridREx.n_rules, sum([p is None for p in gridREx.predict(test)])\n",
+    "\n",
+    "def cart(model, train, test, normalization):\n",
+    "    CART = Extractor.cart(model, max_depth=10, max_leaves=10, normalization=normalization)\n",
+    "    CART.extract(train)\n",
+    "    return CART.predict(test), CART.n_rules, sum([p is None for p in CART.predict(test)])\n",
+    "\n",
+    "def cosmik(model, train, test, normalization):\n",
+    "    COSMiK = Extractor.cosmik(model, max_components=4, k=125, patience=12, close_to_center=True,\n",
+    "                              output=Target.REGRESSION, normalization=normalization)\n",
+    "    COSMiK.extract(train)\n",
+    "    return COSMiK.brute_predict(test, 'default'), COSMiK.n_rules, sum([p is None for p in COSMiK.predict(test)])\n",
+    "\n",
+    "def cream(model, train, test, normalization):\n",
+    "    CReEPy = Extractor.creepy(model, clustering=Clustering.cream, depth=5, error_threshold=.5, gauss_components=10,\n",
+    "                              output=Target.REGRESSION, normalization=normalization)\n",
+    "    CReEPy.extract(train)\n",
+    "    return CReEPy.brute_predict(test), CReEPy.n_rules, sum([p is None for p in CReEPy.predict(test)])\n",
+    "\n",
+    "def exact(model, train, test, normalization):\n",
+    "    CReEPy = Extractor.creepy(model, clustering=Clustering.exact, depth=5, error_threshold=.5, gauss_components=10,\n",
+    "                              output=Target.REGRESSION, normalization=normalization)\n",
+    "    CReEPy.extract(train)\n",
+    "    return CReEPy.brute_predict(test), CReEPy.n_rules, sum([p is None for p in CReEPy.predict(test)])"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ITER performance (9 rules):\n",
-      "MAE = 0.04\n",
-      "MAE fidelity = 0.04\n",
-      "R2 = 0.88\n",
-      "R2 fidelity = 0.88\n",
-      "\n",
-      "ITER extracted rules:\n",
-      "\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.46], Y in [0.00, 0.43].\n",
-      "z(X, Y, 0.59) :-\n",
-      "    X in [0.00, 0.46], Y in [0.43, 0.53].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.46], Y in [0.53, 0.99].\n",
-      "z(X, Y, 0.47) :-\n",
-      "    X in [0.46, 0.56], Y in [0.00, 0.43].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.56, 0.99], Y in [0.00, 0.43].\n",
-      "z(X, Y, 0.18) :-\n",
-      "    X in [0.46, 0.56], Y in [0.53, 0.99].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.56, 0.99], Y in [0.53, 0.99].\n",
-      "z(X, Y, 0.32) :-\n",
-      "    X in [0.46, 0.56], Y in [0.43, 0.53].\n",
-      "z(X, Y, 0.20) :-\n",
-      "    X in [0.56, 0.99], Y in [0.43, 0.53].\n"
-     ]
-    }
-   ],
-   "source": [
-    "it = Extractor.iter(predictor, min_update=1.0 / 10, n_points=1, max_iterations=600,\n",
-    "                    min_examples=100, threshold=.4, normalization=normalization)\n",
-    "theory_from_iter = it.extract(train)\n",
-    "evaluate('ITER', it, true, predicted)\n",
-    "print('ITER extracted rules:\\n\\n' + pretty_theory(theory_from_iter))"
-   ]
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CReEPy performance (3 rules):\n",
-      "MAE = 0.08\n",
-      "MAE fidelity = 0.08\n",
-      "R2 = 0.81\n",
-      "R2 fidelity = 0.81\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.49], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.49], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.145238).\n"
+      "2491\n",
+      "GridEx\n",
+      "GridREx\n",
+      "CART\n",
+      "COSMiK\n",
+      "ExACT\n",
+      "CREAM\n",
+      "2492\n",
+      "GridEx\n",
+      "GridREx\n",
+      "CART\n",
+      "COSMiK\n",
+      "ExACT\n",
+      "CREAM\n",
+      "2493\n",
+      "GridEx\n",
+      "GridREx\n",
+      "CART\n",
+      "COSMiK\n",
+      "ExACT\n",
+      "CREAM\n"
      ]
     }
    ],
    "source": [
-    "creepy = Extractor.creepy(predictor, depth=3, error_threshold=0.02, output=Target.CONSTANT,\n",
-    "                          normalization=normalization, clustering=Clustering.exact)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "evaluate('CReEPy', creepy, true, predicted)\n",
-    "print('CReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+    "bartels = pd.read_csv(\"data/bartels.csv\", parse_dates = [1, 2])\n",
+    "\n",
+    "extractors = ['GridEx', 'GridREx', 'CART', 'COSMiK', 'ExACT', 'CREAM']\n",
+    "\n",
+    "TESTB = [i for i in range(2491, 2509)]\n",
+    "\n",
+    "predicted = {name: [] for name in ['index', 'V', 'model'] + extractors}\n",
+    "\n",
+    "rules = {name: [] for name in ['BR'] + extractors}\n",
+    "\n",
+    "missed = {name: [] for name in ['BR'] + extractors}\n",
+    "\n",
+    "for testB in TESTB[:3]:\n",
+    "    rules['BR'].append(testB)\n",
+    "    missed['BR'].append(testB)\n",
+    "    print(testB)\n",
+    "\n",
+    "    data = pd.read_csv(f'data/pruned.csv', parse_dates=[0], index_col=0)\n",
+    "    train, test = getTrainTest(data, testB)\n",
+    "\n",
+    "    predicted['index'] += list(test.index.values)\n",
+    "    predicted['V'] += list(test.V.values)\n",
+    "\n",
+    "    scaler, scaledTrain, normalization = getScaler(train, f\"test{testB}\")\n",
+    "    scaledTest = pd.DataFrame(scaler.transform(test), columns=test.columns).iloc[:, :-1]\n",
+    "    m, s = normalization[test.columns[-1]]\n",
+    "\n",
+    "    model = KNN(200, weights='distance', p=1).fit(scaledTrain.iloc[:, :-1], scaledTrain.iloc[:, -1])\n",
+    "    #dump(model, f\"models/RF/{k}_{name}_{testB}.joblib\")\n",
+    "    predicted['model'] += list(model.predict(scaledTest) * s + m)\n",
+    "\n",
+    "    for name, fun in zip(extractors, [cart, cart, cart, cosmik, exact, cream]):\n",
+    "        print(name)\n",
+    "        #if name in ['GridREx', 'CART', 'COSMiK']:\n",
+    "        #    continue\n",
+    "        pred, n, miss = fun(model, scaledTrain, scaledTest, normalization)\n",
+    "        predicted[name] += list(pred)\n",
+    "        rules[name].append(n)\n",
+    "        missed[name].append(miss)"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CReEPy performance (3 rules):\n",
-      "MAE = 0.03\n",
-      "MAE fidelity = 0.03\n",
-      "R2 = 0.94\n",
-      "R2 fidelity = 0.94\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.00, 0.49], Y in [0.00, 0.49], Z is 0.7.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.00, 0.49], Y in [0.00, 0.99], Z is 0.4.\n",
-      "z(X, Y, Z) :-\n",
-      "    Y in [0.00, 0.99], Z is 0.38 - 0.09 * X - 1.73 * Y.\n"
+      "90.20644923758856 47.26039351140736\n",
+      "90.20644923758856 47.26039351140736\n",
+      "90.20644923758856 47.26039351140736\n",
+      "73.38650661174208 31.857409982273143\n",
+      "71.27591988138312 36.1748132343631\n",
+      "70.26005494494277 33.783890819165826\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": "     BR  GridEx  GridREx  CART  COSMiK  ExACT  CREAM\n0  2491      10       10    10       4      6      6\n1  2492      10       10    10       8      6      6\n2  2493      10       10    10       9      6      7",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BR</th>\n      <th>GridEx</th>\n      <th>GridREx</th>\n      <th>CART</th>\n      <th>COSMiK</th>\n      <th>ExACT</th>\n      <th>CREAM</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2491</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>4</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2492</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>8</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2493</td>\n      <td>10</td>\n      <td>10</td>\n      <td>10</td>\n      <td>9</td>\n      <td>6</td>\n      <td>7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "creepy = Extractor.creepy(predictor, depth=3, error_threshold=0.02, output=Target.REGRESSION,\n",
-    "                          normalization=normalization, clustering=Clustering.exact)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "evaluate('CReEPy', creepy, true, predicted)\n",
-    "print('CReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+    "p = pd.DataFrame(predicted)\n",
+    "for e in extractors:\n",
+    "    print(abs(p.V - p[e]).mean(), abs(p.model - p[e]).mean())\n",
+    "\n",
+    "b = 2493\n",
+    "#plot(b, p['index'], p.model, p.COSMiK)\n",
+    "#plot(b, p['index'], p.model, p.ExACT)\n",
+    "#plot(b, p['index'], p.model, p.CREAM)\n",
+    "\n",
+    "pd.DataFrame(rules)"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CReEPy performance (4 rules):\n",
-      "MAE = 0.01\n",
-      "MAE fidelity = 0.01\n",
-      "R2 = 0.94\n",
-      "R2 fidelity = 0.94\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.51, 0.99], Y in [0.00, 0.49], Z is 0.3.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.98], Z is 0.0.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.00, 0.49], Y in [0.50, 0.99], Z is 0.4.\n",
-      "z(X, Y, Z) :-\n",
-      "    Y in [0.00, 0.99], Z is 0.7.\n"
-     ]
-    }
-   ],
-   "source": [
-    "creepy = Extractor.creepy(predictor, depth=2, error_threshold=0.02, output=Target.REGRESSION,\n",
-    "                          normalization=normalization, clustering=Clustering.cream)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "evaluate('CReEPy', creepy, true, predicted)\n",
-    "print('CReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(predicted).to_csv(\"results/pred1.csv\")\n",
+    "pd.DataFrame(rules).to_csv('results/rules1.csv')\n",
+    "pd.DataFrame(missed).to_csv('results/missed1.csv')"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.00. MAE = 0.06, 2 rules\n",
-      "Algorithm.ExACT. Depth: 1. Threshold = 0.00. MAE = 0.06, 2 rules\n",
-      "\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.00. MAE = 0.06, 2 rules\n",
-      "Algorithm.ExACT. Depth: 2. Threshold = 0.00. MAE = 0.06, 2 rules\n",
-      "\n",
-      "**********************\n",
-      "Best Algorithm.ExACT\n",
-      "**********************\n",
-      "MAE = 0.06, 2 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 2\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 0.06, 2 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 2\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 0.06, 2 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 2\n",
-      "\n",
-      "CReEPy performance (3 rules):\n",
-      "MAE = 0.69\n",
-      "MAE fidelity = 0.69\n",
-      "R2 = -8.82\n",
-      "R2 fidelity = -8.82\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [-1.70, 0.01], Y in [-1.69, 0.02], Z is 1.37.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [-1.70, 0.01], Y in [-1.69, 1.75], Z is 0.19.\n",
-      "z(X, Y, Z) :-\n",
-      "    Y in [-1.69, 1.75], Z is -0.76 - 0.02 * X - 0.50 * Y.\n"
-     ]
-    }
-   ],
-   "source": [
-    "crash = CRASH(predictor, train, max_depth=3, patience=1, readability_tradeoff=.5,\n",
-    "              algorithm=CRASH.Algorithm.ExACT, output=Target.REGRESSION, normalization=normalization)\n",
-    "crash.search()\n",
-    "(_, _, depth, threshold) = crash.get_best()[0]\n",
-    "\n",
-    "creepy = Extractor.creepy(predictor, depth=depth, error_threshold=threshold, output=Target.REGRESSION,\n",
-    "                          clustering=Clustering.exact)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "evaluate('CReEPy', creepy, true, predicted)\n",
-    "print('CReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.00. MAE = 0.12, 2 rules\n",
-      "Algorithm.CREAM. Depth: 1. Threshold = 0.00. MAE = 0.12, 2 rules\n",
-      "\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.00. MAE = 0.02, 3 rules\n",
-      "Algorithm.CREAM. Depth: 2. Threshold = 0.00. MAE = 0.02, 3 rules\n",
-      "\n",
-      "**********************\n",
-      "Best Algorithm.CREAM\n",
-      "**********************\n",
-      "MAE = 0.02, 3 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 2\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 0.02, 3 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 2\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 0.12, 2 rules\n",
-      "Threshold = 0.00\n",
-      "Depth = 1\n",
-      "\n",
-      "CReEPy performance (4 rules):\n",
-      "MAE = 0.69\n",
-      "MAE fidelity = 0.69\n",
-      "R2 = -9.40\n",
-      "R2 fidelity = -9.40\n",
-      "\n",
-      "CReEPy extracted rules:\n",
-      "\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.07, 1.75], Y in [-1.69, 0.00], Z is -0.19.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.02, 1.75], Y in [-1.69, 1.71], Z is -1.37.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [-1.69, 0.01], Y in [0.03, 1.75], Z is 0.19.\n",
-      "z(X, Y, Z) :-\n",
-      "    Y in [-1.69, 1.75], Z is 1.37.\n"
-     ]
-    }
-   ],
-   "source": [
-    "crash = CRASH(predictor, train, max_depth=3, patience=1, readability_tradeoff=.75, algorithm=CRASH.Algorithm.CREAM,\n",
-    "              normalization=normalization)\n",
-    "crash.search()\n",
-    "(_, _, depth, threshold) = crash.get_best()[0]\n",
-    "\n",
-    "creepy = Extractor.creepy(predictor, depth=depth, error_threshold=threshold, output=Target.REGRESSION,\n",
-    "                          clustering=Clustering.cream)\n",
-    "theory_from_creepy = creepy.extract(train)\n",
-    "evaluate('CReEPy', creepy, true, predicted)\n",
-    "print('CReEPy extracted rules:\\n\\n' + pretty_theory(theory_from_creepy))"
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "[\n",
+    "    ('half2', 1.0),\n",
+    "    ('Bmin', 0.9014926374248455),\n",
+    "    ('Bmedian', 0.5931779258652916),\n",
+    "    ('Bmax', 0.5580664110760513),\n",
+    "\n",
+    "    ('trend', 0.441328536097858),\n",
+    "    ('max', 0.19391808653199277),\n",
+    "    ('median', 0.16157357528452515),\n",
+    "\n",
+    "    ('halfB2', 0.06672545822303622),\n",
+    "    ('min', 0.042323799412967836),\n",
+    "    ('B', 0.014457395044254808),\n",
+    "    ('halfB1', 0.005159557201584083),\n",
+    "    ('half1', 0.0023286124765232786),\n",
+    "]"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Algorithm.GRIDEX. Grid (1). Fixed (2). Threshold = 0.00. MAE = 0.00, 4 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Fixed (2). Threshold = 0.00. MAE = 0.00, 8 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Fixed (3). Threshold = 0.00. MAE = 0.00, 17 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Fixed (3). Threshold = 0.00. MAE = 0.00, 26 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 2)]). Threshold = 0.00. MAE = 0.00, 28 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 2)]). Threshold = 0.00. MAE = 0.00, 30 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 3)]). Threshold = 0.00. MAE = 0.00, 33 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 3)]). Threshold = 0.00. MAE = 0.00, 36 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 5)]). Threshold = 0.00. MAE = 0.00, 41 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 5)]). Threshold = 0.00. MAE = 0.00, 46 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 10)]). Threshold = 0.00. MAE = 0.00, 56 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.99, 10)]). Threshold = 0.00. MAE = 0.00, 66 rules\n",
-      "\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.33, 2), (0.67, 3)]). Threshold = 0.00. MAE = 0.00, 72 rules\n",
-      "Algorithm.GRIDEX. Grid (1). Adaptive ([(0.33, 2), (0.67, 3)]). Threshold = 0.00. MAE = 0.00, 78 rules\n",
-      "\n",
-      "**********************\n",
-      "Best Algorithm.GRIDEX\n",
-      "**********************\n",
-      "MAE = 0.00, 4 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 0.00, 72 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Adaptive ([(0.33, 2), (0.67, 3)])\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 0.00, 4 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "GridEx performance (82 rules):\n",
-      "MAE = 0.00\n",
-      "MAE fidelity = 0.00\n",
-      "R2 = 0.99\n",
-      "R2 fidelity = 0.99\n",
-      "\n",
-      "GridEx extracted rules:\n",
-      "\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.39) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.39) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.00, 0.33], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.48) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.33, 0.66], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.00, 0.33], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.48) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.33, 0.66], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.35) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.35) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.20, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.40, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.60, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.20, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.40, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.60, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.10], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.10, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.20, 0.30], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.30, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.40, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.50, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.60, 0.70], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.13) :-\n",
-      "    X in [0.70, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.89], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.89, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.10], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.10, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.20, 0.30], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.30, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.40, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.50, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.60, 0.70], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.13) :-\n",
-      "    X in [0.70, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.89], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.89, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.52) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.29) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.52) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.29) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.39) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99].\n"
-     ]
-    }
-   ],
-   "source": [
-    "pedro = PEDRO(predictor, train, max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1,\n",
-    "              max_depth=1, patience=1, algorithm=PEDRO.Algorithm.GRIDEX, objective=Objective.MODEL,\n",
-    "              normalization=normalization)\n",
-    "pedro.search()\n",
-    "(_, _, threshold, grid) = pedro.get_best()[0]\n",
-    "\n",
-    "gridEx = Extractor.gridex(predictor, grid, threshold=threshold, normalization=normalization)\n",
-    "theory_from_gridEx = gridEx.extract(train)\n",
-    "evaluate('GridEx', gridEx, true, predicted)\n",
-    "print('GridEx extracted rules:\\n\\n' + pretty_theory(theory_from_gridEx))"
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "p = pd.read_csv('results/cartpredpruned.csv')\n",
+    "p = p[p.columns[2:]]\n",
+    "for c in p.columns[2:]:\n",
+    "    p[c] = abs(p[c] - p.model)\n",
+    "p.describe().loc['mean']"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "**********************\n",
-      "Best Algorithm.GRIDEX\n",
-      "**********************\n",
-      "MAE = 0.00, 4 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "**********************\n",
-      "Best   MAE  \n",
-      "**********************\n",
-      "MAE = 0.00, 72 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Adaptive ([(0.33, 2), (0.67, 3)])\n",
-      "\n",
-      "**********************\n",
-      "Best N rules\n",
-      "**********************\n",
-      "MAE = 0.00, 4 rules\n",
-      "Threshold = 0.00\n",
-      "Iterations = 1\n",
-      "Strategy = Fixed (2)\n",
-      "\n",
-      "GridREx performance (86 rules):\n",
-      "MAE = 0.00\n",
-      "MAE fidelity = 0.00\n",
-      "R2 = 0.99\n",
-      "R2 fidelity = 0.99\n",
-      "\n",
-      "GridREx extracted rules:\n",
-      "\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.39) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.39) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.00, 0.33], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.48) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.33, 0.66], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.00, 0.33], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.48) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.33, 0.66], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.33].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.33, 0.66].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.66, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.35) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.35) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.20, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.40, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.60, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.20, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.37) :-\n",
-      "    X in [0.40, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.60, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.10], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.10, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.20, 0.30], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.30, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.40, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.50, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.60, 0.70], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.13) :-\n",
-      "    X in [0.70, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.89], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.89, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.56) :-\n",
-      "    X in [0.00, 0.10], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.10, 0.20], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.54) :-\n",
-      "    X in [0.20, 0.30], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.30, 0.40], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.55) :-\n",
-      "    X in [0.40, 0.50], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.50, 0.60], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.15) :-\n",
-      "    X in [0.60, 0.70], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.13) :-\n",
-      "    X in [0.70, 0.80], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.80, 0.89], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.14) :-\n",
-      "    X in [0.89, 0.99], Y in [0.00, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.52) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.29) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.33], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.4) :-\n",
-      "    X in [0.00, 0.33], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.52) :-\n",
-      "    X in [0.33, 0.66], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.17) :-\n",
-      "    X in [0.33, 0.66], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.29) :-\n",
-      "    X in [0.66, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.66, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.7) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.39) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99].\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49].\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99].\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.00, 0.50], Y in [0.00, 0.49], Z is 0.7.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.00, 0.50], Y in [0.49, 0.99], Z is 0.40 - 0.07 * X + 0.00 * Y.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.50, 0.99], Y in [0.00, 0.49], Z is 0.3.\n",
-      "z(X, Y, Z) :-\n",
-      "    X in [0.50, 0.99], Y in [0.49, 0.99], Z is 0.0.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#pedro = PEDRO(predictor, train, max_mae_increase=1.2, min_rule_decrease=0.9, readability_tradeoff=0.1,\n",
-    "#              max_depth=2, patience=1, algorithm=PEDRO.Algorithm.GRIDREX, objective=Objective.MODEL)\n",
-    "#pedro.search()\n",
-    "(_, _, threshold, grid) = pedro.get_best()[0]\n",
-    "\n",
-    "gridREx = Extractor.gridrex(predictor, grid, threshold=threshold, normalization=normalization)\n",
-    "theory_from_gridREx = gridREx.extract(train)\n",
-    "evaluate('GridREx', gridREx, true, predicted)\n",
-    "print('GridREx extracted rules:\\n\\n' + pretty_theory(theory_from_gridREx))"
-   ]
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CART performance (4 rules):\n",
-      "MAE = 0.00\n",
-      "MAE fidelity = 0.00\n",
-      "R2 = 1.00\n",
-      "R2 fidelity = 1.00\n",
-      "\n",
-      "CART extracted rules:\n",
-      "\n",
-      "z(X, Y, 0.3) :-\n",
-      "    X > 0.50, Y =< 0.49.\n",
-      "z(X, Y, 0.0) :-\n",
-      "    X > 0.50, Y > 0.49.\n",
-      "z(X, Y, 0.7) :-\n",
-      "    Y =< 0.50.\n",
-      "z(X, Y, 0.4).\n"
-     ]
-    }
-   ],
-   "source": [
-    "cart = Extractor.cart(predictor, max_depth=4, max_leaves=4, simplify=True, normalization=normalization)\n",
-    "theory_from_cart = cart.extract(train)\n",
-    "evaluate('CART', cart, true, predicted)\n",
-    "print('CART extracted rules:\\n\\n' + pretty_theory(theory_from_cart))"
-   ]
+   }
   }
  ],
  "metadata": {
diff --git a/demo/README.md b/demo/README.md
deleted file mode 100644
index a67d14ac..00000000
--- a/demo/README.md
+++ /dev/null
@@ -1,40 +0,0 @@
-## About Notebooks files in this directory
-
-Notebook files (`.ipynb`) in this directory contain demos of PSyKE.
-
-These are NOT meant to be executed directly, but rather via some container started from the [PSyKE Image](https://hub.docker.com/r/pikalab/psyke) on DockerHub.
-
-To do so, please ensure 
-- [Docker](https://docs.docker.com/engine/install/) is installed on your system
-- the Docker service is up and running
-
-and then follow these steps:
-
-1. Choose a target version of PSyKE, say `X.Y.Z`
-    - cf. [DockerHub image tags](https://hub.docker.com/r/pikalab/psyke/tags)
-    - cf. [PyPi releases history](https://pypi.org/project/psyke/#history)
-    - cf. [GitHub releases](https://github.com/psykei/psyke-python/releases)
-
-2. Pull the corresponding image:
-    ```bash
-    docker pull pikalab/psyke:X.Y.Z
-    ```
-
-2. Run the image into a container:
-    ```bash
-    docker run --rm -it -p 8888:8888 --name psyke pikalab/psyke:X.Y.Z
-    ```
-
-3. Some logs such as the following ones should eventually be printed
-    ```
-    To access the notebook, open this file in a browser:
-        file:///root/.local/share/jupyter/runtime/nbserver-7-open.html
-    Or copy and paste one of these URLs:
-        http://66fa9b93bbe7:8888/?token=<LONG TOKEN HERE>
-     or http://127.0.0.1:8888/?token=<LONG TOKEN HERE>
-    ```
-
-4. Open your browser and browse to `http://localhost:8888/?token=<LONG TOKEN HERE>`
-    - use the token from the logs above, if requested by the Web page
-
-5. Enjoy PSyKE-powered notebook!
diff --git a/demo/demo.py b/demo/demo.py
deleted file mode 100644
index c4d2d124..00000000
--- a/demo/demo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
-from sklearn.neighbors import KNeighborsClassifier
-import pandas as pd
-from psyke import Extractor
-from psyke.utils.dataframe import get_discrete_features_equal_frequency, get_discrete_dataset
-from psyke.utils.logic import pretty_theory
-
-x, y = load_iris(return_X_y=True, as_frame=True)
-x.columns = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth']
-iris_features = get_discrete_features_equal_frequency(x, bins=3, output=False)
-x = get_discrete_dataset(x, iris_features)
-y = pd.DataFrame(y).replace({"target": {0: 'setosa', 1: 'virginica', 2: 'versicolor'}})
-dataset = x.join(y)
-dataset.columns = [*dataset.columns[:-1], 'iris']
-train, test = train_test_split(dataset, test_size=0.5, random_state=0)
-
-predictor = KNeighborsClassifier(n_neighbors=7)
-predictor.fit(train.iloc[:, :-1], train.iloc[:, -1])
-
-real = Extractor.real(predictor, iris_features)
-theory_from_real = real.extract(train)
-print('REAL extracted rules:\n' + pretty_theory(theory_from_real))
-
-trepan = Extractor.trepan(predictor, iris_features)
-theory_from_trepan = trepan.extract(train)
-print('\nTrepan extracted rules:\n' + pretty_theory(theory_from_trepan))
diff --git a/psyke/__init__.py b/psyke/__init__.py
index eb71036c..77bb12e8 100644
--- a/psyke/__init__.py
+++ b/psyke/__init__.py
@@ -56,14 +56,27 @@ def predict(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None) -> It
         :param mapping: for one-hot encoding.
         :return: a list of predictions.
         """
-        ys = self._predict(dataframe)
+        return self.__convert(self._predict(dataframe), mapping)
+
+    def _predict(self, dataframe: pd.DataFrame) -> Iterable:
+        raise NotImplementedError('predict')
+
+    def __convert(self, ys: Iterable, mapping: dict[str: int] = None) -> Iterable:
         if mapping is not None:
             inverse_mapping = {v: k for k, v in mapping.items()}
             ys = [inverse_mapping[y] for y in ys]
+        if self.normalization is not None:
+            m, s = self.normalization[list(self.normalization.keys())[-1]]
+            ys = [prediction if prediction is None else prediction * s + m for prediction in ys]
         return ys
 
-    def _predict(self, dataframe: pd.DataFrame) -> Iterable:
-        raise NotImplementedError('predict')
+    def brute_predict(self, dataframe: pd.DataFrame, criterion: str = 'corner', n: int = 2,
+                      mapping: dict[str: int] = None) -> Iterable:
+        return self.__convert(self._brute_predict(dataframe, criterion, n, mapping), mapping)
+
+    def _brute_predict(self, dataframe: pd.DataFrame, criterion: str = 'corner', n: int = 2,
+                       mapping: dict[str: int] = None) -> Iterable:
+        raise NotImplementedError('brute_predict')
 
     def unscale(self, values, name):
         if self.normalization is None or isinstance(values, LinearRegression):
@@ -76,9 +89,13 @@ def unscale(self, values, name):
         return values
 
     def score(self, dataframe: pd.DataFrame, predictor=None, fidelity: bool = False, completeness: bool = True,
+              brute: bool = False, criterion: str = 'corners', n: int = 2,
               task: EvaluableModel.Task = Task.CLASSIFICATION,
               scoring_function: Iterable[EvaluableModel.Score] = [ClassificationScore.ACCURACY]):
-        extracted = np.array(self.predict(dataframe.iloc[:, :-1]))
+        extracted = np.array(
+            self.predict(dataframe.iloc[:, :-1]) if not brute else
+            self.brute_predict(dataframe.iloc[:, :-1], criterion, n)
+        )
         idx = [prediction is not None for prediction in extracted]
         y_extracted = extracted[idx]
         true = [dataframe.iloc[idx, -1]]
@@ -153,59 +170,81 @@ def extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort:
         """
         raise NotImplementedError('extract')
 
-    def mae(self, dataframe: pd.DataFrame, predictor=None) -> float:
+    def mae(self, dataframe: pd.DataFrame, predictor=None, brute: bool = False, criterion: str = 'center',
+            n: int = 3) -> float:
         """
         Calculates the predictions' MAE w.r.t. the instances given as input.
 
         :param dataframe: is the set of instances to be used to calculate the mean absolute error.
         :param predictor: if provided, its predictions on the dataframe are taken instead of the dataframe instances.
+        :param brute: if True, a brute prediction is executed.
+        :param criterion: creterion for brute prediction.
+        :param n: number of points for brute prediction with 'perimeter' criterion.
         :return: the mean absolute error (MAE) of the predictions.
         """
-        return self.score(dataframe, predictor, predictor is not None, False, Extractor.Task.REGRESSION,
-                          [Extractor.RegressionScore.MAE])[Extractor.RegressionScore.MAE][-1]
+        return self.score(dataframe, predictor, predictor is not None, False, brute, criterion, n,
+                          Extractor.Task.REGRESSION, [Extractor.RegressionScore.MAE])[Extractor.RegressionScore.MAE][-1]
 
-    def mse(self, dataframe: pd.DataFrame, predictor=None) -> float:
+    def mse(self, dataframe: pd.DataFrame, predictor=None, brute: bool = False, criterion: str = 'center',
+            n: int = 3) -> float:
         """
         Calculates the predictions' MSE w.r.t. the instances given as input.
 
         :param dataframe: is the set of instances to be used to calculate the mean squared error.
         :param predictor: if provided, its predictions on the dataframe are taken instead of the dataframe instances.
+        :param brute: if True, a brute prediction is executed.
+        :param criterion: creterion for brute prediction.
+        :param n: number of points for brute prediction with 'perimeter' criterion.
         :return: the mean squared error (MSE) of the predictions.
         """
-        return self.score(dataframe, predictor, predictor is not None, False, Extractor.Task.REGRESSION,
-                          [Extractor.RegressionScore.MSE])[Extractor.RegressionScore.MSE][-1]
+        return self.score(dataframe, predictor, predictor is not None, False, brute, criterion, n,
+                          Extractor.Task.REGRESSION, [Extractor.RegressionScore.MSE])[Extractor.RegressionScore.MSE][-1]
 
-    def r2(self, dataframe: pd.DataFrame, predictor=None) -> float:
+    def r2(self, dataframe: pd.DataFrame, predictor=None, brute: bool = False, criterion: str = 'center',
+            n: int = 3) -> float:
         """
         Calculates the predictions' R2 score w.r.t. the instances given as input.
 
         :param dataframe: is the set of instances to be used to calculate the R2 score.
         :param predictor: if provided, its predictions on the dataframe are taken instead of the dataframe instances.
+        :param brute: if True, a brute prediction is executed.
+        :param criterion: creterion for brute prediction.
+        :param n: number of points for brute prediction with 'perimeter' criterion.
         :return: the R2 score of the predictions.
         """
-        return self.score(dataframe, predictor, predictor is not None, False,
+        return self.score(dataframe, predictor, predictor is not None, False, brute, criterion, n,
                           Extractor.Task.REGRESSION, [Extractor.RegressionScore.R2])[Extractor.RegressionScore.R2][-1]
 
-    def accuracy(self, dataframe: pd.DataFrame, predictor=None) -> float:
+    def accuracy(self, dataframe: pd.DataFrame, predictor=None, brute: bool = False, criterion: str = 'center',
+            n: int = 3) -> float:
         """
         Calculates the predictions' accuracy classification score w.r.t. the instances given as input.
 
         :param dataframe: is the set of instances to be used to calculate the accuracy classification score.
         :param predictor: if provided, its predictions on the dataframe are taken instead of the dataframe instances.
+        :param brute: if True, a brute prediction is executed.
+        :param criterion: creterion for brute prediction.
+        :param n: number of points for brute prediction with 'perimeter' criterion.
         :return: the accuracy classification score of the predictions.
         """
-        return self.score(dataframe, predictor, predictor is not None, False, Extractor.Task.CLASSIFICATION,
+        return self.score(dataframe, predictor, predictor is not None, False, brute, criterion, n,
+                          Extractor.Task.CLASSIFICATION,
                           [Extractor.ClassificationScore.ACCURACY])[Extractor.ClassificationScore.ACCURACY][-1]
 
-    def f1(self, dataframe: pd.DataFrame, predictor=None) -> float:
+    def f1(self, dataframe: pd.DataFrame, predictor=None, brute: bool = False, criterion: str = 'center',
+            n: int = 3) -> float:
         """
         Calculates the predictions' F1 score w.r.t. the instances given as input.
 
         :param dataframe: is the set of instances to be used to calculate the F1 score.
         :param predictor: if provided, its predictions on the dataframe are taken instead of the dataframe instances.
+        :param brute: if True, a brute prediction is executed.
+        :param criterion: creterion for brute prediction.
+        :param n: number of points for brute prediction with 'perimeter' criterion.
         :return: the F1 score of the predictions.
         """
-        return self.score(dataframe, predictor, predictor is not None, False, Extractor.Task.CLASSIFICATION,
+        return self.score(dataframe, predictor, predictor is not None, False, brute, criterion, n,
+                          Extractor.Task.CLASSIFICATION,
                           [Extractor.ClassificationScore.F1])[Extractor.ClassificationScore.F1][-1]
 
     @staticmethod
@@ -218,6 +257,27 @@ def cart(predictor, max_depth: int = 3, max_leaves: int = 3,
         return Cart(predictor, max_depth, max_leaves, discretization=discretization, normalization=normalization,
                     simplify=simplify)
 
+    @staticmethod
+    def divine(predictor, k: int = 5, patience: int = 15, close_to_center: bool = True,
+               discretization: Iterable[DiscreteFeature] = None, normalization=None) -> Extractor:
+        """
+        Creates a new DiViNE extractor.
+        """
+        from psyke.extraction.hypercubic.divine import DiViNE
+        return DiViNE(predictor, k=k, patience=patience, close_to_center=close_to_center,
+                      discretization=discretization, normalization=normalization)
+
+    @staticmethod
+    def cosmik(predictor, max_components: int = 4, k: int = 5, patience: int = 15, close_to_center: bool = True,
+               output: Target = Target.CONSTANT,
+               discretization: Iterable[DiscreteFeature] = None, normalization=None) -> Extractor:
+        """
+        Creates a new COSMiK extractor.
+        """
+        from psyke.extraction.hypercubic.cosmik import COSMiK
+        return COSMiK(predictor, max_components=max_components, k=k, patience=patience, close_to_center=close_to_center,
+                      output=output, discretization=discretization, normalization=normalization)
+
     @staticmethod
     def iter(predictor, min_update: float = 0.1, n_points: int = 1, max_iterations: int = 600, min_examples: int = 250,
              threshold: float = 0.1, fill_gaps: bool = True, normalization: dict[str, tuple[float, float]] = None,
@@ -250,15 +310,15 @@ def gridrex(predictor, grid, min_examples: int = 250, threshold: float = 0.1,
         return GridREx(predictor, grid, min_examples, threshold, normalization, seed)
 
     @staticmethod
-    def creepy(predictor, clustering, depth: int, error_threshold: float, output, gauss_components: int = 2,
-               ranks: [(str, float)] = [], ignore_threshold: float = 0.0,
-               normalization: dict[str, tuple[float, float]] = None) -> Extractor:
+    def creepy(predictor, clustering, depth: int, error_threshold: float, output: Target = Target.CONSTANT,
+               gauss_components: int = 2, ranks: [(str, float)] = [], ignore_threshold: float = 0.0,
+               discretization=None, normalization: dict[str, tuple[float, float]] = None) -> Extractor:
         """
         Creates a new CReEPy extractor.
         """
         from psyke.extraction.hypercubic.creepy import CReEPy
         return CReEPy(predictor, depth, error_threshold, output, gauss_components, ranks, ignore_threshold,
-                      normalization, clustering)
+                      discretization, normalization, clustering)
 
     @staticmethod
     def real(predictor, discretization=None) -> Extractor:
@@ -314,6 +374,7 @@ def __init__(self, predictor, discretization=None, normalization=None):
         Extractor.__init__(self, predictor=predictor, discretization=discretization, normalization=normalization)
 
     def extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort: bool = True) -> Theory:
+        from psyke.extraction.hypercubic import HyperCubeExtractor, HyperCube
         new_y = self.predictor.predict(dataframe.iloc[:, :-1])
         if mapping is not None:
             if hasattr(new_y[0], 'shape'):
@@ -326,7 +387,11 @@ def extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort:
         new_y = pd.DataFrame(new_y).set_index(dataframe.index)
         data = dataframe.iloc[:, :-1].copy().join(new_y)
         data.columns = dataframe.columns
-        return self._extract(data, mapping, sort)
+        theory = self._extract(data, mapping, sort)
+        if isinstance(self, HyperCubeExtractor):
+            self._surrounding = HyperCube.create_surrounding_cube(dataframe, output=self._output)
+            self._surrounding.update(dataframe, self.predictor)
+        return theory
 
     def _extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort: bool = True) -> Theory:
         raise NotImplementedError('extract')
diff --git a/psyke/extraction/cart/__init__.py b/psyke/extraction/cart/__init__.py
index c2ceabff..444045ed 100644
--- a/psyke/extraction/cart/__init__.py
+++ b/psyke/extraction/cart/__init__.py
@@ -77,8 +77,8 @@ def _extract(self, data: pd.DataFrame, mapping: dict[str: int] = None, sort: boo
         self._cart_predictor.predictor.fit(data.iloc[:, :-1], data.iloc[:, -1])
         return self._create_theory(data, mapping, sort)
 
-    def _predict(self, data) -> Iterable:
-        return self._cart_predictor.predict(data)
+    def _predict(self, dataframe: pd.DataFrame) -> Iterable:
+        return self._cart_predictor.predict(dataframe)
 
     @property
     def n_rules(self) -> int:
diff --git a/psyke/extraction/hypercubic/__init__.py b/psyke/extraction/hypercubic/__init__.py
index 7d7783a5..7a5ee046 100644
--- a/psyke/extraction/hypercubic/__init__.py
+++ b/psyke/extraction/hypercubic/__init__.py
@@ -9,26 +9,78 @@
 from sklearn.linear_model import LinearRegression
 from tuprolog.core import Var, Struct, clause
 from tuprolog.theory import Theory, mutable_theory
-from psyke import logger, PedagogicalExtractor
-from psyke.extraction.hypercubic.hypercube import HyperCube, RegressionCube, ClassificationCube, ClosedCube
+from psyke import logger, PedagogicalExtractor, EvaluableModel
+from psyke.extraction.hypercubic.hypercube import HyperCube, RegressionCube, ClassificationCube, ClosedCube, Point, \
+    GenericCube
 from psyke.utils.logic import create_variable_list, create_head, to_var, Simplifier
 from psyke.utils import Target, get_int_precision
 from psyke.extraction.hypercubic.strategy import Strategy, FixedStrategy
+from sklearn.neighbors import BallTree
 
 
-class HyperCubePredictor:
-    def __init__(self, cubes=[], output=Target.CONSTANT, normalization=None):
-        self._hypercubes = cubes
+class HyperCubePredictor(EvaluableModel):
+    def __init__(self, output=Target.CONSTANT, normalization=None):
+        super().__init__(normalization)
+        self._hypercubes = []
         self._output = output
-        self.normalization = normalization
 
     def _predict(self, dataframe: pd.DataFrame) -> Iterable:
-        return np.array([self._predict_from_cubes(dict(row.to_dict())) for _, row in dataframe.iterrows()])
+        return np.array([self._predict_from_cubes(row.to_dict()) for _, row in dataframe.iterrows()])
+
+    def _brute_predict(self, dataframe: pd.DataFrame, criterion: str = 'corner', n: int = 2,
+                       mapping: dict[str: int] = None) -> Iterable:
+        predictions = self._predict(dataframe)
+        idx = [prediction is None for prediction in predictions]
+        if sum(idx) > 0:
+            if criterion == 'default':
+                if not isinstance(self, HyperCubeExtractor):
+                    raise ValueError("'default' criterion only available for instances of HyperCubeExtractor")
+                predictions[idx] = np.array([HyperCubePredictor._get_cube_output(
+                    self._surrounding, row
+                ) for _, row in dataframe[idx].iterrows()])
+            elif criterion == 'surface':
+                if not isinstance(self, HyperCubeExtractor):
+                    raise ValueError("'surface' criterion only available for instances of HyperCubeExtractor")
+                predictions[idx] = np.array([HyperCubePredictor._get_cube_output(self._brute_predict_surface(row), row)
+                                             for _, row in dataframe[idx].iterrows()])
+            else:
+                tree, cubes = self._create_brute_tree(criterion, n)
+                predictions[idx] = np.array([HyperCubePredictor._brute_predict_from_cubes(
+                    row.to_dict(), tree, cubes
+                ) for _, row in dataframe[idx].iterrows()])
+        return np.array(predictions)
 
-    def _predict_from_cubes(self, data: dict[str, float]) -> float | None:
-        data = {k: v for k, v in data.items()}
+    @staticmethod
+    def _brute_predict_from_cubes(row: dict[str, float], tree: BallTree,
+                                  cubes: list[GenericCube]) -> float | str:
+        idx = tree.query([list(row.values())], k=1)[1][0][0]
+        return HyperCubePredictor._get_cube_output(cubes[idx], row)
+
+    def _brute_predict_surface(self, row: dict[str, float]) -> GenericCube:
+        distances = [(
+            cube.surface_distance(Point(list(row.keys()), list(row.values))), cube.volume(), cube
+        ) for cube in self._hypercubes]
+        return min(distances)[-1]
+
+    def _create_brute_tree(self, criterion: str = 'center', n: int = 2) -> (BallTree, list[GenericCube]):
+        admissible_criteria = ['surface', 'center', 'corner', 'perimeter', 'density', 'default']
+        if criterion not in admissible_criteria:
+            raise NotImplementedError(
+                "'criterion' should be chosen in " + str(admissible_criteria)
+            )
+
+        points = [(cube.center, cube) for cube in self._hypercubes] if criterion == 'center' else \
+            [(cube.barycenter, cube) for cube in self._hypercubes] if criterion == 'density' else \
+            [(corner, cube) for cube in self._hypercubes for corner in cube.corners()] if criterion == 'corner' else \
+            [(point, cube) for cube in self._hypercubes for point in cube.perimeter_samples(n)] \
+            if criterion == 'perimeter' else None
+
+        return BallTree(pd.concat([point[0].to_dataframe() for point in points], ignore_index=True)), \
+            [point[1] for point in points]
+
+    def _predict_from_cubes(self, data: dict[str, float]) -> float | str | None:
         for cube in self._hypercubes:
-            if cube.__contains__(data):
+            if data in cube:
                 if self._output == Target.CLASSIFICATION:
                     return HyperCubePredictor._get_cube_output(cube, data)
                 else:
@@ -50,9 +102,10 @@ def _get_cube_output(cube, data: dict[str, float]) -> float:
 
 
 class HyperCubeExtractor(HyperCubePredictor, PedagogicalExtractor, ABC):
-    def __init__(self, predictor, output, normalization):
-        PedagogicalExtractor.__init__(self, predictor, normalization=normalization)
+    def __init__(self, predictor, output, discretization=None, normalization=None):
         HyperCubePredictor.__init__(self, output=output, normalization=normalization)
+        PedagogicalExtractor.__init__(self, predictor, discretization=discretization, normalization=normalization)
+        self._surrounding = None
 
     def _default_cube(self) -> HyperCube | RegressionCube | ClassificationCube:
         if self._output == Target.CONSTANT:
@@ -61,6 +114,11 @@ def _default_cube(self) -> HyperCube | RegressionCube | ClassificationCube:
             return RegressionCube()
         return ClassificationCube()
 
+    def _sort_cubes(self):
+        cubes = [(cube.diversity, i, cube) for i, cube in enumerate(self._hypercubes)]
+        cubes.sort()
+        self._hypercubes = [cube[2] for cube in cubes]
+
     @staticmethod
     def _create_head(dataframe: pd.DataFrame, variables: list[Var], output: float | LinearRegression) -> Struct:
         return create_head(dataframe.columns[-1], variables[:-1], output) \
@@ -70,7 +128,11 @@ def _create_head(dataframe: pd.DataFrame, variables: list[Var], output: float |
     def _ignore_dimensions(self) -> Iterable[str]:
         return []
 
-    def _create_theory(self, dataframe: pd.DataFrame, sort: bool = True) -> Theory:
+    def __drop(self, dataframe: pd.DataFrame):
+        self._hypercubes = [cube for cube in self._hypercubes if cube.count(dataframe) > 1]
+
+    def _create_theory(self, dataframe: pd.DataFrame, sort: bool = False) -> Theory:
+        self.__drop(dataframe)
         new_theory = mutable_theory()
         for cube in self._hypercubes:
             logger.info(cube.output)
diff --git a/psyke/extraction/hypercubic/cosmik/__init__.py b/psyke/extraction/hypercubic/cosmik/__init__.py
new file mode 100644
index 00000000..c7be3a89
--- /dev/null
+++ b/psyke/extraction/hypercubic/cosmik/__init__.py
@@ -0,0 +1,43 @@
+import pandas as pd
+from sklearn.mixture import GaussianMixture
+from tuprolog.theory import Theory
+
+from psyke import Target, Extractor
+from psyke.clustering.utils import select_gaussian_mixture
+from psyke.extraction.hypercubic import HyperCube, HyperCubeExtractor, RegressionCube
+
+
+class COSMiK(HyperCubeExtractor):
+    """
+    Explanator implementing COSMiK algorithm.
+    """
+
+    def __init__(self, predictor, max_components: int = 4, k: int = 5, patience: int = 15, close_to_center: bool = True,
+                 output: Target = Target.CONSTANT, discretization=None, normalization=None):
+        super().__init__(predictor, Target.REGRESSION, discretization, normalization)
+        self.max = max_components
+        self.k = k
+        self.patience = patience
+        self.output = output
+        self.close_to_center = close_to_center
+
+    def _extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort: bool = True) -> Theory:
+        X, y = dataframe.iloc[:, :-1], dataframe.iloc[:, -1]
+
+        _, n, _ = select_gaussian_mixture(dataframe, self.max)
+        gmm = GaussianMixture(n)
+        gmm.fit(X, y)
+
+        divine = Extractor.divine(gmm, self.k, self.patience, self.close_to_center,
+                                  self.discretization, self.normalization)
+        df = X.join(pd.DataFrame(gmm.predict(X)))
+        df.columns = dataframe.columns
+        divine.extract(df)
+
+        self._hypercubes = [HyperCube(cube.dimensions.copy()) if self.output == Target.CONSTANT else
+                            RegressionCube(cube.dimensions.copy()) for cube in divine._hypercubes]
+        for cube in self._hypercubes:
+            cube.update(dataframe, self.predictor)
+
+        self._sort_cubes()
+        return self._create_theory(dataframe, sort)
\ No newline at end of file
diff --git a/psyke/extraction/hypercubic/creepy/__init__.py b/psyke/extraction/hypercubic/creepy/__init__.py
index 8bdba96f..1d72f759 100644
--- a/psyke/extraction/hypercubic/creepy/__init__.py
+++ b/psyke/extraction/hypercubic/creepy/__init__.py
@@ -11,18 +11,19 @@
 from psyke.clustering import HyperCubeClustering
 from psyke.extraction.hypercubic import HyperCubeExtractor
 from psyke.utils import Target
+from psyke.utils.logic import last_in_body
 
 
-class CReEPy(HyperCubeExtractor, ABC):
+class CReEPy(HyperCubeExtractor):
     """
     Explanator implementing CReEPy algorithm.
     """
 
     def __init__(self, predictor, depth: int, error_threshold: float, output: Target = Target.CONSTANT,
                  gauss_components: int = 5, ranks: list[(str, float)] = [], ignore_threshold: float = 0.0,
-                 normalization=None, clustering=Clustering.exact):
+                 discretization=None, normalization=None, clustering=Clustering.exact):
         super().__init__(predictor, Target.CLASSIFICATION if isinstance(predictor, ClassifierMixin) else output,
-                         normalization)
+                         discretization, normalization)
         self.clustering = clustering(depth, error_threshold, self._output, gauss_components)
         self.ranks = ranks
         self.ignore_threshold = ignore_threshold
@@ -40,7 +41,7 @@ def _extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort
         last_clause = list(theory.clauses)[-1]
         theory.retract(last_clause)
         theory.assertZ(clause(
-            last_clause.head, [list(last_clause.body)[-1]] if self._output is Target.REGRESSION else []))
+            last_clause.head, [last_in_body(last_clause.body)] if self._output is Target.REGRESSION else []))
         last_cube = self._hypercubes[-1]
         for dimension in last_cube.dimensions.keys():
             last_cube[dimension] = [-np.inf, np.inf]
diff --git a/psyke/extraction/hypercubic/divine/__init__.py b/psyke/extraction/hypercubic/divine/__init__.py
new file mode 100644
index 00000000..08c80e74
--- /dev/null
+++ b/psyke/extraction/hypercubic/divine/__init__.py
@@ -0,0 +1,83 @@
+import numpy as np
+import pandas as pd
+from tuprolog.theory import Theory
+
+from psyke import Target
+from psyke.extraction.hypercubic import HyperCubeExtractor
+from psyke.extraction.hypercubic.hypercube import Point, GenericCube, HyperCube
+
+from sklearn.neighbors import BallTree
+
+
+class DiViNE(HyperCubeExtractor):
+    """
+    Explanator implementing DiViNE algorithm.
+    """
+
+    def __init__(self, predictor, k: int = 5, patience: int = 15, close_to_center: bool = True,
+                 discretization=None, normalization=None):
+        super().__init__(predictor, Target.CLASSIFICATION, discretization, normalization)
+        self.k = k
+        self.patience = patience
+        self.vicinity_function = DiViNE.closest_to_center if close_to_center else DiViNE.closest_to_corners
+
+    @staticmethod
+    def __pop(data: pd.DataFrame, idx: int = None) -> (Point, pd.DataFrame):
+        if idx is None:
+            idx = data.sample(1).index.values[0]
+        t = data.T
+        return DiViNE.__to_point(t.pop(idx)), t.T.reset_index(drop=True)
+
+    @staticmethod
+    def __to_point(instance) -> Point:
+        point = Point(instance.index.values, instance.values)
+        return point
+
+    def __to_cube(self, point: Point) -> GenericCube:
+        cube = HyperCube.cube_from_point(point.dimensions, self._output)
+        cube._output = list(point.dimensions.values())[-1]
+        return cube
+
+    def __clean(self, data: pd.DataFrame) -> pd.DataFrame:
+        _, idx = BallTree(data.iloc[:, :-1]).query(data.iloc[:, :-1], k=self.k)
+        # how many output classes are associated with the k neighbors
+        count = np.array(list(map(lambda indices: len(data.iloc[indices].iloc[:, -1].unique()), idx)))
+        # instances with neighbors of different classes are discarded
+        return data[count == 1]
+
+    def __closest(self, data: pd.DataFrame, cube: GenericCube) -> (Point, pd.DataFrame):
+        return DiViNE.__pop(data, self.vicinity_function(BallTree(data.iloc[:, :-1]), cube))
+
+    @staticmethod
+    def closest_to_center(tree: BallTree, cube: GenericCube):
+        return tree.query([list(cube.center.dimensions.values())], k=1)[1][0][-1]
+
+    @staticmethod
+    def closest_to_corners(tree: BallTree, cube: GenericCube):
+        distance, idx = tree.query([list(point.dimensions.values()) for point in cube.corners()], k=1)
+        return idx[np.argmin(distance)][-1]
+
+    def _extract(self, dataframe: pd.DataFrame, mapping: dict[str: int] = None, sort: bool = True) -> Theory:
+        data = self.__clean(dataframe)
+
+        while len(data) > 0:
+            discarded = []
+            patience = self.patience
+            point, data = self.__pop(data)
+            cube = self.__to_cube(point)
+
+            while patience > 0 and len(data) > 0:
+                other, data = self.__closest(data, cube)
+                if cube.output == list(other.dimensions.values())[-1]:
+                    cube = cube.merge_with_point(other)
+                    data = data[~(cube.filter_indices(data.iloc[:, :-1]))].reset_index(drop=True)
+                else:
+                    patience -= 1
+                    discarded.append(other)
+            if cube.volume() > 0:
+                cube.update(dataframe, self.predictor)
+                self._hypercubes.append(cube)
+            if len(discarded) > 0:
+                data = pd.concat([data] + [d.to_dataframe() for d in discarded]).reset_index(drop=True)
+        self._sort_cubes()
+        return self._create_theory(dataframe, sort)
diff --git a/psyke/extraction/hypercubic/gridex/__init__.py b/psyke/extraction/hypercubic/gridex/__init__.py
index 09272ee0..664554b6 100644
--- a/psyke/extraction/hypercubic/gridex/__init__.py
+++ b/psyke/extraction/hypercubic/gridex/__init__.py
@@ -17,7 +17,7 @@ class GridEx(HyperCubeExtractor):
 
     def __init__(self, predictor, grid: Grid, min_examples: int, threshold: float, normalization=None,
                  seed=get_default_random_seed()):
-        super().__init__(predictor, Target.CONSTANT, normalization)
+        super().__init__(predictor, Target.CONSTANT, normalization=normalization)
         self.grid = grid
         self.min_examples = min_examples
         self.threshold = threshold
diff --git a/psyke/extraction/hypercubic/hypercube.py b/psyke/extraction/hypercubic/hypercube.py
index 7c8d2d2b..342d1da7 100644
--- a/psyke/extraction/hypercubic/hypercube.py
+++ b/psyke/extraction/hypercubic/hypercube.py
@@ -30,25 +30,31 @@ class Point:
 
     EPSILON = get_default_precision()
 
-    def __init__(self, dimensions: list[str], values: list[float]):
+    def __init__(self, dimensions: list[str], values: list[float | str]):
         self._dimensions = {dimension: value for (dimension, value) in zip(dimensions, values)}
 
-    def __getitem__(self, feature: str) -> float:
+    def __getitem__(self, feature: str) -> float | str:
         if feature in self._dimensions.keys():
             return self._dimensions[feature]
         else:
             raise FeatureNotFoundException(feature)
 
-    def __setitem__(self, key: str, value: float) -> None:
+    def __setitem__(self, key: str, value: float | str) -> None:
         self._dimensions[key] = value
 
     def __eq__(self, other: Point) -> bool:
         return all([abs(self[dimension] - other[dimension]) < Point.EPSILON for dimension in self._dimensions])
 
     @property
-    def dimensions(self) -> dict[str, float]:
+    def dimensions(self) -> dict[str, float | str]:
         return self._dimensions
 
+    def to_dataframe(self) -> pd.DataFrame:
+        return pd.DataFrame(data=[self.dimensions.values()], columns=list(self.dimensions.keys()))
+
+    def copy(self) -> Point:
+        return Point(list(self._dimensions.keys()), list(self._dimensions.values()))
+
 
 class HyperCube:
     """
@@ -59,11 +65,12 @@ class HyperCube:
     INT_PRECISION = get_int_precision()
 
     def __init__(self, dimension: dict[str, tuple[float, float]] = None, limits: set[Limit] = None,
-                 output: float | LinearRegression = 0.0):
+                 output: float | LinearRegression | str = 0.0):
         self._dimensions = self._fit_dimension(dimension) if dimension is not None else {}
         self._limits = limits if limits is not None else set()
         self._output = output
         self._diversity = 0.0
+        self._barycenter = Point([], [])
 
     def __contains__(self, point: dict[str, float]) -> bool:
         """
@@ -101,13 +108,17 @@ def limit_count(self) -> int:
         return len(self._limits)
 
     @property
-    def output(self) -> float | LinearRegression:
+    def output(self) -> float | str | LinearRegression:
         return self._output
 
     @property
     def diversity(self) -> float:
         return self._diversity
 
+    @property
+    def barycenter(self) -> Point:
+        return self._barycenter
+
     def _fit_dimension(self, dimension: dict[str, tuple[float, float]]) -> dict[str, tuple[float, float]]:
         new_dimension: dict[str, tuple[float, float]] = {}
         for key, value in dimension.items():
@@ -125,12 +136,11 @@ def filter_indices(self, dataset: pd.DataFrame) -> ndarray:
         ds = dataset.to_numpy(copy=True)
         return np.all((v[:, 0] <= ds) & (ds < v[:, 1]), axis=1)
 
-    def _filter_dataframe(self, dataset: pd.DataFrame) -> pd.DataFrame:
+    def filter_dataframe(self, dataset: pd.DataFrame) -> pd.DataFrame:
         return dataset[self.filter_indices(dataset)]
 
-    def _zip_dimensions(self, hypercube: HyperCube) -> list[ZippedDimension]:
-        return [ZippedDimension(dimension, self[dimension], hypercube[dimension])
-                for dimension in self._dimensions.keys()]
+    def _zip_dimensions(self, other: HyperCube) -> list[ZippedDimension]:
+        return [ZippedDimension(dimension, self[dimension], other[dimension]) for dimension in self._dimensions.keys()]
 
     def add_limit(self, limit_or_feature: Limit | str, direction: str = None) -> None:
         if isinstance(limit_or_feature, Limit):
@@ -167,7 +177,7 @@ def copy(self) -> HyperCube:
         return HyperCube(self.dimensions.copy(), self._limits.copy(), self.output)
 
     def count(self, dataset: pd.DataFrame) -> int:
-        return self._filter_dataframe(dataset.iloc[:, :-1]).shape[0]
+        return self.filter_dataframe(dataset.iloc[:, :-1]).shape[0]
 
     def body(self, variables: dict[str, Var], ignore: list[str], unscale=None, normalization=None) -> Iterable[Struct]:
         dimensions = dict(self.dimensions)
@@ -202,7 +212,7 @@ def _create_tuple(self, generator: Random) -> dict:
         return {k: generator.uniform(self.get_first(k), self.get_second(k)) for k in self._dimensions.keys()}
 
     @staticmethod
-    def cube_from_point(point: dict, output=None) -> GenericCube:
+    def cube_from_point(point: dict[str, float], output=None) -> GenericCube:
         if output is Target.CLASSIFICATION:
             return ClassificationCube({k: (v, v) for k, v in list(point.items())[:-1]})
         if output is Target.REGRESSION:
@@ -249,6 +259,7 @@ def diagonal(self) -> float:
             lambda a, b: a + b, [(dimension[1] - dimension[0]) ** 2 for dimension in self._dimensions.values()], 0
         ) ** 0.5
 
+    @property
     def center(self) -> Point:
         return Point(list(self._dimensions.keys()),
                      [(interval[0] + interval[1]) / 2 for interval in self._dimensions.values()])
@@ -258,6 +269,50 @@ def corners(self) -> Iterable[Point]:
             Point(list(self._dimensions.keys()), values) for values in itertools.product(*self._dimensions.values())
         ]
 
+    def surface_distance(self, point: Point) -> float:
+        s = 0
+        for d in self.dimensions.keys():
+            lower, upper = self[d]
+            p = point[d]
+            if p > upper:
+                s += (p - upper)**2
+            elif p < lower:
+                s += (lower - p)**2
+        return s**0.5
+
+    def perimeter_samples(self, n: int = 5) -> Iterable[Point]:
+        def duplicate(point: Point, feature: str) -> Iterable[Point]:
+            new_point_a = point.copy()
+            new_point_b = point.copy()
+            new_point_a[feature] = self.get_first(feature)
+            new_point_b[feature] = self.get_second(feature)
+            return [new_point_a, new_point_b]
+
+        def remove_duplicates(points: Iterable[Point]) -> Iterable[Point]:
+            new_points = []
+            for point in points:
+                if point not in new_points:
+                    new_points.append(point)
+            return new_points
+
+        def split(point: Point, feature: str, n: int):
+            points = []
+            a, b = self.get_first(feature), self.get_second(feature)
+            for value in np.linspace(a, b, n) if n > 1 else [(a + b) / 2]:
+                new_point = point.copy()
+                new_point[feature] = value
+                points.append(new_point)
+            return points
+
+        points = []
+        for primary in self._dimensions:
+            new_points = [Point([], [])]
+            for secondary in self._dimensions:
+                new_points = np.array([duplicate(point, secondary) if primary != secondary else
+                                       split(point, primary, n) for point in new_points]).flatten()
+            points = points + list(new_points)
+        return remove_duplicates(points)
+
     def is_adjacent(self, cube: HyperCube) -> str | None:
         adjacent = None
         for (feature, [a1, b1]) in self._dimensions.items():
@@ -276,6 +331,15 @@ def merge_along_dimension(self, cube: HyperCube, feature: str) -> HyperCube:
         new_cube.update_dimension(feature, (min(a1, a2), max(b1, b2)))
         return new_cube
 
+    def merge(self, other: HyperCube) -> HyperCube:
+        new_cube = self.copy()
+        for dimension in self.dimensions.keys():
+            new_cube = new_cube.merge_along_dimension(other, dimension)
+        return new_cube
+
+    def merge_with_point(self, other: Point) -> HyperCube:
+        return self.merge(HyperCube.cube_from_point(other.dimensions))
+
     # TODO: maybe two different methods are more readable and easier to debug
     def overlap(self, hypercubes: Iterable[HyperCube] | HyperCube) -> HyperCube | bool | None:
         if isinstance(hypercubes, Iterable):
@@ -298,10 +362,12 @@ def update_dimension(self, feature: str, lower: float | tuple[float, float], upp
             self.update_dimension(feature, (lower, upper))
 
     def update(self, dataset: pd.DataFrame, predictor) -> None:
-        filtered = self._filter_dataframe(dataset.iloc[:, :-1])
+        filtered = self.filter_dataframe(dataset.iloc[:, :-1])
         predictions = predictor.predict(filtered)
         self._output = np.mean(predictions)
         self._diversity = np.std(predictions)
+        means = filtered.describe().loc['mean']
+        self._barycenter = Point(means.index.values, means.values)
 
     # TODO: why this is not a property?
     def init_diversity(self, std: float) -> None:
@@ -313,40 +379,45 @@ def __init__(self, dimension: dict[str, tuple] = None):
         super().__init__(dimension=dimension, output=LinearRegression())
 
     def update(self, dataset: pd.DataFrame, predictor) -> None:
-        filtered = self._filter_dataframe(dataset.iloc[:, :-1])
+        filtered = self.filter_dataframe(dataset.iloc[:, :-1])
         if len(filtered > 0):
             predictions = predictor.predict(filtered)
             self._output.fit(filtered, predictions)
             self._diversity = (abs(self._output.predict(filtered) - predictions)).mean()
+            means = filtered.describe().loc['mean']
+            self._barycenter = Point(means.index.values, means.values)
 
     def copy(self) -> RegressionCube:
         return RegressionCube(self.dimensions.copy())
 
     def body(self, variables: dict[str, Var], ignore: list[str], unscale=None, normalization=None) -> Iterable[Struct]:
-        intercept = self.output.intercept_ if normalization is None else \
-            unscale(sum([-self.output.coef_[i] * normalization[name][0] / normalization[name][1] for i, name in
-                         enumerate(self.dimensions.keys())], self.output.intercept_), list(normalization.keys())[-1])
-        coefs = self.output.coef_ if normalization is None else \
-            [self.output.coef_[i] / normalization[name][1] for i, name in enumerate(self.dimensions.keys())]
+        intercept = self.output.intercept_ if normalization is None else unscale(sum(
+            [-self.output.coef_[i] * normalization[name][0] / normalization[name][1] for i, name in
+             enumerate(self.dimensions.keys())], self.output.intercept_), list(normalization.keys())[-1])
+        coefs = self.output.coef_ if normalization is None else [
+            self.output.coef_[i] / normalization[name][1] * normalization[list(normalization.keys())[-1]][1] for
+            i, name in enumerate(self.dimensions.keys())
+        ]
         return list(super().body(variables, ignore, unscale, normalization)) + [linear_function_creator(
-            list(variables.values()), [to_rounded_real(v) for v in coefs],
-            to_rounded_real(intercept)
+            list(variables.values()), [to_rounded_real(v) for v in coefs], to_rounded_real(intercept)
         )]
 
 
 class ClassificationCube(HyperCube):
-    def __init__(self, dimension: dict[str, tuple] = None):
-        super().__init__(dimension=dimension)
+    def __init__(self, dimension: dict[str, tuple] = None, limits: set[Limit] = None, output: str = ""):
+        super().__init__(dimension=dimension, limits=limits, output=output)
 
     def update(self, dataset: pd.DataFrame, predictor) -> None:
-        filtered = self._filter_dataframe(dataset.iloc[:, :-1])
+        filtered = self.filter_dataframe(dataset.iloc[:, :-1])
         if len(filtered > 0):
             predictions = predictor.predict(filtered)
             self._output = mode(predictions)
             self._diversity = 1 - sum(prediction == self.output for prediction in predictions) / len(filtered)
+            means = filtered.describe().loc['mean']
+            self._barycenter = Point(means.index.values, means.values)
 
     def copy(self) -> ClassificationCube:
-        return ClassificationCube(self.dimensions.copy())
+        return ClassificationCube(self.dimensions.copy(), self._limits.copy(), self._output)
 
 
 class ClosedCube(HyperCube):
diff --git a/psyke/extraction/hypercubic/iter/__init__.py b/psyke/extraction/hypercubic/iter/__init__.py
index 7f2ca76d..1e6d8237 100644
--- a/psyke/extraction/hypercubic/iter/__init__.py
+++ b/psyke/extraction/hypercubic/iter/__init__.py
@@ -5,7 +5,6 @@
 import pandas as pd
 from sklearn.base import ClassifierMixin
 from tuprolog.theory import Theory
-from psyke import PedagogicalExtractor
 from psyke.extraction.hypercubic import HyperCube, HyperCubeExtractor
 from psyke.extraction.hypercubic.hypercube import GenericCube
 from psyke.extraction.hypercubic.utils import MinUpdate, Expansion
diff --git a/psyke/tuning/orchid/__init__.py b/psyke/tuning/orchid/__init__.py
index d1dfe6c8..cf76eefa 100644
--- a/psyke/tuning/orchid/__init__.py
+++ b/psyke/tuning/orchid/__init__.py
@@ -57,7 +57,8 @@ def __search_threshold(self, depth):
                 (EvaluableModel.Task.CLASSIFICATION, EvaluableModel.ClassificationScore.INVERSE_ACCURACY) \
                 if self.output == Target.CLASSIFICATION else \
                 (EvaluableModel.Task.REGRESSION, EvaluableModel.RegressionScore.MAE)
-            p, n = clustering.score(self.dataframe, None, False, False, task, [metric])[metric][0], clustering.n_rules
+            p, n = clustering.score(self.dataframe, None, False, False, task=task,
+                                    scoring_function=[metric])[metric][0], clustering.n_rules
 
             print(f"Predictive loss = {p:.2f}, {n} rules")
 
diff --git a/psyke/tuning/pedro/__init__.py b/psyke/tuning/pedro/__init__.py
index feb57e5e..9e6aae3b 100644
--- a/psyke/tuning/pedro/__init__.py
+++ b/psyke/tuning/pedro/__init__.py
@@ -4,10 +4,10 @@
 from psyke import Extractor
 from psyke.extraction.hypercubic import Grid, FeatureRanker
 from psyke.extraction.hypercubic.strategy import AdaptiveStrategy, FixedStrategy
-from psyke.tuning import Objective, Optimizer
+from psyke.tuning import Objective, GridOptimizer
 
 
-class PEDRO(Optimizer):
+class PEDRO(GridOptimizer):
     class Algorithm(Enum):
         GRIDEX = 1,
         GRIDREX = 2
@@ -71,7 +71,7 @@ def __search_depth(self, strategy, critical, max_partitions):
         for iterations in range(self.max_depth):
             grid = Grid(iterations + 1, strategy)
             p = self.__search_threshold(grid, critical, max_partitions)
-            b = Optimizer._best(p)[1]
+            b = GridOptimizer._best(p)[1]
             print()
             improvement = self._depth_improvement(
                 [best[0], best[1]], [b[0], b[1]]
diff --git a/psyke/utils/logic.py b/psyke/utils/logic.py
index d606252d..e56da90a 100644
--- a/psyke/utils/logic.py
+++ b/psyke/utils/logic.py
@@ -134,6 +134,10 @@ def create_variable_list(features: list[DiscreteFeature], dataset: pd.DataFrame
     return values
 
 
+def last_in_body(body: Struct) -> Struct:
+    return body.args[-1] if body.args[-1].functor == 'is' else last_in_body(body.args[-1])
+
+
 def create_head(functor: str, variables: Iterable[Var], output) -> Struct:
     if isinstance(output, Var):
         variables += [output]
diff --git a/psyke/utils/plot.py b/psyke/utils/plot.py
index 2bcbca82..3d69f60f 100644
--- a/psyke/utils/plot.py
+++ b/psyke/utils/plot.py
@@ -7,10 +7,81 @@
 from matplotlib.lines import Line2D
 from tuprolog.solve.prolog import prolog_solver
 from tuprolog.theory import Theory, mutable_theory
-from psyke.utils.logic import data_to_struct, pretty_theory, get_in_rule, get_not_in_rule
+
+from psyke.extraction.hypercubic import HyperCubeExtractor
+from psyke.utils.logic import data_to_struct, get_in_rule, get_not_in_rule
 
 import matplotlib
-matplotlib.use('TkAgg')
+#matplotlib.use('TkAgg')
+
+
+def plot_init(xlim, ylim, xlabel, ylabel, size=(4, 3), equal=False):
+    plt.figure(figsize=size)
+    if equal:
+        plt.gca().set_aspect(1)
+    plt.xlim(xlim)
+    plt.ylim(ylim)
+    plt.gca().set_xlabel(xlabel)
+    plt.gca().set_ylabel(ylabel)
+    plt.gca().set_rasterized(True)
+
+
+def plot_point(x, y, color, marker):
+    plt.scatter(x, y, c=color, marker=marker)
+
+
+def plot_classification_samples(dataframe, classes, colors, markers, labels, loc, name, show=True):
+    marks = [Line2D([0], [0], color=c, marker=m, lw="0") for c, m in zip(colors, markers)]
+
+    for cl, c, m in zip(classes, colors, markers):
+        df = dataframe[dataframe.target == cl]
+        plot_point(df["petal length"], df["petal width"], c, m)
+
+    plt.gca().legend(marks, labels, loc=loc)
+    plt.savefig("plot/{}.pdf".format(name), dpi=500, bbox_inches='tight')
+    if show:
+        plt.show()
+
+
+def plot_boundaries(extractor: HyperCubeExtractor, x: str, y: str, colors: dict[str, str],
+                    a: float = .5, h: str = '////////', ls='-', e=.05):
+    for cube in extractor._hypercubes:
+        plt.gca().fill_between((cube[x][0] - e, cube[x][1] + e), cube[y][0] - e, cube[y][1] + e,
+                               fc='none', ec=colors[cube.output], alpha=a, hatch=h, linestyle=ls)
+
+
+def plot_surfaces(extractor: HyperCubeExtractor, x: str, y: str, colors: dict[str, str], ec='r', e=.05):
+    for cube in extractor._hypercubes:
+        plt.gca().fill_between((cube[x][0] - e, cube[x][1] + e), cube[y][0] - e, cube[y][1] + e,
+                               fc='none', ec=ec)
+
+
+def plot_perimeters(extractor: HyperCubeExtractor, x: str, y: str, colors: dict[str, str], n: int = 5,
+                    ec: str = 'r', m: str = '*', s: int = 60, z: float = 1e10, lw: float = 0.8):
+    for cube in extractor._hypercubes:
+        for corner in cube.perimeter_samples(n):
+            plt.scatter(corner[x], corner[y], c=colors[cube.output], marker=m, edgecolor=ec, s=s, zorder=z, linewidth=lw)
+
+
+def plot_centers(extractor: HyperCubeExtractor, x: str, y: str, colors: dict[str, str],
+                 ec: str = 'r', m: str = '*', s: int = 60, z: float = 1e10, lw: float = 0.8):
+    for cube in extractor._hypercubes:
+        center = cube.center
+        plt.scatter(center[x], center[y], c=colors[cube.output], marker=m, edgecolor=ec, s=s, zorder=z, linewidth=lw)
+
+
+def plot_corners(extractor: HyperCubeExtractor, x: str, y: str, colors: dict[str, str],
+                 ec: str = 'r', m: str = '*', s: int = 60, z: float = 1e10, lw: float = 0.8):
+    for cube in extractor._hypercubes:
+        for corner in cube.corners():
+            plt.scatter(corner[x], corner[y], c=colors[cube.output], marker=m, edgecolor=ec, s=s, zorder=z, linewidth=lw)
+
+
+def plot_barycenters(extractor: HyperCubeExtractor, x: str, y: str, colors: dict[str, str],
+                 ec: str = 'r', m: str = '*', s: int = 60, z: float = 1e10, lw: float = 0.8):
+    for cube in extractor._hypercubes:
+        center = cube.barycenter
+        plt.scatter(center[x], center[y], c=colors[cube.output], marker=m, edgecolor=ec, s=s, zorder=z, linewidth=lw)
 
 
 def predict_from_theory(theory: Theory, data: pd.DataFrame) -> list[float or str]:
@@ -95,6 +166,7 @@ def color_fader(v: float = 0., c1: str = 'green', c2: str = 'red'):
         pass
         # ax.text2D(0., 0.88, pretty_theory(theory, new_line=False), transform=ax.transAxes, fontsize=8)
     if isinstance(ys[0], str):
-        custom_lines = [Line2D([0], [0], marker='o', markerfacecolor=get_color(c), markersize=20, color='w') for c in classes]
+        custom_lines = [Line2D([0], [0], marker='o', markerfacecolor=get_color(c),
+                               markersize=20, color='w') for c in classes]
         ax.legend(custom_lines, classes, loc='upper left', numpoints=1, ncol=3, fontsize=18, bbox_to_anchor=(0, 0))
     plt.savefig(output, format='pdf')
diff --git a/requirements.txt b/requirements.txt
index e85d46d2..bee085c2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,4 +11,4 @@ parameterized==0.9.0
 protobuf==4.24.3
 setuptools==68.2.2
 kneed==0.8.5
-sympy==1.12
\ No newline at end of file
+sympy==1.12
diff --git a/test/psyke/extraction/hypercubic/test_hypercube.py b/test/psyke/extraction/hypercubic/test_hypercube.py
index d1ee273a..ef4fcec4 100644
--- a/test/psyke/extraction/hypercubic/test_hypercube.py
+++ b/test/psyke/extraction/hypercubic/test_hypercube.py
@@ -162,7 +162,7 @@ def test_filter_dataframe(self):
         expected = (self.dataset.X >= self.x[0]) & (self.dataset.X < self.x[1]) & \
                    (self.dataset.Y >= self.y[0]) & (self.dataset.Y < self.y[1])
         expected = self.dataset[expected].iloc[:, :-1]
-        filtered = self.cube._filter_dataframe(self.dataset.iloc[:, :-1])
+        filtered = self.cube.filter_dataframe(self.dataset.iloc[:, :-1])
         self.assertTrue(all(expected == filtered))
 
     def test_update(self):
@@ -225,8 +225,8 @@ def test_diagonal(self):
         self.assertEqual(self.cube.diagonal(), ((self.x[1] - self.x[0])**2 + (self.y[1] - self.y[0])**2)**0.5)
 
     def test_center(self):
-        self.assertEqual(self.cube.center(), Point(list(self.dimensions.keys()),
-                                                   [(val[0] + val[1]) / 2 for val in self.dimensions.values()]))
+        self.assertEqual(self.cube.center, Point(list(self.dimensions.keys()),
+                                                 [(val[0] + val[1]) / 2 for val in self.dimensions.values()]))
 
     def test_corners(self):
         self.assertEqual(self.cube.corners(), [