Repository: mozilla/PRESC (language: python)

Issue:
ML Classifier Copying package release (2 nits)
Two changes were missed/added during merging of #281:
* The ML copies prototype example was not removed and it can be, because the ML copies package has already been implemented.
* There was an additional line in the `generate_synthetic_data` function of the `copying.py` file ( `df_generated = labeling(X_generated, self.original, label_col=self.label_col)` ), which was overriding the condition in the previous line and which will generate a problem in the case of balancer samplers. These samplers already label the data to do the balancing, so the output should NOT be labelled again.


Discussion / hints:

Fix patch:
diff --git a/docs/ml_copies.md b/docs/ml_copies.md
index bab87861..d169697f 100644
--- a/docs/ml_copies.md
+++ b/docs/ml_copies.md
@@ -34,6 +34,28 @@ When instantiating the `presc.copies.copying.ClassifierCopy` class, an instance
 
 The `presc.copies.copying.ClassifierCopy.copy_classifier` method will generate synthetic data in the feature space using the sampling function and options specified on instantiation, will label it using the original classifier, and then will use it to train the desired copy model. The generated synthetic training data can be saved in this step if needed but it can also be recovered later using the `presc.copies.copying.ClassifierCopy.generate_synthetic_data` method simply using the same random seed.
 
-After the copy has been obtained, an evaluation of the copy can be carried out using the  `presc.copies.copying.ClassifierCopy.compute_fidelity_error` and the `presc.copies.copying.ClassifierCopy.replacement_capability` methods. The evaluation methods need data with which to perform the evaluation, so an unlabeled array-like parameter should be specified when calling them. If original test data is available, it can be used as a test for the copy evaluation. Otherwise, synthetic test data can be generated with the `presc.copies.copying.ClassifierCopy.generate_synthetic_data` method simply using another random seed. However, interpretation of the results will of course have a different meaning than with the original test data.
 
+## Evaluation of the copy
+
+After the copy has been obtained, an evaluation of the copy can be carried out using the  `presc.copies.copying.ClassifierCopy.compute_fidelity_error` and the `presc.copies.copying.ClassifierCopy.replacement_capability` methods. A more complete overview can also be obtained using `presc.copies.copying.ClassifierCopy.evaluation_summary`.
+
+The evaluation methods need data with which to perform the evaluation, so an unlabeled array-like parameter should be specified when calling them. If original test data is available, it can be used as a test for the copy evaluation. Otherwise, synthetic test data can be generated with the `presc.copies.copying.ClassifierCopy.generate_synthetic_data` method simply using another random seed. However, interpretation of the results will of course have a different meaning than with the original test data.
+
+### Empirical Fidelity Error
+
+When performing a copy, we do not aim for an improvement of the original model performace, but to obtain the exact same behavior. Hence, the copy will be of higher quality when it mimics the orginal model exactly, including its misclassifications. To evaluate this, the best metric to use is the Empirical Fidelity Error.
+
+The Fidelity Error captures the loss of copying, and in its general form is the probability that the copy resembles the model. It evaluates the disagreement between the prediction of the original model and the prediction of the copy model through the percentage error of the copy over a given set of data points, taking the predictions of the original model as ground truth. In the ideal case, the fidelity error is zero and the accuracy of the copy is the same as that of the original classifier. 
+
+Since the synthetic dataset is always separable, theoretically it is always possible to achieve zero empirical error, given we choose a copy model with enough capacity. Hence, copying can be in theory carried out without any loss. However, in practice the generated dataset is invariably finite.
+
+A low Empirical Fidelity Error does not guarantee a good copy. In addition to that, the generated dataset to perform de copy must ensure a good coverage of the input space, and any volume imbalance effect needs to be accounted for as well.
+
+### Replacement Capability
+
+Sometimes we do not really need an exact copy of the original model yielding identical predictions, but we just need to obtain a new model that guarantees the same performance as the original. In these cases we are not concerned with mimicking the predicted classification of specific data points as long as the generalization ability of the copy model is good enough. In this scenario, the best way to evaluate the performance of the copy is to use the Replacement Capability.
+
+The Replacement Capability is the ratio between the accuracy of the copy model with respect to the accuracy of the original model, and it quantifies the ability of a copy to replace the original without any loss in performance. This means that the replacement capability will be one if the copy model matches the accuracy of the original, and that it can have a high value even if the performance of the copy model is not very good, as long as the copy is at least as good as the original. 
+
+The Replacement Capability can also yield in some cases values much larger than one if the copy model generalizes better than the original. This is not normally the case, but it might happen if the original model was poorly chosen and the copy model family is better suited to describe the boundary profile of the original problem.
 
diff --git a/examples/MLcopies_prototype.ipynb b/examples/MLcopies_prototype.ipynb
deleted file mode 100644
index 3d1948b3..00000000
--- a/examples/MLcopies_prototype.ipynb
+++ /dev/null
@@ -1,1657 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ML Classifier Copies (v1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.metrics import plot_confusion_matrix, classification_report, accuracy_score\n",
-    "from sklearn.datasets import make_blobs, make_moons"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Original datasets\n",
-    "### Blobs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-2.5, 3.5)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Xb, yb = make_blobs(n_samples=200, centers=[(0,0), (2,1)], n_features=2, random_state=0)\n",
-    "plt.scatter(Xb[:,0],Xb[:,1], c=yb)\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Moons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-2.5, 3.5)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Xm, ym = make_moons(n_samples=200, noise=0.2, random_state=0)\n",
-    "plt.scatter(Xm[:,0],Xm[:,1], c=ym)\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit SVC classifier\n",
-    "### Blobs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained SVC classifier: 0.8\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.75      0.83      0.79        18\n",
-      "           1       0.85      0.77      0.81        22\n",
-      "\n",
-      "    accuracy                           0.80        40\n",
-      "   macro avg       0.80      0.80      0.80        40\n",
-      "weighted avg       0.81      0.80      0.80        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train/test split\n",
-    "X_train_blobs, X_test_blobs, y_train_blobs, y_test_blobs = train_test_split(Xb, yb, test_size=0.20, random_state=0)\n",
-    "\n",
-    "# SVC classifier\n",
-    "classifier_blobs = SVC(kernel='linear', decision_function_shape='ovo')\n",
-    "classifier_blobs.fit(X_train_blobs, y_train_blobs)\n",
-    "y_predicted_blobs = classifier_blobs.predict(X_test_blobs)\n",
-    "\n",
-    "score = classifier_blobs.score(X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nScore of trained SVC classifier: {score}\")\n",
-    "plot_confusion_matrix(classifier_blobs, X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_blobs, y_predicted_blobs))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Moons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained SVC classifier: 0.85\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.86      0.86      0.86        22\n",
-      "           1       0.83      0.83      0.83        18\n",
-      "\n",
-      "    accuracy                           0.85        40\n",
-      "   macro avg       0.85      0.85      0.85        40\n",
-      "weighted avg       0.85      0.85      0.85        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Train/test split\n",
-    "X_train_moons, X_test_moons, y_train_moons, y_test_moons = train_test_split(Xm, ym, test_size=0.20, random_state=0)\n",
-    "\n",
-    "# SVC classifier\n",
-    "classifier_moons = SVC(kernel='linear', decision_function_shape='ovo')\n",
-    "classifier_moons.fit(X_train_moons, y_train_moons)\n",
-    "y_predicted_moons = classifier_moons.predict(X_test_moons)\n",
-    "\n",
-    "score = classifier_moons.score(X_test_moons, y_test_moons)\n",
-    "print(f\"\\nScore of trained SVC classifier: {score}\")\n",
-    "plot_confusion_matrix(classifier_moons, X_test_moons, y_test_moons)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_moons, y_predicted_moons))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Feature dynamical ranges"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Blobs dataset:\n",
-      "X min: -2.5530   X max: 4.3039    Interval: 6.8569\n",
-      "Y min: -2.2234   Y max: 3.2567    Interval: 5.4801\n",
-      "\n",
-      "Moons dataset:\n",
-      "X min: -1.3546   X max: 2.2554    Interval: 3.6100\n",
-      "Y min: -0.9321   Y max: 1.3416    Interval: 2.2737\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Blobs dataset:\")\n",
-    "print(f\"X min: {Xb[:,0].min():.4f}   X max: {Xb[:,0].max():.4f}    Interval: {Xb[:,0].max()- Xb[:,0].min():.4f}\")\n",
-    "print(f\"Y min: {Xb[:,1].min():.4f}   Y max: {Xb[:,1].max():.4f}    Interval: {Xb[:,1].max()- Xb[:,1].min():.4f}\")\n",
-    "\n",
-    "print(\"\\nMoons dataset:\")\n",
-    "print(f\"X min: {Xm[:,0].min():.4f}   X max: {Xm[:,0].max():.4f}    Interval: {Xm[:,0].max()- Xm[:,0].min():.4f}\")\n",
-    "print(f\"Y min: {Xm[:,1].min():.4f}   Y max: {Xm[:,1].max():.4f}    Interval: {Xm[:,1].max()- Xm[:,1].min():.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate distribution\n",
-    "###  Grid-like uniform "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Blobs dataset\n",
-    "X0_generated_gb = np.linspace(Xb[:,0].min(), Xb[:,0].max(), 12)\n",
-    "X1_generated_gb = np.linspace(Xb[:,1].min(), Xb[:,1].max(), 16)\n",
-    "X_generated_gb = np.array(np.meshgrid(X0_generated_gb, X1_generated_gb)).T.reshape(-1, 2)\n",
-    "\n",
-    "# Moons dataset\n",
-    "X0_generated_gm = np.linspace(Xm[:,0].min(), Xm[:,0].max(), 12)\n",
-    "X1_generated_gm = np.linspace(Xm[:,1].min(), Xm[:,1].max(), 16)\n",
-    "X_generated_gm = np.array(np.meshgrid(X0_generated_gm, X1_generated_gm)).T.reshape(-1, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_gb[:,0], X_generated_gb[:,1])\n",
-    "plt.scatter(X_generated_gm[:,0], X_generated_gm[:,1])\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Random uniform\n",
-    "We generate 2 sets of uniform random data because the dynamic range of the features is different in each case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Blobs datasetfrom matplotlib import cm\n",
-    "X0_generated_ub = np.random.uniform(low=Xb[:,0].min(), high=Xb[:,0].max(), size=200)\n",
-    "X1_generated_ub = np.random.uniform(low=Xb[:,1].min(), high=Xb[:,1].max(), size=200)\n",
-    "X_generated_ub = np.stack((X0_generated_ub, X1_generated_ub), axis=1)\n",
-    "\n",
-    "# Moons dataset\n",
-    "X0_generated_um = np.random.uniform(low=Xm[:,0].min(), high=Xm[:,0].max(), size=200)\n",
-    "X1_generated_um = np.random.uniform(low=Xm[:,1].min(), high=Xm[:,1].max(), size=200)\n",
-    "X_generated_um = np.stack((X0_generated_um, X1_generated_um), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X0_generated_ub, X1_generated_ub)\n",
-    "plt.scatter(X0_generated_um, X1_generated_um)\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Normal distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Blobs dataset\n",
-    "mean_b = [Xb[:,0].min()+(Xb[:,0].max()-Xb[:,0].min())/2, \n",
-    "          Xb[:,1].min()+(Xb[:,1].max()-Xb[:,1].min())/2]\n",
-    "cov_b = [[0.2*(Xb[:,0].max()-Xb[:,0].min()), 0], \n",
-    "         [0, 0.2*(Xb[:,1].max()-Xb[:,1].min())]] \n",
-    "X0_generated_nb, X1_generated_nb = np.random.multivariate_normal(mean_b, cov_b, 200).T\n",
-    "X_generated_nb = np.stack((X0_generated_nb, X1_generated_nb), axis=1)\n",
-    "\n",
-    "# Moons dataset\n",
-    "mean_m = [Xm[:,0].min()+(Xm[:,0].max()-Xm[:,0].min())/2, \n",
-    "          Xm[:,1].min()+(Xm[:,1].max()-Xm[:,1].min())/2]\n",
-    "cov_m = [[0.2*(Xm[:,0].max()-Xm[:,0].min()), 0], \n",
-    "         [0,0.2*(Xm[:,1].max()-Xm[:,1].min())]   ]\n",
-    "X0_generated_nm, X1_generated_nm = np.random.multivariate_normal(mean_m, cov_m, 200).T\n",
-    "X_generated_nm = np.stack((X0_generated_nm, X1_generated_nm), axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X0_generated_nb, X1_generated_nb)\n",
-    "plt.scatter(X0_generated_nm, X1_generated_nm)\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Label synthetic data with original classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Grid-like uniform\n",
-    "y_generated_gb = classifier_blobs.predict(X_generated_gb)\n",
-    "y_generated_gm = classifier_moons.predict(X_generated_gm)\n",
-    "\n",
-    "# Random uniform\n",
-    "y_generated_ub = classifier_blobs.predict(X_generated_ub)\n",
-    "y_generated_um = classifier_moons.predict(X_generated_um)\n",
-    "\n",
-    "# Normal distribution\n",
-    "y_generated_nb = classifier_blobs.predict(X_generated_nb)\n",
-    "y_generated_nm = classifier_moons.predict(X_generated_nm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "----\n",
-    "\n",
-    "## Copy classifier with synthetic data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Blobs SVC classifier + grid-like synthetic data + decision tree copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train/test split\n",
-    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(X_generated_gb, y_generated_gb, \n",
-    "                                                                    test_size=0.20, random_state=0)\n",
-    "# Decision tree classifier copy\n",
-    "classifier_copy = DecisionTreeClassifier(max_depth=3)\n",
-    "classifier_copy.fit(X_gen_train, y_gen_train)\n",
-    "y_gen_predicted = classifier_copy.predict(X_gen_test)\n",
-    "y_predicted = classifier_copy.predict(X_test_blobs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classification of synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_gb[:,0], X_generated_gb[:,1], c=y_generated_gb )\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.97\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      0.95      0.97        19\n",
-      "           1       0.95      1.00      0.98        20\n",
-      "\n",
-      "    accuracy                           0.97        39\n",
-      "   macro avg       0.98      0.97      0.97        39\n",
-      "weighted avg       0.98      0.97      0.97        39\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_gen_test, y_gen_test)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_gen_test, y_gen_test)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_gen_test, y_gen_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.75\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.72      0.72      0.72        18\n",
-      "           1       0.77      0.77      0.77        22\n",
-      "\n",
-      "    accuracy                           0.75        40\n",
-      "   macro avg       0.75      0.75      0.75        40\n",
-      "weighted avg       0.75      0.75      0.75        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAAEGCAYAAADmLRl+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVG0lEQVR4nO3dfZBcVZ3G8e+TSTJ5IRlCElhAkMjrRlxejLxZYhCV4KLgFr6AuIhaEVzAAi3UtcqsVq2LqxbqqmiELKAYBAXBAgLKgkFKAgEBIUCBAULAGJLwGkKSmfntH30nDCHpvnfm9vQ9Pc+n6la6b3ef+8uk5sk55557WxGBmVnKRrS6ADOzwXKQmVnyHGRmljwHmZklz0FmZskb2eoC+hvZNS46d+hqdRlWQMcj61tdghXwCmvZEOs1mDaOOmJ8rF7Tk+u9d923/oaImDWY4+VRqSDr3KGLN3//E60uwwroet+jrS7BClgUNw26jdVrerjjhl1zvbdjx0emDPqAOVQqyMys+gLopbfVZbyGg8zMCgmCjZFvaDlUHGRmVph7ZGaWtCDoqdiljQ4yMyusFweZmSUsgB4HmZmlzj0yM0taABs9R2ZmKQvCQ0szS1xAT7VyzEFmZsXUVvZXi4PMzAoSPQzquvPSOcjMrJDaZH+1gsz3IzOzQmrryJRra0TSPEkrJd2/2f4zJD0k6QFJ/92oHffIzKyw3vJ6ZBcBPwAu6dsh6QjgWGC/iFgvaftGjTjIzKyQvh5ZKW1FLJS022a7TwPOjYj12XtWNmrHQ0szKyQQPYzItQFTJC3ut83OcYi9gHdIWiTpD5Le1ugD7pGZWWEFhparImJGweZHAtsBhwBvAy6X9Kao823iDjIzKyQQG6KjmYdYDlyZBdcdknqBKcAzW/uAh5ZmVkhtQeyIXNsA/QY4AkDSXsBoYFW9D7hHZmaFlTXZL2k+MJPaXNpyYA4wD5iXLcnYAJxcb1gJDjIzKyhC9EQ5g7mIOGErL51UpB0HmZkV1utLlMwsZbXJ/mpFR7WqMbPK65vsrxIHmZkV1lOxi8YdZGZWSN/K/ipxkJlZYb0lnbUsi4PMzAqpXTTuIDOzhAViY3MvUSrMQWZmhURQ2oLYsjjIzKwgeUGsmaUtcI/MzNqAJ/vNLGmByrxnfykcZGZWSO3r4KoVHdWqxswS4C/oNbPEBV7Zb2ZtwD0yM0tahNwjM7O01Sb7fYmSmSWtvHv2l8VBZmaF1Cb7PUdmZonzyn4zS5pX9ptZW/CXj5hZ0iJgY6+DzMwSVhtaOsjMLHFe2d/mxp73d0be8TKxbQcvnb8rAJ2XrGbU7WthBPR2dbDu7B2Iyf7RV9HFi5aw7qUOenuhp1uccfRerS6pcobd8gtJs4DvAR3ABRFxbjOPVwUb3j2R9e/vYtx3Vm7at/74Saz/18kAjL76OTp/sYZXzti+VSVaA+d8aHdeWOP/aLauvKGlpHnAMcDKiNh3s9c+D3wbmBoRq+q107SBrqQO4IfA0cB04ARJ05t1vKroectYYsJml2+Me/XHrFd6qViv3Kyw3uy+/Y22HC4CZm2+U9IuwHuBZXkaaeZ/OwcBj0bE0qywy4BjgSVNPGZldV68mtE3vUiMH8Hac3dudTm2NSG+MX8pBFz7s8lcf+nkVldUObWzluVcaxkRCyXttoWXzgPOAa7O004zg2xn4Ml+z5cDB2/+JkmzgdkAo7ef2MRyWmv9yZNZf/JkOn+5htG/fY71J/kXpIrOPm4PVq8YRdfkjZx72VKefLST+xdt0+qyKqXggtgpkhb3ez43IubW+4CkY4GnIuJeKd9xWn4ONSLmRsSMiJgxsmtcq8tpug1HTGDUbWtbXYZtxeoVowB4fvUoblvQxT4HvNziiqqpwNByVd/vd7Y1CrFxwL8DXy1STzOD7Clgl37P35DtG3ZGPLVh0+NRt6+l9w2jWliNbU3n2B7Gju/Z9Pit73yRxx8a0+KqqqfvrGWebQB2B6YB90p6nFpu3C3pH+p9qJlDyzuBPSVNoxZgHwVObOLxKmHsN1cw8r516IUeJnz8MV45aTKj7lzLiKc2gqB3+5GsO91nLKto0tRu5lz4OAAdI4Obr5rE4lvad7pjMJq1IDYi/gJs+gXJwmxGo7OWTQuyiOiWdDpwA7XlF/Mi4oFmHa8q1n3x9f9xbDzKvwwpWLGsk9Pes3ery6i8CNFd3vKL+cBManNpy4E5EXFh0XaaulgmIq4DrmvmMcxs6JW1IDYiTmjw+m552vGqPzMrZNit7Dez9uQgM7Ok+caKZtYWcl5+NGQcZGZWSAR0+8aKZpY6Dy3NLGmeIzOzthAOMjNLnSf7zSxpEZ4jM7PkiR6ftTSz1HmOzMyS5mstzSx9UZsnqxIHmZkV5rOWZpa08GS/mbUDDy3NLHk+a2lmSYtwkJlZG/DyCzNLnufIzCxpgej1WUszS13FOmQOMjMryJP9ZtYWKtYlc5CZWWHJ9Mgk/Q91cjcizmxKRWZWaQH09iYSZMDiIavCzNIRQEk9MknzgGOAlRGxb7bvW8D7gQ3AX4FTIuK5eu1sNcgi4uLNDjguIl4eZN1m1gZKXEd2EfAD4JJ++34HfDkiuiV9E/gy8MV6jTRcDCLpUElLgIey5/tJ+tFAqzazNhA5t0bNRCwE1my278aI6M6e3g68oVE7eVa1fRc4ClidHeRe4PAcnzOztiQi8m3AFEmL+22zCx7sk8D1jd6U66xlRDwpvWZM3FOwGDNrJ/mHlqsiYsZADiHpK0A3cGmj9+YJsiclHQaEpFHA54AHB1KYmbWBgGjyWUtJn6B2EuDIiMYzcnmGlqcC/wbsDDwN7J89N7NhSzm3AbQszQLOAT6Q9wRjwx5ZRKwCPjagisysPZV01lLSfGAmtbm05cAcamcpO4HfZVNat0fEqfXaaRhkkt4EfA84hFr5fwLOioilg/kLmFnCSgqyiDhhC7svLNpOnqHlL4DLgR2BnYArgPlFD2RmbaJvQWyebYjkCbJxEfGziOjOtp8DY5pdmJlVV0S+bajUu9Zyu+zh9ZK+BFxGLYs/Alw3BLWZWVUldK3lXdSCq6/iz/R7LahNyJnZMKRUbuMTEdOGshAzS0TOy4+GUq6V/ZL2BabTb24sIi7Z+ifMrH0N7UR+HnmWX8yhts5jOrW5saOBP/Laq9XNbDipWI8sz1nL44EjgRURcQqwH9DV1KrMrNp6c25DJM/Qcl1E9ErqljQRWAns0uS6zKyqSryxYlnyBNliSdsCP6V2JvMlaqv7zWyYSuasZZ+I+Gz28MeSFgATI+K+5pZlZpWWSpBJOrDeaxFxd3NKMjMrpl6P7Dt1XgvgXSXXQscj6+l636NlN2tNdMPT97S6BCvgoKPK+dqNZIaWEXHEUBZiZokIkrpEycxsy1LpkZmZbU0yQ0szs62qWJDl+V5LSTpJ0lez57tKOqj5pZlZZZX0vZZlyXOJ0o+AQ4G+W9K+CPywaRWZWaUp8m9DJc/Q8uCIOFDSnwEi4llJo5tcl5lVWYJnLTdK6iDrKEqaypBeDmpmVVO1yf48Q8vvA1cB20v6T2q38PlGU6sys2qr2BxZnmstL5V0F7Vb+Qg4LiL8TeNmw9UQz3/lkefGirsCLwO/7b8vIpY1szAzq7DUggy4lle/hGQMMA14GHhzE+syswpTxWbJ8wwt39L/eXZXjM9u5e1mZkOu8Mr+iLhb0sHNKMbMEpHa0FLS2f2ejgAOBJ5uWkVmVm0lTvZLmgccA6yMiH2zfdsBvwR2Ax4HPhwRz9ZrJ8/yiwn9tk5qc2bHDrRwM2sD5S2/uAiYtdm+LwE3RcSewE3Z87rq9siyhbATIuILuUoys+GhpB5ZRCyUtNtmu4+l9hWUABcDtwBfrNdOvVtdj4yIbklvH3iZZtZuRKGzllMkLe73fG5EzG3wmR0i4m/Z4xXADo0OUq9Hdge1+bB7JF0DXAGs7XsxIq5s1LiZtaFic2SrImLGgA8VEVLjo+U5azkGWE3tHv1968kCcJCZDVfNPWv5d0k7RsTfJO1I7bt066oXZNtnZyzv59UA61Oxk69mNqSamwDXACcD52Z/Xt3oA/WCrAPYhtcGWB8HmdkwVuLyi/nUJvanSFoOzKEWYJdL+hTwBPDhRu3UC7K/RcTXS6jVzNpNeWctT9jKS0cWaadekFXrzmlmVg2R1rWWhRLRzIaRik0u1fuC3jVDWYiZpSO5+5GZmb2Og8zMkjbEt7HOw0FmZoUIDy3NrA04yMwsfQ4yM0ueg8zMkpbi18GZmb2Og8zMUpfSJUpmZlvkoaWZpc0LYs2sLTjIzCxlXtlvZm1BvdVKMgeZmRXjOTIzawceWppZ+hxkZpY698jMLH0OMjNLWmLfomRm9jpeR2Zm7SGqlWQOMjMrzD2yYebiRUtY91IHvb3Q0y3OOHqvVpdk/XznrF1Y9PuJbDulm7k3PwzAf37mjSz/6xgA1r7QwfiJPZz/+4dbWWa1DKcFsZLmAccAKyNi32YdJwXnfGh3Xljj/zOq6L0fWcMHTlnFtz6366Z9X/nJE5se/+RrOzF+Qk8rSqu0sib7JZ0FfJpaNP4FOCUiXinazohyytmii4BZTWzfbNDecshaJkzaclBFwMJrtuWI454d4qqqT735trptSDsDZwIzss5OB/DRgdTTtG5CRCyUtFuz2k9GiG/MXwoB1/5sMtdfOrnVFVlO9y8az6Sp3ez8pg2tLqVagjIn+0cCYyVtBMYBTw+0kZaSNBuYDTCGcS2upnxnH7cHq1eMomvyRs69bClPPtrJ/Yu2aXVZlsPNv5nETPfGtqjAZP8USYv7PZ8bEXMBIuIpSd8GlgHrgBsj4saB1NPMoWUuETE3ImZExIxRdLa6nNKtXjEKgOdXj+K2BV3sc8DLLa7I8ujphtuu6+KdH3iu1aVUU+TcYFXf73e2ze1rQtIk4FhgGrATMF7SSQMpp+VB1s46x/YwdnzPpsdvfeeLPP7QmBZXZXncfesEdtljPVN32tjqUiqnb0Fsnq2BdwOPRcQzEbERuBI4bCA1tXxo2c4mTe1mzoWPA9AxMrj5qkksvmVia4uy1/iv097IfX/ahufXjORjb53Oxz+/glknruEPV3tYuVURZd1YcRlwiKRx1IaWRwKL639ky5q5/GI+MJPaGHk5MCciLmzW8apoxbJOTnvP3q0uw+r48vlPbHH/F767bIgrSUwJORYRiyT9Crgb6Ab+DMyt/6kta+ZZyxOa1baZtVZZK/sjYg4wZ7DteGhpZsUE4Hv2m1nyqpVjDjIzK84XjZtZ8vx1cGaWtuF09wsza0+1BbHVSjIHmZkV53v2m1nq3CMzs7R5jszM0lfatZalcZCZWXEeWppZ0vwFvWbWFtwjM7PkVSvHHGRmVpx6qzW2dJCZWTGBF8SaWdpEeEGsmbUBB5mZJc9BZmZJ8xyZmbUDn7U0s8SFh5ZmlrjAQWZmbaBaI0sHmZkV53VkZpY+B5mZJS0Ceqo1tnSQmVlxFeuRjWh1AWaWoIh8WwOStpX0K0kPSXpQ0qEDKcc9MjMrJoDy7tn/PWBBRBwvaTQwbiCNOMjMrKCAGPwcmaQu4HDgEwARsQHYMJC2PLQ0s2KC2mR/ng2mSFrcb5vdr6VpwDPA/0r6s6QLJI0fSEkOMjMrLv8c2aqImNFvm9uvlZHAgcD5EXEAsBb40kDKcZCZWXHlTPYvB5ZHxKLs+a+oBVthDjIzKyhniDUIsohYATwpae9s15HAkoFU5Ml+MysmgPJu43MGcGl2xnIpcMpAGnGQmVlxJS2IjYh7gBmDbcdBZmYF+RIlM0tdQJSwjqxMDjIzK668lf2lcJCZWXEVu2jcQWZmxUSUedayFA4yMyvOPTIzS1sQPT2tLuI1HGRmVky5t/EphYPMzIrz8gszS1kA4R6ZmSUtyrmxYpkcZGZWWNUm+xUVOo0q6RngiVbX0QRTgFWtLsIKadd/szdGxNTBNCBpAbWfTx6rImLWYI6XR6WCrF1JWhwRg77C34aO/83S4hsrmlnyHGRmljwH2dCY2/gtVjH+N0uI58jMLHnukZlZ8hxkZpY8B1kTSZol6WFJj0oa0BeP2tCSNE/SSkn3t7oWy89B1iSSOoAfAkcD04ETJE1vbVWWw0VA0xdwWrkcZM1zEPBoRCyNiA3AZcCxLa7JGoiIhcCaVtdhxTjImmdn4Ml+z5dn+8ysZA4yM0ueg6x5ngJ26ff8Ddk+MyuZg6x57gT2lDRN0mjgo8A1La7JrC05yJokIrqB04EbgAeByyPigdZWZY1Img/8Cdhb0nJJn2p1TdaYL1Eys+S5R2ZmyXOQmVnyHGRmljwHmZklz0FmZslzkCVEUo+keyTdL+kKSeMG0dZFko7PHl9Q74J2STMlHTaAYzwu6XXftrO1/Zu956WCx/oPSV8oWqO1BwdZWtZFxP4RsS+wATi1/4uSBvQ9pRHx6YhYUuctM4HCQWY2VBxk6boV2CPrLd0q6RpgiaQOSd+SdKek+yR9BkA1P8juj/Z7YPu+hiTdImlG9niWpLsl3SvpJkm7UQvMs7Le4DskTZX06+wYd0p6e/bZyZJulPSApAsANfpLSPqNpLuyz8ze7LXzsv03SZqa7dtd0oLsM7dK2qeUn6Ylzd80nqCs53U0sCDbdSCwb0Q8loXB8xHxNkmdwG2SbgQOAPamdm+0HYAlwLzN2p0K/BQ4PGtru4hYI+nHwEsR8e3sfb8AzouIP0raldrVC/8IzAH+GBFfl/TPQJ5V8Z/MjjEWuFPSryNiNTAeWBwRZ0n6atb26dS+FOTUiHhE0sHAj4B3DeDHaG3EQZaWsZLuyR7fClxIbch3R0Q8lu1/L/BPffNfQBewJ3A4MD8ieoCnJf3fFto/BFjY11ZEbO2+XO8GpkubOlwTJW2THeNfss9eK+nZHH+nMyV9MHu8S1braqAX+GW2/+fAldkxDgOu6HfszhzHsDbnIEvLuojYv/+O7Bd6bf9dwBkRccNm73tfiXWMAA6JiFe2UEtukmZSC8VDI+JlSbcAY7by9siO+9zmPwMzz5G1nxuA0ySNApC0l6TxwELgI9kc2o7AEVv47O3A4ZKmZZ/dLtv/IjCh3/tuBM7oeyJp/+zhQuDEbN/RwKQGtXYBz2Yhtg+1HmGfEUBfr/JEakPWF4DHJH0oO4Yk7dfgGDYMOMjazwXU5r/uzr5A4yfUet5XAY9kr11C7Q4PrxERzwCzqQ3j7uXVod1vgQ/2TfYDZwIzspMJS3j17OnXqAXhA9SGmMsa1LoAGCnpQeBcakHaZy1wUPZ3eBfw9Wz/x4BPZfU9gG8fbvjuF2bWBtwjM7PkOcjMLHkOMjNLnoPMzJLnIDOz5DnIzCx5DjIzS97/A7dkjiujqjlnAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_blobs, y_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Empirical fidelity error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Empirical fidelity error\n",
-      "Synthetic dataset: 0.0256\n",
-      "Original dataset:  0.0500\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Synthetic data\n",
-    "error_synthetic = 1 - accuracy_score(y_gen_test, y_gen_predicted)\n",
-    "# Original data\n",
-    "error_original = 1 - accuracy_score(y_predicted_blobs, y_predicted)\n",
-    "\n",
-    "print(\"Empirical fidelity error\")\n",
-    "print(f\"Synthetic dataset: {error_synthetic:.4f}\")\n",
-    "print(f\"Original dataset:  {error_original:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "----\n",
-    "### Blobs SVC classifier + random uniform synthetic data + decision tree copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train/test split\n",
-    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(X_generated_ub, y_generated_ub, \n",
-    "                                                                    test_size=0.20, random_state=0)\n",
-    "# Decision tree classifier copy\n",
-    "classifier_copy = DecisionTreeClassifier(max_depth=3)\n",
-    "classifier_copy.fit(X_gen_train, y_gen_train)\n",
-    "y_gen_predicted = classifier_copy.predict(X_gen_test)\n",
-    "y_predicted = classifier_copy.predict(X_test_blobs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classification of synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_ub[:,0], X_generated_ub[:,1], c=y_generated_ub )\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.95\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      0.91      0.95        22\n",
-      "           1       0.90      1.00      0.95        18\n",
-      "\n",
-      "    accuracy                           0.95        40\n",
-      "   macro avg       0.95      0.95      0.95        40\n",
-      "weighted avg       0.96      0.95      0.95        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_gen_test, y_gen_test)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_gen_test, y_gen_test)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_gen_test, y_gen_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.78\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.85      0.61      0.71        18\n",
-      "           1       0.74      0.91      0.82        22\n",
-      "\n",
-      "    accuracy                           0.78        40\n",
-      "   macro avg       0.79      0.76      0.76        40\n",
-      "weighted avg       0.79      0.78      0.77        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_blobs, y_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Empirical fidelity error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Empirical fidelity error\n",
-      "Synthetic dataset: 0.0500\n",
-      "Original dataset:  0.1750\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Synthetic data\n",
-    "error_synthetic = 1 - accuracy_score(y_gen_test, y_gen_predicted)\n",
-    "# Original data\n",
-    "error_original = 1 - accuracy_score(y_predicted_blobs, y_predicted)\n",
-    "\n",
-    "print(\"Empirical fidelity error\")\n",
-    "print(f\"Synthetic dataset: {error_synthetic:.4f}\")\n",
-    "print(f\"Original dataset:  {error_original:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-----\n",
-    "### Blobs SVC classifier + normally distributed synthetic data + decision tree copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train/test split\n",
-    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(X_generated_nb, y_generated_nb, \n",
-    "                                                                    test_size=0.20, random_state=0)\n",
-    "# Decision tree classifier copy\n",
-    "classifier_copy = DecisionTreeClassifier(max_depth=3)\n",
-    "classifier_copy.fit(X_gen_train, y_gen_train)\n",
-    "y_gen_predicted = classifier_copy.predict(X_gen_test)\n",
-    "y_predicted = classifier_copy.predict(X_test_blobs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classification of synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_nb[:,0], X_generated_nb[:,1], c=y_generated_nb )\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.93\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      0.84      0.91        19\n",
-      "           1       0.88      1.00      0.93        21\n",
-      "\n",
-      "    accuracy                           0.93        40\n",
-      "   macro avg       0.94      0.92      0.92        40\n",
-      "weighted avg       0.93      0.93      0.92        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_gen_test, y_gen_test)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_gen_test, y_gen_test)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_gen_test, y_gen_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.78\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.85      0.61      0.71        18\n",
-      "           1       0.74      0.91      0.82        22\n",
-      "\n",
-      "    accuracy                           0.78        40\n",
-      "   macro avg       0.79      0.76      0.76        40\n",
-      "weighted avg       0.79      0.78      0.77        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_test_blobs, y_test_blobs)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_blobs, y_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Empirical fidelity error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Empirical fidelity error\n",
-      "Synthetic dataset: 0.0750\n",
-      "Original dataset:  0.1750\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Synthetic data\n",
-    "error_synthetic = 1 - accuracy_score(y_gen_test, y_gen_predicted)\n",
-    "# Original data\n",
-    "error_original = 1 - accuracy_score(y_predicted_blobs, y_predicted)\n",
-    "\n",
-    "print(\"Empirical fidelity error\")\n",
-    "print(f\"Synthetic dataset: {error_synthetic:.4f}\")\n",
-    "print(f\"Original dataset:  {error_original:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-----\n",
-    "### Moons SVC classifier + grid-like synthetic data + decision tree copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train/test split\n",
-    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(X_generated_gm, y_generated_gm, \n",
-    "                                                                    test_size=0.20, random_state=0)\n",
-    "# Decision tree classifier copy\n",
-    "classifier_copy = DecisionTreeClassifier(max_depth=3)\n",
-    "classifier_copy.fit(X_gen_train, y_gen_train)\n",
-    "y_gen_predicted = classifier_copy.predict(X_gen_test)\n",
-    "y_predicted = classifier_copy.predict(X_test_moons)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classification of synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_gm[:,0], X_generated_gm[:,1], c=y_generated_gm )\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.87\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.94      0.79      0.86        19\n",
-      "           1       0.83      0.95      0.88        20\n",
-      "\n",
-      "    accuracy                           0.87        39\n",
-      "   macro avg       0.88      0.87      0.87        39\n",
-      "weighted avg       0.88      0.87      0.87        39\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_gen_test, y_gen_test)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_gen_test, y_gen_test)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_gen_test, y_gen_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.85\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.83      0.91      0.87        22\n",
-      "           1       0.88      0.78      0.82        18\n",
-      "\n",
-      "    accuracy                           0.85        40\n",
-      "   macro avg       0.85      0.84      0.85        40\n",
-      "weighted avg       0.85      0.85      0.85        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_test_moons, y_test_moons)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_test_moons, y_test_moons)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_moons, y_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Empirical fidelity error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Empirical fidelity error\n",
-      "Synthetic dataset: 0.1282\n",
-      "Original dataset:  0.0500\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Synthetic data\n",
-    "error_synthetic = 1 - accuracy_score(y_gen_test, y_gen_predicted)\n",
-    "# Original data\n",
-    "error_original = 1 - accuracy_score(y_predicted_moons, y_predicted)\n",
-    "\n",
-    "print(\"Empirical fidelity error\")\n",
-    "print(f\"Synthetic dataset: {error_synthetic:.4f}\")\n",
-    "print(f\"Original dataset:  {error_original:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-----\n",
-    "### Moons SVC classifier + random uniform synthetic data + decision tree copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train/test split\n",
-    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(X_generated_um, y_generated_um, \n",
-    "                                                                    test_size=0.20, random_state=0)\n",
-    "# Decision tree classifier copy\n",
-    "classifier_copy = DecisionTreeClassifier(max_depth=3)\n",
-    "classifier_copy.fit(X_gen_train, y_gen_train)\n",
-    "y_gen_predicted = classifier_copy.predict(X_gen_test)\n",
-    "y_predicted = classifier_copy.predict(X_test_moons)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classification of synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_um[:,0], X_generated_um[:,1], c=y_generated_um )\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.85\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.81      0.89      0.85        19\n",
-      "           1       0.89      0.81      0.85        21\n",
-      "\n",
-      "    accuracy                           0.85        40\n",
-      "   macro avg       0.85      0.85      0.85        40\n",
-      "weighted avg       0.85      0.85      0.85        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_gen_test, y_gen_test)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_gen_test, y_gen_test)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_gen_test, y_gen_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.88\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.87      0.91      0.89        22\n",
-      "           1       0.88      0.83      0.86        18\n",
-      "\n",
-      "    accuracy                           0.88        40\n",
-      "   macro avg       0.88      0.87      0.87        40\n",
-      "weighted avg       0.88      0.88      0.87        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_test_moons, y_test_moons)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_test_moons, y_test_moons)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_moons, y_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Empirical fidelity error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Empirical fidelity error\n",
-      "Synthetic dataset: 0.1500\n",
-      "Original dataset:  0.0750\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Synthetic data\n",
-    "error_synthetic = 1 - accuracy_score(y_gen_test, y_gen_predicted)\n",
-    "# Original data\n",
-    "error_original = 1 - accuracy_score(y_predicted_moons, y_predicted)\n",
-    "\n",
-    "print(\"Empirical fidelity error\")\n",
-    "print(f\"Synthetic dataset: {error_synthetic:.4f}\")\n",
-    "print(f\"Original dataset:  {error_original:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-----\n",
-    "### Moons SVC classifier + normally distributed synthetic data + decision tree copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train/test split\n",
-    "X_gen_train, X_gen_test, y_gen_train, y_gen_test = train_test_split(X_generated_nm, y_generated_nm, \n",
-    "                                                                    test_size=0.20, random_state=0)\n",
-    "# Decision tree classifier copy\n",
-    "classifier_copy = DecisionTreeClassifier(max_depth=3)\n",
-    "classifier_copy.fit(X_gen_train, y_gen_train)\n",
-    "y_gen_predicted = classifier_copy.predict(X_gen_test)\n",
-    "y_predicted = classifier_copy.predict(X_test_moons)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Classification of synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X_generated_nm[:,0], X_generated_nm[:,1], c=y_generated_nm )\n",
-    "plt.axis('equal')\n",
-    "plt.xlim(-3,5)\n",
-    "plt.ylim(-2.5,3.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.95\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.95      0.95      0.95        20\n",
-      "           1       0.95      0.95      0.95        20\n",
-      "\n",
-      "    accuracy                           0.95        40\n",
-      "   macro avg       0.95      0.95      0.95        40\n",
-      "weighted avg       0.95      0.95      0.95        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_gen_test, y_gen_test)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_gen_test, y_gen_test)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_gen_test, y_gen_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Copy performance with respect to original data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Score of trained decision tree classifier: 0.85\n",
-      "\n",
-      "Classification report: \n",
-      "\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.81      0.95      0.88        22\n",
-      "           1       0.93      0.72      0.81        18\n",
-      "\n",
-      "    accuracy                           0.85        40\n",
-      "   macro avg       0.87      0.84      0.84        40\n",
-      "weighted avg       0.86      0.85      0.85        40\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "score_copy = classifier_copy.score(X_test_moons, y_test_moons)\n",
-    "print(f\"\\nScore of trained decision tree classifier: {score_copy:.2f}\")\n",
-    "plot_confusion_matrix(classifier_copy, X_test_moons, y_test_moons)\n",
-    "print(f\"\\nClassification report: \\n\\n\", classification_report(y_test_moons, y_predicted))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Empirical fidelity error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Empirical fidelity error\n",
-      "Synthetic dataset: 0.0500\n",
-      "Original dataset:  0.1000\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Synthetic data\n",
-    "error_synthetic = 1 - accuracy_score(y_gen_test, y_gen_predicted)\n",
-    "# Original data\n",
-    "error_original = 1 - accuracy_score(y_predicted_moons, y_predicted)\n",
-    "\n",
-    "print(\"Empirical fidelity error\")\n",
-    "print(f\"Synthetic dataset: {error_synthetic:.4f}\")\n",
-    "print(f\"Original dataset:  {error_original:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "--------"
-   ]
-  }
- ],
- "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": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/presc/copies/copying.py b/presc/copies/copying.py
index a118866d..59cad06f 100644
--- a/presc/copies/copying.py
+++ b/presc/copies/copying.py
@@ -1,6 +1,10 @@
 from presc.dataset import Dataset
 from presc.copies.sampling import labeling
-from presc.copies.evaluations import empirical_fidelity_error
+from presc.copies.evaluations import (
+    empirical_fidelity_error,
+    replacement_capability,
+    summary_metrics,
+)
 
 
 class ClassifierCopy:
@@ -119,7 +123,6 @@ def generate_synthetic_data(self, **k_mod_sampling_parameters):
             df_generated = labeling(
                 X_generated, self.original, label_col=self.label_col
             )
-        df_generated = labeling(X_generated, self.original, label_col=self.label_col)
 
         return df_generated
 
@@ -146,3 +149,69 @@ def compute_fidelity_error(self, test_data):
         y_pred_copy = self.copy.predict(test_data)
 
         return empirical_fidelity_error(y_pred_original, y_pred_copy)
+
+    def replacement_capability(self, test_data):
+        """Computes the replacement capability of a classifier copy.
+
+        Quantifies the ability of the copy model to substitute the original
+        model, i.e. maintaining the same accuracy in its predictions. This value
+        is one when the accuracy of the copy model is the same as the original
+        model, although the individual predictions may be different, approaching
+        zero if the accuracy of the copy is much smaller than the original, and
+        it can even take values larger than one if the copy model is better than
+        the original.
+
+        Parameters
+        ----------
+        test_data : presc.dataset.Dataset
+            Subset of the original data reserved to evaluate the resemblance of
+            the copy to the original classifier. Or synthetic data generated
+            from the original model with the same purpose.
+
+        Returns
+        -------
+        float
+            The numerical value of the replacement capability.
+        """
+        y_pred_original = self.original.predict(test_data.features)
+        y_pred_copy = self.copy.predict(test_data.features)
+        return replacement_capability(test_data.labels, y_pred_original, y_pred_copy)
+
+    def evaluation_summary(self, test_data=None, synthetic_data=None):
+        """Computes several metrics to evaluate the classifier copy.
+
+        Summary of metrics that evaluate the quality of a classifier copy, not
+        only to assess its performance as classifier but to quantify its
+        resemblance to the original classifier. Accuracy of the original and the
+        copy models (using the original test data), and the empirical fidelity
+        error and replacement capability of the copy (using the original test
+        data and/or the generated synthetic data). This is a wrapper of the
+        `summary_metrics` function applied to the copy and original models in
+        this instance.
+
+        Parameters
+        ----------
+        original_model : sklearn-type classifier
+            Original ML classifier to be copied.
+        copy_model : presc.copies.copying.ClassifierCopy
+            ML classifier copy from the original ML classifier.
+        test_data : presc.dataset.Dataset
+            Subset of the original data reserved for testing.
+        synthetic_data : presc.dataset.Dataset
+            Synthetic data generated using the original model.
+        show_results : bool
+            If `True` the metrics are also printed.
+
+        Returns
+        -------
+        dict
+            The values of all metrics.
+        """
+        results = summary_metrics(
+            original_model=self.original,
+            copy_model=self,
+            test_data=test_data,
+            synthetic_data=synthetic_data,
+            show_results=True,
+        )
+        return results
diff --git a/presc/copies/evaluations.py b/presc/copies/evaluations.py
index 3de3186d..27e730f6 100644
--- a/presc/copies/evaluations.py
+++ b/presc/copies/evaluations.py
@@ -22,3 +22,125 @@ def empirical_fidelity_error(y_pred_original, y_pred_copy):
     """
     error = 1 - accuracy_score(y_pred_original, y_pred_copy)
     return error
+
+
+def replacement_capability(y_true, y_pred_original, y_pred_copy):
+    """Computes the replacement capability of a classifier copy.
+
+    Quantifies the ability of the copy model to substitute the original model,
+    i.e. maintaining the same accuracy in its predictions. This value is one
+    when the accuracy of the copy model is the same as the original model,
+    although the individual predictions may be different, approaching zero if
+    the accuracy of the copy is much smaller than the original, and it can even
+    take values larger than one if the copy model is better than the original.
+
+    Parameters
+    ----------
+    y_true : list or 1d array-like
+        True labels from the data.
+    y_pred_original : list or 1d array-like
+        Predicted labels, as returned by the original classifier.
+    y_pred_copy : list or 1d array-like
+        Predicted labels, as returned by the classifier copy.
+
+    Returns
+    -------
+    float
+        The numerical value of the replacement capability.
+    """
+    accuracy_original = accuracy_score(y_true, y_pred_original)
+    accuracy_copy = accuracy_score(y_true, y_pred_copy)
+    rcapability = accuracy_copy / accuracy_original
+    return rcapability
+
+
+def summary_metrics(
+    original_model=None,
+    copy_model=None,
+    test_data=None,
+    synthetic_data=None,
+    show_results=True,
+):
+    """Computes several metrics to evaluate the classifier copy.
+
+    Summary of metrics that evaluate the quality of a classifier copy, not only
+    to assess its performance as classifier but to quantify its resemblance to
+    the original classifier. Accuracy of the original and the copy models (using
+    the original test data), and the empirical fidelity error and replacement
+    capability of the copy (using the original test data and/or the generated
+    synthetic data).
+
+    Parameters
+    ----------
+    original_model : sklearn-type classifier
+        Original ML classifier to be copied.
+    copy_model : presc.copies.copying.ClassifierCopy
+        ML classifier copy from the original ML classifier.
+    test_data : presc.dataset.Dataset
+        Subset of the original data reserved for testing.
+    synthetic_data : presc.dataset.Dataset
+        Synthetic data generated using the original model.
+    show_results : bool
+        If `True` the metrics are also printed.
+
+    Returns
+    -------
+    dict
+        The values of all metrics.
+    """
+
+    results = {
+        "Original Model Accuracy (test)": None,
+        "Copy Model Accuracy (test)": None,
+        "Empirical Fidelity Error (synthetic)": None,
+        "Empirical Fidelity Error (test)": None,
+        "Replacement Capability (synthetic)": None,
+        "Replacement Capability (test)": None,
+    }
+
+    if test_data is not None:
+        if original_model is not None:
+            y_pred_original_test = original_model.predict(test_data.features)
+
+            original_accuracy = accuracy_score(test_data.labels, y_pred_original_test)
+            results["Original Model Accuracy (test)"] = original_accuracy
+
+        if copy_model is not None:
+            y_pred_copy_test = copy_model.copy.predict(test_data.features)
+
+            copy_accuracy = accuracy_score(test_data.labels, y_pred_copy_test)
+            results["Copy Model Accuracy (test)"] = copy_accuracy
+
+            efe_test = copy_model.compute_fidelity_error(test_data=test_data.features)
+            results["Empirical Fidelity Error (test)"] = efe_test
+
+        if (original_model is not None) and (copy_model is not None):
+            rc_test = replacement_capability(
+                test_data.labels, y_pred_original_test, y_pred_copy_test
+            )
+            results["Replacement Capability (test)"] = rc_test
+
+    if synthetic_data is not None:
+        if original_model is not None:
+            y_pred_original_synth = original_model.predict(synthetic_data.features)
+
+        if copy_model is not None:
+            y_pred_copy_synth = copy_model.copy.predict(synthetic_data.features)
+
+            efe_synthetic = copy_model.compute_fidelity_error(
+                test_data=synthetic_data.features
+            )
+            results["Empirical Fidelity Error (synthetic)"] = efe_synthetic
+
+        if (original_model is not None) and (copy_model is not None):
+            rc_synthetic = replacement_capability(
+                synthetic_data.labels, y_pred_original_synth, y_pred_copy_synth
+            )
+            results["Replacement Capability (synthetic)"] = rc_synthetic
+
+    if show_results:
+        for name, value in results.items():
+            if value is not None:
+                print(f"{name:<37}   {value:.4f}")
+
+    return results


Test patch:
diff --git a/tests/test_copies.py b/tests/test_copies.py
index 5decd0c3..bb9c467c 100644
--- a/tests/test_copies.py
+++ b/tests/test_copies.py
@@ -14,7 +14,11 @@
     normal_sampling,
     labeling,
 )
-from presc.copies.evaluations import empirical_fidelity_error
+from presc.copies.evaluations import (
+    empirical_fidelity_error,
+    replacement_capability,
+    summary_metrics,
+)
 from presc.copies.copying import ClassifierCopy
 from presc.copies.examples import multiclass_gaussians
 
@@ -142,6 +146,77 @@ def test_empirical_fidelity_error():
     assert efe2 == 0.25
 
 
+def test_replacement_capability():
+    y_true = [1, 0, 1, 0, 1]
+    y_pred_original = [1, 0, 1, 1, 0]  # 3 right predictions
+    y_pred_copy1 = [1, 1, 0, 0, 0]  # 2 right predictions
+    y_pred_copy2 = [1, 0, 1, 0, 0]  # 4 right predictions
+    rc1 = replacement_capability(y_true, y_pred_original, y_pred_copy1)
+    rc2 = replacement_capability(y_true, y_pred_original, y_pred_copy2)
+
+    np.testing.assert_almost_equal(rc1, 2.0 / 3.0, decimal=14)
+    np.testing.assert_almost_equal(rc2, 4.0 / 3.0, decimal=14)
+
+
+def test_summary_metrics():
+    random_seed = 42
+    # Original data
+    train_data = pd.DataFrame(
+        {"x": [0, 1, 0, 2, 1], "y": [1, 0, 2, 0, 1], "label": [0, 0, 1, 1, 1]},
+        columns=["x", "y", "label"],
+    )
+    test_data = Dataset(
+        pd.DataFrame(
+            {"x": [2, 0, 0, 1, 2], "y": [1, 0, 2, 0, 2], "label": [0, 0, 1, 0, 1]},
+            columns=["x", "y", "label"],
+        ),
+        label_col="label",
+    )
+
+    # Original classifier
+    original_classifier = SVC(kernel="linear", random_state=random_seed)
+    original_classifier.fit(train_data[["x", "y"]], train_data["label"])
+
+    # Copy classifier
+    feature_parameters = {"x": {"min": 0, "max": 2}, "y": {"min": 0, "max": 2}}
+    classifier_copy = DecisionTreeClassifier(max_depth=2, random_state=random_seed)
+    copy_grid = ClassifierCopy(
+        original_classifier,
+        classifier_copy,
+        grid_sampling,
+        nsamples=20,
+        label_col="label",
+        feature_parameters=feature_parameters,
+    )
+    copy_grid.copy_classifier()
+
+    # Generated data
+    synthetic_test_data = copy_grid.generate_synthetic_data(
+        generated_nsamples=5, random_state=random_seed, label_col="label"
+    )
+
+    metrics = summary_metrics(
+        original_model=original_classifier,
+        copy_model=copy_grid,
+        test_data=test_data,
+        synthetic_data=synthetic_test_data,
+        show_results=True,
+    )
+
+    expected_results = {
+        "Original Model Accuracy (test)": 0.6,
+        "Copy Model Accuracy (test)": 0.8,
+        "Empirical Fidelity Error (synthetic)": 0.0625,
+        "Empirical Fidelity Error (test)": 0.2,
+        "Replacement Capability (synthetic)": 0.9375,
+        "Replacement Capability (test)": 1.33333333,
+    }
+
+    metric_names = metrics.keys()
+    for name in metric_names:
+        np.testing.assert_almost_equal(metrics[name], expected_results[name], decimal=6)
+
+
 def test_ClassifierCopy_copy_classifier():
     # Original classifier
     train_data = pd.DataFrame(