diff --git a/MOA_API_Examples.ipynb b/MOA_API_Examples.ipynb
new file mode 100644
index 00000000..36e9133b
--- /dev/null
+++ b/MOA_API_Examples.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "be91302b-3d48-449a-9eeb-4444d86d8e7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MOA jar path location (config.ini): /Users/gomeshe/Dropbox/ciencia_computacao/dev/Using-MOA-API/moa.jar\n",
+      "JVM Location (system): \n",
+      "/Users/gomeshe/Library/Java/JavaVirtualMachines/openjdk-20.0.1/Contents/Home\n",
+      "Sucessfully started the JVM and added MOA jar to the class path\n"
+     ]
+    }
+   ],
+   "source": [
+    "from prepare_jpype import start_jpype\n",
+    "start_jpype()\n",
+    "\n",
+    "from evaluation import test_train_loop_MOA, test_train_loop_RIVER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "60bb1d0d-66e3-45f1-b7cc-15ae08cbe81a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Datasets paths\n",
+    "csv_elec_tiny_path = '/Users/gomeshe/Desktop/data/electricity_tiny.csv'\n",
+    "arff_elec_tiny_path = '/Users/gomeshe/Desktop/data/electricity_tiny.arff'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "517c68a0-3951-4202-b3eb-0b648f0e33ba",
+   "metadata": {},
+   "source": [
+    "## Basic classification examples\n",
+    "\n",
+    "* Using the ```test_train_loop_MOA``` for MOA and ```test_train_loop_RIVER``` helper functions (see module test_train_loop)\n",
+    "\n",
+    "* These are just examples on how to run several algorithms, it is kind of repetitive, but might be useful to have an example. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7835c1a-35d0-42bb-9a4d-d990773cc0f6",
+   "metadata": {},
+   "source": [
+    "## Examples using River"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3d366b78-feeb-497a-8a05-578437600343",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "# Read the file to a dataframe, used by all examples\n",
+    "river_elec_tiny = pd.read_csv(csv_elec_tiny_path).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d56e186b-a288-4953-9d95-74f25cfeff5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ARFClassifier, 0.8670, 0.3444, 0.3445\n"
+     ]
+    }
+   ],
+   "source": [
+    "from river.forest import ARFClassifier\n",
+    "\n",
+    "river_arf5 = ARFClassifier(\n",
+    "    n_models=5,\n",
+    "    max_features=0.60\n",
+    ")\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_arf5, river_elec_tiny)\n",
+    "print(f\"{river_arf5}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0d9c9d33-52b2-4442-a577-396de0bf31e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HoeffdingTreeClassifier, 0.8230, 0.0372, 0.0379\n"
+     ]
+    }
+   ],
+   "source": [
+    "from river.tree import HoeffdingTreeClassifier\n",
+    "\n",
+    "river_ht = HoeffdingTreeClassifier()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_ht, river_elec_tiny)\n",
+    "print(f\"{river_ht}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "84f29ddd-2e97-4c31-945a-1129e8ca1833",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNNClassifier, 0.7560, 1.9004, 1.8937\n"
+     ]
+    }
+   ],
+   "source": [
+    "from river.neighbors import KNNClassifier\n",
+    "\n",
+    "river_knn = KNNClassifier()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_knn, river_elec_tiny)\n",
+    "print(f\"{river_knn}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "47e22fbe-c42a-4cf0-9f40-0c408fc8185b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GaussianNB, 0.8490, 0.0285, 0.0285\n"
+     ]
+    }
+   ],
+   "source": [
+    "from river.naive_bayes import GaussianNB\n",
+    "\n",
+    "river_gnb = GaussianNB()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_gnb, river_elec_tiny)\n",
+    "print(f\"{river_gnb}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7f59847d-b33b-431e-8ddb-ee0a3a5375b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SRPClassifier(HoeffdingTreeClassifier), 0.8940, 0.7320, 0.7318\n"
+     ]
+    }
+   ],
+   "source": [
+    "from river.ensemble.streaming_random_patches import SRPClassifier\n",
+    "\n",
+    "river_srp = SRPClassifier(\n",
+    "    n_models=5,\n",
+    "    subspace_size=0.6\n",
+    ")\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_srp, river_elec_tiny)\n",
+    "print(f\"{river_srp}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e25a96f4-7612-445b-b210-838d474fba2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ExtremelyFastDecisionTreeClassifier, 0.8230, 0.2255, 0.2264\n"
+     ]
+    }
+   ],
+   "source": [
+    "from river.tree import ExtremelyFastDecisionTreeClassifier\n",
+    "\n",
+    "river_EFDT = ExtremelyFastDecisionTreeClassifier()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_EFDT, river_elec_tiny)\n",
+    "print(f\"{river_EFDT}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15079978-5afa-4876-a0cc-ab0fcf3ab6ce",
+   "metadata": {},
+   "source": [
+    "## Examples using MOA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "389f00c6-22e0-4ed6-a73a-7c18d01c3fb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from moa.streams import ArffFileStream\n",
+    "\n",
+    "elec_tiny_arff = ArffFileStream(arff_elec_tiny_path, -1)\n",
+    "elec_tiny_arff.prepareForUse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9cf0778d-942f-44e2-bd79-e754e6c7735e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moa.classifiers.meta.AdaptiveRandomForest, 89.4500, 0.3876, 1.1303\n"
+     ]
+    }
+   ],
+   "source": [
+    "from moa.classifiers.meta import AdaptiveRandomForest\n",
+    "\n",
+    "arf10 = AdaptiveRandomForest()\n",
+    "arf10.getOptions().setViaCLIString(\"-s 5\")\n",
+    "arf10.prepareForUse()\n",
+    "\n",
+    "elec_tiny_arff.restart()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, arf10, maxInstances=2000, sampleFrequency=2000)\n",
+    "print(f\"{arf10.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1270cf7b-3bf1-4092-9498-f5e6f91aea64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moa.classifiers.trees.HoeffdingTree, 86.0500, 0.0282, 0.0883\n"
+     ]
+    }
+   ],
+   "source": [
+    "from moa.classifiers.trees import HoeffdingTree\n",
+    "\n",
+    "ht_moa = HoeffdingTree()\n",
+    "ht_moa.prepareForUse()\n",
+    "\n",
+    "elec_tiny_arff.restart()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, ht_moa, maxInstances=2000, sampleFrequency=2000)\n",
+    "print(f\"{ht_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c8ac0b21-a3bf-488b-b5cb-ce6289298ee1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moa.classifiers.lazy.kNN, 84.2167, 0.1976, 0.5912\n"
+     ]
+    }
+   ],
+   "source": [
+    "from moa.classifiers.lazy import kNN\n",
+    "\n",
+    "knn_moa = kNN()\n",
+    "knn_moa.prepareForUse()\n",
+    "\n",
+    "elec_tiny_arff.restart()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, knn_moa, maxInstances=2000, sampleFrequency=2000)\n",
+    "print(f\"{knn_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "15ccd3c4-f0d6-479d-b773-df230cfdf2aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moa.classifiers.trees.EFDT, 83.8375, 0.0246, 0.0775\n"
+     ]
+    }
+   ],
+   "source": [
+    "from moa.classifiers.trees import EFDT\n",
+    "\n",
+    "EFDT_moa = EFDT()\n",
+    "EFDT_moa.prepareForUse()\n",
+    "\n",
+    "elec_tiny_arff.restart()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, EFDT_moa, maxInstances=2000, sampleFrequency=2000)\n",
+    "print(f\"{EFDT_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e264098a-588a-456b-ae11-dced5b5ceb7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moa.classifiers.bayes.NaiveBayes, 83.8700, 0.0181, 0.0508\n"
+     ]
+    }
+   ],
+   "source": [
+    "from moa.classifiers.bayes import NaiveBayes\n",
+    "\n",
+    "NaiveBayes_moa = NaiveBayes()\n",
+    "NaiveBayes_moa.prepareForUse()\n",
+    "\n",
+    "elec_tiny_arff.restart()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, NaiveBayes_moa, maxInstances=2000, sampleFrequency=2000)\n",
+    "print(f\"{NaiveBayes_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6fa952e8-08e3-4e6f-88ad-dac1aaea94ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moa.classifiers.meta.StreamingRandomPatches, 84.9417, 0.4210, 1.1560\n"
+     ]
+    }
+   ],
+   "source": [
+    "from moa.classifiers.meta import StreamingRandomPatches\n",
+    "\n",
+    "SRP_moa = StreamingRandomPatches()\n",
+    "SRP_moa.getOptions().setViaCLIString(\"-s 10\")\n",
+    "SRP_moa.prepareForUse()\n",
+    "\n",
+    "elec_tiny_arff.restart()\n",
+    "\n",
+    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, SRP_moa, maxInstances=2000, sampleFrequency=2000)\n",
+    "print(f\"{SRP_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
+   ]
+  }
+ ],
+ "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": "3.8.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MOA_River_Comparison.ipynb b/MOA_Comparison.ipynb
similarity index 100%
rename from MOA_River_Comparison.ipynb
rename to MOA_Comparison.ipynb
diff --git a/MOA_River_Example.ipynb b/MOA_River_Example.ipynb
deleted file mode 100644
index c3ed147a..00000000
--- a/MOA_River_Example.ipynb
+++ /dev/null
@@ -1,2544 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "be91302b-3d48-449a-9eeb-4444d86d8e7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MOA jar path location (config.ini): /Users/gomeshe/Dropbox/ciencia_computacao/dev/Using-MOA-API/moa.jar\n",
-      "JVM Location (system): \n",
-      "/Users/gomeshe/Library/Java/JavaVirtualMachines/openjdk-20.0.1/Contents/Home\n",
-      "JVM already started\n"
-     ]
-    }
-   ],
-   "source": [
-    "from prepare_jpype import start_jpype\n",
-    "start_jpype()\n",
-    "\n",
-    "from evaluation import test_train_loop_MOA, test_train_loop_RIVER"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "60bb1d0d-66e3-45f1-b7cc-15ae08cbe81a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Datasets paths\n",
-    "csv_elec_tiny_path = '/Users/gomeshe/Desktop/data/electricity_tiny.csv'\n",
-    "arff_elec_tiny_path = '/Users/gomeshe/Desktop/data/electricity_tiny.arff'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "517c68a0-3951-4202-b3eb-0b648f0e33ba",
-   "metadata": {},
-   "source": [
-    "## Basic classification examples\n",
-    "\n",
-    "* Using the ```test_train_loop_MOA``` for MOA and ```test_train_loop_RIVER``` helper functions (see module test_train_loop)\n",
-    "\n",
-    "* These are just examples on how to run several algorithms, it is kind of repetitive, but might be useful to have an example. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7835c1a-35d0-42bb-9a4d-d990773cc0f6",
-   "metadata": {},
-   "source": [
-    "## Examples using River"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3d366b78-feeb-497a-8a05-578437600343",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "# Read the file to a dataframe, used by all examples\n",
-    "river_elec_tiny = pd.read_csv(csv_elec_tiny_path).to_numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d56e186b-a288-4953-9d95-74f25cfeff5e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ARFClassifier, 0.8720, 0.3055, 0.3054\n"
-     ]
-    }
-   ],
-   "source": [
-    "from river.forest import ARFClassifier\n",
-    "\n",
-    "river_arf5 = ARFClassifier(\n",
-    "    n_models=5,\n",
-    "    max_features=0.60\n",
-    ")\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_arf5, river_elec_tiny)\n",
-    "print(f\"{river_arf5}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0d9c9d33-52b2-4442-a577-396de0bf31e7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HoeffdingTreeClassifier, 0.8230, 0.0388, 0.0391\n"
-     ]
-    }
-   ],
-   "source": [
-    "from river.tree import HoeffdingTreeClassifier\n",
-    "\n",
-    "river_ht = HoeffdingTreeClassifier()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_ht, river_elec_tiny)\n",
-    "print(f\"{river_ht}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "84f29ddd-2e97-4c31-945a-1129e8ca1833",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "KNNClassifier, 0.7550, 2.1145, 2.1065\n"
-     ]
-    }
-   ],
-   "source": [
-    "from river.neighbors import KNNClassifier\n",
-    "\n",
-    "river_knn = KNNClassifier()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_knn, river_elec_tiny)\n",
-    "print(f\"{river_knn}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "47e22fbe-c42a-4cf0-9f40-0c408fc8185b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "GaussianNB, 0.8490, 0.0294, 0.0294\n"
-     ]
-    }
-   ],
-   "source": [
-    "from river.naive_bayes import GaussianNB\n",
-    "\n",
-    "river_gnb = GaussianNB()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_gnb, river_elec_tiny)\n",
-    "print(f\"{river_gnb}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "7f59847d-b33b-431e-8ddb-ee0a3a5375b7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SRPClassifier(HoeffdingTreeClassifier), 0.8850, 0.7937, 0.7863\n"
-     ]
-    }
-   ],
-   "source": [
-    "from river.ensemble.streaming_random_patches import SRPClassifier\n",
-    "\n",
-    "river_srp = SRPClassifier(\n",
-    "    n_models=5,\n",
-    "    subspace_size=0.6\n",
-    ")\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_srp, river_elec_tiny)\n",
-    "print(f\"{river_srp}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e25a96f4-7612-445b-b210-838d474fba2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ExtremelyFastDecisionTreeClassifier, 0.8230, 0.2274, 0.2280\n"
-     ]
-    }
-   ],
-   "source": [
-    "from river.tree import ExtremelyFastDecisionTreeClassifier\n",
-    "\n",
-    "river_EFDT = ExtremelyFastDecisionTreeClassifier()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_RIVER(river_EFDT, river_elec_tiny)\n",
-    "print(f\"{river_EFDT}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15079978-5afa-4876-a0cc-ab0fcf3ab6ce",
-   "metadata": {},
-   "source": [
-    "## Examples using MOA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "389f00c6-22e0-4ed6-a73a-7c18d01c3fb8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "elec_tiny_arff = ArffFileStream(arff_elec_tiny_path, -1)\n",
-    "elec_tiny_arff.prepareForUse()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9cf0778d-942f-44e2-bd79-e754e6c7735e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "moa.classifiers.meta.AdaptiveRandomForest, 89.4500, 0.4188, 0.8300\n"
-     ]
-    }
-   ],
-   "source": [
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "\n",
-    "arf10 = AdaptiveRandomForest()\n",
-    "arf10.getOptions().setViaCLIString(\"-s 5\")\n",
-    "arf10.prepareForUse()\n",
-    "\n",
-    "elec_tiny_arff.restart()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, arf10, maxInstances=2000, sampleFrequency=2000)\n",
-    "print(f\"{arf10.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1270cf7b-3bf1-4092-9498-f5e6f91aea64",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "moa.classifiers.trees.HoeffdingTree, 86.0500, 0.0264, 0.0536\n"
-     ]
-    }
-   ],
-   "source": [
-    "from moa.classifiers.trees import HoeffdingTree\n",
-    "\n",
-    "ht_moa = HoeffdingTree()\n",
-    "ht_moa.prepareForUse()\n",
-    "\n",
-    "elec_tiny_arff.restart()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, ht_moa, maxInstances=2000, sampleFrequency=2000)\n",
-    "print(f\"{ht_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c8ac0b21-a3bf-488b-b5cb-ce6289298ee1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "moa.classifiers.lazy.kNN, 84.2167, 0.2200, 0.4583\n"
-     ]
-    }
-   ],
-   "source": [
-    "from moa.classifiers.lazy import kNN\n",
-    "\n",
-    "knn_moa = kNN()\n",
-    "knn_moa.prepareForUse()\n",
-    "\n",
-    "elec_tiny_arff.restart()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, knn_moa, maxInstances=2000, sampleFrequency=2000)\n",
-    "print(f\"{knn_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "15ccd3c4-f0d6-479d-b773-df230cfdf2aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "moa.classifiers.trees.EFDT, 83.8375, 0.0261, 0.0578\n"
-     ]
-    }
-   ],
-   "source": [
-    "from moa.classifiers.trees import EFDT\n",
-    "\n",
-    "EFDT_moa = EFDT()\n",
-    "EFDT_moa.prepareForUse()\n",
-    "\n",
-    "elec_tiny_arff.restart()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, EFDT_moa, maxInstances=2000, sampleFrequency=2000)\n",
-    "print(f\"{EFDT_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "e264098a-588a-456b-ae11-dced5b5ceb7f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "moa.classifiers.bayes.NaiveBayes, 83.8700, 0.0193, 0.0365\n"
-     ]
-    }
-   ],
-   "source": [
-    "from moa.classifiers.bayes import NaiveBayes\n",
-    "\n",
-    "NaiveBayes_moa = NaiveBayes()\n",
-    "NaiveBayes_moa.prepareForUse()\n",
-    "\n",
-    "elec_tiny_arff.restart()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, NaiveBayes_moa, maxInstances=2000, sampleFrequency=2000)\n",
-    "print(f\"{NaiveBayes_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "6fa952e8-08e3-4e6f-88ad-dac1aaea94ef",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "moa.classifiers.meta.StreamingRandomPatches, 84.9417, 0.5412, 1.0869\n"
-     ]
-    }
-   ],
-   "source": [
-    "from moa.classifiers.meta import StreamingRandomPatches\n",
-    "\n",
-    "SRP_moa = StreamingRandomPatches()\n",
-    "SRP_moa.getOptions().setViaCLIString(\"-s 10\")\n",
-    "SRP_moa.prepareForUse()\n",
-    "\n",
-    "elec_tiny_arff.restart()\n",
-    "\n",
-    "acc, wallclock, cpu_time, df = test_train_loop_MOA(elec_tiny_arff, SRP_moa, maxInstances=2000, sampleFrequency=2000)\n",
-    "print(f\"{SRP_moa.getClass().getName()}, {acc:.4f}, {wallclock:.4f}, {cpu_time:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0886e57-bcd3-46be-9d56-d04888831add",
-   "metadata": {},
-   "source": [
-    "# Experiments MOA vs River using ARF\n",
-    "\n",
-    "* These tests were using the old test_train_loop() and run_experiment() methods. \n",
-    "\n",
-    "* They are safe to delete or update (but the river ones take too long to execute).\n",
-    "\n",
-    "* **SAVED THE RESULTS BECAUSE RIVER TAKES TOO LONG TO EXECUTE! Especially for ARF100!**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f744b99-2661-462a-bde4-a53b4de3a694",
-   "metadata": {},
-   "source": [
-    "## MOA ARF10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "94bafd08-94cd-4577-b9b6-e8f39d24ae97",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 50.8 s, sys: 367 ms, total: 51.1 s\n",
-      "Wall time: 50.2 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(83.88189655172414,\n",
-       " 50.219346046447754,\n",
-       " 51.13412499999999,\n",
-       "     classified instances  classifications correct (percent)  \\\n",
-       " 0                17000.0                          86.317647   \n",
-       " 1                18000.0                          86.577778   \n",
-       " 2                19000.0                          86.794737   \n",
-       " 3                20000.0                          87.030000   \n",
-       " 4                21000.0                          87.266667   \n",
-       " ..                   ...                                ...   \n",
-       " 95              112000.0                          84.234821   \n",
-       " 96              113000.0                          84.133628   \n",
-       " 97              114000.0                          84.052632   \n",
-       " 98              115000.0                          83.971304   \n",
-       " 99              116000.0                          83.881897   \n",
-       " \n",
-       "     Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       " 0                   71.151605                            7.293743   \n",
-       " 1                   71.304855                           13.929462   \n",
-       " 2                   71.387396                           18.855110   \n",
-       " 3                   71.509604                           22.935235   \n",
-       " 4                   71.645880                           26.538462   \n",
-       " ..                        ...                                 ...   \n",
-       " 95                  76.055835                           66.396422   \n",
-       " 96                  75.870843                           66.291269   \n",
-       " 97                  75.714576                           66.224501   \n",
-       " 98                  75.552421                           66.136973   \n",
-       " 99                  75.376769                           66.039415   \n",
-       " \n",
-       "     Kappa M Statistic (percent)  \n",
-       " 0                     64.187837  \n",
-       " 1                     63.772680  \n",
-       " 2                     63.388297  \n",
-       " 3                     63.053696  \n",
-       " 4                     62.799110  \n",
-       " ..                          ...  \n",
-       " 95                    68.073411  \n",
-       " 96                    67.855350  \n",
-       " 97                    67.669079  \n",
-       " 98                    67.474238  \n",
-       " 99                    67.257412  \n",
-       " \n",
-       " [100 rows x 5 columns])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf10 = AdaptiveRandomForest()\n",
-    "arf10.getOptions().setViaCLIString(\"-s 10\")\n",
-    "arf10.setRandomSeed(1)\n",
-    "arf10.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "# Updated this one. \n",
-    "test_train_loop_MOA(rtg_2abrupt, arf10, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46f4d235-220d-44b9-abf9-07269030c175",
-   "metadata": {},
-   "source": [
-    "CPU times: user 48.2 s, sys: 193 ms, total: 48.4 s\n",
-    "Wall time: 47.4 s\n",
-    "\n",
-    "83.527000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "63df8448-9053-448e-83e2-43456e5b0451",
-   "metadata": {},
-   "source": [
-    "## RIVER ARF10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e3dda62f-23b1-40f4-8dc8-913215fded24",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learner:  ARFClassifier  Result:  (0.79017, 379)\n",
-      "CPU times: user 6min 17s, sys: 8.05 s, total: 6min 25s\n",
-      "Wall time: 6min 21s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "from river.forest import ARFClassifier\n",
-    "\n",
-    "rtg_2abrupt_path = '/Users/gomeshe/Desktop/data/RTG_2abrupt.csv'\n",
-    "\n",
-    "river_arf10 = ARFClassifier(\n",
-    "    n_models=10,\n",
-    "    max_features=0.60\n",
-    ")\n",
-    "\n",
-    "river_rtg_2abrupt = pd.read_csv(rtg_2abrupt_path).to_numpy()\n",
-    "\n",
-    "result = run_experiment(river_arf10, river_rtg_2abrupt)\n",
-    "print('Learner: ', river_arf10, ' Result: ', result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83a8b293-8ad5-4b8b-9f7f-7b930eac1f4a",
-   "metadata": {},
-   "source": [
-    "CPU times: user 6min 5s, sys: 3.75 s, total: 6min 8s\n",
-    "Wall time: 6min 9s\n",
-    "\n",
-    "Learner:  ARFClassifier  Result:  (0.80196, 368)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "172377ea-923e-455f-b3f1-7808ab327fc6",
-   "metadata": {},
-   "source": [
-    "## MOA ARF30"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "c0aba450-349a-4bb1-ac46-daf3dda0238b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 2min 40s, sys: 508 ms, total: 2min 40s\n",
-      "Wall time: 2min 36s\n"
-     ]
-    },
-    {
-     "data": {
-      "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>classified instances</th>\n",
-       "      <th>classifications correct (percent)</th>\n",
-       "      <th>Kappa Statistic (percent)</th>\n",
-       "      <th>Kappa Temporal Statistic (percent)</th>\n",
-       "      <th>Kappa M Statistic (percent)</th>\n",
-       "      <th>Recall for class 0 (percent)</th>\n",
-       "      <th>Recall for class 1 (percent)</th>\n",
-       "      <th>Recall for class 2 (percent)</th>\n",
-       "      <th>Recall for class 3 (percent)</th>\n",
-       "      <th>Recall for class 4 (percent)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>88.900000</td>\n",
-       "      <td>47.856968</td>\n",
-       "      <td>58.426966</td>\n",
-       "      <td>31.901840</td>\n",
-       "      <td>98.685783</td>\n",
-       "      <td>38.650307</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>89.550000</td>\n",
-       "      <td>54.067347</td>\n",
-       "      <td>63.008850</td>\n",
-       "      <td>37.982196</td>\n",
-       "      <td>98.496693</td>\n",
-       "      <td>45.400593</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>89.500000</td>\n",
-       "      <td>55.650876</td>\n",
-       "      <td>62.941176</td>\n",
-       "      <td>39.539347</td>\n",
-       "      <td>98.305768</td>\n",
-       "      <td>47.600768</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4000.0</td>\n",
-       "      <td>90.050000</td>\n",
-       "      <td>58.071433</td>\n",
-       "      <td>64.590747</td>\n",
-       "      <td>42.235123</td>\n",
-       "      <td>98.399275</td>\n",
-       "      <td>49.927431</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>90.160000</td>\n",
-       "      <td>58.288796</td>\n",
-       "      <td>64.806867</td>\n",
-       "      <td>42.523364</td>\n",
-       "      <td>98.455598</td>\n",
-       "      <td>50.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>96000.0</td>\n",
-       "      <td>84.034375</td>\n",
-       "      <td>75.052102</td>\n",
-       "      <td>69.530644</td>\n",
-       "      <td>68.703163</td>\n",
-       "      <td>96.978332</td>\n",
-       "      <td>70.715536</td>\n",
-       "      <td>58.354866</td>\n",
-       "      <td>17.525163</td>\n",
-       "      <td>90.082503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97000.0</td>\n",
-       "      <td>83.921649</td>\n",
-       "      <td>74.852764</td>\n",
-       "      <td>69.387194</td>\n",
-       "      <td>68.457245</td>\n",
-       "      <td>96.988813</td>\n",
-       "      <td>70.620877</td>\n",
-       "      <td>58.025742</td>\n",
-       "      <td>17.515924</td>\n",
-       "      <td>89.952536</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>98000.0</td>\n",
-       "      <td>83.835714</td>\n",
-       "      <td>74.693075</td>\n",
-       "      <td>69.290865</td>\n",
-       "      <td>68.253873</td>\n",
-       "      <td>97.006299</td>\n",
-       "      <td>70.590061</td>\n",
-       "      <td>57.839428</td>\n",
-       "      <td>17.362887</td>\n",
-       "      <td>89.764364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>99000.0</td>\n",
-       "      <td>83.754545</td>\n",
-       "      <td>74.535105</td>\n",
-       "      <td>69.184933</td>\n",
-       "      <td>68.051251</td>\n",
-       "      <td>97.024250</td>\n",
-       "      <td>70.551270</td>\n",
-       "      <td>57.618305</td>\n",
-       "      <td>17.163555</td>\n",
-       "      <td>89.620224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>83.666000</td>\n",
-       "      <td>74.362581</td>\n",
-       "      <td>69.072009</td>\n",
-       "      <td>67.828091</td>\n",
-       "      <td>97.034268</td>\n",
-       "      <td>70.486585</td>\n",
-       "      <td>57.339922</td>\n",
-       "      <td>17.144417</td>\n",
-       "      <td>89.467704</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classified instances  classifications correct (percent)  \\\n",
-       "0                 1000.0                          88.900000   \n",
-       "1                 2000.0                          89.550000   \n",
-       "2                 3000.0                          89.500000   \n",
-       "3                 4000.0                          90.050000   \n",
-       "4                 5000.0                          90.160000   \n",
-       "..                   ...                                ...   \n",
-       "95               96000.0                          84.034375   \n",
-       "96               97000.0                          83.921649   \n",
-       "97               98000.0                          83.835714   \n",
-       "98               99000.0                          83.754545   \n",
-       "99              100000.0                          83.666000   \n",
-       "\n",
-       "    Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       "0                   47.856968                           58.426966   \n",
-       "1                   54.067347                           63.008850   \n",
-       "2                   55.650876                           62.941176   \n",
-       "3                   58.071433                           64.590747   \n",
-       "4                   58.288796                           64.806867   \n",
-       "..                        ...                                 ...   \n",
-       "95                  75.052102                           69.530644   \n",
-       "96                  74.852764                           69.387194   \n",
-       "97                  74.693075                           69.290865   \n",
-       "98                  74.535105                           69.184933   \n",
-       "99                  74.362581                           69.072009   \n",
-       "\n",
-       "    Kappa M Statistic (percent)  Recall for class 0 (percent)  \\\n",
-       "0                     31.901840                     98.685783   \n",
-       "1                     37.982196                     98.496693   \n",
-       "2                     39.539347                     98.305768   \n",
-       "3                     42.235123                     98.399275   \n",
-       "4                     42.523364                     98.455598   \n",
-       "..                          ...                           ...   \n",
-       "95                    68.703163                     96.978332   \n",
-       "96                    68.457245                     96.988813   \n",
-       "97                    68.253873                     97.006299   \n",
-       "98                    68.051251                     97.024250   \n",
-       "99                    67.828091                     97.034268   \n",
-       "\n",
-       "    Recall for class 1 (percent)  Recall for class 2 (percent)  \\\n",
-       "0                      38.650307                           NaN   \n",
-       "1                      45.400593                           NaN   \n",
-       "2                      47.600768                           NaN   \n",
-       "3                      49.927431                           NaN   \n",
-       "4                      50.000000                           NaN   \n",
-       "..                           ...                           ...   \n",
-       "95                     70.715536                     58.354866   \n",
-       "96                     70.620877                     58.025742   \n",
-       "97                     70.590061                     57.839428   \n",
-       "98                     70.551270                     57.618305   \n",
-       "99                     70.486585                     57.339922   \n",
-       "\n",
-       "    Recall for class 3 (percent)  Recall for class 4 (percent)  \n",
-       "0                            NaN                           NaN  \n",
-       "1                            NaN                           NaN  \n",
-       "2                            NaN                           NaN  \n",
-       "3                            NaN                           NaN  \n",
-       "4                            NaN                           NaN  \n",
-       "..                           ...                           ...  \n",
-       "95                     17.525163                     90.082503  \n",
-       "96                     17.515924                     89.952536  \n",
-       "97                     17.362887                     89.764364  \n",
-       "98                     17.163555                     89.620224  \n",
-       "99                     17.144417                     89.467704  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf30 = AdaptiveRandomForest()\n",
-    "arf30.getOptions().setViaCLIString(\"-s 30\")\n",
-    "arf30.setRandomSeed(1)\n",
-    "arf30.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "evaluator = BasicClassificationPerformanceEvaluator()\n",
-    "evaluator.recallPerClassOption.set()\n",
-    "evaluator.prepareForUse()\n",
-    "\n",
-    "test_train_loop(rtg_2abrupt, arf30, evaluator, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1640e8c9-0353-4b04-b448-856073ef6d0f",
-   "metadata": {},
-   "source": [
-    "CPU times: user 2min 40s, sys: 508 ms, total: 2min 40s\n",
-    "Wall time: 2min 36s\n",
-    "\n",
-    "83.666000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "906b04c6-c5a4-4035-ba5d-31fba17e1118",
-   "metadata": {},
-   "source": [
-    "## RIVER ARF30"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "0665dae0-d21d-4a1d-9a46-0cbfdc356ed9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learner:  ARFClassifier  Result:  (0.80162, 1145)\n",
-      "CPU times: user 18min 56s, sys: 11.7 s, total: 19min 7s\n",
-      "Wall time: 19min 6s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "river_arf30 = ARFClassifier(\n",
-    "    n_models=30,\n",
-    "    max_features=0.60\n",
-    ")\n",
-    "\n",
-    "river_rtg_2abrupt = pd.read_csv(rtg_2abrupt_path).to_numpy()\n",
-    "\n",
-    "result = run_experiment(river_arf30, river_rtg_2abrupt)\n",
-    "print('Learner: ', river_arf30, ' Result: ', result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d9588fa-d162-48e6-9ffd-e6833bbb9562",
-   "metadata": {},
-   "source": [
-    "CPU times: user 18min 56s, sys: 11.7 s, total: 19min 7s\n",
-    "Wall time: 19min 6s\n",
-    "\n",
-    "Learner:  ARFClassifier  Result:  (0.80162, 1145)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9436f4b5-209d-4661-8fa1-9875932e6b2b",
-   "metadata": {},
-   "source": [
-    "## ARF100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "fce1ac7e-d62f-4196-87bc-22dfb5a44c74",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 11min 15s, sys: 2.25 s, total: 11min 17s\n",
-      "Wall time: 10min 28s\n"
-     ]
-    },
-    {
-     "data": {
-      "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>classified instances</th>\n",
-       "      <th>classifications correct (percent)</th>\n",
-       "      <th>Kappa Statistic (percent)</th>\n",
-       "      <th>Kappa Temporal Statistic (percent)</th>\n",
-       "      <th>Kappa M Statistic (percent)</th>\n",
-       "      <th>Recall for class 0 (percent)</th>\n",
-       "      <th>Recall for class 1 (percent)</th>\n",
-       "      <th>Recall for class 2 (percent)</th>\n",
-       "      <th>Recall for class 3 (percent)</th>\n",
-       "      <th>Recall for class 4 (percent)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>88.900000</td>\n",
-       "      <td>48.185077</td>\n",
-       "      <td>58.426966</td>\n",
-       "      <td>31.901840</td>\n",
-       "      <td>98.566308</td>\n",
-       "      <td>39.263804</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>89.600000</td>\n",
-       "      <td>54.086419</td>\n",
-       "      <td>63.185841</td>\n",
-       "      <td>38.278932</td>\n",
-       "      <td>98.616957</td>\n",
-       "      <td>45.103858</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>89.466667</td>\n",
-       "      <td>55.139083</td>\n",
-       "      <td>62.823529</td>\n",
-       "      <td>39.347409</td>\n",
-       "      <td>98.467124</td>\n",
-       "      <td>46.641075</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4000.0</td>\n",
-       "      <td>89.875000</td>\n",
-       "      <td>56.827170</td>\n",
-       "      <td>63.967972</td>\n",
-       "      <td>41.219158</td>\n",
-       "      <td>98.550287</td>\n",
-       "      <td>48.185776</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>90.120000</td>\n",
-       "      <td>57.742296</td>\n",
-       "      <td>64.663805</td>\n",
-       "      <td>42.289720</td>\n",
-       "      <td>98.624517</td>\n",
-       "      <td>48.948598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>96000.0</td>\n",
-       "      <td>83.966667</td>\n",
-       "      <td>74.936690</td>\n",
-       "      <td>69.401427</td>\n",
-       "      <td>68.570437</td>\n",
-       "      <td>96.937929</td>\n",
-       "      <td>70.651886</td>\n",
-       "      <td>58.936262</td>\n",
-       "      <td>16.074600</td>\n",
-       "      <td>89.859058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97000.0</td>\n",
-       "      <td>83.862887</td>\n",
-       "      <td>74.750844</td>\n",
-       "      <td>69.275311</td>\n",
-       "      <td>68.341963</td>\n",
-       "      <td>96.961477</td>\n",
-       "      <td>70.542352</td>\n",
-       "      <td>58.642698</td>\n",
-       "      <td>16.184134</td>\n",
-       "      <td>89.718076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>98000.0</td>\n",
-       "      <td>83.781633</td>\n",
-       "      <td>74.598578</td>\n",
-       "      <td>69.188120</td>\n",
-       "      <td>68.147658</td>\n",
-       "      <td>96.983431</td>\n",
-       "      <td>70.496975</td>\n",
-       "      <td>58.448928</td>\n",
-       "      <td>16.197783</td>\n",
-       "      <td>89.530439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>99000.0</td>\n",
-       "      <td>83.696970</td>\n",
-       "      <td>74.434459</td>\n",
-       "      <td>69.075720</td>\n",
-       "      <td>67.938021</td>\n",
-       "      <td>97.003699</td>\n",
-       "      <td>70.428652</td>\n",
-       "      <td>58.169527</td>\n",
-       "      <td>16.160490</td>\n",
-       "      <td>89.392473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>83.616000</td>\n",
-       "      <td>74.274225</td>\n",
-       "      <td>68.977335</td>\n",
-       "      <td>67.729609</td>\n",
-       "      <td>97.015987</td>\n",
-       "      <td>70.335001</td>\n",
-       "      <td>57.978176</td>\n",
-       "      <td>16.270816</td>\n",
-       "      <td>89.234790</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classified instances  classifications correct (percent)  \\\n",
-       "0                 1000.0                          88.900000   \n",
-       "1                 2000.0                          89.600000   \n",
-       "2                 3000.0                          89.466667   \n",
-       "3                 4000.0                          89.875000   \n",
-       "4                 5000.0                          90.120000   \n",
-       "..                   ...                                ...   \n",
-       "95               96000.0                          83.966667   \n",
-       "96               97000.0                          83.862887   \n",
-       "97               98000.0                          83.781633   \n",
-       "98               99000.0                          83.696970   \n",
-       "99              100000.0                          83.616000   \n",
-       "\n",
-       "    Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       "0                   48.185077                           58.426966   \n",
-       "1                   54.086419                           63.185841   \n",
-       "2                   55.139083                           62.823529   \n",
-       "3                   56.827170                           63.967972   \n",
-       "4                   57.742296                           64.663805   \n",
-       "..                        ...                                 ...   \n",
-       "95                  74.936690                           69.401427   \n",
-       "96                  74.750844                           69.275311   \n",
-       "97                  74.598578                           69.188120   \n",
-       "98                  74.434459                           69.075720   \n",
-       "99                  74.274225                           68.977335   \n",
-       "\n",
-       "    Kappa M Statistic (percent)  Recall for class 0 (percent)  \\\n",
-       "0                     31.901840                     98.566308   \n",
-       "1                     38.278932                     98.616957   \n",
-       "2                     39.347409                     98.467124   \n",
-       "3                     41.219158                     98.550287   \n",
-       "4                     42.289720                     98.624517   \n",
-       "..                          ...                           ...   \n",
-       "95                    68.570437                     96.937929   \n",
-       "96                    68.341963                     96.961477   \n",
-       "97                    68.147658                     96.983431   \n",
-       "98                    67.938021                     97.003699   \n",
-       "99                    67.729609                     97.015987   \n",
-       "\n",
-       "    Recall for class 1 (percent)  Recall for class 2 (percent)  \\\n",
-       "0                      39.263804                           NaN   \n",
-       "1                      45.103858                           NaN   \n",
-       "2                      46.641075                           NaN   \n",
-       "3                      48.185776                           NaN   \n",
-       "4                      48.948598                           NaN   \n",
-       "..                           ...                           ...   \n",
-       "95                     70.651886                     58.936262   \n",
-       "96                     70.542352                     58.642698   \n",
-       "97                     70.496975                     58.448928   \n",
-       "98                     70.428652                     58.169527   \n",
-       "99                     70.335001                     57.978176   \n",
-       "\n",
-       "    Recall for class 3 (percent)  Recall for class 4 (percent)  \n",
-       "0                            NaN                           NaN  \n",
-       "1                            NaN                           NaN  \n",
-       "2                            NaN                           NaN  \n",
-       "3                            NaN                           NaN  \n",
-       "4                            NaN                           NaN  \n",
-       "..                           ...                           ...  \n",
-       "95                     16.074600                     89.859058  \n",
-       "96                     16.184134                     89.718076  \n",
-       "97                     16.197783                     89.530439  \n",
-       "98                     16.160490                     89.392473  \n",
-       "99                     16.270816                     89.234790  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf100 = AdaptiveRandomForest()\n",
-    "arf100.getOptions().setViaCLIString(\"-s 100\")\n",
-    "arf100.setRandomSeed(1)\n",
-    "arf100.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "evaluator = BasicClassificationPerformanceEvaluator()\n",
-    "evaluator.recallPerClassOption.set()\n",
-    "evaluator.prepareForUse()\n",
-    "\n",
-    "test_train_loop(rtg_2abrupt, arf100, evaluator, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02a8856b-6664-407e-83c2-3d78c48d9343",
-   "metadata": {},
-   "source": [
-    "CPU times: user 11min 15s, sys: 2.25 s, total: 11min 17s\n",
-    "Wall time: 10min 28s\n",
-    "\n",
-    "83.616000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d568b5b6-5836-4fe9-8cb0-eca28b8e9082",
-   "metadata": {},
-   "source": [
-    "## RIVER ARF100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "647edb8e-fcad-401d-acc3-33aeb34b71c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learner:  ARFClassifier  Result:  (0.80145, 4384)\n",
-      "CPU times: user 1h 13min 34s, sys: 42.7 s, total: 1h 14min 17s\n",
-      "Wall time: 2h 5s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "river_arf100 = ARFClassifier(\n",
-    "    n_models=100,\n",
-    "    max_features=0.60\n",
-    ")\n",
-    "\n",
-    "river_rtg_2abrupt = pd.read_csv(rtg_2abrupt_path).to_numpy()\n",
-    "\n",
-    "result = run_experiment(river_arf100, river_rtg_2abrupt)\n",
-    "print('Learner: ', river_arf100, ' Result: ', result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4dd276b8-9bb2-4feb-b59d-60aff8f21900",
-   "metadata": {},
-   "source": [
-    "CPU times: user 1h 13min 34s, sys: 42.7 s, total: 1h 14min 17s\n",
-    "Wall time: 2h 5s\n",
-    "\n",
-    "Learner:  ARFClassifier  Result:  (0.80145, 4384)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa9a71e2-5730-4309-96ed-a7c6aa4e8509",
-   "metadata": {},
-   "source": [
-    "## ARF100 -j 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "28c4e3b0-a0f9-4755-862e-ee4f3c8b7683",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 8min 25s, sys: 8.7 s, total: 8min 34s\n",
-      "Wall time: 3min 45s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\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>classified instances</th>\n",
-       "      <th>classifications correct (percent)</th>\n",
-       "      <th>Kappa Statistic (percent)</th>\n",
-       "      <th>Kappa Temporal Statistic (percent)</th>\n",
-       "      <th>Kappa M Statistic (percent)</th>\n",
-       "      <th>Recall for class 0 (percent)</th>\n",
-       "      <th>Recall for class 1 (percent)</th>\n",
-       "      <th>Recall for class 2 (percent)</th>\n",
-       "      <th>Recall for class 3 (percent)</th>\n",
-       "      <th>Recall for class 4 (percent)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>88.900000</td>\n",
-       "      <td>48.185077</td>\n",
-       "      <td>58.426966</td>\n",
-       "      <td>31.901840</td>\n",
-       "      <td>98.566308</td>\n",
-       "      <td>39.263804</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>89.600000</td>\n",
-       "      <td>54.086419</td>\n",
-       "      <td>63.185841</td>\n",
-       "      <td>38.278932</td>\n",
-       "      <td>98.616957</td>\n",
-       "      <td>45.103858</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>89.466667</td>\n",
-       "      <td>55.139083</td>\n",
-       "      <td>62.823529</td>\n",
-       "      <td>39.347409</td>\n",
-       "      <td>98.467124</td>\n",
-       "      <td>46.641075</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4000.0</td>\n",
-       "      <td>89.875000</td>\n",
-       "      <td>56.827170</td>\n",
-       "      <td>63.967972</td>\n",
-       "      <td>41.219158</td>\n",
-       "      <td>98.550287</td>\n",
-       "      <td>48.185776</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>90.120000</td>\n",
-       "      <td>57.742296</td>\n",
-       "      <td>64.663805</td>\n",
-       "      <td>42.289720</td>\n",
-       "      <td>98.624517</td>\n",
-       "      <td>48.948598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>96000.0</td>\n",
-       "      <td>83.966667</td>\n",
-       "      <td>74.936690</td>\n",
-       "      <td>69.401427</td>\n",
-       "      <td>68.570437</td>\n",
-       "      <td>96.937929</td>\n",
-       "      <td>70.651886</td>\n",
-       "      <td>58.936262</td>\n",
-       "      <td>16.074600</td>\n",
-       "      <td>89.859058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97000.0</td>\n",
-       "      <td>83.862887</td>\n",
-       "      <td>74.750844</td>\n",
-       "      <td>69.275311</td>\n",
-       "      <td>68.341963</td>\n",
-       "      <td>96.961477</td>\n",
-       "      <td>70.542352</td>\n",
-       "      <td>58.642698</td>\n",
-       "      <td>16.184134</td>\n",
-       "      <td>89.718076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>98000.0</td>\n",
-       "      <td>83.781633</td>\n",
-       "      <td>74.598578</td>\n",
-       "      <td>69.188120</td>\n",
-       "      <td>68.147658</td>\n",
-       "      <td>96.983431</td>\n",
-       "      <td>70.496975</td>\n",
-       "      <td>58.448928</td>\n",
-       "      <td>16.197783</td>\n",
-       "      <td>89.530439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>99000.0</td>\n",
-       "      <td>83.696970</td>\n",
-       "      <td>74.434459</td>\n",
-       "      <td>69.075720</td>\n",
-       "      <td>67.938021</td>\n",
-       "      <td>97.003699</td>\n",
-       "      <td>70.428652</td>\n",
-       "      <td>58.169527</td>\n",
-       "      <td>16.160490</td>\n",
-       "      <td>89.392473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>83.616000</td>\n",
-       "      <td>74.274225</td>\n",
-       "      <td>68.977335</td>\n",
-       "      <td>67.729609</td>\n",
-       "      <td>97.015987</td>\n",
-       "      <td>70.335001</td>\n",
-       "      <td>57.978176</td>\n",
-       "      <td>16.270816</td>\n",
-       "      <td>89.234790</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classified instances  classifications correct (percent)  \\\n",
-       "0                 1000.0                          88.900000   \n",
-       "1                 2000.0                          89.600000   \n",
-       "2                 3000.0                          89.466667   \n",
-       "3                 4000.0                          89.875000   \n",
-       "4                 5000.0                          90.120000   \n",
-       "..                   ...                                ...   \n",
-       "95               96000.0                          83.966667   \n",
-       "96               97000.0                          83.862887   \n",
-       "97               98000.0                          83.781633   \n",
-       "98               99000.0                          83.696970   \n",
-       "99              100000.0                          83.616000   \n",
-       "\n",
-       "    Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       "0                   48.185077                           58.426966   \n",
-       "1                   54.086419                           63.185841   \n",
-       "2                   55.139083                           62.823529   \n",
-       "3                   56.827170                           63.967972   \n",
-       "4                   57.742296                           64.663805   \n",
-       "..                        ...                                 ...   \n",
-       "95                  74.936690                           69.401427   \n",
-       "96                  74.750844                           69.275311   \n",
-       "97                  74.598578                           69.188120   \n",
-       "98                  74.434459                           69.075720   \n",
-       "99                  74.274225                           68.977335   \n",
-       "\n",
-       "    Kappa M Statistic (percent)  Recall for class 0 (percent)  \\\n",
-       "0                     31.901840                     98.566308   \n",
-       "1                     38.278932                     98.616957   \n",
-       "2                     39.347409                     98.467124   \n",
-       "3                     41.219158                     98.550287   \n",
-       "4                     42.289720                     98.624517   \n",
-       "..                          ...                           ...   \n",
-       "95                    68.570437                     96.937929   \n",
-       "96                    68.341963                     96.961477   \n",
-       "97                    68.147658                     96.983431   \n",
-       "98                    67.938021                     97.003699   \n",
-       "99                    67.729609                     97.015987   \n",
-       "\n",
-       "    Recall for class 1 (percent)  Recall for class 2 (percent)  \\\n",
-       "0                      39.263804                           NaN   \n",
-       "1                      45.103858                           NaN   \n",
-       "2                      46.641075                           NaN   \n",
-       "3                      48.185776                           NaN   \n",
-       "4                      48.948598                           NaN   \n",
-       "..                           ...                           ...   \n",
-       "95                     70.651886                     58.936262   \n",
-       "96                     70.542352                     58.642698   \n",
-       "97                     70.496975                     58.448928   \n",
-       "98                     70.428652                     58.169527   \n",
-       "99                     70.335001                     57.978176   \n",
-       "\n",
-       "    Recall for class 3 (percent)  Recall for class 4 (percent)  \n",
-       "0                            NaN                           NaN  \n",
-       "1                            NaN                           NaN  \n",
-       "2                            NaN                           NaN  \n",
-       "3                            NaN                           NaN  \n",
-       "4                            NaN                           NaN  \n",
-       "..                           ...                           ...  \n",
-       "95                     16.074600                     89.859058  \n",
-       "96                     16.184134                     89.718076  \n",
-       "97                     16.197783                     89.530439  \n",
-       "98                     16.160490                     89.392473  \n",
-       "99                     16.270816                     89.234790  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf100 = AdaptiveRandomForest()\n",
-    "arf100.getOptions().setViaCLIString(\"-s 100 -j 4\")\n",
-    "arf100.setRandomSeed(1)\n",
-    "arf100.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "evaluator = BasicClassificationPerformanceEvaluator()\n",
-    "evaluator.recallPerClassOption.set()\n",
-    "evaluator.prepareForUse()\n",
-    "\n",
-    "test_train_loop(rtg_2abrupt, arf100, evaluator, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f459bd5e-c70d-4065-a20e-5c04e24eac9a",
-   "metadata": {},
-   "source": [
-    "## ARF100 -j 8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fb87016c-9df1-4d9f-aa9f-08decb704a83",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 9min 43s, sys: 12.7 s, total: 9min 55s\n",
-      "Wall time: 4min 18s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>classified instances</th>\n",
-       "      <th>classifications correct (percent)</th>\n",
-       "      <th>Kappa Statistic (percent)</th>\n",
-       "      <th>Kappa Temporal Statistic (percent)</th>\n",
-       "      <th>Kappa M Statistic (percent)</th>\n",
-       "      <th>Recall for class 0 (percent)</th>\n",
-       "      <th>Recall for class 1 (percent)</th>\n",
-       "      <th>Recall for class 2 (percent)</th>\n",
-       "      <th>Recall for class 3 (percent)</th>\n",
-       "      <th>Recall for class 4 (percent)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>88.900000</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>89.600000</td>\n",
-       "      <td>54.086419</td>\n",
-       "      <td>63.185841</td>\n",
-       "      <td>38.278932</td>\n",
-       "      <td>98.616957</td>\n",
-       "      <td>45.103858</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>89.466667</td>\n",
-       "      <td>55.139083</td>\n",
-       "      <td>62.823529</td>\n",
-       "      <td>39.347409</td>\n",
-       "      <td>98.467124</td>\n",
-       "      <td>46.641075</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4000.0</td>\n",
-       "      <td>89.875000</td>\n",
-       "      <td>56.827170</td>\n",
-       "      <td>63.967972</td>\n",
-       "      <td>41.219158</td>\n",
-       "      <td>98.550287</td>\n",
-       "      <td>48.185776</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>90.120000</td>\n",
-       "      <td>57.742296</td>\n",
-       "      <td>64.663805</td>\n",
-       "      <td>42.289720</td>\n",
-       "      <td>98.624517</td>\n",
-       "      <td>48.948598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>96000.0</td>\n",
-       "      <td>83.966667</td>\n",
-       "      <td>74.936690</td>\n",
-       "      <td>69.401427</td>\n",
-       "      <td>68.570437</td>\n",
-       "      <td>96.937929</td>\n",
-       "      <td>70.651886</td>\n",
-       "      <td>58.936262</td>\n",
-       "      <td>16.074600</td>\n",
-       "      <td>89.859058</td>\n",
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-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97000.0</td>\n",
-       "      <td>83.862887</td>\n",
-       "      <td>74.750844</td>\n",
-       "      <td>69.275311</td>\n",
-       "      <td>68.341963</td>\n",
-       "      <td>96.961477</td>\n",
-       "      <td>70.542352</td>\n",
-       "      <td>58.642698</td>\n",
-       "      <td>16.184134</td>\n",
-       "      <td>89.718076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>98000.0</td>\n",
-       "      <td>83.781633</td>\n",
-       "      <td>74.598578</td>\n",
-       "      <td>69.188120</td>\n",
-       "      <td>68.147658</td>\n",
-       "      <td>96.983431</td>\n",
-       "      <td>70.496975</td>\n",
-       "      <td>58.448928</td>\n",
-       "      <td>16.197783</td>\n",
-       "      <td>89.530439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>99000.0</td>\n",
-       "      <td>83.696970</td>\n",
-       "      <td>74.434459</td>\n",
-       "      <td>69.075720</td>\n",
-       "      <td>67.938021</td>\n",
-       "      <td>97.003699</td>\n",
-       "      <td>70.428652</td>\n",
-       "      <td>58.169527</td>\n",
-       "      <td>16.160490</td>\n",
-       "      <td>89.392473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>83.616000</td>\n",
-       "      <td>74.274225</td>\n",
-       "      <td>68.977335</td>\n",
-       "      <td>67.729609</td>\n",
-       "      <td>97.015987</td>\n",
-       "      <td>70.335001</td>\n",
-       "      <td>57.978176</td>\n",
-       "      <td>16.270816</td>\n",
-       "      <td>89.234790</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classified instances  classifications correct (percent)  \\\n",
-       "0                 1000.0                          88.900000   \n",
-       "1                 2000.0                          89.600000   \n",
-       "2                 3000.0                          89.466667   \n",
-       "3                 4000.0                          89.875000   \n",
-       "4                 5000.0                          90.120000   \n",
-       "..                   ...                                ...   \n",
-       "95               96000.0                          83.966667   \n",
-       "96               97000.0                          83.862887   \n",
-       "97               98000.0                          83.781633   \n",
-       "98               99000.0                          83.696970   \n",
-       "99              100000.0                          83.616000   \n",
-       "\n",
-       "    Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       "0                   48.185077                           58.426966   \n",
-       "1                   54.086419                           63.185841   \n",
-       "2                   55.139083                           62.823529   \n",
-       "3                   56.827170                           63.967972   \n",
-       "4                   57.742296                           64.663805   \n",
-       "..                        ...                                 ...   \n",
-       "95                  74.936690                           69.401427   \n",
-       "96                  74.750844                           69.275311   \n",
-       "97                  74.598578                           69.188120   \n",
-       "98                  74.434459                           69.075720   \n",
-       "99                  74.274225                           68.977335   \n",
-       "\n",
-       "    Kappa M Statistic (percent)  Recall for class 0 (percent)  \\\n",
-       "0                     31.901840                     98.566308   \n",
-       "1                     38.278932                     98.616957   \n",
-       "2                     39.347409                     98.467124   \n",
-       "3                     41.219158                     98.550287   \n",
-       "4                     42.289720                     98.624517   \n",
-       "..                          ...                           ...   \n",
-       "95                    68.570437                     96.937929   \n",
-       "96                    68.341963                     96.961477   \n",
-       "97                    68.147658                     96.983431   \n",
-       "98                    67.938021                     97.003699   \n",
-       "99                    67.729609                     97.015987   \n",
-       "\n",
-       "    Recall for class 1 (percent)  Recall for class 2 (percent)  \\\n",
-       "0                      39.263804                           NaN   \n",
-       "1                      45.103858                           NaN   \n",
-       "2                      46.641075                           NaN   \n",
-       "3                      48.185776                           NaN   \n",
-       "4                      48.948598                           NaN   \n",
-       "..                           ...                           ...   \n",
-       "95                     70.651886                     58.936262   \n",
-       "96                     70.542352                     58.642698   \n",
-       "97                     70.496975                     58.448928   \n",
-       "98                     70.428652                     58.169527   \n",
-       "99                     70.335001                     57.978176   \n",
-       "\n",
-       "    Recall for class 3 (percent)  Recall for class 4 (percent)  \n",
-       "0                            NaN                           NaN  \n",
-       "1                            NaN                           NaN  \n",
-       "2                            NaN                           NaN  \n",
-       "3                            NaN                           NaN  \n",
-       "4                            NaN                           NaN  \n",
-       "..                           ...                           ...  \n",
-       "95                     16.074600                     89.859058  \n",
-       "96                     16.184134                     89.718076  \n",
-       "97                     16.197783                     89.530439  \n",
-       "98                     16.160490                     89.392473  \n",
-       "99                     16.270816                     89.234790  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf100 = AdaptiveRandomForest()\n",
-    "arf100.getOptions().setViaCLIString(\"-s 100 -j 8\")\n",
-    "arf100.setRandomSeed(1)\n",
-    "arf100.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "evaluator = BasicClassificationPerformanceEvaluator()\n",
-    "evaluator.recallPerClassOption.set()\n",
-    "evaluator.prepareForUse()\n",
-    "\n",
-    "test_train_loop(rtg_2abrupt, arf100, evaluator, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6114fd77-9d53-492d-8906-f2e0c6fb40a1",
-   "metadata": {},
-   "source": [
-    "CPU times: user 9min 43s, sys: 12.7 s, total: 9min 55s\n",
-    "Wall time: 4min 18s\n",
-    "\n",
-    "83.616000\t"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cbee199-c4e2-4907-b110-7b64a66458d0",
-   "metadata": {},
-   "source": [
-    "## ARF100 -j 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "68a0b070-0ba8-4122-b33f-132b366a4fe3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 9min 42s, sys: 8.26 s, total: 9min 50s\n",
-      "Wall time: 6min 20s\n"
-     ]
-    },
-    {
-     "data": {
-      "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>classified instances</th>\n",
-       "      <th>classifications correct (percent)</th>\n",
-       "      <th>Kappa Statistic (percent)</th>\n",
-       "      <th>Kappa Temporal Statistic (percent)</th>\n",
-       "      <th>Kappa M Statistic (percent)</th>\n",
-       "      <th>Recall for class 0 (percent)</th>\n",
-       "      <th>Recall for class 1 (percent)</th>\n",
-       "      <th>Recall for class 2 (percent)</th>\n",
-       "      <th>Recall for class 3 (percent)</th>\n",
-       "      <th>Recall for class 4 (percent)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>88.900000</td>\n",
-       "      <td>48.185077</td>\n",
-       "      <td>58.426966</td>\n",
-       "      <td>31.901840</td>\n",
-       "      <td>98.566308</td>\n",
-       "      <td>39.263804</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>89.600000</td>\n",
-       "      <td>54.086419</td>\n",
-       "      <td>63.185841</td>\n",
-       "      <td>38.278932</td>\n",
-       "      <td>98.616957</td>\n",
-       "      <td>45.103858</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>89.466667</td>\n",
-       "      <td>55.139083</td>\n",
-       "      <td>62.823529</td>\n",
-       "      <td>39.347409</td>\n",
-       "      <td>98.467124</td>\n",
-       "      <td>46.641075</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4000.0</td>\n",
-       "      <td>89.875000</td>\n",
-       "      <td>56.827170</td>\n",
-       "      <td>63.967972</td>\n",
-       "      <td>41.219158</td>\n",
-       "      <td>98.550287</td>\n",
-       "      <td>48.185776</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>90.120000</td>\n",
-       "      <td>57.742296</td>\n",
-       "      <td>64.663805</td>\n",
-       "      <td>42.289720</td>\n",
-       "      <td>98.624517</td>\n",
-       "      <td>48.948598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>96000.0</td>\n",
-       "      <td>83.966667</td>\n",
-       "      <td>74.936690</td>\n",
-       "      <td>69.401427</td>\n",
-       "      <td>68.570437</td>\n",
-       "      <td>96.937929</td>\n",
-       "      <td>70.651886</td>\n",
-       "      <td>58.936262</td>\n",
-       "      <td>16.074600</td>\n",
-       "      <td>89.859058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97000.0</td>\n",
-       "      <td>83.862887</td>\n",
-       "      <td>74.750844</td>\n",
-       "      <td>69.275311</td>\n",
-       "      <td>68.341963</td>\n",
-       "      <td>96.961477</td>\n",
-       "      <td>70.542352</td>\n",
-       "      <td>58.642698</td>\n",
-       "      <td>16.184134</td>\n",
-       "      <td>89.718076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>98000.0</td>\n",
-       "      <td>83.781633</td>\n",
-       "      <td>74.598578</td>\n",
-       "      <td>69.188120</td>\n",
-       "      <td>68.147658</td>\n",
-       "      <td>96.983431</td>\n",
-       "      <td>70.496975</td>\n",
-       "      <td>58.448928</td>\n",
-       "      <td>16.197783</td>\n",
-       "      <td>89.530439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>99000.0</td>\n",
-       "      <td>83.696970</td>\n",
-       "      <td>74.434459</td>\n",
-       "      <td>69.075720</td>\n",
-       "      <td>67.938021</td>\n",
-       "      <td>97.003699</td>\n",
-       "      <td>70.428652</td>\n",
-       "      <td>58.169527</td>\n",
-       "      <td>16.160490</td>\n",
-       "      <td>89.392473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>83.616000</td>\n",
-       "      <td>74.274225</td>\n",
-       "      <td>68.977335</td>\n",
-       "      <td>67.729609</td>\n",
-       "      <td>97.015987</td>\n",
-       "      <td>70.335001</td>\n",
-       "      <td>57.978176</td>\n",
-       "      <td>16.270816</td>\n",
-       "      <td>89.234790</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classified instances  classifications correct (percent)  \\\n",
-       "0                 1000.0                          88.900000   \n",
-       "1                 2000.0                          89.600000   \n",
-       "2                 3000.0                          89.466667   \n",
-       "3                 4000.0                          89.875000   \n",
-       "4                 5000.0                          90.120000   \n",
-       "..                   ...                                ...   \n",
-       "95               96000.0                          83.966667   \n",
-       "96               97000.0                          83.862887   \n",
-       "97               98000.0                          83.781633   \n",
-       "98               99000.0                          83.696970   \n",
-       "99              100000.0                          83.616000   \n",
-       "\n",
-       "    Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       "0                   48.185077                           58.426966   \n",
-       "1                   54.086419                           63.185841   \n",
-       "2                   55.139083                           62.823529   \n",
-       "3                   56.827170                           63.967972   \n",
-       "4                   57.742296                           64.663805   \n",
-       "..                        ...                                 ...   \n",
-       "95                  74.936690                           69.401427   \n",
-       "96                  74.750844                           69.275311   \n",
-       "97                  74.598578                           69.188120   \n",
-       "98                  74.434459                           69.075720   \n",
-       "99                  74.274225                           68.977335   \n",
-       "\n",
-       "    Kappa M Statistic (percent)  Recall for class 0 (percent)  \\\n",
-       "0                     31.901840                     98.566308   \n",
-       "1                     38.278932                     98.616957   \n",
-       "2                     39.347409                     98.467124   \n",
-       "3                     41.219158                     98.550287   \n",
-       "4                     42.289720                     98.624517   \n",
-       "..                          ...                           ...   \n",
-       "95                    68.570437                     96.937929   \n",
-       "96                    68.341963                     96.961477   \n",
-       "97                    68.147658                     96.983431   \n",
-       "98                    67.938021                     97.003699   \n",
-       "99                    67.729609                     97.015987   \n",
-       "\n",
-       "    Recall for class 1 (percent)  Recall for class 2 (percent)  \\\n",
-       "0                      39.263804                           NaN   \n",
-       "1                      45.103858                           NaN   \n",
-       "2                      46.641075                           NaN   \n",
-       "3                      48.185776                           NaN   \n",
-       "4                      48.948598                           NaN   \n",
-       "..                           ...                           ...   \n",
-       "95                     70.651886                     58.936262   \n",
-       "96                     70.542352                     58.642698   \n",
-       "97                     70.496975                     58.448928   \n",
-       "98                     70.428652                     58.169527   \n",
-       "99                     70.335001                     57.978176   \n",
-       "\n",
-       "    Recall for class 3 (percent)  Recall for class 4 (percent)  \n",
-       "0                            NaN                           NaN  \n",
-       "1                            NaN                           NaN  \n",
-       "2                            NaN                           NaN  \n",
-       "3                            NaN                           NaN  \n",
-       "4                            NaN                           NaN  \n",
-       "..                           ...                           ...  \n",
-       "95                     16.074600                     89.859058  \n",
-       "96                     16.184134                     89.718076  \n",
-       "97                     16.197783                     89.530439  \n",
-       "98                     16.160490                     89.392473  \n",
-       "99                     16.270816                     89.234790  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf100 = AdaptiveRandomForest()\n",
-    "arf100.getOptions().setViaCLIString(\"-s 100 -j 2\")\n",
-    "arf100.setRandomSeed(1)\n",
-    "arf100.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "evaluator = BasicClassificationPerformanceEvaluator()\n",
-    "evaluator.recallPerClassOption.set()\n",
-    "evaluator.prepareForUse()\n",
-    "\n",
-    "test_train_loop(rtg_2abrupt, arf100, evaluator, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17ed7480-582f-479b-ab4b-80445b147841",
-   "metadata": {},
-   "source": [
-    "CPU times: user 9min 42s, sys: 8.26 s, total: 9min 50s\n",
-    "Wall time: 6min 20s\n",
-    "\n",
-    "83.616000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed024403-f9a3-4d1f-8f07-f39adcb034ab",
-   "metadata": {},
-   "source": [
-    "## ARF100 -j 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "75c69c1e-c379-4850-805e-ea9d94caef84",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 9min 39s, sys: 8.91 s, total: 9min 48s\n",
-      "Wall time: 4min 37s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>classified instances</th>\n",
-       "      <th>classifications correct (percent)</th>\n",
-       "      <th>Kappa Statistic (percent)</th>\n",
-       "      <th>Kappa Temporal Statistic (percent)</th>\n",
-       "      <th>Kappa M Statistic (percent)</th>\n",
-       "      <th>Recall for class 0 (percent)</th>\n",
-       "      <th>Recall for class 1 (percent)</th>\n",
-       "      <th>Recall for class 2 (percent)</th>\n",
-       "      <th>Recall for class 3 (percent)</th>\n",
-       "      <th>Recall for class 4 (percent)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>88.900000</td>\n",
-       "      <td>48.185077</td>\n",
-       "      <td>58.426966</td>\n",
-       "      <td>31.901840</td>\n",
-       "      <td>98.566308</td>\n",
-       "      <td>39.263804</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>89.600000</td>\n",
-       "      <td>54.086419</td>\n",
-       "      <td>63.185841</td>\n",
-       "      <td>38.278932</td>\n",
-       "      <td>98.616957</td>\n",
-       "      <td>45.103858</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3000.0</td>\n",
-       "      <td>89.466667</td>\n",
-       "      <td>55.139083</td>\n",
-       "      <td>62.823529</td>\n",
-       "      <td>39.347409</td>\n",
-       "      <td>98.467124</td>\n",
-       "      <td>46.641075</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4000.0</td>\n",
-       "      <td>89.875000</td>\n",
-       "      <td>56.827170</td>\n",
-       "      <td>63.967972</td>\n",
-       "      <td>41.219158</td>\n",
-       "      <td>98.550287</td>\n",
-       "      <td>48.185776</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>90.120000</td>\n",
-       "      <td>57.742296</td>\n",
-       "      <td>64.663805</td>\n",
-       "      <td>42.289720</td>\n",
-       "      <td>98.624517</td>\n",
-       "      <td>48.948598</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>96000.0</td>\n",
-       "      <td>83.966667</td>\n",
-       "      <td>74.936690</td>\n",
-       "      <td>69.401427</td>\n",
-       "      <td>68.570437</td>\n",
-       "      <td>96.937929</td>\n",
-       "      <td>70.651886</td>\n",
-       "      <td>58.936262</td>\n",
-       "      <td>16.074600</td>\n",
-       "      <td>89.859058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>97000.0</td>\n",
-       "      <td>83.862887</td>\n",
-       "      <td>74.750844</td>\n",
-       "      <td>69.275311</td>\n",
-       "      <td>68.341963</td>\n",
-       "      <td>96.961477</td>\n",
-       "      <td>70.542352</td>\n",
-       "      <td>58.642698</td>\n",
-       "      <td>16.184134</td>\n",
-       "      <td>89.718076</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>98000.0</td>\n",
-       "      <td>83.781633</td>\n",
-       "      <td>74.598578</td>\n",
-       "      <td>69.188120</td>\n",
-       "      <td>68.147658</td>\n",
-       "      <td>96.983431</td>\n",
-       "      <td>70.496975</td>\n",
-       "      <td>58.448928</td>\n",
-       "      <td>16.197783</td>\n",
-       "      <td>89.530439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>99000.0</td>\n",
-       "      <td>83.696970</td>\n",
-       "      <td>74.434459</td>\n",
-       "      <td>69.075720</td>\n",
-       "      <td>67.938021</td>\n",
-       "      <td>97.003699</td>\n",
-       "      <td>70.428652</td>\n",
-       "      <td>58.169527</td>\n",
-       "      <td>16.160490</td>\n",
-       "      <td>89.392473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>100000.0</td>\n",
-       "      <td>83.616000</td>\n",
-       "      <td>74.274225</td>\n",
-       "      <td>68.977335</td>\n",
-       "      <td>67.729609</td>\n",
-       "      <td>97.015987</td>\n",
-       "      <td>70.335001</td>\n",
-       "      <td>57.978176</td>\n",
-       "      <td>16.270816</td>\n",
-       "      <td>89.234790</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    classified instances  classifications correct (percent)  \\\n",
-       "0                 1000.0                          88.900000   \n",
-       "1                 2000.0                          89.600000   \n",
-       "2                 3000.0                          89.466667   \n",
-       "3                 4000.0                          89.875000   \n",
-       "4                 5000.0                          90.120000   \n",
-       "..                   ...                                ...   \n",
-       "95               96000.0                          83.966667   \n",
-       "96               97000.0                          83.862887   \n",
-       "97               98000.0                          83.781633   \n",
-       "98               99000.0                          83.696970   \n",
-       "99              100000.0                          83.616000   \n",
-       "\n",
-       "    Kappa Statistic (percent)  Kappa Temporal Statistic (percent)  \\\n",
-       "0                   48.185077                           58.426966   \n",
-       "1                   54.086419                           63.185841   \n",
-       "2                   55.139083                           62.823529   \n",
-       "3                   56.827170                           63.967972   \n",
-       "4                   57.742296                           64.663805   \n",
-       "..                        ...                                 ...   \n",
-       "95                  74.936690                           69.401427   \n",
-       "96                  74.750844                           69.275311   \n",
-       "97                  74.598578                           69.188120   \n",
-       "98                  74.434459                           69.075720   \n",
-       "99                  74.274225                           68.977335   \n",
-       "\n",
-       "    Kappa M Statistic (percent)  Recall for class 0 (percent)  \\\n",
-       "0                     31.901840                     98.566308   \n",
-       "1                     38.278932                     98.616957   \n",
-       "2                     39.347409                     98.467124   \n",
-       "3                     41.219158                     98.550287   \n",
-       "4                     42.289720                     98.624517   \n",
-       "..                          ...                           ...   \n",
-       "95                    68.570437                     96.937929   \n",
-       "96                    68.341963                     96.961477   \n",
-       "97                    68.147658                     96.983431   \n",
-       "98                    67.938021                     97.003699   \n",
-       "99                    67.729609                     97.015987   \n",
-       "\n",
-       "    Recall for class 1 (percent)  Recall for class 2 (percent)  \\\n",
-       "0                      39.263804                           NaN   \n",
-       "1                      45.103858                           NaN   \n",
-       "2                      46.641075                           NaN   \n",
-       "3                      48.185776                           NaN   \n",
-       "4                      48.948598                           NaN   \n",
-       "..                           ...                           ...   \n",
-       "95                     70.651886                     58.936262   \n",
-       "96                     70.542352                     58.642698   \n",
-       "97                     70.496975                     58.448928   \n",
-       "98                     70.428652                     58.169527   \n",
-       "99                     70.335001                     57.978176   \n",
-       "\n",
-       "    Recall for class 3 (percent)  Recall for class 4 (percent)  \n",
-       "0                            NaN                           NaN  \n",
-       "1                            NaN                           NaN  \n",
-       "2                            NaN                           NaN  \n",
-       "3                            NaN                           NaN  \n",
-       "4                            NaN                           NaN  \n",
-       "..                           ...                           ...  \n",
-       "95                     16.074600                     89.859058  \n",
-       "96                     16.184134                     89.718076  \n",
-       "97                     16.197783                     89.530439  \n",
-       "98                     16.160490                     89.392473  \n",
-       "99                     16.270816                     89.234790  \n",
-       "\n",
-       "[100 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "from moa.classifiers.meta import AdaptiveRandomForest\n",
-    "from moa.core import Example\n",
-    "from moa.evaluation import BasicClassificationPerformanceEvaluator\n",
-    "from moa.streams import ArffFileStream\n",
-    "\n",
-    "arf100 = AdaptiveRandomForest()\n",
-    "arf100.getOptions().setViaCLIString(\"-s 100 -j 5\")\n",
-    "arf100.setRandomSeed(1)\n",
-    "arf100.prepareForUse()\n",
-    "\n",
-    "rtg_2abrupt = ArffFileStream(\"/Users/gomeshe/Desktop/data/RTG_2abrupt.arff\", -1)\n",
-    "rtg_2abrupt.prepareForUse()\n",
-    "\n",
-    "evaluator = BasicClassificationPerformanceEvaluator()\n",
-    "evaluator.recallPerClassOption.set()\n",
-    "evaluator.prepareForUse()\n",
-    "\n",
-    "test_train_loop(rtg_2abrupt, arf100, evaluator, maxInstances=100000, sampleFrequency=1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bdc7aae-643e-451f-a62e-3ac0acb9606b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.8.17"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}