college-datascience/project/ProjectDataScienceBeppeVanrolleghem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datascience project\n",
    "\n",
    "## Voorwoord\n",
    "\n",
    "Jammer genoeg heb ik niet zoveel tijd kunnen steken in deze opgave als ik wou. Dit komt namelijk omdat ik de opdracht niet goed gelezen had en de opgave verkeerd gemaakt heb voor meerendeels van de tijd die ik hierin gestoken heb. Dit  andere project is meegegeven en kan gevonden worden in de notebook \"VoorspellenVanSignaalSterkteADVPositie\".\n",
    "\n",
    "## Inlezen van de data\n",
    "\n",
    "Er wordt begonnen met het inlezen van de data als een array van de lijnen. Door gebruik van enkele if functies kunnen we kiezen welke datasets we willen inlezen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time=12/03 06:08:53& Sender=44:6E:E5:C5:8F:4F& Location=gang@0.61875;0.13758& WifiInfo=ODISEE@88-1d-fc-30-d4-40:-74,campusroam@88-1d-fc-30-d4-43:-74,ODISEE@88-1d-fc-30-d5-50:-72,eduroam@88-1d-fc-30-d4-42:-74,eduroam@88-1d-fc-30-d5-52:-72,campusroam@88-1d-fc-30-d5-53:-73,ODISEEGuest@88-1d-fc-30-d4-41:-75,ODISEEGuest@88-1d-fc-30-d5-51:-73,CiscoC5976@58-6d-8f-19-14-38:-82,rechts@58-6d-8f-19-10-fc:-59,ODISEE@88-1d-fc-41-dc-50:-81,eduroam@88-1d-fc-41-dc-52:-81,campusroam@88-1d-fc-41-dc-53:-67,eduroam@88-1d-fc-2c-c0-02:-78,campusroam@88-1d-fc-2c-c0-03:-71,ODISEE@88-1d-fc-2c-c0-00:-77,telenet-5467D@dc-53-7c-85-46-82:-87,ODISEEGuest@88-1d-fc-41-dc-51:-80,ODISEEGuest@88-1d-fc-2c-c0-01:-73,CiscoC5959@58-6d-8f-19-13-f4:-81,TELENETHOMESPOT@02-53-7c-85-46-83:-86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "lines = []\n",
    "\n",
    "if True:\n",
    "    with open(\"DataScienceData01.txt\",\"r\") as infile:\n",
    "        lines = infile.readlines()\n",
    "if True:\n",
    "    with open(\"DataScienceData02.txt\", \"r\") as infile:\n",
    "        lines.extend(infile.readlines())\n",
    "    \n",
    "\n",
    "if False:\n",
    "    with open(\"DataScienceData03.txt\", \"r\") as infile:\n",
    "        lines.extend(infile.readlines())\n",
    "\n",
    "print(lines[1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De lijnen zullen meerdere keren gesplit moeten worden om zo een uiteindelijke dataset te krijgen.\n",
    "Dit gebeurt door het gebruik van de dataParse functie:\n",
    "\n",
    "Deze zal de data splitten en parsen naar dictionary objecten. Vorm in json:\n",
    "```json\n",
    "[\n",
    "    {\n",
    "        sender = '',\n",
    "        location = '',\n",
    "        time = '',\n",
    "        x = '',\n",
    "        y = '',\n",
    "        px = '',\n",
    "        py = '',\n",
    "        xmax = '',\n",
    "        ymax = '',\n",
    "        WifiInfo= [\n",
    "            {\n",
    "                ssid = '',\n",
    "                mac = '',\n",
    "                routerid = '',\n",
    "                signal = ''\n",
    "            },\n",
    "            ...\n",
    "        ]\n",
    "    },\n",
    "    ...\n",
    "]\n",
    "```\n",
    "Deze worden daarna in een dataframe gestoken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Sender                Time  \\\n",
      "0  44:6E:E5:C5:8F:4F 1900-03-12 06:08:41   \n",
      "1  44:6E:E5:C5:8F:4F 1900-03-12 06:08:53   \n",
      "2  44:6E:E5:C5:8F:4F 1900-03-12 06:09:03   \n",
      "3  44:6E:E5:C5:8F:4F 1900-03-12 06:09:17   \n",
      "4  44:6E:E5:C5:8F:4F 1900-03-12 06:09:41   \n",
      "\n",
      "                                            WifiInfo location       px  \\\n",
      "0  [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'...     gang  0.65625   \n",
      "1  [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'...     gang  0.61875   \n",
      "2  [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'...     gang  0.26250   \n",
      "3  [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'...     gang  0.63333   \n",
      "4  [{'ssid': 'rechts', 'mac': '58-6d-8f-19-10-fc'...     gang  0.63958   \n",
      "\n",
      "        py          x  xmax          y  ymax  \n",
      "0  0.04449  186.37500   284   49.51737  1113  \n",
      "1  0.13758  175.72500   284  153.12654  1113  \n",
      "2  0.13826   74.55000   284  153.88338  1113  \n",
      "3  0.31006  179.86572   284  345.09678  1113  \n",
      "4  0.49555  181.64072   284  551.54715  1113  \n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "wifiSignals = []\n",
    "\n",
    "def dataParse2(l):\n",
    "    objs = l.split(\"& \")\n",
    "    dic = {}\n",
    "    for obj in objs:\n",
    "        items = obj.split(\"=\")\n",
    "        title = items[0]\n",
    "        data = items[1].split(\",\")\n",
    "        if len(data) == 1:\n",
    "            data = data[0]\n",
    "        if title == \"Time\":\n",
    "            dic[title] = datetime.strptime(data, \"%d/%m %H:%M:%S\")\n",
    "            continue\n",
    "        if title == \"Location\":\n",
    "            temp = data.split(\"@\")\n",
    "            naam = temp[0].lower()\n",
    "            x, y = temp[1].split(\";\")\n",
    "            dic[\"location\"] = naam\n",
    "            img = plt.imread(naam+'.png')\n",
    "            height, width, channels = img.shape\n",
    "            dic[\"x\"] = float(x) * width\n",
    "            dic[\"y\"] = float(y) * height\n",
    "            dic[\"px\"] = float(x)\n",
    "            dic[\"py\"] = float(y)\n",
    "            dic[\"xmax\"] = width\n",
    "            dic[\"ymax\"] = height\n",
    "            continue\n",
    "        if title == \"WifiInfo\":\n",
    "            appendable = []\n",
    "            for f in data:\n",
    "                append = {}\n",
    "                temp = f.replace(\"\\n\",'').split('@')\n",
    "                ti = temp[0]\n",
    "                append[\"ssid\"] = ti\n",
    "                temp = temp[1].split(\":\")\n",
    "                append[\"mac\"] = temp[0]\n",
    "                append[\"routerId\"] = \"\".join(temp[0].split('-'))\n",
    "                append[\"routerId\"] = append[\"routerId\"][:-4]\n",
    "                if append[\"routerId\"] not in wifiSignals:\n",
    "                    wifiSignals.append(append[\"routerId\"])\n",
    "                append[\"signal\"] = float(temp[1])\n",
    "                appendable.append(append)\n",
    "            dic[title] = sorted(appendable, key=lambda k: k[\"signal\"], reverse=True)\n",
    "            continue\n",
    "        dic[title] = data\n",
    "    return dic\n",
    "\n",
    "\n",
    "data = []\n",
    "for l in lines:\n",
    "   data.append(dataParse2(l))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "d = pd.DataFrame(data)\n",
    "print(d.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selectie van de data\n",
    "\n",
    "Nadat de data ingelezen wordt is het een goed idee om het in beeld te brengen zodat we een idee hebben van met wat we gaan werken. Dit wordt gedaan door de meetpunten te displayen in een scatterplot overheen de images. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lokalen = d.location.unique().tolist()\n",
    "\n",
    "for i in lokalen:\n",
    "    temp = d.loc[d[\"location\"] == i]\n",
    "    plt.scatter(temp.x, temp.y)\n",
    "    #print(np.column_stack((temp.x, temp.y)))\n",
    "    #print(i)\n",
    "    img = plt.imread(i+'.png')\n",
    "    #print(img)\n",
    "    plt.imshow(img)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoals men kan zien zijn er sommige meetpunten die zeer dicht bij elkaar liggen. In dit geval worden deze gefilterd en moesten ze gelijkaardige wifi info hebben eruit gehaald."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def removeIrelevant(df, minSampleSize=50):\n",
    "    rdf = []\n",
    "    returnable = pd.DataFrame()\n",
    "    for i, v in df.iterrows():\n",
    "        rdf = CloseToOthers(v, rdf)\n",
    "    rdf = pd.DataFrame(rdf)\n",
    "    return rdf\n",
    "\n",
    "def CloseToOthers(i, df, SpacePerc = .1):\n",
    "    tdf = pd.DataFrame(df)\n",
    "    approved = []\n",
    "    if \"location\" in tdf:\n",
    "        l = tdf.loc[tdf[\"location\"] == i[\"location\"]]\n",
    "        for index, dataframe in l.iterrows():\n",
    "            temp = abs(dataframe.px - i.px) \n",
    "            temp2 = abs(dataframe.py - i.py)\n",
    "            if temp <= SpacePerc and temp2 <= SpacePerc and len(dataframe[\"WifiInfo\"]) > len(i[\"WifiInfo\"]):\n",
    "                return df\n",
    "        df.append(i)\n",
    "        return df\n",
    "    else:\n",
    "        df.append(i)\n",
    "        return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oud: 217 \n",
      "Nieuw: 203\n"
     ]
    }
   ],
   "source": [
    "g = removeIrelevant(d)\n",
    "print(\"oud: {} \\nNieuw: {}\".format(len(d), len(g)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training data:\n",
    "\n",
    "Nadat de data gefilterd geweest is kan er begonnen worden aan de voorbereiding van de trainings data. Er wordt ook nog een functie aangemaakt voor de modellen te evalueren.\n",
    "\n",
    "Voor de x waarden gebruikt deze opgave een lijst van de top 2 bereikbare modems.De y waarden werden de coordinaten van het meetpunt + een nummer die afhankelijk is van het lokaal gegeven. Dit nummer werd vermenigvuldigd zodat het niet kan samenspelen met de percentages (float 0-1). Uit testen bleek dit beter te gaan dan 3 verschillende y waarden te proberen predicten. En om dit te parsen haalt men gewoon het tiental (het eerste/eerste twee getallen) van de return values en de overblijvende nummers zijn percentages (tussen 0 en 1 ideaal) die vermenigvuldigd moeten worden met de breedte / lengte van het lokaal.\n",
    "\n",
    "Daarna werd er geexperimenteerd met bepaalde scalers om het beste resultaat te halen waaruit bleek dat de normalizer de beste test results gaf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "\n",
    "def prepTrainingOLD(df, l=7):\n",
    "    x = []\n",
    "    y = []\n",
    "    #scaler = MinMaxScaler(feature_range=(0,1))\n",
    "    #scaler = StandardScaler()\n",
    "    #scaler = MaxAbsScaler()\n",
    "    scaler = Normalizer()\n",
    "    for i, dataframe in df.iterrows():\n",
    "        tx = []\n",
    "        for i in sorted(dataframe[\"WifiInfo\"], key=lambda x: x[\"signal\"], reverse=True):\n",
    "            if i[\"routerId\"] not in tx:\n",
    "                tx.append(wifiSignals.index(i[\"routerId\"]))\n",
    "            if len(tx) >= 2:\n",
    "                break\n",
    "        #for ij in dataframe[\"WifiInfo\"]:\n",
    "        #    tx[ij[\"routerId\"]] = ij[\"signal\"]\n",
    "        #print(tx)\n",
    "        x.append(tx)\n",
    "        ty = (lokalen.index(dataframe[\"location\"])/len(lokalen),dataframe[\"px\"], dataframe[\"py\"])\n",
    "        #x.append(tx)\n",
    "        y.append(ty)\n",
    "    fx = pd.DataFrame(x).fillna(0)\n",
    "    fy =  pd.DataFrame(y)\n",
    "    #print(fx)\n",
    "    #print(fy)\n",
    "    xtrain, xtest, ytrain, ytest = train_test_split(fx, fy)\n",
    "    scaler.fit(xtrain)\n",
    "    xtrain = scaler.transform(xtrain)\n",
    "    xtest = scaler.transform(xtest)\n",
    "    return xtrain, xtest, ytrain, ytest\n",
    "\n",
    "\n",
    "def prepTraining(df, scaler=Normalizer(), l=2):\n",
    "    x = []\n",
    "    y = []\n",
    "    scaler = Normalizer()\n",
    "    for i, dataframe in df.iterrows():\n",
    "        tx = []\n",
    "        for i in sorted(dataframe[\"WifiInfo\"], key=lambda x: x[\"signal\"], reverse=True):\n",
    "            if i[\"routerId\"] not in tx:\n",
    "                tx.append(wifiSignals.index(i[\"routerId\"]))\n",
    "            if len(tx) >= l:\n",
    "                break\n",
    "        x.append(tx)\n",
    "        ty = (dataframe[\"px\"]+lokalen.index(dataframe[\"location\"])*10, dataframe[\"py\"]+lokalen.index(dataframe[\"location\"])*10)\n",
    "        y.append(ty)\n",
    "    fx = pd.DataFrame(x).fillna(0)\n",
    "    fy =  pd.DataFrame(y)\n",
    "    xtrain, xtest, ytrain, ytest = train_test_split(fx, fy, random_state=3)\n",
    "    scaler.fit(xtrain)\n",
    "    xtrain = scaler.transform(xtrain)\n",
    "    xtest = scaler.transform(xtest)\n",
    "    return xtrain, xtest, ytrain, ytest\n",
    "\n",
    "\n",
    "def score(mod, cv=3):\n",
    "    kfold = KFold(n_splits=3, shuffle=True, random_state=2)\n",
    "    print(\"Model score {}\\nCrosValScore {}\\nMean {}\\n\\n\".format(mod.score(xtest, ytest), cross_val_score(mod, xtest, ytest, cv = cv),cross_val_score(mod, xtest, ytest, cv = cv).mean()))\n",
    "    print(\"Kfold:\\nScore: {}\\nMean: {}\".format(cross_val_score(mod, xtest, ytest, cv=kfold),cross_val_score(mod, xtest, ytest, cv=kfold).mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modellen\n",
    "\n",
    "![alt text](https://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png \"Vormen van plotting\")\n",
    "\n",
    "\n",
    "### Lineare regressie\n",
    "\n",
    "Het eenvoudigste model. Dit komt vooral omdat er niet veel parameters zijn die dit model aanpassen t.o.v. andere modellen die hier gebruikt worden. Het reflecteerd ook dus zeer goed de kwaliteit van de trainings set die gebruikt wordt. Dit is waarom het de baseline is van deze opgave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model score 0.053549619325076785\n",
      "CrosValScore [0.08107485 0.05643525 0.26221046]\n",
      "Mean 0.1332401864126653\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [0.25199567 0.00721587 0.08458426]\n",
      "Mean: 0.11459860042677367\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
    "\n",
    "\n",
    "\n",
    "def LinReg():\n",
    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
    "    lr = LinearRegression().fit(xtrain, ytrain)\n",
    "    score(lr)\n",
    "    return lr\n",
    "\n",
    "model = LinReg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian process\n",
    "\n",
    "Dit was een model waar er meer geexperimenteerd werd in de notebook (eerder vermeld). De mogelijkheid om kernels te kiezen die het model zou gebruiken leek mij zeer interessant om de resultaten hiervan te kunnen zien.\n",
    "\n",
    "\n",
    "#### White kernel\n",
    "\n",
    "Op zich zelf is deze kernel redelijk onbruikbaar. Het is een white noise kernel, wat betekend dat het willekeurige en onverwachte resultaten zal geven, maar dit is handig als je ze in gebruik zet met andere kernels om een meer gevarieerd resultaat te geven.\n",
    "\n",
    "\n",
    "#### DotProduct en RBF kernels\n",
    "\n",
    "De dotproduct kernel is een kernel die meer decision tree achtige resultaten geeft, terwijl de RBF kernel meer gevarieerd zal zijn. Dit is ook te zien in de notebook waar ze oorspronkelijk geimplementeerd werden, maar het leek interessant om ze in deze opgave ook te implementeren.\n",
    "\n",
    "Jammer genoeg is het niet gelukt om de White noise die deze kernel genereerd te onderdrukken. (((***Goede***))) resultaten zijn bereikbaar via de RBF en DotProduct kernel op zichzelf, maar van zodra dat de white noise erbij komt is dit teveel. Dit is zichtbaar door de testscores die exact dezelfde zijn als die van de whiteKernel op zichzelf.\n",
    "\n",
    "![alt text](https://scikit-learn.org/stable/_images/sphx_glr_plot_gpc_xor_001.png \"Kernels\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "GeneratingOptimalAlphaRBF\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.33373797e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.67252169e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal alpha for rbf kernel 1.275\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "GeneratingOptimalAlphaDotProduct\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([84.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 78, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([348.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.5]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 63, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([11.125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([15.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-38.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.25]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([9.375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([5.35546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 71, 'nit': 5, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([63.90625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-8.34375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 56, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([20.6875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 68, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([65.1875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.3125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.75]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-24.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 75, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.453125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.296875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.8125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 51, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([3.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 6, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-30.1640625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 59, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([18.078125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.71875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 46, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-23.609375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 88, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.1171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.671875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 42, 'nit': 1, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([1.41796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-14.09765625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 58, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([26.01953125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.10546875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-26.19140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-16.40234375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.6796875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-6.94140625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 117, 'nit': 6, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-11.21679688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 87, 'nit': 6, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.37890625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.22265625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([2.03515625]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 99, 'nit': 5, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.69921875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.7578125]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 73, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.99804688]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 50, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.85742188]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.59667969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 44, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.58984375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 28, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-4.69726562]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.68945312]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 82, 'nit': 6, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.3359375]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-2.52978516]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 69, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.96875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 48, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.04345703]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.72167969]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 74, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-1.03710938]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.6171875]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 47, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.61621094]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 79, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.97216797]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 49, 'nit': 4, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.68652344]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.66503906]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 64, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.76708984]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 62, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.73803711]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.48022461]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 3, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([0.17260742]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 43, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal alpha for dotproduct kernel 0.04\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "Generating white kernel noise level for rbf\n",
      "\n",
      "Optimal noise level for rbf 1\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "Generating white kernel noise for DotProd\n",
      "\n",
      "Optimal noise level for dot 1\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "White Kernel\n",
      "Model score -1.3040243750461273\n",
      "CrosValScore [-1.80544992 -1.09822479 -1.12201902]\n",
      "Mean -1.3418979083128233\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [-1.59551739 -1.24587679 -1.11763551]\n",
      "Mean: -1.3196765647323825\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "DotProduct\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n",
      "/home/beppe/.local/lib/python3.7/site-packages/sklearn/gaussian_process/gpr.py:480: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-10.]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 45, 'nit': 2, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model score 0.05304424748634964\n",
      "CrosValScore [0.08041413 0.05850993 0.26145889]\n",
      "Mean 0.1334609854955265\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [0.25166842 0.00850081 0.08625134]\n",
      "Mean: 0.11547352452721255\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "Rbf\n",
      "Model score 0.0485491868165403\n",
      "CrosValScore [0.06244162 0.10407524 0.32431861]\n",
      "Mean 0.16361182171401337\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [ 0.26362672 -0.25184796  0.09040504]\n",
      "Mean: 0.034061269824504775\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "DotWhite\n",
      "Model score -0.013242764071176399\n",
      "CrosValScore [-0.10300226 -0.03773465 -0.00324282]\n",
      "Mean -0.04799324044247397\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [-0.04027678 -0.00891338 -0.00084781]\n",
      "Mean: -0.01667932332465106\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "---\n",
      "RbfWhite\n",
      "Model score -1.2725604583700336\n",
      "CrosValScore [-1.79311122 -1.08885394 -1.1123998 ]\n",
      "Mean -1.3314549845918533\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [-1.58380539 -1.23608991 -1.10878677]\n",
      "Mean: -1.3095606923592664\n"
     ]
    }
   ],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, DotProduct, WhiteKernel\n",
    "\n",
    "def GaussProc(alpha=.08, kernel=RBF()):\n",
    "    xtrain, xtest, ytrain, ytest = prepTraining(d)\n",
    "    gp = GaussianProcessRegressor(kernel=kernel,alpha=alpha).fit(xtrain, ytrain)\n",
    "    #print(\"Model score:{}\".format(gp.score(xtest, ytest)))\n",
    "    return gp, cross_val_score(gp, xtest, ytest, cv = 3).mean()\n",
    "    \n",
    "\n",
    "    \n",
    "lastScore = 0\n",
    "optimal = 1\n",
    "kern = RBF()\n",
    "pr=False #set to true for every score it gets\n",
    "print(\"---\\nGeneratingOptimalAlphaRBF\\n\")\n",
    "\n",
    "for i in np.arange(0.005,5, .005):\n",
    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
    "    if sc > lastScore:\n",
    "        lastScore = sc\n",
    "        optimal = i\n",
    "        if pr:\n",
    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
    "\n",
    "rbfAlpha = optimal\n",
    "print(\"Optimal alpha for rbf kernel {}\\n\\n\\n\".format(rbfAlpha))\n",
    "\n",
    "\n",
    "\n",
    "print(\"---\\nGeneratingOptimalAlphaDotProduct\\n\")\n",
    "\n",
    "lastScore = 0\n",
    "optimal = 1\n",
    "kern = DotProduct()\n",
    "        \n",
    "for i in np.arange(0.005,5, .005):\n",
    "    model, sc = GaussProc(alpha=i, kernel=kern)\n",
    "    if sc > lastScore:\n",
    "        lastScore = sc\n",
    "        optimal = i\n",
    "        if pr:\n",
    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
    "        \n",
    "dotAlpha = optimal\n",
    "print(\"Optimal alpha for dotproduct kernel {} with score {}\\n\\n\\n\".format(dotAlpha, lastScore))\n",
    "\n",
    "\n",
    "\n",
    "print(\"---\\nGenerating white kernel noise level for rbf\\n\")\n",
    "kern = RBF()\n",
    "    \n",
    "lastScore = 0\n",
    "optimal = 1\n",
    "\n",
    "for i in np.arange(0.005,5, .005):\n",
    "    model, sc = GaussProc(alpha=rbfAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
    "    if sc > lastScore:\n",
    "        lastScore = sc\n",
    "        optimal = i\n",
    "        if pr:\n",
    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
    "\n",
    "rbfWhite = optimal    \n",
    "\n",
    "print(\"Optimal noise level for rbf {} with score {}\\n\\n\\n\".format(rbfWhite, lastScore))\n",
    "\n",
    "print(\"---\\nGenerating white kernel noise for DotProd\\n\")\n",
    "\n",
    "kern = DotProduct()\n",
    "    \n",
    "lastScore = 0\n",
    "optimal = 1\n",
    "        \n",
    "for i in np.arange(0.005,5, .005):\n",
    "    model, sc = GaussProc(alpha=dotAlpha, kernel=kern+WhiteKernel(noise_level=i))\n",
    "    if sc > lastScore:\n",
    "        lastScore = sc\n",
    "        optimal = i\n",
    "        if pr:\n",
    "            print(\"Last Score: {}\\nOptimal num: {}\".format(lastScore, optimal))\n",
    "\n",
    "dotWhite = optimal        \n",
    "print(\"Optimal noise level for dot {} with score {}\\n\\n\\n\".format(dotWhite, lastScore))\n",
    "\n",
    "\n",
    "\n",
    "print(\"---\\nWhite Kernel\")\n",
    "model, dump = GaussProc(alpha=1, kernel=WhiteKernel())\n",
    "score(model)\n",
    "print(\"\\n\\n\\n\")\n",
    "print(\"---\\nDotProduct\")\n",
    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct())\n",
    "score(model)\n",
    "print(\"\\n\\n\\n\")\n",
    "print(\"---\\nRbf\")\n",
    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF())\n",
    "score(model)\n",
    "print(\"\\n\\n\\n\")\n",
    "print(\"---\\nDotWhite\")\n",
    "model, dump = GaussProc(alpha=dotAlpha, kernel=DotProduct() + WhiteKernel(noise_level = dotWhite))\n",
    "score(model)\n",
    "print(\"\\n\\n\\n\")\n",
    "print(\"---\\nRbfWhite\")\n",
    "model, dump = GaussProc(alpha=rbfAlpha, kernel=RBF() + WhiteKernel(noise_level = rbfWhite))\n",
    "score(model)\n",
    "#a, b = GaussProc(alpha=0.04908, kernel=DotProduct() * WhiteKernel())\n",
    "#print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "\n",
    "Dit model werd gekozen omdat een decision tree beter past bij het voorspellen van welk lokaal een bepaalde value in komt, moest dit een klassificatie probleem zijn (dit is het jammer genoeg niet). Door dus de lokalen op hogere waarden te steken (tientallen ipv values tussen 0 en 1) kunnen we het model nog simpele decisions geven (bv tussen 0 en 10 is 1 lokaal, tussen 10 en 20 is gang etc). Jammer genoeg wordt dit niet gereflecteerd in de resultaten.\n",
    "\n",
    "Er was nog geen vorm van decision tree aanwezig in het project, en Random Forest had de beste resultaten voor deze opgave. Door for loops te maken kan er gekeken worden wat de beste waarden zijn voor de n_estimators en max_depth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.03583447235599587 en 0.15000000000000002 voor dep\n",
      "0.08730520188582598 en 1.0 voor dep\n",
      "0.11835171292421753 en 1.05 voor dep\n",
      "0.12800809141118277 en 1.1 voor dep\n",
      "0.1309682618539385 en 1.2000000000000002 voor dep\n",
      "0.1325868840100242 en 1.8 voor dep\n",
      "0.18389298719551705 en 2.0500000000000003 voor dep\n",
      "0.2033019128173553 en 2.6500000000000004 voor dep\n",
      "0.024595002239385173 en 1 voor est\n",
      "0.1414475584387812 en 2 voor est\n",
      "0.19844434825486879 en 6 voor est\n",
      "0.2045974213542903 en 23 voor est\n",
      "\n",
      "\n",
      "\n",
      "Optimal Estimations 23 en optimal depth 2.6500000000000004\n",
      "\n",
      "\n",
      "\n",
      "Model score 0.05451023322649307\n",
      "CrosValScore [0.11198684 0.02559293 0.35556681]\n",
      "Mean 0.16299764209737205\n",
      "\n",
      "\n",
      "Kfold:\n",
      "Score: [ 0.35889054 -0.01211664  0.08784591]\n",
      "Mean: 0.11315903629642887\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "\n",
    "def rfor(est=5, dep=50):\n",
    "    xtrain, xtest, ytrain, ytest = prepTraining(d, scaler=MinMaxScaler())\n",
    "    lr =  RandomForestRegressor(n_estimators=est, max_depth=dep)\n",
    "    lr.fit(xtrain, ytrain)\n",
    "    return lr, cross_val_score(lr, xtest, ytest, cv = 3).mean()\n",
    "#Calculating optimal depth\n",
    "lastScore = 0\n",
    "optimal = 1\n",
    "for i in np.arange(0,20,.05):\n",
    "    if i == 0:\n",
    "        model, lastScore = rfor(dep=0.05, est=25)\n",
    "        continue\n",
    "    model, sc = rfor(dep=i, est=25)\n",
    "    if sc > lastScore:\n",
    "        lastScore = sc\n",
    "        optimal = i\n",
    "        print(\"{} en {} voor dep\".format(lastScore, optimal))\n",
    "\n",
    "de = optimal\n",
    "lastScore = 0\n",
    "optimal = 1\n",
    "\n",
    "for i in range(1,140):\n",
    "    model, sc = rfor(est=i, dep=de)\n",
    "    if sc > lastScore:\n",
    "        lastScore = sc\n",
    "        optimal = i\n",
    "        print(\"{} en {} voor est\".format(lastScore, optimal))\n",
    "\n",
    "print(\"\\n\\n\\nOptimal Estimations {} en optimal depth {}\\n\\n\\n\".format(optimal, de))\n",
    "optimal, sc = rfor(est=optimal, dep=de)\n",
    "score(optimal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusie\n",
    "\n",
    "Uit deze vindingen kunnen we afleiden dat meeste modellen redelijk gelijkaardige scores halen voor deze trainingsdata. Hoewel theoretisch random forest het beste van de bovenstaande modellen zou zijn is de score maar een kleine verbetering op de lineare regressie die we als baseline gebruikten. \n",
    "\n",
    "## Post-mortem/wat kon beter\n",
    "\n",
    "Er moest zeker meer tijd gestoken worden in het selecteren van de trainings data en features. Deze hebben de rest van het project sterk beinvloed en gezorgd voor lage en gelijkaardige scores. Meer tijd om te experimenteren met de gaussian methode zou ook betere testscores kunnen genereren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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