commit 6845db4d2a867f488b8ecb282dda85cedd0af05a
Author: aboelhamd <aboelhamd.abotreka@gmail.com>
Date:   Fri Jul 26 23:44:10 2019 +0200

    pyhthon script to load models and encoders, to predict new data

diff --git a/loadmodels.ipynb b/loadmodels.ipynb
new file mode 100644
index 0000000..0531b26
--- /dev/null
+++ b/loadmodels.ipynb
@@ -0,0 +1,15683 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "path='/home/aboelhamd/Downloads/sklearn-svm/sklearn-nobad'\n",
+    "files = {}\n",
+    "# r=root, d=directories, f=files\n",
+    "for r, d, f in os.walk(path):\n",
+    "    for file in f:\n",
+    "        files[file]=os.path.join(r, file)\n",
+    "\n",
+    "# os.mkdir('models')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "file name : 40+68_63+.csv\n",
+      "Rules(classes) number : 2\n",
+      "Words(features) number : 2\n",
+      "Records number : 276"
+     ]
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+       "0     0     0.5  haber   ser\n",
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
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+     "text": [
+      "model : NaiveBayes , score = 0.45654482962107334\n",
+      "model : LinearSVM , score = 0.4637975614023888\n",
+      "model : RBFSVM , score = 0.31829418475530613\n",
+      "model : DecisionTree , score = 0.3836062964213818\n",
+      "model : RandomForest , score = 0.38350158147992314\n",
+      "model : AdaBoost , score = 0.3617831020952113\n",
+      "----------------------------------------------\n",
+      "\n",
+      "file name : 27+21_106_29+10_30_106_29+10_110_29_106_29+11_30_106_29+11_110_29_106_29+33_1_30_106_29+33_1_110_29_106_29+.csv\n",
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+      "model : NaiveBayes , score = 0.06703474247602752\n",
+      "model : LinearSVM , score = 0.10093532974509636\n",
+      "model : RBFSVM , score = 0.11605573183596676\n",
+      "model : DecisionTree , score = 0.0\n",
+      "model : RandomForest , score = 0.0\n",
+      "model : AdaBoost , score = 0.033775670441412345\n",
+      "----------------------------------------------\n",
+      "\n",
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+      "model : RandomForest , score = 0.1712896936630942\n",
+      "model : AdaBoost , score = 0.1694033114534203\n",
+      "----------------------------------------------\n",
+      "\n",
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+      "----------------------------------------------\n",
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+      "model : LinearSVM , score = 0.05853263185872462\n",
+      "model : RBFSVM , score = 0.09487956768291234\n",
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+      "----------------------------------------------\n",
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+       "      <th>3</th>\n",
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+       "    <tr>\n",
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+       "    <tr>\n",
+       "      <th>5</th>\n",
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+       "      <td>a</td>\n",
+       "      <td>trabajar</td>\n",
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+       "    <tr>\n",
+       "      <th>6</th>\n",
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+       "      <td>0.066377</td>\n",
+       "      <td>ni_siquiera</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>empezar</td>\n",
+       "      <td>a</td>\n",
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+       "    <tr>\n",
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+       "    <tr>\n",
+       "      <th>8</th>\n",
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+       "      <td>ni_siquiera</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>empezar</td>\n",
+       "      <td>a</td>\n",
+       "      <td>trabajar</td>\n",
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+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.066377</td>\n",
+       "      <td>ni_siquiera</td>\n",
+       "      <td>haber</td>\n",
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+       "      <td>haber</td>\n",
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+      "model : AdaBoost , score = 0.06637753982652389\n",
+      "----------------------------------------------\n",
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+      "----------------------------------------------\n",
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+      "----------------------------------------------\n",
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+       "      <td>haber</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.058393</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
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+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.057981</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
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+       "      <td>haber</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.057981</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.058962</td>\n",
+       "      <td>haber</td>\n",
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+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
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+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.057254</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
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+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.058962</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.057597</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.058962</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.058962</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.059888</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
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+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0.058557</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
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+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0.059888</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>decir</td>\n",
+       "      <td>de</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>adoptar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0.058393</td>\n",
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+       "    <tr>\n",
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+       "    rule    weight  word1  word2 word3  word4    word5\n",
+       "0      0  0.058557  haber  decir    de  haber  adoptar\n",
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+       "16    16  0.059888  haber  decir    de  haber  adoptar"
+      ]
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+      "model : NaiveBayes , score = 0.05855728243281527\n",
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+      "model : RBFSVM , score = 0.0598877820336654\n",
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+      "model : RandomForest , score = 0.05839268248219526\n",
+      "model : AdaBoost , score = 0.0598877820336654\n",
+      "----------------------------------------------\n",
+      "\n",
+      "file name : 23_29+24_14+24_1_29+14_106_29_14+14_106_29_1_29+1_29_106_29_14+1_29_106_29_1_29+.csv\n",
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+      "  n_ij = - 0.5 * np.sum(np.log(2. * np.pi * self.sigma_[i, :]))\n",
+      "/home/aboelhamd/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:438: RuntimeWarning: invalid value encountered in true_divide\n",
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+      "model : NaiveBayes , score = 0.1438888561111439\n",
+      "model : LinearSVM , score = 0.14400785599214402\n",
+      "model : RBFSVM , score = 0.14400785599214402\n",
+      "model : DecisionTree , score = 0.14400785599214402\n",
+      "model : RandomForest , score = 0.14400785599214402\n",
+      "model : AdaBoost , score = 0.14400785599214402\n",
+      "----------------------------------------------\n",
+      "\n",
+      "file name : 10+11+33_1+.csv\n"
+     ]
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+      "/home/aboelhamd/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:436: RuntimeWarning: divide by zero encountered in log\n",
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+      "/home/aboelhamd/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:438: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  (self.sigma_[i, :]), 1)\n"
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+      "model : AdaBoost , score = 0.3321084830109897\n",
+      "----------------------------------------------\n",
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+      "model : AdaBoost , score = 0.07641758281833691\n",
+      "----------------------------------------------\n",
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+      "model : AdaBoost , score = 0.019361885843466525\n",
+      "----------------------------------------------\n",
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+      "----------------------------------------------\n",
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+      "  n_ij = - 0.5 * np.sum(np.log(2. * np.pi * self.sigma_[i, :]))\n",
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+      "----------------------------------------------\n",
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+      "model : AdaBoost , score = 0.1985863322341775\n",
+      "----------------------------------------------\n",
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+      "model : AdaBoost , score = 0.16665342755583534\n",
+      "----------------------------------------------\n",
+      "\n",
+      "file name : 94+97_108_113+108_95+108_98_108_113+108_33_96+108_33_99_108_113+108_33_113_108_1_108_113+.csv\n",
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+       "   rule    weight word1 word2  word3 word4  word5 word6  word7\n",
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+      "model : NaiveBayes , score = 0.07477421659088763\n",
+      "model : LinearSVM , score = 0.10496404562553276\n",
+      "model : RBFSVM , score = 0.15827958403992717\n",
+      "model : DecisionTree , score = 0.00769779337779194\n",
+      "model : RandomForest , score = 0.02123447408436888\n",
+      "model : AdaBoost , score = 0.06372186683106934\n",
+      "----------------------------------------------\n",
+      "\n",
+      "file name : 24+14_106_29+1_29_106_29+.csv\n",
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+       "   rule    weight  word1  word2 word3     word4\n",
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+      "model : NaiveBayes , score = 0.32481851581061016\n",
+      "model : LinearSVM , score = 0.3165909792196462\n",
+      "model : RBFSVM , score = 0.3488859231380752\n",
+      "model : DecisionTree , score = 0.2606002301328052\n",
+      "model : RandomForest , score = 0.20003356990093957\n",
+      "model : AdaBoost , score = 0.22003922441900595\n",
+      "----------------------------------------------\n",
+      "\n",
+      "file name : 60_63+47_40+47_68_63+.csv\n",
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+      ],
+      "text/plain": [
+       "   rule    weight word1  word2  word3 word4\n",
+       "0     0  0.314649    lo  poder  haber   ser\n",
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model : NaiveBayes , score = 0.3146486853513146\n",
+      "model : LinearSVM , score = 0.34267565732434274\n",
+      "model : RBFSVM , score = 0.34267565732434274\n",
+      "model : DecisionTree , score = 0.34267565732434274\n",
+      "model : RandomForest , score = 0.3146486853513146\n",
+      "model : AdaBoost , score = 0.34267565732434274\n",
+      "----------------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aboelhamd/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:436: RuntimeWarning: divide by zero encountered in log\n",
+      "  n_ij = - 0.5 * np.sum(np.log(2. * np.pi * self.sigma_[i, :]))\n",
+      "/home/aboelhamd/anaconda3/lib/python3.7/site-packages/sklearn/naive_bayes.py:438: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  (self.sigma_[i, :]), 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "\n",
+    "\n",
+    "for file in files:\n",
+    "    # These are the classifiers that permit training data with sample weights!\n",
+    "    names = [\"NaiveBayes\", \"LinearSVM\", \"RBFSVM\", \"DecisionTree\",\n",
+    "         \"RandomForest\", \"AdaBoost\"]\n",
+    "    \n",
+    "    print(\"file name :\", file)\n",
+    "    data = pd.read_csv(files[file], delimiter=r\"\\s+\", header=None).dropna()\n",
+    "    \n",
+    "    # words (features) encoding\n",
+    "    # load the encoder\n",
+    "    enc = joblib.load('models/'+'encoder'+'-'+file[:-4])\n",
+    "    # remove records with unseen word, will return always 0 for that record\n",
+    "    # this will be solved later\n",
+    "    \n",
+    "    \n",
+    "    features = enc.fit_transform(data.iloc[:,2:])\n",
+    "#     display(enc.categories_)\n",
+    "#     display(data.iloc[:,2:],features)\n",
+    "    # target and weights\n",
+    "    target = data.iloc[:,0]\n",
+    "    weights = data.iloc[:,1].values\n",
+    "    \n",
+    "#     print(\"file name :\", file)\n",
+    "    print(\"Rules(classes) number :\",target.nunique())\n",
+    "    print(\"Words(features) number :\",features.shape[1])\n",
+    "    print(\"Records number :\",features.shape[0], end = '')\n",
+    "    display(data.iloc[:target.nunique(),:])\n",
+    "    \n",
+    "    # split to train and test\n",
+    "    X_train, X_test, y_train, y_test, w_train, w_test = \\\n",
+    "        train_test_split(features, target, weights, test_size=.5, random_state=0, stratify=target)\n",
+    "#     display(features, target, weights)\n",
+    "#     display(X_train, X_test, y_train, y_test, w_train, w_test)\n",
+    "    \n",
+    "    # train models and print their scores\n",
+    "    for name in names:\n",
+    "        print(\"model :\", name, \",\", end = '')\n",
+    "        modelname = 'sklearn-models/'+name+'-'+file[:-4]+'.model'\n",
+    "        loaded_model = joblib.load(modelname)\n",
+    "        score = loaded_model.score(X=X_test, y=y_test, sample_weight=w_test)\n",
+    "        print(\" score =\", score)\n",
+    "        \n",
+    "        # save models\n",
+    "#         name+'-'+file[:-4]+'.model'\n",
+    "#         modelname = 'sklearn-models/'+name+'-'+file[:-4]+'.model'\n",
+    "#         joblib.dump(clf, filename)\n",
+    "    print(\"----------------------------------------------\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0],\n",
+       "       [1, 1]], dtype=int32)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# enc = OrdinalEncoder(dtype=np.int32)\n",
+    "# features = enc.fit_transform(data.iloc[:,2:])\n",
+    "enc.fit_transform([['a','b'],['c','d']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(['a','b']).issubset(enc.categories_[0])\n",
+    "# enc.transform([['lo','poder']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rule</td>\n",
+       "      <td>weight</td>\n",
+       "      <td>word1</td>\n",
+       "      <td>word2</td>\n",
+       "      <td>word3</td>\n",
+       "      <td>word4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.314649</td>\n",
+       "      <td>lo</td>\n",
+       "      <td>poder</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>ser</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.342676</td>\n",
+       "      <td>lo</td>\n",
+       "      <td>poder</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>ser</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.342676</td>\n",
+       "      <td>lo</td>\n",
+       "      <td>poder</td>\n",
+       "      <td>haber</td>\n",
+       "      <td>ser</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0         1      2      3      4      5\n",
+       "0  rule    weight  word1  word2  word3  word4\n",
+       "1     0  0.314649     lo  poder  haber    ser\n",
+       "2     1  0.342676     lo  poder  haber    ser\n",
+       "3     2  0.342676     lo  poder  haber    ser"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(files[file], delimiter=r\"\\s+\", header=None).dropna()\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file='60_63+47_40+47_68_63+.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}