model_training_RF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cde48c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "import joblib\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pickle\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eefb9d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.load(\"data_np/x.npy\",allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d38bf360",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.load(\"data_np/y.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e19b0e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b7c1232",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "[60347 rows x 63 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1250c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        J\n",
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       "4        J\n",
       "        ..\n",
       "60342    Z\n",
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       "60346    Z\n",
       "Length: 60347, dtype: object"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.Series(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c24fab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8ba9faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"SGD\":SGDClassifier(average = True),\n",
    "    \"KNN_3\":KNeighborsClassifier(3),\n",
    "    \"KNN_5\":KNeighborsClassifier(5),\n",
    "    \"KNN_7\":KNeighborsClassifier(7),\n",
    "    \"SVC\": SVC(),\n",
    "    \"RandomForest\": RandomForestClassifier(),\n",
    "    \"LogisticRegresion\": LogisticRegression()   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc9ab3f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************SGD************\n",
      "Accuracy: 0.9464788732394366, Precision: 0.9464788732394366, Recall: 0.9464788732394366, F1: 0.9464788732394366\n",
      "************KNN_3************\n",
      "Accuracy: 0.9851698425849212, Precision: 0.9851698425849212, Recall: 0.9851698425849212, F1: 0.9851698425849212\n",
      "************KNN_5************\n",
      "Accuracy: 0.980115990057995, Precision: 0.980115990057995, Recall: 0.980115990057995, F1: 0.980115990057995\n",
      "************KNN_7************\n",
      "Accuracy: 0.9735708367854184, Precision: 0.9735708367854184, Recall: 0.9735708367854184, F1: 0.9735708367854184\n",
      "************SVC************\n",
      "Accuracy: 0.976139188069594, Precision: 0.976139188069594, Recall: 0.976139188069594, F1: 0.976139188069594\n",
      "************RandomForest************\n",
      "Accuracy: 0.9829328914664457, Precision: 0.9829328914664457, Recall: 0.9829328914664457, F1: 0.9829328914664457\n",
      "************LogisticRegresion************\n",
      "Accuracy: 0.9688483844241922, Precision: 0.9688483844241922, Recall: 0.9688483844241922, F1: 0.9688483844241922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bikai\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "for model_tag, model in models.items():\n",
    "    print(f\"************{model_tag}************\")\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, average='micro')\n",
    "    recall = recall_score(y_test, y_pred, average='micro')\n",
    "    f1 = f1_score(y_test, y_pred, average='micro')\n",
    "    \n",
    "    print(f\"Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}, F1: {f1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a3cd4d",
   "metadata": {},
   "source": [
    "Guardo el modelo que mejores métricas ha tenido en una variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d72ff02f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:   57.3s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_features=&#x27;log2&#x27;, n_estimators=150, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=&#x27;log2&#x27;, n_estimators=150, verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_features='log2', n_estimators=150, verbose=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=150, verbose=1, max_features = \"log2\")\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c1a2e261",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.983678541839271"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "66f28a9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.6s finished\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a6b2d979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W', 'W', 'B', ..., 'A', 'W', 'C'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "74a0eb31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.983678541839271"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f40d1a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9838170515228605"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e941c978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.983678541839271"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "edf50259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9837021220032922"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f7674f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "conf = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c9bdba97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 132.0, 'Predicted')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x1224 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['A', 'B', 'C', 'D','E', 'F', 'G', 'H ', 'I', 'J', 'K','L' ,'M','N','O','P','Q','R','S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n",
    "plt.figure(figsize=(17,17))\n",
    "plt.title(\"Sign_language\")\n",
    "np.fill_diagonal(conf,0)\n",
    "sns.heatmap(conf,\n",
    "            annot=True, \n",
    "            xticklabels=labels, \n",
    "            yticklabels=labels,\n",
    "            cmap=\"Reds\")\n",
    "plt.ylabel(\"Ground_truth\")\n",
    "plt.xlabel(\"Predicted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0a9736af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 132.0, 'Predicted')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x1224 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "letters = ['A', 'B', 'C', 'D','E', 'F', 'G', 'H ', 'I', 'J', 'K','L' ,'M','N','O','P','Q','R','S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n",
    "aux=conf\n",
    "conf_norm= aux/aux.sum(axis=1)[:, np.newaxis]\n",
    "plt.figure(figsize=(17,17))\n",
    "plt.title(\"Sign_language\")\n",
    "np.fill_diagonal(conf,0)\n",
    "sns.heatmap(conf/np.sum(conf),\n",
    "            annot=True,\n",
    "            xticklabels=labels, \n",
    "            yticklabels=labels,\n",
    "            fmt='.2f',\n",
    "            cmap=\"Blues\")\n",
    "plt.ylabel(\"Ground_truth\")\n",
    "plt.xlabel(\"Predicted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782c1e3f",
   "metadata": {},
   "source": [
    "Guardo el modelo para poder probarlo con otro dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0c0bc63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RF_model.pkl','wb') as fw:\n",
    "    pickle.dump(rf,fw)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "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": "3.10.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "9ecfd48840a2dfbab88768852dcbcba3d4a3dd6e3302abfd80a63a47d87986f3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}