3 copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>D1</th>\n",
       "      <th>D4</th>\n",
       "      <th>D6</th>\n",
       "      <th>M1</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.92575</td>\n",
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       "      <td>9</td>\n",
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       "      <td>-0.9</td>\n",
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       "      <td>1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.030911</td>\n",
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       "      <td>35</td>\n",
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      "text/plain": [
       "        F1       F2  F3   D1        D4       D6  M1\n",
       "0 -0.92575 -0.34309   1  0.0  0.189970  0.66667   9\n",
       "1 -0.92575 -0.34309   1 -0.8 -1.000000 -0.66667  67\n",
       "2 -0.90100 -0.46830   1 -0.7 -0.349560 -0.66667  10\n",
       "3 -0.86940 -0.57487  -1 -0.9 -0.719870 -0.83333  36\n",
       "4 -0.93839 -0.25525   1 -0.4 -0.030911 -0.33333  35"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"./x_train_money_after_pca.csv\", names=['F1','F2','F3','D1','D4','D6','M1'])\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y_train=np.array(train['M1'])\n",
    "x_train=train.drop(['M1'], axis=1)\n",
    "x_train=np.array(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size = 0.2, random_state = 1234) # 학습데이터와 평가데이터의 비율을 8:2 로 분할| "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import accuracy_score # 정확도 함수\n",
    "\n",
    "clf = SVR().fit(x_train,y_train)\n",
    "predict1 = clf.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [50766, 18392]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\hooki\\Documents\\GitHub\\AutoCrawler\\3 copy.ipynb Cell 6'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/hooki/Documents/GitHub/AutoCrawler/3%20copy.ipynb#ch0000013?line=0'>1</a>\u001b[0m \u001b[39mprint\u001b[39m(accuracy_score(y_test,predict1))\n",
      "File \u001b[1;32mc:\\Users\\hooki\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:211\u001b[0m, in \u001b[0;36maccuracy_score\u001b[1;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[0;32m    145\u001b[0m \u001b[39m\"\"\"Accuracy classification score.\u001b[39;00m\n\u001b[0;32m    146\u001b[0m \n\u001b[0;32m    147\u001b[0m \u001b[39mIn multilabel classification, this function computes subset accuracy:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[39m0.5\u001b[39;00m\n\u001b[0;32m    208\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    210\u001b[0m \u001b[39m# Compute accuracy for each possible representation\u001b[39;00m\n\u001b[1;32m--> 211\u001b[0m y_type, y_true, y_pred \u001b[39m=\u001b[39m _check_targets(y_true, y_pred)\n\u001b[0;32m    212\u001b[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001b[0;32m    213\u001b[0m \u001b[39mif\u001b[39;00m y_type\u001b[39m.\u001b[39mstartswith(\u001b[39m\"\u001b[39m\u001b[39mmultilabel\u001b[39m\u001b[39m\"\u001b[39m):\n",
      "File \u001b[1;32mc:\\Users\\hooki\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:84\u001b[0m, in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_targets\u001b[39m(y_true, y_pred):\n\u001b[0;32m     58\u001b[0m     \u001b[39m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[0;32m     59\u001b[0m \n\u001b[0;32m     60\u001b[0m \u001b[39m    This converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[39m    y_pred : array or indicator matrix\u001b[39;00m\n\u001b[0;32m     83\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 84\u001b[0m     check_consistent_length(y_true, y_pred)\n\u001b[0;32m     85\u001b[0m     type_true \u001b[39m=\u001b[39m type_of_target(y_true)\n\u001b[0;32m     86\u001b[0m     type_pred \u001b[39m=\u001b[39m type_of_target(y_pred)\n",
      "File \u001b[1;32mc:\\Users\\hooki\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:332\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    330\u001b[0m uniques \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(lengths)\n\u001b[0;32m    331\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(uniques) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 332\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    333\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m    334\u001b[0m         \u001b[39m%\u001b[39m [\u001b[39mint\u001b[39m(l) \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m lengths]\n\u001b[0;32m    335\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [50766, 18392]"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,predict1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits=5, shuffle = True, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(clf , x_train , y_train ,cv=kfold)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>D1</th>\n",
       "      <th>D4</th>\n",
       "      <th>D6</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.71301</td>\n",
       "      <td>-0.79765</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.19115</td>\n",
       "      <td>-0.94596</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-1.00000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.65350</td>\n",
       "      <td>-0.83488</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-1.00000</td>\n",
       "      <td>-0.50000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.90100</td>\n",
       "      <td>-0.46830</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>-0.86414</td>\n",
       "      <td>-0.58888</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.9</td>\n",
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      ],
      "text/plain": [
       "        F1       F2  F3   D1       D4       D6\n",
       "0 -0.71301 -0.79765  -1 -0.8 -1.00000 -0.66667\n",
       "1 -0.19115 -0.94596  -1 -0.7 -1.00000 -0.66667\n",
       "2 -0.65350 -0.83488  -1 -0.7 -1.00000 -0.50000\n",
       "3 -0.90100 -0.46830   1 -0.7 -0.71987 -0.66667\n",
       "4 -0.86414 -0.58888  -1 -0.9 -0.71987 -0.83333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv(\"./x_test_money_after_pca.csv\", names=['F1','F2','F3','D1','D4','D6'])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hooki\\anaconda3\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but SVR was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_test = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_o = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_o.to_csv(\"C:/1234/y_test_out_money_pca_logistc_regression.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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