titanic_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
      "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
      "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
      "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
      "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
      "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
      "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
      "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
      "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
      "\n",
      "            Parch        Fare  \n",
      "count  891.000000  891.000000  \n",
      "mean     0.381594   32.204208  \n",
      "std      0.806057   49.693429  \n",
      "min      0.000000    0.000000  \n",
      "25%      0.000000    7.910400  \n",
      "50%      0.000000   14.454200  \n",
      "75%      0.000000   31.000000  \n",
      "max      6.000000  512.329200  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "titanic = pd.read_csv(\"d:\\BaiduNetdiskDownload/titanic_train.csv\")\n",
    "print(titanic.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
      "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
      "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
      "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
      "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
      "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
      "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
      "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
      "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
      "\n",
      "            Parch        Fare  \n",
      "count  891.000000  891.000000  \n",
      "mean     0.381594   32.204208  \n",
      "std      0.806057   49.693429  \n",
      "min      0.000000    0.000000  \n",
      "25%      0.000000    7.910400  \n",
      "50%      0.000000   14.454200  \n",
      "75%      0.000000   31.000000  \n",
      "max      6.000000  512.329200  \n"
     ]
    }
   ],
   "source": [
    "titanic['Age'] = titanic['Age'].fillna(titanic['Age'].mean())\n",
    "print(titanic.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['male' 'female']\n"
     ]
    }
   ],
   "source": [
    "print(titanic['Sex'].unique())#几种可能性\n",
    "titanic.loc[titanic['Sex']=='male','Sex'] = 0\n",
    "titanic.loc[titanic['Sex']=='female','Sex'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['S' 'C' 'Q' nan]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891\n"
     ]
    }
   ],
   "source": [
    "print(titanic['Embarked'].unique())\n",
    "plt.bar(np.arange(len(titanic['Embarked'].value_counts()))+0.5,titanic['Embarked'].value_counts())\n",
    "plt.xticks(np.arange(len(titanic['Embarked'].value_counts()))+0.5,titanic['Embarked'].value_counts().index)\n",
    "for x,y in zip(np.arange(len(titanic['Embarked'].value_counts()))+0.5,titanic['Embarked'].value_counts()):\n",
    "               plt.text(x,y,y,ha=\"center\", va=\"bottom\")\n",
    "plt.show()\n",
    "#把最多的填充给空白处\n",
    "titanic['Embarked'] = titanic['Embarked'].fillna('S')\n",
    "titanic.loc[titanic['Embarked']=='S','Embarked'] = 1\n",
    "titanic.loc[titanic['Embarked']=='C','Embarked'] = 2\n",
    "titanic.loc[titanic['Embarked']=='Q','Embarked'] = 3\n",
    "print(len(titanic['Embarked']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
      "0       3    0  22.0      1      0   7.2500         1\n",
      "1       1    1  38.0      1      0  71.2833         2\n",
      "2       3    1  26.0      0      0   7.9250         1\n",
      "3       1    1  35.0      1      0  53.1000         1\n",
      "4       3    0  35.0      0      0   8.0500         1\n"
     ]
    }
   ],
   "source": [
    "#进行学习\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import KFold\n",
    "predictors = titanic.drop(['PassengerId','Survived','Name','Ticket','Cabin'],axis=1)\n",
    "x = predictors\n",
    "print(x.head())\n",
    "y = titanic['Survived']\n",
    "#print(predictors.head()),还剩6个特征量\n",
    "lr = LinearRegression()\n",
    "kf=KFold(n_splits=3,random_state=1)\n",
    "kf.get_n_splits(x)#交叉验证的倍数\n",
    "predictions = []\n",
    "#for train_index, test_index in kf.split(x):\n",
    " #   x_train, x_test = x[train_index], x[test_index]\n",
    "  #  y_train, y_test = y[train_index], y[test_index]\n",
    "   # lr.fit(x_train,y_train)\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "534 179 178\n"
     ]
    }
   ],
   "source": [
    "#进行学习\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "predictors = titanic.drop(['PassengerId','Survived','Name','Ticket','Cabin'],axis=1)\n",
    "x = predictors\n",
    "y = titanic['Survived']\n",
    "lr = LinearRegression()\n",
    "x_tt,x_validation,y_tt,y_validation = train_test_split(x,y,test_size = 0.2)\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_tt,y_tt,test_size = 0.25)\n",
    "print(len(x_train),len(x_validation),len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coef:拟合参数 [-1.73404235e-01  4.99472800e-01 -5.86896720e-03 -4.16160575e-02\n",
      " -1.40861513e-02  3.46106921e-04  3.94070202e-02]\n",
      "MSE: 0.142453316769581\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "features= x\n",
    "label = y\n",
    "regr=LinearRegression()\n",
    "regr=Ridge(alpha=1)\n",
    "regr=Lasso(alpha=0.001)\n",
    "regr.fit(features.values,label.values)\n",
    "y_pred=regr.predict(features.values)\n",
    "print(\"Coef:拟合参数\",regr.coef_)\n",
    "from sklearn.metrics import mean_squared_error#判断好坏\n",
    "print(\"MSE:\",mean_squared_error(y_pred,label.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACC: 0.7877094972067039\n",
      "REC: 0.6805555555555556\n",
      "F-Score: 0.7205882352941176\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lgr = LogisticRegression(solver=\"sag\",max_iter=10000)\n",
    "#可见这个不是单纯的线性可以解决，需要高维解决\n",
    "lgr.fit(x_train,y_train)\n",
    "y_pred = lgr.predict(x_validation)\n",
    "print(\"ACC:\",accuracy_score(y_validation,y_pred))\n",
    "print(\"REC:\",recall_score(y_validation,y_pred))\n",
    "print(\"F-Score:\",f1_score(y_validation,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest:\n",
      "ACC: 0.8156424581005587\n",
      "REC: 0.7638888888888888\n",
      "F-Score: 0.7692307692307692\n",
      "decide tree:\n",
      "ACC: 0.7821229050279329\n",
      "REC: 0.7083333333333334\n",
      "F-Score: 0.723404255319149\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "rfc_clf = RandomForestClassifier(n_estimators=10,min_samples_split=4,\\\n",
    "                                 min_samples_leaf=2,max_features=None)\n",
    "rfc_clf.fit(x_train,y_train)\n",
    "y_pred = rfc_clf.predict(x_validation)\n",
    "print(\"random forest:\\nACC:\",accuracy_score(y_validation,y_pred))\n",
    "print(\"REC:\",recall_score(y_validation,y_pred))\n",
    "print(\"F-Score:\",f1_score(y_validation,y_pred))\n",
    "dtc_clf = DecisionTreeClassifier()\n",
    "dtc_clf.fit(x_train,y_train)\n",
    "y_pred = dtc_clf.predict(x_validation)\n",
    "print(\"decide tree:\\nACC:\",accuracy_score(y_validation,y_pred))\n",
    "print(\"REC:\",recall_score(y_validation,y_pred))\n",
    "print(\"F-Score:\",f1_score(y_validation,y_pred))#在不知道哪个更好的时候就先带入随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mr          517\n",
      "Miss        182\n",
      "Mrs         125\n",
      "Master       40\n",
      "Dr            7\n",
      "Rev           6\n",
      "Major         2\n",
      "Mlle          2\n",
      "Col           2\n",
      "Jonkheer      1\n",
      "Countess      1\n",
      "Mme           1\n",
      "Sir           1\n",
      "Ms            1\n",
      "Lady          1\n",
      "Capt          1\n",
      "Don           1\n",
      "Name: Name, dtype: int64\n",
      "1     517\n",
      "2     183\n",
      "3     125\n",
      "4      40\n",
      "5       7\n",
      "6       6\n",
      "7       5\n",
      "10      3\n",
      "8       3\n",
      "9       2\n",
      "Name: Name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#由于准确率过低，考虑增加特征\n",
    "import re\n",
    "titanic['FamilySize']=titanic['SibSp']+titanic['Parch']\n",
    "titanic['NameLength']=titanic['Name'].apply(lambda x:len(x))\n",
    "\n",
    "def get_title(name):\n",
    "     # Use a regular expression to search for a title.  Titles always consist of capital and lowercase letters, and end with a period.\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "# Get all the titles and print how often each one occurs.\n",
    "titles = titanic[\"Name\"].apply(get_title)\n",
    "print(pd.value_counts(titles))\n",
    "\n",
    "# Map each title to an integer.  Some titles are very rare, and are compressed into the same codes as other titles.\n",
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Dr\": 5, \"Rev\": 6, \"Major\": 7, \"Col\": 7, \"Mlle\": 8, \"Mme\": 8, \"Don\": 9, \"Lady\": 10, \"Countess\": 10, \"Jonkheer\": 10, \"Sir\": 9, \"Capt\": 7, \"Ms\": 2}\n",
    "for k,v in title_mapping.items():\n",
    "    titles[titles == k] = v\n",
    "\n",
    "# Verify that we converted everything.\n",
    "print(pd.value_counts(titles))\n",
    "\n",
    "# Add in the title column.\n",
    "titanic[\"Title\"] = titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_selection import SelectKBest, f_classif #choose best\n",
    "import matplotlib.pyplot as plt\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"FamilySize\", \"Title\", \"NameLength\"]\n",
    "\n",
    "# Perform feature selection\n",
    "selector = SelectKBest(f_classif, k=5)\n",
    "selector.fit(titanic[predictors], titanic[\"Survived\"])\n",
    "\n",
    "# Get the raw p-values for each feature, and transform from p-values into scores\n",
    "scores = -np.log10(selector.pvalues_)\n",
    "\n",
    "# Plot the scores.  See how \"Pclass\", \"Sex\", \"Title\", and \"Fare\" are the best?\n",
    "plt.bar(range(len(predictors)), scores)\n",
    "plt.xticks(range(len(predictors)), predictors, rotation='vertical')\n",
    "plt.show()\n",
    "\n",
    "# Pick only the four best features.\n",
    "predictors = [\"Pclass\", \"Sex\", \"Fare\", \"Title\"]\n",
    "\n",
    "alg = RandomForestClassifier(random_state=1, n_estimators=50, min_samples_split=8, min_samples_leaf=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [7, 891]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-121d73da9a31>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtitanic\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Survived'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mlr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mx_tt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_validation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_tt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_tt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_tt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_validation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[1;34m(*arrays, **options)\u001b[0m\n\u001b[0;32m   2094\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid parameters passed: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2095\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2096\u001b[1;33m     \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2097\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2098\u001b[0m     \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mindexable\u001b[1;34m(*iterables)\u001b[0m\n\u001b[0;32m    228\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    229\u001b[0m             \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 230\u001b[1;33m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    231\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    203\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    204\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[1;32m--> 205\u001b[1;33m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[0;32m    206\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [7, 891]"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import numpy as np\n",
    "\n",
    "# The algorithms we want to ensemble.\n",
    "# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.\n",
    "algorithms = [\n",
    "    [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\", \"FamilySize\", \"Title\",]],\n",
    "    [LogisticRegression(random_state=1), [\"Pclass\", \"Sex\", \"Fare\", \"FamilySize\", \"Title\", \"Age\", \"Embarked\"]]\n",
    "]\n",
    "from sklearn.model_selection import train_test_split\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\",\"Fare\",  \"FamilySize\", \"Title\", \"NameLength\"]\n",
    "x = predictors\n",
    "y = titanic['Survived']\n",
    "lr = LinearRegression()\n",
    "x_tt,x_validation,y_tt,y_validation = train_test_split(x,y,test_size = 0.2)\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_tt,y_tt,test_size = 0.25)\n",
    "print(len(x_train),len(x_validation),len(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got multiple values for argument 'n_splits'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-101-f3136c81b14b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# Initialize the cross validation folds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mkf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitanic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[0mkf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_n_splits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitanic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() got multiple values for argument 'n_splits'"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import numpy as np\n",
    "\n",
    "# The algorithms we want to ensemble.\n",
    "# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.\n",
    "algorithms = [\n",
    "    [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\", \"FamilySize\", \"Title\",]],\n",
    "    [LogisticRegression(random_state=1), [\"Pclass\", \"Sex\", \"Fare\", \"FamilySize\", \"Title\", \"Age\", \"Embarked\"]]\n",
    "]\n",
    "\n",
    "# Initialize the cross validation folds\n",
    "kf = KFold(titanic.shape[0], n_splits=3, random_state=1)\n",
    "kf.get_n_splits(titanic.shape[0])\n",
    "predictions = []\n",
    "for train, test in kf.split(titanic.shape[0]):\n",
    "    train_target = titanic[\"Survived\"].iloc[train]\n",
    "    full_test_predictions = []\n",
    "    # Make predictions for each algorithm on each fold\n",
    "    for alg, predictors in algorithms:\n",
    "        # Fit the algorithm on the training data.\n",
    "        alg.fit(titanic[predictors].iloc[train,:], train_target)\n",
    "        # Select and predict on the test fold.  \n",
    "        # The .astype(float) is necessary to convert the dataframe to all floats and avoid an sklearn error.\n",
    "        test_predictions = alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]\n",
    "        full_test_predictions.append(test_predictions)\n",
    "    # Use a simple ensembling scheme -- just average the predictions to get the final classification.\n",
    "    test_predictions = (full_test_predictions[0] + full_test_predictions[1]) / 2\n",
    "    # Any value over .5 is assumed to be a 1 prediction, and below .5 is a 0 prediction.\n",
    "    test_predictions[test_predictions <= .5] = 0\n",
    "    test_predictions[test_predictions > .5] = 1\n",
    "    predictions.append(test_predictions)\n",
    "\n",
    "# Put all the predictions together into one array.\n",
    "predictions = np.concatenate(predictions, axis=0)\n",
    "\n",
    "# Compute accuracy by comparing to the training data.\n",
    "accuracy = sum(predictions[predictions == titanic[\"Survived\"]]) / len(predictions)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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