kaggle/competitions/getting-started/titanic/introduction-to-EnsemblingStacking-in-Python.ipynb

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    "# Introduction\n",
    "\n",
    "This notebook is a very basic and simple introductory primer to the method of ensembling (combining) base learning models, in particular the variant of ensembling known as Stacking. In a nutshell stacking uses as a first-level (base), the predictions of a few basic classifiers and then uses another model at the second-level to predict the output from the earlier first-level predictions.\n",
    "\n",
    "The Titanic dataset is a prime candidate for introducing this concept as many newcomers to Kaggle start out here. Furthermore even though stacking has been responsible for many a team winning Kaggle competitions there seems to be a dearth of kernels on this topic so I hope this notebook can fill somewhat of that void.\n",
    "\n",
    "I myself am quite a newcomer to the Kaggle scene as well and the first proper ensembling/stacking script that I managed to chance upon and study was one written in the AllState Severity Claims competition by the great Faron. The material in this notebook borrows heavily from Faron's script although ported to factor in ensembles of classifiers whilst his was ensembles of regressors. Anyway please check out his script here:\n",
    "\n",
    "[Stacking Starter][1] : by Faron \n",
    "\n",
    "\n",
    "Now onto the notebook at hand and I hope that it manages to do justice and convey the concept of ensembling in an intuitive and concise manner.  My other standalone Kaggle [script][2] which implements exactly the same ensembling steps (albeit with different parameters) discussed below gives a Public LB score of 0.808 which is good enough to get to the top 9% and runs just under 4 minutes. Therefore I am pretty sure there is a lot of room to improve and add on to that script. Anyways please feel free to leave me any comments with regards to how I can improve\n",
    "\n",
    "\n",
    "  [1]: https://www.kaggle.com/mmueller/allstate-claims-severity/stacking-starter/run/390867\n",
    "  [2]: https://www.kaggle.com/arthurtok/titanic/simple-stacking-with-xgboost-0-808"
   ]
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   "source": [
    "# Load in our libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import sklearn\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#plotly库Anaconda并未包含 需要自行下载\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Going to use these 5 base models for the stacking\n",
    "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n",
    "                              GradientBoostingClassifier, ExtraTreesClassifier)\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cross_validation import KFold"
   ]
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   "source": [
    "# Feature Exploration, Engineering and Cleaning \n",
    "\n",
    "Now we will proceed much like how most kernels in general are structured, and that is to first explore the data on hand, identify possible feature engineering opportunities as well as numerically encode any categorical features."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  "
      ]
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   ],
   "source": [
    "# Load in the train and test datasets\n",
    "#改为你的数据文件的相对路径\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "\n",
    "# Store our passenger ID for easy access\n",
    "PassengerId = test['PassengerId']\n",
    "\n",
    "train.head(3)"
   ]
  },
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   "source": [
    "Well it is no surprise that our task is to somehow extract the information out of the categorical variables \n",
    "\n",
    "**Feature Engineering**\n",
    "\n",
    "Here, credit must be extended to Sina's very comprehensive and well-thought out notebook for the feature engineering ideas so please check out his work \n",
    "\n",
    "[Titanic Best Working Classfier][1] : by Sina\n",
    "\n",
    "\n",
    "  [1]: https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier"
   ]
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    "full_data = [train, test]\n",
    "\n",
    "# Some features of my own that I have added in\n",
    "# Gives the length of the name\n",
    "train['Name_length'] = train['Name'].apply(len)\n",
    "test['Name_length'] = test['Name'].apply(len)\n",
    "# Feature that tells whether a passenger had a cabin on the Titanic\n",
    "train['Has_Cabin'] = train[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n",
    "test['Has_Cabin'] = test[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n",
    "\n",
    "# Feature engineering steps taken from Sina\n",
    "# Create new feature FamilySize as a combination of SibSp and Parch\n",
    "for dataset in full_data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "# Create new feature IsAlone from FamilySize\n",
    "for dataset in full_data:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "# Remove all NULLS in the Embarked column\n",
    "for dataset in full_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n",
    "# Remove all NULLS in the Fare column and create a new feature CategoricalFare\n",
    "for dataset in full_data:\n",
    "    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())\n",
    "train['CategoricalFare'] = pd.qcut(train['Fare'], 4)\n",
    "# Create a New feature CategoricalAge\n",
    "for dataset in full_data:\n",
    "    age_avg = dataset['Age'].mean()\n",
    "    age_std = dataset['Age'].std()\n",
    "    age_null_count = dataset['Age'].isnull().sum()\n",
    "    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n",
    "    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "train['CategoricalAge'] = pd.cut(train['Age'], 5)\n",
    "# Define function to extract titles from passenger names\n",
    "def get_title(name):\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",
    "# Create a new feature Title, containing the titles of passenger names\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Name'].apply(get_title)\n",
    "# Group all non-common titles into one single grouping \"Rare\"\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "for dataset in full_data:\n",
    "    # Mapping Sex\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "    \n",
    "    # Mapping titles\n",
    "    title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "    \n",
    "    # Mapping Embarked\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "    \n",
    "    # Mapping Fare\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] \t\t\t\t\t\t        = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] \t\t\t\t\t\t\t        = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    \n",
    "    # Mapping Age\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] \t\t\t\t\t       = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4 ;"
   ]
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   "source": [
    "# Feature selection\n",
    "drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\n",
    "train = train.drop(drop_elements, axis = 1)\n",
    "train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)\n",
    "test  = test.drop(drop_elements, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e9814168-b7cd-d4e4-1b1d-e21c6637a663",
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   "source": [
    "All right so now having cleaned the features and extracted relevant information and dropped the categorical columns our features should now all be numeric, a format suitable to feed into our Machine Learning models. However before we proceed let us generate some simple correlation and distribution plots of our transformed dataset to observe ho\n",
    "\n",
    "## Visualisations "
   ]
  },
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Parch</th>\n",
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       "      <th>Name_length</th>\n",
       "      <th>Has_Cabin</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Title</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Parch  Fare  Embarked  Name_length  Has_Cabin  \\\n",
       "0         0       3    1    1      0     0         0           23          0   \n",
       "1         1       1    0    2      0     3         1           51          1   \n",
       "2         1       3    0    1      0     1         0           22          0   \n",
       "\n",
       "   FamilySize  IsAlone  Title  \n",
       "0           2        0      1  \n",
       "1           2        0      3  \n",
       "2           1        1      2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.head(3)"
   ]
  },
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    "_uuid": "3544b03040a1d691f6c48433c84d1e57f3c15e3d"
   },
   "source": [
    "**Pearson Correlation Heatmap**\n",
    "\n",
    "let us generate some correlation plots of the features to see how related one feature is to the next. To do so, we will utilise the Seaborn plotting package which allows us to plot heatmaps very conveniently as follows"
   ]
  },
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cyDJ5QNyhI0SVyt6VL6ZMKeIOHc5S5jCR7i5hZSPC8Pf3w8/Pj5s6tmT91j8z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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207ee17a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.RdBu\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0, \n",
    "            square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ccc92745-0680-df4d-d709-10003475d8e3",
    "_uuid": "b6c7a1ea3866d177016a1e13c5d9e7341c49c147"
   },
   "source": [
    "**Takeaway from the Plots**\n",
    "\n",
    "One thing that that the Pearson Correlation plot can tell us is that there are not too many features strongly correlated with one another. This is good from a point of view of feeding these features into your learning model because this means that there isn't much redundant or superfluous data in our training set and we are happy that each feature carries with it some unique information. Here are two most correlated features are that of Family size and Parch (Parents and Children). I'll still leave both features in for the purposes of this exercise.\n",
    "\n",
    "**Pairplots**\n",
    "\n",
    "Finally let us generate some pairplots to observe the distribution of data from one feature to the other. Once again we use Seaborn to help us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "ea6b0a8f-5a33-666f-8057-c0d689f370f5",
    "_execution_state": "idle",
    "_uuid": "624446543aafd518025fd3f5346d32ee1aab6f9a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x207ee17a668>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xgvPOW/1dnD/7M3dltS1b1l96ulf3im633VZ4UwEAANAwvn8MNDVjzAeMMQ8b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\nSK2okyMiIiIiIrWiTo6IiIiIiNTK/wfpufb/YRKxWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207ee17a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(train[[u'Survived', u'Pclass', u'Sex', u'Age', u'Parch', u'Fare', u'Embarked',\n",
    "       u'FamilySize', u'Title']], hue='Survived', palette = 'seismic',size=1.2,diag_kind = 'kde',diag_kws=dict(shade=True),plot_kws=dict(s=10) )\n",
    "g.set(xticklabels=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "32ac3ce1-42bc-9a7d-44f3-4b7c025a921c",
    "_uuid": "b74f445f5d9fb9622c4609c00679254abb3c91b1"
   },
   "source": [
    "# Ensembling & Stacking models\n",
    "\n",
    "Finally after that brief whirlwind detour with regards to feature engineering and formatting, we finally arrive at the meat and gist of the this notebook.\n",
    "\n",
    "Creating a Stacking ensemble!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3e922821-5a10-040b-305a-c2d47d633c49",
    "_uuid": "4e5fda8c6c92fc3cdf8e906089494f80e7c37245"
   },
   "source": [
    "### Helpers via Python Classes\n",
    "\n",
    "Here we invoke the use of Python's classes to help make it more convenient for us. For any newcomers to programming, one normally hears Classes being used in conjunction with Object-Oriented Programming (OOP). In short, a class helps to extend some code/program for creating objects (variables for old-school peeps) as well as to implement functions and methods specific to that class.\n",
    "\n",
    "In the section of code below, we essentially write a class *SklearnHelper* that allows one to extend the inbuilt methods (such as train, predict and fit) common to all the Sklearn classifiers. Therefore this cuts out redundancy as  won't need to write the same methods five times if we wanted to invoke five different classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "c017c078-172d-16e9-65f2-4a01c6e0626f",
    "_uuid": "04d921ea89a0560cf010e956e4065bb2eaf21619",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Some useful parameters which will come in handy later on\n",
    "ntrain = train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "SEED = 0 # for reproducibility\n",
    "NFOLDS = 5 # set folds for out-of-fold prediction\n",
    "kf = KFold(ntrain, n_folds= NFOLDS, random_state=SEED)\n",
    "\n",
    "# Class to extend the Sklearn classifier\n",
    "class SklearnHelper(object):\n",
    "    def __init__(self, clf, seed=0, params=None):\n",
    "        params['random_state'] = seed\n",
    "        self.clf = clf(**params)\n",
    "\n",
    "    def train(self, x_train, y_train):\n",
    "        self.clf.fit(x_train, y_train)\n",
    "\n",
    "    def predict(self, x):\n",
    "        return self.clf.predict(x)\n",
    "    \n",
    "    def fit(self,x,y):\n",
    "        return self.clf.fit(x,y)\n",
    "    \n",
    "    def feature_importances(self,x,y):\n",
    "        print(self.clf.fit(x,y).feature_importances_)\n",
    "    \n",
    "# Class to extend XGboost classifer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "75f59fa7-65a4-e53e-95df-4c747a608408",
    "_uuid": "634199a5b36cba6b4d85db2c0401426a58b95279"
   },
   "source": [
    "Bear with me for those who already know this but for people who have not created classes or objects in Python before, let me explain what the code given above does. In creating my base classifiers, I will only use the models already present in the Sklearn library and therefore only extend the class for that.\n",
    "\n",
    "**def init** : Python standard for invoking the default constructor for the class. This means that when you want to create an object (classifier), you have to give it the parameters of clf (what sklearn classifier you want), seed (random seed) and params (parameters for the classifiers).\n",
    "\n",
    "The rest of the code are simply methods of the class which simply call the corresponding methods already existing within the sklearn classifiers. Essentially, we have created a wrapper class to extend the various Sklearn classifiers so that this should help us reduce having to write the same code over and over when we implement multiple learners to our stacker."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6f67620d-b531-a2fa-c297-e951970c3c28",
    "_uuid": "4d193c581df258e823aff2796bf015cf906aac99"
   },
   "source": [
    "### Out-of-Fold Predictions\n",
    "\n",
    "Now as alluded to above in the introductory section, stacking uses predictions of base classifiers as input for training to a second-level model. However one cannot simply train the base models on the full training data, generate predictions on the full test set and then output these for the second-level training. This runs the risk of your base model predictions already having \"seen\" the test set and therefore overfitting when feeding these predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "406d0494-1d0c-3126-19d9-bc53127c4249",
    "_uuid": "46a93dc062e973832cecd50246d0d7581aafb02b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_oof(clf, x_train, y_train, x_test):\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "\n",
    "    for i, (train_index, test_index) in enumerate(kf):\n",
    "        x_tr = x_train[train_index]\n",
    "        y_tr = y_train[train_index]\n",
    "        x_te = x_train[test_index]\n",
    "\n",
    "        clf.train(x_tr, y_tr)\n",
    "\n",
    "        oof_train[test_index] = clf.predict(x_te)\n",
    "        oof_test_skf[i, :] = clf.predict(x_test)\n",
    "\n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3cd92196-f7ba-4f14-0fc4-36520fbcb2ca",
    "_uuid": "7b1a7767ae61b6b217a3311e89190b05ab0a4891"
   },
   "source": [
    "# Generating our Base First-Level Models \n",
    "\n",
    "So now let us prepare five learning models as our first level classification. These models can all be conveniently invoked via the Sklearn library and are listed as follows:\n",
    "\n",
    " 1. Random Forest classifier\n",
    " 2. Extra Trees classifier\n",
    " 3. AdaBoost classifer\n",
    " 4. Gradient Boosting classifer\n",
    " 5. Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0ef6862a-b5cc-6829-f040-d2b2b2c817f3",
    "_uuid": "12e3a5f76fb118ff6906431fc60e7010e33106ad"
   },
   "source": [
    "**Parameters**\n",
    "\n",
    "Just a quick summary of the parameters that we will be listing here for completeness,\n",
    "\n",
    "**n_jobs** : Number of cores used for the training process. If set to -1, all cores are used.\n",
    "\n",
    "**n_estimators** : Number of classification trees in your learning model ( set to 10 per default)\n",
    "\n",
    "**max_depth** : Maximum depth of tree, or how much a node should be expanded. Beware if set to too high  a number would run the risk of overfitting as one would be growing the tree too deep\n",
    "\n",
    "**verbose** : Controls whether you want to output any text during the learning process. A value of 0 suppresses all text while a value of 3 outputs the tree learning process at every iteration.\n",
    "\n",
    " Please check out the full description via the official Sklearn website. There you will find that there are a whole host of other useful parameters that you can play around with. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "6e634aba-90b6-0620-eceb-3e1a39fbfedc",
    "_uuid": "d77772886c0125e022d1fbb39cd484c95121d74d",
    "collapsed": true
   },
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   "source": [
    "# Put in our parameters for said classifiers\n",
    "# Random Forest parameters\n",
    "rf_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators': 500,\n",
    "     'warm_start': True, \n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 6,\n",
    "    'min_samples_leaf': 2,\n",
    "    'max_features' : 'sqrt',\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# Extra Trees Parameters\n",
    "et_params = {\n",
    "    'n_jobs': -1,\n",
    "    'n_estimators':500,\n",
    "    #'max_features': 0.5,\n",
    "    'max_depth': 8,\n",
    "    'min_samples_leaf': 2,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# AdaBoost parameters\n",
    "ada_params = {\n",
    "    'n_estimators': 500,\n",
    "    'learning_rate' : 0.75\n",
    "}\n",
    "\n",
    "# Gradient Boosting parameters\n",
    "gb_params = {\n",
    "    'n_estimators': 500,\n",
    "     #'max_features': 0.2,\n",
    "    'max_depth': 5,\n",
    "    'min_samples_leaf': 2,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "# Support Vector Classifier parameters \n",
    "svc_params = {\n",
    "    'kernel' : 'linear',\n",
    "    'C' : 0.025\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ee6325d7-a7c1-c767-fcba-4c59eaa83787",
    "_uuid": "4a49a6c0cbe7028035b6efb7f9374084f3fa152a"
   },
   "source": [
    "Furthermore, since having mentioned about Objects and classes within the OOP framework, let us now create 5 objects that represent our 5 learning models via our Helper Sklearn Class we defined earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "6798243f-5ff9-527b-01b1-09cfe62284bb",
    "_uuid": "3bd54b4dfbbedc86d6c415cf83936bb8cd6c0973",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create 5 objects that represent our 4 models\n",
    "rf = SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=rf_params)\n",
    "et = SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=et_params)\n",
    "ada = SklearnHelper(clf=AdaBoostClassifier, seed=SEED, params=ada_params)\n",
    "gb = SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=gb_params)\n",
    "svc = SklearnHelper(clf=SVC, seed=SEED, params=svc_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "46e1e6fc-92e2-e7e2-1ab9-470e6c7039ce",
    "_uuid": "cd3f527543e61b3841f242b744c3c5d9af608941"
   },
   "source": [
    "**Creating NumPy arrays out of our train and test sets**\n",
    "\n",
    "Great. Having prepared our first layer base models as such, we can now ready the training and test test data for input into our classifiers by generating NumPy arrays out of their original dataframes as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "968cbd7f-80b2-7f8d-2ad6-b68b3aeae671",
    "_uuid": "95fdd1e8cd9f23cfcef3bed92511da084a323c55",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create Numpy arrays of train, test and target ( Survived) dataframes to feed into our models\n",
    "y_train = train['Survived'].ravel()\n",
    "train = train.drop(['Survived'], axis=1)\n",
    "x_train = train.values # Creates an array of the train data\n",
    "x_test = test.values # Creats an array of the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "606b619c-2301-8aaa-a5de-781d981c4a6f",
    "_uuid": "d30ac9528b69cdaec565b647fee98a92391112c9"
   },
   "source": [
    "**Output of the First level Predictions** \n",
    "\n",
    "We now feed the training and test data into our 5 base classifiers and use the Out-of-Fold prediction function we defined earlier to generate our first level predictions. Allow a handful of minutes for the chunk of code below to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "79bd2a86-82e2-648a-e816-9660e89794ad",
    "_uuid": "114750e2d5e4fdd234ccd8647fc349463a56fa09"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is complete\n"
     ]
    }
   ],
   "source": [
    "# Create our OOF train and test predictions. These base results will be used as new features\n",
    "et_oof_train, et_oof_test = get_oof(et, x_train, y_train, x_test) # Extra Trees\n",
    "rf_oof_train, rf_oof_test = get_oof(rf,x_train, y_train, x_test) # Random Forest\n",
    "ada_oof_train, ada_oof_test = get_oof(ada, x_train, y_train, x_test) # AdaBoost \n",
    "gb_oof_train, gb_oof_test = get_oof(gb,x_train, y_train, x_test) # Gradient Boost\n",
    "svc_oof_train, svc_oof_test = get_oof(svc,x_train, y_train, x_test) # Support Vector Classifier\n",
    "\n",
    "print(\"Training is complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3f292e65-fe8a-d662-6ace-41a19866d671",
    "_uuid": "4cf41b3d9a541c9d39b645a66c8f1116eaf76861"
   },
   "source": [
    "**Feature importances generated from the different classifiers**\n",
    "\n",
    "Now having learned our the first-level classifiers, we can utilise a very nifty feature of the Sklearn models and that is to output the importances of the various features in the training and test sets with one very simple line of code.\n",
    "\n",
    "As per the Sklearn documentation, most of the classifiers are built in with an attribute which returns feature importances by simply typing in **.feature_importances_**. Therefore we will invoke this very useful attribute via our function earliand plot the feature importances as such"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "ed9cf8b5-95a4-d974-fb11-592214949d1f",
    "_uuid": "b3b0356c8bef0dceb5fcfa7fb7a11359010b2098"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.1253263   0.19961467  0.03065075  0.02176063  0.07232234  0.0233847\n",
      "  0.10815212  0.06343066  0.06800938  0.01334406  0.27400439]\n",
      "[ 0.12184652  0.38116024  0.02688926  0.01689099  0.05603155  0.02864036\n",
      "  0.04538795  0.0825402   0.04422112  0.02197225  0.17441957]\n",
      "[ 0.028  0.012  0.02   0.068  0.04   0.01   0.68   0.012  0.054  0.004\n",
      "  0.072]\n",
      "[ 0.07372906  0.03593002  0.09681701  0.03202122  0.09604092  0.06201702\n",
      "  0.39868163  0.01843962  0.07046661  0.02307459  0.0927823 ]\n"
     ]
    }
   ],
   "source": [
    "rf_feature = rf.feature_importances(x_train,y_train)\n",
    "et_feature = et.feature_importances(x_train, y_train)\n",
    "ada_feature = ada.feature_importances(x_train, y_train)\n",
    "gb_feature = gb.feature_importances(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f1be8fbb-34c0-3f92-c7d2-757fceced507",
    "_uuid": "b0c66aa58b45b917157f47c811e7d222513ddb0d"
   },
   "source": [
    "So I have not yet figured out how to assign and store the feature importances outright. Therefore I'll print out the values from the code above and then simply copy and paste into Python lists as below (sorry for the lousy hack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "bde9b5fd-3100-8f21-053e-45015b99cf65",
    "_uuid": "527425374ec8cd66edc015842f352a95041821c6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_features = [0.10474135,  0.21837029,  0.04432652,  0.02249159,  0.05432591,  0.02854371\n",
    "  ,0.07570305,  0.01088129 , 0.24247496,  0.13685733 , 0.06128402]\n",
    "et_features = [ 0.12165657,  0.37098307  ,0.03129623 , 0.01591611 , 0.05525811 , 0.028157\n",
    "  ,0.04589793 , 0.02030357 , 0.17289562 , 0.04853517,  0.08910063]\n",
    "ada_features = [0.028 ,   0.008  ,      0.012   ,     0.05866667,   0.032 ,       0.008\n",
    "  ,0.04666667 ,  0.     ,      0.05733333,   0.73866667,   0.01066667]\n",
    "gb_features = [ 0.06796144 , 0.03889349 , 0.07237845 , 0.02628645 , 0.11194395,  0.04778854\n",
    "  ,0.05965792 , 0.02774745,  0.07462718,  0.4593142 ,  0.01340093]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2a03ad8f-0ea1-5afa-a6e8-56284482c646",
    "_uuid": "d59169f89c5541177f537200a90805420b449001"
   },
   "source": [
    "Create a dataframe from the lists containing the feature importance data for easy plotting via the Plotly package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "635a063f-281d-66d4-6572-587ebecd6b4b",
    "_uuid": "6f68b3033a8f185f61d83e80323c2486024f5d4d",
    "collapsed": true
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   "outputs": [],
   "source": [
    "cols = train.columns.values\n",
    "# Create a dataframe with features\n",
    "feature_dataframe = pd.DataFrame( {'features': cols,\n",
    "     'Random Forest feature importances': rf_features,\n",
    "     'Extra Trees  feature importances': et_features,\n",
    "      'AdaBoost feature importances': ada_features,\n",
    "    'Gradient Boost feature importances': gb_features\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "06b9f410-f93e-0206-b029-24df035eea2b",
    "_uuid": "7e25675f239b0ab008e0264917abff497795681a"
   },
   "source": [
    "**Interactive feature importances via Plotly scatterplots**\n",
    "\n",
    "I'll use the interactive Plotly package at this juncture to visualise the feature importances values of the different classifiers  via a plotly scatter plot by calling \"Scatter\" as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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    "_uuid": "d8ee9114cd391433835f1272ef81d0a729c78b71"
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   ],
   "source": [
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Random Forest feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Random Forest feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Random Forest Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Extra Trees  feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Extra Trees  feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Extra Trees Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['AdaBoost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['AdaBoost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'AdaBoost Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')\n",
    "\n",
    "# Scatter plot \n",
    "trace = go.Scatter(\n",
    "    y = feature_dataframe['Gradient Boost feature importances'].values,\n",
    "    x = feature_dataframe['features'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        sizeref = 1,\n",
    "        size = 25,\n",
    "#       size= feature_dataframe['AdaBoost feature importances'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = feature_dataframe['Gradient Boost feature importances'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = feature_dataframe['features'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Gradient Boosting Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatter2010')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "553828f0-c994-5ee1-695f-9373f11a1a7b",
    "_uuid": "9d7b8fdd0c3102d7e3ddcffaf26ce19b02e5ad74"
   },
   "source": [
    "Now let us calculate the mean of all the feature importances and store it as a new column in the feature importance dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "06847850-a829-0858-b12c-7b66e53e030a",
    "_uuid": "f611812e2c9de3773df2264dfb2b13c0995807ac"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AdaBoost feature importances</th>\n",
       "      <th>Extra Trees  feature importances</th>\n",
       "      <th>Gradient Boost feature importances</th>\n",
       "      <th>Random Forest feature importances</th>\n",
       "      <th>features</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028</td>\n",
       "      <td>0.121657</td>\n",
       "      <td>0.067961</td>\n",
       "      <td>0.104741</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>0.080590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.370983</td>\n",
       "      <td>0.038893</td>\n",
       "      <td>0.218370</td>\n",
       "      <td>Sex</td>\n",
       "      <td>0.159062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.012</td>\n",
       "      <td>0.031296</td>\n",
       "      <td>0.072378</td>\n",
       "      <td>0.044327</td>\n",
       "      <td>Age</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AdaBoost feature importances  Extra Trees  feature importances  \\\n",
       "0                         0.028                          0.121657   \n",
       "1                         0.008                          0.370983   \n",
       "2                         0.012                          0.031296   \n",
       "\n",
       "   Gradient Boost feature importances  Random Forest feature importances  \\\n",
       "0                            0.067961                           0.104741   \n",
       "1                            0.038893                           0.218370   \n",
       "2                            0.072378                           0.044327   \n",
       "\n",
       "  features      mean  \n",
       "0   Pclass  0.080590  \n",
       "1      Sex  0.159062  \n",
       "2      Age  0.040000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the new column containing the average of values\n",
    "\n",
    "feature_dataframe['mean'] = feature_dataframe.mean(axis= 1) # axis = 1 computes the mean row-wise\n",
    "feature_dataframe.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5645e647-c517-7822-f881-b8d7e38ef5da",
    "_uuid": "1f0aff0896fef90b326fff7816393fef0e0cb992"
   },
   "source": [
    "**Plotly Barplot of Average Feature Importances**\n",
    "\n",
    "Having obtained the mean feature importance across all our classifiers, we can plot them into a Plotly bar plot as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "63d86121-8c29-4b7f-b2ad-12b0a593f1d6",
    "_uuid": "0bd069388b419fe45306c01825aa3e6f5466ba2b"
   },
   "outputs": [
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          "colorscale": "Portland",
          "reversescale": false,
          "showscale": true
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         "opacity": 0.6,
         "type": "bar",
         "width": 0.5,
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       "<div id=\"37730de1-b847-44c5-87fe-0978e568a2e8\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"37730de1-b847-44c5-87fe-0978e568a2e8\", [{\"type\": \"bar\", \"x\": [\"Pclass\", \"Sex\", \"Age\", \"Parch\", \"Fare\", \"Embarked\", \"Name_length\", \"Has_Cabin\", \"FamilySize\", \"IsAlone\", \"Title\"], \"y\": [0.08058984000000001, 0.1590617125, 0.0400003, 0.030840205000000002, 0.0633819925, 0.0281223125, 0.0569813925, 0.0147330775, 0.1368327725, 0.3458433425, 0.0436130625], \"width\": 0.5, \"marker\": {\"color\": [0.08058984000000001, 0.1590617125, 0.0400003, 0.030840205000000002, 0.0633819925, 0.0281223125, 0.0569813925, 0.0147330775, 0.1368327725, 0.3458433425, 0.0436130625], \"colorscale\": \"Portland\", \"showscale\": true, \"reversescale\": false}, \"opacity\": 0.6}], {\"autosize\": true, \"title\": \"Barplots of Mean Feature Importance\", \"hovermode\": \"closest\", \"yaxis\": {\"title\": \"Feature Importance\", \"ticklen\": 5, \"gridwidth\": 2}, \"showlegend\": false}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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       "<div id=\"37730de1-b847-44c5-87fe-0978e568a2e8\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"37730de1-b847-44c5-87fe-0978e568a2e8\", [{\"type\": \"bar\", \"x\": [\"Pclass\", \"Sex\", \"Age\", \"Parch\", \"Fare\", \"Embarked\", \"Name_length\", \"Has_Cabin\", \"FamilySize\", \"IsAlone\", \"Title\"], \"y\": [0.08058984000000001, 0.1590617125, 0.0400003, 0.030840205000000002, 0.0633819925, 0.0281223125, 0.0569813925, 0.0147330775, 0.1368327725, 0.3458433425, 0.0436130625], \"width\": 0.5, \"marker\": {\"color\": [0.08058984000000001, 0.1590617125, 0.0400003, 0.030840205000000002, 0.0633819925, 0.0281223125, 0.0569813925, 0.0147330775, 0.1368327725, 0.3458433425, 0.0436130625], \"colorscale\": \"Portland\", \"showscale\": true, \"reversescale\": false}, \"opacity\": 0.6}], {\"autosize\": true, \"title\": \"Barplots of Mean Feature Importance\", \"hovermode\": \"closest\", \"yaxis\": {\"title\": \"Feature Importance\", \"ticklen\": 5, \"gridwidth\": 2}, \"showlegend\": false}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = feature_dataframe['mean'].values\n",
    "x = feature_dataframe['features'].values\n",
    "data = [go.Bar(\n",
    "            x= x,\n",
    "             y= y,\n",
    "            width = 0.5,\n",
    "            marker=dict(\n",
    "               color = feature_dataframe['mean'].values,\n",
    "            colorscale='Portland',\n",
    "            showscale=True,\n",
    "            reversescale = False\n",
    "            ),\n",
    "            opacity=0.6\n",
    "        )]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= 'Barplots of Mean Feature Importance',\n",
    "    hovermode= 'closest',\n",
    "#     xaxis= dict(\n",
    "#         title= 'Pop',\n",
    "#         ticklen= 5,\n",
    "#         zeroline= False,\n",
    "#         gridwidth= 2,\n",
    "#     ),\n",
    "    yaxis=dict(\n",
    "        title= 'Feature Importance',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='bar-direct-labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c1201ecc-b07d-f8a1-0870-b8d78c89ebc0",
    "_uuid": "bbb76d189e8d03921caaacfa9545cef894348c7d"
   },
   "source": [
    "# Second-Level Predictions from the First-level Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6b901750-ccdd-38ca-d8ea-1c361121ec4f",
    "_uuid": "fed132782b73dda8d265065867e7f57c0aed7f50"
   },
   "source": [
    "**First-level output as new features**\n",
    "\n",
    "Having now obtained our first-level predictions, one can think of it as essentially building a new set of features to be used as training data for the next classifier. As per the code below, we are therefore having as our new columns the first-level predictions from our earlier classifiers and we train the next classifier on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "7330a71c-0b71-87c2-1f4d-dd0f6d6fa586",
    "_uuid": "a5945e93337b87a1a8ee5580856768bbb14c07cd"
   },
   "outputs": [
    {
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       "      <th>AdaBoost</th>\n",
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       "   AdaBoost  ExtraTrees  GradientBoost  RandomForest\n",
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   "source": [
    "base_predictions_train = pd.DataFrame( {'RandomForest': rf_oof_train.ravel(),\n",
    "     'ExtraTrees': et_oof_train.ravel(),\n",
    "     'AdaBoost': ada_oof_train.ravel(),\n",
    "      'GradientBoost': gb_oof_train.ravel()\n",
    "    })\n",
    "base_predictions_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "_uuid": "2a6f987bd9b8ffc32a72e21cb8c43a6bc43ba200"
   },
   "source": [
    "**Correlation Heatmap of the Second Level Training set**"
   ]
  },
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       "<div id=\"82d4201d-fb91-45cd-8be2-a455983efcab\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"82d4201d-fb91-45cd-8be2-a455983efcab\", [{\"type\": \"heatmap\", \"z\": [[1.0, 0.7848873951270243, 0.6715294459312147, 0.7734817230194352], [0.7848873951270243, 1.0, 0.7210083816131495, 0.8707780084438146], [0.6715294459312147, 0.7210083816131495, 1.0, 0.7273400136793821], [0.7734817230194352, 0.8707780084438146, 0.7273400136793821, 1.0]], \"x\": [\"AdaBoost\", \"ExtraTrees\", \"GradientBoost\", \"RandomForest\"], \"y\": [\"AdaBoost\", \"ExtraTrees\", \"GradientBoost\", \"RandomForest\"], \"colorscale\": \"Viridis\", \"showscale\": true, \"reversescale\": true}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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   "source": [
    "data = [\n",
    "    go.Heatmap(\n",
    "        z= base_predictions_train.astype(float).corr().values ,\n",
    "        x=base_predictions_train.columns.values,\n",
    "        y= base_predictions_train.columns.values,\n",
    "          colorscale='Viridis',\n",
    "            showscale=True,\n",
    "            reversescale = True\n",
    "    )\n",
    "]\n",
    "py.iplot(data, filename='labelled-heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4d6b61c0-5d72-b02a-3b37-cbf6518d71b6",
    "_uuid": "4d9ef0298b568e43da6925f385403e0d77bd6e33"
   },
   "source": [
    "There have been quite a few articles and Kaggle competition winner stories about the merits of having trained models that are more uncorrelated with one another producing better scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "6685fa11-497f-3fc2-ab1f-97f92d6eca61",
    "_uuid": "fef365199854ca3fff754399b4699d941b7e43b8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = np.concatenate(( et_oof_train, rf_oof_train, ada_oof_train, gb_oof_train, svc_oof_train), axis=1)\n",
    "x_test = np.concatenate(( et_oof_test, rf_oof_test, ada_oof_test, gb_oof_test, svc_oof_test), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a02a94ab-3c9c-a824-7168-e964c5a0f5d5",
    "_uuid": "65727ae393d3f7118215fde76e4fd5a9d0e9dd6c"
   },
   "source": [
    "Having now concatenated and joined both the first-level train and test predictions as x_train and x_test, we can now fit a second-level learning model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "628a03ea-933c-7075-a589-0ff7af237dfd",
    "_uuid": "dc4a32e9a8e7c9e611124cba676e5d28240b38be"
   },
   "source": [
    "### Second level learning model via XGBoost\n",
    "\n",
    "Here we choose the eXtremely famous library for boosted tree learning model, XGBoost. It was built to optimize large-scale boosted tree algorithms. For further information about the algorithm, check out the [official documentation][1].\n",
    "\n",
    "  [1]: https://xgboost.readthedocs.io/en/latest/\n",
    "\n",
    "Anyways, we call an XGBClassifier and fit it to the first-level train and target data and use the learned model to predict the test data as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "3a7c7517-b9a3-3a21-3a7b-299ca37c6843",
    "_uuid": "5155d370069fe6de0fe5105309342ce55130dae8",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gbm = xgb.XGBClassifier(\n",
    "    #learning_rate = 0.02,\n",
    " n_estimators= 2000,\n",
    " max_depth= 4,\n",
    " min_child_weight= 2,\n",
    " #gamma=1,\n",
    " gamma=0.9,                        \n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread= -1,\n",
    " scale_pos_weight=1).fit(x_train, y_train)\n",
    "predictions = gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0a8152d8-6842-ed00-6bc5-47a511adce1c",
    "_uuid": "0101e6b843f6378838874ccfb844ed464b81d627"
   },
   "source": [
    "Just a quick run down of the XGBoost parameters used in the model:\n",
    "\n",
    "**max_depth** : How deep you want to grow your tree. Beware if set to too high a number might run the risk of overfitting.\n",
    "\n",
    "**gamma** : minimum loss reduction required to make a further partition on a leaf node of the tree. The larger, the more conservative the algorithm will be.\n",
    "\n",
    "**eta** : step size shrinkage used in each boosting step to prevent overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6b4a5c81-e968-d41e-27e4-871481019867",
    "_uuid": "52ac0cd99cee0099d86a180127da42ff7fff960a"
   },
   "source": [
    "**Producing the Submission file**\n",
    "\n",
    "Finally having trained and fit all our first-level and second-level models, we can now output the predictions into the proper format for submission to the Titanic competition as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "f5a31787-5fe1-a559-bee9-ad6b6d83ae14",
    "_uuid": "9d607d829dbadd6c72ee01c9735a642435eb53e6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate Submission File \n",
    "StackingSubmission = pd.DataFrame({ 'PassengerId': PassengerId,\n",
    "                            'Survived': predictions })\n",
    "StackingSubmission.to_csv(\"StackingSubmission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1e56c738-b8f3-95e4-d642-c483f9757ed8",
    "_uuid": "9db5fd7cbb0d406ab0ef9aa08cf56532c51ec8b5"
   },
   "source": [
    "**Steps for Further Improvement**\n",
    "\n",
    "As a closing remark it must be noted that the steps taken above just show a very simple way of producing an ensemble stacker. You hear of ensembles created at the highest level of Kaggle competitions which involves monstrous combinations of stacked classifiers as well as levels of stacking which go to more than 2 levels. \n",
    "\n",
    "Some additional steps that may be taken to improve one's score could be:\n",
    "\n",
    " 1. Implementing a good cross-validation strategy in training the models to find optimal parameter values\n",
    " 2. Introduce a greater variety of base models for learning. The more uncorrelated the results, the better the final score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9a8f83fd-d0e8-035a-cf7f-25c9012e9373",
    "_uuid": "c32d1d64e1a5f8fbe5f51a0a7afd952ccfdec57e"
   },
   "source": [
    "### Conclusion\n",
    "\n",
    "I have this notebook has been helpful somewhat in introducing a working script for stacking learning models. Again credit must be extended to Faron and Sina. \n",
    "\n",
    "For other excellent material on stacking or ensembling in general, refer to the de-facto Must read article on the website MLWave: [Kaggle Ensembling Guide][1]. \n",
    "\n",
    "Till next time, Peace Out\n",
    "\n",
    "  [1]: http://mlwave.com/kaggle-ensembling-guide/"
   ]
  }
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