10.1_One_hot_encoding.ipynb

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    "## Engineer labels of categorical variables\n",
    "\n",
    "In this section, I will describe a variety of methods to transform the strings of categorical variables into numbers, so that we can feed these variables in machine learning algorithms using sklearn.\n",
    "\n",
    "## One Hot Encoding\n",
    "\n",
    "One hot encoding, consists of replacing the categorical variable by different boolean variables, which take value 0 or 1, to indicate whether or not a certain category / label of the variable was present for that observation.\n",
    "\n",
    "Each one of the boolean variables are also known as **dummy variables** or binary variables.\n",
    "\n",
    "For example, from the categorical variable \"Gender\", with labels 'female' and 'male', we can generate the boolean variable \"female\", which takes 1 if the person is female or 0 otherwise. We can also generate the variable male, which takes 1 if the person is \"male\" and 0 otherwise. \n",
    "\n",
    "See below:\n"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex\n",
       "0    male\n",
       "1  female\n",
       "2  female\n",
       "3  female\n",
       "4    male"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('titanic.csv', usecols=['Sex'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex_female  Sex_male\n",
       "0           0         1\n",
       "1           1         0\n",
       "2           1         0\n",
       "3           1         0\n",
       "4           0         1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "\n",
    "pd.get_dummies(data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Sex_female  Sex_male\n",
       "0    male           0         1\n",
       "1  female           1         0\n",
       "2  female           1         0\n",
       "3  female           1         0\n",
       "4    male           0         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for better visualisation\n",
    "pd.concat([data, pd.get_dummies(data)], axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may have noticed, we only need 1 of the 2 dummy variables to represent the original categorical variable Sex. Any of the 2 will suffice, and it doesn't matter which one we select, since they are equivalent.\n",
    "\n",
    "Therefore, to encode a categorical variable with 2 labels, we need 1 dummy variable. \n",
    "\n",
    "To extend this concept, to encode  categorical variable with k labels, we need k-1 dummy variables.\n",
    "\n",
    "How can we get this using pandas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Sex_male\n",
       "0         1\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# obtaining k-1 labels\n",
    "pd.get_dummies(data, drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Embarked\n",
       "0        S\n",
       "1        C\n",
       "2        S\n",
       "3        S\n",
       "4        S"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# Let's now look at an example with more than 2 labels\n",
    "\n",
    "data=pd.read_csv('titanic.csv', usecols=['Embarked'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the number of different labels\n",
    "data.Embarked.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked_C  Embarked_Q  Embarked_S\n",
       "0           0           0           1\n",
       "1           1           0           0\n",
       "2           0           0           1\n",
       "3           0           0           1\n",
       "4           0           0           1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get whole set of dummy variables\n",
    "\n",
    "pd.get_dummies(data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Embarked_Q  Embarked_S\n",
       "0           0           1\n",
       "1           0           0\n",
       "2           0           1\n",
       "3           0           1\n",
       "4           0           1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get k-1 dummy variables\n",
    "\n",
    "pd.get_dummies(data, drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Embarked_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Embarked_Q  Embarked_S  Embarked_nan\n",
       "0           0           1             0\n",
       "1           0           0             0\n",
       "2           0           1             0\n",
       "3           0           1             0\n",
       "4           0           1             0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also add an additional dummy variable to indicate whether there was missing data\n",
    "\n",
    "pd.get_dummies(data, drop_first=True, dummy_na=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked_Q       77\n",
       "Embarked_S      644\n",
       "Embarked_nan      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by summing the number of 1s per boolean variable over the rows of the dataset, we get to know how\n",
    "# many observations we have for each variable (i.e., each category)\n",
    "\n",
    "pd.get_dummies(data, drop_first=True, dummy_na=True).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notes\n",
    "\n",
    "Both pandas and sklearn will provide a whole set of dummy variables from a categorical variable. This is, instead of returning k-1 binary variables, they will return k, with the option in pandas of dropping the first binary variable and obtain k-1.\n",
    "\n",
    "### When should you use k and when k-1?\n",
    "\n",
    "When the original variable is binary, that is, when the original variable has only 2 labels, then you should create **one and only one** binary variable.\n",
    "\n",
    "When the original variable has more than 2 labels, the following is important:\n",
    "\n",
    "#### One hot encoding into k-1:\n",
    "One hot encoding into k-1 binary variables takes into account that we can use 1 less dimension and still represent the whole information: if the observation is 0 in all the binary variables, then it must be 1 in the final (removed) binary variable. As an example, for the variable gender encoded into male, if the observation is 0, then it has to be female. We do not need the additional female variable to explain that. \n",
    "\n",
    "One hot encoding with k-1 binary variables should be used in linear regression, to keep the correct number of degrees of freedom (k-1). The linear regression has access to all of the features as it is being trained, and therefore examines altogether the whole set of dummy variables. This means that k-1 binary variables give the whole information about (represent completely) the original categorical variable to the linear regression.\n",
    "\n",
    "And the same is true for all machine learning algorithms that look at ALL the features at the same time during training. For example, support vector machines and neural networks as well. And clustering algorithms.\n",
    "\n",
    "#### One hot encoding into k dummy variables\n",
    "\n",
    "However, tree based models select at each iteration only a group of features to make a decision. This is to separate the data at each node. Therefore, the last category, the one that was removed in the one hot encoding into k-1 variables, would only be taken into account by those splits or even trees, that use the entire set of binary variables at a time. And this would rarely happen, because each split usually uses 1-3 features to make a decision. So, tree based methods will never consider that additional label, the one that was dropped. Thus, if the categorical variables will be used in a tree based learning algorithm, it is good practice to encode it into k binary variables instead of k-1.\n",
    "\n",
    "Finally, if you are planning to do feature selection, you will also need the entire set of binary variables (k) to let the machine learning model select which ones have the most predictive power.\n",
    "\n",
    "Let's train a model on data with categorical variables re-encoded with One Hot Encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "    </tr>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       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",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      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  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('titanic.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch  Survived  male  Q  S\n",
       "0       3  22.0      1      0         0     1  0  1\n",
       "1       1  38.0      1      0         1     0  0  0\n",
       "2       3  26.0      0      0         1     0  0  1\n",
       "3       1  35.0      1      0         1     0  0  1\n",
       "4       3  35.0      0      0         0     1  0  1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's make a copy of the dataset, in which we encode the categorical variables using OHE\n",
    "\n",
    "data_OHE = pd.concat([data[['Pclass', 'Age', 'SibSp','Parch', 'Survived']], # numerical variables \n",
    "                      pd.get_dummies(data.Sex, drop_first=True),   # binary categorical variable\n",
    "                      pd.get_dummies(data.Embarked, drop_first=True)],  # k categories in categorical\n",
    "                    axis=1)\n",
    "\n",
    "data_OHE.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 7), (268, 7))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now let's separate into train and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_OHE[['Pclass', 'Age', 'SibSp',\n",
    "                                                              'Parch', 'male', 'Q', 'S']].fillna(0),\n",
    "                                                    data_OHE.Survived,\n",
    "                                                    test_size=0.3,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "Random Forests roc-auc: 0.8696177957094205\n",
      "Test set\n",
      "Random Forests roc-auc: 0.8606250000000001\n"
     ]
    }
   ],
   "source": [
    "# let's build a random forest\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=200, random_state=39, max_depth=3)\n",
    "rf.fit(X_train, y_train)\n",
    "print('Train set')\n",
    "pred = rf.predict_proba(X_train)\n",
    "print('Random Forests roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "print('Test set')\n",
    "pred = rf.predict_proba(X_test)\n",
    "print('Random Forests roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "AdaBoost roc-auc: 0.9010433613153728\n",
      "Test set\n",
      "AdaBoost roc-auc: 0.8474404761904762\n"
     ]
    }
   ],
   "source": [
    "# and now an AdaBoost classifier\n",
    "\n",
    "ada = AdaBoostClassifier(n_estimators=200, random_state=44)\n",
    "ada.fit(X_train, y_train)\n",
    "print('Train set')\n",
    "pred = ada.predict_proba(X_train)\n",
    "print('AdaBoost roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "print('Test set')\n",
    "pred = ada.predict_proba(X_test)\n",
    "print('AdaBoost roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "Logistic Regression roc-auc: 0.8452745059760092\n",
      "Test set\n",
      "Logistic Regression roc-auc: 0.8424404761904761\n"
     ]
    }
   ],
   "source": [
    "# and finally a logistic regression\n",
    "\n",
    "logit = LogisticRegression(random_state=44)\n",
    "logit.fit(X_train, y_train)\n",
    "print('Train set')\n",
    "pred = logit.predict_proba(X_train)\n",
    "print('Logistic Regression roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "print('Test set')\n",
    "pred = logit.predict_proba(X_test)\n",
    "print('Logistic Regression roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression and Adaboost show the highest performance using the current datasets.\n",
    "\n",
    "\n",
    "## One Hot encoding: final notes\n",
    "\n",
    "### Advantages\n",
    "\n",
    "- Straightforward to implement\n",
    "- Makes no assumption\n",
    "- Keeps all the information of the categorical variable\n",
    "\n",
    "### Disadvantages\n",
    "\n",
    "- Does not add any information that may make the variable more predictive\n",
    "- If the variable has loads of categories, then OHE increases the feature space dramatically\n",
    "\n",
    "See below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin\n",
       "0   NaN\n",
       "1   C85\n",
       "2   NaN\n",
       "3  C123\n",
       "4   NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's load the variable Cabin of the titanic dataset\n",
    "\n",
    "data = pd.read_csv('titanic.csv', usecols = ['Cabin'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of labels: 148\n"
     ]
    }
   ],
   "source": [
    "# let's inspect the number of different labels in Cabin\n",
    "\n",
    "print('number of labels: {}'.format(len(data.Cabin.unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 147)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see how many features we would create if we did OHE for Cabin\n",
    "\n",
    "Cabin_OHE = pd.get_dummies(data.Cabin)\n",
    "Cabin_OHE.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   A10  A14  A16  A19  A20  A23  A24  A26  A31  A32 ...  E8  F E69  F G63  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0 ...   0      0      0   \n",
       "1    0    0    0    0    0    0    0    0    0    0 ...   0      0      0   \n",
       "2    0    0    0    0    0    0    0    0    0    0 ...   0      0      0   \n",
       "3    0    0    0    0    0    0    0    0    0    0 ...   0      0      0   \n",
       "4    0    0    0    0    0    0    0    0    0    0 ...   0      0      0   \n",
       "\n",
       "   F G73  F2  F33  F38  F4  G6  T  \n",
       "0      0   0    0    0   0   0  0  \n",
       "1      0   0    0    0   0   0  0  \n",
       "2      0   0    0    0   0   0  0  \n",
       "3      0   0    0    0   0   0  0  \n",
       "4      0   0    0    0   0   0  0  \n",
       "\n",
       "[5 rows x 147 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "Cabin_OHE.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we performed OHE in the variable Cabin that contains 148 different labels, we would end up with 147 variables, where originally there was one. If we have a few categorical variables like this, we would end up with huge datasets. Therefore, OHE is not always the best option to encode categorical variables.\n",
    "\n",
    "### Notes\n",
    "\n",
    "If our datasets have a few multi-label variables, we will end up very soon with datasets with thousands of columns or more. And this may make training of our algorithms slow.\n",
    "\n",
    "In addition, many of these dummy variables may be similar to each other, since it is not unusual for 2 or more variables to share the same combinations of 1 and 0s."
   ]
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