examples/titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic example\n",
    "\n",
    "## Main imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import recipipe as r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dowload data\n",
    "\n",
    "In this example we are going to use the Titanic dataset.\n",
    "Train and test CSVs should be in `data/titanic/*.csv`.\n",
    "\n",
    "You can download it from the Kaggle web page (https://www.kaggle.com/c/titanic/data) or, if you have the Kaggle CLI installed (https://github.com/Kaggle/kaggle-api), you can execute the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "from shutil import which\n",
    "\n",
    "\n",
    "if which(\"kaggle\") is not None:\n",
    "    !kaggle competitions download titanic -p data\n",
    "    !unzip -o data/titanic.zip -d data/titanic\n",
    "else:\n",
    "    pass  # Kaggle CLI not installed :( Download the data manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender_submission.csv  test.csv  train.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls data/titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<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",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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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       "      <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",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"data/titanic/train.csv\")\n",
    "df_test = pd.read_csv(\"data/titanic/test.csv\")\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply transformations with Recipipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = r.recipipe([\n",
    "    # Drop unnecessary columns.\n",
    "    r.drop(\"PassengerId\", \"Ticket\"),\n",
    "    # Impute categorical features.\n",
    "    r.impute(\"Embarked\", strategy=\"most_frequent\"),\n",
    "    # Impute numerical features.\n",
    "    r.impute(\"Age\", \"Fare\", strategy=\"mean\"),\n",
    "    # Categorical to numeric using one-hot.\n",
    "    r.onehot(\"Embarked\", \"Pclass\", \"Sex\"),\n",
    "    # Standarize numeric features.\n",
    "    r.scale(\"Age\", \"Fare\"),\n",
    "    # Extract deck name from the cabin.\n",
    "    r.extract(\"Cabin\", pattern=r\"([A-Z])\\d*\", col_format=\"Deck\"),\n",
    "    r.impute(\"Deck\", fill_value=\"Unknown\"),\n",
    "    r.target_encoder(\"Deck\", target=\"Survived\"),\n",
    "    # Extract the English honorific title from the name.\n",
    "    r.extract(\"Name\", pattern=[\"Mr\\.\", \"Mrs\\.\", \"Miss\\.\"], indicator=True, col_format=\"Title={value}\"),\n",
    "    # Total number of family members onboard.\n",
    "    r.sum(\"SibSp\", \"Parch\", col_format=\"FamilyOnboard\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Survived  Pclass=1  Pclass=2  Pclass=3  Title=Mr  Title=Mrs  Title=Miss  \\\n",
       "0         0       0.0       0.0       1.0         1          0           0   \n",
       "1         1       1.0       0.0       0.0         0          1           0   \n",
       "2         1       0.0       0.0       1.0         0          0           1   \n",
       "3         1       1.0       0.0       0.0         0          1           0   \n",
       "4         0       0.0       0.0       1.0         1          0           0   \n",
       "\n",
       "   Sex=female  Sex=male       Age  FamilyOnboard      Fare      Deck  \\\n",
       "0         0.0       1.0 -0.592481              1 -0.502445  0.299854   \n",
       "1         1.0       0.0  0.638789              1  0.786845  0.593220   \n",
       "2         1.0       0.0 -0.284663              0 -0.488854  0.299854   \n",
       "3         1.0       0.0  0.407926              1  0.420730  0.593220   \n",
       "4         0.0       1.0  0.407926              0 -0.486337  0.299854   \n",
       "\n",
       "   Embarked=C  Embarked=Q  Embarked=S  \n",
       "0         0.0         0.0         1.0  \n",
       "1         1.0         0.0         0.0  \n",
       "2         0.0         0.0         1.0  \n",
       "3         0.0         0.0         1.0  \n",
       "4         0.0         0.0         1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_transformed = pipe.fit_transform(df_train)\n",
    "df_train_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "   Pclass=1  Pclass=2  Pclass=3  Title=Mr  Title=Mrs  Title=Miss  Sex=female  \\\n",
       "0       0.0       0.0       1.0         1          0           0         0.0   \n",
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       "4       0.0       0.0       1.0         0          1           0         1.0   \n",
       "\n",
       "   Sex=male       Age  FamilyOnboard      Fare      Deck  Embarked=C  \\\n",
       "0       1.0  0.369449              0 -0.490783  0.299854         0.0   \n",
       "1       0.0  1.331378              1 -0.507479  0.299854         0.0   \n",
       "2       1.0  2.485693              0 -0.453367  0.299854         0.0   \n",
       "3       1.0 -0.207709              0 -0.474005  0.299854         0.0   \n",
       "4       0.0 -0.592481              2 -0.401017  0.299854         0.0   \n",
       "\n",
       "   Embarked=Q  Embarked=S  \n",
       "0         1.0         0.0  \n",
       "1         0.0         1.0  \n",
       "2         1.0         0.0  \n",
       "3         0.0         1.0  \n",
       "4         0.0         1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_transformed = pipe.transform(df_test)\n",
    "df_test_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a Logistic Regression model\n",
    "\n",
    "As an example of the use of transformed data, we are going to train a very simple model using SKLearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train model.\n",
    "model = LogisticRegression()\n",
    "target = \"Survived\"\n",
    "X, y = df_train_transformed.drop(target, axis=1).values, df_train[target].values\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run inference.\n",
    "X_test = df_test_transformed.values\n",
    "predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save outputs.\n",
    "df_output = pd.read_csv(\"data/titanic/gender_submission.csv\")\n",
    "df_output.Survived = predicted\n",
    "df_output.to_csv(\"output/submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uploading `data/titanic/submission.csv` to Kaggle would get you about 78.5% accuracy. Creating a good model is not part of this tutorial, it's only an example of use of the Recipipe transformations.\n",
    "\n",
    "\n",
    "## What's next?\n",
    "\n",
    "You can continue learning about Recipipe by:\n",
    "\n",
    "* Following this [fun tutorial about Paranoids](https://github.com/guiferviz/recipipe/blob/master/examples/paranoids.ipynb).\n",
    "* Reading a list of [features with code](https://github.com/guiferviz/recipipe/blob/master/examples/features.ipynb).\n",
    "* Reading the [official documentation](https://guiferviz.com/recipipe/).\n",
    "* Reading the source code from the [GitHub repository](https://github.com/guiferviz/recipipe) :)."
   ]
  }
 ],
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