notebooks/bird-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement:\n",
    "\n",
    "Scientists have determined that a known species of bird should be divided into 3 distinct and separate species. These species are endemic to a particular region of the country and their populations must be tracked and estimated with as much precision as possible. As such, a non-profit conservation society has taken up the task. They need to be able to log which species they have encountered based on the characteristics that their field officers observe in the wild. \n",
    "\n",
    "Using certain genetic traits and location data, can you predict the species of bird that has been observed?\n",
    "\n",
    "This is a beginner-level practice competition and your goal is to predict the bird species (A, B, or C) based on attributes or location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "matplotlib.rcParams['figure.dpi'] = 100\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.set(style=\"whitegrid\")\n",
    "%matplotlib inline\n",
    "\n",
    "# ml\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the necessary libraries we need."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_vals(df):\n",
    "    \"\"\"prints out columns with perc of missing values\"\"\"\n",
    "    missing = [\n",
    "        (df.columns[idx], perc)\n",
    "        for idx, perc in enumerate(df.isna().mean() * 100)\n",
    "        if perc > 0\n",
    "    ]\n",
    "\n",
    "    if len(missing) == 0:\n",
    "        return \"no missing values\"\n",
    "        \n",
    "\n",
    "    # sort desc by perc\n",
    "    missing.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    print(f\"There are a total of {len(missing)} variables with missing values\\n\")\n",
    "\n",
    "    for tup in missing:\n",
    "        print(str.ljust(f\"{tup[0]:<20} => {round(tup[1], 3)}%\", 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above is a helper function to show missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"dataset/training_set/training_set.csv\")\n",
    "labels = pd.read_csv(\"dataset/training_set/training_target.csv\")\n",
    "\n",
    "# join target variable to training set\n",
    "train = train.merge(labels, on=\"ID\")\n",
    "\n",
    "test = pd.read_csv(\"dataset/test_set/test_set.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load the train and test data using the `read_csv` function.\n",
    "\n",
    "We also merge `training_set.csv` (containing the features) with `training_target.csv` (containing the target variable) and form the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_depth</th>\n",
       "      <th>bill_length</th>\n",
       "      <th>wing_length</th>\n",
       "      <th>location</th>\n",
       "      <th>mass</th>\n",
       "      <th>sex</th>\n",
       "      <th>ID</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.300000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>loc_2</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>284</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.400000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>loc_2</td>\n",
       "      <td>4625.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>loc_3</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.982114</td>\n",
       "      <td>47.504878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.982114</td>\n",
       "      <td>38.259307</td>\n",
       "      <td>217.186992</td>\n",
       "      <td>loc_3</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bill_depth  bill_length  wing_length location    mass  sex   ID species\n",
       "0   14.300000    48.200000   210.000000    loc_2  4600.0  0.0  284       C\n",
       "1   14.400000    48.400000   203.000000    loc_2  4625.0  0.0  101       C\n",
       "2   18.400000          NaN   200.000000    loc_3  3400.0  0.0  400       B\n",
       "3   14.982114    47.504878          NaN      NaN  4800.0  0.0   98       C\n",
       "4   18.982114    38.259307   217.186992    loc_3  5200.0  0.0  103       C"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the head function we can peek into the first 5 rows of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_cols = \"species\"\n",
    "num_cols = [\"bill_depth\", \"bill_length\", \"wing_length\", \"mass\"]\n",
    "cat_cols = [\"location\", \"sex\"]\n",
    "all_cols = num_cols + cat_cols + [target_cols]\n",
    "\n",
    "train = train[all_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n",
    "This is where we look into the trends and patterns of the data, both numerical and categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 435 entries, 0 to 434\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   bill_depth   434 non-null    float64\n",
      " 1   bill_length  295 non-null    float64\n",
      " 2   wing_length  298 non-null    float64\n",
      " 3   mass         433 non-null    float64\n",
      " 4   location     405 non-null    object \n",
      " 5   sex          379 non-null    float64\n",
      " 6   species      435 non-null    object \n",
      "dtypes: float64(5), object(2)\n",
      "memory usage: 27.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `info` function we can see the number of rows and data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[num_cols].hist(figsize=(20, 14));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the histograms of the numerical variables.\n",
    "\n",
    "- bill_depth peaks around 15 and 19\n",
    "- bill length peaks around 39 and 47\n",
    "- wing length peaks around 190 and 216\n",
    "- mass is right skewed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x700 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_plot = cat_cols + [target_cols]\n",
    "fig, axes = plt.subplots(1, 3, figsize=(20, 7), dpi=100)\n",
    "\n",
    "for i, col_name in enumerate(train[to_plot].columns):\n",
    "    sns.countplot(x = col_name, data = train, palette=\"Set1\", ax=axes[i % 3])\n",
    "    axes[i % 3].set_title(f\"{col_name}\", fontsize=13)\n",
    "    plt.subplots_adjust(hspace=0.45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see location and species seemingly matches for their respective locations and species (loc2 & species C, loc3 & species A). We also see there are slightly more female (1) birds than the male counterpart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    182\n",
       "A    160\n",
       "B     93\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.species.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a closer look, we see the target variable is imbalanced, where class B is almost 100 class lower than C, and around 70 lower than A.\n",
    "\n",
    "Imbalanced class is an issue because it biases the model to place more importance on classes with more samples ie. C is predicted a lot more often than B."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentages of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are a total of 6 variables with missing values\n",
      "\n",
      "bill_length          => 32.184%\n",
      "wing_length          => 31.494%\n",
      "sex                  => 12.874%\n",
      "location             => 6.897%\n",
      "mass                 => 0.46%\n",
      "bill_depth           => 0.23%\n"
     ]
    }
   ],
   "source": [
    "missing_vals(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our helper function, we find out that bill_length and wing_length has over 30% of missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis', cbar=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot a heatmap to visualize the missing values and see if there are any patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute categorical columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's first see how many missing variables are in our categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    195\n",
       "0.0    184\n",
       "NaN     56\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sex.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loc_2    181\n",
       "loc_3    141\n",
       "loc_1     83\n",
       "NaN       30\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.location.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the simple imputer to deal with them by replacing them with the most frequent value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_imp = SimpleImputer(strategy=\"most_frequent\")\n",
    "\n",
    "train[cat_cols] = cat_imp.fit_transform(train[cat_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    251\n",
       "0.0    184\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sex.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, by the 'most_frequent' strategy, the missing values were imputed with 1.0, which was the most frequent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute numerical columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the median value to impute our numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_imp = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "train[num_cols] = num_imp.fit_transform(train[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'no missing values'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    182\n",
       "A    160\n",
       "B     93\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.species.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### encode categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the label encoder, we can encode our categorical variables (and target variable) into numerical values. WE do this becaues most ML models don't work with string values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 0, 'B': 1, 'C': 2}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(train['species'])\n",
    "le_name_map = dict(zip(le.classes_, le.transform(le.classes_)))\n",
    "le_name_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fit the encoder on the variable first, and view what the mappping is like, so we can invert the mapping later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['species'] = le.fit_transform(train['species'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For other columns with string variables (non-numeric), we also do the same encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_cols:\n",
    "    if train[col].dtype == \"object\":\n",
    "        train[col] = le.fit_transform(train[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_depth</th>\n",
       "      <th>bill_length</th>\n",
       "      <th>wing_length</th>\n",
       "      <th>mass</th>\n",
       "      <th>location</th>\n",
       "      <th>sex</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.300000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.400000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>4625.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.400000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.982114</td>\n",
       "      <td>47.504878</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.982114</td>\n",
       "      <td>38.259307</td>\n",
       "      <td>217.186992</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bill_depth  bill_length  wing_length    mass  location  sex  species\n",
       "0   14.300000    48.200000   210.000000  4600.0         1    0        2\n",
       "1   14.400000    48.400000   203.000000  4625.0         1    0        2\n",
       "2   18.400000    47.500000   200.000000  3400.0         2    0        1\n",
       "3   14.982114    47.504878   199.000000  4800.0         1    0        2\n",
       "4   18.982114    38.259307   217.186992  5200.0         2    0        2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert cat_features to pd.Categorical dtype\n",
    "for col in cat_cols:\n",
    "    train[col] = pd.Categorical(train[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also convert categorical features into the pd.Categorical dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bill_depth      float64\n",
       "bill_length     float64\n",
       "wing_length     float64\n",
       "mass            float64\n",
       "location       category\n",
       "sex            category\n",
       "species           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['b_depth_length_ratio'] = train['bill_depth'] / train['bill_length']\n",
    "train['b_length_depth_ratio'] = train['bill_length'] / train['bill_depth']\n",
    "train['w_length_mass_ratio'] = train['wing_length'] / train['mass']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create some features with division to form ratios of variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_depth</th>\n",
       "      <th>bill_length</th>\n",
       "      <th>wing_length</th>\n",
       "      <th>mass</th>\n",
       "      <th>location</th>\n",
       "      <th>sex</th>\n",
       "      <th>species</th>\n",
       "      <th>b_depth_length_ratio</th>\n",
       "      <th>b_length_depth_ratio</th>\n",
       "      <th>w_length_mass_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.300000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.296680</td>\n",
       "      <td>3.370629</td>\n",
       "      <td>0.045652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.400000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>4625.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.297521</td>\n",
       "      <td>3.361111</td>\n",
       "      <td>0.043892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.400000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.387368</td>\n",
       "      <td>2.581522</td>\n",
       "      <td>0.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.982114</td>\n",
       "      <td>47.504878</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.315381</td>\n",
       "      <td>3.170773</td>\n",
       "      <td>0.041458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.982114</td>\n",
       "      <td>38.259307</td>\n",
       "      <td>217.186992</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.496144</td>\n",
       "      <td>2.015545</td>\n",
       "      <td>0.041767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bill_depth  bill_length  wing_length    mass location sex  species  \\\n",
       "0   14.300000    48.200000   210.000000  4600.0        1   0        2   \n",
       "1   14.400000    48.400000   203.000000  4625.0        1   0        2   \n",
       "2   18.400000    47.500000   200.000000  3400.0        2   0        1   \n",
       "3   14.982114    47.504878   199.000000  4800.0        1   0        2   \n",
       "4   18.982114    38.259307   217.186992  5200.0        2   0        2   \n",
       "\n",
       "   b_depth_length_ratio  b_length_depth_ratio  w_length_mass_ratio  \n",
       "0              0.296680              3.370629             0.045652  \n",
       "1              0.297521              3.361111             0.043892  \n",
       "2              0.387368              2.581522             0.058824  \n",
       "3              0.315381              3.170773             0.041458  \n",
       "4              0.496144              2.015545             0.041767  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split\n",
    "\n",
    "Now it's time to build the model, we first split it into X (features) and y (target variable), and then split it into training and evaluation set. \n",
    "\n",
    "Training is where we train the model, evaluation is where we test the model before fitting it to the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = train.drop([\"species\"], axis=1), train[[\"species\"]].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_eval, y_train, y_eval = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit a simple Decision Tree Classifier\n",
    "\n",
    "Here we fit a baseline model with a simple hyperparameter of max_depth = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree_model = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we fit the data, we can use it to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree_pred = dtree_model.predict(X_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.70      0.82        57\n",
      "           1       0.71      0.92      0.80        13\n",
      "           2       0.75      1.00      0.86        39\n",
      "\n",
      "    accuracy                           0.83       109\n",
      "   macro avg       0.82      0.87      0.83       109\n",
      "weighted avg       0.88      0.83      0.83       109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(dtree_pred, y_eval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classification report shows us useful metrics for our classifier. \n",
    "\n",
    "For example, the f1-score of our model is 0.83"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix\n",
    "\n",
    "We can also build a confusion matrix to visualize what our classifier is doing well/bad at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[40  0  0]\n",
      " [ 5 12  0]\n",
      " [12  1 39]]\n",
      "Normalized confusion matrix\n",
      "[[1.         0.         0.        ]\n",
      " [0.29411765 0.70588235 0.        ]\n",
      " [0.23076923 0.01923077 0.75      ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# save the target variable classes\n",
    "class_names = le_name_map.keys()\n",
    "\n",
    "titles_options = [\n",
    "    (\"Confusion matrix, without normalization\", None),\n",
    "    (\"Normalized confusion matrix\", \"true\"),\n",
    "]\n",
    "for title, normalize in titles_options:\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(\n",
    "        dtree_model,\n",
    "        X_eval,\n",
    "        y_eval,\n",
    "        display_labels=class_names,\n",
    "        cmap=plt.cm.Blues,\n",
    "        normalize=normalize,\n",
    "        ax = ax\n",
    "    )\n",
    "    disp.ax_.set_title(title)\n",
    "    disp.ax_.grid(False)\n",
    "\n",
    "    print(title)\n",
    "    print(disp.confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix shows us that it is predicting more classes A and C, which is not surprising since we had more samples.\n",
    "\n",
    "It also shows the model is predicting more A classes when it should be B/C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp = pd.DataFrame(sorted(zip(dtree_model.feature_importances_,X.columns)), columns=['Value','Feature'])\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
    "plt.title('LightGBM Features')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('lightgbm_fimp.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the feature importance, it seems mass is the best at predicting species, second is bill length. Other variables seem to have zero importance in the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25,20))\n",
    "_ = tree.plot_tree(dtree_model, \n",
    "                   feature_names=X.columns,  \n",
    "                   class_names=list(class_names),\n",
    "                   filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see how the feature importance is used in this visualization of our decision tree classifier.\n",
    "\n",
    "In root node, if the mass is lower than around 4600, it then checks for bill_length, else it checks for bill_depth, and then at the leaf is where it predicts the classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to do the same feature preprocessing and engineering we did to the train data, before we fit our model on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "cat_imp = SimpleImputer(strategy=\"most_frequent\")\n",
    "num_imp = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "test[cat_cols] = cat_imp.fit_transform(test[cat_cols])\n",
    "test[num_cols] = num_imp.fit_transform(test[num_cols])\n",
    "\n",
    "for col in cat_cols:\n",
    "    if test[col].dtype == \"object\":\n",
    "        test[col] = le.fit_transform(test[col])\n",
    "\n",
    "# Convert cat_features to pd.Categorical dtype\n",
    "for col in cat_cols:\n",
    "    test[col] = pd.Categorical(test[col])\n",
    "\n",
    "# save ID column\n",
    "test_id = test[\"ID\"]\n",
    "\n",
    "all_cols.remove('species')\n",
    "test = test[all_cols]\n",
    "\n",
    "test['b_depth_length_ratio'] = test['bill_depth'] / test['bill_length']\n",
    "test['b_length_depth_ratio'] = test['bill_length'] / test['bill_depth']\n",
    "test['w_length_mass_ratio'] = test['wing_length'] / test['mass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  species\n",
       "0   2        2\n",
       "1   5        0\n",
       "2   7        0\n",
       "3   8        0\n",
       "4   9        0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds = dtree_model.predict(test)\n",
    "submission_df = pd.concat([test_id, pd.DataFrame(test_preds, columns=['species'])], axis=1)\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the species value are numerical, we have to convert it back to the string values. with the label encoder with fit earlier, we can do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 0, 'B': 1, 'C': 2}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le_name_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'A', 1: 'B', 2: 'C'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_map = {v: k for k, v in le_name_map.items()}\n",
    "inv_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID species\n",
       "0   2       C\n",
       "1   5       A\n",
       "2   7       A\n",
       "3   8       A\n",
       "4   9       A"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df['species'] = submission_df['species'].map(inv_map)  \n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df.to_csv('solution.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we write the data frame to a csv file and submit it!"
   ]
  }
 ],
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