notebooks/process/basic-process.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting a sense of the ML training/testing procedure\n",
    "\n",
    "(*By testing, I really mean validation but it's easier to say \"test set\" until we get into more detail on testing.*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data into dataframe\n",
    "\n",
    "`rent-ideal.csv` is in the data folder in canvas. Do not distribute that file as we don't have the rights to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>40.7145</td>\n",
       "      <td>-73.9425</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7947</td>\n",
       "      <td>-73.9667</td>\n",
       "      <td>5465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7388</td>\n",
       "      <td>-74.0018</td>\n",
       "      <td>2850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7539</td>\n",
       "      <td>-73.9677</td>\n",
       "      <td>3275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8241</td>\n",
       "      <td>-73.9493</td>\n",
       "      <td>3350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  latitude  longitude  price\n",
       "0         3        1.5   40.7145   -73.9425   3000\n",
       "1         2        1.0   40.7947   -73.9667   5465\n",
       "2         1        1.0   40.7388   -74.0018   2850\n",
       "3         1        1.0   40.7539   -73.9677   3275\n",
       "4         4        1.0   40.8241   -73.9493   3350"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd # Import the library and give a short alias: pd\n",
    "df_rent = pd.read_csv(\"data/rent-ideal.csv\")\n",
    "df_rent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>40.7145</td>\n",
       "      <td>-73.9425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7947</td>\n",
       "      <td>-73.9667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7388</td>\n",
       "      <td>-74.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7539</td>\n",
       "      <td>-73.9677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8241</td>\n",
       "      <td>-73.9493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  latitude  longitude\n",
       "0         3        1.5   40.7145   -73.9425\n",
       "1         2        1.0   40.7947   -73.9667\n",
       "2         1        1.0   40.7388   -74.0018\n",
       "3         1        1.0   40.7539   -73.9677\n",
       "4         4        1.0   40.8241   -73.9493"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_rent.drop('price', axis=1)\n",
    "y = df_rent['price']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=10) # use 10 trees\n",
    "rf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown_x = [2, 1, 40.7957, -73.97] # 2 bedrooms, 1 bathroom, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rf.predict(unknown_x) # gets error \"Expected 2D array, got 1D array instead...\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4313.38616878])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict([unknown_x]) # send in list of list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, We can convert to a NumPy array and reshape it into a two-dimensional matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.    ,   1.    ,  40.7957, -73.97  ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "unknown_x = np.array(unknown_x).reshape(1,-1)\n",
    "unknown_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4313.38616878])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict(unknown_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Must encode everything to numbers\n",
    "\n",
    "We will deal with this specifically later, but you cannot pass in nonnumeric features to sklearn's random forest or any other model (as far as I know)."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df = df_rent.copy()\n",
    "df['address'] = '101 Howard' # Causes \"could not convert string to float: '101 Howard'\"\n",
    "X = df.drop('price', axis=1)\n",
    "y = df['price']\n",
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True)\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get an error \"*could not convert string to float: '101 Howard'*\" because it's not a number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training set error\n",
    "\n",
    "Does the model capture relationships in the data between X and y? If the model can't make accurate predictions for apartments it trained on, then there's no hope the model will generalize to previously-unseen apartments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.943129423723678"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X,y) # R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188.47075350730844"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict(X)\n",
    "np.mean(np.abs(y - y_pred)) # mean absolute error (MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3438.297950310559"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise-to-noise training error\n",
    "\n",
    "The training error tells us very little, other than the \"do not exceed speed\" of our model. For example, here's a model trained purely on noise that looks pretty accurate with regards to the training error. For the most part, though, the training error isn't that useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7870420017127995, 0.10494261326914205)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "rf_noise = RandomForestRegressor(n_estimators=10)\n",
    "X = np.random.rand(1000, 3) # 100 rows of 3-column feature vectors full of noise\n",
    "y = np.random.rand(1000)\n",
    "\n",
    "rf_noise.fit(X, y)\n",
    "rf_noise.score(X,y), mean_absolute_error(y, rf_noise.predict(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RFs are so powerful they can't even find a relationship in the noise training data, but it doesn't mean they are general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2673654436521671, 0.2817307393694486)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) \n",
    "rf_noise.fit(X_train, y_train)\n",
    "rf_noise.score(X_test,y_test), mean_absolute_error(y_test, rf_noise.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to the rent data set now, we see that generality is actually pretty good on the 20% test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8555418058933696, 297.7491919679805)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_rent.drop('price', axis=1)\n",
    "y = df_rent['price']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) \n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "rf.score(X_test,y_test), mean_absolute_error(y_test, rf.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OOB\n",
    "\n",
    "Compare the out of bag score from the random forest with the hold-out test set R^2.  Very close. This is very handy because we don't have to worry about validation or test sets during development work.  It's the only model I'm aware of that has this nice feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8631207597828239"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True)\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unpredictive features\n",
    "\n",
    "Adding noise or superfluous columns can introduce a little bit of error but RFs are pretty good about ignoring them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8179705745267823"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_rent.copy()\n",
    "for i in range(5):\n",
    "    df[f'noise{i}'] = np.random.random(len(df))\n",
    "X = df.drop('price', axis=1)\n",
    "y = df['price']\n",
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True, n_jobs=-1) # n_jobs=-1 makes it parallel\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing to the test error, we see that OOB R^2 continues to be an excellent estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8147338764170683, 376.1518495514147, 0.804868097297829)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) \n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True, n_jobs=-1)\n",
    "rf.fit(X_train, y_train)\n",
    "rf.score(X_test,y_test), mean_absolute_error(y_test, rf.predict(X_test)), rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've reduced accuracy (increased bias) by the introduction of all these extra columns, but we can compensate partially by increasing the number of trees in the forest  which helps to dilute the value of superfluous columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8314772631741696"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100, oob_score=True, n_jobs=-1) # n_jobs=-1 makes it parallel\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Duplicate or codependent features\n",
    "\n",
    "RFs are very good about dealing with codependent features. We don't increase bias by adding a new feature that is linear combination of other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>beds_and_baths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>40.7145</td>\n",
       "      <td>-73.9425</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7947</td>\n",
       "      <td>-73.9667</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7388</td>\n",
       "      <td>-74.0018</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7539</td>\n",
       "      <td>-73.9677</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8241</td>\n",
       "      <td>-73.9493</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  latitude  longitude  beds_and_baths\n",
       "0         3        1.5   40.7145   -73.9425             4.5\n",
       "1         2        1.0   40.7947   -73.9667             3.0\n",
       "2         1        1.0   40.7388   -74.0018             2.0\n",
       "3         1        1.0   40.7539   -73.9677             2.0\n",
       "4         4        1.0   40.8241   -73.9493             5.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_rent.copy()\n",
    "df['beds_and_baths'] = df['bedrooms'] + df['bathrooms']\n",
    "X = df.drop('price', axis=1)\n",
    "y = df['price']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8643514077219694"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True)\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RFs are insensitive to the scale and center (X and y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8639440595559142"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_rent.drop('price', axis=1)\n",
    "y = df_rent['price'] * 1000 + 99\n",
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True)\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.862627250455746"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_rent.drop('price', axis=1) * 1000 + 99\n",
    "y = df_rent['price']\n",
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True)\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What the model says about the data"
   ]
  },
  {
   "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>Importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>0.529879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.516242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0.439342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.431596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Importance\n",
       "Feature              \n",
       "longitude    0.529879\n",
       "bedrooms     0.516242\n",
       "latitude     0.439342\n",
       "bathrooms    0.431596"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rfpimp import *\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) \n",
    "rf = RandomForestRegressor(n_estimators=100)\n",
    "rf.fit(X_train, y_train)\n",
    "I = importances(rf, X_test, y_test)\n",
    "I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I = importances(rf, X_test, y_test,\n",
    "                features=['bedrooms','bathrooms',['latitude','longitude']])\n",
    "plot_importances(I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pitfalls\n",
    "\n",
    "### Pitfall: RF models don't extrapolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x113821358>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X = np.linspace(0, 10, num=50)\n",
    "y = X + np.random.random(size=len(X))\n",
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x= 0 -> y=0.35\n",
      "x= 1 -> y=1.58\n",
      "x= 2 -> y=2.60\n",
      "x= 3 -> y=3.86\n",
      "x= 4 -> y=4.70\n",
      "x= 5 -> y=5.63\n",
      "x= 6 -> y=6.58\n",
      "x= 7 -> y=7.50\n",
      "x= 8 -> y=8.36\n",
      "x= 9 -> y=9.20\n",
      "x=10 -> y=10.22\n",
      "x=11 -> y=10.22\n",
      "x=12 -> y=10.22\n",
      "x=13 -> y=10.22\n",
      "x=14 -> y=10.22\n"
     ]
    }
   ],
   "source": [
    "X = X.reshape(-1,1) # make 2D\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=10)\n",
    "rf.fit(X, y)\n",
    "for x in range(0,15):\n",
    "    print(f\"x={x:2d} -> y={rf.predict([[x]])[0]:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pitfall: When to split validation/test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/parrt/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8625001994978165"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_rent.drop('price', axis=1)\n",
    "y = df_rent['price']\n",
    "rf = RandomForestRegressor(n_estimators=30, oob_score=True, n_jobs=-1)\n",
    "rf.fit(X, y)\n",
    "rf.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9427713108580272"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) \n",
    "rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's wrong here? Why is the test score so good?  When you're OOB and test scores don't match, look for bugs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pitfall: instability of prediction error\n",
    "\n",
    "#### Regressor\n",
    "\n",
    "Any time you split out data, you'll get a different subset, which might look different. You must consider variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 scores: 0.52 0.41 0.44 0.01 -2.32 0.78 -0.07 -284.07 0.58 0.19 0.94 -3.66 0.60 0.53 -98.14 "
     ]
    }
   ],
   "source": [
    "df_rent = pd.read_json(\"data/rent.json\")\n",
    "X = df_rent[['bedrooms','bathrooms','latitude','longitude']]\n",
    "y = df_rent['price']\n",
    "scores = []\n",
    "print(\"R^2 scores: \", end='')\n",
    "for i in range(15):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    cl = RandomForestRegressor(n_estimators=10)\n",
    "    cl.fit(X_train, y_train)\n",
    "    s = cl.score(X_test, y_test)\n",
    "    print(f\"{s:.2f} \", end='')\n",
    "    scores.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean R^2 score -25.55, stddev 73.28\n"
     ]
    }
   ],
   "source": [
    "print(f\"Mean R^2 score {np.mean(scores):.2f}, stddev {np.std(scores):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, using MAE, easier to see it bounce around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MAE trials: $897 $602 $385 $649 $635 $389 $571 $520 $372 $379\n",
      "Average validation MAE $540, stddev 159.66\n"
     ]
    }
   ],
   "source": [
    "errors = []\n",
    "print(f\"Validation MAE trials:\", end='')\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = \\\n",
    "        train_test_split(X, y, test_size=0.20)\n",
    "    rf = RandomForestRegressor(n_estimators=10, n_jobs=-1)\n",
    "    rf.fit(X_train, y_train)\n",
    "    y_predicted = rf.predict(X_test)\n",
    "    e = mean_absolute_error(y_test, y_predicted)\n",
    "    print(f\" ${e:.0f}\", end='')\n",
    "    errors.append(e)\n",
    "print()\n",
    "noisy_avg_mae = np.mean(errors)\n",
    "print(f\"Average validation MAE ${noisy_avg_mae:.0f}, stddev {np.std(errors):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bouncing around means that different test subsamples of the data set have different characteristics (pulled from different distribution); i.e., there are outliers or data is just plain differently distributed. (Or, we could have a bug)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.92 0.96 0.91 0.96 0.97 0.95 0.96 0.97 0.96 0.94 0.94 0.95 0.96 0.96 0.92"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X = cancer.data\n",
    "y = cancer.target\n",
    "scores = []\n",
    "\n",
    "print(f\"Accuracy:\", end='')\n",
    "for i in range(15):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "    cl = RandomForestClassifier(n_estimators=10)\n",
    "    cl.fit(X_train, y_train)\n",
    "    s = cl.score(X_test, y_test)\n",
    "    print(f\" {s:.2f}\", end='')\n",
    "    scores.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean score 0.95, stddev 0.02\n"
     ]
    }
   ],
   "source": [
    "print(f\"Mean score {np.mean(scores):.2f}, stddev {np.std(scores):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample classifier (breast cancer data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius error</th>\n",
       "      <th>texture error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst symmetry</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.0950</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>17.33</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.4601</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>23.41</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.2750</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.7869</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>25.53</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.3613</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4956</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>26.50</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.6638</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>16.67</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2364</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   radius error  texture error  concave points error  symmetry error  \\\n",
       "0        1.0950         0.9053               0.01587         0.03003   \n",
       "1        0.5435         0.7339               0.01340         0.01389   \n",
       "2        0.7456         0.7869               0.02058         0.02250   \n",
       "3        0.4956         1.1560               0.01867         0.05963   \n",
       "4        0.7572         0.7813               0.01885         0.01756   \n",
       "\n",
       "   worst texture  worst smoothness  worst symmetry  \n",
       "0          17.33            0.1622          0.4601  \n",
       "1          23.41            0.1238          0.2750  \n",
       "2          25.53            0.1444          0.3613  \n",
       "3          26.50            0.2098          0.6638  \n",
       "4          16.67            0.1374          0.2364  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "import pandas as pd\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X = cancer.data\n",
    "y = cancer.target\n",
    "df = pd.DataFrame(X, columns=cancer.feature_names)\n",
    "\n",
    "\n",
    "features = ['radius error', 'texture error', 'concave points error',\n",
    "            'symmetry error', 'worst texture', 'worst smoothness',\n",
    "            'worst symmetry']\n",
    "df = df[features] # select just these features\n",
    "X = df\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(357, 569)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y), len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.956140350877193"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "\n",
    "cl = RandomForestClassifier(n_estimators=100, n_jobs=-1)\n",
    "cl.fit(X_train, y_train)\n",
    "cl.score(X_test, y_test) # this is accuracy score for classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I = importances(cl, X_test, y_test)\n",
    "plot_importances(I, vscale=1.4)"
   ]
  }
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