data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a660828c",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed7670c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "import datetime as dt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn import linear_model\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.preprocessing import scale\n",
    "from collections import Counter\n",
    "import ziptotimezone as z\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import graphviz \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "606aaa56",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('eBay_ML_Challenge_Dataset_2021_train.csv')\n",
    "\n",
    "b2c_c2c = np.array(dataset[\"b2c_c2c\"])\n",
    "seller_id = np.array(dataset[\"seller_id\"])\n",
    "declared_handling_days = np.array(dataset[\"declared_handling_days\"])\n",
    "acceptance_scan_timestamp = np.array(dataset[\"acceptance_scan_timestamp\"])\n",
    "shipment_method_id = np.array(dataset[\"shipment_method_id\"])\n",
    "shipping_fee = np.array(dataset[\"shipping_fee\"])\n",
    "carrier_min_estimate = np.array(dataset[\"carrier_min_estimate\"])\n",
    "carrier_max_estimate = np.array(dataset[\"carrier_max_estimate\"])\n",
    "item_zip = dataset[\"item_zip\"]\n",
    "buyer_zip = dataset[\"buyer_zip\"]\n",
    "category_id = np.array(dataset[\"category_id\"])\n",
    "item_price = np.array(dataset[\"item_price\"])\n",
    "quantity = np.array(dataset[\"quantity\"])\n",
    "payment_datetime = np.array(dataset[\"payment_datetime\"])\n",
    "delivery_date = np.array(dataset[\"delivery_date\"])\n",
    "weight = np.array(dataset[\"weight\"])\n",
    "weight_units = np.array(dataset[\"weight_units\"])\n",
    "package_size = np.array(dataset[\"package_size\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32808e64",
   "metadata": {},
   "source": [
    "Encode b2c_c2c as numeric binary [0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d8d02af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def b2c_c2c_to_binary(arr):\n",
    "    if arr[0] in [0,1]:\n",
    "        print(\"Array has already been converted to numeric binary!\")\n",
    "    else:\n",
    "        for i in range(arr.shape[0]):\n",
    "            if arr[i][0] == \"B\":\n",
    "                arr[i] = 0\n",
    "            else:\n",
    "                arr[i] = 1\n",
    "            \n",
    "b2c_c2c_to_binary(b2c_c2c)\n",
    "b2c_c2c = np.array(b2c_c2c, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd1d1e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "310141ad",
   "metadata": {},
   "source": [
    "Convert all times to the timezome of the buyer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e64a76b",
   "metadata": {},
   "source": [
    "Create labels for handling time (acceptance_scan_timestamp - payment_datetime), shipment time (delivery_date - acceptance_scan_timestamp), and total time (delivery_date - payment_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eefa5dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def round_datetime_to_date(datetime):\n",
    "    days = datetime.days\n",
    "    hours = datetime.seconds // 3600\n",
    "    if hours > 12:\n",
    "        return days + 1\n",
    "    else:\n",
    "        return days\n",
    "\n",
    "def calculate_handling_and_delivery_days(acceptance_timestamps, payment_timestamps, delivery_date):\n",
    "    handling_labels = []\n",
    "    shipping_labels = []\n",
    "    delivery_labels = []\n",
    "    for i in range(acceptance_timestamps.shape[0]):\n",
    "        raw_payment = payment_timestamps[i]\n",
    "        raw_acceptance = acceptance_timestamps[i]\n",
    "        #parse raw_payment time string to separate year, month, date, and time\n",
    "        p_year, p_month, p_date = int(raw_payment[0:4]), int(raw_payment[5:7]), int(raw_payment[8:10])\n",
    "        p_hour, p_min, p_sec = int(raw_payment[11:13]), int(raw_payment[14:16]), int(raw_payment[17:19])\n",
    "        p_datetime = dt.datetime(year=p_year, month=p_month, day=p_date, hour=p_hour, minute=p_min, second=p_sec)\n",
    "            \n",
    "        #parse raw_acceptance time string to separate year, month, date, and time\n",
    "        raw_acceptance = acceptance_timestamps[i]\n",
    "        a_year, a_month, a_date = int(raw_acceptance[0:4]), int(raw_acceptance[5:7]), int(raw_acceptance[8:10])\n",
    "        a_hour, a_min, a_sec = int(raw_acceptance[11:13]), int(raw_acceptance[14:16]), int(raw_acceptance[17:19])\n",
    "        a_datetime = dt.datetime(year=a_year, month=a_month, day=a_date, hour=a_hour, minute=a_min, second=a_sec)\n",
    "        \n",
    "        raw_delivery = delivery_date[i]\n",
    "        d_year, d_month, d_date = int(raw_delivery[0:4]), int(raw_delivery[5:7]), int(raw_delivery[8:10])\n",
    "        d_date = dt.datetime(year=d_year, month=d_month, day=d_date, hour=17)\n",
    "        \n",
    "        #handling days = acceptance time - payment time; shipping days = delivery date - acceptance time\n",
    "        handling_days = a_datetime - p_datetime\n",
    "        shipping_days = d_date - a_datetime\n",
    "        delivery_days = d_date - p_datetime\n",
    "        \n",
    "        #round to nearest day\n",
    "        rounded_handling_days = round_datetime_to_date(handling_days)\n",
    "        rounded_shipping_days = round_datetime_to_date(shipping_days)\n",
    "        rounded_delivery_days = round_datetime_to_date(delivery_days)\n",
    "        \n",
    "        handling_labels.append(rounded_handling_days)\n",
    "        shipping_labels.append(rounded_shipping_days)\n",
    "        delivery_labels.append(rounded_delivery_days)\n",
    "        \n",
    "    return np.array(handling_labels), np.array(shipping_labels), np.array(delivery_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12335f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "handling_days, shipping_days, delivery_days = calculate_handling_and_delivery_days(acceptance_scan_timestamp, payment_datetime, delivery_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb563e8e",
   "metadata": {},
   "source": [
    "Quantify distance between buyer and seller using zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eddcabf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45.45, -122.7\n",
      "41.97, -85.34\n",
      "40.71, -73.83\n",
      "39.8, -89.0\n",
      "35.7, -80.2\n",
      "43.6, -88.3\n",
      "33.87, -118.07\n",
      "39.9, -104.8\n",
      "33.87, -118.07\n",
      "45.4, -93.4\n",
      "39.97, -83.03\n",
      "29.74, -95.52\n",
      "34.23, -118.63\n",
      "41.72, -88.11\n",
      "45.0, -84.6\n",
      "34.7, -81.6\n",
      "41.67, -83.6\n",
      "27.8, -80.49\n",
      "33.66, -86.88\n",
      "37.64, -112.1\n",
      "35.35, -119.06\n",
      "38.89, -76.77\n",
      "42.8, -96.2\n",
      "35.5, -81.3\n",
      "29.65, -95.48\n",
      "40.0, -84.2\n",
      "34.02, -117.85\n",
      "35.8, -79.8\n",
      "29.68, -82.39\n",
      "25.82, -80.31\n",
      "44.2, -70.6\n",
      "38.0, -92.1\n",
      "38.89, -76.9\n",
      "37.33, -121.84\n",
      "29.5, -95.9\n",
      "44.83, -93.27\n",
      "43.6, -116.4\n",
      "29.3, -96.1\n",
      "39.6, -74.6\n",
      "39.9, -78.6\n",
      "39.7, -121.8\n",
      "39.9, -121.8\n",
      "29.16, -81.03\n",
      "43.3, -112.4\n",
      "33.71, -117.31\n",
      "41.49, -73.94\n",
      "None, None\n",
      "39.4, -104.5\n",
      "None, None\n",
      "37.9, -97.3\n",
      "34.07, -118.01\n",
      "31.8, -97.1\n",
      "34.07, -118.01\n",
      "27.1, -80.5\n",
      "34.1, -118.42\n",
      "34.15, -118.49\n",
      "44.3, -88.6\n",
      "32.78, -79.93\n",
      "39.8, -76.0\n",
      "42.9, -89.6\n",
      "28.9, -82.6\n",
      "33.4, -84.6\n",
      "45.9, -89.5\n",
      "45.6, -70.3\n",
      "43.1, -78.96\n",
      "36.7, -86.6\n",
      "29.86, -95.34\n",
      "28.73, -81.51\n",
      "42.8, -96.2\n",
      "40.1, -89.4\n",
      "42.8, -96.2\n",
      "40.2, -84.2\n",
      "35.6, -82.6\n",
      "33.2, -95.6\n",
      "40.96, -74.12\n",
      "34.06, -118.31\n",
      "33.75, -117.17\n",
      "25.85, -80.18\n",
      "42.8, -96.2\n",
      "39.23, -77.15\n",
      "36.22, -80.84\n",
      "34.0, -118.29\n",
      "29.75, -95.49\n",
      "38.41, -78.86\n",
      "44.5, -93.6\n",
      "32.71, -97.34\n",
      "39.74, -86.17\n",
      "33.63, -78.96\n",
      "25.99, -80.15\n",
      "38.42, -82.73\n",
      "44.2, -73.6\n",
      "31.98, -102.13\n",
      "33.51, -86.74\n",
      "32.4, -80.5\n",
      "33.74, -117.81\n",
      "42.9, -75.0\n",
      "41.67, -88.22\n",
      "None, None\n",
      "34.0, -117.98\n",
      "27.79, -82.68\n",
      "40.72, -74.15\n",
      "30.82, -97.6\n",
      "34.44, -117.62\n",
      "41.28, -75.89\n",
      "26.19, -80.11\n",
      "33.28, -111.74\n",
      "None, None\n",
      "41.63, -83.75\n",
      "34.0, -117.98\n",
      "43.7, -86.3\n",
      "44.7, -93.6\n",
      "38.61, -121.45\n",
      "42.3, -124.1\n",
      "42.53, -73.84\n",
      "43.01, -78.96\n",
      "33.64, -86.7\n",
      "44.3, -85.5\n",
      "42.91, -82.48\n",
      "42.79, -71.13\n",
      "40.82, -74.37\n",
      "33.74, -117.81\n",
      "34.06, -118.08\n",
      "33.74, -117.81\n",
      "43.2, -84.1\n",
      "42.6, -83.18\n",
      "44.3, -89.1\n",
      "34.02, -117.85\n",
      "25.82, -80.31\n",
      "33.81, -117.81\n",
      "37.2, -86.3\n",
      "40.06, -75.08\n",
      "44.3, -83.5\n",
      "33.84, -118.35\n",
      "47.5, -111.4\n",
      "42.93, -85.65\n",
      "32.5, -96.4\n",
      "42.01, -87.99\n",
      "40.71, -73.24\n",
      "41.67, -88.22\n",
      "30.38, -97.96\n",
      "43.06, -73.93\n",
      "41.46, -72.81\n",
      "45.41, -122.8\n",
      "40.9, -111.9\n",
      "41.6, -72.5\n",
      "43.0, -110.0\n",
      "45.16, -93.32\n",
      "44.2, -86.2\n",
      "43.4, -86.3\n",
      "34.24, -83.89\n",
      "30.4, -87.1\n",
      "37.57, -121.99\n",
      "35.5, -81.8\n",
      "33.6, -98.0\n",
      "38.4, -91.0\n",
      "35.7, -95.5\n",
      "31.3, -81.5\n",
      "35.25, -97.46\n",
      "30.4, -87.1\n",
      "42.0, -88.7\n",
      "42.79, -73.72\n",
      "28.99, -82.52\n",
      "28.9, -82.6\n",
      "27.32, -80.35\n",
      "26.82, -80.21\n",
      "None, None\n",
      "42.63, -71.39\n",
      "33.94, -117.95\n",
      "33.6, -88.4\n",
      "32.75, -96.77\n",
      "42.8, -96.2\n",
      "42.65, -71.92\n",
      "26.82, -80.21\n",
      "40.9, -81.43\n",
      "32.5, -95.9\n",
      "43.13, -77.61\n",
      "32.5, -114.7\n",
      "32.81, -97.36\n",
      "35.6, -118.9\n",
      "39.89, -75.24\n",
      "38.2, -75.2\n",
      "32.1, -95.9\n",
      "36.1, -96.4\n",
      "48.8, -116.5\n",
      "43.6, -116.4\n",
      "39.5, -84.2\n",
      "40.6, -111.83\n",
      "41.6, -86.3\n",
      "40.6, -111.83\n",
      "39.28, -76.53\n",
      "40.6, -111.83\n",
      "32.6, -117.0\n",
      "33.85, -117.79\n",
      "36.3, -88.4\n",
      "33.85, -117.79\n",
      "48.1, -113.8\n",
      "42.87, -85.66\n",
      "37.8, -122.1\n",
      "33.75, -117.17\n",
      "41.24, -96.0\n",
      "35.4, -119.12\n",
      "48.4, -122.4\n",
      "35.6, -82.6\n",
      "39.22, -94.75\n",
      "31.3, -81.5\n",
      "42.13, -87.77\n",
      "35.23, -80.84\n",
      "29.77, -95.16\n",
      "33.62, -86.76\n",
      "37.69, -97.11\n",
      "25.6, -80.49\n",
      "34.01, -118.11\n",
      "32.61, -96.87\n",
      "37.8, -84.9\n",
      "32.61, -96.87\n",
      "37.1, -77.4\n",
      "32.61, -96.87\n",
      "39.96, -76.73\n",
      "41.5, -72.7\n",
      "40.53, -75.12\n",
      "40.06, -104.95\n",
      "37.4, -122.3\n",
      "28.7, -81.5\n",
      "None, None\n",
      "41.78, -111.81\n",
      "40.5, -111.5\n",
      "33.84, -118.35\n",
      "32.0, -106.9\n",
      "38.4, -91.0\n",
      "32.5, -96.8\n",
      "42.01, -87.99\n",
      "39.3, -84.6\n",
      "42.01, -87.99\n",
      "26.57, -81.94\n",
      "42.01, -87.99\n",
      "34.6, -97.4\n",
      "42.01, -87.99\n",
      "42.65, -82.93\n",
      "35.6, -118.9\n",
      "32.96, -96.65\n",
      "37.44, -121.84\n",
      "40.72, -73.86\n",
      "34.04, -117.99\n",
      "35.6, -80.4\n",
      "34.04, -117.99\n",
      "37.32, -121.93\n",
      "25.6, -80.6\n",
      "33.93, -117.36\n",
      "40.76, -73.32\n",
      "32.37, -90.17\n",
      "33.76, -84.65\n",
      "47.87, -122.26\n",
      "37.44, -121.84\n",
      "34.16, -117.71\n",
      "30.37, -97.64\n",
      "40.48, -74.44\n",
      "34.29, -118.5\n",
      "30.1, -96.1\n",
      "41.6, -70.56\n",
      "33.87, -118.07\n",
      "33.71, -116.99\n",
      "38.4, -84.5\n",
      "37.69, -122.44\n",
      "43.07, -76.15\n",
      "41.71, -87.76\n",
      "38.9, -121.3\n",
      "40.1, -83.01\n",
      "32.0, -102.0\n",
      "30.37, -97.64\n",
      "39.95, -104.8\n",
      "32.95, -96.72\n",
      "40.87, -74.84\n",
      "37.44, -121.84\n",
      "28.21, -82.3\n",
      "26.61, -80.06\n",
      "41.84, -87.83\n",
      "43.18, -86.19\n",
      "35.7, -80.9\n",
      "40.76, -73.32\n",
      "35.4, -96.1\n",
      "37.44, -121.84\n",
      "35.9, -80.1\n",
      "36.15, -80.33\n",
      "35.9, -94.3\n",
      "26.38, -80.17\n",
      "42.2, -85.9\n",
      "40.4, -74.11\n",
      "37.4, -120.5\n",
      "40.2, -74.17\n",
      "40.19, -75.15\n",
      "27.79, -82.73\n",
      "30.0, -93.2\n",
      "34.76, -92.29\n",
      "40.57, -74.13\n",
      "37.52, -121.9\n",
      "34.06, -117.18\n",
      "42.6, -83.9\n",
      "29.41, -97.61\n",
      "None, None\n",
      "37.7, -109.9\n",
      "34.8, -82.7\n",
      "43.76, -87.71\n",
      "41.5, -72.7\n",
      "32.1, -96.0\n",
      "40.64, -74.02\n",
      "33.89, -117.33\n",
      "41.17, -73.28\n",
      "40.69, -73.82\n",
      "40.76, -73.32\n",
      "43.3, -122.6\n",
      "34.09, -117.97\n",
      "41.8, -88.35\n",
      "34.09, -117.97\n",
      "40.2, -105.2\n",
      "34.09, -117.97\n",
      "35.44, -97.44\n",
      "34.09, -117.97\n",
      "32.86, -83.67\n",
      "42.8, -96.2\n",
      "42.7, -91.5\n",
      "27.33, -82.35\n",
      "33.5, -98.5\n",
      "42.31, -71.8\n",
      "36.84, -119.8\n",
      "33.12, -96.69\n",
      "40.64, -80.08\n",
      "46.6, -120.8\n",
      "47.37, -122.72\n",
      "40.76, -73.77\n",
      "32.6, -117.0\n",
      "40.76, -73.77\n",
      "41.12, -74.05\n",
      "39.8, -105.3\n",
      "34.13, -77.86\n",
      "40.7, -73.88\n",
      "38.92, -94.7\n",
      "34.16, -118.55\n",
      "39.04, -95.73\n",
      "42.8, -96.2\n",
      "33.4, -90.7\n",
      "39.79, -86.29\n",
      "46.32, -119.4\n",
      "34.42, -118.61\n",
      "30.23, -94.2\n",
      "33.74, -117.81\n",
      "41.39, -81.64\n",
      "28.9, -82.6\n",
      "29.6, -83.1\n",
      "32.5, -86.1\n",
      "None, None\n",
      "32.5, -86.1\n",
      "34.02, -117.85\n",
      "41.95, -87.75\n",
      "40.58, -73.99\n",
      "47.36, -122.87\n",
      "41.93, -87.89\n",
      "40.81, -72.85\n",
      "33.85, -117.96\n",
      "36.9, -119.6\n",
      "38.35, -76.57\n",
      "36.9, -119.6\n",
      "39.48, -77.89\n",
      "36.9, -119.6\n",
      "35.7, -78.5\n",
      "36.9, -119.6\n",
      "35.17, -85.21\n",
      "40.2, -74.17\n",
      "36.7, -90.5\n",
      "35.35, -119.06\n",
      "37.76, -122.16\n",
      "37.27, -122.05\n",
      "36.15, -80.33\n",
      "41.64, -81.37\n",
      "41.7, -80.7\n",
      "44.3, -85.5\n",
      "36.2, -83.0\n",
      "25.79, -80.36\n",
      "31.8, -106.1\n",
      "41.3, -74.2\n",
      "44.08, -92.51\n",
      "46.5, -90.2\n",
      "45.02, -93.24\n",
      "39.11, -77.25\n",
      "42.08, -87.82\n",
      "33.92, -118.27\n",
      "35.57, -80.89\n",
      "33.92, -118.27\n",
      "36.6, -119.9\n",
      "28.7, -81.5\n",
      "39.04, -76.81\n",
      "27.22, -82.47\n",
      "42.2, -89.2\n",
      "40.09, -75.04\n",
      "41.0, -78.2\n",
      "40.09, -75.04\n",
      "34.2, -97.4\n",
      "41.9, -87.64\n",
      "40.26, -74.76\n",
      "34.29, -118.5\n",
      "33.8, -82.4\n",
      "29.68, -82.39\n",
      "46.9, -96.1\n",
      "40.75, -73.99\n",
      "43.75, -70.27\n",
      "33.7, -86.9\n",
      "42.9, -76.8\n",
      "25.94, -80.14\n",
      "29.91, -95.64\n",
      "40.57, -80.15\n",
      "40.55, -80.05\n",
      "25.79, -80.36\n",
      "40.78, -73.19\n",
      "34.1, -118.05\n",
      "44.45, -73.18\n",
      "34.07, -117.7\n",
      "44.1, -70.2\n",
      "40.04, -75.07\n",
      "25.7, -80.41\n",
      "41.4, -87.3\n",
      "38.4, -77.4\n",
      "28.1, -82.69\n",
      "35.09, -106.51\n",
      "35.81, -78.89\n",
      "40.69, -73.93\n",
      "42.95, -87.94\n",
      "39.0, -85.6\n",
      "39.78, -75.05\n",
      "39.84, -84.11\n",
      "26.02, -80.15\n",
      "39.28, -85.77\n",
      "26.02, -80.15\n",
      "40.58, -73.95\n",
      "34.044, -118.252\n",
      "29.3, -81.2\n",
      "40.82, -74.37\n",
      "33.63, -117.8\n",
      "37.51, -121.99\n",
      "34.0, -118.21\n",
      "40.7, -73.88\n",
      "38.3, -77.49\n",
      "41.71, -85.8\n",
      "41.5, -81.0\n",
      "28.61, -81.3\n",
      "32.3, -86.1\n",
      "40.76, -73.32\n",
      "25.77, -80.23\n",
      "34.03, -118.4\n",
      "32.8, -83.69\n",
      "26.25, -80.09\n",
      "39.64, -85.99\n",
      "36.9, -119.6\n",
      "39.3, -75.9\n",
      "41.93, -87.89\n",
      "35.2, -90.2\n",
      "26.07, -80.22\n",
      "32.81, -117.14\n",
      "37.3, -93.3\n",
      "41.4, -85.3\n",
      "32.5, -86.1\n",
      "33.61, -111.89\n",
      "47.39, -122.31\n",
      "39.2, -120.4\n",
      "34.2, -88.0\n",
      "32.77, -117.21\n",
      "38.76, -77.31\n",
      "34.01, -118.11\n",
      "33.74, -117.81\n",
      "41.66, -93.63\n",
      "26.25, -80.09\n",
      "47.1, -122.4\n",
      "33.71, -117.76\n",
      "42.27, -83.13\n",
      "40.86, -74.05\n",
      "41.67, -70.94\n",
      "30.4, -87.1\n",
      "38.2, -86.2\n",
      "30.4, -87.1\n",
      "29.3, -81.2\n",
      "35.4, -119.12\n",
      "30.43, -97.6\n",
      "35.4, -119.12\n",
      "39.54, -104.88\n",
      "35.4, -119.12\n",
      "39.5, -104.8\n",
      "35.4, -119.12\n",
      "39.49, -76.66\n",
      "34.044, -118.252\n",
      "34.76, -92.29\n",
      "36.6, -76.3\n",
      "45.8, -92.7\n",
      "36.6, -76.3\n",
      "35.8, -79.2\n",
      "37.51, -121.99\n",
      "44.0, -75.6\n",
      "37.51, -121.99\n",
      "39.1, -78.8\n",
      "42.95, -87.94\n",
      "33.3, -96.6\n",
      "38.38, -75.64\n",
      "31.65, -106.27\n",
      "33.7, -85.7\n",
      "33.7, -88.7\n",
      "42.8, -85.7\n",
      "28.18, -80.6\n",
      "36.46, -94.3\n",
      "35.2, -91.7\n",
      "34.09, -118.1\n",
      "33.78, -117.84\n",
      "33.95, -117.73\n",
      "47.67, -122.34\n",
      "33.02, -96.75\n",
      "42.2, -123.3\n",
      "32.6, -82.4\n",
      "40.41, -75.52\n",
      "None, None\n",
      "36.27, -115.11\n",
      "42.63, -71.39\n",
      "33.9, -118.17\n",
      "34.01, -117.7\n",
      "45.5, -90.7\n",
      "46.2, -123.1\n",
      "47.02, -122.29\n",
      "30.82, -97.6\n",
      "None, None\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "string index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1433270/2669380781.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mzips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0mzips\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_zip_distance_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem_zip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuyer_zip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_1433270/2669380781.py\u001b[0m in \u001b[0;36madd_zip_distance_column\u001b[0;34m(item_zip, buyer_zip)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mzips\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem_zip_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuyer_zip_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0mzips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzips\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_zip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuyer_zip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mzips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   7766\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7767\u001b[0m         )\n\u001b[0;32m-> 7768\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7770\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_1433270/2669380781.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mzips\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem_zip_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuyer_zip_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0mzips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzips\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_zip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuyer_zip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mzips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_1433270/2669380781.py\u001b[0m in \u001b[0;36mget_distance\u001b[0;34m(item_zip, buyer_zip)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlat1\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlat2\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlong1\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlong2\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0mlat1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlong1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzip_to_central_lat_lon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem_zip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m             \u001b[0mlat2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlong2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzip_to_central_lat_lon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuyer_zip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhaversine_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlat1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlong1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlat2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlong2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/eBay_ML_Challenge_Dataset_2021/ziptotimezone.py\u001b[0m in \u001b[0;36mzip_to_central_lat_lon\u001b[0;34m(z)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;31m# %%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mzip_to_central_lat_lon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m     \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzipcode_to_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m     \u001b[0mcoordinates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate_to_lat_long\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/eBay_ML_Challenge_Dataset_2021/ziptotimezone.py\u001b[0m in \u001b[0;36mzipcode_to_state\u001b[0;34m(z)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"0\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring_zip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mdigit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstring_zip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mleading_zeros\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring_zip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mleading_zeros\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
     ]
    }
   ],
   "source": [
    "import mpu\n",
    "from uszipcode import SearchEngine\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def get_distance(item_zip, buyer_zip):\n",
    "    \"\"\"\n",
    "    Haversine formula using 'mpu' library which determines the\n",
    "    great-circle distance between two points on a sphere.\n",
    "    \"\"\"\n",
    "    if item_zip is not None and buyer_zip is not None:\n",
    "        search = SearchEngine(simple_zipcode=True)\n",
    "\n",
    "        zip1 = search.by_zipcode(item_zip[0:5])\n",
    "        lat1 =zip1.lat\n",
    "        long1 =zip1.lng\n",
    "        print(str(lat1) + \", \" + str(long1))\n",
    "\n",
    "        zip2 =search.by_zipcode(buyer_zip[0:5])\n",
    "        lat2 =zip2.lat\n",
    "        long2 =zip2.lng\n",
    "        print(str(lat2) + \", \" + str(long2))\n",
    "\n",
    "        if lat1 is None or lat2 is None or long1 is None or long2 is None:\n",
    "            lat1, long1 = z.zip_to_central_lat_lon(int(item_zip))\n",
    "            lat2, long2 = z.zip_to_central_lat_lon(int(buyer_zip))\n",
    "        return mpu.haversine_distance((lat1,long1),(lat2,long2)) \n",
    "    else:\n",
    "        return None\n",
    "\n",
    "\n",
    "def add_zip_distance_column(item_zip, buyer_zip):\n",
    "    item_zip_str = item_zip.apply(lambda x: str(x))\n",
    "    buyer_zip_str = buyer_zip.apply(lambda x: str(x))\n",
    "\n",
    "    zips = pd.concat([item_zip_str, buyer_zip_str], axis=1)\n",
    "\n",
    "    zips['distance'] = zips.apply(lambda x: get_distance(x.item_zip, x.buyer_zip), axis=1)\n",
    "    \n",
    "    return zips['distance']\n",
    "\n",
    "zips = add_zip_distance_column(item_zip, buyer_zip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6066ef6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d6371c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ea0f2caf",
   "metadata": {},
   "source": [
    "Use weight_units to convert all weights to the same unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9037f28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_weights():\n",
    "    for i, unit in enumerate(weight_units):\n",
    "        if unit == 2:\n",
    "            #convert weight to lbs; 1 kg = 2.20462 lbs.\n",
    "            weight[i] *= 2.20462\n",
    "convert_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce9b63e",
   "metadata": {},
   "source": [
    "Replace missing weight values with the average weight by category ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "42c26198",
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_weight_averages_by_category_id():\n",
    "    category_id_weights = {}\n",
    "    for i, w in enumerate(weight):\n",
    "        category = category_id[i]\n",
    "        if category not in category_id_weights:\n",
    "            category_id_weights[category] = [w]\n",
    "        else:\n",
    "            category_id_weights[category].append(w)\n",
    "    \n",
    "    category_id_weight_means = {}\n",
    "    for category in category_id_weights:\n",
    "        weights = category_id_weights[category]\n",
    "        average_weight = np.mean(weights)\n",
    "        category_id_weight_means[category] = average_weight\n",
    "    \n",
    "    return category_id_weight_means\n",
    "\n",
    "def fill_missing_weights():\n",
    "    weight_means = determine_weight_averages_by_category_id()\n",
    "    overall_mean = np.mean(weight)\n",
    "    for i, w in enumerate(weight):\n",
    "        if w == 0:\n",
    "            #weight is missing, replace with average weight across same category id\n",
    "            category = category_id[i]\n",
    "            if category in weight_means:\n",
    "                weight[i] = weight_means[category]\n",
    "            else:\n",
    "                #don't have records for this category id, so replace with overall average\n",
    "                weight[i] = overall_mean\n",
    "fill_missing_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefa3ba9",
   "metadata": {},
   "source": [
    "Encode package_size as discrete numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b0b5b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def string_to_numeric_package_size():\n",
    "    if type(package_size[0]) == int:\n",
    "        print(\"Already converted to discrete numeric values\")\n",
    "    else:\n",
    "        encodings = {\"LETTER\": 0, \"PACKAGE_THICK_ENVELOPE\": 1, \"LARGE_ENVELOPE\": 2,\"VERY_LARGE_PACKAGE\": 3, \n",
    "                     \"LARGE_PACKAGE\": 4, \"EXTRA_LARGE_PACKAGE\": 5, \"NONE\": -1}\n",
    "        for i, size in enumerate(package_size):\n",
    "            package_size[i] = encodings[size]\n",
    "string_to_numeric_package_size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a70435",
   "metadata": {},
   "source": [
    "Replace missing package_size values with the most likely size using weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77f12b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_average_weight_by_package_size():\n",
    "    package_size_weights = {}\n",
    "    for i, w in enumerate(weight):\n",
    "        p_size = package_size[i]\n",
    "        if p_size not in package_size_weights:\n",
    "            package_size_weights[p_size] = [w]\n",
    "        else:\n",
    "            package_size_weights[p_size].append(w)\n",
    "    \n",
    "    package_id_weight_means = {}\n",
    "    for p_size in package_size_weights:\n",
    "        weights = package_size_weights[p_size]\n",
    "        average_weight = np.mean(weights)\n",
    "        package_id_weight_means[p_size] = average_weight\n",
    "    \n",
    "    return package_id_weight_means\n",
    "\n",
    "def fill_missing_package_sizes():\n",
    "    weight_means = determine_average_weight_by_package_size()\n",
    "    weight_means.pop(-1, None)\n",
    "    weight_means_list = [weight_means[key] for key in weight_means]\n",
    "    for i, s in enumerate(package_size):\n",
    "        if s == -1:\n",
    "            #package size is missing, replace with package size it's weight is closest to the average of\n",
    "            w = weight[i]\n",
    "            abs_function = lambda value: abs(value-w)\n",
    "            closest_value = min(weight_means_list, key=abs_function)\n",
    "            closest_p_size = weight_means_list.index(closest_value)\n",
    "            package_size[i] = closest_p_size\n",
    "\n",
    "fill_missing_package_sizes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb6e2ca",
   "metadata": {},
   "source": [
    "Replace missing carrier_min_estimate and carrier_max_estimate with averages from the same shipment_method_id. If there are no records for that shipment_method_id, replace with overall average across all shipment_method_id's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e26ae11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_average_shipping_estimates_by_shipment_method():    \n",
    "    carrier_min_by_shipment_method = {}\n",
    "    carrier_max_by_shipment_method = {}\n",
    "    for i, method_id in enumerate(shipment_method_id):\n",
    "        carrier_min = carrier_min_estimate[i]\n",
    "        carrier_max = carrier_max_estimate[i]\n",
    "        if method_id not in carrier_min_by_shipment_method:\n",
    "            carrier_min_by_shipment_method[method_id] = [carrier_min]\n",
    "        else:\n",
    "            carrier_min_by_shipment_method[method_id].append(carrier_min)\n",
    "\n",
    "        if method_id not in carrier_max_by_shipment_method:\n",
    "            carrier_max_by_shipment_method[method_id] = [carrier_max]\n",
    "        else:\n",
    "            carrier_max_by_shipment_method[method_id].append(carrier_max)\n",
    "    \n",
    "    carrier_min_means = {}\n",
    "    for method_id in carrier_min_by_shipment_method:\n",
    "        min_estimates = carrier_min_by_shipment_method[method_id]\n",
    "        mean_min_estimate = np.mean(min_estimates)\n",
    "        carrier_min_means[method_id] = mean_min_estimate\n",
    "    \n",
    "    carrier_max_means = {}\n",
    "    for method_id in carrier_max_by_shipment_method:\n",
    "        max_estimates = carrier_max_by_shipment_method[method_id]\n",
    "        mean_max_estimate = np.mean(max_estimates)\n",
    "        carrier_max_means[method_id] = mean_max_estimate \n",
    "    \n",
    "    return carrier_min_means, carrier_max_means\n",
    "\n",
    "def fill_missing_carrier_estimates():\n",
    "    carrier_min_means, carrier_max_means = determine_average_shipping_estimates_by_shipment_method()\n",
    "    overall_min_mean, overall_max_mean = np.mean(carrier_min_estimate), np.mean(carrier_max_estimate)\n",
    "    for i, estimate in enumerate(carrier_min_estimate):\n",
    "        if estimate < 0:\n",
    "            #need to fill value \n",
    "            method_id = shipment_method_id[i]\n",
    "            if method_id in carrier_min_means:\n",
    "                carrier_min_estimate[i] = carrier_min_means[method_id]\n",
    "            else:\n",
    "                carrier_min_estimate[i] = overall_min_mean\n",
    "    for i, estimate in enumerate(carrier_max_estimate):\n",
    "        if estimate < 0:\n",
    "            #need to fill value\n",
    "            method_id = shipment_method_id[i]\n",
    "            if method_id in carrier_max_means:\n",
    "                carrier_max_estimate[i] = carrier_max_means[method_id]\n",
    "            else:\n",
    "                carrier_max_estimate[i] = overall_max_mean\n",
    "\n",
    "fill_missing_carrier_estimates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c52d4c",
   "metadata": {},
   "source": [
    "Replace missing declared_handling_days with average from the same seller_id. If there are no records for that seller_id, replace with overall average across all sellers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e71ad5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_missing_declared_handling_days():\n",
    "    overall_mean = np.mean(declared_handling_days)\n",
    "    seller_counts = Counter(seller_id)\n",
    "    for i, days in enumerate(declared_handling_days):\n",
    "        if np.isnan(days):\n",
    "            #need to fill\n",
    "            declared_handling_days[i] = overall_mean\n",
    "#COULD MAKE THIS BETTER BY DOING BY SELLER ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efad3e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = np.column_stack((b2c_c2c, seller_id, declared_handling_days, shipment_method_id, shipping_fee,\n",
    "                             carrier_min_estimate, carrier_max_estimate, category_id,\n",
    "                             item_price, weight, package_size, handling_days))\n",
    "labels = np.array(delivery_days)\n",
    "handling_labels = np.array(handling_days)\n",
    "shipping_labels = np.array(shipping_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c181463",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(features, labels)\n",
    "handling_X_train, handling_X_test, handling_Y_train, handling_Y_test = train_test_split(features, handling_labels)\n",
    "shipping_X_train, shipping_X_test, shipping_Y_train, shipping_Y_test = train_test_split(features, shipping_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9a593bc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:194: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "  warnings.warn(\"Numerical issues were encountered \"\n",
      "/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:194: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "  warnings.warn(\"Numerical issues were encountered \"\n",
      "/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:194: UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.\n",
      "  warnings.warn(\"Numerical issues were encountered \"\n"
     ]
    }
   ],
   "source": [
    "X_train = scale(X_train, with_mean=True, with_std=True)\n",
    "X_test = scale(X_test, with_mean=True, with_std=True)\n",
    "handling_X_train = scale(handling_X_train, with_mean=True, with_std=True)\n",
    "handling_X_test = scale(handling_X_test, with_mean=True, with_std=True)\n",
    "shipping_X_train = scale(shipping_X_train, with_mean=True, with_std=True)\n",
    "shipping_X_test = scale(shipping_X_test, with_mean=True, with_std=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4553d00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,\n",
      "             colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
      "             gpu_id=None, importance_type='gain', interaction_constraints=None,\n",
      "             learning_rate=None, max_delta_step=None, max_depth=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "             random_state=None, reg_alpha=None, reg_lambda=None,\n",
      "             scale_pos_weight=None, subsample=None, tree_method=None,\n",
      "             validate_parameters=None, verbosity=0)\n"
     ]
    }
   ],
   "source": [
    "xgbr = xgb.XGBRegressor(verbosity=0)\n",
    "print(xgbr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0418258b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=40, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='approx', validate_parameters=1, verbosity=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d6d769e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6863894542225488\n"
     ]
    }
   ],
   "source": [
    "train_score = xgbr.score(X_train, Y_train)\n",
    "print(train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3ad3b36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9188312931786387\n"
     ]
    }
   ],
   "source": [
    "pred = xgbr.predict(X_test)\n",
    "mse = mean_squared_error(Y_test, pred)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8822dbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_loss(preds, actual):\n",
    "    early_loss, late_loss = 0,0 \n",
    "    for i in range(len(preds)):\n",
    "        if preds[i] < actual[i]:\n",
    "            #early shipment\n",
    "            early_loss += actual[i] - preds[i]\n",
    "        elif preds[i] > actual[i]:\n",
    "            #late shipment\n",
    "            late_loss += preds[i] - actual[i]\n",
    "    loss = (1/len(preds)) * (0.4 * (early_loss) + 0.6 * (late_loss))\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "459dd196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5300786547326438\n"
     ]
    }
   ],
   "source": [
    "print(evaluate_loss(pred, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a4391c92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_tree(xgbr)\n",
    "plt.savefig('tree.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "34794a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00470502 0.01391438 0.0124221  0.01212137 0.00642627 0.0412479\n",
      " 0.08207865 0.00904437 0.00842107 0.00675844 0.01387058 0.78898984]\n"
     ]
    }
   ],
   "source": [
    "print(xgbr.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "18502922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(xgbr.max_depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d95286d",
   "metadata": {},
   "source": [
    "[0.00716152 0.0138678  0.0125318  0.01243079 0.00656817 0.04646778\n",
    " 0.08607896 0.00716838 0.00732436 0.00689182 0.01321472 0.7802939 ]\n",
    " \n",
    "b2c_c2c, seller_id, declared_handling_days, shipment_method_id, shipping_fee,\n",
    "                             carrier_min_estimate, carrier_max_estimate, category_id,\n",
    "                             item_price, weight, package_size, handling_days\n",
    "\n",
    "importance < 0.01: b2c_c2c, shipping_fee, category_id, item_price, weight\n",
    "0.01 < importance < 0.05: seller_id, declared_handling_days, shipment_method_id, carrier_min_estimate, package_size\n",
    "0.05 < importance: carrier_max_estimate, handling_days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f22ce4e",
   "metadata": {},
   "source": [
    "Try random forest with and without unimportant features\n",
    "visualize correlation matrix as a heap map (can use seaborn)\n",
    "Knock out experiments:\n",
    "- knock out one feature and train \n",
    "- compare models (the one that has the largest hit from removing 1 feature tells that that feature is most important)\n",
    "Try turning zip codes into floating point numbers and passing that into neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680be5d8",
   "metadata": {},
   "source": [
    "Let's use XGBoost to determine which features are important only for the handling period, and which are important only for the shipping period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4a839c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,\n",
      "             colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
      "             gpu_id=None, importance_type='gain', interaction_constraints=None,\n",
      "             learning_rate=None, max_delta_step=None, max_depth=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "             random_state=None, reg_alpha=None, reg_lambda=None,\n",
      "             scale_pos_weight=None, subsample=None, tree_method=None,\n",
      "             validate_parameters=None, verbosity=0)\n",
      "MSE:\n",
      "22.76756258453385\n",
      "Loss:\n",
      "0.43506425964805373\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.66874991e-02 1.57523658e-02 1.91025324e-02 4.13970882e-03\n",
      " 1.33468891e-02 2.09106889e-04 3.63922194e-02 1.04670925e-02\n",
      " 1.02126561e-02 9.62290820e-03 4.79271309e-03 8.49274337e-01]\n"
     ]
    }
   ],
   "source": [
    "handling_xgbr = xgb.XGBRegressor(verbosity=0)\n",
    "print(handling_xgbr)\n",
    "handling_xgbr.fit(handling_X_train, handling_Y_train)\n",
    "pred = handling_xgbr.predict(handling_X_test)\n",
    "mse = mean_squared_error(handling_Y_test, pred)\n",
    "print(\"MSE:\")\n",
    "print(mse)\n",
    "print(\"Loss:\")\n",
    "print(evaluate_loss(pred, handling_Y_test))\n",
    "xgb.plot_tree(handling_xgbr)\n",
    "plt.savefig('handling_tree.png', dpi=300)\n",
    "plt.show()\n",
    "print(handling_xgbr.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9439cd66",
   "metadata": {},
   "source": [
    "[2.66874991e-02 1.57523658e-02 1.91025324e-02 4.13970882e-03\n",
    " 1.33468891e-02 2.09106889e-04 3.63922194e-02 1.04670925e-02\n",
    " 1.02126561e-02 9.62290820e-03 4.79271309e-03 8.49274337e-01]\n",
    " \n",
    " b2c_c2c, seller_id, declared_handling_days, shipment_method_id, shipping_fee,\n",
    "                             carrier_min_estimate, carrier_max_estimate, category_id,\n",
    "                             item_price, weight, package_size, handling_days\n",
    "                            \n",
    "                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d5131c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,\n",
      "             colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
      "             gpu_id=None, importance_type='gain', interaction_constraints=None,\n",
      "             learning_rate=None, max_delta_step=None, max_depth=None,\n",
      "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "             random_state=None, reg_alpha=None, reg_lambda=None,\n",
      "             scale_pos_weight=None, subsample=None, tree_method=None,\n",
      "             validate_parameters=None, verbosity=0)\n",
      "MSE:\n",
      "18.531836014004373\n",
      "Loss:\n",
      "0.8092401947733605\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03684181 0.05246348 0.07678234 0.03377131 0.02894857 0.26008287\n",
      " 0.3404752  0.02395581 0.04662873 0.02663036 0.03651725 0.0369023 ]\n"
     ]
    }
   ],
   "source": [
    "shipping_xgbr = xgb.XGBRegressor(verbosity=0)\n",
    "print(shipping_xgbr)\n",
    "shipping_xgbr.fit(shipping_X_train, shipping_Y_train)\n",
    "pred = shipping_xgbr.predict(shipping_X_test)\n",
    "mse = mean_squared_error(shipping_Y_test, pred)\n",
    "print(\"MSE:\")\n",
    "print(mse)\n",
    "print(\"Loss:\")\n",
    "print(evaluate_loss(pred, shipping_Y_test))\n",
    "xgb.plot_tree(shipping_xgbr)\n",
    "plt.savefig('shipping_tree.png', dpi=300)\n",
    "plt.show()\n",
    "print(shipping_xgbr.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c8bda8",
   "metadata": {},
   "source": [
    "[0.00974114 0.05542793 0.05909049 0.0481035  0.03052319 0.26555276\n",
    " 0.3525968  0.01976914 0.04382701 0.03989847 0.03796855 0.03750111]\n",
    " \n",
    "b2c_c2c, seller_id, declared_handling_days, shipment_method_id, shipping_fee, carrier_min_estimate, carrier_max_estimate, category_id, item_price, weight, package_size, handling_days\n",
    "\n",
    "importance < 0.01: b2c_c2c\n",
    "\n",
    "0.01 < importance < 0.05: shipment_method_id, shipping_fee, category_id, item_price, weight, package_size, handling days\n",
    "\n",
    "importance > 0.05: seller_id, declared_handling_days, carrier_min_estimate, carrier_max_estimate\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3bae550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('shipping_tree.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "608386d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 15000000)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a755be43",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1433270/555612633.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(x, y, rowvar, bias, ddof)\u001b[0m\n\u001b[1;32m   2549\u001b[0m         warnings.warn('bias and ddof have no effect and are deprecated',\n\u001b[1;32m   2550\u001b[0m                       DeprecationWarning, stacklevel=3)\n\u001b[0;32m-> 2551\u001b[0;31m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrowvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2552\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2553\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcov\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[1;32m   2454\u001b[0m             \u001b[0mw\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0maweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2456\u001b[0;31m     \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2457\u001b[0m     \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw_sum\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36maverage\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/opt/mambaforge/envs/cs152/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36maverage\u001b[0;34m(a, axis, weights, returned)\u001b[0m\n\u001b[1;32m    413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mscl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m             \u001b[0mscl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "corr = np.corrcoef(np.array(features.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8a9d14f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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