Model Building.ipynb

{
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  {
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   "source": [
    "# Ride Hailing Service: Model Building\n",
    "### Benjamen Simon\n",
    "\n",
    "The goal of this document is to explore the processes for the Ride Hailing service, using the probabilities of acceptance for each Pay estimated in the Data Modelling document, to explore different strategies for maximising profit."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import random"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PAY</th>\n",
       "      <th>PROB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.001807</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.001812</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.001816</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.001821</td>\n",
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       "    <tr>\n",
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       "      <td>0.04</td>\n",
       "      <td>0.001826</td>\n",
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       "      <td>59.96</td>\n",
       "      <td>0.999864</td>\n",
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       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>59.97</td>\n",
       "      <td>0.999864</td>\n",
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       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>59.98</td>\n",
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       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>59.99</td>\n",
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       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>60.00</td>\n",
       "      <td>0.999865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6001 rows × 2 columns</p>\n",
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       "        PAY      PROB\n",
       "0      0.00  0.001807\n",
       "1      0.01  0.001812\n",
       "2      0.02  0.001816\n",
       "3      0.03  0.001821\n",
       "4      0.04  0.001826\n",
       "...     ...       ...\n",
       "5996  59.96  0.999864\n",
       "5997  59.97  0.999864\n",
       "5998  59.98  0.999865\n",
       "5999  59.99  0.999865\n",
       "6000  60.00  0.999865\n",
       "\n",
       "[6001 rows x 2 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pay_prob_df = pd.read_csv('Data/probs.csv', names = ['PAY', 'PROB'], header = 0)\n",
    "\n",
    "pay_prob_df"
   ]
  },
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   "metadata": {},
   "source": [
    "## The process for 1 month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_riders = 12000"
   ]
  },
  {
   "cell_type": "code",
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       "       USER_ID  ACTIVE  REQ_M1  REQ_M2  REQ_M3  REQ_M4  REQ_M5  REQ_M6  \\\n",
       "0            1       0       0       0       0       0       0       0   \n",
       "1            2       0       0       0       0       0       0       0   \n",
       "2            3       0       0       0       0       0       0       0   \n",
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       "...        ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "11995    11996       0       0       0       0       0       0       0   \n",
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       "11997    11998       0       0       0       0       0       0       0   \n",
       "11998    11999       0       0       0       0       0       0       0   \n",
       "11999    12000       0       0       0       0       0       0       0   \n",
       "\n",
       "       REQ_M7  REQ_M8  ...  RATE_M4  RATE_M5  RATE_M6  RATE_M7  RATE_M8  \\\n",
       "0           0       0  ...        0        0        0        0        0   \n",
       "1           0       0  ...        0        0        0        0        0   \n",
       "2           0       0  ...        0        0        0        0        0   \n",
       "3           0       0  ...        0        0        0        0        0   \n",
       "4           0       0  ...        0        0        0        0        0   \n",
       "...       ...     ...  ...      ...      ...      ...      ...      ...   \n",
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       "11996       0       0  ...        0        0        0        0        0   \n",
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       "11998       0       0  ...        0        0        0        0        0   \n",
       "11999       0       0  ...        0        0        0        0        0   \n",
       "\n",
       "       RATE_M9  RATE_M10  RATE_M11  RATE_M12  RATE_M13  \n",
       "0            0         0         0         0         0  \n",
       "1            0         0         0         0         0  \n",
       "2            0         0         0         0         0  \n",
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       "...        ...       ...       ...       ...       ...  \n",
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       "\n",
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = {'USER_ID': np.arange(1, num_riders+1),\n",
    "          'ACTIVE': np.repeat(0, num_riders),\n",
    "          'REQ_M1': np.repeat(0, num_riders),\n",
    "          'REQ_M2': np.repeat(0, num_riders),\n",
    "          'REQ_M3': np.repeat(0, num_riders),\n",
    "          'REQ_M4': np.repeat(0, num_riders),\n",
    "          'REQ_M5': np.repeat(0, num_riders),\n",
    "          'REQ_M6': np.repeat(0, num_riders),\n",
    "          'REQ_M7': np.repeat(0, num_riders),\n",
    "          'REQ_M8': np.repeat(0, num_riders),\n",
    "          'REQ_M9': np.repeat(0, num_riders),\n",
    "          'REQ_M10': np.repeat(0, num_riders),\n",
    "          'REQ_M11': np.repeat(0, num_riders),\n",
    "          'REQ_M12': np.repeat(0, num_riders),\n",
    "          'RATE_M1': np.repeat(0, num_riders),\n",
    "          'RATE_M2': np.repeat(0, num_riders),\n",
    "          'RATE_M3': np.repeat(0, num_riders),\n",
    "          'RATE_M4': np.repeat(0, num_riders),\n",
    "          'RATE_M5': np.repeat(0, num_riders),\n",
    "          'RATE_M6': np.repeat(0, num_riders),\n",
    "          'RATE_M7': np.repeat(0, num_riders),\n",
    "          'RATE_M8': np.repeat(0, num_riders),\n",
    "          'RATE_M9': np.repeat(0, num_riders),\n",
    "          'RATE_M10': np.repeat(0, num_riders),\n",
    "          'RATE_M11': np.repeat(0, num_riders),\n",
    "          'RATE_M12': np.repeat(0, num_riders),\n",
    "          'RATE_M13': np.repeat(0, num_riders)\n",
    "          }\n",
    "\n",
    "users_df = pd.DataFrame(users)\n",
    "\n",
    "users_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>RIDE_ID</th>\n",
       "      <th>ACCEPTED</th>\n",
       "      <th>PAY</th>\n",
       "      <th>PROFIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [USER_ID, MONTH, RIDE_ID, ACCEPTED, PAY, PROFIT]\n",
       "Index: []"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides = {'USER_ID': [],\n",
    "          'MONTH': [],\n",
    "          'RIDE_ID': [],\n",
    "          'ACCEPTED': [],\n",
    "          'PAY': [],\n",
    "          'PROFIT': [],\n",
    "          }\n",
    "\n",
    "rides_df = pd.DataFrame(rides)\n",
    "\n",
    "rides_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Month 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate new active users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flag new users as active\n",
    "users_df.ACTIVE[0:1000] = 1\n",
    "\n",
    "# This is the rate at which the new users request rides\n",
    "users_df.RATE_M1[0:1000] = 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find active users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These users are active and will generate requests\n",
    "active_users = users_df.index[users_df.ACTIVE == 1]\n",
    "\n",
    "# This is the number of active users\n",
    "num_active_users = len(active_users)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the rate at which active users request new rides\n",
    "rate = users_df.RATE_M1[active_users]\n",
    "\n",
    "# These are the number of requests per user this month\n",
    "users_df.REQ_M1[active_users] = random.poisson(lam=rate, size = num_active_users)\n",
    "\n",
    "# These are the active users with 0 requests\n",
    "non_returning_users = active_users[users_df.REQ_M1[active_users] == 0]\n",
    "\n",
    "# Set these users to never active again\n",
    "users_df.ACTIVE[non_returning_users] = -1\n",
    "\n",
    "# These are the users with requests greater than 0\n",
    "users_requesting = users_df.index[users_df.REQ_M1 > 0]\n",
    "\n",
    "# These are the requests that we need to generate acceptances for\n",
    "requests_oi = users_df.REQ_M1[users_requesting]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate acceptances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the acceptances\n",
    "users_df.RATE_M2[users_requesting] = random.binomial(size = len(requests_oi), n = requests_oi, p = 0.7)\n",
    "\n",
    "# These are the users that had none of their ride requests accepted this month\n",
    "non_returning_users_2 = users_requesting[users_df.RATE_M2[users_requesting] == 0]\n",
    "\n",
    "# Set these users to never active again\n",
    "users_df.ACTIVE[non_returning_users_2] = -1\n",
    "\n",
    "\n",
    "users_df[['USER_ID', 'ACTIVE', 'REQ_M1', 'REQ_M2', 'RATE_M1', 'RATE_M2']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['USER_ID', 'MONTH', 'RIDE_ID', 'ACCEPTED', 'PAY', 'PROFIT'], dtype='object')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate acceptances (individually)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>USER_ID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>RIDE_ID</th>\n",
       "      <th>ACCEPTED</th>\n",
       "      <th>PAY</th>\n",
       "      <th>PROFIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5010</th>\n",
       "      <td>995.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5011</th>\n",
       "      <td>996.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5012</th>\n",
       "      <td>997.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5013</th>\n",
       "      <td>998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>998.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5015 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      USER_ID  MONTH  RIDE_ID  ACCEPTED   PAY  PROFIT\n",
       "0         1.0    1.0      1.0       1.0  25.0     5.0\n",
       "1         1.0    1.0      2.0       0.0  25.0     5.0\n",
       "2         2.0    1.0      1.0       1.0  25.0     5.0\n",
       "3         3.0    1.0      1.0       1.0  25.0     5.0\n",
       "4         4.0    1.0      1.0       0.0  25.0     5.0\n",
       "...       ...    ...      ...       ...   ...     ...\n",
       "5010    995.0    1.0      1.0       0.0  25.0     5.0\n",
       "5011    996.0    1.0      1.0       1.0  25.0     5.0\n",
       "5012    997.0    1.0      1.0       1.0  25.0     5.0\n",
       "5013    998.0    1.0      1.0       1.0  25.0     5.0\n",
       "5014    998.0    1.0      2.0       0.0  25.0     5.0\n",
       "\n",
       "[5015 rows x 6 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the number of rides of each user\n",
    "ids_and_requests = users_df.loc[users_requesting, ['USER_ID', \"REQ_M1\"]]\n",
    "\n",
    "# Calculate the total number of rides requested\n",
    "num_rides = sum(ids_and_requests.REQ_M1)\n",
    "\n",
    "# Set up an array to store the results\n",
    "rides_M1 = np.zeros((num_rides, len(rides_df.columns)))\n",
    "\n",
    "# A count of the number of rides processed\n",
    "ride_row = 0\n",
    "\n",
    "# Loop through each ride and generate if it accepted or not\n",
    "# and how the Pay, and store the results in the temp array\n",
    "for row in range(0, len(ids_and_requests)):\n",
    "    user_id_j = ids_and_requests.USER_ID.iloc[row]\n",
    "    requests_j = ids_and_requests.REQ_M1.iloc[row]\n",
    "    \n",
    "    for ride in range(1, requests_j+1):\n",
    "        acc_j_r = random.random() < 0.75\n",
    "        rides_M1[ride_row, :] = [user_id_j, 1, ride, acc_j_r, 25, 30-25]\n",
    "        ride_row = ride_row + 1\n",
    "    \n",
    "# Convert the temp array to a data frame        \n",
    "rides_M1_df = pd.DataFrame(rides_M1, columns=rides_df.columns)        \n",
    "\n",
    "# Append the temp array to the rides results df\n",
    "rides_df = pd.concat([rides_df, rides_M1_df], ignore_index=True)\n",
    "\n",
    "rides_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping it in a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise the results dataframes\n",
    "\n",
    "def create_users_df(num_riders):\n",
    "    \n",
    "    users = {'USER_ID': np.arange(1, num_riders+1),\n",
    "          'ACTIVE': np.repeat(0, num_riders),\n",
    "          'REQ_M1': np.repeat(0, num_riders),\n",
    "          'REQ_M2': np.repeat(0, num_riders),\n",
    "          'REQ_M3': np.repeat(0, num_riders),\n",
    "          'REQ_M4': np.repeat(0, num_riders),\n",
    "          'REQ_M5': np.repeat(0, num_riders),\n",
    "          'REQ_M6': np.repeat(0, num_riders),\n",
    "          'REQ_M7': np.repeat(0, num_riders),\n",
    "          'REQ_M8': np.repeat(0, num_riders),\n",
    "          'REQ_M9': np.repeat(0, num_riders),\n",
    "          'REQ_M10': np.repeat(0, num_riders),\n",
    "          'REQ_M11': np.repeat(0, num_riders),\n",
    "          'REQ_M12': np.repeat(0, num_riders),\n",
    "          'RATE_M1': np.repeat(0, num_riders),\n",
    "          'RATE_M2': np.repeat(0, num_riders),\n",
    "          'RATE_M3': np.repeat(0, num_riders),\n",
    "          'RATE_M4': np.repeat(0, num_riders),\n",
    "          'RATE_M5': np.repeat(0, num_riders),\n",
    "          'RATE_M6': np.repeat(0, num_riders),\n",
    "          'RATE_M7': np.repeat(0, num_riders),\n",
    "          'RATE_M8': np.repeat(0, num_riders),\n",
    "          'RATE_M9': np.repeat(0, num_riders),\n",
    "          'RATE_M10': np.repeat(0, num_riders),\n",
    "          'RATE_M11': np.repeat(0, num_riders),\n",
    "          'RATE_M12': np.repeat(0, num_riders),\n",
    "          'RATE_M13': np.repeat(0, num_riders)\n",
    "          }\n",
    "\n",
    "    users_df = pd.DataFrame(users) \n",
    "    \n",
    "    return(users_df)\n",
    "\n",
    "\n",
    "def create_rides_df():\n",
    "    \n",
    "    rides = {'USER_ID': [],\n",
    "          'MONTH': [],\n",
    "          'RIDE_ID': [],\n",
    "          'ACCEPTED': [],\n",
    "          'PAY': [],\n",
    "          'PROFIT': []\n",
    "          }\n",
    "\n",
    "    rides_df = pd.DataFrame(rides)\n",
    "    \n",
    "    return(rides_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_new_active_users(month, users_df):\n",
    "    \n",
    "    lower = (month - 1) * 1000\n",
    "    upper = (month) * 1000\n",
    "    \n",
    "    # Flag new users as active\n",
    "    users_df.ACTIVE[lower:upper] = 1\n",
    "\n",
    "    # This is the rate at which the new users request rides\n",
    "    users_df[\"RATE_M\"+f\"{month}\"][lower:upper] = 1\n",
    "    \n",
    "    return(users_df)\n",
    "\n",
    "\n",
    "def find_active_users(users_df):\n",
    "    \n",
    "    # These users are active and will generate requests\n",
    "    active_users = users_df.index[users_df.ACTIVE == 1]\n",
    "\n",
    "    # This is the number of active users\n",
    "    num_active_users = len(active_users)\n",
    "    \n",
    "    return(active_users, num_active_users, users_df)\n",
    "\n",
    "\n",
    "def generate_requests(active_users, num_active_users, month, users_df):\n",
    "    \n",
    "    # This is the rate at which active users request new rides\n",
    "    rate = users_df[\"RATE_M\"+f\"{month}\"][active_users]\n",
    "\n",
    "    # These are the number of requests per user this month\n",
    "    users_df[\"REQ_M\"+f\"{month}\"][active_users] = random.poisson(lam=rate, size = num_active_users)\n",
    "\n",
    "    # These are the active users with 0 requests\n",
    "    non_returning_users = active_users[users_df[\"REQ_M\"+f\"{month}\"][active_users] == 0]\n",
    "\n",
    "    # Set these users to never active again\n",
    "    users_df.ACTIVE[non_returning_users] = -1\n",
    "\n",
    "    # These are the users with requests greater than 0\n",
    "    users_requesting = users_df.index[users_df[\"REQ_M\"+f\"{month}\"] > 0]\n",
    "\n",
    "    # These are the requests that we need to generate acceptances for\n",
    "    requests_oi = users_df[\"REQ_M\"+f\"{month}\"][users_requesting]\n",
    "    \n",
    "    return(users_requesting, requests_oi, users_df)\n",
    "    \n",
    "    \n",
    "def generate_acceptances(users_requesting, requests_oi, month, users_df):\n",
    "        \n",
    "    # Generate the acceptances\n",
    "    users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting] = random.binomial(size = len(requests_oi), n = requests_oi, p = 0.9)\n",
    "\n",
    "    # These are the users that had none of their ride requests accepted this month\n",
    "    non_returning_users_2 = users_requesting[users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting] == 0]\n",
    "\n",
    "    # Set these users to never active again\n",
    "    users_df.ACTIVE[non_returning_users_2] = -1\n",
    "    \n",
    "    return(users_df)\n",
    "\n",
    "\n",
    "def generate_acceptances_individually(users_requesting, pay, pay_probs, month, users_df, rides_df):\n",
    "    \n",
    "    # Extract the number of rides of each user\n",
    "    ids_and_requests = users_df.loc[users_requesting, ['USER_ID', \"REQ_M\"+f\"{month}\"]]\n",
    "\n",
    "    # Calculate the total number of rides requested\n",
    "    num_rides = sum(ids_and_requests[\"REQ_M\"+f\"{month}\"])\n",
    "\n",
    "    # Set up an array to store the results\n",
    "    rides_this_month = np.zeros((num_rides, len(rides_df.columns)))\n",
    "\n",
    "    # A count of the number of rides processed\n",
    "    ride_row = 0\n",
    "    \n",
    "    # Look up the acceptance probability of the Pay\n",
    "    prob_acc = pay_probs.PROB[pay_probs.PAY == pay].values[0]\n",
    "\n",
    "    # Loop through each ride and generate if it accepted or not\n",
    "    # and how the Pay, and store the results in the temp array\n",
    "    for row in range(0, len(ids_and_requests)):\n",
    "        user_id_j = ids_and_requests.USER_ID.iloc[row]\n",
    "        requests_j = ids_and_requests[\"REQ_M\"+f\"{month}\"].iloc[row]\n",
    "        \n",
    "        total_acc_j = 0\n",
    "        \n",
    "        for ride in range(1, requests_j+1):\n",
    "            acc_j_r = random.random() < prob_acc\n",
    "            rides_this_month[ride_row, :] = [user_id_j, month, ride, acc_j_r, pay, 30-pay]\n",
    "            ride_row = ride_row + 1\n",
    "            total_acc_j = total_acc_j + acc_j_r\n",
    "            \n",
    "        users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting[row]] = total_acc_j\n",
    "            \n",
    "    # These are the users that had none of their ride requests accepted this month\n",
    "    non_returning_users_2 = users_requesting[users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting] == 0]\n",
    "\n",
    "    # Set these users to never active again\n",
    "    users_df.ACTIVE[non_returning_users_2] = -1        \n",
    "        \n",
    "    # Convert the temp ride array to a data frame        \n",
    "    rides_this_month_df = pd.DataFrame(rides_this_month, columns=rides_df.columns)        \n",
    "\n",
    "    # Append the temp array to the rides results df\n",
    "    rides_df = pd.concat([rides_df, rides_this_month_df], ignore_index=True)\n",
    "    \n",
    "    return(users_df, rides_df)\n",
    "\n",
    "\n",
    "def generate_acceptances_adaptive(users_requesting, pays, probs, pay_probs, month, users_df, rides_df):\n",
    "    \n",
    "    # Extract the number of rides of each user\n",
    "    ids_and_requests = users_df.loc[users_requesting, ['USER_ID', \"REQ_M\"+f\"{month}\"]]\n",
    "\n",
    "    # Calculate the total number of rides requested\n",
    "    num_rides = sum(ids_and_requests[\"REQ_M\"+f\"{month}\"])\n",
    "\n",
    "    # Set up an array to store the results\n",
    "    rides_this_month = np.zeros((num_rides, len(rides_df.columns)))\n",
    "\n",
    "    # A count of the number of rides processed\n",
    "    ride_row = 0\n",
    "    \n",
    "\n",
    "    # Loop through each ride and generate if it accepted or not\n",
    "    # and how the Pay, and store the results in the temp array\n",
    "    for row in range(0, len(ids_and_requests)):\n",
    "        user_id_j = ids_and_requests.USER_ID.iloc[row]\n",
    "        requests_j = ids_and_requests[\"REQ_M\"+f\"{month}\"].iloc[row]\n",
    "        \n",
    "        total_acc_j = 0\n",
    "\n",
    "        for ride in range(1, requests_j+1):\n",
    "            if total_acc_j > 0:\n",
    "                pay = pays[1]\n",
    "                prob_acc = probs[1]\n",
    "            else:\n",
    "                pay = pays[0]\n",
    "                prob_acc = probs[0]\n",
    "                \n",
    "            acc_j_r = random.random() < prob_acc\n",
    "            rides_this_month[ride_row, :] = [user_id_j, month, ride, acc_j_r, pay, 30-pay]\n",
    "            ride_row = ride_row + 1\n",
    "            total_acc_j = total_acc_j + acc_j_r\n",
    "            \n",
    "        users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting[row]] = total_acc_j\n",
    "            \n",
    "    # These are the users that had none of their ride requests accepted this month\n",
    "    non_returning_users_2 = users_requesting[users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting] == 0]\n",
    "\n",
    "    # Set these users to never active again\n",
    "    users_df.ACTIVE[non_returning_users_2] = -1        \n",
    "        \n",
    "    # Convert the temp ride array to a data frame        \n",
    "    rides_this_month_df = pd.DataFrame(rides_this_month, columns=rides_df.columns)        \n",
    "\n",
    "    # Append the temp array to the rides results df\n",
    "    rides_df = pd.concat([rides_df, rides_this_month_df], ignore_index=True)\n",
    "    \n",
    "    return(users_df, rides_df)\n",
    "\n",
    "\n",
    "\n",
    "def generate_acceptances_half(users_requesting, pays, probs, pay_probs, month_change, month, users_df, rides_df):\n",
    "    \n",
    "    # Extract the number of rides of each user\n",
    "    ids_and_requests = users_df.loc[users_requesting, ['USER_ID', \"REQ_M\"+f\"{month}\"]]\n",
    "\n",
    "    # Calculate the total number of rides requested\n",
    "    num_rides = sum(ids_and_requests[\"REQ_M\"+f\"{month}\"])\n",
    "\n",
    "    # Set up an array to store the results\n",
    "    rides_this_month = np.zeros((num_rides, len(rides_df.columns)))\n",
    "\n",
    "    # A count of the number of rides processed\n",
    "    ride_row = 0\n",
    "    \n",
    "\n",
    "    # Loop through each ride and generate if it accepted or not\n",
    "    # and how the Pay, and store the results in the temp array\n",
    "    for row in range(0, len(ids_and_requests)):\n",
    "        user_id_j = ids_and_requests.USER_ID.iloc[row]\n",
    "        requests_j = ids_and_requests[\"REQ_M\"+f\"{month}\"].iloc[row]\n",
    "        \n",
    "        total_acc_j = 0\n",
    "\n",
    "        for ride in range(1, requests_j+1):\n",
    "            if month > month_change:\n",
    "                pay = pays[1]\n",
    "                prob_acc = probs[1]\n",
    "            else:\n",
    "                pay = pays[0]\n",
    "                prob_acc = probs[0]\n",
    "                \n",
    "            acc_j_r = random.random() < prob_acc\n",
    "            rides_this_month[ride_row, :] = [user_id_j, month, ride, acc_j_r, pay, 30-pay]\n",
    "            ride_row = ride_row + 1\n",
    "            total_acc_j = total_acc_j + acc_j_r\n",
    "            \n",
    "        users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting[row]] = total_acc_j\n",
    "            \n",
    "    # These are the users that had none of their ride requests accepted this month\n",
    "    non_returning_users_2 = users_requesting[users_df[\"RATE_M\"+f\"{month+1}\"][users_requesting] == 0]\n",
    "\n",
    "    # Set these users to never active again\n",
    "    users_df.ACTIVE[non_returning_users_2] = -1        \n",
    "        \n",
    "    # Convert the temp ride array to a data frame        \n",
    "    rides_this_month_df = pd.DataFrame(rides_this_month, columns=rides_df.columns)        \n",
    "\n",
    "    # Append the temp array to the rides results df\n",
    "    rides_df = pd.concat([rides_df, rides_this_month_df], ignore_index=True)\n",
    "    \n",
    "    return(users_df, rides_df)\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1: Hard coded probability (90%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>USER_ID</th>\n",
       "      <th>ACTIVE</th>\n",
       "      <th>REQ_M1</th>\n",
       "      <th>RATE_M1</th>\n",
       "      <th>REQ_M2</th>\n",
       "      <th>RATE_M2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>-1</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
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       "      <th>8</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   USER_ID  ACTIVE  REQ_M1  RATE_M1  REQ_M2  RATE_M2\n",
       "0        1       1       1        1       2        1\n",
       "1        2      -1       0        1       0        0\n",
       "2        3      -1       2        1       2        2\n",
       "3        4      -1       0        1       0        0\n",
       "4        5      -1       1        1       0        0\n",
       "5        6      -1       2        1       2        2\n",
       "6        7      -1       1        1       0        1\n",
       "7        8      -1       0        1       0        0\n",
       "8        9      -1       1        1       2        1\n",
       "9       10       1       2        1       2        2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_df_test = create_users_df(12000)\n",
    "rides_df_test = create_rides_df()\n",
    "\n",
    "\n",
    "for m in range(1, 13):\n",
    "    \n",
    "    users_df_test = generate_new_active_users(m, users_df_test)\n",
    "    \n",
    "    active_users_m, num_active_users_m, users_df_test = find_active_users(users_df_test)\n",
    "    \n",
    "    users_requesting_m, requests_oi_m, users_df_test = generate_requests(active_users_m, num_active_users_m, m, users_df_test)\n",
    "    \n",
    "    users_df_test = generate_acceptances(users_requesting_m, requests_oi_m, m, users_df_test)\n",
    "\n",
    "\n",
    "users_df_test.iloc[0:10, [0,1,2,14,3,15]]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 2: Fixed Pay point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>RIDE_ID</th>\n",
       "      <th>ACCEPTED</th>\n",
       "      <th>PAY</th>\n",
       "      <th>PROFIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21595</th>\n",
       "      <td>11996.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21596</th>\n",
       "      <td>11997.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>21597</th>\n",
       "      <td>11998.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21598</th>\n",
       "      <td>11998.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21599</th>\n",
       "      <td>11999.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       USER_ID  MONTH  RIDE_ID  ACCEPTED   PAY  PROFIT\n",
       "0          1.0    1.0      1.0       1.0  25.0     5.0\n",
       "1          3.0    1.0      1.0       0.0  25.0     5.0\n",
       "2          3.0    1.0      2.0       1.0  25.0     5.0\n",
       "3          5.0    1.0      1.0       1.0  25.0     5.0\n",
       "4          7.0    1.0      1.0       0.0  25.0     5.0\n",
       "...        ...    ...      ...       ...   ...     ...\n",
       "21595  11996.0   12.0      1.0       0.0  25.0     5.0\n",
       "21596  11997.0   12.0      1.0       0.0  25.0     5.0\n",
       "21597  11998.0   12.0      1.0       1.0  25.0     5.0\n",
       "21598  11998.0   12.0      2.0       0.0  25.0     5.0\n",
       "21599  11999.0   12.0      1.0       1.0  25.0     5.0\n",
       "\n",
       "[21600 rows x 6 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_df_test_2 = create_users_df(12000)\n",
    "rides_df_test_2 = create_rides_df()\n",
    "\n",
    "for m in range(1, 13):\n",
    "    \n",
    "    users_df_test_2 = generate_new_active_users(m, users_df_test_2)\n",
    "    \n",
    "    active_users_m, num_active_users_m, users_df_test_2 = find_active_users(users_df_test_2)\n",
    "    \n",
    "    users_requesting_m, requests_oi_m, users_df_test_2 = generate_requests(active_users_m, num_active_users_m, m, users_df_test_2)\n",
    "    \n",
    "    users_df_test_2, rides_df_test_2 = generate_acceptances_individually(users_requesting_m, 25, pay_prob_df, m, users_df_test_2, rides_df_test_2)\n",
    "\n",
    "\n",
    "rides_df_test_2\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 3: Adaptive to accepted status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>-3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29212</th>\n",
       "      <td>11996.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.01</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29213</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.54</td>\n",
       "      <td>-3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29214</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.01</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29215 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       USER_ID  MONTH  RIDE_ID  ACCEPTED    PAY  PROFIT\n",
       "0          2.0    1.0      1.0       1.0  33.54   -3.54\n",
       "1          3.0    1.0      1.0       1.0  33.54   -3.54\n",
       "2          3.0    1.0      2.0       0.0  20.01    9.99\n",
       "3          3.0    1.0      3.0       0.0  20.01    9.99\n",
       "4          4.0    1.0      1.0       1.0  33.54   -3.54\n",
       "...        ...    ...      ...       ...    ...     ...\n",
       "29210  11994.0   12.0      1.0       0.0  33.54   -3.54\n",
       "29211  11996.0   12.0      1.0       1.0  33.54   -3.54\n",
       "29212  11996.0   12.0      2.0       0.0  20.01    9.99\n",
       "29213  12000.0   12.0      1.0       1.0  33.54   -3.54\n",
       "29214  12000.0   12.0      2.0       0.0  20.01    9.99\n",
       "\n",
       "[29215 rows x 6 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_df_test_3 = create_users_df(12000)\n",
    "rides_df_test_3 = create_rides_df()\n",
    "\n",
    "pay_first, prob_acc_first = pay_prob_df[pay_prob_df.PROB > 0.9].iloc[0]\n",
    "pay_extra, prob_acc_extra = pay_prob_df[pay_prob_df.PAY > 20].iloc[0]\n",
    "\n",
    "pays = [pay_first, pay_extra]\n",
    "probs = [prob_acc_first, prob_acc_extra]\n",
    "\n",
    "for m in range(1, 13):\n",
    "    \n",
    "    users_df_test_3 = generate_new_active_users(m, users_df_test_3)\n",
    "    \n",
    "    active_users_m, num_active_users_m, users_df_test_3 = find_active_users(users_df_test_3)\n",
    "    \n",
    "    users_requesting_m, requests_oi_m, users_df_test_3 = generate_requests(active_users_m, num_active_users_m, m, users_df_test_3)\n",
    "    \n",
    "    users_df_test_3, rides_df_test_3 = generate_acceptances_adaptive(users_requesting_m, pays, probs, pay_prob_df, m, users_df_test_3, rides_df_test_3)\n",
    "\n",
    "\n",
    "rides_df_test_3\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 4: Lock in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>USER_ID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>RIDE_ID</th>\n",
       "      <th>ACCEPTED</th>\n",
       "      <th>PAY</th>\n",
       "      <th>PROFIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.49</td>\n",
       "      <td>-6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.49</td>\n",
       "      <td>-6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.49</td>\n",
       "      <td>-6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.49</td>\n",
       "      <td>-6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.49</td>\n",
       "      <td>-6.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50850</th>\n",
       "      <td>11996.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.01</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50851</th>\n",
       "      <td>11997.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.01</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50852</th>\n",
       "      <td>11998.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.01</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50853</th>\n",
       "      <td>11999.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.01</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50854</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.01</td>\n",
       "      <td>24.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50855 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       USER_ID  MONTH  RIDE_ID  ACCEPTED    PAY  PROFIT\n",
       "0          1.0    1.0      1.0       1.0  36.49   -6.49\n",
       "1          3.0    1.0      1.0       1.0  36.49   -6.49\n",
       "2          3.0    1.0      2.0       1.0  36.49   -6.49\n",
       "3          5.0    1.0      1.0       1.0  36.49   -6.49\n",
       "4          6.0    1.0      1.0       1.0  36.49   -6.49\n",
       "...        ...    ...      ...       ...    ...     ...\n",
       "50850  11996.0   12.0      2.0       0.0   5.01   24.99\n",
       "50851  11997.0   12.0      1.0       0.0   5.01   24.99\n",
       "50852  11998.0   12.0      1.0       0.0   5.01   24.99\n",
       "50853  11999.0   12.0      1.0       0.0   5.01   24.99\n",
       "50854  12000.0   12.0      1.0       0.0   5.01   24.99\n",
       "\n",
       "[50855 rows x 6 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_df_test_4 = create_users_df(12000)\n",
    "rides_df_test_4 = create_rides_df()\n",
    "\n",
    "pay_first_half, prob_acc_first_half = pay_prob_df[pay_prob_df.PROB > 0.95].iloc[0]\n",
    "pay_last_half, prob_acc_last_half = pay_prob_df[pay_prob_df.PAY > 5].iloc[0]\n",
    "\n",
    "pays = [pay_first_half, pay_last_half]\n",
    "probs = [prob_acc_first_half, prob_acc_last_half]\n",
    "\n",
    "month_change = 9\n",
    "\n",
    "for m in range(1, 13):\n",
    "    \n",
    "    users_df_test_4 = generate_new_active_users(m, users_df_test_4)\n",
    "    \n",
    "    active_users_m, num_active_users_m, users_df_test_4 = find_active_users(users_df_test_4)\n",
    "    \n",
    "    users_requesting_m, requests_oi_m, users_df_test_4 = generate_requests(active_users_m, num_active_users_m, m, users_df_test_4)\n",
    "    \n",
    "    users_df_test_4, rides_df_test_4 = generate_acceptances_half(users_requesting_m, pays, probs, pay_prob_df, month_change, m, users_df_test_4, rides_df_test_4)\n",
    "\n",
    "\n",
    "rides_df_test_4\n"
   ]
  }
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