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# SBA Campaign Budget Pacing - Minimize Lost IS (Budget)
#
# Allocates according to:
# * Remaining budget for each Salesforce Item ID Budget Group
# * Remaining weekdays in month
# * Historical spend and spend potential
# * Campaigns with spend in lookback period
# * Minimum daily budget
#
# Author: Michael S. Huang, Adam Scott
#
# Created: 2023-09-30
# Updated: 2024-02-10
#

##### Configurable Param #####

MINIMUM_DAILY_BUDGET = 10

##############################

########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=True
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
# pickle_path = ''
pickle_path = '/Users/mhuang/Downloads/pickle/avb_marketing_datasource_dict_1702622906522.pkl'
# Step 3: Copy this script into local IDE with Python virtual env loaded with pandas and numpy.
# Step 4: Run locally with below code to init dataSourceDict

# determine if code is running on server or locally
def is_executing_on_server():
    try:
        # Attempt to access a known restricted builtin
        dict_items = dataSourceDict.items()
        return True
    except NameError:
        # NameError: dataSourceDict object is missing (indicating not on server)
        return False

if is_executing_on_server():
    print("Code is executing on server. Skip init.")
elif len(pickle_path) > 3:
    print("Code is NOT executing on server. Doing init.")
    # load dataSourceDict via pickled file
    import pickle
    dataSourceDict = pickle.load(open(pickle_path, 'rb'))

    # print shape and first 5 rows for each entry in dataSourceDict
    for key, value in dataSourceDict.items():
        print(f"Shape of dataSourceDict[{key}]: {value.shape}")
        # print(f"First 5 rows of dataSourceDict[{key}]:\n{value.head(5)}")

    # set outputDf same as inputDf
    inputDf = dataSourceDict["1"]
    outputDf = inputDf.copy()

    # setup timezone
    import datetime
    # Chicago Timezone is GMT-5. Adjust as needed.
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=-5))

    # import pandas
    import pandas as pd
    import numpy as np

    # other imports
    import re
    import urllib

    # import Marin util functions
else:
   from marin_scripts_utils import tableize, select_changed
   print("Running locally but no pickle path defined. dataSourceDict not loaded.")
   exit(1)
########### END - Local Mode Setup ###########



RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_PUBLISHER_NAME = 'Publisher Name'
RPT_COL_STRATEGY = 'Strategy'
RPT_COL_DAILY_BUDGET = 'Daily Budget'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_CONV = 'Conv.'
RPT_COL_IMPR_SHARE = 'Impr. share %'
RPT_COL_LOST_IMPR_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'
RPT_COL_LOST_IMPR_SHARE_RANK = 'Lost Impr. Share (Rank) %'
RPT_COL_SBA_STRATEGY = 'SBA Strategy'
RPT_COL_SBA_CAMPAIGN_BUDGET = 'SBA Campaign Budget'
RPT_COL_SBA_ALLOCATION = 'SBA Allocation'
RPT_COL_REC_DAILY_BUDGET = 'Rec. Daily Budget'
RPT_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
RPT_COL_SBA_TRAFFIC = 'SBA Traffic'
RPT_COL_PROGRAM_END_Date = 'Program End Date'

BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_DAILY_BUDGET = 'Daily Budget'
BULK_COL_SBA_ALLOCATION = 'SBA Allocation'
BULK_COL_SBA_BUDGET_PACING = 'SBA Budget Pacing'
BULK_COL_REC_DAILY_BUDGET = 'Rec. Daily Budget'

COL_SPEND_FULL_POTENTIAL = 'spend_lookback_full_potential'
COL_SPEND_FULL_POTENTIAL_CAPPED = 'spend_lookback_full_potential_capped'
COL_SPEND_MTD = 'spend_mtd'
COL_SBA_ALLOCATION_NEW_FLOAT = RPT_COL_SBA_ALLOCATION + '_new_float'
COL_SBA_ALLOCATION_NEW = RPT_COL_SBA_ALLOCATION + '_new'
COL_SBA_STRATEGY_BUDGET_REMAINING = 'SBA_Campaign_budget_remaining'
COL_SBA_BUDGET_PACING_NEW = RPT_COL_SBA_BUDGET_PACING + '_new'
COL_BUDGET_REMAINING = 'budget_remaining'
COL_DAILY_BUDGET_NEW = RPT_COL_DAILY_BUDGET + '_new'
COL_REC_DAILY_BUDGET_NEW = RPT_COL_REC_DAILY_BUDGET + '_new'
COL_DAYS_REMAINING= 'weekdays_remaining'
COL_DAYS_TOTAL= 'weekdays_total'
COL_PACING_CALC = 'pacing_calc'

outputDf[BULK_COL_DAILY_BUDGET] = "<<YOUR VALUE>>"

today = datetime.datetime.now(CLIENT_TIMEZONE).date()
print("inputDf shape", inputDf.shape)
print("inputDf dtypes", inputDf.dtypes)

# change back to percent string
if inputDf[RPT_COL_SBA_ALLOCATION].dtype == "float":
    inputDf[RPT_COL_SBA_ALLOCATION] = round(inputDf[RPT_COL_SBA_ALLOCATION] * 100.0, 0).astype(str) + '%'
if inputDf[BULK_COL_SBA_BUDGET_PACING].dtype == "float":
    inputDf[BULK_COL_SBA_BUDGET_PACING] = round(inputDf[BULK_COL_SBA_BUDGET_PACING] * 100.0, 0).astype(str) + '%'

# coerce Program End Date into Date type
inputDf[RPT_COL_PROGRAM_END_Date] = pd.to_datetime(inputDf[RPT_COL_PROGRAM_END_Date], errors='coerce')

inputDf = inputDf.set_index([RPT_COL_SBA_STRATEGY])
group_by_salesforce_item_ID = inputDf.groupby(RPT_COL_SBA_STRATEGY)

# ## Calculate Full-Potential Spend
# * Adjust Historical Spend by _Lost Impression Share due to Budget_ (see [Formula](https://docs.google.com/document/d/1EbCQ5z9Up8TZ6GISEeCaRSB3Fc15vCPCfeIydree23M/edit#bookmark=id.5fsx7jlseze6))
# 

adj_ratio = 1 + (inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET] / (1 - inputDf[RPT_COL_LOST_IMPR_SHARE_BUDGET]))

inputDf[COL_SPEND_FULL_POTENTIAL] = round(inputDf[RPT_COL_PUB_COST] * adj_ratio, 2)

# ## Remove Date Segmentation
# * Calculate MTD Spend

# SUM Series with Date index and only includes current month
def current_month_sum(x):
    x = x.sort_index()
    mtd = x[ (x.index.month == today.month) & (x.values > 0)]
    return mtd.sum()

groupby_cols = [ \
    RPT_COL_SBA_STRATEGY, \
    RPT_COL_STRATEGY, \
    RPT_COL_PUBLISHER_NAME, \
    RPT_COL_ACCOUNT, \
    RPT_COL_CAMPAIGN, \
]


agg_spec = {
    RPT_COL_CAMPAIGN_STATUS: 'last', \
    RPT_COL_DAILY_BUDGET: 'last', \
    RPT_COL_SBA_CAMPAIGN_BUDGET: 'last', \
    RPT_COL_SBA_ALLOCATION: 'last', \
    RPT_COL_REC_DAILY_BUDGET: 'last', \
    RPT_COL_SBA_BUDGET_PACING: 'last', \
    RPT_COL_SBA_TRAFFIC: 'last', \
    RPT_COL_CLICKS: 'sum', \
    RPT_COL_CONV: 'sum', \
    RPT_COL_PUB_COST: 'sum', \
    COL_SPEND_MTD: current_month_sum, \
    COL_SPEND_FULL_POTENTIAL: 'sum', \
    RPT_COL_PROGRAM_END_Date: 'last', \
}

inputDf[COL_SPEND_MTD] = inputDf[RPT_COL_PUB_COST]

df_campaign_agg = inputDf.reset_index() \
                         .set_index(RPT_COL_DATE) \
                         .groupby(groupby_cols) \
                         .agg(agg_spec) \
                         .reset_index() \
                         .set_index(RPT_COL_SBA_STRATEGY)


# ## Only allocate budget for recently trafficking campaigns
# * Exclude Campaigns that are:
# ** not ACTIVE 
# ** without spend in lookback period
# ** Program Date is in the past

inactive_campaigns = (df_campaign_agg[RPT_COL_CAMPAIGN_STATUS] != 'Active') & (df_campaign_agg[RPT_COL_PUB_COST] == 0)
expired_campaigns = df_campaign_agg[RPT_COL_PROGRAM_END_Date].notnull() & (df_campaign_agg[RPT_COL_PROGRAM_END_Date] < pd.to_datetime(today))
df_campaign_agg = df_campaign_agg.loc[ ~(inactive_campaigns | expired_campaigns) ]


# ## Calculate Budget Allocation Ratio 
# * Cap full potential spend at 2X (don't spend twice as much as before)
# * Compare full potential spend for each campaign to total spend within same SALESFORCE_ITEM_ID budget group

df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] = df_campaign_agg \
    .apply(lambda row: min(row[COL_SPEND_FULL_POTENTIAL], 2 * row[RPT_COL_PUB_COST]), axis=1)


# use transform to calculate sum for each SALESFORCE_ITEM_ID and make it available on every row
# note: no need to build aggregate DataFrame and JOIN back to original

df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] = 100.0 * \
        df_campaign_agg[COL_SPEND_FULL_POTENTIAL_CAPPED] / \
        df_campaign_agg.groupby(RPT_COL_SBA_STRATEGY)[COL_SPEND_FULL_POTENTIAL_CAPPED].transform('sum')

df_campaign_agg[COL_SBA_ALLOCATION_NEW] = round(df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT],0).astype(str) + '%'


# 
# ## Calculate Remaining Budget
# * For each SALESFORCE_ITEM_ID budget group, calculate how much Budget is left by substracting SBA Monthly budget from MTD SALESFORCE_ITEM_ID spend

df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] =  \
        df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET] - \
        df_campaign_agg.groupby(by=[RPT_COL_SBA_STRATEGY])[COL_SPEND_MTD].sum()


# ## Allocate Budget
# * Allocate remaining budget to each campaign according to ratio calculated above

df_campaign_agg[COL_BUDGET_REMAINING] = round(df_campaign_agg[COL_SBA_STRATEGY_BUDGET_REMAINING] * df_campaign_agg[COL_SBA_ALLOCATION_NEW_FLOAT] / 100.0, 1)


# ## Calculate SBA Daily Budget
# 
# * Calcualte next day Daily Budget by dividing allocated budget by number of Business Days left in the current month


today_numpy = pd.to_datetime(today).to_numpy().astype('datetime64[D]')
next_month_start = (today_numpy + pd.offsets.BMonthBegin()).to_numpy().astype('datetime64[D]')

# for months ending on weekends, use max(1,x) to avoid dividing by zero
days_left = max(1, (next_month_start - today_numpy).astype('timedelta64[D]').astype(int))

df_campaign_agg[COL_DAYS_REMAINING] = days_left
df_campaign_agg[COL_REC_DAILY_BUDGET_NEW] = round(df_campaign_agg[COL_BUDGET_REMAINING] / days_left, 0)

# ### Apply Minimum Rule
# * Bump allocated budget above minimum

allocated_below_min = (df_campaign_agg[COL_REC_DAILY_BUDGET_NEW] < MINIMUM_DAILY_BUDGET)
df_campaign_agg.loc[allocated_below_min, COL_REC_DAILY_BUDGET_NEW] = MINIMUM_DAILY_BUDGET


# ### Traffic Budget
df_campaign_agg[COL_DAILY_BUDGET_NEW] = np.nan

# campaigns to traffic
to_traffic = df_campaign_agg[RPT_COL_SBA_TRAFFIC].notnull() & \
            (df_campaign_agg[RPT_COL_SBA_TRAFFIC].astype(str).str.lower() == 'traffic')
print("Not weekend. Traffic count", to_traffic.sum())

# copy budgets over
df_campaign_agg[COL_DAILY_BUDGET_NEW] = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]
# then blank out budget for non-traffic campaigns
df_campaign_agg.loc[~to_traffic, COL_DAILY_BUDGET_NEW] = np.nan


# ## Calculate Salesforece Item ID -level Pacing compliance percentage. Ideally should be 100% each day.

# number of elapsed workdays
current_month_start = pd.to_datetime(today.replace(day=1)).to_numpy().astype('datetime64[D]')
total_days_in_month = (next_month_start - current_month_start).astype('timedelta64[D]').astype(int)
df_campaign_agg[COL_DAYS_TOTAL] = total_days_in_month
prorated_ratio = (total_days_in_month - days_left) / total_days_in_month

print("today", today)
print("current_month_start", current_month_start)
print("next_month_start", next_month_start)
print("weekdays_in_month", total_days_in_month)
print("weekdays_left", days_left)
print("prorated_ratio", prorated_ratio)

# divide MTD spend by prorated total budget
mask = df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET] > 0
df_campaign_agg[COL_PACING_CALC] = round(100.0 * \
                                    df_campaign_agg.groupby(by=[RPT_COL_SBA_STRATEGY])[COL_SPEND_MTD].sum() / \
                                    (prorated_ratio * df_campaign_agg[RPT_COL_SBA_CAMPAIGN_BUDGET]), \
                                    0).astype(str) + '%'
df_campaign_agg.loc[mask, COL_SBA_BUDGET_PACING_NEW] = df_campaign_agg.loc[mask, COL_PACING_CALC]



# Debug DF with full details
df_SALESFORCE_ITEM_ID_budget = group_by_salesforce_item_ID[[RPT_COL_SBA_CAMPAIGN_BUDGET]].transform('max').dropna().drop_duplicates()
print("Salesforce Item ID budgets", df_SALESFORCE_ITEM_ID_budget.to_string())


# ## Generate outputDf

# Check for changes
changed = df_campaign_agg[COL_REC_DAILY_BUDGET_NEW].notnull() & \
    ( \
       (df_campaign_agg[RPT_COL_REC_DAILY_BUDGET] != df_campaign_agg[COL_REC_DAILY_BUDGET_NEW]) | \
       (df_campaign_agg[RPT_COL_DAILY_BUDGET] != df_campaign_agg[COL_DAILY_BUDGET_NEW]) | \
       (df_campaign_agg[RPT_COL_SBA_ALLOCATION] != df_campaign_agg[COL_SBA_ALLOCATION_NEW]) | \
       (df_campaign_agg[RPT_COL_SBA_BUDGET_PACING] != df_campaign_agg[COL_SBA_BUDGET_PACING_NEW]) \
    )

print("Changed rows:", changed.sum())

# Debug
debugDf = df_campaign_agg.loc[changed] \
      .reset_index() \
      .sort_values(by=[RPT_COL_SBA_STRATEGY, COL_DAILY_BUDGET_NEW, COL_REC_DAILY_BUDGET_NEW], ascending=False) 

# print("debugDf", tableize(debugDf))

# Only emit output for changed campaigns
if changed.sum() > 0:

    # construct outputDf
    outputDf = df_campaign_agg.loc[changed, [RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN, COL_DAILY_BUDGET_NEW, COL_REC_DAILY_BUDGET_NEW, COL_SBA_BUDGET_PACING_NEW, COL_SBA_ALLOCATION_NEW]] \
                      .copy() \
                      .rename(columns={ \
                            COL_DAILY_BUDGET_NEW: BULK_COL_DAILY_BUDGET, \
                            COL_REC_DAILY_BUDGET_NEW: BULK_COL_REC_DAILY_BUDGET, \
                            COL_SBA_BUDGET_PACING_NEW: BULK_COL_SBA_BUDGET_PACING, \
                            COL_SBA_ALLOCATION_NEW: BULK_COL_SBA_ALLOCATION, \
                        }) \
                      .reset_index() \
                      .sort_values(by=[RPT_COL_SBA_STRATEGY, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET], ascending=False) \
                      .drop(RPT_COL_SBA_STRATEGY, axis=1)

    print("outputDf shape", outputDf.shape)
    print("outputDf", tableize(outputDf.head()))
else:
    print("No changes detected, returning an empty dataframe")
    outputDf = pd.DataFrame(columns=[BULK_COL_ACCOUNT, BULK_COL_CAMPAIGN, BULK_COL_DAILY_BUDGET, BULK_COL_REC_DAILY_BUDGET, BULK_COL_SBA_BUDGET_PACING, BULK_COL_SBA_ALLOCATION])