Script 1007: Intraday Budget Cap Based on Strategy Target

Purpose

Intraday Budget Cap via Strategy

To Elaborate

The Python script solves the problem of pausing campaigns when the month-to-date spend reaches the monthly budget stored in the strategy. It calculates the monthly spend for each budget group and compares it to the monthly budget to determine which campaigns should be paused or resumed. The script also handles traffic pausing and resuming based on the recommended campaign status.

Walking Through the Code

The script starts by defining a configurable parameter for the safety margin of the budget cap.
It then checks if the code is running on the server or locally and loads the dataSourceDict accordingly.
The script sets up the necessary imports and defines the primary data source and columns.
It makes a copy of the original inputDf to keep the dataSourceDict.pkl pristine.
The script defines some intermediate columns and status values.
It converts the necessary columns to their expected types.
The script calculates the month-to-date budget group spend.
It recommends pausing campaigns with a month-to-date budget group spend over the monthly budget (by a margin).
The script recommends resuming campaigns with a month-to-date budget group spend under the monthly group budget (by a margin) and a populated SBA Pause Date.
It handles traffic pausing and resuming based on the recommended campaign status.
The script selects only the changed rows for the outputDf.
It renames the column header for the status in the outputDf.
The script outputs the final outputDf and debugDf.
If running locally, it also writes the outputDf and debugDf to CSV files for local debugging.

Vitals

Script ID : 1007
Client ID / Customer ID: 1306926629 / 60270083
Action Type: Bulk Upload (Preview)
Item Changed: Campaign
Output Columns: Account, Campaign, Status, Auto Pause Date, Auto Pause Rec. Campaign Status
Linked Datasource: M1 Report
Reference Datasource: None
Owner: ascott@marinsoftware.com (ascott@marinsoftware.com)
Created by ascott@marinsoftware.com on 2024-04-26 20:38
Last Updated by ascott@marinsoftware.com on 2024-04-26 20:38

> See it in Action

Python Code

## name: Intraday Budget Cap via Strategy
## description:
##  Pause campaigns when MTD spend reaches Monthly Budget (stored in Strategy)
## 
## author: Adam Scott
## created: 2024-04-26
## 

##### Configurable Param #####
# Define how close MTD spend can get to Monthly Budget before being Paused
#  - compensates for lag in system
#  - compendates for non-linearity in intraday spend
BUDGET_CAP_SAFETY_MARGIN = 0.02 # set to 2%
##############################


########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=False
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
pickle_path = '/Users/mhuang/Downloads/pickle/generic_budget_cap_intraday_datasource_dict.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

local_dev = 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.")
    local_dev = True
    # 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

    # Printing out the version of Python, Pandas and Numpy
    # import sys
    # python_version = sys.version
    # pandas_version = pd.__version__
    # numpy_version = np.__version__

    # print(f"python version: {python_version}")
    # print(f"pandas version: {pandas_version}")
    # print(f"numpy version: {numpy_version}")

    # other imports
    import re
    import urllib

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

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_AUTO_PAUSE_STATUS = 'Auto Pause Status'
RPT_COL_BUDGET_GROUP = 'Strategy'
RPT_COL_GROUP_MONTHLY_BUDGET = 'Strategy Target'
RPT_COL_PAUSE_DATE = 'Auto Pause Date'
RPT_COL_RECOMMENDED_STATUS = 'Auto Pause Rec. Campaign Status'

# output columns and initial values
BULK_COL_ACCOUNT = 'Account'
BULK_COL_CAMPAIGN = 'Campaign'
BULK_COL_STATUS = 'Status'
BULK_COL_SBA_PAUSE_DATE = 'Auto Pause Date'
BULK_COL_SBA_RECOMMENDED_STATUS = 'Auto Pause Rec. Campaign Status'

# Make inputDf a copy of original to keep dataSourceDict.pkl pristine
originalDf = dataSourceDict["1"]
inputDf = originalDf.copy()

# define some intermediate columns
COL_MTD_BUDGET_GROUP_SPEND = 'mtd_budget_group_spend'

# define Status values
VAL_STATUS_ACTIVE = 'Active'
VAL_STATUS_PAUSED = 'Paused'
VAL_BLANK = ''

print("inputDf shape", inputDf.shape)
print("inputDf info", inputDf.info())

## force expected types
# Convert RPT_COL_SBA_MONTHLY_BUDGET to numeric, coercing errors to NaN
inputDf[RPT_COL_GROUP_MONTHLY_BUDGET] = pd.to_numeric(inputDf[RPT_COL_GROUP_MONTHLY_BUDGET], errors='coerce')
# Replace NaN values with 0.0 if that's the desired behavior
inputDf[RPT_COL_GROUP_MONTHLY_BUDGET].fillna(0.0, inplace=True)
# Force RPT_COL_PAUSE_DATE to be Date type
inputDf[RPT_COL_PAUSE_DATE] = pd.to_datetime(inputDf[RPT_COL_PAUSE_DATE], errors='coerce').dt.date

# HACK: replace nan with empty strings so comparison doesn't fail
inputDf.fillna(VAL_BLANK, inplace=True)





# Calculate MTD Budget Group Spend
inputDf[COL_MTD_BUDGET_GROUP_SPEND] = inputDf.groupby(RPT_COL_BUDGET_GROUP)[RPT_COL_PUB_COST].transform('sum')

# Recommend to Pause camapigns with MTD Budget Group Spend over Monthly Budget (by a margin)
has_monthly_group_budget = inputDf[RPT_COL_GROUP_MONTHLY_BUDGET] > 0.0
over_spent_campaigns = inputDf[COL_MTD_BUDGET_GROUP_SPEND] >= inputDf[RPT_COL_GROUP_MONTHLY_BUDGET] * (1 - BUDGET_CAP_SAFETY_MARGIN)
campaigns_to_pause = has_monthly_group_budget & over_spent_campaigns

inputDf.loc[campaigns_to_pause, 'pause'] = 1
print(f"campaigns_to_pause count: {sum(campaigns_to_pause)}")
if campaigns_to_pause.any():
    print("campaigns_to_pause campaigns", tableize(inputDf.loc[campaigns_to_pause].head()))

inputDf.loc[ campaigns_to_pause, \
             RPT_COL_RECOMMENDED_STATUS \
           ] = VAL_STATUS_PAUSED
 
# Recommend to reactivate campaigns with MTD Budget Group Spend under Monthly Group Budget (by a margin)
# but limited to campaigns with SBA Pause Date populated 10 digit date
under_spent_campaigns = inputDf[COL_MTD_BUDGET_GROUP_SPEND] < inputDf[RPT_COL_GROUP_MONTHLY_BUDGET] * (1 - BUDGET_CAP_SAFETY_MARGIN)
sba_paused_campaigns = inputDf[RPT_COL_PAUSE_DATE].astype('str').str.len() >= 10
campaigns_to_resume = under_spent_campaigns & sba_paused_campaigns

inputDf.loc[campaigns_to_resume, 'resume'] = 1
print(f"campaigns_to_resume count: {sum(campaigns_to_resume)}")
if campaigns_to_resume.any():
    print("campaigns_to_resume", tableize(inputDf.loc[campaigns_to_resume].head()))

inputDf.loc[ campaigns_to_resume, \
             RPT_COL_RECOMMENDED_STATUS  \
           ] = VAL_STATUS_ACTIVE

## Actually taffic PAUSE

should_traffic = inputDf[RPT_COL_AUTO_PAUSE_STATUS].astype(str).str.lower() == 'traffic'

should_traffic_pause = should_traffic & \
                       (inputDf[RPT_COL_RECOMMENDED_STATUS] == VAL_STATUS_PAUSED) & \
                       (inputDf[RPT_COL_RECOMMENDED_STATUS] != inputDf[RPT_COL_CAMPAIGN_STATUS])


inputDf.loc[should_traffic_pause, 'traffic_pause'] = 1
print(f"should_traffic_pause count: {sum(should_traffic_pause)}")
if should_traffic_pause.any():
    print("should_traffic_pause campaigns", tableize(inputDf.loc[should_traffic_pause].head()))


inputDf.loc[should_traffic_pause, RPT_COL_CAMPAIGN_STATUS] = inputDf.loc[should_traffic_pause, RPT_COL_RECOMMENDED_STATUS]
inputDf.loc[should_traffic_pause, RPT_COL_PAUSE_DATE] = today.strftime('%Y-%m-%d')

## Actually taffic RESUME

should_traffic_resume = should_traffic & \
                       (inputDf[RPT_COL_RECOMMENDED_STATUS] == VAL_STATUS_ACTIVE) & \
                       (inputDf[RPT_COL_RECOMMENDED_STATUS] != inputDf[RPT_COL_CAMPAIGN_STATUS]) & \
                       sba_paused_campaigns

inputDf.loc[should_traffic_resume, 'traffic_resume'] = 1
print(f"should_traffic_resume count: {sum(should_traffic_resume)}")
if should_traffic_resume.any():
    print("should_traffic_resume campaigns", tableize(inputDf.loc[should_traffic_resume].head()))

inputDf.loc[should_traffic_resume, RPT_COL_CAMPAIGN_STATUS] = inputDf.loc[should_traffic_resume, RPT_COL_RECOMMENDED_STATUS]
inputDf.loc[should_traffic_resume, RPT_COL_PAUSE_DATE] = VAL_BLANK

print(f"select_changed with inputDf shape {inputDf.shape} and originalDf shape {originalDf.shape}")


# only include changed rows in bulk file
(outputDf, debugDf) = select_changed(inputDf, \
                                    originalDf, \
                                    diff_cols = [ \
                                        RPT_COL_CAMPAIGN_STATUS, \
                                        RPT_COL_RECOMMENDED_STATUS, \
                                    ], \
                                    select_cols = [ \
                                        RPT_COL_ACCOUNT, \
                                        RPT_COL_CAMPAIGN, \
                                        RPT_COL_CAMPAIGN_STATUS, \
                                        RPT_COL_RECOMMENDED_STATUS, \
                                        RPT_COL_PAUSE_DATE, \
                                    ], \
                                    merged_cols=[RPT_COL_ACCOUNT, RPT_COL_CAMPAIGN] \
                                    )


changed = (debugDf[RPT_COL_CAMPAIGN_STATUS+'_new'] != debugDf[RPT_COL_CAMPAIGN_STATUS+'_orig']) | \
          (debugDf[RPT_COL_RECOMMENDED_STATUS+'_new'] != debugDf[RPT_COL_RECOMMENDED_STATUS+'_orig'])

debugDf.loc[changed, 'changed'] = 1
print(f"changed count: {sum(changed)}")
if changed.any():
    print("changed campaigns", tableize(debugDf.loc[changed].head()))

# remember to use Bulk column header for Status
outputDf = outputDf.rename(columns = { \
                RPT_COL_CAMPAIGN_STATUS: BULK_COL_STATUS \
                })

print("outputDf shape", outputDf.shape)
print("outputDf", tableize(outputDf.tail(5)))

## local debug
if local_dev:
    output_filename = 'outputDf.csv'
    outputDf.to_csv(output_filename, index=False)
    print(f"Local Dev: Output written to: {output_filename}")

    debug_filename = 'debugDf.csv'
    debugDf.to_csv(debug_filename, index=False)
    print(f"Local Dev: Debug written to: {debug_filename}")
    

Post generated on 2024-05-15 07:44:05 GMT

26 Apr 2024

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