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##
## name: Keyword Click-weighted Quality Calls
## description:
##  Attribute Quality Call Conv to Keywords via Click Weight
## 
## author: Michael S. Huang
## created: 2024-11-09
## 

########### 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 = ''
pickle_path = '/Users/mhuang/Downloads/pickle/mass_general_quality_call_attribution_20241115.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

    # other imports
    import re
    import urllib

    # import Marin util functions
    from marin_scripts_utils import tableize, select_changed

    # pandas settings
    pd.set_option('display.max_columns', None)  # Display all columns
    pd.set_option('display.max_colwidth', None)  # Display full content of each column

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_KEYWORD = 'Keyword'
RPT_COL_DATE = 'Date'
RPT_COL_KEYWORD_ID = 'Keyword ID'
RPT_COL_PUB_ID = 'Pub. ID'
RPT_COL_GROUP_PUB_ID = 'Group Pub. ID'
RPT_COL_CAMPAIGN_PUB_ID = 'Campaign Pub. ID'
RPT_COL_GROUP = 'Group'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'
RPT_COL_CLICK_WTD_QUALITY_CALL_CONV = 'Click Wtd Quality Call Conv.'

# reference data source and columns
reportDf = dataSourceDict["2"]  # report dataframe
RPT_COL_GROUP = 'Group'
RPT_COL_DATE = 'Date'
RPT_COL_PUB_ID = 'Pub. ID'
RPT_COL_GROUP_ID = 'Group ID'
RPT_COL_CAMPAIGN_PUB_ID = 'Campaign Pub. ID'
RPT_COL_CAMPAIGN_ID = 'Campaign ID'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_QUALITY_CALLS_CONV = 'Quality Calls Conv.'

# output columns and initial values
REVENUE_COL_DATE = 'Date'
REVENUE_COL_GROUP_ID = 'Group ID'
REVENUE_COL_TARGET_IDS = 'Target IDs'
REVENUE_COL_CLICK_WTD_QUALITY_CALL_CONV = 'Click Wtd Quality Call Conv'
outputDf[REVENUE_COL_DATE] = "<<YOUR VALUE>>"
outputDf[REVENUE_COL_GROUP_ID] = "<<YOUR VALUE>>"
outputDf[REVENUE_COL_TARGET_IDS] = "<<YOUR VALUE>>"
outputDf[REVENUE_COL_CLICK_WTD_QUALITY_CALL_CONV] = "<<YOUR VALUE>>"

COL_CLICK_RATIO = 'click_ratio'

####  user code starts here

print("keyword report shape", inputDf.shape)
print("keyword sample\n", tableize(inputDf.head(5)))
print("group report shape", reportDf.shape)
print("group sample\n", tableize(reportDf.head(5)))

## cleanup
# drop extra columns used for debugging
inputDf = inputDf.drop(columns=[RPT_COL_CLICK_WTD_QUALITY_CALL_CONV])
# rename colliding columns
reportDf = reportDf.rename(columns={RPT_COL_PUB_ID: RPT_COL_GROUP_PUB_ID})


## calculate click-weight for each adgroup
# Calculate the total clicks for each AdGroup
adgroup_clicks = inputDf.groupby([RPT_COL_CAMPAIGN_PUB_ID, RPT_COL_GROUP_PUB_ID, RPT_COL_DATE])[RPT_COL_CLICKS].transform('sum')
# Calculate the click ratio for each keyword
inputDf[COL_CLICK_RATIO] = inputDf[RPT_COL_CLICKS] / adgroup_clicks

## calculate click-weights
# Merge inputDf with reportDf to get the RPT_COL_QUALITY_CALLS_CONV for each GROUP_PUB_ID
mergedDf = inputDf.merge(reportDf[[RPT_COL_CAMPAIGN_PUB_ID, RPT_COL_GROUP_PUB_ID, RPT_COL_GROUP_ID, RPT_COL_DATE, RPT_COL_QUALITY_CALLS_CONV]], 
                         left_on=[RPT_COL_CAMPAIGN_PUB_ID, RPT_COL_GROUP_PUB_ID, RPT_COL_DATE], 
                         right_on=[RPT_COL_CAMPAIGN_PUB_ID, RPT_COL_GROUP_PUB_ID, RPT_COL_DATE],
                         how='inner')

print("mergedDf shape", mergedDf.shape)
print("mergedDf sample\n", mergedDf.head(5))

# Calculate the keyword-level Call Quality Conv
mergedDf[RPT_COL_CLICK_WTD_QUALITY_CALL_CONV] = mergedDf[RPT_COL_QUALITY_CALLS_CONV] * mergedDf[COL_CLICK_RATIO]

debugDf = mergedDf.round(2)

# only output rows with non-zero click-weighted quality call conv
output_cols = [RPT_COL_DATE, RPT_COL_GROUP_PUB_ID, RPT_COL_PUB_ID, RPT_COL_CLICK_WTD_QUALITY_CALL_CONV]
outputDf = mergedDf.loc[mergedDf[RPT_COL_CLICK_WTD_QUALITY_CALL_CONV] > 0, output_cols].round(2)

# TargetID for keywords need prefix `kwd-`
#     outputCol['Target IDs'] = 'kwd-' + inputCol['Pub. ID']
outputDf[RPT_COL_PUB_ID] = 'kwd-' + outputDf[RPT_COL_PUB_ID].astype(str)

# rename columns for Revenue Upload
outputDf = outputDf.rename(columns={
    RPT_COL_DATE: REVENUE_COL_DATE,
    RPT_COL_GROUP_PUB_ID: REVENUE_COL_GROUP_ID,
    RPT_COL_PUB_ID: REVENUE_COL_TARGET_IDS,
    RPT_COL_CLICK_WTD_QUALITY_CALL_CONV: REVENUE_COL_CLICK_WTD_QUALITY_CALL_CONV
})

# DEBUG: limit to one group
# outputDf = outputDf.loc[outputDf[REVENUE_COL_GROUP_ID] == 151266388237]

print("outputDf.shape", outputDf.shape)

## 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}")
else:
    print("outputDf", outputDf.head(5))