Script 811: Campaigns Anomaly Detection 4 All Promos

Purpose

Python script that identifies performance anomalies in PPC campaigns by comparing actual data against forecasts based on historical trends.

To Elaborate

The Python script analyzes the performance of PPC campaigns by comparing the actual data with the forecasted data based on historical trends. It calculates an anomaly score for each metric and identifies campaigns with significant deviations from the forecast. The script highlights the metrics that have the highest anomaly scores and provides a summary of the anomalies in a clear and concise manner.

Walking Through the Code

  1. The script defines column constants and user-changeable parameters.
  2. It checks if the code is running on a server or locally and loads the necessary data accordingly.
  3. The script reduces the input data by dropping unnecessary columns and removing the latest N days due to conversion lag.
  4. It groups the reduced data by account and campaign and calculates the forecast, interquartile range, and actual values for each metric.
  5. The script calculates the anomaly scores for each metric based on the forecast, actual, and interquartile range values.
  6. It identifies campaigns with outlier anomaly scores and sorts them based on the trailing cost.
  7. The script constructs a prompt for summarizing the anomaly results in a clear and concise manner.
  8. It generates the output dataframe for further analysis and debugging.
  9. If running locally, the script writes the prompt, output dataframe, and debug dataframe to separate files.

Vitals

  • Script ID : 811
  • Client ID / Customer ID: 314475863 / 63970
  • Action Type: Email Report
  • Item Changed: None
  • Output Columns:
  • Linked Datasource: M1 Report
  • Reference Datasource: None
  • Owner: dwaidhas@marinsoftware.com (dwaidhas@marinsoftware.com)
  • Created by dwaidhas@marinsoftware.com on 2024-03-14 14:30
  • Last Updated by dwaidhas@marinsoftware.com on 2024-03-14 17:26
> See it in Action

Python Code

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
##
## name: Campaign Performance Anomaly Report with Summary
## description:
##  * Identify anomalies based on day-of-week forecast
##  * Calculate anomaly via adjustable IQR and Deviation Thresholds
## 
## author: Dana Waidhas
## created: 2024-03-01
## 

RPT_COL_DATE = 'Date'
RPT_COL_ACCOUNT = 'Account'
RPT_COL_CAMPAIGN = 'Campaign'
RPT_COL_CAMPAIGN_TYPE = 'Campaign Type'
RPT_COL_CAMPAIGN_STATUS = 'Campaign Status'

RPT_COL_IMPR = 'Impr.'
RPT_COL_CLICKS = 'Clicks'
RPT_COL_PUB_COST = 'Pub. Cost $'

RPT_COL_CONV = 'Pub. Conv.'
RPT_COL_REVENUE = 'Pub. Revenue'

RPT_COL_IMPRESSION_SHARE = 'Impr. share %'
RPT_COL_IMPRESSION_SHARE_TOP = 'Impr. Share (Top) %'
RPT_COL_LOST_IMPRESSION_SHARE_BUDGET = 'Lost Impr. Share (Budget) %'

COL_CONV_RATE = 'CVR'
COL_COST_PER_CLICK = 'CPC'
COL_CTR = 'CTR %'
COL_COST_PER_LEAD = 'CPL'
COL_SEARCHES = 'Searches'
COL_IMPRESSIONS_TOP = 'IMPRESSIONS_TOP'
COL_IMPRESSIONS_LOST_BUDGET = 'IMPRESSIONS_LOST_BUDGET'

# column names
COL_FORECAST = 'forecast'
COL_ACTUAL = 'actual'
COL_TRAILING = 'trailing'
COL_IQR = 'z_iqr'
COL_DEVIATION = 'z_deviation'
COL_DEVIATION_PCT = 'deviation_pct'

COL_DEVIATION_RATIO = 'z_deviation_ratio'
COL_DEVIATION_RATIO_FLAG_COUNT = COL_DEVIATION_RATIO + '_flagged'

COL_OUTLIER_SCORE = 'z_outlier_score'
COL_OUTLIER_SCORE_FLAG_COUNT = COL_OUTLIER_SCORE + '_flagged'

COL_OUTLIER_DEVIATION_FLAG_COUNT = 'zz_outlier_deviation_flagged'
COL_OUTLIER_DEVIATION_FLAG_COUNT_SCALED = COL_OUTLIER_DEVIATION_FLAG_COUNT + '_scaled'

COL_MOST_UPWARD_OUTLIER_METRIC = 'zz_most_upward_outlier_metric'
COL_MOST_DOWNWARD_OUTLIER_METRIC = 'zz_most_downward_outlier_metric'

COL_TOTAL_FLAG_COUNT_SCALED = 'zz_total_flag_count_scaled'
COL_TRAILING_COST = RPT_COL_PUB_COST + '_' + COL_TRAILING

########### START - User Params ###########

CONVERSION_LAG_DAYS = 1
MIN_FORECAST_LOOKBACK_WEEKS = 7

# Metrics to include in Report
# Format: (Metric, Outlier Threshold, Deviation Threshold)
# Metric = metrics to analyze
# Outlier Threshold: IQR multiplier; 1.5 is equivalent to 97.5% percentile
# Deviation Threshold: deviation threshold (in decimal; 0.20 = 20%)
REPORT_METRICS = [
    (RPT_COL_CONV,          1.5,    0.20),
    (RPT_COL_PUB_COST,      1.5,    0.20),
    (RPT_COL_CLICKS,        1.5,    0.20),
    (RPT_COL_REVENUE,       1.5,    0.20),
]

########### END - User Params ###########

########### 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/universitysouthwest_campaign_anomaly.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 ###########


########### Anomaly Detection Libray Functions #############

### Forecast and Anomaly functions

# get forecast via exponential smoothing of previous weeks
def get_forecasts(data, decimals=0):
    if len(data) >= MIN_FORECAST_LOOKBACK_WEEKS * 7:
        # print("data len: ", len(data))
        # print("data.index", data.index)
        # print("data", data)
        index_previous_weeks = list(range(-8, -len(data)-1, -7))
        index_previous_weeks_ordered = index_previous_weeks[::-1]
        # print(index_previous_weeks_ordered)
        hist_data = data.iloc[index_previous_weeks_ordered]
        # forecasts = np.mean(hist_data, axis=0)

        # exponential smoothing
        alpha = 0.5 # smoothing factor
        forecasts = hist_data.ewm(alpha=alpha).mean().iloc[-1]
        if decimals == 0:
            forecasts = forecasts.astype(int)
        else:
            forecasts = forecasts.round(decimals)

        return forecasts
    else:
        print("not enough data. skipping: ", data.index)
    
    return None

# get interquartile range from previous weeks
def get_inter_quartile_ranges(data):
    if len(data) >= MIN_FORECAST_LOOKBACK_WEEKS * 7:
        # print("data.index", data.index)
        # print("data", data)

        index_previous_weeks = list(range(-8, -len(data), -7))
        index_previous_weeks_ordered = index_previous_weeks[::-1]
        hist_data = data.iloc[index_previous_weeks_ordered]

        if np.isnan(hist_data).any():
            print("fixing nan in hist_data")
            hist_data = hist_data.fillna(0)
        

        # iqrs = np.std(hist_data, axis=0)

        # calculate interquartile range (IQR)
        Q1 = hist_data.quantile(0.25)
        Q3 = hist_data.quantile(0.75)
        IQR = Q3 - Q1

        return IQR
    else:
        print("not enough data. skipping: ", data.index)
    
    return None


# most recent data point is the last item
get_actuals = lambda x: x.iloc[[-1]]

# get trailing total
def get_trailing_total(data, window=7):
    if len(data) >= window:
        index_previous_days = list(range(-1, -(window+1), -1))
        hist_data = data.iloc[index_previous_days]
        return hist_data.sum()
    else:
        print("not enough data. skipping: ", data.index)
    
    return None

# ### Calculate anomaly score

# calc anomaly score across list of metrics
def calc_anomaly_scores(df, metrics_and_weights):
    df = df.copy()

    deviation_ratio_list = []
    outlier_score_list = []

    for (metric, _, _) in metrics_and_weights:
        forecast = df.loc[:, (metric, COL_FORECAST)]
        actual = df.loc[:, (metric, COL_ACTUAL)]
        iqr = df.loc[:, (metric, COL_IQR)]

        if np.isnan(forecast).any():
            print("nan in forecast")
            forecast = np.nan_to_num(forecast)
            
        if np.isnan(actual).any():
            print("nan in actual")
            actual = np.nan_to_num(actual)

        if np.isnan(iqr).any():
            print("nan in iqr")
            iqr = np.nan_to_num(iqr)

        # negative deviation when less than forecasted
        deviation = np.subtract(actual, forecast)
        
        df.loc[:, (metric, COL_DEVIATION)] = deviation

        # when both forecasted and actual values are ZERO, deviation should be ZERO
        # when forecasted is ZERO but actual is not, set to 100% deviation
        deviation_ratio = np.where(forecast > 0, \
                            deviation/forecast, \
                            np.where(actual > 0, 1.0, 0.0))
        deviation_ratio_list.append(deviation_ratio)
        df.loc[:, (metric, COL_DEVIATION_RATIO)] = deviation_ratio
        df.loc[:, (metric, COL_DEVIATION_PCT)] = np.char.add(np.char.mod('%0.0f', deviation_ratio * 100), '%')

        # anomaly score is ratio of deviation with Inter Quartile Range; score of 1.5 would be 97.5% percentile
        # positive score means exceeding forecast
        # if IQR is 0, default anomaly score to 0 so it won't trigger any alerts
        score = np.where(abs(iqr) > 0, deviation / iqr, 0.0)

        outlier_score_list.append(score)
        df.loc[:, (metric, COL_OUTLIER_SCORE)] = score

    # flag scores that exceed the anomaly threshold
    anomaly_thresholds = np.array([threshold for (_, threshold, _) in metrics_and_weights])
    scores_stack = np.stack(outlier_score_list, axis=0)
    flagged_outlier_score_list = np.where(np.abs(scores_stack) > anomaly_thresholds[:, None], 1, 0)
    # sum across metrics and save for output
    df[COL_OUTLIER_SCORE_FLAG_COUNT] = np.sum(flagged_outlier_score_list, axis=0)

    # flag deviation ratios that exceed the deviation threshold
    deviation_thresholds = np.array([threshold for (_, _, threshold) in metrics_and_weights])
    deviation_ratios_stack = np.stack(deviation_ratio_list, axis=0)
    flagged_deviation_ratio_list = np.where(np.abs(deviation_ratios_stack) > deviation_thresholds[:, None], 1, 0)
    # sum across metrics and save for output
    df[COL_DEVIATION_RATIO_FLAG_COUNT] = np.sum(flagged_deviation_ratio_list, axis=0)

    # flag anomalous deviations by combining both flags above
    # AND flags in flagged_outlier_score_list and flagged_deviation_ratio_list
    # get the count of metrics where both are 1
    combined_flags = np.logical_and(flagged_outlier_score_list, flagged_deviation_ratio_list)
    df[COL_OUTLIER_DEVIATION_FLAG_COUNT] = np.sum(combined_flags, axis=0)

    # scaled score highlights larger spenders with more anomaly or deviation flags
    forecast_cost = df.loc[:, (RPT_COL_PUB_COST, COL_FORECAST)]
    actual_cost = df.loc[:, (RPT_COL_PUB_COST, COL_ACTUAL)]
    nominal_cost = np.maximum(forecast_cost, actual_cost)
    df[COL_TOTAL_FLAG_COUNT_SCALED] = np.round((df[COL_OUTLIER_SCORE_FLAG_COUNT] + df[COL_DEVIATION_RATIO_FLAG_COUNT]) * nominal_cost, 0)
    
    # another version that highlights larger spenders with anomalous deviation flags
    df[COL_OUTLIER_DEVIATION_FLAG_COUNT_SCALED] = np.round(df[COL_OUTLIER_DEVIATION_FLAG_COUNT] * nominal_cost, 0)


    # calc best & worst changes; scale change by outlier score (use absolute value to avoid change sign of deviation)
    # Using numpy.nan_to_num to fill in NA
    change_score = np.nan_to_num(np.multiply(deviation_ratio_list, np.abs(outlier_score_list)), copy=False)
    scores_stack = np.stack(change_score, axis=0)
    max_scores = np.maximum.reduce(scores_stack, axis=0)
    min_scores = np.minimum.reduce(scores_stack, axis=0)
    max_score_indices = np.argmax(scores_stack, axis=0)
    min_score_indices = np.argmin(scores_stack, axis=0)

    # fill in corresponding metric names
    metric_names = [metric for (metric, weight, _) in metrics_and_weights]
    df[COL_MOST_UPWARD_OUTLIER_METRIC] = [metric_names[idx] if score > 0 else '' for (score, idx) in zip(max_scores, max_score_indices)]
    df[COL_MOST_DOWNWARD_OUTLIER_METRIC] = [metric_names[idx] if score < 0 else '' for (score, idx) in zip(min_scores, min_score_indices)]

    # resort columns
    df.columns = df.columns.swaplevel(0, 1)
    df.sort_index(axis=1, inplace=True)
    df.columns = df.columns.swaplevel(1, 0)
    df.sort_index(axis=1, inplace=True)

    # return everything for debugging
    # return (df.sort_values(by=COL_OUTLIER_SCORE_FLAG_COUNT, axis=0, ascending=False), max_scores, min_scores, max_score_indices, min_score_indices)
    return df.sort_values(by=COL_OUTLIER_SCORE_FLAG_COUNT, axis=0, ascending=False)


# convert to percentage units with 2 decimal places
def safe_percentage(numerator, denominator):
    return np.where(denominator > 0, \
                    round(numerator / denominator * 100, 2), \
                    0)


########### END Functions ###########


#### User Starts Here

print('inputDf.info\n',inputDf.info())

min_input_date = min(inputDf[RPT_COL_DATE])
max_input_date = max(inputDf[RPT_COL_DATE])
print(f"Input date range: {min_input_date.date()} to {max_input_date.date()}")

### Reduce columns and drop latest N days due to converion lag

report_date = (pd.to_datetime(max_input_date - datetime.timedelta(days=CONVERSION_LAG_DAYS)))
print(f"Most recent input date is {max_input_date.date()}. Using conversion lag of {CONVERSION_LAG_DAYS} days, set Report Date to {report_date.date()} and discard more recent dates.")

min_hist_date = min_input_date
max_hist_date = report_date - datetime.timedelta(days=1)
hist_days = (max_hist_date - min_hist_date).days + 1

print(f"Input has {hist_days} days of historical data from {min_hist_date.date()} to {max_hist_date.date()}")

inputDf_reduced = inputDf.loc[ inputDf[RPT_COL_DATE] <= report_date ] \
                         .drop([RPT_COL_CAMPAIGN_TYPE, RPT_COL_CAMPAIGN_STATUS], axis=1) \
                         .reset_index() \
                         .set_index([RPT_COL_CAMPAIGN])

print(f"Reduced from {inputDf.shape[0]} to {inputDf_reduced.shape[0]} rows")
print(f"Reduced input date range: {min(inputDf_reduced[RPT_COL_DATE]).date()} to {max(inputDf_reduced[RPT_COL_DATE]).date()}")

# calculate impression counts since impression share cannot be directly aggregated
inputDf_reduced[COL_SEARCHES] =  np.where(inputDf_reduced[RPT_COL_IMPRESSION_SHARE] > 0, \
                    np.round(inputDf_reduced[RPT_COL_IMPR] / inputDf_reduced[RPT_COL_IMPRESSION_SHARE], 0), \
                    inputDf_reduced[RPT_COL_IMPR])
inputDf_reduced[COL_IMPRESSIONS_TOP] = np.round(inputDf_reduced[COL_SEARCHES] * inputDf_reduced[RPT_COL_IMPRESSION_SHARE_TOP], 0)
inputDf_reduced[COL_IMPRESSIONS_LOST_BUDGET] = np.round(inputDf_reduced[COL_SEARCHES] * inputDf_reduced[RPT_COL_LOST_IMPRESSION_SHARE_BUDGET], 0)


### Use gropuby and agg to calculate forecast, iqr, and actual

inputDf_reduced[COL_CONV_RATE] = safe_percentage(inputDf_reduced[RPT_COL_CONV], inputDf_reduced[RPT_COL_CLICKS])
inputDf_reduced[COL_COST_PER_CLICK] = inputDf_reduced[RPT_COL_PUB_COST]/inputDf_reduced[RPT_COL_CLICKS]
inputDf_reduced[COL_CTR] = safe_percentage(inputDf_reduced[RPT_COL_CLICKS], inputDf_reduced[RPT_COL_IMPR])


df_campaign = inputDf_reduced \
                        .fillna(0) \
                        .replace([np.inf, -np.inf], 0) \
                        .groupby([RPT_COL_ACCOUNT,RPT_COL_CAMPAIGN]) \
                        .agg({ \
                            RPT_COL_REVENUE:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_CONV:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_CLICKS:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            COL_CONV_RATE:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_PUB_COST:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals), (COL_TRAILING, get_trailing_total)],
                            COL_COST_PER_CLICK:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            COL_CTR:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_IMPR:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            COL_SEARCHES:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_IMPRESSION_SHARE:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_IMPRESSION_SHARE_TOP:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                            RPT_COL_LOST_IMPRESSION_SHARE_BUDGET:[(COL_FORECAST, get_forecasts), (COL_IQR, get_inter_quartile_ranges), (COL_ACTUAL, get_actuals)],
                        })

### Compute Anomaly Scores

df_campaign_anomaly = calc_anomaly_scores(df_campaign, REPORT_METRICS)


### Find Outlier Trafficking Campaigns

highlight_campaigns = df_campaign_anomaly.loc[(df_campaign_anomaly[(RPT_COL_PUB_COST, COL_ACTUAL)] > 0) & \
                                 (df_campaign_anomaly[COL_OUTLIER_DEVIATION_FLAG_COUNT] > 0)
                                ] \
                  .sort_values(by=[(RPT_COL_PUB_COST, COL_TRAILING)], ascending=[False])

print("highlight_campaigns: ", highlight_campaigns.shape[0])

### Construct Complete Prompt

def get_anomaly_results_for_prompt_string(df):
    if df.empty:
        return pd.DataFrame()
    else:
        trailing = df.xs(key=COL_TRAILING, level=1, axis=1, drop_level=False).round(0).astype(int)
        forecast = df.xs(key=COL_FORECAST, level=1, axis=1, drop_level=False).round(2)
        deviation_pct = df.xs(key=COL_DEVIATION_PCT, level=1, axis=1, drop_level=False)
        anomaly_score = df.xs(key=COL_OUTLIER_SCORE, level=1, axis=1, drop_level=False).round(2)
        outlier_metrics = df[[COL_MOST_UPWARD_OUTLIER_METRIC, COL_MOST_DOWNWARD_OUTLIER_METRIC]]
        table = pd.concat([trailing, forecast, deviation_pct, anomaly_score, outlier_metrics], axis=1)
        # sorting removes level 1 col name for each column and confuses GPT
        # table = table.sort_index(axis=1, level=0)
        table = table.reset_index()
        table.columns = ['{}_{}'.format(col[0], col[1]) if col[1] else col[0] for col in table.columns]

        return table.to_string(index=False, formatters={col: lambda x: f'"{x}"' for col in table.columns})

def get_anomaly_results_for_human_dataframe(df):
    if df.empty:
        return pd.DataFrame()
    else:
        deviation_pct = df.xs(key=COL_DEVIATION_PCT, level=1, axis=1, drop_level=False)
        forecast = df.xs(key=COL_FORECAST, level=1, axis=1, drop_level=False).round(2)
        table = pd.concat([deviation_pct, forecast], axis=1)
        table = table.sort_index(axis=1, level=0)
        trailing = df.xs(key=COL_TRAILING, level=1, axis=1, drop_level=False).round(0).astype(int)
        table = pd.concat([trailing, table], axis=1)
        table = table.reset_index()
        table.columns = ['{}_{}'.format(col[0], col[1]) if col[1] else col[0] for col in table.columns]
        return table

prompt_header = f'''
You are a helpful pay-per-click marketing data analyst with deep understanding of common performance issues.

You are working with the output of a performance anomaly report.
Please summarize the results in a clear, easy to understand, and concise manner.
Make the report useful and insightful to read by using language from the higher education sector while keeping the tone professional.
Please make sure the report is not alarming while still pointing out the anomalies.

The anomaly report examines this list of METRIC (from most important to least important): {', '.join(["'" + m[0] + "'" for m in REPORT_METRICS])}.
Anomaly score (`METRIC_{COL_OUTLIER_SCORE}`) of a metric is calclated by taking the difference (`METRIC_{COL_DEVIATION}`) between forecast and actual values, and divided by the Inter Quartile Range of historical data.
Forecasted value for each metric is in `METRIC_{COL_FORECAST}`.
For each metric, deviation percentage from forecast is calculated in `METRIC_{COL_DEVIATION_PCT}`.
Metric with anomaly score (`METRIC_{COL_OUTLIER_SCORE}`) greater than 1.5 or less than -1.5 are outliers that may require attention, especially when percentage change `METRIC_{COL_DEVIATION_PCT}` is also greater than 15% or less than -15%.
For these metrics, positive percentage change is good: Conversion Rate (CVR), Click Through Rate (CTR), Searches, Impression Share, Conversions (Conv), Revenue, Clicks
For these metrics, negative percentage change is good: Cost Per Click (CPC), Publisher Cost (Pub. Cost)
Don't use column names like `METRIC_{COL_DEVIATION_PCT}` in the response.
Don't include anomaly scores in the response.
State that a metric that increased/decreased certain percentage (`METRIC_{COL_DEVIATION_PCT}`) from the forecasted value (`METRIC_{COL_FORECAST}`) when highlighting an anomalous metric.
Highlight all METRIC names in bold using Markdown `__` notation.
Use the Account and Campaign DataFrame data (blocks surrounded by triple hyphens '---') to highlight issues and summarize trends.
Output at most 5 bullet points for each section, but review all the data given to analyze for trends.

Generate output in Markdown, using this format:

# Performance Anomaly Report for {report_date.date()}

'''

emailSummaryPrompt = f'''
{prompt_header}

## short headline of main trend. focus on metric with `METRIC_{COL_OUTLIER_SCORE}` greater than 1.5 or less than -1.5 and `METRIC_{COL_DEVIATION_PCT}` greater than 15% or less than -15%.


### Notable Campaigns

* provide the campaign name ('{RPT_COL_CAMPAIGN}') with trailing cost ('{COL_TRAILING_COST}' with currency symbol) in parenthesis and all in bold: __{RPT_COL_CAMPAIGN}__ (__{COL_TRAILING_COST}__)
  * summary of metric anomalies, with highlights on metric listed under `{COL_MOST_UPWARD_OUTLIER_METRIC}` and `{COL_MOST_DOWNWARD_OUTLIER_METRIC}`. When highlighting an anomalous metric, use the form: __METRIC__ increased/decreased X% (`METRIC_{COL_DEVIATION_PCT}`) from the forecasted value Y (`METRIC_{COL_FORECAST}`).
  * EXAMPLE: Experienced a substantial increase of __Clicks__ by 60% (forecast: 134), while __Conversions__ decreased by 17% (forecast: 12). __Publisher Cost__ also rose by 53% (forecast: 220).

  
### Trends 
* short summary of positive trends with Campaigns. Highlight names in bold.
* short summary of negative trends with Campaigns. Highlight names in bold.

===

DataFrame of Accounts with at least one large anomalous deviation:
---
{get_anomaly_results_for_prompt_string(highlight_campaigns.head(20))}
---


'''

# blank out prompt if there is no actual output
if highlight_campaigns.empty:
    emailSummaryPrompt = ''

print(f"Prompt has ({len(emailSummaryPrompt)} chars)")

#### email output

# TODO: combine account and campaign into output
outputDf = get_anomaly_results_for_human_dataframe(highlight_campaigns)

debugDf = highlight_campaigns.reset_index().round(2)
debugDf.columns = ['{}_{}'.format(col[0], col[1]) if col[1] else col[0] for col in debugDf.columns]

print(f"OutputDf has {outputDf.shape[0]} rows")

## local debug
if local_dev:
    with open('prompt.txt', 'w') as file:
        file.write(emailSummaryPrompt)
        print(f"Local Dev: Prompt written to: {file.name}")

    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("====== Prompt =====")
    print(emailSummaryPrompt)
    print("===========")

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

comments powered by Disqus