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
##
## name: Campaigns Anomaly 
## description:
##  
## 
## author: 
## created: 2024-04-18
## 

# Data processing functions (replace with your actual logic)
def load_data_from_publisher(data_source):
  """
  Loads data from the ad publisher using the specified data source.

  Args:
      data_source (str): The data source to use (e.g., API endpoint, database connection string).

  Returns:
      pd.DataFrame: The loaded data as a pandas DataFrame.
  """
  # Replace with your implementation to fetch data from the ad publisher API/database
  # Example placeholder data
  data = {
      "date": ["2024-04-10", "2024-04-11", "2024-04-12", "2024-04-13", "2024-04-14"],
      "metric": [100, 120, 95, 150, 80]
  }
  return pd.DataFrame(data)

def calculate_rolling_average(data, window_size):
  """
  Calculates the rolling average for the specified metric in the data.

  Args:
      data (pd.DataFrame): The data containing the metric column.
      window_size (int): The window size for the rolling average calculation.

  Returns:
      pd.DataFrame: The data with an added column for rolling average.
  """
  data["rolling_average"] = data["metric"].rolling(window=window_size).mean()
  return data

# Anomaly detection function (replace with your desired logic)
def identify_anomalies(data, threshold):
  """
  Identifies anomalies based on the difference between the metric and rolling average.

  Args:
      data (pd.DataFrame): The data containing metric and rolling average columns.
      threshold (float): The threshold for anomaly detection.

  Returns:
      list: A list of anomaly indices in the data.
  """
  anomaly_indices = []
  for i in range(len(data)):
    if abs(data.loc[i, "metric"] - data.loc[i, "rolling_average"]) > threshold:
      anomaly_indices.append(i)
  return anomaly_indices

# Report generation function (replace with your actual logic)
def generate_report(data, anomalies):
  """
  Generates a report based on the data and identified anomalies.

  Args:
      data (pd.DataFrame): The data used for the report.
      anomalies (list): A list of anomaly indices in the data.
  """
  # Replace with your implementation to generate a report (e.g., CSV, email)
  # Example: Print a summary to the console
  print(f"Total records: {len(data)}")
  print(f"Anomaly count: {len(anomalies)}")
  for i in anomalies:
    print(f"Anomaly detected at index: {i}, value: {data.loc[i, 'metric']}")

# Main execution
data = load_data_from_publisher("your_data_source")  # Replace with actual data source
data = calculate_rolling_average(data.copy(), window_size=3)  # Calculate rolling average on a copy
anomalies = identify_anomalies(data, threshold=10)
generate_report(data, anomalies)