Add peer review multiplier calclulator

scripts/teammates/README.md

@@ -0,0 +1,10 @@
+# Scripts for using TEAMMATES (Peer Evaluation Software)
+
+Note: Requires python v3.0 or higher.
+
+## `reviewmultiplier.py`
+
+To use, call:
+`python3 reviewmultiplier.py <filepath to TEAMMATES .csv file>`
+
+Given a .csv file exported by a team review session at https://teammatesv4.appspot.com, calculates multipliers proportional to the amount of work a teammate has done relative to the number of members in a team. Exports a folder with a question-by-question breakdown and a combined multiplier in multipliers.csv. By Tyler Lam (@TydaJL)

scripts/teammates/reviewmultiplier.py

@@ -0,0 +1,106 @@
+"""
+Takes in an exported .csv file from https://teammatesv4.appspot.com and outputs
+a .csv file (multipliers.csv) of multipliers to assign student grades to. Also outputs
+question-by-question breakdowns.
+
+How to call from command line:
+python3 peer_multiplier.py <filepath of TEAMMATES.org exported csv>
+"""
+
+import csv, re, sys, os, datetime, pandas as pd
+
+def main(): 
+    filepath = str(sys.argv[1])
+    equalize_csv(filepath)
+    questions_list = split_csv(filepath)
+    folder_name = str(datetime.datetime.now().replace(microsecond = 0))
+    os.mkdir(folder_name)
+    multiplier_df = pd.DataFrame()
+    for question in questions_list: #Recombining the csv's after they've been split by question to give equal weighting
+        calculate_multiplier(question)
+        os.rename(question, os.path.join(folder_name, question))
+        if len(multiplier_df) == 0:
+            multiplier_df = pd.read_csv(os.path.join(folder_name, question))
+        else:
+            question_df = pd.read_csv(os.path.join(folder_name, question))
+            multiplier_df = multiplier_df.merge(question_df, left_on = ["Team", "Recipient", "Recipient Email"], right_on = ["Team", "Recipient", "Recipient Email"])
+    multiplier_df["Multiplier"] = multiplier_df[['Multiplier_x', 'Multiplier_y']].mean(axis=1).round(2) #Future improvements: Need to delete the dependence on the column names being "Multiplier_x"
+    multiplier_df = multiplier_df.drop(["Multiplier_x", "Multiplier_y"], axis = 1)
+    with open(os.path.join(folder_name, "multipliers.csv"), 'w', newline='') as file:
+        output_csv = multiplier_df.to_csv(index=False)
+        file.write(output_csv)
+
+
+def equalize_csv(filepath): #Adds commas to a csv file to equalize column length
+    # Step 1: Read the CSV file and determine the maximum number of entries in a row
+    with open(filepath, 'r') as file:
+        reader = csv.reader(file)
+        rows = list(reader)
+
+    max_entries = max(len(row) for row in rows)
+
+    # Step 2: Iterate over the rows and append empty values to make them equal length
+    equalized_rows = []
+    for row in rows:
+        diff = max_entries - len(row)
+        equalized_row = row + [''] * diff
+        equalized_rows.append(equalized_row)
+
+    # Step 3: Write the updated rows to a new CSV file
+    with open(filepath, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerows(equalized_rows)
+
+def split_csv(filepath): #Takes in a TEAMMATES csv file and splits it into relevant questions
+    outputFilepaths = []
+    with open(filepath, 'r') as file:
+        lines = file.readlines()
+
+    header = lines[0]  # Assuming the first line is the header
+
+    question_indices = [i for i, line in enumerate(lines[1:], start=1) if re.search('Question\s\d+', line)]
+
+    question_count = 0
+
+    for i in range(len(question_indices)):
+        question_index = question_indices[i]
+        if len(lines) < question_index + 3 or 'Summary Statistics' not in lines[question_index + 2]:
+            continue
+
+        question_count += 1
+        destinationpath = f'{datetime.datetime.now().replace(microsecond = 0)}question_{question_count}.csv'
+        outputFilepaths.append(destinationpath)
+        with open(destinationpath, 'w') as output_file:
+            output_file.write(header)
+            output_file.write(lines[question_index])
+
+            team_indices = [j for j, line in enumerate(lines[question_index + 1:], start=question_index + 1) if 'Team' in line]
+            if len(team_indices) >= 2:
+                second_team_index = team_indices[1]
+            else:
+                second_team_index = len(lines)
+
+            for line in lines[question_index + 1:second_team_index]:
+                output_file.write(line)
+    return outputFilepaths
+
+def calculate_multiplier(filepath): #Takes in a TEAMMATES .CSV file and outputs a .CSV file with the multipliers calculated
+    csv_converted = pd.read_csv(filepath)
+
+    team_index = csv_converted.index[csv_converted.iloc[:, 0].str.contains('Team', na=False)].tolist()[0]
+    df = csv_converted.iloc[team_index:]
+    df = df.rename(columns=df.iloc[0]).drop(df.index[0]).reset_index(drop=True)
+
+    relevant_columns = ["Team", "Recipient", "Recipient Email", "Total Points", "Average Points"]
+    df = df[relevant_columns].dropna(how='all')
+    df["Average Points"] = df["Average Points"].astype(float).astype(int)
+
+    team_count = df.groupby('Team')['Team'].transform('count')
+    df["Multiplier"] = df["Average Points"] / (1 / team_count) / 100
+    df = df.drop(columns = ["Total Points", "Average Points"]).sort_values("Recipient")
+
+    output_csv = df.to_csv(index=False)
+    with open(filepath, 'w') as output_file:
+        output_file.write(output_csv)
+
+main()