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import re
import pandas as pd
import os
from huggingface_hub import InferenceClient
class DataProcessor:
INTERVENTION_COLUMN_OPTIONS = [
'Did the intervention happen today?',
'Did the intervention take place today?'
]
ENGAGED_STR = 'Engaged'
PARTIALLY_ENGAGED_STR = 'Partially Engaged'
NOT_ENGAGED_STR = 'Not Engaged'
def __init__(self, student_metrics_df=None):
self.hf_api_key = os.getenv('HF_API_KEY')
if not self.hf_api_key:
raise ValueError("HF_API_KEY not set in environment variables")
self.client = InferenceClient(api_key=self.hf_api_key)
self.student_metrics_df = student_metrics_df
self.intervention_column = None # Will be set when processing data
def read_excel(self, uploaded_file):
return pd.read_excel(uploaded_file)
def format_session_data(self, df):
date_column = next((col for col in df.columns if col in ["Date of Session", "Date"]), None)
if date_column:
df[date_column] = pd.to_datetime(df[date_column], errors='coerce').dt.date
else:
print("Warning: Neither 'Date of Session' nor 'Date' column found in the dataframe.")
df['Timestamp'] = self.safe_convert_to_datetime(df['Timestamp'], '%I:%M %p')
df['Session Start Time'] = self.safe_convert_to_time(df['Session Start Time'], '%I:%M %p')
df['Session End Time'] = self.safe_convert_to_time(df['Session End Time'], '%I:%M %p')
return df
def safe_convert_to_time(self, series, format_str='%I:%M %p'):
try:
converted = pd.to_datetime(series, format='%H:%M:%S', errors='coerce')
if format_str:
return converted.dt.strftime(format_str)
return converted
except Exception as e:
print(f"Error converting series to time: {e}")
return series
def safe_convert_to_datetime(self, series, format_str=None):
try:
converted = pd.to_datetime(series, errors='coerce')
if format_str:
return converted.dt.strftime(format_str)
return converted
except Exception as e:
print(f"Error converting series to datetime: {e}")
return series
def replace_student_names_with_initials(self, df):
updated_columns = []
for col in df.columns:
if col.startswith('Student Attendance'):
match = re.match(r'Student Attendance \[(.+?)\]', col)
if match:
name = match.group(1)
initials = ''.join([part[0] for part in name.split()])
updated_columns.append(f'Student Attendance [{initials}]')
else:
updated_columns.append(col)
else:
updated_columns.append(col)
df.columns = updated_columns
return df
def find_intervention_column(self, df):
for column in self.INTERVENTION_COLUMN_OPTIONS:
if column in df.columns:
self.intervention_column = column
return column
raise ValueError("No intervention column found in the dataframe.")
def get_intervention_column(self, df):
if self.intervention_column is None:
self.intervention_column = self.find_intervention_column(df)
return self.intervention_column
def compute_intervention_statistics(self, df):
intervention_column = self.get_intervention_column(df)
total_days = len(df)
sessions_held = df[intervention_column].str.strip().str.lower().eq('yes').sum()
intervention_frequency = (sessions_held / total_days) * 100 if total_days > 0 else 0
return pd.DataFrame({
'Intervention Dosage (%)': [round(intervention_frequency, 0)],
'Intervention Sessions Held': [sessions_held],
'Intervention Sessions Not Held': [total_days - sessions_held],
'Total Number of Days Available': [total_days]
})
def classify_engagement(self, engagement_str):
engagement_str = str(engagement_str).lower()
if engagement_str.startswith(self.ENGAGED_STR.lower()):
return self.ENGAGED_STR
elif engagement_str.startswith(self.PARTIALLY_ENGAGED_STR.lower()):
return self.PARTIALLY_ENGAGED_STR
elif engagement_str.startswith(self.NOT_ENGAGED_STR.lower()):
return self.NOT_ENGAGED_STR
else:
return 'Unknown'
def compute_student_metrics(self, df):
intervention_column = self.get_intervention_column(df)
intervention_df = df[df[intervention_column].str.strip().str.lower() == 'yes']
intervention_sessions_held = len(intervention_df)
student_columns = [col for col in df.columns if col.startswith('Student Attendance')]
student_metrics = {}
for col in student_columns:
student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
student_data = intervention_df[[col]].copy()
student_data[col] = student_data[col].fillna('Absent')
attendance_values = student_data[col].apply(lambda x: 1 if self.classify_engagement(x) in [
self.ENGAGED_STR,
self.PARTIALLY_ENGAGED_STR,
self.NOT_ENGAGED_STR
] else 0)
sessions_attended = attendance_values.sum()
attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
attendance_pct = round(attendance_pct)
engagement_counts = {
self.ENGAGED_STR: 0,
self.PARTIALLY_ENGAGED_STR: 0,
self.NOT_ENGAGED_STR: 0,
'Absent': 0
}
for x in student_data[col]:
classified_engagement = self.classify_engagement(x)
if classified_engagement in engagement_counts:
engagement_counts[classified_engagement] += 1
else:
engagement_counts['Absent'] += 1 # Count as Absent if not engaged
total_sessions = sum(engagement_counts.values())
engaged_pct = (engagement_counts[self.ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
engaged_pct = round(engaged_pct)
partially_engaged_pct = (engagement_counts[self.PARTIALLY_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
partially_engaged_pct = round(partially_engaged_pct)
not_engaged_pct = (engagement_counts[self.NOT_ENGAGED_STR] / total_sessions * 100) if total_sessions > 0 else 0
not_engaged_pct = round(not_engaged_pct)
absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
absent_pct = round(absent_pct)
# Engagement percentage is based on Engaged and Partially Engaged sessions
engagement_pct = ((engagement_counts[self.ENGAGED_STR] + engagement_counts[self.PARTIALLY_ENGAGED_STR]) / total_sessions * 100) if total_sessions > 0 else 0
engagement_pct = round(engagement_pct)
# Determine if the student attended ≥ 90% of sessions
attended_90 = "Yes" if attendance_pct >= 90 else "No"
# Determine if the student was engaged ≥ 80% of the time
engaged_80 = "Yes" if engagement_pct >= 80 else "No"
# Store metrics in the required order
student_metrics[student_name] = {
'Attended ≥ 90%': attended_90,
'Engagement ≥ 80%': engaged_80,
'Attendance (%)': attendance_pct,
'Engagement (%)': engagement_pct,
f'{self.ENGAGED_STR} (%)': engaged_pct,
f'{self.PARTIALLY_ENGAGED_STR} (%)': partially_engaged_pct,
f'{self.NOT_ENGAGED_STR} (%)': not_engaged_pct,
'Absent (%)': absent_pct
}
# Create a DataFrame from student_metrics
student_metrics_df = pd.DataFrame.from_dict(student_metrics, orient='index').reset_index()
student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
return student_metrics_df
def compute_average_metrics(self, student_metrics_df):
# Calculate the attendance and engagement average percentages across students
attendance_avg_stats = student_metrics_df['Attendance (%)'].mean() # Average attendance percentage
engagement_avg_stats = student_metrics_df['Engagement (%)'].mean() # Average engagement percentage
# Round the averages to whole numbers
attendance_avg_stats = round(attendance_avg_stats)
engagement_avg_stats = round(engagement_avg_stats)
return attendance_avg_stats, engagement_avg_stats
def evaluate_student(self, row, attendance_threshold=90, engagement_threshold=80):
if row["Attended ≥ 90%"] == "No":
return "Address Attendance"
elif row["Engagement ≥ 80%"] == "No":
return "Address Engagement"
else:
return "Consider barriers, fidelity, and progress monitoring" |