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import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from _config import config
class CreateCombinedDataFrame(BaseEstimator, TransformerMixin):
def __init__(self, time_window, label_columns=None):
self.time_window = time_window
self.label_columns = label_columns
def fit(self, X, y=None):
return self
def transform(self, X):
df_reports, df_accel = X
print(f"PreprocesssingCombined initialized with label_columns: {self.label_columns}")
# Ensure the chosen label columns exist in the dataset
valid_conditions = (df_reports['timeOfEngagement'] != 0)
for label in self.label_columns:
valid_conditions &= (df_reports[label] != "NONE")
df_reports = df_reports[valid_conditions].copy()
# No datetime conversion needed; timestamps remain as integers
df_accel.rename(columns={'timestamp': 'timeOfNotification'}, inplace=True)
print(f"ExtractAccelData initialized with time_window: {self.time_window}")
df_reports['accel_data'] = df_reports.apply(lambda row: self._extract_accel_data(row, df_accel), axis=1)
print(f"Combining called with label_columns: {self.label_columns}")
combined_data = []
for _, row in df_reports.iterrows():
accel_data = row['accel_data']
for _, accel_row in accel_data.iterrows():
combined_row = {
'participantId': row['participantId'], # Participant ID
'selfreport_time': row['timeOfNotification'], # Self-report time
'accel_time': accel_row['timeOfNotification'], # Accelerometer data time
'x': accel_row['x'], # x-axis accelerometer data
'y': accel_row['y'], # y-axis accelerometer data
'z': accel_row['z'] # z-axis accelerometer data
}
# Dynamically add emotion labels to the combined row
for label in self.label_columns:
combined_row[label] = row[label]
combined_data.append(combined_row)
combined_df = pd.DataFrame(combined_data)
# Convert integer timestamps back to datetime format for the CSV
combined_df['selfreport_time'] = pd.to_datetime(combined_df['selfreport_time'], unit='ms')
combined_df['accel_time'] = pd.to_datetime(combined_df['accel_time'], unit='ms')
# Create groupid column (unique identifier based on participantId and selfreport_time)
combined_df['groupid'] = combined_df.groupby(['participantId', 'selfreport_time']).ngroup() + 1
col = combined_df.pop("groupid") # Move groupid to the first column
combined_df.insert(0, col.name, col)
# Export the combined dataframe to CSV
time_window_str = str(self.time_window)
label_columns_str = "_".join(self.label_columns)
file_name = f"combined_data_timewindow_{time_window_str}min_labels_{label_columns_str}.csv"
combined_df.to_csv(file_name, index=False)
print(f"Combined dataframe exported successfully to {file_name}.")
return combined_df
def _extract_accel_data(self, row, accel_data):
time_delta = self.time_window * 60 * 1000 # Convert minutes to milliseconds
start_time = row['timeOfNotification'] - time_delta # Keep as integer
end_time = row['timeOfNotification'] + time_delta # Keep as integer
participant_id = row['participantId']
# Ensure accel_data['timeOfNotification'] is also an integer
accel_data['timeOfNotification'] = accel_data['timeOfNotification'].astype(np.int64) # Ensure integer format
# Log a warning if the desired time range exceeds the available data range
if start_time < accel_data['timeOfNotification'].min() or end_time > accel_data['timeOfNotification'].max():
print(
f"Warning: Data does not cover the full {self.time_window}-minute window for participant {participant_id}. "
f"Available range: {accel_data['timeOfNotification'].min()} to {accel_data['timeOfNotification'].max()}. "
f"Requested range: {start_time} to {end_time}."
)
# Apply the filtering mask
mask = (
(accel_data['participantId'] == participant_id) &
(accel_data['timeOfNotification'] >= max(start_time, accel_data['timeOfNotification'].min())) &
(accel_data['timeOfNotification'] <= min(end_time, accel_data['timeOfNotification'].max()))
)
print("Start Time (ms):", start_time)
print("End Time (ms):", end_time)
print("Filtered Rows:\n", accel_data[mask])
return accel_data[mask]
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