To save and parse selected features in `tsfresh`:

# Saving Selected Features

1. Extract Features:
“`python
from tsfresh import extract_features
extracted_features = extract_features(data, column_id=’id’, column_sort=’time’)
“`

2. Select Relevant Features:
“`python
from tsfresh import select_features
from tsfresh.utilities.dataframe_functions import impute
impute(extracted_features)
selected_features = select_features(extracted_features, y)
“`

3. Save Selected Features:
“`python
selected_features.to_csv(‘selected_features.csv’)
“`

# Parsing and Converting Feature Names

1. Load Selected Features:
“`python
import pandas as pd
selected_features = pd.read_csv(‘selected_features.csv’, index_col=0)
“`

2. Convert to Compatible Format:
“`python
from tsfresh.feature_extraction import ComprehensiveFCParameters
import re

def convert_feature_names(selected_features):
new_params = {}
for feature in selected_features.columns:
match = re.match(r'(.+?)__([^_]+)_(.+)’, feature)
if match:
feature_name, param, value = match.groups()
if feature_name not in new_params:
new_params[feature_name] = {param: [value]}
else:
new_params[feature_name].setdefault(param, []).append(value)
return new_params

compatible_format = convert_feature_names(selected_features)
Explanation

– Extract Features: Initial feature extraction.
– Select Features: Choose relevant features.
– Save to CSV: Save for future use.
– Convert Names: Format for `ComprehensiveFCParameters`.

This prepares features for further analysis or model training.