# Interpolation-based missing value imputation using linear estimation strategy.
#
# **Importing and Using the Interpolation Class in a Python Program**
#
# import pandas as pd
#
# from geoanalytics.imputation import Interpolation
#
# df = pd.read_csv('input.csv')
#
# ip = Interpolation(df)
#
# output = ip.impute()
#
# ip.save('Interpolation.csv')
#
__copyright__ = """
Copyright (C) 2022 Rage Uday Kiran
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import time
import psutil
from tqdm import tqdm
import pandas as pd
[docs]
class Interpolation:
"""
**About this algorithm**
:**Description**: Interpolation is a missing data imputation technique that estimates NaN values using linear interpolation between known values. It fills values in both forward and backward directions to ensure completeness.
:**Parameters**: - Dataset (pandas DataFrame) must be provided during object initialization.
- No other parameters are required during instantiation.
:**Attributes**: - **df** (*pd.DataFrame*) -- The input data with 'x', 'y' coordinates and feature columns.
- **imputedDF** (*pd.DataFrame*) -- DataFrame containing 'x', 'y', and imputed values.
**Execution methods**
**Calling from a Python program**
.. code-block:: python
import pandas as pd
from geoanalytics.imputation import Interpolation
df = pd.read_csv("input.csv")
ip = Interpolation(df)
output = ip.impute()
ip.save('Interpolation.csv')
**Credits**
This implementation was created by and revised by under the guidance of Professor Rage Uday Kiran.
"""
def __init__(self, dataframe):
"""
Constructor to initialize the Interpolation object with the input DataFrame.
:param dataframe: pandas DataFrame containing at least columns ['x', 'y'] and feature columns.
"""
self.df = dataframe.copy()
self.df.columns = ['x', 'y'] + list(self.df.columns[2:])
self.imputedDF = None
self.startTime = None
self.endTime = None
self.memoryUSS = None
self.memoryRSS = None
[docs]
def getRuntime(self):
"""
Prints the total runtime of the algorithm.
"""
print("Total Execution time of proposed Algorithm:", self.endTime - self.startTime, "seconds")
[docs]
def getMemoryUSS(self):
"""
Prints the memory usage (USS) of the process in kilobytes.
"""
print("Memory (USS) of proposed Algorithm in KB:", self.memoryUSS)
[docs]
def run(self):
"""
Performs linear interpolation to fill missing values in the dataset.
The method:
- Separates 'x' and 'y' columns from the rest of the data.
- Applies linear interpolation along each column.
- Uses both forward and backward filling to handle edge NaNs.
- Reconstructs the complete DataFrame with imputed values.
:return: Imputed DataFrame with original 'x', 'y' columns and interpolated features.
"""
self.startTime = time.time()
xy = self.df[['x', 'y']].reset_index(drop=True)
data = self.df.drop(['x', 'y'], axis=1).reset_index(drop=True)
interpolatedData = data.interpolate(method='linear', limit_direction='both')
self.imputedDF = pd.concat([xy, interpolatedData], axis=1)
self.endTime = time.time()
process = psutil.Process()
self.memoryUSS = process.memory_full_info().uss / 1024
self.memoryRSS = process.memory_full_info().rss / 1024
return self.imputedDF
[docs]
def save(self, outputFile='Interpolation.csv'):
"""
Saves the imputed DataFrame to a CSV file.
:param outputFile: The filename for saving the DataFrame. Defaults to 'Interpolation.csv'.
"""
if self.imputedDF is not None:
try:
self.imputedDF.to_csv(outputFile, index=False)
print(f"Imputed data saved to: {outputFile}")
except Exception as e:
print(f"Failed to save labels: {e}")
else:
print("No imputed data to save. Run impute() first")