# MeanImputation fills missing values in a DataFrame using column-wise mean substitution, with performance tracking and optional CSV export.
#
# **Importing and Using the MeanImputation Class in a Python Program**
#
# import pandas as pd
#
# from geoanalytics.imputation import MeanImputation
#
# df = pd.read_csv('data_with_nans.csv')
#
# obj = MeanImputation(df)
#
# imputed_df = obj.impute()
#
# obj.save('MeanImputation.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 MeanImputation:
"""
**About this algorithm**
:**Description**: MeanImputation fills missing values in a dataset by replacing them with the mean of their respective columns.
:**Parameters**: - **dataframe** (*pd.DataFrame*) -- A Pandas DataFrame containing missing values.
- The first two columns must represent spatial/positional attributes, typically 'x' and 'y'.
:**Attributes**: - **df** (*pd.DataFrame*) -- Original dataframe with renamed first two columns ('x', 'y') and copied features.
- **imputedDF** (*pd.DataFrame*) -- Stores the resulting dataframe after mean imputation.
**Execution methods**
**Calling from a Python program**
.. code-block:: python
import pandas as pd
from geoanalytics.imputation import MeanImputation
df = pd.read_csv('data_with_nans.csv')
obj = MeanImputation(df)
imputed_df = obj.impute()
obj.save('MeanImputation.csv')
**Credits**
The complete program was written by and revised by under the supervision of Professor Rage Uday Kiran.
"""
def __init__(self, dataframe):
"""
Constructor to initialize the MeanImputation object.
:param dataframe: Input dataframe where missing values need to be imputed.
:type dataframe: pd.DataFrame
"""
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 mean imputation on all feature columns (excluding x and y).
:return: DataFrame with 'x', 'y', and imputed features.
:rtype: pd.DataFrame
"""
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)
imputedData = data.fillna(data.mean())
self.imputedDF = pd.concat([xy, imputedData], 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='MeanImputation.csv'):
"""
Saves the imputed DataFrame to a CSV file.
:param outputFile: File path to save the output. Defaults to 'MeanImputation.csv'.
:type outputFile: str
"""
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")