# CrossDistance Class for Computing Mean Pairwise Distances Between Two Datasets
# **Importing and Using the CrossDistance Class in a Python Program**
#
# import pandas as pd
#
# from geoanalytics.scoreCalculator import CrossDistance
#
# topk_df = pd.read_csv("topk.csv")
#
# train_df = pd.read_csv("train.csv")
#
# cd = CrossDistance(topk_df, train_df, startBandTopkDF=2, startBandTrainDF=2)
#
# mean_distance = cd.run(metric='cosine')
#
__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 numpy as np
from scipy.spatial.distance import cdist
[docs]
class CrossDistance:
"""
**About this algorithm**
:**Description**:
CrossDistance computes the average pairwise distances between two datasets using a specified distance metric.
This is useful for evaluating the similarity between two sets of feature vectors, such as in retrieval,
matching, or clustering validation.
:**Parameters**:
- `TopkDF` (*pd.DataFrame*): First dataset (e.g., top-k retrieved samples).
- `TrainDF` (*pd.DataFrame*): Second dataset (e.g., original training samples).
- `startBandTopkDF` (*int*): Column index from which to start using features in `TopkDF` (default: 2).
- `startBandTrainDF` (*int*): Column index from which to start using features in `TrainDF` (default: 2).
:**Attributes**:
- **TopkDF** (*np.ndarray*) -- Sliced feature matrix from `TopkDF`.
- **TrainDF** (*np.ndarray*) -- Sliced feature matrix from `TrainDF`.
**Execution methods**
**Calling from a Python program**
.. code-block:: python
import pandas as pd
from geoanalytics.scoreCalculator import CrossDistance
topk_df = pd.read_csv("topk.csv")
train_df = pd.read_csv("train.csv")
cd = CrossDistance(topk_df, train_df, startBandTopkDF=2, startBandTrainDF=2)
mean_distance = cd.run(metric='euclidean')
**Credits**
This implementation was created by Raashika and revised by M. Charan Teja under the guidance of Professor Rage Uday Kiran.
"""
def __init__(self, TopkDF, TrainDF, startBandTopkDF = 2, startBandTrainDF = 2):
self.TopkDF = TopkDF.iloc[:,startBandTopkDF:]
self.TrainDF = TrainDF.iloc[:,startBandTrainDF:]
if self.TopkDF.shape[1] != self.TrainDF.shape[1]:
raise ValueError("TopkDF and TrainDF must have the same number of columns after slicing.")
[docs]
def run(self, metric='euclidean'):
"""
Computes and returns the mean pairwise distance between the rows of two datasets.
Args:
metric (str): Distance metric to use (default: 'euclidean').
Returns:
float: Mean of all computed distances.
"""
distances = cdist(self.TopkDF, self.TrainDF, metric=metric)
return np.mean(distances)