From a6c1de649e615892380766e06c8e73927eb94c31 Mon Sep 17 00:00:00 2001
From: Rustam <rustambhadouriya7@gmail.com>
Date: Fri, 28 Aug 2026 15:07:39 +0530
Subject: [PATCH] Added rslearn-py to package list

---
 kaggle_requirements.txt |  2 ++
 tests/test_numpy.py     |  3 ++-
 tests/test_rslearn.py   | 30 ++++++++++++++++++++++++++++++
 3 files changed, 34 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_rslearn.py

diff --git a/kaggle_requirements.txt b/kaggle_requirements.txt
index c7eb18e5..ed97210b 100644
--- a/kaggle_requirements.txt
+++ b/kaggle_requirements.txt
@@ -136,3 +136,5 @@ vtk
 wavio
 xvfbwrapper
 ydata-profiling
+rslearn-py==1.0.5
+# A Machine Learning Library with Automated Evaluations with one liner code for `sklearn`, `rslearn`, `numpy arrays`
\ No newline at end of file
diff --git a/tests/test_numpy.py b/tests/test_numpy.py
index ab7ec03c..46657261 100644
--- a/tests/test_numpy.py
+++ b/tests/test_numpy.py
@@ -1,6 +1,7 @@
 import unittest
 
-from distutils.version import StrictVersion
+# from distutils.version import StrictVersion
+# Removed since python version 3.12
 
 import numpy as np
 import io
diff --git a/tests/test_rslearn.py b/tests/test_rslearn.py
new file mode 100644
index 00000000..d662ed9a
--- /dev/null
+++ b/tests/test_rslearn.py
@@ -0,0 +1,30 @@
+import unittest
+
+from sklearn import datasets
+from rslearn.linear_model import LogisticRegression
+from rslearn.neighbors import KNNClassifier
+from rslearn.metrics import evaluate_model # Evaluate any Kindof Model with return type of numpy array.
+
+class TestRslearn(unittest.TestCase):
+    def test_KNN_classifier(self):
+        iris = datasets.load_iris()
+        X, y = iris.data, iris.target
+        clf1 = KNNClassifier(k_neighbors=3)
+        clf1.fit(X,y, scale=True) # Auto Scale Data by default=True
+
+    def test_logistic_classifier(self):
+        iris = datasets.load_iris()
+        X, y = iris.data, iris.target
+        lr1 = LogisticRegression(solver="saga", lr=0.03)
+        lr1.fit(X,y)
+
+    def test_evaluates(self):
+        iris = datasets.load_iris()
+        X, y = iris.data, iris.target
+        lr1 = LogisticRegression()
+        lr1.fit(X,y)
+
+        evaluations = lr1.evaluate(X=X, y_true=y)
+        evals = evaluate_model(model=lr1, X=X, y_true=y, task="classification")
+
+