Added rslearn-py

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`

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

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")
+
+