Rename DecisionBoundaries_LearningCurves_Algorithms.py to DB_LC_Algorithms.py

DecisionBoundaries_LearningCurves_Algorithms.py → DB_LC_Algorithms.py

@@ -13,7 +13,7 @@ from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_sc
 from mlxtend.plotting import plot_decision_regions
 
 # Image
-st.image("https://huggingface.co/spaces/varshitha22/DecisionBoundaries_Learningcurves_Algorithms/resolve/main/logo.png")
+st.image("https://huggingface.co/spaces/varshitha22/KNN/resolve/main/logo.png")
 st.markdown("<br>", unsafe_allow_html=True)
 
 # Sidebar for dataset selection
@@ -25,33 +25,37 @@ noise = st.sidebar.slider("Add Noise:", 0.0, 1.0, 0.2, step=0.05)
 st.sidebar.header("Model Selection")
 model_name = st.sidebar.radio("Choose a Model:", ["KNN", "Decision Tree", "Naive Bayes", "Logistic Regression", "SVC"])
 
-# Display number of neighbors selector only if KNN is selected
-neighbors = None
+# Display KNN specific settings only if KNN is selected
 if model_name == "KNN":
     neighbors = st.sidebar.number_input("Neighbors", min_value=1, max_value=25, value=5, step=1)
     knn_weights = st.sidebar.radio("KNN Weights:", ["uniform", "distance"])
 
-# KNN Algorithm
-st.sidebar.subheader("KNN Algorithm")
-algorithms_selected = []
-if st.sidebar.checkbox("auto", value=True):
-    algorithms_selected.append("auto")
-if st.sidebar.checkbox("ball_tree"):
-    algorithms_selected.append("ball_tree")
-if st.sidebar.checkbox("kd_tree"):
-    algorithms_selected.append("kd_tree")
-if st.sidebar.checkbox("brute"):
-    algorithms_selected.append("brute")
-
-# KNN Metric
-st.sidebar.subheader("KNN Metric")
-metrics_selected = []
-if st.sidebar.checkbox("euclidean", value=True):
-    metrics_selected.append("euclidean")
-if st.sidebar.checkbox("manhattan"):
-    metrics_selected.append("manhattan")
-if st.sidebar.checkbox("minkowski"):
-    metrics_selected.append("minkowski")
+    # KNN Algorithm
+    st.sidebar.subheader("KNN Algorithm")
+    algorithms_selected = []
+    if st.sidebar.checkbox("auto", value=True):
+        algorithms_selected.append("auto")
+    if st.sidebar.checkbox("ball_tree"):
+        algorithms_selected.append("ball_tree")
+    if st.sidebar.checkbox("kd_tree"):
+        algorithms_selected.append("kd_tree")
+    if st.sidebar.checkbox("brute"):
+        algorithms_selected.append("brute")
+
+    # KNN Metric
+    st.sidebar.subheader("KNN Metric")
+    metrics_selected = []
+    if st.sidebar.checkbox("euclidean", value=True):
+        metrics_selected.append("euclidean")
+    if st.sidebar.checkbox("manhattan"):
+        metrics_selected.append("manhattan")
+    if st.sidebar.checkbox("minkowski"):
+        metrics_selected.append("minkowski")
+else:
+    neighbors = None
+    knn_weights = None
+    algorithms_selected = []
+    metrics_selected = []
 
 # Generate dataset
 if data_type == "Blobs":
@@ -123,3 +127,4 @@ ax.set_xlabel("Training Size")
 ax.set_ylabel("Accuracy")
 ax.legend()
 st.pyplot(fig)
+