commit

app.py

@@ -9,21 +9,21 @@ st.subheader("K nearest neighbor (KNN) Regressor")
 
 st_col = st.columns(1)[0]
 
-K = st.slider('Number of nearest neighbors (K)', min_value=1, max_value=10, value=5, step=1)
+K = st.slider(
+    "Number of nearest neighbors (K)", min_value=1, max_value=10, value=5, step=1
+)
 
 
+X, y = make_regression(n_samples=1000, n_features=1, noise=0.1, random_state=42)
 
-
-X, y = make_regression(n_samples=100, n_features=1, noise=0.1,random_state=42)
-
-ntrain = 100
+ntrain = 700
 
 x_train = X[:ntrain]
 y_train = y[:ntrain]
 x_test = X[ntrain:]
 y_test = y[ntrain:]
 
-knn = KNN(n_neighbors=K)
+knn = KNeighborsRegressor(n_neighbors=K)
 knn.fit(x_train, y_train)
 plt.figure()
 
@@ -33,21 +33,23 @@ xx, yy = np.meshgrid(x, y)
 xy = np.c_[xx.ravel(), yy.ravel()]
 
 y_predicted = knn.predict(xy)
-#plt.pcolormesh(y_predicted.reshape(200, 200), cmap='jet')
-plt.pcolormesh(xx, yy, y_predicted.reshape(200, 200), cmap='jet', alpha=0.2)
+# plt.pcolormesh(y_predicted.reshape(200, 200), cmap='jet')
+plt.pcolormesh(xx, yy, y_predicted.reshape(200, 200), cmap="jet", alpha=0.2)
 y_unique = np.unique(y_train)
-markers = '*x+'
-colors = 'bgr'
+markers = "*x+"
+colors = "bgr"
 for i in range(len(y_unique)):
-  plt.scatter(x_train[y_train == y_unique[i], 0],
-               x_train[y_train == y_unique[i], 1],
-               marker=markers[i],
-               c=colors[i])
-               
+    plt.scatter(
+        x_train[y_train == y_unique[i], 0],
+        x_train[y_train == y_unique[i], 1],
+        marker=markers[i],
+        c=colors[i],
+    )
+
 
 with st_col:
-  st.pyplot(plt)
-  
+    st.pyplot(plt)
+
 hide_streamlit_style = """
             <style>
             #MainMenu {visibility: hidden;}
@@ -55,10 +57,12 @@ hide_streamlit_style = """
             subheader {alignment: center;}
             </style>
             """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
 
-st.markdown("""
+st.markdown(
+    """
             There are several points to note on the effect of K on the quality of model fit:
             * Models with extremely small values of K learn the local patterns and do not generalize well thus they have a high variance or overfitting effect.
             * Models with extremely high values of K suffer from averaging effect over the entire space and thus do not do well even on the train points. This is known as a high bias or underfitting effect.
-            """)
+            """
+)