Create voting_regressor_viz.py

voting_regressor_viz.py

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+import matplotlib.pyplot as plt
+import streamlit as st
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import VotingRegressor
+from sklearn.model_selection import train_test_split
+import numpy as np
+from sklearn.metrics import r2_score,mean_absolute_error
+
+def train_voting_regressor(algos):
+
+    vr = VotingRegressor(algos)
+    vr.fit(X_train,y_train)
+
+    y_pred = vr.predict(X_test1)
+
+    r2 = r2_score(y_test1,y_pred)
+    mae = mean_absolute_error(y_test1,y_pred)
+
+    return vr,r2,mae
+
+plt.style.use('seaborn-bright')
+
+# Create a random dataset
+rng = np.random.RandomState(1)
+X = np.sort(5 * rng.rand(80, 1), axis=0)
+y = np.sin(X).ravel()
+y[::5] += 3 * (0.5 - rng.rand(16))
+
+# Random state - 8
+X_train,X_test1,y_train,y_test1 = train_test_split(X,y,test_size=0.1,random_state=8)
+
+# Predict
+X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
+
+
+st.sidebar.markdown("# Voting Regressor")
+
+# Estimator Multi-select
+estimators = st.sidebar.multiselect(
+    'Estimators',
+    [
+        'Linear Regression',
+        'SVR',
+        'Decision Tree Regressor'
+    ]
+)
+
+# Build estimators
+algos = []
+
+if 'Linear Regression' in estimators:
+    lr_reg = LinearRegression()
+    algos.append(('lr', lr_reg))
+if 'SVR' in estimators:
+    svr_reg = SVR()
+    algos.append(('svr', svr_reg))
+if 'Decision Tree Regressor' in estimators:
+    dt_reg = DecisionTreeRegressor(max_depth=5)
+    algos.append(('dt', dt_reg))
+
+fig, ax = plt.subplots()
+ax.scatter(X, y, s=100,color="yellow", edgecolor="black")
+orig = st.pyplot(fig)
+
+
+if st.sidebar.button("Run Algorithm"):
+    vr,r2,mae = train_voting_regressor(algos)
+    y_2 = vr.predict(X_test)
+    ax.plot(X_test, y_2, linewidth=3,label="Voting Regressor")
+    ax.legend()
+    orig.pyplot(fig)
+    figs = []
+    r2_scores = []
+    maes = []
+    for i in algos:
+        i[1].fit(X_train,y_train)
+        y_pred = i[1].predict(X_test)
+        y_pred1 = i[1].predict(X_test1)
+        r2_scores.append(r2_score(y_test1,y_pred1))
+        maes.append(mean_absolute_error(y_test1,y_pred1))
+        ax.plot(X_test, y_pred, linewidth=1,label=i[0],linestyle='dashdot')
+        ax.legend()
+
+    counter = 0
+    for i in st.beta_columns(len(algos)):
+        with i:
+            orig.pyplot(fig)
+        counter += 1
+
+    st.sidebar.subheader("Regression Metrics")
+    st.sidebar.text("R2 score Voting Regressor " + str(round(r2,2)))
+    st.sidebar.text("MAE Voting Regressor " + str(round(mae,2)))
+
+    for i in range(len(algos)):
+        st.sidebar.text("*"*35)
+        st.sidebar.text("R2 score for " + algos[i][0] + " " + str(round(r2_scores[i],2)))
+        st.sidebar.text("MAE score for " + algos[i][0] + " " + str(round(maes[i], 2)))