Initial

A3.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split, KFold
+from sklearn.ensemble import BaggingRegressor, GradientBoostingRegressor, AdaBoostRegressor
+from sklearn.linear_model import LinearRegression, Lasso, Ridge
+from sklearn.svm import SVR
+from sklearn.metrics import mean_squared_error
+import matplotlib.pyplot as plt
+
+
+st.title('Boosting in Regression')
+
+DATE_COLUMN = 'date/time'
+DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
+            'streamlit-demo-data/uber-raw-data-sep14.csv.gz')
+
+@st.cache_data
+def load_data(nrows):
+    data = pd.read_csv(DATA_URL, nrows=nrows)
+    lowercase = lambda x: str(x).lower()
+    data.rename(lowercase, axis='columns', inplace=True)
+    data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
+    return data
+
+@st.cache_data
+def make_data(dataset_option):
+    opt = dataset_option.split()[0]
+    if opt == "100":
+        X, y = make_regression(n_samples=100,
+                               n_features=10, n_informative=2,
+                               random_state=2)
+    elif opt == "200":
+        X, y = make_regression(n_samples=200,
+                               n_features=5, n_informative=2,
+                               random_state=4)
+    elif opt == "150":
+        X, y = make_regression(n_samples=150,
+                               n_features=7,n_informative=2,
+                               random_state=2)
+    else:
+        X, y = make_regression(random_state=10)
+    return X, y
+
+def estimator_model(estimator_type):
+    if estimator_type == "Linear regressor":
+        model = LinearRegression()
+    elif estimator_type == "Ridge regressor":
+        model = Ridge()
+    elif estimator_type == "Lasso regressor":
+        model = Lasso()
+    elif estimator_type == "SVR":
+        model = SVR()
+    else:
+        model = LinearRegression()
+    return model
+
+options = ['100 samples with 10 features and 1 target', '200 samples with 5 features and 1 target', '150 samples with 7 features and 1 target']
+dataset_option = st.selectbox('Select dataset size:', options)
+X, y = make_data(dataset_option)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4)
+fig = plt.figure()
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("Dataset")
+plt.scatter(X[:,0], y)
+st.pyplot(fig)
+
+options = ['Linear regressor', 'Ridge regressor', 'Lasso regressor', 'SVR']
+model_type = st.selectbox('Select model type to use:', options)
+options = ['boosting', 'bagging', 'gradient descent']
+ensemble_type = st.selectbox('Select the ensemble type:', options)
+estimator_number = st.slider('n_estimators', 1, 20, 4)
+
+fig = plt.figure()
+if ensemble_type == "bagging":
+    estimator_ = estimator_model(model_type)
+    test_loss = []
+    train_loss = []
+    for i in range(1, estimator_number):
+        model = BaggingRegressor( n_estimators=i, random_state=45)
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        temp = mean_squared_error(y_test, y_pred)
+        test_loss.append(temp)
+        y_pred = model.predict(X_train)
+        temp = mean_squared_error(y_train, y_pred)
+        train_loss.append(temp)
+    plt.plot(range(1, estimator_number), test_loss, label="test loss")
+    plt.plot(range(1, estimator_number), train_loss, label="train loss")
+elif ensemble_type == "gradient descent":
+    test_loss = []
+    estimator_ = estimator_model(model_type)
+    for i in range(1, estimator_number):
+        model = GradientBoostingRegressor( n_estimators=i, learning_rate=0.1, random_state=45)
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        test_loss.append(mean_squared_error(y_test, y_pred))
+    plt.plot(range(1, estimator_number), test_loss, label="test loss")
+elif ensemble_type == "boosting":
+    test_loss = []
+    estimator_ = estimator_model(model_type)
+    for i in range(1, estimator_number):
+        model = AdaBoostRegressor(n_estimators=i)
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        test_loss.append(mean_squared_error(y_test, y_pred))
+    plt.plot(range(1, estimator_number), test_loss, label="test loss")
+
+plt.legend()
+plt.title("loss plot")
+plt.xlabel("n_estimators")
+plt.ylabel("loss")
+st.pyplot(fig)
+
+if st.button('Magic'):
+    loss = []
+    n_splits=5
+    opts = ['Linear regressor', 'Ridge regressor', 'Lasso regressor', 'SVR']
+    for opt in opts:
+        kf = KFold(n_splits=n_splits, shuffle=True, random_state=32)
+        cv_scores = []
+        for train_index, val_index in kf.split(X_train):
+            model = estimator_model(opt)
+            X_train_cv, X_val_cv = X_train[train_index], X_train[val_index]
+            y_train_cv, y_val_cv = y_train[train_index], y_train[val_index]
+            model.fit(X_train_cv, y_train_cv)
+            y_val_pred = model.predict(X_val_cv)
+            cv_scores.append(mean_squared_error(y_val_cv, y_val_pred))
+        loss.append(np.mean(cv_scores))
+    best_model = estimator_model(opts[np.argmin(loss)])
+    best_model.fit(X_train, y_train)
+    y_pred = best_model.predict(X_test)
+    fig = plt.figure()
+    plt.title(f"Best model fit is of {opts[np.argmin(loss)]}")
+    plt.scatter(X_test[:,0], y_pred)
+    plt.scatter(X_test[:,0], y_test)
+    st.pyplot(fig)
+
+