update

app.py

@@ -1,44 +1,32 @@
 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.linear_model import LinearRegression
+from sklearn.tree import DecisionTreeRegressor
 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.write("Over here, we will try to visualise the effect of number of estimators in the ensembling methods for the regression and could tryout with different basic estimators")
+st.write("Magic button will help you to find the best individual estimator on selected dataset.")
 @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)
+                               n_features=2,
+                               random_state=42)
     elif opt == "200":
         X, y = make_regression(n_samples=200,
-                               n_features=5, n_informative=2,
-                               random_state=4)
+                               n_features=2,
+                               random_state=56)
     elif opt == "150":
         X, y = make_regression(n_samples=150,
-                               n_features=7,n_informative=2,
-                               random_state=2)
+                               n_features=2,
+                               random_state=25)
     else:
         X, y = make_regression(random_state=10)
     return X, y
@@ -46,10 +34,8 @@ def make_data(dataset_option):
 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 == "Decision Tree regressor":
+        model = DecisionTreeRegressor()
     elif estimator_type == "SVR":
         model = SVR()
     else:
@@ -67,7 +53,31 @@ plt.title("Dataset")
 plt.scatter(X[:,0], y)
 st.pyplot(fig)
 
-options = ['Linear regressor', 'Ridge regressor', 'Lasso regressor', 'SVR']
+if st.button('Magic'):
+    loss = []
+    n_splits=5
+    opts = ['Linear regressor', 'Decision Tree 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)
+
+options = ['Linear regressor', 'Decision Tree 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)
@@ -75,11 +85,11 @@ estimator_number = st.slider('n_estimators', 1, 20, 4)
 
 fig = plt.figure()
 if ensemble_type == "bagging":
-    estimator_ = estimator_model(model_type)
+    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 = BaggingRegressor(estimator=estimator, 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)
@@ -91,7 +101,7 @@ if ensemble_type == "bagging":
     plt.plot(range(1, estimator_number), train_loss, label="train loss")
 elif ensemble_type == "gradient descent":
     test_loss = []
-    estimator_ = estimator_model(model_type)
+    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)
@@ -100,9 +110,9 @@ elif ensemble_type == "gradient descent":
     plt.plot(range(1, estimator_number), test_loss, label="test loss")
 elif ensemble_type == "boosting":
     test_loss = []
-    estimator_ = estimator_model(model_type)
+    estimator = estimator_model(model_type)
     for i in range(1, estimator_number):
-        model = AdaBoostRegressor(n_estimators=i)
+        model = AdaBoostRegressor(n_estimators=i, estimator=estimator)
         model.fit(X_train, y_train)
         y_pred = model.predict(X_test)
         test_loss.append(mean_squared_error(y_test, y_pred))
@@ -111,31 +121,5 @@ elif ensemble_type == "boosting":
 plt.legend()
 plt.title("loss plot")
 plt.xlabel("n_estimators")
-plt.ylabel("loss")
+plt.ylabel("mean squared error 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)
-
-