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q01_myXGBoost/build.py

@@ -1,3 +1,4 @@
+# %load q01_myXGBoost/build.py
 import pandas as pd
 from xgboost import XGBClassifier
 from sklearn.model_selection import train_test_split
@@ -11,13 +12,45 @@
 y = dataset.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=9)
 
-param_grid1 = {"max_depth": [2, 3, 4, 5, 6, 7, 9, 11],
-               "min_child_weight": [4, 6, 7, 8],
-               "subsample": [0.6, .7, .8, .9, 1],
-               "colsample_bytree": [0.6, .7, .8, .9, 1]
+param_grid1 = {'max_depth': [2, 3, 4, 5, 6, 7, 9, 11],
+               'min_child_weight': [4, 6, 7, 8],
+               'subsample': [0.6, .7, .8, .9, 1],
+               'colsample_bytree': [0.6, .7, .8, .9, 1]
                }
 
 
 # Write your solution here :
 
+def myXGBoost(X_train, X_test, y_train,y_test, model, param_grid, KFold=3, **kwargs):
+    if kwargs:
+        model.set_params(**kwargs)
+    gs_cv = GridSearchCV(model, param_grid=param_grid, cv=KFold, verbose=0)
+    gs_cv.fit(X_train, y_train)
+    best_params = gs_cv.best_params_
+    y_pred = gs_cv.predict(X_test)
+    accuracy = accuracy_score(y_pred, y_test)
+
+    return accuracy, best_params
+param_grid1 = {'max_depth': [2, 3, 4, 5, 6, 7, 9, 11],
+               'min_child_weight': [4, 6, 7, 8],
+               'subsample': [0.6, .7, .8, .9, 1],
+               'colsample_bytree': [0.6, .7, .8, .9, 1]
+               }
+
+
+def myXGBoost(X_train, X_test, y_train,y_test, model, param_grid, KFold=3, **kwargs):
+    if kwargs:
+        model.set_params(**kwargs)
+    gs_cv = GridSearchCV(model, param_grid=param_grid, cv=KFold, verbose=0)
+    gs_cv.fit(X_train, y_train)
+    best_params = gs_cv.best_params_
+    y_pred = gs_cv.predict(X_test)
+    accuracy = accuracy_score(y_pred, y_test)
+
+    return accuracy, best_params
+#accuracy, best_params = myXGBoost(X_train, X_test, y_train, y_test, XGBClassifier(seed=9), param_grid1, 3)
+
+#print (accuracy)
+#print (best_params)
+