commit-live-students · kvin15 · May 17, 2018 · May 17, 2018 · May 17, 2018
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_myXGBoost/__pycache__/__init__.cpython-36.pyc b/q01_myXGBoost/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_myXGBoost/__pycache__/build.cpython-36.pyc b/q01_myXGBoost/__pycache__/build.cpython-36.pyc
diff --git a/q01_myXGBoost/build.py b/q01_myXGBoost/build.py
@@ -19,5 +19,10 @@
 
 
 # Write your solution here :
-
-
+def myXGBoost(X_train, X_test, y_train, y_test, model, param_grid, KFold=3, **kwargs):
+    grid_cv = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=3, cv=KFold)
+    grid_cv.fit(X_train, y_train)
+    y_pred_test = grid_cv.predict(X_test)
+    acc_score = accuracy_score(y_true=y_test, y_pred=y_pred_test)
+    best_params = grid_cv.best_params_
+    return acc_score, best_params
diff --git a/q01_myXGBoost/tests/__pycache__/__init__.cpython-36.pyc b/q01_myXGBoost/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_myXGBoost/tests/__pycache__/test_q01_myXGBoost.cpython-36.pyc b/q01_myXGBoost/tests/__pycache__/test_q01_myXGBoost.cpython-36.pyc
diff --git a/q02_param2/__pycache__/__init__.cpython-36.pyc b/q02_param2/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_param2/__pycache__/build.cpython-36.pyc b/q02_param2/__pycache__/build.cpython-36.pyc
diff --git a/q02_param2/build.py b/q02_param2/build.py
@@ -18,3 +18,23 @@
 
 
 # Write your solution here :
+def param2(X_train, X_test, y_train, y_test, model, param_grid):
+    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]
+               }
+    acc_score, best_params = myXGBoost(X_train, X_test, y_train, y_test, model=model, param_grid=param_grid1, KFold=3)
+    # Get the best parameters from iteration-1
+    # Append the best parameters and the new parameters to create
+    # new set of parameters
+    param_grid3 = { key_: [val_] for key_, val_ in best_params.items() }
+    param_grid3.update(param_grid)
+    # Use previous function with new set of parameters (iteration-1 best params and new params param-grid)
+    acc_score1, best_params1 = myXGBoost(X_train, X_test, y_train, y_test, model=model, param_grid=param_grid3)
+    # Return only specific_params that were passed as part of param_grid in dictionary
+    specific_params = {}
+    for key_, value_ in best_params1.items():
+        if key_ not in best_params.keys():
+            specific_params[key_] = best_params1[key_]
+    return acc_score1, specific_params
diff --git a/q02_param2/tests/__pycache__/__init__.cpython-36.pyc b/q02_param2/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_param2/tests/__pycache__/test_q02_param2.cpython-36.pyc b/q02_param2/tests/__pycache__/test_q02_param2.cpython-36.pyc
diff --git a/q03_xgboost/__pycache__/__init__.cpython-36.pyc b/q03_xgboost/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_xgboost/__pycache__/build.cpython-36.pyc b/q03_xgboost/__pycache__/build.cpython-36.pyc
diff --git a/q03_xgboost/build.py b/q03_xgboost/build.py
@@ -2,6 +2,7 @@
 from sklearn.model_selection import train_test_split
 from xgboost import XGBClassifier
 import pandas as pd
+from sklearn.model_selection import GridSearchCV
 from sklearn.metrics import accuracy_score
 
 # load data
@@ -13,5 +14,10 @@
 
 
 # Write your solution here :
-
-
+# exercise -3
+def xgboost(X_train, X_test, y_train, y_test, **kwargs):
+    kwargs['random_state'] = 9
+    xgb = XGBClassifier(**kwargs)
+    xgb.fit(X_train, y_train)
+    y_pred_test = xgb.predict(X_test)
+    return accuracy_score(y_true=y_test, y_pred=y_pred_test)
diff --git a/q03_xgboost/tests/__pycache__/__init__.cpython-36.pyc b/q03_xgboost/tests/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_xgboost/tests/__pycache__/test_q03_xgboost.cpython-36.pyc b/q03_xgboost/tests/__pycache__/test_q03_xgboost.cpython-36.pyc