commit-live-students · rushabh-mehta · Dec 30, 2018 · Dec 30, 2018 · Dec 30, 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
@@ -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 +12,18 @@
 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):
+    grid_search = GridSearchCV(estimator=model,param_grid=param_grid,cv=KFold)
+    grid_search.fit(X_train,y_train)
+    return grid_search.score(X_test,y_test),grid_search.best_params_
+myXGBoost(X_train,X_test,y_train,y_test,XGBClassifier(seed=9),param_grid1,3)
 
 
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
@@ -1,20 +1,27 @@
+# %load q02_param2/build.py
 # Default imports
 from sklearn.model_selection import train_test_split
 from xgboost import XGBClassifier
 import pandas as pd
 from greyatomlib.Xgboost_project.q01_myXGBoost.build import myXGBoost
-
+from sklearn.model_selection import GridSearchCV
 # load data
 dataset = pd.read_csv('data/loan_clean_data.csv')
 # split data into X and y
 X = dataset.iloc[:, :-1]
 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_grid2 = {"gamma": [0, 0.05, 0.1, 0.3, 0.7, 0.9, 1],
-               "reg_alpha": [0, 0.001, 0.005, 0.01, 0.05, 0.1],
-               "reg_lambda": [0.05, 0.1, 0.5, 1.0]
+param_grid2 = {'gamma': [0, 0.05, 0.1, 0.3, 0.7, 0.9, 1],
+               'reg_alpha': [0, 0.001, 0.005, 0.01, 0.05, 0.1],
+               'reg_lambda': [0.05, 0.1, 0.5, 1.0]
                }
 
 
 # Write your solution here :
+def param2(X_train, X_test, y_train, y_test, model, param_grid2):
+    return myXGBoost(X_train, X_test, y_train, y_test, model, param_grid2,
+                     colsample_bytree=0.7, subsample=0.8, max_depth=2, min_child_weight=4)
+param2(X_train,X_test,y_train,y_test,XGBClassifier(seed=9),param_grid2)
+
+
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
@@ -1,3 +1,4 @@
+# %load q03_xgboost/build.py
 # Default imports
 from sklearn.model_selection import train_test_split
 from xgboost import XGBClassifier
@@ -13,5 +14,15 @@
 
 
 # Write your solution here :
+def xgboost(X_train,X_test,y_train,y_test,**kwargs):
+    xgb = XGBClassifier(**kwargs,seed=9)
+    xgb.fit(X_train,y_train)
+    return xgb.score(X_test,y_test)
+
+xgboost(X_train, X_test, y_train, y_test,subsample=0.8,
+      colsample_bytree=0.7, max_depth=2, 
+      min_child_weight=4, reg_alpha=0, reg_lambda=1.0,
+     gamma=0,n_estimators=100,learning_rate=0.1)
+
 
 
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