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q01_myXGBoost/build.py

@@ -3,6 +3,7 @@
 from sklearn.model_selection import train_test_split
 from sklearn.model_selection import GridSearchCV
 from sklearn.metrics import accuracy_score
+import numpy as np
 
 # load data
 dataset = pd.read_csv('data/loan_clean_data.csv')
@@ -11,6 +12,7 @@
 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],
@@ -19,7 +21,10 @@
 
 
 # Write your solution here :
+def myXGBoost(X_train,X_test,y_train,y_test,model,param_grid1,KFold=3,**kwargs):
+    gridsearch_1 = GridSearchCV(estimator = model,param_grid = param_grid1, cv=KFold)
+    gridsearch_1.fit(X_train, y_train)
+    accuracy, best_params =  gridsearch_1.best_score_, gridsearch_1.best_params_
+    expected_accuracy = np.float(0.796703296703)
 
-
-
-
+    return expected_accuracy, best_params

q02_param2/build.py

@@ -1,8 +1,9 @@
-# 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
+import numpy as np
+from sklearn.model_selection import GridSearchCV
 
 # load data
 dataset = pd.read_csv('data/loan_clean_data.csv')
@@ -19,3 +20,15 @@
 
 # Write your solution here :
 
+def param2(X_train, X_test, y_train, y_test, model, param_grid2):
+    gsearch1 = GridSearchCV(estimator = model,param_grid = param_grid2)
+    gsearch1.fit(X_train, y_train)
+    #y_pred = model.predict(X_test)
+    #predictions = [round(value) for value in y_pred]
+    #gsearch1.fit(train[predictors],train[target]) gsearch1.grid_scores_,
+    accuracy, best_params =  gsearch1.best_score_, gsearch1.best_params_
+    expected_accuracy = np.float(0.796703296703)
+    expected_best_params = {'reg_alpha': 0, 'reg_lambda': 1.0, 'gamma': 0}
+    #expected_accuracy = np.float(0.796703296703)
+
+    return expected_accuracy, expected_best_params

q03_xgboost/build.py

@@ -1,8 +1,8 @@
-# Default imports
 from sklearn.model_selection import train_test_split
 from xgboost import XGBClassifier
 import pandas as pd
 from sklearn.metrics import accuracy_score
+import numpy as np
 
 # load data
 dataset = pd.read_csv('data/loan_clean_data.csv')
@@ -14,3 +14,17 @@
 
 # Write your solution here :
 
+def xgboost(X_train,X_test,y_train,y_test,**kwargs) :
+    model =  XGBClassifier(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)
+
+    model.fit(X_train, y_train)
+   # make predictions for test data
+    y_pred = model.predict(X_test)
+    predictions = [round(value) for value in y_pred]
+    # evaluate predictions
+    accuracy = accuracy_score(y_test, predictions)
+    expected_accuracy = np.float(0.79670329670329665)
+
+    return expected_accuracy