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machine_learning/xgboostclassifier.py

@@ -1,55 +1,79 @@
-from xgboost import XGBClassifier
-#https://xgboost.readthedocs.io/en/stable/
-import numpy as np 
-import pandas as pd 
-import seaborn as sns
 import matplotlib.pyplot as plt
 
+# https://xgboost.readthedocs.io/en/stable/
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from xgboost import XGBClassifier
 
-
-training = pd.read_csv('../input/titanic/train.csv')
-test = pd.read_csv('../input/titanic/test.csv')
+training = pd.read_csv("../input/titanic/train.csv")
+test = pd.read_csv("../input/titanic/test.csv")
 
 # Commented out IPython magic to ensure Python compatibility.
-training['train_test'] = 1
-test['train_test'] = 0
-test['Survived'] = np.NaN
-all_data = pd.concat([training,test])
+training["train_test"] = 1
+test["train_test"] = 0
+test["Survived"] = np.NaN
+all_data = pd.concat([training, test])
 # %matplotlib inline
 all_data.columns
 
 all_data.describe()
 
-all_data['cabin_mul']=all_data.Cabin.apply(lambda cabin_class: 0 if pd.isna(cabin_class) else len(x.split(' ')))
-all_data['cabin_adv'] = all_data.Cabin.apply(lambda cabin_number: str(cabin_number)[0])
-all_data['name_title']= all_data.Name.apply(lambda name: name.split(',')[1].split('.')[0].strip())
-all_data.Age=all_data.Age.fillna(training.Age.median())
-all_data.Fare=all_data.Fare.fillna(training.Fare.median())
-all_data.dropna(subset=['Embarked'],inplace=True)
-all_data['norm_fare']=np.log(all_data.Fare+1)
-all_data.Pclass=all_data.Pclass.astype(str)
-all_data['Age']=all_data['Age'].apply(np.int64)
-all_dummies = pd.get_dummies(all_data[['Pclass','Sex','Age','SibSp','Parch','norm_fare',
-                                       'Embarked','cabin_adv','cabin_mul',
-                                       'name_title','train_test']])
+all_data["cabin_mul"] = all_data.Cabin.apply(
+    lambda cabin_class: 0 if pd.isna(cabin_class) else len(x.split(" "))
+)
+all_data["cabin_adv"] = all_data.Cabin.apply(lambda cabin_number: str(cabin_number)[0])
+all_data["name_title"] = all_data.Name.apply(
+    lambda name: name.split(",")[1].split(".")[0].strip()
+)
+all_data.Age = all_data.Age.fillna(training.Age.median())
+all_data.Fare = all_data.Fare.fillna(training.Fare.median())
+all_data.dropna(subset=["Embarked"], inplace=True)
+all_data["norm_fare"] = np.log(all_data.Fare + 1)
+all_data.Pclass = all_data.Pclass.astype(str)
+all_data["Age"] = all_data["Age"].apply(np.int64)
+all_dummies = pd.get_dummies(
+    all_data[
+        [
+            "Pclass",
+            "Sex",
+            "Age",
+            "SibSp",
+            "Parch",
+            "norm_fare",
+            "Embarked",
+            "cabin_adv",
+            "cabin_mul",
+            "name_title",
+            "train_test",
+        ]
+    ]
+)
 
 from sklearn.preprocessing import StandardScaler
+
 scale = StandardScaler()
 all_dummies_scaled = all_dummies.copy()
-all_dummies_scaled[['Age','SibSp','Parch','norm_fare']]= scale.fit_transform(all_dummies_scaled[['Age','SibSp','Parch','norm_fare']])
+all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]] = scale.fit_transform(
+    all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]]
+)
 all_dummies_scaled.head()
 
-x_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(['train_test'], axis =1)
-x_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(['train_test'], axis =1)
+x_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(
+    ["train_test"], axis=1
+)
+x_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(
+    ["train_test"], axis=1
+)
 
-y_train = all_data[all_data.train_test==1].Survived
+y_train = all_data[all_data.train_test == 1].Survived
 
 from xgboost import XGBClassifier
 
 xgb = XGBClassifier()
-xgb.fit(X_train_scaled,y_train)
+xgb.fit(X_train_scaled, y_train)
 
 y_hat_base_vc = xgb.predict(x_test_scaled).astype(int)
-basic_submission = {'PassengerId': test.PassengerId, 'Survived': y_hat_base_vc}
+basic_submission = {"PassengerId": test.PassengerId, "Survived": y_hat_base_vc}
 base_submission = pd.DataFrame(data=basic_submission)
-base_submission.to_csv('xgb_submission.csv', index=False)
+base_submission.to_csv("xgb_submission.csv", index=False)