Big Data Analytics SecondHomework

本次作業主要目的在於藉由sklearn的GridSearchCV測試怎樣的參數可以在xgboost得到最好的分類效果。

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(1) 首先先測試'max_depth'和'min_child_weight'的參數

param_test1 = {
    'max_depth':list(range(3,10,2)),
    'min_child_weight':list(range(1,6,2))
}
gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=5,
                                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,
                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), 
                       param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
gsearch1.fit(train[predictors],train[target])

得到最好的參數為max_depth = 5，min_child_weight = 1

(2) 使用(1)的結果修改參數後，接著測試'gamma'參數

param_test2 = {
    'gamma':list([i/10.0 for i in range(0,10)])
}
gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=5,
                                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,
                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), 
                       param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
gsearch2.fit(train[predictors],train[target])

得到最好的參數為'gamma' = 0.0

(3) 延續(2)的結果，tune'subsample'與'colsample_bytree'參數

param_test3 = {
     'subsample':list([i/10.0 for i in range(6,10)]),
     'colsample_bytree':list([i/10.0 for i in range(6,10)])
}
gsearch3 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=5,
                                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,
                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), 
                       param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
gsearch3.fit(train[predictors],train[target])

得到最好的subsample = 0.8，colsample_bytree = 0.8。

(4) 最後是'reg_alpha'

param_test4 = {
    'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]
}
gsearch4 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=1000, max_depth=5,
                                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,
                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), 
                       param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
gsearch4.fit(train[predictors],train[target])

得到最好的reg_alpha = 1e-05。

經過這一連串的測試後可以使用剛剛產生的參數，可以得到最好的預測率。

最後再使用confusion matrix 驗證分析結果

import itertools
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from xgboost.sklearn import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


data = pd.read_csv("./dataset/LargeTrain.csv")
label = 'Class'
features = [x for x in data.columns if x != label]
class_names = [ 'Class'+ str(x) for x in range(1,10)] 
X = data[features]
y = data[label]

#cross validation
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = XGBClassifier(max_depth=5,min_child_weight=1,gamma=0.0,
subsample=0.8,colsample_bytree=0.8,reg_alpha=1e-05,reg_lambda=0.01)
clf.fit(X_train,y_train)
y_pred = clf.predict(X_test)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plt.figure(figsize=(20, 20), dpi=200)
plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title='Confusion matrix, without normalization')

plt.show()


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Reference

1. XGBoostのパラメータチューニング実践 with Python
2. Complete Guide to Parameter Tuning in Gradient Boosting (GBM) in Python
3. Complete Guide to Parameter Tuning in XGBoost (with codes in Python)
4. Confusion matrix