通过网格搜索完善模型

在这个迷你 Lab 练习中，我们将为决策树模型拟合一些样本数据。这个初始模型会过拟合。然后，我们将使用网格搜索为这个模型找到更好的参数，以减少过拟合。

首先，导入：



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%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

1.阅读并绘制数据

现在，这个函数将帮助我们读取 csv 文件并绘制数据。



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def load_pts(csv_name):
    data = np.asarray(pd.read_csv(csv_name, header=None))
    X = data[:,0:2]
    y = data[:,2]

    plt.scatter(X[np.argwhere(y==0).flatten(),0], X[np.argwhere(y==0).flatten(),1],s = 50, color = 'blue', edgecolor = 'k')
    plt.scatter(X[np.argwhere(y==1).flatten(),0], X[np.argwhere(y==1).flatten(),1],s = 50, color = 'red', edgecolor = 'k')
    
    plt.xlim(-2.05,2.05)
    plt.ylim(-2.05,2.05)
    plt.grid(False)
    plt.tick_params(
    axis='x',
    which='both',
    bottom='off',
    top='off')

    return X,y

X, y = load_pts('data.csv')
plt.show()

该函数将帮助我们绘制模型。



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def plot_model(X, y, clf):
    plt.scatter(X[np.argwhere(y==0).flatten(),0],X[np.argwhere(y==0).flatten(),1],s = 50, color = 'blue', edgecolor = 'k')
    plt.scatter(X[np.argwhere(y==1).flatten(),0],X[np.argwhere(y==1).flatten(),1],s = 50, color = 'red', edgecolor = 'k')

    plt.xlim(-2.05,2.05)
    plt.ylim(-2.05,2.05)
    plt.grid(False)
    plt.tick_params(
    axis='x',
    which='both',
    bottom='off',
    top='off')

    r = np.linspace(-2.1,2.1,300)
    s,t = np.meshgrid(r,r)
    s = np.reshape(s,(np.size(s),1))
    t = np.reshape(t,(np.size(t),1))
    h = np.concatenate((s,t),1)

    z = clf.predict(h)

    s.shape = (np.size(r),np.size(r))
    t.shape = (np.size(r),np.size(r))
    z.shape = (np.size(r),np.size(r))

    plt.contourf(s,t,z,colors = ['blue','red'],alpha = 0.2,levels = range(-1,2))
    if len(np.unique(z)) > 1:
        plt.contour(s,t,z,colors = 'k', linewidths = 2)
    plt.show()

2. 将我们的数据分为训练和测试集



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from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, make_scorer

#Fixing a random seed
import random
random.seed(42)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. 拟合一个决策树模型



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from sklearn.tree import DecisionTreeClassifier

# Define the model (with default hyperparameters)
clf = DecisionTreeClassifier(random_state=42)

# Fit the model
clf.fit(X_train, y_train)

# Make predictions using the unoptimized and model
train_predictions = clf.predict(X_train)
test_predictions = clf.predict(X_test)

Now let's plot the model, and find the testing f1_score, to see how we did.



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plot_model(X, y, clf)
print('The Training F1 Score is', f1_score(train_predictions, y_train))
print('The Testing F1 Score is', f1_score(test_predictions, y_test))



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The Training F1 Score is 1.0
The Testing F1 Score is 0.7

现在有一些过拟合。我们不仅仅是看图表，还需要看看高训练分（1.0）和低测试分（0.7）之间的差异。思考一下，我们是否可以找到更好的超参数来让这个模型做得更好？接下来我们将使用网格搜索。

4.（解决方案）使用网格搜索来完善模型

现在，我们将执行以下步骤：

1.首先，定义一些参数来执行网格搜索。我们建议使用max_depth, min_samples_leaf, 和 min_samples_split。

2.使用f1_score，为模型制作记分器。

3.使用参数和记分器，在分类器上执行网格搜索。

4.将数据拟合到新的分类器中。

5.绘制模型并找到 f1_score。

6.如果模型不太好，请尝试更改参数的范围并再次拟合。



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from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV

clf = DecisionTreeClassifier(random_state=42)

# TODO: Create the parameters list you wish to tune.
parameters = {'max_depth':[2,4,6,8,10],'min_samples_leaf':[2,4,6,8,10], 'min_samples_split':[2,4,6,8,10]}

# TODO: Make an fbeta_score scoring object.
scorer = make_scorer(f1_score)

# TODO: Perform grid search on the classifier using 'scorer' as the scoring method.
grid_obj = GridSearchCV(clf, parameters, scoring=scorer)

# TODO: Fit the grid search object to the training data and find the optimal parameters.
grid_fit = grid_obj.fit(X_train, y_train)

# Get the estimator.
best_clf = grid_fit.best_estimator_

# Fit the new model.
best_clf.fit(X_train, y_train)

# Make predictions using the new model.
best_train_predictions = best_clf.predict(X_train)
best_test_predictions = best_clf.predict(X_test)

# Calculate the f1_score of the new model.
print('The training F1 Score is', f1_score(best_train_predictions, y_train))
print('The testing F1 Score is', f1_score(best_test_predictions, y_test))

# Plot the new model.
plot_model(X, y, best_clf)

# Let's also explore what parameters ended up being used in the new model.
best_clf



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The training F1 Score is 0.814814814815
The testing F1 Score is 0.8



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DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=2, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=42,
            splitter='best')

5. 总结

注意，通过使用网格搜索，我们将 F1 分数从 0.7 提高到 0.8（同时我们失去了一些训练分数，但这没问题）。另外，如果你看绘制的图，第二个模型的边界更为简单，这意味着它不太可能过拟合。