小编fpe*_*syn的帖子

我正在尝试从with绘制一个plot_tree对象，但我的树图看起来不太好。我的树图看起来被压扁了：sklearnmatplotlib

下面是我的代码：

from sklearn import tree
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

# create tree object 
model_gini_class = tree.DecisionTreeClassifier(criterion='gini')

# train the model using the training sets and check score
model_gini_class.fit(X_train, y_train)
model_gini_class.score(X_train, y_train)

# predict output
predicted_gini_class = model_gini_class.predict(X_test)

plt.figure()
tree.plot_tree(model_gini_class, filled=True)
plt.title("Decision trees on the Shakespear dataset (Gini)")
plt.show() # the tree looks squished?

所以我的问题是：

• 有人可以告诉我如何调整sklearnplot_tree对象的大小，使其看起来不被压扁吗？

谢谢你，

我正在尝试使用 GCP 文档中提供的 Google Cloud Tasks 代码示例： https: //cloud.google.com/tasks/docs/creating-http-target-tasks（请参阅下面的代码）。但是，从文档中尚不清楚我需要哪个包才能使其工作。有任何想法吗？

但是，我收到以下错误。

ImportError: cannot import name 'tasks_v2' from 'google.cloud'

代码示例：

from google.cloud import tasks_v2
from google.protobuf import timestamp_pb2

# Create a client.
client = tasks_v2.CloudTasksClient()

# TODO(developer): Uncomment these lines and replace with your values.
# project = 'my-project-id'
# queue = 'my-appengine-queue'
# location = 'us-central1'
# payload = 'hello'

# Construct the fully qualified queue name.
parent = client.queue_path(project, location, queue)

# Construct the request body.
task = { …

每当我尝试将 statsmodels.formula.api 作为 smf 导入时，我都会收到以下错误。

import statsmodels.formula.api as smf

Traceback (most recent call last):

File "<ipython-input-257-268d740a5877>", line 1, in <module>
   import statsmodels.formula.api as smf

File "C:\Users\ldresl\Anaconda3\lib\site- 
    packages\statsmodels\formula\__init__.py", line 1, in <module>
  from statsmodels import PytestTester

 ImportError: cannot import name 'PytestTester'