使用 Python 的随机森林特征重要性图表
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Random Forest Feature Importance Chart using Python
提问by user348547
I am working with RandomForestRegressor in python and I want to create a chart that will illustrate the ranking of feature importance. This is the code I used:
我正在 python 中使用 RandomForestRegressor，我想创建一个图表来说明特征重要性的排名。这是我使用的代码：
from sklearn.ensemble import RandomForestRegressor
MT= pd.read_csv("MT_reduced.csv")
df = MT.reset_index(drop = False)
columns2 = df.columns.tolist()
# Filter the columns to remove ones we don't want.
columns2 = [c for c in columns2 if c not in["Violent_crime_rate","Change_Property_crime_rate","State","Year"]]
# Store the variable we'll be predicting on.
target = "Property_crime_rate"
# Let's randomly split our data with 80% as the train set and 20% as the test set:
# Generate the training set. Set random_state to be able to replicate results.
train2 = df.sample(frac=0.8, random_state=1)
#exclude all obs with matching index
test2 = df.loc[~df.index.isin(train2.index)]
print(train2.shape) #need to have same number of features only difference should be obs
print(test2.shape)
# Initialize the model with some parameters.
model = RandomForestRegressor(n_estimators=100, min_samples_leaf=8, random_state=1)
#n_estimators= number of trees in forrest
#min_samples_leaf= min number of samples at each leaf
# Fit the model to the data.
model.fit(train2[columns2], train2[target])
# Make predictions.
predictions_rf = model.predict(test2[columns2])
# Compute the error.
mean_squared_error(predictions_rf, test2[target])#650.4928
Feature Importance
特征重要性
features=df.columns[[3,4,6,8,9,10]]
importances = model.feature_importances_
indices = np.argsort(importances)
plt.figure(1)
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), features[indices])
plt.xlabel('Relative Importance')
This feature importance code was altered from an example found on http://www.agcross.com/2015/02/random-forests-in-python-with-scikit-learn/
此功能重要性代码是从http://www.agcross.com/2015/02/random-forests-in-python-with-scikit-learn/上的示例中更改的
I receive the following error when I attempt to replicate the code with my data:
当我尝试用我的数据复制代码时收到以下错误：
IndexError: index 6 is out of bounds for axis 1 with size 6
Also, only one feature shows up on my chart with 100% importance where there are no labels.
此外，在没有标签的情况下，我的图表上仅显示一项具有 100% 重要性的功能。
Any help solving this issue so I can create this chart will be greatly appreciated.
任何帮助解决此问题以便我可以创建此图表将不胜感激。
回答by spies006
Here is an example using the iris data set.
这是一个使用 iris 数据集的示例。
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)
>>> rnd_clf.fit(iris["data"], iris["target"])
>>> for name, importance in zip(iris["feature_names"], rnd_clf.feature_importances_):
... print(name, "=", importance)
sepal length (cm) = 0.112492250999
sepal width (cm) = 0.0231192882825
petal length (cm) = 0.441030464364
petal width (cm) = 0.423357996355
Plotting feature importance
绘制特征重要性
>>> features = iris['feature_names']
>>> importances = rnd_clf.feature_importances_
>>> indices = np.argsort(importances)
>>> plt.title('Feature Importances')
>>> plt.barh(range(len(indices)), importances[indices], color='b', align='center')
>>> plt.yticks(range(len(indices)), [features[i] for i in indices])
>>> plt.xlabel('Relative Importance')
>>> plt.show()
回答by fordy
Load the feature importances into a pandas series indexed by your column names, then use its plot method. e.g. for an sklearn RF classifier/regressor modeltrained using df:
将特征重要性加载到由列名索引的 Pandas 系列中，然后使用其 plot 方法。例如，对于model使用df以下方法训练的 sklearn RF 分类器/回归器：
feat_importances = pd.Series(model.feature_importances_, index=df.columns)
feat_importances.nlargest(4).plot(kind='barh')
回答by seralouk
A barplotwould be more than usefulin order to visualizethe importanceof the features.
一个barplot会超过有用的，以可视化的重要的功能。
Use this (example using Iris Dataset):
使用这个（使用虹膜数据集的例子）：
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Create decision tree classifer object
clf = RandomForestClassifier(random_state=0, n_jobs=-1)
# Train model
model = clf.fit(X, y)
# Calculate feature importances
importances = model.feature_importances_
# Sort feature importances in descending order
indices = np.argsort(importances)[::-1]
# Rearrange feature names so they match the sorted feature importances
names = [iris.feature_names[i] for i in indices]
# Barplot: Add bars
plt.bar(range(X.shape[1]), importances[indices])
# Add feature names as x-axis labels
plt.xticks(range(X.shape[1]), names, rotation=20, fontsize = 8)
# Create plot title
plt.title("Feature Importance")
# Show plot
plt.show()
回答by Kuang Liang
The y-ticks are not correct. To fix it, it should be
y 刻度不正确。要修复它，它应该是
plt.yticks(range(len(indices)), [features[i] for i in indices])
回答by Miguel Gutierrez
This code from spies006 dont work : plt.yticks(range(len(indices)), features[indices])so you have to change it for plt.yticks(range(len(indices)),features.columns[indices])
spies006 中的此代码不起作用：plt.yticks(range(len(indices)), features[indices])因此您必须将其更改为plt.yticks(range(len(indices)),features.columns[indices])
回答by Aanish
In the above code from spies006, "feature_names" didn't work for me. A generic solution would be to use name_of_the_dataframe.columns.
在上面来自 spies006 的代码中，“feature_names”对我不起作用。一个通用的解决方案是使用 name_of_the_dataframe.columns。
