小编Rox*_*lia的帖子

当我试图用seaborn保存我的情节时,像这样:

import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
from pylab import savefig

array = [[100,0], 
        [33,67]]

df_cm = pd.DataFrame(array)

svm = sn.heatmap(df_cm, annot=True,cmap='coolwarm', linecolor='white', linewidths=1)

svm.savefig('svm_conf.png', dpi=400)

我收到这个错误

AttributeError                            Traceback (most recent call last)
<ipython-input-71-5c0ae9cda020> in <module>()
----> 1 svm.savefig('svm_conf.png', dpi=400)

AttributeError: 'AxesSubplot' object has no attribute 'savefig'

我以前用相同的代码保存了一些箱图,但这一次,它不起作用.

尝试实施 XGBoost 以确定最重要的变量时，我对数组有一些错误。

我的完整代码如下

from numpy import loadtxt
from numpy import sort
import pandas as pd
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.feature_selection import SelectFromModel


df = pd.read_csv('data.txt')
array=df.values
X= array[:,0:330]
Y = array[:,330]

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=7)


model = XGBClassifier()
model.fit(X_train, y_train)


y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]

我收到以下错误：

TypeError: type str doesn't define __round__ method

我能做什么？

我尝试运行这段代码：

from cdlib import algorithms
import networkx as nx
G = nx.karate_club_graph()
coms = algorithms.louvain(G, resolution=1., randomize=False)

但错误仍然相同。我已尝试 AttributeError: module 'networkx.algorithms.community' has no attribute 'best_partition'给出的所有选项

但它不起作用。

另外，我在 Google Colab 工作，并且安装了 cdlib。

当我绘制功能重要性图时，会出现混乱的图。我有7000多个变量。我了解内置功能只会选择最重要的功能，尽管最终图形不可读。这是完整的代码：

import numpy as np
import pandas as pd
df = pd.read_csv('ricerice.csv')
array=df.values
X = array[:,0:7803]
Y = array[:,7804]
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
seed=0
test_size=0.30
X_train, X_test, y_train, y_test = train_test_split(X,Y,test_size=test_size, random_state=seed)
from xgboost import XGBClassifier
model = XGBClassifier()
model.fit(X, Y)
import matplotlib.pyplot as plt
from matplotlib import pyplot
from xgboost import plot_importance
fig1=plt.gcf()
plot_importance(model)
plt.draw()
fig1.savefig('xgboost.png', figsize=(50, 40), dpi=1000)

尽管该图的尺寸很大，但该图难以辨认。

当我尝试使用 Pipeline 和 param_grid 运行 RandomForestClassifier 时：

pipeline = Pipeline([("scaler" , StandardScaler()),
                     ("rf",RandomForestClassifier())])
from sklearn.model_selection import GridSearchCV
param_grid = {
    'max_depth': [4, 5, 10],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'n_estimators': [100, 200, 300]
}
# initialize
grid_pipeline = GridSearchCV(pipeline,param_grid,n_jobs=-1, verbose=1, cv=3, scoring='f1')
# fit
grid_pipeline.fit(X_train,y_train)
grid_pipeline.best_params_

我收到以下错误：

ValueError: Invalid parameter max_depth for estimator Pipeline(memory=None,
         steps=[('scaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('rf',
                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                        class_weight=None, criterion='gini',
                                        max_depth=None, max_features='auto',
                                        max_leaf_nodes=None, max_samples=None,
                                        min_impurity_decrease=0.0,
                                        min_impurity_split=None,
                                        min_samples_leaf=1, min_samples_split=2,
                                        min_weight_fraction_leaf=0.0,
                                        n_estimators=100, n_jobs=None,
                                        oob_score=False, random_state=None,
                                        verbose=0, warm_start=False))], …