Matplotlib錯誤：'高度'必須是長度爲5或標量

Question

我試圖在Python 2.7中使用matplotlib將腳本的輸出繪製成2系列條形圖。

我的劇本打印 '味精'，這將產生以下的輸出：

KNN：90.000000（0.322734）

LDA：83.641395（0.721210）

購物車：92.600996（0.399870）

NB：29.214167（1.743959）

隨機森林：92.617598（0.323824）

代碼後輸出「味精」的結果，我嘗試使用matplotlib繪製結果成2系列條形圖和我然後用下面的返回錯誤：

Traceback (most recent call last): 
    File "comparison.py", line 113, in <module> 
    label='mean') 
    File "C:\Users\Scot\Anaconda2\lib\site-packages\matplotlib\pyplot.py", line 2650, in bar 
    **kwargs) 
    File "C:\Users\Scot\Anaconda2\lib\site-packages\matplotlib\__init__.py", line 1818, in inner 
    return func(ax, *args, **kwargs) 
    File "C:\Users\Scot\Anaconda2\lib\site-packages\matplotlib\axes\_axes.py", line 2038, in bar 
    "must be length %d or scalar" % nbars) 
ValueError: incompatible sizes: argument 'height' must be length 5 or scalar 

林不知道如何解決這個，我認爲這可能是由於結果的值是一個浮點值？任何幫助將非常感激。 這裏是我的代碼：

# Modules 
import pandas 
import numpy 
import os 
from pandas.tools.plotting import scatter_matrix 
import matplotlib.pyplot as plt 
from matplotlib import style 
plt.rcdefaults() 
from sklearn import preprocessing 
from sklearn import cross_validation 
from sklearn.metrics import classification_report 
from sklearn.metrics import confusion_matrix 
from sklearn.metrics import accuracy_score, precision_recall_curve, average_precision_score 
from sklearn.linear_model import LogisticRegression 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis 
from sklearn.naive_bayes import GaussianNB 
from scipy.stats import ttest_ind, ttest_ind_from_stats 
from scipy.special import stdtr 
from sklearn.svm import SVC 
from collections import defaultdict 
from sklearn.preprocessing import LabelEncoder 
import warnings 

# Load KDD dataset 
data_set = "NSL-KDD/KDDTest+.arff" 
import os 
os.system("cls") 

print "Loading: ", data_set 

with warnings.catch_warnings(): 
    warnings.simplefilter("ignore") 

    names = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'su_attempted', 'num_root', 'num_file_creations', 
      'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 
      'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'class', 
      'dst_host_srv_rerror_rate'] 

    dataset = pandas.read_csv(data_set, names=names) 

    for column in dataset.columns: 
     if dataset[column].dtype == type(object): 
      le = LabelEncoder() 
      dataset[column] = le.fit_transform(dataset[column]) 

    array = dataset.values 
    X = array[:, 0:40] 
    Y = array[:, 40] 

    # Split-out validation dataset 
    validation_size = 0.20 
    seed = 7 
    X_train, X_validation, Y_train, Y_validation = cross_validation.train_test_split(
     X, Y, test_size=validation_size, random_state=seed) 

    # Test options and evaluation metric 
    num_folds = 10 
    num_instances = len(X_train) 
    seed = 10 
    scoring = 'accuracy' 

    # Algorithms 
    models = [] 
    models.append(('KNN', KNeighborsClassifier())) 
    models.append(('LDA', LinearDiscriminantAnalysis())) 
    models.append(('CART', DecisionTreeClassifier())) 
    models.append(('NB', GaussianNB())) 
    models.append(('Random Forest', RandomForestClassifier())) 
    # models.append(('LR', LogisticRegression())) 

    # evaluate each model in turn 
    results = [] 
    names = [] 
    for name, model in models: 
     kfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed) 
     cv_results = cross_validation.cross_val_score(
      model, X_train, Y_train, cv=kfold, scoring=scoring) 
     results.append(cv_results) 
     names.append(name) 
     msg = "%s: %f (%f)" % (name, cv_results.mean() * 100, cv_results.std() 
           * 100) # multiplying by 100 to show percentage 
     print(msg) 
     # print cv_results * 100 # plots all values that make the average 

    print ("\n") 

    # Perform T Test on each iteration of models. 
    for i in range(len(results) - 1): 
     for j in range(i, len(results)): 
      t, p = ttest_ind(results[i], results[j], equal_var=False) 
      print("T_Test between {} & {}: T Value = {}, P Value = {}".format(
       names[i], names[j], t, p)) 
      print("\n") 

    plt.style.use('ggplot') 
    n_groups = 5 
    # create plot 
    fig, ax = plt.subplots() 
    index = numpy.arange(n_groups) 
    bar_width = 0.35 
    opacity = 0.8 

    rects1 = plt.bar(index, cv_results, bar_width, 
        alpha=opacity, 
        # color='b', 
        label='mean') # Line 113 

    rects2 = plt.bar(index + bar_width, cv_results.std(), bar_width, 
        alpha=opacity, 
        color='g', 
        label='standard_d') 

    plt.xlabel('Models') 
    plt.ylabel('Percentage') 
    plt.title('All Model Performance') 
    plt.xticks(index + bar_width, (names)) 
    plt.legend() 

    plt.tight_layout() 
    plt.show() 

編輯

印刷cv_results顯示如下，併爲7個或8位小數：

[ 90.48146099 90.48146099 89.42999447 89.5960155 90.03873824 
    89.9833979 89.9833979 89.76203652 90.09407858 90.14941893] 

[ 83.34255672 84.94742667 82.2910902 83.78527947 84.3386829 
    83.9513005 82.78915329 84.06198118 83.39789707 83.50857775] 

[ 93.1931378 92.69507471 91.92030991 92.52905368 92.69507471 
    92.41837299 92.58439402 92.25235196 92.19701162 92.14167128] 

[ 29.05368013 26.89540675 31.54399557 28.22357499 29.27504151 
    27.94687327 33.20420587 28.99833979 28.55561704 28.44493636] 

[ 93.35915883 93.02711677 92.25235196 91.69894853 93.02711677 
    92.63973437 92.58439402 92.14167128 92.47371334 92.69507471] 

Answer 1

，如果你想繪製的cv_results手段，您需要使用.mean()來計算平均值，就像您在第二個圖中使用.std()一樣。

而且，你通過追加cv_results每個模型results的過程，但是當你來幹什麼繪圖，好像你還在使用cv_results，但是這可能只是將是對cv_results在循環中訪問的最後一個模型。

看起來你的results將會是一個包含5個numpy數組的列表。所以，你可以遍歷該列表，計算出每個數組的平均值，並用它來繪製你的barplot：

mean_results = [res.mean() for res in results] 
rects1 = plt.bar(index, mean_results, bar_width, 
       alpha=opacity, 
       # color='b', 
       label='mean') 

或者，您也可以在你原有的循環中只是追加cv_results.mean()到列表，並使用該列表讓你的酒吧情節。