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I'm looking for someone who can help me to plot my Confusion Matrix. I need this for a term paper at the university. However I have very little experience in programming.
In the pictures you can see the classification report and the structure of my y_test and X_test in my case dtree_predictions.
I would be happy if someone can help me, because I have tried so many things but I just don't get a solution, only error messages.
X_train, X_test, y_train, y_test = train_test_split(X, Y_profile, test_size = 0.3, random_state = 30)
dtree_model = DecisionTreeClassifier().fit(X_train,y_train)
dtree_predictions = dtree_model.predict(X_test)
print(metrics.classification_report(dtree_predictions, y_test))
precision recall f1-score support
0 1.00 1.00 1.00 222
1 1.00 1.00 1.00 211
2 1.00 1.00 1.00 229
3 0.96 0.97 0.96 348
4 0.89 0.85 0.87 93
5 0.86 0.86 0.86 105
6 0.94 0.93 0.94 116
7 1.00 1.00 1.00 364
8 0.99 0.97 0.98 139
9 0.98 0.99 0.99 159
10 0.97 0.96 0.97 189
11 0.92 0.92 0.92 124
12 0.92 0.92 0.92 119
13 0.95 0.96 0.95 230
14 0.98 0.96 0.97 452
15 0.91 0.96 0.93 210
micro avg 0.96 0.96 0.96 3310
macro avg 0.95 0.95 0.95 3310
weighted avg 0.97 0.96 0.96 3310
samples avg 0.96 0.96 0.96 3310
next I print the metris of the multilabel confusion matrix
from sklearn.metrics import multilabel_confusion_matrix
multilabel_confusion_matrix(y_test, dtree_predictions)
array([[[440, 0],
[ 0, 222]],
[[451, 0],
[ 0, 211]],
[[433, 0],
[ 0, 229]],
[[299, 10],
[ 15, 338]],
[[559, 14],
[ 10, 79]],
[[542, 15],
[ 15, 90]],
[[539, 8],
[ 7, 108]],
[[297, 0],
[ 1, 364]],
[[522, 4],
[ 1, 135]],
[[500, 1],
[ 3, 158]],
[[468, 8],
[ 5, 181]],
[[528, 10],
[ 10, 114]],
[[534, 9],
[ 9, 110]],
[[420, 9],
[ 12, 221]],
[[201, 19],
[ 9, 433]],
[[433, 9],
[ 19, 201]]])
and the structure of y_test and dtree_predictons
print(dtree_predictions)
print(dtree_predictions.shape)
[[0. 0. 1. ... 0. 1. 0.]
[1. 0. 0. ... 0. 1. 0.]
[0. 0. 1. ... 0. 1. 0.]
...
[1. 0. 0. ... 0. 0. 1.]
[0. 1. 0. ... 1. 0. 1.]
[0. 1. 0. ... 1. 0. 1.]]
(662, 16)
print(y_test)
Cooler close to failure Cooler reduced effiency Cooler full effiency \
1985 0.0 0.0 1.0
322 1.0 0.0 0.0
2017 0.0 0.0 1.0
1759 0.0 0.0 1.0
1602 0.0 0.0 1.0
... ... ... ...
128 1.0 0.0 0.0
321 1.0 0.0 0.0
53 1.0 0.0 0.0
859 0.0 1.0 0.0
835 0.0 1.0 0.0
valve optimal valve small lag valve severe lag \
1985 0.0 0.0 0.0
322 0.0 1.0 0.0
2017 1.0 0.0 0.0
1759 0.0 0.0 0.0
1602 1.0 0.0 0.0
... ... ... ...
128 1.0 0.0 0.0
321 0.0 1.0 0.0
53 1.0 0.0 0.0
859 1.0 0.0 0.0
835 1.0 0.0 0.0
valve close to failure pump no leakage pump weak leakage \
1985 1.0 0.0 1.0
322 0.0 1.0 0.0
2017 0.0 0.0 1.0
1759 1.0 1.0 0.0
1602 0.0 1.0 0.0
... ... ... ...
128 0.0 1.0 0.0
321 0.0 1.0 0.0
53 0.0 1.0 0.0
859 0.0 1.0 0.0
835 0.0 1.0 0.0
pump severe leakage accu optimal pressure \
1985 0.0 0.0
322 0.0 1.0
2017 0.0 0.0
1759 0.0 1.0
1602 0.0 0.0
... ... ...
128 0.0 1.0
321 0.0 1.0
53 0.0 1.0
859 0.0 0.0
835 0.0 0.0
accu slightly reduced pressure accu severly reduced pressure \
1985 0.0 1.0
322 0.0 0.0
2017 0.0 1.0
1759 0.0 0.0
1602 0.0 0.0
... ... ...
128 0.0 0.0
321 0.0 0.0
53 0.0 0.0
859 0.0 0.0
835 0.0 0.0
accu close to failure stable flag stable stable flag not stable
1985 0.0 1.0 0.0
322 0.0 1.0 0.0
2017 0.0 1.0 0.0
1759 0.0 1.0 0.0
1602 1.0 0.0 1.0
... ... ... ...
128 0.0 0.0 1.0
321 0.0 1.0 0.0
53 0.0 0.0 1.0
859 1.0 0.0 1.0
835 1.0 0.0 1.0
[662 rows x 16 columns]
372
Unlike binary classification, there is no negative class. It is a perception that TP, TN, and other metrics are difficult to derive out of the confusion matrix for multi-class but actually, it is quite easy.
Confusion Matrix gives a comparison between Actual and predicted values. The confusion matrix is a N x N matrix, where N is the number of classes or outputs. For 2 class ,we get 2 x 2 confusion matrix. For 3 class ,we get 3 X 3 confusion matrix.
Summary: The best way to plot a Confusion Matrix with labels, is to use the ConfusionMatrixDisplay object from the sklearn. metrics module. Another simple and elegant way is to use the seaborn. heatmap() function.
Usually, a confusion matrix is visualized via a heatmap. A function is also created in github to pretty print a confusion matrix. Inspired from it, I have adapted into multilabel scenario where each of the class with the binary predictions (Y, N) are added into the matrix and visualized via heat map.
Here, is the example taking some of the output from the posted code:
import numpy as np
vis_arr = np.asarray([[[440, 0],
[ 0, 222]],
[[451, 0],
[ 0, 211]],
[[433, 0],
[ 0, 229]],
[[299, 10],
[ 15, 338]],
[[559, 14],
[ 10, 79]],
[[542, 15],
[ 15, 90]],
[[539, 8],
[ 7, 108]],
[[297, 0],
[ 1, 364]],
[[522, 4],
[ 1, 135]],
[[500, 1],
[ 3, 158]],
[[468, 8],
[ 5, 181]],
[[528, 10],
[ 10, 114]],
[[534, 9],
[ 9, 110]],
[[420, 9],
[ 12, 221]],
[[201, 19],
[ 9, 433]],
[[433, 9],
[ 19, 201]]])
labels = ["".join("c" + str(i)) for i in range(0, 16)]
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def print_confusion_matrix(confusion_matrix, axes, class_label, class_names, fontsize=14):
df_cm = pd.DataFrame(
confusion_matrix, index=class_names, columns=class_names,
)
try:
heatmap = sns.heatmap(df_cm, annot=True, fmt="d", cbar=False, ax=axes)
except ValueError:
raise ValueError("Confusion matrix values must be integers.")
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
axes.set_ylabel('True label')
axes.set_xlabel('Predicted label')
axes.set_title("Confusion Matrix for the class - " + class_label)
Extending the basic confusion matrix to plot of a grid of subplots with the title as each of the classes. Here the [Y, N] are the defined class labels and can be extended.
fig, ax = plt.subplots(4, 4, figsize=(12, 7))
for axes, cfs_matrix, label in zip(ax.flatten(), vis_arr, labels):
print_confusion_matrix(cfs_matrix, axes, label, ["N", "Y"])
fig.tight_layout()
plt.show()
Note: This plot is constructed based on wiki article on confusion matrix
Output:
159
You could use the ConfusionMatrixDisplay option in sklearn.metrics.
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_multilabel_classification
from sklearn.tree import DecisionTreeClassifier
X, y = make_multilabel_classification(n_samples=1000,
n_classes=15, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=42)
tree = DecisionTreeClassifier(random_state=42).fit(X_train, y_train)
y_pred = tree.predict(X_test)
f, axes = plt.subplots(3, 5, figsize=(25, 15))
axes = axes.ravel()
for i in range(15):
disp = ConfusionMatrixDisplay(confusion_matrix(y_test[:, i],
y_pred[:, i]),
display_labels=[0, i])
disp.plot(ax=axes[i], values_format='.4g')
disp.ax_.set_title(f'class {i}')
if i<10:
disp.ax_.set_xlabel('')
if i%5!=0:
disp.ax_.set_ylabel('')
disp.im_.colorbar.remove()
plt.subplots_adjust(wspace=0.10, hspace=0.1)
f.colorbar(disp.im_, ax=axes)
plt.show()
13
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