From c10b01291c57dc05d1b62013c676672b242db285 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 24 Apr 2024 15:39:18 -0500
Subject: [PATCH] snapshot...
---
modules/machine_learning/classification.py | 36 ++++++++++++++++++++++
1 file changed, 36 insertions(+)
diff --git a/modules/machine_learning/classification.py b/modules/machine_learning/classification.py
index dcbfa2de..9b532038 100644
--- a/modules/machine_learning/classification.py
+++ b/modules/machine_learning/classification.py
@@ -10,10 +10,46 @@ from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
+import itertools
import sklearn.tree as tree
from sklearn.tree import export_graphviz
+def plot_confusion_matrix(cm, classes,
+ normalize=False,
+ title='Confusion matrix',
+ cmap=plt.cm.Blues):
+ """
+ This function prints and plots the confusion matrix.
+ Normalization can be applied by setting `normalize=True`.
+ """
+ if normalize:
+ cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+ print("Normalized confusion matrix")
+ else:
+ print('Confusion matrix, without normalization')
+
+ print(cm)
+
+ plt.imshow(cm, interpolation='nearest', cmap=cmap)
+ plt.title(title)
+ plt.colorbar()
+ tick_marks = np.arange(len(classes))
+ plt.xticks(tick_marks, classes, rotation=45)
+ plt.yticks(tick_marks, classes)
+
+ fmt = '.2f' if normalize else 'd'
+ thresh = cm.max() / 2.
+ for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+ plt.text(j, i, format(cm[i, j], fmt),
+ horizontalalignment="center",
+ color="white" if cm[i, j] > thresh else "black")
+
+ plt.tight_layout()
+ plt.ylabel('True label')
+ plt.xlabel('Predicted label')
+
+
def get_csv_as_dataframe(csv_file, reduce_frac=None):
icing_df = pd.read_csv(csv_file)
# Random selection of reduce_frac of the rows
--
GitLab