From bfcfa753b235bc16f50f470b1c934b9de2630bcb Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 24 Apr 2024 12:37:35 -0500
Subject: [PATCH] snapshot...
---
modules/machine_learning/classification.py | 24 ++++++++++++++++++++++
1 file changed, 24 insertions(+)
diff --git a/modules/machine_learning/classification.py b/modules/machine_learning/classification.py
index 035ef544..757bafe8 100644
--- a/modules/machine_learning/classification.py
+++ b/modules/machine_learning/classification.py
@@ -8,6 +8,8 @@ from sklearn.metrics import confusion_matrix, classification_report, accuracy_sc
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+import sklearn.tree as tree
def get_csv_as_dataframe(csv_file):
@@ -82,6 +84,28 @@ def k_nearest_neighbors(x, y, k=4):
yhat = KN_C.predict(x_test)
yhat_prob = KN_C.predict_proba(x_test)
+ print('Accuracy: ', "{:.4f}".format(accuracy_score(y_test, yhat)))
+ print('Jaccard Idx: ', "{:.4f}".format(jaccard_score(y_test, yhat)))
+ print('Precision: ', "{:.4f}".format(precision_score(y_test, yhat)))
+ print('Recall: ', "{:.4f}".format(recall_score(y_test, yhat)))
+ print('F1: ', "{:.4f}".format(f1_score(y_test, yhat)))
+ print('AUC: ', "{:.4f}".format(roc_auc_score(y_test, yhat_prob[:, 1])))
+
+
+def decision_tree(x, y, max_depth=4):
+ x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=4)
+ print('Train set:', x_train.shape, y_train.shape)
+ print('Test set:', x_test.shape, y_test.shape)
+
+ x_train = np.where(np.isnan(x_train), 0, x_train)
+ x_test = np.where(np.isnan(x_test), 0, x_test)
+ print('num no icing test: ', np.sum(y_test == 0))
+ print('num icing test: ', np.sum(y_test == 1))
+
+ DT = DecisionTreeClassifier(criterion="entropy", max_depth=max_depth).fit(x_train, y_train)
+ yhat = DT.predict(x_test)
+ yhat_prob = DT.predict_proba(x_test)
+
print('Accuracy: ', "{:.4f}".format(accuracy_score(y_test, yhat)))
print('Jaccard Idx: ', "{:.4f}".format(jaccard_score(y_test, yhat)))
print('Precision: ', "{:.4f}".format(precision_score(y_test, yhat)))
--
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