From 3e18193a36fee20f41caac4e7c4832195c59e9f3 Mon Sep 17 00:00:00 2001
From: Tanushree Tunstall <tanu@tunstall.in>
Date: Thu, 7 Jul 2022 17:46:06 +0100
Subject: [PATCH] added examples

---
 scripts/ml/dummy_classifier.py | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/scripts/ml/dummy_classifier.py b/scripts/ml/dummy_classifier.py
index e084fd5..bbc4015 100644
--- a/scripts/ml/dummy_classifier.py
+++ b/scripts/ml/dummy_classifier.py
@@ -12,17 +12,29 @@ from sklearn.dummy import DummyClassifier
 X_eg = np.array([-1, 1, 1, 1])
 y_eg = np.array([0, 1, 1, 1])
 dummy_clf = DummyClassifier(strategy="most_frequent")
+dummy_clf = DummyClassifier(strategy="stratified")
+dummy_clf = DummyClassifier(strategy="stratified")
+
 dummy_clf.fit(X_eg, y_eg)
-DummyClassifier(strategy='most_frequent')
+#DummyClassifier(strategy='most_frequent')
 dummy_clf.predict(X_eg)
-np.array([1, 1, 1, 1])
+dummy_clf.predict(np.array([1,1,1,1,1,1,1,1,1,1]))
+dummy_clf.predict_proba(X_eg)
+
 dummy_clf.score(X_eg, y_eg)
 0.75
 
-dummy_clf.matthews_corrcoef(X_eg, y_eg)
+df2['X']
 
+dummy_clf.fit(df2['X'], df2['y'])
+dummy_clf.predict(df2['X'])
+dummy_clf.predict_proba(df2['X'])
+ypred = dummy_clf.predict(df2['X'])
 
+dummy_clf.score(df2['X'], df2['y'])
 
+confusion_matrix(df2['y'], ypred)
+matthews_corrcoef(df2['y'], ypred)
 #%%
 df['dst_mode']
 y_all_tt = df.loc[:,'dst']