109 lines
2.2 KiB
Python
109 lines
2.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jul 7 10:54:09 2022
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@author: tanu
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"""
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import numpy as np
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from sklearn.dummy import DummyClassifier
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X_eg = np.array([-1, 1, 1, 1])
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y_eg = np.array([0, 1, 1, 1])
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dummy_clf = DummyClassifier(strategy="most_frequent")
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dummy_clf = DummyClassifier(strategy="stratified")
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dummy_clf = DummyClassifier(strategy="stratified")
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dummy_clf.fit(X_eg, y_eg)
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#DummyClassifier(strategy='most_frequent')
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dummy_clf.predict(X_eg)
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dummy_clf.predict(np.array([1,1,1,1,1,1,1,1,1,1]))
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dummy_clf.predict_proba(X_eg)
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dummy_clf.score(X_eg, y_eg)
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0.75
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df2['X']
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dummy_clf.fit(df2['X'], df2['y'])
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dummy_clf.predict(df2['X'])
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dummy_clf.predict_proba(df2['X'])
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ypred = dummy_clf.predict(df2['X'])
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dummy_clf.score(df2['X'], df2['y'])
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confusion_matrix(df2['y'], ypred)
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matthews_corrcoef(df2['y'], ypred)
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#%%
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df['dst_mode']
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y_all_tt = df.loc[:,'dst']
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y_all_tt.value_counts()
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#Counter(y_all_tt)
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#0: 71, 1: 114
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y_all_tt.value_counts(normalize = True)
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df2['y']
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y_train_tt = df2['y']
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Counter(y_train_tt)
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##0: 41, 1: 82
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y_train_tt.value_counts(normalize = True)
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df2['y_bts']
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y_bts_tt = df2['y_bts']
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Counter(y_bts_tt)
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#0: 21, 1: 41
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y_bts_tt.value_counts(normalize = True)
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#%%
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df_clean = df[df['dst'].notna()]
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X = df_clean.iloc[:,0:171]
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X.columns
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y = df_clean.iloc[:,171] # dst
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y.value_counts()
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y2 = df_clean.iloc[:,172] #dst_mode
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y2.value_counts()
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X_train_tt,X_test_tt, y_train_tt, y_test_tt = train_test_split(X, y, test_size=0.30, random_state=42, stratify = y)
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y2.value_counts()
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round(y.value_counts(normalize = True),2)
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y_train_tt.value_counts()
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round(y_train_tt.value_counts(normalize = True),2)
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y_test_tt.value_counts()
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round(y_test_tt.value_counts(normalize = True),2)
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dummy_clf = DummyClassifier(strategy="most_frequent")
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dummy_clf.fit(X_train_tt, y_train_tt)
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DummyClassifier(strategy='most_frequent')
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dummy_clf.predict(X_test_tt)
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# pnca: split 0/30
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=======================
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Total y count in data:
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1.0 114 (62%)
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0.0 71 (38%)
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=======================
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=======================
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Train y count in data:
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1.0 79 (61%)
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0.0 50 (39%)
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=======================
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=======================
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Test y count in data:
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1.0 35 (62%)
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0.0 21 (38%)
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=======================
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acccuracy:
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TP+TN/TP+TN+FP+FN
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114/71
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