141 lines
No EOL
3.1 KiB
Python
141 lines
No EOL
3.1 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Thu Jul 7 10:54:09 2022
|
|
|
|
@author: tanu
|
|
"""
|
|
|
|
import numpy as np
|
|
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')
|
|
dummy_clf.predict(X_eg)
|
|
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
|
|
|
|
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']
|
|
y_all_tt.value_counts()
|
|
#Counter(y_all_tt)
|
|
#0: 71, 1: 114
|
|
y_all_tt.value_counts(normalize = True)
|
|
|
|
df2['y']
|
|
y_train_tt = df2['y']
|
|
Counter(y_train_tt)
|
|
##0: 41, 1: 82
|
|
y_train_tt.value_counts(normalize = True)
|
|
|
|
df2['y_bts']
|
|
y_bts_tt = df2['y_bts']
|
|
Counter(y_bts_tt)
|
|
#0: 21, 1: 41
|
|
y_bts_tt.value_counts(normalize = True)
|
|
|
|
#%%
|
|
df_clean = df[df['dst'].notna()]
|
|
X = df_clean.iloc[:,0:171]
|
|
X.columns
|
|
|
|
y = df_clean.iloc[:,171] # dst
|
|
y.value_counts()
|
|
#########################
|
|
|
|
y2 = df_clean.iloc[:,172] #dst_mode
|
|
y2.value_counts()
|
|
|
|
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)
|
|
|
|
y2.value_counts()
|
|
round(y.value_counts(normalize = True),2)
|
|
|
|
y_train_tt.value_counts()
|
|
round(y_train_tt.value_counts(normalize = True),2)
|
|
|
|
y_test_tt.value_counts()
|
|
round(y_test_tt.value_counts(normalize = True),2)
|
|
|
|
dummy_clf = DummyClassifier(strategy="most_frequent")
|
|
dummy_clf.fit(X_train_tt, y_train_tt)
|
|
DummyClassifier(strategy='most_frequent')
|
|
dummy_clf.predict(X_test_tt)
|
|
|
|
# pnca: split 0/30
|
|
=======================
|
|
Total y count in data:
|
|
1.0 114 (62%)
|
|
0.0 71 (38%)
|
|
=======================
|
|
|
|
=======================
|
|
Train y count in data:
|
|
1.0 79 (61%)
|
|
0.0 50 (39%)
|
|
=======================
|
|
|
|
=======================
|
|
Test y count in data:
|
|
1.0 35 (62%)
|
|
0.0 21 (38%)
|
|
=======================
|
|
|
|
|
|
acccuracy:
|
|
|
|
TP+TN/TP+TN+FP+FN
|
|
|
|
114/71
|
|
|
|
######################################
|
|
# try with CV
|
|
|
|
X_eg = np.array([-1, 1, 1, 1, -2, 9, 4, 4, 1, -1, 3, 0])
|
|
y_eg = np.array([0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])
|
|
dummy_clf = DummyClassifier(strategy="most_frequent")
|
|
dummy_clf.fit(X_eg, y_eg)
|
|
#DummyClassifier(strategy='most_frequent')
|
|
dummy_clf.predict(X_eg)
|
|
#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)
|
|
|
|
|
|
cv_DummyD = cross_validate(dummy_clf
|
|
, X_eg
|
|
, y_eg
|
|
, cv = 5
|
|
#, groups = group
|
|
, scoring = scoring_fn
|
|
, return_train_score = True)
|
|
|
|
cv_dummyD_ALL= {}
|
|
cv_dummyD_ALL['DUMMY'] = {}
|
|
|
|
for key, value in cv_DummyD.items():
|
|
print('\nkey:', key, '\nvalue:', value)
|
|
print('\nmean value:', np.mean(value))
|
|
cv_dummyD_ALL['DUMMY'][key] = round(np.mean(value),2) |