saved the UQ_FS_eg with almost complete output, still some minor fixes to do
This commit is contained in:
parent
1436557287
commit
a420822a93
3 changed files with 173 additions and 45 deletions
195
UQ_FS_eg.py
195
UQ_FS_eg.py
|
|
@ -53,45 +53,190 @@ clf2.get_feature_names(
|
|||
|
||||
|
||||
clf3 = clf2.best_estimator_ #
|
||||
clf3._final_estimator
|
||||
clf3._final_estimator_
|
||||
clf3._final_estimator.C
|
||||
clf3._final_estimator.solver
|
||||
|
||||
|
||||
fs_bmod = clf2.best_estimator_
|
||||
print('\nbest model with feature selection:', fs_bmod)
|
||||
|
||||
#########################################################
|
||||
# my data
|
||||
|
||||
# my data: Feature Selelction + GridSearch CV + Pipeline
|
||||
pipe = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('selector', RFECV(LogisticRegression(**rs), cv = skf_cv, scoring = 'matthews_corrcoef'))
|
||||
, ('classifier', LogisticRegression(**rs))])
|
||||
, ('fs', RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', LogisticRegression(**rs))])
|
||||
|
||||
search_space = [{'selector__min_features_to_select': [1,2]},
|
||||
{'classifier': [LogisticRegression()],
|
||||
#'classifier__C': np.logspace(0, 4, 10),
|
||||
'classifier__C': [2, 2.8],
|
||||
'classifier__max_iter': [100],
|
||||
'classifier__penalty': ['l1', 'l2'],
|
||||
'classifier__solver': ['saga']
|
||||
}] #,
|
||||
#{'classifier': [RandomForestClassifier(n_estimators=100)],
|
||||
# 'classifier__max_depth': [5, 10, None]},
|
||||
#{'classifier': [KNeighborsClassifier()],
|
||||
# 'classifier__n_neighbors': [3, 7, 11],
|
||||
# 'classifier__weights': ['uniform', 'distance']
|
||||
#}]
|
||||
search_space = [{'fs__min_features_to_select': [1,2]
|
||||
# ,'fs__cv': [rskf_cv]
|
||||
},
|
||||
{
|
||||
#'clf': [LogisticRegression()],
|
||||
#'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__C': [1],
|
||||
'clf__max_iter': [100],
|
||||
'clf__penalty': ['l1', 'l2'],
|
||||
'clf__solver': ['saga']
|
||||
},
|
||||
|
||||
clf = GridSearchCV(pipe, search_space, cv=skf_cv, scoring = mcc_score_fn, refit = 'mcc', verbose=0)
|
||||
{
|
||||
#'clf': [LogisticRegression()],
|
||||
#'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__C': [2, 2.5],
|
||||
'clf__max_iter': [100],
|
||||
'clf__penalty': ['l1', 'l2'],
|
||||
'clf__solver': ['saga']
|
||||
},
|
||||
#{'clf': [RandomForestclf(n_estimators=100)],
|
||||
# 'clf__max_depth': [5, 10, None]},
|
||||
#{'clf': [KNeighborsclf()],
|
||||
# 'clf__n_neighbors': [3, 7, 11],
|
||||
# 'clf__weights': ['uniform', 'distance']
|
||||
#}
|
||||
]
|
||||
|
||||
clf.fit(X, y)
|
||||
clf.best_params_
|
||||
clf.best_score_
|
||||
gscv_fs = GridSearchCV(pipe
|
||||
, search_space
|
||||
, cv = skf_cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 1
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
gscv_fs.fit(X, y)
|
||||
#Fitting 10 folds for each of 8 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
tp = clf.predict(X_bts)
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
# sanity check
|
||||
if train_bscore == round(gscv_tr_resD['mean_test_mcc'].max(),2):
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
|
||||
# create a dict with all scores
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
, 'bts_recall':None
|
||||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
lr_btsD
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_mcc'] = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
output_modelD = {'model_name': model_name
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
|
||||
, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
|
||||
, 'all_feature_names': all_features
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_features
|
||||
, 'train_score (MCC)': train_bscore}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# json.dump(output_modelD, f)
|
||||
# #
|
||||
# with open(file, 'r') as f:
|
||||
# data = json.load(f)
|
||||
|
|
@ -84,6 +84,7 @@ from imblearn.under_sampling import EditedNearestNeighbours
|
|||
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.base import BaseEstimator
|
||||
import json
|
||||
|
||||
scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
|
||||
, 'fscore' : make_scorer(f1_score)
|
||||
|
|
@ -101,10 +102,8 @@ skf_cv = StratifiedKFold(n_splits = 10
|
|||
, shuffle = True,**rs)
|
||||
|
||||
rskf_cv = RepeatedStratifiedKFold(n_splits = 10
|
||||
, n_repeats=3
|
||||
#, shuffle = False, random_state= None)
|
||||
#, shuffle = True
|
||||
,**rs)
|
||||
, n_repeats = 3
|
||||
, **rs)
|
||||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
|
|
|||
|
|
@ -179,22 +179,6 @@ print(test_predict)
|
|||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, test_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, test_predict),2))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Now get the features out
|
||||
all_features = gs_final.feature_names_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue