finally made the fs work within class and without
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3 changed files with 95 additions and 110 deletions
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@ -25,6 +25,7 @@ X_eg, y_eg = make_classification(n_samples=1000,
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pipe = Pipeline([('scaler', StandardScaler()),
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('selector', SelectKBest(mutual_info_classif, k=9)),
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('classifier', LogisticRegression())])
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search_space = [{'selector__k': [5, 6, 7, 10]},
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107
UQ_LR_FS_p2.py
107
UQ_LR_FS_p2.py
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@ -21,18 +21,25 @@ class ClfSwitcher(BaseEstimator):
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def __init__(
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self,
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estimator = SGDClassifier(),
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#feature = RFECV()
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#feature = RFECV(SGDClassifier())
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):
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"""
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A Custom BaseEstimator that can switch between classifiers.
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:param estimator: sklearn object - The classifier
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"""
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self.estimator = estimator
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#self.feature = feature
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def fit(self, X, y=None, **kwargs):
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self.estimator.fit(X, y)
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#self.feature.fit(X, y)
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return self
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# def transform(self, X, y=None):
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# #self.estimator.transform(X, y)
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# self.feature.transform(X)
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# return self
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def predict(self, X, y=None):
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return self.estimator.predict(X)
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@ -42,23 +49,26 @@ class ClfSwitcher(BaseEstimator):
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def score(self, X, y):
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return self.estimator.score(X, y)
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#%%
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parameters = [
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# {'feature__fs__estimator': LogisticRegression(**rs)
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# , 'feature__fs__cv': [10]
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# , 'feature__fs__scoring': ['matthews_corrcoef']
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# },
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# {'fs__feature__min_features_to_select': [1]
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# , 'fs__feature__scoring': ['matthews_corrcoef']
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# , 'fs__feature__cv': [skf_cv]},
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{'fs__min_features_to_select': [1]
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#, 'fs__scoring': ['matthews_corrcoef']
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, 'fs__cv': [skf_cv]},
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{
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'clf__estimator': [LogisticRegression(**rs)],
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'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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#'clf__estimator__C': np.logspace(0, 4, 10),
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'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['saga']
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'clf__estimator': [LogisticRegression(**rs)],
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#'clf__estimator__C': np.logspace(0, 4, 10),
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'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['saga']
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}#,
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# {
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# 'clf__estimator': [MODEL2(**rs)],
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# #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__estimator__C': np.logspace(0, 4, 10),
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# 'clf__estimator__penalty': ['l2', 'none'],
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# 'clf__estimator__max_iter': list(range(100,800,100)),
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@ -68,13 +78,14 @@ parameters = [
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#%% Create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler())
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# , ('fs', RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
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, ('selector', SelectKBest(mutual_info_classif, k=6))
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, ('clf', ClfSwitcher())
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])
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, ('fs', RFECV(LogisticRegression(**rs), scoring = 'matthews_corrcoef'))#cant be my mcc_fn
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# , ('fs', ClfSwitcher())
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, ('clf', ClfSwitcher())
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])
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#%% Grid search i.e hyperparameter tuning and refitting on mcc
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mod_fs = GridSearchCV(pipeline
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#%%
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# Grid search i.e hyperparameter tuning and refitting on mcc
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gscv_lr = GridSearchCV(pipeline
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, parameters
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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@ -82,6 +93,66 @@ mod_fs = GridSearchCV(pipeline
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, return_train_score = False
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, verbose = 3)
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# Fit
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gscv_lr.fit(X, y)
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####
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gscv_lr_fit = gscv_lr.fit(X, y)
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gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
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gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
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#%% Grid search i.e hyperparameter tuning and refitting on mcc
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param_grid2 = [
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{'fs__min_features_to_select': [1]
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, 'fs__cv': [skf_cv]
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l2'],
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'clf__max_iter': list(range(100,200,100)),
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#'clf__solver': ['newton-cg', 'lbfgs', 'sag']
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'clf__solver': ['sag']
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l1', 'l2'],
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'clf__max_iter': list(range(100,200,100)),
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'clf__solver': ['liblinear']
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}
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]
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# step 4: create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler())
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#, ('fs', model_rfecv)
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, ('fs', RFECV(LogisticRegression(**rs), scoring = 'matthews_corrcoef'))
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, ('clf', LogisticRegression(**rs))])
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# step 5: Perform Gridsearch CV
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gs_final = GridSearchCV(pipeline
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, param_grid2
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, cv = skf_cv
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, scoring = mcc_score_fn, refit = 'mcc'
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, verbose = 1
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, return_train_score = False
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, **njobs)
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#%% Fit
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mod_fs_fit = mod_fs.fit(X, y)
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mod_fs_fbm = mod_fs_fit.best_params_
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@ -12,85 +12,6 @@ Created on Tue Mar 15 11:09:50 2022
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@author: tanu
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"""
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#%% Import libs
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jaccard' : make_scorer(jaccard_score)
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})
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% Get data
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y.to_frame().value_counts().plot(kind = 'bar')
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blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
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# %% Logistic Regression + FS + hyperparameter
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# https://www.tomasbeuzen.com/post/scikit-learn-gridsearch-pipelines/
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# from sklearn.feature_selection import SelectKBest, mutual_info_classif
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# # Create pipeline
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# pipe = Pipeline([
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# ('pre', MinMaxScaler())
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# , ('fs', RFECV( LogisticRegression(**rs), cv = skf_cv, scoring = 'matthews_corrcoef', **njobs,verbose = 3))
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# #, ('fs', SelectKBest(mutual_info_classif, k=5))
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# , ('clf', LogisticRegression(**rs))
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# ])
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# # Create search space
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# param_grid = [{'fs__step': [1]},
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# {
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# 'clf': [LogisticRegression(**rs)],
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# #'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__C': np.logspace(0, 4, 10),
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# 'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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# 'clf__max_iter': list(range(100,800,100)),
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# 'clf__solver': ['saga']
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# },
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# {
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# 'clf': [LogisticRegression(**rs)],
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# #'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__C': np.logspace(0, 4, 10),
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# 'clf__penalty': ['l2', 'none'],
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# 'clf__max_iter': list(range(100,800,100)),
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# 'clf__solver': ['newton-cg', 'lbfgs', 'sag']
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# },
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# {
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# 'clf': [LogisticRegression(**rs)],
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# #'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__C': np.logspace(0, 4, 10),
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# 'clf__penalty': ['l1', 'l2'],
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# 'clf__max_iter': list(range(100,800,100)),
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# 'clf__solver': ['liblinear']
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# }]
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# # Run Grid search
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# gscv_fs_lr = GridSearchCV(pipe
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# , param_grid
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# , cv = skf_cv
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# , scoring = mcc_score_fn, refit = 'mcc'
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# , verbose = 3)
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# gscv_fs_lr_fit = gscv_fs_lr.fit(X, y)
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# gscv_fs_lr_fit_be_mod = gscv_fs_lr_fit.best_params_
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# gscv_fs_lr_fit_be_res = gscv_fs_lr_fit.cv_results_
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# print('Best model:\n', gscv_fs_lr_fit_be_mod)
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# print('Best models score:\n', gscv_fs_lr_fit.best_score_, ':' , round(gscv_fs_lr_fit.best_score_, 2))
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# #print('\nMean test score from fit results:', round(mean(gscv_fs_lr_fit_be_res['mean_test_mcc']),2))
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# print('\nMean test score from fit results:', round(np.nanmean(gscv_fs_lr_fit_be_res['mean_test_mcc']),2))
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##############################################################################
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#MANUAL
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#%% Logistic Regression + hyperparam + FS: BaseEstimator: ClfSwitcher()
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model_lr = LogisticRegression(**rs)
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model_rfecv = RFECV(estimator = model_lr
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@ -99,15 +20,6 @@ model_rfecv = RFECV(estimator = model_lr
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, scoring = 'matthews_corrcoef'
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)
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# model_rfecv = SequentialFeatureSelector(estimator = model_lr
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# , n_features_to_select = 'auto'
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# , tol = None
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# # , cv = 10
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# , cv = rskf_cv
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# # , direction ='backward'
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# , direction ='forward'
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# , **njobs)
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param_grid2 = [
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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@ -155,14 +67,15 @@ pipeline = Pipeline([('pre', MinMaxScaler())
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# Fit
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lr_fs_fit = pipeline.fit(X,y)
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lr_fs_fit_be_mod = lr_fs_fit.best_params_
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lr_fs_fit_be_res = lr_fs_fit.cv_results_
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#lr_fs_fit_be_mod = lr_fs_fit.best_params_
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#lr_fs_fit_be_res = lr_fs_fit.cv_results_
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dir(lr_fs_fit)
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print('Best model:\n', lr_fs_fit_be_mod)
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print('Best models score:\n', lr_fs_fit.best_score_, ':' , round(lr_fs_fit.best_score_, 2))
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pipeline.predict(X_bts)
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lr_fs.predict(X_bts)
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lr_fs_fit.predict(X_bts)
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test_predict = pipeline.predict(X_bts)
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print(test_predict)
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@ -238,4 +151,4 @@ lr_df
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#FIXME: tidy the index of the formatted df
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###############################################################################
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###############################################################################
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