added validated params for all classifiers
This commit is contained in:
parent
00633872f5
commit
b5d29dd449
5 changed files with 245 additions and 617 deletions
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@ -25,8 +25,16 @@ ds_lrD = fsgs(input_df = X
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, estimator = LogisticRegression(**rs)
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, var_type = 'mixed')
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# RF: without fs + hyperparam
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rf_allF = fsgs(input_df = X
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, target = y
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, param_gridLd = param_grid_rf
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)
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, var_type = 'mixed')
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#Fitting 10 folds for each of 31104 candidates, totalling 311040 fits
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@ -44,4 +52,5 @@ ds_lrD = fsgs(input_df = X
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# file = 'LR_FS.json'
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# with open(file, 'r') as f:
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# data = json.load(f)
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##############################################################################
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##############################################################################
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@ -37,6 +37,7 @@ col_transform = ColumnTransformer(transformers = t
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# print(col_transform.get_feature_names_out())
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# foo = col_transform.fit_transform(X)
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Xm = col_transform.fit_transform(X)
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# (foo == test).all()
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#-----------------------
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@ -49,7 +49,6 @@ from sklearn.compose import make_column_transformer
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score
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from sklearn.metrics import jaccard_score
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from sklearn.metrics import make_scorer
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from sklearn.metrics import classification_report
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@ -62,8 +61,8 @@ from sklearn.model_selection import StratifiedKFold
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from sklearn.pipeline import Pipeline
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_selection import RFE
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from sklearn.feature_selection import RFECV
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#from sklearn.feature_selection import RFE
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#from sklearn.feature_selection import RFECV
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import itertools
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#import seaborn as sns
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import matplotlib.pyplot as plt
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@ -1,479 +0,0 @@
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########################################################################
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#======================
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# AdaBoostClassifier()
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#======================
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estimator = AdaBoostClassifier(**rs)
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# Define pipleline with steps
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pipe_abc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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# , ('clf', AdaBoostClassifier(**rs))])
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_abc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [AdaBoostClassifier(**rs)],
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'clf__n_estimators': [1, 2, 5, 10]
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# , 'clf__base_estimator' : ['SVC']
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# , 'clf__splitter' : ["best", "random"]
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}
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]
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########################################################################
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#======================
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# BaggingClassifier()
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#======================
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estimator = BaggingClassifier(**rs
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, **njobs
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, bootstrap = True
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, oob_score = True)
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# Define pipleline with steps
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pipe_bc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_bc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [BaggingClassifier(**rs, **njobs , bootstrap = True, oob_score = True)],
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'clf__n_estimators' : [10, 25, 50, 100, 150, 200, 500, 700, 1000]
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# , 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()'] # if none, DT is used
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}
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]
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########################################################################
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#======================
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# BernoulliNB ()
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#======================
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# Define estimator
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estimator = BernoulliNB()
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# Define pipleline with steps
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pipe_bnb = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_bnb = [
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{'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [BernoulliNB()],
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'clf__alpha': [1, 0]
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, 'clf__binarize':[None, 0]
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, 'clf__fit_prior': [True]
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, 'clf__class_prior': [None]
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}
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]
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########################################################################
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#===========================
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# DecisionTreeClassifier()
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#===========================
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# Define estimator
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estimator = DecisionTreeClassifier(**rs)
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# Define pipleline with steps
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pipe_dt = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_dt = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [DecisionTreeClassifier(**rs)],
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'clf__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
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, 'clf__class_weight':['balanced']
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, 'clf__criterion': ['gini', 'entropy', 'log_loss']
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, 'clf__max_features': [None, 'sqrt', 'log2']
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, 'clf__min_samples_leaf': [1, 2, 3, 4, 5, 10]
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, 'clf__min_samples_split': [2, 5, 15, 20]
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}
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]
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#########################################################################
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#==============================
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# GradientBoostingClassifier()
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#==============================
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# Define estimator
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estimator = GradientBoostingClassifier(**rs)
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# Define pipleline with steps
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pipe_gbc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_gbc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [GradientBoostingClassifier(**rs)],
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'clf__n_estimators' : [10, 100, 200, 500, 1000]
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, 'clf__learning_rate': [0.001, 0.01, 0.1]
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, 'clf__subsample' : [0.5, 0.7, 1.0]
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, 'clf__max_depth' : [3, 7, 9]
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}
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]
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#########################################################################
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#===========================
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# GaussianNB ()
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#===========================
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# Define estimator
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estimator = GaussianNB()
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# Define pipleline with steps
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pipe_gnb = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_gnb = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [GaussianNB()],
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'clf__priors': [None]
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, 'clf__var_smoothing': np.logspace(0,-9, num=100)
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}
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]
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#########################################################################
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#===========================
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# GaussianProcessClassifier()
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#===========================
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# Define estimator
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estimator = GaussianProcessClassifier(**rs)
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# Define pipleline with steps
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pipe_gbc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_gbc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [GaussianProcessClassifier(**rs)],
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'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
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}
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]
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#########################################################################
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#===========================
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# KNeighborsClassifier ()
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#===========================
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# Define estimator
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estimator = KNeighborsClassifier(**njobs)
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# Define pipleline with steps
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pipe_knn = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_knn = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [KNeighborsClassifier(**njobs)],
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'clf__n_neighbors': range(21, 51, 2)
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#, 'clf__n_neighbors': [5, 7, 11]
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, 'clf__metric' : ['euclidean', 'manhattan', 'minkowski']
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, 'clf__weights' : ['uniform', 'distance']
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}
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]
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#########################################################################
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#===========================
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# LogisticRegression ()
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#===========================
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# Define estimator
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estimator = LogisticRegression(**rs)
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# Define pipleline with steps
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pipe_lr = 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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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)])
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# Define hyperparmeter space to search for
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param_grid_lr = [
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{'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [rskf_cv]
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},
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{
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# 'clf': [LogisticRegression(**rs)],
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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': 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': 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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]
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#########################################################################
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#==================
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# MLPClassifier()
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#==================
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# Define estimator
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estimator = MLPClassifier(**rs)
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# Define pipleline with steps
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pipe_mlp = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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param_grid_mlp = [ {
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [MLPClassifier(**rs, max_iter = 1000)],
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'clf__max_iter': [1000, 2000]
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, 'clf__hidden_layer_sizes': [(1), (2), (3), (5), (10)]
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, 'clf__solver': ['lbfgs', 'sgd', 'adam']
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, 'clf__learning_rate': ['constant', 'invscaling', 'adaptive']
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#, 'clf__learning_rate': ['constant']
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}
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]
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#########################################################################
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#==================================
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# QuadraticDiscriminantAnalysis()
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#==================================
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# Define estimator
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estimator = QuadraticDiscriminantAnalysis(**rs)
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# Define pipleline with steps
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pipe_qda = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_qda = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [QuadraticDiscriminantAnalysis()],
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'clf__priors': [None]
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}
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]
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#########################################################################
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#====================
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# RidgeClassifier()
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#====================
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# Define estimator
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estimator = RidgeClassifier(**rs)
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# Define pipleline with steps
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pipe_rc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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param_grid_rc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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#'clf' : [RidgeClassifier(**rs)],
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'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
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}
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]
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#######################################################################
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#===========================
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# RandomForestClassifier()
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#===========================
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# Define estimator
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estimator = [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
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# Define pipleline with steps
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pipe_rf = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_rf = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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# 'clf': [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)],
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'clf__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
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, 'clf__class_weight':['balanced','balanced_subsample']
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, 'clf__n_estimators': [10, 25, 50, 100, 200, 300] # go upto a 100
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, 'clf__criterion': ['gini', 'entropy', 'log_loss']
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, 'clf__max_features': ['sqrt', 'log2', None] #deafult is sqrt
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, 'clf__min_samples_leaf': [1, 2, 3, 4, 5, 10]
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, 'clf__min_samples_split': [2, 5, 15, 20]
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}
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]
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#######################################################################
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#========
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# SVC()
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#========
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estimator = SVC(**rs)
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# Define pipleline with steps
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pipe_svc = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_svc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [SVC(**rs)],
|
||||
'clf__kernel': ['poly', 'rbf', 'sigmoid']
|
||||
#, 'clf__kernel': ['linear']
|
||||
, 'clf__C' : [50, 10, 1.0, 0.1, 0.01]
|
||||
, 'clf__gamma': ['scale', 'auto']
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
#######################################################################
|
||||
#=================
|
||||
# XGBClassifier ()
|
||||
#=================
|
||||
|
||||
# Define estimator
|
||||
#https://www.datatechnotes.com/2019/07/classification-example-with.html
|
||||
# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
|
||||
# colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
|
||||
# max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
|
||||
# n_estimators=100, n_jobs=1, nthread=None,
|
||||
# objective='multi:softprob', random_state=0, reg_alpha=0,
|
||||
# reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
|
||||
# subsample=1, verbosity=1)
|
||||
estimator = XGBClassifier(**rs, **njobs, verbose = 3)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_xgb = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_xgb = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [XGBClassifier(**rs , **njobs, verbose = 3)],
|
||||
'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
|
||||
, 'clf__max_depth' : [4, 6, 8, 10, 12, 16, 20]
|
||||
, 'clf__n_estimators': [10, 25, 50, 100, 200, 300]
|
||||
#, 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
|
||||
#, 'clf__max_features': ['auto', 'sqrt']
|
||||
}
|
||||
]
|
||||
|
||||
#######################################################################
|
||||
|
||||
|
|
@ -1,6 +1,31 @@
|
|||
# Date: 25/05/2020
|
||||
# https://scikit-learn.org/stable/supervised_learning.html
|
||||
|
||||
|
||||
# try features from Autosklearn:
|
||||
# autosklearn --> pipleine --> components --> classification
|
||||
# https://github.com/automl/auto-sklearn/tree/master/autosklearn/pipeline/components/classification
|
||||
|
||||
# TOADD:
|
||||
# Extra Trees
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/extra_trees.py
|
||||
# LDA
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/lda.py
|
||||
# Multinomial_nb
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/multinomial_nb.py
|
||||
# passive_aggressive
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/passive_aggressive.py
|
||||
# SGD
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/sgd.py
|
||||
|
||||
|
||||
######https://scikit-learn.org/stable/supervised_learning.html
|
||||
|
||||
########################################################################
|
||||
#======================
|
||||
# AdaBoostClassifier()
|
||||
#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/adaboost.py
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
|
||||
#======================
|
||||
estimator = AdaBoostClassifier(**rs)
|
||||
|
||||
|
|
@ -22,14 +47,162 @@ param_grid_abc = [
|
|||
|
||||
{
|
||||
# 'clf': [AdaBoostClassifier(**rs)],
|
||||
'clf__n_estimators': [1, 2, 5, 10]
|
||||
# , 'clf__base_estimator' : ['SVC']
|
||||
# , 'clf__splitter' : ["best", "random"]
|
||||
# 'clf__n_estimators': [50, 100, 150, 200, 250, 300, 350, 400, 450, 500]
|
||||
'clf__n_estimators': [50, 100, 200, 300, 400, 500],
|
||||
'clf__learning_rate': [0.01, 0.1, 1, 1.5, 2],
|
||||
'clf__max_depth': [1, 5, 10],
|
||||
# 'clf__base_estimator' : ['SVC']
|
||||
}
|
||||
]
|
||||
#======================
|
||||
# Extra TreesClassifier()
|
||||
#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/extra_trees.py
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
|
||||
#======================
|
||||
estimator = ExtraTreesClassifier**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_abc = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('clf', ExtraTreesClassifier(**rs))])
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_abc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
# 'clf': [ExtraTreesClassifier(**rs)],
|
||||
'clf__n_estimators': [100, 300, 500], # sklearn has no tuning
|
||||
'clf__max_depth': [None],
|
||||
'clf__criterion': ['gini', 'entropy'],
|
||||
'clf__max_features': [None, 'sqrt', 'log2', 0.5, 1],
|
||||
'clf__min_samples_leaf': [1, 5, 10, 15, 20],
|
||||
'clf__min_samples_split': [2, 5, 10, 15, 20]
|
||||
}
|
||||
]
|
||||
|
||||
#===========================
|
||||
# DecisionTreeClassifier()
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/decision_tree.py
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = DecisionTreeClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_dt = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_dt = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [DecisionTreeClassifier(**rs)],
|
||||
# 'clf__max_depth': [None, 2, 6, 10, 14, 16, 20],
|
||||
'clf__max_depth': [None, 0, 0.2, 0.5],
|
||||
'clf__class_weight':[None, 'balanced'],
|
||||
'clf__criterion': ['gini', 'entropy'],
|
||||
'clf__max_features': [None, 'sqrt', 'log2', 1],
|
||||
'clf__min_samples_leaf': [1, 5, 10, 15, 20],
|
||||
'clf__min_samples_split': [2, 5, 10, 15, 20]
|
||||
}
|
||||
]
|
||||
|
||||
########################################################################
|
||||
#===========================
|
||||
# RandomForestClassifier()
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/random_forest.py
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
|
||||
#===========================
|
||||
|
||||
# Define estimator
|
||||
estimator = [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_rf = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_rf = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)],
|
||||
# 'clf__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
|
||||
'clf__max_depth': [None, 2, 6, 10, 14, 16, 20] #autosk: None
|
||||
, 'clf__class_weight':[None, 'balanced']
|
||||
, 'clf__n_estimators': [50, 100, 200, 300] # autodesk: no
|
||||
, 'clf__criterion': ['gini', 'entropy']
|
||||
, 'clf__max_features': ['sqrt', 'log2', None, 0, 0.5, 1]
|
||||
, 'clf__min_samples_leaf': [1, 5, 10, 15, 20]
|
||||
, 'clf__min_samples_split': [2, 5, 15, 20]
|
||||
}
|
||||
]
|
||||
|
||||
#=================
|
||||
# XGBClassifier ()
|
||||
#=================
|
||||
# https://www.kaggle.com/code/stuarthallows/using-xgboost-with-scikit-learn/notebook
|
||||
# Define estimator
|
||||
#https://www.datatechnotes.com/2019/07/classification-example-with.html
|
||||
# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
|
||||
# colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
|
||||
# max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
|
||||
# n_estimators=100, n_jobs=1, nthread=None,
|
||||
# objective='multi:softprob', random_state=0, reg_alpha=0,
|
||||
# reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
|
||||
# subsample=1, verbosity=1)
|
||||
estimator = XGBClassifier(**rs, **njobs, verbose = 3)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_xgb = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_xgb = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [XGBClassifier(**rs , **njobs, verbose = 3)],
|
||||
'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
|
||||
, 'clf__max_depth' : [3, 8, 10, 12, 16, 20]
|
||||
, 'clf__n_estimators': [50, 100, 200, 300]
|
||||
}
|
||||
]
|
||||
|
||||
#######################################################################
|
||||
|
||||
|
||||
########################################################################
|
||||
#======================
|
||||
# BaggingClassifier()
|
||||
# BaggingClassifier()*
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html
|
||||
#======================
|
||||
estimator = BaggingClassifier(**rs
|
||||
, **njobs
|
||||
|
|
@ -53,7 +226,7 @@ param_grid_bc = [
|
|||
},
|
||||
|
||||
{
|
||||
# 'clf': [BaggingClassifier(**rs, **njobs , bootstrap = True, oob_score = True)],
|
||||
# 'clf': [BaggingClassifier(**rs, **njobs , bootstrap = True, oob_score = True)],
|
||||
'clf__n_estimators' : [10, 25, 50, 100, 150, 200, 500, 700, 1000]
|
||||
# , 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()'] # if none, DT is used
|
||||
}
|
||||
|
|
@ -61,6 +234,8 @@ param_grid_bc = [
|
|||
########################################################################
|
||||
#======================
|
||||
# BernoulliNB ()
|
||||
#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/bernoulli_nb.py
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html
|
||||
#======================
|
||||
# Define estimator
|
||||
estimator = BernoulliNB()
|
||||
|
|
@ -81,49 +256,18 @@ param_grid_bnb = [
|
|||
|
||||
{
|
||||
# 'clf': [BernoulliNB()],
|
||||
'clf__alpha': [1, 0]
|
||||
, 'clf__binarize':[None, 0]
|
||||
'clf__alpha': [0.01, 0, 1, 10, 100]
|
||||
, 'clf__binarize':[None, 0] # autosk has no, maybe just use None
|
||||
, 'clf__fit_prior': [True]
|
||||
, 'clf__class_prior': [None]
|
||||
}
|
||||
]
|
||||
########################################################################
|
||||
#===========================
|
||||
# DecisionTreeClassifier()
|
||||
#===========================
|
||||
|
||||
# Define estimator
|
||||
estimator = DecisionTreeClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_dt = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_dt = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [DecisionTreeClassifier(**rs)],
|
||||
'clf__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
|
||||
, 'clf__class_weight':['balanced']
|
||||
, 'clf__criterion': ['gini', 'entropy', 'log_loss']
|
||||
, 'clf__max_features': [None, 'sqrt', 'log2']
|
||||
, 'clf__min_samples_leaf': [1, 2, 3, 4, 5, 10]
|
||||
, 'clf__min_samples_split': [2, 5, 15, 20]
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#==============================
|
||||
# GradientBoostingClassifier()
|
||||
#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/gradient_boosting.py
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
|
||||
#==============================
|
||||
# Define estimator
|
||||
estimator = GradientBoostingClassifier(**rs)
|
||||
|
|
@ -144,17 +288,26 @@ param_grid_gbc = [
|
|||
},
|
||||
{
|
||||
# 'clf': [GradientBoostingClassifier(**rs)],
|
||||
'clf__n_estimators' : [10, 100, 200, 500, 1000]
|
||||
, 'clf__learning_rate': [0.001, 0.01, 0.1]
|
||||
, 'clf__subsample' : [0.5, 0.7, 1.0]
|
||||
, 'clf__max_depth' : [3, 7, 9]
|
||||
'clf__loss' : ['log_loss', 'exponential'],
|
||||
'clf__n_estimators' : [10, 100, 200, 500, 1000], # autosklearn: not there
|
||||
'clf__learning_rate' : [0.01,0.1, 0, 0.5, 1],
|
||||
'clf__subsample' : [0.5, 0.7, 1.0],
|
||||
'clf__max_depth' : [3, 7, 9],
|
||||
'clf__min_samples_leaf' : [1, 20, 50, 100, 150, 200],
|
||||
'clf__max_depth' : [None],
|
||||
'clf__max_leaf_nodes' : [3, 31, 51, 331, 2047] # autosklearn: log = T
|
||||
'clf__l2_regularization' : [0.0000000001, 0.000001, 0.0001, 0.01, 0.1, 1], #lower=1E-10, upper=1, log = T
|
||||
'n_iter_no_change' : [None, 1, 5, 10, 15, 20], # autsk: 1, 20
|
||||
'validation_fraction' : [0.01, 0.03, 0.2, 0.3, 0.4] # autosk: 0.01, 0.4
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#===========================
|
||||
# GaussianNB ()
|
||||
# GaussianNB()
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/gaussian_nb.py
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = GaussianNB()
|
||||
|
|
@ -183,7 +336,8 @@ param_grid_gnb = [
|
|||
|
||||
#########################################################################
|
||||
#===========================
|
||||
# GaussianProcessClassifier()
|
||||
# GaussianProcessClassifier() *
|
||||
# https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = GaussianProcessClassifier(**rs)
|
||||
|
|
@ -212,6 +366,8 @@ param_grid_gbc = [
|
|||
#########################################################################
|
||||
#===========================
|
||||
# KNeighborsClassifier ()
|
||||
#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/k_nearest_neighbors.py
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = KNeighborsClassifier(**njobs)
|
||||
|
|
@ -233,16 +389,18 @@ param_grid_knn = [
|
|||
|
||||
{
|
||||
# 'clf': [KNeighborsClassifier(**njobs)],
|
||||
'clf__n_neighbors': range(21, 51, 2)
|
||||
#, 'clf__n_neighbors': [5, 7, 11]
|
||||
, 'clf__metric' : ['euclidean', 'manhattan', 'minkowski']
|
||||
, 'clf__weights' : ['uniform', 'distance']
|
||||
#'clf__n_neighbors': list(range(21, 51, 4),)
|
||||
'clf__n_neighbors' : [1, 11, 21, 51, 71, 101],
|
||||
'clf__metric' : ['euclidean', 'manhattan', 'minkowski'],
|
||||
'clf__weights' : ['uniform', 'distance']
|
||||
|
||||
}
|
||||
]
|
||||
#########################################################################
|
||||
#===========================
|
||||
# LogisticRegression ()
|
||||
# LogisticRegression () *
|
||||
# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = LogisticRegression(**rs)
|
||||
|
|
@ -287,6 +445,8 @@ param_grid_lr = [
|
|||
#########################################################################
|
||||
#==================
|
||||
# MLPClassifier()
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/mlp.py
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
|
||||
#==================
|
||||
# Define estimator
|
||||
estimator = MLPClassifier(**rs)
|
||||
|
|
@ -306,11 +466,10 @@ param_grid_mlp = [ {
|
|||
|
||||
{
|
||||
# 'clf': [MLPClassifier(**rs, max_iter = 1000)],
|
||||
'clf__max_iter': [1000, 2000]
|
||||
, 'clf__hidden_layer_sizes': [(1), (2), (3), (5), (10)]
|
||||
, 'clf__solver': ['lbfgs', 'sgd', 'adam']
|
||||
, 'clf__learning_rate': ['constant', 'invscaling', 'adaptive']
|
||||
#, 'clf__learning_rate': ['constant']
|
||||
'clf__max_iter': [200, 500, 1000, 2000], # no autosklearn
|
||||
'clf__hidden_layer_sizes': [(100), (1), (2), (3), (5), (10) ], #no autosklearn
|
||||
'clf__solver': ['lbfgs', 'sgd', 'adam'], #no autosklearn
|
||||
'clf__learning_rate': ['constant', 'invscaling', 'adaptive'] #no autosklearn
|
||||
|
||||
}
|
||||
]
|
||||
|
|
@ -318,6 +477,8 @@ param_grid_mlp = [ {
|
|||
#########################################################################
|
||||
#==================================
|
||||
# QuadraticDiscriminantAnalysis()
|
||||
https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/qda.py
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
|
||||
#==================================
|
||||
# Define estimator
|
||||
estimator = QuadraticDiscriminantAnalysis(**rs)
|
||||
|
|
@ -339,14 +500,15 @@ param_grid_qda = [
|
|||
|
||||
{
|
||||
# 'clf': [QuadraticDiscriminantAnalysis()],
|
||||
'clf__priors': [None]
|
||||
|
||||
'clf__priors': [None],
|
||||
'clf__reg_param': [0, 1]
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#====================
|
||||
# RidgeClassifier()
|
||||
# RidgeClassifier() *
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html
|
||||
#====================
|
||||
|
||||
# Define estimator
|
||||
|
|
@ -372,41 +534,14 @@ param_grid_rc = [
|
|||
}
|
||||
]
|
||||
#######################################################################
|
||||
#===========================
|
||||
# RandomForestClassifier()
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_rf = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_rf = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)],
|
||||
'clf__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
|
||||
, 'clf__class_weight':['balanced','balanced_subsample']
|
||||
, 'clf__n_estimators': [10, 25, 50, 100, 200, 300] # go upto a 100
|
||||
, 'clf__criterion': ['gini', 'entropy', 'log_loss']
|
||||
, 'clf__max_features': ['sqrt', 'log2', None] #deafult is sqrt
|
||||
, 'clf__min_samples_leaf': [1, 2, 3, 4, 5, 10]
|
||||
, 'clf__min_samples_split': [2, 5, 15, 20]
|
||||
}
|
||||
]
|
||||
#######################################################################
|
||||
#========
|
||||
# SVC()
|
||||
# https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/libsvm_svc.py
|
||||
# paper that supports libSVM/SVC param searching
|
||||
# https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
|
||||
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
|
||||
|
||||
##########https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/liblinear_svc.py (NOT the one used, but they are very similar!)
|
||||
#========
|
||||
|
||||
estimator = SVC(**rs)
|
||||
|
|
@ -428,52 +563,15 @@ param_grid_svc = [
|
|||
|
||||
{
|
||||
# 'clf': [SVC(**rs)],
|
||||
'clf__kernel': ['poly', 'rbf', 'sigmoid']
|
||||
#, 'clf__kernel': ['linear']
|
||||
, 'clf__C' : [50, 10, 1.0, 0.1, 0.01]
|
||||
, 'clf__gamma': ['scale', 'auto']
|
||||
# 'clf__kernel': ['poly', 'rbf', 'sigmoid']
|
||||
, 'clf__kernel': ['rbf']
|
||||
, 'clf__C' : [50, 10, 1.0, 0.1, 0.01]
|
||||
, 'clf__C' : [1, 0.03, 10, 100, 1000, 10000, 32768]
|
||||
, 'clf__gamma' : ['scale', 'auto']
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
#######################################################################
|
||||
#=================
|
||||
# XGBClassifier ()
|
||||
#=================
|
||||
|
||||
# Define estimator
|
||||
#https://www.datatechnotes.com/2019/07/classification-example-with.html
|
||||
# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
|
||||
# colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
|
||||
# max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
|
||||
# n_estimators=100, n_jobs=1, nthread=None,
|
||||
# objective='multi:softprob', random_state=0, reg_alpha=0,
|
||||
# reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
|
||||
# subsample=1, verbosity=1)
|
||||
estimator = XGBClassifier(**rs, **njobs, verbose = 3)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_xgb = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_xgb = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [XGBClassifier(**rs , **njobs, verbose = 3)],
|
||||
'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
|
||||
, 'clf__max_depth' : [4, 6, 8, 10, 12, 16, 20]
|
||||
, 'clf__n_estimators': [10, 25, 50, 100, 200, 300]
|
||||
#, 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
|
||||
#, 'clf__max_features': ['auto', 'sqrt']
|
||||
}
|
||||
]
|
||||
|
||||
#######################################################################
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue