added file containing model names and hyperaprams to run for all models inc FS
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6 changed files with 536 additions and 299 deletions
38
UQ_FS_fn.py
38
UQ_FS_fn.py
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@ -10,19 +10,26 @@ Created on Mon May 23 23:25:26 2022
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def fsgs(input_df
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, target
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, blind_test_df = pd.DataFrame()
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, blind_test_target = pd.Series(dtype = 'int64')
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#, y_trueS = pd.Series()
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, estimator = LogisticRegression(**rs)
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, param_gridLd = {}
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, cv_method = 10
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, cv_method = StratifiedKFold(n_splits = 10
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, shuffle = True,**rs)
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, var_type = ['numerical'
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, 'categorical'
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, 'mixed']
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, fs_estimator = [LogisticRegression(**rs)]
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, fs = RFECV(DecisionTreeClassifier(**rs) , cv = 10, scoring = 'matthews_corrcoef')
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, fs = RFECV(DecisionTreeClassifier(**rs)
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, cv = StratifiedKFold(n_splits = 10
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, shuffle = True,**rs)
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, scoring = 'matthews_corrcoef')
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):
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'''
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returns
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Dict containing results from FS and hyperparam tuning
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Dict containing results from FS and hyperparam tuning for a given estiamtor
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>>> ADD MORE <<<
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optimised/selected based on mcc
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'''
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# Determine categorical and numerical features
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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@ -68,7 +75,6 @@ def fsgs(input_df
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############################################################################
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# Create Pipeline object
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pipe = Pipeline([
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#('pre', MinMaxScaler()),
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('pre', col_transform),
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('fs', fs),
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#('clf', LogisticRegression(**rs))])
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@ -119,8 +125,8 @@ def fsgs(input_df
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#tp = gscv_fs.predict(X_bts)
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tp = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
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print('\nMCC on Blind test:' , round(matthews_corrcoef(blind_test_target, tp),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, tp),2))
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#=================
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# info extraction
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@ -191,9 +197,9 @@ def fsgs(input_df
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#bts_predict = gscv_fs.predict(X_bts)
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bts_predict = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(blind_test_target, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
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# Diff b/w train and bts test scores
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train_test_diff = train_bscore - bts_mcc_score
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@ -213,12 +219,12 @@ def fsgs(input_df
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lr_btsD
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#lr_btsD['bts_mcc'] = bts_mcc_score
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lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
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lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
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lr_btsD['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_jaccard'] = round(jaccard_score(blind_test_target, bts_predict),2)
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lr_btsD
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#===========================
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@ -229,7 +235,7 @@ def fsgs(input_df
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fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
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all_featuresL = list(all_features)
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fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
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fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
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fs_res_array_rankf = str(list( gscv_fs.best_estimator_.named_steps['fs'].ranking_))
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sel_featuresf = list(sel_features)
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n_sf = int(n_sf)
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@ -6,9 +6,27 @@ Created on Tue May 24 08:11:05 2022
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@author: tanu
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"""
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# my function
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#import fsgs from UQ_FS_fn
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fsgs(input_df = X
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, target = y
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, param_gridLd = param_grid_abc
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = AdaBoostClassifier(**rs)
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, var_type = 'mixed')
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ds_lrD = fsgs(input_df = X
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, target = y
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, param_gridLd = param_grid_lr
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = LogisticRegression(**rs)
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, var_type = 'mixed')
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import fsgs from
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fsgs(X,y,param_gridLd=param_grid_abc, blind_test_df = X_bts, estimator=AdaBoostClassifier(**rs), var_type = 'mixed')
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@ -17,13 +35,13 @@ fsgs(X,y,param_gridLd=param_grid_abc, blind_test_df = X_bts, estimator=AdaBoostC
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# Write final output file
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# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
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#========================================
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#output final dict as a json
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outFile = 'LR_FS.json'
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with open(outFile, 'w') as f:
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f.write(json.dumps(output_modelD,cls=NpEncoder))
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# #output final dict as a json
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# outFile = 'LR_FS.json'
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# with open(outFile, 'w') as f:
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# f.write(json.dumps(output_modelD,cls=NpEncoder))
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# read json
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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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# # read json
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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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@ -1,266 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 18 09:47:48 2022
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@author: tanu
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"""
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#%% Useful links
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# https://stackoverflow.com/questions/41844311/list-of-all-classification-algorithms
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# https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
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# https://github.com/davidsbatista/machine-learning-notebooks/blob/master/hyperparameter-across-models.ipynb
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# https://scikit-learn.org/stable/modules/svm.html#classification
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# https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/ # [params]
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# https://uk.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html [ algo]
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# As a general rule of thumb, it is required to run baseline models on the dataset. I know H2O- AutoML and other AutoML packages do this. But I want to try using Scikit-learn Pipeline,
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# https://codereview.stackexchange.com/questions/256934/model-pipeline-to-run-multiple-classifiers-for-ml-classification
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# https://uk.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html
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# QDA: https://www.geeksforgeeks.org/quadratic-discriminant-analysis/
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names = [
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"Nearest Neighbors",
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"Linear SVM",
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"RBF SVM",
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"Gaussian Process",
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"Decision Tree",
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"Random Forest",
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"Neural Net",
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"AdaBoost",
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"Naive Bayes",
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"QDA",
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]
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classifiers = [
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KNeighborsClassifier(5),
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SVC(kernel="linear", C=0.025),
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SVC(gamma=2, C=1),
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GaussianProcessClassifier(1.0 * RBF(1.0)),
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DecisionTreeClassifier(max_depth=5),
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RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
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MLPClassifier(alpha=1, max_iter=1000),
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AdaBoostClassifier(),
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GaussianNB(),
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QuadraticDiscriminantAnalysis(),
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]
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# NOTE Logistic regression
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# The choice of the algorithm depends on the penalty chosen: Supported penalties by solver:
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# ‘newton-cg’ - [‘l2’, ‘none’]
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# ‘lbfgs’ - [‘l2’, ‘none’]
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# ‘liblinear’ - [‘l1’, ‘l2’]
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# ‘sag’ - [‘l2’, ‘none’]
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# ‘saga’ - [‘elasticnet’, ‘l1’, ‘l2’, ‘none’]
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# SVR?
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# estimator=SVR(kernel='rbf')
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# param_grid={
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# 'C': [1.1, 5.4, 170, 1001],
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# 'epsilon': [0.0003, 0.007, 0.0109, 0.019, 0.14, 0.05, 8, 0.2, 3, 2, 7],
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# 'gamma': [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]
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# }
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#%% Classification algorithms param grid
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#%% LogisticRegression()
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#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
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gs_lr = Pipeline((
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('pre' , MinMaxScaler())
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,('clf', LogisticRegression(**rs
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, **njobs))
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))
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gs_lr_params = {
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'clf__C' : [0.0001, 0.001, 0.01, 0.1 ,1, 10, 100]
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#'C': np.logspace(-4, 4, 50)
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, 'clf__penalty': ['l1', 'l2', 'elasticnet', 'none']
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, 'clf__solver' : ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']
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}
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#%% DecisionTreeClassifier()
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gs_dt = Pipeline((
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('pre' , MinMaxScaler())
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, ('clf', DecisionTreeClassifier(**rs
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, **njobs))
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))
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gs_dt_params = {
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'clf__max_depth': [ 2, 4, 6, 8, 10]
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, 'clf__criterion':['gini','entropy']
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, "clf__max_features":["auto", None]
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, "clf__max_leaf_nodes":[10,20,30,40]
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}
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#%% KNeighborsClassifier()
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#https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
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gs_knn = Pipeline((
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('pre' , MinMaxScaler())
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,('clf', KNeighborsClassifier(**rs
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, **njobs))
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))
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gs_knn_params = {
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'clf__n_neighbors': [5, 7, 11]
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#, 'clf__n_neighbors': range(1, 21, 2)
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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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#%% RandomForestClassifier()
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gs_rf = Pipeline((
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('pre' , MinMaxScaler())
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,('clf', RandomForestClassifier(**rs
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, **njobs
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, bootstrap = True
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, oob_score = True))
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))
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gs_rf_params = {
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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, 100, 1000]
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, 'clf__criterion': ['gini', 'entropy']
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, 'clf__max_features': ['auto', 'sqrt']
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, 'clf__min_samples_leaf': [2, 4, 8, 50]
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, 'clf__min_samples_split': [10, 20]
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}
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#%% XGBClassifier() # SPNT
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# https://stackoverflow.com/questions/34674797/xgboost-xgbclassifier-defaults-in-python
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# https://stackoverflow.com/questions/34674797/xgboost-xgbclassifier-defaults-in-python
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gs_xgb = Pipeline((
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('pre' , MinMaxScaler())
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,('clf', XGBClassifier(**rs
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, **njobs))
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))
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gs_xgb_params = {
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'clf__learning_rate': [0.01, 0.05, 0.1, 0.2]
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, 'clf__max_depth': [4, 6, 8, 10, 12, 16, 20]
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, 'clf__min_samples_leaf': [4, 8, 12, 16, 20]
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, 'clf__max_features': ['auto', 'sqrt']
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}
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#%% MLPClassifier()
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# https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
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gs_mlp = Pipeline((
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('pre' , MinMaxScaler())
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,('clf', MLPClassifier(**rs
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, **njobs
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, max_iter = 500))
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))
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gs_mlp_params = {
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'clf__hidden_layer_sizes': [(1), (2), (3)]
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, 'clf__max_features': ['auto', 'sqrt']
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, 'clf__min_samples_leaf': [2, 4, 8]
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, 'clf__min_samples_split': [10, 20]
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}
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#%% RidgeClassifier()
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# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html
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gs_rc = Pipeline((
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('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
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,('clf', RidgeClassifier(**rs
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, **njobs))
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))
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gs_rc_params = {
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'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
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}
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#%% SVC()
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# https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
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gs_svc = Pipeline((
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('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
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,('clf', SVC(**rs
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, **njobs))
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))
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gs_svc_params = {
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'clf__kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}
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, 'clf__C' : [50, 10, 1.0, 0.1, 0.01]
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, 'clf__gamma': ['scale', 'auto'] }
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#%% BaggingClassifier()
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#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html
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gs_bdt = Pipeline((
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('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
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,('clf', BaggingClassifier(**rs
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, **njobs
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, bootstrap = True
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, oob_score = True))
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))
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gs_bdt_params = {
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'clf__n_estimators' : [10, 100, 1000]
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# If None, then the base estimator is a DecisionTreeClassifier.
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, 'clf__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()']# if none, DT is used
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, 'clf__gamma': ['scale', 'auto'] }
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#%% GradientBoostingClassifier()
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# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
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gs_gb = Pipeline((
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('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
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,('clf', GradientBoostingClassifier(**rs))
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))
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gs_bdt_params = {
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'clf__n_estimators' : [10, 100, 200, 500, 1000]
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, 'clf__n_estimators' : [10, 100, 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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#%% AdaBoostClassifier()
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#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier
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gs_gb = Pipeline((
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('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
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,('clf', AdaBoostClassifier(**rs))
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))
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gs_bdt_params = {
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'clf__n_estimators': [none, 1, 2]
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, 'clf__base_estiamtor' : ['None', 1*SVC(), 1*KNeighborsClassifier()]
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#, 'clf___splitter' : ["best", "random"]
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}
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#%% GaussianProcessClassifier()
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# https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html
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#GaussianProcessClassifier(1.0 * RBF(1.0)),
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gs_gpc = Pipeline((
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('pre' , MinMaxScaler()) # CHECK if it wants -1 to 1
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,('clf', GaussianProcessClassifier(**rs))
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))
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gs_gpc_params = {
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'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
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}
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#%% GaussianNB()
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# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
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gs_gnb = Pipeline((
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('pre' , MinMaxScaler())
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, ('pca', PCA() )# CHECK if it wants -1 to 1
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,('clf', GaussianNB(**rs))
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))
|
||||
|
||||
gs_gnb_params = {
|
||||
'clf__priors': [None]
|
||||
, 'clf__var_smoothing': np.logspace(0,-9, num=100)
|
||||
}
|
||||
|
||||
#%% QuadraticDiscriminantAnalysis()
|
||||
#https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
|
||||
|
||||
gs_qda = Pipeline((
|
||||
('pre' , MinMaxScaler())
|
||||
#, ('pca', PCA() )# CHECK if it wants -1 to 1
|
||||
,('clf', QuadraticDiscriminantAnalysis())
|
||||
))
|
||||
#%% BernoulliNB()
|
||||
# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html
|
||||
gs_gnb = Pipeline((
|
||||
('pre' , MinMaxScaler())
|
||||
,('clf', BernoulliNB())
|
||||
))
|
||||
BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
|
||||
gs_gnb_params = {
|
||||
'clf__alpha': [0, 1]
|
||||
, 'clf__binarize':['None', 0]
|
||||
, 'clf__fit_prior': [True]
|
||||
, 'clf__class_prior': ['None']
|
||||
}
|
||||
480
classification_params_FS.py
Normal file
480
classification_params_FS.py
Normal file
|
|
@ -0,0 +1,480 @@
|
|||
########################################################################
|
||||
#======================
|
||||
# AdaBoostClassifier()
|
||||
#======================
|
||||
estimator = AdaBoostClassifier(**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', AdaBoostClassifier(**rs))])
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_abc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [AdaBoostClassifier(**rs)],
|
||||
'clf__n_estimators': [1, 2, 5, 10]
|
||||
# , 'clf__base_estimator' : ['SVC']
|
||||
# , 'clf__splitter' : ["best", "random"]
|
||||
}
|
||||
]
|
||||
########################################################################
|
||||
#======================
|
||||
# BaggingClassifier()
|
||||
#======================
|
||||
estimator = BaggingClassifier(**rs
|
||||
, **njobs
|
||||
, bootstrap = True
|
||||
, oob_score = True)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_bc = 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_bc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# '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
|
||||
}
|
||||
]
|
||||
########################################################################
|
||||
#======================
|
||||
# BernoulliNB ()
|
||||
#======================
|
||||
# Define estimator
|
||||
estimator = BernoulliNB()
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_bnb = 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_bnb = [
|
||||
{'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [BernoulliNB()],
|
||||
'clf__alpha': [1, 0]
|
||||
, 'clf__binarize':[None, 0]
|
||||
, '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()
|
||||
#==============================
|
||||
# Define estimator
|
||||
estimator = GradientBoostingClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_gbc = 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_gbc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [GradientBoostingClassifier(**rs)],
|
||||
'clf__n_estimators' : [10, 100, 200, 500, 1000]
|
||||
, 'clf__n_estimators' : [10, 100, 1000]
|
||||
, 'clf__learning_rate': [0.001, 0.01, 0.1]
|
||||
, 'clf__subsample' : [0.5, 0.7, 1.0]
|
||||
, 'clf__max_depth' : [3, 7, 9]
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#===========================
|
||||
# GaussianNB ()
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = GaussianNB()
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_gnb = 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_gnb = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
{
|
||||
# 'clf': [GaussianNB()],
|
||||
'clf__priors': [None]
|
||||
, 'clf__var_smoothing': np.logspace(0,-9, num=100)
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#===========================
|
||||
# GaussianProcessClassifier()
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = GaussianProcessClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_gbc = 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_gbc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [GaussianProcessClassifier(**rs)],
|
||||
'clf__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#===========================
|
||||
# KNeighborsClassifier ()
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = KNeighborsClassifier(**njobs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_knn = 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_knn = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# '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']
|
||||
|
||||
}
|
||||
]
|
||||
#########################################################################
|
||||
#===========================
|
||||
# LogisticRegression ()
|
||||
#===========================
|
||||
# Define estimator
|
||||
estimator = LogisticRegression(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_lr = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)])
|
||||
|
||||
# Define hyperparmeter space to search for
|
||||
param_grid_lr = [
|
||||
|
||||
{'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [rskf_cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [LogisticRegression(**rs)],
|
||||
'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
|
||||
'clf__max_iter': list(range(100,800,100)),
|
||||
'clf__solver': ['saga']
|
||||
},
|
||||
{
|
||||
# 'clf': [LogisticRegression(**rs)],
|
||||
'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__penalty': ['l2', 'none'],
|
||||
'clf__max_iter': list(range(100,800,100)),
|
||||
'clf__solver': ['newton-cg', 'lbfgs', 'sag']
|
||||
},
|
||||
{
|
||||
# 'clf': [LogisticRegression(**rs)],
|
||||
'clf__C': np.logspace(0, 4, 10),
|
||||
'clf__penalty': ['l1', 'l2'],
|
||||
'clf__max_iter': list(range(100,800,100)),
|
||||
'clf__solver': ['liblinear']
|
||||
}
|
||||
|
||||
]
|
||||
#########################################################################
|
||||
#==================
|
||||
# MLPClassifier()
|
||||
#==================
|
||||
# Define estimator
|
||||
estimator = MLPClassifier(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_mlp = Pipeline([
|
||||
('pre', MinMaxScaler())
|
||||
, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
|
||||
# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
|
||||
, ('clf', estimator)
|
||||
])
|
||||
|
||||
param_grid_mlp = [ {
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# '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']
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#==================================
|
||||
# QuadraticDiscriminantAnalysis()
|
||||
#==================================
|
||||
# Define estimator
|
||||
estimator = QuadraticDiscriminantAnalysis(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
pipe_qda = 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_qda = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
# 'clf': [QuadraticDiscriminantAnalysis()],
|
||||
'clf__priors': [None]
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
#########################################################################
|
||||
#====================
|
||||
# RidgeClassifier()
|
||||
#====================
|
||||
|
||||
# Define estimator
|
||||
estimator = RidgeClassifier(**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', estimator)
|
||||
])
|
||||
|
||||
param_grid_rc = [
|
||||
{
|
||||
'fs__min_features_to_select' : [1,2]
|
||||
# , 'fs__cv': [cv]
|
||||
},
|
||||
|
||||
{
|
||||
#'clf' : [RidgeClassifier(**rs)],
|
||||
'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
|
||||
}
|
||||
]
|
||||
#######################################################################
|
||||
#===========================
|
||||
# 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()
|
||||
#========
|
||||
|
||||
estimator = SVC(**rs)
|
||||
|
||||
# Define pipleline with steps
|
||||
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
|
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#https://www.datatechnotes.com/2019/07/classification-example-with.html
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# XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
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# colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,
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# max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,
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# n_estimators=100, n_jobs=1, nthread=None,
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# objective='multi:softprob', random_state=0, reg_alpha=0,
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||||
# reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,
|
||||
# subsample=1, verbosity=1)
|
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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']
|
||||
}
|
||||
]
|
||||
|
||||
#######################################################################
|
||||
|
||||
|
|
@ -16,7 +16,6 @@ Created on Tue Mar 15 11:09:50 2022
|
|||
cv = skf_cv
|
||||
|
||||
# LogisticRegression: Feature Selelction + GridSearch CV + Pipeline
|
||||
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = LogisticRegression(**rs)
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ Created on Wed May 18 06:03:24 2022
|
|||
#cv = rskf_cv
|
||||
cv = skf_cv
|
||||
|
||||
# AdaBoostClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
# RandomForestClassifier: Feature Selelction + GridSearch CV + Pipeline
|
||||
###############################################################################
|
||||
# Define estimator
|
||||
estimator = [RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)](**rs)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue