moved earlier version for model and param names to earlier_versions
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earlier_versions/classification_names_params.py
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earlier_versions/classification_names_params.py
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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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))
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gs_gnb_params = {
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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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#%% QuadraticDiscriminantAnalysis()
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#https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html
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gs_qda = 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', QuadraticDiscriminantAnalysis())
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))
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#%% BernoulliNB()
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# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html
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gs_gnb = Pipeline((
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('pre' , MinMaxScaler())
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,('clf', BernoulliNB())
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))
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BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
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gs_gnb_params = {
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'clf__alpha': [0, 1]
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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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