#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 18 09:47:48 2022

@author: tanu
"""

#%% Useful links
# https://stackoverflow.com/questions/41844311/list-of-all-classification-algorithms
# https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
# https://github.com/davidsbatista/machine-learning-notebooks/blob/master/hyperparameter-across-models.ipynb
# https://scikit-learn.org/stable/modules/svm.html#classification
# https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/ # [params]
# https://uk.mathworks.com/help/stats/hyperparameter-optimization-in-classification-learner-app.html [ algo]
# 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,
    #  https://codereview.stackexchange.com/questions/256934/model-pipeline-to-run-multiple-classifiers-for-ml-classification

names = [
    "Nearest Neighbors",
    "Linear SVM",
    "RBF SVM",
    "Gaussian Process",
    "Decision Tree",
    "Random Forest",
    "Neural Net",
    "AdaBoost",
    "Naive Bayes",
    "QDA",
]

classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    GaussianProcessClassifier(1.0 * RBF(1.0)),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    MLPClassifier(alpha=1, max_iter=1000),
    AdaBoostClassifier(),
    GaussianNB(),
    QuadraticDiscriminantAnalysis(),
]