91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
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#!/usr/bin/env python3
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import os
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import pandas as pd
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import glob
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from sklearn import preprocessing
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from sklearn.metrics import f1_score, precision_score, recall_score, make_scorer
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from sklearn.model_selection import GridSearchCV
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from sklearn.neural_network import MLPClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.ensemble import RandomForestClassifier
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DIR: str = os.path.dirname(os.path.realpath(__file__))
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IN_DIR: str = DIR + '/metrics'
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OUT_DIR: str = DIR + '/models'
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RANDOM: int = 42
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def clean_output():
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filelist = glob.glob(OUT_DIR + '/models.csv')
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for f in filelist:
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os.remove(f)
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def get_classifiers() -> list:
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return [
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(GaussianNB(), {}),
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(SVC(), {
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'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
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'gamma': ['scale', 'auto']
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}),
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(DecisionTreeClassifier(), {
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'criterion': ['gini', 'entropy'],
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'splitter': ['best', 'random']
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}),
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(RandomForestClassifier(), {
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'criterion': ['gini', 'entropy'],
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'max_features': ['sqrt', 'log2'],
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'class_weight': ['balanced', 'balanced_subsample']
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}),
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(MLPClassifier(), {
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'max_iter': [2000],
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'hidden_layer_sizes': [(10,), (50,), (100, 20,)],
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'activation': ['identity', 'logistic', 'tanh', 'relu'],
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'solver': ['lbfgs', 'sgd', 'adam'],
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'learning_rate': ['constant', 'invscaling', 'adaptive']
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})
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]
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def main():
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clean_output()
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# load dataset
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df = pd.read_csv(IN_DIR + '/feature_vectors_labeled.csv')
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df = df.drop('class_name', axis=1)
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X = df.iloc[:, :-1].to_numpy()
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y = df.iloc[:, -1].to_numpy()
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scaler = preprocessing.StandardScaler().fit(X)
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X_scaled = scaler.transform(X)
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df = None
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for classifier, grid in get_classifiers():
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# cross-validation splits are same across calls as data is not shuffled
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# see: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
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clf = GridSearchCV(classifier, grid, cv=5, scoring={
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'precision': make_scorer(precision_score, average='binary', zero_division=0),
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'recall': make_scorer(recall_score, average='binary'),
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'f1': make_scorer(f1_score, average='binary')
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}, refit='f1')
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clf.fit(X_scaled, y)
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df_classifier = pd.DataFrame(clf.cv_results_)
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df_classifier['classifier'] = type(classifier).__name__
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df = df_classifier if df is None else pd.concat(
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[df, df_classifier], ignore_index=True)
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df.to_csv(OUT_DIR + '/models.csv', index=False)
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if __name__ == '__main__':
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main()
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