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November 3, 2022 12:14
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| """ | |
| This is an adjusted version of the following file: https://github.com/outerbounds/dsbook/blob/main/chapter-3/classifier_train.py | |
| """ | |
| from metaflow import FlowSpec, step, project, conda_base | |
| @project(name="dummy_example") | |
| @conda_base(python="3.8.13", libraries={"pandas": "1.5", "scikit-learn": "1.1.2"}) | |
| class ClassifierTrainFlow(FlowSpec): | |
| @step | |
| def start(self): | |
| from sklearn import datasets | |
| from sklearn.model_selection import train_test_split | |
| X, y = datasets.load_wine(return_X_y=True) | |
| self.train_data,\ | |
| self.test_data,\ | |
| self.train_labels,\ | |
| self.test_labels = train_test_split(X, y, test_size=0.2, random_state=0) | |
| self.next(self.train_knn, self.train_svm) | |
| @step | |
| def train_knn(self): | |
| from sklearn.neighbors import KNeighborsClassifier | |
| self.model = KNeighborsClassifier() | |
| self.model.fit(self.train_data, self.train_labels) | |
| self.next(self.choose_model) | |
| @step | |
| def train_svm(self): | |
| from sklearn import svm | |
| self.model = svm.SVC(kernel="poly") | |
| self.model.fit(self.train_data, self.train_labels) | |
| self.next(self.choose_model) | |
| @step | |
| def choose_model(self, inputs): | |
| def score(inp): | |
| return inp.model, inp.model.score(inp.test_data, inp.test_labels) | |
| self.results = sorted(map(score, inputs), key=lambda x: -x[1]) | |
| self.model = self.results[0][0] | |
| self.next(self.end) | |
| @step | |
| def end(self): | |
| print('Scores:') | |
| print('\n'.join('%s %f' % res for res in self.results)) | |
| if __name__ == '__main__': | |
| ClassifierTrainFlow() |
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