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| import numpy as np | |
| from sklearn.base import TransformerMixin, BaseEstimator | |
| from utils.param_keys import N_COMPONENTS | |
| class MyModel(TransformerMixin, BaseEstimator): | |
| """ | |
| This class and some child classes are partly copied from: | |
| https://towardsdatascience.com/implementing-pca-from-scratch-fb434f1acbaa | |
| and commented with the help of: | |
| https://www.askpython.com/python/examples/principal-component-analysis | |
| """ | |
| def __init__(self): | |
| self.explained_variance_ = None | |
| self.components_ = None | |
| self._standardized_data = None | |
| self.n_components = None | |
| self.n_samples = None | |
| self._covariance_matrix = None | |
| def __str__(self): | |
| return f'{self.__class__.__name__}:\ncomponents={self.n_components}' | |
| def fit_transform(self, data_ndarray, **fit_params): | |
| self.fit(data_ndarray, **fit_params) | |
| return self.transform(data_ndarray) | |
| def fit(self, data_matrix, **fit_params): | |
| self.n_samples = data_matrix.shape[0] | |
| self.n_components = fit_params.get(N_COMPONENTS, 2) | |
| self._standardized_data = self._standardize_data(data_matrix) | |
| self._covariance_matrix = self.get_covariance_matrix() | |
| self.components_ = self.get_eigenvectors() | |
| return self | |
| def _standardize_data(matrix): | |
| """ | |
| Subtract mean and divide by standard deviation column-wise. | |
| Doing this proves to be very helpful when calculating the covariance matrix. | |
| https://towardsdatascience.com/understanding-the-covariance-matrix-92076554ea44 | |
| Mean-Center the data | |
| :param matrix: Data as matrix | |
| :return: Standardized data matrix | |
| """ | |
| numerator = matrix - np.mean(matrix, axis=0) | |
| denominator = np.std(matrix, axis=0) | |
| return numerator / denominator | |
| def get_covariance_matrix(self): | |
| """ | |
| Calculate covariance matrix with standardized matrix A | |
| :return: Covariance Matrix | |
| """ | |
| return np.cov(self._standardized_data.T) | |
| def get_eigenvectors(self): | |
| pass | |
| def transform(self, data_matrix): | |
| """ | |
| Project the data to the lower dimension with the help of the eigenvectors. | |
| :return: Data reduced to lower dimensions from higher dimensions | |
| """ | |
| data_matrix_standardized = self._standardize_data(data_matrix) | |
| return np.dot(data_matrix_standardized, self.components_[:, :self.n_components]) | |