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from sklearn.decomposition import PCA, IncrementalPCA |
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from sklearn.decomposition import TruncatedSVD |
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import numpy as np |
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import pickle |
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import os |
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from tqdm import tqdm |
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from numpy import save, load |
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import math |
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from PIL import Image |
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from numpy import save, load |
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class PCAUtility: |
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eigenvalues_prefix = "_eigenvalues_" |
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eigenvectors_prefix = "_eigenvectors_" |
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meanvector_prefix = "_meanvector_" |
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def create_pca_from_npy(self, dataset_name, labels_npy_path, pca_percentages): |
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""" |
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generate and save eigenvalues, eigenvectors, meanvector |
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:param labels_npy_path: the path to the normalized labels that are save in npy format. |
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:param pca_percentages: % of eigenvalues that will be used |
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:return: generate |
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""" |
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path = labels_npy_path |
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print('PCA calculation started: loading labels') |
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lbl_arr = [] |
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for file in tqdm(os.listdir(path)): |
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if file.endswith(".npy"): |
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npy_file = os.path.join(path, file) |
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lbl_arr.append(load(npy_file)) |
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lbl_arr = np.array(lbl_arr) |
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reduced_lbl_arr, eigenvalues, eigenvectors = self._func_PCA(lbl_arr, pca_percentages) |
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mean_lbl_arr = np.mean(lbl_arr, axis=0) |
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eigenvectors = eigenvectors.T |
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save('./pca_obj/' + dataset_name + self.eigenvalues_prefix + str(pca_percentages), eigenvalues) |
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save('./pca_obj/' + dataset_name + self.eigenvectors_prefix + str(pca_percentages), eigenvectors) |
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save('./pca_obj/' + dataset_name + self.meanvector_prefix + str(pca_percentages), mean_lbl_arr) |
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def load_pca_obj(self, dataset_name, pca_percentages): |
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eigenvalues = np.load('./pca_obj/' + dataset_name + self.eigenvalues_prefix + str(pca_percentages)) |
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eigenvectors = np.load('./pca_obj/' + dataset_name + self.eigenvectors_prefix + str(pca_percentages)) |
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meanvector = np.load('./pca_obj/' + dataset_name + self.meanvector_prefix + str(pca_percentages)) |
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return eigenvalues, eigenvectors, meanvector |
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def calculate_b_vector(self, predicted_vector, correction, eigenvalues, eigenvectors, meanvector): |
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tmp1 = predicted_vector - meanvector |
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b_vector = np.dot(eigenvectors.T, tmp1) |
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if correction: |
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i = 0 |
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for b_item in b_vector: |
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lambda_i_sqr = 3 * math.sqrt(eigenvalues[i]) |
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if b_item > 0: |
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b_item = min(b_item, lambda_i_sqr) |
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else: |
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b_item = max(b_item, -1 * lambda_i_sqr) |
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b_vector[i] = b_item |
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i += 1 |
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return b_vector |
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def _func_PCA(self, input_data, pca_postfix): |
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input_data = np.array(input_data) |
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pca = PCA(n_components=pca_postfix / 100) |
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pca.fit(input_data) |
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pca_input_data = pca.transform(input_data) |
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eigenvalues = pca.explained_variance_ |
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eigenvectors = pca.components_ |
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return pca_input_data, eigenvalues, eigenvectors |
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