remove RPS dataset, irelevant

skluc/main/data/mldatasets/RPSDataset.py

-import os
-import zipfile
-
-import numpy as np
-import imageio
-import matplotlib.pyplot as plt
-
-from skluc.utils import LabeledData, create_directory
-from skluc.data.mldatasets.ImageDataset import ImageDataset
-from skluc.utils import logger, check_files
-
-
-class RPSDataset(ImageDataset):
-    data_groups_private = ["_train"]
-    HEIGHT = 50
-    WIDTH = 50
-    DEPTH = 3
-    TRAIN_SIZE = 600
-
-    def __init__(self, validation_size=0, seed=None, s_download_dir=None):
-        self.__s_url = ["https://pageperso.lif.univ-mrs.fr/~luc.giffon/datasets/rps_data_resize.zip"]
-        self.meta = None
-        name = "rps"
-        if s_download_dir is not None:
-            super().__init__(self.__s_url, name, s_download_dir, validation_size=validation_size, seed=seed)
-        else:
-            super().__init__(self.__s_url, name, validation_size=validation_size, seed=seed)
-
-        self.__extracted_dirs = [
-            os.path.join(self.s_download_dir, "images_background"),
-            os.path.join(self.s_download_dir, "images_evaluation")
-        ]
-
-    def get_rps_data(self):
-        data_dirname = "rps_data_resize"
-        data_dirpath = os.path.join(self.s_download_dir, data_dirname)
-        class_index = 0
-        list_of_images = []
-        list_of_labels = []
-        for symbol_name in os.listdir(data_dirpath):
-            data_symbol_path = os.path.join(data_dirpath, symbol_name)
-            for symbol_image_file in os.listdir(data_symbol_path):
-                symbol_image_path = os.path.join(data_symbol_path, symbol_image_file)
-                im = imageio.imread(symbol_image_path)
-                list_of_images.append(im)
-                list_of_labels.append(class_index)
-            class_index += 1
-        data = np.array(list_of_images)
-        labels = np.array(list_of_labels)
-        data = data.reshape(data.shape[0], self.WIDTH*self.HEIGHT, self.DEPTH, order="C")
-        data = data.reshape(data.shape[0], self.WIDTH*self.HEIGHT*self.DEPTH, order="F")
-        return data, labels
-
-    def read(self):
-        npzdir_path = os.path.join(self.s_download_dir, "npzfiles")
-        lst_npzfile_paths = [os.path.join(npzdir_path, kw + ".npz")
-                               for kw in self.data_groups_private]
-        create_directory(npzdir_path)
-        if check_files(lst_npzfile_paths):
-            # case npz files already exist
-            logger.debug("Files {} already exists".format(lst_npzfile_paths))
-            logger.info("Loading transformed data from files {}".format(lst_npzfile_paths))
-            for kw in self.data_groups_private:
-                npzfile_path = os.path.join(npzdir_path, kw + ".npz")
-                logger.debug("Loading {}".format(npzfile_path))
-                npzfile = np.load(npzfile_path)
-                data = npzfile[kw + "_data"]
-                logger.debug("Shape of {} set: {}".format(kw, data.shape))
-                labels = npzfile[kw + "_labels"]
-                setattr(self, kw, LabeledData(data=data, labels=labels))
-        else:
-
-            if not check_files(self.__extracted_dirs):
-                # case zip files dont even exist
-                logger.debug("Extracting {} ...".format(self.l_filepaths))
-                for zip_file in self.l_filepaths:
-                    zip_ref = zipfile.ZipFile(zip_file, 'r')
-                    zip_ref.extractall(self.s_download_dir)
-                    zip_ref.close()
-            else:
-                logger.debug("Files {} have already been extracted".format(self.l_filepaths))
-
-            full_data, full_labels = self.get_rps_data()
-            logger.debug("Get training data of dataset {}".format(self.s_name))
-            self._train = LabeledData(data=full_data, labels=full_labels)
-            # self._test = LabeledData(data=np.array([]), labels=np.array([]))
-            #
-            # logger.debug("Get testing data of dataset {}".format(self.s_name))
-            # self._test = LabeledData(*self.get_omniglot_data('evaluation'))
-            #
-            self._check_validation_size(self._train[0].shape[0])
-
-            self.save_npz()
-
-
-    @property
-    def train(self):
-        indexes = self.permuted_index_train[:self.TRAIN_SIZE - self.validation_size]
-        return LabeledData(data=self._train.data[indexes],
-                           labels=self._train.labels[indexes])
-
-    @property
-    def test(self):
-        indexes = self.permuted_index_train[self.TRAIN_SIZE:]
-        return LabeledData(data=self._train.data[indexes],
-                           labels=self._train.labels[indexes])
-
-    @property
-    def validation(self):
-        indexes = self.permuted_index_train[(self.TRAIN_SIZE - self.validation_size):self.TRAIN_SIZE]
-        return LabeledData(data=self._train.data[indexes],
-                           labels=self._train.labels[indexes])
-
-
-if __name__ == "__main__":
-    import time
-    d = RPSDataset(validation_size=100)
-    d.load()
-    d.to_image()
-    print(d.train.data.shape)
-    for i, im in enumerate(d.train.data):
-        plt.imshow(im)
-        plt.show()
-        print(d.train.labels[i])
-        time.sleep(1)
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