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import time |
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import logging |
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import xlsxwriter |
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import collections |
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from . import overall_performance_index, per_class_performance_index, get_performance_array |
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from ..logging import logging_name |
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from ..CASAS.fuel import CASASFuel |
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logger = logging.getLogger(__file__) |
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class LearningResult: |
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"""LearningResult is a class that stores results of a learning run. |
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It may be a single-shot run or a time-based analysis |
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The result structure holds the parameters for the model as well as |
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the evaluation result for easy plot. |
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Parameters: |
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name (:obj:`str`): Name of the learning run |
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data (:obj:`str`): Name of the dataset or description of the dataset |
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mode (:obj:`str`): valid choices are `single_shot`, `by_week` or `by_day` |
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Attributes: |
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name (:obj:`str`): Name of the learning run |
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data (:obj:`str`): Path to the h5py dataset directory |
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mode (:obj:`str`): valid choices are `single_shot`, `by_week` or `by_day` |
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created_time (:obj:`float`): created time since Epoch in seconds |
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modified_time (:obj:`float`): record modified time since Epoch in seconds |
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overall_performance (:class:`numpy.array`): overall performance of the learning |
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per_class_performance (:class:`numpy.array`): overall per-class performance of the learning |
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confusion_matrix (:class:`numpy.array`): overall confusion matrix |
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records (:obj:`collections.OrderedDict`): Ordered dictionary storing all records |
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""" |
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def __init__(self, name='', data='', mode='single_shot'): |
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cur_time = time.time() |
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self.name = name |
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self.data = data |
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self.mode = mode |
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self.created_time = cur_time |
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self.modified_time = cur_time |
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self.overall_performance = None |
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self.per_class_performance = None |
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self.confusion_matrix = None |
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self.records = collections.OrderedDict() |
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def get_num_records(self): |
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"""Get the length of result records in current instance |
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""" |
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if self.records is None: |
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return 0 |
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else: |
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return len(self.records) |
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def get_record_keys(self): |
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"""Get List of keys to all the records |
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""" |
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if self.records is None: |
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return [] |
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else: |
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return self.records.keys() |
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def add_record(self, model, key='single_shot', confusion_matrix=None): |
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"""Add a learning milestone record |
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Args: |
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model (:obj:`object`): snap shot of learning model parameters |
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key (:obj:`str`): key string to represent current record |
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confusion_matrix (:obj:`numpy.array`): Confusion Matrix |
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""" |
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if self.get_num_records() == 0: |
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self.confusion_matrix = confusion_matrix.copy() |
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else: |
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# Check confusion matrix size |
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if confusion_matrix.shape != self.confusion_matrix.shape: |
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logger.error(logging_name(self) + ': confusion matrix shape mismatch. Original shape %s. New shape %s' |
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% (str(self.confusion_matrix.shape), str(confusion_matrix.shape))) |
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else: |
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self.confusion_matrix += confusion_matrix |
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self.overall_performance, self.per_class_performance = get_performance_array(self.confusion_matrix) |
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overall_performance, per_class_performance = get_performance_array(confusion_matrix) |
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cur_result = { |
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'model': model, |
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'confusion_matrix': confusion_matrix, |
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'per_class_performance': per_class_performance, |
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'overall_performance': overall_performance |
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} |
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self.records[key] = cur_result |
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def get_record_by_key(self, key): |
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""" |
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Get result corresponding to specific key |
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:param key: |
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:return: |
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""" |
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if key in self.records.keys(): |
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return self.records[key] |
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else: |
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logger.error(logging_name(self) + ': Cannot find record %s' % key) |
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return None |
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def export_to_xlsx(self, filename, home_info=None): |
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"""Export to XLSX |
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Args: |
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filename (:obj:`str`): path to the file |
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home_info (:class:`pyActLearn.CASAS.fuel.CASASFuel`): dataset information |
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""" |
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if home_info is None: |
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home_info = CASASFuel(dir_name=self.data) |
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workbook = xlsxwriter.Workbook(filename) |
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records = self.get_record_keys() |
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num_performance = len(per_class_performance_index) |
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num_classes = self.confusion_matrix.shape[0] |
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# Overall Performance Summary |
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overall_sheet = workbook.add_worksheet('overall') |
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overall_sheet.merge_range(0, 0, 0, len(overall_performance_index) - 1, 'Overall Performance') |
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for c in range(len(overall_performance_index)): |
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overall_sheet.write(1, c, str(overall_performance_index[c])) |
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overall_sheet.write(2, c, self.overall_performance[c]) |
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overall_sheet.merge_range(4, 0, 4, len(per_class_performance_index), 'Per-Class Performance') |
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overall_sheet.write(5, 0, 'Activities') |
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for c in range(len(per_class_performance_index)): |
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overall_sheet.write(5, c + 1, str(per_class_performance_index[c])) |
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for r in range(num_classes): |
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label = home_info.get_activity_by_index(r) |
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overall_sheet.write(r + 6, 0, label) |
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for c in range(num_performance): |
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overall_sheet.write(r + 6, c + 1, self.per_class_performance[r][c]) |
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overall_sheet.merge_range(8 + num_classes, 0, 8 + num_classes, num_classes, 'Confusion Matrix') |
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for i in range(num_classes): |
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label = home_info.get_activity_by_index(i) |
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overall_sheet.write(9 + num_classes, i + 1, label) |
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overall_sheet.write(10 + num_classes + i, 0, label) |
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for r in range(num_classes): |
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for c in range(num_classes): |
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overall_sheet.write(10 + num_classes + r, c + 1, self.confusion_matrix[r][c]) |
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# Weekly Performance Summary |
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weekly_sheet = workbook.add_worksheet('weekly') |
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weekly_list_title = ['dataset', '#week'] + overall_performance_index |
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for c in range(len(weekly_list_title)): |
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weekly_sheet.write(0, c, str(weekly_list_title[c])) |
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r = 1 |
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for record_id in records: |
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weekly_sheet.write(r, 0, 'b1') |
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weekly_sheet.write(r, 1, record_id) |
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for c in range(len(overall_performance_index)): |
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weekly_sheet.write(r, c + 2, '%.5f' % self.get_record_by_key(record_id)['overall_performance'][c]) |
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r += 1 |
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dataset_list_title = ['activities'] + per_class_performance_index |
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# Per Week Per Class Summary |
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for record_id in self.get_record_keys(): |
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cur_sheet = workbook.add_worksheet(record_id) |
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for c in range(0, len(dataset_list_title)): |
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cur_sheet.write(0, c, str(dataset_list_title[c])) |
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for r in range(num_classes): |
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label = home_info.get_activity_by_index(r) |
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cur_sheet.write(r+1, 0, label) |
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for c in range(num_performance): |
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cur_sheet.write(r + 1, c + 1, self.get_record_by_key(record_id)['per_class_performance'][r][c]) |
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workbook.close() |
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TinghuiWang /
pyActLearn
