diff --git a/examples/reliability/concept_drift_time_series.py b/examples/reliability/concept_drift_time_series.py index 07ddbf0..143809a 100644 --- a/examples/reliability/concept_drift_time_series.py +++ b/examples/reliability/concept_drift_time_series.py @@ -34,7 +34,6 @@ Please choose one column or multiple columns. import numpy as np from mindarmour import ConceptDriftCheckTimeSeries - # input data DATA_FILE = r'archive/individual_stocks_5yr/individual_stocks_5yr/AEE_data.csv' data = np.loadtxt(DATA_FILE, str, delimiter=",") diff --git a/mindarmour/reliability/concept_drift/README.md b/mindarmour/reliability/concept_drift/README.md index 70d118f..8a98927 100644 --- a/mindarmour/reliability/concept_drift/README.md +++ b/mindarmour/reliability/concept_drift/README.md @@ -53,11 +53,11 @@ concept = ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10, step=1 need_label=False) ``` ->window_size(int): Size of a concept window, belongs to [10, 1/3*len(input data)]. If the data is periodic, usually window_size equals 2-5 periods, such as, for monthly/weekly data, the data volume of 30/7 days is a period. Default: 100. +>window_size(int): Size of a concept window, no less than 10. If given the input data, window_size belongs to [10, 1/3*len(input data)]. If the data is periodic, usually window_size equals 2-5 periods, such as, for monthly/weekly data, the data volume of 30/7 days is a period. Default: 100. rolling_window(int): Smoothing window size, belongs to [1, window_size]. Default:10. step(int): The jump length of the sliding window, belongs to [1,window_size]. Default:10. -threshold_index(float): The threshold index, (-∞,+∞), Default: 1.5. -need_label(bool）: False or True. If need_label=True, concept drift labels are needed. Default: False. +threshold_index(float): The threshold index. Default: 1.5. +need_label(bool): False or True. If need_label=True, concept drift labels are needed. Default: False. ### Data @@ -74,19 +74,22 @@ data = data[1:, 2].astype('float64') # here we choose one column or multiple co ```python drift_score, threshold, concept_drift_location = concept.concept_check(data) +# the result is saved as pdf named 'concept_drift_check.pdf' ``` >drift_score(numpy.ndarray): The concept drift score of the example series. threshold(float): The threshold to judge concept drift. -concept_drift_location(list): The location of the concept drift. +concept_drift_location(list): The location of the concept drift. + ## Script Description -```python +```bash ├── mindarmour ├── reliability # descriptions about GhostNet # shell script for evaluation with CPU, GPU or Ascend ├──concept_drift - ├──concept_drift.py - ├──readme.md + ├──__init__.py + ├──concept_drift_check_time_series.py + ├──README.md ``` diff --git a/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py b/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py index 7e6b2db..b086a3a 100644 --- a/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py +++ b/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py @@ -24,23 +24,24 @@ from mindarmour.utils._check_param import check_param_type, check_param_in_range class ConceptDriftCheckTimeSeries: """ - Concept is used for example series distribution change detection. + ConceptDriftCheckTimeSeries is used for example series distribution change detection. Args: - window_size(int): Size of a concept window, belongs to [10, 1/3*len(input data)]. - If the data is periodic, usually window_size equals 2-5 periods, such as, - for monthly/weekly data, the data volume of 30/7 days is a period. Default: 100. + window_size(int): Size of a concept window, no less than 10. If given the input data, + window_size belongs to [10, 1/3*len(input data)]. If the data is periodic, usually + window_size equals 2-5 periods, such as, for monthly/weekly data, the data volume + of 30/7 days is a period. Default: 100. rolling_window(int): Smoothing window size, belongs to [1, window_size]. Default:10. step(int): The jump length of the sliding window, belongs to [1, window_size]. Default:10. - threshold_index(float): The threshold index, (-∞, +∞), Default: 1.5. + threshold_index(float): The threshold index, :math:`(-\infty, +\infty)`. Default: 1.5. need_label(bool): False or True. If need_label=True, concept drift labels are needed. Default: False. Examples: >>> concept = ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10, >>> step=10, threshold_index=1.5, need_label=False) - >>> data_example = np.array([np.random.rand(1000), - >>> np.random.rand(1000),np.random.rand(1000)]).T + >>> data_example = 5*np.random.rand(1000) + >>> data_example[200: 800] = 20*np.random.rand(600) >>> score, threshold, concept_drift_location = concept.concept_check(data_example) """ @@ -48,10 +49,7 @@ class ConceptDriftCheckTimeSeries: step=10, threshold_index=1.5, need_label=False): self.window_size = check_param_type('window_size', window_size, int) self.rolling_window = check_param_type('rolling_window', rolling_window, int) - self.rolling_window = check_param_in_range('rolling_window', - rolling_window, 1, window_size) self.step = check_param_type('step', step, int) - self.step = check_param_in_range('step', step, 1, window_size) self.threshold_index = check_param_type('threshold_index', threshold_index, float) self.need_label = check_param_type('need_label', need_label, bool) self._in_size = window_size @@ -66,8 +64,8 @@ class ConceptDriftCheckTimeSeries: window_data(numpy.ndarray): The input data (in one window). Returns: - w_out(numpy.ndarray): The output weight of reservoir model. - x_state(numpy.ndarray): The state of the reservoir model in the latent space. + - numpy.ndarray, the output weight of reservoir model. + - numpy.ndarray, the state of the reservoir model in the latent space. Examples: >>> input_data = np.random.rand(100) @@ -99,7 +97,7 @@ class ConceptDriftCheckTimeSeries: data_y(numpy.ndarray): Data y. Returns: - distance_score_mean(float): Distance between data_x and data_y. + - float, distance between data_x and data_y. Examples: >>> x = np.random.rand(100) @@ -125,7 +123,7 @@ class ConceptDriftCheckTimeSeries: data(numpy.ndarray): Input data. Returns: - smooth_data(numpy.ndarray): Data after smoothing. + - numpy.ndarray, data after smoothing. Examples: >>> data_example = np.random.rand(100) @@ -150,28 +148,30 @@ class ConceptDriftCheckTimeSeries: def concept_check(self, data): """ - Find concept drift locations in a example series. + Find concept drift locations in a data series. Args: data(numpy.ndarray): Input data. The shape of data could be (n,1) or (n,m). Note that each column (m columns) is one data series. Returns: - drift_score(numpy.ndarray): The concept drift score of the example series. - threshold(float): The threshold to judge concept drift. - concept_drift_location(list): The location of the concept drift. + - numpy.ndarray, the concept drift score of the example series. + - float, the threshold to judge concept drift. + - list, the location of the concept drift. Examples： >>> concept = ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10, >>> step=10, threshold_index=1.5, need_label=False) - >>> data_example = np.array([np.random.rand(1000), - >>> np.random.rand(1000), np.random.rand(1000)]).T - >>> score, drift_threshold, point = concept.concept_check(data_example) + >>> data_example = 5*np.random.rand(1000) + >>> data_example[200: 800] = 20*np.random.rand(600) + >>> score, drift_threshold, drift_location = concept.concept_check(data_example) """ # data check data = _check_array_not_empty('data', data) data = check_param_type('data', data, np.ndarray) check_param_in_range('window_size', self.window_size, 10, int((1 / 3)*len(data))) + check_param_in_range('rolling_window', self.rolling_window, 1, self.window_size) + check_param_in_range('step', self.step, 1, self.window_size) original_data = data data = self._data_process(data) # calculate drift score @@ -190,39 +190,38 @@ class ConceptDriftCheckTimeSeries: # find drift blocks concept_drift_location, drift_point = _drift_blocks(drift_score, label_continue, label_location) - # show result - _plot_show(original_data, threshold, concept_drift_location, - drift_point, drift_score) + # save result + _result_save(original_data, threshold, concept_drift_location, drift_point, drift_score) return drift_score, threshold, concept_drift_location -def _plot_show(original_data, threshold, concept_location, drift_point, drift_score): +def _result_save(original_data, threshold, concept_location, drift_point, drift_score): """ - To show the result. + To save the result. Args: original_data(numpy.ndarray): The input data. threshold(float): The concept drift threshold. concept_location(list): The concept drift locations(x-axis). - drift_point(list): The precise drift location of a drift. + drift_point(list): The precise drift point of a drift. drift_score(numpy.ndarray): The drift score of input data. """ plt.figure(figsize=(20, 8)) plt.subplot(2, 1, 1) # Plot input data and drift points plt.plot(original_data, label="data") - plt.title('concept drift check, threshold=' + str(threshold)) + plt.title('concept drift check, threshold=' + str(threshold), fontsize=25) plt.scatter(concept_location, np.ones(len(concept_location)), marker='*', s=200, color="b", label="concept drift occurred") for _, i in enumerate(drift_point): plt.axvline(x=i, color='r', linestyle='--') - plt.legend() + plt.legend(fontsize=15) plt.subplot(2, 1, 2) # Plot drift score plt.plot(drift_score, label="drift_score") plt.axhline(y=threshold, color='r', linestyle='--', label="threshold") - plt.legend() - plt.show() + plt.legend(fontsize=15) + plt.savefig('concept_drift_check.pdf') def _original_label(original_data, threshold, drift_score, window_size, step_size): @@ -238,9 +237,9 @@ def _original_label(original_data, threshold, drift_score, window_size, step_siz step_size(int): The jump length of the sliding window. Returns: - label(list): The drift label of input data. + - list, the drift label of input data. 0 means no drift, and 1 means drift happens. - label_location(list): The locations of drifts(x-axis). + - list, the locations of drifts(x-axis). """ label = [] label_location = [] @@ -262,7 +261,7 @@ def _label_continue_process(label): label(list): The original drift label. Returns: - label_continue(numpy.ndarray): The continual drift label. + - numpy.ndarray, The continual drift label. The drift may happen occasionally, we hope to avoid frequent alarms, so label continue process is necessary. """ @@ -290,7 +289,7 @@ def _continue_block(location): location(numpy.ndarray): The locations of concept drift. Returns: - area(list): Continue blocks of concept drift. + - list, continue blocks of concept drift. """ area = [] for _, loc in groupby(enumerate(location), _find_loc): @@ -309,8 +308,8 @@ def _drift_blocks(drift_score, label_continue, label_location): label_location(numpy.ndarray): The locations of concept drift(x-axis). Returns: - concept_location(list): The concept drift locations(x-axis) after continual blocks finding. - drift_point(list): Return a precise beginning location of a drift. + - list, the concept drift locations(x-axis) after continual blocks finding. + - list, return a precise beginning location of a drift. """ # Find drift blocks area = _continue_block(np.where(label_continue == 1)[0]) @@ -341,7 +340,7 @@ def _w_generate(res_size, in_size, input_data): input_data(numpy.ndarray): Input data. Returns: - x_state(numpy.ndarray): The state of reservoir. + - numpy.ndarray, the state of reservoir. """ # Weight generates randomly np.random.seed(42) @@ -373,7 +372,7 @@ def _cal_distance(matrix1, matrix2): matrix2(numpy.ndarray): Input array. Returns: - distance(numpy.ndarray): Distance between two arrays. + - numpy.ndarray, distance between two arrays. """ w_mean_x = np.mean(matrix1, axis=0) w_mean_y = np.mean(matrix2, axis=0) @@ -390,7 +389,7 @@ def _cal_threshold(distance, threshold_index): threshold_index(float): Threshold adjusted index, [-∞, +∞]. Returns: - threshold(float): [0, 1]. + - float, [0, 1]. """ distance = distance[distance > 0] # Threshold calculation diff --git a/setup.py b/setup.py index d92df61..ae33a24 100644 --- a/setup.py +++ b/setup.py @@ -24,17 +24,6 @@ version = '1.2.0' cur_dir = os.path.dirname(os.path.realpath(__file__)) pkg_dir = os.path.join(cur_dir, 'build') -try: - from wheel.bdist_wheel import bdist_wheel as _bdist_wheel - - - class bdist_wheel(_bdist_wheel): - def finalize_options(self): - _bdist_wheel.finalize_options(self) - self.root_is_pure = False -except ImportError: - bdist_wheel = None - def write_version(file): file.write("__version__ = '{}'\n".format(version)) @@ -122,7 +111,6 @@ setup( cmdclass={ 'egg_info': EggInfo, 'build_py': BuildPy, - 'bdist_wheel': bdist_wheel }, install_requires=[ 'scipy >= 1.5.3',