fix concept drift feature

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=",")

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
 ```
 

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

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',