add base class and some possible plot functions

src/dackar/anomalies/AnomalyBase.py

@@ -0,0 +1,123 @@
+"""
+  Created on Dec. 19, 2024
+
+  @author: wangc, mandd
+  Base Class for Anomaly Detection
+"""
+
+import abc
+from sklearn.base import BaseEstimator
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import RobustScaler
+import logging
+
+logger = logging.getLogger(__name__)
+
+class AnomalyBase(BaseEstimator):
+  """Anomaly detection base class
+  """
+
+  def __init__(self, norm='robust'):
+    """Constructor
+    """
+    self.print_tag = type(self).__name__
+    self.is_fitted = False
+    self._features = None
+    self._targets = None
+    self._norm = norm
+    if norm is None:
+      logger.info('Use standard scalar (z-score) to transform input data.')
+      self._scalar = StandardScaler()
+    elif norm.lower() == 'robust':
+      logger.info('Use robust scalar to transform input data.')
+      self._scalar = RobustScaler()
+    else:
+      logger.warning('Unrecognized value for param "norm", using default "RobustScalar"')
+      self._scalar = RobustScaler()
+    self._meta = {}
+
+  def reset(self):
+    """reset
+    """
+    self.is_fitted = False
+    self._features = None
+    self._targets = None
+    self._norm = 'robust'
+    self._scalar = RobustScaler()
+    self._meta = {}
+
+  def get_params(self):
+    """
+    Get parameters for this estimator.
+
+    Returns
+    -------
+    params : dict
+        Parameter names mapped to their values.
+    """
+    params = super().get_params()
+    return params
+
+  def set_params(self, **params):
+    """Set the parameters of this estimator.
+
+    Parameters
+    ----------
+    **params : dict
+        Estimator parameters.
+
+    Returns
+    -------
+    self : estimator instance
+        Estimator instance.
+    """
+    super().set_params(**params)
+
+
+  def fit(self, X, y=None):
+    """perform fitting
+
+    Args:
+        X (array-like): (n_samples, n_features)
+        y (array-like, optional): (n_samples, n_features). Defaults to None.
+    """
+    logger.info('Train model.')
+    self.is_fitted = True
+    self._features = X
+    self._targets = y
+    X_transform = self._scalar.fit_transform(X)
+    fit_obj = self._fit(X_transform, y)
+    return fit_obj
+
+  def predict(self, X):
+    """perform prediction
+
+    Args:
+        X (array-like): (n_samples, n_features)
+    """
+    logger.info('Perform model forecast.')
+    X_transform = self._scalar.fit_transform(X)
+    y_new = self._predict(X_transform)
+
+    return y_new
+
+  #################################################################################
+  # To be implemented in subclasses
+
+  @abc.abstractmethod
+  def _fit(self, X, y=None):
+    """perform fitting
+
+    Args:
+        X (array-like): (n_samples, n_features)
+        y (array-like, optional): (n_samples, n_features). Defaults to None.
+    """
+
+
+  @abc.abstractmethod
+  def _predict(self, X):
+    """perform prediction
+
+    Args:
+        X (array-like): (n_samples, n_features)
+    """

src/dackar/anomalies/MatrixProfile.py

@@ -0,0 +1,40 @@
+import numpy as np
+
+from .AnomalyBase import AnomalyBase
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class MatrixProfile(AnomalyBase):
+  """_summary_
+
+  Args:
+      AnomalyBase (_type_): _description_
+  """
+
+
+  def __init__(self, norm='robust'):
+    """Constructor
+    """
+    super().__init__(norm=norm)
+
+
+
+  def _fit(self, X, y=None):
+    """perform fitting
+
+    Args:
+        X (array-like): (n_samples, n_features)
+        y (array-like, optional): ignored, (n_samples, n_features). Defaults to None.
+    """
+
+
+
+  def _predict(self, X):
+    """perform prediction
+
+    Args:
+        X (array-like): (n_samples, n_features)
+    """

src/dackar/anomalies/plotUtils.py

@@ -0,0 +1,63 @@
+import logging
+logger = logging.getLogger(__name__)
+
+try:
+  from matplotlib import pyplot as plt
+  from matplotlib.ticker import FuncFormatter
+except ImportError:
+  logger.error('Importing matplotlib failed. Plotting will not work.')
+
+try:
+  import plotly.graph_objs as go
+  from plotly.subplots import make_subplots
+except ImportError:
+  logger.error('Importing plotly failed. Interactive plots will not work.')
+
+
+def plot(x, y, ax=None, xlabel='time', ylabel='ds', figsize=(10,6), include_legend=False):
+  """_summary_
+
+  Args:
+      ax (_type_, optional): _description_. Defaults to None.
+      xlabel (str, optional): _description_. Defaults to 'time'.
+      ylabel (str, optional): _description_. Defaults to 'ds'.
+      figsize (tuple, optional): _description_. Defaults to (10,6).
+      include_legend (bool, optional): _description_. Defaults to False.
+  """
+  user_provided_ax = False if ax is None else True
+  if ax is None:
+    fig = plt.figure(facecolor='w', figsize=figsize)
+    ax = fig.add_subplot(111)
+  else:
+    fig = ax.get_figure()
+
+  ax.plot(x, y, 'k.')
+  ax.set_xlabel(xlabel)
+  ax.set_ylabel(ylabel)
+  if include_legend:
+    ax.legend()
+  if not user_provided_ax:
+    fig.tight_layout()
+  return fig
+
+def plot_plotly(x, y, xlabel='time', ylabel='ds', figsize=(900, 600)):
+  """_summary_
+
+  Args:
+      x (_type_): _description_
+      y (_type_): _description_
+      figsize (_type_): _description_
+      xlabel (str, optional): _description_. Defaults to 'time'.
+      ylabel (str, optional): _description_. Defaults to 'ds'.
+  """
+  line_width = 2
+  marker_size = 4
+
+  data = []
+
+  data.append(go.Scatter(name='', x=x, y=y, model='lines'))
+  layout = go.Layout(width=figsize[0], height=figsize[1], showlegend=False)
+  fig = go.Figure(data=data, layout=layout)
+
+  return fig
+