class WindowClassificationMeasurements(BaseObject):
""" This class will maintain a fixed sized window of the newest information
about one classifier. It can provide, as requested, any of the relevant
current metrics about the classifier, measured inside the window.
To keep track of statistics inside a window, the class will use a
ConfusionMatrix object, alongside FastBuffers, to simulate fixed sized
windows of the important classifier's attributes.
Its functionality is somewhat similar to those of the
ClassificationMeasurements class. The difference is that the statistics
kept by this class are local, or partial, while the statistics kept by
the ClassificationMeasurements class are global.
At any given moment, it can compute the following statistics: accuracy,
kappa, kappa_t, kappa_m, majority_class and error rate.
Parameters
----------
targets: list
A list containing the possible labels.
dtype: data type (Default: numpy.int64)
The data type of the existing labels.
window_size: int (Default: 200)
The width of the window. Determines how many samples the object
can see.
Examples
--------
"""
def __init__(self, targets=None, dtype=np.int64, window_size=200):
super().__init__()
if targets is not None:
self.n_targets = len(targets)
else:
self.n_targets = 0
self.confusion_matrix = ConfusionMatrix(self.n_targets, dtype)
self.last_class = None
self.targets = targets
self.window_size = window_size
self.true_labels = FastBuffer(window_size)
self.predictions = FastBuffer(window_size)
self.temp = 0
self.last_prediction = None
self.last_true_label = None
self.last_sample = None
self.majority_classifier = 0
self.correct_no_change = 0
self.majority_classifier_correction = FastBuffer(window_size)
self.correct_no_change_correction = FastBuffer(window_size)
def reset(self):
if self.targets is not None:
self.n_targets = len(self.targets)
else:
self.n_targets = 0
self.true_labels = FastBuffer(self.window_size)
self.predictions = FastBuffer(self.window_size)
self.temp = 0
self.last_prediction = None
self.last_true_label = None
self.last_sample = None
self.majority_classifier = 0
self.correct_no_change = 0
self.confusion_matrix.restart(self.n_targets)
self.majority_classifier_correction = FastBuffer(self.window_size)
self.correct_no_change_correction = FastBuffer(self.window_size)
def add_result(self, y_true, y_pred):
""" Updates its statistics with the results of a prediction.
If needed it will remove samples from the observation window.
Parameters
----------
y_true: int
The true label.
y_pred: int
The classifier's prediction
"""
true_y = self._get_target_index(y_true, True)
pred = self._get_target_index(y_pred, True)
old_true = self.true_labels.add_element(np.array([y_true]))
old_predict = self.predictions.add_element(np.array([y_pred]))
# Verify if it's needed to decrease the count of any label
# pair in the confusion matrix
if (old_true is not None) and (old_predict is not None):
self.temp += 1
self.confusion_matrix.remove(
self._get_target_index(old_true[0]),
self._get_target_index(old_predict[0]))
self.correct_no_change += self.correct_no_change_correction.peek()
self.majority_classifier += self.majority_classifier_correction.peek(
)
# Verify if it's needed to decrease the majority_classifier count
if (self.get_majority_class()
== y_true) and (self.get_majority_class() is not None):
self.majority_classifier += 1
self.majority_classifier_correction.add_element([-1])
else:
self.majority_classifier_correction.add_element([0])
# Verify if it's needed to decrease the correct_no_change
if (self.last_true_label == y_true) and (self.last_true_label
is not None):
self.correct_no_change += 1
self.correct_no_change_correction.add_element([-1])
else:
self.correct_no_change_correction.add_element([0])
self.confusion_matrix.update(true_y, pred)
self.last_true_label = y_true
self.last_prediction = y_pred
def get_last(self):
return self.last_true_label, self.last_prediction
def get_majority_class(self):
""" Computes the window/current true majority class.
Returns
-------
int
The true window/current majority class.
"""
if (self.n_targets is None) or (self.n_targets == 0):
return None
majority_class = 0
max_prob = 0.0
for i in range(self.n_targets):
sum_value = 0.0
for j in range(self.n_targets):
sum_value += self.confusion_matrix.value_at(i, j)
sum_value = sum_value / self.true_labels.get_current_size()
if sum_value > max_prob:
max_prob = sum_value
majority_class = i
return majority_class
def get_accuracy(self):
""" Computes the window/current accuracy.
Returns
-------
float
The window/current accuracy.
"""
sum_value = 0.0
n, _ = self.confusion_matrix.shape()
for i in range(n):
sum_value += self.confusion_matrix.value_at(i, i)
try:
return sum_value / self.true_labels.get_current_size()
except ZeroDivisionError:
return 0.0
def get_incorrectly_classified_ratio(self):
return 1.0 - self.get_accuracy()
def _get_target_index(self, target, add=False):
""" Computes the index of an element in the self.targets list.
Also reshapes the ConfusionMatrix and adds new found targets
if add is True.
Parameters
----------
target: int
A class label.
add: bool
Either to add new found labels to the targets list or not.
Returns
-------
int
The target index in the self.targets list.
"""
if (self.targets is None) and add:
self.targets = []
self.targets.append(target)
self.n_targets = len(self.targets)
self.confusion_matrix.reshape(len(self.targets), len(self.targets))
elif (self.targets is None) and (not add):
return None
if target not in self.targets and add:
self.targets.append(target)
self.n_targets = len(self.targets)
self.confusion_matrix.reshape(len(self.targets), len(self.targets))
for i in range(len(self.targets)):
if self.targets[i] == target:
return i
return None
def get_kappa(self):
""" Computes the window/current Cohen's kappa coefficient.
Returns
-------
float
The window/current Cohen's kappa coefficient.
"""
p0 = self.get_accuracy()
pc = 0.0
n_rows, n_cols = self.confusion_matrix.shape()
for i in range(n_rows):
row = self.confusion_matrix.row(i)
column = self.confusion_matrix.column(i)
sum_row = np.sum(row) / self.true_labels.get_current_size()
sum_column = np.sum(column) / self.true_labels.get_current_size()
pc += sum_row * sum_column
if pc == 1:
return 1
return (p0 - pc) / (1.0 - pc)
def get_kappa_t(self):
""" Computes the window/current Cohen's kappa T coefficient. This measures
the temporal correlation between samples.
Returns
-------
float
The window/current Cohen's kappa T coefficient.
"""
p0 = self.get_accuracy()
if self.sample_count != 0:
pc = self.correct_no_change / self.sample_count
else:
pc = 0
if pc == 1:
return 1
return (p0 - pc) / (1.0 - pc)
def get_kappa_m(self):
""" Computes the window/current Cohen's kappa M coefficient.
Returns
-------
float
The window/current Cohen's kappa M coefficient.
"""
p0 = self.get_accuracy()
if self.sample_count != 0:
pc = self.majority_classifier / self.sample_count
else:
pc = 0
if pc == 1:
return 1
return (p0 - pc) / (1.0 - pc)
@property
def _matrix(self):
return self.confusion_matrix.matrix
@property
def sample_count(self):
return self.true_labels.get_current_size()
def get_class_type(self):
return 'measurement'
def get_info(self):
return '{}:'.format(type(self).__name__) + \
' - sample_count: {}'.format(self.sample_count) + \
' - window_size: {}'.format(self.window_size) + \
' - accuracy: {:.6f}'.format(self.get_accuracy()) + \
' - kappa: {:.6f}'.format(self.get_kappa()) + \
' - kappa_t: {:.6f}'.format(self.get_kappa_t()) + \
' - kappa_m: {:.6f}'.format(self.get_kappa_m()) + \
' - majority_class: {}'.format(self.get_majority_class())
class WindowRegressionMeasurements(BaseObject):
""" This class is used to keep updated statistics over a regression
learner in a regression problem context inside a fixed sized window.
It uses FastBuffer objects to simulate the fixed sized windows.
It will keep track of partial metrics, that can be provided at
any moment. The relevant metrics kept by an instance of this class
are: MSE (mean square error) and MAE (mean absolute error).
"""
def __init__(self, window_size=200):
super().__init__()
self.total_square_error = 0.0
self.average_error = 0.0
self.last_true_label = None
self.last_prediction = None
self.total_square_error_correction = FastBuffer(window_size)
self.average_error_correction = FastBuffer(window_size)
self.window_size = window_size
def reset(self):
self.total_square_error = 0.0
self.average_error = 0.0
self.last_true_label = None
self.last_prediction = None
self.total_square_error_correction = FastBuffer(self.window_size)
self.average_error_correction = FastBuffer(self.window_size)
def add_result(self, y_true, y_pred):
""" Use the true value and the prediction to update the statistics.
Parameters
----------
y_true: float
The true value.
y_pred: float
The predicted value.
"""
self.last_true_label = y_true
self.last_prediction = y_pred
self.total_square_error += (y_true - y_pred) * (y_true - y_pred)
self.average_error += np.absolute(y_true - y_pred)
old_square = self.total_square_error_correction.add_element(
np.array([-1 * ((y_true - y_pred) * (y_true - y_pred))]))
old_average = self.average_error_correction.add_element(
np.array([-1 * (np.absolute(y_true - y_pred))]))
if (old_square is not None) and (old_average is not None):
self.total_square_error += old_square[0]
self.average_error += old_average[0]
def get_mean_square_error(self):
""" Computes the window/current mean square error.
Returns
-------
float
The window/current mean square error.
"""
if self.sample_count == 0:
return 0.0
else:
return self.total_square_error / self.sample_count
def get_average_error(self):
""" Computes the window/current average error.
Returns
-------
float
The window/current average error.
"""
if self.sample_count == 0:
return 0.0
else:
return self.average_error / self.sample_count
def get_last(self):
return self.last_true_label, self.last_prediction
@property
def sample_count(self):
return self.total_square_error_correction.get_current_size()
def get_class_type(self):
return 'measurement'
def get_info(self):
return '{}:'.format(type(self).__name__) + \
' - sample_count: {}'.format(self.sample_count) + \
' - mean_square_error: {:.6f}'.format(self.get_mean_square_error()) + \
' - mean_absolute_error: {:.6f}'.format(self.get_average_error())
class WindowMultiTargetRegressionMeasurements(BaseObject):
""" This class is used to keep updated statistics over a multi-target regression
learner in a multi-target regression problem context inside a fixed sized
window. It uses FastBuffer objects to simulate the fixed sized windows.
It will keep track of partial metrics, that can be provided at
any moment. The relevant metrics kept by an instance of this class
are: AMSE (average mean square error) and AMAE (average mean absolute error).
"""
def __init__(self, window_size=200):
super().__init__()
self.n_targets = 0
self.total_square_error = 0.0
self.average_error = 0.0
self.last_true_label = None
self.last_prediction = None
self.total_square_error_correction = FastBuffer(window_size)
self.average_error_correction = FastBuffer(window_size)
self.window_size = window_size
def reset(self):
self.total_square_error = 0.0
self.average_error = 0.0
self.last_true_label = None
self.last_prediction = None
self.total_square_error_correction = FastBuffer(self.window_size)
self.average_error_correction = FastBuffer(self.window_size)
def add_result(self, y, prediction):
""" Use the true value and the prediction to update the statistics.
Parameters
----------
y: float or list or np.ndarray
The true value(s).
prediction: float or list or np.ndarray
The predicted value(s).
"""
self.last_true_label = y
self.last_prediction = prediction
m = 0
if hasattr(y, 'size'):
m = y.size
elif hasattr(y, 'append'):
m = len(y)
self.n_targets = m
self.total_square_error += (y - prediction)**2
self.average_error += np.absolute(y - prediction)
old_square = self.total_square_error_correction.add_element(
np.array([-1 * ((y - prediction)**2)]))
old_average = self.average_error_correction.add_element(
np.array([-1 * (np.absolute(y - prediction))]))
if (old_square is not None) and (old_average is not None):
self.total_square_error += old_square[0]
self.average_error += old_average[0]
def get_average_mean_square_error(self):
""" Computes the window/current average mean square error.
Returns
-------
float
The window/current average mean square error.
"""
if self._sample_count == 0:
return 0.0
else:
return np.sum(self.total_square_error / self._sample_count) \
/ self.n_targets
def get_average_absolute_error(self):
""" Computes the window/current average mean absolute error.
Returns
-------
float
The window/current average mean absolute error.
"""
if self._sample_count == 0:
return 0.0
else:
return np.sum(self.average_error / self._sample_count) \
/ self.n_targets
def get_average_root_mean_square_error(self):
""" Computes the mean square error.
Returns
-------
float
The average mean square error.
"""
if self._sample_count == 0:
return 0.0
else:
return np.sum(np.sqrt(self.total_square_error /
self._sample_count)) \
/ self.n_targets
def get_last(self):
return self.last_true_label, self.last_prediction
@property
def _sample_count(self):
return self.total_square_error_correction.get_current_size()
def get_class_type(self):
return 'measurement'
def get_info(self):
return 'MultiTargetRegressionMeasurements: sample_count: ' + \
str(self._sample_count) + ' - average_mean_square_error: ' + \
str(self.get_average_mean_square_error()) + ' - average_mean_absolute_error: ' + \
str(self.get_average_absolute_error()) + ' - average_root_mean_square_error: ' + \
str(self.get_average_root_mean_square_error())
