class MissingValuesCleaner(StreamTransform):
""" This is a transform object. It provides a simple way to replace missing
values in samples with another value, which can be chosen from a set of
replacing strategies.
Parameters
----------
missing_value: int, float or list (Default: numpy.nan)
Missing value to replace
strategy: string (Default: 'zero')
The strategy adopted to find the missing value replacement. It can
be one of the following: 'zero', 'mean', 'median', 'mode', 'custom'.
window_size: int (Default: 200)
Defines the window size for the 'mean', 'median' and 'mode' strategies.
new_value: int (Default: 1)
This is the replacement value in case the chosen strategy is 'custom'.
Examples
--------
>>> # Imports
>>> import numpy as np
>>> from skmultiflow.data.file_stream import FileStream
>>> from skmultiflow.transform.missing_values_cleaner import MissingValuesCleaner
>>> # Setting up a stream
>>> stream = FileStream('skmultiflow/data/datasets/covtype.csv', -1, 1)
>>> stream.prepare_for_use()
>>> # Setting up the filter to substitute values -47 by the median of the
>>> # last 10 samples
>>> cleaner = MissingValuesCleaner(-47, 'median', 10)
>>> X, y = stream.next_sample(10)
>>> X[9, 0] = -47
>>> # We will use this list to keep track of values
>>> data = []
>>> # Iterate over the first 9 samples, to build a sample window
>>> for i in range(9):
>>> X_transf = cleaner.partial_fit_transform([X[i].tolist()])
>>> data.append(X_transf[0][0])
>>>
>>> # Transform last sample. The first feature should be replaced by the list's
>>> # median value
>>> X_transf = cleaner.partial_fit_transform([X[9].tolist()])
>>> np.median(data)
Notes
-----
A missing value in a sample can be coded in many different ways, but the
most common one is to use numpy's NaN, that's why that is the default
missing value parameter.
The user should choose the correct substitution strategy for his use
case, as each strategy has its pros and cons. The strategy can be chosen
from a set of predefined strategies, which are: 'zero', 'mean', 'median',
'mode', 'custom'.
Notice that `MissingValuesCleaner` can actually be used to replace arbitrary
values.
"""
def __init__(self,
missing_value=np.nan,
strategy='zero',
window_size=200,
new_value=1):
super().__init__()
if isinstance(missing_value, list):
self.missing_value = missing_value
else:
self.missing_value = [missing_value]
self.strategy = strategy
self.window_size = window_size
self.window = None
self.new_value = new_value
self.__configure()
def __configure(self):
if self.strategy in ['mean', 'median', 'mode']:
self.window = FastBuffer(max_size=self.window_size)
def transform(self, X):
""" transform
Does the transformation process in the samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The sample or set of samples that should be transformed.
"""
r, c = get_dimensions(X)
for i in range(r):
if self.strategy in ['mean', 'median', 'mode']:
self.window.add_element([X[i][:]])
for j in range(c):
if X[i][j] in self.missing_value or np.isnan(X[i][j]):
X[i][j] = self._get_substitute(j)
return X
def _get_substitute(self, column_index):
""" _get_substitute
Computes the replacement for a missing value.
Parameters
----------
column_index: int
The index from the column where the missing value was found.
Returns
-------
int or float
The replacement.
"""
if self.strategy == 'zero':
return 0
elif self.strategy == 'mean':
if not self.window.is_empty():
return np.nanmean(
np.array(self.window.get_queue())[:, column_index])
else:
return self.new_value
elif self.strategy == 'median':
if not self.window.is_empty():
return np.nanmedian(
np.array(self.window.get_queue())[:, column_index])
else:
return self.new_value
elif self.strategy == 'mode':
if not self.window.is_empty():
return stats.mode(np.array(
self.window.get_queue())[:, column_index],
nan_policy='omit')[0]
else:
return self.new_value
elif self.strategy == 'custom':
return self.new_value
def partial_fit_transform(self, X, y=None):
""" partial_fit_transform
Partially fits the model and then apply the transform to the data.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The sample or set of samples that should be transformed.
y: Array-like
The true labels.
Returns
-------
numpy.ndarray of shape (n_samples, n_features)
The transformed data.
"""
X = self.transform(X)
return X
def partial_fit(self, X, y=None):
""" partial_fit
Partial fits the model.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The sample or set of samples that should be transformed.
y: Array-like
The true labels.
Returns
-------
MissingValuesCleaner
self
"""
X = np.asarray(X)
if self.strategy in ['mean', 'median', 'mode']:
self.window.add_element(X)
return self
def get_info(self):
info = '{}:'.format(type(self).__name__)
info += ' - strategy: {}'.format(self.strategy)
info += ' - window_size: {}'.format(self.window_size)
info += ' - new_value: {}'.format(self.new_value)
return info
class Goowe(StreamModel):
#class Goowe(BaseEstimator):
""" GOOWE (Geometrically Optimum Online Weighted Ensemble), as it is
described in Bonab and Can (2017). Common notation in the code is
as follows:
K for maximum number of classifiers in the ensemble.
N for data instances.
A, d as they are, in the aforementioned paper.
Parameters
----------
n_max_components: int
Ensemble size limit. Maximum number of component classifiers.
chunk_size: int
The amount of instances necessary for ensemble to learn concepts from.
At each chunk_size many instances, some training is done.
window_size: int
Size of sliding window, which keeps record of the last k instances
that are encountered in the data stream.
"""
def __init__(self,
n_max_components: int = 10,
chunk_size: int = 500,
window_size: int = 100,
logging=True):
super().__init__()
self._num_of_max_classifiers = n_max_components
self._chunk_size = chunk_size
self._Logging = logging
self._num_of_current_classifiers = 0
self._num_of_processed_instances = 0
self._classifiers = np.empty((self._num_of_max_classifiers),
dtype=object)
self._weights = np.zeros((self._num_of_max_classifiers, ))
# What to save from current Data Chunk --> will be used for
# adjusting weights, pruning purposes and so on.
# Individual predictions of components, overall prediction of ensemble,
# and ground truth info.
self._chunk_comp_preds = FastBuffer(max_size=chunk_size)
self._chunk_ensm_preds = FastBuffer(max_size=chunk_size)
# chunk_data has instances in the chunk and their ground truth.
# To be initialized after receiving n_features, n_targets
self._chunk_data = None
# self._chunk_truths = FastBuffer(max_size=chunk_size)
# some external stuff that is about the data we are dealing with
# but useful for recording predictions
self._num_classes = None
self._target_values = None # Required to correctly train HTs
self._record = False # Boolean for keeping records to files
# TODO: Implement Sliding Window Continuous Evaluator.
# What to save at Sliding Window (last n instances) --> will be
# used for continuous evaluation.
# self._sliding_window_ensemble_preds =FastBuffer(max_size=window_size)
# self._sliding_window_truths = FastBuffer(max_size=window_size)
def prepare_post_analysis_req(self,
num_features,
num_targets,
num_classes,
target_values,
record=False):
# Need to get the dataset information but we do not want to
# take it as an argument to the classifier itself, nor we do want to
# ask it at each data instance. Hence we take dataset info from user
# explicitly to create _chunk_data entries.
#chunk_size = self._chunk_size
self._chunk_data = InstanceWindow(n_features=num_features,
n_targets=num_targets,
max_size=self._chunk_size)
#self._chunk_data = chunk_data
# num_targets shows how many columns you want to predict in the data.
# num classes is eqv to possible number of values that that column
# can have.
self._num_classes = num_classes
self._target_values = target_values
self._record = record
if (self._record):
# Create files that keeps record of:
# - weights at each chunk
# - individual component results for every instance
# - ground truths for every instance.
self._f_comp_preds = open("component_predictions.csv", "w+")
self._f_truths = open("ground_truths.csv", "w+")
self._f_weights = open("weights.csv", "w+")
self._f_comp_preds.write(str(self._chunk_size) + '\n')
self._f_comp_preds.close()
self._f_truths.close()
self._f_weights.close()
return
def _get_components_predictions_for_instance(self, inst):
""" For a given data instance, takes predictions of
individual components from the ensemble as a matrix.
Parameters
----------
inst: data instance for which votes of components are delivered.
Returns
----------
numpy.array
A 2-d numpy array where each row corresponds to predictions of
each classifier.
"""
preds = np.zeros((self._num_of_current_classifiers, self._num_classes))
# print(np.shape(preds))
for k in range(len(preds)):
kth_comp_pred = self._classifiers[k].predict_proba(inst)
# print(kth_comp_pred[0])
# print(preds)
# print("Component {}'s Prediction: {}".format(k, kth_comp_pred))
preds[k, :] = kth_comp_pred[0]
if (self._Logging):
print('Component Predictions:')
print(preds)
return preds
def _adjust_weights(self):
""" Weight adustment by solving linear least squares, as it is
described in Bonab and Can (2017).
"""
# Prepare variables for Weight Adjustment
# print('number of current classifiers: {}'.format(self._num_of_current_classifiers))
A = np.zeros(shape=(self._num_of_current_classifiers,
self._num_of_current_classifiers))
d = np.zeros(shape=(self._num_of_current_classifiers, ))
# Go over all the data chunk, calculate values of (S_i x S_j) for A.
# (S_i x O) for d.
y_all = self._chunk_data.get_targets_matrix().astype(int)
# print(y_all)
for i in range(len(y_all)):
class_index = y_all[i]
comp_preds = self._chunk_comp_preds.get_next_element()
#print("{} components predictions:".format(i))
#print(comp_preds)
A = A + comp_preds.dot(comp_preds.T)
d = d + comp_preds[0][class_index]
# A and d are filled. Now, the linear system Aw=d to be solved
# to get our desired weights. w is of size K.
# print("Solving Aw=d")
# print(A)
# print(d)
w = np.linalg.lstsq(A, d, rcond=None)[0]
# _weights has maximum size but what we found can be
# smaller. Therefore, need to put the values of w to global weights
if (self._num_of_current_classifiers < self._num_of_max_classifiers):
for i in range(len(w)):
self._weights[i] = w[i]
else: # If full size, there is no problem.
self._weights = w
# print("After solving Aw=d weights:")
# print(self._weights)
return
def _normalize_weights(self):
""" Normalizes the weights of the ensemble to (0, 1) range.
Performs (x_i - min(x)) / (max(x) - min(x)) on the nonzero elements
of the weight vector.
"""
min = np.amin(self._weights[:self._num_of_current_classifiers])
max = np.amax(self._weights[:self._num_of_current_classifiers])
if (min == max): # all weights are the same
for i in range(self._num_of_current_classifiers):
self._weights[i] = 1. / self._num_of_current_classifiers
else:
for i in range(self._num_of_current_classifiers):
self._weights[i] = (self._weights[i] - min) / (max - min)
return
def _normalize_weights_softmax(self):
""" Normalizes the weights of the ensemble to (0, 1) range.
Performs (x_i - min(x)) / (max(x) - min(x)) on the nonzero elements
of the weight vector.
"""
cur_weights = self._weights[:self._num_of_current_classifiers]
self._weights[:self._num_of_current_classifiers] = np.exp(
cur_weights) / sum(np.exp(cur_weights))
return
def _process_chunk(self):
""" A subroutine that runs at the end of each chunk, allowing
the components to be trained and ensemble weights to be adjusted.
Until the first _process_chunk call, the ensemble is not yet ready.
At first call, the first component is learned.
At the rest of the calls, new components are formed, and the older ones
are trained by the given chunk.
If the ensemble size is reached, then the lowest weighted component is
removed from the ensemble.
"""
new_clf = HoeffdingTree() # with default parameters for now
new_clf.reset()
# Save records of previous chunk
if (self._record and self._num_of_current_classifiers > 0):
self._record_truths_this_chunk()
self._record_comp_preds_this_chunk()
self._record_weights_this_chunk()
# Case 1: No classifier in the ensemble yet, first chunk:
if (self._num_of_current_classifiers == 0):
self._classifiers[0] = new_clf
self._weights[0] = 1.0 # weight is 1 for the first clf
self._num_of_current_classifiers += 1
else:
# First, adjust the weights of the old component classifiers
# according to what happened in this chunk.
self._adjust_weights()
# Case 2: There are classifiers in the ensemble but
# the ensemble size is still not capped.
if (self._num_of_current_classifiers <
self._num_of_max_classifiers):
# Put the new classifier to ensemble with the weight of 1
self._classifiers[self._num_of_current_classifiers] = new_clf
self._weights[self._num_of_current_classifiers] = float(1.0)
self._num_of_current_classifiers += 1
# Case 3: Ensemble size is capped. Need to replace the component
# with lowest weight.
else:
assert (self._num_of_current_classifiers ==
self._num_of_max_classifiers), "Ensemble not full."
index_of_lowest_weight = np.argmin(self._weights)
self._classifiers[index_of_lowest_weight] = new_clf
self._weights[index_of_lowest_weight] = 1.0
# Normalizing weigths to simplify numbers
self._normalize_weights_softmax() # maybe useful. we'll see.
if (self._Logging):
print("After normalization weights: ")
print(self._weights)
# Ensemble maintenance is done. Now train all classifiers
# in the ensemble from the current chunk.
# Can be parallelized.
data_features = self._chunk_data.get_attributes_matrix()
data_truths = self._chunk_data.get_targets_matrix()
data_truths = data_truths.astype(int).flatten()
if (self._Logging):
print("Starting training the components with the current chunk...")
for k in range(self._num_of_current_classifiers):
print("Training classifier {}".format(k))
self._classifiers[k].partial_fit(data_features,
data_truths,
classes=self._target_values)
print(
"Training the components with the current chunk completed...")
else:
for k in range(self._num_of_current_classifiers):
self._classifiers[k].partial_fit(data_features,
data_truths,
classes=self._target_values)
return
def _record_truths_this_chunk(self):
f = open("ground_truths.csv", "ab")
data_truths = self._chunk_data.get_targets_matrix()
data_truths = data_truths.astype(int).flatten()
# Default behaviour is to store list of lists for savetxt.
# Hence, to prevent newline after each element of list, we surround
# the truth array with one more set of bracketts.
np.savetxt(f, [data_truths], delimiter=",", fmt='%d')
f.close()
return
def _record_comp_preds_this_chunk(self):
f = open("component_predictions.csv", "a+")
np.savetxt(f, [self._num_of_current_classifiers], fmt='%d')
comp_preds = np.array(self._chunk_comp_preds.get_queue())
for i in range(len(comp_preds)):
np.savetxt(f, comp_preds[i], delimiter=',', fmt='%1.5f')
f.close()
return
def _record_weights_this_chunk(self):
f = open("weights.csv", "a+")
np.savetxt(f, [self._num_of_current_classifiers], fmt='%d')
weights = self._weights
np.savetxt(f, [weights], delimiter=',', fmt='%1.5f')
f.close()
return
# --------------------------------------------------
# Overridden methods from the parent (StreamModel)
# --------------------------------------------------
def fit(self, X, y, classes=None, weight=None):
raise NotImplementedError("For now, only the stream version "
"is implemented. Use partial_fit()")
def partial_fit(self, X, y, classes=None, weight=None):
# This method should work with individual instances, as well as bunch
# of instances, since there can be pre-training for warm start.
# If an individual instance is inputted, then just save X and y to
# train from them later.
if (len(X) == 1):
# Save X and y to train classifiers later
# y is required to be 1x1, and hence the square bracketts.
y_i = np.array([y])
# print(type(X))
# print(type(y_i))
# print(X)
# print(y_i)
self._chunk_data.add_element(X, y_i)
# If still filling the chunk, then just add the instance to the
# current data chunk, wait for it to be filled.
self._num_of_processed_instances += 1
# If at the end of a chunk, start training components
# and adjusting weights using information in this chunk.
if (self._num_of_processed_instances % self._chunk_size == 0):
print("Instance {}".format(self._num_of_processed_instances))
self._process_chunk()
elif (len(X) > 1):
# Input is a chunk. Add them individually.
for i in range(len(X)):
X_i = np.array([X[i]])
y_i = np.array([[y[i]]])
# print(X_i)
# print(y_i)
self._chunk_data.add_element(X_i, y_i)
self._num_of_processed_instances += 1
# If at the end of a chunk, start training components
# and adjusting weights using information in this chunk.
if (self._num_of_processed_instances % self._chunk_size == 0):
print("Instance {}".format(
self._num_of_processed_instances))
self._process_chunk()
else:
print("Something wrong with the data...")
print("len(X) is: {}".format(len(X)))
return
def predict(self, X):
""" For a given data instance, yields the prediction values.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
Samples for which we want to predict the labels.
Returns
-------
numpy.array
Predicted labels for all instances in X.
"""
predictions = []
if (len(X) == 1):
predictions.append(np.argmax(self.predict_proba(X)))
elif (len(X) > 1):
# Add many predictions
for i in range(len(X)):
relevance_scores = self.predict_proba(X[i])
predictions.append(np.argmax(relevance_scores))
# print(np.argmax(relevance_scores))
if (self._Logging):
print('Ensemble Prediction:')
print(np.array(predictions))
return np.array(predictions) #, one_hot
def predict_proba(self, X):
""" For a given data instance, takes WEIGHTED combination
of components to get relevance scores for each class.
Parameters
----------
X: data instance for which weighted combination is delivered.
Returns
----------
numpy.array
A vector with number_of_classes elements where each element
represents class score of corresponding class for this instance.
"""
weights = np.array(self._weights)
# get only the useful weights
weights = weights[:self._num_of_current_classifiers]
components_preds = self._get_components_predictions_for_instance(X)
#print('*****************************')
#print(components_preds)
#print('*****************************')
# Save individual component predictions and ensemble prediction
# for later analysis.
self._chunk_comp_preds.add_element([components_preds])
#print(weights)
#print(components_preds)
#print(self.get_classifiers())
weighted_ensemble_vote = np.dot(weights, components_preds)
# print("Weighted Ensemble vote: {}".format(weighted_ensemble_vote))
self._chunk_ensm_preds.add_element(weighted_ensemble_vote)
return weighted_ensemble_vote
def reset(self):
pass
def score(self, X, y):
pass
def get_info(self):
return 'The Ensemble GOOWE (Bonab and Can, 2017) with' + \
' - n_max_components: ' + str(self._num_of_max_classifiers) + \
' - num_of_current_components: ' + str(self._num_of_current_classifiers) + \
' - chunk_size: ' + str(self._chunk_size) + \
' - num_dimensions_in_label_space(num_classes): ' + str(self._num_classes) + \
' - recording: ' + str(self._record)
def get_class_type(self):
pass
# Some getters and setters..
def get_number_of_current_classifiers(self):
return self._num_of_current_classifiers
def get_number_of_max_classifiers(self):
return self._num_of_max_classifiers
# Helper methods for GooweMS
def get_classifiers(self):
return self._classifiers
def set_classifiers(self, classifiers):
self._classifiers = classifiers
def get_weights(self):
return self._weights
