The example above is using the VFDTClassifier, no difference between it and ARFClassifier aside from which classifier is being used.
does not affect the result
thanks for the examples and further analysis
I agree that results seem to be fine, the warning seems to be coming from a mishandled corner case
sorry for the delay in my answer, while I was reviewing the code I actually found a bug that impacts (as long as I can see) some variants of the ARF. I will create an issue with a clear explanation.
After I finish my dissertation report in May, I'm planning on exploring GPU parallelization possibilities with scikit. I think I've just about maxed out the 72 cores on my server with the current scikit architecture. My nieve thought is that perhaps NVidia or Radeon have parallelization libraries that can be easily imported into my python projects (based on scikit).
That is a very interesting topic and would be really nice to see how it can be applied to stream learning. I am going to drop here a reference to https://dask.org/ which seems promissing but we have not enough resources to explore at the moment.
New to skmultiflow. When I load the 'covtype.csv' using filestream and after stream.prepare_for_use(), it was interpreted as a regression problem, with y=1.0/2.0/3.0/4.0/5.0. But it actually a multi-classification problem, and y=1/2/3/4/5 in the 'covtype.csv' . How can I make the filestream interpret the data in a right way?
Hi @JoanYinCQ. I am not able to reproduce this error, can you share a MWE?
This is what I used:
This is what I used:
from skmultiflow.data import FileStream
stream = FileStream("./src/skmultiflow/data/datasets/covtype.csv")
stream.prepare_for_use()
stream.n_classes # Output: 7
stream.target_values # Output: [1, 2, 3, 4, 5, 6, 7]
@barnettjv https://github.com/scikit-multiflow/scikit-multiflow/tree/master/src/skmultiflow/data/datasets I download the data and test scripts from the githib repo of scikit-multiflow.
Thanks @jacobmontiel . I run exactly the same codes with you. But the error still exists. However, when I redownload the data, the error is fixed. So, maybe the data source is not saved properly at first. Thanks for your reply.
Glad to hear that it is working. It is strange that it just went away, in any case we will keep it in mind in case somebody else gets the same error.
Hi @jacobmontiel I want to know if all methods for Concept Drift Detection included in skmultiflow.drift_detection only support 1-D data stream. For example, when using a 2-d (size=[2000,5]) data_stream in the following codes, an error will arise.
Imports
import numpy as np
from skmultiflow.drift_detection import PageHinkley
ph = PageHinkley()
Simulating a data stream as a normal distribution of 1's and 0's
data_stream = np.random.randint(2, size=[2000,5])
Changing the data concept from index 999 to 2000
for i in range(999, 2000):
data_stream[i] = np.random.randint(4, high=8,size=5)
Adding stream elements to the PageHinkley drift detector and verifying if drift occurred
for i in range(2000):
ph.add_element(data_stream[i])
if ph.detected_change():
print('Change has been detected in data: ' + str(data_stream[i]) + ' - of index: ' + str(i))
you code should look like this:
from skmultiflow.drift_detection import PageHinkley
data_stream = np.concatenate((np.random.randint(2, size=1000), np.random.randint(4, size=1000)))
ph = PageHinkley()
for i, val in enumerate(data_stream):
ph.add_element(val)
if ph.detected_change():
print('Change has been detected in data: ' + str(data_stream[i]) + ' - of index: ' + str(i))
ph.reset()
Short anwer, no. However, there is a loader in scipy: (scipy.io.arff.loadarff). So you can load the data and then convert it using
skmultiflow.data.DataStream
hi , i have a problem . i want to compute kernel density estimation and i use scikit-learn.neighbor.KernelDensity ; multivariate data is comming in a stream and i want to change and update my estimated density as streaming data come in. so i choose scikit-multiflow , i discover that multiflow supports streaming data and has drift detection method in window; but i want to find out if multiflow core can join to sklearn.neighbor.KernelDensity and can update densities as data come in, and if it can not work with this so how should i use adwin() for multivariate data to save part of data in window and change saved part every time drift detected and compute kernel density estimation again in new part of data? any other advice will be useful, thanks in advance!
Hello @farzane30788946_twitter . You can use
sklearn.neighbor.KernelDensity alongisde scikit-multiflow. As you mention the sklearn implementation works in batches of data, if you wan to update the densities you have to define data update strategy. This is very similar to how the KNNClassifier is implemented. You will see there that the data is stored in a sliding window. Regarding drift detection, ADWIN as all other drift detectors take as input 1-dimensional data. You can check the KNNADWINClassifier which uses ADWIN to monitor the classification performance of the basic KNN model. If ADWIN detects a change in classification performance, then the model is reset.
hi guys , one question ; i have a distribution of a data set ; i want to measure the probability of how much the new instance belongs to this distribution. it is the same things generative adversarial networks do for discriminate real data and fake data, but in python i am wandering how can i do that ? i am not sure there is methods in scikit or not but if there is i prefer use this instead of other libraries.
every ideas even from other libraries such as stats or .. will be useful! thanks in advance.
every ideas even from other libraries such as stats or .. will be useful! thanks in advance.
i find my question answer myself ! binary cross entropy loss function is useful here , but i am wander if i can use this function as a learner model in skmultiflow.evaluation.evaluate_prequential.EvaluatePrequential .
@jacobmontiel you answer my last question;much obliged for your answer . do you have any idea here too? if you have some experiences let me know ; thanks in advance.
@jacobmontiel you answer my last question;much obliged for your answer . do you have any idea here too? if you have some experiences let me know ; thanks in advance.
the important element is to process one sample at a time
Hi @dossy , here is one
from skmultiflow.data import DataStream
import numpy as np
n_features = 10
n_samples = 50
X = np.random.random(size=(n_samples, n_feature
y = np.random.randint(2, size=n_samples)
stream = DataStream(data=X, y=y)
# stream.prepare_for_use() # if using the stable version (0.4.1)
stream.n_remaining_samples()Last line return 50
This is for example if you have your data in different
numpy.ndarray
You can also a single
np.ndarray as long as you define the index of the target column (last column by default). pandas.DataFrame are also supported, following the same indications.
How do you actually implement this as a stream processor? How do you operationalize this so it implements online learning, and periodically checkpoints to disk and reloads when restarted, etc.? I want to stream log data at it and have it classify events in the stream ... looks like
scikit-multiflow is only a small part of the puzzle and there’s a lot of stuff you have to develop yourself around it?
I know this is a n00b question, but if I’m working with strings, I have to vectorize them first? I can’t just pass in a
pandas.DataFramecontaining strings - only real/int values?
All questions are welcomed. Currently, we only support numerical data. Your data must be pre-processed. As you mention, scikit-multiflow is focused on the learning part. The idea is that you can take it and integrate it as part of your workflow.
scikit-multiflow processes data one sample at a time. We provide the FileStream and DataStream classes for the case when you have data in a file or in memory. Both are extensions of the Stream class. If you want to read from log file you could process each log as it arrives (convert to numerical values) and the pass it to a model. The operation to receive and process the log entry can be wrapped in an extension of the Stream class. The most relevant method is next_sample.
