High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

Hi

I am not sure if we expect that but the following example predict different results each time I run it:

import numpy as np
import xlearn as xl
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split


iris_data = load_iris()
X = iris_data['data']
y = (iris_data['target'] == 2)

X_train,   \
X_val,     \
y_train,   \
y_val = train_test_split(X, y, test_size=0.3, random_state=0)

linear_model = xl.LRModel(task='binary', init=0.1,
                          epoch=10, lr=0.1,
                          reg_lambda=1.0, opt='sgd')


linear_model.fit(X_train, y_train,
                 eval_set=[X_val, y_val],
                 is_lock_free=False)


y_pred = linear_model.predict(X_val)

This is version 0.4.2 executed via sklearn api on windows: y_pred is never the same. Are we expecting that?

Thank you

when I install from pip , Exception: Please install CMake first but I the centOS has cmake 2.8.12.2

when I build from source code ./build.sh CMake Error at CMakeLists.txt:24 (cmake_minimum_required): CMake 3.0 or higher is required. You are running version 2.8.12.2

can have a simple way install xlearn ? or have a simple way update cmake to 3.0

Please see discussion in https://github.com/aksnzhy/xlearn/issues/68. Currently, the sklearn interface converts numpy array to libsvm format internally with the use of temporary files. Further improvement could be done once the in-memory conversion is ready.

I installed a new version with the build.sh in MacOS (Xcode and CMake installed first). When running the run_example.sh, it aborted with the error message:

MacOS: Mojave 10.14.4 xlearn: 0.43

(the pip installed one in version 0.40a works on my computer, but has a bug just fixed in latest version)

Hi, aksnzhy! Thank you for this library. Can you please guide me a bit? I have a dataset with four columns: transaction_count, user, item, item_colour. I want to recommend some items to users, based on transaction_count. I can use ALS with transaction_count, user and item columns, for example with "implicit" library. But if i want to take in account item_colour i need to use for example ffm. So, i create ffm-formatted file:

transaction_count user_id:value_id:1 item_id:value_id:1 item_colour_id:value_id:1

5 0:0:1 1:3:1 2:5:1
3 0:1:1 1:4:1 2:6:1
8 0:2:1 1:3:1 2:7:1

and train my model. But, if i want to recommend top-5 items with some colours to a user, i need to create all combinations of user:item:colour rows, score them and then sort among each user all variants of item:colour by modeled probabilities and select 5 best among them. The problem is that such a list of all possible combinations explodes with my dimensions (users=80000, items=14000, colours=5), and impossible to operate. Is there any hack for implementation?

xLearn for Windows

Build on VS2017(x64), and pass all existing tests on windows10. Then check xLearn for unix-like version on WSL. There are some information about this pull request.

Finished:

• xLearn for Windows including CLI and Python-package

Unfinished:

• test unittests with pthread.
• compile x86 release version(because fm_score_test haven't been passed for x86 release version).

Supplement:

I did not find what changes with the latest update for common.h, so I modify the program base on the last one before the latest update. There is only format change in some files.

Thanks to your wonderful works! Here is my failed message.

I don't have other information to check the error point. Could you help me?

I ran the same code for three times and get the same error.

My environment is Python 3.5.2, Ubuntu 16.04. The RAM is 32GB.

HERE is my error messages:

[ ACTION     ] Read Problem ...
[------------] First check if the text file has been already converted to binary format.
[------------] Binary file (../data/cfg/ffm_train.txt.bin) found. Skip converting text to binary.
[------------] First check if the text file has been already converted to binary format.
[------------] Binary file (../data/cfg/ffm_valid.txt.bin) found. Skip converting text to binary.
[------------] Number of Feature: 32870706
[------------] Number of Field: 50
[------------] Time cost for reading problem: 38.60 (sec)
[ ACTION     ] Initialize model ...
[1]    26156 segmentation fault (core dumped)  python3 train_xlearn.py

Seems there is a limit in featureid count in one line in sample in training file. max size is 10000?

[ ACTION ] Read Problem ... [------------] First check if the text file has been already converted to binary format. [------------] Binary file (train.problem.5.bin) NOT found. Convert text file to binary file.

例如: v_1025805_41 中的 1025805 和输入的特征中的特征id是对应的吗?

如果输入样本中没有出现的feature id 就应该不计算在内吧~~那我输入的feature id 的编码岂不是与model.txt中的 id 会有差别吗? (输入的样本的feature id会考虑全部的feature,有部分可能不会在样本中出现)

举个例子: 输入的样本中特征编码(libffm格式) 1 1:1:1 2:3:1 0 1:1:1 2:1:1 如果field2 中的特征有 1, 2, 3三个 但是样本中只出现了 1,3 那么model.txt中对应的 v_featureid_fields 会考虑 fields2中的 2 特征?

L2 regularization seems to be reduplicated for FTRL optimization. Take LR as an example. Proximal operator in FTRL has cover the L2 regularization, so the former one seems to be reduplicated. FM and FFM have similar problem.

首先我在windows文件夹用 Visual Studio2017编译成功了，然后我进入xlearn\python-package, 打开命令行， 输入python setup.py install,结果报错如下： Traceback (most recent call last): File "setup.py", line 29, in LIB_PATH = os.path.relpath(libfile, CURRENT_DIR) for libfile in libpa nd_lib_path'] File "xlearn/libpath.py", line 63, in find_lib_path 'Cannot find xlearn Library in the candidate path' XLearnLibraryNotFound: Cannot find xlearn Library in the candidate path 我看也有其他用户遇到这个问题，请问怎么解决？

I got an error when I run "example_FM_wine.py" file.

Failed to convert feature matrix X and label y to xlearn data format During handling of the above exception, another exception occurred: File "D:\Src\example_FM_wine.py", line 50, in fm_model.fit(X_train,

The same error occurs in"example_LR_iris.py" file. The error does not occur in "example_FFM_criteo.py" file. (Because this file uses a txt file as a parameter when calling the fit function.)

how do I fix it? Thanks a lot.

In the model training step, specifically when we are standing in the "fit" step, we use the command and it crashes the kernel.

train and test are txt files readed from hdfs.

train = train.decode() test = test.decode() ffm_model = xl.create_ffm() ffm_model.setTrain(train) ffm_model.fit(param,str)

where str is the hdfs path to our production folder.

I am sorry if the issue is badly explained and/or contextualized , is the first time I report something as an issue to the library's owner.

What have we tried? import subprocess cat = subprocess.Popen(["hadoop", "fs", "-cat", str], stdout=subprocess.PIPE) ffm_model.fit(param,cat)

The file specified in str, already exists but is empty.

At present, the metric indicators calculated by the algorithm are all output in the log, and the indicator values ​​cannot be obtained, so I additionally calculated logloss, acc, and prec. In the calculation process, it is found that logloss needs a value of 0 to 1, and acc and prec need a label value of 0 and 1, so it is necessary to call the setSign and setSigmoid methods, but after continuous calls, it is found that these two functions do not cover the last time. value, but add a new value, so add disableSigmoid, disableSign these two methods

你好,在使用xlearn时，我们会把数据X传给python的fit接口，python会一层层的调用，直到使用底层的c++的train接口，我看这个c++接口使用的是reader数据类型，所以在从python侧调用c++侧的过程中一定会进行数据的处理，我对通过python调用c++这个过程不熟悉，请问如何才能看到这个数据处理都过程?它会包含在哪个文件里吗？最后，像“_LIB.XLearnSetTrain”这样的函数定义在哪个文件里啊？我找不到它，希望您能回复一下，不胜感谢！ Thank！

xlearn was installed on windows anaconda successfully but is not working error is

import xlearn as xl Traceback (most recent call last): File "", line 1, in File "C:\Users\sndr\anaconda3_Apr_2020\lib\site-packages\xlearn_init_.py", line 18, in from .xlearn import * File "C:\Users\sndr\anaconda3_Apr_2020\lib\site-packages\xlearn\xlearn.py", line 19, in from .base import _LIB, XLearnHandle File "C:\Users\sndr\anaconda3_Apr_2020\lib\site-packages\xlearn\base.py", line 34, in _LIB = _load_lib() File "C:\Users\sndr\anaconda3_Apr_2020\lib\site-packages\xlearn\base.py", line 27, in _load_lib lib_path = find_lib_path() File "C:\Users\sndr\anaconda3_Apr_2020\lib\site-packages\xlearn\libpath.py", line 59, in find_lib_path 'Cannot find xlearn Library in the candidate path' xlearn.libpath.XLearnLibraryNotFound: Cannot find xlearn Library in the candidate path

I was getting strange resource exhausted bug when running xlearn fm model predict method for a while.

When I profiled the processes via htop, I have noticed that the number of threads gradually increases by 8 when invoking model.predict("model/model.out", f"output/output.txt") which leads to resource exhausted when the number of threads reaches a critical level.

One solution, I found to solve this problem is invoke the model.predict in a separate process via the multiprocessing module, however this solution is extremely slow in cases when model.predict needs to be invoked many times.

Is there a way to kill the created threads after the execution of the predict method has completed?