I am figuring out which is the best machine learning approach to use for a pedometer project. So far I have gyro and accelerometer data of walking and no walking. When I train and test a Naive Bayes model in my machine I get nearly 70 of accuracy. However, when I port to java and add it to my android app and start using the implementation it is just predicting the same label. Several question arise from this: why is this happening?... Do I need to use an online learning algorithm for this scenario?, the balance of my classes is wrong?

Sorry to bother you again, but when attempting to run: python3 -m sklearn_porter -i model_notokenizer.pkl -l javaI get:

Traceback (most recent call last):
  File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.5/dist-packages/sklearn_porter/__main__.py", line 71, in <module>
    main()
  File "/usr/local/lib/python3.5/dist-packages/sklearn_porter/__main__.py", line 49, in main
    porter = Porter(model, language=language)
  File "/usr/local/lib/python3.5/dist-packages/sklearn_porter/Porter.py", line 65, in __init__
    raise ValueError(error)
ValueError: The given model 'Pipeline(memory=None,
     steps=[('vect', TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
        lowercase=True, max_df=0.5, max_features=None, min_df=0.001,
        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True...ax_iter=1000,
     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
     verbose=0))])' isn't supported.

I'm running python 3.5.2, numpy 1.13.1, and sklearn 0.19.0.

when i try to export "porter = Porter(model, language='java', method= 'predict_proba')" from SVC model it returns "Currently the chosen model method 'predict_proba' isn't supported." is there any solution to get the probability of the predicted class ?!

Attempted to port a somewhat large random forest classifier (7.2 MB) for Java and compiling the Java class ended up giving a "too many constants" error, because of the number of hardcoded values to compose the tree. I circumvented this by using a simple script to separate out all (static) methods into individual classes and files. Is there a cleaner way internally to get around this problem or achieve this effect?

The command: pip install sklearn-porter produces the following:

Collecting sklearn-porter
  Using cached sklearn-porter-0.3.2.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-build-2k6e9qlh/sklearn-porter/setup.py", line 6, in <module>
        from sklearn_porter import Porter
      File "/tmp/pip-build-2k6e9qlh/sklearn-porter/sklearn_porter/__init__.py", line 3, in <module>
        from Porter import Porter
    ImportError: No module named 'Porter'
    
    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-2k6e9qlh/sklearn-porter/

I am using the wrapper of scikit-learn Multilayer Perceptron in Python https://github.com/aigamedev/scikit-neuralnetwork to train the neural network and save it to a file. Now, I want to expose it on production to predict in real time. So, I was thinking to use Java for better concurrency than Python. Hence, my question is whether can we read the model using this library written using Python or above wrapper? The code below I am using for training the model and last three lines I want to port to Java to expose it on production

import pickle
import numpy as np
import pandas as pd
from sknn.mlp import Classifier, Layer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

f = open("TrainLSDataset.csv")
data = np.loadtxt(f,delimiter = ',')

x = data[:, 1:]
y = data[:, 0]
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)

nn = Classifier(
    layers=[            	    
        Layer("Rectifier", units=5),
        Layer("Softmax")],
    learning_rate=0.001,
    n_iter=100)

nn.fit(X_train, y_train)
filename = 'finalized_model.txt'
pickle.dump(nn, open(filename, 'wb')) 

**Below code i want to write in Java/GoLang for exposing it on Production** :

loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(X_test, y_test)
y_pred = loaded_model.predict(X_test)

Hi, I know sklearn-porter doesn't support nu-SVCs, but those are mathematically equivalent to svm.SVC models (see http://scikit-learn.org/stable/modules/svm.html#nusvc). I was wondering if there was a workaround for this ? Thank you

Hi,

I am trying to run the command: python -m sklearn_porter -i estimator.pkl --js as instructed on the github readme, with a sklearn random forest classifier that I saved into estimator.pkl as instructed. I am using Python 3.6 from Anaconda on a Ubuntu 16.04 LTS. But it fails with following error: Traceback (most recent call last): File "/home/user/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/home/user/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/user/anaconda3/lib/python3.6/site-packages/sklearn_porter/main.py", line 153, in main() File "/home/user/anaconda3/lib/python3.6/site-packages/sklearn_porter/main.py", line 105, in main estimator = joblib.load(input_path) File "/home/user/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/numpy_pickle.py", line 578, in load obj = _unpickle(fobj, filename, mmap_mode) File "/home/user/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/numpy_pickle.py", line 508, in _unpickle obj = unpickler.load() File "/home/user/anaconda3/lib/python3.6/pickle.py", line 1050, in load dispatchkey[0] KeyError: 239

Hi @nok, the libsvm implementation seems to be using subtraction while the sklearn-porter's JavaScript predict method is using addition in the same place. I'm guessing both are the same if the intercepts are having opposite sign, but, I'm not sure. Could you please shed some light on this?

I am unable to install sklearn-porter for python3 through pip.

Collecting sklearn-porter
  Using cached sklearn-porter-0.5.0.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-build-bdmsnkqx/sklearn-porter/setup.py", line 18, in <module>
        with open(requirements_path) as f:
    FileNotFoundError: [Errno 2] No such file or directory: '/tmp/pip-build-bdmsnkqx/sklearn-porter/requirements.txt'
    
    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-bdmsnkqx/sklearn-porter/

I am running python 3.6 on Arch Linux.

Functions predict_* were named predict_1, predict_2, ..., predict_9, but when they were called the format was the following _01, _02, ... _09. This patch unifies the naming structure. I don't understand why the developer wished to name them _01 and not directly _1.

I was having difficulties with some of the template combinations and realized that the .jinja2 files were not being included in the package. This change fixes the examples on my machine when running with a pip installed porter cli

Here are my versions : The scikit-learn 0.21.3 Python 3.7.13

And here is my code. I used the same example as in the notebook.

# 1. Load data and train a dummy classifier:
X, y = load_iris(return_X_y=True)
clf = tree.DecisionTreeClassifier()
clf.fit(X, y)

I tried this code:

# 2. Port or transpile an estimator:
est = Estimator(clf, language='java', template='combined')
output = est.port()
print(output)

This is my error:

---------------------------------------------------------------------------
TemplateNotFound                          Traceback (most recent call last)
[<ipython-input-38-6d9e6b3013b2>](https://localhost:8080/#) in <module>()
      1 # 2. Port or transpile an estimator:
      2 est = Estimator(clf, language='java', template='combined')
----> 3 output = est.port()
      4 print(output)

5 frames
[/usr/local/lib/python3.7/dist-packages/jinja2/loaders.py](https://localhost:8080/#) in get_source(self, environment, template)
    305             source = self.mapping[template]
    306             return source, None, lambda: source == self.mapping.get(template)
--> 307         raise TemplateNotFound(template)
    308 
    309     def list_templates(self):

TemplateNotFound: combined.class

Can someone help me ?

This pull request updates the version of scikit-learn used by sklearn-porter to the current stable release (>-1.0.1).

Changes comprise:

• updating the specified version of scikit-learn in requirements.txt
• fixed broken imports due to changes to scikit-learn
• commenting out (minimal) broken tests (which may be due to changes in joblib, but I'm not 100% sure)

All tests (other than the few mentions above) pass on my local system:

• platform linux -- Python 3.8.10, pytest-6.2.5, py-1.11.0, pluggy-1.0.0 -- /usr/bin/python3
• gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
• java openjdk version "17.0.1" 2021-10-19
• javac 17.0.1
• PHP 7.4.3 (cli) (built: Nov 25 2021 23:16:22) ( NTS )
• ruby 2.7.0p0 (2019-12-25 revision 647ee6f091) [x86_64-linux-gnu]
• go version go1.13.8 linux/amd64
• node v16.13.2

Let be know if any details are required.

I have implemented the feature of transforming multilabel/multi-output decision tree Classifier into c language. Each output of decision tree or each dimension of predicted vector is binary-class (0 or 1). "examples/estimator/classifier/DecisionTreeClassifier/c/basics.pct.multilabel.py" file is test.

I'm using porter 0.7.4. and scikit-learn 0.20.0 (using conda) and When I used “from sklearn_porter import Porter” I got the error: ImportError: cannot import name 'Porter' Why about it?