[scikit-learn] (no subject)

[Ulderico Santarelli]

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[scikit-learn] (no subject)

[yoshizawa ryota]

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Re: [scikit-learn] (no subject)

[Guillaume Lemaître]

You can refer to https://scikit-learn.org/stable/about.html#citing-scikit-learn 
 depending what 
is the scope of your research paper.
--
Guillaume Lemaitre
Scikit-learn @ Inria Foundation
https://glemaitre.github.io/

> On 10 Nov 2021, at 15:19, 杨哈哈  wrote:
> 
> 
> 
> -- 转发的邮件 -
> 发件人： 杨哈哈 mailto:yanghaha...@gmail.com>>
> 日期：2021年11月10日 周三下午10:08
> 主题：
> 收件人： mailto:scikit-learn@python.org>>
> 
> 
> Excuse me, I'm a Chinese student, Yang.I want to cite sklearn package in my 
> paper, because it's very efficient to machine learning.
> But I'm doubt about references in official webpage. For example, the 
> references in 
> 'https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
>  
> '
>  is LIBSVM:A Library for Support Vector Machines and Probabilistic outputs 
> for support vector machines and comparison to regularizedlikelihood methods. 
> May I ask you what are these references for?For the webpage or the package? 
> Or I just need to cite Scikit-learn: Machine Learning in Python, Pedregosa et 
> al., JMLR 12, pp. 2825-2830, 2011 in 
> 'https://scikit-learn.org/stable/about.html#citing-scikit-learn 
> '?
> I would be appreciated if you can reply me.
>
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[scikit-learn] (no subject)

[Aco Jugo]

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Re: [scikit-learn] (no subject)

[James Melenkevitz via scikit-learn]

 I did some more tests.  My issue that I brought up may be related to the 
custom kernel.  

On Thursday, May 24, 2018, 12:49:34 PM PDT, Gael Varoquaux 
 wrote:  
 
 On Thu, May 24, 2018 at 09:35:00PM +0530, aijaz qazi wrote:
> scikit- multi learn is misleading.

Yes, but I am not sure what scikit-learn should do about this.

Gaël
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Re: [scikit-learn] (no subject)

[Gael Varoquaux]

On Thu, May 24, 2018 at 09:35:00PM +0530, aijaz qazi wrote:
> scikit- multi learn is misleading.

Yes, but I am not sure what scikit-learn should do about this.

Gaël
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[scikit-learn] (no subject)

[aijaz qazi]

 scikit- multi learn is misleading.



*Regards,*
*Aijaz A.Qazi *
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Re: [scikit-learn] (no subject)

[Andreas Mueller]

Try this:
https://jakevdp.github.io/PythonDataScienceHandbook/

On 03/28/2018 11:49 PM, PARK Jinwoo wrote:

Dear scikit-learn experts

Hello, I am a graduate school student majoring in doping control
analysis in Korea.
Now I'm in a research institute that carries out doping control analyses.

I received a project by my advising doctor. It's about operating an AI project.
A workshop is scheduled in April, so it needs to be done in a month.
However, I haven't learn computer science at all and I'm totally ignorant of it.
So I desperately need your advice.

To be specific, the 3 xml files shown in the picture are analysis results
named positive, negative, and unknown from top to bottom.
We'd like to let AI learn positive and negative data,
input unknown datum, and then see what result will turn out.

I came to know that there's a module called 'iris calssification' in
scikit-learn
and I'm thinking of utilizing that as it seems similar with my assignment
However, while the database of iris is a csv file with 150 data and
labels inside,
what I have are 3 xml files each one of which represents one data,
which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata
The training process is not shuffling randomly the 150 data and
dividing into training set and test set. The data are already assigned
into training ones and testing one.
Also, when training the program, training labels naming positive and
negative should be inserted on my own.

What I know all is that it will be appropriate to use fit() function
and predict() function to train and test.
But I have no idea on what to import, how to write codes correctly, and so on

It will be thankful to give me some help


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[scikit-learn] (no subject)

[PARK Jinwoo]

Dear scikit-learn experts

Hello, I am a graduate school student majoring in doping control
analysis in Korea.
Now I'm in a research institute that carries out doping control analyses.

I received a project by my advising doctor. It's about operating an AI project.
A workshop is scheduled in April, so it needs to be done in a month.
However, I haven't learn computer science at all and I'm totally ignorant of it.
So I desperately need your advice.

To be specific, the 3 xml files shown in the picture are analysis results
named positive, negative, and unknown from top to bottom.
We'd like to let AI learn positive and negative data,
input unknown datum, and then see what result will turn out.

I came to know that there's a module called 'iris calssification' in
scikit-learn
and I'm thinking of utilizing that as it seems similar with my assignment
However, while the database of iris is a csv file with 150 data and
labels inside,
what I have are 3 xml files each one of which represents one data,
which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata
The training process is not shuffling randomly the 150 data and
dividing into training set and test set. The data are already assigned
into training ones and testing one.
Also, when training the program, training labels naming positive and
negative should be inserted on my own.

What I know all is that it will be appropriate to use fit() function
and predict() function to train and test.
But I have no idea on what to import, how to write codes correctly, and so on

It will be thankful to give me some help
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[scikit-learn] (no subject)

[Erik Fransson]

Hi everyone,
I have a simple questions in regards to how LassoCV works.
In the documentation it states that the best model is selected via
cross-validation, however I'm wondering how is this best model constructed.

Is it simply running a normal lasso fit with all of the training data
available using the optimal alpha?
And are these the parameters stored in .coef_?

Regards,
Erik
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[scikit-learn] (no subject)

[Carlton Banks]

I am currently struggling with getting good results with my CNN in which i 
decided to optimize parameter using grid search.  I am currently trying to use 
scikit-learn GridSearchCV.

def 
create_model(init_mode='uniform',activation_mode='linear',optimizer_mode="adam",
 activation_mode_conv = 'linear'):
model = Sequential()


model.add(ZeroPadding2D((6,4),input_shape=(6,3,3)))
model.add(Convolution2D(32,3,3 , activation=activation_mode_conv))
print model.output_shape
model.add(Convolution2D(32, 3,3, activation=activation_mode_conv))
print model.output_shape
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,1)))
print model.output_shape
model.add(Convolution2D(64, 3,3 , activation=activation_mode_conv))
print model.output_shape
model.add(Convolution2D(64, 3,3 , activation=activation_mode_conv))
print model.output_shape
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,1)))
model.add(Flatten())
print model.output_shape
model.add(Dense(output_dim=32, input_dim=64, 
init=init_mode,activation=activation_mode))
model.add(Dense(output_dim=13, input_dim=50, 
init=init_mode,activation=activation_mode))
model.add(Dense(output_dim=1, input_dim=13, 
init=init_mode,activation=activation_mode))
model.add(Dense(output_dim=1,  init=init_mode, 
activation=activation_mode))
#print model.summary()
model.compile(loss='mean_squared_error',optimizer=optimizer_mode)

return model
#reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.01, 
patience=3, verbose=1, mode='auto', epsilon=0.1, cooldown=0, 
min_lr=0.01)
#stop  = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, 
verbose=1, mode='auto')

#log=csv_logger = CSVLogger('training_'+str(i)+'.csv')
#print "Model Train"

#hist_current = model.fit(np.array(data_train_input),
#np.array(data_train_output),
#shuffle=False,
#
validation_data=(np.array(data_test_input),np.array(data_test_output)),
#validation_split=0.1,
#nb_epoch=15,
#verbose=1,
#callbacks=[reduce_lr,log,stop])

#print()
#print model.summary()
#print "Model stored"
#model.save(spectogram_path+"Model"+str(feature)+".h5")

#model.save_weights(spectogram_path+"Model"+str(feature)+"_weights.h5")
#del model



## Make it work for other feature ranges
## Add the CNN part and test it
## Try with gabor kernels as suggested by the other paper..

input_train, input_test, output_train, output_test =  
model(0,train_input_data_interweawed_normalized[:-(len(train_input_data_interweawed_normalized)-1000)],output_data_train[:-(len(output_data_train)-1000)],test_input_data_interweawed_normalized[:-(len(test_input_data_interweawed_normalized)-1000)],output_data_test[:-(len(output_data_test)-1000)])

del test_input_data
del test_name
del test_input_data_normalized
del test_name_normalized
del test_input_data_interweawed
del test_name_interweawed
del test_input_data_interweawed_normalized
del test_name_interweawed_normalized

del train_input_data
del train_name
del train_input_data_normalized
del train_name_normalized
del train_input_data_interweawed
del train_name_interweawed
del train_input_data_interweawed_normalized
del train_name_interweawed_normalized


seed = 7
np.random.seed(seed)
print "Regressor"
model = KerasRegressor(build_fn = create_model, verbose = 10)
init_mode_list = ['uniform', 'lecun_uniform', 'normal', 'zero', 
'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']
activation_mode_list = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 
'sigmoid', 'hard_sigmoid', 'linear']
activation_mode_list_conv =  ['softplus', 'softsign', 'relu', 'tanh', 
'sigmoid', 'hard_sigmoid', 'linear']
optimizer_mode_list = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 
'Adamax', 'Nadam']
batch_size_list = [10, 20, 40, 60, 80, 100]
epochs = [10, 50, 100]
param_grid = dict(init_mode=init_mode_list, batch_size=batch_size_list, 
nb_epoch=epochs, activation_mode=activation_mode_list, optimizer_mode = 
optimizer_mode_list, activation_mode_conv =  activation_mode_list_conv)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1)
print "Grid fit"
grid_result = grid.fit(np.asarray(input_train), np.array(output_train))

# summarize results
print("Best: %f using %s" % 

[scikit-learn] Renaming subject lines if you get a digest

[Dale T Smith]

Please rename subjects if you use the digest – now the thread is not complete 
in the archive. Others will have a harder time benefitting from answers.


__
Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science
5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.sm...@macys.com

From: scikit-learn 
[mailto:scikit-learn-bounces+dale.t.smith=macys@python.org] On Behalf Of 
Graham Arthur Mackenzie
Sent: Tuesday, December 13, 2016 5:02 PM
To: scikit-learn@python.org
Subject: Re: [scikit-learn] scikit-learn Digest, Vol 9, Issue 42

⚠ EXT MSG:
Thanks for the speedy and helpful responses!

Actually, the thrust of my question was, "I'm assuming the fit() method for all 
three modules work the same way, so how come the example code for DTs differs 
from NB, SVMs?" Since you seem to be saying that it'll work either way, I'm 
assuming there's no real reason behind it, which was my suspicion, but just 
wanted to have it confirmed, as the inconsistency was conspicuous.

Thanks!
GAM

ps, My apologies if this is the improper way to respond to responses. I am 
receiving the Digest rather than individual messages, so this was the best I 
could think to do...

On Tue, Dec 13, 2016 at 12:38 PM, 
<scikit-learn-requ...@python.org<mailto:scikit-learn-requ...@python.org>> wrote:
Send scikit-learn mailing list submissions to
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Today's Topics:

   1. Why do DTs have a different fit protocol than NB and SVMs?
  (Graham Arthur Mackenzie)
   2. Re: Why do DTs have a different fit protocol than NB and
  SVMs? (Jacob Schreiber)
   3. Re: Why do DTs have a different fit protocol than NB and
  SVMs? (Stuart Reynolds)
   4. Re: Why do DTs have a different fit protocol than NB and
  SVMs? (Vlad Niculae)


--

Message: 1
Date: Tue, 13 Dec 2016 12:14:43 -0800
From: Graham Arthur Mackenzie 
<graham.arthur.macken...@gmail.com<mailto:graham.arthur.macken...@gmail.com>>
To: scikit-learn@python.org<mailto:scikit-learn@python.org>
Subject: [scikit-learn] Why do DTs have a different fit protocol than
NB and SVMs?
Message-ID:

<caguvbb72xozgcuiuaxucmz9fckirzllnv3zm4aw5tcsf-6g...@mail.gmail.com<mailto:caguvbb72xozgcuiuaxucmz9fckirzllnv3zm4aw5tcsf-6g...@mail.gmail.com>>
Content-Type: text/plain; charset="utf-8"

Hello All,

I hope this is the right way to ask a question about documentation.

In the doc for Decision Trees
<http://scikit-learn.org/stable/modules/tree.html#tree>, the fit statement
is assigned back to the classifier:

clf = clf.fit(X, Y)

Whereas, for Naive Bayes
<http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html>
 and Support Vector Machines
<http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>,
it's just:

clf.fit(X, Y)

I assumed this was a typo, but thought I should try and verify such before
proceeding under that assumption. I appreciate any feedback you can provide.

Thank You and Be Well,
Graham
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Message: 2
Date: Tue, 13 Dec 2016 12:23:00 -0800
From: Jacob Schreiber <jmschreibe...@gmail.com<mailto:jmschreibe...@gmail.com>>
To: Scikit-learn user and developer mailing list
<scikit-learn@python.org<mailto:scikit-learn@python.org>>
Subject: Re: [scikit-learn] Why do DTs have a different fit protocol
than NB and SVMs?
Message-ID:

<CA+ad8Ev87kFsDfkX8xUsoLFpa9x=W3Kr_wG3ue47WosO=jo...@mail.gmail.com<mailto:jo...@mail.gmail.com>>
Content-Type: text/plain; charset="utf-8"

The fit method returns the object itself, so regardless of which way you do
it, it will work. The reason the fit method returns itself is so that you
can chain methods, like "preds = clf.fit(X, y).predict(X)"

On Tue, Dec 13, 2016 at 12:14 PM, Graham Arthur Mackenzie <
graham.arthur.macken...@gmail.com<mailto:graham.arthur.macken...@gmail.com>> 
wrote:

> Hello All,
>
> I hope