Re: [scikit-learn] I’m in trouble and I need your advice on operating scikit-learn

[Andrew Howe]

Hi Jinwoo

It is true that scikit-learn has many models for supervised classification
tasks, and it should be relatively trivial for you to munge your 3 data
files into the X (data) y (labels) format required for these methods.
Examples are k-means, Support Vector Machines, Decision Trees, and
Discriminant Analysis.  However, these are typically considered "machine
learning" techniques; when someone says "AI", they typically mean a Neural
Network.  If you wish to use scikit-learn for Neural Network
classification, you are limited to the Multilayer Perceptron:
http://scikit-learn.org/stable/modules/neural_networks_supervised.html#.
If you want to be able to use more advanced Neural Networks, here are some
options:

*Deep neural networks etc.*

   - pylearn2  A deep learning
   and neural network library build on theano with scikit-learn like interface.
   - sklearn_theano  scikit-learn
   compatible estimators, transformers, and datasets which use Theano
   internally
   - nolearn  A number of wrappers and
   abstractions around existing neural network libraries
   - keras  Deep Learning library
   capable of running on top of either TensorFlow or Theano.
   - lasagne  A lightweight library to
   build and train neural networks in Theano.

I personally use Google's TensorFlow.  Hope this helps.

Andrew

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On Thu, Mar 29, 2018 at 7:06 AM, 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.
>
> 
> ___
> scikit-learn mailing list
> scikit-learn@python.org
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>
>
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[scikit-learn] I’m in trouble and I need your advice on operating scikit-learn

[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)

[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] How to apply LR in gridded (for multiple location datasets) time series datasets?

[Vishal Singh]

Hello,

This code is written for multivariate (multiple independent variables
x1,x2,x3..xn and a dependent variable y) time series analysis using
logistic regression (correlation and prediction).

#Import Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Import Dataset
dataset = pd.read_csv(‘precipitation.csv’)
x = dataset.iloc[:,[2,3]].values
y =dataset.iloc[:,4].values

#Split Training Set and Testing Set
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test =train_test_split(x,y,test_size=0.25)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
x_train=sc_X.fit_transform(x_train)
x_test=sc_X.transform(x_test)

#Training the Logistic Model
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(x_train, y_train)

#Predicting the Test Set Result
y_pred = classifier.predict(x_test)


This code is based on one point location (one lat/long) datasets. Suppose,
I am having gridded datasets (which has many points/locations, lat/long,
varying in space and time) then How I will implement this code. I am not
expertise in python. If somebody can help me in this? If somebody can give
me an example or idea so I can implement this code as per my requirement.

Thank you in advance.

Vishu
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