It seems as if I have to import scipy optimize explicitly to get it.
An example:
If I do
import scipy as sp
sp.optimize.somefunction()
I get an AttributeError.
But if I do
import scipy as sp
from scipy import optimize
sp.optimize.somefunction()
Everything works fine.
Is this desired behaviour? How do I enforce python to load the whole namespace of scipy by doing import scipy as sp?
You don't. The import directives in scipy don't allow that. By design it is modular. You have to use the from scipy import optimize kind of statements.
With from scipy import optimize you can then use optimize.somefunction().
This has been raised a number of times before. A good duplicate is
Why does from scipy import spatial work, while scipy.spatial doesn't work after import scipy?
Related
I'm following along an older tutorial with an SVM optimized with a genetic algorithm. I originally thought the issue was just with versions of python and/or scikit, but I'm now unsure of what the issue could be as it continues displaying the same error. I'm currently using python-scikit-learn 0.20.3-1 on Antergos and found a link here that unfortunately didn't seem to help.
So far I've found a few links and examples that have had me alter different aspects of the code, which overall just jumbled everything up. This GitHub page was useful in at least understanding the version difference, as was the first link. This blog post was also neat, but again didn't really help me narrow down the exact issue as to why it's reading out the error. I even tried looking at the sklearn documentation but I still couldn't get it.
These are what I've imported:
import numpy as np
import pandas as pd
import random as rd
from sklearn.model_selection import cross_validate
from sklearn import preprocessing
from sklearn import svm
I had "kfold" defined earlier in the program as such:
kfold = 3
As well, this is the exact line it seem to be having issues with:
kf = cross_validate.KFold(Cnt1,n_splits=kfold)
What it should be doing is simply applying cross validation. However, the error reads:
AttributeError: 'function' object has no attribue 'KFold'
I can't tell if the issue is that I'm not understanding what I should be altering via the links I've given, or if it's a different error born of ignorance. Is there something I'm missing in order to get this to work?
The KFold function is in the sklearn.model_selection module not in sklearn.model_selection.cross_validate
So you sould import
from sklearn import model_selection
and then using like
model_selection.KFold(...)
or you can import the function
from sklearn.model_selection import KFold
just like in the KFold Doc example.
Can't import multinomialNB and make_pipeline from sklearn.naive_bayes and sklearn.pipeline respectively, screenshot is attached.I'm using python3. I uninstalled and installed anaconda from "https://conda.io/docs/user-guide/install/index.html" last time.
I installed and uninstalled from separate sources too.
I tried installing packages separately also. sklearn,scipy or other packages are installed and upgraded but this piece of code is giving the same error again and again.
I tried every possible solutions on internet and stackoverflow.
#importing necessary packages
from sklearn.feature_extraction.text import TfidVectorizer
from sklearn.naive_bayes import multinomialNB
from sklearn.pipeline import make_pipeline
#creating a model based on multinomial naive-bayes
model = make_pipeline(TfidVectorizer(), multinomialNB())
#training the model with train data
model.fit(train.data, train.target)
#creating labels for test data
labels = model.predict(test.data)
You have some spelling mistakes in your imports. Also, include the error messages the next time you have an error.
from sklearn.feature_extraction.text import TfidfVectorizer # notice the spelling with the f before Vectorizer
from sklearn.naive_bayes import MultinomialNB # notice the Caps on the M
from sklearn.pipeline import make_pipeline
EDIT: Also, please read this about a minimum example, it will make your life a lot easier when trying to get answers from SO in the future.
Welcome to SO!
I tried to understand it by myself using the doc , but I don't get why, from the module scipy, I can import linspace which is a function of the module numpy. They both work well.
Also, I can't find it in the scipy doc.
Scipy imports all Numpy functions in its namespace.
Granted, it is not easy to know where to find this in the documentation, but it is in the first page of the tutorial that is also the first section of the documentation.
In addition, many basic array functions from numpy are also available
at the top-level of the scipy package.
When I first changed my code to "model_selection", the warnings quit. Over the weekend I updated Anaconda and now any import of sklearn triggers the "cross_validation" warning.
I've found several samples of this error on the net, none addressing this specific issues. It if has been, it's due to my old brain not being able properly form the question. Apologies in advance. Clarity greatly appreciated.
#!/usr/bin/env python
# tpot pipeline
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split
import numpy as np
from numpy import loadtxt
I am new to the "scikit-Learn" API and wish to implement a multilabel classification problem. After importing the following packages:
import numpy as np
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
I get an error which says 'Unresolved import: MultiLabelBinarizer'. But other related packages imported seem to work fine. I wonder why the 'MultiLabelBinarizer' cannot be imported, given the fact that the 'sklearn ' package was properly installed. Any help will be appreciated.
I found out the reason, in case someone comes across the same problem. The error was due to fact that I was running the above code on 'sklearn' version 0.14 (which was installed by default on Ubuntu 14.04 LTS) instead of 0.16. I also think the MultiLabelBinarizer Class is only available on 'sklearn' version 0.16 (I have not tried the 0.15 - in case there is any).