GUI and Desktop Applications int64 0 1 | A_Id int64 5.3k 72.5M | Networking and APIs int64 0 1 | Python Basics and Environment int64 0 1 | Other int64 0 1 | Database and SQL int64 0 1 | Available Count int64 1 13 | is_accepted bool 2
classes | Q_Score int64 0 1.72k | CreationDate stringlengths 23 23 | Users Score int64 -11 327 | AnswerCount int64 1 31 | System Administration and DevOps int64 0 1 | Title stringlengths 15 149 | Q_Id int64 5.14k 60M | Score float64 -1 1.2 | Tags stringlengths 6 90 | Answer stringlengths 18 5.54k | Question stringlengths 49 9.42k | Web Development int64 0 1 | Data Science and Machine Learning int64 1 1 | ViewCount int64 7 3.27M |
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0 | 31,226,011 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2015-07-04T23:30:00.000 | 1 | 2 | 0 | Random Forest for multi-label classification | 31,225,935 | 0.099668 | python,machine-learning,svm,random-forest,text-classification | It is very hard to answer this question without looking at the data in question.
SVM does have a history of working better with text classification - but machine learning by definition is context dependent.
Consider the parameters by which you are running the random forest algorithm. What are your number and depth of t... | I am making an application for multilabel text classification .
I've tried different machine learning algorithm.
No doubt the SVM with linear kernel gets the best results.
I have also tried to sort through the algorithm Radom Forest and the results I have obtained have been very bad, both the recall and precision are v... | 0 | 1 | 2,289 |
0 | 31,234,627 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2015-07-04T23:30:00.000 | 2 | 2 | 0 | Random Forest for multi-label classification | 31,225,935 | 0.197375 | python,machine-learning,svm,random-forest,text-classification | The ensemble of the random forest performs well across many domains and types of data. They are excellent at reducing error from variance and don't over fit if trees are kept simple enough.
I would expect a forest to perform comparably to a SVM with a linear kernel.
The SVM will tend to overfit more because it does... | I am making an application for multilabel text classification .
I've tried different machine learning algorithm.
No doubt the SVM with linear kernel gets the best results.
I have also tried to sort through the algorithm Radom Forest and the results I have obtained have been very bad, both the recall and precision are v... | 0 | 1 | 2,289 |
0 | 38,479,294 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2015-07-10T12:44:00.000 | 1 | 2 | 0 | Matplotlib with multiprocessing freeze computer | 31,341,127 | 0.099668 | python,python-2.7,matplotlib,multiprocessing | I just had a very similar issue in which I have a class which produces plots in parallel. The first time I create a new instance of that class and run the plotting function, everything works perfectly. But if I create a new instance and plot, everything freezes.
I fixed it by writing a bash script which will in turn ru... | I have an issue with matplotlib and multiprocessing.
I launch a first process, where I display an image and select an area, and close the figure. Then I launch another process, where I call a graph function that is regularly updated. Up this point, eveything works fine.
Then when I try to launch another process with th... | 0 | 1 | 1,823 |
0 | 43,903,727 | 0 | 0 | 0 | 0 | 2 | false | 15 | 2015-07-11T22:43:00.000 | 10 | 2 | 0 | Performance difference in pandas read_table vs. read_csv vs. from_csv vs. read_excel? | 31,362,573 | 1 | python,performance,csv,pandas,dataframe | I've found that CSV and tab-delimited text (.txt) are equivalent in read and write speed, both are much faster than reading and writing MS Excel files. However, Excel format compresses the file size a lot.
For the same 320 MB CSV file (16 MB .xlsx)
(i7-7700k, SSD, running Anaconda Python 3.5.3, Pandas 0.19.2)
Using t... | I tend to import .csv files into pandas, but sometimes I may get data in other formats to make DataFrame objects.
Today, I just found out about read_table as a "generic" importer for other formats, and wondered if there were significant performance differences between the various methods in pandas for reading .csv file... | 0 | 1 | 20,524 |
0 | 31,362,987 | 0 | 0 | 0 | 0 | 2 | true | 15 | 2015-07-11T22:43:00.000 | 28 | 2 | 0 | Performance difference in pandas read_table vs. read_csv vs. from_csv vs. read_excel? | 31,362,573 | 1.2 | python,performance,csv,pandas,dataframe | read_table is read_csv with sep=',' replaced by sep='\t', they are two thin wrappers around the same function so the performance will be identical. read_excel uses the xlrd package to read xls and xlsx files into a DataFrame, it doesn't handle csv files.
from_csv calls read_table, so no. | I tend to import .csv files into pandas, but sometimes I may get data in other formats to make DataFrame objects.
Today, I just found out about read_table as a "generic" importer for other formats, and wondered if there were significant performance differences between the various methods in pandas for reading .csv file... | 0 | 1 | 20,524 |
0 | 69,462,087 | 0 | 0 | 0 | 1 | 1 | false | 106 | 2015-07-13T13:56:00.000 | 0 | 9 | 0 | How to export a table dataframe in PySpark to csv? | 31,385,363 | 0 | python,apache-spark,dataframe,apache-spark-sql,export-to-csv | try display(df) and use the download option in the results. Please note: only 1 million rows can be downloaded with this option but its really quick. | I am using Spark 1.3.1 (PySpark) and I have generated a table using a SQL query. I now have an object that is a DataFrame. I want to export this DataFrame object (I have called it "table") to a csv file so I can manipulate it and plot the columns. How do I export the DataFrame "table" to a csv file?
Thanks! | 0 | 1 | 340,481 |
0 | 31,619,724 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2015-07-13T18:32:00.000 | 0 | 2 | 0 | Using semantic word representation (e.g. word2vec) to build a classifier | 31,390,838 | 0 | python,classification,word2vec | For a multi-class classification problem of sentences, doc2vec can work just fine, since context rarely changes a lot within the sentence.
If you want to use only python, I will recommend, doc2vec (for building features) followed by xgboost (for training classifier), which has worked for me in similar problems. | I want to build a classifier for forum posts that will automatically categorize these
posts into some defined categories(so multiclass classification not only binary
classification) by using semantic word representations. For this task I want to make use
of word2vec and doc2vec and check the feasability of using these ... | 0 | 1 | 1,109 |
0 | 31,393,600 | 0 | 0 | 0 | 0 | 2 | true | 1 | 2015-07-13T18:32:00.000 | 2 | 2 | 0 | Using semantic word representation (e.g. word2vec) to build a classifier | 31,390,838 | 1.2 | python,classification,word2vec | The issue with things like word2vec/doc2vec and so on - actually any usupervised classifier - is that it just uses context. So, for example if I have a sentence like "Today is a hot day" and another like "Today is a cold day" it thinks hot and cold are very very similar and should be in the same cluster.
This makes it ... | I want to build a classifier for forum posts that will automatically categorize these
posts into some defined categories(so multiclass classification not only binary
classification) by using semantic word representations. For this task I want to make use
of word2vec and doc2vec and check the feasability of using these ... | 0 | 1 | 1,109 |
0 | 31,399,878 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2015-07-14T04:30:00.000 | 0 | 1 | 0 | Cannot import python-numpy using Ubuntu | 31,397,675 | 0 | python,linux,ubuntu | try pip freeze to find whether the module numpy is installed, If its not then try pip install numpy, also check weather in the environmental variable that the /Scripts is added because that's where all the packages reside. | Currently I am trying to import numpy using the python command but I am getting the error which is:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: No module named numpy
I have already installed the latest version of numpy and my python version is 2.7.10. | 0 | 1 | 103 |
0 | 31,429,440 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2015-07-14T20:51:00.000 | 0 | 1 | 0 | Python pickle dump memory error | 31,417,091 | 0 | python,list,pickle | If you really want to keep it simple and use something like pickle, the best thing is to use cPickle. This library is written in C and can handle bigger files and is faster than pickle. | I have a very large list that I want to write to file. My list is 2 dimensional, and each element of the list is a 1 dimensional list. Different elements of the 2 dimensional list has 1 dimensional lists of varying size.
When my 2D list was small, pickle dump worked great. But now it just gives me memory error.
Any s... | 0 | 1 | 1,136 |
0 | 31,417,912 | 0 | 0 | 0 | 0 | 1 | true | 29 | 2015-07-14T21:12:00.000 | 16 | 7 | 0 | sklearn LogisticRegression and changing the default threshold for classification | 31,417,487 | 1.2 | python,scikit-learn,classification,regression | That is not a built-in feature. You can "add" it by wrapping the LogisticRegression class in your own class, and adding a threshold attribute which you use inside a custom predict() method.
However, some cautions:
The default threshold is actually 0. LogisticRegression.decision_function() returns a signed distance to ... | I am using LogisticRegression from the sklearn package, and have a quick question about classification. I built a ROC curve for my classifier, and it turns out that the optimal threshold for my training data is around 0.25. I'm assuming that the default threshold when creating predictions is 0.5. How can I change this ... | 0 | 1 | 39,588 |
0 | 31,422,570 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2015-07-15T01:39:00.000 | 1 | 1 | 0 | How to make dataframe in pandas as numeric? | 31,420,095 | 1.2 | python,numpy,pandas | It seems that you have a list of int in your data frame. To convert it to you need to select the value inside and form data frame.
I suggest you this code to convert
for col in df:
df[col] = df[col].apply(lambda x: x[0]) | I am learning the book Python for Data Analysis, after running the code from the book I got a pandas dataframe diversity like this:
sex F M
year
1880 [38] [14]
1881 [38] [14]
When I want to use diversity.plot() to draw some pictures, there is TypeError:
Empty 'DataFrame': no numeric data to plot
So, my question ... | 0 | 1 | 352 |
0 | 31,441,162 | 0 | 0 | 0 | 0 | 1 | true | 9 | 2015-07-15T15:42:00.000 | 7 | 2 | 0 | Using Numba with scikit-learn | 31,435,024 | 1.2 | python,scikit-learn,numba | Scikit-learn makes heavy use of numpy, most of which is written in C and already compiled (hence not eligible for JIT optimization).
Further, the LogisticRegression model is essentially LinearSVC with the appropriate loss function. I could be slightly wrong about that, but in any case, it uses LIBLINEAR to do the solv... | Has anyone succeeded in speeding up scikit-learn models using numba and jit compilaition. The specific models I am looking at are regression models such as Logistic Regressions.
I am able to use numba to optimize the functions I write using sklearn models, but the model functions themselves are not affected by this an... | 0 | 1 | 6,532 |
0 | 31,493,214 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2015-07-18T13:20:00.000 | 3 | 1 | 0 | How can I get randomized grid search to be more verbose? (seems stopped, but can't diagnose) | 31,491,583 | 1.2 | python,scikit-learn,random-forest,cross-validation,grid-search | As a first step, adding the verbose parameter to the RandomForestClassifier as well could let you see if the search is really stuck. It will display progress in fitting the trees (building tree 88 out of 100 ...).
I don't really know why your search got stuck, but thinking about it removing the search on n_estimators ... | I'm running a relatively large job, which involves doing a randomized grid search on a dataset, which (with a small n_iter_search) already takes a long time.
I'm running it on a 64 core machine, and for about 2 hours it kept 2000 threads active working on the first folds. It then stopped reporting completely into the ... | 0 | 1 | 6,252 |
0 | 31,493,846 | 0 | 1 | 0 | 0 | 1 | true | 6 | 2015-07-18T17:24:00.000 | 4 | 1 | 0 | How to 'partially' install a Python package | 31,493,649 | 1.2 | python,numpy,pip,python-wheel | This is probably not worth the hassle but it's up to you to make that trade-off. numpy.random.choice is not implemented in Python but in a .pyx file which needs to be compiled to C using Cython.
You could refactor it and construct a new package which implements only that functionality (possibly with a few related data ... | I need to use a function in numpy package, say numpy.random.choice (another Python lib function random.choice samples the list uniformly while I want it to do that from some discrete distributions).
My program will be distributed to a lot of people to develop and test. So that means they should also install numpy befo... | 0 | 1 | 1,071 |
0 | 31,497,506 | 0 | 0 | 0 | 0 | 2 | false | 2 | 2015-07-18T22:03:00.000 | 2 | 4 | 0 | ImportError on cv2.so | 31,496,020 | 0.099668 | python,opencv,caffe | The problem has been solved by some tryings.
Since I installed under my /.~local path, it should be noticed that [include],[bin] and [lib] should all point to the local version by modifying the bashrc.
I just change the lib path while the other 2 paths remained unchanged, which point to the cluster's opencv version 2.4... | I am trying to run fast-rcnn on a cluster, where cv2.so is not installed for public use. So I directly move the cv2.so into a PATH, but it turns as:
/lib64/libc.so.6: version `GLIBC_2.14' not found
So I have to install the opencv on my local path again, this time it says:
ImportError: /home/username/.local/lib/python2... | 0 | 1 | 12,670 |
0 | 42,237,346 | 0 | 0 | 0 | 0 | 2 | false | 2 | 2015-07-18T22:03:00.000 | 3 | 4 | 0 | ImportError on cv2.so | 31,496,020 | 0.148885 | python,opencv,caffe | I know this is a little late, but I just got this same error with python 2.7 and opencv 3.1.0 on Ubuntu. Turns out I had to reinstall opencv-python. Running sudo pip install opencv-python did the trick. | I am trying to run fast-rcnn on a cluster, where cv2.so is not installed for public use. So I directly move the cv2.so into a PATH, but it turns as:
/lib64/libc.so.6: version `GLIBC_2.14' not found
So I have to install the opencv on my local path again, this time it says:
ImportError: /home/username/.local/lib/python2... | 0 | 1 | 12,670 |
0 | 31,499,284 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2015-07-19T07:02:00.000 | 6 | 1 | 0 | Performance difference between filling existing numpy array and creating a new one | 31,498,784 | 1 | python,numpy | The answer depends on the size of your arrays. While allocating a new memory region takes nearly a fixed amount of time, the time to fill this memory region grows linear with size.
But, filling a new allocated memory with numpy.zeros is nearly twice as fast, as filling an existing array with numpy.fill, and three times... | In iterative algorithms, it is common to use large numpy arrays many times. Frequently the arrays need to be manually "reset" on each iteration. Is there a performance difference between filling an existing array (with nans or 0s) and creating a new array? If so, why? | 0 | 1 | 1,368 |
0 | 31,504,403 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-07-19T16:55:00.000 | 0 | 1 | 0 | Distributed mapping and lookup with Spark | 31,503,613 | 0 | python,apache-spark,pyspark | Although the effect depends on your data set and operations, here are options I come up with
Optimize text format (Avro, Kryo etc) so that hashmap can build quickly
Combine the files into one HDFS file and increase block replication factor of the file, so that many Spark executors can get read the files locally
Use Sp... | I need to build a hashmap using text files and map values using that hashmap.
The files are already in HDFS.
I want to map data using this hashmap.
The text files are fairly small (I have around 10 files each few MB that I need to use for building the hashmap).
If the files are already on HDFS is there anything else t... | 0 | 1 | 502 |
0 | 31,528,542 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2015-07-20T22:10:00.000 | 1 | 1 | 0 | FFT in Python with Explanations | 31,527,206 | 0.197375 | python,audio,signal-processing,fft | FFT data is in units of normalized frequency where the first point is 0 Hz and one past the last point is fs Hz. You can create the frequency axis yourself with linspace(0.0, (1.0 - 1.0/n)*fs, n). You can also use fftfreq but the components will be negative.
These are the same if n is even. You can also use rfftfreq I ... | I have a WAV file which I would like to visualize in the frequency domain. Next, I would like to write a simple script that takes in a WAV file and outputs whether the energy at a certain frequency "F" exceeds a threshold "Z" (whether a certain tone has a strong presence in the WAV file). There are a bunch of code snip... | 0 | 1 | 1,122 |
0 | 31,540,754 | 0 | 0 | 0 | 1 | 1 | false | 1 | 2015-07-21T13:27:00.000 | 0 | 3 | 0 | Creating arrays in Python by using excel data sheet | 31,540,437 | 0 | python | I might, but as a former user of the excellent xlrd package, I really would recommend switching to pyopenxl. Quite apart from other benefits, each worksheet has a columns attribute that is a list of columns, each column being a list of cells. (There is also a rows) attribute.
Converting your code would be relatively pa... | I have an excel file with 234 rows and 5 columns. I want to create an array for each column so that when I can read each column separately in xlrd. Does anyone can help please? | 0 | 1 | 898 |
0 | 31,581,102 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2015-07-23T07:10:00.000 | 1 | 2 | 0 | Creating a disk-based data structure | 31,580,478 | 1.2 | python,file,data-structures,disk | There's quite a number of problems you have to solve, some are quite straight forward and some are a little bit more elaborate, but since you want to do it yourself I don't think you minding about filling out details yourself (so I'll skip some parts).
First simple step is to serialize and deserialize nodes (in order t... | I couldn't find any resources on this topic. There are a few questions with good answers describing solutions to problems which call for data stored on disk (pickle, shelve, databases in general), but I want to learn how to implement my own.
1) If I were to create a disk based graph structure in Python, I'd have to im... | 0 | 1 | 1,607 |
0 | 31,582,822 | 0 | 0 | 0 | 1 | 1 | false | 4 | 2015-07-23T09:04:00.000 | 0 | 3 | 0 | How to open an excel file with multiple sheets in pandas? | 31,582,821 | 0 | python,excel,import | exFile = ExcelFile(f) #load file f
data = ExcelFile.parse(exFile) #this creates a dataframe out of the first sheet in file | I have an excel file composed of several sheets. I need to load them as separate dataframes individually. What would be a similar function as pd.read_csv("") for this kind of task?
P.S. due to the size I cannot copy and paste individual sheets in excel | 0 | 1 | 15,976 |
0 | 63,382,231 | 0 | 0 | 0 | 0 | 1 | false | 13 | 2015-07-24T09:53:00.000 | 1 | 4 | 0 | How to add clipboard support to Matplotlib figures? | 31,607,458 | 0.049958 | python,matplotlib,plot,scipy,clipboard | The last comment is very useful.
Install the package with
pip install addcopyfighandler
Import the module after importing matplotlib, for instance:
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from matplotlib.cm import get_cmap
import addcopyfighandler
Use ctr + C to copy the Figure to the ... | In MATLAB, there is a very convenient option to copy the current figure to the clipboard. Although Python/numpy/scipy/matplotlib is a great alternative to MATLAB, such an option is unfortunately missing.
Can this option easily be added to Matplotlib figures? Preferably, all MPL figures should automatically benefit from... | 0 | 1 | 12,554 |
0 | 66,269,162 | 0 | 0 | 0 | 0 | 2 | false | 4 | 2015-07-24T12:54:00.000 | 0 | 2 | 0 | Random Forest pruning | 31,611,075 | 0 | python,machine-learning,scikit-learn,random-forest,pruning | You could try ensemble pruning. This boils down to removing from your random forest a number of the decision trees that make it up.
If you remove trees at random, the expected outcome is that the performance of the ensemble will gradually deteriorate with the number of removed trees. However, you can do something more ... | I have sklearn random forest regressor. It's very heavy, 1.6 GBytes, and works very long time when predicting values.
I want to prune it to make lighter. As I know pruning is not implemented for decision trees and forests. I can't implement it by myself since tree code is written on C and I don't know it.
Does anyone k... | 0 | 1 | 5,467 |
0 | 31,611,419 | 0 | 0 | 0 | 0 | 2 | false | 4 | 2015-07-24T12:54:00.000 | 3 | 2 | 0 | Random Forest pruning | 31,611,075 | 0.291313 | python,machine-learning,scikit-learn,random-forest,pruning | The size of the trees can be a solution for you. Try to limit the size of the trees in the forest (max leaf noders, max depth, min samples split...). | I have sklearn random forest regressor. It's very heavy, 1.6 GBytes, and works very long time when predicting values.
I want to prune it to make lighter. As I know pruning is not implemented for decision trees and forests. I can't implement it by myself since tree code is written on C and I don't know it.
Does anyone k... | 0 | 1 | 5,467 |
0 | 31,646,627 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-07-24T12:54:00.000 | 0 | 1 | 0 | How to identify objects related to KD Tree data? | 31,611,089 | 0 | python,tree,kdtree | A typical KD tree node contains a reference to the data point.
A KD tree that only keeps the coordinates is much less useful.
This way, you can easily identify them. | I've been studying KD Trees and KNN searching in 2D & 3D space. The thing I cannot seem to find a good explanation of is how to identify which objects are being referenced by each node of the tree.
Example would be an image comparison database. If you generated descriptors for all the images, would you push all the de... | 0 | 1 | 245 |
0 | 31,778,280 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-07-24T13:39:00.000 | 0 | 1 | 0 | Theano continue training | 31,612,074 | 0 | python,theano,deep-learning | When pickling models, it is always better to save the parameters and when loading re-create the shared variable and rebuild the graph out of this. This allow to swap the device between CPU and GPU.
But you can pickle Theano functions. If you do that, pickle all associated function at the same time. Otherwise, they will... | I am looking for some suggestions about how to do continue training in theano. For example, I have the following:
classifier = my_classifier()
cost = ()
updates = []
train_model = theano.function(...)
eval_model = theano.function(...)
best_accuracy = 0
while (epoch < n_epochs):
train_model()
current_accura... | 0 | 1 | 246 |
0 | 53,998,316 | 0 | 1 | 0 | 0 | 1 | false | 22 | 2015-07-26T23:19:00.000 | 1 | 6 | 0 | Finding if two strings are almost similar | 31,642,940 | 0.033321 | python,regex,string | You could split the string and check to see if it contains at least one first/last name that is correct. | I want to find out if you strings are almost similar. For example, string like 'Mohan Mehta' should match 'Mohan Mehte' and vice versa. Another example, string like 'Umesh Gupta' should match 'Umash Gupte'.
Basically one string is correct and other one is a mis-spelling of it. All my strings are names of people.
Any s... | 0 | 1 | 17,280 |
0 | 31,643,427 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-07-26T23:41:00.000 | 1 | 1 | 0 | Spatstat: Given a list of 2-d points, how to connect them into a polygon, and further make it the study region? | 31,643,100 | 0.197375 | python,r,opencv,computational-geometry,spatstat | Do you want it to be a spatstat study region (of class owin) since you have the spatstat tag on there? In that case you can just use owin(poly=x) where x is your nx2 matrix (after loading the spatstat library of course). The rows in this matrix should contain the vertices of the polygon in the order that you want them ... | Please allow me to start the question with a simplest task：If I have four points which are vertices of a rectangle, stored in a 4x2 matrix, how can I turn this into a rectangular window? (Please do not use any special command specific to drawing rectangles as the rectangle is raised just to represent a general class of... | 0 | 1 | 422 |
0 | 31,667,553 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2015-07-27T18:07:00.000 | 1 | 1 | 0 | How to save a graph that is generated by GNU Radio? | 31,660,214 | 1.2 | python,gnuradio,gnuradio-companion | The "QT GUI Frequency Sink" block will display the frequency domain representation of a signal. You can save a static image of the spectrum by accessing the control panel using center-click and choosing "Save". | I have generated the spectrogram with GNU Radio and want to save the output graph but have no idea how to do it. | 0 | 1 | 791 |
0 | 31,671,206 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2015-07-28T07:27:00.000 | 1 | 1 | 0 | How to set output file permissions using matplotlib savefig()? | 31,669,940 | 1.2 | python,matplotlib,permissions | matplotlib.pyplot.savefig() does not have capability to change file permissions. It has to be done afterwards with os.chmod(path, mode) for example os.chmod(fname, 0o400). | How can I specify the *nix read/write permissions for an output file (e.g. PDF) produced by the matplotlib savefig() command from within a Python script? i.e. without having to use chmod after the file has been produced. | 0 | 1 | 1,542 |
0 | 31,676,562 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2015-07-28T11:38:00.000 | 1 | 1 | 1 | Using mpi4py (or any python module) without installing | 31,675,214 | 0.197375 | python,python-2.7,numpy,mpi4py | Did you try pip install --user mpi4py?
However, I think the best solution would be to just talk to the people in charge of the cluster and see if they will install it. It seems pretty useless to have a cluster without mpi4py installed. | I have some parallel code I have written using numpy and mpi4py modules. Till now I was running it on my laptop but now I want to attack bigger problem sizes by using the computing clusters at my university. The trouble is that they don't have mpi4py installed. Is there anyway to use the module by copying the necessar... | 0 | 1 | 283 |
0 | 31,699,669 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-07-28T21:17:00.000 | -1 | 2 | 0 | supervised dimensionality redunction/topic model using sklearn or gensim | 31,687,263 | -0.099668 | python,machine-learning,gensim,dimensionality-reduction | you can only perform dimensionality reduction in an unsupervised manner OR supervised but with different labels than your target labels.
For example you could train a logistic regression classifier with a dataset containing 100 topics. the output of this classifier (100 values) using your training data could be your di... | I've got BOW vectors and I'm wondering if there's a supervised dimensionality reduction algorithm in sklearn or gensim capable of taking high-dimensional, supervised data and projecting it into a lower dimensional space which preserves the variance between these classes.
Actually I'm trying to find a proper metric for ... | 0 | 1 | 468 |
0 | 32,441,545 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2015-07-29T06:57:00.000 | 0 | 2 | 0 | Avoid deepcopy() in Python when changing dictionary | 31,693,319 | 1.2 | python,parsing,runtime | As @dmargol1 suggested in the comments, I was able to avoid deepcopy() and copy() by instead building the graph from scratch, rather than copying and modifying it, which was actually a lot faster.
If that is possible: do it!
If copying is neccessary, there are two ways. If you don't need to alter the values, copy() is ... | I am looking for a solution to avoid deepcopy() in my task using Python.
I am implementing a statistical dependency parser using chu-liu-edmonds algorithm. I have a graph represented as a dictionary with every head node stored as a key with each having a list containing one or more objects of the class arc as the value... | 0 | 1 | 1,493 |
0 | 31,742,947 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-07-29T21:34:00.000 | 0 | 2 | 0 | random forest with specified false positive and sensitivity | 31,711,555 | 0 | python,r,machine-learning,random-forest | You can do a grid serarch over the 'regularazation' parameters to best match your target behavior.
Parameters of interest:
max depth
number of features | Using the randomForest package in R, I was able to train a random forest that minimized overall error rate. However, what I want to do is train two random forests, one that first minimizes false positive rate (~ 0) and then overall error rate, and one that first maximizes sensitivity (~1), and then overall error. Anoth... | 0 | 1 | 1,366 |
0 | 37,935,771 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-07-31T07:20:00.000 | 0 | 1 | 0 | Regarding opencv 3.0.0 and updating sift feature module in anaconda IDE | 31,740,332 | 0 | python,opencv | This is happening because SIFT (which is considered patent-encumbered or non-free) has been moved from the opencv package to the opencv "contrib" repo. You need a version of cv2 that has been compiled specifically with the contrib included.
Alternatively in cv2, use ORB instead of SIFT. | Currently I am using windows 8.1 64 bit machine and anaconda as IDE. I am getting the error as shown below. please help me how to update module. Import cv2 is working fine but not with sift features.
File "C:\Users\conquistador\Anaconda\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 71, in ex... | 0 | 1 | 366 |
0 | 33,232,261 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2015-07-31T07:32:00.000 | 2 | 1 | 0 | Random number generator using Joblib | 31,740,561 | 0.379949 | python,random,parallel-processing,joblib | This is expected, although unfortunate.
The reason is that joblib (based on the standard multiprocessing Python tool) relies on forking under Unix. Forking creates the exact same processes and thus the same pseudo-random number generation.
The right way to solve this problem is to pass to the function that you are call... | I need to generate random number in a function which is paralleled using Joblib. However, the random number generated from the cores are exactly the same.
Currently I solved the problem by assigning random seeds for different cores. Is there any simple way to solve this problem? | 0 | 1 | 591 |
0 | 31,763,839 | 0 | 0 | 0 | 0 | 1 | true | 3 | 2015-08-01T16:33:00.000 | 5 | 1 | 0 | Reassigning index in pandas DataFrame | 31,763,800 | 1.2 | python,pandas,dataframe | df.reset_index(drop=True, inplace=True) | I'm fairly sure this is a duplicate, but suppose I have a pandas DataFrame and I've sorted the rows based on the values of some column. Originally the indices were the integers 0, 1, …, n-1 but now they're out of order. How do I reassign these indices to be in the proper order for the new sorted DataFrame? | 0 | 1 | 3,763 |
0 | 66,423,995 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2015-08-03T11:29:00.000 | 0 | 1 | 0 | Scipy.generic_filter - window translation to 1D | 31,786,076 | 0 | python,scipy,ndimage | This is really late, but the data that gets passed to your filter function is a numpy array. You should just be able to reshape the data like normal
arr = arr.reshape((y, x)) | I am trying to use scipy.generic_filter to process an image. However, I need to further subset the window within the function I am applying. In another words I need to know the process (function) used to convert the 2D window to 1D array within the generic filter, so I can recreate the 2D array within the applied funct... | 0 | 1 | 64 |
0 | 31,794,556 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-08-03T18:22:00.000 | 2 | 1 | 0 | Python adaptive histogram widths | 31,794,158 | 0.379949 | python,numpy | I think you should first remap your data, then create the histogram, and then interpret the histogram knowing the values have been transformed. One possibility would be to tweak the histogram tick labels so that they display mapped values.
One possible way of doing it, for example, would be:
Sort one dimension of data... | I am currently working on a project where I have to bin up to 10-dimensional data. This works totally fine with numpy.histogramdd, however with one have a serious obstacle:
My parameter space is pretty large, but only a fraction is actually inhabited by data (say, maybe a few % or so...). In these regions, the data is ... | 0 | 1 | 740 |
0 | 31,815,098 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-08-03T23:02:00.000 | 0 | 1 | 0 | Fit a level set field to CAD data | 31,798,097 | 0 | python,autocad,euclidean-distance,freecad | You might look into the DXF file type. (drawing exchange format)
AutoCAD and most freeCAD will read/write DXF files. | I'm looking for software that can fit a signed distance function to a vector image or output from either AutoCAD or FreeCAD. Preferable CAD data because that output is 3D. I'm looking at coding something in either C or Python but I thought I'd check to see if there was anything out there because I couldn't find anyth... | 0 | 1 | 122 |
0 | 31,822,646 | 0 | 1 | 0 | 0 | 1 | false | 4 | 2015-08-05T02:39:00.000 | 0 | 4 | 0 | Logic behind Python indexing | 31,822,523 | 0 | python,indexing | In python: 0 == -0, so x[0] == x[-0].
Why is sequence indexing zero based instead of one based? It is a choice the language designer should do. Most languages I know of use 0 based indexing. Xpath uses 1 based for selection.
Using negative indexing is also a convention for the language. Not sure why it was chosen, but ... | I'm curious in Python why x[0] retrieves the first element of x while x[-1] retrieves the first element when reading in the reverse order. The syntax seems inconsistent to me since in the one case we're counting distance from the first element, whereas we don't count distance from the last element when reading backwar... | 0 | 1 | 180 |
0 | 31,881,089 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2015-08-07T14:53:00.000 | 0 | 3 | 0 | Python - Pick from Array based on price with a caveat | 31,880,759 | 0 | python,arrays | From your description, it might be enough to have a counter associated with each sub-array as to how many of the items in that sub-array have already been bought. Give that you haven't shown any details as to your representation of these things, I can't give more details as to how to implement this. | I'm relatively new to Python and have already written a code to randomly select from two tables based on user input but the next function I need to create is more complex and I'm having trouble wrapping my head around.
I'm going to have some code that's going to take user input and generate an amount of money I'm going... | 0 | 1 | 45 |
0 | 31,881,413 | 0 | 1 | 0 | 0 | 2 | false | 0 | 2015-08-07T14:53:00.000 | 0 | 3 | 0 | Python - Pick from Array based on price with a caveat | 31,880,759 | 0 | python,arrays | It's difficult to understand what you're getting at, because you're not expressing your ideas very well. You're finding this general programming difficult as programming can be considered as the precise expression of ideas.
So, in very general terms you are trying to simulate some sort of curated shopping experience. Y... | I'm relatively new to Python and have already written a code to randomly select from two tables based on user input but the next function I need to create is more complex and I'm having trouble wrapping my head around.
I'm going to have some code that's going to take user input and generate an amount of money I'm going... | 0 | 1 | 45 |
0 | 31,888,691 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2015-08-08T01:08:00.000 | 0 | 3 | 0 | Calculating distances on grid | 31,888,624 | 0 | python,python-2.7,numpy,grid,distance | The simplest way I know of to calculate the distance between two points on a plane is using the Pythagorean theorem.
That is, picture a right angle triangle where the hypotenuse goes between the two points and the base of the triangle is parallel to the x axis and the height is parallel to the y axis. We then know that... | I have a 10 x 10 grid of cells (as a numpy array). I also have a list of 3 points on that grid. For each cell on the grid, I need to find the closest of the three points. I can do this in series of nested loops in python (2.7) which works but is slow (especially if I upscale to larger grids) but I suspect there is a... | 0 | 1 | 2,279 |
0 | 53,851,540 | 0 | 0 | 0 | 0 | 1 | false | 33 | 2015-08-08T14:08:00.000 | -1 | 7 | 0 | Download CSV from an iPython Notebook | 31,893,930 | -0.028564 | csv,pandas,ipython-notebook | My simple approach to download all the files from the jupyter notebook would be by simply using this wonderful command
!tar cvfz my_compressed_file_name.tar.gz *
This will download all the files of the server including the notebooks.
In case if your server has multiple folders, you might be willing to use the following... | I run an iPython Notebook server, and would like users to be able to download a pandas dataframe as a csv file so that they can use it in their own environment. There's no personal data, so if the solution involves writing the file at the server (which I can do) and then downloading that file, I'd be happy with that. | 0 | 1 | 50,655 |
0 | 31,909,251 | 0 | 0 | 0 | 0 | 1 | false | 4 | 2015-08-09T21:21:00.000 | 1 | 3 | 0 | Numpy conditional multiply data in array (if true multiply A, false multiply B) | 31,908,956 | 0.066568 | python,arrays,numpy | np.where is the answer. I spend time messing with np.place without knowing its existence. | Say I have a large array of value 0~255. I wanted every element in this array that is higher than 100 got multiplied by 1.2, otherwise, got multiplied by 0.8.
It sounded simple but I could not find anyway other than iterate through all the variable and multiply it one by one. | 0 | 1 | 6,219 |
0 | 31,915,492 | 0 | 1 | 0 | 0 | 1 | false | 2 | 2015-08-10T08:39:00.000 | 2 | 2 | 0 | Python save variable to file like save() in MATLAB | 31,915,120 | 0.197375 | python-2.7 | Use pickle.dump in Python 3.x, or cPickle.dump in Python 2.x. | I just new in python. How to save variable data to file like save command in MATLAB.
Thank you | 0 | 1 | 1,761 |
0 | 32,591,030 | 0 | 0 | 0 | 0 | 1 | true | 0 | 2015-08-11T13:18:00.000 | 1 | 1 | 0 | How to show star rating on ckan for datasets | 31,942,911 | 1.2 | python,ckan | I'm not aware of any extensions that do this.
You could write one to add this info in a dataset extra field. You may wish to store it as JSON and record the ratings given by each user.
Alternatively you could try the rating_create API function - this is old functionality which has no UI, but it may just do what you wan... | I have used ckanext-qa but its seems its not as per my requirement I am looking for extension by which Logged in user can be able to rate form 1 to 5 for each dataset over ckan.
Anybody have an idea how to do like that | 0 | 1 | 261 |
0 | 31,945,716 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2015-08-11T14:37:00.000 | 3 | 1 | 0 | How does one interpret class weight when working with non linear SVMs? | 31,944,799 | 1.2 | python,machine-learning,scikit-learn,svm | Using class weighting has nothing to do w linear/non-linear kernel. It simply controls the cost of missclassifing particular sample during training. Per-class weight simply put constant weight on each sample in a given class. When you use auto, classes samples gets weightes invertibly proportional to the class size. So... | I'm using Scikit-learn SVM classifier to make predictions and i'm using a rbf kernel. I have set the class_weight = 'auto'. Am I right in thinking that classes that appear more often will get lower weights? Say I had two classes, A and B. If A appeared a lot more than B does that mean that later on when making the pred... | 0 | 1 | 847 |
0 | 31,951,440 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2015-08-11T20:02:00.000 | 0 | 1 | 0 | Plotting in real time | 31,950,928 | 0 | python,multithreading,csv,plot,real-time | Short answer: it depends on your use case.
If you simply want a static graph that updates in real time, then your approach is totally fine.
On the other hand, threading is useful when the user needs to somehow interact with the plot (like zoom in/out, etc) because otherwise I/O operations would block the main routine ... | In python, is it necessary to use threading when plotting in real time? In my specific case, I wish to continuously plot from a .csv that is having lines of data added to it indefinitely. Can't I just create a function that re-reads the data source with every iteration of the plot update? | 0 | 1 | 82 |
0 | 31,952,102 | 0 | 1 | 0 | 0 | 2 | false | 120 | 2015-08-11T21:08:00.000 | 127 | 3 | 0 | What exactly does numpy.exp() do? | 31,951,980 | 1 | python,numpy,statistics,exp | The exponential function is e^x where e is a mathematical constant called Euler's number, approximately 2.718281. This value has a close mathematical relationship with pi and the slope of the curve e^x is equal to its value at every point. np.exp() calculates e^x for each value of x in your input array. | I'm very confused as to what np.exp() actually does. In the documentation it says that it: "Calculates the exponential of all elements in the input array." I'm confused as to what exactly this means. Could someone give me more information to what it actually does? | 0 | 1 | 221,837 |
0 | 31,952,092 | 0 | 1 | 0 | 0 | 2 | false | 120 | 2015-08-11T21:08:00.000 | 62 | 3 | 0 | What exactly does numpy.exp() do? | 31,951,980 | 1 | python,numpy,statistics,exp | It calculates ex for each x in your list where e is Euler's number (approximately 2.718). In other words, np.exp(range(5)) is similar to [math.e**x for x in range(5)]. | I'm very confused as to what np.exp() actually does. In the documentation it says that it: "Calculates the exponential of all elements in the input array." I'm confused as to what exactly this means. Could someone give me more information to what it actually does? | 0 | 1 | 221,837 |
0 | 36,915,460 | 0 | 0 | 0 | 0 | 1 | true | 8 | 2015-08-12T20:38:00.000 | 16 | 3 | 0 | pandas - plot sorted column to increasing integer index | 31,974,942 | 1.2 | python,pandas | This will first sort the series and then plot, ignoring the index of the series:
ts = pd.Series(np.random.randn(100), index=pd.date_range('1/1/2000', periods=100))
ts.sort_values().plot(use_index=False) | Let's say I have a pandas series with numerical values. What's the shortest way to plot the sorted series against an increasing integer index?
The plot should show:
x-axis: 0,1,2,3,4,...
y-axis: the sorted values of the series.
(please notice that I cannot plot it against the series' index, because the index is not nec... | 0 | 1 | 21,421 |
0 | 31,977,464 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-08-12T21:30:00.000 | 0 | 2 | 0 | gensim LDA: How can i generate topics with different words for each topic? | 31,975,754 | 0 | python,algorithm,api,lda,gensim | LDA provides for each topic and each word a probability that the topic generates that word. You can try assigning words to topics by just taking the max over all topics of the probability to generate the word. In other words if topic A generates "monkey" with probability 0.01 and topic B generates the word monkey with ... | I'm using the LDA algorithm from the gensim package to find topics in a given text.
I've been asked that the resulting topics will include different words for each topic, E.G If topic A has the word 'monkey' in it then no other topic should include the word 'monkey' in its list.
My thoughts so far: run it multiple time... | 0 | 1 | 925 |
0 | 31,990,326 | 0 | 0 | 0 | 0 | 1 | true | 4 | 2015-08-13T13:39:00.000 | 6 | 2 | 0 | Local mean filter in numpy | 31,989,903 | 1.2 | python-3.x,numpy,scipy | scipy.ndimage.filters.convolve() with a weight: np.full((3, 3, 3), 1.0/27). | I have a 512x512x512 numpy array. Is there any efficient way to perform a mean filter where every array value is substituted by all 3x3x3 local values?
We are seeking somethin similar to scipy.ndimage.filters.median_filter but insted of median with mean. | 0 | 1 | 9,128 |
0 | 70,254,029 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2015-08-17T21:06:00.000 | 0 | 2 | 0 | In scipy, what's the point of the two different distance functions used in hierarchical clustering? | 32,059,711 | 0 | python,scipy,hierarchical-clustering | The parameter 'method' is used to measure the similarities between clusters through the hierarchical clustering process. The parameter 'metric' is used to measure the distance between two objects in the dataset.
The 'metric' is closely related to the nature of the data (e.g., you could want to use 'euclidean' distance ... | There is one distance function I can pass to pdist use to create the distance matrix that is given to linkage. There is a second distance function that I can pass to linkage as the metric.
Why are there two possible distance functions?
If they are different, how are they used? For instance, does linkage use the dista... | 0 | 1 | 206 |
0 | 32,062,644 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2015-08-18T00:14:00.000 | 3 | 1 | 0 | How to view a saved matplotlib plot | 32,061,658 | 0.53705 | python,matplotlib,graph | Vi is a text editor, and can't view images as images. The Windows Paint program should be able to view them, however, or on a Mac, Preview should work. | I have successfully saved my graphs using the plt.savefig() function in the matplotlib library.
When I try to open my graph using vi, the file is there but there are a lot of strange characters. I guess I'm viewing the code and other info rather than the visualization of the graph. How do I see the graph in its picto... | 0 | 1 | 204 |
0 | 32,094,372 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-08-18T15:03:00.000 | 0 | 1 | 0 | Why does scikit neural network reshape my input array automatically? | 32,076,040 | 0 | python,arrays,numpy,scikit-learn,neural-network | Looking at the source, scikit will reshape input arrays if the X (input samples) you pass in when initializing is a different size from creating/splitting the dataset in the MLP backend. This is to reduce overfitting by training and validating on the same samples. | When I pass my training data to the scikit neural network it is a NumPy array of all my 24*24 image matrices. I check and this is the shape of the data: (3237, 24, 24) - 3237 24*24 images.
However, once I feed it into the neural network, I get this warning:
WARNING:sknn: - Reshaping input array from (3237, 24, 24) to ... | 0 | 1 | 218 |
0 | 32,119,557 | 0 | 0 | 0 | 1 | 1 | true | 1 | 2015-08-18T16:17:00.000 | 1 | 1 | 0 | Use Pyxl to read Excel spreadsheet data up to a certain row | 32,077,627 | 1.2 | python,excel,openpyxl | To be honest I'd be tempted to suggest you use openpyxl all the way if there is something that xlsxwriter doesn't do, though I think that it's formatting options are pretty extensive. The most recent version of openpyxl is as fast as xlsxwriter if lxml is installed.
However, it's worth noting that Pandas has tended to ... | I created an Excel spreadsheet using Pandas and xlsxwriter, which has all the data in the right rows and columns. However, the formatting in xlsxwriter is pretty basic, so I want to solve this problem by writing my Pandas spreadsheet on top of a template spreadsheet with Pyxl.
First, however, I need to get Pyxl to only... | 0 | 1 | 256 |
0 | 62,125,080 | 0 | 0 | 0 | 0 | 1 | false | 91 | 2015-08-20T03:58:00.000 | 1 | 9 | 0 | How to implement the ReLU function in Numpy | 32,109,319 | 0.022219 | python,numpy,machine-learning,neural-network | ReLU(x) also is equal to (x+abs(x))/2 | I want to make a simple neural network which uses the ReLU function. Can someone give me a clue of how can I implement the function using numpy. | 0 | 1 | 165,706 |
0 | 32,126,371 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2015-08-20T18:56:00.000 | 1 | 2 | 0 | How to calculate correlation on large number of records? | 32,126,189 | 0.099668 | python,statistics,correlation,pyspark | Firstly, make sure you're applying the right formula for correlation. Remember, given vectors x and y, correlation is ((x-mean(x)) * (y - mean(y)))/(length(x)*length(y)), where * represents the dot-product and length(x) is the square root of the sum of the squares of the terms in x. (I know that's silly, but noticing... | I am trying to calculate correlation amongst three columns in a dataset. The dataset is relatively large (4 GB in size). When I calculate correlation among the columns of interest, I get small values like 0.0024, -0.0067 etc. I am not sure this result makes any sense or not. Should I sample the data and then try calcul... | 0 | 1 | 116 |
0 | 32,127,507 | 0 | 0 | 0 | 0 | 2 | false | 0 | 2015-08-20T18:56:00.000 | 0 | 2 | 0 | How to calculate correlation on large number of records? | 32,126,189 | 0 | python,statistics,correlation,pyspark | There is nothing special about correlation of large data sets. All you need to do is some simple aggregation.
If you want to improve your numerical precision (remember that floating point math is lossy) you can use Kahan summation and similar techniques, in particular for values close to 0.
But maybe your data justt do... | I am trying to calculate correlation amongst three columns in a dataset. The dataset is relatively large (4 GB in size). When I calculate correlation among the columns of interest, I get small values like 0.0024, -0.0067 etc. I am not sure this result makes any sense or not. Should I sample the data and then try calcul... | 0 | 1 | 116 |
0 | 32,147,241 | 0 | 1 | 0 | 0 | 1 | true | 5 | 2015-08-20T23:19:00.000 | 3 | 1 | 0 | what is the difference between rdd.repartition() and partition size in sc.parallelize(data, partitions) | 32,130,000 | 1.2 | python,apache-spark,rdd | By calling repartition(N) spark will do a shuffle to change the number of partitions (and will by default result in a HashPartitioner with that number of partitions). When you call sc.parallelize with a desired number of partitions it splits your data (more or less) equally up amongst the slices (effectively similar to... | I was going through the documentation of spark. I got a bit confused with rdd.repartition() function and the number of partitions we pass during context initialization in sc.parallelize().
I have 4 cores on my machine, if I sc.parallelize(data, 4) everything works fine, but when I rdd.repartition(4) and apply rdd.mappa... | 0 | 1 | 2,919 |
0 | 32,138,958 | 0 | 0 | 0 | 0 | 1 | false | 2 | 2015-08-21T11:00:00.000 | 0 | 4 | 0 | Finding the points nearest to a given x? | 32,138,651 | 0 | python,algorithm,function | You can compute the distance of chosen points.
1) Search for the minimun distance X value (left and right).
2) Search each points corresponding with X_MIN_LEFT and X_MIN_RIGHT. At the same time you can check the distance with Y and find the minimum Y distance.
That's it. | I need to interpolate a linear function and I can't use numpy or scipy.
I'm given this data points ((0,10), (1,4), (2,3), (3,5), (4,12)) and a point on `x = 2.8. the data points is polyline (linear between 2 coordinates)
of course I need to use the closest x-points from the data for 2.8. which is from (2,3) and (3,5) ... | 0 | 1 | 3,022 |
0 | 32,236,196 | 0 | 0 | 0 | 0 | 1 | false | 1 | 2015-08-21T19:46:00.000 | 2 | 1 | 0 | Truncated SVD vs Partial SVD | 32,148,219 | 0.379949 | python-2.7,scikit-learn,pca | Truncated or partial means that you only calculate a certain number of components/singular vector-value pairs (the strongest ones).
In scikit-learn parlance, "partial" usually refers to the fact that a method is on line, meaning that it can be fed with partial data. The more data you give it the better it will converge... | Can somebody tell me the difference between truncated SVD as implemented in sklearn and partial SVD as implemented in, say, fbpca?
I couldn't find a definitive answer as I haven't seen anybody use truncated SVD for principal component pursuit (PCP). | 0 | 1 | 1,242 |
0 | 32,192,487 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-08-24T14:19:00.000 | 1 | 1 | 1 | Apache Spark RDD transformations with 2 elements as input | 32,184,638 | 0.197375 | python,tcp,apache-spark,pyspark,rdd | It seems like what your looking for might be best done with something like reduceByKey where you can remove the duplicates as you go for each sequence (assuming that the resulting amount of data for each sequence isn't too large, in your example it seems pretty small). Sorting the results can be done with the standard ... | I am trying to 'follow-tcp-stream' in Hadoop sequence file that structured as follows:
i. Time stamp as key
ii. Raw Ethernet frame as value
The file contains a single TCP session, and because the record is very long, sequence-id of TCP frame overflows (which means that seq-id not necessarily unique and data cannot be... | 0 | 1 | 237 |
0 | 34,873,978 | 0 | 0 | 0 | 0 | 1 | false | 20 | 2015-08-24T14:32:00.000 | 1 | 1 | 0 | Calculating eigen values of very large sparse matrices in python | 32,184,915 | 0.197375 | python,scipy,sparse-matrix,eigenvector,eigenvalue | I agree with @pv. If your matrix S was symmetric, you could see it as a laplacian matrix of the matrix I - S. The number of connected components of I - S is the number of zero-eigenvalues of this matrix (i.e, the dimension of the space associated to eigenvalue 1 of S). You could check the number of connected components... | I have a very large sparse matrix which represents a transition martix in a Markov Chain, i.e. the sum of each row of the matrix equals one and I'm interested in finding the first eigenvalue and its corresponding vector which is smaller than one. I know that the eigenvalues are bounded in the section [-1, 1] and they a... | 0 | 1 | 1,334 |
0 | 32,235,511 | 0 | 0 | 0 | 0 | 1 | true | 5 | 2015-08-26T19:37:00.000 | 4 | 2 | 0 | Normalizing a list of restaurant dishes | 32,235,272 | 1.2 | python,machine-learning,nlp,nltk | You might want look for TFIDF and cosine similarity.
There are challenging cases, however. Let's say you have the following three dishes:
Pulled pork
Pulled egg
Egg sandwich
Which of the two you are going to combine?
Pulled pork and pulled egg
Pulled egg and egg sandwich
Using TFIDF, you can find the most repres... | I have a large data set of restaurant dishes (for example, "Pulled Pork", "Beef Brisket"...)
I am trying to "normalize" (wrong word) the dishes. I want "Pulled Pork" and "Pulled Pork Sandwich" and "Jumbo Pork Slider" all to map to a single dish, "Pulled Pork".
So far I have gotten started with NLTK using Python and ha... | 0 | 1 | 183 |
0 | 32,281,179 | 0 | 0 | 0 | 0 | 1 | true | 1 | 2015-08-28T23:23:00.000 | 2 | 1 | 0 | Computational Logistic Regression With Python, Different Sample Sizes | 32,281,130 | 1.2 | python-2.7,machine-learning,statistics,logistic-regression | How much of a problem it is depends on the nature of your data. The bigger issue will be that you simply have a huge class imbalance (50 As for every B). If you end up getting good classification accuracy anyway, then fine - nothing to do. What to do next depends on your data and the nature of the problem and what is a... | Currently, I am trying to implement a basic logistic regression algorithm in Python to differentiate between A vs. B.
For my training and test data, I have ~50,000 samples of A vs. 1000 samples of B. Is this a problem if I use half the data of each to train the algorithm and the other half as testing data (25000 train ... | 0 | 1 | 69 |
0 | 32,290,589 | 0 | 0 | 0 | 0 | 1 | false | 22 | 2015-08-29T20:09:00.000 | 10 | 2 | 0 | Choosing between numpy.interp vs scipy.interpolate.interp1d (with kind='linear') | 32,290,233 | 1 | python,numpy,scipy | While numpy returns an array with discrete datapoints, 'interp1d' returns a function. You can use the generated function later in your code as often as you want. Furthermore, you can choose other methods than linear interpoationn | I'm trying to choose between numpy.interp vs scipy.interpolate.interp1d (with kind='linear' of course). I realize they have different interfaces but that doesn't matter much to me (I can code around either interface). I'm wondering whether there are other differences I should be aware of. Thanks. | 0 | 1 | 7,258 |
0 | 32,304,062 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-08-30T20:04:00.000 | 0 | 1 | 0 | Passing objects to Spark | 32,300,616 | 0 | python,apache-spark,cloud | If your object's aren't picklable your options are pretty limited. If you can create them on the executor side though (frequently a useful option for things like database connections), you can parallelize a regular list (e.g. maybe a list of the constructor parameters) and then use map if your dostuff function returns... | I try to understand the capabilities of Spark, but I fail to see if the following is possible in Python.
I have some objects that are non Pickable (wrapped from C++ with SWIG).
I have a list of those objects obj_list = [obj1, obj2, ...]
All those objects have a member function called .dostuff
I'd like to parallelized t... | 0 | 1 | 335 |
0 | 32,347,770 | 0 | 0 | 0 | 0 | 1 | false | 14 | 2015-09-02T06:00:00.000 | 4 | 3 | 0 | Simulink for Python | 32,345,638 | 0.26052 | python,matlab,simulink | Until now there is no library like Simulink in Python. The closest match is the Modelica language with OpenModelica and a python implementation JModelica. | I have been searching and found many libraries (scipy, numpy, matplotlib) for Python that lets a user easily shift from MATLAB to Python. However, I am unable to find any library that is related to the Simulink in MATLAB. I would like to know if such a library exists or something else that resembles Simulink in it's GU... | 0 | 1 | 28,386 |
0 | 32,363,759 | 0 | 0 | 1 | 0 | 1 | false | 1 | 2015-09-02T21:47:00.000 | 0 | 1 | 0 | openCV track object in video and obtain a better image from multiple frames | 32,363,678 | 0 | python,opencv,raspberry-pi | You need to ensure that your frame rate is fast enough to get a decent still of the moving car. When filming, each frame will most likely be blurry, and our brain pieces together the number plate on playback. Of course a blurry frame is no good for letter recognition, so is something you'll need to deal with on the har... | I'm working on detecting license plates with openCV, python and a raspberry pi. Most of it is already covered. What I want is to detect the ROI (Region of interest) of the plate, track it on a few frames and add those together to get a more clear and crisp image of the plate.
I want to get a better image of the plate b... | 0 | 1 | 330 |
0 | 32,409,317 | 0 | 0 | 0 | 0 | 3 | false | 3 | 2015-09-05T03:38:00.000 | 4 | 5 | 0 | Algo to find duplicates in a very large array | 32,409,274 | 0.158649 | javascript,java,python,c,algorithm | You can do it in O(nlog(n)):
Sort the array
find the duplicates (they will be next to each other) in one pass.
I think that is what the interviewer wanted to hear.
if you did a merge sort or a quick sort, finding the duplicates could be done at merging in hidden time.
These can be implemented "in-place", or "by-part"... | During one of technical interview got this question.
I know way to solve this problem using (in java) HashSet.
But i could not understand when interviewer forced on the word "a very large array let's say 10 million elements in the given array".
Do i need to change the approach? If not, what should be the efficient to a... | 0 | 1 | 3,410 |
0 | 32,409,400 | 0 | 0 | 0 | 0 | 3 | false | 3 | 2015-09-05T03:38:00.000 | 4 | 5 | 0 | Algo to find duplicates in a very large array | 32,409,274 | 0.158649 | javascript,java,python,c,algorithm | There were some key things, which interviewer expected you to ask back like: if you can not load the array in memory, then how much I can load. These are the steps to solve the problem:
you need to divide the array in how much memory is available to you.
Let's say you can load 1M number at a time. You have split the d... | During one of technical interview got this question.
I know way to solve this problem using (in java) HashSet.
But i could not understand when interviewer forced on the word "a very large array let's say 10 million elements in the given array".
Do i need to change the approach? If not, what should be the efficient to a... | 0 | 1 | 3,410 |
0 | 32,409,590 | 0 | 0 | 0 | 0 | 3 | true | 3 | 2015-09-05T03:38:00.000 | 3 | 5 | 0 | Algo to find duplicates in a very large array | 32,409,274 | 1.2 | javascript,java,python,c,algorithm | One thing to keep in mind is that O-notation doesn't necessarily tell you what algorithm is fastest. If one algorithm is O(n log n) and another algorithm is O(n2), then there is some value M such that the first algorithm is faster for all n > M. But M could be much larger than the amount of data you'll ever have to d... | During one of technical interview got this question.
I know way to solve this problem using (in java) HashSet.
But i could not understand when interviewer forced on the word "a very large array let's say 10 million elements in the given array".
Do i need to change the approach? If not, what should be the efficient to a... | 0 | 1 | 3,410 |
0 | 32,428,486 | 0 | 1 | 0 | 0 | 1 | false | 1 | 2015-09-06T21:06:00.000 | 0 | 1 | 0 | Break into debugger on numpy exception in PTVS | 32,428,313 | 0 | python,numpy,ptvs | You can add your own exception types in the Exceptions dialog (in Debug -> Exceptions), and then check them to have it break on them. | I'm using PTVS to debug some code I've written. I'd like to get it to break into the debugger whenever a numpy exception is raised. Currently, it only breaks into the debugger when a standard Python exception is raised; whenever a numpy exception is raised, all I get is a traceback print out. | 0 | 1 | 92 |
0 | 32,554,802 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-09-07T09:02:00.000 | 0 | 2 | 0 | POS tagging - NLTK- Python | 32,434,810 | 0 | python,nltk | Your question confuses the nltk itself with nltk_data. You can't really download just part of the nltk (though you could manually trim it down, carefully, if you need to save space). But I think you're trying to avoid downloading all of the nltk data. As @barny wrote, you can see the IDs of different resources when you... | I want to use word_tokenize, pos_tag, FreqDist. I don't want to download all nltk as default. I want to use nltk.download(info_or_id=''). What options I should put in info_or_id to get the POS tagging and its frequency. POS tagging - Penn Treebank POS. | 0 | 1 | 283 |
0 | 32,510,602 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2015-09-09T20:41:00.000 | 1 | 3 | 0 | Use Histogram data to generate random samples in scipy | 32,488,808 | 0.066568 | python,statistics,scipy | You can also use the histogram, piecewise uniform distribution directly, then you get exactly the corresponding random numbers instead of an approximation.
The inverse cdf, ppf, is piecewise linear and linear interpolation can be used to transform uniform random numbers appropriately. | Suppose I have a process where I push a button, and after a certain amount of time (from 1 to 30 minutes), an event occurs. I then run a very large number of trials, and record how long it takes the event to occur for each trial. This raw data is then reduced to a set of 30 data points where the x value is the number... | 0 | 1 | 2,122 |
0 | 32,490,655 | 0 | 1 | 0 | 0 | 1 | true | 0 | 2015-09-09T23:12:00.000 | 4 | 2 | 0 | Change date format when importing from CSV | 32,490,561 | 1.2 | python,excel,date,csv,import-from-csv | Option 3. Import it properly
Use DATA, Get External Data, From Text and when the wizard prompts you choose the appropriate DMY combination (Step 3 of 3, Under Column data format, and Date). | I have a CSV file where the date is formatted as yy/mm/dd, but Excel is reading it wrongly as dd/mm/yyyy (e.g. 8th September 2015 is read as 15th of September 2008).
I know how to change the format that Excel outputs, but how can I change the format it uses to interpret the CSV data?
I'd like to keep it to Excel if... | 0 | 1 | 1,588 |
0 | 32,502,985 | 0 | 0 | 0 | 0 | 1 | false | 22 | 2015-09-10T03:17:00.000 | 8 | 2 | 0 | What is the difference between sample weight and class weight options in scikit learn? | 32,492,550 | 1 | python,machine-learning,scikit-learn,classification | sample_weight and class_weight have a similar function, that is to make your estimator pay more attention to some samples.
Actual sample weights will be sample_weight * weights from class_weight.
This serves the same purpose as under/oversampling but the behavior is likely to be different: say you have an algorithm th... | I have class imbalance problem and want to solve this using cost sensitive learning.
under sample and over sample
give weights to class to use a modified loss function
Question
Scikit learn has 2 options called class weights and sample weights. Is sample weight actually doing option 2) and class weight options 1)... | 0 | 1 | 12,531 |
0 | 46,094,876 | 0 | 0 | 0 | 0 | 1 | false | 6 | 2015-09-10T20:40:00.000 | 1 | 1 | 0 | Does scikit-learn have Bayes Net ? If yes is there an implementation for reference | 32,511,232 | 0.197375 | python,scikit-learn,bayesian-networks | You can use Weka for classify the data using BayesNet in Python.You can train your data using Weka and save your model as XML then you can write prediction API's in python for that saved model. | I need to classify the data using BayesNet in Python. I have used scikit learn for other classifiers like Random Forests, SVM etc. I know it has Naive Bayes but I am looking for Bayesian Network alone. If anyone could help me with it it would be very helpful Also, if there is an implementation of it for reference that ... | 0 | 1 | 1,288 |
0 | 32,912,189 | 0 | 0 | 0 | 0 | 1 | false | 3 | 2015-09-11T08:40:00.000 | 3 | 2 | 0 | Scipy installation cygwin64 Windows10 fails at late stage | 32,519,166 | 0.291313 | python-2.7,scipy,cygwin,windows-10 | I suffered for days with the same issue. My final solution was to install scipy0.15.1: pip install scipy==0.15.1. Hope it works for you too. | Installed cygwin64, including Python 2.7, on my new computer running Windows10.
Python runs fine, adding modules like matplotlib or bitstream goes fine, but when trying to add scipy the build eventually, after about an hour, having successfully compiled lots of fortran and C/C++ files, fails with:
error: Setup script... | 0 | 1 | 620 |
0 | 32,574,361 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2015-09-12T23:49:00.000 | 1 | 1 | 0 | Cannot import scikit-learn | 32,544,925 | 0.197375 | python,numpy,matplotlib,scikit-learn,anaconda | The MacHD/Library/Frameworks/python.framework/versions/3.4/site-packages/sklearn is for Python 3.4 (note the 3.4 in the path) and the MacHD/Library/Python/2.7/ is for Python 2.7. The packages for each are independent of each other. | Beginner here, please be gentle! I’m receiving an error that reads
ImportError: No module named sklearn when using pycharm.
I’m trying to import matplotlib, numpy, and sklearn. I’ve downloaded scikit_learn. I’ve also downloaded anaconda.
I have “two” pythons. Looks like this…
MacHD/Library/Frameworks/python.fr... | 0 | 1 | 809 |
0 | 32,551,334 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2015-09-13T06:35:00.000 | 4 | 1 | 0 | Which classifiers handle missing values in scikit learn 0.16.1 | 32,546,992 | 1.2 | python,scikit-learn | RandomForests in scikit-learn don't handle missing values at the moment [as of 0.16 and upcoming 0.17], and you do need to impute the values before. | I have data with missing values and I would like to build a classifier for it. I know that scikit-learn will help you impute values for the missing data. However, in my case it is not clear this is the right thing to do or even easy. The problem is that the features in the data are correlated so it's not obvious now to... | 0 | 1 | 2,290 |
0 | 32,555,599 | 0 | 0 | 0 | 0 | 2 | true | 1 | 2015-09-13T19:39:00.000 | 2 | 2 | 0 | If my entire training set of documents is class A, how can I use TF-IDF to find other documents of class A? | 32,553,806 | 1.2 | python,machine-learning,tf-idf,text-classification | What I think you have is an unsupervised learning application. Clustering. Using the combined X & Y dataset, generate clusters. Then overlay the X boundary; the boundary that contains all X samples. All items from Y in the X boundary can be considered X. And the X-ness of a given sample from Y is the distance from... | I have a collection X of documents, all of which are of class A (the only class in which I'm interested or know anything about). I also have a much larger collection Y of documents that I know nothing about. The documents in X and Y come from the same source and have similar formats and somewhat similar subject matters... | 0 | 1 | 264 |
0 | 32,604,626 | 0 | 0 | 0 | 0 | 2 | false | 1 | 2015-09-13T19:39:00.000 | 1 | 2 | 0 | If my entire training set of documents is class A, how can I use TF-IDF to find other documents of class A? | 32,553,806 | 0.099668 | python,machine-learning,tf-idf,text-classification | The easiest thing to do is what was already proposed - clustering. More specifically, you extract a single feature vector from set X and then apply K-means clustering to the whole X & Y set.
ps: Be careful not to confuse k-means with kNN (k-nearest neighbors). You are able to apply only unsupervised learning methods. | I have a collection X of documents, all of which are of class A (the only class in which I'm interested or know anything about). I also have a much larger collection Y of documents that I know nothing about. The documents in X and Y come from the same source and have similar formats and somewhat similar subject matters... | 0 | 1 | 264 |
0 | 32,574,697 | 0 | 0 | 0 | 0 | 1 | true | 2 | 2015-09-14T21:09:00.000 | 1 | 1 | 1 | How can I reference libraries for ApacheSpark using IPython Notebook only? | 32,573,995 | 1.2 | python,apache-spark,ipython,ibm-cloud,jupyter | You cannot add 3rd party libraries at this point in the beta. This will most certainly be coming later in the beta as it's a popular requirement ;-) | I'm currently playing around with the Apache Spark Service in IBM Bluemix. There is a quick start composite application (Boilerplate) consisting of the Spark Service itself, an OpenStack Swift service for the data and an IPython/Jupyter Notebook.
I want to add some 3rd party libraries to the system and I'm wondering h... | 0 | 1 | 157 |
0 | 32,641,163 | 0 | 1 | 0 | 0 | 1 | false | 0 | 2015-09-17T22:16:00.000 | 0 | 2 | 0 | In Python, can you change how a method from class 1 acts on class 2 from within class 2? | 32,640,830 | 0 | python,numpy,multidimensional-array,scipy,subclass | No. Numpy's asarray() is coded to instantiate a regular numpy array, and you can't change that without editing asarray() or changing the caller's code to call your special method instead of asarray() | Basically I have a class which subclasses ndarray and has additional information. When I call np.asarray() on my object, it returns just the numpy array and destroys my additional information.
My question is then this: Is there a way in Python to change how np.asarray() acts on my subclass of ndarray from within my sub... | 0 | 1 | 54 |
0 | 32,664,411 | 0 | 1 | 0 | 0 | 1 | true | 8 | 2015-09-18T20:00:00.000 | 28 | 3 | 0 | How to install OpenCV on Windows and enable it for PyCharm without using the package manager | 32,660,114 | 1.2 | python,windows,opencv,installation,pycharm | I finally figured out on how to solve this issue:
Steps to follow:
Install Python 2.7.10
Install Pycharm(If you have not done it already)
Download and install the OpenCV executable.
Add OpenCV in the system path(%OPENCV_DIR% = /path/of/opencv/directory)
Goto C:\opencv\build\python\2.7\x86 folder and copy cv2.pyd file.... | I am trying to install and use OpenCV library for python development. I want to use it for PyCharm IDE. I am trying to do it without the package manager.
The environment is a windows 64 bit architecture. For Python I am using Python 2.7.10.
I have already included the OpenCV directory in the system path.
I am using py... | 0 | 1 | 66,287 |
0 | 65,357,723 | 0 | 1 | 0 | 0 | 4 | false | 24 | 2015-09-20T02:07:00.000 | 1 | 8 | 0 | Getting PyCharm to import sklearn | 32,675,024 | 0.024995 | python,scikit-learn,python-import,anaconda | SOLVED:
reinstalled Python 3.7.9 (not the latet)
installed numpy 1.17.5 (not the latest)
installed scikit-learn (latest)
sklearn works now! | Beginner here.
I’m trying to use sklearn in pycharm. When importing sklearn I get an error that reads “Import error: No module named sklearn”
The project interpreter in pycharm is set to 2.7.10 (/anaconda/bin/python.app), which should be the right one.
Under default preferenes, project interpreter, I see all of anacon... | 0 | 1 | 67,839 |
0 | 53,357,368 | 0 | 1 | 0 | 0 | 4 | false | 24 | 2015-09-20T02:07:00.000 | 0 | 8 | 0 | Getting PyCharm to import sklearn | 32,675,024 | 0 | python,scikit-learn,python-import,anaconda | For Mac OS:
PyCharm --> Preferences --> Project Interpreter --> Double Click on pip (a new window will open with search option) --> mention 'Scikit-learn' on the search bar --> Install Packages --> Once installed, close that new window --> OK on the existing window
and you are done. | Beginner here.
I’m trying to use sklearn in pycharm. When importing sklearn I get an error that reads “Import error: No module named sklearn”
The project interpreter in pycharm is set to 2.7.10 (/anaconda/bin/python.app), which should be the right one.
Under default preferenes, project interpreter, I see all of anacon... | 0 | 1 | 67,839 |
0 | 54,702,828 | 0 | 1 | 0 | 0 | 4 | false | 24 | 2015-09-20T02:07:00.000 | 8 | 8 | 0 | Getting PyCharm to import sklearn | 32,675,024 | 1 | python,scikit-learn,python-import,anaconda | please notice that, in the packages search 'Scikit-learn', instead 'sklearn' | Beginner here.
I’m trying to use sklearn in pycharm. When importing sklearn I get an error that reads “Import error: No module named sklearn”
The project interpreter in pycharm is set to 2.7.10 (/anaconda/bin/python.app), which should be the right one.
Under default preferenes, project interpreter, I see all of anacon... | 0 | 1 | 67,839 |
0 | 50,808,255 | 0 | 1 | 0 | 0 | 4 | false | 24 | 2015-09-20T02:07:00.000 | 1 | 8 | 0 | Getting PyCharm to import sklearn | 32,675,024 | 0.024995 | python,scikit-learn,python-import,anaconda | Same error occurs to me i have fixed by selecting File Menu-> Default Settings-> Project Interpreter -> Press + button and type 'sklearn' Press install button. Installation will be done in 10 to 20 seconds.
If issue not resolved please check you PyCharm Interpreter path. Sometimes your machine have Python 2.7 and Pytho... | Beginner here.
I’m trying to use sklearn in pycharm. When importing sklearn I get an error that reads “Import error: No module named sklearn”
The project interpreter in pycharm is set to 2.7.10 (/anaconda/bin/python.app), which should be the right one.
Under default preferenes, project interpreter, I see all of anacon... | 0 | 1 | 67,839 |
0 | 32,709,770 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-09-21T14:22:00.000 | 0 | 1 | 0 | Face detection for two faces-Cascade opencv python | 32,697,663 | 0 | python,opencv,face-detection | Sort the detected faces by size and keep the biggest one only? | I am working on with the Face detection using cascade Classifier in opencv python. Its working fine. But i want to develop my code to detect only one face and also the largest face only to detect. | 0 | 1 | 216 |
0 | 32,797,781 | 0 | 0 | 0 | 0 | 1 | false | 0 | 2015-09-24T07:54:00.000 | 2 | 1 | 0 | OpenCV can't find the right version of CUDA | 32,756,140 | 0.379949 | python,c++,opencv,cuda,nvidia | Upgrading from CMake 2.8 to CMake 3.2.2 seems to have solved this particular issue.
[This answer has been added from information gleaned from comments in order to get the question off the unanswered list] | I installed OpenCV 3.0.0 but I'm having troubles any C++ or Python code using OpenCV. For testing, I went into the directory opencv-3.0.0/samples and ran cmake to build the samples. I got the following error:
CMake Error at /usr/share/cmake-2.8/Modules/FindPackageHandleStandardArgs.cmake:108 (message):
Could NOT find C... | 0 | 1 | 1,214 |
0 | 55,287,554 | 0 | 1 | 0 | 0 | 2 | false | 19 | 2015-09-24T12:38:00.000 | 8 | 3 | 0 | Python scikit learn n_jobs | 32,761,556 | 1 | python,parallel-processing,scikit-learn,joblib | what is the point of using n-jobs (and joblib) if the the library uses all cores anyway?
It does not, if you specify n_jobs to -1, it will use all cores. If it is set to 1 or 2, it will use one or two cores only (test done scikit-learn 0.20.3 under Linux). | This is not a real issue, but I'd like to understand:
running sklearn from Anaconda distrib on a Win7 4 cores 8 GB system
fitting a KMeans model on a 200.000 samples*200 values table.
running with n-jobs = -1: (after adding the if __name__ == '__main__': line to my script) I see the script starting 4 processes with
10... | 0 | 1 | 46,321 |
0 | 59,874,972 | 0 | 1 | 0 | 0 | 2 | false | 19 | 2015-09-24T12:38:00.000 | -1 | 3 | 0 | Python scikit learn n_jobs | 32,761,556 | -0.066568 | python,parallel-processing,scikit-learn,joblib | You should either use n_jobs or joblib, don't use both simultaneously. | This is not a real issue, but I'd like to understand:
running sklearn from Anaconda distrib on a Win7 4 cores 8 GB system
fitting a KMeans model on a 200.000 samples*200 values table.
running with n-jobs = -1: (after adding the if __name__ == '__main__': line to my script) I see the script starting 4 processes with
10... | 0 | 1 | 46,321 |
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