From 4121205cf3db7b8fb66d45384934d8a6cf0b2e1a Mon Sep 17 00:00:00 2001 From: Spring Operator Date: Wed, 20 Mar 2019 01:34:45 -0500 Subject: [PATCH] URL Cleanup This commit updates URLs to prefer the https protocol. Redirects are not followed to avoid accidentally expanding intentionally shortened URLs (i.e. if using a URL shortener). # HTTP URLs that Could Not Be Fixed These URLs were unable to be fixed. Please review them to see if they can be manually resolved. * http://hardlifeofapo.com/psycopg2-and-postgresql-9-1-on-snow-leopard/ (200) with 1 occurrences could not be migrated: ([https](https://hardlifeofapo.com/psycopg2-and-postgresql-9-1-on-snow-leopard/) result NotSslRecordException). * http://www.eithoncadag.com/files/pyroc.txt (200) with 1 occurrences could not be migrated: ([https](https://www.eithoncadag.com/files/pyroc.txt) result SSLHandshakeException). * http://aimotion.bogspot.com (302) with 1 occurrences could not be migrated: ([https](https://aimotion.bogspot.com) result AnnotatedConnectException). # Fixed URLs ## Fixed But Review Recommended These URLs were fixed, but the https status was not OK. However, the https status was the same as the http request or http redirected to an https URL, so they were migrated. Your review is recommended. * http://madlib.net (301) with 1 occurrences migrated to: https://madlib.apache.org/ ([https](https://madlib.net) result SSLHandshakeException). * http://doc.madlib.net/v0.5/ (301) with 4 occurrences migrated to: https://madlib.apache.org/docs/v0.5/ ([https](https://doc.madlib.net/v0.5/) result SSLHandshakeException). * http://doc.madlib.net/v0.5/group__grp__kernmach.html (301) with 1 occurrences migrated to: https://madlib.apache.org/docs/v0.5/group__grp__kernmach.html ([https](https://doc.madlib.net/v0.5/group__grp__kernmach.html) result SSLHandshakeException). * http://doc.madlib.net/v0.5/group__grp__kmeans.html (301) with 1 occurrences migrated to: https://madlib.apache.org/docs/v0.5/group__grp__kmeans.html ([https](https://doc.madlib.net/v0.5/group__grp__kmeans.html) result SSLHandshakeException). * http://doc.madlib.net/v0.5/group__grp__linreg.html (301) with 1 occurrences migrated to: https://madlib.apache.org/docs/v0.5/group__grp__linreg.html ([https](https://doc.madlib.net/v0.5/group__grp__linreg.html) result SSLHandshakeException). * http://doc.madlib.net/v0.5/group__grp__logreg.html (301) with 1 occurrences migrated to: https://madlib.apache.org/docs/v0.5/group__grp__logreg.html ([https](https://doc.madlib.net/v0.5/group__grp__logreg.html) result SSLHandshakeException). * http://doc.madlib.net/v0.5/group__grp__plda.html (301) with 1 occurrences migrated to: https://madlib.apache.org/docs/v0.5/group__grp__plda.html ([https](https://doc.madlib.net/v0.5/group__grp__plda.html) result SSLHandshakeException). * http://pythonpaste.org (301) with 1 occurrences migrated to: https://web.archive.org/web/http%3A//pythonpaste.org/ ([https](https://pythonpaste.org) result ConnectTimeoutException). * http://www.initd.org/psycopg/articles/2010/11/11/links-about-building-psycopg-mac-os-x/ (UnknownHostException) with 1 occurrences migrated to: https://www.initd.org/psycopg/articles/2010/11/11/links-about-building-psycopg-mac-os-x/ ([https](https://www.initd.org/psycopg/articles/2010/11/11/links-about-building-psycopg-mac-os-x/) result UnknownHostException). ## Fixed Success These URLs were switched to an https URL with a 2xx status. While the status was successful, your review is still recommended. * http://archive.ics.uci.edu/ml/datasets/Auto+MPG with 2 occurrences migrated to: https://archive.ics.uci.edu/ml/datasets/Auto+MPG ([https](https://archive.ics.uci.edu/ml/datasets/Auto+MPG) result 200). * http://archive.ics.uci.edu/ml/datasets/Wine+Quality with 3 occurrences migrated to: https://archive.ics.uci.edu/ml/datasets/Wine+Quality ([https](https://archive.ics.uci.edu/ml/datasets/Wine+Quality) result 200). * http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names with 1 occurrences migrated to: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names ([https](https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names) result 200). * http://en.wikipedia.org/wiki/Categorical_variable with 2 occurrences migrated to: https://en.wikipedia.org/wiki/Categorical_variable ([https](https://en.wikipedia.org/wiki/Categorical_variable) result 200). * http://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd with 1 occurrences migrated to: https://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd ([https](https://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd) result 200). * http://pivotalsoftware.github.io/pymadlib/ with 1 occurrences migrated to: https://pivotalsoftware.github.io/pymadlib/ ([https](https://pivotalsoftware.github.io/pymadlib/) result 200). * http://postgresapp.com/ with 1 occurrences migrated to: https://postgresapp.com/ ([https](https://postgresapp.com/) result 200). * http://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg with 1 occurrences migrated to: https://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg ([https](https://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg) result 200). * http://networkx.github.com/download.html with 2 occurrences migrated to: https://networkx.github.com/download.html ([https](https://networkx.github.com/download.html) result 301). * http://vatsan.github.com/pymadlib with 1 occurrences migrated to: https://vatsan.github.com/pymadlib ([https](https://vatsan.github.com/pymadlib) result 301). * http://www.opensource.org/licenses/mit-license.php with 1 occurrences migrated to: https://www.opensource.org/licenses/mit-license.php ([https](https://www.opensource.org/licenses/mit-license.php) result 301). --- README.md | 18 +++++++++--------- README.txt | 10 +++++----- pymadlib/doc/PyMADlib Tutorial.ipynb | 2 +- pymadlib/example.py | 2 +- pymadlib/pymadlib.py | 12 ++++++------ pymadlib/pyroc.py | 2 +- pymadlib/utils.py | 4 ++-- setup.py | 8 ++++---- 8 files changed, 29 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index 54b151a..5acdf90 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ A Python wrapper for MADlib - an open source library for scalable in-database machine learning algorithms. -You can visit [PyMADlib's webpage](http://pivotalsoftware.github.io/pymadlib/) for installation and usage tutorials. +You can visit [PyMADlib's webpage](https://pivotalsoftware.github.io/pymadlib/) for installation and usage tutorials. ## Algorithms @@ -11,14 +11,14 @@ PyMADlib currently has wrappers for the following algorithms in MADlib (version 1. K-Means 1. LDA -Refer [MADlib User Docs](http://doc.madlib.net/v0.5/ ) for MADlib's user documentation. Please note that PyMADlib as of now is only compatible with MADlib v0.5. You can obtain MADlib v0.5 from [MADlib v0.5](https://github.com/madlib/madlib/archive/v0.5.tar.gz). We might add support to more recent versions of MADlib depending on adoption rate. Please email me if you have a strong case for an upgrade. +Refer [MADlib User Docs](https://madlib.apache.org/docs/v0.5/ ) for MADlib's user documentation. Please note that PyMADlib as of now is only compatible with MADlib v0.5. You can obtain MADlib v0.5 from [MADlib v0.5](https://github.com/madlib/madlib/archive/v0.5.tar.gz). We might add support to more recent versions of MADlib depending on adoption rate. Please email me if you have a strong case for an upgrade. ## Dependencies 1. You'll need the python extension _**psycopg2**_ to use PyMADlib. 1. If you have matplotlib installed, you'll see Matplotlib visualizations for Linear Regression demo. -1. If you have installed [networkx](http://networkx.github.com/download.html), you'll see a visualization of the k-means demo +1. If you have installed [networkx](https://networkx.github.com/download.html), you'll see a visualization of the k-means demo 1. [PyROC](https://github.com/marcelcaraciolo/PyROC) is included in the source of this distribution with permission from its developer. You'll see a visualization of the ROC curves for Logistic Regression. @@ -53,7 +53,7 @@ PyMADlib depends on `MADlib`, `psycopg2` and `Pandas`. It is easiest to work wit ## Build Environment Setup on Mac OS X 10.8 -* Download & install [Anaconda-1.9.0-MacOSX-x86_64.pkg] (http://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg) +* Download & install [Anaconda-1.9.0-MacOSX-x86_64.pkg] (https://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg) * Open a terminal and check if you have Anaconda Python & the package manager conda @@ -62,7 +62,7 @@ PyMADlib depends on `MADlib`, `psycopg2` and `Pandas`. It is easiest to work wit > vatsan-mac$ which conda > /Users/vatsan/anaconda/bin/conda -* If you haven't installed PostgreSQL on your Mac already, you'll have to download & install `PostGreSQL` for Mac. This is so that we get some required libraries to compile the SQL Engine: psycopg2. The easiest way to install `PostGreSQL` on Mac is via `http://postgresapp.com/`. Once you've downloaded and installed PostGreSQL on Mac, it should typically be found under `/Library/PostgreSQL` +* If you haven't installed PostgreSQL on your Mac already, you'll have to download & install `PostGreSQL` for Mac. This is so that we get some required libraries to compile the SQL Engine: psycopg2. The easiest way to install `PostGreSQL` on Mac is via `https://postgresapp.com/`. Once you've downloaded and installed PostGreSQL on Mac, it should typically be found under `/Library/PostgreSQL` > vatsan-mac$ ls /Library/PostgreSQL/9.2/ > Library include pg_env.sh uninstall-postgresql.app @@ -98,7 +98,7 @@ If the above command did not error out, then installation was successful. ## Usage Tutorial -Visit [PyMADlib Tutorial](http://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd) for a tutorial on using PyMADlib +Visit [PyMADlib Tutorial](https://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd) for a tutorial on using PyMADlib Also visit [PyMADlib IPython NB](https://gist.github.com/vatsan/dd88abb47c2fbd9e16bd) to download the IPython NB tutorial @@ -137,9 +137,9 @@ Remember to close the Matplotlib windows that pop-up to continue with the rest o PyMADlib packages publicly available datasets from the UCI machine learning repository and other sources. -1. [Wine quality dataset from UCI Machine Learning repository](http://archive.ics.uci.edu/ml/datasets/Wine+Quality) -1. [Auto MPG dataset from UCI ML repository from UCI Machine Learning repository](http://archive.ics.uci.edu/ml/datasets/Auto+MPG) -1. [Wine quality dataset from UCI Machine Learning repository](http://archive.ics.uci.edu/ml/datasets/Wine+Quality) +1. [Wine quality dataset from UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets/Wine+Quality) +1. [Auto MPG dataset from UCI ML repository from UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG) +1. [Wine quality dataset from UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets/Wine+Quality) 1. Obama-Romney second presidential debate (2012) transcripts diff --git a/README.txt b/README.txt index 24344be..7f653dc 100644 --- a/README.txt +++ b/README.txt @@ -3,14 +3,14 @@ Python wrapper for MADlib Srivatsan Ramanujam , 3 Jan 2013 This currently implements Linear regression, Logistic Regression, SVM (regression & classification), K-Means and LDA algorithms of MADlib. -Refer : http://doc.madlib.net/v0.5/ for MADlib's user documentation. +Refer : https://madlib.apache.org/docs/v0.5/ for MADlib's user documentation. ================================================================================ Dependencies : =============== You'll need the python extension : psycopg2 to use PyMADlib. (i) If you have matplotlib installed, you'll see Matplotlib visualizations for Linear Regression demo. - (ii) If you have installed networkx (http://networkx.github.com/download.html), you'll see a visualization of the k-means demo + (ii) If you have installed networkx (https://networkx.github.com/download.html), you'll see a visualization of the k-means demo (iii) PyROC (https://github.com/marcelcaraciolo/PyROC) is included in the source of this distribution with permission from its developer. You'll see a visualization of the ROC curves for Logistic Regression. Configurations: @@ -56,8 +56,8 @@ Datasets packaged with this installation : ========================================= PyMADlib packages publicly available datasets from the UCI machine learning repository and other sources. -1) Wine quality dataset from UCI Machine Learning repository : http://archive.ics.uci.edu/ml/datasets/Wine+Quality -2) Auto MPG dataset from UCI ML repository : http://archive.ics.uci.edu/ml/datasets/Auto+MPG +1) Wine quality dataset from UCI Machine Learning repository : https://archive.ics.uci.edu/ml/datasets/Wine+Quality +2) Auto MPG dataset from UCI ML repository : https://archive.ics.uci.edu/ml/datasets/Auto+MPG 3) Obama-Romney second presidential debate (2012) transcripts for the LDA models. @@ -71,6 +71,6 @@ with installing psycopg2. Here are some blogs which discuss the issue and offer solutions: http://hardlifeofapo.com/psycopg2-and-postgresql-9-1-on-snow-leopard/ -http://www.initd.org/psycopg/articles/2010/11/11/links-about-building-psycopg-mac-os-x/ +https://www.initd.org/psycopg/articles/2010/11/11/links-about-building-psycopg-mac-os-x/ diff --git a/pymadlib/doc/PyMADlib Tutorial.ipynb b/pymadlib/doc/PyMADlib Tutorial.ipynb index e7176b9..ede04b0 100644 --- a/pymadlib/doc/PyMADlib Tutorial.ipynb +++ b/pymadlib/doc/PyMADlib Tutorial.ipynb @@ -28,7 +28,7 @@ "1. K-Means \n", "1. LDA \n", "\n", - "Refer [MADlib User Docs](http://doc.madlib.net/v0.5/ ) for MADlib's user documentation.\n", + "Refer [MADlib User Docs](https://madlib.apache.org/docs/v0.5/ ) for MADlib's user documentation.\n", "\n", "We can employ it to push the heavy number crunching to MADlib, while allowing us to work with awesomeness of Python in the front end." ] diff --git a/pymadlib/example.py b/pymadlib/example.py index 0ceb801..37ea10c 100644 --- a/pymadlib/example.py +++ b/pymadlib/example.py @@ -86,7 +86,7 @@ def linearRegressionDemo(conn): smat = scatter_matrix(predictions.get(['quality','prediction']), diagonal='kde') # 1 b) Linear Regression with categorical variables - # We'll use the auto_mpg dataset from UCI : http://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names + # We'll use the auto_mpg dataset from UCI : https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names # make, fuel_type, fuel_system are all categorical variables, rest are real. #Train Linear Regression Model on a mixture of Numeric and Categorical Variables mdl_dict, mdl_params = lreg.train('public.auto_mpg_train',['1','height','width','length','highway_mpg','engine_size','make','fuel_type','fuel_system'],'price') diff --git a/pymadlib/pymadlib.py b/pymadlib/pymadlib.py index bad2c83..a570ff2 100644 --- a/pymadlib/pymadlib.py +++ b/pymadlib/pymadlib.py @@ -7,7 +7,7 @@ 3) SVM (regression & classification) and 4) K-Means & 5) PLDA - Refer : http://doc.madlib.net/v0.5/ for MADlib's user documentation. + Refer : https://madlib.apache.org/docs/v0.5/ for MADlib's user documentation. ''' from utils import pivotCategoricalColumns, convertsColsToArray import psycopg2 @@ -98,7 +98,7 @@ def predict(self, *args): class LinearRegression(SupervisedLearning): ''' Python Wrapper to invoke MADlib's Linear Regression Algorithm - http://doc.madlib.net/v0.5/group__grp__linreg.html + https://madlib.apache.org/docs/v0.5/group__grp__linreg.html ''' def __init__(self,conn): super(LinearRegression,self).__init__(conn) @@ -184,7 +184,7 @@ def predict(self, predict_table_name, actual_label_col=''): class LogisticRegression(SupervisedLearning): ''' Python Wrapper to invoke MADlib's Logistic Regression Algorithm - http://doc.madlib.net/v0.5/group__grp__logreg.html + https://madlib.apache.org/docs/v0.5/group__grp__logreg.html ''' def __init__(self,conn): super(LogisticRegression,self).__init__(conn) @@ -293,7 +293,7 @@ def predict(self, predict_table_name,actual_label_col='',threshold=0.5): class SVM(SupervisedLearning): ''' Python Wrapper to invoke MADlib's SVM Algorithm - http://doc.madlib.net/v0.5/group__grp__kernmach.html + https://madlib.apache.org/docs/v0.5/group__grp__kernmach.html ''' def __init__(self,conn): super(SVM,self).__init__(conn) @@ -494,7 +494,7 @@ def predict_batch(self, predict_table, output_table, id_col, data_col): class KMeans(object): ''' Python Wrapper to invoke MADlib's KMeans Algorithm - http://doc.madlib.net/v0.5/group__grp__kmeans.html + https://madlib.apache.org/docs/v0.5/group__grp__kmeans.html ''' def __init__(self,conn): self.dbconn = conn @@ -611,7 +611,7 @@ def generateClusters( class PLDA(object): ''' Python Wrapper to invoke MADlib's PLDA Algorithm - http://doc.madlib.net/v0.5/group__grp__plda.html + https://madlib.apache.org/docs/v0.5/group__grp__plda.html ''' def __init__(self,conn): self.dbconn = conn diff --git a/pymadlib/pyroc.py b/pymadlib/pyroc.py index 877f84d..f62d154 100644 --- a/pymadlib/pyroc.py +++ b/pymadlib/pyroc.py @@ -351,7 +351,7 @@ def _calculate_counts(self,pos_data,neg_data): if __name__ == '__main__': print "PyRoC - ROC Curve Generator" print "By Marcel Pinheiro Caraciolo (@marcelcaraciolo)" - print "http://aimotion.bogspot.com\n" + print "http://ww1.bogspot.com\n" from optparse import OptionParser parser = OptionParser() diff --git a/pymadlib/utils.py b/pymadlib/utils.py index 530cd8d..504f563 100644 --- a/pymadlib/utils.py +++ b/pymadlib/utils.py @@ -184,7 +184,7 @@ def __getColNamesAndTypesList__(cols,col_types_dict, col_distinct_vals_dict): ''' Return a list of column names and types, where any categorical column in the original table have been 'binarized'. Dummy coding is used to convert categorical columns into dummy variables. - Refer: http://en.wikipedia.org/wiki/Categorical_variable#Dummy_coding + Refer: https://en.wikipedia.org/wiki/Categorical_variable#Dummy_coding Inputs: ======= @@ -278,7 +278,7 @@ def pivotCategoricalColumns(conn,table_name,cols,label='',col_distinct_vals_dict Take a table_name and a set of columns (some of which may be categorical and return a new table, where the categorical columns have been pivoted. This method uses the "Dummy Coding" approach: - http://en.wikipedia.org/wiki/Categorical_variable#Dummy_coding + https://en.wikipedia.org/wiki/Categorical_variable#Dummy_coding Inputs: ======= diff --git a/setup.py b/setup.py index f4d0630..32b8ad9 100644 --- a/setup.py +++ b/setup.py @@ -10,8 +10,8 @@ './dist', 'EGG-INFO', '*.egg-info') -# (c) 2005 Ian Bicking and contributors; written for Paste (http://pythonpaste.org) -# Licensed under the MIT license: http://www.opensource.org/licenses/mit-license.php +# (c) 2005 Ian Bicking and contributors; written for Paste (https://web.archive.org/web/http%3A//pythonpaste.org/) +# Licensed under the MIT license: https://www.opensource.org/licenses/mit-license.php # Note: you may want to copy this into your setup.py file verbatim, as # you can't import this from another package, when you don't know if # that package is installed yet. @@ -98,12 +98,12 @@ def find_package_data( version='1.0', author='Srivatsan Ramanujam', author_email='vatsan.cs@utexas.edu', - url='http://vatsan.github.com/pymadlib', + url='https://vatsan.github.com/pymadlib', packages=find_packages(), package_data=find_package_data(only_in_packages=False,show_ignored=True), include_package_data=True, license='LICENSE.txt', - description='A Python wrapper for MADlib (http://madlib.net) - an open source library for scalable in-database machine learning algorithms', + description='A Python wrapper for MADlib (https://madlib.apache.org/) - an open source library for scalable in-database machine learning algorithms', long_description=open('README.txt').read(), install_requires=[ "psycopg2 >= 2.4.5",