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@drazenz
drazenz / step_1.py
Created April 14, 2019 19:27
# Step 1 - Make a scatter plot with square markers, set column names as labels
def heatmap(x, y, size):
fig, ax = plt.subplots()
# Mapping from column names to integer coordinates
x_labels = [v for v in sorted(x.unique())]
y_labels = [v for v in sorted(y.unique())]
x_to_num = {p[1]:p[0] for p in enumerate(x_labels)}
y_to_num = {p[1]:p[0] for p in enumerate(y_labels)}
@kauffmanes
kauffmanes / install_anaconda.md
Last active May 24, 2024 08:51
Install Anaconda on Windows Subsystem for Linux (WSL)

Thanks everyone for commenting/contributing! I made this in college for a class and I no longer really use the technology. I encourage you all to help each other, but I probably won't be answering questions anymore.

This article is also on my blog: https://emilykauffman.com/blog/install-anaconda-on-wsl

Note: $ denotes the start of a command. Don't actually type this.

Steps to Install Anaconda on Windows Ubuntu Terminal

  1. Install WSL (Ubuntu for Windows - can be found in Windows Store). I recommend the latest version (I'm using 18.04) because there are some bugs they worked out during 14/16 (microsoft/WSL#785)
  2. Go to https://repo.continuum.io/archive to find the list of Anaconda releases
  3. Select the release you want. I have a 64-bit computer, so I chose the latest release ending in x86_64.sh. If I had a 32-bit computer, I'd select the x86.sh version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose `Anaconda3-5.2.0-Li
@W4ngatang
W4ngatang / download_glue_data.py
Last active May 23, 2024 12:55
Script for downloading data of the GLUE benchmark (gluebenchmark.com)
''' Script for downloading all GLUE data.
Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized,
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).
mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC
@moustaki
moustaki / Faster save-load for word2vec
Created February 19, 2014 12:37
Faster save-load for word2vec
import dbm, os
import cPickle as pickle
from gensim.models import Word2Vec
import numpy as np
def save_model(model, directory):
model.init_sims() # making sure syn0norm is initialised
if not os.path.exists(directory):
os.makedirs(directory)
# Saving indexes as DBM'ed dictionary