A "Best of the Best Practices" (BOBP) guide to developing in Python.
- "Build tools for others that you want to be built for you." - Kenneth Reitz
- "Simplicity is alway better than functionality." - Pieter Hintjens
"""Information Retrieval metrics | |
Useful Resources: | |
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
http://www.nii.ac.jp/TechReports/05-014E.pdf | |
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
""" | |
import numpy as np |
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
#!/usr/bin/env python | |
# | |
# Simple script showing how to read a mitmproxy dump file | |
# | |
### UPD: this feature is now avaiable in mitmproxy: https://github.com/mitmproxy/mitmproxy/pull/619 | |
from libmproxy import flow | |
import json, sys |
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
A curated list of AWS resources to prepare for the AWS Certifications
A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.
#determine the labels | |
import pyvw #vw python interface | |
DEST = 1 | |
PROP = 2 | |
FAC = 3 | |
... | |
#create the class for the Sequence Labeler | |
class SequenceLabeler(pyvw.SearchTask): |
import pandas as pd | |
from collections import Counter | |
import tensorflow as tf | |
from tffm import TFFMRegressor | |
from sklearn.metrics import mean_squared_error | |
from sklearn.model_selection import train_test_split | |
import numpy as np | |
# Loading datasets' |
# -*- coding: utf-8 -*- | |
from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import argparse | |
import numpy as np | |
import os | |
import random |