bwhite

"""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

mminer

"""An example of a cache decorator."""

import json
from functools import wraps
from redis import StrictRedis


redis = StrictRedis()

def cached(func):

fchollet

import theano
from keras.models import Sequential
from keras.layers.core import Dense, Activation

X_train, y_train = ... # load some training data
X_batch = ... # a batch of test data

# this is your initial model
model = Sequential()
model.add(Dense(20, 64))

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

vadimkantorov

# Example:
# $ python argparse_dict_argument.py --env a=b --env aa=bb
# Namespace(env={'a': 'b', 'aa': 'bb'})

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--env', action = type('', (argparse.Action, ), dict(__call__ = lambda a, p, n, v, o: getattr(n, a.dest).update(dict([v.split('=')])))), default = {}) # anonymously subclassing argparse.Action
print(parser.parse_args())

philipperemy

from __future__ import print_function

import os

import numpy as np
from keras.layers import RepeatVector
from keras.layers.core import Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.models import load_model

lampts

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

gyglim

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

kevvzub

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'

m-root

import talib
import statsmodels.api as sm
import pandas as pd

def initialize(context):
    context.security = symbol('AAPL')
    #set_universe(universe.DollarVolumeUniverse(floor_percentile=98.0,ceiling_percentile=100.0))
    
def bar_data(OHLC_type, bars_nr):
    bar_data_func = (history((bars_nr + 1), '1d', OHLC_type).iloc[0]).astype('float')