kretes

import akka.NotUsed
import akka.actor.ActorSystem
import akka.stream.ActorMaterializer
import akka.stream.scaladsl.{Flow, Sink, Source}
import org.scalatest.mockito.MockitoSugar
import org.scalatest.{FlatSpec, Matchers}

import scala.collection.immutable.Seq
import scala.concurrent.duration._

kretes

import akka.NotUsed
import akka.actor.ActorSystem
import akka.stream.ActorMaterializer
import akka.stream.scaladsl.{Flow, Keep, Sink, Source}
import pl.allegro.offer.imagescore.floki.streams.TupledFlow

import scala.concurrent.duration.DurationInt
import scala.concurrent.{Await, ExecutionContext, Future}

implicit val as = ActorSystem()

kretes

from striatum.storage import history
from striatum.storage import model
from striatum.bandit import linucb
from striatum.storage.action import ActionStorage, MemoryActionStorage, Action

historystorage = history.MemoryHistoryStorage()
modelstorage = model.MemoryModelStorage()
actionstorage = MemoryActionStorage()

actionstorage.add([Action(1),Action(2),Action(3)])

kretes

from tensorflow.contrib.keras import layers
from tensorflow.contrib.keras import models
from tensorflow.contrib.keras import backend as K
import numpy as np

input_shape = (10,10, 1)
input_data = layers.Input(name='the_input', shape=input_shape, dtype='float32')
inner = layers.Flatten()(input_data)
binary = layers.Dense(1, kernel_initializer='he_normal', name='densebin')(inner)
y_pred = layers.Activation('sigmoid', name='output_bin')(binary)

kretes

FROM gcr.io/tensorflow/tensorflow:latest-gpu

RUN add-apt-repository ppa:webupd8team/java && apt-get update
RUN echo debconf shared/accepted-oracle-license-v1-1 select true | debconf-set-selections && echo debconf shared/accepted-oracle-license-v1-1 seen true | debconf-set-selections
RUN apt-get install -y oracle-java8-installer
RUN echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | tee /etc/apt/sources.list.d/bazel.list && curl https://bazel.build/bazel-release.pub.gpg | apt-key add -
RUN apt-get update && apt-get install -y bazel
RUN pip install -U nltk

RUN python -m nltk.downloader -d /usr/local/share/nltk_data all

kretes

import xgboost as xgb
import pandas as pd
import numpy as np
import random
import os

print(xgb.__version__)

def generate_observations(n_rows, n_cols):
    return [[random.randint(0,100) for _ in range(n_cols)] for _ in range(n_rows)]

kretes

0 qid:1369666032782981875 0:1.2172619104385376
0 qid:1369666032782981875 0:1.5916666984558105
1 qid:1369666032782981875 0:1.3103448152542114
0 qid:1369666032782981875 0:0.7198443412780762
0 qid:1369666032782981875 0:0.6421052813529968
0 qid:1369666032782981875 0:2.450000047683716
0 qid:1369666032782981875 0:0.3511904776096344
0 qid:1369666032782981875 0:2.110119104385376
0 qid:1369666032782981875 0:1.7380952835083008
0 qid:1369666032782981875 0:1.2692307233810425

kretes

a,b
at_the_end\,1
in_\_side,1
"comma,at_the_end\\",1
"comma,in_\\_side",1

kretes

import xgboost as xgb
from scipy.sparse.csr import csr_matrix
import numpy as np
from sklearn.datasets import load_svmlight_file

num_rows = int(np.iinfo(np.int32).max / 1000)
num_cols = 1001

more_than_int32_count = num_rows * num_cols - np.iinfo(np.int32).max
print(more_than_int32_count)

kretes

from multiprocessing import Process
import os
from tokenizers.implementations import ByteLevelBPETokenizer
import tokenizers

print(tokenizers.__version__)

# works:
tok = ByteLevelBPETokenizer()
print(tok.encode_batch(['ala']))