MechCoder

class Model(object):

MechCoder

from hpolib.benchmarks.ml import svm_benchmark
from ConfigSpace.hyperparameters import FloatHyperparameter, IntegerHyperparameter
from skopt import gp_minimize

svm_bench = svm_benchmark.SvmOnMnist()
bounds = svm_bench.get_meta_information()["bounds"]
hyperparams = svm_bench.configuration_space.get_hyperparameters()

new_bounds = []
for i, (l_b, u_b) in enumerate(bounds):

MechCoder

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor

boston = load_boston()
X, y = boston.data, boston.target
X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=0.6, test_size=0.4, random_state=0)
X_train = np.array(X_train, dtype=np.float32)

MechCoder

[array([  1.93743793,   2.30290486,  14.52240377,   2.30262299,
         1.95685067,   2.30586127,   1.14091716,   1.91978845,
         2.30250415,   1.53107564,   2.30258038,   2.30215575,
         2.3025886 ,   2.26822514,   2.1281669 ,   2.32953785,
         2.18480186,   2.31662579,   2.30259784,   2.11116244,
         2.30262142,   2.30337757,   2.06764852,   2.31965076,
         2.3030167 ,   2.0474523 ,   2.12057801,   2.06735832,
         2.30270004,   2.31166795,   2.30260678,   2.16387814,
         2.10967536,   2.30661086,   2.11461189,   2.3029234 ,
         2.07641528,   2.08500446,   2.0747728 ,   2.30185462,

MechCoder

#!/bin/bash
#PBS -l nodes=1:ppn=1:gpus=1
#PBS -l walltime=8:00:00
#PBS -l mem=50GB
#PBS -N name_of_script
#PBS -M emailid
#PBS -m b -m e -m a -m abe
#PBS -j oe

module purge

MechCoder

1. ssh mks542@hpc.nyu.edu
2. ssh -X mercer

To create an interactive job on the GPU:
# You should increase the mem and walltime argument depending on the requirement.
3. qsub -I -l nodes=1:ppn=1:gpus=1,mem=32GB,walltime=03:00:00
4. module load librosa/0.4.1
5. module avail tensorflow
# Choose the version you would like.
6. module load tensorflow/python2.7/20161207

MechCoder

[array([-0.2792, -0.2379, -0.2677, -0.1   , -0.1   , -0.1   , -0.0927,
       -0.2448, -0.3269, -0.1142, -0.1002, -0.6134, -0.1902, -0.1   ,
       -0.0988, -0.1   , -0.1   , -0.1   , -0.1058, -0.1   , -0.1073,
       -0.4381, -0.1824, -0.1832, -0.1785, -0.106 , -0.2397, -0.1648,
       -0.1088, -0.1   , -0.163 , -0.1   , -0.1531, -0.1   , -0.1   ,
       -0.1   , -0.1   , -0.1049, -0.1   , -0.1   , -0.1509, -0.144 ,
       -0.1056, -0.1207, -0.1779, -0.1   , -0.1213, -0.1   , -0.496 ,
       -0.1816])]

Mean optimum: -0.6134

MechCoder

import tensorflow as tf

class test(object):
    def test_func(self, pass=1):
        to_hidden_W = tf.get_variable(
                    "to_hidden_W",
                    shape=[2, 2])

with tf.variable_scope("Model", reuse=True):
    a = test()

MechCoder

import os

import numpy as np
import scipy.io.wavfile as wav


def convert_mp3_to_wav(folder, sampling_freq=44.1):
    """
    Converts a directory with mp3 files to wav files.

MechCoder

Image features:
array([1971, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988,
       1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,
       2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
       2011, 2012, 2013, 2014])

Voice traits (Continuous)
array([1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
       2009, 2010, 2011, 2012])