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from sigopt_sklearn.search import SigOptSearchCV

client_token = "My Token"
net_parameters = {
                'parameters':(range),
                }
clf = SigOptSearchCV(net, net_parameters, cv=train_test_splitter, client_token=client_token,
                    n_jobs=1, n_iter=50, scoring=get_scorer('neg_mean_squared_error'))

clf.fit(SDT_training, target_training)

Error: No available formula for gcc
GCC is now maintained in homebrew-versions, with major version
number in formula name as suffix. Please tap using:

    brew tap homebrew/versions

and then install GCC based on its version, e.g., 'brew install gcc47'.

clf = SigOptSearchCV(net, net_parameters, cv=train_test_splitter, client_token=client_token,
                    n_jobs=1, n_iter=100, scoring=get_scorer('neg_mean_squared_error'),experiment=experiment)

from predictor.sigopt_creds import client_token
from predictor.stand_alone import run_example
# Warning, this can take a very long time (many hours)
EXPERIMENT_ID = run_example(client_token)
# EXPERIMENT_ID = run_example(sigopt_depth=0, sigopt_width=0)
#EXPERIMENT_ID = 1545 # Put your experiment ID here if you have already run stand_alone

from predictor.stand_alone import run_example
# Warning, this can take a very long time (many hours)
EXPERIMENT_ID = run_example(sigopt_depth=0, sigopt_width=0)
#EXPERIMENT_ID = 1545 # Put your experiment ID here if you have already run stand_alone

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-2-6a242b8ff34b> in <module>()
----> 1 from predictor.stand_alone import run_example
      2 # Warning, this can take a very long time (many hours)
      3 EXPERIMENT_ID = run_example(sigopt_depth=0, sigopt_width=0)
      4 #EXPERIMENT_ID = 1545 # Put your experiment ID here if you have already run stand_alone

/Users/jlent/github_projects/sigopt-examples/sigopt-beats-vegas/predictor/stand_alone.py in <module>()
      4 
      5 import bet_reader
----> 6 import evaluator
      7 import read_data
      8 from constant import SEASON_1314_END

/Users/jlent/github_projects/sigopt-examples/sigopt-beats-vegas/predictor/evaluator.py in <module>()
      2 
      3 import bet_reader
----> 4 from model import get_features
      5 from constant import SEASON_1415_START, SEASON_1415_END
      6 

/Users/jlent/github_projects/sigopt-examples/sigopt-beats-vegas/predictor/model.py in <module>()
----> 1 from sklearn import ensemble
      2 import numpy
      3 
      4 from team_stats import TeamStats
      5 

/Users/jlent/anaconda/envs/sigopt_vegas/lib/python2.7/site-packages/sklearn/ensemble/__init__.py in <module>()
      5 
      6 from .base import BaseEnsemble
----> 7 from .forest import RandomForestClassifier
      8 from .forest import RandomForestRegressor
      9 from .forest import RandomTreesEmbedding

/Users/jlent/anaconda/envs/sigopt_vegas/lib/python2.7/site-packages/sklearn/ensemble/forest.py in <module>()
     53 from ..externals.joblib import Parallel, delayed
     54 from ..externals import six
---> 55 from ..feature_selection.from_model import _LearntSelectorMixin
     56 from ..metrics import r2_score
     57 from ..preprocessing import OneHotEncoder

/Users/jlent/anaconda/envs/sigopt_vegas/lib/python2.7/site-packages/sklearn/feature_selection/__init__.py in <module>()
      5 """
      6 
----> 7 from .univariate_selection import chi2
      8 from .univariate_selection import f_classif
      9 from .univariate_selection import f_oneway

/Users/jlent/anaconda/envs/sigopt_vegas/lib/python2.7/site-packages/sklearn/feature_selection/univariate_selection.py in <module>()
     13 
     14 from ..base import BaseEstimator
---> 15 from ..preprocessing import LabelBinarizer
     16 from ..utils import (as_float_array, check_array, check_X_y, safe_sqr,
     17                      safe_mask)

/Users/jlent/anaconda/envs/sigopt_vegas/lib/python2.7/site-packages/sklearn/preprocessing/__init__.py in <module>()
      6 from ._function_transformer import FunctionTransformer
      7 
----> 8 from .data import Binarizer
      9 from .data import KernelCenterer
     10 from .data import MinMaxScaler

/Users/jlent/anaconda/envs/sigopt_vegas/lib/python2.7/site-packages/sklearn/preprocessing/data.py in <module>()
     23 from ..utils.sparsefuncs_fast import (inplace_csr_row_normalize_l1,
     24                                       inplace_csr_row_normalize_l2)
---> 25 from ..utils.sparsefuncs import (inplace_column_scale,
     26                                  mean_variance_axis, incr_mean_variance_axis,
     27                                  min_max_axis)

ImportError: cannot import name inplace_column_scale