Update installation instructions, fix for Python 3

Author: AlexanderFabisch

README.md

@@ -1,25 +1,26 @@
-Summary
-=======
+# Summary
+
 This repository contains some basic code for Bayesian optimization
 
-Installation
-============
 
-Install the current development version of scikit-learn (or sklearn version 0.18 once this is available)
+## Installation
 
-    git clone [email protected]:scikit-learn/scikit-learn.git
-    cd sklearn
-    sudo python setup.py install
+Install dependencies
+
+1. Install [nlopt](https://github.com/stevengj/nlopt) for your Python version.
+2. Install dependencies with pip: `[sudo] pip[3] install -r requirements.txt`
+   (requirements.txt can be found in the repository)
+3. (Optional:) install [BOLeRo](https://github.com/rock-learning/bolero)
 
 Install `bayesian_optimization`
 
-    git clone [email protected]:jmetzen/bayesian_optimization.git
+    git clone https://github.com/rock-learning/bayesian_optimization.git
     cd bayesian_optimization
     sudo python setup.py install
 
 
-Usage
-=====
+## Usage
+
 Some usage examples are contained in the folder "examples". To reproduce the results from the ICML 2016 paper
 "Minimum Regret Search for Single- and Multi-Task Optimization", please execute the jupyter notebook "examples/mrs_evaluation.ipynb." 
 

bayesian_optimization/__init__.py

@@ -1,6 +1,7 @@
-from bayesian_optimization import (BayesianOptimizer, REMBOOptimizer,
-    InterleavedREMBOOptimizer)
-from model import GaussianProcessModel
-from acquisition_functions import (ProbabilityOfImprovement,
-    ExpectedImprovement, UpperConfidenceBound, GPUpperConfidenceBound,
-    EntropySearch, MinimalRegretSearch, create_acquisition_function)
+from .bayesian_optimization import (BayesianOptimizer, REMBOOptimizer,
+                                    InterleavedREMBOOptimizer)
+from .model import GaussianProcessModel
+from .acquisition_functions import (
+    ProbabilityOfImprovement, ExpectedImprovement, UpperConfidenceBound,
+    GPUpperConfidenceBound, EntropySearch, MinimalRegretSearch,
+    create_acquisition_function)

bayesian_optimization/acquisition_functions.py

@@ -5,9 +5,6 @@
 from scipy.stats import norm, entropy
 from scipy.special import erf
 
-from sklearn.cluster import KMeans
-from sklearn.neighbors import NearestNeighbors
-
 
 class AcquisitionFunction(object):
     """ Abstract base class for acquisition functions."""

bayesian_optimization/bayesian_optimization.py

@@ -333,7 +333,7 @@ def __init__(self, interleaved_runs=2, *args, **kwargs):
                                       *args, **kwargs)
                        for run in range(interleaved_runs)]
         self.rembos = cycle(self.rembos)
-        self.current_rembo = self.rembos.next()
+        self.current_rembo = next(self.rembos)
 
         self.X_ = []
         self.y_ = []
@@ -361,4 +361,4 @@ def update(self, X, y):
         self.y_.append(y)
 
         self.current_rembo.update(X, y)
-        self.current_rembo = self.rembos.next()
+        self.current_rembo = next(self.rembos)

requirements.txt

@@ -0,0 +1,2 @@
+scikit-learn
+scipy