Merge branch 'master' of https://github.com/maxpumperla/hyperas

hyperas/optim.py

@@ -5,13 +5,14 @@
 
 import nbformat
 import numpy as np
-from hyperopt import fmin, space_eval
+from hyperopt import fmin
 from nbconvert import PythonExporter
 
 from .ensemble import VotingModel
 from .utils import (
     remove_imports, remove_all_comments, extract_imports, temp_string,
-    write_temp_files, determine_indent, with_line_numbers)
+    write_temp_files, determine_indent, with_line_numbers, unpack_hyperopt_vals,
+    eval_hyperopt_space)
 
 sys.path.append(".")
 
@@ -52,7 +53,7 @@ def minimize(model,
     -------
     If `return_space` is False: A pair consisting of the results dictionary of the best run and the corresponding
     keras model.
-    If `return_space` is True: The pair of best result and correspondeing keras model, and the hyperopt search space
+    If `return_space` is True: The pair of best result and corresponding keras model, and the hyperopt search space
     """
     best_run, space = base_minimizer(model=model,
                                      data=data,
@@ -68,14 +69,15 @@ def minimize(model,
     best_model = None
     for trial in trials:
         vals = trial.get('misc').get('vals')
-        for key in vals.keys():
-            vals[key] = vals[key][0]
-        if trial.get('misc').get('vals') == best_run and 'model' in trial.get('result').keys():
+        # unpack the values from lists without overwriting the mutable dict within 'trial'
+        unpacked_vals = unpack_hyperopt_vals(vals)
+        # identify the best_run (comes with unpacked values from the hyperopt function `base.Trials.argmin`)
+        if unpacked_vals == best_run and 'model' in trial.get('result').keys():
             best_model = trial.get('result').get('model')
 
     if eval_space is True:
         # evaluate the search space
-        best_run = space_eval(space, best_run)
+        best_run = eval_hyperopt_space(space, best_run)
 
     if return_space is True:
         # return the space as well
@@ -114,7 +116,8 @@ def base_minimizer(model, data, functions, algo, max_evals, trials,
                  algo=algo,
                  max_evals=max_evals,
                  trials=trials,
-                 rseed=rseed),
+                 rseed=rseed,
+                 return_argmin=True),
             get_space()
         )
     except TypeError:
@@ -126,7 +129,8 @@ def base_minimizer(model, data, functions, algo, max_evals, trials,
              algo=algo,
              max_evals=max_evals,
              trials=trials,
-             rstate=np.random.RandomState(rseed)),
+             rstate=np.random.RandomState(rseed),
+             return_argmin=True),
         get_space()
     )
 

hyperas/utils.py

@@ -3,17 +3,18 @@
 import warnings
 from operator import attrgetter
 
+from hyperopt import space_eval
+
 
 class ImportParser(ast.NodeVisitor):
     def __init__(self):
         self.lines = []
         self.line_numbers = []
 
-
-    def visit_Import(self,node):
+    def visit_Import(self, node):
         line = 'import {}'.format(self._import_names(node.names))
-        if (self._import_asnames(node.names)!=''):
-            line += ' as {}'.format(self._import_asnames(node.names) )
+        if (self._import_asnames(node.names) != ''):
+            line += ' as {}'.format(self._import_asnames(node.names))
         self.line_numbers.append(node.lineno)
         self.lines.append(line)
 
@@ -22,7 +23,7 @@ def visit_ImportFrom(self, node):
             node.level * '.',
             node.module or '',
             self._import_names(node.names))
-        if (self._import_asnames(node.names)!=''):
+        if (self._import_asnames(node.names) != ''):
             line += " as {}".format(self._import_asnames(node.names))
         self.line_numbers.append(node.lineno)
         self.lines.append(line)
@@ -91,7 +92,7 @@ def with_line_numbers(code):
 
     Parameters
     ----------
-    str : string
+    code : string
        any multiline text, such as as (fragments) of source code
 
     Returns
@@ -149,3 +150,34 @@ def determine_indent(str):
                               indent, len(indent), new_indent, len(new_indent)))
         indent = new_indent
     return indent
+
+
+def unpack_hyperopt_vals(vals):
+    """
+    Unpack values from a hyperopt return dictionary where values are wrapped in a list.
+    :param vals: dict
+    :return: dict
+        copy of the dictionary with unpacked values
+    """
+    assert isinstance(vals, dict), "Parameter must be given as dict."
+    ret = {}
+    for k, v in list(vals.items()):
+        try:
+            ret[k] = v[0]
+        except (TypeError, IndexError):
+            ret[k] = v
+    return ret
+
+
+def eval_hyperopt_space(space, vals):
+    """
+    Evaluate a set of parameter values within the hyperopt space.
+    Optionally unpacks the values, if they are wrapped in lists.
+    :param space: dict
+        the hyperopt space dictionary
+    :param vals: dict
+        the values from a hyperopt trial
+    :return: evaluated space
+    """
+    unpacked_vals = unpack_hyperopt_vals(vals)
+    return space_eval(space, unpacked_vals)

tests/test_utils.py

@@ -1,7 +1,9 @@
 import os
+from hyperopt import hp
 from hyperas.utils import (
     extract_imports, remove_imports, remove_all_comments, temp_string,
-    write_temp_files, with_line_numbers, determine_indent)
+    write_temp_files, with_line_numbers, determine_indent, unpack_hyperopt_vals,
+    eval_hyperopt_space)
 
 TEST_SOURCE = """
 from __future__ import print_function
@@ -100,3 +102,73 @@ def test_determine_indent():
     assert determine_indent(code) == '  '
     code = "def do_stuff(x):\n\tfoo"
     assert determine_indent(code) == '\t'
+
+
+def test_unpack_hyperopt_vals():
+    test_vals = {
+        'filters_conv_A': [0],
+        'filters_conv_B': [1],
+        'rate': [0.1553971698387464],
+        'units': [1],
+        'rate_1': [0.4114807190252343],
+        'lr': [2.0215692016654265e-05],
+        'momentum': [2],
+        'nesterov': [0]
+    }
+    result = {
+        'filters_conv_A': 0,
+        'filters_conv_B': 1,
+        'rate': 0.1553971698387464,
+        'units': 1,
+        'rate_1': 0.4114807190252343,
+        'lr': 2.0215692016654265e-05,
+        'momentum': 2,
+        'nesterov': 0
+    }
+    assert unpack_hyperopt_vals(test_vals) == result
+
+
+def test_eval_hyperopt_space():
+    space = {
+        'filters_conv_A': hp.choice('filters_conv_A', [8, 16]),
+        'filters_conv_B': hp.choice('filters_conv_B', [16, 24]),
+        'rate': hp.uniform('rate', 0, 1),
+        'units': hp.choice('units', [96, 128, 192]),
+        'rate_1': hp.uniform('rate_1', 0, 1),
+        'lr': hp.uniform('lr', 1e-5, 1e-4),
+        'momentum': hp.choice('momentum', [0.5, 0.9, 0.999]),
+        'nesterov': hp.choice('nesterov', [True, False])
+    }
+    test_vals = {
+        'filters_conv_A': [0],
+        'filters_conv_B': [1],
+        'rate': [0.1553971698387464],
+        'units': [1],
+        'rate_1': [0.4114807190252343],
+        'lr': [2.0215692016654265e-05],
+        'momentum': [2],
+        'nesterov': [0]
+    }
+    test_vals_unpacked = {
+        'filters_conv_A': 0,
+        'filters_conv_B': 1,
+        'rate': 0.1553971698387464,
+        'units': 1,
+        'rate_1': 0.4114807190252343,
+        'lr': 2.0215692016654265e-05,
+        'momentum': 2,
+        'nesterov': 0
+    }
+    result = {
+        'filters_conv_A': 8,
+        'filters_conv_B': 24,
+        'rate': 0.1553971698387464,
+        'units': 128,
+        'rate_1': 0.4114807190252343,
+        'lr': 2.0215692016654265e-05,
+        'momentum': 0.999,
+        'nesterov': True
+    }
+
+    assert eval_hyperopt_space(space, test_vals) == result
+    assert eval_hyperopt_space(space, test_vals_unpacked) == result