Fix doctest (#243)

* Fix: Remove test for output of `file.write`

* Fix: Issues with docs

* Add: Fix docs title and change to code blocks

Author: eddiebergman

ConfigSpace/read_and_write/json.py

@@ -295,20 +295,18 @@ def write(configuration_space, indent=2):
     :class:`~ConfigSpace.configuration_space.ConfigurationSpace` in json format.
     This string can be written to file.
 
-    Example
-    -------
-    .. doctest::
+    Example:
 
-        >>> from ConfigSpace import ConfigurationSpace
-        >>> import ConfigSpace.hyperparameters as CSH
-        >>> from ConfigSpace.read_and_write import json
-        >>> cs = ConfigurationSpace()
-        >>> cs.add_hyperparameter(CSH.CategoricalHyperparameter('a', choices=[1, 2, 3]))
-        a, Type: Categorical, Choices: {1, 2, 3}, Default: 1
+    ..code:: python
+
+        from ConfigSpace import ConfigurationSpace
+        import ConfigSpace.hyperparameters as CSH
+        from ConfigSpace.read_and_write import json
+        cs = ConfigurationSpace()
+        cs.add_hyperparameter(CSH.CategoricalHyperparameter('a', choices=[1, 2, 3]))
 
-        >>> with open('configspace.json', 'w') as f:
-        ...      f.write(json.write(cs))
-        299
+        with open('configspace.json', 'w') as f:
+            f.write(json.write(cs))
 
     Parameters
     ----------

docs/source/User-Guide.rst

@@ -246,36 +246,48 @@ To read it from file
 
 
 5th Example: Placing priors on the hyperparameters
--------------------------
+--------------------------------------------------
 
-If you want to conduct black-box optimization in SMAC (https://arxiv.org/abs/2109.09831), and you have prior knowledge about the which regions of the search space are more likely to contain the optimum, you may include this knowledge when designing the configuration space. More specifically, you place prior distributions over the optimum on the parameters, either by a (log)-normal or (log)-Beta distribution. SMAC then considers the given priors through the optimization by using PiBO (https://openreview.net/forum?id=MMAeCXIa89). 
+If you want to conduct black-box optimization in SMAC (https://arxiv.org/abs/2109.09831), and you have prior knowledge about the which regions of the search space are more likely to contain the optimum, you may include this knowledge when designing the configuration space. More specifically, you place prior distributions over the optimum on the parameters, either by a (log)-normal or (log)-Beta distribution. SMAC then considers the given priors through the optimization by using PiBO (https://openreview.net/forum?id=MMAeCXIa89).
 
 Consider the case of optimizing the accuracy of an MLP with three hyperparameters: learning rate [1e-5, 1e-1], dropout [0, 0.99] and activation {Tanh, ReLU}. From prior experience, you believe the optimal learning rate to be around 1e-3, a good dropout to be around 0.25, and the optimal activation function to be ReLU about 80% of the time. This can be represented accordingly:
 
->>> import numpy as np
->>> import ConfigSpace.hyperparameters as CSH
->>> from ConfigSpace.configuration_space import ConfigurationSpace
->>> # convert 10 log to natural log for learning rate, mean 1e-3
->>> logmean = np.log(1e-3)
->>> # two standard deviations on either side of the mean to cover the search space
->>> logstd = np.log(10.0) 
->>> learning_rate = CSH.NormalFloatHyperparameter(name='learning_rate', lower=1e-5, upper=1e-1, default_value=1e-3, mu=logmean, sigma=logstd, log=True)
->>> dropout = CSH.BetaFloatHyperparameter(name='dropout', lower=0, upper=0.99, default_value=0.25, alpha=2, beta=4, log=False)
->>> activation = CSH.CategoricalHyperparameter(name='activation', choices=['tanh', 'relu'], weights=[0.2, 0.8])
-
->>> cs = ConfigurationSpace()
->>> cs.add_hyperparameters([learning_rate, dropout, activation])
-[learning_rate, Type: NormalFloat, Mu: -6.907755278982137 Sigma: 2.302585092994046, Range: [1e-05, 0.1], Default: 0.001, on log-scale, dropout, Type: BetaFloat, Alpha: 2.0 Beta: 4.0, Range: [0.0, 0.99], Default: 0.25, activation, Type: Categorical, Choices: {tanh, relu}, Default: tanh, Probabilities: (0.2, 0.8)]
+.. code-block:: python
+
+    import numpy as np
+    import ConfigSpace.hyperparameters as CSH
+    from ConfigSpace.configuration_space import ConfigurationSpace
+
+    # convert 10 log to natural log for learning rate, mean 1e-3
+    logmean = np.log(1e-3)
+    # two standard deviations on either side of the mean to cover the search space
+    logstd = np.log(10.0)
+
+    learning_rate = CSH.NormalFloatHyperparameter(name='learning_rate', lower=1e-5, upper=1e-1, default_value=1e-3, mu=logmean, sigma=logstd, log=True)
+    dropout = CSH.BetaFloatHyperparameter(name='dropout', lower=0, upper=0.99, default_value=0.25, alpha=2, beta=4, log=False)
+    activation = CSH.CategoricalHyperparameter(name='activation', choices=['tanh', 'relu'], weights=[0.2, 0.8])
+
+    cs = ConfigurationSpace()
+
+    cs.add_hyperparameters([learning_rate, dropout, activation])
+    # [learning_rate, Type: NormalFloat, Mu: -6.907755278982137 Sigma: 2.302585092994046, Range: [1e-05, 0.1], Default: 0.001, on log-scale, dropout, Type: BetaFloat, Alpha: 2.0 Beta: 4.0, Range: [0.0, 0.99], Default: 0.25, activation, Type: Categorical, Choices: {tanh, relu}, Default: tanh, Probabilities: (0.2, 0.8)]
 
 To check that your prior makes sense for each hyperparameter, you can easily do so with the __pdf__ method. There, you will see that the probability of the optimal learning rate peaks at 10^-3, and decays as we go further away from it:
 
->>> test_points = np.logspace(-5, -1, 5) 
->>> test_points
-array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01])
+.. code-block:: python
+
+    test_points = np.logspace(-5, -1, 5)
+
+    print(test_points)
+    # array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01])
+
+The pdf function accepts an (N, ) numpy array as input.
+
+.. code-block:: python
 
-# the pdf function accepts an (N, ) numpy array as input.                 
->>> learning_rate.pdf(test_points)
-array([0.02456573, 0.11009594, 0.18151753, 0.11009594, 0.02456573])
+    test_points_pdf = learning_rate.pdf(test_points)
+    print(test_points_pdf)
+    # array([0.02456573, 0.11009594, 0.18151753, 0.11009594, 0.02456573])