Move docs/source/wiki/ to docs/source/manuals/

docs/source/index.rst

@@ -68,15 +68,15 @@ Manuals
 The following manuals illustrate several aspects of QuaPy through examples:
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
 
-   wiki/Datasets
-   wiki/Evaluation
-   wiki/ExplicitLossMinimization
-   wiki/Methods
-   wiki/Model-Selection
-   wiki/Plotting
-   wiki/Protocols
+   manuals/datasets
+   manuals/evaluation
+   manuals/explicit-loss-minimization
+   manuals/methods
+   manuals/model-selection
+   manuals/plotting
+   manuals/protocols
 
 .. toctree::
    :hidden:

docs/source/manuals/datasets.md

@@ -67,9 +67,8 @@ for method in methods:
 ```
 
 However, generating samples for evaluation purposes is tackled in QuaPy
-by means of the evaluation protocols (see the dedicated entries in the Wiki
-for [evaluation](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation) and 
-[protocols](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)).
+by means of the evaluation protocols (see the dedicated entries in the manuals
+for [evaluation](./evaluation) and [protocols](./protocols)).
 
 
 ## Reviews Datasets

docs/source/manuals/methods.md

@@ -29,7 +29,7 @@ instance in a sample-- while in quantification the output for a sample
 is one single array of class prevalences).
 Quantifiers also extend from scikit-learn's `BaseEstimator`, in order
 to simplify the use of `set_params` and `get_params` used in 
-[model selector](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection).
+[model selection](./model-selection).
 
 ## Aggregative Methods
 
@@ -96,7 +96,7 @@ classifier, and then _clones_ these classifiers and explores the combinations
 of hyperparameters that are specific to the quantifier (this can result in huge
 time savings).
 Concerning the inference phase, this two-step process allow the evaluation of many 
-standard protocols (e.g., the [artificial sampling protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)) to be
+standard protocols (e.g., the [artificial sampling protocol](./evaluation)) to be
 carried out very efficiently. The reason is that the entire set can be pre-classified
 once, and the quantification estimations for different samples can directly
 reuse these predictions, without requiring to classify each element every time.
@@ -484,8 +484,7 @@ the performance estimated for each member of the ensemble in terms of that evalu
 When using any of the above options, it is important to set the `red_size` parameter, which 
 informs of the number of members to retain.
 
-Please, check the [model selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
-wiki if you want to optimize the hyperparameters of ensemble for classification or quantification.
+Please, check the [model selection manual](./model-selection) if you want to optimize the hyperparameters of ensemble for classification or quantification.
 
 ### The QuaNet neural network
 

docs/source/manuals/model-selection.md

@@ -33,11 +33,11 @@ of scenarios exhibiting different degrees of prior
 probability shift.
 
 The class _qp.model_selection.GridSearchQ_ implements a grid-search exploration over the space of 
-hyper-parameter combinations that [evaluates](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation) 
+hyper-parameter combinations that [evaluates](./evaluation) 
 each combination of hyper-parameters by means of a given quantification-oriented
 error metric (e.g., any of the error functions implemented
 in _qp.error_) and according to a 
-[sampling generation protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols).
+[sampling generation protocol](./protocols).
 
 The following is an example (also included in the examples folder) of model selection for quantification:
 

docs/source/manuals/plotting.md

@@ -43,7 +43,7 @@ quantification methods across different scenarios showcasing
 the accuracy of the quantifier in predicting class prevalences
 for a wide range of prior distributions. This can easily be
 achieved by means of the 
-[artificial sampling protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)
+[artificial sampling protocol](./protocols)
 that is implemented in QuaPy.
 
 The following code shows how to perform one simple experiment
@@ -113,7 +113,7 @@ are '.png' or '.pdf'). If this path is not provided, then the plot
 will be shown but not saved. 
 The resulting plot should look like:
 
-![diagonal plot on Kindle](./wiki_examples/selected_plots/bin_diag.png)
+![diagonal plot on Kindle](./plots/bin_diag.png)
 
 Note that in this case, we are also indicating the training 
 prevalence, which is plotted in the diagonal a as cyan dot.
@@ -138,7 +138,7 @@ qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, savepath='./pl
 
 and should look like:
 
-![bias plot on Kindle](./wiki_examples/selected_plots/bin_bias.png)
+![bias plot on Kindle](./plots/bin_bias.png)
 
 The box plots show some interesting facts:
 * all methods are biased towards the training prevalence but specially
@@ -181,7 +181,7 @@ def gen_data():
 
 and the plot should now look like:
 
-![bias plot on IMDb](./wiki_examples/selected_plots/bin_bias_cc.png)
+![bias plot on IMDb](./plots/bin_bias_cc.png)
 
 which clearly shows a negative bias for CC variants trained on
 data containing more negatives (i.e., < 50%) and positive biases
@@ -195,7 +195,7 @@ To this aim, an argument _nbins_ is passed which indicates
 how many isometric subintervals to take. For example 
 the following plot is produced for _nbins=3_:
 
-![bias plot on IMDb](./wiki_examples/selected_plots/bin_bias_bin_cc.png)
+![bias plot on IMDb](./plots/bin_bias_bin_cc.png)
 
 Interestingly enough, the seemingly unbiased estimator (CC at 50%) happens to display
 a positive bias (or a tendency to overestimate) in cases of low prevalence 
@@ -205,7 +205,7 @@ and a negative bias (or a tendency to underestimate) in cases of high prevalence
 
 Out of curiosity, the diagonal plot for this experiment looks like:
 
-![diag plot on IMDb](./wiki_examples/selected_plots/bin_diag_cc.png)
+![diag plot on IMDb](./plots/bin_diag_cc.png)
 
 showing pretty clearly the dependency of CC on the prior probabilities
 of the labeled set it was trained on.
@@ -234,7 +234,7 @@ qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
     error_name='ae', n_bins=10, savepath='./plots/err_drift.png')
 ```
 
-![diag plot on IMDb](./wiki_examples/selected_plots/err_drift.png)
+![diag plot on IMDb](./plots/err_drift.png)
 
 Note that all methods work reasonably well in cases of low prevalence
 drift (i.e., any CC-variant is a good quantifier whenever the IID