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diff --git a/docs/_images/plotter_3_obsvspred.png b/docs/_images/plotter_3_obsvspred.png
index e1b94d3086fd325ebc301f7114f2b82645303777..4e4dfff17f894c1a160dad98d1b21e10e8604729 100644
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diff --git a/docs/userguide/model.rst b/docs/userguide/model.rst
index 1fbf1b11250bfa686713e276ed433a5aa0fcc6ff..ec7ae5fe2a79746065f62e67d17dbe4301157a88 100644
--- a/docs/userguide/model.rst
+++ b/docs/userguide/model.rst
@@ -283,22 +283,34 @@ Once defined a request, we just need to call the generator as follows:
The method prompts a message with an overview of the results, in terms of the errors incurred for the number of samples requested, for a 10% of the best samples, and for the best sample.
- # Error during generation
+.. code-block:: python
- * Errors when requesting: sun_occlusion = [20.0, 30.0], surface_platf_1 = [35], surface_platf_3 = [50]
- - Mean for 10 samples: 0.0, 0.66272086, 0.9577557
- - Mean for 1.0 sample(s): 0.0, 0.4821968, 0.39183807
+ Generator: Accuracy
+ -------------------
+ Requested attributes: | y1 | y2 |
+ Requested values: | 5 | 140 |
+ Best generated sample: | 4.999994964440804 | 137.59591324644316 |
+ Mean error of generated samples: | ----- | ----- |
+ .. of all returned 1000 samples: | +/- 0.08159927114184601 | +/- 2.152879584234029 |
+ .. of the best 100 samples: | +/- 0.007120085617553693 | +/- 2.3875806416660854 |
- Best sample: 24.619483947753906, 34.51780319213867, 50.39183807373047
Besides, variable ``df_out`` combines the generated designs :math:`x` with the requested values :math:`y`. If there is a direct mapping between ``InputML`` and ``DesignParameters``, this can be fed to the CAD or FEM software for further evaluation.
-Furthermore, ``all_results`` is a dictionary containing all the results derived from the internal operations of the ``Generator``. Per se it is not useful to the user, but it can be harnessed by other methods. In particular, the plotter contains a method that allows to more visually represent the generated samples. For example, we can provide the output of 3 different requests as follows:
+Furthermore, ``all_results`` is a dictionary containing all the results derived from the internal operations of the ``Generator``.
+Per se it is not useful to the user, but it can be harnessed by other methods.
+In particular, the plotter contains a method that allows us to more visually represent the generated samples.
+For example, we can provide results from multiple requests ("experiments"),
+as shown here in an example taken from the use case `Semiramis`:
+
.. code-block:: python
+ request1 = {"rain_total": 50, "surface_total": [210,230]}
+ request2 = {"rain_total": 60, "surface_total": 250}
+ ...
plotter = Plotter(dataset, model=cae, datamodule=datamodule, output='show')
- plotter.generation_scatter([dict_results1, dict_results2, dict_results3], n_samples = 2)
+ plotter.generation_scatter([dict_results1, dict_results2], n_samples = 10)
Leading to the following plot:
@@ -308,13 +320,6 @@ Leading to the following plot:
:align: center
:width: 800
-This plot results from the use case `Semiramis`, and the attributes requested are the sun oclussion, and the surfaces for platform 1 and 3. However, the request are different in each case:
-
-.. code-block:: python
-
- request1 = {"sun_occlusion": 27}
- request2 = {"sun_occlusion": [20,23], "surface_platf_3": [40, 50]}
- request3 = {"sun_occlusion": 25, "surface_platf_1": [30, 38]}
We can observe then in the plot how each sample is just plotted in some particular contour plots and histogram, those that correspond to the attributes requested. This graph provides a visual representation of the performance of the generated geometries, and may allow understanding more in detail the behavior for more challenging requests, e.g. closer to the edges of the attributes’ distribution, where the model has observed less data during training.
diff --git a/docs/userguide/plotter.rst b/docs/userguide/plotter.rst
index 570d4ae84b81d5056eb975b3985932121eedd4ed..08e97a8b75c185fdc9f93a5af1d0fa9f2e4f1ac5 100644
--- a/docs/userguide/plotter.rst
+++ b/docs/userguide/plotter.rst
@@ -30,17 +30,6 @@ Most of the following plotting methods ask the user to specify the name of the d
Some methods allow also to limit the plot to selected data objects within that block(s) by providing their names to the ``attributes`` argument.
-
-
-.. caution::
-
- The name of the :ref:`data block <Data Blocks>` must match the blocks used in the :ref:`dataset <dataset>` or the :ref:`DataModule <datamodule>`!
-
- - Default names of data blocks used in the ``Dataset`` are `'design_parameters`' and `'performance_attributes'`, unless the user named them differently.
- - Default names of data blocks used in the ``DataModule`` are `'inputML`' and `'outputML`', unless the user named them differently.
-
-
-
.. hint::
When instantiating the ``Plotter``, you get a hint on the attributes and blocks that can be submitted as arguments.
@@ -153,10 +142,10 @@ One method to evaluate the trained ML model ist to compare the model predictions
.. code-block:: python
- plotter.attributes_obs_vs_pred(block="inputML", attributes=["x1"], datamodule=datamodule)
+ plotter.attributes_obs_vs_pred(block="inputML", datamodule=datamodule)
-.. image:: ../_images/plotter_3_obsvspred.png
+.. image:: ../_images/gettingstarted_7_45degreeplot_x.png
:alt: The image cannot be displayed.
:align: center
:width: 600
diff --git a/examples/gridshell_structure/gridshell_structure_example.ipynb b/examples/gridshell_structure/gridshell_structure_example.ipynb
index 1735b45796c997614cfe0b6eb51fe5fea54d7e67..1ca414c0e0e654c346a3c76efc93b179b7a0ed52 100644
--- a/examples/gridshell_structure/gridshell_structure_example.ipynb
+++ b/examples/gridshell_structure/gridshell_structure_example.ipynb
@@ -589,8 +589,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.21"
+ "pygments_lexer": "ipython3"
}
},
"nbformat": 4,
diff --git a/examples/semiramis/from_dataset/semiramis_from_dataset.ipynb b/examples/semiramis/from_dataset/semiramis_from_dataset.ipynb
index 4dfca0ebeaadd52d5699e374cb094f853e794eb3..c6d0ef3a8db209d646a41aed60592f3cb57c0706 100644
--- a/examples/semiramis/from_dataset/semiramis_from_dataset.ipynb
+++ b/examples/semiramis/from_dataset/semiramis_from_dataset.ipynb
@@ -425,7 +425,7 @@
"metadata": {},
"outputs": [],
"source": [
- "n_samples = 10\n",
+ "n_samples = 20\n",
"\n",
"request = {\n",
" \"rain_total\": 50,\n",
@@ -464,7 +464,22 @@
"metadata": {},
"outputs": [],
"source": [
- "plotter.generation_scatter([detailed_results], n_samples=2)"
+ "\n",
+ "request1 = {\n",
+ " \"rain_total\": 50,\n",
+ " \"surface_total\": [210,230],\n",
+ "}\n",
+ "\n",
+ "request2 = {\n",
+ " \"rain_total\": 60,\n",
+ " \"surface_total\": 250,\n",
+ "}\n",
+ "\n",
+ "generated_samples1, detailed_results1 = gen.generate(request=request1, n_samples=n_samples, format_out=\"df\")\n",
+ "generated_samples2, detailed_results2 = gen.generate(request=request2, n_samples=n_samples, format_out=\"df\")\n",
+ "\n",
+ "\n",
+ "plotter.generation_scatter([detailed_results1, detailed_results2], n_samples=10,downsamp=1)"
]
}
],
@@ -484,7 +499,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.18"
+ "version": "3.10.16"
}
},
"nbformat": 4,
diff --git a/examples/simple_example/simple_analytical_example.ipynb b/examples/simple_example/simple_analytical_example.ipynb
index 83c446170d6745b0f1da2752057aac1695dc5bd1..7e0394a58816ce75b7919f6c52de80c348bd4cb0 100644
--- a/examples/simple_example/simple_analytical_example.ipynb
+++ b/examples/simple_example/simple_analytical_example.ipynb
@@ -951,8 +951,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.21"
+ "pygments_lexer": "ipython3"
}
},
"nbformat": 4,