diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index deac156f7e0e578edd688a132fc5d25165341876..a728988ed5ff55fa67feaa7fe0fdb57daf315f1c 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -351,7 +351,9 @@ build-gnu-gcov:
pages:
stage: doc_builds
script:
- - source cpp_sisso_gnu_py_env/bin/activate
+ - source cpp_sisso_gnu_param_py_env/bin/activate
+ - export LD_LIBRARY_PATH=$HOME/intel/oneapi/intelpython/latest/lib/:$HOME/intel/oneapi/intelpython/latest/lib/python3.7:$LD_LIBRARY_PATH
+ - export PYTHONPATH=$HOME/intel/oneapi/intelpython/latest/lib/python3.7/site-packages/:cpp_sisso_gnu_param_py_env/lib/python3.7/site-packages/
- cd docs/
- make html
- mv _build/html/ ../public
diff --git a/docs/development/Credits.md b/docs/development/Credits.md
index 5c5626c328ca7ab784d21796897bb85ca596f8ab..97c6cc934d3339e40c43c2d0e80ec153cb79a01d 100644
--- a/docs/development/Credits.md
+++ b/docs/development/Credits.md
@@ -1,5 +1,4 @@
-Acknowledgements
----
+# Acknowledgements
`SISSO++` would not be possible without the following packages:
@@ -9,7 +8,7 @@ Acknowledgements
- [googletest](https://github.com/google/googletest)
- [NLopt](http://github.com/stevengj/nlopt)
-# How to cite these packages:
+## How to cite these packages:
Please make sure to give credit to the right people when using `SISSO++`:
For classification problems cite:
diff --git a/docs/tutorial/1_command_line.md b/docs/tutorial/1_command_line.md
index 65656a07c3dbd6f175cfa617f9e32f30f448bbbe..8e0890723e9dcaa77789399bec4d07fb49b8d5d9 100644
--- a/docs/tutorial/1_command_line.md
+++ b/docs/tutorial/1_command_line.md
@@ -69,7 +69,6 @@ Below are reconstructions of both files for this calculation (To see the file cl
<details>
<summary>feature_space/SIS_summary.txt</summary>
- ```text
# FEAT_ID Score Feature Expression
0 0.920868624862486329 ((E_HOMO_B / r_p_A) / (r_sigma + r_p_B))
1 0.919657911026942054 ((|r_pi - r_s_A|) / (r_s_A^3))
@@ -115,12 +114,10 @@ Below are reconstructions of both files for this calculation (To see the file cl
38 0.262777418218664849 ((E_LUMO_A^6) / (r_p_B^3))
39 0.253659279222423484 ((E_LUMO_A / r_p_B) * (E_LUMO_B * E_LUMO_A))
#-----------------------------------------------------------------------
- ```
</details>
<details>
<summary>feature_space/selected_features.txt</summary>
- ```text
# FEAT_ID Feature Postfix Expression (RPN)
0 9|14|div|18|15|add|div
1 19|12|abd|12|cb|div
@@ -166,7 +163,7 @@ Below are reconstructions of both files for this calculation (To see the file cl
38 10|sp|15|cb|div
39 10|15|div|11|10|mult|mult
#-----------------------------------------------------------------------
- ```
+
</details>
In both files the change in rung is represented by the commented out dashed (--) line.
@@ -183,7 +180,6 @@ An example of these files is provided here:
<details>
<summary>models/train_dim_2_model_0.dat</summary>
- ```csv
# c0 + a0 * ((EA_B - IP_A) * (|r_sigma - r_s_B|)) + a1 * ((E_HOMO_B / r_p_A) / (r_sigma + r_p_B))
# Property Label: $E_{RS} - E_{ZB}$; Unit of the Property: eV
# RMSE: 0.0931540779192557; Max AE: 0.356632500670745
@@ -281,7 +277,6 @@ An example of these files is provided here:
SeZn , 2.631368992806530e-01, 2.463580576975095e-01, 7.384497385908948e-01, -2.320488278555971e+00
TeZn , 2.450012951740060e-01, 1.776248032825628e-01, 2.763715059556858e+00, -2.304848319397327e+00
- ```
</details>
@@ -295,7 +290,6 @@ To do this we have to modify the `sisso.json` file to automatically leave out a
<details>
<summary> updated sisso.json file</summary>
- ```json
{
"data_file": "data.csv",
"property_key": "E_RS - E_ZB",
@@ -311,7 +305,7 @@ To do this we have to modify the `sisso.json` file to automatically leave out a
"leave_out_inds": [],
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"]
}
- ```
+
</details>
Now lets make ten cross validation directories in the working directory and copy the `data.csv` and `sisso.json` into them and run separate calculations for each run.
@@ -339,7 +333,6 @@ A full example of the testing set output file is reproduced below:
<details>
<summary>The test data file cv_0/models/test_dim_2_model_0.dat</summary>
- ```csv
# c0 + a0 * ((E_HOMO_B / r_p_A) / (r_sigma + r_p_B))
# Property Label: $E_{RS} - E_{ZB}$; Unit of the Property: eV
# RMSE: 0.212994478440008; Max AE: 0.442277221520276
@@ -363,7 +356,6 @@ A full example of the testing set output file is reproduced below:
BrNa , -1.264287278827400e-01, -1.888626375989341e-01, -8.644123624074346e-01
CSi , 6.690237272359810e-01, 3.948280949265375e-01, -3.351692484156472e+00
- ```
</details>
## Analyzing the Results with python
@@ -388,7 +380,9 @@ To visualize these results we will also use `plot_validation_rmse` at the end, a
Here is an example of the `plot_validation_rmse` output:
<details>
<summary> Cross-Validation results </summary>
-
+
+
+
</details>
These initial results, particularly the high standard error of the mean for the 1D and 3D models, indicate that more cross-validation samples are needed (Note: you will have different values as the random samples will be different), so lets increase the total number of samples to 100, and redo the analysis
@@ -417,7 +411,8 @@ As can be seen from the standard error measurements the results are now reasonab
<details>
<summary> Converged cross-validation results </summary>
-
+
+
</details>
Because the validation error for the three and four dimensional models are within each others error bars and the standard error increases when going to the fourth dimension, we conclude that the three-dimensional model has the ideal complexity.
@@ -433,7 +428,8 @@ To see the distributions for this system we run
<details>
<summary> Distribution of Errors </summary>
-
+
+
</details>
One thing that stands out in the plot is the large error seen in a single point for both the one and two dimensional models.
@@ -596,7 +592,7 @@ The result of which is shown below:
<details>
<summary> Final 3D model </summary>
-
+
</details>
Additionally you can generate a output the model as a Matlab function or a LaTeX string using the following commands.
@@ -610,9 +606,9 @@ Additionally you can generate a output the model as a Matlab function or a LaTeX
A copy of the generated matlab function is below.
<details>
-<summary> Final 3D model </summary>
+<summary> Matlab function of the Final 3D model </summary>
+
- ```matlab
function P = model(X)
% Returns the value of E_{RS} - E_{ZB} = c0 + a0 * ((r_d_B / r_d_A) * (r_p_B * E_HOMO_A)) + a1 * ((IP_A^3) * (|r_sigma - r_s_B|)) + a2 * ((IP_A / r_p_A) / (r_p_B + r_p_A))
%
@@ -650,5 +646,5 @@ A copy of the generated matlab function is below.
P = reshape(c0 + a0 * f0 + a1 * f1 + a2 * f2, [], 1);
end
- ```
+
</details>
diff --git a/docs/tutorial/3_classification.md b/docs/tutorial/3_classification.md
index 2c1a9650015b284d16448bcce049f100e77639ca..cdfe924041825bfed776211273d881a257c1379e 100644
--- a/docs/tutorial/3_classification.md
+++ b/docs/tutorial/3_classification.md
@@ -1,5 +1,5 @@
-Performing Classification with SISSO++
----
+# Performing Classification with SISSO++
+
inally, besides regression problems, `SISSO++` can be used to solve classification problems.
As an example of this we will adapt the previous example by replacing the property with the identifier of if the material favors the rock-salt or zinc-blende structure, and change the calculation type to be `classification`.
It is important to note that while this problem only has two classes, multi-class classification is also possible.
@@ -10,7 +10,6 @@ Here is the updated data file, with the property `E_RS - E_ZB (eV)` replaced wit
<details>
<summary>Here is the full data_class.csv file for the calculation</summary>
- ```
# Material,Class,Z_A (nuc_charge) ,Z_B (nuc_charge) ,period_A,period_B,IP_A (eV_IP) ,IP_B (eV_IP) ,EA_A (eV_IP),EA_B (eV_IP) ,E_HOMO_A (eV) ,E_HOMO_B (eV) ,E_LUMO_A (eV),E_LUMO_B (eV) ,r_s_A ,r_s_B ,r_p_A ,r_p_B ,r_d_A ,r_d_B,r_sigma ,r_pi
AgBr,0,47,35,5,4,-8.0580997467,-12.649600029,-1.66659998894,-3.73930001259,-4.71000003815,-8.00100040436,-0.479000002146,0.708000004292,1.32000005245,0.75,1.87999999523,0.879999995232,2.97000002861,1.87000000477,1.570000052448,0.689999938012
AgCl,0,47,17,5,3,-8.0580997467,-13.9018001556,-1.66659998894,-3.97079992294,-4.71000003815,-8.69999980927,-0.479000002146,0.574000000954,1.32000005245,0.680000007153,1.87999999523,0.759999990463,2.97000002861,1.66999995708,1.760000050064,0.63999992609
@@ -94,7 +93,7 @@ Here is the updated data file, with the property `E_RS - E_ZB (eV)` replaced wit
SZn,1,30,16,4,3,-10.1354999542,-11.7951002121,1.08070003986,-2.84489989281,-6.21700000763,-7.10599994659,-1.19400000572,0.64200001955,1.10000002384,0.740000009537,1.54999995232,0.850000023842,2.25,2.36999988556,1.059999942781,0.559999942785
SeZn,1,30,34,4,4,-10.1354999542,-10.9460000992,1.08070003986,-2.75099992752,-6.21700000763,-6.65399980545,-1.19400000572,1.31599998474,1.10000002384,0.800000011921,1.54999995232,0.949999988079,2.25,2.18000006676,0.89999997616,0.599999904638
TeZn,1,30,52,4,5,-10.1354999542,-9.86670017242,1.08070003986,-2.66599988937,-6.21700000763,-6.10900020599,-1.19400000572,0.0989999994636,1.10000002384,0.939999997616,1.54999995232,1.13999998569,2.25,1.83000004292,0.569999992854,0.649999916554
- ```
+
</details>
## Running `SISSO++` for Classification problems
@@ -148,7 +147,6 @@ The two output files stored in `feature_space/` are also very similar, with the
<details>
<summary>feature_space/SIS_summary.txt</summary>
- ```text
# FEAT_ID Score Feature Expression
0 2.00218777423865069 (r_sigma + r_p_B)
1 2.0108802733799549 (r_pi - r_p_A)
@@ -192,7 +190,8 @@ The two output files stored in `feature_space/` are also very similar, with the
38 -0.999999633027590651 (period_A * Z_B)
39 -0.999999625788926316 (Z_B / EA_A)
#-----------------------------------------------------------------------
- ```
+
+</details>
Additionally the model files change to better represent the classifier.
The largest changes are in the header, where the coefficients now represent the linear decision boundaries calculated using support-vector machines (SVM).
@@ -200,7 +199,6 @@ The estimated property vector in this case refers to the predicted class from SV
<details>
<summary>models/train_dim_2_model_0.dat</summary>
- ```csv
# [(EA_B * Z_A), (r_sigma + r_p_B)]
# Property Label: $$Class$$; Unit of the Property: Unitless
# # Samples in Convex Hull Overlap Region: 0;# Samples SVM Misclassified: 0
@@ -298,7 +296,6 @@ The estimated property vector in this case refers to the predicted class from SV
SeZn , 1.000000000000000e+00, 1.000000000000000e+00, -8.252999782560001e+01, 1.849999964239000e+00
TeZn , 1.000000000000000e+00, 1.000000000000000e+00, -7.997999668110000e+01, 1.709999978544000e+00
- ```
</details>
## Updating the SVM Model Using `sklearn`
@@ -332,11 +329,14 @@ These changes are a result of different SVM libraries leading to slightly differ
<summary> `SISSO++` Classification </summary>
+
</details>
+
<details>
<summary> sklearn SVM </summary>
+
</details>
However as we decrease the value of `c` an increasing number of points becomes miss classified, suggesting the model is potentially over-fitting the data .
diff --git a/docs/tutorial/command_line/cv/cv_100_error.png b/docs/tutorial/command_line/cv/cv_100_error.png
new file mode 100644
index 0000000000000000000000000000000000000000..2e7271503dadf66ccd096c55697ab812e097175d
Binary files /dev/null and b/docs/tutorial/command_line/cv/cv_100_error.png differ
diff --git a/docs/tutorial/command_line/cv/cv_10_error.png b/docs/tutorial/command_line/cv/cv_10_error.png
new file mode 100644
index 0000000000000000000000000000000000000000..90379f77e0075197d889253f86402484e5f8249f
Binary files /dev/null and b/docs/tutorial/command_line/cv/cv_10_error.png differ
diff --git a/docs/tutorial/command_line/cv/error_cv_dist.png b/docs/tutorial/command_line/cv/error_cv_dist.png
new file mode 100644
index 0000000000000000000000000000000000000000..a93be2fccfdc369706b18f4bb445f8883e3d585a
Binary files /dev/null and b/docs/tutorial/command_line/cv/error_cv_dist.png differ