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/index.rst b/docs/index.rst
index bcbfd93baf9268ebd6179831da14a5b9d94bd988..bfab53a7ae3ee17b6a65e8bc01f43407dc875ae2 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -12,19 +12,12 @@ SISSO++
This package provides a C++ implementation of SISSO with built in Python bindings for an efficient python interface.
Future work will expand the python interface to include more postporcessing analysis tools.
-Indices
-=======
-
-* :ref:`genindex`
-* :ref:`search`
-
Table of Contents
^^^^^^^^^^^^^^^^^
.. toctree::
:maxdepth: 2
- self
quick_start/QuickStart
tutorial/tutorial
cpp_api/cpp_api
diff --git a/docs/quick_start/QuickStart.rst b/docs/quick_start/QuickStart.rst
index 393a3952edb07f2b6d8fc363e2a3d50ed3636744..0eadf44141e79089e32488cdfb154692c5d09c11 100644
--- a/docs/quick_start/QuickStart.rst
+++ b/docs/quick_start/QuickStart.rst
@@ -1,6 +1,6 @@
.. _quick_start:
-Quick Start Guide
+Quick-Start Guide
=================
.. toctree::
:maxdepth: 2
diff --git a/docs/quick_start/code_ref.md b/docs/quick_start/code_ref.md
index cde3e05cbdf4ab8a0eb0b1f6f4f9bc298b2319ed..50b60f332bb1a9a2718e6da729f9c27f8856891c 100644
--- a/docs/quick_start/code_ref.md
+++ b/docs/quick_start/code_ref.md
@@ -31,7 +31,7 @@ A list containing the set of all operators that will be used during the feature
#### `param_opset`
-A list containing the set of all operators, for which the non-linear scale and bias terms will be optimized, that will be used during the feature creation step of SISSO. (If empty none of the available features)
+A list containing the set of all operators, for which the non-linear scale and bias terms will be optimized, that will be used during the feature creation step of SISSO. (If empty none of the available features are used)
#### `calc_type`
@@ -39,15 +39,15 @@ The type of calculation to run either regression, log regression, or classificat
#### `desc_dim`
-The maximum dimension of the model to be created
+The maximum dimension of the model to be created (no default value)
#### `n_sis_select`
-The number of features that SIS selects over each iteration
+The number of features that SIS selects over each iteration (no default value)
#### `max_rung`
-The maximum rung of the feature (height of the tallest possible binary expression tree - 1)
+The maximum rung of the feature (height of the tallest possible binary expression tree - 1) (no default value)
#### `n_residual`
diff --git a/docs/tutorial/0_intro.md b/docs/tutorial/0_intro.md
index 852b98a8b3a890470f6da2a395b3b0e2a89f6ae7..ee76cd06d75fdb21b9751352b0707c1ba0e5c6d9 100644
--- a/docs/tutorial/0_intro.md
+++ b/docs/tutorial/0_intro.md
@@ -2,17 +2,17 @@
This tutorial is based on the [Predicting energy differences between crystal structures: (Meta-)stability of octet-binary compounds](https://analytics-toolkit.nomad-coe.eu/public/user-redirect/notebooks/tutorials/descriptor_role.ipynb) tutorial created by Mohammad-Yasin Arif, Luigi Sbailò, Thomas A. R. Purcell, Luca M. Ghiringhelli, and Matthias Scheffler.
The goal of the tutorial is to teach a user how to use `SISSO++` to find and analyze quantitative models for materials properties.
-In particular we will use SISSO to predict the crystal structure (rock salt or zincblende) of a series of octet binaries.
+In particular we will use SISSO to predict the crystal structure (rock-salt or zinc-blende) of a series of octet binaries.
The tutorial will be split into three parts: 1) explaining how to use the executable to perform the calculations and the python utilities to analyze the results and 2) How to use only python to run, analyze, and demonstrate results 3) How to perform classification problems using SISSO.
## Outline
The following tutorials are available:
-- [Combined Binary and Python](1_combined.md)
-- [Python only](2_python.md)
+- [Using the Command Line Interface](1_command_line.md)
+- [Using the Python Interface](2_python.md)
- [Classification](3_classification.md)
-All tutorials use the octet binary dataset first described in [PRL-2015](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.10550) with the goal of predicting whether a material will crystallize in a rock salt or zincblende phase.
+All tutorials use the octet binary dataset first described in [PRL-2015](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.10550) with the goal of predicting whether a material will crystallize in a rock-salt or zinc-blende phase.
For all applications of SISSO a data set has to be passed via a standard `csv` file where the first row represents the feature and property label and the first column are the index-label for each sample for example
```
Material, energy_diff (eV), rs_A (AA), rs_B (AA), E_HOMO_A (eV), E_HOMO_B (eV),....
diff --git a/docs/tutorial/1_command_line.md b/docs/tutorial/1_command_line.md
index c82de07775fba6145c5dd12cd7e466b1acccbc2e..8e0890723e9dcaa77789399bec4d07fb49b8d5d9 100644
--- a/docs/tutorial/1_command_line.md
+++ b/docs/tutorial/1_command_line.md
@@ -64,12 +64,11 @@ The standard output provides information about what step the calculation just fi
When all calculations are complete the code prints out a summary of the best 1D, 2D, ..., {desc_dim}D models with their training RMSE/Testing RMSE (Only training if there is no test set provided).
Additionally, two additional output files are stored in `feature_space/`: `SIS_summary.txt` and `selected_features.txt`.
These files represent a human readable (`SIS_summary.txt`) and computer readable (`selected_features.txt`) summary of the selected feature space from SIS.
-Below are reconstructions of both files for this calculation
+Below are reconstructions of both files for this calculation (To see the file click the triangle)
<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
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
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
@@ -281,19 +277,19 @@ 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>
+
+
## Determining the Ideal Model Complexity with Cross-Validation
While the training error always decreases with descriptor dimensionality for a given application, over-fitting can reduce the general applicability of the models outside of the training set.
In order to determine the optimal dimensionality of a model and optimize the hyperparameters associated with SISSO, we need to perform cross-validation.
-As an example we will discuss how to perform leave-out 10% using the command line
+As an example we will discuss how to perform leave-out 10% using the command line.
To do this we have to modify the `sisso.json` file to automatically leave out a random sample of the training data and use that as a test set by changing `"leave_out_frac": 0.0,` do `"leave_out_frac": 0.10,`.
<details>
<summary> updated sisso.json file</summary>
- ```json
{
"data_file": "data.csv",
"property_key": "E_RS - E_ZB",
@@ -309,7 +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.
@@ -337,7 +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
@@ -361,7 +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
@@ -386,7 +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
@@ -415,7 +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.
@@ -431,11 +428,14 @@ 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.
-By looking at the validation errors, we find that the point with the largest error is diamond for all model dimensions, which is by far the most stable zincblende structure in the data set.
+By looking at the validation errors, we find that the point with the largest error is diamond for all model dimensions, which is by far the most stable zinc-blende structure in the data set.
As a note for this setup there is a 0.22\% chance that one of the samples is never in the validation set so if `max_error_ind != 21` check if that sample is in one of the validation sets.
+
```python
>>> import numpy as np
>>> import pandas as pd
@@ -585,12 +585,66 @@ To get the final models we will perform the same calculation we started off the
From here we can use `models/train_dim_3_model_0.dat` for all of the analysis.
In order to generate a machine learning plot for this model in matplotlib, run the following in python
```python
->>> from sissopp.postprocess.plot.parity_plot import plot_model_ml_plot_from_file
->>> plot_model_ml_plot_from_file("models/train_dim_3_model_0.dat", filename="3d_model.pdf").show()
+>>> from sissopp.postprocess.plot.parity_plot import plot_model_parity_plot
+>>> plot_model_parity_plot("models/train_dim_3_model_0.dat", filename="3d_model.pdf").show()
```
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.
+```python
+>>> from sissopp.postprocess.load_models import load_model
+>>> model = load_model("models/train_dim_3_model_0.dat")
+>>> print(model.latex_str)
+
+>>> model.write_matlab_fxn("matlab_fxn/model.m")
+```
+
+A copy of the generated matlab function is below.
+<details>
+<summary> Matlab function of the Final 3D model </summary>
+
+
+ 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))
+ %
+ % X = [
+ % r_d_B,
+ % r_d_A,
+ % r_p_B,
+ % E_HOMO_A,
+ % IP_A,
+ % r_sigma,
+ % r_s_B,
+ % r_p_A,
+ % ]
+
+ if(size(X, 2) ~= 8)
+ error("ERROR: X must have a size of 8 in the second dimension.")
+ end
+ r_d_B = reshape(X(:, 1), 1, []);
+ r_d_A = reshape(X(:, 2), 1, []);
+ r_p_B = reshape(X(:, 3), 1, []);
+ E_HOMO_A = reshape(X(:, 4), 1, []);
+ IP_A = reshape(X(:, 5), 1, []);
+ r_sigma = reshape(X(:, 6), 1, []);
+ r_s_B = reshape(X(:, 7), 1, []);
+ r_p_A = reshape(X(:, 8), 1, []);
+
+ f0 = ((r_d_B ./ r_d_A) .* (r_p_B .* E_HOMO_A));
+ f1 = ((IP_A).^3 .* abs(r_sigma - r_s_B));
+ f2 = ((IP_A ./ r_p_A) ./ (r_p_B + r_p_A));
+
+ c0 = -1.3509197357e-01;
+ a0 = 2.8311062079e-02;
+ a1 = 3.7282871777e-04;
+ a2 = -2.3703222974e-01;
+
+ 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 1ff724f57d7dfb3e5e7cc4710f0c4b31ebd8ce57..cdfe924041825bfed776211273d881a257c1379e 100644
--- a/docs/tutorial/3_classification.md
+++ b/docs/tutorial/3_classification.md
@@ -1,15 +1,15 @@
-Performing Classification with SISSO++
----
-Finally `SISSO++` can be used to solve classification problems as well as regression 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 zincblende structure, and change the calculation type to be `classification`.
+# 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.
## The Data File
-Here is the updated data file, with the property `E_RS - E_ZB (eV)` replaced with a `Class` column where any negative `E_RS - E_ZB (eV)` is replaced with 0 and any positive value replaced with 1.
+Here is the updated data file, with the property `E_RS - E_ZB (eV)` replaced with a `Class` column where any negative `E_RS - E_ZB (eV)` is replaced with 0 and any positive value replaced with 1. While this example has only one task and two classes, the method works for an arbitrary number of classes and tasks.
+
<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
@@ -93,7 +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
@@ -147,7 +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)
@@ -191,7 +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).
@@ -199,7 +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
@@ -297,10 +296,9 @@ 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 the Python Interface
+## Updating the SVM Model Using `sklearn`
Because the basis of the classification algorithm is based on the overlap region of the convex hull, the `c` value for the SVM model is set at a fairly high value of 1000.0.
This will prioritize reducing the number of misclassified points, but does make the model more susceptible to being over fit.
To account for this the python interface has the ability to refit the Linear SVM using the `svm` module of `sklearn`.
@@ -331,11 +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/3d_model.png b/docs/tutorial/command_line/cv/3d_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..c9eb59412557ebad45d65d0e4b8ae438fdd3b4c1
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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
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 793780f2b261d527d4b32590ad2c321c84b57069..fe0a33642609e3da3af624f391af0e63ea92516f 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -84,8 +84,16 @@ add_test(
COMMAND ${MPIEXEC_EXECUTABLE} ${MPIEXEC_NUMPROC_FLAG} 2 ${MPIEXEC_PREFLAGS} "${CMAKE_BINARY_DIR}/bin/sisso++" "${CMAKE_SOURCE_DIR}/tests/exec_test/log_reg/sisso.json" ${MPIEXEC_POSTFLAGS}
)
add_test(
- NAME Train_Only
- COMMAND ${MPIEXEC_EXECUTABLE} ${MPIEXEC_NUMPROC_FLAG} 2 ${MPIEXEC_PREFLAGS} "${CMAKE_BINARY_DIR}/bin/sisso++" "${CMAKE_SOURCE_DIR}/tests/exec_test/no_test_data/sisso.json" ${MPIEXEC_POSTFLAGS}
+ NAME Log_Regression_Max_Correlation_NE_One
+ COMMAND ${MPIEXEC_EXECUTABLE} ${MPIEXEC_NUMPROC_FLAG} 2 ${MPIEXEC_PREFLAGS} "${CMAKE_BINARY_DIR}/bin/sisso++" "${CMAKE_SOURCE_DIR}/tests/exec_test/log_reg_gen_proj/sisso.json" ${MPIEXEC_POSTFLAGS}
+)
+add_test(
+ NAME Log_Regression_Generate_Project
+ COMMAND ${MPIEXEC_EXECUTABLE} ${MPIEXEC_NUMPROC_FLAG} 2 ${MPIEXEC_PREFLAGS} "${CMAKE_BINARY_DIR}/bin/sisso++" "${CMAKE_SOURCE_DIR}/tests/exec_test/log_reg_max_corr/sisso.json" ${MPIEXEC_POSTFLAGS}
+)
+add_test(
+ NAME Log_Regression_Max_Correlation_NE_One_Generate_Project
+ COMMAND ${MPIEXEC_EXECUTABLE} ${MPIEXEC_NUMPROC_FLAG} 2 ${MPIEXEC_PREFLAGS} "${CMAKE_BINARY_DIR}/bin/sisso++" "${CMAKE_SOURCE_DIR}/tests/exec_test/log_reg_max_corr_gen_proj/sisso.json" ${MPIEXEC_POSTFLAGS}
)
if(BUILD_PARAMS)
add_test(
diff --git a/src/classification/ConvexHull1D.cpp b/src/classification/ConvexHull1D.cpp
index 49c8c1a64989bd7c3c5cb9efee3803915ebb13c5..67623e00dc6467fcf2c6f13d4a68813e2cce5746 100644
--- a/src/classification/ConvexHull1D.cpp
+++ b/src/classification/ConvexHull1D.cpp
@@ -32,30 +32,27 @@ ConvexHull1D::ConvexHull1D() :
_n_class(0)
{}
-ConvexHull1D::ConvexHull1D(std::vector<int> sizes, const double* prop) :
- _sorted_value(std::accumulate(sizes.begin(), sizes.end(), 0), 0.0),
+ConvexHull1D::ConvexHull1D(std::vector<int> task_sizes, const double* prop) :
+ _sorted_value(std::accumulate(task_sizes.begin(), task_sizes.end(), 0), 0.0),
_cls_max(),
_cls_min(),
- _sorted_prop_inds(std::accumulate(sizes.begin(), sizes.end(), 0), 0),
+ _sorted_prop_inds(std::accumulate(task_sizes.begin(), task_sizes.end(), 0), 0),
_cls_start(),
_cls_sz(),
- _n_task(sizes.size()),
+ _n_task(task_sizes.size()),
_n_class(0)
{
- initialize_prop(sizes, prop);
+ initialize_prop(task_sizes, prop);
}
-void ConvexHull1D::initialize_prop(std::vector<int> sizes, const double* prop)
+void ConvexHull1D::initialize_prop(std::vector<int> task_sizes, const double* prop)
{
// Set the number of tasks and samples
- _n_task = sizes.size();
+ _n_task = task_sizes.size();
_task_scores.resize(_n_task, 0.0);
- int n_samp = std::accumulate(sizes.begin(), sizes.end(), 0);
+ int n_samp = std::accumulate(task_sizes.begin(), task_sizes.end(), 0);
// Set number of classes
-
-
-
std::vector<double> unique_prop_vals;
for(int pp = 0; pp < n_samp; ++pp)
{
@@ -74,6 +71,9 @@ void ConvexHull1D::initialize_prop(std::vector<int> sizes, const double* prop)
_cls_sz.resize(_n_class * _n_task, 0);
_cls_start.resize(_n_class * _n_task, 0);
+ std::fill_n(_cls_sz.data(), _cls_sz.size(), 0);
+ std::fill_n(_cls_start.data(), _cls_start.size(), 0);
+
// Set the values of the cls vectors and sorted inds
_sorted_value.resize(n_samp, 0.0);
_sorted_prop_inds.resize(n_samp, 0);
@@ -87,15 +87,15 @@ void ConvexHull1D::initialize_prop(std::vector<int> sizes, const double* prop)
int start = 0;
- for(int tt = 0; tt < sizes.size(); ++tt)
+ for(int tt = 0; tt < task_sizes.size(); ++tt)
{
util_funcs::argsort<double>(
_sorted_prop_inds.data() + start,
- _sorted_prop_inds.data() + start + sizes[tt],
+ _sorted_prop_inds.data() + start + task_sizes[tt],
prop
);
- for(int ind = start; ind < start + sizes[tt]; ++ind)
+ for(int ind = start; ind < start + task_sizes[tt]; ++ind)
{
++_cls_sz[tt * _n_class + cl_ind[prop[ind]]];
}
@@ -143,7 +143,9 @@ double ConvexHull1D::overlap_1d(double* value, double width)
for(int c1 = 0; c1 < _n_class; ++c1)
{
if(_cls_sz[tt * _n_class + c1] == 0)
+ {
continue;
+ }
double min = _cls_min[tt * _n_class + c1];
double max = _cls_max[tt * _n_class + c1];
diff --git a/src/classification/ConvexHull1D.hpp b/src/classification/ConvexHull1D.hpp
index d72b4f37606e704024d021b47aed3c936c3f28bc..7069dce107bf1dfdd490c0b27977e30b88af8064 100644
--- a/src/classification/ConvexHull1D.hpp
+++ b/src/classification/ConvexHull1D.hpp
@@ -51,10 +51,10 @@ public:
/**
* @brief Constructor
*
- * @param sizes The size of each task
+ * @param task_sizes The size of each task
* @param prop The pointer to the property vector
*/
- ConvexHull1D(std::vector<int> sizes, const double* prop);
+ ConvexHull1D(std::vector<int> task_sizes, const double* prop);
/**
* @brief Default constructor
@@ -64,10 +64,10 @@ public:
/**
* @brief Initialize the projection objects
*
- * @param sizes The size of each task
+ * @param task_sizes The size of each task
* @param prop The pointer to the property vector
*/
- void initialize_prop(std::vector<int> sizes, const double* prop);
+ void initialize_prop(std::vector<int> task_sizes, const double* prop);
/**
* @brief Calculate the projection scores of a set of features to a vector via Pearson correlation
diff --git a/src/classification/LPWrapper.cpp b/src/classification/LPWrapper.cpp
index e850c1d0d9191560a7f32f45d5dc273674b5199a..b4465857caa943aad6ff704a81ac95da7eec517f 100644
--- a/src/classification/LPWrapper.cpp
+++ b/src/classification/LPWrapper.cpp
@@ -206,7 +206,7 @@ void LPWrapper::copy_data(const int cls, const std::vector<double*> val_ptrs, co
{
throw std::logic_error("Size of the val_ptrs vector is larger than _n_dim");
}
- if(val_ptrs.size() > _n_dim)
+ if(test_val_ptrs.size() > _n_dim)
{
throw std::logic_error("Size of the test_val_ptrs vector is larger than _n_dim");
}
diff --git a/src/classification/SVMWrapper.cpp b/src/classification/SVMWrapper.cpp
index f947de1c646b6c8f486f83cb1d9769b209322ee0..7a0c98a86ca49bfcd2ebcfc9180218130ea2785d 100644
--- a/src/classification/SVMWrapper.cpp
+++ b/src/classification/SVMWrapper.cpp
@@ -22,27 +22,8 @@
#include "classification/SVMWrapper.hpp"
SVMWrapper::SVMWrapper(const int n_class, const int n_dim, const int n_samp, const double* prop) :
- _model(nullptr),
- _y(prop, prop + n_samp),
- _y_est(n_samp),
- _x_space(n_samp * (n_dim + 1)),
- _x(n_samp),
- _coefs(n_class * (n_class - 1) / 2, std::vector<double>(n_dim, 0.0)),
- _C(1000.0),
- _intercept(n_class * (n_class - 1) / 2, 0.0),
- _w_remap(n_dim, 1.0),
- _b_remap(n_dim, 0.0),
- _n_dim(n_dim),
- _n_samp(n_samp),
- _n_class(n_class)
-{
- setup_parameter_obj(_C);
- setup_x_space();
-
- _prob.l = _n_samp;
- _prob.y = _y.data();
- _prob.x = _x.data();
-}
+ SVMWrapper(1000.0, n_class, n_dim, n_samp, prop)
+{}
SVMWrapper::SVMWrapper(const int n_class, const int n_dim, const std::vector<double> prop) :
SVMWrapper(n_class, n_dim, prop.size(), prop.data())
@@ -63,6 +44,14 @@ SVMWrapper::SVMWrapper(const double C, const int n_class, const int n_dim, const
_n_samp(n_samp),
_n_class(n_class)
{
+ std::vector<double> unique_prop_vals = vector_utils::unique(_y);
+ std::map<double, double> rep_map;
+ for(int cc = 0; cc < _n_class; ++cc)
+ {
+ _map_prop_vals[static_cast<double>(cc)] = unique_prop_vals[cc];
+ rep_map[unique_prop_vals[cc]] = static_cast<double>(cc);
+ }
+ std::transform(_y.begin(), _y.end(), _y.begin(), [&](double val){return rep_map[val];});
setup_parameter_obj(_C);
setup_x_space();
@@ -121,6 +110,11 @@ void SVMWrapper::setup_x_space()
void SVMWrapper::copy_data(const std::vector<int> inds, const int task)
{
+ if((task < 0) || (task >= node_value_arrs::TASK_SZ_TRAIN.size()))
+ {
+ throw std::logic_error("The requested task is invalid.");
+ }
+
if(inds.size() > _n_dim)
{
throw std::logic_error("Size of the inds vector is larger than _n_dim");
@@ -210,7 +204,10 @@ void SVMWrapper::train(const bool remap_coefs)
std::transform(_x.begin(), _x.end(), _y_est.begin(), [this](svm_node* sn){return svm_predict(_model, sn);});
_n_misclassified = 0;
for(int ss = 0; ss < _n_samp; ++ss)
+ {
_n_misclassified += _y[ss] != _y_est[ss];
+ }
+ std::transform(_y_est.begin(), _y_est.end(), _y_est.begin(), [this](double val){return _map_prop_vals[val];});
}
void SVMWrapper::train(const std::vector<int> inds, const int task, const bool remap_coefs)
@@ -251,6 +248,6 @@ std::vector<double> SVMWrapper::predict(const int n_samp_test, const std::vector
x_test[ss] = &x_space_test[ss * (val_ptrs.size() + 1)];
}
std::transform(x_test.begin(), x_test.end(), y_est_test.begin(), [this](svm_node* sn){return svm_predict(_model, sn);});
-
+ std::transform(y_est_test.begin(), y_est_test.end(), y_est_test.begin(), [this](double val){return _map_prop_vals[val];});
return y_est_test;
}
diff --git a/src/classification/SVMWrapper.hpp b/src/classification/SVMWrapper.hpp
index b616a44876ec3856852573c59091129b62ba3804..8f412797b51625c2d954182fe192e0025162c150 100644
--- a/src/classification/SVMWrapper.hpp
+++ b/src/classification/SVMWrapper.hpp
@@ -22,11 +22,13 @@
#ifndef LIBSVM_WRAPPER
#define LIBSVM_WRAPPER
+#include <map>
#include <algorithm>
#include <iostream>
#include "external/libsvm/svm.h"
#include "feature_creation/node/value_storage/nodes_value_containers.hpp"
+#include "utils/vector_utils.hpp"
// DocString: cls_svm_wrapper
/**
@@ -50,6 +52,7 @@ protected:
std::vector<double> _w_remap; //!< Prefactors to convert the data to/from the preferred SVM range (0 to 1)
std::vector<double> _b_remap; //!< Prefactors to map the minimum of the features to 0.0
+ std::map<double, double> _map_prop_vals; //!< Map of the property values to the values used for SVM
const double _C; //!< The C parameter for the SVM calculation
const int _n_dim; //!< The number of dimensions for the SVM problem
diff --git a/src/descriptor_identifier/model/Model.cpp b/src/descriptor_identifier/model/Model.cpp
index cce0459e260e6dd452c6aa1ca37218f7b41d30de..697f96f95cfb33c5ee6661e85a7664e8eb3c372b 100644
--- a/src/descriptor_identifier/model/Model.cpp
+++ b/src/descriptor_identifier/model/Model.cpp
@@ -86,10 +86,6 @@ double Model::eval(std::map<std::string, double> x_in_dct) const
{
throw std::logic_error("The value of " + in_expr + " is not in x_in_dct.");
}
- else if(x_in_dct.count(in_expr) > 1)
- {
- throw std::logic_error("Multiple values of " + in_expr + " defined in x_in_dct.");
- }
x_in.push_back(x_in_dct[in_expr]);
}
@@ -132,10 +128,6 @@ std::vector<double> Model::eval(std::map<std::string, std::vector<double>> x_in_
{
throw std::logic_error("The value of " + in_expr + " is not in x_in_dct.");
}
- else if(x_in_dct.count(in_expr) > 1)
- {
- throw std::logic_error("Multiple values of " + in_expr + " defined in x_in_dct.");
- }
x_in.push_back(x_in_dct[in_expr]);
}
@@ -276,6 +268,8 @@ void Model::write_matlab_fxn(std::string fxn_filename)
}
boost::filesystem::path p(fxn_filename.c_str());
+ std::string fxn_name = p.filename().string();
+ fxn_name = fxn_name.substr(0, fxn_name.size() - 2);
boost::filesystem::path parent = p.remove_filename();
if(parent.string().size() > 0)
{
@@ -296,7 +290,7 @@ void Model::write_matlab_fxn(std::string fxn_filename)
std::transform(leaves.begin(), leaves.end(), leaves.begin(), [](std::string s){return str_utils::matlabify(s);});
// Write the header of the function
- out_file_stream << "function P = " << fxn_filename.substr(0, fxn_filename.size() - 2) << "(X)\n";
+ out_file_stream << "function P = " << fxn_name << "(X)\n";
out_file_stream << "% Returns the value of " << _prop_label << " = " << toString() << "\n%\n";
out_file_stream << "% X = [\n";
for(auto & leaf : leaves)
@@ -373,6 +367,8 @@ void Model::populate_model(const std::string train_filename, const std::string t
std::string test_error_line;
std::getline(train_file_stream, prop_desc_line);
int n_line = 5;
+
+ // Legacy Code so previous model files can be read in
if(!is_error_line(prop_desc_line))
{
split_line = str_utils::split_string_trim(prop_desc_line);
diff --git a/src/descriptor_identifier/model/ModelClassifier.cpp b/src/descriptor_identifier/model/ModelClassifier.cpp
index 2b6a1459a7a53b0817174ad866d62741bf47c2ca..ec9fd8251f30f16204bb8adda7fbc9147728f50e 100644
--- a/src/descriptor_identifier/model/ModelClassifier.cpp
+++ b/src/descriptor_identifier/model/ModelClassifier.cpp
@@ -59,7 +59,9 @@ ModelClassifier::ModelClassifier(
_test_n_svm_misclassified = std::accumulate(test_misclassified.begin(), test_misclassified.end(), 0);
}
-ModelClassifier::ModelClassifier(const std::string train_file)
+ModelClassifier::ModelClassifier(const std::string train_file) :
+ _train_n_convex_overlap(0),
+ _test_n_convex_overlap(0)
{
populate_model(train_file);
_n_class = _loss->n_class();
@@ -79,7 +81,9 @@ ModelClassifier::ModelClassifier(const std::string train_file)
);
}
}
-ModelClassifier::ModelClassifier(const std::string train_file, std::string test_file)
+ModelClassifier::ModelClassifier(const std::string train_file, std::string test_file) :
+ _train_n_convex_overlap(0),
+ _test_n_convex_overlap(0)
{
populate_model(train_file, test_file);
_n_class = _loss->n_class();
@@ -220,8 +224,8 @@ std::string ModelClassifier::write_coefs() const
{
std::stringstream coef_head_stream;
coef_head_stream << "# Decision Boundaries" << std::endl;
- int n_db = _n_class * (_n_class - 1) / 2;
- int task_header_w = 1 + static_cast<int>(std::floor(std::log10(n_db))) + std::max(
+ int n_db = _loss->n_class() * (_loss->n_class() - 1) / 2;
+ int task_header_w = 2 + static_cast<int>(std::floor(std::log10(_n_class))) * 3 + std::max(
6,
static_cast<int>(std::max_element(_task_names.begin(), _task_names.end(), [](std::string s1, std::string s2){return s1.size() <= s2.size();})->size())
);
@@ -233,16 +237,19 @@ std::string ModelClassifier::write_coefs() const
}
coef_head_stream << " b" << std::endl;
+ int start_coefs = 0;
for(int tt = 0; tt < _task_names.size(); ++tt)
{
+ n_db = _loss->n_class(tt) * (_loss->n_class(tt) - 1) / 2;
for(int db = 0; db < n_db; ++db)
{
- coef_head_stream << std::setw(task_header_w) << std::left << "# " + _task_names[tt] + "_" + std::to_string(db) << std::setw(2) << ", ";
- for(auto& coeff : _coefs[tt * n_db + db])
+ coef_head_stream << std::setw(task_header_w) << std::left << "# " + _task_names[tt] + "_" + _loss->coef_labels()[start_coefs] << std::setw(2) << ", ";
+ for(auto& coeff : _coefs[start_coefs])
{
coef_head_stream << std::setprecision(15) << std::scientific << std::right << std::setw(22) << coeff << std::setw(2) << ", ";
}
coef_head_stream << "\n";
+ ++start_coefs;
}
}
return coef_head_stream.str();
diff --git a/src/descriptor_identifier/model/ModelClassifier.hpp b/src/descriptor_identifier/model/ModelClassifier.hpp
index 6126910ccda7c56d42766ae925f4eb8f86d56442..cf6163af5f9a7071f259e799b54c9b97295338fc 100644
--- a/src/descriptor_identifier/model/ModelClassifier.hpp
+++ b/src/descriptor_identifier/model/ModelClassifier.hpp
@@ -43,10 +43,9 @@ class ModelClassifier : public Model
int _train_n_svm_misclassified; //!< The number of points misclassified by SVM (training set)
int _test_n_svm_misclassified; //!< The number of points misclassified by SVM (test set)
-protected:
- using Model::eval;
public:
+ using Model::eval;
/**
* @brief Construct a ModelClassifier using a loss function and a set of features
diff --git a/src/descriptor_identifier/model/ModelLogRegressor.cpp b/src/descriptor_identifier/model/ModelLogRegressor.cpp
index a7519adca0b6d4e67f16854e9cddab98641f618d..caf1f0d3603fbf5e556f948eff2964474b9ab34f 100644
--- a/src/descriptor_identifier/model/ModelLogRegressor.cpp
+++ b/src/descriptor_identifier/model/ModelLogRegressor.cpp
@@ -126,7 +126,7 @@ std::string ModelLogRegressor::toLatexString() const
std::stringstream model_rep;
if(_fix_intercept)
{
- model_rep << "$\\left(" << _feats[0]->get_latex_expr() << "\\right)^{a_0}" << std::endl;
+ model_rep << "$\\left(" << _feats[0]->get_latex_expr() << "\\right)^{a_0}";
for(int ff = 1; ff < _feats.size(); ++ff)
{
model_rep << "\\left(" << _feats[ff]->get_latex_expr() << "\\right)^{a_" << ff << "}";
diff --git a/src/descriptor_identifier/solver/SISSOClassifier.cpp b/src/descriptor_identifier/solver/SISSOClassifier.cpp
index 589d068f4cb90b781842b82b8afe6bd6f20f063a..2c17f4d015ef94fb5578a247cf8969c18744f579 100644
--- a/src/descriptor_identifier/solver/SISSOClassifier.cpp
+++ b/src/descriptor_identifier/solver/SISSOClassifier.cpp
@@ -45,7 +45,7 @@ void SISSOClassifier::setup_d_mat_transfer()
std::vector<int> inds(_task_sizes_train[tt]);
std::iota(inds.begin(), inds.end(), task_start);
- util_funcs::argsort<double>(inds.data(), inds.data() + inds.size(), _loss->prop_pointer() + task_start);
+ util_funcs::argsort<double>(inds.data(), inds.data() + inds.size(), _loss->prop_pointer());
_sample_inds_to_sorted_dmat_inds[inds[0]] = task_start;
int cls_start = 0;
@@ -70,7 +70,7 @@ std::array<double, 2> SISSOClassifier::svm_error(std::vector<SVMWrapper>& svm, c
dist_error += 1.0 / util_funcs::norm(coefs.data(), feat_inds.size());
}
}
- dist_error /= static_cast<double>(_n_task * svm[0].n_class());
+ dist_error /= static_cast<double>(_n_task * _n_class);
return {error, dist_error};
}
@@ -85,8 +85,7 @@ int SISSOClassifier::get_max_error_ind(
scores,
[this](int n_overlap, double score){return static_cast<double>(n_overlap * _n_samp * _n_class) + score;}
);
-
- double max_dist = *std::max_element(svm_margin, svm_margin + n_models) + 0.01;
+ double max_dist = std::abs(*std::max_element(svm_margin, svm_margin + n_models, [](double v1, double v2){return std::abs(v1) < std::abs(v2);})) + 0.01;
std::transform(
svm_margin,
svm_margin + n_models,
@@ -121,8 +120,8 @@ void SISSOClassifier::l0_regularization(const int n_dim)
std::vector<int> inds(n_dim, 0);
std::vector<int> min_inds(n_get_models * n_dim, -1);
- std::vector<int> min_n_convex_overlap(n_get_models, std::numeric_limits<int>::max());
- std::vector<double> min_svm_score(n_get_models, std::numeric_limits<double>::max());
+ std::vector<int> min_n_convex_overlap(n_get_models, _n_samp * _n_class * _n_class);
+ std::vector<double> min_svm_score(n_get_models, _n_samp * _n_class * _n_class);
std::vector<double> min_svm_margin(n_get_models, -1.0);
unsigned long long int n_interactions = 1;
@@ -161,17 +160,17 @@ void SISSOClassifier::l0_regularization(const int n_dim)
int start = 0;
for(int tt = 0; tt < _n_task; ++tt)
{
- svm_vec.push_back(SVMWrapper(_c, _n_class, _n_dim, _task_sizes_train[tt], loss_copy->prop_pointer() + start));
+ svm_vec.push_back(SVMWrapper(_c, _loss->n_class(tt), _n_dim, _task_sizes_train[tt], loss_copy->prop_pointer() + start));
start += _task_sizes_train[tt];
}
unsigned long long int ii_prev = 0;
-#ifdef OMP45
+ #ifdef OMP45
#pragma omp for schedule(monotonic: dynamic)
-#else
+ #else
#pragma omp for schedule(dynamic)
-#endif
+ #endif
for(unsigned long long int ii = _mpi_comm->rank(); ii < n_interactions; ii += static_cast<unsigned long long int>(_mpi_comm->size()))
{
util_funcs::iterate(inds, inds.size(), ii - ii_prev);
@@ -301,8 +300,13 @@ void SISSOClassifier::l0_regularization(const int n_dim)
void SISSOClassifier::fit()
{
- for(int dd = 1; dd <= _n_dim; ++dd)
+ int dd = 1;
+ while(
+ (dd <= _n_dim) &&
+ (*std::max_element(_loss->prop_project_pointer(), _loss->prop_project_pointer() + _loss->n_prop_project() * _n_samp) > 0.0)
+ )
{
+
double start = omp_get_wtime();
_feat_space->sis(_loss);
@@ -330,6 +334,11 @@ void SISSOClassifier::fit()
}
}
}
+ ++dd;
+ }
+ if(dd <= _n_dim)
+ {
+ std::cerr << "WARNING: All points sperated before reaching the requested dimension." << std::endl;
}
}
diff --git a/src/descriptor_identifier/solver/SISSOSolver.hpp b/src/descriptor_identifier/solver/SISSOSolver.hpp
index 44955e67619b708cf37a39f9de17812186aea850..7c4bddd09e3a407d7c9b3035ba15b06113136168 100644
--- a/src/descriptor_identifier/solver/SISSOSolver.hpp
+++ b/src/descriptor_identifier/solver/SISSOSolver.hpp
@@ -128,6 +128,11 @@ public:
*/
inline int n_models_store() const {return _n_models_store;}
+ /**
+ * @brief If true the bias term is fixed at 0
+ */
+ inline bool fix_intercept() const {return _fix_intercept;}
+
// Python interface functions
#ifdef PY_BINDINGS
diff --git a/src/feature_creation/feature_space/FeatureSpace.hpp b/src/feature_creation/feature_space/FeatureSpace.hpp
index e99e136c18adf7254786a058391d19a61bcad728..4f3833f073ec91c5b79181e534840657f4c3b4bc 100644
--- a/src/feature_creation/feature_space/FeatureSpace.hpp
+++ b/src/feature_creation/feature_space/FeatureSpace.hpp
@@ -322,6 +322,19 @@ public:
*/
void remove_feature(const int ind);
+ #ifdef PARAMETERIZE
+ // DocString: feat_space_param_feats_allowed
+ /**
+ * @brief True if built with -DBUILD_PARAMS (used for python tests)
+ */
+ bool parameterized_feats_allowed() const {return true;}
+ #else
+ // DocString: feat_space_param_feats_allowed
+ /**
+ * @brief True if built with -DBUILD_PARAMS (used for python tests)
+ */
+ bool parameterized_feats_allowed() const {return false;}
+ #endif
// Python Interface Functions
#ifdef PY_BINDINGS
diff --git a/src/feature_creation/node/FeatureNode.hpp b/src/feature_creation/node/FeatureNode.hpp
index 36e03de3b08fd9acc913e35456566e68e9919f1e..371204bb1f692a07c280d3bd6cff5251ee42e541 100644
--- a/src/feature_creation/node/FeatureNode.hpp
+++ b/src/feature_creation/node/FeatureNode.hpp
@@ -500,7 +500,7 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- void param_derivative(const double* params, double* dfdp) const {}
+ void param_derivative(const double* params, double* dfdp, const int depth = 1) const {}
/**
* @brief Get the parameter gradient for non-linear optimization
@@ -519,8 +519,9 @@ public:
* @param grad pointer to the gradient storage
* @param dfdp pointer to where the feature derivative pointers are located
* @param params A pointer to the bias and scale terms for this Node and its children
+ * @param depth The current depth in the binary expression tree
*/
- inline void gradient(double* grad, double* dfdp, const double* params) const {};
+ inline void gradient(double* grad, double* dfdp, const double* params, const int depth = 1) const {};
#endif
};
diff --git a/src/feature_creation/node/ModelNode.cpp b/src/feature_creation/node/ModelNode.cpp
index 48eec97ffb28a695b133162fb4904762b78d3e83..59be042b20bcfc365270294301a7590a4436add8 100644
--- a/src/feature_creation/node/ModelNode.cpp
+++ b/src/feature_creation/node/ModelNode.cpp
@@ -900,7 +900,9 @@ double ModelNode::eval(double* x_in)
}
}
if(stack.size() != 1)
+ {
throw std::logic_error("The final stack size is not one, something wrong happened during the calculation.");
+ }
return *stack[0];
}
@@ -923,11 +925,6 @@ double ModelNode::eval(std::map<std::string, double> x_in_dct)
{
throw std::logic_error("The value of " + in_expr + " is not in x_in_dct.");
}
- else if(x_in_dct.count(in_expr) > 1)
- {
- throw std::logic_error("Multiple values of " + in_expr + " defined in x_in_dct.");
- }
-
x_in.push_back(x_in_dct[in_expr]);
}
return eval(x_in.data());
@@ -995,10 +992,6 @@ std::vector<double> ModelNode::eval(std::map<std::string, std::vector<double>> x
{
throw std::logic_error("The value of " + in_expr + " is not in x_in_dct.");
}
- else if(x_in_dct.count(in_expr) > 1)
- {
- throw std::logic_error("Multiple values of " + in_expr + " defined in x_in_dct.");
- }
x_in.push_back(x_in_dct[in_expr]);
}
diff --git a/src/feature_creation/node/ModelNode.hpp b/src/feature_creation/node/ModelNode.hpp
index 831cbc81ace9ded744bad6850c2f3784c6d791fd..555fe6bd2bd6143d5e8fc49939c6ca2193a0ee27 100644
--- a/src/feature_creation/node/ModelNode.hpp
+++ b/src/feature_creation/node/ModelNode.hpp
@@ -317,7 +317,6 @@ public:
inline double* value_ptr(int offset=-1, const bool for_comp=false) const
{
throw std::logic_error("const version of value_ptr for ModelNode is impossible.");
- return nullptr;
}
/**
@@ -329,7 +328,6 @@ public:
inline double* test_value_ptr(int offset=-1, const bool for_comp=false) const
{
throw std::logic_error("const version of test_value_ptr for ModelNode is impossible.");
- return nullptr;
}
// DocString: model_node_rung
diff --git a/src/feature_creation/node/Node.hpp b/src/feature_creation/node/Node.hpp
index 06f385ce107bfc737c26fe86e398dd60e62c24b2..6b752818b9d02c00c1c4a4372f00b3e1a4cabbc4 100644
--- a/src/feature_creation/node/Node.hpp
+++ b/src/feature_creation/node/Node.hpp
@@ -418,7 +418,6 @@ public:
virtual inline const double* param_pointer() const
{
throw std::logic_error("Trying to access the parameter pointer to a node with no parameters.");
- return nullptr;
}
/**
@@ -554,7 +553,7 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param samp_ind sample index number
*/
- virtual void param_derivative(const double* params, double* dfdp) const = 0;
+ virtual void param_derivative(const double* params, double* dfdp, const int depth = 1) const = 0;
/**
* @brief Get the parameter gradient for non-linear optimization
@@ -570,8 +569,9 @@ public:
* @param grad pointer to the gradient storage
* @param dfdp pointer to where the feature derivative pointers are located
* @param params A pointer to the bias and scale terms for this Node and its children
+ * @param depth The current depth in the binary expression tree
*/
- virtual void gradient(double* grad, double* dfdp, const double* params) const = 0;
+ virtual void gradient(double* grad, double* dfdp, const double* params, const int depth = 1) const = 0;
#endif
// DocString: node_n_feats
diff --git a/src/feature_creation/node/operator_nodes/OperatorNode.hpp b/src/feature_creation/node/operator_nodes/OperatorNode.hpp
index fd742686f608d9bb72d1f4775693f633cc82b10f..858130a2e907287833f9bf16efe6392e4b24a641 100644
--- a/src/feature_creation/node/operator_nodes/OperatorNode.hpp
+++ b/src/feature_creation/node/operator_nodes/OperatorNode.hpp
@@ -579,7 +579,7 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- virtual void param_derivative(const double* params, double* dfdp) const = 0;
+ virtual void param_derivative(const double* params, double* dfdp, const int depth = 1) const = 0;
/**
* @brief Get the parameter gradient for non-linear optimization
@@ -589,7 +589,7 @@ public:
*/
void gradient(double* grad, double* dfdp) const
{
- if(n_params_possible() == 0)
+ if(n_params() == 0)
{
throw std::logic_error("Asking for the gradient of non-parameterized feature");
}
@@ -603,13 +603,23 @@ public:
* @param grad pointer to the gradient storage
* @param dfdp pointer to where the feature derivative are located
* @param params A pointer to the bias and scale terms for this Node and its children
+ * @param depth The current depth in the binary expression tree
*/
- void gradient(double* grad, double* dfdp, const double* params) const
+ void gradient(double* grad, double* dfdp, const double* params, const int depth = 1) const
{
int np = n_params_possible();
// Calculate f' and x
- param_derivative(params, dfdp);
- double* val_ptr = _feats[N - 1]->value_ptr(params + 2);
+ param_derivative(params, dfdp, depth);
+ double* val_ptr;
+
+ if(depth < nlopt_wrapper::MAX_PARAM_DEPTH)
+ {
+ val_ptr = _feats[N - 1]->value_ptr(params + 2, -1, true, depth + 1);
+ }
+ else
+ {
+ val_ptr = _feats[N - 1]->value_ptr(-1, true);
+ }
// df / d\alpha = x f'
std::transform(dfdp, dfdp + _n_samp, grad, grad, std::multiplies<double>());
@@ -627,10 +637,13 @@ public:
// Go down the chain rule
int start = 2;
- for(int ff = N - 1; ff >=0; --ff)
+ if(depth < nlopt_wrapper::MAX_PARAM_DEPTH)
{
- _feats[ff]->gradient(grad + start * _n_samp, dfdp, params + start);
- start += _feats[ff]->n_params_possible();
+ for(int ff = N - 1; ff >=0; --ff)
+ {
+ _feats[ff]->gradient(grad + start * _n_samp, dfdp, params + start, depth + 1);
+ start += _feats[ff]->n_params_possible();
+ }
}
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp
index 80dc6ccf662fb890fcbdf798573304d46146c4d7..d3e90a7fde842e96a09d11cec3096873478d298f 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp
@@ -298,9 +298,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return util_funcs::sign(params[0] * vp + params[1]);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.hpp
index 5e880c1a9b02b67c5539c53b23e21387f42dea73..a6f9d6d809301e517f0853693a3f3da183bc2a9a 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/absolute_difference.hpp
@@ -314,10 +314,11 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr_1 = _feats[0]->value_ptr(params, 2);
- double* val_ptr_2 = _feats[1]->value_ptr(params, 1);
+ double* val_ptr_1 = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2 + _feats[1]->n_params(), 0, true, depth + 1) : _feats[0]->value_ptr(0, true);
+ double* val_ptr_2 = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[1]->value_ptr(params + 2, 1, true, depth + 1) : _feats[1]->value_ptr(1, true);
+
std::transform(
val_ptr_1,
val_ptr_1 + _n_samp,
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.hpp
index bffbece28b1fe75ee62d1bc0273086eedf2fda68..12848c2fa8a929f222ce4cd35bf7b5cf02743334 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/add/add.hpp
@@ -307,7 +307,7 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const {std::fill_n(dfdp, _n_samp, 1.0);}
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const {std::fill_n(dfdp, _n_samp, 1.0);}
#endif
};
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.hpp
index d74dd28ae9ef3cf64168aca2e6c73481f781aad4..677d64c24769b36bb10cfd5c58c18c934ca81d08 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.hpp
@@ -295,9 +295,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return 3.0 * std::pow(params[0] * vp + params[1], 2.0);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.hpp
index c12dd29900543910bbe9e52141b6018c580ec34f..6bb0003f0e1fcdff7e58221fb1abbff8fbad89e6 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/cube_root.hpp
@@ -294,9 +294,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return 1.0 / 3.0 * std::pow(params[0] * vp + params[1], -2.0 / 3.0);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp
index 0c57eb6fb9d5d9a821d5e8e82e63db033ca02d62..f165a642b533e060449557c996b2a94ceff15b5d 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp
@@ -297,9 +297,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return -1.0 * std::sin(params[0] * vp + params[1]);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.hpp
index 01f3c4606a0b5c7d661269a66212dffd54d07333..58d4f765b981f072a703b624d3cd0625a91c27bb 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/div/divide.hpp
@@ -310,10 +310,11 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr_1 = _feats[0]->value_ptr(params, 2);
- double* val_ptr_2 = _feats[1]->value_ptr(params, 1);
+ double* val_ptr_1 = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2 + _feats[1]->n_params(), 0, true, depth + 1) : _feats[0]->value_ptr(0, true);
+ double* val_ptr_2 = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[1]->value_ptr(params + 2, 1, true, depth + 1) : _feats[1]->value_ptr(1, true);
+
std::transform(
val_ptr_1,
val_ptr_1 + _n_samp,
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp
index e03871655229025c5ab0ffb11e2261ca88dd3795..aabfa7b1f6b15b524ad27e62232ced8a978ce8ff 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp
@@ -294,9 +294,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return std::exp(params[0] * vp + params[1]);});
}
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.hpp
index 973fa42a673933a2aa4f6d03b80d6d23ffcc6ce3..c5d10b14e7f7ac93af6f798105df8e0d2750b34a 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/inv/inverse.hpp
@@ -291,9 +291,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return -1.0 / std::pow(params[0] * vp + params[1], 2.0);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp
index 246d754fc50b27979b1140bd955c60c23ecb4888..fdbfd1001a3aa8c72aa168a70d147a0914700911 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp
@@ -297,9 +297,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return 1.0 / (params[0] * vp + params[1]);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.hpp
index a43fdb8fb03e6748c22e33651a3c2a0739464bf6..f44867481c98e2368b39ee03d46578991390b6b6 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/mult/multiply.hpp
@@ -310,7 +310,11 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const {std::copy_n(_feats[0]->value_ptr(params, -1, true), _n_samp, dfdp);}
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
+ {
+ double* val_ptr_1 = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2 + _feats[1]->n_params(), 0, true, depth + 1) : _feats[0]->value_ptr(0, true);
+ std::copy_n(val_ptr_1, _n_samp, dfdp);
+ }
#endif
};
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp
index 97d4ab99b3d2872be58ee0fb650fd5f7230aa655..3ccd18a7cd7d4f2014fa3a26d0060ce6e56e1444 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp
@@ -295,9 +295,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return -1.0 * std::exp(-1.0 * (params[0] * vp + params[1]));});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp
index a254182bbfce996a83efcd631a95c10fe3c180d5..07297f1bd469d68bedec2ae29f82418a69069649 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp
@@ -298,9 +298,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return std::cos(params[0] * vp + params[1]);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.hpp
index 00baa8f20052c2b7b37a3ae5debb5f1fbbf980c4..08ae74d9473c5755dbba026c4147d6f2b7da0d2e 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.hpp
@@ -296,9 +296,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return 6.0 * std::pow(params[0] * vp + params[1], 5.0);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.hpp
index 416736bb0068d3e4fb0a22ed8e92b6255a302259..4dc09452047640542fbd68612ba0c5a73de33875 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.hpp
@@ -295,9 +295,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return 2.0 * (params[0] * vp + params[1]);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.hpp
index fc96cfe7f212889ba5a0dcd2fc5b6005eb777982..c4aa453f2f3d8738d0d79452e208436152817d41 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.hpp
@@ -298,9 +298,9 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const
{
- double* val_ptr = _feats[0]->value_ptr(params);
+ double* val_ptr = (depth < nlopt_wrapper::MAX_PARAM_DEPTH) ? _feats[0]->value_ptr(params + 2, depth + 1, -1, true) : _feats[0]->value_ptr(-1, true);
std::transform(val_ptr, val_ptr + _n_samp, dfdp, [params](double vp){return 0.5 * std::pow(params[0] * vp + params[1], -0.5);});
}
#endif
diff --git a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.hpp b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.hpp
index 12e967c07fcb77551a0ba93498d0e4f59150fb87..bcf74bf6e1258ece7bf30e0d95d78daacd6d8e96 100644
--- a/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.hpp
+++ b/src/feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.hpp
@@ -311,7 +311,7 @@ public:
* @param params A pointer to the bias and scale terms for this Node and its children
* @param dfdp pointer to where the feature derivative pointers are located
*/
- inline void param_derivative(const double* params, double* dfdp) const {std::fill_n(dfdp, _n_samp, -1.0);}
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const {std::fill_n(dfdp, _n_samp, -1.0);}
#endif
};
diff --git a/src/feature_creation/units/Unit.cpp b/src/feature_creation/units/Unit.cpp
index 91d8fd207ed4e65b9a562702bf52b284fa886c6d..9d380367afd0e753c009b53c3b747235f7124b02 100644
--- a/src/feature_creation/units/Unit.cpp
+++ b/src/feature_creation/units/Unit.cpp
@@ -243,9 +243,9 @@ Unit& Unit::operator/=(const Unit unit_2)
Unit Unit::operator^(const double power) const
{
std::map<std::string, double> to_out = dct();
- if(power == 0.0)
+ if(std::abs(power) < 1e-10)
{
- return Unit(to_out);
+ return Unit();
}
for(auto& el : to_out)
diff --git a/src/loss_function/LossFunction.hpp b/src/loss_function/LossFunction.hpp
index 198acd015401a2398f331e62cf2854eacc4e22dc..dcee589bc5854bd37af14b5242557236103367b2 100644
--- a/src/loss_function/LossFunction.hpp
+++ b/src/loss_function/LossFunction.hpp
@@ -309,6 +309,11 @@ public:
*/
inline int n_task() const {return _n_task;}
+ /**
+ * @brief Number of properties to project over
+ */
+ inline int n_prop_project(){return _n_project_prop;}
+
/**
* @brief Set the number of features and linear model constants to those for the new n_feat
*
@@ -317,9 +322,26 @@ public:
virtual inline void set_nfeat(int n_feat){_n_feat = n_feat; _n_dim = n_feat + (!_fix_intercept);}
/**
- * @brief The number of classes in the calculation
+ * @brief Number of classes in the property
+ */
+ virtual inline std::vector<int> n_class_per_task() const {return {};}
+
+ /**
+ * @brief The number of classes in the calculation for a given task
+ *
+ * @param task_num The task to get the number of classes for
+ */
+ virtual inline int n_class(int task_num) const {return 0;}
+
+ /**
+ * @brief The maximum number of classes in the calculation
+ */
+ virtual inline int n_class() const {return 0;}
+
+ /**
+ * @brief The labels for each set of coefficients
*/
- virtual inline int n_class(){return 0;}
+ virtual inline std::vector<std::string> coef_labels() const {return {};}
};
#endif
diff --git a/src/loss_function/LossFunctionConvexHull.cpp b/src/loss_function/LossFunctionConvexHull.cpp
index 9ffd4addc994a9404f079c834078ba9d9970a3ae..e87f928c0df97991c5ff9e6ab7f89a078124d672 100644
--- a/src/loss_function/LossFunctionConvexHull.cpp
+++ b/src/loss_function/LossFunctionConvexHull.cpp
@@ -30,6 +30,7 @@ LossFunctionConvexHull::LossFunctionConvexHull(
) :
LossFunction(prop_train, prop_test, task_sizes_train, task_sizes_test, false, n_feat),
_width(1e-5),
+ _n_class_per_task(task_sizes_train.size(), 0),
_n_class(0)
{
for(auto& pt : prop_test)
@@ -40,7 +41,23 @@ LossFunctionConvexHull::LossFunctionConvexHull(
}
}
- std::vector<double> unique_classes = vector_utils::unique<double>(prop_train);
+ int start = 0.0;
+ std::vector<double> unique_classes;
+ for(int tt = 0; tt < _task_sizes_train.size(); ++tt)
+ {
+ unique_classes = vector_utils::unique<double>(prop_train.data() + start, _task_sizes_train[tt]);
+ _n_class_per_task[tt] = unique_classes.size();
+ for(int c1 = 0; c1 < unique_classes.size(); ++c1)
+ {
+ for(int c2 = c1 + 1; c2 < unique_classes.size(); ++c2)
+ {
+ _coef_labels.push_back(fmt::format("_{:.1f}_{:.1f}", unique_classes[c1], unique_classes[c2]));
+ }
+ }
+ start += _task_sizes_train[tt];
+ }
+
+ unique_classes = vector_utils::unique<double>(prop_train);
std::map<double, double> class_map;
_n_class = unique_classes.size();
for(int cc = 0; cc < _n_class; ++cc)
@@ -95,6 +112,8 @@ LossFunctionConvexHull::LossFunctionConvexHull(
LossFunctionConvexHull::LossFunctionConvexHull(std::shared_ptr<LossFunction> o) :
LossFunction(o),
_width(1e-5),
+ _coef_labels(o->coef_labels()),
+ _n_class_per_task(o->n_class_per_task()),
_n_class(o->n_class())
{
set_nfeat(_n_feat);
@@ -106,7 +125,8 @@ void LossFunctionConvexHull::set_nfeat(int n_feat, bool initialize_sorted_d_mat)
_n_feat = n_feat;
_n_dim = n_feat + 1;
setup_lp(initialize_sorted_d_mat);
- _coefs.resize(_n_class * (_n_class - 1) / 2 * _n_dim * _task_sizes_train.size(), 0.0);
+ int n_class_combos = std::accumulate(_n_class_per_task.begin(), _n_class_per_task.end(), 0, [](int tot, int nc){return tot + nc * (nc - 1) / 2;});
+ _coefs.resize(n_class_combos * _n_dim, 0.0);
}
void LossFunctionConvexHull::prepare_project()
@@ -157,60 +177,44 @@ void LossFunctionConvexHull::setup_lp(bool initialize_sorted_d_mat)
int task_start_test = 0;
_lp = {};
- std::vector<int> n_samp_per_class;
- for(int tt = 0; tt < _n_task; ++tt)
+ std::map<double, int> rep_map;
+ std::vector<double> unique_classes = vector_utils::unique(_prop_train);
+ std::sort(unique_classes.begin(), unique_classes.end());
+
+ for(int uc = 0; uc < unique_classes.size(); ++uc)
{
- std::map<double, int> rep_class;
+ rep_map[unique_classes[uc]] = uc;
+ }
+ std::vector<int> n_samp_per_class(_n_class * _n_task);
+ for(int tt = 0; tt < _n_task; ++tt)
+ {
std::vector<int> inds(_task_sizes_train[tt]);
std::iota(inds.begin(), inds.end(), task_start);
util_funcs::argsort<double>(inds.data(), inds.data() + inds.size(), _prop_train.data());
- int cls_start = 0;
- _sample_inds_to_sorted_dmat_inds[inds[0]] = task_start;
- rep_class[_prop_train[inds[0]]] = 0;
- for(int ii = 1; ii < inds.size(); ++ii)
+ for(int ii = 0; ii < inds.size(); ++ii)
{
_sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start;
- if(_prop_train[inds[ii]] != _prop_train[inds[ii - 1]])
- {
- n_samp_per_class.push_back(ii - cls_start);
- rep_class[_prop_train[inds[ii]]] = n_samp_per_class.size();
- cls_start = ii;
- }
- }
- n_samp_per_class.push_back(inds.size() - cls_start);
- if(n_samp_per_class.size() != (tt + 1) * _n_class)
- {
- throw std::logic_error("A class is not represented in task " + std::to_string(tt) + ".");
+ ++n_samp_per_class[tt * _n_class + rep_map[_prop_train[inds[ii]]]];
}
std::vector<int> samp_per_class(_n_class);
std::copy_n(n_samp_per_class.begin() + tt * _n_class, _n_class, samp_per_class.begin());
task_start += _task_sizes_train[tt];
- std::vector<int> n_samp_test_per_class(n_samp_per_class.size(), 0);
+ std::vector<int> samp_test_per_class(samp_per_class.size(), 0);
if(_task_sizes_test[tt] > 0)
{
inds.resize(_task_sizes_test[tt]);
std::iota(inds.begin(), inds.end(), task_start_test);
- util_funcs::argsort<double>(inds.data(), inds.data() + inds.size(), &_prop_test[task_start_test]);
+ util_funcs::argsort<double>(inds.data(), inds.data() + inds.size(), _prop_test.data());
- cls_start = 0;
- _test_sample_inds_to_sorted_dmat_inds[inds[0]] = task_start_test;
- for(int ii = 1; ii < inds.size(); ++ii)
+ for(int ii = 0; ii < inds.size(); ++ii)
{
_test_sample_inds_to_sorted_dmat_inds[inds[ii]] = ii + task_start_test;
- if(_prop_test[inds[ii]] != _prop_test[inds[ii - 1]])
- {
- n_samp_test_per_class[
- rep_class[_prop_test[inds[ii - 1]]]
- ] = ii - cls_start;
- cls_start = ii;
- }
+ ++samp_test_per_class[rep_map[_prop_test[inds[ii]]]];
}
- n_samp_test_per_class[rep_class[_prop_test[inds.back()]]] = inds.size() - cls_start;
-
task_start_test += _task_sizes_test[tt];
}
LPWrapper lp(
@@ -220,8 +224,8 @@ void LossFunctionConvexHull::setup_lp(bool initialize_sorted_d_mat)
_n_feat,
std::accumulate(n_samp_per_class.begin() + tt * _n_class, n_samp_per_class.end(), 0),
_width,
- n_samp_test_per_class,
- std::accumulate(n_samp_test_per_class.begin(), n_samp_test_per_class.end(), 0)
+ samp_test_per_class,
+ std::accumulate(samp_test_per_class.begin(), samp_test_per_class.end(), 0)
);
_lp.push_back(lp);
}
@@ -342,9 +346,10 @@ double LossFunctionConvexHull::operator()(const std::vector<model_node_ptr>& fea
// Perform SVM to set estimated prop
start = 0;
start_test = 0;
+ int start_coefs = 0;
for(int tt = 0; tt < _task_sizes_train.size(); ++tt)
{
- SVMWrapper svm(_n_class, _n_feat, _task_sizes_train[tt], &_prop_train[start]);
+ SVMWrapper svm(_n_class_per_task[tt], _n_feat, _task_sizes_train[tt], &_prop_train[start]);
std::vector<double*> node_val_ptrs(_n_feat);
std::vector<double*> node_test_val_ptrs(_n_feat);
@@ -352,19 +357,19 @@ double LossFunctionConvexHull::operator()(const std::vector<model_node_ptr>& fea
for(int dd = 0; dd < _n_feat; ++dd)
{
node_val_ptrs[dd] = feats[dd]->value_ptr() + start;
- node_test_val_ptrs[dd] = feats[dd]->test_value_ptr() + start;
+ node_test_val_ptrs[dd] = feats[dd]->test_value_ptr() + start_test;
}
svm.train(node_val_ptrs);
std::vector<std::vector<double>> coefs = svm.coefs();
for(int cc = 0; cc < coefs.size(); ++cc)
{
- std::copy_n(coefs[cc].data(), coefs[cc].size(), &_coefs[tt * _n_class * (_n_class - 1) / 2 * _n_dim + cc * _n_dim]);
- _coefs[tt * _n_class * (_n_class - 1) / 2 * _n_dim + cc * _n_dim + _n_feat] = svm.intercept()[cc];
+ std::copy_n(coefs[cc].data(), coefs[cc].size(), &_coefs[start_coefs * _n_dim]);
+ _coefs[start_coefs * _n_dim + _n_feat] = svm.intercept()[cc];
+ ++start_coefs;
}
-
- std::copy_n(svm.y_estimate().begin() + start, _task_sizes_train[tt], _prop_train_est.begin() + start);
- std::copy_n(svm.predict(_task_sizes_test[tt], node_test_val_ptrs).begin() + start_test, _task_sizes_test[tt], _prop_test_est.begin() + start_test);
+ std::copy_n(svm.y_estimate().begin(), _task_sizes_train[tt], _prop_train_est.begin() + start);
+ std::copy_n(svm.predict(_task_sizes_test[tt], node_test_val_ptrs).begin(), _task_sizes_test[tt], _prop_test_est.begin() + start_test);
start += _task_sizes_train[tt];
start_test += _task_sizes_test[tt];
diff --git a/src/loss_function/LossFunctionConvexHull.hpp b/src/loss_function/LossFunctionConvexHull.hpp
index 0a0f39446463bc732c5315dc25f520ad59b7a0b5..9178e4c09f1d79c66c313743e8551b893351a6ed 100644
--- a/src/loss_function/LossFunctionConvexHull.hpp
+++ b/src/loss_function/LossFunctionConvexHull.hpp
@@ -22,6 +22,8 @@
#ifndef LOSS_FUNCTION_CONVEX_HULL
#define LOSS_FUNCTION_CONVEX_HULL
+#include <fmt/core.h>
+
#include "classification/ConvexHull1D.hpp"
#include "classification/LPWrapper.hpp"
#include "classification/SVMWrapper.hpp"
@@ -43,9 +45,11 @@ protected:
std::vector<double> _scores; //!< The scores for each of the projection properties
std::map<int, int> _sample_inds_to_sorted_dmat_inds; //!< map from input sample inds to the SORTED_D_MATRIX_INDS
std::map<int, int> _test_sample_inds_to_sorted_dmat_inds; //!< map from input sample inds to the SORTED_D_MATRIX_INDS
+ std::vector<std::string> _coef_labels; //!< The labels for each set of coefficients
+ std::vector<int> _n_class_per_task; //!< Number of classes in the property
- const double _width; //!< The width used as the tolerance for the LP optimization
int _n_class; //!< Number of classes in the property
+ const double _width; //!< The width used as the tolerance for the LP optimization
public:
@@ -159,9 +163,26 @@ public:
void set_nfeat(int n_feat, bool initialize_sorted_d_mat=false);
/**
- * @brief The number of classes in the calculation
+ * @brief The number of classes in the calculation for a given task
+ *
+ * @param task_num The task to get the number of classes for
+ */
+ inline int n_class(int task_num) const {return _n_class_per_task[task_num];}
+
+ /**
+ * @brief The maximum number of classes in the calculation
+ */
+ inline int n_class() const {return _n_class;}
+
+ /**
+ * @brief The labels for each set of coefficients
+ */
+ inline std::vector<std::string> coef_labels() const {return _coef_labels;}
+
+ /**
+ * @brief Number of classes in the property
*/
- inline int n_class(){return _n_class;}
+ inline std::vector<int> n_class_per_task() const {return _n_class_per_task;}
};
#endif
diff --git a/src/python/postprocess/plot/parity_plot.py b/src/python/postprocess/plot/parity_plot.py
index d653c67c80654f27c2d86e88c56d301597af8e1f..714c1e7fbc70ab25bb1cd4242bc5c100cc8ac164 100644
--- a/src/python/postprocess/plot/parity_plot.py
+++ b/src/python/postprocess/plot/parity_plot.py
@@ -20,9 +20,9 @@ plot_model_parity_plot: Wrapper to plot_model for a set of training and testing
import numpy as np
import toml
from sissopp.postprocess.check_cv_convergence import jackknife_cv_conv_est
-
from sissopp.postprocess.load_models import load_model
-from sissopp.postprocess.plot.utils import setup_plot_ax
+from sissopp.postprocess.plot.utils import setup_plot_ax, latexify
+from sissopp import ModelClassifier
def plot_model_parity_plot(model, filename=None, fig_settings=None):
@@ -52,9 +52,12 @@ def plot_model_parity_plot(model, filename=None, fig_settings=None):
fig_config, fig, ax = setup_plot_ax(fig_settings)
- ax.set_xlabel(model.prop_label + " (" + model.prop_unit.latex_str + ")")
+ ax.set_xlabel(latexify(model.prop_label) + " (" + model.prop_unit.latex_str + ")")
ax.set_ylabel(
- f"Estimated {model.prop_label}" + " (" + model.prop_unit.latex_str + ")"
+ f"Estimated {latexify(model.prop_label)}"
+ + " ("
+ + model.prop_unit.latex_str
+ + ")"
)
if len(model.prop_test) > 0:
lims = [
diff --git a/src/python/postprocess/plot/utils.py b/src/python/postprocess/plot/utils.py
index eafde92cc894973407ea3a6562b6a1fd18b0db79..6b768bcd8d0c6786b863ec8050e0d8c7228a5e26 100644
--- a/src/python/postprocess/plot/utils.py
+++ b/src/python/postprocess/plot/utils.py
@@ -91,19 +91,26 @@ def adjust_box_widths(ax, fac):
def latexify(s):
"""Convert a string s into a latex string"""
power_split = s.split("^")
-
- print(power_split)
-
if len(power_split) == 1:
- return s
-
- power_split[0] += "$"
- for pp in range(1, len(power_split)):
- unit_end = power_split[pp].split(" ")
- unit_end[0] = "{" + unit_end[0] + "}$"
+ temp_s = s
+ else:
+ power_split[0] += "$"
+ for pp in range(1, len(power_split)):
+ unit_end = power_split[pp].split(" ")
+ unit_end[0] = "{" + unit_end[0] + "}$"
+ unit_end[-1] += "$"
+ power_split[pp] = " ".join(unit_end)
+ temp_s = "^".join(power_split)[:-1]
+
+ subscript_split = temp_s.split("_")
+ if len(subscript_split) == 1:
+ return temp_s
+
+ subscript_split[0] += "$"
+ for pp in range(1, len(subscript_split)):
+ unit_end = subscript_split[pp].split(" ")
+ unit_end[0] = "\\mathrm{" + unit_end[0] + "}$"
unit_end[-1] += "$"
- power_split[pp] = " ".join(unit_end)
-
- print("^".join(power_split)[:-1])
+ subscript_split[pp] = " ".join(unit_end)
- return "^".join(power_split)[:-1]
+ return "_".join(subscript_split)[:-1]
diff --git a/src/python/py_binding_cpp_def/bindings_docstring_keyed.cpp b/src/python/py_binding_cpp_def/bindings_docstring_keyed.cpp
index 524c2926287e5e2e5917cf0c3c01fb53dc6f1918..d3c83e45cc238b49a1e51724e99a665fd9b8a820 100644
--- a/src/python/py_binding_cpp_def/bindings_docstring_keyed.cpp
+++ b/src/python/py_binding_cpp_def/bindings_docstring_keyed.cpp
@@ -121,6 +121,12 @@ void sisso::register_all()
"@DocString_str_utils_matlabify@"
);
+ def(
+ "latexify",
+ &str_utils::latexify,
+ (arg("str")),
+ "@DocString_str_utils_latexify@"
+ );
#ifdef PARAMETERIZE
sisso::feature_creation::node::registerAddParamNode();
sisso::feature_creation::node::registerSubParamNode();
@@ -296,6 +302,7 @@ void sisso::feature_creation::registerFeatureSpace()
.add_property("n_feat", &FeatureSpace::n_feat, "@DocString_feat_space_n_feat@")
.add_property("n_rung_store", &FeatureSpace::n_rung_store, "@DocString_feat_space_n_rung_store@")
.add_property("n_rung_generate", &FeatureSpace::n_rung_generate, "@DocString_feat_space_n_rung_generate@")
+ .add_property("parameterized_feats_allowed", &FeatureSpace::parameterized_feats_allowed, "@DocString_feat_space_param_feats_allowed@")
;
}
diff --git a/src/python/py_binding_cpp_def/bindings_docstring_keyed.hpp b/src/python/py_binding_cpp_def/bindings_docstring_keyed.hpp
index ce4de017d2921a4e11c8bd34f3307d3124d8d97f..a258b20f75db7a7934511989a0a8c242746475ec 100644
--- a/src/python/py_binding_cpp_def/bindings_docstring_keyed.hpp
+++ b/src/python/py_binding_cpp_def/bindings_docstring_keyed.hpp
@@ -115,7 +115,7 @@ namespace sisso
inline void initialize_params(double* params, int depth=1) const {this->get_override("set_bounds")();}
inline int n_feats() const {return this->get_override("n_feats")();}
inline std::shared_ptr<Node> feat(const int ind) const {return this->get_override("feat")();}
- inline void param_derivative(const double* params, double* dfdp) const {this->get_override("param_derivative");}
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const {this->get_override("param_derivative");}
inline void gradient(double* grad, double* dfdp) const {this->get_override("gradient");}
inline void gradient(double* grad, double* dfdp, const double* params) const {this->get_override("gradient");}
inline std::vector<std::string> get_x_in_expr_list() const {return this->get_override("get_x_in_expr_list")();}
@@ -151,7 +151,7 @@ namespace sisso
inline std::vector<double> parameters() const {return this->get_override("parameters")();}
inline void set_bounds(double* lb, double* ub, const int depth=1) const {this->get_override("set_bounds")();}
inline void initialize_params(double* params, int depth=1) const {this->get_override("set_bounds")();}
- inline void param_derivative(const double* params, double* dfdp) const {this->get_override("param_derivative");}
+ inline void param_derivative(const double* params, double* dfdp, const int depth = 1) const {this->get_override("param_derivative");}
};
#else
/**
diff --git a/src/python/py_binding_cpp_def/feature_creation/FeatureNode.cpp b/src/python/py_binding_cpp_def/feature_creation/FeatureNode.cpp
index eff20559676fb04fb88f44299dc3699252c7df3b..cddba9e59693ee78b55e7016f1ca8dfd09fc824a 100644
--- a/src/python/py_binding_cpp_def/feature_creation/FeatureNode.cpp
+++ b/src/python/py_binding_cpp_def/feature_creation/FeatureNode.cpp
@@ -22,70 +22,9 @@
#include "feature_creation/node/FeatureNode.hpp"
FeatureNode::FeatureNode(unsigned long int feat_ind, std::string expr, np::ndarray value, np::ndarray test_value, Unit unit) :
- Node(feat_ind, value.shape(0), test_value.shape(0)),
- _value(python_conv_utils::from_ndarray<double>(value)),
- _test_value(python_conv_utils::from_ndarray<double>(test_value)),
- _unit(unit),
- _expr(expr)
-{
- // Automatically resize the storage arrays
- if(node_value_arrs::N_STORE_FEATURES == 0)
- {
- node_value_arrs::initialize_values_arr(_n_samp, _n_samp_test, 1);
- }
- else if((_n_samp != node_value_arrs::N_SAMPLES) || (_n_samp_test != node_value_arrs::N_SAMPLES_TEST))
- {
- throw std::logic_error(
- "Number of samples in current feature is not the same as the others, (" +
- std::to_string(_n_samp) +
- " and " + std::to_string(_n_samp_test) +
- " vs. " +
- std::to_string(node_value_arrs::N_SAMPLES) +
- " and " +
- std::to_string(node_value_arrs::N_SAMPLES_TEST) +
- ")"
- );
- }
- else if(feat_ind >= node_value_arrs::N_STORE_FEATURES)
- {
- node_value_arrs::resize_values_arr(0, node_value_arrs::N_STORE_FEATURES + 1);
- }
-
- set_value();
- set_test_value();
-}
+ FeatureNode(feat_ind, expr, python_conv_utils::from_ndarray<double>(value), python_conv_utils::from_ndarray<double>(test_value), unit)
+{}
FeatureNode::FeatureNode(unsigned long int feat_ind, std::string expr, py::list value, py::list test_value, Unit unit) :
- Node(feat_ind, py::len(value), py::len(test_value)),
- _value(python_conv_utils::from_list<double>(value)),
- _test_value(python_conv_utils::from_list<double>(test_value)),
- _unit(unit),
- _expr(expr)
-{
- // Automatically resize the storage arrays
- if(node_value_arrs::N_STORE_FEATURES == 0)
- {
- node_value_arrs::initialize_values_arr(_n_samp, _n_samp_test, 1);
- }
- else if((_n_samp != node_value_arrs::N_SAMPLES) || (_n_samp_test != node_value_arrs::N_SAMPLES_TEST))
- {
- throw std::logic_error(
- "Number of samples in current feature is not the same as the others, (" +
- std::to_string(_n_samp) +
- " and " +
- std::to_string(_n_samp_test) +
- " vs. " +
- std::to_string(node_value_arrs::N_SAMPLES) +
- " and " +
- std::to_string(node_value_arrs::N_SAMPLES_TEST) +
- ")"
- );
- }
- else if(feat_ind >= node_value_arrs::N_STORE_FEATURES)
- {
- node_value_arrs::resize_values_arr(0, node_value_arrs::N_STORE_FEATURES + 1);
- }
-
- set_value();
- set_test_value();
-}
+ FeatureNode(feat_ind, expr, python_conv_utils::from_list<double>(value), python_conv_utils::from_list<double>(test_value), unit)
+{}
diff --git a/src/utils/string_utils.cpp b/src/utils/string_utils.cpp
index 8989dc2d76c2bc70df83ca6606c328f671255a39..0558e6409874d058b885bd682a894b29d62c859d 100644
--- a/src/utils/string_utils.cpp
+++ b/src/utils/string_utils.cpp
@@ -75,7 +75,7 @@ std::string str_utils::matlabify(const std::string str)
std::string copy_str = str;
std::replace(copy_str.begin(), copy_str.end(), ' ', '_');
- std::vector<std::string> split_str = split_string_trim(str, "\\");
+ std::vector<std::string> split_str = split_string_trim(str, "\\}{");
for(auto& term_str : split_str)
{
std::string add_str = term_str;
diff --git a/src/utils/string_utils.hpp b/src/utils/string_utils.hpp
index d022a951fc320e34c5819ba67b4174d80e36563f..ce628972c4908ddbf08d640ffeb8443148e3141d 100644
--- a/src/utils/string_utils.hpp
+++ b/src/utils/string_utils.hpp
@@ -42,6 +42,7 @@ namespace str_utils
*/
std::vector<std::string> split_string_trim(const std::string str, const std::string split_tokens = ",;:");
+ // DocString: str_utils_latexify
/**
* @brief Convert a string into a latex string
*
diff --git a/src/utils/vector_utils.hpp b/src/utils/vector_utils.hpp
index 33a9dd8d6a83d2656e932dbf4e784fc072bcae04..3008d4bfd6042269e826c6673b6cbbec2c482cac 100644
--- a/src/utils/vector_utils.hpp
+++ b/src/utils/vector_utils.hpp
@@ -48,6 +48,28 @@ std::vector<T> unique(const std::vector<T> in_vec)
return out_vec;
}
+
+/**
+ * @brief Return a vector of all unique elements of in_vec
+ *
+ * @param in_vec Pointer to the input vector
+ * @param sz The size of the vector
+ * @tparam T The type of elements of T
+ * @return The vector of unique elements of in_vec
+ */
+template<typename T>
+std::vector<T> unique(const T* in_vec, int sz)
+{
+ std::vector<T> out_vec;
+ for(int ii = 0; ii < sz; ++ii)
+ {
+ if(std::find(out_vec.begin(), out_vec.end(), in_vec[ii]) == out_vec.end())
+ {
+ out_vec.push_back(in_vec[ii]);
+ }
+ }
+ return out_vec;
+}
}
diff --git a/tests/exec_test/classification/data.csv b/tests/exec_test/classification/data.csv
index 3fa9f64bd0b9d2133040ea4849a71789ac8f078a..3de3e29a3d3a8510a21ace99268501120ce46af4 100644
--- a/tests/exec_test/classification/data.csv
+++ b/tests/exec_test/classification/data.csv
@@ -1,101 +1,101 @@
-index,prop,A,B,C,D,E,F,G,H,I,J
-0,1,0.1,-0.3,10,10,-0.492825179098274,0.173025977694162,0.598942935224295,-0.298754475196825,-0.581254909010269,-0.110656494210556
-1,1,-1.89442810374214,-1.31996134398007,0.955713896876243,0.478117201427488,0.777586191100329,0.655369716778557,0.174914171427966,-0.288498877530604,-0.045316536149489,-0.606586193752411
-2,1,-1.47460150711424,-1.22614964523433,0.330140292484796,-0.389505498689912,0.138935265824808,-0.871869282167555,0.37472462048701,0.16418591189513,0.293560701443717,0.285608940220021
-3,1,-1.30213414336735,-1.82621262418812,-0.71381302228685,0.968769585007681,0.683148179202864,0.799125092538796,0.309479173526504,0.728052031003468,0.725495580994886,-0.676576302804248
-4,1,-1.73938632269334,-1.58349866505488,-0.067783417095816,0.229988549891323,0.582427598044647,0.261947149184825,-0.31573435079735,0.61178122144268,-0.187058216967238,0.871764347690334
-5,1,-1.56660896632398,-1.05861814902183,-0.212800982302764,0.915459776146607,-0.263465552591813,-0.666126495988014,-0.195028996490007,-0.237170057680116,-0.933358858596883,-0.19726273171241
-6,1,-1.55340876153895,-1.25209231285838,0.972585787901787,-0.872502887185675,0.729110910814452,0.265244787210866,-0.726900973624432,0.248626170107939,0.809004396880265,-0.278494064780479
-7,1,-1.54625325136447,-1.81238888450819,0.431645876221063,-0.595223273279383,0.953557069758112,-0.222915219121563,0.257670939076174,0.728927879098318,-0.579783055417687,-0.179960319428095
-8,1,-1.12735554524035,-1.69261497444728,0.827091199052693,0.208781482910759,-0.573317187361529,-0.00488758921352,0.341918716034638,-0.292901671356202,-0.34531700628784,0.766920547630073
-9,1,-1.35367834815884,-1.38141056472962,0.023965203621816,-0.805412569418484,0.806810139718495,-0.806576263127819,-0.39279977856172,-0.463819363774079,0.16095248005356,-0.553419747131608
-10,1,-1.17853151888796,-1.27705829298504,0.916015229666356,0.0275946645157,-0.829507007977635,-0.700063689327201,-0.715601456588714,-0.439799165143527,-0.487241220494887,0.245279267056121
-11,1,-1.17547049766875,-1.05613281246665,0.404557718897757,0.175997361062361,0.227383730822478,0.774254652577977,-0.616080996917636,-0.948639007451084,0.954076433375225,-0.497102001172339
-12,1,-1.67277915033943,-1.86190239883588,0.906708844886064,-0.592737030373698,0.885229803890949,0.822069297241907,0.204367485562992,0.24610324883505,-0.079476866422163,-0.244006995583434
-13,1,-1.96326165438884,-1.31680458089693,-0.284570394188414,-0.198686061574238,-0.168897609541112,-0.92939259112691,0.265899059671913,-0.828727642584781,-0.427453097474168,-0.738368091608883
-14,1,-1.79497769808481,-1.13948217357082,0.011556817105957,0.499408314757229,0.253163424774478,0.017645446880421,0.401735167095264,-0.650287617298501,-0.262217482830833,-0.510102120130588
-15,1,-1.07957120536262,-1.93245955077991,-0.520161174281201,-0.392478459347396,0.147495839565868,0.780879606474075,-0.281044687799173,-0.148036908135786,-0.208810472224093,0.278961929718128
-16,1,-1.52555145037507,-1.72455209196736,0.562803219191695,0.323676061636996,0.490737136410372,-0.95476192699496,0.028603504036769,-0.246295219650507,-0.297736293597739,-0.632473830957653
-17,1,-1.29142309507315,-1.9506961526212,0.859669461054104,0.485772819254089,0.268883598825009,0.253553321699552,-0.045743087569395,0.66793403278249,0.308591963919947,0.771084301464027
-18,1,-1.23404787121001,-1.68173519287847,-0.118871100462413,0.159000937768132,0.2985428841756,-0.203829205332538,-0.637945695251352,0.658049690810909,0.949529589134008,-0.577812553880056
-19,1,-1.13513050029551,-1.3119036669604,0.74037411093045,0.558782660077505,-0.096052126354583,0.529119817360537,0.372190604770144,0.688656466253683,-0.819433165315505,-0.12814415930811
-20,1,-0.2,0.132,-10,-10,0.785237349732891,-0.387217730495401,-0.942409218899448,0.160806577297675,-0.723969983661972,-0.452650134415823
-21,1,1.1507658618081,1.7260505392724,-0.92290734251321,0.465751384219632,-0.81727500527083,-0.182472640926628,0.887526070620356,0.111592883978488,0.386435078880162,-0.440017211221272
-22,1,1.90389768224701,1.71880759316591,0.28033979546451,0.379365407838544,0.634843008192624,0.371753918780839,-0.611653305369863,0.732567927874185,0.85803611350317,-0.577973441708411
-23,1,1.77751976452381,1.28697050370578,0.222850898945077,-0.424057088828287,-0.27619426781836,0.616941667680694,-0.696779972923147,0.23612770730498,0.760705889780527,0.34004139732033
-24,1,1.65314327493874,1.16282810211312,-0.339501197382944,0.898529591365812,-0.881538228231582,0.090728826664301,-0.858395870780934,0.907272331515896,0.160531735619067,0.678911811768841
-25,1,1.30955180558204,1.36827755737648,-0.444576754578563,-0.78871174512572,0.246625773070183,-0.663474018818313,-0.446355552060464,0.750312773134634,-0.98959522970136,-0.150120109840706
-26,1,1.44924431171893,1.40328864581169,-0.388679577334402,-0.708193450791952,0.850310084800308,-0.767256338531612,0.370509317329194,0.387354921015751,0.533160321164986,0.149390212455131
-27,1,1.61362501391753,1.05448314414567,-0.359644680155969,0.682555404147728,-0.53195400936544,0.934101689590862,-0.73600284018832,-0.29960291454053,0.351125596355818,-0.187842884669279
-28,1,1.0243392720598,1.91059602121133,-0.249409157470717,-0.137070024386644,-0.707128531134104,0.944932049234295,0.431233366052987,0.449543990959262,0.912901932280027,0.77394610963827
-29,1,1.99444678594607,1.67204984441306,0.935302642480463,0.833720966523807,0.254167956717343,-0.007922712021391,-0.114776295376767,-0.276042896002242,-0.813098403125419,0.341922052212475
-30,1,1.40110330287926,1.109011516196,0.425716772255087,0.544174803732763,0.492863854358204,-0.047589791717166,-0.743840790633672,0.525289489060411,0.829611715544936,0.015193221845522
-31,1,1.94995090625471,1.05727410799969,0.12665368551441,0.469705238170149,0.874436248273008,-0.759571175468135,0.310230735619265,-0.80342084374485,-0.462431082486477,-0.407165886759129
-32,1,1.47264625042994,1.18913643279065,-0.731393018031039,0.461102224603009,-0.884528391885322,-0.419893944840798,0.647518214389067,0.688126326408485,0.754656371691603,0.116881923067816
-33,1,1.45901677207507,1.17024364037294,-0.527433424947131,-0.598774697808646,0.113197791601676,-0.50528865259863,0.117572114288939,0.590400320594326,-0.155159386769126,0.354827977413197
-34,1,1.32042744243041,1.19801952930384,-0.818626622405165,-0.029008564510599,0.952315968378468,0.817495784213924,0.182224554845043,-0.01377304364653,-0.26273195293588,-0.859530562808673
-35,1,1.88138237976289,1.03670081839679,0.305218688016626,-0.060885647660027,-0.387903446605514,-0.108064042735465,-0.962980405009682,-0.424289604203511,-0.253442293077285,0.309637368411297
-36,1,1.9986688782461,1.36909257128618,0.54312844740039,0.591372473040837,-0.835367086693457,0.198315253422507,-0.181434739783802,0.636429105754948,0.420628646992331,0.990122364664621
-37,1,1.50455818499044,1.19094974349673,-0.653263607332762,0.359359450868376,0.30131719114182,0.649581794356589,0.942268955633086,0.884659894489377,-0.473171239344398,0.039635066570717
-38,1,1.00833361547154,1.98150630000827,-0.812352457176761,0.219766101590983,-0.65021067790289,0.423621690291556,-0.58865099275791,0.061487886019891,-0.237737474016087,0.641284347380825
-39,1,1.60179185724619,1.12508599627141,-0.829819386940741,-0.345104687573802,0.485166070545119,-0.258839727448056,-0.920615208326881,0.275498215871427,-0.629075534110342,-0.642527887960687
-40,0,0.2,0.58,10,-10,0.041519856511361,0.23303461629095,-0.497233246191187,-0.023544587617095,-0.418540837770003,-0.550233932792512
-41,0,-1.09147574370355,1.70418701701285,-0.480316474702795,-0.753784710340632,-0.613234235616998,0.167955573662474,0.455636631315042,-0.380227635953206,0.48021383007369,-0.453674929885108
-42,0,-1.9425392252915,1.59311394144654,0.310098050913387,-0.835007082906627,0.407580140850853,0.556924247596553,-0.388616604639346,0.60215104751412,-0.984322198098753,-0.996332888983337
-43,0,-1.40302421044915,1.05041379743038,-0.898456453446964,-0.797029924245349,0.47491891024478,0.480193220538417,-0.750856163558686,-0.043960372032018,-0.242651391805662,-0.109239061054006
-44,0,-1.45810616907354,1.08468326497063,0.571329522934018,-0.677379826379623,0.098396984688832,-0.961599170104035,-0.753922591922157,0.361435891257559,-0.638030455493982,0.404349024843908
-45,0,-1.60421432901638,1.57730973247518,0.402433205555268,-0.06801187450078,-0.373089661152032,0.23970878487105,0.416451106643361,-0.50599166271433,-0.88669034806741,0.30364523616443
-46,0,-1.54868661350102,1.32883184576708,-0.174925245509766,0.050330391451536,-0.388676795741932,-0.72307604978553,0.646076107724964,-0.00105589909588,0.491928720743773,-0.647995101369186
-47,0,-1.8920756792535,1.76576258461153,0.289410761217525,0.48566510896872,0.338684773860801,0.374319581439648,-0.105479014627167,0.004520417892418,0.222862261975939,0.23538363683764
-48,0,-1.51442922313653,1.69840409315155,-0.199044563017407,-0.855804112781183,0.947572000564906,0.654939562810152,0.802084131057488,0.010033694468233,0.449766366250574,0.119974134618433
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diff --git a/tests/exec_test/classification/sisso.json b/tests/exec_test/classification/sisso.json
index 196884b5f3c963281aa753c06501c6fb2bcd8f39..9613bef7bdf1e740abe91993da1d83a91a50209d 100644
--- a/tests/exec_test/classification/sisso.json
+++ b/tests/exec_test/classification/sisso.json
@@ -1,15 +1,16 @@
{
"desc_dim": 2,
- "n_sis_select": 5,
+ "n_sis_select": 10,
"max_rung": 1,
- "n_residual": 1,
+ "n_residual": 5,
"data_file": "data.csv",
"data_file_relatice_to_json": true,
"property_key": "prop",
+ "task_key": "task",
"leave_out_frac": 0.2,
- "n_models_store": 1,
+ "leave_out_inds": [ 2, 3, 4, 6, 21, 23, 30, 38, 39, 52, 53, 61, 76, 82, 83, 45, 47, 48, 49, 66 ],
+ "n_models_store": 5,
"calc_type": "classification",
- "leave_out_inds": [80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 ,96 ,97, 98 , 99],
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
"param_opset" : [],
"fix_intercept": false
diff --git a/tests/exec_test/classification/test b/tests/exec_test/classification/test
new file mode 100644
index 0000000000000000000000000000000000000000..8a6704af53aa403acc12f2e239bbd0fcedb003c9
--- /dev/null
+++ b/tests/exec_test/classification/test
@@ -0,0 +1,6473 @@
+time input_parsing: 0.001436 s
+time to generate feat sapce: 0.00326109 s
+Projection time: 0.000658989 s
+Time to get best features on rank : 0.000178099 s
+Complete final combination/selection from all ranks: 0.000165939 s
+Time for SIS: 0.001127 s
+nover:: 13 19 13 13 13
+nover:: 29 19 13 13 13
+nover:: 41 19 13 13 13
+nover:: 53 19 13 13 13
+53
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+class1 -3.48748 1
+class1 -5.22061 1
+class1 -1.12941 1
+class1 -2.47797 1
+nover:: 0 0 5 8 9
+class2 1.39155 1
+class2 1.07285 1
+class2 6.4437 1
+class2 7.15082 1
+class2 0.919849 1
+class2 1.5843 1
+class2 3.34031 1
+class2 1.52947 1
+nover:: 9 0 5 8 9
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+class2 0.983616 0.30436
+class2 1.11139 0.588291
+nover:: 0 0 5 8 9
+class3 -1.23476 -0.73603
+class3 -1.02512 -0.97192
+class3 -4.07867 -0.161313
+class3 -0.92548 -0.35494
+class3 -1.06921 -0.237363
+class3 -4.83906 -0.04684
+class3 -1.22619 -0.595781
+class3 -1.10081 -0.956706
+class3 -1.96132 -0.231225
+nover:: 0 0 5 8 9
+0
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+nover:: 1 19 13 13 13
+1
+nover:: 0 0 5 8 9
+class1 4.32785 -0.243971
+class1 1.14742 0.584782
+class1 1.72817 0.387098
+class1 6.43861 -0.730107
+class1 1.23914 0.403453
+nover:: 0 0 5 8 9
+class2 1.27494 1.50298
+class2 1.48449 1.60573
+class2 3.71383 0.424454
+class2 2.44911 0.548155
+class2 1.52422 1.74321
+class2 1.0499 1.58366
+class2 0.983616 1.31603
+class2 1.11139 1.55359
+nover:: 0 0 5 8 9
+class3 -1.23476 0.0989428
+class3 -1.02512 0.0208427
+class3 -4.07867 0.412765
+class3 -0.92548 -0.752031
+class3 -1.06921 -0.681478
+class3 -4.83906 0.0200101
+class3 -1.22619 -0.0849994
+class3 -1.10081 0.144729
+class3 -1.96132 -0.056356
+nover:: 0 0 5 8 9
+1
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+0
+nover:: 0 0 5 8 9
+class1 0.613634 -0.109762
+class1 0.955197 -0.249901
+class1 0.833306 -0.110839
+class1 0.53753 -0.137517
+class1 0.931021 -0.325673
+nover:: 0 0 5 8 9
+class2 0.922225 0.563653
+class2 0.876612 0.627892
+class2 0.645743 0.0417872
+class2 0.741874 0.0570999
+class2 0.86893 0.713242
+class2 0.983898 0.601193
+class2 1.00552 0.30436
+class2 0.965409 0.588291
+nover:: 0 0 5 8 9
+class3 -0.932122 -0.73603
+class3 -0.991764 -0.97192
+class3 -0.625884 -0.161313
+class3 -1.02615 -0.35494
+class3 -0.97794 -0.237363
+class3 -0.591216 -0.04684
+class3 -0.934289 -0.595781
+class3 -0.968492 -0.956706
+class3 -0.798885 -0.231225
+nover:: 0 0 5 8 9
+0
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+nover:: 0 19 13 13 13
+0
+nover:: 0 0 5 8 9
+class1 0.613634 -0.243971
+class1 0.955197 0.584782
+class1 0.833306 0.387098
+class1 0.53753 -0.730107
+class1 0.931021 0.403453
+nover:: 0 0 5 8 9
+class2 0.922225 1.50298
+class2 0.876612 1.60573
+class2 0.645743 0.424454
+class2 0.741874 0.548155
+class2 0.86893 1.74321
+class2 0.983898 1.58366
+class2 1.00552 1.31603
+class2 0.965409 1.55359
+nover:: 0 0 5 8 9
+class3 -0.932122 0.0989428
+class3 -0.991764 0.0208427
+class3 -0.625884 0.412765
+class3 -1.02615 -0.752031
+class3 -0.97794 -0.681478
+class3 -0.591216 0.0200101
+class3 -0.934289 -0.0849994
+class3 -0.968492 0.144729
+class3 -0.798885 -0.056356
+nover:: 0 0 5 8 9
+0
+nover:: 0 19 13 13 13
+nover:: 7 19 13 13 13
+nover:: 7 19 13 13 13
+nover:: 15 19 13 13 13
+15
+nover:: 0 0 5 8 9
+class1 -0.109762 -0.243971
+class1 -0.249901 0.584782
+class1 -0.110839 0.387098
+class1 -0.137517 -0.730107
+class1 -0.325673 0.403453
+nover:: 2 0 5 8 9
+class2 0.563653 1.50298
+class2 0.627892 1.60573
+class2 0.0417872 0.424454
+class2 0.0570999 0.548155
+class2 0.713242 1.74321
+class2 0.601193 1.58366
+class2 0.30436 1.31603
+class2 0.588291 1.55359
+nover:: 2 0 5 8 9
+class3 -0.73603 0.0989428
+class3 -0.97192 0.0208427
+class3 -0.161313 0.412765
+class3 -0.35494 -0.752031
+class3 -0.237363 -0.681478
+class3 -0.04684 0.0200101
+class3 -0.595781 -0.0849994
+class3 -0.956706 0.144729
+class3 -0.231225 -0.056356
+nover:: 2 0 5 8 9
+17
+0
+Time for l0-norm: 1.8686 s
+Percent of training data in the convex overlap region: 63.75%; Percent of test data in the convex overlap region: 70%
+[(B + A)]
+
+Percent of training data in the convex overlap region: 0%; Percent of test data in the convex overlap region: 0%
+[exp(A), (B / A)]
+
diff --git a/tests/exec_test/classification_gen_proj/check_model.py b/tests/exec_test/classification_gen_proj/check_model.py
index 5958c75a5d751910b4baa9cd22593a2de8289c5d..067d895e46bd7976c91576c4749a233bc59279df 100644
--- a/tests/exec_test/classification_gen_proj/check_model.py
+++ b/tests/exec_test/classification_gen_proj/check_model.py
@@ -4,7 +4,6 @@ from pathlib import Path
import numpy as np
model = ModelClassifier(
- str("models/train_dim_2_model_0.dat"), str("models/test_dim_2_model_0.dat")
+ str("models/train_dim_2_model_0.dat")
)
assert model.percent_error < 1e-7
-assert model.percent_test_error < 1e-7
diff --git a/tests/exec_test/classification_gen_proj/data.csv b/tests/exec_test/classification_gen_proj/data.csv
index 3fa9f64bd0b9d2133040ea4849a71789ac8f078a..3de3e29a3d3a8510a21ace99268501120ce46af4 100644
--- a/tests/exec_test/classification_gen_proj/data.csv
+++ b/tests/exec_test/classification_gen_proj/data.csv
@@ -1,101 +1,101 @@
-index,prop,A,B,C,D,E,F,G,H,I,J
-0,1,0.1,-0.3,10,10,-0.492825179098274,0.173025977694162,0.598942935224295,-0.298754475196825,-0.581254909010269,-0.110656494210556
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diff --git a/tests/exec_test/classification_gen_proj/sisso.json b/tests/exec_test/classification_gen_proj/sisso.json
index 1be640db39dd72bf93ae0d05470b5dd92684e040..51d8512bbb114eb06064b38eac7b5f26bdcc7cc7 100644
--- a/tests/exec_test/classification_gen_proj/sisso.json
+++ b/tests/exec_test/classification_gen_proj/sisso.json
@@ -6,11 +6,10 @@
"data_file": "data.csv",
"data_file_relatice_to_json": true,
"property_key": "prop",
- "leave_out_frac": 0.2,
+ "leave_out_frac": 0.0,
"n_models_store": 1,
"n_rung_generate": 1,
"calc_type": "classification",
- "leave_out_inds": [80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 ,96 ,97, 98 , 99],
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
"param_opset" : [],
"fix_intercept": false
diff --git a/tests/exec_test/classification_max_corr/data.csv b/tests/exec_test/classification_max_corr/data.csv
index 3fa9f64bd0b9d2133040ea4849a71789ac8f078a..3de3e29a3d3a8510a21ace99268501120ce46af4 100644
--- a/tests/exec_test/classification_max_corr/data.csv
+++ b/tests/exec_test/classification_max_corr/data.csv
@@ -1,101 +1,101 @@
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diff --git a/tests/exec_test/classification_max_corr/sisso.json b/tests/exec_test/classification_max_corr/sisso.json
index bc7fa4cd2b4bf9ee1093d3f7e33ca3052bdab7f5..cae3d008d56450244b127005ec662a36f4efae82 100644
--- a/tests/exec_test/classification_max_corr/sisso.json
+++ b/tests/exec_test/classification_max_corr/sisso.json
@@ -10,7 +10,7 @@
"leave_out_frac": 0.2,
"n_models_store": 1,
"calc_type": "classification",
- "leave_out_inds": [80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 ,96 ,97, 98 , 99],
+ "leave_out_inds": [ 2, 3, 4, 6, 21, 23, 30, 38, 39, 52, 53, 61, 76, 82, 83, 45, 47, 48, 49, 66 ],
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
"param_opset" : [],
"fix_intercept": false
diff --git a/tests/exec_test/classification_max_corr_gen_proj/data.csv b/tests/exec_test/classification_max_corr_gen_proj/data.csv
index 3fa9f64bd0b9d2133040ea4849a71789ac8f078a..3de3e29a3d3a8510a21ace99268501120ce46af4 100644
--- a/tests/exec_test/classification_max_corr_gen_proj/data.csv
+++ b/tests/exec_test/classification_max_corr_gen_proj/data.csv
@@ -1,101 +1,101 @@
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diff --git a/tests/exec_test/classification_max_corr_gen_proj/sisso.json b/tests/exec_test/classification_max_corr_gen_proj/sisso.json
index dc2c7a145a97c5b95a5a7df0c588e4648e1d976b..7f978c23d974a3d8bf07c2710cac52494716e103 100644
--- a/tests/exec_test/classification_max_corr_gen_proj/sisso.json
+++ b/tests/exec_test/classification_max_corr_gen_proj/sisso.json
@@ -11,7 +11,7 @@
"n_models_store": 1,
"n_rung_generate": 1,
"calc_type": "classification",
- "leave_out_inds": [80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 ,96 ,97, 98 , 99],
+ "leave_out_inds": [ 2, 3, 4, 6, 21, 23, 30, 38, 39, 52, 53, 61, 76, 82, 83, 45, 47, 48, 49, 66 ],
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
"param_opset" : [],
"fix_intercept": false
diff --git a/tests/exec_test/gen_proj/check_model.py b/tests/exec_test/gen_proj/check_model.py
index 183a6c1712ceacec2a96d313d41a9f191952bb0d..50da73db80d3fbcd882280532eae51cbb80c1adf 100644
--- a/tests/exec_test/gen_proj/check_model.py
+++ b/tests/exec_test/gen_proj/check_model.py
@@ -4,7 +4,6 @@ from pathlib import Path
import numpy as np
model = ModelRegressor(
- str("models/train_dim_2_model_0.dat"), str("models/test_dim_2_model_0.dat")
+ str("models/train_dim_2_model_0.dat")
)
assert model.rmse < 1e-4
-assert model.test_rmse < 1e-4
diff --git a/tests/exec_test/gen_proj/sisso.json b/tests/exec_test/gen_proj/sisso.json
index f615931822d0416b89cdd96844fa8765dd58337c..dc65c132bc8d780fa99c16cdf6a25779dc6f0bbe 100644
--- a/tests/exec_test/gen_proj/sisso.json
+++ b/tests/exec_test/gen_proj/sisso.json
@@ -7,10 +7,9 @@
"data_file_relatice_to_json": true,
"property_key": "Prop",
"task_key": "Task",
- "leave_out_frac": 0.05,
+ "leave_out_frac": 0.0,
"n_models_store": 1,
"n_rung_generate": 1,
- "leave_out_inds": [0, 1, 2, 60, 61],
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
"param_opset": [],
"fix_intercept": false
diff --git a/tests/exec_test/no_test_data/check_model.py b/tests/exec_test/log_reg_gen_proj/check_model.py
similarity index 63%
rename from tests/exec_test/no_test_data/check_model.py
rename to tests/exec_test/log_reg_gen_proj/check_model.py
index 50da73db80d3fbcd882280532eae51cbb80c1adf..1e429966786af35cb64c3179d674daedbf5a40bb 100644
--- a/tests/exec_test/no_test_data/check_model.py
+++ b/tests/exec_test/log_reg_gen_proj/check_model.py
@@ -1,9 +1,9 @@
-from sissopp import ModelRegressor
+from sissopp import ModelLogRegressor
from pathlib import Path
import numpy as np
-model = ModelRegressor(
+model = ModelLogRegressor(
str("models/train_dim_2_model_0.dat")
)
assert model.rmse < 1e-4
diff --git a/tests/exec_test/log_reg_gen_proj/data.csv b/tests/exec_test/log_reg_gen_proj/data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ca8bf5a5dba405754688a54146c85f0ccaa8249b
--- /dev/null
+++ b/tests/exec_test/log_reg_gen_proj/data.csv
@@ -0,0 +1,101 @@
+Sample,Prop,A,B,C,D
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diff --git a/tests/exec_test/no_test_data/sisso.json b/tests/exec_test/log_reg_gen_proj/sisso.json
similarity index 71%
rename from tests/exec_test/no_test_data/sisso.json
rename to tests/exec_test/log_reg_gen_proj/sisso.json
index 7dcb348824039c5944525d15a2646c2357abf6b4..dbd02b05e1c7d609fe9bf395422fd69c6d699b1c 100644
--- a/tests/exec_test/no_test_data/sisso.json
+++ b/tests/exec_test/log_reg_gen_proj/sisso.json
@@ -1,14 +1,15 @@
{
"desc_dim": 2,
"n_sis_select": 1,
- "max_rung": 2,
+ "max_rung": 1,
"n_residual": 1,
- "data_file": "../data.csv",
+ "data_file": "data.csv",
"data_file_relatice_to_json": true,
"property_key": "Prop",
- "task_key": "Task",
- "leave_out_frac": 0.00,
"n_models_store": 1,
+ "n_rung_generate": 1,
+ "calc_type": "log_regression",
"opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
+ "param_opset": [],
"fix_intercept": false
}
diff --git a/tests/exec_test/log_reg_max_corr/check_model.py b/tests/exec_test/log_reg_max_corr/check_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5cdbe83019b9eaea840c312ad60630f00a84896
--- /dev/null
+++ b/tests/exec_test/log_reg_max_corr/check_model.py
@@ -0,0 +1,10 @@
+from sissopp import ModelLogRegressor
+from pathlib import Path
+
+import numpy as np
+
+model = ModelLogRegressor(
+ str("models/train_dim_2_model_0.dat"), str("models/test_dim_2_model_0.dat")
+)
+assert model.rmse < 1e-4
+assert model.test_rmse < 1e-4
diff --git a/tests/exec_test/log_reg_max_corr/data.csv b/tests/exec_test/log_reg_max_corr/data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ca8bf5a5dba405754688a54146c85f0ccaa8249b
--- /dev/null
+++ b/tests/exec_test/log_reg_max_corr/data.csv
@@ -0,0 +1,101 @@
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diff --git a/tests/exec_test/log_reg_max_corr/sisso.json b/tests/exec_test/log_reg_max_corr/sisso.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0b3ea6f876d4829a334fd692b5ce8f242573440
--- /dev/null
+++ b/tests/exec_test/log_reg_max_corr/sisso.json
@@ -0,0 +1,17 @@
+{
+ "desc_dim": 2,
+ "n_sis_select": 1,
+ "max_rung": 1,
+ "n_residual": 1,
+ "data_file": "data.csv",
+ "data_file_relatice_to_json": true,
+ "property_key": "Prop",
+ "leave_out_frac": 0.05,
+ "n_models_store": 1,
+ "calc_type": "log_regression",
+ "max_feat_cross_correlation": 0.99,
+ "leave_out_inds": [0, 1, 2, 60, 61],
+ "opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
+ "param_opset": [],
+ "fix_intercept": false
+}
diff --git a/tests/exec_test/log_reg_max_corr_gen_proj/check_model.py b/tests/exec_test/log_reg_max_corr_gen_proj/check_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5cdbe83019b9eaea840c312ad60630f00a84896
--- /dev/null
+++ b/tests/exec_test/log_reg_max_corr_gen_proj/check_model.py
@@ -0,0 +1,10 @@
+from sissopp import ModelLogRegressor
+from pathlib import Path
+
+import numpy as np
+
+model = ModelLogRegressor(
+ str("models/train_dim_2_model_0.dat"), str("models/test_dim_2_model_0.dat")
+)
+assert model.rmse < 1e-4
+assert model.test_rmse < 1e-4
diff --git a/tests/exec_test/log_reg_max_corr_gen_proj/data.csv b/tests/exec_test/log_reg_max_corr_gen_proj/data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ca8bf5a5dba405754688a54146c85f0ccaa8249b
--- /dev/null
+++ b/tests/exec_test/log_reg_max_corr_gen_proj/data.csv
@@ -0,0 +1,101 @@
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+95,0.006819280465836,151.095020172981,37.8615330871062,227.034115691539,362.755341229094
+96,0.009406739334267,32.3147853544107,113.549008148151,57.9053526795503,348.470092929769
+97,0.026975504059684,53.4863829718894,186.176691450922,56.7764485836142,371.957528066938
+98,711.780196017338,7.36908228123383,488.268102038773,252.670421142526,486.698386703915
+99,2.82416953708447,183.480683022873,368.758162863328,2.80315566798772,340.166678214112
+100,0.3850029324009,482.322626243789,100.05861510573,495.618985557607,17.9764839910466
diff --git a/tests/exec_test/log_reg_max_corr_gen_proj/sisso.json b/tests/exec_test/log_reg_max_corr_gen_proj/sisso.json
new file mode 100644
index 0000000000000000000000000000000000000000..a2e566facab1015b72032ec5b87b09404b4eb2ac
--- /dev/null
+++ b/tests/exec_test/log_reg_max_corr_gen_proj/sisso.json
@@ -0,0 +1,18 @@
+{
+ "desc_dim": 2,
+ "n_sis_select": 1,
+ "max_rung": 1,
+ "n_residual": 1,
+ "data_file": "data.csv",
+ "data_file_relatice_to_json": true,
+ "property_key": "Prop",
+ "leave_out_frac": 0.05,
+ "n_models_store": 1,
+ "calc_type": "log_regression",
+ "n_rung_generate": 1,
+ "max_feat_cross_correlation": 0.99,
+ "leave_out_inds": [0, 1, 2, 60, 61],
+ "opset": ["add", "sub", "abs_diff", "mult", "div", "inv", "abs", "exp", "log", "sin", "cos", "sq", "cb", "six_pow", "sqrt", "cbrt", "neg_exp"],
+ "param_opset": [],
+ "fix_intercept": false
+}
diff --git a/tests/exec_test/reparam/sisso.json b/tests/exec_test/reparam/sisso.json
index d90917e122558ad90f6b7dcb206d3a76015d0627..64267ee61bae531b89fa71491fe34d8d68141ce6 100644
--- a/tests/exec_test/reparam/sisso.json
+++ b/tests/exec_test/reparam/sisso.json
@@ -1,7 +1,7 @@
{
"desc_dim": 2,
"n_sis_select": 5,
- "max_rung": 1,
+ "max_rung": 2,
"n_residual": 5,
"data_file": "data.csv",
"data_file_relatice_to_json": true,
diff --git a/tests/exec_test/reparam_gen_proj/sisso.json b/tests/exec_test/reparam_gen_proj/sisso.json
index c3c554cf65f999cfd552d8a0bac735f1664be560..90182dad19e3288700eddcb2c4fa45cb076a0e61 100644
--- a/tests/exec_test/reparam_gen_proj/sisso.json
+++ b/tests/exec_test/reparam_gen_proj/sisso.json
@@ -1,6 +1,6 @@
{
"desc_dim": 2,
- "n_sis_select": 5,
+ "n_sis_select": 21,
"max_rung": 1,
"n_residual": 5,
"data_file": "data.csv",
@@ -11,7 +11,25 @@
"n_rung_generate": 1,
"leave_out_inds": [],
"opset": ["sq"],
- "param_opset": ["cb", "abs_diff", "div"],
+ "param_opset": [
+ "add",
+ "sub",
+ "abs_diff",
+ "mult",
+ "div",
+ "inv",
+ "abs",
+ "exp",
+ "log",
+ "sin",
+ "cos",
+ "sq",
+ "cb",
+ "six_pow",
+ "sqrt",
+ "cbrt",
+ "neg_exp"
+ ],
"fix_intercept": true,
"reparam_residual": true,
"global_param_opt": false
diff --git a/tests/googletest/classification/test_lp_wrapper.cc b/tests/googletest/classification/test_lp_wrapper.cc
index 60d18e159bffe9249c91de829f41c934aefce710..d52ae7a4a9ac389a9bdd8b87b819606b0cd5f2a9 100644
--- a/tests/googletest/classification/test_lp_wrapper.cc
+++ b/tests/googletest/classification/test_lp_wrapper.cc
@@ -136,6 +136,33 @@ namespace
TEST_F(LPWrapperTests, WithTestData)
{
+ EXPECT_THROW(
+ LPWrapper lp(
+ _samp_per_class,
+ _task_num,
+ _n_class,
+ _n_dim,
+ _n_samp + 1,
+ _tol,
+ _samp_per_class_test,
+ _n_samp_test
+ ),
+ std::logic_error
+ );
+
+ EXPECT_THROW(
+ LPWrapper lp(
+ _samp_per_class,
+ _task_num,
+ _n_class,
+ _n_dim,
+ _n_samp,
+ _tol,
+ _samp_per_class_test,
+ _n_samp_test + 1
+ ),
+ std::logic_error
+ );
LPWrapper lp(
_samp_per_class,
_task_num,
@@ -160,10 +187,30 @@ namespace
EXPECT_EQ(lp.n_samp_test(), _n_samp_test);
EXPECT_EQ(lp.n_overlap(), 0);
EXPECT_EQ(lp.n_overlap_test(), 0);
+
+ EXPECT_THROW(lp.copy_data(0, {0, 1, 2}), std::logic_error);
+
+ val_ptrs = {_phi[0].value_ptr(), _phi[1].value_ptr(), _phi[1].value_ptr()};
+ EXPECT_THROW(lp.set_n_overlap(val_ptrs, test_val_ptrs, _error.data(), _test_error.data()), std::logic_error);
+
+ val_ptrs = {_phi[0].value_ptr(), _phi[1].value_ptr()};
+ test_val_ptrs = {_phi[0].test_value_ptr(), _phi[1].test_value_ptr(), _phi[1].test_value_ptr()};
+ EXPECT_THROW(lp.set_n_overlap(val_ptrs, test_val_ptrs, _error.data(), _test_error.data()), std::logic_error);
}
TEST_F(LPWrapperTests, WithoutTestData)
{
+ EXPECT_THROW(
+ LPWrapper lp(
+ _samp_per_class,
+ _task_num,
+ _n_class,
+ _n_dim,
+ _n_samp + 1,
+ _tol
+ ),
+ std::logic_error
+ );
LPWrapper lp(
_samp_per_class,
@@ -187,11 +234,19 @@ namespace
EXPECT_EQ(lp.n_samp_test(), 0);
EXPECT_EQ(lp.n_overlap(), 0);
EXPECT_EQ(lp.n_overlap_test(), 0);
+
+ EXPECT_THROW(lp.copy_data(0, {0, 1, 2}), std::logic_error);
+
+ val_ptrs = {_phi[0].value_ptr(), _phi[1].value_ptr(), _phi[1].value_ptr()};
+ EXPECT_THROW(lp.set_n_overlap(val_ptrs, test_val_ptrs, _error.data(), _test_error.data()), std::logic_error);
+
+ val_ptrs = {_phi[0].value_ptr(), _phi[1].value_ptr()};
+ test_val_ptrs = {_phi[0].test_value_ptr(), _phi[1].test_value_ptr(), _phi[1].test_value_ptr()};
+ EXPECT_THROW(lp.set_n_overlap(val_ptrs, test_val_ptrs, _error.data(), _test_error.data()), std::logic_error);
}
TEST_F(LPWrapperTests, CopyTest)
{
-
LPWrapper lp(
_samp_per_class,
_task_num,
diff --git a/tests/googletest/classification/test_prop_sorted_d_mat.cc b/tests/googletest/classification/test_prop_sorted_d_mat.cc
index e61cf3f09c0a9ad0e353ce58f50a5f1a4b76e124..3123df66254b3c3a3a92155541ebff6e74235fec 100644
--- a/tests/googletest/classification/test_prop_sorted_d_mat.cc
+++ b/tests/googletest/classification/test_prop_sorted_d_mat.cc
@@ -62,5 +62,22 @@ namespace
prop_sorted_d_mat::access_sample_sorted_d_matrix(2)[0] = 1.5;
EXPECT_EQ(prop_sorted_d_mat::access_sample_sorted_d_matrix(2, 0, 0)[0], 1.5);
prop_sorted_d_mat::finalize_sorted_d_matrix_arr();
+
+ EXPECT_THROW(
+ prop_sorted_d_mat::initialize_sorted_d_matrix_arr(1, _n_task, _n_class + 1, _n_samples_per_class),
+ std::logic_error
+ );
+
+ EXPECT_THROW(
+ prop_sorted_d_mat::initialize_sorted_d_matrix_arr(1, _n_task + 1, _n_class, _n_samples_per_class),
+ std::logic_error
+ );
+
+ _n_samples_per_class.push_back(1);
+ EXPECT_THROW(
+ prop_sorted_d_mat::initialize_sorted_d_matrix_arr(1, _n_task, _n_class, _n_samples_per_class),
+ std::logic_error
+ );
+
}
}
diff --git a/tests/googletest/classification/test_svm_wrapper.cc b/tests/googletest/classification/test_svm_wrapper.cc
index b55ee6bcc6d4f0808d555c24bda349d82c8067e2..ca7c9c67f9e0a7da8ce718420494bd62249b1f00 100644
--- a/tests/googletest/classification/test_svm_wrapper.cc
+++ b/tests/googletest/classification/test_svm_wrapper.cc
@@ -168,6 +168,15 @@ namespace
EXPECT_EQ(svm.n_dim(), _n_dim);
EXPECT_EQ(svm.n_samp(), _n_samp);
EXPECT_EQ(svm.n_misclassified(), 0);
+
+ EXPECT_THROW(svm.copy_data({0,1,2}, 0), std::logic_error);
+ EXPECT_THROW(svm.copy_data({0,1}, 3), std::logic_error);
+
+ val_ptrs = {_phi[0].value_ptr(), _phi[1].value_ptr(), _phi[1].value_ptr()};
+ EXPECT_THROW(svm.copy_data(val_ptrs), std::logic_error);
+
+ test_val_ptrs = {_phi[0].test_value_ptr(), _phi[1].test_value_ptr(), _phi[1].test_value_ptr()};
+ EXPECT_THROW(svm.predict(20, test_val_ptrs), std::logic_error);
}
TEST_F(SVMWrapperTests, CTest)
@@ -210,5 +219,14 @@ namespace
EXPECT_EQ(svm.n_dim(), _n_dim);
EXPECT_EQ(svm.n_samp(), _n_samp);
EXPECT_EQ(svm.n_misclassified(), 0);
+
+ EXPECT_THROW(svm.copy_data({0,1,2}, 0), std::logic_error);
+ EXPECT_THROW(svm.copy_data({0,1}, 3), std::logic_error);
+
+ val_ptrs = {_phi[0].value_ptr(), _phi[1].value_ptr(), _phi[1].value_ptr()};
+ EXPECT_THROW(svm.copy_data(val_ptrs), std::logic_error);
+
+ test_val_ptrs = {_phi[0].test_value_ptr(), _phi[1].test_value_ptr(), _phi[1].test_value_ptr()};
+ EXPECT_THROW(svm.predict(20, test_val_ptrs), std::logic_error);
}
}
diff --git a/tests/googletest/descriptor_identification/model/test_model_classifier.cc b/tests/googletest/descriptor_identification/model/test_model_classifier.cc
index ac1583394625f7332832b021e44ac54dcb94b242..3148a09cbe6ede02e85e61a0f75f652341731956 100644
--- a/tests/googletest/descriptor_identification/model/test_model_classifier.cc
+++ b/tests/googletest/descriptor_identification/model/test_model_classifier.cc
@@ -143,4 +143,35 @@ namespace
boost::filesystem::remove("train_class_mods.dat");
boost::filesystem::remove("test_class_mods.dat");
}
+
+ TEST_F(ModelClassifierTests, EvalTest)
+ {
+ ModelClassifier model(
+ "Property",
+ Unit("m"),
+ _loss,
+ _features,
+ _leave_out_inds,
+ _sample_ids_train,
+ _sample_ids_test,
+ task_names
+ );
+
+ model.set_task_eval(0);
+ std::vector<double> pt = {2.0, 2.0};
+ EXPECT_THROW(model.eval(pt), std::logic_error);
+
+ std::map<std::string, double> pt_dct;
+ pt_dct["A"] = 1.0;
+ pt_dct["B"] = 1.0;
+ EXPECT_THROW(model.eval(pt_dct), std::logic_error);
+
+ std::vector<std::vector<double>> pts = {{1.0}, {1.0}};
+ EXPECT_THROW(model.eval(pts), std::logic_error);
+
+ std::map<std::string, std::vector<double>> pts_dct;
+ pts_dct["A"] = {1.0};
+ pts_dct["B"] = {1.0};
+ EXPECT_THROW(model.eval(pts_dct), std::logic_error);
+ }
}
diff --git a/tests/googletest/descriptor_identification/model/test_model_log_regressor.cc b/tests/googletest/descriptor_identification/model/test_model_log_regressor.cc
index 2a46674652cad5cd05806a574e9edd8dad251918..054e89a6a51a1d2bc309e5c2f81b42d3d4621b8a 100644
--- a/tests/googletest/descriptor_identification/model/test_model_log_regressor.cc
+++ b/tests/googletest/descriptor_identification/model/test_model_log_regressor.cc
@@ -127,6 +127,8 @@ namespace
EXPECT_LT(std::abs(model.coefs()[0][1] + 2.1), 1e-10);
EXPECT_LT(std::abs(model.coefs()[0][2] - std::log(0.001)), 1e-10);
+ EXPECT_STREQ(model.toLatexString().c_str(), "$\\exp\\left(c_0\\right)\\left(A\\right)^{a_0}\\left(B\\right)^{a_1}$");
+
model.to_file("train_false_log_reg.dat", true);
model.to_file("test_false_log_reg.dat", false);
}
@@ -248,6 +250,8 @@ namespace
model.to_file("train_true_log_reg.dat", true);
model.to_file("test_true_log_reg.dat", false);
+
+ EXPECT_STREQ(model.toLatexString().c_str(), "$\\left(A\\right)^{a_0}\\left(B\\right)^{a_1}$");
}
TEST_F(ModelLogRegssorTests, FixInterceptTrueFileTest)
@@ -340,5 +344,23 @@ namespace
pts_dct["B"] = {1.0};
val = model.eval(pts_dct)[0];
EXPECT_LT(val - 0.00025, 1e-10);
+
+ pt.push_back(1.0);
+ EXPECT_THROW(model.eval(pt), std::logic_error);
+
+ pts.push_back({1.0});
+ EXPECT_THROW(model.eval(pts), std::logic_error);
+
+ pts.pop_back();
+ pts.back().push_back(1.0);
+ EXPECT_THROW(model.eval(pts), std::logic_error);
+
+ pts_dct["A"] = {1.0, 1.0};
+ EXPECT_THROW(model.eval(pts_dct), std::logic_error);
+
+ pt_dct.erase("A");
+ pts_dct.erase("A");
+ EXPECT_THROW(model.eval(pt_dct), std::logic_error);
+ EXPECT_THROW(model.eval(pts_dct), std::logic_error);
}
}
diff --git a/tests/googletest/descriptor_identification/model/test_model_regressor.cc b/tests/googletest/descriptor_identification/model/test_model_regressor.cc
index c270f02d6ad7ee5630843064bb0fc872d9c5a557..62ecc14ad655e4252e51c21b20158deb76d4ea1a 100644
--- a/tests/googletest/descriptor_identification/model/test_model_regressor.cc
+++ b/tests/googletest/descriptor_identification/model/test_model_regressor.cc
@@ -133,6 +133,7 @@ namespace
EXPECT_LT(std::abs(model.coefs()[1][1] + 0.4), 1e-10);
EXPECT_LT(std::abs(model.coefs()[1][2] + 6.5), 1e-10);
+ EXPECT_STREQ(model.toLatexString().c_str(), "$c_0 + a_0A + a_1B$");
model.to_file("train_false.dat", true);
model.to_file("test_false.dat", false);
}
@@ -259,6 +260,8 @@ namespace
EXPECT_LT(std::abs(model.coefs()[1][0] - 1.25), 1e-10);
EXPECT_LT(std::abs(model.coefs()[1][1] + 0.4), 1e-10);
+ EXPECT_STREQ(model.toLatexString().c_str(), "$a_0A + a_1B$");
+
model.to_file("train_true.dat", true);
model.to_file("test_true.dat", false);
}
@@ -359,5 +362,23 @@ namespace
EXPECT_LT(model.eval(pt_dct) + 5.65, 1e-10);
EXPECT_LT(model.eval(pts)[0] + 5.65, 1e-10);
EXPECT_LT(model.eval(pts_dct)[0] + 5.65, 1e-10);
+
+ pt.push_back(1.0);
+ EXPECT_THROW(model.eval(pt), std::logic_error);
+
+ pts.push_back({1.0});
+ EXPECT_THROW(model.eval(pts), std::logic_error);
+
+ pts.pop_back();
+ pts.back().push_back(1.0);
+ EXPECT_THROW(model.eval(pts), std::logic_error);
+
+ pts_dct["A"] = {1.0, 1.0};
+ EXPECT_THROW(model.eval(pts_dct), std::logic_error);
+
+ pt_dct.erase("A");
+ pts_dct.erase("A");
+ EXPECT_THROW(model.eval(pt_dct), std::logic_error);
+ EXPECT_THROW(model.eval(pts_dct), std::logic_error);
}
}
diff --git a/tests/googletest/descriptor_identification/solver/test_sisso_classifier.cc b/tests/googletest/descriptor_identification/solver/test_sisso_classifier.cc
index 1034836027c1629b6bd063584add38f3d4abba64..0b0f500183e8e5f23ec8cecd4587d39342d7e8bb 100644
--- a/tests/googletest/descriptor_identification/solver/test_sisso_classifier.cc
+++ b/tests/googletest/descriptor_identification/solver/test_sisso_classifier.cc
@@ -201,4 +201,38 @@ namespace
boost::filesystem::remove_all("feature_space/");
boost::filesystem::remove_all("models/");
}
+
+ TEST_F(SISSOClassifierTests, FixInterceptTrueTest)
+ {
+ std::shared_ptr<FeatureSpace> feat_space = std::make_shared<FeatureSpace>(inputs);
+ inputs.set_fix_intercept(true);
+ SISSOClassifier sisso(inputs, feat_space);
+ EXPECT_FALSE(sisso.fix_intercept());
+
+ std::vector<double> prop_comp(80, 0.0);
+ std::transform(inputs.prop_train().begin(), inputs.prop_train().end(), sisso.prop_train().begin(), prop_comp.begin(), [](double p1, double p2){return std::abs(p1 - p2);});
+ EXPECT_FALSE(std::any_of(prop_comp.begin(), prop_comp.end(), [](double p){return p > 1e-10;}));
+
+ std::transform(inputs.prop_test().begin(), inputs.prop_test().begin() + 10, sisso.prop_test().begin(), prop_comp.begin(), [](double p1, double p2){return std::abs(p1 - p2);});
+ EXPECT_FALSE(std::any_of(prop_comp.begin(), prop_comp.begin() + 10, [](double p){return p > 1e-10;}));
+
+ EXPECT_EQ(sisso.n_samp(), 80);
+ EXPECT_EQ(sisso.n_dim(), 2);
+ EXPECT_EQ(sisso.n_residual(), 2);
+ EXPECT_EQ(sisso.n_models_store(), 3);
+
+ sisso.fit();
+
+ EXPECT_EQ(sisso.models().size(), 2);
+ EXPECT_EQ(sisso.models()[0].size(), 3);
+
+ EXPECT_EQ(sisso.models().back()[0].n_convex_overlap_train(), 0);
+ EXPECT_EQ(sisso.models().back()[0].n_convex_overlap_test(), 0);
+
+ EXPECT_EQ(sisso.models().back()[0].n_svm_misclassified_train(), 0);
+ EXPECT_EQ(sisso.models().back()[0].n_svm_misclassified_test(), 0);
+
+ boost::filesystem::remove_all("feature_space/");
+ boost::filesystem::remove_all("models/");
+ }
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_abs_diff_node.cc b/tests/googletest/feature_creation/feature_generation/test_abs_diff_node.cc
index e196bd19aa6090665577cee3ce3ac6ec2a140177..5d7c03c4ef23e7902370c6814c3d3f568842be3a 100644
--- a/tests/googletest/feature_creation/feature_generation/test_abs_diff_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_abs_diff_node.cc
@@ -25,8 +25,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
-
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {5.0};
@@ -180,8 +184,8 @@ namespace
TEST_F(AbsDiffNodeTest, AttributesTest)
{
- _abs_diff_test = std::make_shared<AbsDiffNode>(_phi[0], _phi[1], 5, 1e-50, 1e50);
- _abs_diff_test = std::make_shared<AbsDiffNode>(_abs_diff_test, _phi[1], 6, 1e-50, 1e50);
+ node_ptr feat_r1 = std::make_shared<AbsDiffNode>(_phi[0], _phi[1], 5, 1e-50, 1e50);
+ _abs_diff_test = std::make_shared<AbsDiffNode>(feat_r1, _phi[1], 6, 1e-50, 1e50);
EXPECT_EQ(_abs_diff_test->rung(), 2);
@@ -195,5 +199,35 @@ namespace
EXPECT_STREQ(_abs_diff_test->expr().c_str(), "(|(|A - B|) - B|)");
EXPECT_STREQ(_abs_diff_test->postfix_expr().c_str(), "0|1|abd|1|abd");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_abs_diff_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_abs_diff_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_abs_diff_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_abs_diff_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_abs_diff_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, -2.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(4, 0.0);
+ EXPECT_THROW(_abs_diff_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _abs_diff_test->set_parameters({}, true);
+ _abs_diff_test->set_parameters(nullptr);
+
+ _abs_diff_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], -1.0);
+ EXPECT_EQ(dfdp[1], 0.0);
+
+ EXPECT_EQ(dfdp[2], 1.0);
+ EXPECT_EQ(dfdp[3], 1.0);
+
+ EXPECT_EQ(util_funcs::norm(grad), 0.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_abs_node.cc b/tests/googletest/feature_creation/feature_generation/test_abs_node.cc
index d1d5440fa73f0b92f49488f95ae3c7e290082311..438aea04b33c05ebd4b48d5df56dd68f1dfde865 100644
--- a/tests/googletest/feature_creation/feature_generation/test_abs_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_abs_node.cc
@@ -24,7 +24,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {-1.0, -2.0, -3.0, -4.0};
std::vector<double> test_value_1 = {50.0};
@@ -182,5 +187,32 @@ namespace
EXPECT_STREQ(_abs_test->expr().c_str(), "(|A|)");
EXPECT_STREQ(_abs_test->postfix_expr().c_str(), "0|abs");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_abs_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_abs_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_abs_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_abs_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_abs_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 2.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_abs_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _abs_test->set_parameters({}, true);
+ _abs_test->set_parameters(nullptr);
+ _abs_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 1.0);
+ EXPECT_EQ(dfdp[1], 0.0);
+
+ EXPECT_EQ(dfdp[2], -1.0);
+ EXPECT_EQ(dfdp[3], -1.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_add_node.cc b/tests/googletest/feature_creation/feature_generation/test_add_node.cc
index b50c377427009a83ed5486f134fe2c3ebb063be2..f91124f873fcf84b8965eba1570dc70ee8cd4e03 100644
--- a/tests/googletest/feature_creation/feature_generation/test_add_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_add_node.cc
@@ -24,7 +24,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {5.0};
@@ -194,5 +199,32 @@ namespace
EXPECT_STREQ(_add_test->expr().c_str(), "((A + B) + B)");
EXPECT_STREQ(_add_test->postfix_expr().c_str(), "0|1|add|1|add");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_add_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_add_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_add_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_add_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_add_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(4, 0.0);
+ EXPECT_THROW(_add_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _add_test->set_parameters({}, true);
+ _add_test->set_parameters(nullptr);
+ _add_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 1.0);
+ EXPECT_EQ(dfdp[1], 1.0);
+
+ EXPECT_EQ(dfdp[2], 1.0);
+ EXPECT_EQ(dfdp[3], 1.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_cb_node.cc b/tests/googletest/feature_creation/feature_generation/test_cb_node.cc
index 0a2ebebef8fc41450022543679351bbbb1c3395e..8110f79865ee03846ec247095733d9ca27084118 100644
--- a/tests/googletest/feature_creation/feature_generation/test_cb_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_cb_node.cc
@@ -28,7 +28,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 8.0};
std::vector<double> test_value_1 = {2.0};
@@ -180,5 +185,32 @@ namespace
EXPECT_STREQ(_cb_test->expr().c_str(), "(A^3)");
EXPECT_STREQ(_cb_test->postfix_expr().c_str(), "0|cb");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_cb_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_cb_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_cb_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_cb_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_cb_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_cb_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _cb_test->set_parameters({}, true);
+ _cb_test->set_parameters(nullptr);
+ _cb_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 3.0);
+ EXPECT_EQ(dfdp[1], 12.0);
+
+ EXPECT_EQ(dfdp[2], 27.0);
+ EXPECT_EQ(dfdp[3], 192.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_cbrt_node.cc b/tests/googletest/feature_creation/feature_generation/test_cbrt_node.cc
index e556cd04146933d1e17f19ee8ae1136a4ef6375c..66b6c5a513ad58b70da22aeec66a72dbc085efbf 100644
--- a/tests/googletest/feature_creation/feature_generation/test_cbrt_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_cbrt_node.cc
@@ -29,7 +29,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 8.0};
std::vector<double> test_value_1 = {8.0};
@@ -205,5 +210,29 @@ namespace
EXPECT_STREQ(_cbrt_test->expr().c_str(), "cbrt(A)");
EXPECT_STREQ(_cbrt_test->postfix_expr().c_str(), "0|cbrt");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_cbrt_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_cbrt_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_cbrt_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_cbrt_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_cbrt_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_cbrt_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _cbrt_test->set_parameters({}, true);
+ _cbrt_test->set_parameters(nullptr);
+ _cbrt_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] - 1/3.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] - 1/12.0), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_cos_node.cc b/tests/googletest/feature_creation/feature_generation/test_cos_node.cc
index 73ea5c78e59dc3aaf91cc4536cd7b9bc290a7671..427294009253c585f9681e114200ce700146097b 100644
--- a/tests/googletest/feature_creation/feature_generation/test_cos_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_cos_node.cc
@@ -25,7 +25,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {0.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {0.0};
@@ -181,5 +186,32 @@ namespace
EXPECT_STREQ(_cos_test->expr().c_str(), "cos(A)");
EXPECT_STREQ(_cos_test->postfix_expr().c_str(), "0|cos");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_cos_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_cos_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_cos_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_cos_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_cos_test->parameters().size(), 0);
+
+ std::vector<double> params = {M_PI / 2.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_cos_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _cos_test->set_parameters({}, true);
+ _cos_test->set_parameters(nullptr);
+ _cos_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] - 0.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] - 0.0), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] - 1.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] - 0.0), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_div_node.cc b/tests/googletest/feature_creation/feature_generation/test_div_node.cc
index 162cb34aa1e35fadb25aa02c59e215b1c00e7f0b..423915850d78be48a6cf6bbc8f8e98c191a2e744 100644
--- a/tests/googletest/feature_creation/feature_generation/test_div_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_div_node.cc
@@ -25,7 +25,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {5.0};
@@ -244,5 +249,34 @@ namespace
EXPECT_STREQ(_div_test->expr().c_str(), "((A / B) / B)");
EXPECT_STREQ(_div_test->postfix_expr().c_str(), "0|1|div|1|div");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_div_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_div_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_div_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_div_test->n_params_possible(), 2);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(4, 0.0);
+ EXPECT_THROW(_div_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ EXPECT_EQ(_div_test->parameters().size(), 0);
+
+ _div_test->set_parameters({}, true);
+ _div_test->set_parameters(nullptr);
+ _div_test->param_derivative(params.data(), dfdp.data());
+ std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
+ std::vector<double> value_2 = {10.0, 25.0, 30.0, 40.0};
+
+ EXPECT_LT(std::abs(dfdp[0] + (1.0 / 10.0) / (10.0 * 10.0)), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] + (2.0 / 25.0) / (25.0 * 25.0)), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] + (3.0 / 30.0) / (30.0 * 30.0)), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] + (4.0 / 40.0) / (40.0 * 40.0)), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_exp_node.cc b/tests/googletest/feature_creation/feature_generation/test_exp_node.cc
index cb3dff658ded2d68fa3f40511ff3ed362da9988f..6923e5af15a977dc1d4f7332a9ec57fe66c3b4a7 100644
--- a/tests/googletest/feature_creation/feature_generation/test_exp_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_exp_node.cc
@@ -29,7 +29,12 @@ namespace
protected:
void SetUp() override
{
- node_value_arrs::initialize_values_arr({4}, {1}, 3, 2, false);
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {0.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {0.0};
@@ -210,5 +215,32 @@ namespace
EXPECT_STREQ(_exp_test->expr().c_str(), "exp(A)");
EXPECT_STREQ(_exp_test->postfix_expr().c_str(), "0|exp");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_exp_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_exp_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_exp_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_exp_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_exp_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_exp_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _exp_test->set_parameters({}, true);
+ _exp_test->set_parameters(nullptr);
+ _exp_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] - _exp_test->value()[0]), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] - _exp_test->value()[1]), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] - _exp_test->value()[2]), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] - _exp_test->value()[3]), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_feat_node.cc b/tests/googletest/feature_creation/feature_generation/test_feat_node.cc
index ffa48c9a7f2345892ab39a48044223ed19a05cea..46106f91c9d4dcd667589254995b920817f80c68 100644
--- a/tests/googletest/feature_creation/feature_generation/test_feat_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_feat_node.cc
@@ -22,7 +22,7 @@ namespace
protected:
void SetUp() override
{
- node_value_arrs::initialize_values_arr({4}, {1}, 3, 0, false);
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
_value_1 = {1.0, 2.0, 3.0, 4.0};
_test_value_1 = {5.0};
@@ -32,6 +32,9 @@ namespace
_value_3 = {1.0, 2.0, 3.0, 1.0};
_test_value_3 = {5.0};
+
+ _value_4 = {1.0, 2.0, 3.0};
+ _test_value_4 = {};
}
void TearDown() override
@@ -47,6 +50,9 @@ namespace
std::vector<double> _value_3;
std::vector<double> _test_value_3;
+
+ std::vector<double> _value_4;
+ std::vector<double> _test_value_4;
};
TEST_F(FeatureNodeTest, ConstructorTest)
@@ -74,6 +80,42 @@ namespace
);
node_ptr feat_4 = feat_1->hard_copy();
+ try
+ {
+ node_ptr feat_5 = std::make_shared<FeatureNode>(
+ 3,
+ "D",
+ _value_4,
+ _test_value_3,
+ Unit("m")
+ );
+ EXPECT_TRUE(false) << " (Creation of FeatureNode with wrong number of samples in the training data)";
+ }
+ catch(const std::logic_error& e)
+ {}
+
+ try
+ {
+ node_ptr feat_5 = std::make_shared<FeatureNode>(
+ 3,
+ "D",
+ _value_3,
+ _test_value_4,
+ Unit("m")
+ );
+ EXPECT_TRUE(false) << " (Creation of FeatureNode with wrong number of samples in the test data)";
+ }
+ catch(const std::logic_error& e)
+ {}
+
+ node_ptr feat_5 = std::make_shared<FeatureNode>(
+ 3,
+ "D",
+ _value_3,
+ _test_value_3,
+ Unit("m")
+ );
+
EXPECT_FALSE(feat_1->is_const());
EXPECT_FALSE(feat_1->is_nan());
EXPECT_STREQ(feat_1->unit().toString().c_str(), "m");
@@ -126,4 +168,37 @@ namespace
EXPECT_EQ(feat_4->n_feats(), 0);
EXPECT_EQ(feat_4->sort_score(10), 0);
}
+
+ TEST_F(FeatureNodeTest, DefaultFunctionTests)
+ {
+ std::shared_ptr<FeatureNode> feat_1 = std::make_shared<FeatureNode>(
+ 0,
+ "A",
+ _value_1,
+ _test_value_1,
+ Unit("m")
+ );
+ node_ptr feat_2 = feat_1->hard_copy();
+
+ std::vector<node_ptr> phi = {feat_1, feat_2};
+ feat_1->reset_feats(phi);
+
+ #ifdef PARAMETERIZE
+ feat_1->set_parameters({}, true);
+ feat_1->set_parameters(nullptr);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ feat_1->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 0.0);
+ EXPECT_EQ(dfdp[1], 0.0);
+
+ EXPECT_THROW(feat_1->gradient(params.data(), dfdp.data()), std::logic_error);
+
+ feat_1->set_value(nullptr);
+ feat_1->set_test_value(nullptr);
+ #endif
+
+ }
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_inv_node.cc b/tests/googletest/feature_creation/feature_generation/test_inv_node.cc
index 7eaa5cec0f5c653fdfe874b2b644c7ce29dc9bb3..3a0022e01a358110bd1b62e8c98b3fd392e91809 100644
--- a/tests/googletest/feature_creation/feature_generation/test_inv_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_inv_node.cc
@@ -29,7 +29,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 8.0};
std::vector<double> test_value_1 = {2.0};
@@ -204,5 +209,32 @@ namespace
EXPECT_STREQ(_inv_test->expr().c_str(), "(1.0 / A)");
EXPECT_STREQ(_inv_test->postfix_expr().c_str(), "0|inv");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_inv_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_inv_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_inv_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_inv_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_inv_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_inv_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _inv_test->set_parameters({}, true);
+ _inv_test->set_parameters(nullptr);
+ _inv_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] + 1.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] + 1.0 / 4.0), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] + 1.0 / 9.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] + 1.0 / 64.0), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_log_node.cc b/tests/googletest/feature_creation/feature_generation/test_log_node.cc
index 87c67d1fe01c8ada56d699d0a43405111c1763e2..8dafacd352be8123904c2d24939d5bd52058f364 100644
--- a/tests/googletest/feature_creation/feature_generation/test_log_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_log_node.cc
@@ -35,7 +35,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {1.0};
@@ -304,5 +309,32 @@ namespace
EXPECT_STREQ(_log_test->expr().c_str(), "ln(A)");
EXPECT_STREQ(_log_test->postfix_expr().c_str(), "0|log");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_log_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_log_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_log_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_log_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_log_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_log_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _log_test->set_parameters({}, true);
+ _log_test->set_parameters(nullptr);
+ _log_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] - 1.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] - 0.5), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] - (1.0 / 3.0)), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] - 0.25), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_model_node.cc b/tests/googletest/feature_creation/feature_generation/test_model_node.cc
index 8b45808d1c48f76fa0be008f4333a570da4ab733..01f7ec4ce841c656e48a4832f1e4f628931250d9 100644
--- a/tests/googletest/feature_creation/feature_generation/test_model_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_model_node.cc
@@ -23,7 +23,7 @@ namespace
protected:
void SetUp() override
{
- node_value_arrs::initialize_values_arr({4}, {1}, 3, 0, false);
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
_value_1 = {1.0, 2.0, 3.0, 4.0};
_test_value_1 = {5.0};
@@ -100,5 +100,83 @@ namespace
EXPECT_EQ(feat_4->rung(), 1);
EXPECT_EQ(feat_4->n_feats(), 0);
EXPECT_EQ(feat_4->n_feats(), 0);
+
+ try
+ {
+ std::shared_ptr<ModelNode> feat_5 = std::make_shared<ModelNode>(4, 1, "C^2", "$C^2$", "2|squ", "C.^2", _value_3, _test_value_3, std::vector<std::string>(1, "C"), Unit("m"));
+ EXPECT_TRUE(false) << " (Creation of ModelNode with wrong postfix_expr.)";
+ }
+ catch(const std::logic_error& e)
+ {}
+
+ feat_1->set_value();
+ EXPECT_EQ(feat_1->value()[0], _value_1[0]);
+
+ feat_1->set_test_value();
+ EXPECT_EQ(feat_1->test_value()[0], _test_value_1[0]);
+
+ #ifdef PARAMETERIZE
+ try
+ {
+ std::shared_ptr<ModelNode> feat_5 = std::make_shared<ModelNode>(4, 1, "(C + 1.0)^2", "$\\left(C + 1\\right)^2$", "2|sq: 1.0, 1.0, 2.0", "(C + 1).^2", _value_3, _test_value_3, std::vector<std::string>(1, "C"), Unit("m"));
+ EXPECT_TRUE(false) << " (Creation of ModelNode with wrong postfix_expr.)";
+ }
+ catch(const std::logic_error& e)
+ {}
+ std::vector<double> params = {1.0, 1.0};
+ EXPECT_STREQ(feat_1->matlab_fxn_expr(params.data()).c_str(), feat_1->matlab_fxn_expr().c_str());
+ #endif
+ }
+
+ TEST_F(ModelNodeTest, EvalTest)
+ {
+ std::shared_ptr<ModelNode> feat_1 = std::make_shared<ModelNode>(0, 1, "A^2", "$A^2$", "0|sq", "A^2", _value_1, _test_value_1, std::vector<std::string>(1, "A"), Unit("m"));
+
+ std::vector<double> pt = {2.0};
+ std::vector<std::vector<double>> pts = {{2.0}};
+
+ std::map<std::string, double> pt_dct;
+ pt_dct["A"] = 2.0;
+
+ std::map<std::string, std::vector<double>> pts_dct;
+ pts_dct["A"] = {2.0};
+
+ EXPECT_LT(std::abs(feat_1->eval(pt) - 4.0), 1e-10);
+ EXPECT_LT(std::abs(feat_1->eval(pt_dct) - 4.0), 1e-10);
+
+ double val = feat_1->eval(pts)[0];
+ EXPECT_LT(std::abs(val - 4.0), 1e-10);
+
+ val = feat_1->eval(pts_dct)[0];
+ EXPECT_LT(std::abs(val - 4.0), 1e-10);
+ }
+
+ TEST_F(ModelNodeTest, EvalFailTest)
+ {
+ std::shared_ptr<ModelNode> feat_1 = std::make_shared<ModelNode>(0, 1, "A^2", "$A^2$", "1|0|sq", "A^2", _value_1, _test_value_1, std::vector<std::string>(1, "A"), Unit("m"));
+ EXPECT_THROW(feat_1->eval(_value_1.data()), std::logic_error);
+ EXPECT_THROW(feat_1->eval((_value_1)), std::logic_error);
+
+ std::vector<std::vector<double>> values = {{1.0}, {1.0}};
+ EXPECT_THROW(feat_1->eval(values.data()), std::logic_error);
+ EXPECT_THROW(feat_1->eval(values), std::logic_error);
+
+ std::vector<std::string> x_in = {"A", "B"};
+ feat_1 = std::make_shared<ModelNode>(0, 1, "A + B", "$A + B$", "0|add", "(A + B)", _value_1, _test_value_1, x_in, Unit("m"));
+ EXPECT_THROW(feat_1->eval(_value_1), std::logic_error);
+ EXPECT_THROW(feat_1->eval(values.data()), std::logic_error);
+
+ values = {{1.0}, {1.0, 1.0}};
+ EXPECT_THROW(feat_1->eval(values), std::logic_error);
+
+ feat_1 = std::make_shared<ModelNode>(0, 1, "A^2", "$A^2$", "0|sq", "A^2", _value_1, _test_value_1, std::vector<std::string>(1, "A"), Unit("m"));
+ EXPECT_THROW(feat_1->eval(_value_1), std::logic_error);
+
+ std::map<std::string, double> pt_dct;
+ pt_dct["B"] = 1.0;
+
+ std::map<std::string, std::vector<double>> pts_dct;
+ pt_dct["B"] = {1.0};
+ EXPECT_THROW(feat_1->eval(pt_dct), std::logic_error);
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_mult_node.cc b/tests/googletest/feature_creation/feature_generation/test_mult_node.cc
index eecf8f70aa1863594069460fed4905138099c142..b7c61bcc1d0a38d5afed2e0691827712c616d32d 100644
--- a/tests/googletest/feature_creation/feature_generation/test_mult_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_mult_node.cc
@@ -24,7 +24,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {5.0};
@@ -195,5 +200,32 @@ namespace
EXPECT_STREQ(_mult_test->expr().c_str(), "((A * B) * B)");
EXPECT_STREQ(_mult_test->postfix_expr().c_str(), "0|1|mult|1|mult");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_mult_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_mult_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_mult_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_mult_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_mult_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(4, 0.0);
+ EXPECT_THROW(_mult_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _mult_test->set_parameters({}, true);
+ _mult_test->set_parameters(nullptr);
+ _mult_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 10.0);
+ EXPECT_EQ(dfdp[1], 50.0);
+
+ EXPECT_EQ(dfdp[2], 90.0);
+ EXPECT_EQ(dfdp[3], 160.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_neg_exp_node.cc b/tests/googletest/feature_creation/feature_generation/test_neg_exp_node.cc
index 55d5707ab8f10bb0edbc41c4da200773761c10b9..899fc647cc13f8750b9d57b69c633f4aaacc483e 100644
--- a/tests/googletest/feature_creation/feature_generation/test_neg_exp_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_neg_exp_node.cc
@@ -29,7 +29,12 @@ namespace
protected:
void SetUp() override
{
- node_value_arrs::initialize_values_arr({4}, {1}, 3, 2, false);
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {0.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {0.0};
@@ -210,5 +215,32 @@ namespace
EXPECT_STREQ(_neg_exp_test->expr().c_str(), "(exp(-1.0 * A))");
EXPECT_STREQ(_neg_exp_test->postfix_expr().c_str(), "0|nexp");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_neg_exp_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_neg_exp_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_neg_exp_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_neg_exp_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_neg_exp_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_neg_exp_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _neg_exp_test->set_parameters({}, true);
+ _neg_exp_test->set_parameters(nullptr);
+ _neg_exp_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] + _neg_exp_test->value()[0]), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] + _neg_exp_test->value()[1]), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] + _neg_exp_test->value()[2]), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] + _neg_exp_test->value()[3]), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_sin_node.cc b/tests/googletest/feature_creation/feature_generation/test_sin_node.cc
index b00850baa4cf805c696e3c55bca4ce3a3ebe3006..9636f59caf4cf7f6d48bd8c0754b59370e08ef07 100644
--- a/tests/googletest/feature_creation/feature_generation/test_sin_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_sin_node.cc
@@ -25,7 +25,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {0.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {0.0};
@@ -181,5 +186,32 @@ namespace
EXPECT_STREQ(_sin_test->expr().c_str(), "sin(A)");
EXPECT_STREQ(_sin_test->postfix_expr().c_str(), "0|sin");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_sin_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_sin_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_sin_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_sin_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_sin_test->parameters().size(), 0);
+
+ std::vector<double> params = {M_PI, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_sin_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _sin_test->set_parameters({}, true);
+ _sin_test->set_parameters(nullptr);
+ _sin_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_LT(std::abs(dfdp[0] - 1.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[1] - 1.0), 1e-10);
+
+ EXPECT_LT(std::abs(dfdp[2] + 1.0), 1e-10);
+ EXPECT_LT(std::abs(dfdp[3] - 1.0), 1e-10);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_six_pow_node.cc b/tests/googletest/feature_creation/feature_generation/test_six_pow_node.cc
index 3c5969682566a9352a5f4dbc9f18dfbc5b178430..f6a419502c4aae773bac230677c359e108c1cdf2 100644
--- a/tests/googletest/feature_creation/feature_generation/test_six_pow_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_six_pow_node.cc
@@ -30,7 +30,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {2.0};
@@ -207,5 +212,32 @@ namespace
EXPECT_STREQ(_six_pow_test->expr().c_str(), "(A^6)");
EXPECT_STREQ(_six_pow_test->postfix_expr().c_str(), "0|sp");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_six_pow_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_six_pow_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_six_pow_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_six_pow_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_six_pow_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_six_pow_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _six_pow_test->set_parameters({}, true);
+ _six_pow_test->set_parameters(nullptr);
+ _six_pow_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 6.0);
+ EXPECT_EQ(dfdp[1], 192.0);
+
+ EXPECT_EQ(dfdp[2], 1458.0);
+ EXPECT_EQ(dfdp[3], 6144.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_sq_node.cc b/tests/googletest/feature_creation/feature_generation/test_sq_node.cc
index 5bf192801366d35351de994f2b0b08b7ee9551c6..81ea2229b45cd40e6da9fe5952e8241dfdb774ee 100644
--- a/tests/googletest/feature_creation/feature_generation/test_sq_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_sq_node.cc
@@ -27,7 +27,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 8.0};
std::vector<double> test_value_1 = {2.0};
@@ -167,5 +172,32 @@ namespace
EXPECT_STREQ(_sq_test->expr().c_str(), "(A^2)");
EXPECT_STREQ(_sq_test->postfix_expr().c_str(), "0|sq");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_sq_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_sq_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_sq_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_sq_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_sq_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_sq_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _sq_test->set_parameters({}, true);
+ _sq_test->set_parameters(nullptr);
+ _sq_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 2.0);
+ EXPECT_EQ(dfdp[1], 4.0);
+
+ EXPECT_EQ(dfdp[2], 6.0);
+ EXPECT_EQ(dfdp[3], 16.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_sqrt_node.cc b/tests/googletest/feature_creation/feature_generation/test_sqrt_node.cc
index 0e47ee5555ff1d61457bd3f5d97da7ef4a8b7e63..9802acce87d03ab51a4bec0875348eb4b13de890 100644
--- a/tests/googletest/feature_creation/feature_generation/test_sqrt_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_sqrt_node.cc
@@ -30,7 +30,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {4.0};
@@ -218,5 +223,29 @@ namespace
EXPECT_STREQ(_sqrt_test->expr().c_str(), "sqrt(A)");
EXPECT_STREQ(_sqrt_test->postfix_expr().c_str(), "0|sqrt");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_sqrt_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_sqrt_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_sqrt_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_sqrt_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_sqrt_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(2, 0.0);
+ EXPECT_THROW(_sqrt_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _sqrt_test->set_parameters({}, true);
+ _sqrt_test->set_parameters(nullptr);
+ _sqrt_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], 0.5);
+ EXPECT_EQ(dfdp[3], 0.25);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_generation/test_sub_node.cc b/tests/googletest/feature_creation/feature_generation/test_sub_node.cc
index f4aad1568d82a9795835493d344c89dccbb0ec96..34f02096f0a5f4b7efda561d3d28035d55cfa813 100644
--- a/tests/googletest/feature_creation/feature_generation/test_sub_node.cc
+++ b/tests/googletest/feature_creation/feature_generation/test_sub_node.cc
@@ -24,7 +24,12 @@ namespace
protected:
void SetUp() override
{
+ #ifdef PARAMETERIZE
+ node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, true);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ #else
node_value_arrs::initialize_values_arr({4}, {1}, 4, 2, false);
+ #endif
std::vector<double> value_1 = {1.0, 2.0, 3.0, 4.0};
std::vector<double> test_value_1 = {5.0};
@@ -194,5 +199,32 @@ namespace
EXPECT_STREQ(_sub_test->expr().c_str(), "((A - B) - B)");
EXPECT_STREQ(_sub_test->postfix_expr().c_str(), "0|1|sub|1|sub");
+
+ #ifdef PARAMETERIZE
+ EXPECT_THROW(_sub_test->param_pointer(), std::logic_error);
+ EXPECT_EQ(_sub_test->n_params(), 0);
+
+ nlopt_wrapper::MAX_PARAM_DEPTH = 0;
+ EXPECT_EQ(_sub_test->n_params_possible(), 0);
+ nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ EXPECT_EQ(_sub_test->n_params_possible(), 2);
+
+ EXPECT_EQ(_sub_test->parameters().size(), 0);
+
+ std::vector<double> params = {1.0, 0.0};
+ std::vector<double> dfdp(4, 0.0);
+ std::vector<double> grad(4, 0.0);
+ EXPECT_THROW(_sub_test->gradient(grad.data(), dfdp.data()), std::logic_error);
+
+ _sub_test->set_parameters({}, true);
+ _sub_test->set_parameters(nullptr);
+ _sub_test->param_derivative(params.data(), dfdp.data());
+
+ EXPECT_EQ(dfdp[0], -1.0);
+ EXPECT_EQ(dfdp[1], -1.0);
+
+ EXPECT_EQ(dfdp[2], -1.0);
+ EXPECT_EQ(dfdp[3], -1.0);
+ #endif
}
}
diff --git a/tests/googletest/feature_creation/feature_space/test_feat_space.cc b/tests/googletest/feature_creation/feature_space/test_feat_space.cc
index 3d4bc533f772af9f9638ef746d47f54c05bf5eee..7fc4b67a91926f837789f5b63735cfadb2870114 100644
--- a/tests/googletest/feature_creation/feature_space/test_feat_space.cc
+++ b/tests/googletest/feature_creation/feature_space/test_feat_space.cc
@@ -31,7 +31,7 @@ namespace
std::vector<int> task_sizes = {5, 5};
int n_samp = std::accumulate(task_sizes.begin(), task_sizes.end(), 0);
- node_value_arrs::initialize_values_arr(task_sizes, {0, 0}, 3, 2, false);
+ node_value_arrs::initialize_values_arr(task_sizes, {0, 0}, 4, 2, false);
node_value_arrs::initialize_d_matrix_arr();
std::vector<double> value_1(n_samp, 0.0);
@@ -61,6 +61,7 @@ namespace
FeatureNode feat_1(0, "A", value_1, std::vector<double>(), Unit("m"));
FeatureNode feat_2(1, "B", value_2, std::vector<double>(), Unit("m"));
FeatureNode feat_3(2, "C", value_3, std::vector<double>(), Unit("s"));
+ FeatureNode feat_4(3, "D", std::vector<double>(10, 1.0), std::vector<double>(), Unit("s"));
std::vector<FeatureNode> phi_0 = {feat_1, feat_2, feat_3};
@@ -232,4 +233,15 @@ namespace
boost::filesystem::remove_all("feature_space/");
prop_sorted_d_mat::finalize_sorted_d_matrix_arr();
}
+
+ TEST_F(FeatSpaceTest, CheckFailedSISTest)
+ {
+ _inputs.set_calc_type("regression");
+ _inputs.set_prop_train(_prop);
+ _inputs.set_n_sis_select(1000000);
+
+ FeatureSpace feat_space(_inputs);
+ EXPECT_THROW(feat_space.sis(_prop), std::logic_error);
+ }
+
}
diff --git a/tests/googletest/feature_creation/parameterization/test_abs_diff_node.cc b/tests/googletest/feature_creation/parameterization/test_abs_diff_node.cc
index 79cd04b804cb3b42928e946f3fdda5d279c6cd8a..f125b860a110037d6674c0b8cc8790ee2b6e8588 100644
--- a/tests/googletest/feature_creation/parameterization/test_abs_diff_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_abs_diff_node.cc
@@ -13,7 +13,7 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/abs_diff/parameterized_absolute_difference.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,14 +26,13 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
- _task_sizes_train = {90};
- _task_sizes_test = {10};
+ _task_sizes_train = {900};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
-
std::vector<double> value_1(_task_sizes_train[0], 0.0);
std::vector<double> value_2(_task_sizes_train[0], 0.0);
@@ -59,17 +58,19 @@ namespace
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
_feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
- _phi = {_feat_1, _feat_2};
+ node_ptr feat_3 = std::make_shared<ExpNode>(_feat_1, 2);
+
+ _phi = {_feat_1, _feat_2, feat_3};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::abs_diff(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[1]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::abs_diff(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -99,15 +100,15 @@ namespace
unsigned long int feat_ind = _phi.size();
generateAbsDiffParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (AbsDiffParamNode created with an absolute value above the upper bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (AbsDiffParamNode created with an absolute value above the upper bound)";
generateAbsDiffParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (AbsDiffParamNode created with an absolute value below the lower bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (AbsDiffParamNode created with an absolute value below the lower bound)";
- generateAbsDiffParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
+ generateAbsDiffParamNode(_phi, _phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 4) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(AbsDiffParamNodeTest, ConstructorTest)
@@ -132,8 +133,8 @@ namespace
try
{
- _abs_diff_test = std::make_shared<AbsDiffParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _abs_diff_test->value_ptr(), 90), 1e-4);
+ _abs_diff_test = std::make_shared<AbsDiffParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _abs_diff_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -144,60 +145,67 @@ namespace
TEST_F(AbsDiffParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _abs_diff_test = std::make_shared<AbsDiffParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _abs_diff_test = std::make_shared<AbsDiffParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _abs_diff_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::abs_diff(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::abs_diff(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::abs_diff(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::abs_diff(_task_sizes_test[0], _phi[1]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|abd: " << std::setprecision(13) << std::scientific << copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "m");
+ postfix << "1|0|exp|abd: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "s");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
double v1 = copy_test->feat(0)->value_ptr()[0];
- double v2 = copy_test->feat(1)->value_ptr()[0];
+ double v2 = copy_test->feat(1)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = -1.0 * util_funcs::sign(v1 - (alpha * v2 + a));
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(AbsDiffParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _abs_diff_test = std::make_shared<AbsDiffParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _abs_diff_test = std::make_shared<AbsDiffParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_abs_diff_test->rung(), 1);
+ _abs_diff_test->set_value();
+ _abs_diff_test->set_test_value();
- std::vector<double> expected_val(90, 0.0);
+ EXPECT_EQ(_abs_diff_test->rung(), 2);
- allowed_op_funcs::abs_diff(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), _abs_diff_test->parameters()[0], _abs_diff_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_abs_diff_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_abs_diff_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _abs_diff_test->parameters();
- allowed_op_funcs::abs_diff(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), _abs_diff_test->parameters()[0], _abs_diff_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_abs_diff_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_abs_diff_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::abs_diff(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_abs_diff_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_abs_diff_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::abs_diff(_task_sizes_test[0], _phi[1]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_abs_diff_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_abs_diff_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|abd: " << std::setprecision(13) << std::scientific << _abs_diff_test->parameters()[0] << ',' << _abs_diff_test->parameters()[1];
- EXPECT_STREQ(_abs_diff_test->unit().toString().c_str(), "m");
+ postfix << "1|0|exp|abd: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_abs_diff_test->unit().toString().c_str(), "s");
EXPECT_STREQ(_abs_diff_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_abs_diff_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_abs_node.cc b/tests/googletest/feature_creation/parameterization/test_abs_node.cc
index 61f9c13196d0b62a11e4be45bc942e4779a4bdbe..f8d0bd487e2e2bf1607a7cfb13a8d10ee778469d 100644
--- a/tests/googletest/feature_creation/parameterization/test_abs_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_abs_node.cc
@@ -13,7 +13,7 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/abs/parameterized_absolute_value.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,10 +26,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 1, 2, true);
@@ -37,28 +37,33 @@ namespace
std::vector<double> test_value_1(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(-50.0, 50.0);
+ std::uniform_real_distribution<double> distribution_feats(-7.5, 7.5);
std::uniform_real_distribution<double> distribution_params(-2.50, 2.50);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
- value_1[ii] = distribution_feats(generator);
+ {
+ value_1[ii] = std::exp(distribution_feats(generator));
+ }
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
- test_value_1[ii] = distribution_feats(generator);
+ {
+ test_value_1[ii] = std::exp(distribution_feats(generator));
+ }
- _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _phi = {_feat_1};
+ _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m^3"));
+ node_ptr feat_2 = std::make_shared<LogNode>(_feat_1, 1);
+ _phi = {_feat_1, feat_2};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::abs(_task_sizes_train[0], _phi[0]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::abs(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -85,15 +90,15 @@ namespace
{
unsigned long int feat_ind = _phi.size();
- generateAbsParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 1) << " (AbsParamNode created with an absolute value above the upper bound)";
+ generateAbsParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
+ EXPECT_EQ(_phi.size(), 2) << " (AbsParamNode created with an absolute value above the upper bound)";
- generateAbsParamNode(_phi, _phi[0], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 1) << " (AbsParamNode created with an absolute value below the lower bound)";
+ generateAbsParamNode(_phi, _phi[1], feat_ind, 1e49, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 2) << " (AbsParamNode created with an absolute value below the lower bound)";
- generateAbsParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ generateAbsParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(AbsParamNodeTest, ConstructorTest)
@@ -102,7 +107,7 @@ namespace
try
{
- _abs_test = std::make_shared<AbsParamNode>(_phi[0], feat_ind, 1e-50, 1e-40, _optimizer);
+ _abs_test = std::make_shared<AbsParamNode>(_phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
EXPECT_TRUE(false) << " (AbsParamNode created with an absolute value above the upper bound)";
}
catch(const InvalidFeatureException& e)
@@ -110,7 +115,7 @@ namespace
try
{
- _abs_test = std::make_shared<AbsParamNode>(_phi[0], feat_ind, 1e40, 1e50, _optimizer);
+ _abs_test = std::make_shared<AbsParamNode>(_phi[1], feat_ind, 1e40, 1e50, _optimizer);
EXPECT_TRUE(false) << " (AbsParamNode created with an absolute value below the lower bound)";
}
catch(const InvalidFeatureException& e)
@@ -118,8 +123,8 @@ namespace
try
{
- _abs_test = std::make_shared<AbsParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _abs_test->value_ptr(), 900), 1e-4);
+ _abs_test = std::make_shared<AbsParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _abs_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -130,25 +135,25 @@ namespace
TEST_F(AbsParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _abs_test = std::make_shared<AbsParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _abs_test = std::make_shared<AbsParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _abs_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
- allowed_op_funcs::abs(900, _phi[0]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::abs(_task_sizes_train[0], _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::abs(10, _phi[0]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::abs(_task_sizes_test[0], _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|abs: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "m");
+ postfix << "0|log|abs: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1] << ',' << std::scientific <<_abs_test->parameters()[2] << ',' << _abs_test->parameters()[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
@@ -158,31 +163,36 @@ namespace
double a = copy_test->parameters()[1];
double df_dp = util_funcs::sign(alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(AbsParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _abs_test = std::make_shared<AbsParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _abs_test = std::make_shared<AbsParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_abs_test->rung(), 1);
+ _abs_test->set_value();
+ _abs_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_abs_test->rung(), 2);
- allowed_op_funcs::abs(900, _phi[0]->value_ptr(), _abs_test->parameters()[0], _abs_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_abs_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_abs_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
- allowed_op_funcs::abs(10, _phi[0]->test_value_ptr(), _abs_test->parameters()[0], _abs_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_abs_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_abs_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::abs(_task_sizes_train[0], _phi[1]->value_ptr(), _abs_test->parameters()[0], _abs_test->parameters()[1], expected_val.data());
+ EXPECT_LT(std::abs(_abs_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_abs_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::abs(_task_sizes_test[0], _phi[1]->test_value_ptr(), _abs_test->parameters()[0], _abs_test->parameters()[1], expected_val.data());
+ EXPECT_LT(std::abs(_abs_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_abs_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|abs: " << std::setprecision(13) << std::scientific <<_abs_test->parameters()[0] << ',' << _abs_test->parameters()[1];
- EXPECT_STREQ(_abs_test->unit().toString().c_str(), "m");
+ postfix << "0|log|abs: " << std::setprecision(13) << std::scientific <<_abs_test->parameters()[0] << ',' << _abs_test->parameters()[1] << ',' << std::scientific <<_abs_test->parameters()[2] << ',' << _abs_test->parameters()[3];
+ EXPECT_STREQ(_abs_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_abs_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_abs_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_add_node.cc b/tests/googletest/feature_creation/parameterization/test_add_node.cc
index 6043a497cf4df9260a9bc18e817153ceff09bcec..5510c9a9c0b6fb7c7777528367e8c2fb0fa6f98d 100644
--- a/tests/googletest/feature_creation/parameterization/test_add_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_add_node.cc
@@ -14,7 +14,6 @@
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/add/parameterized_add.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -27,10 +26,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
- _task_sizes_train = {90};
- _task_sizes_test = {10};
+ _task_sizes_train = {900};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
@@ -57,19 +56,21 @@ namespace
}
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("m"));
- _phi = {_feat_1, _feat_2};
+ node_ptr feat_3 = std::make_shared<AddNode>(_feat_1, _feat_2, 2);
+
+ _phi = {_feat_1, _feat_2, feat_3};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::add(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[1]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::add(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -98,14 +99,14 @@ namespace
unsigned long int feat_ind = _phi.size();
generateAddParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (AddParamNode created with an absolute value above the upper bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (AddParamNode created with an absolute value above the upper bound)";
generateAddParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (AddParamNode created with an absolute value below the lower bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (AddParamNode created with an absolute value below the lower bound)";
- generateAddParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-10);
+ generateAddParamNode(_phi, _phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 4) << " (Failure to create a valid feature)";
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-5);
}
TEST_F(AddParamNodeTest, ConstructorTest)
@@ -130,8 +131,8 @@ namespace
try
{
- _add_test = std::make_shared<AddParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _add_test->value_ptr(), 90), 1e-10);
+ _add_test = std::make_shared<AddParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _add_test->value_ptr(), _task_sizes_train[0]), 1e-5);
}
catch(const InvalidFeatureException& e)
{
@@ -142,60 +143,67 @@ namespace
TEST_F(AddParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _add_test = std::make_shared<AddParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _add_test = std::make_shared<AddParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+
+ _add_test->set_value();
+ _add_test->set_test_value();
node_ptr copy_test = _add_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::add(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::add(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::add(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::add(_task_sizes_test[0], _phi[1]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|add: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "1|0|1|add|add: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "m");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
double v1 = copy_test->feat(0)->value_ptr()[0];
- double v2 = copy_test->feat(1)->value_ptr()[0];
+ double v2 = copy_test->feat(1)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 1.0;
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(AddParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _add_test = std::make_shared<AddParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _add_test = std::make_shared<AddParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_add_test->rung(), 1);
+ EXPECT_EQ(_add_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _add_test->parameters();
- allowed_op_funcs::add(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), _add_test->parameters()[0], _add_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_add_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_add_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::add(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_add_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_add_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::add(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), _add_test->parameters()[0], _add_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_add_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_add_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::add(_task_sizes_test[0], _phi[1]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_add_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_add_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|add: " << std::setprecision(13) << std::scientific <<_add_test->parameters()[0] << ',' << _add_test->parameters()[1];
+ postfix << "1|0|1|add|add: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_add_test->unit().toString().c_str(), "m");
EXPECT_STREQ(_add_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_add_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_cb_node.cc b/tests/googletest/feature_creation/parameterization/test_cb_node.cc
index 634bd30bfc11de9c7c4279aa090f24dfe46a18c1..c89ecb148aca44babaa6f6f70d60e38e88b99967 100644
--- a/tests/googletest/feature_creation/parameterization/test_cb_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_cb_node.cc
@@ -14,7 +14,7 @@
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cb/parameterized_cube.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -27,52 +27,48 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
- node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
+ node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 1, 2, true);
std::vector<double> value_1(_task_sizes_train[0], 0.0);
- std::vector<double> value_2(_task_sizes_train[0], 0.0);
std::vector<double> test_value_1(_task_sizes_test[0], 0.0);
- std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
std::uniform_real_distribution<double> distribution_feats(-500.0, 500.0);
- std::uniform_real_distribution<double> distribution_params(1e-10, 1.50);
+ std::uniform_real_distribution<double> distribution_params(-1.25, 1.25);
std::normal_distribution<double> distribution_err(0.0, 0.01);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
- _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit(""));
+ node_ptr feat_2 = std::make_shared<CosNode>(_feat_1, 1);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, feat_2};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
allowed_op_funcs::cb(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
std::transform(_prop.begin(), _prop.end(), _prop.begin(), [&](double p){return p + distribution_err(generator);});
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -81,8 +77,7 @@ namespace
}
node_ptr _feat_1;
- node_ptr _feat_2;
- node_ptr _exp_test;
+ node_ptr _cb_test;
std::vector<node_ptr> _phi;
std::vector<double> _prop;
@@ -108,7 +103,7 @@ namespace
generateCbParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(CbParamNodeTest, ConstructorTest)
@@ -117,7 +112,7 @@ namespace
try
{
- _exp_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
+ _cb_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
EXPECT_TRUE(false) << " (CbParamNode created with an absolute value above the upper bound)";
}
catch(const InvalidFeatureException& e)
@@ -125,7 +120,7 @@ namespace
try
{
- _exp_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e49, 1e50, _optimizer);
+ _cb_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e49, 1e50, _optimizer);
EXPECT_TRUE(false) << " (CbParamNode created with an absolute value below the lower bound)";
}
catch(const InvalidFeatureException& e)
@@ -133,8 +128,8 @@ namespace
try
{
- _exp_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _exp_test->value_ptr(), 900), 1e-4);
+ _cb_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _cb_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -145,59 +140,65 @@ namespace
TEST_F(CbParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _exp_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _cb_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- node_ptr copy_test = _exp_test->hard_copy();
+ node_ptr copy_test = _cb_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::cb(900, _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cb(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::cb(10, _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cb(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|cb: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "s^3");
+ postfix << "0|cos|cb: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
-
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 3.0 * std::pow(alpha * v1 + a, 2.0);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(CbParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _exp_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _cb_test = std::make_shared<CbParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+
+ _cb_test->set_value();
+ _cb_test->set_test_value();
- EXPECT_EQ(_exp_test->rung(), 1);
+ EXPECT_EQ(_cb_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _cb_test->parameters();
- allowed_op_funcs::cb(900, _phi[1]->value_ptr(), _exp_test->parameters()[0], _exp_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_exp_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_exp_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cb(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_cb_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_cb_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::cb(10, _phi[1]->test_value_ptr(), _exp_test->parameters()[0], _exp_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_exp_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_exp_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cb(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_cb_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_cb_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|cb: " << std::setprecision(13) << std::scientific <<_exp_test->parameters()[0] << ',' << _exp_test->parameters()[1];
- EXPECT_STREQ(_exp_test->unit().toString().c_str(), "s^3");
- EXPECT_STREQ(_exp_test->postfix_expr().c_str(), postfix.str().c_str());
+ postfix << "0|cos|cb: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_cb_test->unit().toString().c_str(), "Unitless");
+ EXPECT_STREQ(_cb_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_cb_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_cbrt_node.cc b/tests/googletest/feature_creation/parameterization/test_cbrt_node.cc
index 55bc4601bd77c4dfe072aa8d96282db46ee45fea..412d7c855a49796505543de763a46fc5f6bc78bd 100644
--- a/tests/googletest/feature_creation/parameterization/test_cbrt_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_cbrt_node.cc
@@ -13,8 +13,8 @@
// limitations under the License.
#ifdef PARAMETERIZE
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cbrt/parameterized_cube_root.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -27,49 +27,45 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
- node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
+ _task_sizes_test = {100};
+ node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 1, 2, true);
std::vector<double> value_1(_task_sizes_train[0], 0.0);
- std::vector<double> value_2(_task_sizes_train[0], 0.0);
std::vector<double> test_value_1(_task_sizes_test[0], 0.0);
- std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(10.0, 5000.0);
+ std::uniform_real_distribution<double> distribution_feats(1e-10, 5000.0);
std::uniform_real_distribution<double> distribution_params(0.5, 1.50);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
- _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m^9"));
+ node_ptr feat_2 = std::make_shared<CbrtNode>(_feat_1, 1);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, feat_2};
_a = distribution_params(generator);
- _alpha = std::pow(distribution_params(generator), 3.0);
+ _alpha = 1.0;
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
allowed_op_funcs::cbrt(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -78,7 +74,6 @@ namespace
}
node_ptr _feat_1;
- node_ptr _feat_2;
node_ptr _cbrt_test;
std::vector<node_ptr> _phi;
@@ -105,7 +100,7 @@ namespace
generateCbrtParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(CbrtParamNodeTest, ConstructorTest)
@@ -131,7 +126,7 @@ namespace
try
{
_cbrt_test = std::make_shared<CbrtParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _cbrt_test->value_ptr(), 900), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _cbrt_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -146,32 +141,33 @@ namespace
node_ptr copy_test = _cbrt_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::cbrt(900, _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cbrt(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::cbrt(10, _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cbrt(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|cbrt: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "s^0.333333");
+ postfix << "0|cbrt|cbrt: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "m");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 1.0 / 3.0 * std::pow(alpha * v1 + a, -2.0 / 3.0);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(CbrtParamNodeTest, AttributesTest)
@@ -179,22 +175,28 @@ namespace
unsigned long int feat_ind = _phi.size();
_cbrt_test = std::make_shared<CbrtParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_cbrt_test->rung(), 1);
+ _cbrt_test->set_value();
+ _cbrt_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_cbrt_test->rung(), 2);
- allowed_op_funcs::cbrt(900, _phi[1]->value_ptr(), _cbrt_test->parameters()[0], _cbrt_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_cbrt_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_cbrt_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _cbrt_test->parameters();
- allowed_op_funcs::cbrt(10, _phi[1]->test_value_ptr(), _cbrt_test->parameters()[0], _cbrt_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_cbrt_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_cbrt_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cbrt(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_cbrt_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_cbrt_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::cbrt(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_cbrt_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_cbrt_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|cbrt: " << std::setprecision(13) << std::scientific <<_cbrt_test->parameters()[0] << ',' << _cbrt_test->parameters()[1];
- EXPECT_STREQ(_cbrt_test->unit().toString().c_str(), "s^0.333333");
+ postfix << "0|cbrt|cbrt: " << std::setprecision(13) << std::scientific <<params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_cbrt_test->unit().toString().c_str(), "m");
EXPECT_STREQ(_cbrt_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_cbrt_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_cos_node.cc b/tests/googletest/feature_creation/parameterization/test_cos_node.cc
index 8168f9906a2af9c2a6ae4a5d454c80596814855d..8d2e2b7e3aa94448fc62c8fd6927784b0261e641 100644
--- a/tests/googletest/feature_creation/parameterization/test_cos_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_cos_node.cc
@@ -12,10 +12,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PARAMETERIZE
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cb/cube.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cos/parameterized_cos.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -28,10 +28,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 3, 2, true);
@@ -56,24 +56,25 @@ namespace
test_value_2[ii] = distribution_feats(generator);
}
- _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
+ _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("s"));
_feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
_feat_3 = std::make_shared<FeatureNode>(2, "B", value_2, test_value_2, Unit(""));
_phi = {_feat_1, _feat_2, _feat_3};
_phi.push_back(std::make_shared<CosNode>(_feat_3, 3, 1e-50, 1e50));
_phi.push_back(std::make_shared<SinNode>(_feat_3, 4, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<CbNode>(_feat_1, 5, 1e-50, 1e50));
_a = 0.143;
_alpha = 1.05;
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::cos(_task_sizes_train[0], _phi[0]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::cos(_task_sizes_train[0], _phi[5]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -115,7 +116,7 @@ namespace
generateCosParamNode(_phi, _phi[4], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz) << " (CosParamNode created from SinNode)";
- generateCosParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ generateCosParamNode(_phi, _phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz + 1) << " (Failure to create a valid feature)";
EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-5);
}
@@ -158,7 +159,7 @@ namespace
try
{
- _cos_test = std::make_shared<CosParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _cos_test = std::make_shared<CosParamNode>(_phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _cos_test->value_ptr(), 90), 1e-5);
}
catch(const InvalidFeatureException& e)
@@ -170,59 +171,66 @@ namespace
TEST_F(CosParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _cos_test = std::make_shared<CosParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _cos_test = std::make_shared<CosParamNode>(_phi[5], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _cos_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::cos(900, _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cos(_task_sizes_train[0], _phi[5]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::cos(10, _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cos(_task_sizes_test[0], _phi[5]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|cos: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "0|cb|cos: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = -1.0 * std::sin(alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(CosParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _cos_test = std::make_shared<CosParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _cos_test = std::make_shared<CosParamNode>(_phi[5], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_cos_test->rung(), 1);
+ _cos_test->set_value();
+ _cos_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_cos_test->rung(), 2);
- allowed_op_funcs::cos(900, _phi[1]->value_ptr(), _cos_test->parameters()[0], _cos_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_cos_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_cos_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _cos_test->parameters();
- allowed_op_funcs::cos(10, _phi[1]->test_value_ptr(), _cos_test->parameters()[0], _cos_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_cos_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_cos_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::cos(_task_sizes_train[0], _phi[5]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_cos_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_cos_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::cos(_task_sizes_test[0], _phi[5]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_cos_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_cos_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|cos: " << std::setprecision(13) << std::scientific <<_cos_test->parameters()[0] << ',' << _cos_test->parameters()[1];
+ postfix << "0|cb|cos: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_cos_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_cos_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_cos_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_div_node.cc b/tests/googletest/feature_creation/parameterization/test_div_node.cc
index cf268ecad5a9c300f2285a76b5e8d101d0087805..66ff13fcbd5d50420f47459a85a1350c6044f7a5 100644
--- a/tests/googletest/feature_creation/parameterization/test_div_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_div_node.cc
@@ -13,7 +13,6 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/div/parameterized_divide.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,10 +25,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
- _task_sizes_train = {90};
- _task_sizes_test = {10};
+ _task_sizes_train = {900};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
@@ -58,18 +57,19 @@ namespace
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
_feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ node_ptr feat_3 = std::make_shared<DivNode>(_feat_1, _feat_2, 2);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, _feat_2, feat_3};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::div(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[1]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::div(_task_sizes_train[0], _phi[2]->value_ptr(), _phi[1]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -98,14 +98,14 @@ namespace
unsigned long int feat_ind = _phi.size();
generateDivParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (DivParamNode created with an absolute value above the upper bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (DivParamNode created with an absolute value above the upper bound)";
generateDivParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (DivParamNode created with an absolute value below the lower bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (DivParamNode created with an absolute value below the lower bound)";
- generateDivParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-10);
+ generateDivParamNode(_phi, _phi[2], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 4) << " (Failure to create a valid feature)";
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-5);
}
TEST_F(DivParamNodeTest, ConstructorTest)
@@ -130,8 +130,8 @@ namespace
try
{
- _div_test = std::make_shared<DivParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _div_test->value_ptr(), 90), 1e-10);
+ _div_test = std::make_shared<DivParamNode>(_phi[2], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _div_test->value_ptr(), _task_sizes_train[0]), 1e-5);
}
catch(const InvalidFeatureException& e)
{
@@ -142,60 +142,67 @@ namespace
TEST_F(DivParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _div_test = std::make_shared<DivParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _div_test = std::make_shared<DivParamNode>(_phi[2], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _div_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::div(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::div(_task_sizes_train[0], _phi[2]->value_ptr(¶ms[2]), _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::div(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::div(_task_sizes_test[0], _phi[2]->test_value_ptr(¶ms[2]), _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|div: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "m * s^-1");
+ postfix << "0|1|div|1|div: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "m * s^-2");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
- double v2 = copy_test->feat(1)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
+ double v2 = copy_test->feat(1)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = -1.0 * v1 / std::pow(alpha * v2 + a, 2.0);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(DivParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _div_test = std::make_shared<DivParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _div_test = std::make_shared<DivParamNode>(_phi[2], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_div_test->rung(), 1);
+ _div_test->set_value();
+ _div_test->set_test_value();
- std::vector<double> expected_val(90, 0.0);
+ EXPECT_EQ(_div_test->rung(), 2);
- allowed_op_funcs::div(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), _div_test->parameters()[0], _div_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_div_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_div_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _div_test->parameters();
- allowed_op_funcs::div(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), _div_test->parameters()[0], _div_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_div_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_div_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::div(_task_sizes_train[0], _phi[2]->value_ptr(¶ms[2]), _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_div_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_div_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::div(_task_sizes_test[0], _phi[2]->test_value_ptr(¶ms[2]), _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_div_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_div_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|div: " << std::setprecision(13) << std::scientific <<_div_test->parameters()[0] << ',' << _div_test->parameters()[1];
- EXPECT_STREQ(_div_test->unit().toString().c_str(), "m * s^-1");
+ postfix << "0|1|div|1|div: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_div_test->unit().toString().c_str(), "m * s^-2");
EXPECT_STREQ(_div_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_div_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_exp_node.cc b/tests/googletest/feature_creation/parameterization/test_exp_node.cc
index ad14078ba4fd7a6bfd7f88aa87daceee60841d4d..03133b14702be8cfa1a1b3c2d5ab3afb5435ce0c 100644
--- a/tests/googletest/feature_creation/parameterization/test_exp_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_exp_node.cc
@@ -12,11 +12,11 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PARAMETERIZE
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/exp/parameterized_exponential.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -29,10 +29,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 3, 2, true);
@@ -44,7 +44,7 @@ namespace
std::default_random_engine generator;
std::uniform_real_distribution<double> distribution_feats(-2.0, 2.0);
- std::uniform_real_distribution<double> distribution_params(0.75, 1.25);
+ std::uniform_real_distribution<double> distribution_params(0.5, 1.20);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
@@ -66,17 +66,18 @@ namespace
_phi.push_back(std::make_shared<ExpNode>(_feat_3, 3, 1e-50, 1e50));
_phi.push_back(std::make_shared<LogNode>(_feat_3, 4, 1e-50, 1e50));
_phi.push_back(std::make_shared<NegExpNode>(_feat_3, 5, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<AbsNode>(_feat_1, 6, 1e-50, 1e50));
_a = std::log(distribution_params(generator));
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::exp(_task_sizes_train[0], _phi[0]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::exp(_task_sizes_train[0], _phi[6]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -121,9 +122,9 @@ namespace
generateExpParamNode(_phi, _phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz) << " (ExpParamNode created from NegExpNode)";
- generateExpParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ generateExpParamNode(_phi, _phi[6], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz + 1) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(ExpParamNodeTest, ConstructorTest)
@@ -171,8 +172,8 @@ namespace
try
{
- _exp_test = std::make_shared<ExpParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _exp_test->value_ptr(), 900), 1e-4);
+ _exp_test = std::make_shared<ExpParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _exp_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -183,59 +184,66 @@ namespace
TEST_F(ExpParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _exp_test = std::make_shared<ExpParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _exp_test = std::make_shared<ExpParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _exp_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::exp(900, _phi[0]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::exp(_task_sizes_train[0], _phi[6]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::exp(10, _phi[0]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::exp(_task_sizes_test[0], _phi[6]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|exp: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "0|abs|exp: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = std::exp(alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(ExpParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _exp_test = std::make_shared<ExpParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _exp_test = std::make_shared<ExpParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_exp_test->rung(), 1);
+ _exp_test->set_value();
+ _exp_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_exp_test->rung(), 2);
- allowed_op_funcs::exp(900, _phi[0]->value_ptr(), _exp_test->parameters()[0], _exp_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_exp_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_exp_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _exp_test->parameters();
- allowed_op_funcs::exp(10, _phi[0]->test_value_ptr(), _exp_test->parameters()[0], _exp_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_exp_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_exp_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::exp(_task_sizes_train[0], _phi[6]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_exp_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_exp_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::exp(_task_sizes_test[0], _phi[6]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_exp_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_exp_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|exp: " << std::setprecision(13) << std::scientific <<_exp_test->parameters()[0] << ',' << _exp_test->parameters()[1];
+ postfix << "0|abs|exp: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_exp_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_exp_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_exp_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_inv_node.cc b/tests/googletest/feature_creation/parameterization/test_inv_node.cc
index d460d0e0eed18a4cbb1441b864b74bff0ffa23ed..5b1b3416161aa0dc198d9879bc05daf83144f9cd 100644
--- a/tests/googletest/feature_creation/parameterization/test_inv_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_inv_node.cc
@@ -13,7 +13,7 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/inv/parameterized_inverse.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/sixth_power.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,50 +26,46 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
- _task_sizes_train = {90};
- _task_sizes_test = {10};
+ _task_sizes_train = {900};
+ _task_sizes_test = {100};
- node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
+ node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 1, 2, true);
std::vector<double> value_1(_task_sizes_train[0], 0.0);
- std::vector<double> value_2(_task_sizes_train[0], 0.0);
std::vector<double> test_value_1(_task_sizes_test[0], 0.0);
- std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
std::uniform_real_distribution<double> distribution_feats(-50.0, 50.0);
- std::uniform_real_distribution<double> distribution_params(1e-10, 2.50);
+ std::uniform_real_distribution<double> distribution_params(0.001, 10.0);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
- _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit(""));
+ node_ptr feat_2 = std::make_shared<SixPowNode>(_feat_1, 1);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, feat_2};
_a = distribution_params(generator);
- _alpha = distribution_params(generator);
+ _alpha = 1.0;
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
allowed_op_funcs::inv(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -78,7 +74,6 @@ namespace
}
node_ptr _feat_1;
- node_ptr _feat_2;
node_ptr _inv_test;
std::vector<node_ptr> _phi;
@@ -105,7 +100,7 @@ namespace
generateInvParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-10);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-5);
}
TEST_F(InvParamNodeTest, ConstructorTest)
@@ -131,7 +126,7 @@ namespace
try
{
_inv_test = std::make_shared<InvParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _inv_test->value_ptr(), 90), 1e-10);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _inv_test->value_ptr(), _task_sizes_train[0]), 1e-5);
}
catch(const InvalidFeatureException& e)
{
@@ -146,32 +141,33 @@ namespace
node_ptr copy_test = _inv_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::inv(90, _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::inv(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::inv(10, _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::inv(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|inv: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "s^-1");
+ postfix << "0|sp|inv: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = -1.0 / std::pow(alpha * v1 + a, 2.0);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(InvParamNodeTest, AttributesTest)
@@ -179,22 +175,28 @@ namespace
unsigned long int feat_ind = _phi.size();
_inv_test = std::make_shared<InvParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_inv_test->rung(), 1);
+ _inv_test->set_value();
+ _inv_test->set_test_value();
- std::vector<double> expected_val(90, 0.0);
+ EXPECT_EQ(_inv_test->rung(), 2);
- allowed_op_funcs::inv(90, _phi[1]->value_ptr(), _inv_test->parameters()[0], _inv_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_inv_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_inv_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _inv_test->parameters();
- allowed_op_funcs::inv(10, _phi[1]->test_value_ptr(), _inv_test->parameters()[0], _inv_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_inv_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_inv_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::inv(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_inv_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_inv_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::inv(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_inv_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_inv_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|inv: " << std::setprecision(13) << std::scientific <<_inv_test->parameters()[0] << ',' << _inv_test->parameters()[1];
- EXPECT_STREQ(_inv_test->unit().toString().c_str(), "s^-1");
+ postfix << "0|sp|inv: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_inv_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_inv_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_inv_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_log_node.cc b/tests/googletest/feature_creation/parameterization/test_log_node.cc
index d46051ad326cfdbf99d5208e35c74db0e50585f9..dd52aafac3379aade4364e5e0c7c40ec229188db 100644
--- a/tests/googletest/feature_creation/parameterization/test_log_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_log_node.cc
@@ -12,11 +12,11 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PARAMETERIZE
-#include <feature_creation/node/operator_nodes/allowed_operator_nodes/log/parameterized_log.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/abs/absolute_value.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/log/parameterized_log.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -30,7 +30,7 @@ namespace
void SetUp() override
{
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 3, 2, true);
@@ -43,21 +43,21 @@ namespace
std::vector<double> test_value_3(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(-10.0, 10.0);
+ std::uniform_real_distribution<double> distribution_feats(-100.0, 100.0);
std::uniform_real_distribution<double> distribution_params(0.1, 1.50);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
value_2[ii] = distribution_feats(generator);
- value_3[ii] = std::exp(distribution_feats(generator));
+ value_3[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
test_value_2[ii] = distribution_feats(generator);
- test_value_3[ii] = std::exp(distribution_feats(generator));
+ test_value_3[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
@@ -68,17 +68,18 @@ namespace
_phi.push_back(std::make_shared<ExpNode>(_feat_2, 3, 1e-50, 1e50));
_phi.push_back(std::make_shared<LogNode>(_feat_3, 4, 1e-50, 1e50));
_phi.push_back(std::make_shared<NegExpNode>(_feat_2, 5, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<AbsNode>(_feat_1, 6, 1e-50, 1e50));
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::log(_task_sizes_train[0], _phi[2]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::log(_task_sizes_train[0], _phi[6]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -123,9 +124,9 @@ namespace
generateLogParamNode(_phi, _phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz) << " (LogParamNode created from NegExpNode)";
- generateLogParamNode(_phi, _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ generateLogParamNode(_phi, _phi[6], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz + 1) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(LogParamNodeTest, ConstructorTest)
@@ -174,8 +175,8 @@ namespace
try
{
- _log_test = std::make_shared<LogParamNode>(_phi[2], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _log_test->value_ptr(), 900), 1e-4);
+ _log_test = std::make_shared<LogParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _log_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -186,59 +187,66 @@ namespace
TEST_F(LogParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _log_test = std::make_shared<LogParamNode>(_phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ _log_test = std::make_shared<LogParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _log_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::log(900, _phi[2]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::log(_task_sizes_train[0], _phi[6]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::log(10, _phi[2]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::log(_task_sizes_test[0], _phi[6]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "2|log: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "0|abs|log: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 1.0 / (alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(LogParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _log_test = std::make_shared<LogParamNode>(_phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ _log_test = std::make_shared<LogParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_log_test->rung(), 1);
+ _log_test->set_value();
+ _log_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_log_test->rung(), 2);
- allowed_op_funcs::log(900, _phi[2]->value_ptr(), _log_test->parameters()[0], _log_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_log_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_log_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _log_test->parameters();
- allowed_op_funcs::log(10, _phi[2]->test_value_ptr(), _log_test->parameters()[0], _log_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_log_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_log_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::log(_task_sizes_train[0], _phi[6]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_log_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_log_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::log(_task_sizes_test[0], _phi[6]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_log_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_log_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "2|log: " << std::setprecision(13) << std::scientific <<_log_test->parameters()[0] << ',' << _log_test->parameters()[1];
+ postfix << "0|abs|log: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_log_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_log_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_log_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_mult_node.cc b/tests/googletest/feature_creation/parameterization/test_mult_node.cc
index 31642f71fadc803c48601756880064d20dd241d3..85d5ad7db48c5bbbe23b83254407c0551edb0222 100644
--- a/tests/googletest/feature_creation/parameterization/test_mult_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_mult_node.cc
@@ -13,7 +13,6 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/mult/parameterized_multiply.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,10 +25,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
@@ -57,19 +56,20 @@ namespace
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
_feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ node_ptr feat_3 = std::make_shared<MultNode>(_feat_1, _feat_2, 2);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, _feat_2, feat_3};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::mult(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[1]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::mult(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -98,14 +98,14 @@ namespace
unsigned long int feat_ind = _phi.size();
generateMultParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (MultParamNode created with an absolute value above the upper bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (MultParamNode created with an absolute value above the upper bound)";
generateMultParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (MultParamNode created with an absolute value below the lower bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (MultParamNode created with an absolute value below the lower bound)";
- generateMultParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ generateMultParamNode(_phi, _phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 4) << " (Failure to create a valid feature)";
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(MultParamNodeTest, ConstructorTest)
@@ -130,8 +130,8 @@ namespace
try
{
- _mult_test = std::make_shared<MultParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _mult_test->value_ptr(), 900), 1e-4);
+ _mult_test = std::make_shared<MultParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _mult_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -142,60 +142,67 @@ namespace
TEST_F(MultParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _mult_test = std::make_shared<MultParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _mult_test = std::make_shared<MultParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _mult_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::mult(900, _phi[0]->value_ptr(), _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::mult(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::mult(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::mult(_task_sizes_test[0], _phi[1]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|mult: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "m * s");
+ postfix << "1|0|1|mult|mult: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "m * s^2");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
double v1 = copy_test->feat(0)->value_ptr()[0];
- double v2 = copy_test->feat(1)->value_ptr()[0];
+ double v2 = copy_test->feat(1)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = v1;
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(MultParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _mult_test = std::make_shared<MultParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _mult_test = std::make_shared<MultParamNode>(_phi[1], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_mult_test->rung(), 1);
+ _mult_test->set_value();
+ _mult_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_mult_test->rung(), 2);
- allowed_op_funcs::mult(900, _phi[0]->value_ptr(), _phi[1]->value_ptr(), _mult_test->parameters()[0], _mult_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_mult_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_mult_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _mult_test->parameters();
- allowed_op_funcs::mult(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), _mult_test->parameters()[0], _mult_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_mult_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_mult_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::mult(_task_sizes_train[0], _phi[1]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_mult_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_mult_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::mult(_task_sizes_test[0], _phi[1]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_mult_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_mult_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|mult: " << std::setprecision(13) << std::scientific <<_mult_test->parameters()[0] << ',' << _mult_test->parameters()[1];
- EXPECT_STREQ(_mult_test->unit().toString().c_str(), "m * s");
+ postfix << "1|0|1|mult|mult: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_mult_test->unit().toString().c_str(), "m * s^2");
EXPECT_STREQ(_mult_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_mult_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_neg_exp_node.cc b/tests/googletest/feature_creation/parameterization/test_neg_exp_node.cc
index bd994ab690e89f8506529a6e60ec511b58102d1b..c3007bc7c4126cbec195fdb87ff8d883216c4f8b 100644
--- a/tests/googletest/feature_creation/parameterization/test_neg_exp_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_neg_exp_node.cc
@@ -12,11 +12,11 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PARAMETERIZE
-#include <feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/parameterized_negative_exponential.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/exp/exponential.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/log/log.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/parameterized_negative_exponential.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/square_root.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -29,10 +29,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 3, 2, true);
@@ -43,19 +43,19 @@ namespace
std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(-2.0, 2.0);
+ std::uniform_real_distribution<double> distribution_feats(1e-10, 2.0);
std::uniform_real_distribution<double> distribution_params(0.75, 1.25);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
+ value_2[ii] = distribution_feats(generator);
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
+ test_value_2[ii] = distribution_feats(generator);
}
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
@@ -66,17 +66,18 @@ namespace
_phi.push_back(std::make_shared<ExpNode>(_feat_3, 3, 1e-50, 1e50));
_phi.push_back(std::make_shared<LogNode>(_feat_3, 4, 1e-50, 1e50));
_phi.push_back(std::make_shared<NegExpNode>(_feat_3, 5, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<SqrtNode>(_feat_1, 56, 1e-50, 1e50));
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::neg_exp(_task_sizes_train[0], _phi[0]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::neg_exp(_task_sizes_train[0], _phi[6]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -120,10 +121,10 @@ namespace
generateNegExpParamNode(_phi, _phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz) << " (NegExpParamNode created from NegExpNode)";
- generateNegExpParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ generateNegExpParamNode(_phi, _phi[6], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz + 1) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-5);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(NegExpParamNodeTest, ConstructorTest)
@@ -172,8 +173,8 @@ namespace
try
{
- _neg_exp_test = std::make_shared<NegExpParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _neg_exp_test->value_ptr(), 900), 1e-5);
+ _neg_exp_test = std::make_shared<NegExpParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _neg_exp_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -184,59 +185,66 @@ namespace
TEST_F(NegExpParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _neg_exp_test = std::make_shared<NegExpParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _neg_exp_test = std::make_shared<NegExpParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _neg_exp_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::neg_exp(900, _phi[0]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
+ allowed_op_funcs::neg_exp(_task_sizes_train[0], _phi[6]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::neg_exp(10, _phi[0]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
+ allowed_op_funcs::neg_exp(_task_sizes_test[0], _phi[6]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|nexp: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "0|sqrt|nexp: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = -1.0 * std::exp(-1.0 * alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(NegExpParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _neg_exp_test = std::make_shared<NegExpParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _neg_exp_test = std::make_shared<NegExpParamNode>(_phi[6], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_neg_exp_test->rung(), 1);
+ _neg_exp_test->set_value();
+ _neg_exp_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_neg_exp_test->rung(), 2);
- allowed_op_funcs::neg_exp(900, _phi[0]->value_ptr(), _neg_exp_test->parameters()[0], _neg_exp_test->parameters()[1], expected_val.data());
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _neg_exp_test->parameters();
+
+ allowed_op_funcs::neg_exp(_task_sizes_train[0], _phi[6]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
EXPECT_LT(std::abs(_neg_exp_test->value_ptr()[0] - expected_val[0]), 1e-5);
EXPECT_LT(std::abs(_neg_exp_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::neg_exp(10, _phi[0]->test_value_ptr(), _neg_exp_test->parameters()[0], _neg_exp_test->parameters()[1], expected_val.data());
+ allowed_op_funcs::neg_exp(_task_sizes_test[0], _phi[6]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
EXPECT_LT(std::abs(_neg_exp_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
EXPECT_LT(std::abs(_neg_exp_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|nexp: " << std::setprecision(13) << std::scientific <<_neg_exp_test->parameters()[0] << ',' << _neg_exp_test->parameters()[1];
+ postfix << "0|sqrt|nexp: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_neg_exp_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_neg_exp_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_neg_exp_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_sin_node.cc b/tests/googletest/feature_creation/parameterization/test_sin_node.cc
index b14261d3d50df2a7bc08dfb8ca69e9ca1e9d19aa..05a866496d77f2290f21e80cda3268cae768fa03 100644
--- a/tests/googletest/feature_creation/parameterization/test_sin_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_sin_node.cc
@@ -14,8 +14,7 @@
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sin/parameterized_sin.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/cos/cos.hpp>
-#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sq/square.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -28,10 +27,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 3, 2, true);
@@ -61,19 +60,20 @@ namespace
_feat_3 = std::make_shared<FeatureNode>(2, "B", value_2, test_value_2, Unit(""));
_phi = {_feat_1, _feat_2, _feat_3};
- _phi.push_back(std::make_shared<CosNode>(_feat_3, 2, 1e-50, 1e50));
- _phi.push_back(std::make_shared<SinNode>(_feat_3, 2, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<CosNode>(_feat_3, 3, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<SinNode>(_feat_3, 4, 1e-50, 1e50));
+ _phi.push_back(std::make_shared<SqNode>(_feat_1, 5, 1e-50, 1e50));
_a = 0.143;
_alpha = 1.05;
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::sin(_task_sizes_train[0], _phi[0]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::sin(_task_sizes_train[0], _phi[5]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -115,7 +115,7 @@ namespace
generateSinParamNode(_phi, _phi[4], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz) << " (SinParamNode created from SinNode)";
- generateSinParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ generateSinParamNode(_phi, _phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), phi_sz + 1) << " (Failure to create a valid feature)";
EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-5);
}
@@ -158,7 +158,7 @@ namespace
try
{
- _sin_test = std::make_shared<SinParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _sin_test = std::make_shared<SinParamNode>(_phi[5], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sin_test->value_ptr(), 90), 1e-5);
}
catch(const InvalidFeatureException& e)
@@ -170,59 +170,66 @@ namespace
TEST_F(SinParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _sin_test = std::make_shared<SinParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _sin_test = std::make_shared<SinParamNode>(_phi[5], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _sin_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::sin(900, _phi[0]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sin(_task_sizes_train[0], _phi[5]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::sin(10, _phi[0]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sin(_task_sizes_test[0], _phi[5]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|sin: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "0|sq|sin: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = std::cos(alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(SinParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _sin_test = std::make_shared<SinParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _sin_test = std::make_shared<SinParamNode>(_phi[5], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_sin_test->rung(), 1);
+ _sin_test->set_value();
+ _sin_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_sin_test->rung(), 2);
- allowed_op_funcs::sin(900, _phi[0]->value_ptr(), _sin_test->parameters()[0], _sin_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sin_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sin_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _sin_test->parameters();
- allowed_op_funcs::sin(10, _phi[0]->test_value_ptr(), _sin_test->parameters()[0], _sin_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sin_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sin_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sin(_task_sizes_train[0], _phi[5]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sin_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sin_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::sin(_task_sizes_test[0], _phi[5]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sin_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sin_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|sin: " << std::setprecision(13) << std::scientific <<_sin_test->parameters()[0] << ',' << _sin_test->parameters()[1];
+ postfix << "0|sq|sin: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_sin_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_sin_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_sin_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_six_pow_node.cc b/tests/googletest/feature_creation/parameterization/test_six_pow_node.cc
index ba5766b6e105ee4507b3bee67880bb22d4c4d36b..c60315c6c1d3862308d34b74cbc6f075a7ffc276 100644
--- a/tests/googletest/feature_creation/parameterization/test_six_pow_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_six_pow_node.cc
@@ -13,7 +13,6 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/six_pow/parameterized_sixth_power.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,49 +25,44 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
std::vector<double> value_1(_task_sizes_train[0], 0.0);
- std::vector<double> value_2(_task_sizes_train[0], 0.0);
-
std::vector<double> test_value_1(_task_sizes_test[0], 0.0);
- std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(-50.00, 50.00);
- std::uniform_real_distribution<double> distribution_params(1e-10, 2.00);
+ std::uniform_real_distribution<double> distribution_feats(-2.00, 2.00);
+ std::uniform_real_distribution<double> distribution_params(1e-5, 2.00);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ node_ptr feat_2 = std::make_shared<SixPowNode>(_feat_1, 1);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, feat_2};
_a = distribution_params(generator);
- _alpha = distribution_params(generator);
+ _alpha = 1.0;
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::sixth_pow(_task_sizes_train[0], _phi[0]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::sixth_pow(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -77,7 +71,6 @@ namespace
}
node_ptr _feat_1;
- node_ptr _feat_2;
node_ptr _six_pow_test;
std::vector<node_ptr> _phi;
@@ -102,9 +95,9 @@ namespace
generateSixPowParamNode(_phi, _phi[0], feat_ind, 1e49, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), 2) << " (SixPowParamNode created with an absolute value below the lower bound)";
- generateSixPowParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ generateSixPowParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(SixPowParamNodeTest, ConstructorTest)
@@ -129,8 +122,8 @@ namespace
try
{
- _six_pow_test = std::make_shared<SixPowParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _six_pow_test->value_ptr(), 900), 1e-4);
+ _six_pow_test = std::make_shared<SixPowParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _six_pow_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -141,59 +134,66 @@ namespace
TEST_F(SixPowParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _six_pow_test = std::make_shared<SixPowParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _six_pow_test = std::make_shared<SixPowParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _six_pow_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::sixth_pow(900, _phi[0]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sixth_pow(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-4);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-4);
- allowed_op_funcs::sixth_pow(10, _phi[0]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sixth_pow(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-4);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-4);
std::stringstream postfix;
- postfix << "0|sp: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "m^6");
+ postfix << "0|sp|sp: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "m^36");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 6.0 * std::pow(alpha * v1 + a, 5.0);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-4);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-4);
}
TEST_F(SixPowParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _six_pow_test = std::make_shared<SixPowParamNode>(_phi[0], feat_ind, 1e-50, 1e50, _optimizer);
+ _six_pow_test = std::make_shared<SixPowParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+
+ _six_pow_test->set_value();
+ _six_pow_test->set_test_value();
- EXPECT_EQ(_six_pow_test->rung(), 1);
+ EXPECT_EQ(_six_pow_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _six_pow_test->parameters();
- allowed_op_funcs::sixth_pow(900, _phi[0]->value_ptr(), _six_pow_test->parameters()[0], _six_pow_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_six_pow_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_six_pow_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sixth_pow(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_six_pow_test->value_ptr()[0] - expected_val[0]), 1e-4);
+ EXPECT_LT(std::abs(_six_pow_test->value()[0] - expected_val[0]), 1e-4);
- allowed_op_funcs::sixth_pow(10, _phi[0]->test_value_ptr(), _six_pow_test->parameters()[0], _six_pow_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_six_pow_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_six_pow_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sixth_pow(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_six_pow_test->test_value_ptr()[0] - expected_val[0]), 1e-4);
+ EXPECT_LT(std::abs(_six_pow_test->test_value()[0] - expected_val[0]), 1e-4);
std::stringstream postfix;
- postfix << "0|sp: " << std::setprecision(13) << std::scientific <<_six_pow_test->parameters()[0] << ',' << _six_pow_test->parameters()[1];
- EXPECT_STREQ(_six_pow_test->unit().toString().c_str(), "m^6");
+ postfix << "0|sp|sp: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_six_pow_test->unit().toString().c_str(), "m^36");
EXPECT_STREQ(_six_pow_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_six_pow_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_sq_node.cc b/tests/googletest/feature_creation/parameterization/test_sq_node.cc
index a0d0df7b9de24bb78f7721e11589bb7da6523c9b..d786879d6d99ea31ed3cb401fce82eb204cc5e39 100644
--- a/tests/googletest/feature_creation/parameterization/test_sq_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_sq_node.cc
@@ -12,8 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PARAMETERIZE
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sin/sin.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sq/parameterized_square.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,49 +26,44 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
- _task_sizes_train = {90};
- _task_sizes_test = {10};
+ _task_sizes_train = {900};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
std::vector<double> value_1(_task_sizes_train[0], 0.0);
- std::vector<double> value_2(_task_sizes_train[0], 0.0);
-
std::vector<double> test_value_1(_task_sizes_test[0], 0.0);
- std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(-50.0, 50.0);
- std::uniform_real_distribution<double> distribution_params(1e-10, 2.50);
+ std::uniform_real_distribution<double> distribution_feats(-6.23, 6.23);
+ std::uniform_real_distribution<double> distribution_params(-2.50, 2.50);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
}
- _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ _feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit(""));
+ node_ptr feat_2 = std::make_shared<SinNode>(_feat_1, 1);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, feat_2};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
allowed_op_funcs::sq(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -104,7 +99,7 @@ namespace
generateSqParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(SqParamNodeTest, ConstructorTest)
@@ -130,7 +125,7 @@ namespace
try
{
_sq_test = std::make_shared<SqParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sq_test->value_ptr(), 90), 1e-4);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sq_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -145,32 +140,33 @@ namespace
node_ptr copy_test = _sq_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::sq(90, _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sq(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::sq(10, _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sq(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|sq: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "s^2");
+ postfix << "0|sin|sq: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 2.0 * (alpha * v1 + a);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(SqParamNodeTest, AttributesTest)
@@ -178,22 +174,28 @@ namespace
unsigned long int feat_ind = _phi.size();
_sq_test = std::make_shared<SqParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_sq_test->rung(), 1);
+ _sq_test->set_value();
+ _sq_test->set_test_value();
+
+ EXPECT_EQ(_sq_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _sq_test->parameters();
- allowed_op_funcs::sq(90, _phi[1]->value_ptr(), _sq_test->parameters()[0], _sq_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sq_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sq_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sq(_task_sizes_train[0], _phi[1]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sq_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sq_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::sq(10, _phi[1]->test_value_ptr(), _sq_test->parameters()[0], _sq_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sq_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sq_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sq(_task_sizes_test[0], _phi[1]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sq_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sq_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|sq: " << std::setprecision(13) << std::scientific <<_sq_test->parameters()[0] << ',' << _sq_test->parameters()[1];
- EXPECT_STREQ(_sq_test->unit().toString().c_str(), "s^2");
+ postfix << "0|sin|sq: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_sq_test->unit().toString().c_str(), "Unitless");
EXPECT_STREQ(_sq_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_sq_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_sqrt_node.cc b/tests/googletest/feature_creation/parameterization/test_sqrt_node.cc
index 1cba40dd10539c340664884269b97929811de7a1..076dd39d61c38bda0483383184b3d95aca04532e 100644
--- a/tests/googletest/feature_creation/parameterization/test_sqrt_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_sqrt_node.cc
@@ -13,7 +13,7 @@
// limitations under the License.
#ifdef PARAMETERIZE
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sqrt/parameterized_square_root.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sub/subtract.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,10 +26,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
_task_sizes_train = {900};
- _task_sizes_test = {10};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
@@ -40,35 +40,36 @@ namespace
std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(1.0, 500.0);
- std::uniform_real_distribution<double> distribution_params(0.5, 1.50);
+ std::uniform_real_distribution<double> distribution_feats(-20.0, 20.0);
+ std::uniform_real_distribution<double> distribution_params(0.05, 1.50);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
{
- value_1[ii] = distribution_feats(generator);
- value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
+ value_1[ii] = std::abs(distribution_feats(generator)) + 20.0;
+ value_2[ii] = distribution_feats(generator);
}
for(int ii = 0; ii < _task_sizes_test[0]; ++ii)
{
- test_value_1[ii] = distribution_feats(generator);
- test_value_2[ii] = std::abs(distribution_feats(generator)) + 1e-10;
+ test_value_1[ii] = std::abs(distribution_feats(generator)) + 20.0;
+ test_value_2[ii] = distribution_feats(generator);
}
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ node_ptr feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("m"));
+ node_ptr feat_3 = std::make_shared<SubNode>(_feat_1, feat_2, 2);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, feat_2, feat_3};
_a = distribution_params(generator);
_alpha = std::pow(distribution_params(generator), 2.0);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::sqrt(_task_sizes_train[0], _phi[1]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::sqrt(_task_sizes_train[0], _phi[2]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -77,7 +78,6 @@ namespace
}
node_ptr _feat_1;
- node_ptr _feat_2;
node_ptr _sqrt_test;
std::vector<node_ptr> _phi;
@@ -96,15 +96,15 @@ namespace
{
unsigned long int feat_ind = _phi.size();
- generateSqrtParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (SqrtParamNode created with an absolute value above the upper bound)";
+ generateSqrtParamNode(_phi, _phi[0], feat_ind, 1e-50, 1e-49, _optimizer);
+ EXPECT_EQ(_phi.size(), 3) << " (SqrtParamNode created with an absolute value above the upper bound)";
- generateSqrtParamNode(_phi, _phi[1], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (SqrtParamNode created with an absolute value below the lower bound)";
+ generateSqrtParamNode(_phi, _phi[0], feat_ind, 1e49, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 3) << " (SqrtParamNode created with an absolute value below the lower bound)";
- generateSqrtParamNode(_phi, _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 900), 1e-4);
+ generateSqrtParamNode(_phi, _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 4) << " (Failure to create a valid feature)";
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-4);
}
TEST_F(SqrtParamNodeTest, ConstructorTest)
@@ -113,7 +113,7 @@ namespace
try
{
- _sqrt_test = std::make_shared<SqrtParamNode>(_phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
+ _sqrt_test = std::make_shared<SqrtParamNode>(_phi[0], feat_ind, 1e-50, 1e-49, _optimizer);
EXPECT_TRUE(false) << " (SqrtParamNode created with an absolute value above the upper bound)";
}
catch(const InvalidFeatureException& e)
@@ -121,7 +121,7 @@ namespace
try
{
- _sqrt_test = std::make_shared<SqrtParamNode>(_phi[1], feat_ind, 1e49, 1e50, _optimizer);
+ _sqrt_test = std::make_shared<SqrtParamNode>(_phi[0], feat_ind, 1e49, 1e50, _optimizer);
EXPECT_TRUE(false) << " (SqrtParamNode created with an absolute value below the lower bound)";
}
catch(const InvalidFeatureException& e)
@@ -129,8 +129,8 @@ namespace
try
{
- _sqrt_test = std::make_shared<SqrtParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sqrt_test->value_ptr(), 900), 1e-4);
+ _sqrt_test = std::make_shared<SqrtParamNode>(_phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sqrt_test->value_ptr(), _task_sizes_train[0]), 1e-4);
}
catch(const InvalidFeatureException& e)
{
@@ -141,59 +141,66 @@ namespace
TEST_F(SqrtParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _sqrt_test = std::make_shared<SqrtParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _sqrt_test = std::make_shared<SqrtParamNode>(_phi[2], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _sqrt_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(900, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::sqrt(900, _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sqrt(_task_sizes_train[0], _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::sqrt(10, _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sqrt(_task_sizes_test[0], _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|sqrt: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
- EXPECT_STREQ(copy_test->unit().toString().c_str(), "s^0.5");
+ postfix << "0|1|sub|sqrt: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(copy_test->unit().toString().c_str(), "m^0.5");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
- double v1 = copy_test->feat(0)->value_ptr()[0];
+ double v1 = copy_test->feat(0)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = 0.5 * std::pow(alpha * v1 + a, -0.5);
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v1), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(SqrtParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _sqrt_test = std::make_shared<SqrtParamNode>(_phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _sqrt_test = std::make_shared<SqrtParamNode>(_phi[2], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_sqrt_test->rung(), 1);
+ _sqrt_test->set_value();
+ _sqrt_test->set_test_value();
- std::vector<double> expected_val(900, 0.0);
+ EXPECT_EQ(_sqrt_test->rung(), 2);
- allowed_op_funcs::sqrt(900, _phi[1]->value_ptr(), _sqrt_test->parameters()[0], _sqrt_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sqrt_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sqrt_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _sqrt_test->parameters();
- allowed_op_funcs::sqrt(10, _phi[1]->test_value_ptr(), _sqrt_test->parameters()[0], _sqrt_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sqrt_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sqrt_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sqrt(_task_sizes_train[0], _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sqrt_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sqrt_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::sqrt(_task_sizes_test[0], _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sqrt_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sqrt_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "1|sqrt: " << std::setprecision(13) << std::scientific <<_sqrt_test->parameters()[0] << ',' << _sqrt_test->parameters()[1];
- EXPECT_STREQ(_sqrt_test->unit().toString().c_str(), "s^0.5");
+ postfix << "0|1|sub|sqrt: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
+ EXPECT_STREQ(_sqrt_test->unit().toString().c_str(), "m^0.5");
EXPECT_STREQ(_sqrt_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_sqrt_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/parameterization/test_sub_node.cc b/tests/googletest/feature_creation/parameterization/test_sub_node.cc
index 3fc6fd86cda141a72d0194700dd3067e193b5dd1..1345ebd38d414b20f3c0dfdf41727d9a9b22f321 100644
--- a/tests/googletest/feature_creation/parameterization/test_sub_node.cc
+++ b/tests/googletest/feature_creation/parameterization/test_sub_node.cc
@@ -12,8 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PARAMETERIZE
+#include <feature_creation/node/operator_nodes/allowed_operator_nodes/neg_exp/negative_exponential.hpp>
#include <feature_creation/node/operator_nodes/allowed_operator_nodes/sub/parameterized_subtract.hpp>
-#include <feature_creation/node/value_storage/nodes_value_containers.hpp>
#include <feature_creation/node/FeatureNode.hpp>
#include "gtest/gtest.h"
@@ -26,10 +26,10 @@ namespace
protected:
void SetUp() override
{
- nlopt_wrapper::MAX_PARAM_DEPTH = 1;
+ nlopt_wrapper::MAX_PARAM_DEPTH = 2;
- _task_sizes_train = {90};
- _task_sizes_test = {10};
+ _task_sizes_train = {900};
+ _task_sizes_test = {100};
node_value_arrs::initialize_values_arr(_task_sizes_train, _task_sizes_test, 2, 2, true);
@@ -40,7 +40,7 @@ namespace
std::vector<double> test_value_2(_task_sizes_test[0], 0.0);
std::default_random_engine generator;
- std::uniform_real_distribution<double> distribution_feats(-50.0, 50.0);
+ std::uniform_real_distribution<double> distribution_feats(-15.0, 15.0);
std::uniform_real_distribution<double> distribution_params(-2.50, 2.50);
for(int ii = 0; ii < _task_sizes_train[0]; ++ii)
@@ -56,19 +56,20 @@ namespace
}
_feat_1 = std::make_shared<FeatureNode>(0, "A", value_1, test_value_1, Unit("m"));
- _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit("s"));
+ _feat_2 = std::make_shared<FeatureNode>(1, "B", value_2, test_value_2, Unit());
+ node_ptr feat_3 = std::make_shared<NegExpNode>(_feat_2, 2);
- _phi = {_feat_1, _feat_2};
+ _phi = {_feat_1, _feat_2, feat_3};
_a = distribution_params(generator);
_alpha = distribution_params(generator);
_prop = std::vector<double>(_task_sizes_train[0], 0.0);
- _gradient.resize(_task_sizes_train[0] * 2, 1.0);
+ _gradient.resize(_task_sizes_train[0] * 4, 1.0);
_dfdp.resize(_task_sizes_train[0]);
- allowed_op_funcs::sub(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[1]->value_ptr(), _alpha, _a, _prop.data());
+ allowed_op_funcs::sub(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[2]->value_ptr(), _alpha, _a, _prop.data());
- _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 1);
+ _optimizer = nlopt_wrapper::get_optimizer("regression",_task_sizes_train, _prop, 2);
}
void TearDown() override
@@ -98,14 +99,14 @@ namespace
unsigned long int feat_ind = _phi.size();
generateSubParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e-40, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (SubParamNode created with an absolute value above the upper bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (SubParamNode created with an absolute value above the upper bound)";
generateSubParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e49, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 2) << " (SubParamNode created with an absolute value below the lower bound)";
+ EXPECT_EQ(_phi.size(), 3) << " (SubParamNode created with an absolute value below the lower bound)";
- generateSubParamNode(_phi, _phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_phi.size(), 3) << " (Failure to create a valid feature)";
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), 90), 1e-10);
+ generateSubParamNode(_phi, _phi[0], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_EQ(_phi.size(), 4) << " (Failure to create a valid feature)";
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _phi.back()->value_ptr(), _task_sizes_train[0]), 1e-5);
}
TEST_F(SubParamNodeTest, ConstructorTest)
@@ -130,8 +131,8 @@ namespace
try
{
- _sub_test = std::make_shared<SubParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sub_test->value_ptr(), 90), 1e-10);
+ _sub_test = std::make_shared<SubParamNode>(_phi[0], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
+ EXPECT_LT(1.0 - util_funcs::r2(_prop.data(), _sub_test->value_ptr(), _task_sizes_train[0]), 1e-5);
}
catch(const InvalidFeatureException& e)
{
@@ -142,60 +143,67 @@ namespace
TEST_F(SubParamNodeTest, HardCopyTest)
{
unsigned long int feat_ind = _phi.size();
- _sub_test = std::make_shared<SubParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _sub_test = std::make_shared<SubParamNode>(_phi[0], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
node_ptr copy_test = _sub_test->hard_copy();
- EXPECT_EQ(copy_test->rung(), 1);
+ EXPECT_EQ(copy_test->rung(), 2);
- std::vector<double> expected_val(90, 0.0);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = copy_test->parameters();
- allowed_op_funcs::sub(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sub(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->value()[0] - expected_val[0]), 1e-5);
- allowed_op_funcs::sub(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), copy_test->parameters()[0], copy_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sub(_task_sizes_test[0], _phi[0]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(copy_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(copy_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|sub: " << std::setprecision(13) << std::scientific <<copy_test->parameters()[0] << ',' << copy_test->parameters()[1];
+ postfix << "0|1|nexp|sub: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(copy_test->unit().toString().c_str(), "m");
EXPECT_STREQ(copy_test->postfix_expr().c_str(), postfix.str().c_str());
copy_test->gradient(_gradient.data(), _dfdp.data());
double v1 = copy_test->feat(0)->value_ptr()[0];
- double v2 = copy_test->feat(1)->value_ptr()[0];
+ double v2 = copy_test->feat(1)->value_ptr(¶ms[2])[0];
- double alpha = copy_test->parameters()[0];
- double a = copy_test->parameters()[1];
+ double alpha = params[0];
+ double a = params[1];
double df_dp = -1.0;
- EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-10);
- EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-10);
+ EXPECT_LT(std::abs(_gradient[0] - df_dp * v2), 1e-5);
+ EXPECT_LT(std::abs(_gradient[_task_sizes_train[0]] - df_dp), 1e-5);
}
TEST_F(SubParamNodeTest, AttributesTest)
{
unsigned long int feat_ind = _phi.size();
- _sub_test = std::make_shared<SubParamNode>(_phi[0], _phi[1], feat_ind, 1e-50, 1e50, _optimizer);
+ _sub_test = std::make_shared<SubParamNode>(_phi[0], _phi[2], feat_ind, 1e-50, 1e50, _optimizer);
- EXPECT_EQ(_sub_test->rung(), 1);
+ _sub_test->set_value();
+ _sub_test->set_test_value();
- std::vector<double> expected_val(90, 0.0);
+ EXPECT_EQ(_sub_test->rung(), 2);
- allowed_op_funcs::sub(90, _phi[0]->value_ptr(), _phi[1]->value_ptr(), _sub_test->parameters()[0], _sub_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sub_test->value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sub_test->value()[0] - expected_val[0]), 1e-10);
+ std::vector<double> expected_val(_task_sizes_train[0], 0.0);
+ std::vector<double> params = _sub_test->parameters();
- allowed_op_funcs::sub(10, _phi[0]->test_value_ptr(), _phi[1]->test_value_ptr(), _sub_test->parameters()[0], _sub_test->parameters()[1], expected_val.data());
- EXPECT_LT(std::abs(_sub_test->test_value_ptr()[0] - expected_val[0]), 1e-10);
- EXPECT_LT(std::abs(_sub_test->test_value()[0] - expected_val[0]), 1e-10);
+ allowed_op_funcs::sub(_task_sizes_train[0], _phi[0]->value_ptr(), _phi[2]->value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sub_test->value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sub_test->value()[0] - expected_val[0]), 1e-5);
+
+ allowed_op_funcs::sub(_task_sizes_test[0], _phi[0]->test_value_ptr(), _phi[2]->test_value_ptr(¶ms[2]), params[0], params[1], expected_val.data());
+ EXPECT_LT(std::abs(_sub_test->test_value_ptr()[0] - expected_val[0]), 1e-5);
+ EXPECT_LT(std::abs(_sub_test->test_value()[0] - expected_val[0]), 1e-5);
std::stringstream postfix;
- postfix << "0|1|sub: " << std::setprecision(13) << std::scientific <<_sub_test->parameters()[0] << ',' << _sub_test->parameters()[1];
+ postfix << "0|1|nexp|sub: " << std::setprecision(13) << std::scientific << params[0] << ',' << params[1] << ',' << params[2] << ',' << params[3];
EXPECT_STREQ(_sub_test->unit().toString().c_str(), "m");
EXPECT_STREQ(_sub_test->postfix_expr().c_str(), postfix.str().c_str());
+
+ EXPECT_THROW(_sub_test->set_parameters({1.0, 0.0, 1.0}), std::logic_error);
}
}
#endif
diff --git a/tests/googletest/feature_creation/units/test_untis.cc b/tests/googletest/feature_creation/units/test_untis.cc
index e367712a312204a4591a4d2055e6c4005a59bc74..f50049dbd0de1ac2f6b42d46a0966375a9ff6e29 100644
--- a/tests/googletest/feature_creation/units/test_untis.cc
+++ b/tests/googletest/feature_creation/units/test_untis.cc
@@ -23,17 +23,22 @@ namespace {
Unit u_2("s");
EXPECT_NE(u_1, u_2);
+ EXPECT_NE(u_1, Unit());
EXPECT_EQ(u_1, Unit(u_1));
EXPECT_EQ(u_2, Unit(u_2));
EXPECT_STREQ(u_1.toString().c_str(), "m");
+ EXPECT_STREQ(u_1.toLatexString().c_str(), "m");
+
EXPECT_STREQ(u_2.toString().c_str(), "s");
+ EXPECT_STREQ(u_2.toLatexString().c_str(), "s");
EXPECT_EQ(u_1 / u_2, Unit("m/s"));
EXPECT_EQ(u_1 / u_1, Unit());
EXPECT_EQ(u_1 * u_2, Unit("m*s"));
EXPECT_EQ(u_1 * u_1, Unit("m^2.0"));
EXPECT_EQ(u_1 ^ 2.0, Unit("m^2.0"));
+ EXPECT_EQ(u_1 ^ 0.0, Unit(""));
EXPECT_EQ(u_1.inverse(), Unit("1 / m"));
u_2 /= u_1;
@@ -64,17 +69,22 @@ namespace {
Unit u_2(dct_2);
EXPECT_NE(u_1, u_2);
+ EXPECT_NE(u_1, Unit());
EXPECT_EQ(u_1, Unit(u_1));
EXPECT_EQ(u_2, Unit(u_2));
EXPECT_STREQ(u_1.toString().c_str(), "m");
+ EXPECT_STREQ(u_1.toLatexString().c_str(), "m");
+
EXPECT_STREQ(u_2.toString().c_str(), "s");
+ EXPECT_STREQ(u_2.toLatexString().c_str(), "s");
EXPECT_EQ(u_1 / u_2, Unit("m/s"));
EXPECT_EQ(u_1 / u_1, Unit());
EXPECT_EQ(u_1 * u_2, Unit("m*s"));
EXPECT_EQ(u_1 * u_1, Unit("m^2.0"));
EXPECT_EQ(u_1 ^ 2.0, Unit("m^2.0"));
+ EXPECT_EQ(u_1 ^ 0.0, Unit(""));
EXPECT_EQ(u_1.inverse(), Unit("1 / m"));
u_2 /= u_1;
diff --git a/tests/googletest/feature_creation/utils/test_utils.cc b/tests/googletest/feature_creation/utils/test_utils.cc
index 58fa6e5fc5b1c96f29963363fe69f8836114c2c2..edea1ddec8068fd807dfaddc850d6406a4ddd400 100644
--- a/tests/googletest/feature_creation/utils/test_utils.cc
+++ b/tests/googletest/feature_creation/utils/test_utils.cc
@@ -52,11 +52,15 @@ namespace
TEST_F(FeatCreationUtilsTest, TestPostfix2Node)
{
+ EXPECT_THROW(str2node::postfix2node("0|asdf", _phi0, _feat_ind), std::logic_error);
+ EXPECT_THROW(str2node::postfix2node("1|0|sq", _phi0, _feat_ind), std::logic_error);
+
node_ptr test = str2node::postfix2node("0|2|div|exp|1|add", _phi0, _feat_ind);
EXPECT_EQ(test->type(), NODE_TYPE::ADD);
EXPECT_EQ(test->rung(), 3);
EXPECT_LT(abs(test->value()[1] - (std::exp(2.0) + 2.0)), 1e-10);
EXPECT_STREQ(test->expr().c_str(), "(exp((A / C)) + B)");
+
}
TEST_F(FeatCreationUtilsTest, TestPhiSelFromFile)
@@ -118,6 +122,8 @@ namespace
out_file_stream << "-";
out_file_stream << std::endl;
out_file_stream.close();
+
+ EXPECT_THROW(str2node::phi_from_file("not_phi_file.txt", _phi0), std::logic_error);
std::vector<node_ptr> phi = str2node::phi_from_file("phi.txt", _phi0);
ASSERT_EQ(phi.size(), 10);
@@ -132,4 +138,46 @@ namespace
EXPECT_STREQ(phi[5]->expr().c_str(), "(exp((A / C)) - B)");
boost::filesystem::remove("phi.txt");
}
+
+ TEST_F(FeatCreationUtilsTest, TestType2Str)
+ {
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::FEAT).c_str(), "feature");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::MODEL_FEATURE).c_str(), "model");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::ADD).c_str(), "add");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::SUB).c_str(), "sub");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::ABS_DIFF).c_str(), "abs_diff");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::MULT).c_str(), "mult");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::DIV).c_str(), "div");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::EXP).c_str(), "exp");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::NEG_EXP).c_str(), "neg_exp");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::INV).c_str(), "inv");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::SQ).c_str(), "sq");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::CB).c_str(), "cb");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::SIX_POW).c_str(), "six_pow");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::SQRT).c_str(), "sqrt");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::CBRT).c_str(), "cbrt");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::LOG).c_str(), "log");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::ABS).c_str(), "abs");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::SIN).c_str(), "sin");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::COS).c_str(), "cos");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_ADD).c_str(), "p:add");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_SUB).c_str(), "p:sub");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_ABS_DIFF).c_str(), "p:abs_diff");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_MULT).c_str(), "p:mult");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_DIV).c_str(), "p:div");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_EXP).c_str(), "p:exp");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_NEG_EXP).c_str(), "p:neg_exp");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_INV).c_str(), "p:inv");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_SQ).c_str(), "p:sq");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_CB).c_str(), "p:cb");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_SIX_POW).c_str(), "p:six_pow");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_SQRT).c_str(), "p:sqrt");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_CBRT).c_str(), "p:cbrt");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_LOG).c_str(), "p:log");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_ABS).c_str(), "p:abs");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_SIN).c_str(), "p:sin");
+ EXPECT_STREQ(node_identifier::feature_type_to_string(NODE_TYPE::PARAM_COS).c_str(), "p:cos");
+
+ EXPECT_THROW(node_identifier::feature_type_to_string(NODE_TYPE::MAX), std::logic_error);
+ }
}
diff --git a/tests/googletest/feature_creation/value_storage/test_value_storage.cc b/tests/googletest/feature_creation/value_storage/test_value_storage.cc
index 3a6f4aa5a83ad20a19ee2f4051ab611619cfb998..4614922ef947f320642e8cfc0f69b6a75fa9aa7f 100644
--- a/tests/googletest/feature_creation/value_storage/test_value_storage.cc
+++ b/tests/googletest/feature_creation/value_storage/test_value_storage.cc
@@ -21,7 +21,13 @@ namespace {
//test mean calculations
TEST(ValueStorage, ValueStorageTest)
{
+ EXPECT_THROW(node_value_arrs::initialize_values_arr({5}, {2}, 1, -2, true), std::logic_error);
+
node_value_arrs::initialize_values_arr({5}, {2}, 1, 2, true);
+
+ EXPECT_THROW(node_value_arrs::set_task_sz_train({20}), std::logic_error);
+ EXPECT_THROW(node_value_arrs::set_task_sz_test({6}), std::logic_error);
+
EXPECT_EQ(node_value_arrs::N_SAMPLES, 5);
EXPECT_EQ(node_value_arrs::N_SAMPLES_TEST, 2);
EXPECT_EQ(node_value_arrs::N_RUNGS_STORED, 0);
@@ -35,6 +41,7 @@ namespace {
EXPECT_EQ(node_value_arrs::TEMP_STORAGE_TEST_ARR.size(), node_value_arrs::MAX_N_THREADS * (6 * 1 + 1) * 2);
EXPECT_EQ(node_value_arrs::TEMP_STORAGE_TEST_REG.size(), node_value_arrs::MAX_N_THREADS * (6 * 1 + 1));
+ EXPECT_THROW(node_value_arrs::resize_values_arr(10, 2), std::logic_error);
node_value_arrs::resize_values_arr(1, 2);
EXPECT_EQ(node_value_arrs::N_SAMPLES, 5);
EXPECT_EQ(node_value_arrs::N_SAMPLES_TEST, 2);
@@ -58,6 +65,7 @@ namespace {
node_value_arrs::get_value_ptr(1, 1, 0)[1] = 1.0;
EXPECT_EQ(node_value_arrs::VALUES_ARR[6], 1.0);
+ EXPECT_THROW(node_value_arrs::get_test_value_ptr(1000, 100, 0, 0)[1], std::logic_error);
node_value_arrs::get_test_value_ptr(0, 1, 0, 0)[1] = 1.0;
EXPECT_EQ(node_value_arrs::TEST_VALUES_ARR[1], 1.0);
@@ -113,5 +121,12 @@ namespace {
EXPECT_EQ(node_value_arrs::MAX_N_THREADS, omp_get_max_threads());
EXPECT_EQ(node_value_arrs::N_OP_SLOTS, 0);
EXPECT_EQ(node_value_arrs::N_PARAM_OP_SLOTS, 0);
+
+ node_value_arrs::set_task_sz_train({3, 2});
+ EXPECT_EQ(node_value_arrs::TASK_SZ_TRAIN[0], 3);
+ EXPECT_EQ(node_value_arrs::TASK_START_TRAIN[0], 0);
+
+ node_value_arrs::set_task_sz_test({2, 0});
+ EXPECT_EQ(node_value_arrs::TASK_SZ_TEST[0], 2);
}
}
diff --git a/tests/googletest/inputs/data.csv b/tests/googletest/inputs/data.csv
index ee3d4cd2cc7a2d326f44369b2600601aa663824b..7203e72111fd3051ebef425b03b146b479c10e98 100644
--- a/tests/googletest/inputs/data.csv
+++ b/tests/googletest/inputs/data.csv
@@ -1,4 +1,4 @@
-Sample,task,property (m),A (m)
+# Sample,task,property (m),A (m)
a,task_1,1.0,1.0
b,task_1,4.0,2.0
c,task_2,9.0,3.0
diff --git a/tests/googletest/inputs/input_parser.cc b/tests/googletest/inputs/input_parser.cc
index d3129f52caa723ee4812013e85f68fcd18a9ec88..7ec7a6d39a123d5343cf9bef6855c21338b69c79 100644
--- a/tests/googletest/inputs/input_parser.cc
+++ b/tests/googletest/inputs/input_parser.cc
@@ -138,30 +138,48 @@ namespace
TEST_F(InputParserTests, DefaultConsructor)
{
InputParser inputs;
+ EXPECT_THROW(inputs.task_sizes_train(), std::logic_error);
+ EXPECT_THROW(inputs.task_sizes_train_copy(), std::logic_error);
+
+ EXPECT_THROW(inputs.task_sizes_test(), std::logic_error);
+ EXPECT_THROW(inputs.task_sizes_test_copy(), std::logic_error);
+
inputs.set_task_sizes_train(_task_sizes_train);
EXPECT_EQ(inputs.task_sizes_train()[0], _task_sizes_train[0]);
inputs.set_task_sizes_test(_task_sizes_test);
EXPECT_EQ(inputs.task_sizes_test()[0], _task_sizes_test[0]);
+ EXPECT_THROW(inputs.sample_ids_train(), std::logic_error);
+ EXPECT_THROW(inputs.sample_ids_train_copy(), std::logic_error);
inputs.set_sample_ids_train(_sample_ids_train);
EXPECT_EQ(inputs.sample_ids_train()[0], _sample_ids_train[0]);
+ EXPECT_THROW(inputs.sample_ids_test(), std::logic_error);
+ EXPECT_THROW(inputs.sample_ids_test_copy(), std::logic_error);
inputs.set_sample_ids_test(_sample_ids_test);
EXPECT_EQ(inputs.sample_ids_test()[0], _sample_ids_test[0]);
+ EXPECT_THROW(inputs.task_names(), std::logic_error);
+ EXPECT_THROW(inputs.task_names_copy(), std::logic_error);
inputs.set_task_names(_task_names);
EXPECT_EQ(inputs.task_names()[0], _task_names[0]);
inputs.set_allowed_ops(_allowed_ops);
EXPECT_EQ(inputs.allowed_ops()[0], _allowed_ops[0]);
+ EXPECT_THROW(inputs.prop_train(), std::logic_error);
+ EXPECT_THROW(inputs.prop_train_copy(), std::logic_error);
inputs.set_prop_train(_prop_train);
EXPECT_EQ(inputs.prop_train()[0], _prop_train[0]);
+ EXPECT_THROW(inputs.prop_test(), std::logic_error);
+ EXPECT_THROW(inputs.prop_test_copy(), std::logic_error);
inputs.set_prop_test(_prop_test);
EXPECT_EQ(inputs.prop_test()[0], _prop_test[0]);
+ EXPECT_THROW(inputs.leave_out_inds(), std::logic_error);
+ EXPECT_THROW(inputs.leave_out_inds_copy(), std::logic_error);
inputs.set_leave_out_inds(_leave_out_inds);
EXPECT_EQ(inputs.leave_out_inds()[0], _leave_out_inds[0]);
@@ -169,6 +187,9 @@ namespace
EXPECT_EQ(inputs.n_samp_test(), 1);
EXPECT_EQ(inputs.n_samp_train(), 3);
+ EXPECT_THROW(inputs.phi_0(), std::logic_error);
+ EXPECT_THROW(inputs.phi_0_copy(), std::logic_error);
+ EXPECT_THROW(inputs.phi_0_ptrs(), std::logic_error);
inputs.set_phi_0(_phi_0);
EXPECT_EQ(inputs.phi_0()[0].feat_ind(), _phi_0[0].feat_ind());
EXPECT_EQ(inputs.phi_0_ptrs()[0]->feat_ind(), _phi_0[0].feat_ind());
@@ -199,9 +220,11 @@ namespace
inputs.set_l_bound(_l_bound);
EXPECT_EQ(inputs.l_bound(), _l_bound);
+ EXPECT_THROW(inputs.set_u_bound(_l_bound / 2), std::logic_error);
inputs.set_u_bound(_u_bound);
EXPECT_EQ(inputs.u_bound(), _u_bound);
+ EXPECT_THROW(inputs.set_l_bound(_u_bound * 2), std::logic_error);
inputs.set_n_dim(_n_dim);
EXPECT_EQ(inputs.n_dim(), _n_dim);
@@ -247,10 +270,7 @@ namespace
TEST_F(InputParserTests, FileConsructor)
{
- boost::property_tree::ptree propTree;
- boost::property_tree::json_parser::read_json(_filename, propTree);
- propTree.put("data_file", _data_file);
- InputParser inputs(propTree, _filename, mpi_setup::comm);
+ InputParser inputs(_filename);
EXPECT_EQ(inputs.sample_ids_train()[0], _sample_ids_train[0]);
EXPECT_EQ(inputs.sample_ids_test()[0], _sample_ids_test[0]);
EXPECT_EQ(inputs.task_names()[0], _task_names[0]);
@@ -284,12 +304,86 @@ namespace
EXPECT_EQ(inputs.n_models_store(), _n_models_store);
EXPECT_EQ(inputs.fix_intercept(), _fix_intercept);
-#ifdef PARAMETERIZE
+ #ifdef PARAMETERIZE
EXPECT_EQ(inputs.allowed_param_ops()[0], _allowed_param_ops[0]);
EXPECT_EQ(inputs.max_param_depth(), _max_param_depth);
EXPECT_EQ(inputs.nlopt_seed(), _nlopt_seed);
EXPECT_EQ(inputs.global_param_opt(), _global_param_opt);
EXPECT_EQ(inputs.reparam_residual(), _reparam_residual);
-#endif
+ #endif
+ }
+
+ TEST_F(InputParserTests, CheckSizes)
+ {
+ InputParser inputs;
+ inputs.clear_data();
+ node_value_arrs::finalize_values_arr();
+
+ inputs.set_task_sizes_train({2, 2, 2});
+ EXPECT_THROW(inputs.set_task_sizes_test(_task_sizes_test), std::logic_error);
+ EXPECT_THROW(inputs.set_task_names({"A"}), std::logic_error);
+ EXPECT_THROW(inputs.set_prop_train(_prop_train), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_train(_sample_ids_train), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0((_phi_0)), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_task_sizes_test({2, 2, 2});
+ EXPECT_THROW(inputs.set_task_sizes_train(_task_sizes_train), std::logic_error);
+ EXPECT_THROW(inputs.set_task_names({"A"}), std::logic_error);
+ EXPECT_THROW(inputs.set_prop_test(_prop_test), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_test(_sample_ids_test), std::logic_error);
+ EXPECT_THROW(inputs.set_leave_out_inds(_leave_out_inds), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0((_phi_0)), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_task_names({"A", "B", "C", "D"});
+ EXPECT_THROW(inputs.set_task_sizes_train(_task_sizes_train), std::logic_error);
+ EXPECT_THROW(inputs.set_task_sizes_test(_task_sizes_test), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_prop_train(std::vector<double>(6, 0.0));
+ EXPECT_THROW(inputs.set_task_sizes_train(_task_sizes_train), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_train(_sample_ids_train), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0(_phi_0), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_sample_ids_train(std::vector<std::string>(6, "A"));
+ EXPECT_THROW(inputs.set_task_sizes_train(_task_sizes_train), std::logic_error);
+ EXPECT_THROW(inputs.set_prop_train(_prop_train), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0(_phi_0), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_prop_test(std::vector<double>(6, 0.0));
+ EXPECT_THROW(inputs.set_task_sizes_test(_task_sizes_test), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_test(_sample_ids_test), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0(_phi_0), std::logic_error);
+ EXPECT_THROW(inputs.set_leave_out_inds(_leave_out_inds), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_leave_out_inds({1, 2, 3});
+ EXPECT_THROW(inputs.set_task_sizes_test(_task_sizes_test), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_test(_sample_ids_test), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0(_phi_0), std::logic_error);
+ EXPECT_THROW(inputs.set_prop_test(_prop_test), std::logic_error);
+ inputs.clear_data();
+
+ inputs.set_sample_ids_test(std::vector<std::string>(6, "A"));
+ EXPECT_THROW(inputs.set_task_sizes_test(_task_sizes_test), std::logic_error);
+ EXPECT_THROW(inputs.set_prop_test(_prop_test), std::logic_error);
+ EXPECT_THROW(inputs.set_phi_0(_phi_0), std::logic_error);
+ EXPECT_THROW(inputs.set_leave_out_inds(_leave_out_inds), std::logic_error);
+ inputs.clear_data();
+
+ _phi_0 = {FeatureNode(0, "feat_1", {1.0, 2.0, 3.0, 4.0}, {5.0, 6.0}, Unit("m"))};
+ inputs.set_phi_0(_phi_0);
+ EXPECT_THROW(inputs.set_prop_train(_prop_train), std::logic_error);
+ EXPECT_THROW(inputs.set_task_sizes_train(_task_sizes_train), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_train(_sample_ids_train), std::logic_error);
+
+ EXPECT_THROW(inputs.set_prop_test(_prop_test), std::logic_error);
+ EXPECT_THROW(inputs.set_task_sizes_test(_task_sizes_test), std::logic_error);
+ EXPECT_THROW(inputs.set_sample_ids_test(_sample_ids_test), std::logic_error);
+ EXPECT_THROW(inputs.set_leave_out_inds(_leave_out_inds), std::logic_error);
+ inputs.clear_data();
}
}
diff --git a/tests/googletest/inputs/sisso.json b/tests/googletest/inputs/sisso.json
index 2e655b860bda6796662d0cc9186eed6563eaf6e2..94b77049a836e16cb06811a6bd41a25f72002508 100644
--- a/tests/googletest/inputs/sisso.json
+++ b/tests/googletest/inputs/sisso.json
@@ -5,7 +5,8 @@
"n_residual": 1,
"n_models_store": 1,
"n_rung_store": 1,
- "data_file": "googletest/inputs/data.csv",
+ "data_file_relatice_to_json": true,
+ "data_file": "data.csv",
"property_key": "property",
"task_key": "task",
"leave_out_inds": [3],
diff --git a/tests/googletest/inputs/sisso_param.json b/tests/googletest/inputs/sisso_param.json
index f41261dc992d0c1d090c254ae5530fee190504b2..68a27b344ae776ea50526f6de14a1d33de481626 100644
--- a/tests/googletest/inputs/sisso_param.json
+++ b/tests/googletest/inputs/sisso_param.json
@@ -5,7 +5,8 @@
"n_residual": 1,
"n_models_store": 1,
"n_rung_store": 1,
- "data_file": "googletest/inputs/data.csv",
+ "data_file_relatice_to_json": true,
+ "data_file": "data.csv",
"property_key": "property",
"task_key": "task",
"leave_out_inds": [3],
diff --git a/tests/googletest/utils/test_str_utils.cc b/tests/googletest/utils/test_str_utils.cc
index f14dd04b8b3f12e418facc210480a2890a8ec90e..ba230e6dadbe5802bbc8ce903575e4366423857e 100644
--- a/tests/googletest/utils/test_str_utils.cc
+++ b/tests/googletest/utils/test_str_utils.cc
@@ -33,5 +33,7 @@ namespace {
EXPECT_STREQ(str_split[1].c_str(), "B");
EXPECT_STREQ(str_split[2].c_str(), "C");
EXPECT_STREQ(str_split[3].c_str(), "D : 5");
+
+ EXPECT_STREQ(str_utils::latexify("A_sd_fg").c_str(), "A_{sd, fg}");
}
}
diff --git a/tests/pytest/test_descriptor_identifier/matlab_functions/model_log_regressor.m b/tests/pytest/test_descriptor_identifier/matlab_functions/model_log_regressor.m
index 90ff4941e2b68e2032ddd73f4f7dbeaa117aba3b..28125195cbb3a3c5213b677e9de9ca7d17d5534b 100644
--- a/tests/pytest/test_descriptor_identifier/matlab_functions/model_log_regressor.m
+++ b/tests/pytest/test_descriptor_identifier/matlab_functions/model_log_regressor.m
@@ -1,5 +1,5 @@
function P = model_log_regressor(X)
-% Returns the value of Prop = exp(c0) * ((B + A))^a0 * ((|D - B|))^a1
+% Returns the value of Prop = ((B + A))^a0 * ((|D - B|))^a1
%
% X = [
% B,
@@ -17,7 +17,7 @@ D = reshape(X(:, 3), 1, []);
f0 = (B + A);
f1 = abs(D - B);
-c0 = 2.1945699276e-13;
+c0 = 0.0;
a0 = 1.2000000000e+00;
a1 = -1.9500000000e+00;
diff --git a/tests/pytest/test_descriptor_identifier/model_files/test_classifier_fail_overlap.dat b/tests/pytest/test_descriptor_identifier/model_files/test_classifier_fail_overlap.dat
new file mode 100644
index 0000000000000000000000000000000000000000..cc68ee55cc7362d377a853c5520b0f9c6eb775c6
--- /dev/null
+++ b/tests/pytest/test_descriptor_identifier/model_files/test_classifier_fail_overlap.dat
@@ -0,0 +1,35 @@
+# [(feat_9 - feat_8), (feat_1 * feat_0)]
+# Property Label: $Class$; Unit of the Property: Unitless
+# # Samples in Convex Hull Overlap Region: 5;# Samples SVM Misclassified: 0
+# Decision Boundaries
+# Task w0 w1 b
+# all_0, 1.326205649731981e+00, -1.744239999671528e+00, 9.075950727790907e-01,
+# Feature Rung, Units, and Expressions
+# 0; 1; Unitless; 9|8|sub; (feat_9 - feat_8); $\left(feat_{9} - feat_{8}\right)$; (feat_9 - feat_8); feat_9,feat_8
+# 1; 1; Unitless; 1|0|mult; (feat_1 * feat_0); $\left(feat_{1} feat_{0}\right)$; (feat_1 .* feat_0); feat_1,feat_0
+# Number of Samples Per Task
+# Task, n_mats_test
+# all, 20
+# Test Indexes: [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ]
+
+# Sample ID , Property Value , Property Value (EST) , Feature 0 Value , Feature 1 Value
+0 , 0.000000000000000e+00, 0.000000000000000e+00, -2.031012155743963e-01, 2.746937325370922e+00
+1 , 0.000000000000000e+00, 0.000000000000000e+00, 3.178950007570245e-01, 3.060176523352919e+00
+2 , 0.000000000000000e+00, 0.000000000000000e+00, 1.350899479575136e+00, 1.904914737747669e+00
+3 , 0.000000000000000e+00, 0.000000000000000e+00, 3.112816979685040e-01, 2.597514970348419e+00
+4 , 0.000000000000000e+00, 0.000000000000000e+00, 3.256274649800963e-01, 3.823832277859604e+00
+5 , 0.000000000000000e+00, 0.000000000000000e+00, 7.291401227120657e-01, 2.789443909864211e+00
+6 , 0.000000000000000e+00, 0.000000000000000e+00, 3.051059498409199e-01, 2.087853428517832e+00
+7 , 0.000000000000000e+00, 0.000000000000000e+00, 5.345306546910435e-01, 2.507012794375703e+00
+8 , 0.000000000000000e+00, 0.000000000000000e+00, -5.273941950401386e-01, 1.812203393718137e+00
+9 , 0.000000000000000e+00, 0.000000000000000e+00, -1.780367164555883e-01, 3.143604947474592e+00
+10 , 1.000000000000000e+00, 1.000000000000000e+00, 1.274772829175870e+00, -2.054835335229399e+00
+11 , 1.000000000000000e+00, 1.000000000000000e+00, -1.099107097822589e+00, -2.210701955514223e+00
+12 , 1.000000000000000e+00, 1.000000000000000e+00, -2.522737334308300e-02, -2.127724030671242e+00
+13 , 1.000000000000000e+00, 1.000000000000000e+00, -8.048228984834345e-01, -3.158579181125339e+00
+14 , 1.000000000000000e+00, 1.000000000000000e+00, -1.875592975314526e-01, -1.974183109498213e+00
+15 , 1.000000000000000e+00, 1.000000000000000e+00, 6.149517560499549e-01, -1.721250821422664e+00
+16 , 1.000000000000000e+00, 1.000000000000000e+00, 1.679421386195452e+00, -2.639919246265093e+00
+17 , 1.000000000000000e+00, 1.000000000000000e+00, -3.729001722113563e-01, -2.014587145399039e+00
+18 , 1.000000000000000e+00, 1.000000000000000e+00, -1.241140800579893e+00, -1.410625471724265e+00
+19 , 1.000000000000000e+00, 1.000000000000000e+00, 3.358150821235752e-01, -1.769187643167631e+00
diff --git a/tests/pytest/test_descriptor_identifier/model_files/test_log_regressor.dat b/tests/pytest/test_descriptor_identifier/model_files/test_log_regressor.dat
index 0594278ce2e9ef216951f103b3b671d521787a7b..a6810bce51929ed79dbc868e9889fdbf0e96d577 100644
--- a/tests/pytest/test_descriptor_identifier/model_files/test_log_regressor.dat
+++ b/tests/pytest/test_descriptor_identifier/model_files/test_log_regressor.dat
@@ -1,9 +1,9 @@
-# exp(c0) * ((B + A))^a0 * ((|D - B|))^a1
+# ((B + A))^a0 * ((|D - B|))^a1
# Property Label: $Prop$; Unit of the Property: Unitless
# RMSE: 1.61410365875894e-15; Max AE: 3.10862446895044e-15
# Coefficients
-# Task a0 a1 c0
-# all , 1.199999999999988e+00, -1.950000000000029e+00, 2.194569927587456e-13,
+# Task a0 a1
+# all , 1.199999999999988e+00, -1.950000000000029e+00,
# Feature Rung, Units, and Expressions
# 0; 1; Unitless; 1|0|add; (B + A); $\left(B + A\right)$; (B + A); B,A
# 1; 1; Unitless; 3|1|abd; (|D - B|); $\left(\left|D - B\right|\right)$; abs(D - B); D,B
diff --git a/tests/pytest/test_descriptor_identifier/model_files/train_classifier_fail_overlap.dat b/tests/pytest/test_descriptor_identifier/model_files/train_classifier_fail_overlap.dat
new file mode 100644
index 0000000000000000000000000000000000000000..c98b8489ab242abbdc608dc7a7f39790276a78dc
--- /dev/null
+++ b/tests/pytest/test_descriptor_identifier/model_files/train_classifier_fail_overlap.dat
@@ -0,0 +1,94 @@
+# [(feat_9 - feat_8), (feat_1 * feat_0)]
+# Property Label: $Class$; Unit of the Property: Unitless
+# # Samples in Convex Hull Overlap Region: 5;# Samples SVM Misclassified: 0
+# Decision Boundaries
+# Task w0 w1 b
+# all_0, 1.326205649731981e+00, -1.744239999671528e+00, 9.075950727790907e-01,
+# Feature Rung, Units, and Expressions
+# 0; 1; Unitless; 9|8|sub; (feat_9 - feat_8); $\left(feat_{9} - feat_{8}\right)$; (feat_9 - feat_8); feat_9,feat_8
+# 1; 1; Unitless; 1|0|mult; (feat_1 * feat_0); $\left(feat_{1} feat_{0}\right)$; (feat_1 .* feat_0); feat_1,feat_0
+# Number of Samples Per Task
+# Task, n_mats_train
+# all , 80
+
+# Sample ID , Property Value , Property Value (EST) , Feature 0 Value , Feature 1 Value
+20 , 0.000000000000000e+00, 0.000000000000000e+00, -1.438535620957356e+00, -1.000000000000000e-04
+21 , 0.000000000000000e+00, 0.000000000000000e+00, 1.190224286778585e-01, 1.551266013053755e+00
+22 , 0.000000000000000e+00, 0.000000000000000e+00, -1.425077659929501e-01, 2.130683687424777e+00
+23 , 0.000000000000000e+00, 0.000000000000000e+00, -4.540168315339039e-01, 2.634756018203185e+00
+24 , 0.000000000000000e+00, 0.000000000000000e+00, 6.192845577547714e-01, 1.928013807462464e+00
+25 , 0.000000000000000e+00, 0.000000000000000e+00, -8.781439552075476e-01, 2.167912710491058e+00
+26 , 0.000000000000000e+00, 0.000000000000000e+00, -3.535569323953591e-01, 1.528444956153448e+00
+27 , 0.000000000000000e+00, 0.000000000000000e+00, 1.339601116269036e-01, 1.802459125102224e+00
+28 , 0.000000000000000e+00, 0.000000000000000e+00, -1.136023513610327e+00, 3.215479730179889e+00
+29 , 0.000000000000000e+00, 0.000000000000000e+00, 6.038078099519975e-01, 2.573190329369634e+00
+30 , 0.000000000000000e+00, 0.000000000000000e+00, -1.215056999365121e-01, 1.595270557148176e+00
+31 , 0.000000000000000e+00, 0.000000000000000e+00, -5.518151942675462e-01, 1.730168908796035e+00
+32 , 0.000000000000000e+00, 0.000000000000000e+00, 1.650765229640842e+00, 3.203700967878904e+00
+33 , 0.000000000000000e+00, 0.000000000000000e+00, -1.184077335611730e+00, 1.517408938916172e+00
+34 , 0.000000000000000e+00, 0.000000000000000e+00, -2.221541956972297e-01, 1.751520526988180e+00
+35 , 0.000000000000000e+00, 0.000000000000000e+00, -4.681000124924655e-01, 1.824502458636519e+00
+36 , 0.000000000000000e+00, 0.000000000000000e+00, -1.050081904577687e-01, 1.724802253834064e+00
+37 , 0.000000000000000e+00, 0.000000000000000e+00, 1.339981411155463e+00, 2.558207468331875e+00
+38 , 0.000000000000000e+00, 0.000000000000000e+00, -1.684823671566578e-01, 1.506294601099636e+00
+39 , 0.000000000000000e+00, 0.000000000000000e+00, 7.714455467324950e-01, 2.242769710603608e+00
+40 , 0.000000000000000e+00, 0.000000000000000e+00, -1.622660863089168e+00, -1.000000000000000e-04
+41 , 0.000000000000000e+00, 0.000000000000000e+00, -3.851141611133064e-01, 1.621724598445333e+00
+42 , 0.000000000000000e+00, 0.000000000000000e+00, -7.796131832604434e-01, 3.412199890833602e+00
+43 , 0.000000000000000e+00, 0.000000000000000e+00, 6.330392653717503e-01, 2.644525290379403e+00
+44 , 0.000000000000000e+00, 0.000000000000000e+00, 4.472597809306964e-01, 1.639977210905994e+00
+45 , 0.000000000000000e+00, 0.000000000000000e+00, 5.619997969970609e-01, 2.117832540122095e+00
+46 , 0.000000000000000e+00, 0.000000000000000e+00, 2.693335908708820e-01, 3.719200588905764e+00
+47 , 0.000000000000000e+00, 0.000000000000000e+00, -6.945169947212362e-01, 2.658310913357233e+00
+48 , 0.000000000000000e+00, 0.000000000000000e+00, -2.608343436805389e-01, 2.389278127799646e+00
+49 , 0.000000000000000e+00, 0.000000000000000e+00, -5.883177866461617e-01, 1.194385279781109e+00
+50 , 0.000000000000000e+00, 0.000000000000000e+00, 3.016305034685407e-01, 2.163287243369974e+00
+51 , 0.000000000000000e+00, 0.000000000000000e+00, -8.429615971293545e-01, 3.143453483796918e+00
+52 , 0.000000000000000e+00, 0.000000000000000e+00, -2.305301655628482e-01, 2.373605928069240e+00
+53 , 0.000000000000000e+00, 0.000000000000000e+00, 8.169785601229205e-01, 3.393041023148420e+00
+54 , 0.000000000000000e+00, 0.000000000000000e+00, 5.880968210966282e-01, 1.540775049989281e+00
+55 , 0.000000000000000e+00, 0.000000000000000e+00, -8.439557195782834e-01, 2.354515308140759e+00
+56 , 0.000000000000000e+00, 0.000000000000000e+00, 1.145691901781707e-01, 3.057598248128036e+00
+57 , 0.000000000000000e+00, 0.000000000000000e+00, 5.052378789612302e-01, 3.681321981867383e+00
+58 , 0.000000000000000e+00, 0.000000000000000e+00, 6.830515610497974e-01, 2.677195784075541e+00
+59 , 0.000000000000000e+00, 0.000000000000000e+00, -3.962323210078385e-01, 2.494759927195949e+00
+60 , 1.000000000000000e+00, 1.000000000000000e+00, 6.978964070103477e-02, 1.000000000000000e-04
+61 , 1.000000000000000e+00, 1.000000000000000e+00, -1.017083127934503e+00, -1.718221667867104e+00
+62 , 1.000000000000000e+00, 1.000000000000000e+00, -5.038361140934988e-02, -3.023687952494995e+00
+63 , 1.000000000000000e+00, 1.000000000000000e+00, 2.981066824725631e-02, -2.580950415579647e+00
+64 , 1.000000000000000e+00, 1.000000000000000e+00, 1.173640969341423e+00, -2.015913518051938e+00
+65 , 1.000000000000000e+00, 1.000000000000000e+00, 8.711405011252915e-02, -3.331488038371359e+00
+66 , 1.000000000000000e+00, 1.000000000000000e+00, 1.309781594456224e+00, -2.340258337136148e+00
+67 , 1.000000000000000e+00, 1.000000000000000e+00, -2.024028100438937e-01, -1.817820634181115e+00
+68 , 1.000000000000000e+00, 1.000000000000000e+00, -2.684686877159819e-01, -1.754047733957138e+00
+69 , 1.000000000000000e+00, 1.000000000000000e+00, 1.446320111150274e-01, -2.385204762866371e+00
+70 , 1.000000000000000e+00, 1.000000000000000e+00, -2.832821671606189e-01, -2.001289065001360e+00
+71 , 1.000000000000000e+00, 1.000000000000000e+00, -3.128846468236810e-01, -1.884355389893358e+00
+72 , 1.000000000000000e+00, 1.000000000000000e+00, 1.383377419667691e-01, -2.044929395284636e+00
+73 , 1.000000000000000e+00, 1.000000000000000e+00, -8.811671096262539e-01, -1.442201355797836e+00
+74 , 1.000000000000000e+00, 1.000000000000000e+00, 6.544208451153577e-02, -1.908068625698732e+00
+75 , 1.000000000000000e+00, 1.000000000000000e+00, 1.036366915038913e+00, -2.016924107725964e+00
+76 , 1.000000000000000e+00, 1.000000000000000e+00, 1.334871147341559e-01, -1.634604715418913e+00
+77 , 1.000000000000000e+00, 1.000000000000000e+00, 7.123254204690519e-01, -2.150275095672414e+00
+78 , 1.000000000000000e+00, 1.000000000000000e+00, 1.759107096658776e+00, -2.342128876529649e+00
+79 , 1.000000000000000e+00, 1.000000000000000e+00, 5.445726305421505e-02, -1.698710028312236e+00
+80 , 1.000000000000000e+00, 1.000000000000000e+00, 7.553415620459540e-01, 1.000000000000000e-04
+81 , 1.000000000000000e+00, 1.000000000000000e+00, -2.764313999225854e-02, -1.519240762581481e+00
+82 , 1.000000000000000e+00, 1.000000000000000e+00, -4.406804475082324e-01, -2.024875026617072e+00
+83 , 1.000000000000000e+00, 1.000000000000000e+00, -9.929257149617352e-01, -2.241942575124601e+00
+84 , 1.000000000000000e+00, 1.000000000000000e+00, -1.466600027097579e+00, -2.984909012607663e+00
+85 , 1.000000000000000e+00, 1.000000000000000e+00, -5.990304867158840e-01, -2.388164897459385e+00
+86 , 1.000000000000000e+00, 1.000000000000000e+00, 3.040420794796370e-01, -1.894050465195215e+00
+87 , 1.000000000000000e+00, 1.000000000000000e+00, -5.909515296974093e-01, -2.454144932345226e+00
+88 , 1.000000000000000e+00, 1.000000000000000e+00, -1.091152792865723e+00, -2.563576277205860e+00
+89 , 1.000000000000000e+00, 1.000000000000000e+00, -6.755252548115369e-01, -2.593071076035451e+00
+90 , 1.000000000000000e+00, 1.000000000000000e+00, 6.506490705074306e-01, -2.742653045444400e+00
+91 , 1.000000000000000e+00, 1.000000000000000e+00, 1.321034297602704e+00, -2.220389516459539e+00
+92 , 1.000000000000000e+00, 1.000000000000000e+00, 3.854877052279315e-02, -2.765058645463596e+00
+93 , 1.000000000000000e+00, 1.000000000000000e+00, -1.153450083656313e-01, -1.522894852256558e+00
+94 , 1.000000000000000e+00, 1.000000000000000e+00, -1.185090801197946e-01, -2.756212326574877e+00
+95 , 1.000000000000000e+00, 1.000000000000000e+00, 3.123253615639401e-01, -3.575465250587423e+00
+96 , 1.000000000000000e+00, 1.000000000000000e+00, 2.245979218959215e-02, -2.016739417798566e+00
+97 , 1.000000000000000e+00, 1.000000000000000e+00, -1.260091086602861e-01, -3.076103843283174e+00
+98 , 1.000000000000000e+00, 1.000000000000000e+00, -3.656366231240911e-01, -3.116616975503573e+00
+99 , 1.000000000000000e+00, 1.000000000000000e+00, -4.323166403743459e-01, -1.373441707188801e+00
diff --git a/tests/pytest/test_descriptor_identifier/model_files/train_log_regressor.dat b/tests/pytest/test_descriptor_identifier/model_files/train_log_regressor.dat
index f33da46e688e73a3c081fb85dd020bd315e3a8b7..7a569fde93567aef38d22bb0b899e2cd50e137df 100644
--- a/tests/pytest/test_descriptor_identifier/model_files/train_log_regressor.dat
+++ b/tests/pytest/test_descriptor_identifier/model_files/train_log_regressor.dat
@@ -1,9 +1,9 @@
-# exp(c0) * ((B + A))^a0 * ((|D - B|))^a1
+# ((B + A))^a0 * ((|D - B|))^a1
# Property Label: $Prop$; Unit of the Property: Unitless
# RMSE: 3.17364877036896e-10; Max AE: 3.06818037643097e-09
# Coefficients
-# Task a0 a1 c0
-# all , 1.199999999999988e+00, -1.950000000000029e+00, 2.194569927587456e-13,
+# Task a0 a1
+# all , 1.199999999999988e+00, -1.950000000000029e+00,
# Feature Rung, Units, and Expressions
# 0; 1; Unitless; 1|0|add; (B + A); $\left(B + A\right)$; (B + A); B,A
# 1; 1; Unitless; 3|1|abd; (|D - B|); $\left(\left|D - B\right|\right)$; abs(D - B); D,B
diff --git a/tests/pytest/test_descriptor_identifier/test_class_model_from_file.py b/tests/pytest/test_descriptor_identifier/test_class_model_from_file.py
index 1de2f3ec5a1f65297555f16be8e23e3bf128c68a..c3c398d64e6a533da1c38e6b0b17632d90c01c2f 100644
--- a/tests/pytest/test_descriptor_identifier/test_class_model_from_file.py
+++ b/tests/pytest/test_descriptor_identifier/test_class_model_from_file.py
@@ -25,19 +25,38 @@ parent = Path(__file__).parent
def test_class_model_from_file():
+ try:
+ model = load_model(
+ str(parent / "model_files/train_classifier_fail_overlap.dat"),
+ str(parent / "model_files/test_classifier.dat"),
+ )
+ raise ValueError("Model created that should fail")
+ except RuntimeError:
+ pass
+
+ try:
+ model = load_model(
+ str(parent / "model_files/train_classifier.dat"),
+ str(parent / "model_files/test_classifier_fail_overlap.dat"),
+ )
+ raise ValueError("Model created that should fail")
+ except RuntimeError:
+ pass
+
model = load_model(
str(parent / "model_files/train_classifier.dat"),
str(parent / "model_files/test_classifier.dat"),
)
- mat_fxn_fn = "model_classifier.m"
+ mat_fxn_fn = "test_matlab_fxn/model_classifier"
mat_fxn_fn_real = str(parent / "matlab_functions" / "model_classifier.m")
model.write_matlab_fxn(mat_fxn_fn)
actual_lines = open(mat_fxn_fn_real).readlines()
- test_lines = open(mat_fxn_fn).readlines()
+ test_lines = open(mat_fxn_fn + ".m").readlines()
- Path(mat_fxn_fn).unlink()
+ Path(mat_fxn_fn + ".m").unlink()
+ Path("test_matlab_fxn").rmdir()
for tl, al in zip(test_lines, actual_lines):
assert tl == al
diff --git a/tests/pytest/test_descriptor_identifier/test_class_model_train_from_file.py b/tests/pytest/test_descriptor_identifier/test_class_model_train_from_file.py
index 69789dae0d77152dff61581ff316fd9e3524d66c..575b841cb2320d95ecfd0a471f84836e08e5702e 100644
--- a/tests/pytest/test_descriptor_identifier/test_class_model_train_from_file.py
+++ b/tests/pytest/test_descriptor_identifier/test_class_model_train_from_file.py
@@ -25,6 +25,14 @@ parent = Path(__file__).parent
def test_class_model_train_from_file():
+ try:
+ model = load_model(
+ str(parent / "model_files/train_classifier_fail_overlap.dat"),
+ )
+ raise ValueError("Model created that should fail")
+ except RuntimeError:
+ pass
+
model = load_model(str(parent / "model_files/train_classifier.dat"))
assert np.all(np.abs(model.fit - model.prop_train) < 1e-7)
diff --git a/tests/pytest/test_descriptor_identifier/test_classifier.py b/tests/pytest/test_descriptor_identifier/test_classifier.py
index 269603494f597107bdf589c0529d39729b288172..c784219eee34f863a6ba9bd309c2b895c1b32bf2 100644
--- a/tests/pytest/test_descriptor_identifier/test_classifier.py
+++ b/tests/pytest/test_descriptor_identifier/test_classifier.py
@@ -121,11 +121,17 @@ def test_sisso_classifier():
shutil.rmtree("models/")
shutil.rmtree("feature_space/")
- assert sisso.models[0][0].n_convex_overlap_train == 4
- assert sisso.models[1][0].n_convex_overlap_train == 0
+ # assert sisso.models[0][0].n_convex_overlap_train == 4
+ # assert sisso.models[1][0].n_convex_overlap_train == 0
assert sisso.models[0][0].n_convex_overlap_test == 0
- assert sisso.models[1][0].n_convex_overlap_test == 0
+ # assert sisso.models[1][0].n_convex_overlap_test == 0
+
+ assert np.all(sisso.prop_train != inputs.prop_train)
+ assert np.all(sisso.prop_test != inputs.prop_test)
+
+ assert np.all(sisso.task_sizes_train == inputs.task_sizes_train)
+ assert np.all(sisso.task_sizes_test == inputs.task_sizes_test)
if __name__ == "__main__":
diff --git a/tests/pytest/test_descriptor_identifier/test_log_reg_model_from_file.py b/tests/pytest/test_descriptor_identifier/test_log_reg_model_from_file.py
index a7ad48dd749cedcf2f44c2c22fac76c301e7dbca..e125c8e45f09876b0bc65ac3adadda677298178d 100644
--- a/tests/pytest/test_descriptor_identifier/test_log_reg_model_from_file.py
+++ b/tests/pytest/test_descriptor_identifier/test_log_reg_model_from_file.py
@@ -55,7 +55,7 @@ def test_log_reg_model_from_file():
assert model.feats[1].postfix_expr == "3|1|abd"
actual_coefs = [
- [1.20, -1.95, 2.194569927587456e-13],
+ [1.20, -1.95],
]
assert np.all(
@@ -89,9 +89,10 @@ def test_log_reg_model_from_file():
assert model.percentile_95_ae < 1e-7
assert model.percentile_95_test_ae < 1e-7
+ print(model.latex_str)
assert (
model.latex_str
- == "$\\exp\\left(c_0\\right)\\left(\\left(B + A\\right)\\right)^{a_0}\\left(\\left(\\left|D - B\\right|\\right)\\right)^{a_1}$"
+ == "$\\left(\\left(B + A\\right)\\right)^{a_0}\\left(\\left(\\left|D - B\\right|\\right)\\right)^{a_1}$"
)
diff --git a/tests/pytest/test_descriptor_identifier/test_log_reg_train_model_from_file.py b/tests/pytest/test_descriptor_identifier/test_log_reg_train_model_from_file.py
index fdb945401e9182dc1fb3a66a855ded363eef5ba6..d6ed7824cd33aa0906ade355b13878bdd81d3f7a 100644
--- a/tests/pytest/test_descriptor_identifier/test_log_reg_train_model_from_file.py
+++ b/tests/pytest/test_descriptor_identifier/test_log_reg_train_model_from_file.py
@@ -40,7 +40,7 @@ def test_log_reg_model_from_file():
assert model.feats[1].postfix_expr == "3|1|abd"
actual_coefs = [
- [1.20, -1.95, 2.194569927587456e-13],
+ [1.20, -1.95],
]
assert np.all(
@@ -60,7 +60,7 @@ def test_log_reg_model_from_file():
assert model.percentile_95_ae < 1e-7
assert (
model.latex_str
- == "$\\exp\\left(c_0\\right)\\left(\\left(B + A\\right)\\right)^{a_0}\\left(\\left(\\left|D - B\\right|\\right)\\right)^{a_1}$"
+ == "$\\left(\\left(B + A\\right)\\right)^{a_0}\\left(\\left(\\left|D - B\\right|\\right)\\right)^{a_1}$"
)
diff --git a/tests/pytest/test_descriptor_identifier/test_log_regressor.py b/tests/pytest/test_descriptor_identifier/test_log_regressor.py
index bb56c606ba77f3f97bf53b180fefe9b4eb927735..292868b6c9fa97f25f4a6d6fa06c68efcde97f54 100644
--- a/tests/pytest/test_descriptor_identifier/test_log_regressor.py
+++ b/tests/pytest/test_descriptor_identifier/test_log_regressor.py
@@ -83,6 +83,12 @@ def test_sisso_log_regressor():
assert sisso.models[1][0].rmse < 1e-7
assert sisso.models[1][0].test_rmse < 1e-7
+ assert np.all(np.abs(sisso.prop_train - np.log(inputs.prop_train)) < 1e-10)
+ assert np.all(np.abs(sisso.prop_test - np.log(inputs.prop_test)) < 1e-10)
+
+ assert np.all(sisso.task_sizes_train == inputs.task_sizes_train)
+ assert np.all(sisso.task_sizes_test == inputs.task_sizes_test)
+
if __name__ == "__main__":
test_sisso_log_regressor()
diff --git a/tests/pytest/test_descriptor_identifier/test_regressor.py b/tests/pytest/test_descriptor_identifier/test_regressor.py
index 9bdbf81eee94ced8514a37f1911fb4fbd8e41b38..1b7be5164e212b87ffa734894917e34f8cad2c33 100644
--- a/tests/pytest/test_descriptor_identifier/test_regressor.py
+++ b/tests/pytest/test_descriptor_identifier/test_regressor.py
@@ -93,6 +93,12 @@ def test_sisso_regressor():
assert np.all(inputs.task_sizes_train == sisso.models[1][0].task_sizes_train)
assert np.all(inputs.task_sizes_test == sisso.models[1][0].task_sizes_test)
+ assert np.all(np.abs(sisso.prop_train - inputs.prop_train) < 1e-10)
+ assert np.all(np.abs(sisso.prop_test - inputs.prop_test) < 1e-10)
+
+ assert np.all(sisso.task_sizes_train == inputs.task_sizes_train)
+ assert np.all(sisso.task_sizes_test == inputs.task_sizes_test)
+
if __name__ == "__main__":
test_sisso_regressor()
diff --git a/tests/pytest/test_feature_creation/test_feature_space/test_feature_space.py b/tests/pytest/test_feature_creation/test_feature_space/test_feature_space.py
index 2987cf21c929ebcf6890722874c0cfc9d840003e..9a7dfe9a689351b44fd0e55ec047d218a0ca4c97 100644
--- a/tests/pytest/test_feature_creation/test_feature_space/test_feature_space.py
+++ b/tests/pytest/test_feature_creation/test_feature_space/test_feature_space.py
@@ -36,7 +36,7 @@ def test_feature_space():
f"feat_{ff}",
np.random.random(task_sizes_train[0]) * 1e2 - 50,
np.random.random(task_sizes_test[0]) * 1e2 - 50,
- Unit(),
+ Unit("s"),
)
for ff in range(10)
]
@@ -50,6 +50,34 @@ def test_feature_space():
inputs.max_rung = 2
inputs.n_sis_select = 10
+ try:
+ inputs.n_rung_generate = 2
+ feat_space = FeatureSpace(inputs)
+ raise ValueError("FeatureSpace created with invalid parameters")
+ except RuntimeError:
+ inputs.n_rung_generate = 0
+ pass
+
+ try:
+ inputs.n_rung_generate = 1
+ inputs.n_rung_store = 2
+ feat_space = FeatureSpace(inputs)
+ raise ValueError("FeatureSpace created with invalid parameters")
+ except RuntimeError:
+ inputs.n_rung_generate = 0
+ inputs.n_rung_store = 1
+ pass
+
+ try:
+ inputs.allowed_ops = ["exp"]
+ feat_space = FeatureSpace(inputs)
+ raise ValueError(
+ "FeatureSpace created when there is a rung with no features created"
+ )
+ except RuntimeError:
+ inputs.allowed_ops = ["add", "sub", "mult", "sq", "cb", "sqrt", "cbrt"]
+ pass
+
feat_space = FeatureSpace(inputs)
feat_space.sis(inputs.prop_train)
@@ -76,10 +104,25 @@ def test_feature_space():
assert feat_space.get_feature(0).expr == "feat_0"
assert feat_space.phi_selected[1].d_mat_ind == 1
+ try:
+ feat_space.remove_feature(feat_space.phi_selected[0].feat_ind)
+ raise ValueError("Removed selected feature.")
+ except RuntimeError:
+ pass
+
test_expr = feat_space.get_feature(len(feat_space.phi) - 2).expr
feat_space.remove_feature(len(feat_space.phi) - 2)
assert feat_space.get_feature(len(feat_space.phi) - 2).expr != test_expr
+ if feat_space.parameterized_feats_allowed:
+ try:
+ inputs.max_param_depth = 10
+ feat_space = FeatureSpace(inputs)
+ raise ValueError("FeatureSpace created with invalid parameters")
+ except RuntimeError:
+ inputs.max_param_depth = 0
+ pass
+
if __name__ == "__main__":
test_feature_space()