diff --git a/Taweret/mix/trees.py b/Taweret/mix/trees.py index fc078c48..909d88d9 100644 --- a/Taweret/mix/trees.py +++ b/Taweret/mix/trees.py @@ -1,7 +1,7 @@ """ Name: trees.py Author: John Yannotty (yannotty.1@osu.edu) -Desc: Defines the tree mixing class, which is an interface for MixBART +Desc: Defines the tree mixing class, which is an interface for BART-BMM Start Date: 10/05/22 Version: 1.0 @@ -15,6 +15,7 @@ import tempfile import shutil import os +import typing import Taweret.core.setup @@ -27,7 +28,7 @@ class Trees(BaseMixer): # Overwrite base constructor - def __init__(self, model_dict, **kwargs): + def __init__(self, model_dict: dict, **kwargs): ''' Constructor for the Trees mixing class. @@ -131,7 +132,7 @@ def map(self): ''' return super().map - # Done + @property def posterior(self): ''' @@ -184,9 +185,9 @@ def prior(self): return hyper_params_dict - # DONE - def set_prior(self, ntree=1,ntreeh=1,k=2,power=2.0,base=0.95,overallsd=None, \ - overallnu=10,inform_prior=True,tauvec=None,betavec=None): + + def set_prior(self, ntree: int = 1,ntreeh:int = 1, k: float=2,power: float=2.0,base:float=0.95,overallsd:float=None, \ + overallnu: int = 10,inform_prior: bool = True,tauvec: bool = None,betavec: bool = None): ''' Sets the hyperparameters in the tree and terminal node priors. Also specfies if an informative or non-informative prior will be used. @@ -266,15 +267,15 @@ def prior_predict(self): raise Exception("Not applicable for trees.") - def train(self,X, y, **kwargs): + def train(self,X: np.ndarray, y: np.ndarray, **kwargs): ''' Train the mixed-model using a set of observations y at inputs x. Parameters: ---------- - X : np.ndarray + X : np.ndarray (n X p) input parameter values. - y : np.1darray + y : np.ndarray (n X 1) observed data at inputs X. kwargs : dict Dictionary of arguments @@ -377,7 +378,7 @@ def train(self,X, y, **kwargs): return res - def predict(self, X, ci = 0.95): + def predict(self, X: np.ndarray, ci: float = 0.95): ''' Obtain posterior predictive distribution of the mixed-model at a set of inputs X. @@ -474,7 +475,7 @@ def predict(self, X, ci = 0.95): return pred_post, pred_mean, pred_credible_interval, pred_sd - def predict_weights(self, X, ci = 0.95): + def predict_weights(self, X: np.ndarray, ci: float = 0.95): ''' Obtain posterior distribution of the weight functions at a set of inputs X. @@ -548,14 +549,14 @@ def predict_weights(self, X, ci = 0.95): posterior = self.wdraws post_mean = self.wts_mean post_sd = self.wts_sd - post_credible_interval = (self.wts_lower,self.wts_upper) + post_credible_interval = [self.wts_lower,self.wts_upper] - return posterior, post_mean, post_sd, post_credible_interval + return posterior, post_mean, post_credible_interval, post_sd # ----------------------------------------------------------- # Plotting - def plot_prediction(self, xdim = 0): + def plot_prediction(self, xdim: int = 0): ''' Plot the predictions of the mixed-model. The x-axis of this plot can be any column of the design matrix X, which is passed into @@ -592,7 +593,7 @@ def plot_prediction(self, xdim = 0): plt.show() - def plot_weights(self, xdim = 0): + def plot_weights(self, xdim: int = 0): ''' Plot the weight functions. The x-axis of this plot can be any column of the design matrix X, which is passed into diff --git a/docs/source/installation.rst b/docs/source/installation.rst index adf937ee..5968d4e8 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -30,3 +30,95 @@ The pip installation is not available yet. We are working on it. .. code-block:: bash pip install Taweret + +Installation +============ + +Taweret requires the following for for basic functionality. + - python + - numpy + - seaborn + - jupyter + - bilby + - ptemcee + +Follow these steps to install Taweret from github. + +.. code-block:: bash + + git clone https://github.com/danOSU/Taweret.git + #If you want to use SMABA toy models please clone samba repo + #git clone https://github.com/asemposki/SAMBA.git + cd Taweret + conda env create -f environment.yml + conda activate test_env + cd doc/source/notebooks + jupyter notebook --browser=safari + +You can look at the available notebooks in the directory and modify it for your own use case. If \ +you need to add a new mixing method please refer to the **For Deveopers** section. + +The pip instalation is not available yet. We are working on it. + +.. code-block:: bash + pip install Taweret + + + +Additional Requirements +----------------------- + +Certain Taweret modules may require additional steps to properly setup an environment which can \ +execute the code. These modules and their respective requirements are listed below. + +**Tree** +^^^^^^^^^ + +The Trees module is a Python interface which calls and executes a number of Ubuntu packages in order \ +to fit the mixing model and obtain the resulting predictions. This package is developed as a part of the \ +Open Bayesian Trees Project (OpenBT)[1,2]. To install the Ubuntu package, please follow the steps below \ +based on the operating system of choice. + + +**Linux:** + +1. Download the OpenBT Ubuntu Linux 20.04 package: +.. code-block:: bash + $ wget -q https://github.com/jcyannotty/OpenBT/raw/main/openbt_mixing0.current_amd64-MPI_Ubuntu_20.04.deb + + +2. Install the package and result the library cache: +.. code-block:: bash + $ cd /location/of/downloaded/.deb + $ dpkg -i openbt_mixing0.current_amd64-MPI_Ubuntu_20.04.deb + $ ldconfig + + +**Mac OS/:X + +1. Install the OS/X OpenMPI package by running the following `brew` commands in a terminal window: +.. code-block:: bash + $ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)" + $ brew install open-mpi + +2. Download the OpenBT OSX binary package: + +3. Install the OpenBT OSX package by double-clicking on the downloaded .pkg file and follow the on-screen instructions. + + +**Windows:** +OpenBT will run within the Windows 10 Windows Subsystem for Linux (WSL) environment. For instructions on installing WSL,\ +please see [Ubuntu on WSL](https://ubuntu.com/wsl). We recommend installing the Ubuntu 20.04 WSL build. \ +There are also instructions \ +[here](https://wiki.ubuntu.com/WSL?action=subscribe&_ga=2.237944261.411635877.1601405226-783048612.1601405226#Installing_Packages_on_Ubuntu) \ +on keeping your Ubuntu WSL up to date, or installing additional features like X support. Once you have \ +installed the WSL Ubuntu layer, start the WSL Ubuntu shell from the start menu and then install the package: + +.. code-block:: bash + $ cd /mnt/c/location/of/downloaded/.deb + $ dpkg -i openbt_mixing0.current_amd64-MPI_Ubuntu_20.04.deb + + +**OpenBT References** +[1. OpenBT Repository](https://bitbucket.org/mpratola/openbt/src/master/). +[2. OpenBT Repository with Model Mixing](https://github.com/jcyannotty/OpenBT). \ No newline at end of file diff --git a/docs/source/notebooks/Trees_BMM_2D.ipynb b/docs/source/notebooks/Trees_BMM_2D.ipynb index 75d77706..df33750c 100644 --- a/docs/source/notebooks/Trees_BMM_2D.ipynb +++ b/docs/source/notebooks/Trees_BMM_2D.ipynb @@ -301,6 +301,16 @@ "The second expansion is shown below. The predicted surface is accurate for points $(x_1,x_2)$ close to the point $(-\\pi,-\\pi)$. Additionally, the expansion of $\\sin(x_1)$ is a high-fidelity approximation of $\\sin(x_1)$ across the interval $[-\\pi,\\pi]$. Hence, from the plot below, $h_2(x)$ can be used to approximate the valley and curvature in the true function for points in the bottom region of the domain. Once again, the surface is truncated for visual purposes in regions where the expansion diverges. " ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The $h_2(x)$ surface\n", + "\n", + "The second expansion is shown below. The predicted surface is accurate for points $(x_1,x_2)$ close to the point $(-\\pi,-\\pi)$. Additionally, the expansion of $\\sin(x_1)$ is a high-fidelity apprixmation of $\\sin(x_1)$ across the interval $[-\\pi,\\pi]$. Hence, from the plot below, $h_2(x)$ can be used to approximate the valley and curvature in the true function for points in the bottom region of the domain. Once again, the surface is truncated for visual purposes in regions where the expansion diverges. " + ] + }, { "cell_type": "code", "execution_count": 8, @@ -445,11 +455,10 @@ "# Define the model set\n", "h1 = sin_cos_exp(7,10,np.pi,np.pi)\n", "h2 = sin_cos_exp(13,6,-np.pi,-np.pi)\n", - "model_dict = {'model1':h1, 'model2':h2}\n" + "model_dict = {'model1':h1, 'model2':h2}" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -897,6 +906,175 @@ "output_type": "display_data" } ], + "source": [ + "cmap_hot = plt.get_cmap('hot')\n", + "w1 = wmean.transpose()[0]\n", + "w2 = wmean.transpose()[1]\n", + "\n", + "w1_mean = wmean.transpose()[0]\n", + "w1_mean = w1_mean.reshape(x1_test.shape).transpose()\n", + "\n", + "w2_mean = wmean.transpose()[1]\n", + "w2_mean\n", + "w2_mean = w2_mean.reshape(x1_test.shape).transpose()\n", + "\n", + "fig, ax = plt.subplots(1,2, figsize = (12,5))\n", + "pcm0 = ax[0].pcolormesh(w1_mean,cmap = cmap_hot, vmin = -0.05, vmax = 1.05)\n", + "ax[0].set_title(\"Posterior Mean of $w_1(x)$\", size = 20)\n", + "ax[0].set(xlabel = \"$x_1$\", ylabel = \"$x_2$\")\n", + "ax[0].xaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[0].xaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "ax[0].yaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[0].yaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "fig.colorbar(pcm1,ax = ax[0])\n", + "\n", + "pcm1 = ax[1].pcolormesh(w2_mean,cmap = cmap_hot, vmin = -0.05, vmax = 1.05)\n", + "ax[1].set_title(\"Posterior Mean of $w_2(x)$\", size = 20)\n", + "ax[1].set(xlabel = \"$x_1$\", ylabel = \"$x_2$\")\n", + "ax[1].xaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[1].xaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "ax[1].yaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[1].yaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "\n", + "fig.colorbar(pcm2,ax = ax[1])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Error Standard Deviation\n", + "Finally, the posterior distribution of the error distribution is shown below. The resulting posterior concentrates about the true value of 0.1. This means, the error standard deviation is accurately recovered with small uncertainty. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Lower bound\n", + "cmap_blues = plt.get_cmap(\"Blues\")\n", + "fig, ax = plt.subplots(1,3, figsize = (18,5))\n", + "\n", + "pcm0 = ax[0].pcolormesh(pci[0].reshape(x1_test.shape).transpose(),cmap = cmap, vmin = -2.5, vmax = 2.5)\n", + "ax[0].set_title(\"Lower Bound of Posterior Prediction\", size = 14)\n", + "ax[0].set(xlabel = \"X1\", ylabel = \"X2\")\n", + "ax[0].xaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[0].xaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "ax[0].yaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[0].yaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "fig.colorbar(pcm0,ax = ax[0])\n", + "\n", + "\n", + "# Upper bound\n", + "pcm1 = ax[1].pcolormesh(pci[1].reshape(x1_test.shape).transpose(),cmap = cmap, vmin = -2.5, vmax = 2.5)\n", + "ax[1].set_title(\"Upper Bound of Posterior Prediction\", size = 14)\n", + "ax[1].set(xlabel = \"X1\", ylabel = \"X2\")\n", + "ax[1].xaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[1].xaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "ax[1].yaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[1].yaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "fig.colorbar(pcm1,ax = ax[1])\n", + "\n", + "\n", + "# CI Width\n", + "pcm2 = ax[2].pcolormesh((pci[1].reshape(x1_test.shape) - pci[0].reshape(x1_test.shape)).transpose(),cmap = cmap_blues, vmin = 0, vmax = 5)\n", + "ax[2].set_title(\"Width of 95% Credible Intervals\", size = 14)\n", + "ax[2].set(xlabel = \"X1\", ylabel = \"X2\")\n", + "ax[2].xaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[2].xaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "ax[2].yaxis.set_major_locator(ticker.FixedLocator(np.round(np.linspace(0, n_test, 6),3)))\n", + "ax[2].yaxis.set_major_formatter(ticker.FixedFormatter(np.round(np.linspace(-np.pi, np.pi, 6),3)))\n", + "fig.colorbar(pcm2,ax = ax[2])\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can plot the mean residual, which is defined as $\\hat{r}(x) = f_\\dagger(x) - \\hat{f}_\\dagger(x)$. Clearly, the majority of the prediction error is atrributed to the upper left corner, wehere both expansions diverge. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "* The BART-BMM model can combine two expansions uisng observational data to obtain improved global prediction and interpretation.\n", + "\n", + "* The mixed-prediction is accurate across the entire domain outisde of the upper left corner, where both expansions diverge.\n", + "\n", + "* The weight functions are adaptively learned by an ensemble of trees.\n", + "\n", + "* A weight function takes values closer to 1 in areas where the corresponding expansion is accurate. Meanwhile, it takes values near 0 when the expansion is innaccurate.\n", + "\n", + "* The true error standard deviation of 0.10 is accurately recovered.\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Weight Functions\n", + "\n", + "The mean weight functions for both simulators are shown below. Both weight functions take higher values when the corresponding simulator provides a higher-fidelity approximation. Note, the patterns in the weight functions match the intuition about where each simulator is locally accurate or inaccurate as previously described. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "cmap_hot = plt.get_cmap('hot')\n", "w1 = wmean.transpose()[0]\n", diff --git a/test/bart_bmm_test_data/2d_pmean.txt b/test/bart_bmm_test_data/2d_pmean.txt new file mode 100644 index 00000000..ba8daefc --- /dev/null +++ b/test/bart_bmm_test_data/2d_pmean.txt @@ -0,0 +1,900 @@ +-1.099629456670000227e+00 +-1.021448453271419865e+00 +-8.922958330557501094e-01 +-6.438679274313500178e-01 +-5.339673436204119827e-01 +-8.033464116226540774e-01 +-5.024940290704160706e-01 +-1.983330964564400112e-01 +1.472453863283919862e-01 +4.789595466899509990e-01 +6.532088367601099899e-01 +7.537260940247698660e-01 +9.578613483636200243e-01 +9.474079046114111113e-01 +7.659682694629700306e-01 +6.724867425911270624e-01 +4.776864956889571023e-01 +2.707882881634169903e-01 +2.003892229805550040e-01 +3.821228746270000087e-01 +-3.027886950691600076e-01 +-5.479491483477729741e-01 +-1.094074908058030093e+00 +-1.066357903502699989e+00 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1.349444167852999936e-03 +7.314811877399999274e-01 -2.715823412416999878e-03 +7.316406913599998729e-01 -3.820918635237000559e-03 +7.310873821800000227e-01 -3.939407842397000051e-03 +7.313726976899999066e-01 -3.618820872457000331e-03 +7.313726976899999066e-01 -3.618820872457000331e-03 diff --git a/test/bart_bmm_test_data/2d_x_test.txt b/test/bart_bmm_test_data/2d_x_test.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/test/bart_bmm_test_data/2d_x_test.txt @@ -0,0 +1 @@ + diff --git a/test/bart_bmm_test_data/2d_x_train.txt b/test/bart_bmm_test_data/2d_x_train.txt new file mode 100644 index 00000000..71830433 --- /dev/null +++ b/test/bart_bmm_test_data/2d_x_train.txt @@ -0,0 +1,32 @@ +-3.025428 -2.07196 -1.524724 -1.151047 -0.03528678 +0.5928028 0.7094715 1.780308 2.17902 2.858839 +-2.794337 -2.363172 -1.407111 -1.143024 -0.3736722 +0.5363003 1.241808 1.398527 2.164437 2.560385 +-2.725709 -2.26977 -1.359123 -1.162074 -0.4101209 +0.3071053 0.7220931 1.480986 2.489803 2.596446 +-3.135049 -2.409826 -1.375897 -0.7107156 -0.3051858 +0.394079 1.158889 1.435627 2.304186 2.607817 +-2.524755 -2.326657 -1.812646 -1.154095 -0.03515957 +0.4994277 1.240733 1.475976 2.200353 3.022462 +-3.137126 -2.504042 -1.455561 -0.6724766 -0.4552789 +0.5101065 1.122101 1.877983 2.270714 2.959497 +-2.65777 -1.955966 -1.49218 -1.093086 -0.08854697 +0.2748818 0.8721971 1.546607 2.022353 2.554703 +-2.968365 -2.318256 -1.858456 -1.140603 -0.5131017 +0.474671 0.8093118 1.80192 2.137943 2.873103 +-2.866199 -2.613806 -3.121918 -2.826569 -2.984531 +-2.468879 -2.378566 -2.887342 -2.565745 -2.874504 +-1.589053 -2.044379 -2.057744 -1.916067 -1.99192 +-2.201646 -2.020874 -2.283133 -2.265631 -2.010594 +-1.412983 -1.234929 -0.8006349 -0.9197624 -1.345408 +-1.102764 -0.8647454 -1.214715 -1.455015 -1.469734 +-0.7660331 -0.2071006 -0.492163 -0.3342836 -0.1371871 +-0.1463231 -0.09998406 -0.6985685 -0.03714915 -0.338505 +0.02895643 0.192651 0.7688143 0.6956564 0.1892675 +0.5947126 0.4420508 0.2396274 0.5447946 0.2638511 +0.9472761 1.507396 0.803315 1.542333 1.033478 +1.308166 1.204442 1.427694 0.9309038 1.099179 +1.710953 1.794976 2.065181 1.806495 1.919257 +2.144295 2.095571 1.815571 1.946671 1.799669 +2.499274 2.639537 3.066037 2.665679 2.968711 +2.579372 3.026705 2.491473 2.418448 2.585582 diff --git a/test/bart_bmm_test_data/2d_y_train.txt b/test/bart_bmm_test_data/2d_y_train.txt new file mode 100644 index 00000000..c2415563 --- /dev/null +++ b/test/bart_bmm_test_data/2d_y_train.txt @@ -0,0 +1,16 @@ +-1.056826 -1.692982 -1.989962 -1.819594 -1.059254 +-0.2111787 -0.1576993 0.05924298 -0.0544788 -0.8149666 +-0.4331506 -1.066078 -1.379559 -1.499199 -1.077917 +-0.07884557 0.4719323 0.1570918 0.2385141 0.1503722 +-0.1369464 -0.3606641 -0.2873712 -0.3460772 -0.1529681 +0.8086101 1.378155 1.289986 0.5853899 0.7594395 +0.8514283 0.3554721 -0.1143837 0.3050791 0.4606621 +1.236615 1.891617 1.763451 1.75127 1.484316 +0.43442 0.08589206 -0.280009 -0.3020369 0.8090201 +1.171527 1.757784 1.880253 1.829187 1.068734 +0.4217746 -0.4694632 -0.2660208 -0.6342783 -0.03601855 +0.7401998 1.206674 1.134931 1.293959 0.5605494 +-0.6211568 -1.160463 -1.515867 -1.095876 -0.5398965 +-0.4047985 0.2852159 0.7608983 0.4887916 0.3669347 +-0.9145546 -1.5618 -2.036541 -1.767452 -1.558593 +-0.3748849 -0.1955595 0.3159026 0.03035052 -0.5602327 diff --git a/test/test_trees.py b/test/test_trees.py index dc3e0d08..d75217dd 100644 --- a/test/test_trees.py +++ b/test/test_trees.py @@ -8,27 +8,108 @@ """ # Imports +import numpy as np + from Taweret.core.base_mixer import BaseMixer from Taweret.core.base_model import BaseModel - +from Taweret.mix import Trees from Taweret.models.polynomial_models import sin_exp, cos_exp, sin_cos_exp - #--------------------------------------------- # Define the test functions #--------------------------------------------- +# Test the constructor with the model set +def test_init(): + # check passing of variables into Multivariate class + assert mix.model_dict == model_dict, "class object self.model_dict not set." + assert mix.nummodels == len(model_dict), "class object self.nummodels not set." + +from Taweret.models.polynomial_models import sin_exp, cos_exp, sin_cos_exp + # Test the mixing fun def test_mixing(): - pass + x_train = np.loadtxt('test/bart_bmm_test_data/2d_x_train.txt').reshape(80,2) + x_train = x_train.reshape(2,80).transpose() + + y_train = np.loadtxt('test/bart_bmm_test_data/2d_y_train.txt').reshape(80,1) + + # Set prior information + mix.set_prior(k=2.5,ntree=30,overallnu=5,overallsd=0.01,inform_prior=False) + + # Check tuning & hyper parameters + assert mix.k == 2.5, "class object k is not set." + assert mix.ntree == 30, "class object ntree is not set." + assert mix.overallnu == 5, "class object nu is not set." + assert mix.overallsd == 0.01, "class object overallsd is not set." + assert mix.overalllambda == 0.01**2, "class object overalllambda is not set." + assert mix.inform_prior == False, "class object inform_prior is not set." + + # Train the model + fit = mix.train(X=x_train, y=y_train, ndpost = 10000, nadapt = 2000, nskip = 2000, adaptevery = 500, minnumbot = 4) + + # Check the mcmc objects + assert mix.ndpost == 10000, "class object ndpost is not set." + assert mix.nadapt == 2000, "class object nadapt is not set." + assert mix.adaptevery == 500, "class object adaptevery is not set." + assert mix.nskip == 2000, "class object nskip is not set." + assert mix.minnumbot == 4, "class object minnumbot is not set." + + # Test the mean predictions def test_predict(): - pass + # Get test data + n_test = 30 + x1_test = np.outer(np.linspace(-3, 3, n_test), np.ones(n_test)) + x2_test = x1_test.copy().transpose() + f0_test = (np.sin(x1_test) + np.cos(x2_test)) + x_test = np.array([x1_test.reshape(x1_test.size,),x2_test.reshape(x1_test.size,)]).transpose() + + # Read in test results + pmean_test = np.loadtxt('test/bart_bmm_test_data/2d_pmean.txt') + eps = 0.05 + + # Get predictions + ppost, pmean, pci, pstd = mix.predict(X = x_test, ci = 0.95) + + # Test the values + perr = np.mean(np.abs(pmean - pmean_test)) + assert perr < eps, "Inaccurate predictions." + # Test posterior of the weights def test_predict_wts(): - pass + # Get weights + n_test = 30 + x1_test = np.outer(np.linspace(-3, 3, n_test), np.ones(n_test)) + x2_test = x1_test.copy().transpose() + x_test = np.array([x1_test.reshape(x1_test.size,),x2_test.reshape(x1_test.size,)]).transpose() + + wpost, wmean, wci, wstd = mix.predict_weights(X = x_test, ci = 0.95) + + # Read in test results + wteps = 0.05 + wmean_test = np.loadtxt('test/bart_bmm_test_data/2d_wmean.txt') + + # Test the values + werr = np.mean(np.abs(wmean - wmean_test)) + assert werr < wteps, "Inaccurate weights." + # Test sigma def test_sigma(): - pass + sig_eps = 0.05 + assert np.abs((np.mean(mix.posterior) - 0.1)) < sig_eps, "Inaccurate sigma calculation." + + + +#--------------------------------------------- +# Initiatilize model set +#--------------------------------------------- +# Define the model set +f1 = sin_cos_exp(7,10,np.pi,np.pi) +f2 = sin_cos_exp(13,6,-np.pi,-np.pi) +model_dict = {'model1':f1, 'model2':f2} + + +mix = Trees(model_dict = model_dict, local_openbt_path = "/home/johnyannotty/Documents/openbt/src") diff --git a/test/test_trees_old.py b/test/test_trees_old.py index 39f0fb39..e4a94cf5 100644 --- a/test/test_trees_old.py +++ b/test/test_trees_old.py @@ -72,12 +72,12 @@ def log_likelihood_elementwise(self): #prior_dict = {'k':1.25,'ntree':20, 'overallnu':5, 'overallsd':np.sqrt(0.1)} # Mixing with the non-informative prior -#mix = Trees(model_dict = model_dict, local_openbt_path = "/home/johnyannotty/Documents/openbt/src") -mix = Trees(model_dict = model_dict) +mix = Trees(model_dict = model_dict, local_openbt_path = "/home/johnyannotty/Documents/openbt/src") +#mix = Trees(model_dict = model_dict) -mix.set_prior(k=1.25,ntree=20,overallnu=5,overallsd=np.sqrt(0.1),inform_prior=False) +mix.set_prior(k=1.25,ntree=2,overallnu=5,overallsd=np.sqrt(0.1),inform_prior=False) mix.prior -fit = mix.train(X=x_train, y=y_train, ndpost = 10000, nadapt = 2000, nskip = 1000, adaptevery = 500, minnumbot = 2) +fit = mix.train(X=x_train, y=y_train, ndpost = 10, nadapt = 2, nskip = 1, adaptevery = 500, minnumbot = 2) post = mix.posterior np.mean(post) @@ -87,6 +87,7 @@ def log_likelihood_elementwise(self): ppost, pmean, pci, pstd = mix.predict(X = x_test, ci = 0.95) wpost, wmean, wci, wstd = mix.predict_weights(X = x_test, ci = 0.95) + # Plot results mix.plot_weights(0) mix.plot_prediction(0)