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Update tutorials, log in debug mode on failure during fitting. (#195)
Summary: - Update tutorials (rerun code, fix some matplotlib DeprecationErrors) - log on `logging.DEBUG` level instead of warning during model fitting Pull Request resolved: #195 Reviewed By: danielrjiang Differential Revision: D16038783 Pulled By: Balandat fbshipit-source-id: 8bc11fcfe5efac2bc26e0af266f6f1715c381291
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‎botorch/fit.py‎

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@@ -100,7 +100,7 @@ def fit_gpytorch_model(
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mll.eval()
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return mll
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retry += 1
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logging.warning(f"Fitting failed on try {retry}.")
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logging.log(logging.DEBUG, f"Fitting failed on try {retry}.")
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warnings.warn("Fitting failed on all retries.", OptimizationWarning)
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return mll.eval()

‎tutorials/batch_mode_cross_validation.ipynb‎

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‎tutorials/closed_loop_botorch_only.ipynb‎

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‎tutorials/compare_mc_analytic_acquisition.ipynb‎

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{
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"data": {
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"text/plain": [
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"tensor([[0.3050, 0.2956, 0.3007, 0.2611, 0.3353, 0.4999]])"
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"tensor([[0.2553, 0.4734, 0.1621, 0.3114, 0.3896, 0.1460]])"
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]
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},
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"execution_count": 5,
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{
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"data": {
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"text/plain": [
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"tensor([[0.3051, 0.2951, 0.3009, 0.2609, 0.3354, 0.5003]])"
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"tensor([[0.2553, 0.4736, 0.1621, 0.3114, 0.3897, 0.1460]])"
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]
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},
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"execution_count": 7,
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{
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"data": {
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"text/plain": [
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"tensor(0.0007)"
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"tensor(0.0002)"
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]
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},
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"execution_count": 8,
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{
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"data": {
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"text/plain": [
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"tensor([[0.3051, 0.2950, 0.3008, 0.2609, 0.3358, 0.5008]])"
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"tensor([[0.2553, 0.4731, 0.1623, 0.3106, 0.3893, 0.1455]])"
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]
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},
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"execution_count": 10,
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{
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"data": {
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"text/plain": [
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"tensor(0.0013)"
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"tensor(0.0010)"
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]
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},
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"execution_count": 11,
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{
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"data": {
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"text/plain": [
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"tensor([[0.3049, 0.2957, 0.3006, 0.2611, 0.3352, 0.4998]])"
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"tensor([[0.2566, 0.4745, 0.1638, 0.3110, 0.3896, 0.1485]])"
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]
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},
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"execution_count": 13,
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{
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"data": {
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"text/plain": [
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"tensor(0.0001)"
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"tensor(0.0035)"
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]
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},
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"execution_count": 14,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

‎tutorials/custom_acquisition.ipynb‎

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{
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"data": {
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"text/plain": [
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"tensor([0.4976], grad_fn=<MeanBackward2>)"
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"tensor([0.4938], grad_fn=<MeanBackward2>)"
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]
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},
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"execution_count": 3,
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{
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"data": {
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"text/plain": [
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"tensor([0.4818, 0.5486], grad_fn=<MeanBackward2>)"
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"tensor([0.5833, 0.5478], grad_fn=<MeanBackward2>)"
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]
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},
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"execution_count": 4,
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{
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"data": {
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"text/plain": [
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"tensor([0.4793], grad_fn=<AddBackward0>)"
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"tensor([0.3583], grad_fn=<AddBackward0>)"
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]
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},
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"execution_count": 7,
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{
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"data": {
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"text/plain": [
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"tensor([0.3744, 0.4234, 0.4092], grad_fn=<AddBackward0>)"
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"tensor([0.3743, 0.5131, 0.3595], grad_fn=<AddBackward0>)"
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]
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},
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"execution_count": 8,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

‎tutorials/custom_botorch_model_in_ax.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

‎tutorials/fit_model_with_torch_optimizer.ipynb‎

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‎tutorials/meta_learning_with_rgpe.ipynb‎

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‎tutorials/optimize_stochastic.ipynb‎

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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"d = 5\n",
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from botorch.acquisition import qExpectedImprovement\n",
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"N = 5\n",
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from botorch.optim.initializers import initialize_q_batch_nonneg\n",
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Iteration 15/75 - Loss: -0.222\n",
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"Iteration 30/75 - Loss: -0.283\n",
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"Iteration 45/75 - Loss: -0.561\n",
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"Iteration 60/75 - Loss: -0.730\n",
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"Iteration 75/75 - Loss: -0.756\n"
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"Iteration 15/75 - Loss: -0.346\n",
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"Iteration 30/75 - Loss: -0.574\n",
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"Iteration 45/75 - Loss: -0.610\n",
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"Iteration 60/75 - Loss: -0.642\n",
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"Iteration 75/75 - Loss: -0.623\n"
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]
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}
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],
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

‎tutorials/optimize_with_cmaes.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(25_w,50)-aCMA-ES (mu_w=14.0,w_1=14%) in dimension 2 (seed=626882, Mon Apr 29 15:20:24 2019)\n"
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"(25_w,50)-aCMA-ES (mu_w=14.0,w_1=14%) in dimension 2 (seed=760451, Thu Jun 27 13:24:05 2019)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"tensor([2.7329e-01, 9.6251e-08])"
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"tensor([1.9858e-01, 3.1043e-07])"
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]
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},
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"execution_count": 12,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.7.3"
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}
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},
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"nbformat": 4,

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