From 0cd000636f35e1669cacbccd0edfdeb7544a8146 Mon Sep 17 00:00:00 2001 From: Chris Fonnesbeck Date: Wed, 15 Dec 2021 15:46:42 -0600 Subject: [PATCH 01/11] Rename pymc3 -> pymc throughout --- .github/PULL_REQUEST_TEMPLATE.md | 2 +- BCM/CaseStudies/ExtrasensoryPerception.ipynb | 4 +- BCM/CaseStudies/HeuristicDecisionMaking.ipynb | 12 +-- BCM/CaseStudies/MemoryRetention.ipynb | 6 +- .../MultinomialProcessingTrees.ipynb | 6 +- .../NumberConceptDevelopment.ipynb | 6 +- BCM/CaseStudies/PsychophysicalFunctions.ipynb | 8 +- BCM/CaseStudies/SignalDetectionTheory.ipynb | 8 +- .../TheBARTModelofRiskTaking.ipynb | 6 +- .../TheGCMModelofCategorization.ipynb | 10 +-- BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb | 6 +- .../ComparingBinomialRates.ipynb | 4 +- .../ComparingGaussianMeans.ipynb | 6 +- BCM/ParameterEstimation/Binomial.ipynb | 10 +-- BCM/ParameterEstimation/DataAnalysis.ipynb | 6 +- BCM/ParameterEstimation/Gaussian.ipynb | 6 +- .../Latent-mixtureModels.ipynb | 18 ++--- BCM/README.md | 8 +- BCM/environment.yml | 4 +- BCM/index.ipynb | 12 +-- BDA3/README.md | 8 +- BDA3/chap_02.ipynb | 8 +- BDA3/chap_03.ipynb | 8 +- BDA3/chap_05.ipynb | 14 ++-- BDA3/chap_06.ipynb | 4 +- BDA3/chap_07.ipynb | 4 +- BDA3/environment.yml | 4 +- ...09_Simple_linear_regression_in_PyMC3.ipynb | 8 +- ...r_03_10_Poisson_gamma_model_in_PyMC3.ipynb | 6 +- ...ce_diagnostics_for_a_ill_posed_model.ipynb | 2 +- ...diagnostics_for_a_well_behaved_model.ipynb | 2 +- ...egression_for_NBA_clutch_free_throws.ipynb | 2 +- ...andom_effects_model_for_the_jaw_data.ipynb | 2 +- BSM/README.md | 8 +- BSM/environment.yml | 4 +- README.md | 16 ++-- Rethinking/Chp_02.ipynb | 10 +-- Rethinking/Chp_03.ipynb | 6 +- Rethinking/Chp_04.ipynb | 20 ++--- Rethinking/Chp_05.ipynb | 8 +- Rethinking/Chp_06.ipynb | 14 ++-- Rethinking/Chp_07.ipynb | 44 +++++----- Rethinking/Chp_08.ipynb | 14 ++-- Rethinking/Chp_10.ipynb | 36 ++++----- Rethinking/Chp_11.ipynb | 8 +- Rethinking/Chp_12.ipynb | 16 ++-- Rethinking/Chp_13.ipynb | 6 +- Rethinking/Chp_14.ipynb | 16 ++-- Rethinking/README.md | 10 +-- .../ch-10.ipynb | 30 +++---- .../ch-11.ipynb | 42 +++++----- .../ch-12.ipynb | 20 ++--- .../ch-13.ipynb | 24 +++--- .../ch-14.ipynb | 52 ++++++------ .../ch-2.ipynb | 2 +- Rethinking/environment.yml | 4 +- Rethinking_2/Chp_02.ipynb | 4 +- Rethinking_2/Chp_03.ipynb | 4 +- Rethinking_2/Chp_04.ipynb | 80 +++++++++---------- Rethinking_2/Chp_05.ipynb | 20 ++--- Rethinking_2/Chp_06.ipynb | 4 +- Rethinking_2/Chp_07.ipynb | 14 ++-- Rethinking_2/Chp_08.ipynb | 8 +- Rethinking_2/Chp_09.ipynb | 6 +- Rethinking_2/Chp_11.ipynb | 40 +++++----- Rethinking_2/Chp_12.ipynb | 20 ++--- Rethinking_2/Chp_13.ipynb | 26 +++--- Rethinking_2/Chp_14.ipynb | 14 ++-- Rethinking_2/Chp_15.ipynb | 6 +- Rethinking_2/Chp_16.ipynb | 6 +- .../End_of_chapter_problems/Chapter_2.ipynb | 8 +- .../End_of_chapter_problems/Chapter_3.ipynb | 10 +-- .../End_of_chapter_problems/Chapter_4.ipynb | 10 +-- .../End_of_chapter_problems/Chapter_5.ipynb | 16 ++-- .../End_of_chapter_problems/Chapter_6.ipynb | 24 +++--- .../End_of_chapter_problems/Chapter_7.ipynb | 44 +++++----- .../End_of_chapter_problems/Chapter_8.ipynb | 24 +++--- .../End_of_chapter_problems/Chapter_9.ipynb | 14 ++-- Rethinking_2/README.md | 12 +-- Rethinking_2/environment.yml | 4 +- 80 files changed, 524 insertions(+), 524 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index a508fbf..28e4145 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,6 +1,6 @@ # Thank you for opening a pull request! -Please check our [style guide](https://github.com/pymc-devs/pymc3/wiki/PyMC's-Jupyter-Notebook-Style), and also make sure that the notebooks you've modified pass the `pre-commit` checks. If, for example, you modified `notebook1.ipynb` and `notebook2.ipynb`, you could do this by running: +Please check our [style guide](https://github.com/pymc-devs/pymc/wiki/PyMC's-Jupyter-Notebook-Style), and also make sure that the notebooks you've modified pass the `pre-commit` checks. If, for example, you modified `notebook1.ipynb` and `notebook2.ipynb`, you could do this by running: ```bash pre-commit run --files notebook1.ipynb notebook2.ipynb diff --git a/BCM/CaseStudies/ExtrasensoryPerception.ipynb b/BCM/CaseStudies/ExtrasensoryPerception.ipynb index a80b0b8..6c0b039 100644 --- a/BCM/CaseStudies/ExtrasensoryPerception.ipynb +++ b/BCM/CaseStudies/ExtrasensoryPerception.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "\n", "from matplotlib import gridspec\n", @@ -623,7 +623,7 @@ "output_type": "stream", "text": [ "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "scipy 1.3.1\n", "numpy 1.17.3\n", "last updated: Mon Apr 27 2020 \n", diff --git a/BCM/CaseStudies/HeuristicDecisionMaking.ipynb b/BCM/CaseStudies/HeuristicDecisionMaking.ipynb index 6b614f2..fd274e9 100644 --- a/BCM/CaseStudies/HeuristicDecisionMaking.ipynb +++ b/BCM/CaseStudies/HeuristicDecisionMaking.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.io as sio\n", "\n", "from scipy import stats\n", @@ -179,7 +179,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -564,7 +564,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/ipykernel_launcher.py:8: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/ipykernel_launcher.py:8: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " \n" ] }, @@ -769,7 +769,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -844,7 +844,7 @@ "text": [ "numpy 1.18.1\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Mon Apr 27 2020 \n", "\n", "CPython 3.7.7\n", @@ -861,7 +861,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/MemoryRetention.ipynb b/BCM/CaseStudies/MemoryRetention.ipynb index 8cdce08..65fb879 100644 --- a/BCM/CaseStudies/MemoryRetention.ipynb +++ b/BCM/CaseStudies/MemoryRetention.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "import theano\n", "\n", @@ -994,7 +994,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "arviz 0.7.0\n", "seaborn 0.10.0\n", @@ -1016,7 +1016,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/MultinomialProcessingTrees.ipynb b/BCM/CaseStudies/MultinomialProcessingTrees.ipynb index ce7c601..65c4b58 100644 --- a/BCM/CaseStudies/MultinomialProcessingTrees.ipynb +++ b/BCM/CaseStudies/MultinomialProcessingTrees.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.special as sp\n", "import theano\n", "\n", @@ -859,7 +859,7 @@ "output_type": "stream", "text": [ "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "theano 1.0.4\n", "arviz 0.7.0\n", "last updated: Mon Apr 27 2020 \n", @@ -878,7 +878,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/NumberConceptDevelopment.ipynb b/BCM/CaseStudies/NumberConceptDevelopment.ipynb index 800546f..1a7be7c 100644 --- a/BCM/CaseStudies/NumberConceptDevelopment.ipynb +++ b/BCM/CaseStudies/NumberConceptDevelopment.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.io as sio\n", "import theano\n", "\n", @@ -1024,7 +1024,7 @@ "text": [ "theano 1.0.4\n", "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "arviz 0.7.0\n", "last updated: Mon Apr 27 2020 \n", "\n", @@ -1042,7 +1042,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/PsychophysicalFunctions.ipynb b/BCM/CaseStudies/PsychophysicalFunctions.ipynb index ef6492c..9498009 100644 --- a/BCM/CaseStudies/PsychophysicalFunctions.ipynb +++ b/BCM/CaseStudies/PsychophysicalFunctions.ipynb @@ -10,11 +10,11 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano\n", "\n", "from matplotlib import gridspec\n", - "from pymc3.step_methods.hmc import quadpotential\n", + "from pymc.step_methods.hmc import quadpotential\n", "from scipy import stats\n", "from theano import tensor as tt\n", "\n", @@ -1169,7 +1169,7 @@ "numpy 1.18.1\n", "theano 1.0.4\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "last updated: Mon Apr 27 2020 \n", "\n", @@ -1187,7 +1187,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/SignalDetectionTheory.ipynb b/BCM/CaseStudies/SignalDetectionTheory.ipynb index b7a9c53..cbe09f6 100644 --- a/BCM/CaseStudies/SignalDetectionTheory.ipynb +++ b/BCM/CaseStudies/SignalDetectionTheory.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from matplotlib.patches import Polygon\n", @@ -672,7 +672,7 @@ "metadata": {}, "source": [ "### Note from Junpeng Lao\n", - "Sampling using HMC (e.g., in STAN and PyMC3), there are better way to diagnose biased inference [[1]](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html), [[2]](http://pymc-devs.github.io/pymc3/notebooks/Diagnosing_biased_Inference_with_Divergences.html)." + "Sampling using HMC (e.g., in STAN and PyMC), there are better way to diagnose biased inference [[1]](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html), [[2]](http://pymc-devs.github.io/pymc/notebooks/Diagnosing_biased_Inference_with_Divergences.html)." ] }, { @@ -819,7 +819,7 @@ "source": [ "As shown above, there are a lot of divergences in the trace, and the energy plot is very different from the energy_diff. This is a strong indication of bias in the estimation, and better reparameterization is needed.\n", "\n", - "Moreover, the reparameterization, which works better in BUGS/JAGS using Gibbs sampler, actually perform worse using NUTS. Again, this demonstrates that many of the tricks and intuition we got using BUGS/JAGS might not translate to PyMC3 and STAN." + "Moreover, the reparameterization, which works better in BUGS/JAGS using Gibbs sampler, actually perform worse using NUTS. Again, this demonstrates that many of the tricks and intuition we got using BUGS/JAGS might not translate to PyMC and STAN." ] }, { @@ -1061,7 +1061,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "seaborn 0.11.0\npymc3 3.9.2\npandas 1.0.3\narviz 0.10.0\nnumpy 1.18.2\nlast updated: Mon Nov 23 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" + "seaborn 0.11.0\npymc 3.9.2\npandas 1.0.3\narviz 0.10.0\nnumpy 1.18.2\nlast updated: Mon Nov 23 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" ] } ], diff --git a/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb b/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb index 9b32f15..575ad25 100644 --- a/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb +++ b/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from theano import tensor as tt\n", @@ -463,7 +463,7 @@ "text": [ "numpy 1.18.1\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "last updated: Tue Apr 28 2020 \n", "\n", @@ -481,7 +481,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/TheGCMModelofCategorization.ipynb b/BCM/CaseStudies/TheGCMModelofCategorization.ipynb index f7ce805..b478256 100644 --- a/BCM/CaseStudies/TheGCMModelofCategorization.ipynb +++ b/BCM/CaseStudies/TheGCMModelofCategorization.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.io as sio\n", "import seaborn as sns\n", "import theano\n", @@ -201,7 +201,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -696,7 +696,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -778,7 +778,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "numpy 1.18.1\n", "arviz 0.7.0\n", @@ -800,7 +800,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb b/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb index 6ab51f4..2dc9361 100644 --- a/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb +++ b/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from theano import tensor as tt\n", @@ -830,7 +830,7 @@ "pandas 1.0.3\n", "numpy 1.18.1\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Tue Apr 28 2020 \n", "\n", "CPython 3.7.7\n", @@ -847,7 +847,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ModelSelection/ComparingBinomialRates.ipynb b/BCM/ModelSelection/ComparingBinomialRates.ipynb index 402a035..4fc5c8d 100644 --- a/BCM/ModelSelection/ComparingBinomialRates.ipynb +++ b/BCM/ModelSelection/ComparingBinomialRates.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from scipy import stats\n", @@ -1246,7 +1246,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.9.2\n", + "pymc 3.9.2\n", "numpy 1.18.2\n", "arviz 0.10.0\n", "last updated: Thu Nov 19 2020 \n", diff --git a/BCM/ModelSelection/ComparingGaussianMeans.ipynb b/BCM/ModelSelection/ComparingGaussianMeans.ipynb index 1945ec9..f257f56 100644 --- a/BCM/ModelSelection/ComparingGaussianMeans.ipynb +++ b/BCM/ModelSelection/ComparingGaussianMeans.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "\n", @@ -632,7 +632,7 @@ "pandas 1.0.3\n", "arviz 0.7.0\n", "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Sat Apr 25 2020 \n", "\n", "CPython 3.7.7\n", @@ -649,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ParameterEstimation/Binomial.ipynb b/BCM/ParameterEstimation/Binomial.ipynb index 3303e15..6f721db 100644 --- a/BCM/ParameterEstimation/Binomial.ipynb +++ b/BCM/ParameterEstimation/Binomial.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from matplotlib import gridspec\n", @@ -262,7 +262,7 @@ "\n", "In the example, we set k1 = 5, n1 = 10 and k2 = 7, n2 = 10 \n", "\n", - "The model involve a deterministic part in pymc3." + "The model involve a deterministic part in pymc." ] }, { @@ -1175,7 +1175,7 @@ "metadata": {}, "source": [ "### Note from Junpeng Lao\n", - "It is obvious from the above posterior plot that the geometry of the posterior is quite nasty. We can see that in the trace as well: the mixing is quite poor, with strong autocorrelation. There is no divergence warning, but it could just be that PyMC3 is mixing Metropolis and NUTS together due to the discrete variable. \n", + "It is obvious from the above posterior plot that the geometry of the posterior is quite nasty. We can see that in the trace as well: the mixing is quite poor, with strong autocorrelation. There is no divergence warning, but it could just be that PyMC is mixing Metropolis and NUTS together due to the discrete variable. \n", "\n", "In this particular case, it is not a big deal as we can visualize the posterior directly. However, when we are sampling larger models, it is definitely going to be a problem.\n", "Actually, we don't necessary need to use `DiscreteUniform` for `TotalN`, as the computation of logp in Binomial doesn't require n to be an integer." @@ -1339,7 +1339,7 @@ "numpy 1.18.1\n", "arviz 0.7.0\n", "seaborn 0.10.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Fri Apr 24 2020 \n", "\n", "CPython 3.7.7\n", @@ -1356,7 +1356,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ParameterEstimation/DataAnalysis.ipynb b/BCM/ParameterEstimation/DataAnalysis.ipynb index ac98014..9835fc9 100644 --- a/BCM/ParameterEstimation/DataAnalysis.ipynb +++ b/BCM/ParameterEstimation/DataAnalysis.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import gridspec\n", "from scipy import corrcoef, stats\n", @@ -55,7 +55,7 @@ "\n", "The observed data take the form _xi_ = (_xi1_, _xi2_) for the ith observation, and, following the theory behind the correlation coefficient, are modeled as draws from a multivariate Gaussian distribution. The parameters of this distribution are the means _μ_ = (_μ1_,_μ2_) and standard deviations _σ_ = (_σ1_,_σ2_) of the two variables, and the correlation coefficient _r_ that links them.\n", "\n", - "**NB: This model runs with PyMC 3.8, but not with the master branch -- probably related to [this issue](https://github.com/pymc-devs/pymc3/issues/3884).**" + "**NB: This model runs with PyMC 3.8, but not with the master branch -- probably related to [this issue](https://github.com/pymc-devs/pymc/issues/3884).**" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "arviz 0.10.0\npymc3 3.9.2\npandas 1.0.3\nnumpy 1.18.2\nlast updated: Sun Nov 15 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" + "arviz 0.10.0\npymc 3.9.2\npandas 1.0.3\nnumpy 1.18.2\nlast updated: Sun Nov 15 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" ] } ], diff --git a/BCM/ParameterEstimation/Gaussian.ipynb b/BCM/ParameterEstimation/Gaussian.ipynb index e519a54..6224b65 100644 --- a/BCM/ParameterEstimation/Gaussian.ipynb +++ b/BCM/ParameterEstimation/Gaussian.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "%config InlineBackend.figure_format = 'retina'\n", "RANDOM_SEED = 8927\n", @@ -827,7 +827,7 @@ "text": [ "arviz 0.7.0\n", "pandas 1.0.3\n", - "pymc3 3.8\n", + "pymc 3.8\n", "numpy 1.18.1\n", "last updated: Sat Apr 25 2020 \n", "\n", @@ -845,7 +845,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ParameterEstimation/Latent-mixtureModels.ipynb b/BCM/ParameterEstimation/Latent-mixtureModels.ipynb index c097915..85db73a 100644 --- a/BCM/ParameterEstimation/Latent-mixtureModels.ipynb +++ b/BCM/ParameterEstimation/Latent-mixtureModels.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from matplotlib import gridspec\n", @@ -38,7 +38,7 @@ "metadata": {}, "source": [ "### Note from Junpeng Lao\n", - "In PyMC3, a discrete latent variable could be very easily expressed as a discrete random variable. PyMC3 will assign this discrete variable with a sampler (usually a Metropolis sampler), while the rest of the (continous) RVs sample with NUTS. However, care must be taken, as the correctness of mixing different sampler is in general not guaranteed. The standard treatment is integrate out the latent variables, as done in Stan." + "In PyMC, a discrete latent variable could be very easily expressed as a discrete random variable. PyMC will assign this discrete variable with a sampler (usually a Metropolis sampler), while the rest of the (continous) RVs sample with NUTS. However, care must be taken, as the correctness of mixing different sampler is in general not guaranteed. The standard treatment is integrate out the latent variables, as done in Stan." ] }, { @@ -263,7 +263,7 @@ } ], "source": [ - "# pymc3 - need some tuning to get the same result as in JAGS\n", + "# pymc - need some tuning to get the same result as in JAGS\n", "k = np.array([21, 17, 21, 18, 22, 31, 31, 34, 34, 35, 35, 36, 39, 36, 35])\n", "p = len(k) # number of people\n", "n = 40 # number of questions\n", @@ -376,7 +376,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, ImputationWarning)\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "CompoundStep\n", @@ -576,7 +576,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, ImputationWarning)\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "CompoundStep\n", @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 8 seconds.\n", - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", " result = getattr(npmodule, name)(values, axis=axis, **kwargs)\n" ] }, @@ -884,7 +884,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.\n", - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", " result = getattr(npmodule, name)(values, axis=axis, **kwargs)\n", "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n", "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n" @@ -1306,7 +1306,7 @@ "numpy 1.18.1\n", "pandas 1.0.3\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Sat Apr 25 2020 \n", "\n", "CPython 3.7.7\n", @@ -1323,7 +1323,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/README.md b/BCM/README.md index 61f9e33..6099406 100644 --- a/BCM/README.md +++ b/BCM/README.md @@ -1,5 +1,5 @@ -# Bayesian Cognitive Modeling in PyMC3 -PyMC3 port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com) +# Bayesian Cognitive Modeling in PyMC +PyMC port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com) All the codes are in jupyter notebooks with the model explained in distributions (as in the book). @@ -8,8 +8,8 @@ All the codes are in jupyter notebooks with the model explained in distributions [](http://nbviewer.jupyter.org/github/pymc-devs/resources/blob/master/BCM/index.ipynb) # Notice: -This repository is tested under [PyMC3](https://github.com/pymc-devs/pymc3) v3.8 master with [theano](https://github.com/Theano/Theano) 1.0.4 +This repository is tested under [PyMC](https://github.com/pymc-devs/pymc) v3.8 master with [theano](https://github.com/Theano/Theano) 1.0.4 --- -Creative Commons License
Bayesian Cognitive Modeling in PyMC3 by Junpeng Lao is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Bayesian Cognitive Modeling in PyMC by Junpeng Lao is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/BCM/environment.yml b/BCM/environment.yml index 99d17e1..299344e 100644 --- a/BCM/environment.yml +++ b/BCM/environment.yml @@ -1,4 +1,4 @@ -name: BCM_pymc3 +name: BCM_pymc channels: - defaults dependencies: @@ -8,5 +8,5 @@ dependencies: - statsmodels - pip - pip: - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" - watermark \ No newline at end of file diff --git a/BCM/index.ipynb b/BCM/index.ipynb index 8d89a64..358a7e2 100644 --- a/BCM/index.ipynb +++ b/BCM/index.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Bayesian Cognitive Modeling in PyMC3\n", - "PyMC3 port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com)\n", + "# Bayesian Cognitive Modeling in PyMC\n", + "PyMC port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com)\n", "\n", "All the codes are in jupyter notebooks with the model explained in distributions (as in the book). Background information of the models please consult the book. You can also compare the result with the original code associated with the book ([WinBUGS and JAGS](https://webfiles.uci.edu/mdlee/Code.zip); [Stan](https://github.com/stan-dev/example-models/tree/master/Bayesian_Cognitive_Modeling))\n", "\n", - "_All the codes are currently tested under PyMC3 v3.8 master with theano 1.0.4_" + "_All the codes are currently tested under PyMC v3.8 master with theano 1.0.4_" ] }, { @@ -141,7 +141,7 @@ "CPython 3.7.7\n", "IPython 7.13.0\n", "\n", - "pymc3 3.8\n", + "pymc 3.8\n", "theano 1.0.4\n", "scipy 1.4.1\n", "numpy 1.18.1\n", @@ -163,13 +163,13 @@ "source": [ "# Python Environment and library version\n", "%load_ext watermark\n", - "%watermark -v -n -u -w -p pymc3,theano,scipy,numpy,pandas,matplotlib,seaborn -m" + "%watermark -v -n -u -w -p pymc,theano,scipy,numpy,pandas,matplotlib,seaborn -m" ] } ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BDA3/README.md b/BDA3/README.md index 7a05fb7..9f0f3d1 100644 --- a/BDA3/README.md +++ b/BDA3/README.md @@ -1,8 +1,8 @@ -# Bayesian Data Analysis with Python and PyMC3 +# Bayesian Data Analysis with Python and PyMC [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/) by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin is a comprehensive, standard, and wonderful textbook on Bayesian Methods. There currently exist code for examples in the book in [R](https://github.com/avehtari/BDA_R_demos), [Python](https://github.com/avehtari/BDA_py_demos), and [Matlab](https://github.com/avehtari/BDA_m_demos), all using the [Stan](http://mc-stan.org/) language. -This repository is a work in progress, organizing work on porting examples and exercises to Python and PyMC3. Please open a pull request on the README to indicate interest in a chapter or section! +This repository is a work in progress, organizing work on porting examples and exercises to Python and PyMC. Please open a pull request on the README to indicate interest in a chapter or section! ## Chapters @@ -116,8 +116,8 @@ Anaconda, run: to install all the dependencies into an isolated environment. You can switch to this environment by running: - source activate bda3-pymc3 + source activate bda3-pymc --- -Creative Commons License
Bayesian Data Analysis with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Bayesian Data Analysis with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/BDA3/chap_02.ipynb b/BDA3/chap_02.ipynb index 1a1643f..1956716 100644 --- a/BDA3/chap_02.ipynb +++ b/BDA3/chap_02.ipynb @@ -24,7 +24,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy.special import expit" ] @@ -247,7 +247,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The true posterior distribution is $\\textsf{Beta}(438, 544)$. Let's compare it with the one we found using `pymc3`." + "The true posterior distribution is $\\textsf{Beta}(438, 544)$. Let's compare it with the one we found using `pymc`." ] }, { @@ -1141,7 +1141,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The true posterior distribution is $\\textsf{Gamma}(6,7)$. Let's compare it with the one we found using `pymc3`." + "The true posterior distribution is $\\textsf{Gamma}(6,7)$. Let's compare it with the one we found using `pymc`." ] }, { @@ -1461,7 +1461,7 @@ "output_type": "stream", "text": [ "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "arviz 0.7.0\n", "CPython 3.6.8\n", "IPython 7.12.0\n", diff --git a/BDA3/chap_03.ipynb b/BDA3/chap_03.ipynb index acb1e9e..1964553 100644 --- a/BDA3/chap_03.ipynb +++ b/BDA3/chap_03.ipynb @@ -36,7 +36,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from scipy.optimize import brentq\n", @@ -131,7 +131,7 @@ { "cell_type": "markdown", "source": [ - "And now, we use `pymc3` to estimate the mean and the standard deviation from the data." + "And now, we use `pymc` to estimate the mean and the standard deviation from the data." ], "metadata": {} }, @@ -1177,7 +1177,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "/home/xyj/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:1689: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/xyj/anaconda3/lib/python3.8/site-packages/pymc/sampling.py:1689: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -1681,7 +1681,7 @@ "Architecture: 64bit\n", "\n", "arviz : 0.11.2\n", - "pymc3 : 3.11.4\n", + "pymc : 3.11.4\n", "matplotlib: 3.3.4\n", "theano : 1.1.2\n", "sys : 3.8.8 (default, Apr 13 2021, 19:58:26) \n", diff --git a/BDA3/chap_05.ipynb b/BDA3/chap_05.ipynb index b8e7e0c..4177377 100644 --- a/BDA3/chap_05.ipynb +++ b/BDA3/chap_05.ipynb @@ -10,7 +10,7 @@ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt" ] }, @@ -454,7 +454,7 @@ "\n", "In order to use this prior, you have to define the logarithm of $p(\\alpha, \\beta)$ and use it in `pm.Potential`, [look here][2]. \n", "\n", - "[1]:https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/GLM-hierarchical-binominal-model.ipynb\n", + "[1]:https://github.com/pymc-devs/pymc/blob/master/docs/source/notebooks/GLM-hierarchical-binominal-model.ipynb\n", "[2]:https://discourse.pymc.io/t/difference-between-densitydist-and-potential/307/4" ] }, @@ -750,7 +750,7 @@ "Attributes:\n", " created_at: 2020-04-24T21:27:00.254444\n", " arviz_version: 0.7.0\n", - " inference_library: pymc3\n", + " inference_library: pymc\n", " inference_library_version: 3.8" ], "text/plain": [ @@ -771,7 +771,7 @@ "Attributes:\n", " created_at: 2020-04-24T21:27:00.254444\n", " arviz_version: 0.7.0\n", - " inference_library: pymc3\n", + " inference_library: pymc\n", " inference_library_version: 3.8" ] }, @@ -1370,7 +1370,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/rpg/src/pymc3/pymc3/distributions/posterior_predictive.py:203: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/rpg/src/pymc/pymc/distributions/posterior_predictive.py:203: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] } @@ -1672,7 +1672,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ok, so the results are not quite good, I mean, there are a lot of discrepancies and the differences are notorious when you compare the two histograms previously showed with the Figure 5.8 (even more if you compare it with Table 5.3). Yes, the question is: Why does this happen? Is the algorithm behind `pymc3` the reason of all this? I would say that priors are really bad and the geometry behind the model is nasty. More info here: https://docs.pymc.io/notebooks/Diagnosing_biased_Inference_with_Divergences.html" + "Ok, so the results are not quite good, I mean, there are a lot of discrepancies and the differences are notorious when you compare the two histograms previously showed with the Figure 5.8 (even more if you compare it with Table 5.3). Yes, the question is: Why does this happen? Is the algorithm behind `pymc` the reason of all this? I would say that priors are really bad and the geometry behind the model is nasty. More info here: https://docs.pymc.io/notebooks/Diagnosing_biased_Inference_with_Divergences.html" ] }, { @@ -2220,7 +2220,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "matplotlib 3.1.3\n", "arviz 0.7.0\n", "numpy 1.18.1\n", diff --git a/BDA3/chap_06.ipynb b/BDA3/chap_06.ipynb index 62312d0..fd91a15 100644 --- a/BDA3/chap_06.ipynb +++ b/BDA3/chap_06.ipynb @@ -18,7 +18,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "import theano.tensor as tt\n", "\n", @@ -735,7 +735,7 @@ "output_type": "stream", "text": [ "numpy 1.15.0\n", - "pymc3 3.5\n", + "pymc 3.5\n", "seaborn 0.9.0\n", "CPython 3.6.6\n", "IPython 7.1.1\n", diff --git a/BDA3/chap_07.ipynb b/BDA3/chap_07.ipynb index 51cbe78..a17f13f 100644 --- a/BDA3/chap_07.ipynb +++ b/BDA3/chap_07.ipynb @@ -20,7 +20,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from scipy import stats\n", @@ -1948,7 +1948,7 @@ "output_type": "stream", "text": [ "numpy 1.16.2\n", - "pymc3 3.6\n", + "pymc 3.6\n", "CPython 3.6.7\n", "IPython 7.3.0\n", "\n", diff --git a/BDA3/environment.yml b/BDA3/environment.yml index 9d06fd4..2e83e66 100644 --- a/BDA3/environment.yml +++ b/BDA3/environment.yml @@ -1,4 +1,4 @@ -name: bda3-pymc3 +name: bda3-pymc channels: - defaults dependencies: @@ -6,4 +6,4 @@ dependencies: - seaborn - pip - pip: - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" diff --git a/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb b/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb index a21ef0b..072a1aa 100644 --- a/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb +++ b/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { @@ -25,11 +25,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using PyMC3 for MCMC sampling\n", + "# Using PyMC for MCMC sampling\n", "\n", - "### Chapter 3.3: Introduction to PyMC3\n", + "### Chapter 3.3: Introduction to PyMC\n", "\n", - "In this example, we use PyMC3 to conduct simple linear regression.\n", + "In this example, we use PyMC to conduct simple linear regression.\n", "\n", "The response is the mass of a T. Rex and the covariate is the age. The model is:\n", "\n", diff --git a/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb b/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb index 6755610..f0f6aa4 100644 --- a/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb +++ b/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { @@ -27,7 +27,7 @@ "source": [ "# Using JAGS for concussions data\n", "\n", - "## Chapter 3.3: Introduction to PyMC3\n", + "## Chapter 3.3: Introduction to PyMC\n", "\n", "The response is the total number of concussions (summing across teams and games) in each year from 2012-2015. We fit the model\n", "\n", @@ -37,7 +37,7 @@ "\n", "\n", "\n", - "We have previously coded Gibbs sampling for this problem, and here e verify that we obtain the same results using PyMC3." + "We have previously coded Gibbs sampling for this problem, and here e verify that we obtain the same results using PyMC." ] }, { diff --git a/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb b/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb index f7684f3..4b54a1b 100644 --- a/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb +++ b/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb b/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb index 86f2593..97ec04b 100644 --- a/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb +++ b/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb b/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb index 4c5471d..8dfc649 100644 --- a/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb +++ b/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb b/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb index b7d586f..5418a81 100644 --- a/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb +++ b/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/README.md b/BSM/README.md index edfc2f4..bdaec56 100644 --- a/BSM/README.md +++ b/BSM/README.md @@ -1,6 +1,6 @@ -# Bayesian Statistical Methods Python and PyMC3 +# Bayesian Statistical Methods Python and PyMC -In this repository we port [the book's original code](https://bayessm.wordpress.ncsu.edu) to Python and PyMC3. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMC3onic_ way. +In this repository we port [the book's original code](https://bayessm.wordpress.ncsu.edu) to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMConic_ way. ## Display notebooks [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pymc-devs/resources/master?filepath=BSM) @@ -23,8 +23,8 @@ to install all the dependencies into an isolated environment. Activate the environment by running: - source activate bsm-pymc3 + source activate bsm-pymc --- -Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/BSM/environment.yml b/BSM/environment.yml index a0e14c5..e64d2ec 100644 --- a/BSM/environment.yml +++ b/BSM/environment.yml @@ -1,4 +1,4 @@ -name: bsm-pymc3 +name: bsm-pymc channels: - defaults dependencies: @@ -7,4 +7,4 @@ dependencies: - pip - pip: - "git+git://github.com/arviz-devs/arviz.git@master" - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" diff --git a/README.md b/README.md index 002ad84..c9c417f 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ -# PyMC3 Resources -PyMC3 educational resources, including the PyMC3 port of the following books (original models in STAN/BUGS/JAGS etc,.): - -- [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking) -- [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking_2) -- [PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/master/BCM) -- [PyMC3 port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh](https://github.com/pymc-devs/resources/tree/master/BSM) -- [PyMC3 port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/master/BDA3) +# PyMC Resources +PyMC educational resources, including the PyMC port of the following books (original models in STAN/BUGS/JAGS etc,.): + +- [PyMC port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking) +- [PyMC port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking_2) +- [PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/master/BCM) +- [PyMC port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh](https://github.com/pymc-devs/resources/tree/master/BSM) +- [PyMC port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/master/BDA3) diff --git a/Rethinking/Chp_02.ipynb b/Rethinking/Chp_02.ipynb index ac77cbc..18dca47 100644 --- a/Rethinking/Chp_02.ipynb +++ b/Rethinking/Chp_02.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -179,8 +179,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", "logp = -1.8075, ||grad|| = 1.5: 100%|██████████| 7/7 [00:00<00:00, 1327.37it/s]\n" ] }, @@ -286,7 +286,7 @@ "This notebook was created using:\n", "Python 3.7.1\n", "IPython 6.2.1\n", - "PyMC3 3.7.rc1\n", + "PyMC 3.7.rc1\n", "ArviZ 0.4.0\n", "NumPy 1.15.4\n", "SciPy 1.1.0\n", @@ -303,7 +303,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_03.ipynb b/Rethinking/Chp_03.ipynb index fdc34f2..0f0ce9d 100644 --- a/Rethinking/Chp_03.ipynb +++ b/Rethinking/Chp_03.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -897,7 +897,7 @@ "This notebook was created using:\n", "Python 3.7.2\n", "IPython 7.9.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.5.1\n", "NumPy 1.17.3\n", "SciPy 1.3.1\n", @@ -914,7 +914,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_04.ipynb b/Rethinking/Chp_04.ipynb index 134922f..1a131df 100644 --- a/Rethinking/Chp_04.ipynb +++ b/Rethinking/Chp_04.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats\n", "\n", "from scipy.interpolate import griddata" @@ -753,11 +753,11 @@ "source": [ "#### Code 4.26\n", "\n", - "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC3 is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", + "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", "\n", - "PyMC3 comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC3 choose the sampler for us. PyMC3 also tries to provide a reasonable starting point for the simulation. By default PyMC3 uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. \n", + "PyMC comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC choose the sampler for us. PyMC also tries to provide a reasonable starting point for the simulation. By default PyMC uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. \n", "\n", - "You can read more details of PyMC3 [here](http://pymc-devs.github.io/pymc3/notebooks/getting_started.html)" + "You can read more details of PyMC [here](http://pymc-devs.github.io/pymc/notebooks/getting_started.html)" ] }, { @@ -1941,7 +1941,7 @@ "\n", " height = pm.Normal('height', mu=alpha + beta * d2.weight, sd=sigma, observed=d2.height)\n", " \n", - "Using PyMC3 there is not too much reason to do this. I personally think that defining mu in a separate line improves readability." + "Using PyMC there is not too much reason to do this. I personally think that defining mu in a separate line improves readability." ] }, { @@ -2697,7 +2697,7 @@ "source": [ "#### Code 4.53\n", "\n", - "Using PyMC3, we do not need to compute anything else. By defining a deterministic variable mu in the model, we add that variable to the trace. Thus we get a matrix with row samples from the posterior and columns values of weights. We can access this matrix directly from the trace or turn it into a DataFrame, it all depends on what we need." + "Using PyMC, we do not need to compute anything else. By defining a deterministic variable mu in the model, we add that variable to the trace. Thus we get a matrix with row samples from the posterior and columns values of weights. We can access this matrix directly from the trace or turn it into a DataFrame, it all depends on what we need." ] }, { @@ -2963,7 +2963,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -3471,7 +3471,7 @@ "text": [ "/home/osvaldo/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: DeprecationWarning: sample_ppc() is deprecated. Please use sample_posterior_predictive()\n", " \n", - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -3701,7 +3701,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.11.1\n", - "PyMC3 3.8\n", + "PyMC 3.8\n", "ArviZ 0.6.1\n", "NumPy 1.17.4\n", "SciPy 1.4.1\n", @@ -3718,7 +3718,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_05.ipynb b/Rethinking/Chp_05.ipynb index d4a0939..4b45a9a 100644 --- a/Rethinking/Chp_05.ipynb +++ b/Rethinking/Chp_05.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "import statsmodels.formula.api as smf\n", "\n", @@ -3995,7 +3995,7 @@ " $$" ], "text/plain": [ - "" + "" ] }, "execution_count": 77, @@ -4020,7 +4020,7 @@ "This notebook was created using:\n", "Python 3.7.1\n", "IPython 6.2.1\n", - "PyMC3 3.7.rc1\n", + "PyMC 3.7.rc1\n", "ArviZ 0.4.0\n", "NumPy 1.15.4\n", "SciPy 1.1.0\n", @@ -4037,7 +4037,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_06.ipynb b/Rethinking/Chp_06.ipynb index 1c0240f..376ca12 100644 --- a/Rethinking/Chp_06.ipynb +++ b/Rethinking/Chp_06.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import statsmodels.api as sm\n", "\n", "# R-like interface, alternatively you can import statsmodels as import statsmodels.api as sm\n", @@ -481,7 +481,7 @@ " \n", " np.concatenate([mm_train, x_train[:, 1:k]], axis=1)\n", " \n", - " #Using pymc3\n", + " #Using pymc\n", " \n", " with pm.Model() as m_sim:\n", " vec_V = pm.MvNormal('vec_V', mu=0, cov=b_sigma * np.eye(n_dim), \n", @@ -1206,7 +1206,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1250,12 +1250,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1701,7 +1701,7 @@ "This notebook was created using:\n", "Python 3.7.1\n", "IPython 6.2.1\n", - "PyMC3 3.7.rc1\n", + "PyMC 3.7.rc1\n", "ArviZ 0.4.0\n", "NumPy 1.15.4\n", "SciPy 1.1.0\n", @@ -1718,7 +1718,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_07.ipynb b/Rethinking/Chp_07.ipynb index a744ea6..f9d0c51 100644 --- a/Rethinking/Chp_07.ipynb +++ b/Rethinking/Chp_07.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import statsmodels.formula.api as smf\n", "\n", "from scipy import stats\n", @@ -313,7 +313,7 @@ "source": [ "#### Code 7.5\n", "\n", - "WAIC values are point estimates and hence is a good idea to include the uncertainty asociated with their estimation when computing weights. PyMC3 uses a Bayesian bootstrapping to do this (read more [here](https://arxiv.org/abs/1704.02030)), and also to compute the standard error (SE) of WAIC/LOO estimates. If you set `bootstrapping = False` weights (and SE) will be computed as in the book." + "WAIC values are point estimates and hence is a good idea to include the uncertainty asociated with their estimation when computing weights. PyMC uses a Bayesian bootstrapping to do this (read more [here](https://arxiv.org/abs/1704.02030)), and also to compute the standard error (SE) of WAIC/LOO estimates. If you set `bootstrapping = False` weights (and SE) will be computed as in the book." ] }, { @@ -325,7 +325,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -578,12 +578,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1491,8 +1491,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", "logp = -175.26, ||grad|| = 0.0011502: 100%|██████████| 24/24 [00:00<00:00, 1082.61it/s] \n" ] }, @@ -1532,8 +1532,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", "logp = -170.17, ||grad|| = 0.012971: 100%|██████████| 54/54 [00:00<00:00, 1531.53it/s] \n" ] }, @@ -1584,8 +1584,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", " 0%| | 0/5000 [00:00" + "" ] }, "execution_count": 39, @@ -2788,8 +2788,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n" + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n" ] }, { @@ -3584,27 +3584,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -4947,7 +4947,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.4\n", "SciPy 1.2.1\n", @@ -4964,7 +4964,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_11.ipynb b/Rethinking/Chp_11.ipynb index 425ad53..4a8e680 100644 --- a/Rethinking/Chp_11.ipynb +++ b/Rethinking/Chp_11.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "\n", "from theano import shared" @@ -358,8 +358,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n" + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n" ] }, { @@ -1261,7 +1261,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"This notebook was createad on a computer {} running {} and using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nNumPy {}\\nPandas {}\\nSciPy {}\\nMatplotlib {}\\n\".format(platform.machine(), ' '.join(platform.linux_distribution()[:2]), sys.version[:5], IPython.__version__, pm.__version__, np.__version__, pd.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"This notebook was createad on a computer {} running {} and using:\\nPython {}\\nIPython {}\\nPyMC {}\\nNumPy {}\\nPandas {}\\nSciPy {}\\nMatplotlib {}\\n\".format(platform.machine(), ' '.join(platform.linux_distribution()[:2]), sys.version[:5], IPython.__version__, pm.__version__, np.__version__, pd.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_12.ipynb b/Rethinking/Chp_12.ipynb index dc49cae..057f147 100644 --- a/Rethinking/Chp_12.ipynb +++ b/Rethinking/Chp_12.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from scipy.special import expit as logistic" @@ -253,7 +253,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -349,7 +349,7 @@ "metadata": {}, "outputs": [], "source": [ - "# extract PyMC3 samples\n", + "# extract PyMC samples\n", "post = pm.trace_to_dataframe(trace_12_2, varnames=['a_tank'])\n", "\n", "# compute median intercept for each tank\n", @@ -839,7 +839,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This part is more Stan and rethinking related. To do the same in PyMC3 (i.e., avoid compiling the same model twice), you need to set up the input data with `theano.shared` or use [sampled](https://github.com/ColCarroll/sampled), a functional decorator for PyMC3." + "This part is more Stan and rethinking related. To do the same in PyMC (i.e., avoid compiling the same model twice), you need to set up the input data with `theano.shared` or use [sampled](https://github.com/ColCarroll/sampled), a functional decorator for PyMC." ] }, { @@ -2246,7 +2246,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.3\n", "SciPy 1.2.1\n", @@ -2262,15 +2262,15 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/Chp_13.ipynb b/Rethinking/Chp_13.ipynb index aa1416c..7a3fe69 100644 --- a/Rethinking/Chp_13.ipynb +++ b/Rethinking/Chp_13.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from theano import tensor as tt" ] @@ -2906,7 +2906,7 @@ "This notebook was created using:\n", "Python 3.7.2\n", "IPython 7.6.1\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.0\n", "SciPy 1.2.0\n", @@ -2922,7 +2922,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_14.ipynb b/Rethinking/Chp_14.ipynb index d058255..816d701 100644 --- a/Rethinking/Chp_14.ipynb +++ b/Rethinking/Chp_14.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { @@ -1239,7 +1239,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1282,7 +1282,7 @@ "# prep data\n", "kcal = d['kcal.per.g'].values.copy()\n", "logmass = d['logmass'].values.copy()\n", - "# PyMC3 can handle missing value quite naturally.\n", + "# PyMC can handle missing value quite naturally.\n", "neocortex = d['neocortex.prop'].values.copy()\n", "mask = np.isfinite(neocortex)\n", "neocortex[~mask] = -999\n", @@ -1653,7 +1653,7 @@ } ], "source": [ - "# the missing value in pymc3 is automatically model as a node with *_missing as name\n", + "# the missing value in pymc is automatically model as a node with *_missing as name\n", "az.summary(trace_14_3, var_names=['neocortex_missing', \n", " 'a', 'bN', 'bM', 'nu', 'sigma_N', 'sigma'],\n", " round_to=2)" @@ -1862,7 +1862,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2287,7 +2287,7 @@ "metadata": {}, "source": [ "#### Code 14.11-14\n", - "Stan related. As you can see above, PyMC3 deal with missing value internally if you represent the observed data using a numpy mask array. The missing/masked value are replaced with a new random variable added to the model (with name `*_missing`)." + "Stan related. As you can see above, PyMC deal with missing value internally if you represent the observed data using a numpy mask array. The missing/masked value are replaced with a new random variable added to the model (with name `*_missing`)." ] }, { @@ -2302,7 +2302,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.4\n", "Matplotlib 3.1.0\n", @@ -2317,7 +2317,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/README.md b/Rethinking/README.md index 2bdb9e5..c62247b 100644 --- a/Rethinking/README.md +++ b/Rethinking/README.md @@ -1,8 +1,8 @@ -# Statistical Rethinking with Python and PyMC3 +# Statistical Rethinking with Python and PyMC [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) is an incredible introductory book to Bayesian Statistics. It follows a [_Jaynesian_](https://en.wikipedia.org/wiki/Edwin_Thompson_Jaynes) and practical approach with very good examples and clear explanations. -In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC3. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMC3onic_ way. +In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMConic_ way. ## Display notebooks [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pymc-devs/resources/master?filepath=Rethinking) @@ -14,7 +14,7 @@ All contributions are welcome! Feel free to send PR to fix errors, improve the code, or make comments that could help users of this repository and readers of the book. -You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC3/Lobby). +You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC/Lobby). ## Installing the dependencies @@ -26,8 +26,8 @@ to install all the dependencies into an isolated environment. Activate the environment by running: - source activate stat-rethink-pymc3 + source activate stat-rethink-pymc --- -Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb index 06fdde5..c6ba00e 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -261,7 +261,7 @@ "\n", "*Can you explain both the differences and the similarities between the approximate and the MCMC distributions?*\n", "\n", - "Related to R and map. See code 10.14 in chapter 10 notebook for model specification and MCMC estimation in PyMC3" + "Related to R and map. See code 10.14 in chapter 10 notebook for model specification and MCMC estimation in PyMC" ] }, { @@ -542,13 +542,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n" ] }, @@ -994,7 +994,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " chain_likelihoods.append(np.stack(log_like))\n" ] }, @@ -1647,16 +1647,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2410,7 +2410,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", "SciPy 1.2.1\n", @@ -2428,7 +2428,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -2442,9 +2442,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb index b5c5883..dfc0dcb 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -655,12 +655,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1097,12 +1097,12 @@ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [bp, bf, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [00:18<00:00, 326.81draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1442,12 +1442,12 @@ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [bd, bf, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [00:09<00:00, 529.37draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1769,22 +1769,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2177,22 +2177,22 @@ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [bd, bf, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [00:10<00:00, 575.35draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2973,12 +2973,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -3383,7 +3383,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -3402,7 +3402,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -3416,9 +3416,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb index abe960a..cbc0c00 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -614,27 +614,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2218,7 +2218,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -2237,7 +2237,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -2251,9 +2251,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb index 62bc956..6afc81b 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb @@ -14,7 +14,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -292,7 +292,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1954,32 +1954,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -3383,7 +3383,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -3402,7 +3402,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -3416,9 +3416,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb index c7e35ba..4dd2cef 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb @@ -14,7 +14,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -384,12 +384,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -678,28 +678,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, mu_N, sigma, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:10<00:00, 366.52draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, mu_N, sigma, bn, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [01:24<00:00, 71.15draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, mu_N, sigma, bm, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:12<00:00, 326.81draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -803,22 +803,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1294,28 +1294,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:16<00:00, 241.38draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, gm, an, sigma, bn, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [01:31<00:00, 36.16draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma, bm, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:15<00:00, 265.01draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1419,32 +1419,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -3799,7 +3799,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in x_est contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in x_est contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -4147,7 +4147,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -4167,7 +4167,7 @@ "\n", "print(\n", " f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\"\n", ")" ] @@ -4182,9 +4182,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb index 3aedb2f..faf3645 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb @@ -26,7 +26,7 @@ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats\n", "\n", "%config InlineBackend.figure_format = 'retina'\n", diff --git a/Rethinking/environment.yml b/Rethinking/environment.yml index f8caf77..b63599b 100644 --- a/Rethinking/environment.yml +++ b/Rethinking/environment.yml @@ -1,4 +1,4 @@ -name: stat-rethink-pymc3 +name: stat-rethink-pymc channels: - defaults dependencies: @@ -9,4 +9,4 @@ dependencies: - pip - pip: - "git+git://github.com/arviz-devs/arviz.git@main" - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" diff --git a/Rethinking_2/Chp_02.ipynb b/Rethinking_2/Chp_02.ipynb index 0a87846..e58a687 100644 --- a/Rethinking_2/Chp_02.ipynb +++ b/Rethinking_2/Chp_02.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -435,7 +435,7 @@ "IPython version : 7.19.0\n", "\n", "arviz : 0.10.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "numpy : 1.19.4\n", "matplotlib: 3.3.3\n", "scipy : 1.5.4\n", diff --git a/Rethinking_2/Chp_03.ipynb b/Rethinking_2/Chp_03.ipynb index 7a35ae6..3dce231 100644 --- a/Rethinking_2/Chp_03.ipynb +++ b/Rethinking_2/Chp_03.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -694,7 +694,7 @@ "arviz : 0.10.0\n", "scipy : 1.5.4\n", "matplotlib: 3.3.3\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "numpy : 1.19.4\n", "\n", "Watermark: 2.1.0\n", diff --git a/Rethinking_2/Chp_04.ipynb b/Rethinking_2/Chp_04.ipynb index 9b015ff..db23321 100644 --- a/Rethinking_2/Chp_04.ipynb +++ b/Rethinking_2/Chp_04.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats\n", "import seaborn as sns\n", "\n", @@ -86,17 +86,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n" ] }, @@ -1037,11 +1037,11 @@ "source": [ "#### Code 4.28\n", "\n", - "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC3 is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", + "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", "\n", - "PyMC3 comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC3 choose the sampler for us. PyMC3 also tries to provide a reasonable starting point for the simulation. By default PyMC3 uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which starts with a identity mass matrix and then adapts a diagonal based on the variance of the tuning samples. \n", + "PyMC comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC choose the sampler for us. PyMC also tries to provide a reasonable starting point for the simulation. By default PyMC uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which starts with a identity mass matrix and then adapts a diagonal based on the variance of the tuning samples. \n", "\n", - "You can read more details of PyMC3 [here](http://pymc-devs.github.io/pymc3/notebooks/getting_started.html)" + "You can read more details of PyMC [here](http://pymc-devs.github.io/pymc/notebooks/getting_started.html)" ] }, { @@ -1053,7 +1053,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1112,7 +1112,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1166,7 +1166,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1247,7 +1247,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1316,7 +1316,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1358,7 +1358,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1718,7 +1718,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1939,7 +1939,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2003,7 +2003,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2074,7 +2074,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -2367,7 +2367,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2652,7 +2652,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] } @@ -2717,7 +2717,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -2771,7 +2771,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] } @@ -2796,7 +2796,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2868,9 +2868,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3015,7 +3015,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -3083,7 +3083,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -3124,7 +3124,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -3221,7 +3221,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -3276,9 +3276,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3341,7 +3341,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -3413,7 +3413,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -3450,9 +3450,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3683,7 +3683,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -3794,7 +3794,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3850,7 +3850,7 @@ "\n", "seaborn : 0.11.1\n", "numpy : 1.19.2\n", - "pymc3 : 3.11.1\n", + "pymc : 3.11.1\n", "scipy : 1.6.0\n", "arviz : 0.11.1\n", "matplotlib: 3.3.4\n", diff --git a/Rethinking_2/Chp_05.ipynb b/Rethinking_2/Chp_05.ipynb index 926bc2f..5b40eac 100644 --- a/Rethinking_2/Chp_05.ipynb +++ b/Rethinking_2/Chp_05.ipynb @@ -21,7 +21,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", "from scipy import stats\n", @@ -867,7 +867,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -881,7 +881,7 @@ } ], "source": [ - "# We can skip most of the code with the posterior predictive plot functionality in pymc3\n", + "# We can skip most of the code with the posterior predictive plot functionality in pymc\n", "with m_5_3:\n", " m_5_3_ppc = pm.sample_posterior_predictive(\n", " m_5_3_trace, var_names=[\"mu\", \"divorce_rate_std\"], samples=1000\n", @@ -1087,7 +1087,7 @@ } ], "source": [ - "# With PyMC3 we have to simulate in each model separately\n", + "# With PyMC we have to simulate in each model separately\n", "\n", "# Simulate the marriage rates at each age first\n", "age_shared.set_value(A_seq)\n", @@ -1264,7 +1264,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -1431,8 +1431,8 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mSamplingError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mm5_5_draft_trace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m \u001b[0mcheck_start_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 428\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/util.py\u001b[0m in \u001b[0;36mcheck_start_vals\u001b[0;34m(start, model)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m raise SamplingError(\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;34m\"Initial evaluation of model at starting point failed!\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\"Starting values:\\n{}\\n\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m \u001b[0mcheck_start_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 428\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/util.py\u001b[0m in \u001b[0;36mcheck_start_vals\u001b[0;34m(start, model)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m raise SamplingError(\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;34m\"Initial evaluation of model at starting point failed!\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\"Starting values:\\n{}\\n\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mSamplingError\u001b[0m: Initial evaluation of model at starting point failed!\nStarting values:\n{'sigma_log__': array(-0.36651292), 'bN': array(0.), 'a': array(0.)}\n\nInitial evaluation results:\nsigma_log__ -1.06\nbN -0.92\na -0.92\nK NaN\nName: Log-probability of test_point, dtype: float64" ] } @@ -1882,7 +1882,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -2133,7 +2133,7 @@ } ], "source": [ - "# With PyMC3 it's easier just to create a deterministic that includes both values\n", + "# With PyMC it's easier just to create a deterministic that includes both values\n", "sex = d[\"male\"].values\n", "\n", "with pm.Model() as m5_8:\n", @@ -2330,7 +2330,7 @@ "\n", "pandas : 1.2.0\n", "scipy : 1.6.0\n", - "pymc3 : 3.10.0\n", + "pymc : 3.10.0\n", "sys : 3.8.5 (default, Sep 4 2020, 07:30:14) \n", "[GCC 7.3.0]\n", "numpy : 1.19.4\n", diff --git a/Rethinking_2/Chp_06.ipynb b/Rethinking_2/Chp_06.ipynb index 01e7326..4d77079 100644 --- a/Rethinking_2/Chp_06.ipynb +++ b/Rethinking_2/Chp_06.ipynb @@ -19,7 +19,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", @@ -3027,7 +3027,7 @@ "arviz 0.7.0\n", "pandas 1.0.3\n", "daft 0.1.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Sun May 10 2020 \n", "\n", "CPython 3.7.6\n", diff --git a/Rethinking_2/Chp_07.ipynb b/Rethinking_2/Chp_07.ipynb index f13f8da..6c4850c 100644 --- a/Rethinking_2/Chp_07.ipynb +++ b/Rethinking_2/Chp_07.ipynb @@ -17,7 +17,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "\n", @@ -741,7 +741,7 @@ "source": [ "# Figure 7.4\n", "\n", - "# this code taken from PyMC3 port of Rethinking/Chp_06.ipynb\n", + "# this code taken from PyMC port of Rethinking/Chp_06.ipynb\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(8, 3))\n", "ax1.scatter(brains.mass, brains.brain, alpha=0.8)\n", @@ -879,7 +879,7 @@ } ], "source": [ - "# PyMC3 does not have a way to calculate LPPD directly, so we use the approach from 7.14\n", + "# PyMC does not have a way to calculate LPPD directly, so we use the approach from 7.14\n", "\n", "sigmas = (np.sum((pred - brains.brain_std.values.reshape(-1, 1)) ** 2, 0) / 7) ** 0.5\n", "ll = np.zeros((n, ns))\n", @@ -990,7 +990,7 @@ "\n", " np.concatenate([mm_train, x_train[:, 1:k]], axis=1)\n", "\n", - " # Using pymc3\n", + " # Using pymc\n", "\n", " with pm.Model() as m_sim:\n", " vec_V = pm.MvNormal(\n", @@ -3119,7 +3119,7 @@ "source": [ "#### Code 7.17\n", "\n", - "Does not apply because multi-threading is automatic in PyMC3." + "Does not apply because multi-threading is automatic in PyMC." ] }, { @@ -3994,7 +3994,7 @@ " Parameters\n", " ----------\n", " dataset_dict : dict\n", - " A dict containing two ore more {'name': pymc3.backends.base.MultiTrace}\n", + " A dict containing two ore more {'name': pymc.backends.base.MultiTrace}\n", " items.\n", " metric : str\n", " The name of the matric to be calculated. Can be any valid column output\n", @@ -4605,7 +4605,7 @@ "text": [ "pandas 1.1.1\n", "numpy 1.19.1\n", - "pymc3 3.9.3\n", + "pymc 3.9.3\n", "arviz 0.9.0\n", "statsmodels.api 0.11.1\n", "last updated: Mon Aug 24 2020 \n", diff --git a/Rethinking_2/Chp_08.ipynb b/Rethinking_2/Chp_08.ipynb index 0f3f157..99869fa 100644 --- a/Rethinking_2/Chp_08.ipynb +++ b/Rethinking_2/Chp_08.ipynb @@ -17,7 +17,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", @@ -1300,7 +1300,7 @@ " lw=1,\n", " edgecolor=\"k\",\n", ")\n", - "# calculating predicted manually because this is a pain with categorical variabiles in PyMC3\n", + "# calculating predicted manually because this is a pain with categorical variabiles in PyMC\n", "pred0 = m_8_3_posterior[\"a\"][:, 0] + rugged_plot.reshape(-1, 1) * m_8_3_posterior[\"b\"][:, 0]\n", "ax0.plot(rugged_plot, pred0.mean(1), color=\"grey\")\n", "az.plot_hdi(rugged_plot, pred0.T, color=\"grey\", hdi_prob=0.97, ax=ax0)\n", @@ -1312,7 +1312,7 @@ " label=\"Africa\",\n", " color=\"b\",\n", ")\n", - "# calculating predicted manually because this is a pain with categorical variabiles in PyMC3\n", + "# calculating predicted manually because this is a pain with categorical variabiles in PyMC\n", "pred1 = m_8_3_posterior[\"a\"][:, 1] + rugged_plot.reshape(-1, 1) * m_8_3_posterior[\"b\"][:, 1]\n", "ax1.plot(rugged_plot, pred1.mean(1), color=\"k\")\n", "az.plot_hdi(\n", @@ -1994,7 +1994,7 @@ "numpy 1.18.5\n", "seaborn 0.10.1\n", "arviz 0.10.0\n", - "pymc3 3.9.3\n", + "pymc 3.9.3\n", "last updated: Mon Oct 19 2020 \n", "\n", "CPython 3.8.3\n", diff --git a/Rethinking_2/Chp_09.ipynb b/Rethinking_2/Chp_09.ipynb index 7e7dc2d..08890ac 100644 --- a/Rethinking_2/Chp_09.ipynb +++ b/Rethinking_2/Chp_09.ipynb @@ -21,7 +21,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "\n", @@ -456,7 +456,7 @@ "source": [ "#### Code 9.12-9.18\n", "\n", - "By using PyMC3 we are already doing everything in these code blocks (No-Uturn sampling, parallell processing).\n", + "By using PyMC we are already doing everything in these code blocks (No-Uturn sampling, parallell processing).\n", "\n", "To translate the results of `summary` to `rethinking`'s `precis`:\n", "\n", @@ -1400,7 +1400,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "numpy 1.18.1\n", "arviz 0.7.0\n", "pandas 1.0.3\n", diff --git a/Rethinking_2/Chp_11.ipynb b/Rethinking_2/Chp_11.ipynb index 5e1b393..8417753 100644 --- a/Rethinking_2/Chp_11.ipynb +++ b/Rethinking_2/Chp_11.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from scipy import stats\n", @@ -1256,7 +1256,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (28). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (28). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -1853,8 +1853,8 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"m11_4\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtrace_11_4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"m11_6\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtrace_11_6\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36mcompare\u001b[0;34m(dataset_dict, ic, method, b_samples, alpha, seed, scale)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"bb-pseudo-bma\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ic_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36m_ic_matrix\u001b[0;34m(ics, ic_i)\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mic\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The number of observations should be the same across all models\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0mic_i_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36mcompare\u001b[0;34m(dataset_dict, ic, method, b_samples, alpha, seed, scale)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"bb-pseudo-bma\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ic_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36m_ic_matrix\u001b[0;34m(ics, ic_i)\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mic\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The number of observations should be the same across all models\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0mic_i_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: The number of observations should be the same across all models" ] } @@ -2366,9 +2366,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3336,9 +3336,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n" ] }, @@ -3431,7 +3431,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n" ] } @@ -3537,13 +3537,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3640,7 +3640,7 @@ "metadata": {}, "source": [ "#### Code 11.49\n", - "The book doesn't pre-process the population data, but if you give them raw to PyMC3, the sampler will break: the scale of these data is too wide. However we can't just standardize the data, as we usually do. Why? Because some data points will then be negative, which doesn't play nice with the `b` exponent (try it if you don't trust me). But we'll do something similar: let's standardize the data, and then just add the absolute value of the minimum, and add yet again an epsilon -- this will ensure that our data stay positive and that the transformation will be easy to reverse when we want to plot on the natural scale:" + "The book doesn't pre-process the population data, but if you give them raw to PyMC, the sampler will break: the scale of these data is too wide. However we can't just standardize the data, as we usually do. Why? Because some data points will then be negative, which doesn't play nice with the `b` exponent (try it if you don't trust me). But we'll do something similar: let's standardize the data, and then just add the absolute value of the minimum, and add yet again an epsilon -- this will ensure that our data stay positive and that the transformation will be easy to reverse when we want to plot on the natural scale:" ] }, { @@ -3839,13 +3839,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4213,7 +4213,7 @@ "source": [ "#### Code 11.56 and 11.57\n", "\n", - "The model described in the book does not sample well in PyMC3. It does slightly better if we change the pivot category to be the first career instead of the third, but this is still suboptimal because we are discarding predictive information from the pivoted category (i.e., its unique career income). \n", + "The model described in the book does not sample well in PyMC. It does slightly better if we change the pivot category to be the first career instead of the third, but this is still suboptimal because we are discarding predictive information from the pivoted category (i.e., its unique career income). \n", "\n", "In fact, it is not necessary to pivot the coefficients of variables that are distinct for each category (what the author calls predictors matched to outcomes), as it is done for the coefficients of shared variables (what the author calles predictors matched to observations). The intercepts belong to the second category, and as such they still need to be pivoted. These two references explain this distinction clearly: \n", "\n", @@ -4933,7 +4933,7 @@ "numpy 1.18.1\n", "theano 1.0.4\n", "matplotlib 3.1.3\n", - "pymc3 3.8\n", + "pymc 3.8\n", "arviz 0.7.0\n", "pandas 0.25.3\n", "last updated: Thu Apr 23 2020 \n", diff --git a/Rethinking_2/Chp_12.ipynb b/Rethinking_2/Chp_12.ipynb index 5b9d64c..a90d65e 100644 --- a/Rethinking_2/Chp_12.ipynb +++ b/Rethinking_2/Chp_12.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import theano.tensor as tt\n", "\n", @@ -739,7 +739,7 @@ "text": [ "Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 19 seconds.\n", "The number of effective samples is smaller than 25% for some parameters.\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n" ] }, @@ -825,13 +825,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3337,7 +3337,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", " \"fill_last and contour will be deprecated. Please use kde_kwargs\", UserWarning,\n" ] }, @@ -3596,7 +3596,7 @@ "theano 1.0.4\n", "numpy 1.18.1\n", "pandas 0.25.3\n", - "pymc3 3.8\n", + "pymc 3.8\n", "scipy 1.4.1\n", "last updated: Fri Apr 24 2020 \n", "\n", @@ -3614,9 +3614,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/Chp_13.ipynb b/Rethinking_2/Chp_13.ipynb index 8a5e7a7..93fabdc 100644 --- a/Rethinking_2/Chp_13.ipynb +++ b/Rethinking_2/Chp_13.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from scipy.special import expit as logistic" @@ -1149,7 +1149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1320: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1320: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " \"For one or more samples the posterior variance of the log predictive \"\n" ] @@ -1804,7 +1804,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This part is Stan related. To do the same in PyMC3 (i.e., avoid compiling the same model twice), you need to set up the input data with `pm.Data`. There are examples in this repository, and you can also take a look at [this tutorial](https://docs.pymc.io/notebooks/data_container.html)" + "This part is Stan related. To do the same in PyMC (i.e., avoid compiling the same model twice), you need to set up the input data with `pm.Data`. There are examples in this repository, and you can also take a look at [this tutorial](https://docs.pymc.io/notebooks/data_container.html)" ] }, { @@ -2866,7 +2866,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", " \"fill_last and contour will be deprecated. Please use kde_kwargs\", UserWarning,\n" ] }, @@ -3091,7 +3091,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", " \"fill_last and contour will be deprecated. Please use kde_kwargs\", UserWarning,\n" ] }, @@ -3810,7 +3810,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3923,7 +3923,7 @@ "Attributes:\n", " created_at: 2020-05-18T15:37:47.591223\n", " arviz_version: 0.7.0\n", - " inference_library: pymc3\n", + " inference_library: pymc\n", " inference_library_version: 3.8" ] }, @@ -4050,7 +4050,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4103,7 +4103,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4149,7 +4149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4231,7 +4231,7 @@ "output_type": "stream", "text": [ "scipy 1.4.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "numpy 1.18.1\n", "pandas 0.25.3\n", "arviz 0.7.0\n", @@ -4252,9 +4252,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/Chp_14.ipynb b/Rethinking_2/Chp_14.ipynb index a7345d9..c99c1d2 100644 --- a/Rethinking_2/Chp_14.ipynb +++ b/Rethinking_2/Chp_14.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib.patches import Ellipse, transforms\n", "from scipy import stats\n", @@ -5370,7 +5370,7 @@ "source": [ "#### Code 14.46\n", "\n", - "Related to Stan. PyMC3's GP module automatically reparametrizes with the Cholesky factor of the covariance matrix under the hood." + "Related to Stan. PyMC's GP module automatically reparametrizes with the Cholesky factor of the covariance matrix under the hood." ] }, { @@ -6193,9 +6193,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is a good opportunity to show a use-case of PyMC3's GP module. In the Oceanic tools example above, we didn't need a mean function (which means it was automatically set to 0 by PyMC3). Now however, we need both a mean function _and_ a covariance function to specify our GP. The covariance function will look familiar. For the mean function, we could use `gp.mean.Linear`, which takes as input a matrix of coefficients and a vector of intercepts. But that mean function would then be evaluated on our distance matrix, as `SIGMA` will be.\n", + "This is a good opportunity to show a use-case of PyMC's GP module. In the Oceanic tools example above, we didn't need a mean function (which means it was automatically set to 0 by PyMC). Now however, we need both a mean function _and_ a covariance function to specify our GP. The covariance function will look familiar. For the mean function, we could use `gp.mean.Linear`, which takes as input a matrix of coefficients and a vector of intercepts. But that mean function would then be evaluated on our distance matrix, as `SIGMA` will be.\n", "\n", - "We don't want that -- we only want the mean function to depend on `M` and `G` not on the phylogenetic distance. We can easily [define a custom mean function](https://docs.pymc.io/notebooks/GP-MeansAndCovs.html#Defining-a-custom-mean-function) in PyMC3. We just need to subclass `pm.gp.mean.Mean` and provide `__call__` and `__init__` methods:" + "We don't want that -- we only want the mean function to depend on `M` and `G` not on the phylogenetic distance. We can easily [define a custom mean function](https://docs.pymc.io/notebooks/GP-MeansAndCovs.html#Defining-a-custom-mean-function) in PyMC. We just need to subclass `pm.gp.mean.Mean` and provide `__call__` and `__init__` methods:" ] }, { @@ -6547,7 +6547,7 @@ "pandas 0.25.3\n", "theano 1.0.4\n", "arviz 0.9.0\n", - "pymc3 3.9.2\n", + "pymc 3.9.2\n", "last updated: Wed Jul 01 2020 \n", "\n", "CPython 3.7.6\n", @@ -6565,9 +6565,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/Chp_15.ipynb b/Rethinking_2/Chp_15.ipynb index 6be3793..d2f4ad2 100644 --- a/Rethinking_2/Chp_15.ipynb +++ b/Rethinking_2/Chp_15.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from numpy.random import default_rng\n", @@ -1385,7 +1385,7 @@ " nu = pm.Normal(\"nu\", 0, 0.5)\n", " a = pm.Normal(\"a\", 0, 0.5)\n", "\n", - " # PyMC3 automatically imputes missing values\n", + " # PyMC automatically imputes missing values\n", " Bi = pm.Normal(\"Bi\", nu, sigma_B, observed=B)\n", "\n", " mu = a + bB * Bi + bM * M\n", @@ -2561,7 +2561,7 @@ "output_type": "stream", "text": [ "arviz 0.10.0\n", - "pymc3 3.9.3\n", + "pymc 3.9.3\n", "pandas 1.0.3\n", "numpy 1.18.1\n", "last updated: Sat Oct 03 2020 \n", diff --git a/Rethinking_2/Chp_16.ipynb b/Rethinking_2/Chp_16.ipynb index e1707ed..ee70636 100644 --- a/Rethinking_2/Chp_16.ipynb +++ b/Rethinking_2/Chp_16.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from numpy.random import default_rng\n", "from theano import tensor as tt" @@ -281,7 +281,7 @@ } ], "source": [ - "# A more native pymc3/Arviz representation\n", + "# A more native pymc/Arviz representation\n", "axes = az.plot_pair(trace_16_1, var_names=[\"k\", \"p\"], kind=\"scatter\", marginals=True)\n", "corr = np.corrcoef(trace_16_1[\"p\"], trace_16_1[\"k\"])[0, 1]\n", "axes[1, 0].text(15, 0.4, f\"ρ = {corr:.2f}\", fontsize=15);" @@ -640,7 +640,7 @@ "text": [ "pandas 1.0.3\n", "numpy 1.18.2\n", - "pymc3 3.9.2\n", + "pymc 3.9.2\n", "arviz 0.10.0\n", "last updated: Wed Dec 09 2020 \n", "\n", diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb index f2fd1ec..96a7146 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb @@ -10,7 +10,7 @@ "source": [ "import arviz as az\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import pylab as plt\n", "from scipy import stats" @@ -587,7 +587,7 @@ "scipy : 1.5.2\n", "matplotlib: 3.3.2\n", "numpy : 1.19.1\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "\n", "Watermark: 2.1.0\n", "\n" @@ -604,9 +604,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb index dfc6e0c..623a3f0 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb @@ -10,7 +10,7 @@ "source": [ "import arviz as az\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import pylab as plt\n", "from scipy import stats" @@ -48,7 +48,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "All of this chapter depends on the posterior distribution for the globe spinning example. I will implement pymc3 to do most of the leg work for me. As before, set the prior to be the uniform and the likelihood to be the binomial. The data given in the book is the set of outcomes W L W W W L W L W, which I'll give a binary representation in the same order.\n", + "All of this chapter depends on the posterior distribution for the globe spinning example. I will implement pymc to do most of the leg work for me. As before, set the prior to be the uniform and the likelihood to be the binomial. The data given in the book is the set of outcomes W L W W W L W L W, which I'll give a binary representation in the same order.\n", "\n", "Pymc3 nicely allows you to state the prior and the likelihood function of the data. You can use its pm.sample function in order to obtain a random sample from the posterior distribution. This is done using MCMC techniques, but they aren't introduced until later in Statistical rethinking. You can run \"chains\" of the sampling procedure to sanity check the MCMC methods that took place. The samples from the posterior distribution are also returned and we can perform inference on the posterior according to this." ] @@ -1350,7 +1350,7 @@ "Python version : 3.8.5\n", "IPython version : 7.18.1\n", "\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "matplotlib: 3.3.2\n", "arviz : 0.9.0\n", "scipy : 1.5.2\n", @@ -1371,9 +1371,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb index e71bcff..35ae1f5 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from matplotlib import pylab as plt\n", @@ -1629,7 +1629,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1678,7 +1678,7 @@ "IPython version : 7.18.1\n", "\n", "arviz : 0.9.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "scipy : 1.5.2\n", "seaborn : 0.11.0\n", "numpy : 1.19.1\n", @@ -1700,9 +1700,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb index 46840d6..d2ab2d7 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -221,11 +221,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", ":12: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n", " plt.tight_layout();\n" @@ -266,7 +266,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now perform our linear regressions on these models. I'll just use pymc3 MCMC tool to estimate this and not bother with Laplace's method for now. I'll fit two linear models to the data, both of which will attempt to predict child IQ. The first model will use just parental income and the second will make use of both parental income and parental IQ. As I generated this data in such a way as for the parental income to be spurious, we should see the first model have a positive slope parameter and the second model should have zero slope parameter for the parental income variable, as we're controlling for the true cause. More formally\n", + "We can now perform our linear regressions on these models. I'll just use pymc MCMC tool to estimate this and not bother with Laplace's method for now. I'll fit two linear models to the data, both of which will attempt to predict child IQ. The first model will use just parental income and the second will make use of both parental income and parental IQ. As I generated this data in such a way as for the parental income to be spurious, we should see the first model have a positive slope parameter and the second model should have zero slope parameter for the parental income variable, as we're controlling for the true cause. More formally\n", "\n", "### Model 1\n", "\n", @@ -3510,7 +3510,7 @@ "Python version : 3.8.5\n", "IPython version : 7.18.1\n", "\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "arviz : 0.9.0\n", "seaborn : 0.11.0\n", "numpy : 1.19.1\n", @@ -3532,9 +3532,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb index cd98ad9..b035922 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -319,9 +319,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n" ] }, @@ -1325,7 +1325,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can use pymc3 to perform the regression conditioned on whether each state is in the south of not." + "We can use pymc to perform the regression conditioned on whether each state is in the south of not." ] }, { @@ -1706,11 +1706,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2551,9 +2551,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2730,9 +2730,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3097,7 +3097,7 @@ "IPython version : 7.18.1\n", "\n", "scipy : 1.5.2\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "matplotlib: 3.3.2\n", "pandas : 1.1.3\n", "arviz : 0.9.0\n", diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb index 70fc750..eb254a1 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -739,7 +739,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1166,7 +1166,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1360,10 +1360,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " warnings.warn(\n" ] } @@ -1383,9 +1383,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1551,7 +1551,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1571,9 +1571,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1760,10 +1760,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " warnings.warn(\n" ] } @@ -1783,9 +1783,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1955,7 +1955,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1975,9 +1975,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2687,7 +2687,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1707: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/pymc/sampling.py:1707: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -3221,7 +3221,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -3512,7 +3512,7 @@ "IPython version : 7.18.1\n", "\n", "arviz : 0.9.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "seaborn : 0.11.0\n", "matplotlib: 3.3.2\n", "pandas : 1.1.3\n", @@ -3534,9 +3534,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb index 6e0c678..6eb5485 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -769,7 +769,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1782,9 +1782,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1829,7 +1829,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1890,9 +1890,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3063,7 +3063,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -4073,7 +4073,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -4127,7 +4127,7 @@ "\n", "matplotlib: 3.3.2\n", "numpy : 1.19.1\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "arviz : 0.9.0\n", "seaborn : 0.11.0\n", "pandas : 1.1.3\n", @@ -4147,9 +4147,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb index 03e3b66..10acaed 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import pylab as plt" ] @@ -727,7 +727,7 @@ "\n", "#### Answers:\n", "\n", - "To translate terms from the book into pymc3:\n", + "To translate terms from the book into pymc:\n", "\n", "n_eff = ess_mean\n", "\n", @@ -1800,7 +1800,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:85: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:85: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -3027,7 +3027,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/pairplot.py:212: UserWarning: rcParams['plot.max_subplots'] (40) is smaller than the number of resulting pair plots with these variables, generating only a 8x8 grid\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/pairplot.py:212: UserWarning: rcParams['plot.max_subplots'] (40) is smaller than the number of resulting pair plots with these variables, generating only a 8x8 grid\n", " warnings.warn(\n" ] }, @@ -3204,7 +3204,7 @@ "IPython version : 7.18.1\n", "\n", "arviz : 0.9.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "pandas : 1.1.3\n", "matplotlib: 3.3.2\n", "numpy : 1.19.1\n", @@ -3224,9 +3224,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/README.md b/Rethinking_2/README.md index 18d941e..992a06f 100644 --- a/Rethinking_2/README.md +++ b/Rethinking_2/README.md @@ -1,8 +1,8 @@ -# Statistical Rethinking (second edition) with Python and PyMC3 +# Statistical Rethinking (second edition) with Python and PyMC [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) is an incredible introductory book to Bayesian Statistics. It follows a [_Jaynesian_](https://en.wikipedia.org/wiki/Edwin_Thompson_Jaynes) and practical approach with very good examples and clear explanations. -In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC3. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMC3onic_ way. +In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMConic_ way. ## Display notebooks [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pymc-devs/resources/master?filepath=Rethinking_2) @@ -13,9 +13,9 @@ In this repository we port [the book's original code in R and Stan](https://gith All contributions are welcome! -Feel free to send PRs to fix errors, improve the code, or make comments that could help users of this repository and readers of the book. When submitting PRs, please make sure the notebooks are formatted according to the [PyMC NB style guide](https://github.com/pymc-devs/pymc3/wiki/PyMC's-Jupyter-Notebook-Style). +Feel free to send PRs to fix errors, improve the code, or make comments that could help users of this repository and readers of the book. When submitting PRs, please make sure the notebooks are formatted according to the [PyMC NB style guide](https://github.com/pymc-devs/pymc/wiki/PyMC's-Jupyter-Notebook-Style). -You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC3/Lobby). +You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC/Lobby). ## Installing the dependencies @@ -27,8 +27,8 @@ to install all the dependencies into an isolated environment. Activate the environment by running: - source activate stat-rethink2-pymc3 + source activate stat-rethink2-pymc --- -Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/Rethinking_2/environment.yml b/Rethinking_2/environment.yml index afbb638..a44c0f0 100644 --- a/Rethinking_2/environment.yml +++ b/Rethinking_2/environment.yml @@ -1,4 +1,4 @@ -name: stat-rethink2-pymc3 +name: stat-rethink2-pymc channels: - defaults dependencies: @@ -9,6 +9,6 @@ dependencies: - pip: - watermark - "git+git://github.com/arviz-devs/arviz.git@main" - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" - causalgraphicalmodels - daft From f4cf1fb6cbdeb76ef1242735120bf5988e1301f6 Mon Sep 17 00:00:00 2001 From: Michael Aydinbas <95412359+maydinbas@users.noreply.github.com> Date: Sun, 20 Mar 2022 18:31:11 +0100 Subject: [PATCH 02/11] fix errors in environment.yml and update README to actually work with notebooks (#170) --- Rethinking_2/README.md | 14 ++++++++++++++ Rethinking_2/environment.yml | 16 ++++++++++------ 2 files changed, 24 insertions(+), 6 deletions(-) diff --git a/Rethinking_2/README.md b/Rethinking_2/README.md index 18d941e..a924f67 100644 --- a/Rethinking_2/README.md +++ b/Rethinking_2/README.md @@ -29,6 +29,20 @@ Activate the environment by running: source activate stat-rethink2-pymc3 +To use the notebooks you first have to register your new environment as a valid notebook kernel: + + python -m ipykernel install --user --name stat-rethink2-pymc3 --display-name "Python 3.10 (stat-rethink2-pymc3)" + +You can start a notebook by running: + + jupyter notebook + +or use the more modern jupyter lab: + + jupyter lab + +from the root directory. + --- Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/Rethinking_2/environment.yml b/Rethinking_2/environment.yml index afbb638..7e6f91c 100644 --- a/Rethinking_2/environment.yml +++ b/Rethinking_2/environment.yml @@ -1,14 +1,18 @@ name: stat-rethink2-pymc3 channels: -- defaults +- conda-forge dependencies: - jupyter + - jupyterlab - seaborn - mkl-service + - watermark + - pymc3 + - arviz + - theano-pymc + - mkl + - mkl-service - pip - pip: - - watermark - - "git+git://github.com/arviz-devs/arviz.git@main" - - "git+git://github.com/pymc-devs/pymc3.git@main" - - causalgraphicalmodels - - daft + - daft + - causalgraphicalmodels From e8b4928d22d1f8456a6cf0d3e6eb9a5bc7c6b734 Mon Sep 17 00:00:00 2001 From: Larry Jones Date: Sun, 20 Mar 2022 12:32:47 -0500 Subject: [PATCH 03/11] Repair "typo" in vampire markdown (#172) Previous commits contained the expression `Pr(positive|mortal)1 - Pr(vampire)` as part of the normalizing constant. I believe the correct expression is `Pr(positive|mortal)(1 - Pr(vampire))`. This commit corrects the markdown of the vampire normalizing constant. Co-authored-by: laj --- Rethinking_2/Chp_03.ipynb | 142 +++++++++++++++++++++++++------------- 1 file changed, 95 insertions(+), 47 deletions(-) diff --git a/Rethinking_2/Chp_03.ipynb b/Rethinking_2/Chp_03.ipynb index 7a35ae6..e82d7dd 100644 --- a/Rethinking_2/Chp_03.ipynb +++ b/Rethinking_2/Chp_03.ipynb @@ -4,7 +4,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" + ] + } + ], "source": [ "import arviz as az\n", "import matplotlib.pyplot as plt\n", @@ -34,7 +42,7 @@ "\n", "$$Pr(vampire|positive) = \\frac{Pr(positive|vampire) Pr(vampire)} {Pr(positive)}$$\n", "\n", - "$$Pr(positive) = Pr(positive|vampire) Pr(vampire) + Pr(positive|mortal) 1 − Pr(vampire)$$" + "$$Pr(positive) = Pr(positive|vampire) Pr(vampire) + Pr(positive|mortal) (1 − Pr(vampire))$$" ] }, { @@ -44,7 +52,9 @@ "outputs": [ { "data": { - "text/plain": "0.08683729433272395" + "text/plain": [ + "0.08683729433272395" + ] }, "execution_count": 3, "metadata": {}, @@ -113,13 +123,15 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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+ "text/plain": [ + "
" + ] }, "metadata": { "image/png": { - "width": 1211, - "height": 611 + "height": 611, + "width": 1211 } }, "output_type": "display_data" @@ -149,7 +161,9 @@ "outputs": [ { "data": { - "text/plain": "0.17183313110747478" + "text/plain": [ + "0.17183313110747475" + ] }, "execution_count": 7, "metadata": {}, @@ -174,7 +188,9 @@ "outputs": [ { "data": { - "text/plain": "0.1699" + "text/plain": [ + "0.1699" + ] }, "execution_count": 8, "metadata": {}, @@ -199,7 +215,9 @@ "outputs": [ { "data": { - "text/plain": "0.6089" + "text/plain": [ + "0.6089" + ] }, "execution_count": 9, "metadata": {}, @@ -224,7 +242,9 @@ "outputs": [ { "data": { - "text/plain": "0.7676767676767677" + "text/plain": [ + "0.7676767676767677" + ] }, "execution_count": 10, "metadata": {}, @@ -249,7 +269,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.45454545, 0.81818182])" + "text/plain": [ + "array([0.45454545, 0.81818182])" + ] }, "execution_count": 11, "metadata": {}, @@ -274,13 +296,15 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "image/png": { - "width": 731, - "height": 491 + "height": 491, + "width": 731 } }, "output_type": "display_data" @@ -307,7 +331,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.70707071, 0.93939394])" + "text/plain": [ + "array([0.70707071, 0.93939394])" + ] }, "execution_count": 13, "metadata": {}, @@ -333,7 +359,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.84848485, 1. ])" + "text/plain": [ + "array([0.84848485, 1. ])" + ] }, "execution_count": 14, "metadata": {}, @@ -358,7 +386,9 @@ "outputs": [ { "data": { - "text/plain": "array([1.])" + "text/plain": [ + "array([1.])" + ] }, "execution_count": 15, "metadata": {}, @@ -383,7 +413,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.96969697])" + "text/plain": [ + "array([0.96969697])" + ] }, "execution_count": 16, "metadata": {}, @@ -408,7 +440,9 @@ "outputs": [ { "data": { - "text/plain": "(0.8039616161616162, 0.8484848484848485)" + "text/plain": [ + "(0.8039616161616162, 0.8484848484848485)" + ] }, "execution_count": 17, "metadata": {}, @@ -433,7 +467,9 @@ "outputs": [ { "data": { - "text/plain": "0.31626874808692995" + "text/plain": [ + "0.31626874808692995" + ] }, "execution_count": 18, "metadata": {}, @@ -458,7 +494,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.84848485])" + "text/plain": [ + "array([0.84848485])" + ] }, "execution_count": 19, "metadata": {}, @@ -484,7 +522,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.09, 0.42, 0.49])" + "text/plain": [ + "array([0.09, 0.42, 0.49])" + ] }, "execution_count": 20, "metadata": {}, @@ -509,7 +549,9 @@ "outputs": [ { "data": { - "text/plain": "array([1])" + "text/plain": [ + "array([1])" + ] }, "execution_count": 21, "metadata": {}, @@ -534,7 +576,9 @@ "outputs": [ { "data": { - "text/plain": "array([2, 2, 1, 1, 1, 2, 2, 0, 2, 1])" + "text/plain": [ + "array([2, 2, 1, 1, 1, 2, 2, 0, 2, 1])" + ] }, "execution_count": 22, "metadata": {}, @@ -559,7 +603,9 @@ "outputs": [ { "data": { - "text/plain": "[0.09088, 0.42142, 0.4877]" + "text/plain": [ + "[0.09088, 0.42142, 0.4877]" + ] }, "execution_count": 23, "metadata": {}, @@ -581,19 +627,19 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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LkiVLjGBbkrp16/aXgm1JatmypanO7/7LyMjQ1q1bTceSk5O1c+fOPPtFR0cbwbaz62bZv3+/7rnnHk2aNElbtmzJM9iWpMuXL2vOnDl65JFHtH79+jzbFobExEQNHz5cvXv31i+//JLneC9evKipU6eqY8eOTkNpV6Smpmro0KEaPXr0DZ/DHdeuXdOwYcPUq1cvrV27Ns9gW5JiYmI0e/Zsvfbaa/me+4cfftD999+vefPm5fraIUkJCQlasmSJOnbsqHnz5rn7FDxi0qRJ6tevX67BtiQdOXJEgwYN0ieffFKEIwMAoHgh3AYAACjGrl27pn/84x85QiibzaaIiAjVr1/fNBPx2rVrev7553XkyJGiHuoNuXTpkvr162cEvzabTZUrV1aDBg2czrCcPXu2vvvuO0nXZ8sOGTJECQkJkq7PgK5WrZrq16+vkJAQU7/09HS99NJLOnnypNtjnDVrlmbMmGHU5cqVU+3atVW7dm2VKVPG1Pb8+fMaMmSI0tLSlJmZqWHDhmndunXG46GhoapXr55q1KiRY5mV33//Xf/6179yHUeXLl3k7+9v1IsXL1ZmZqZbz2XhwoXG1zabTY8//rhb/Z254447TDPON23alGf76OhoXb16Ncfx/Po5hua5hdtpaWmy2+2mY76+vqpcubLq1q2rhg0bqkqVKjm+/1evXtXAgQOL9M2hixcv6u9//7tWrlyZ47GKFSuqXr16qlOnjsqXL2967Pz583r66ae1bds2t685ZswYrVq1yqgDAgJUo0YN1a1bt8CXNoqNjVWvXr2cvrEUEBCgatWqqWHDhqpatarp3nbFggUL9OKLLxq//1nKlCmjGjVqqE6dOipXrpzpsdTUVE2YMMH06ZHi6P3339dnn31m1Fk/o/r16ys4ODhH+//85z+mnykAADcTliUBAAAoxiZNmqTo6GijttlsevLJJ9WvXz/TGs+nTp3SzJkzNX/+fCUkJOjNN9/0xHDdNmnSJMXExMjf31/PPvusnnjiCVWoUMF4/MiRI3rjjTdMywNMmjRJrVq10siRI5WWlqawsDCNGDFCHTt2NMJmu92uX3/9Va+99pr+/PNPSdfXXn7vvfdMS2jk58SJE/roo48kXV8G48UXX1Tz5s2N2c6pqalatGiR3nnnHWPG7eHDh7VgwQIlJCQYM4EfffRRDRw4UDVq1DDOHR8fr48++khff/21cWzRokX6+9//7nSd8KCgIHXs2FFLly6VdH2G64YNG3T33Xe79Fy2bdtmLGsiSa1bt3Z7CRtnfH191bRpU23YsEGStGvXLiUlJeW6xEpuIfbmzZs1fPjwXK+TPXTOumZemjdvrvvuu0+tW7dW1apVc4TZKSkp+u9//6sZM2YYbx5lZmbqpZde0sqVKxUQEJDjnC+//LKuXLkiSerbt69xvFatWhozZkyuY4mKispxLDMzUyNGjDD9fgcHB6t///7q1KmTQkNDTW137Nih//znP8b3ISkpSS+++KKWLFnicij93//+VxcuXJAk1a9fXyNGjFDLli1N35udO3fmuzyLK7JmiB86dMh0vEOHDurbt68aN25s+tRAenq69u3bp59++knLli3L89zR0dGaMGGC6c2dSpUqafTo0brvvvuM5W0yMjL066+/6t133zWNY8qUKWrQoIHatWv3l59nQdu6datOnz4t6fobR8OHD1eLFi2Mn5HdbteWLVv0xhtvmH6f3377bbVv3z7Hfd6mTRvNmTNHkvTZZ58Zv6eS9N5775leb7Nz980GAAA8hXAbAACgmNq5c6cWLFhg1DabTe+++646d+6co21UVJQmTJig+vXra+zYsbkugVHcxMTEqEyZMvr000/VrFmzHI9Xr15dM2fO1OOPP26EU+fOndPTTz+t8+fPq3r16pozZ06OMM5ms6l169b67LPP1KVLFyN4Xrt2reLi4pzOfnTm7NmzkqQHHnhAH3zwQY5N6fz8/NS7d2+VKVNGL7/8snH8s88+08WLFyVJr7zyip555pkc5w4KCtIbb7yh5ORkffvtt5KuB1fffPNNrksy9OrVywi3peszsV0Nt7PP2pak7t27u9TPFS1btjRCs7S0NG3btk1t27Z12jZ7SF23bl3t3btXkrRnzx4lJCQ4DZXT0tJMS8I0atQo1/C8UqVK+v77701vJDjj7++vDh06qH379ho3bpzx/Tl37pyWLl2q3r175+hTv359p+cqX7687rrrrjyv52jWrFn67bffjPqOO+7QtGnTcnzqQLq+xnrTpk01Z84c/fOf/9SXX34p6frM6E8++USvv/66S9fMCrY7duyoyZMnO92ktVGjRm49j9xMmTLF9IkTX19fTZw4UY8++qjT9j4+PmrQoIEaNGigIUOGaNeuXU7b2e12jRkzxrS8Tu3atfXFF1/kmKnt7e2ttm3bqkWLFnr22WdN995rr72mn376SaVKlforT7PAZQXbjz32mN5+++0cywbZbDa1aNFC8+bNU+fOnY2lqs6ePat169apQ4cOpvZhYWEKCwuTpBxvGjRp0qRA3uACAMCTWJYEAACgmPr8889Nyys8+eSTToPt7Hr06FGgoWVRGD16tNNgO0upUqX0/PPPm44dOXJEvr6++uijj/KcZVq9enV17drVqNPS0vTrr7+6Nb6oqCi9++67OYLt7Lp06aLq1asbdUxMjJKTk/Xoo486DbazGzFihLy8/vfP8l9++SXXtk2aNFHNmjWNeu3atUZgmZcrV66Ylr4ICQnJczM5dzkuEZLb7OzU1FRt377dqAcOHGiEi1mhuDNZs8Fzu152ISEh+Qbb2Xl5eWncuHGmzQsXL17scv8bkZSUZFp2IjQ0VJ9++qnTYDs7Ly8vvfrqq6YA+ptvvjFmk7si6352FmwXlPj4eCOAzzJu3Lhcg21Hvr6+uW5KunHjRh08eNCoS5curWnTpuUItrPz8/PTxx9/bJoNf/78eWOJo+Kmfv36mjBhQp7r4QcHB+d4XczrtQMAgJKKcBsAAKAYunLlilavXm3U/v7+Gjp0qEt9R44cmWcQW5xUqlRJPXr0yLddu3btTAGwJD300EOmoDc3jiFu1kxhVw0YMCDH2trO3HvvvabaZrO59DMLDw9XvXr1jPrkyZM51hHOrlevXsbXaWlpLgWx3333nZKTk426S5cuBXqP1KtXzxQu5rZu9Y4dO4xx+Pj4qHXr1mrSpEm+/RzD8rzC7Rvh5+enjh07GvW+fftM36+CtmTJEtMGiEOHDlVQUJBLfb29vTVgwACjTkxMNC01kZ/nnnuu0GcrL1y40LQ56p133unS77mr586uT58+piWaclO2bFkNGTLEdCz7J2OKk2HDhrn0+/nQQw+Zandf2wAAKAkItwEAAIqhHTt2GEtpSNI999zjcvgVHBxcLNeSdaZDhw55zk7MEhAQkGODyQcffNClazgG4FlrcLvCZrPpgQcecKmt42zh2rVrq0qVKi71zT7GzMxMxcbG5tq2U6dOprB90aJFOTZQdFSYS5JI12cUN2/e3Kj37duny5cv52iXPbyuX7++AgMD1apVK+NYXutxZylTpozuuOOOghi2SfblGdLT002zgwta1lrs0vWQ/5FHHnGrf6tWrUxv9ri6saS3t3eOQLQwbNy40VQ/9dRTBXZux+farVs3l/s++uijpmA/Ojra9ImA4qBs2bK5LunjKCgoyBTs5/W6AQBASUW4DQAAUAzt2bPHVGcPDl3hbntPyT5jOT+O4b6rfR37Xbt2zeVrRkZGuvymguOmfnXr1nX5Oo7XyGvmdtmyZfXwww8b9YkTJ0xrNzvas2eP9u3bZ9TNmzdX1apVXR6bq7LPps7MzDRtApole3id1T57v/379+vSpUumPklJSab1l5s0aeLyrPOkpCQtX75c48aNU48ePdSmTRs1btxYtWvXVq1atUz/jRs3ztTXcRwFxW63a8eOHUZdpUoVBQYGunWOMmXKmO6ZI0eOuNSvWrVqbl/LXenp6aafl5eXl8thbX5Onz5tWoYnIiLCtJxMfgIDA03rpmdkZOR4rfW0unXr5viUSl6y7x9w9erVwhgSAADFGhtKAgAAFENZm4plcWcNYSnnbOXiytWNHSXl2EDQ1b6O/dxZbsKd8Tku9fBXnlt+Y+zVq5cWLVpk1AsXLsx1qY7CnrWdxdm62/fff79RJyQk6I8//jDqrBnb9erVU9myZXX16lXZ7Xb99ttvpiVCtm3bZto80JUlSdLS0jRnzhxNnz49zzcK8lJYQeGFCxdMS5IcPnxYtWrV+kvndDZL3pmi2DzwwoULpiVJqlat6nST0Btx5swZU30jr3O1a9c2zf52PKen5bfuuqPsn+IozKV0AAAorpi5DQAAUAw5bhDn6uzhLI6ziIsrf39/j/Qtimv8lb75LTPSoEED08z1VatWmQLTLImJifr++++Nunz58i4v5+KuGjVqqEKFCkbtuMRI9pC6VKlSxlrb3t7epg1FHfs5rsOdX7idnJys/v37a/LkyTccbEsyLQtUkJz9nP4qV4P4ggqZ8+L4/NwNa/Pi+Lp4I69zjn3c2YyzKBTF6xoAACUJM7cBAACKoewzH6WcM3vz4257WE+vXr00duxYSdeD2KVLl6pPnz6mNj/88IMp4O3UqVOhhmctW7Y0wvSjR4/q3LlzCgsLk2QOqZs0aSI/Pz+jbtWqldasWZOjnWQOu8uXL5/vcjRvvvlmjnMEBwerefPmql27tm699VYFBgbK39/ftN77hg0b9Nlnn7nzdG9IYYSp+b0ZkqUoNpp1fEPBlc1Yi/Lcjq+Nf+UNEAAA4HmE2wAAAMWQYwDj7qZnxW2TNBS8Rx55RBMnTjTCuYULF+YItx2XJOnRo0ehjil7uC1dD6Y7d+4syRxaO86+zr6p5PHjxxUbG6uKFSvq8uXLpvXCmzVrlud6xPv27dO3335r1L6+vho1apR69+5tCtOdOXnyZD7PrmA4/m7ffvvteu211/7SOYvTbF/H2eGOb9R5+tyOr41FMZsdAAAUHsJtAACAYqhcuXKm2t3N7dxpb7PZ3Dq3I4J0zwgICFCnTp309ddfS5IOHTqkHTt2qHHjxka9c+dOo32jRo0KfS12x9B68+bN6ty5s+Lj400hdfYwW/rfkiZZmwVu2rRJXbt21ZYtW5SZmZnr+R2tWLHCNIt56NCheuaZZ1wau6vrVv9Vjsti2O123XXXXUVy7aLguITSxYsXC+zcjq+LN7LEi+Nro+M5CwKviQAAFB3W3AYAACiGoqKiTPWhQ4fc6n/gwAGX2zrO+nR3U7K4uDi32qPg9OrVy1QvWLDA6ddS4c/alq7ftxEREUadNVv7t99+M0LnsmXLOl1aJHtwndXP3fW2s4f5Xl5eOb4/eXH3d+xGVahQwbT56JkzZ0wbZlpdhQoVTMuFHDt2rMCW/qhUqZKpdud1Lsv+/ftNdfb7Nbu/8rro7puRAADgxhFuAwAAFEMNGjQw1Vu3bnWrvzvtAwMDTbU7My3j4uIUExPjcnsUrNq1a6tRo0ZGvXLlSl27dk2pqalatmyZcTwwMFAPPfRQkYwpewB95swZnThxwhRSN2/e3LTWtbN+zsLt0NBQ1ahRI89rZ793g4ODVb58eZfGnJmZ6fbvWPZPPLi65rV0famUrM00peuzfHft2uXWtYszHx8f49MD0vXv7X//+98COXdkZKRp09KYmBidOnXK5f7Xrl3TH3/8YRpr/fr1nbZ1fF3M+lSBK/bs2eNy26Lk+Ckdd+5bAACKK8JtAACAYqhx48amzd/WrVvn8kfwL168qPXr17t8rVKlSik4ONioDxw4YFoKIi8rVqxw+TooHD179jS+TkxM1HfffadVq1aZ7pdHH320QDf2y4uzpUmybwrpuCSJs36xsbHasmWLDh8+bBxr0aJFvtfOHta5Mxt6zZo1io2Ndbm9ZF47291lKNq2bWuqv/zyS7f6F3eOz++LL74osHM3a9bMVC9evNjlvsuXLzfNwK5fv36um+86zhLPvqxOXuLi4nJ84qC4cHwNcPdTOgAAFEeE2wAAAMVQuXLldP/99xt1SkqKpkyZ4lLfDz/80O1lDurWrWt8ffnyZW3cuDHfPlevXtXMmTPdug4K3sMPP2yaobxw4UKPLEmSxTHcXrp0qY4dO5br41mioqIUGRlp1B999FGe53Um+6zey5cvm8Lx3CQkJGjixIn5tnOUfW1pdz+90L17d9Nazz/++KPWrVvn9hiKq27dupk2aty2bVuOe/JGde/e3VTPnTvXpTcmrl27luM11PFc2TkunePqG3lTp04ttqGx4ycZTp8+7aGRAABQcAi3AQAAiqmnn37a9DHyefPmmZaacGbhwoVauHCh29dq166dqX7//ffznI2amJioF154QX/++afb10LBKlWqlDp37mzU0dHR+u2334y6Xr16Tte4LixhYWGqVq2aUf/+++/G1/ktLZI9wM7ez/Gx3GRfDkOS3nvvvTw/hZCUlKShQ4e6tbRFlttvv934+tKlS6bveX7Kli2rZ5991qgzMzM1cuRI/fzzz26N4Y8//tALL7zgVp+iUK5cOT399NOmYxMmTNDy5ctd6p+WlqZt27Y5feyuu+5SrVq1jDoxMVHPP/+8rl69muv5UlNTNXz4cJ0/f944Fhoaqr/97W+59mnVqpXp0zMrVqzIdUxZFi5cWKxn4We/Z6Xrb6oAAGB1hNsAAADFVOPGjfX4448btd1u1+jRo/X222/nCJVPnz6t8ePHa+zYsZJy3yQtN3/7299MH8/fv3+/+vbtq71795rapaSkaNWqVXr88ce1YcMG+fr6qmLFiu4+NRSwvDZOzGt2amHJbemR/JYWya1fREREjk1WnenUqZO8vP73J866des0cODAHDO4U1JStHLlSnXu3Nn4lEL16tXzPX92rVu3NtVDhgzR+++/rxUrVmjDhg369ddfjf+chef9+/c3vamUkJCgwYMHa8iQIdq0aZNSU1Nz9ElJSdHu3bs1ffp0PfbYY+rWrVuxXRpo8ODBpvXg09LSNHLkSA0dOlS///67MjIyTO3T09P1xx9/6MMPP9T999+vf//7307Pa7PZ9M4775iC5+joaHXu3Fk//vij6fuWmZmpjRs3qlu3btqwYYPpPP/6179ybBqZXXBwsDp06GA618CBA7V48eIcP5sDBw5o1KhRev3112W321W5cuXcvzEe1KJFC9P37dtvv9WQIUO0aNEirV+/3nTPOr65BABAceXj6QEAAAAgd2PGjFF0dLQRMtvtdn3xxRf68ssvFRkZqaCgIF26dMn08fKAgACNHz9eAwYMcPk6t9xyi4YNG6Z3333XOLZjxw517dpV4eHhCgsLU1JSkk6fPm36yP3rr7+u5cuXu71eMQpW9erV1axZsxybIpYuXTrP2amFpWXLlpo3b16O47mF19n7uXPcUfXq1dWrVy999dVXxrH169dr/fr1uvXWWxUaGqrExETFxMSYPpnQrFkzde7cWa+//rpL15GkLl26aPr06bp06ZIk6cqVK7ku0zNkyBANHTrUdMzLy0uTJ0/W888/ry1btki6/vu9evVqrV69Wn5+fqpUqZLKly+vlJQUXb16VbGxsTlC4eLK19dX//nPf9SvXz8dOnTIOL5q1SqtWrVKAQEBuvXWWxUQEKCrV68qJiZGKSkpRru83syoV6+exo0bp/Hjxxsz82NiYjRs2DAFBAQoIiJC3t7eOnPmjC5fvpyj/9ChQ3X33Xfn+xxeeuklrV+/XomJiZKuL8X0yiuv6K233lLlypXl7e2t2NhY00amzZo1U6dOnYw3GouTkJAQderUSd98841xLOt+cxQREaE1a9YU5fAAALghzNwGAAAoxgIDA/XZZ5+pfv36puN2u12nTp3Snj17TMF2YGCgpk2b5vYsVEl65plnnK7NfPbsWe3Zs0eHDx82gm0vLy+9+uqrec4YRtHKvrFkloceekiBgYFFPpbmzZubZlBnyS/crlChgtNlS1wNtyXplVde0b333pvj+J9//qndu3fr8OHDpmC7RYsWmjp1qry9vV2+hnR9ze0pU6YoJCTErX7ZlS1bVnPmzFHfvn3l42Oed5Samqrjx49r165d2r9/v2JiYpwG27feeusNX7+whYeH6+uvv1b79u1zPJaQkKDDhw9r165dOnr0qCnYdkWPHj00efJk09reWec9ePCg9u3blyPY9vPz07hx4zRkyBCXrhEREaH//Oc/OTadTExM1P79+xUdHW0Ktlu2bKlp06bl+FkWJ6+++mqOTx0AAGBlhNsAAADFXHBwsObPn69Ro0YpNDTUaRtvb289+OCDWrp0ab5LP+TGy8tLb731liZOnKhKlSrl2u7OO+/U//3f/6lPnz43dB0UjgcffDBHkF2UG0lmFxQUpDp16piORUVFubRcjrMg251w28/PT1OnTtUrr7yS6++LdD24HDdunObOnWva3NEdzZo104oVKzRu3Djde++9ioyMVEBAgNNgPzc+Pj4aM2aMVq5cqZ49e7oUlkdERKhnz56aPXt2sZ9dW7ZsWU2bNk2ff/652rRpY1oWw5mqVatq0KBBpk+R5Obhhx/W6tWr9eSTT5o2+HQUEBCgLl26aMWKFXryySfdGn/btm21cOFCtW3b1rQHQnahoaF69dVXNXv2bJUtW9at8xe1rDdMZ82apW7duqlOnToKCgrK9+cCAEBxZbPb7XZPDwIAAACuyczM1O+//67jx4/r4sWL8vPzU1RUlJo2barg4OACu47dbtfevXu1d+9eY9mFihUrqnHjxi6tfYyid/LkST3wwAPK+ud9jRo19P3333t4VJ6Vnp6uPXv26MCBA4qPj5eXl5dCQ0NVp04d1a5d29PDc8put+vQoUM6fPiwLl26pCtXrsjPz09ly5ZVZGSkqlevrvDwcE8P84YlJiZq+/btio2N1aVLl5SZmanAwEBFRkaqVq1aN7yGf0ZGhnbv3q0TJ07o4sWLyszMVHBwsKKiotS4ceMCCW8vXLigLVu26Ny5c0pKSlL58uVVq1YtNWrUyO2Z/wAAoGAQbgMAAAAlwAcffKAZM2YY9auvvsrsegAAAJRoLEsCAAAAWFxaWpppk7hSpUqpc+fOHhwRAAAAUPgItwEAAACLW7JkiS5cuGDUjzzySJ5rEAMAAAAlAeE2AAAAYGEXLlzQRx99ZNQ2m43lSAAAAHBT8PH0AAAAAAC47tdff5UkJSUl6dChQ/ryyy9Ns7Y7duyoWrVqeWp4AAAAQJFhQ0kAAADAQvIKrsuWLavly5crPDy8CEcEAAAAeAbLkgAAAAAlQJkyZTRlyhSCbQAAANw0WJYEAAAAsCg/Pz9VqlRJbdq0Ud++fRUZGenpIQEAAABFhmVJAAAAAAAAAACWw7IkAAAAAAAAAADLIdwGAAAAAAAAAFgO4TYAAAAAAAAAwHIItwEAAAAAAAAAlkO4DQAAAAAAAACwHMJtAAAAAAAAAIDlEG4DAAAAAAAAACyHcBsAAAAAAAAAYDmE2wAAAAAAAAAAyyHcBgAAAAAAAABYDuE2AAAAAAAAAMByCLcBAAAAAAAAAJZDuA0AAAAAAAAAsBzCbQAAAAAAAACA5RBuAwAAAAAAAAAsh3AbAAAAAAAAAGA5hNsAAAAAAAAAAMsh3AYAAAAAAAAAWA7hNgAAAAAAAADAcv4/+2BGqh6ncwwAAAAASUVORK5CYII=\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "image/png": { - "width": 731, - "height": 491 + "height": 491, + "width": 731 } }, "output_type": "display_data" @@ -665,7 +711,9 @@ "outputs": [ { "data": { - "text/plain": "111" + "text/plain": [ + "111" + ] }, "execution_count": 27, "metadata": {}, @@ -685,19 +733,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Sun Dec 20 2020\n", + "Last updated: Fri Mar 18 2022\n", "\n", "Python implementation: CPython\n", - "Python version : 3.8.5\n", - "IPython version : 7.19.0\n", + "Python version : 3.9.7\n", + "IPython version : 8.1.1\n", "\n", - "arviz : 0.10.0\n", - "scipy : 1.5.4\n", - "matplotlib: 3.3.3\n", - "pymc3 : 3.9.3\n", - "numpy : 1.19.4\n", + "matplotlib: 3.5.1\n", + "numpy : 1.21.5\n", + "pymc3 : 3.11.4\n", + "scipy : 1.7.3\n", + "arviz : 0.11.2\n", "\n", - "Watermark: 2.1.0\n", + "Watermark: 2.3.0\n", "\n" ] } @@ -710,7 +758,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -724,9 +772,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } From 50891479ce4d83c7520a73a5f83d11a6caf08ad4 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Sun, 20 Mar 2022 14:44:37 -0700 Subject: [PATCH 04/11] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 002ad84..b734f21 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # PyMC3 Resources PyMC3 educational resources, including the PyMC3 port of the following books (original models in STAN/BUGS/JAGS etc,.): +- ["Bayesian Modeling and Computation in Python" by Osvaldo Martin, Ravin Kumar, Jungpeng Lao](https://bayesiancomputationbook.com/welcome.html) - [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking) - [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking_2) - [PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/master/BCM) @@ -8,6 +9,5 @@ PyMC3 educational resources, including the PyMC3 port of the following books (or - [PyMC3 port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/master/BDA3) - # License Unless otherwise stated in the directory containing the codes, all codes are copyrighted by their author(s) under MIT license. From 60b03a877c67b7a30a96e808e5b7c229741d1c49 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Sun, 20 Mar 2022 14:45:56 -0700 Subject: [PATCH 05/11] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b734f21..5b3ff39 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # PyMC3 Resources PyMC3 educational resources, including the PyMC3 port of the following books (original models in STAN/BUGS/JAGS etc,.): -- ["Bayesian Modeling and Computation in Python" by Osvaldo Martin, Ravin Kumar, Jungpeng Lao](https://bayesiancomputationbook.com/welcome.html) +- ["Bayesian Modeling and Computation in Python" by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao](https://bayesiancomputationbook.com/welcome.html) - [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking) - [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking_2) - [PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/master/BCM) From 7e634a57a0cdfb54af1db918b06e39a246dc70d0 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Mon, 21 Mar 2022 07:24:24 -0700 Subject: [PATCH 06/11] Rename master to main (#174) --- .github/workflows/pre-commit.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 7233479..b52d4af 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -3,7 +3,7 @@ name: pre-commit on: pull_request: push: - branches: [master] + branches: [main] jobs: pre-commit: From 104c47eb78704fa9a0516305fc667ef436f00bd9 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Mon, 21 Mar 2022 07:25:34 -0700 Subject: [PATCH 07/11] Update README.md (#175) --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 5b3ff39..149168e 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,11 @@ PyMC3 educational resources, including the PyMC3 port of the following books (original models in STAN/BUGS/JAGS etc,.): - ["Bayesian Modeling and Computation in Python" by Osvaldo A. Martin, Ravin Kumar, Junpeng Lao](https://bayesiancomputationbook.com/welcome.html) -- [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking) -- [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/master/Rethinking_2) -- [PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/master/BCM) -- [PyMC3 port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh](https://github.com/pymc-devs/resources/tree/master/BSM) -- [PyMC3 port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/master/BDA3) +- [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (first edition)](https://github.com/pymc-devs/resources/tree/main/Rethinking) +- [PyMC3 port of the book "Statistical Rethinking" by Richard McElreath (second edition)](https://github.com/pymc-devs/resources/tree/main/Rethinking_2) +- [PyMC3 port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers](https://github.com/pymc-devs/resources/tree/main/BCM) +- [PyMC3 port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh](https://github.com/pymc-devs/resources/tree/main/BSM) +- [PyMC3 port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/main/BDA3) # License From 8ffee738a0b73905c2392c5f2d62e82278542c33 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Mon, 21 Mar 2022 16:00:53 -0700 Subject: [PATCH 08/11] Add bayes_rule env (#176) * Add bayes_rule env * Rename prefix * Add seaborn --- Bayes_Rules/environment.yml | 182 ++++++++++++++++++++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 Bayes_Rules/environment.yml diff --git a/Bayes_Rules/environment.yml b/Bayes_Rules/environment.yml new file mode 100644 index 0000000..06e2373 --- /dev/null +++ b/Bayes_Rules/environment.yml @@ -0,0 +1,182 @@ +name: bayes_rules +channels: + - defaults + - conda-forge +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=4.5=1_gnu + - anyio=3.5.0=py39h06a4308_0 + - argon2-cffi=21.3.0=pyhd3eb1b0_0 + - argon2-cffi-bindings=21.2.0=py39h7f8727e_0 + - asttokens=2.0.5=pyhd3eb1b0_0 + - attrs=21.4.0=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - blas=1.0=mkl + - bleach=4.1.0=pyhd3eb1b0_0 + - bottleneck=1.3.2=py39hdd57654_1 + - brotli=1.0.9=he6710b0_2 + - brotlipy=0.7.0=py39h27cfd23_1003 + - bzip2=1.0.8=h7b6447c_0 + - c-ares=1.18.1=h7f8727e_0 + - ca-certificates=2022.2.1=h06a4308_0 + - certifi=2021.10.8=py39h06a4308_2 + - cffi=1.15.0=py39hd667e15_1 + - cftime=1.5.1.1=py39hce1f21e_0 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - cryptography=36.0.0=py39h9ce1e76_0 + - curl=7.80.0=h7f8727e_0 + - cycler=0.11.0=pyhd3eb1b0_0 + - dbus=1.13.18=hb2f20db_0 + - debugpy=1.5.1=py39h295c915_0 + - decorator=5.1.1=pyhd3eb1b0_0 + - defusedxml=0.7.1=pyhd3eb1b0_0 + - entrypoints=0.3=py39h06a4308_0 + - executing=0.8.3=pyhd3eb1b0_0 + - expat=2.4.4=h295c915_0 + - fontconfig=2.13.1=h6c09931_0 + - fonttools=4.25.0=pyhd3eb1b0_0 + - freetype=2.11.0=h70c0345_0 + - giflib=5.2.1=h7b6447c_0 + - glib=2.69.1=h4ff587b_1 + - gst-plugins-base=1.14.0=h8213a91_2 + - gstreamer=1.14.0=h28cd5cc_2 + - hdf4=4.2.13=h3ca952b_2 + - hdf5=1.10.6=hb1b8bf9_0 + - icu=58.2=he6710b0_3 + - idna=3.3=pyhd3eb1b0_0 + - importlib-metadata=4.8.2=py39h06a4308_0 + - importlib_metadata=4.8.2=hd3eb1b0_0 + - intel-openmp=2021.4.0=h06a4308_3561 + - ipykernel=6.9.1=py39h06a4308_0 + - ipython=8.1.1=py39h06a4308_0 + - ipython_genutils=0.2.0=pyhd3eb1b0_1 + - jedi=0.18.1=py39h06a4308_1 + - jinja2=3.0.3=pyhd3eb1b0_0 + - jpeg=9d=h7f8727e_0 + - json5=0.9.6=pyhd3eb1b0_0 + - jsonschema=3.2.0=pyhd3eb1b0_2 + - jupyter_client=7.1.2=pyhd3eb1b0_0 + - jupyter_core=4.9.2=py39h06a4308_0 + - jupyter_server=1.13.5=pyhd3eb1b0_0 + - jupyterlab=3.3.2=pyhd3eb1b0_0 + - jupyterlab_pygments=0.1.2=py_0 + - jupyterlab_server=2.10.3=pyhd3eb1b0_1 + - kiwisolver=1.3.2=py39h295c915_0 + - krb5=1.19.2=hac12032_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.35.1=h7274673_9 + - libcurl=7.80.0=h0b77cf5_0 + - libedit=3.1.20210910=h7f8727e_0 + - libev=4.33=h7f8727e_1 + - libffi=3.3=he6710b0_2 + - libgcc-ng=9.3.0=h5101ec6_17 + - libgfortran-ng=7.5.0=ha8ba4b0_17 + - libgfortran4=7.5.0=ha8ba4b0_17 + - libgomp=9.3.0=h5101ec6_17 + - libnetcdf=4.8.1=h42ceab0_1 + - libnghttp2=1.46.0=hce63b2e_0 + - libpng=1.6.37=hbc83047_0 + - libsodium=1.0.18=h7b6447c_0 + - libssh2=1.9.0=h1ba5d50_1 + - libstdcxx-ng=9.3.0=hd4cf53a_17 + - libtiff=4.2.0=h85742a9_0 + - libuuid=1.0.3=h7f8727e_2 + - libwebp=1.2.2=h55f646e_0 + - libwebp-base=1.2.2=h7f8727e_0 + - libxcb=1.14=h7b6447c_0 + - libxml2=2.9.12=h03d6c58_0 + - libzip=1.5.1=h8d318fa_1003 + - lz4-c=1.9.3=h295c915_1 + - markupsafe=2.0.1=py39h27cfd23_0 + - matplotlib=3.5.1=py39h06a4308_1 + - matplotlib-base=3.5.1=py39ha18d171_1 + - matplotlib-inline=0.1.2=pyhd3eb1b0_2 + - mistune=0.8.4=py39h27cfd23_1000 + - mkl=2021.4.0=h06a4308_640 + - mkl-service=2.4.0=py39h7f8727e_0 + - mkl_fft=1.3.1=py39hd3c417c_0 + - mkl_random=1.2.2=py39h51133e4_0 + - munkres=1.1.4=py_0 + - nbclassic=0.3.5=pyhd3eb1b0_0 + - nbclient=0.5.11=pyhd3eb1b0_0 + - nbconvert=6.3.0=py39h06a4308_0 + - nbformat=5.1.3=pyhd3eb1b0_0 + - ncurses=6.3=h7f8727e_2 + - nest-asyncio=1.5.1=pyhd3eb1b0_0 + - netcdf4=1.5.7=py39ha0f2276_1 + - notebook=6.4.8=py39h06a4308_0 + - numexpr=2.8.1=py39h6abb31d_0 + - numpy=1.21.2=py39h20f2e39_0 + - numpy-base=1.21.2=py39h79a1101_0 + - openssl=1.1.1n=h7f8727e_0 + - packaging=21.3=pyhd3eb1b0_0 + - pandas=1.4.1=py39h295c915_0 + - pandocfilters=1.5.0=pyhd3eb1b0_0 + - parso=0.8.3=pyhd3eb1b0_0 + - pcre=8.45=h295c915_0 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pickleshare=0.7.5=pyhd3eb1b0_1003 + - pillow=9.0.1=py39h22f2fdc_0 + - pip=21.2.4=py39h06a4308_0 + - prometheus_client=0.13.1=pyhd3eb1b0_0 + - prompt-toolkit=3.0.20=pyhd3eb1b0_0 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pure_eval=0.2.2=pyhd3eb1b0_0 + - pycparser=2.21=pyhd3eb1b0_0 + - pygments=2.11.2=pyhd3eb1b0_0 + - pyopenssl=22.0.0=pyhd3eb1b0_0 + - pyparsing=3.0.4=pyhd3eb1b0_0 + - pyqt=5.9.2=py39h2531618_6 + - pyrsistent=0.18.0=py39heee7806_0 + - pysocks=1.7.1=py39h06a4308_0 + - python=3.9.7=h12debd9_1 + - python-dateutil=2.8.2=pyhd3eb1b0_0 + - pytz=2021.3=pyhd3eb1b0_0 + - pyzmq=22.3.0=py39h295c915_2 + - qt=5.9.7=h5867ecd_1 + - readline=8.1.2=h7f8727e_1 + - requests=2.27.1=pyhd3eb1b0_0 + - scipy=1.7.3=py39hc147768_0 + - seaborn=0.11.2=pyhd3eb1b0_0 + - send2trash=1.8.0=pyhd3eb1b0_1 + - setuptools=58.0.4=py39h06a4308_0 + - sip=4.19.13=py39h295c915_0 + - six=1.16.0=pyhd3eb1b0_1 + - sniffio=1.2.0=py39h06a4308_1 + - sqlite=3.38.0=hc218d9a_0 + - stack_data=0.2.0=pyhd3eb1b0_0 + - terminado=0.13.1=py39h06a4308_0 + - testpath=0.5.0=pyhd3eb1b0_0 + - tk=8.6.11=h1ccaba5_0 + - tornado=6.1=py39h27cfd23_0 + - traitlets=5.1.1=pyhd3eb1b0_0 + - typing-extensions=3.10.0.2=hd3eb1b0_0 + - typing_extensions=3.10.0.2=pyh06a4308_0 + - tzdata=2021e=hda174b7_0 + - urllib3=1.26.8=pyhd3eb1b0_0 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - webencodings=0.5.1=py39h06a4308_1 + - websocket-client=0.58.0=py39h06a4308_4 + - wheel=0.37.1=pyhd3eb1b0_0 + - xarray=0.20.1=pyhd3eb1b0_1 + - xz=5.2.5=h7b6447c_0 + - zeromq=4.3.4=h2531618_0 + - zipp=3.7.0=pyhd3eb1b0_0 + - zlib=1.2.11=h7f8727e_4 + - zstd=1.4.9=haebb681_0 + - pip: + - aeppl==0.0.27 + - aesara==2.5.1 + - arviz==0.11.4 + - cachetools==5.0.0 + - cloudpickle==2.0.0 + - cons==0.4.5 + - etuples==0.3.4 + - fastprogress==1.0.2 + - filelock==3.6.0 + - logical-unification==0.4.5 + - minikanren==1.0.3 + - multipledispatch==0.6.0 + - pymc==4.0.0b4 + - toolz==0.11.2 From a9a346b66de4921fef654a647dc9e05142005380 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Mon, 21 Mar 2022 16:02:44 -0700 Subject: [PATCH 09/11] Update README.md (#178) * Update README.md * Update README.md Co-authored-by: Oriol Abril-Pla * Update README.md Co-authored-by: Oriol Abril-Pla --- README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/README.md b/README.md index 149168e..aa8b856 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,15 @@ PyMC3 educational resources, including the PyMC3 port of the following books (or - [PyMC3 port of the book "Bayesian Statistical Methods" by Brian J. Reich and Sujit K. Ghosh](https://github.com/pymc-devs/resources/tree/main/BSM) - [PyMC3 port of the book "Bayesian Data Analysis" by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin](https://github.com/pymc-devs/resources/tree/main/BDA3) +## How to contribute +Thanks wanting to contribute! These resources are a community effort and we, and all future resource users, appreciate your help. + +If just starting +1. Reading the [contributing guide for pymc]( https://docs.pymc.io/en/latest/contributing/index.html) is a good place to start. The guide will familiarize you with the high level tools and workflow. + * Some of the instructions will differ so read the below steps first. +2. In this repo the environments are defined per resource. Look into each directory to find the environment file and use that +3. When ready to contribute open a draft PR stating the scope of work as early as possible. This helps avoid duplicate work early. +4. If you have further questions don't hesitate to ask on https://discourse.pymc.io/. # License Unless otherwise stated in the directory containing the codes, all codes are copyrighted by their author(s) under MIT license. From 81babb7c5f32e652e262d8222b5fcbd37d0b1a4c Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Mon, 21 Mar 2022 16:09:41 -0700 Subject: [PATCH 10/11] Update bayes rules environment (#180) --- Bayes_Rules/environment.yml | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/Bayes_Rules/environment.yml b/Bayes_Rules/environment.yml index 06e2373..2b1fa38 100644 --- a/Bayes_Rules/environment.yml +++ b/Bayes_Rules/environment.yml @@ -3,7 +3,7 @@ channels: - defaults - conda-forge dependencies: - - _libgcc_mutex=0.1=main + - _libgcc_mutex=0.1=conda_forge - _openmp_mutex=4.5=1_gnu - anyio=3.5.0=py39h06a4308_0 - argon2-cffi=21.3.0=pyhd3eb1b0_0 @@ -70,10 +70,10 @@ dependencies: - libedit=3.1.20210910=h7f8727e_0 - libev=4.33=h7f8727e_1 - libffi=3.3=he6710b0_2 - - libgcc-ng=9.3.0=h5101ec6_17 + - libgcc-ng=11.2.0=h1d223b6_14 - libgfortran-ng=7.5.0=ha8ba4b0_17 - libgfortran4=7.5.0=ha8ba4b0_17 - - libgomp=9.3.0=h5101ec6_17 + - libgomp=11.2.0=h1d223b6_14 - libnetcdf=4.8.1=h42ceab0_1 - libnghttp2=1.46.0=hce63b2e_0 - libpng=1.6.37=hbc83047_0 @@ -128,10 +128,12 @@ dependencies: - pyopenssl=22.0.0=pyhd3eb1b0_0 - pyparsing=3.0.4=pyhd3eb1b0_0 - pyqt=5.9.2=py39h2531618_6 + - pyreadr=0.4.4=py39h6be6860_0 - pyrsistent=0.18.0=py39heee7806_0 - pysocks=1.7.1=py39h06a4308_0 - python=3.9.7=h12debd9_1 - python-dateutil=2.8.2=pyhd3eb1b0_0 + - python_abi=3.9=2_cp39 - pytz=2021.3=pyhd3eb1b0_0 - pyzmq=22.3.0=py39h295c915_2 - qt=5.9.7=h5867ecd_1 @@ -180,3 +182,4 @@ dependencies: - multipledispatch==0.6.0 - pymc==4.0.0b4 - toolz==0.11.2 + - watermark==2.3.0 From 061ce81c277ac93725beba5bd850a62cd5e81713 Mon Sep 17 00:00:00 2001 From: Chris Fonnesbeck Date: Wed, 15 Dec 2021 15:46:42 -0600 Subject: [PATCH 11/11] Rename pymc3 -> pymc throughout --- BCM/CaseStudies/ExtrasensoryPerception.ipynb | 4 +- BCM/CaseStudies/HeuristicDecisionMaking.ipynb | 12 +-- BCM/CaseStudies/MemoryRetention.ipynb | 6 +- .../MultinomialProcessingTrees.ipynb | 6 +- .../NumberConceptDevelopment.ipynb | 6 +- BCM/CaseStudies/PsychophysicalFunctions.ipynb | 8 +- BCM/CaseStudies/SignalDetectionTheory.ipynb | 8 +- .../TheBARTModelofRiskTaking.ipynb | 6 +- .../TheGCMModelofCategorization.ipynb | 10 +-- BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb | 6 +- .../ComparingBinomialRates.ipynb | 4 +- .../ComparingGaussianMeans.ipynb | 6 +- BCM/ParameterEstimation/Binomial.ipynb | 10 +-- BCM/ParameterEstimation/DataAnalysis.ipynb | 6 +- BCM/ParameterEstimation/Gaussian.ipynb | 6 +- .../Latent-mixtureModels.ipynb | 18 ++--- BCM/README.md | 8 +- BCM/environment.yml | 4 +- BCM/index.ipynb | 12 +-- BDA3/README.md | 8 +- BDA3/chap_02.ipynb | 8 +- BDA3/chap_03.ipynb | 8 +- BDA3/chap_05.ipynb | 14 ++-- BDA3/chap_06.ipynb | 4 +- BDA3/chap_07.ipynb | 4 +- BDA3/environment.yml | 4 +- ...09_Simple_linear_regression_in_PyMC3.ipynb | 8 +- ...r_03_10_Poisson_gamma_model_in_PyMC3.ipynb | 6 +- ...ce_diagnostics_for_a_ill_posed_model.ipynb | 2 +- ...diagnostics_for_a_well_behaved_model.ipynb | 2 +- ...egression_for_NBA_clutch_free_throws.ipynb | 2 +- ...andom_effects_model_for_the_jaw_data.ipynb | 2 +- BSM/README.md | 8 +- BSM/environment.yml | 4 +- Rethinking/Chp_02.ipynb | 10 +-- Rethinking/Chp_03.ipynb | 6 +- Rethinking/Chp_04.ipynb | 20 ++--- Rethinking/Chp_05.ipynb | 8 +- Rethinking/Chp_06.ipynb | 14 ++-- Rethinking/Chp_07.ipynb | 44 +++++----- Rethinking/Chp_08.ipynb | 14 ++-- Rethinking/Chp_10.ipynb | 36 ++++----- Rethinking/Chp_11.ipynb | 8 +- Rethinking/Chp_12.ipynb | 16 ++-- Rethinking/Chp_13.ipynb | 6 +- Rethinking/Chp_14.ipynb | 16 ++-- Rethinking/README.md | 10 +-- .../ch-10.ipynb | 30 +++---- .../ch-11.ipynb | 42 +++++----- .../ch-12.ipynb | 20 ++--- .../ch-13.ipynb | 24 +++--- .../ch-14.ipynb | 52 ++++++------ .../ch-2.ipynb | 2 +- Rethinking/environment.yml | 4 +- Rethinking_2/Chp_02.ipynb | 4 +- Rethinking_2/Chp_03.ipynb | 12 +-- Rethinking_2/Chp_04.ipynb | 80 +++++++++---------- Rethinking_2/Chp_05.ipynb | 20 ++--- Rethinking_2/Chp_06.ipynb | 4 +- Rethinking_2/Chp_07.ipynb | 14 ++-- Rethinking_2/Chp_08.ipynb | 8 +- Rethinking_2/Chp_09.ipynb | 6 +- Rethinking_2/Chp_11.ipynb | 40 +++++----- Rethinking_2/Chp_12.ipynb | 20 ++--- Rethinking_2/Chp_13.ipynb | 26 +++--- Rethinking_2/Chp_14.ipynb | 14 ++-- Rethinking_2/Chp_15.ipynb | 6 +- Rethinking_2/Chp_16.ipynb | 6 +- .../End_of_chapter_problems/Chapter_2.ipynb | 8 +- .../End_of_chapter_problems/Chapter_3.ipynb | 10 +-- .../End_of_chapter_problems/Chapter_4.ipynb | 10 +-- .../End_of_chapter_problems/Chapter_5.ipynb | 16 ++-- .../End_of_chapter_problems/Chapter_6.ipynb | 24 +++--- .../End_of_chapter_problems/Chapter_7.ipynb | 44 +++++----- .../End_of_chapter_problems/Chapter_8.ipynb | 24 +++--- .../End_of_chapter_problems/Chapter_9.ipynb | 14 ++-- Rethinking_2/README.md | 12 +-- Rethinking_2/environment.yml | 7 +- 78 files changed, 523 insertions(+), 518 deletions(-) diff --git a/BCM/CaseStudies/ExtrasensoryPerception.ipynb b/BCM/CaseStudies/ExtrasensoryPerception.ipynb index a80b0b8..6c0b039 100644 --- a/BCM/CaseStudies/ExtrasensoryPerception.ipynb +++ b/BCM/CaseStudies/ExtrasensoryPerception.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "\n", "from matplotlib import gridspec\n", @@ -623,7 +623,7 @@ "output_type": "stream", "text": [ "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "scipy 1.3.1\n", "numpy 1.17.3\n", "last updated: Mon Apr 27 2020 \n", diff --git a/BCM/CaseStudies/HeuristicDecisionMaking.ipynb b/BCM/CaseStudies/HeuristicDecisionMaking.ipynb index 6b614f2..fd274e9 100644 --- a/BCM/CaseStudies/HeuristicDecisionMaking.ipynb +++ b/BCM/CaseStudies/HeuristicDecisionMaking.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.io as sio\n", "\n", "from scipy import stats\n", @@ -179,7 +179,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -564,7 +564,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/ipykernel_launcher.py:8: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/ipykernel_launcher.py:8: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " \n" ] }, @@ -769,7 +769,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -844,7 +844,7 @@ "text": [ "numpy 1.18.1\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Mon Apr 27 2020 \n", "\n", "CPython 3.7.7\n", @@ -861,7 +861,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/MemoryRetention.ipynb b/BCM/CaseStudies/MemoryRetention.ipynb index 8cdce08..65fb879 100644 --- a/BCM/CaseStudies/MemoryRetention.ipynb +++ b/BCM/CaseStudies/MemoryRetention.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "import theano\n", "\n", @@ -994,7 +994,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "arviz 0.7.0\n", "seaborn 0.10.0\n", @@ -1016,7 +1016,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/MultinomialProcessingTrees.ipynb b/BCM/CaseStudies/MultinomialProcessingTrees.ipynb index ce7c601..65c4b58 100644 --- a/BCM/CaseStudies/MultinomialProcessingTrees.ipynb +++ b/BCM/CaseStudies/MultinomialProcessingTrees.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.special as sp\n", "import theano\n", "\n", @@ -859,7 +859,7 @@ "output_type": "stream", "text": [ "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "theano 1.0.4\n", "arviz 0.7.0\n", "last updated: Mon Apr 27 2020 \n", @@ -878,7 +878,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/NumberConceptDevelopment.ipynb b/BCM/CaseStudies/NumberConceptDevelopment.ipynb index 800546f..1a7be7c 100644 --- a/BCM/CaseStudies/NumberConceptDevelopment.ipynb +++ b/BCM/CaseStudies/NumberConceptDevelopment.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.io as sio\n", "import theano\n", "\n", @@ -1024,7 +1024,7 @@ "text": [ "theano 1.0.4\n", "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "arviz 0.7.0\n", "last updated: Mon Apr 27 2020 \n", "\n", @@ -1042,7 +1042,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/PsychophysicalFunctions.ipynb b/BCM/CaseStudies/PsychophysicalFunctions.ipynb index ef6492c..9498009 100644 --- a/BCM/CaseStudies/PsychophysicalFunctions.ipynb +++ b/BCM/CaseStudies/PsychophysicalFunctions.ipynb @@ -10,11 +10,11 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano\n", "\n", "from matplotlib import gridspec\n", - "from pymc3.step_methods.hmc import quadpotential\n", + "from pymc.step_methods.hmc import quadpotential\n", "from scipy import stats\n", "from theano import tensor as tt\n", "\n", @@ -1169,7 +1169,7 @@ "numpy 1.18.1\n", "theano 1.0.4\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "last updated: Mon Apr 27 2020 \n", "\n", @@ -1187,7 +1187,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/SignalDetectionTheory.ipynb b/BCM/CaseStudies/SignalDetectionTheory.ipynb index b7a9c53..cbe09f6 100644 --- a/BCM/CaseStudies/SignalDetectionTheory.ipynb +++ b/BCM/CaseStudies/SignalDetectionTheory.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from matplotlib.patches import Polygon\n", @@ -672,7 +672,7 @@ "metadata": {}, "source": [ "### Note from Junpeng Lao\n", - "Sampling using HMC (e.g., in STAN and PyMC3), there are better way to diagnose biased inference [[1]](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html), [[2]](http://pymc-devs.github.io/pymc3/notebooks/Diagnosing_biased_Inference_with_Divergences.html)." + "Sampling using HMC (e.g., in STAN and PyMC), there are better way to diagnose biased inference [[1]](http://mc-stan.org/documentation/case-studies/divergences_and_bias.html), [[2]](http://pymc-devs.github.io/pymc/notebooks/Diagnosing_biased_Inference_with_Divergences.html)." ] }, { @@ -819,7 +819,7 @@ "source": [ "As shown above, there are a lot of divergences in the trace, and the energy plot is very different from the energy_diff. This is a strong indication of bias in the estimation, and better reparameterization is needed.\n", "\n", - "Moreover, the reparameterization, which works better in BUGS/JAGS using Gibbs sampler, actually perform worse using NUTS. Again, this demonstrates that many of the tricks and intuition we got using BUGS/JAGS might not translate to PyMC3 and STAN." + "Moreover, the reparameterization, which works better in BUGS/JAGS using Gibbs sampler, actually perform worse using NUTS. Again, this demonstrates that many of the tricks and intuition we got using BUGS/JAGS might not translate to PyMC and STAN." ] }, { @@ -1061,7 +1061,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "seaborn 0.11.0\npymc3 3.9.2\npandas 1.0.3\narviz 0.10.0\nnumpy 1.18.2\nlast updated: Mon Nov 23 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" + "seaborn 0.11.0\npymc 3.9.2\npandas 1.0.3\narviz 0.10.0\nnumpy 1.18.2\nlast updated: Mon Nov 23 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" ] } ], diff --git a/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb b/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb index 9b32f15..575ad25 100644 --- a/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb +++ b/BCM/CaseStudies/TheBARTModelofRiskTaking.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from theano import tensor as tt\n", @@ -463,7 +463,7 @@ "text": [ "numpy 1.18.1\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "last updated: Tue Apr 28 2020 \n", "\n", @@ -481,7 +481,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/TheGCMModelofCategorization.ipynb b/BCM/CaseStudies/TheGCMModelofCategorization.ipynb index f7ce805..b478256 100644 --- a/BCM/CaseStudies/TheGCMModelofCategorization.ipynb +++ b/BCM/CaseStudies/TheGCMModelofCategorization.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.io as sio\n", "import seaborn as sns\n", "import theano\n", @@ -201,7 +201,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -696,7 +696,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/sampling.py:1585: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -778,7 +778,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "pandas 1.0.3\n", "numpy 1.18.1\n", "arviz 0.7.0\n", @@ -800,7 +800,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb b/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb index 6ab51f4..2dc9361 100644 --- a/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb +++ b/BCM/CaseStudies/TheSIMPLEModelofMemory.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from theano import tensor as tt\n", @@ -830,7 +830,7 @@ "pandas 1.0.3\n", "numpy 1.18.1\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Tue Apr 28 2020 \n", "\n", "CPython 3.7.7\n", @@ -847,7 +847,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ModelSelection/ComparingBinomialRates.ipynb b/BCM/ModelSelection/ComparingBinomialRates.ipynb index 402a035..4fc5c8d 100644 --- a/BCM/ModelSelection/ComparingBinomialRates.ipynb +++ b/BCM/ModelSelection/ComparingBinomialRates.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from scipy import stats\n", @@ -1246,7 +1246,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.9.2\n", + "pymc 3.9.2\n", "numpy 1.18.2\n", "arviz 0.10.0\n", "last updated: Thu Nov 19 2020 \n", diff --git a/BCM/ModelSelection/ComparingGaussianMeans.ipynb b/BCM/ModelSelection/ComparingGaussianMeans.ipynb index 1945ec9..f257f56 100644 --- a/BCM/ModelSelection/ComparingGaussianMeans.ipynb +++ b/BCM/ModelSelection/ComparingGaussianMeans.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "\n", @@ -632,7 +632,7 @@ "pandas 1.0.3\n", "arviz 0.7.0\n", "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Sat Apr 25 2020 \n", "\n", "CPython 3.7.7\n", @@ -649,7 +649,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ParameterEstimation/Binomial.ipynb b/BCM/ParameterEstimation/Binomial.ipynb index 3303e15..6f721db 100644 --- a/BCM/ParameterEstimation/Binomial.ipynb +++ b/BCM/ParameterEstimation/Binomial.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from matplotlib import gridspec\n", @@ -262,7 +262,7 @@ "\n", "In the example, we set k1 = 5, n1 = 10 and k2 = 7, n2 = 10 \n", "\n", - "The model involve a deterministic part in pymc3." + "The model involve a deterministic part in pymc." ] }, { @@ -1175,7 +1175,7 @@ "metadata": {}, "source": [ "### Note from Junpeng Lao\n", - "It is obvious from the above posterior plot that the geometry of the posterior is quite nasty. We can see that in the trace as well: the mixing is quite poor, with strong autocorrelation. There is no divergence warning, but it could just be that PyMC3 is mixing Metropolis and NUTS together due to the discrete variable. \n", + "It is obvious from the above posterior plot that the geometry of the posterior is quite nasty. We can see that in the trace as well: the mixing is quite poor, with strong autocorrelation. There is no divergence warning, but it could just be that PyMC is mixing Metropolis and NUTS together due to the discrete variable. \n", "\n", "In this particular case, it is not a big deal as we can visualize the posterior directly. However, when we are sampling larger models, it is definitely going to be a problem.\n", "Actually, we don't necessary need to use `DiscreteUniform` for `TotalN`, as the computation of logp in Binomial doesn't require n to be an integer." @@ -1339,7 +1339,7 @@ "numpy 1.18.1\n", "arviz 0.7.0\n", "seaborn 0.10.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Fri Apr 24 2020 \n", "\n", "CPython 3.7.7\n", @@ -1356,7 +1356,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ParameterEstimation/DataAnalysis.ipynb b/BCM/ParameterEstimation/DataAnalysis.ipynb index ac98014..9835fc9 100644 --- a/BCM/ParameterEstimation/DataAnalysis.ipynb +++ b/BCM/ParameterEstimation/DataAnalysis.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import gridspec\n", "from scipy import corrcoef, stats\n", @@ -55,7 +55,7 @@ "\n", "The observed data take the form _xi_ = (_xi1_, _xi2_) for the ith observation, and, following the theory behind the correlation coefficient, are modeled as draws from a multivariate Gaussian distribution. The parameters of this distribution are the means _μ_ = (_μ1_,_μ2_) and standard deviations _σ_ = (_σ1_,_σ2_) of the two variables, and the correlation coefficient _r_ that links them.\n", "\n", - "**NB: This model runs with PyMC 3.8, but not with the master branch -- probably related to [this issue](https://github.com/pymc-devs/pymc3/issues/3884).**" + "**NB: This model runs with PyMC 3.8, but not with the master branch -- probably related to [this issue](https://github.com/pymc-devs/pymc/issues/3884).**" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "arviz 0.10.0\npymc3 3.9.2\npandas 1.0.3\nnumpy 1.18.2\nlast updated: Sun Nov 15 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" + "arviz 0.10.0\npymc 3.9.2\npandas 1.0.3\nnumpy 1.18.2\nlast updated: Sun Nov 15 2020 \n\nCPython 3.8.5\nIPython 7.13.0\nwatermark 2.0.2\n" ] } ], diff --git a/BCM/ParameterEstimation/Gaussian.ipynb b/BCM/ParameterEstimation/Gaussian.ipynb index e519a54..6224b65 100644 --- a/BCM/ParameterEstimation/Gaussian.ipynb +++ b/BCM/ParameterEstimation/Gaussian.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "%config InlineBackend.figure_format = 'retina'\n", "RANDOM_SEED = 8927\n", @@ -827,7 +827,7 @@ "text": [ "arviz 0.7.0\n", "pandas 1.0.3\n", - "pymc3 3.8\n", + "pymc 3.8\n", "numpy 1.18.1\n", "last updated: Sat Apr 25 2020 \n", "\n", @@ -845,7 +845,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/ParameterEstimation/Latent-mixtureModels.ipynb b/BCM/ParameterEstimation/Latent-mixtureModels.ipynb index c097915..85db73a 100644 --- a/BCM/ParameterEstimation/Latent-mixtureModels.ipynb +++ b/BCM/ParameterEstimation/Latent-mixtureModels.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from matplotlib import gridspec\n", @@ -38,7 +38,7 @@ "metadata": {}, "source": [ "### Note from Junpeng Lao\n", - "In PyMC3, a discrete latent variable could be very easily expressed as a discrete random variable. PyMC3 will assign this discrete variable with a sampler (usually a Metropolis sampler), while the rest of the (continous) RVs sample with NUTS. However, care must be taken, as the correctness of mixing different sampler is in general not guaranteed. The standard treatment is integrate out the latent variables, as done in Stan." + "In PyMC, a discrete latent variable could be very easily expressed as a discrete random variable. PyMC will assign this discrete variable with a sampler (usually a Metropolis sampler), while the rest of the (continous) RVs sample with NUTS. However, care must be taken, as the correctness of mixing different sampler is in general not guaranteed. The standard treatment is integrate out the latent variables, as done in Stan." ] }, { @@ -263,7 +263,7 @@ } ], "source": [ - "# pymc3 - need some tuning to get the same result as in JAGS\n", + "# pymc - need some tuning to get the same result as in JAGS\n", "k = np.array([21, 17, 21, 18, 22, 31, 31, 34, 34, 35, 35, 36, 39, 36, 35])\n", "p = len(k) # number of people\n", "n = 40 # number of questions\n", @@ -376,7 +376,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, ImputationWarning)\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "CompoundStep\n", @@ -576,7 +576,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/pymc3/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/pymc/model.py:1515: ImputationWarning: Data in kij contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, ImputationWarning)\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "CompoundStep\n", @@ -752,7 +752,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 8 seconds.\n", - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", " result = getattr(npmodule, name)(values, axis=axis, **kwargs)\n" ] }, @@ -884,7 +884,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.\n", - "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc3/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", + "/Users/alex_andorra/opt/anaconda3/envs/BCM_pymc/lib/python3.7/site-packages/xarray/core/nputils.py:215: RuntimeWarning: All-NaN slice encountered\n", " result = getattr(npmodule, name)(values, axis=axis, **kwargs)\n", "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n", "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n" @@ -1306,7 +1306,7 @@ "numpy 1.18.1\n", "pandas 1.0.3\n", "arviz 0.7.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Sat Apr 25 2020 \n", "\n", "CPython 3.7.7\n", @@ -1323,7 +1323,7 @@ ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BCM/README.md b/BCM/README.md index 61f9e33..6099406 100644 --- a/BCM/README.md +++ b/BCM/README.md @@ -1,5 +1,5 @@ -# Bayesian Cognitive Modeling in PyMC3 -PyMC3 port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com) +# Bayesian Cognitive Modeling in PyMC +PyMC port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com) All the codes are in jupyter notebooks with the model explained in distributions (as in the book). @@ -8,8 +8,8 @@ All the codes are in jupyter notebooks with the model explained in distributions [](http://nbviewer.jupyter.org/github/pymc-devs/resources/blob/master/BCM/index.ipynb) # Notice: -This repository is tested under [PyMC3](https://github.com/pymc-devs/pymc3) v3.8 master with [theano](https://github.com/Theano/Theano) 1.0.4 +This repository is tested under [PyMC](https://github.com/pymc-devs/pymc) v3.8 master with [theano](https://github.com/Theano/Theano) 1.0.4 --- -Creative Commons License
Bayesian Cognitive Modeling in PyMC3 by Junpeng Lao is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Bayesian Cognitive Modeling in PyMC by Junpeng Lao is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/BCM/environment.yml b/BCM/environment.yml index 99d17e1..299344e 100644 --- a/BCM/environment.yml +++ b/BCM/environment.yml @@ -1,4 +1,4 @@ -name: BCM_pymc3 +name: BCM_pymc channels: - defaults dependencies: @@ -8,5 +8,5 @@ dependencies: - statsmodels - pip - pip: - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" - watermark \ No newline at end of file diff --git a/BCM/index.ipynb b/BCM/index.ipynb index 8d89a64..358a7e2 100644 --- a/BCM/index.ipynb +++ b/BCM/index.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Bayesian Cognitive Modeling in PyMC3\n", - "PyMC3 port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com)\n", + "# Bayesian Cognitive Modeling in PyMC\n", + "PyMC port of Lee and Wagenmakers' [Bayesian Cognitive Modeling - A Practical Course](http://bayesmodels.com)\n", "\n", "All the codes are in jupyter notebooks with the model explained in distributions (as in the book). Background information of the models please consult the book. You can also compare the result with the original code associated with the book ([WinBUGS and JAGS](https://webfiles.uci.edu/mdlee/Code.zip); [Stan](https://github.com/stan-dev/example-models/tree/master/Bayesian_Cognitive_Modeling))\n", "\n", - "_All the codes are currently tested under PyMC3 v3.8 master with theano 1.0.4_" + "_All the codes are currently tested under PyMC v3.8 master with theano 1.0.4_" ] }, { @@ -141,7 +141,7 @@ "CPython 3.7.7\n", "IPython 7.13.0\n", "\n", - "pymc3 3.8\n", + "pymc 3.8\n", "theano 1.0.4\n", "scipy 1.4.1\n", "numpy 1.18.1\n", @@ -163,13 +163,13 @@ "source": [ "# Python Environment and library version\n", "%load_ext watermark\n", - "%watermark -v -n -u -w -p pymc3,theano,scipy,numpy,pandas,matplotlib,seaborn -m" + "%watermark -v -n -u -w -p pymc,theano,scipy,numpy,pandas,matplotlib,seaborn -m" ] } ], "metadata": { "kernelspec": { - "display_name": "BCM_pymc3", + "display_name": "BCM_pymc", "language": "python", "name": "bcm_pymc3" }, diff --git a/BDA3/README.md b/BDA3/README.md index 7a05fb7..9f0f3d1 100644 --- a/BDA3/README.md +++ b/BDA3/README.md @@ -1,8 +1,8 @@ -# Bayesian Data Analysis with Python and PyMC3 +# Bayesian Data Analysis with Python and PyMC [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/) by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin is a comprehensive, standard, and wonderful textbook on Bayesian Methods. There currently exist code for examples in the book in [R](https://github.com/avehtari/BDA_R_demos), [Python](https://github.com/avehtari/BDA_py_demos), and [Matlab](https://github.com/avehtari/BDA_m_demos), all using the [Stan](http://mc-stan.org/) language. -This repository is a work in progress, organizing work on porting examples and exercises to Python and PyMC3. Please open a pull request on the README to indicate interest in a chapter or section! +This repository is a work in progress, organizing work on porting examples and exercises to Python and PyMC. Please open a pull request on the README to indicate interest in a chapter or section! ## Chapters @@ -116,8 +116,8 @@ Anaconda, run: to install all the dependencies into an isolated environment. You can switch to this environment by running: - source activate bda3-pymc3 + source activate bda3-pymc --- -Creative Commons License
Bayesian Data Analysis with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Bayesian Data Analysis with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/BDA3/chap_02.ipynb b/BDA3/chap_02.ipynb index 1a1643f..1956716 100644 --- a/BDA3/chap_02.ipynb +++ b/BDA3/chap_02.ipynb @@ -24,7 +24,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy.special import expit" ] @@ -247,7 +247,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The true posterior distribution is $\\textsf{Beta}(438, 544)$. Let's compare it with the one we found using `pymc3`." + "The true posterior distribution is $\\textsf{Beta}(438, 544)$. Let's compare it with the one we found using `pymc`." ] }, { @@ -1141,7 +1141,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The true posterior distribution is $\\textsf{Gamma}(6,7)$. Let's compare it with the one we found using `pymc3`." + "The true posterior distribution is $\\textsf{Gamma}(6,7)$. Let's compare it with the one we found using `pymc`." ] }, { @@ -1461,7 +1461,7 @@ "output_type": "stream", "text": [ "numpy 1.18.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "arviz 0.7.0\n", "CPython 3.6.8\n", "IPython 7.12.0\n", diff --git a/BDA3/chap_03.ipynb b/BDA3/chap_03.ipynb index acb1e9e..1964553 100644 --- a/BDA3/chap_03.ipynb +++ b/BDA3/chap_03.ipynb @@ -36,7 +36,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from scipy.optimize import brentq\n", @@ -131,7 +131,7 @@ { "cell_type": "markdown", "source": [ - "And now, we use `pymc3` to estimate the mean and the standard deviation from the data." + "And now, we use `pymc` to estimate the mean and the standard deviation from the data." ], "metadata": {} }, @@ -1177,7 +1177,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "/home/xyj/anaconda3/lib/python3.8/site-packages/pymc3/sampling.py:1689: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/xyj/anaconda3/lib/python3.8/site-packages/pymc/sampling.py:1689: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -1681,7 +1681,7 @@ "Architecture: 64bit\n", "\n", "arviz : 0.11.2\n", - "pymc3 : 3.11.4\n", + "pymc : 3.11.4\n", "matplotlib: 3.3.4\n", "theano : 1.1.2\n", "sys : 3.8.8 (default, Apr 13 2021, 19:58:26) \n", diff --git a/BDA3/chap_05.ipynb b/BDA3/chap_05.ipynb index b8e7e0c..4177377 100644 --- a/BDA3/chap_05.ipynb +++ b/BDA3/chap_05.ipynb @@ -10,7 +10,7 @@ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt" ] }, @@ -454,7 +454,7 @@ "\n", "In order to use this prior, you have to define the logarithm of $p(\\alpha, \\beta)$ and use it in `pm.Potential`, [look here][2]. \n", "\n", - "[1]:https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/GLM-hierarchical-binominal-model.ipynb\n", + "[1]:https://github.com/pymc-devs/pymc/blob/master/docs/source/notebooks/GLM-hierarchical-binominal-model.ipynb\n", "[2]:https://discourse.pymc.io/t/difference-between-densitydist-and-potential/307/4" ] }, @@ -750,7 +750,7 @@ "Attributes:\n", " created_at: 2020-04-24T21:27:00.254444\n", " arviz_version: 0.7.0\n", - " inference_library: pymc3\n", + " inference_library: pymc\n", " inference_library_version: 3.8" ], "text/plain": [ @@ -771,7 +771,7 @@ "Attributes:\n", " created_at: 2020-04-24T21:27:00.254444\n", " arviz_version: 0.7.0\n", - " inference_library: pymc3\n", + " inference_library: pymc\n", " inference_library_version: 3.8" ] }, @@ -1370,7 +1370,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/rpg/src/pymc3/pymc3/distributions/posterior_predictive.py:203: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/Users/rpg/src/pymc/pymc/distributions/posterior_predictive.py:203: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] } @@ -1672,7 +1672,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ok, so the results are not quite good, I mean, there are a lot of discrepancies and the differences are notorious when you compare the two histograms previously showed with the Figure 5.8 (even more if you compare it with Table 5.3). Yes, the question is: Why does this happen? Is the algorithm behind `pymc3` the reason of all this? I would say that priors are really bad and the geometry behind the model is nasty. More info here: https://docs.pymc.io/notebooks/Diagnosing_biased_Inference_with_Divergences.html" + "Ok, so the results are not quite good, I mean, there are a lot of discrepancies and the differences are notorious when you compare the two histograms previously showed with the Figure 5.8 (even more if you compare it with Table 5.3). Yes, the question is: Why does this happen? Is the algorithm behind `pymc` the reason of all this? I would say that priors are really bad and the geometry behind the model is nasty. More info here: https://docs.pymc.io/notebooks/Diagnosing_biased_Inference_with_Divergences.html" ] }, { @@ -2220,7 +2220,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "matplotlib 3.1.3\n", "arviz 0.7.0\n", "numpy 1.18.1\n", diff --git a/BDA3/chap_06.ipynb b/BDA3/chap_06.ipynb index 62312d0..fd91a15 100644 --- a/BDA3/chap_06.ipynb +++ b/BDA3/chap_06.ipynb @@ -18,7 +18,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "import theano.tensor as tt\n", "\n", @@ -735,7 +735,7 @@ "output_type": "stream", "text": [ "numpy 1.15.0\n", - "pymc3 3.5\n", + "pymc 3.5\n", "seaborn 0.9.0\n", "CPython 3.6.6\n", "IPython 7.1.1\n", diff --git a/BDA3/chap_07.ipynb b/BDA3/chap_07.ipynb index 51cbe78..a17f13f 100644 --- a/BDA3/chap_07.ipynb +++ b/BDA3/chap_07.ipynb @@ -20,7 +20,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from scipy import stats\n", @@ -1948,7 +1948,7 @@ "output_type": "stream", "text": [ "numpy 1.16.2\n", - "pymc3 3.6\n", + "pymc 3.6\n", "CPython 3.6.7\n", "IPython 7.3.0\n", "\n", diff --git a/BDA3/environment.yml b/BDA3/environment.yml index 9d06fd4..2e83e66 100644 --- a/BDA3/environment.yml +++ b/BDA3/environment.yml @@ -1,4 +1,4 @@ -name: bda3-pymc3 +name: bda3-pymc channels: - defaults dependencies: @@ -6,4 +6,4 @@ dependencies: - seaborn - pip - pip: - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" diff --git a/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb b/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb index a21ef0b..072a1aa 100644 --- a/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb +++ b/BSM/Chapter_03_09_Simple_linear_regression_in_PyMC3.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { @@ -25,11 +25,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using PyMC3 for MCMC sampling\n", + "# Using PyMC for MCMC sampling\n", "\n", - "### Chapter 3.3: Introduction to PyMC3\n", + "### Chapter 3.3: Introduction to PyMC\n", "\n", - "In this example, we use PyMC3 to conduct simple linear regression.\n", + "In this example, we use PyMC to conduct simple linear regression.\n", "\n", "The response is the mass of a T. Rex and the covariate is the age. The model is:\n", "\n", diff --git a/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb b/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb index 6755610..f0f6aa4 100644 --- a/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb +++ b/BSM/Chapter_03_10_Poisson_gamma_model_in_PyMC3.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { @@ -27,7 +27,7 @@ "source": [ "# Using JAGS for concussions data\n", "\n", - "## Chapter 3.3: Introduction to PyMC3\n", + "## Chapter 3.3: Introduction to PyMC\n", "\n", "The response is the total number of concussions (summing across teams and games) in each year from 2012-2015. We fit the model\n", "\n", @@ -37,7 +37,7 @@ "\n", "\n", "\n", - "We have previously coded Gibbs sampling for this problem, and here e verify that we obtain the same results using PyMC3." + "We have previously coded Gibbs sampling for this problem, and here e verify that we obtain the same results using PyMC." ] }, { diff --git a/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb b/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb index f7684f3..4b54a1b 100644 --- a/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb +++ b/BSM/Chapter_03_11_Convergence_diagnostics_for_a_ill_posed_model.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb b/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb index 86f2593..97ec04b 100644 --- a/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb +++ b/BSM/Chapter_03_12_Convergence_diagnostics_for_a_well_behaved_model.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb b/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb index 4c5471d..8dfc649 100644 --- a/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb +++ b/BSM/Chapter_04_01_Logistic_regression_for_NBA_clutch_free_throws.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb b/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb index b7d586f..5418a81 100644 --- a/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb +++ b/BSM/Chapter_04_03_One-way_random_effects_model_for_the_jaw_data.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { diff --git a/BSM/README.md b/BSM/README.md index edfc2f4..bdaec56 100644 --- a/BSM/README.md +++ b/BSM/README.md @@ -1,6 +1,6 @@ -# Bayesian Statistical Methods Python and PyMC3 +# Bayesian Statistical Methods Python and PyMC -In this repository we port [the book's original code](https://bayessm.wordpress.ncsu.edu) to Python and PyMC3. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMC3onic_ way. +In this repository we port [the book's original code](https://bayessm.wordpress.ncsu.edu) to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMConic_ way. ## Display notebooks [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pymc-devs/resources/master?filepath=BSM) @@ -23,8 +23,8 @@ to install all the dependencies into an isolated environment. Activate the environment by running: - source activate bsm-pymc3 + source activate bsm-pymc --- -Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/BSM/environment.yml b/BSM/environment.yml index a0e14c5..e64d2ec 100644 --- a/BSM/environment.yml +++ b/BSM/environment.yml @@ -1,4 +1,4 @@ -name: bsm-pymc3 +name: bsm-pymc channels: - defaults dependencies: @@ -7,4 +7,4 @@ dependencies: - pip - pip: - "git+git://github.com/arviz-devs/arviz.git@master" - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" diff --git a/Rethinking/Chp_02.ipynb b/Rethinking/Chp_02.ipynb index ac77cbc..18dca47 100644 --- a/Rethinking/Chp_02.ipynb +++ b/Rethinking/Chp_02.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -179,8 +179,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", "logp = -1.8075, ||grad|| = 1.5: 100%|██████████| 7/7 [00:00<00:00, 1327.37it/s]\n" ] }, @@ -286,7 +286,7 @@ "This notebook was created using:\n", "Python 3.7.1\n", "IPython 6.2.1\n", - "PyMC3 3.7.rc1\n", + "PyMC 3.7.rc1\n", "ArviZ 0.4.0\n", "NumPy 1.15.4\n", "SciPy 1.1.0\n", @@ -303,7 +303,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_03.ipynb b/Rethinking/Chp_03.ipynb index fdc34f2..0f0ce9d 100644 --- a/Rethinking/Chp_03.ipynb +++ b/Rethinking/Chp_03.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -897,7 +897,7 @@ "This notebook was created using:\n", "Python 3.7.2\n", "IPython 7.9.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.5.1\n", "NumPy 1.17.3\n", "SciPy 1.3.1\n", @@ -914,7 +914,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_04.ipynb b/Rethinking/Chp_04.ipynb index 134922f..1a131df 100644 --- a/Rethinking/Chp_04.ipynb +++ b/Rethinking/Chp_04.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats\n", "\n", "from scipy.interpolate import griddata" @@ -753,11 +753,11 @@ "source": [ "#### Code 4.26\n", "\n", - "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC3 is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", + "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", "\n", - "PyMC3 comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC3 choose the sampler for us. PyMC3 also tries to provide a reasonable starting point for the simulation. By default PyMC3 uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. \n", + "PyMC comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC choose the sampler for us. PyMC also tries to provide a reasonable starting point for the simulation. By default PyMC uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. \n", "\n", - "You can read more details of PyMC3 [here](http://pymc-devs.github.io/pymc3/notebooks/getting_started.html)" + "You can read more details of PyMC [here](http://pymc-devs.github.io/pymc/notebooks/getting_started.html)" ] }, { @@ -1941,7 +1941,7 @@ "\n", " height = pm.Normal('height', mu=alpha + beta * d2.weight, sd=sigma, observed=d2.height)\n", " \n", - "Using PyMC3 there is not too much reason to do this. I personally think that defining mu in a separate line improves readability." + "Using PyMC there is not too much reason to do this. I personally think that defining mu in a separate line improves readability." ] }, { @@ -2697,7 +2697,7 @@ "source": [ "#### Code 4.53\n", "\n", - "Using PyMC3, we do not need to compute anything else. By defining a deterministic variable mu in the model, we add that variable to the trace. Thus we get a matrix with row samples from the posterior and columns values of weights. We can access this matrix directly from the trace or turn it into a DataFrame, it all depends on what we need." + "Using PyMC, we do not need to compute anything else. By defining a deterministic variable mu in the model, we add that variable to the trace. Thus we get a matrix with row samples from the posterior and columns values of weights. We can access this matrix directly from the trace or turn it into a DataFrame, it all depends on what we need." ] }, { @@ -2963,7 +2963,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -3471,7 +3471,7 @@ "text": [ "/home/osvaldo/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: DeprecationWarning: sample_ppc() is deprecated. Please use sample_posterior_predictive()\n", " \n", - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/sampling.py:1296: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " \"samples parameter is smaller than nchains times ndraws, some draws \"\n" ] }, @@ -3701,7 +3701,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.11.1\n", - "PyMC3 3.8\n", + "PyMC 3.8\n", "ArviZ 0.6.1\n", "NumPy 1.17.4\n", "SciPy 1.4.1\n", @@ -3718,7 +3718,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_05.ipynb b/Rethinking/Chp_05.ipynb index d4a0939..4b45a9a 100644 --- a/Rethinking/Chp_05.ipynb +++ b/Rethinking/Chp_05.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "import statsmodels.formula.api as smf\n", "\n", @@ -3995,7 +3995,7 @@ " $$" ], "text/plain": [ - "" + "" ] }, "execution_count": 77, @@ -4020,7 +4020,7 @@ "This notebook was created using:\n", "Python 3.7.1\n", "IPython 6.2.1\n", - "PyMC3 3.7.rc1\n", + "PyMC 3.7.rc1\n", "ArviZ 0.4.0\n", "NumPy 1.15.4\n", "SciPy 1.1.0\n", @@ -4037,7 +4037,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_06.ipynb b/Rethinking/Chp_06.ipynb index 1c0240f..376ca12 100644 --- a/Rethinking/Chp_06.ipynb +++ b/Rethinking/Chp_06.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import statsmodels.api as sm\n", "\n", "# R-like interface, alternatively you can import statsmodels as import statsmodels.api as sm\n", @@ -481,7 +481,7 @@ " \n", " np.concatenate([mm_train, x_train[:, 1:k]], axis=1)\n", " \n", - " #Using pymc3\n", + " #Using pymc\n", " \n", " with pm.Model() as m_sim:\n", " vec_V = pm.MvNormal('vec_V', mu=0, cov=b_sigma * np.eye(n_dim), \n", @@ -1206,7 +1206,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1250,12 +1250,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1701,7 +1701,7 @@ "This notebook was created using:\n", "Python 3.7.1\n", "IPython 6.2.1\n", - "PyMC3 3.7.rc1\n", + "PyMC 3.7.rc1\n", "ArviZ 0.4.0\n", "NumPy 1.15.4\n", "SciPy 1.1.0\n", @@ -1718,7 +1718,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_07.ipynb b/Rethinking/Chp_07.ipynb index a744ea6..f9d0c51 100644 --- a/Rethinking/Chp_07.ipynb +++ b/Rethinking/Chp_07.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import statsmodels.formula.api as smf\n", "\n", "from scipy import stats\n", @@ -313,7 +313,7 @@ "source": [ "#### Code 7.5\n", "\n", - "WAIC values are point estimates and hence is a good idea to include the uncertainty asociated with their estimation when computing weights. PyMC3 uses a Bayesian bootstrapping to do this (read more [here](https://arxiv.org/abs/1704.02030)), and also to compute the standard error (SE) of WAIC/LOO estimates. If you set `bootstrapping = False` weights (and SE) will be computed as in the book." + "WAIC values are point estimates and hence is a good idea to include the uncertainty asociated with their estimation when computing weights. PyMC uses a Bayesian bootstrapping to do this (read more [here](https://arxiv.org/abs/1704.02030)), and also to compute the standard error (SE) of WAIC/LOO estimates. If you set `bootstrapping = False` weights (and SE) will be computed as in the book." ] }, { @@ -325,7 +325,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -578,12 +578,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:993: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1491,8 +1491,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", "logp = -175.26, ||grad|| = 0.0011502: 100%|██████████| 24/24 [00:00<00:00, 1082.61it/s] \n" ] }, @@ -1532,8 +1532,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", "logp = -170.17, ||grad|| = 0.012971: 100%|██████████| 54/54 [00:00<00:00, 1531.53it/s] \n" ] }, @@ -1584,8 +1584,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n", " 0%| | 0/5000 [00:00" + "" ] }, "execution_count": 39, @@ -2788,8 +2788,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n" + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n" ] }, { @@ -3584,27 +3584,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/home/osvaldo/proyectos/00_PyMC3/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/home/osvaldo/proyectos/00_PyMC/arviz/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -4947,7 +4947,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.4\n", "SciPy 1.2.1\n", @@ -4964,7 +4964,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_11.ipynb b/Rethinking/Chp_11.ipynb index 425ad53..4a8e680 100644 --- a/Rethinking/Chp_11.ipynb +++ b/Rethinking/Chp_11.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "\n", "from theano import shared" @@ -358,8 +358,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.\n", - " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc3.sample() and it will automatically initialize NUTS in a better way.')\n" + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/tuning/starting.py:61: UserWarning: find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.\n", + " warnings.warn('find_MAP should not be used to initialize the NUTS sampler, simply call pymc.sample() and it will automatically initialize NUTS in a better way.')\n" ] }, { @@ -1261,7 +1261,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"This notebook was createad on a computer {} running {} and using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nNumPy {}\\nPandas {}\\nSciPy {}\\nMatplotlib {}\\n\".format(platform.machine(), ' '.join(platform.linux_distribution()[:2]), sys.version[:5], IPython.__version__, pm.__version__, np.__version__, pd.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"This notebook was createad on a computer {} running {} and using:\\nPython {}\\nIPython {}\\nPyMC {}\\nNumPy {}\\nPandas {}\\nSciPy {}\\nMatplotlib {}\\n\".format(platform.machine(), ' '.join(platform.linux_distribution()[:2]), sys.version[:5], IPython.__version__, pm.__version__, np.__version__, pd.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_12.ipynb b/Rethinking/Chp_12.ipynb index dc49cae..057f147 100644 --- a/Rethinking/Chp_12.ipynb +++ b/Rethinking/Chp_12.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from scipy.special import expit as logistic" @@ -253,7 +253,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -349,7 +349,7 @@ "metadata": {}, "outputs": [], "source": [ - "# extract PyMC3 samples\n", + "# extract PyMC samples\n", "post = pm.trace_to_dataframe(trace_12_2, varnames=['a_tank'])\n", "\n", "# compute median intercept for each tank\n", @@ -839,7 +839,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This part is more Stan and rethinking related. To do the same in PyMC3 (i.e., avoid compiling the same model twice), you need to set up the input data with `theano.shared` or use [sampled](https://github.com/ColCarroll/sampled), a functional decorator for PyMC3." + "This part is more Stan and rethinking related. To do the same in PyMC (i.e., avoid compiling the same model twice), you need to set up the input data with `theano.shared` or use [sampled](https://github.com/ColCarroll/sampled), a functional decorator for PyMC." ] }, { @@ -2246,7 +2246,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.3\n", "SciPy 1.2.1\n", @@ -2262,15 +2262,15 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/Chp_13.ipynb b/Rethinking/Chp_13.ipynb index aa1416c..7a3fe69 100644 --- a/Rethinking/Chp_13.ipynb +++ b/Rethinking/Chp_13.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from theano import tensor as tt" ] @@ -2906,7 +2906,7 @@ "This notebook was created using:\n", "Python 3.7.2\n", "IPython 7.6.1\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.0\n", "SciPy 1.2.0\n", @@ -2922,7 +2922,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nSciPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, scipy.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/Chp_14.ipynb b/Rethinking/Chp_14.ipynb index d058255..816d701 100644 --- a/Rethinking/Chp_14.ipynb +++ b/Rethinking/Chp_14.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm" + "import pymc as pm" ] }, { @@ -1239,7 +1239,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1282,7 +1282,7 @@ "# prep data\n", "kcal = d['kcal.per.g'].values.copy()\n", "logmass = d['logmass'].values.copy()\n", - "# PyMC3 can handle missing value quite naturally.\n", + "# PyMC can handle missing value quite naturally.\n", "neocortex = d['neocortex.prop'].values.copy()\n", "mask = np.isfinite(neocortex)\n", "neocortex[~mask] = -999\n", @@ -1653,7 +1653,7 @@ } ], "source": [ - "# the missing value in pymc3 is automatically model as a node with *_missing as name\n", + "# the missing value in pymc is automatically model as a node with *_missing as name\n", "az.summary(trace_14_3, var_names=['neocortex_missing', \n", " 'a', 'bN', 'bM', 'nu', 'sigma_N', 'sigma'],\n", " round_to=2)" @@ -1862,7 +1862,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/home/osvaldo/proyectos/00_PyMC/pymc/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2287,7 +2287,7 @@ "metadata": {}, "source": [ "#### Code 14.11-14\n", - "Stan related. As you can see above, PyMC3 deal with missing value internally if you represent the observed data using a numpy mask array. The missing/masked value are replaced with a new random variable added to the model (with name `*_missing`)." + "Stan related. As you can see above, PyMC deal with missing value internally if you represent the observed data using a numpy mask array. The missing/masked value are replaced with a new random variable added to the model (with name `*_missing`)." ] }, { @@ -2302,7 +2302,7 @@ "This notebook was created using:\n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "ArviZ 0.4.1\n", "NumPy 1.16.4\n", "Matplotlib 3.1.0\n", @@ -2317,7 +2317,7 @@ "import matplotlib\n", "import scipy\n", "\n", - "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC3 {}\\nArviZ {}\\nNumPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, matplotlib.__version__))" + "print(\"\"\"This notebook was created using:\\nPython {}\\nIPython {}\\nPyMC {}\\nArviZ {}\\nNumPy {}\\nMatplotlib {}\\n\"\"\".format(sys.version[:5], IPython.__version__, pm.__version__, az.__version__, np.__version__, matplotlib.__version__))" ] } ], diff --git a/Rethinking/README.md b/Rethinking/README.md index 2bdb9e5..c62247b 100644 --- a/Rethinking/README.md +++ b/Rethinking/README.md @@ -1,8 +1,8 @@ -# Statistical Rethinking with Python and PyMC3 +# Statistical Rethinking with Python and PyMC [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) is an incredible introductory book to Bayesian Statistics. It follows a [_Jaynesian_](https://en.wikipedia.org/wiki/Edwin_Thompson_Jaynes) and practical approach with very good examples and clear explanations. -In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC3. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMC3onic_ way. +In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMConic_ way. ## Display notebooks [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pymc-devs/resources/master?filepath=Rethinking) @@ -14,7 +14,7 @@ All contributions are welcome! Feel free to send PR to fix errors, improve the code, or make comments that could help users of this repository and readers of the book. -You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC3/Lobby). +You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC/Lobby). ## Installing the dependencies @@ -26,8 +26,8 @@ to install all the dependencies into an isolated environment. Activate the environment by running: - source activate stat-rethink-pymc3 + source activate stat-rethink-pymc --- -Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb index 06fdde5..c6ba00e 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-10.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -261,7 +261,7 @@ "\n", "*Can you explain both the differences and the similarities between the approximate and the MCMC distributions?*\n", "\n", - "Related to R and map. See code 10.14 in chapter 10 notebook for model specification and MCMC estimation in PyMC3" + "Related to R and map. See code 10.14 in chapter 10 notebook for model specification and MCMC estimation in PyMC" ] }, { @@ -542,13 +542,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n" ] }, @@ -994,7 +994,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " chain_likelihoods.append(np.stack(log_like))\n" ] }, @@ -1647,16 +1647,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " return np.stack(logp)\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2410,7 +2410,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", "SciPy 1.2.1\n", @@ -2428,7 +2428,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -2442,9 +2442,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb index b5c5883..dfc0dcb 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-11.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -655,12 +655,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1097,12 +1097,12 @@ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [bp, bf, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [00:18<00:00, 326.81draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1442,12 +1442,12 @@ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [bd, bf, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [00:09<00:00, 529.37draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1769,22 +1769,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2177,22 +2177,22 @@ "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [bd, bf, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [00:10<00:00, 575.35draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2973,12 +2973,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -3383,7 +3383,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -3402,7 +3402,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -3416,9 +3416,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb index abe960a..cbc0c00 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-12.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -614,27 +614,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -2218,7 +2218,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -2237,7 +2237,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -2251,9 +2251,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb index 62bc956..6afc81b 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-13.ipynb @@ -14,7 +14,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -292,7 +292,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1954,32 +1954,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -3383,7 +3383,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -3402,7 +3402,7 @@ "import scipy\n", "\n", "print(f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\")" ] }, @@ -3416,9 +3416,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb index c7e35ba..4dd2cef 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-14.ipynb @@ -14,7 +14,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import seaborn as sns\n", "\n", @@ -384,12 +384,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\"\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1078: UserWarning: For one or more samples the posterior variance of the log predictive\n", " densities exceeds 0.4. This could be indication of WAIC starting to fail see\n", " http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -678,28 +678,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, mu_N, sigma, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:10<00:00, 366.52draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, mu_N, sigma, bn, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [01:24<00:00, 71.15draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, mu_N, sigma, bm, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:12<00:00, 326.81draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -803,22 +803,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -1294,28 +1294,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:16<00:00, 241.38draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma_N, gm, an, sigma, bn, a]\n", "Sampling 2 chains: 100%|██████████| 6000/6000 [01:31<00:00, 36.16draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [neocortex_missing, sigma, bm, a]\n", "Sampling 2 chains: 100%|██████████| 4000/4000 [00:15<00:00, 265.01draws/s]\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in neocortex contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1419,32 +1419,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", " \"\"\")\n", - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n", " log predictive densities exceeds 0.4. This could be indication of\n", " WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n", " \n", @@ -3799,7 +3799,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/envs/stat-rethink-pymc3/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in x_est contains missing values and will be automatically imputed from the sampling distribution.\n", + "/anaconda/envs/stat-rethink-pymc/lib/python3.7/site-packages/pymc/model.py:1331: UserWarning: Data in x_est contains missing values and will be automatically imputed from the sampling distribution.\n", " warnings.warn(impute_message, UserWarning)\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -4147,7 +4147,7 @@ "This notebook was created on a computer x86_64, using: \n", "Python 3.7.3\n", "IPython 7.5.0\n", - "PyMC3 3.7\n", + "PyMC 3.7\n", "Arviz 0.4.1\n", "NumPy 1.16.3\n", "Pandas 0.24.2\n", @@ -4167,7 +4167,7 @@ "\n", "print(\n", " f\"This notebook was created on a computer {platform.machine()}, using: \"\n", - " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC3 {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", + " f\"\\nPython {sys.version[:5]}\\nIPython {IPython.__version__}\\nPyMC {pm.__version__}\\nArviz {az.__version__}\\nNumPy {np.__version__}\"\n", " f\"\\nPandas {pd.__version__}\\nSciPy {scipy.__version__}\\nMatplotlib {matplotlib.__version__}\\n\"\n", ")" ] @@ -4182,9 +4182,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb b/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb index 3aedb2f..faf3645 100644 --- a/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb +++ b/Rethinking/end-of-chapter-practice-problems/ch-2.ipynb @@ -26,7 +26,7 @@ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats\n", "\n", "%config InlineBackend.figure_format = 'retina'\n", diff --git a/Rethinking/environment.yml b/Rethinking/environment.yml index f8caf77..b63599b 100644 --- a/Rethinking/environment.yml +++ b/Rethinking/environment.yml @@ -1,4 +1,4 @@ -name: stat-rethink-pymc3 +name: stat-rethink-pymc channels: - defaults dependencies: @@ -9,4 +9,4 @@ dependencies: - pip - pip: - "git+git://github.com/arviz-devs/arviz.git@main" - - "git+git://github.com/pymc-devs/pymc3.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" diff --git a/Rethinking_2/Chp_02.ipynb b/Rethinking_2/Chp_02.ipynb index 0a87846..e58a687 100644 --- a/Rethinking_2/Chp_02.ipynb +++ b/Rethinking_2/Chp_02.ipynb @@ -9,7 +9,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -435,7 +435,7 @@ "IPython version : 7.19.0\n", "\n", "arviz : 0.10.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "numpy : 1.19.4\n", "matplotlib: 3.3.3\n", "scipy : 1.5.4\n", diff --git a/Rethinking_2/Chp_03.ipynb b/Rethinking_2/Chp_03.ipynb index e82d7dd..24cd90f 100644 --- a/Rethinking_2/Chp_03.ipynb +++ b/Rethinking_2/Chp_03.ipynb @@ -17,7 +17,7 @@ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats" ] }, @@ -739,11 +739,11 @@ "Python version : 3.9.7\n", "IPython version : 8.1.1\n", "\n", - "matplotlib: 3.5.1\n", - "numpy : 1.21.5\n", - "pymc3 : 3.11.4\n", - "scipy : 1.7.3\n", - "arviz : 0.11.2\n", + "arviz : 0.10.0\n", + "scipy : 1.5.4\n", + "matplotlib: 3.3.3\n", + "pymc : 3.9.3\n", + "numpy : 1.19.4\n", "\n", "Watermark: 2.3.0\n", "\n" diff --git a/Rethinking_2/Chp_04.ipynb b/Rethinking_2/Chp_04.ipynb index 9b015ff..db23321 100644 --- a/Rethinking_2/Chp_04.ipynb +++ b/Rethinking_2/Chp_04.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy.stats as stats\n", "import seaborn as sns\n", "\n", @@ -86,17 +86,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", " warnings.warn(msg, FutureWarning)\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/seaborn/distributions.py:1657: FutureWarning: The `bw` parameter is deprecated in favor of `bw_method` and `bw_adjust`. Using 0.01 for `bw_method`, but please see the docs for the new parameters and update your code.\n", " warnings.warn(msg, FutureWarning)\n" ] }, @@ -1037,11 +1037,11 @@ "source": [ "#### Code 4.28\n", "\n", - "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC3 is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", + "We could use a quadratic approximation like McElreath does in his book and we did in code 2.6. But Using PyMC is really simple to just sample from the model using a \"sampler method\". Most common sampler methods are members of the Markov Chain Monte Carlo Method (MCMC) family (for details read Section 2.4.3 and Chapter 8 of Statistical Rethinking).\n", "\n", - "PyMC3 comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC3 choose the sampler for us. PyMC3 also tries to provide a reasonable starting point for the simulation. By default PyMC3 uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which starts with a identity mass matrix and then adapts a diagonal based on the variance of the tuning samples. \n", + "PyMC comes with various samplers. Some samplers are more suited than others for certain type of variable (and/or problems). For now we are going to let PyMC choose the sampler for us. PyMC also tries to provide a reasonable starting point for the simulation. By default PyMC uses the same adaptive procedure as in STAN `'jitter+adapt_diag'`, which starts with a identity mass matrix and then adapts a diagonal based on the variance of the tuning samples. \n", "\n", - "You can read more details of PyMC3 [here](http://pymc-devs.github.io/pymc3/notebooks/getting_started.html)" + "You can read more details of PyMC [here](http://pymc-devs.github.io/pymc/notebooks/getting_started.html)" ] }, { @@ -1053,7 +1053,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1112,7 +1112,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1166,7 +1166,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1247,7 +1247,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1316,7 +1316,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -1358,7 +1358,7 @@ "output_type": "stream", "text": [ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1718,7 +1718,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -1939,7 +1939,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2003,7 +2003,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2074,7 +2074,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -2367,7 +2367,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -2652,7 +2652,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] } @@ -2717,7 +2717,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -2771,7 +2771,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] } @@ -2796,7 +2796,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2868,9 +2868,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3015,7 +3015,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -3083,7 +3083,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -3124,7 +3124,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:96: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -3221,7 +3221,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -3276,9 +3276,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3341,7 +3341,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -3413,7 +3413,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:1688: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -3450,9 +3450,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3683,7 +3683,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/pymc3/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/pymc/sampling.py:466: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n", " warnings.warn(\n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", @@ -3794,7 +3794,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/damian/miniconda3/envs/stat-rethink2-pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/damian/miniconda3/envs/stat-rethink2-pymc/lib/python3.8/site-packages/arviz/stats/stats.py:456: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3850,7 +3850,7 @@ "\n", "seaborn : 0.11.1\n", "numpy : 1.19.2\n", - "pymc3 : 3.11.1\n", + "pymc : 3.11.1\n", "scipy : 1.6.0\n", "arviz : 0.11.1\n", "matplotlib: 3.3.4\n", diff --git a/Rethinking_2/Chp_05.ipynb b/Rethinking_2/Chp_05.ipynb index 926bc2f..5b40eac 100644 --- a/Rethinking_2/Chp_05.ipynb +++ b/Rethinking_2/Chp_05.ipynb @@ -21,7 +21,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", "from scipy import stats\n", @@ -867,7 +867,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -881,7 +881,7 @@ } ], "source": [ - "# We can skip most of the code with the posterior predictive plot functionality in pymc3\n", + "# We can skip most of the code with the posterior predictive plot functionality in pymc\n", "with m_5_3:\n", " m_5_3_ppc = pm.sample_posterior_predictive(\n", " m_5_3_trace, var_names=[\"mu\", \"divorce_rate_std\"], samples=1000\n", @@ -1087,7 +1087,7 @@ } ], "source": [ - "# With PyMC3 we have to simulate in each model separately\n", + "# With PyMC we have to simulate in each model separately\n", "\n", "# Simulate the marriage rates at each age first\n", "age_shared.set_value(A_seq)\n", @@ -1264,7 +1264,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -1431,8 +1431,8 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mSamplingError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mm5_5_draft_trace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m \u001b[0mcheck_start_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 428\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/util.py\u001b[0m in \u001b[0;36mcheck_start_vals\u001b[0;34m(start, model)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m raise SamplingError(\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;34m\"Initial evaluation of model at starting point failed!\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\"Starting values:\\n{}\\n\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m \u001b[0mcheck_start_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 428\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/util.py\u001b[0m in \u001b[0;36mcheck_start_vals\u001b[0;34m(start, model)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m raise SamplingError(\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;34m\"Initial evaluation of model at starting point failed!\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;34m\"Starting values:\\n{}\\n\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mSamplingError\u001b[0m: Initial evaluation of model at starting point failed!\nStarting values:\n{'sigma_log__': array(-0.36651292), 'bN': array(0.), 'a': array(0.)}\n\nInitial evaluation results:\nsigma_log__ -1.06\nbN -0.92\na -0.92\nK NaN\nName: Log-probability of test_point, dtype: float64" ] } @@ -1882,7 +1882,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/joanna/Dropbox/Sketchbook/python/resources/env/lib/python3.8/site-packages/pymc/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -2133,7 +2133,7 @@ } ], "source": [ - "# With PyMC3 it's easier just to create a deterministic that includes both values\n", + "# With PyMC it's easier just to create a deterministic that includes both values\n", "sex = d[\"male\"].values\n", "\n", "with pm.Model() as m5_8:\n", @@ -2330,7 +2330,7 @@ "\n", "pandas : 1.2.0\n", "scipy : 1.6.0\n", - "pymc3 : 3.10.0\n", + "pymc : 3.10.0\n", "sys : 3.8.5 (default, Sep 4 2020, 07:30:14) \n", "[GCC 7.3.0]\n", "numpy : 1.19.4\n", diff --git a/Rethinking_2/Chp_06.ipynb b/Rethinking_2/Chp_06.ipynb index 01e7326..4d77079 100644 --- a/Rethinking_2/Chp_06.ipynb +++ b/Rethinking_2/Chp_06.ipynb @@ -19,7 +19,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", @@ -3027,7 +3027,7 @@ "arviz 0.7.0\n", "pandas 1.0.3\n", "daft 0.1.0\n", - "pymc3 3.8\n", + "pymc 3.8\n", "last updated: Sun May 10 2020 \n", "\n", "CPython 3.7.6\n", diff --git a/Rethinking_2/Chp_07.ipynb b/Rethinking_2/Chp_07.ipynb index f13f8da..6c4850c 100644 --- a/Rethinking_2/Chp_07.ipynb +++ b/Rethinking_2/Chp_07.ipynb @@ -17,7 +17,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "\n", @@ -741,7 +741,7 @@ "source": [ "# Figure 7.4\n", "\n", - "# this code taken from PyMC3 port of Rethinking/Chp_06.ipynb\n", + "# this code taken from PyMC port of Rethinking/Chp_06.ipynb\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(8, 3))\n", "ax1.scatter(brains.mass, brains.brain, alpha=0.8)\n", @@ -879,7 +879,7 @@ } ], "source": [ - "# PyMC3 does not have a way to calculate LPPD directly, so we use the approach from 7.14\n", + "# PyMC does not have a way to calculate LPPD directly, so we use the approach from 7.14\n", "\n", "sigmas = (np.sum((pred - brains.brain_std.values.reshape(-1, 1)) ** 2, 0) / 7) ** 0.5\n", "ll = np.zeros((n, ns))\n", @@ -990,7 +990,7 @@ "\n", " np.concatenate([mm_train, x_train[:, 1:k]], axis=1)\n", "\n", - " # Using pymc3\n", + " # Using pymc\n", "\n", " with pm.Model() as m_sim:\n", " vec_V = pm.MvNormal(\n", @@ -3119,7 +3119,7 @@ "source": [ "#### Code 7.17\n", "\n", - "Does not apply because multi-threading is automatic in PyMC3." + "Does not apply because multi-threading is automatic in PyMC." ] }, { @@ -3994,7 +3994,7 @@ " Parameters\n", " ----------\n", " dataset_dict : dict\n", - " A dict containing two ore more {'name': pymc3.backends.base.MultiTrace}\n", + " A dict containing two ore more {'name': pymc.backends.base.MultiTrace}\n", " items.\n", " metric : str\n", " The name of the matric to be calculated. Can be any valid column output\n", @@ -4605,7 +4605,7 @@ "text": [ "pandas 1.1.1\n", "numpy 1.19.1\n", - "pymc3 3.9.3\n", + "pymc 3.9.3\n", "arviz 0.9.0\n", "statsmodels.api 0.11.1\n", "last updated: Mon Aug 24 2020 \n", diff --git a/Rethinking_2/Chp_08.ipynb b/Rethinking_2/Chp_08.ipynb index 0f3f157..99869fa 100644 --- a/Rethinking_2/Chp_08.ipynb +++ b/Rethinking_2/Chp_08.ipynb @@ -17,7 +17,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from scipy import stats\n", @@ -1300,7 +1300,7 @@ " lw=1,\n", " edgecolor=\"k\",\n", ")\n", - "# calculating predicted manually because this is a pain with categorical variabiles in PyMC3\n", + "# calculating predicted manually because this is a pain with categorical variabiles in PyMC\n", "pred0 = m_8_3_posterior[\"a\"][:, 0] + rugged_plot.reshape(-1, 1) * m_8_3_posterior[\"b\"][:, 0]\n", "ax0.plot(rugged_plot, pred0.mean(1), color=\"grey\")\n", "az.plot_hdi(rugged_plot, pred0.T, color=\"grey\", hdi_prob=0.97, ax=ax0)\n", @@ -1312,7 +1312,7 @@ " label=\"Africa\",\n", " color=\"b\",\n", ")\n", - "# calculating predicted manually because this is a pain with categorical variabiles in PyMC3\n", + "# calculating predicted manually because this is a pain with categorical variabiles in PyMC\n", "pred1 = m_8_3_posterior[\"a\"][:, 1] + rugged_plot.reshape(-1, 1) * m_8_3_posterior[\"b\"][:, 1]\n", "ax1.plot(rugged_plot, pred1.mean(1), color=\"k\")\n", "az.plot_hdi(\n", @@ -1994,7 +1994,7 @@ "numpy 1.18.5\n", "seaborn 0.10.1\n", "arviz 0.10.0\n", - "pymc3 3.9.3\n", + "pymc 3.9.3\n", "last updated: Mon Oct 19 2020 \n", "\n", "CPython 3.8.3\n", diff --git a/Rethinking_2/Chp_09.ipynb b/Rethinking_2/Chp_09.ipynb index 7e7dc2d..08890ac 100644 --- a/Rethinking_2/Chp_09.ipynb +++ b/Rethinking_2/Chp_09.ipynb @@ -21,7 +21,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "\n", @@ -456,7 +456,7 @@ "source": [ "#### Code 9.12-9.18\n", "\n", - "By using PyMC3 we are already doing everything in these code blocks (No-Uturn sampling, parallell processing).\n", + "By using PyMC we are already doing everything in these code blocks (No-Uturn sampling, parallell processing).\n", "\n", "To translate the results of `summary` to `rethinking`'s `precis`:\n", "\n", @@ -1400,7 +1400,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pymc3 3.8\n", + "pymc 3.8\n", "numpy 1.18.1\n", "arviz 0.7.0\n", "pandas 1.0.3\n", diff --git a/Rethinking_2/Chp_11.ipynb b/Rethinking_2/Chp_11.ipynb index 1b7bfa0..4de6fd2 100644 --- a/Rethinking_2/Chp_11.ipynb +++ b/Rethinking_2/Chp_11.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from scipy import stats\n", @@ -1256,7 +1256,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (28). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (28). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -1853,8 +1853,8 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"m11_4\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtrace_11_4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"m11_6\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtrace_11_6\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36mcompare\u001b[0;34m(dataset_dict, ic, method, b_samples, alpha, seed, scale)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"bb-pseudo-bma\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ic_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36m_ic_matrix\u001b[0;34m(ics, ic_i)\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mic\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The number of observations should be the same across all models\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0mic_i_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36mcompare\u001b[0;34m(dataset_dict, ic, method, b_samples, alpha, seed, scale)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"bb-pseudo-bma\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ic_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mic_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic_i_val\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py\u001b[0m in \u001b[0;36m_ic_matrix\u001b[0;34m(ics, ic_i)\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mic\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The number of observations should be the same across all models\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0mic_i_val\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: The number of observations should be the same across all models" ] } @@ -2366,9 +2366,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (12). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3336,9 +3336,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n" ] }, @@ -3431,7 +3431,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n" ] } @@ -3537,13 +3537,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3640,7 +3640,7 @@ "metadata": {}, "source": [ "#### Code 11.49\n", - "The book doesn't pre-process the population data, but if you give them raw to PyMC3, the sampler will break: the scale of these data is too wide. However we can't just standardize the data, as we usually do. Why? Because some data points will then be negative, which doesn't play nice with the `b` exponent (try it if you don't trust me). But we'll do something similar: let's standardize the data, and then just add the absolute value of the minimum, and add yet again an epsilon -- this will ensure that our data stay positive and that the transformation will be easy to reverse when we want to plot on the natural scale:" + "The book doesn't pre-process the population data, but if you give them raw to PyMC, the sampler will break: the scale of these data is too wide. However we can't just standardize the data, as we usually do. Why? Because some data points will then be negative, which doesn't play nice with the `b` exponent (try it if you don't trust me). But we'll do something similar: let's standardize the data, and then just add the absolute value of the minimum, and add yet again an epsilon -- this will ensure that our data stay positive and that the transformation will be easy to reverse when we want to plot on the natural scale:" ] }, { @@ -3839,13 +3839,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4476,7 +4476,7 @@ "source": [ "#### Code 11.56 and 11.57\n", "\n", - "The model described in the book does not sample well in PyMC3. It does slightly better if we change the pivot category to be the first career instead of the third, but this is still suboptimal because we are discarding predictive information from the pivoted category (i.e., its unique career income). \n", + "The model described in the book does not sample well in PyMC. It does slightly better if we change the pivot category to be the first career instead of the third, but this is still suboptimal because we are discarding predictive information from the pivoted category (i.e., its unique career income). \n", "\n", "In fact, it is not necessary to pivot the coefficients of variables that are distinct for each category (what the author calls predictors matched to outcomes), as it is done for the coefficients of shared variables (what the author calles predictors matched to observations). The intercepts belong to the second category, and as such they still need to be pivoted. These two references explain this distinction clearly: \n", "\n", @@ -5196,7 +5196,7 @@ "numpy 1.18.1\n", "theano 1.0.4\n", "matplotlib 3.1.3\n", - "pymc3 3.8\n", + "pymc 3.8\n", "arviz 0.7.0\n", "pandas 0.25.3\n", "last updated: Thu Apr 23 2020 \n", diff --git a/Rethinking_2/Chp_12.ipynb b/Rethinking_2/Chp_12.ipynb index 5b9d64c..a90d65e 100644 --- a/Rethinking_2/Chp_12.ipynb +++ b/Rethinking_2/Chp_12.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import scipy as sp\n", "import theano.tensor as tt\n", "\n", @@ -739,7 +739,7 @@ "text": [ "Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 19 seconds.\n", "The number of effective samples is smaller than 25% for some parameters.\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:613: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n" ] }, @@ -825,13 +825,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (8000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n", - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (50000) than draws (100). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3337,7 +3337,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", " \"fill_last and contour will be deprecated. Please use kde_kwargs\", UserWarning,\n" ] }, @@ -3596,7 +3596,7 @@ "theano 1.0.4\n", "numpy 1.18.1\n", "pandas 0.25.3\n", - "pymc3 3.8\n", + "pymc 3.8\n", "scipy 1.4.1\n", "last updated: Fri Apr 24 2020 \n", "\n", @@ -3614,9 +3614,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/Chp_13.ipynb b/Rethinking_2/Chp_13.ipynb index 8a5e7a7..93fabdc 100644 --- a/Rethinking_2/Chp_13.ipynb +++ b/Rethinking_2/Chp_13.ipynb @@ -10,7 +10,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from scipy import stats\n", "from scipy.special import expit as logistic" @@ -1149,7 +1149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/stats/stats.py:1320: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/stats/stats.py:1320: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " \"For one or more samples the posterior variance of the log predictive \"\n" ] @@ -1804,7 +1804,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This part is Stan related. To do the same in PyMC3 (i.e., avoid compiling the same model twice), you need to set up the input data with `pm.Data`. There are examples in this repository, and you can also take a look at [this tutorial](https://docs.pymc.io/notebooks/data_container.html)" + "This part is Stan related. To do the same in PyMC (i.e., avoid compiling the same model twice), you need to set up the input data with `pm.Data`. There are examples in this repository, and you can also take a look at [this tutorial](https://docs.pymc.io/notebooks/data_container.html)" ] }, { @@ -2866,7 +2866,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", " \"fill_last and contour will be deprecated. Please use kde_kwargs\", UserWarning,\n" ] }, @@ -3091,7 +3091,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/plots/pairplot.py:167: UserWarning: fill_last and contour will be deprecated. Please use kde_kwargs\n", " \"fill_last and contour will be deprecated. Please use kde_kwargs\", UserWarning,\n" ] }, @@ -3810,7 +3810,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -3923,7 +3923,7 @@ "Attributes:\n", " created_at: 2020-05-18T15:37:47.591223\n", " arviz_version: 0.7.0\n", - " inference_library: pymc3\n", + " inference_library: pymc\n", " inference_library_version: 3.8" ] }, @@ -4050,7 +4050,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4103,7 +4103,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4149,7 +4149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc3/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", + "/Users/alex_andorra/opt/anaconda3/envs/stat-rethink-pymc/lib/python3.7/site-packages/arviz/data/base.py:146: UserWarning: More chains (4000) than draws (4). Passed array should have shape (chains, draws, *shape)\n", " UserWarning,\n" ] }, @@ -4231,7 +4231,7 @@ "output_type": "stream", "text": [ "scipy 1.4.1\n", - "pymc3 3.8\n", + "pymc 3.8\n", "numpy 1.18.1\n", "pandas 0.25.3\n", "arviz 0.7.0\n", @@ -4252,9 +4252,9 @@ ], "metadata": { "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/Chp_14.ipynb b/Rethinking_2/Chp_14.ipynb index a7345d9..c99c1d2 100644 --- a/Rethinking_2/Chp_14.ipynb +++ b/Rethinking_2/Chp_14.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib.patches import Ellipse, transforms\n", "from scipy import stats\n", @@ -5370,7 +5370,7 @@ "source": [ "#### Code 14.46\n", "\n", - "Related to Stan. PyMC3's GP module automatically reparametrizes with the Cholesky factor of the covariance matrix under the hood." + "Related to Stan. PyMC's GP module automatically reparametrizes with the Cholesky factor of the covariance matrix under the hood." ] }, { @@ -6193,9 +6193,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is a good opportunity to show a use-case of PyMC3's GP module. In the Oceanic tools example above, we didn't need a mean function (which means it was automatically set to 0 by PyMC3). Now however, we need both a mean function _and_ a covariance function to specify our GP. The covariance function will look familiar. For the mean function, we could use `gp.mean.Linear`, which takes as input a matrix of coefficients and a vector of intercepts. But that mean function would then be evaluated on our distance matrix, as `SIGMA` will be.\n", + "This is a good opportunity to show a use-case of PyMC's GP module. In the Oceanic tools example above, we didn't need a mean function (which means it was automatically set to 0 by PyMC). Now however, we need both a mean function _and_ a covariance function to specify our GP. The covariance function will look familiar. For the mean function, we could use `gp.mean.Linear`, which takes as input a matrix of coefficients and a vector of intercepts. But that mean function would then be evaluated on our distance matrix, as `SIGMA` will be.\n", "\n", - "We don't want that -- we only want the mean function to depend on `M` and `G` not on the phylogenetic distance. We can easily [define a custom mean function](https://docs.pymc.io/notebooks/GP-MeansAndCovs.html#Defining-a-custom-mean-function) in PyMC3. We just need to subclass `pm.gp.mean.Mean` and provide `__call__` and `__init__` methods:" + "We don't want that -- we only want the mean function to depend on `M` and `G` not on the phylogenetic distance. We can easily [define a custom mean function](https://docs.pymc.io/notebooks/GP-MeansAndCovs.html#Defining-a-custom-mean-function) in PyMC. We just need to subclass `pm.gp.mean.Mean` and provide `__call__` and `__init__` methods:" ] }, { @@ -6547,7 +6547,7 @@ "pandas 0.25.3\n", "theano 1.0.4\n", "arviz 0.9.0\n", - "pymc3 3.9.2\n", + "pymc 3.9.2\n", "last updated: Wed Jul 01 2020 \n", "\n", "CPython 3.7.6\n", @@ -6565,9 +6565,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "stat-rethink-pymc3", + "display_name": "stat-rethink-pymc", "language": "python", - "name": "stat-rethink-pymc3" + "name": "stat-rethink-pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/Chp_15.ipynb b/Rethinking_2/Chp_15.ipynb index 6be3793..d2f4ad2 100644 --- a/Rethinking_2/Chp_15.ipynb +++ b/Rethinking_2/Chp_15.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import theano.tensor as tt\n", "\n", "from numpy.random import default_rng\n", @@ -1385,7 +1385,7 @@ " nu = pm.Normal(\"nu\", 0, 0.5)\n", " a = pm.Normal(\"a\", 0, 0.5)\n", "\n", - " # PyMC3 automatically imputes missing values\n", + " # PyMC automatically imputes missing values\n", " Bi = pm.Normal(\"Bi\", nu, sigma_B, observed=B)\n", "\n", " mu = a + bB * Bi + bM * M\n", @@ -2561,7 +2561,7 @@ "output_type": "stream", "text": [ "arviz 0.10.0\n", - "pymc3 3.9.3\n", + "pymc 3.9.3\n", "pandas 1.0.3\n", "numpy 1.18.1\n", "last updated: Sat Oct 03 2020 \n", diff --git a/Rethinking_2/Chp_16.ipynb b/Rethinking_2/Chp_16.ipynb index e1707ed..ee70636 100644 --- a/Rethinking_2/Chp_16.ipynb +++ b/Rethinking_2/Chp_16.ipynb @@ -12,7 +12,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from numpy.random import default_rng\n", "from theano import tensor as tt" @@ -281,7 +281,7 @@ } ], "source": [ - "# A more native pymc3/Arviz representation\n", + "# A more native pymc/Arviz representation\n", "axes = az.plot_pair(trace_16_1, var_names=[\"k\", \"p\"], kind=\"scatter\", marginals=True)\n", "corr = np.corrcoef(trace_16_1[\"p\"], trace_16_1[\"k\"])[0, 1]\n", "axes[1, 0].text(15, 0.4, f\"ρ = {corr:.2f}\", fontsize=15);" @@ -640,7 +640,7 @@ "text": [ "pandas 1.0.3\n", "numpy 1.18.2\n", - "pymc3 3.9.2\n", + "pymc 3.9.2\n", "arviz 0.10.0\n", "last updated: Wed Dec 09 2020 \n", "\n", diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb index f2fd1ec..96a7146 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_2.ipynb @@ -10,7 +10,7 @@ "source": [ "import arviz as az\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import pylab as plt\n", "from scipy import stats" @@ -587,7 +587,7 @@ "scipy : 1.5.2\n", "matplotlib: 3.3.2\n", "numpy : 1.19.1\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "\n", "Watermark: 2.1.0\n", "\n" @@ -604,9 +604,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb index dfc6e0c..623a3f0 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_3.ipynb @@ -10,7 +10,7 @@ "source": [ "import arviz as az\n", "import numpy as np\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import pylab as plt\n", "from scipy import stats" @@ -48,7 +48,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "All of this chapter depends on the posterior distribution for the globe spinning example. I will implement pymc3 to do most of the leg work for me. As before, set the prior to be the uniform and the likelihood to be the binomial. The data given in the book is the set of outcomes W L W W W L W L W, which I'll give a binary representation in the same order.\n", + "All of this chapter depends on the posterior distribution for the globe spinning example. I will implement pymc to do most of the leg work for me. As before, set the prior to be the uniform and the likelihood to be the binomial. The data given in the book is the set of outcomes W L W W W L W L W, which I'll give a binary representation in the same order.\n", "\n", "Pymc3 nicely allows you to state the prior and the likelihood function of the data. You can use its pm.sample function in order to obtain a random sample from the posterior distribution. This is done using MCMC techniques, but they aren't introduced until later in Statistical rethinking. You can run \"chains\" of the sampling procedure to sanity check the MCMC methods that took place. The samples from the posterior distribution are also returned and we can perform inference on the posterior according to this." ] @@ -1350,7 +1350,7 @@ "Python version : 3.8.5\n", "IPython version : 7.18.1\n", "\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "matplotlib: 3.3.2\n", "arviz : 0.9.0\n", "scipy : 1.5.2\n", @@ -1371,9 +1371,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb index e71bcff..35ae1f5 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_4.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from matplotlib import pylab as plt\n", @@ -1629,7 +1629,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1678,7 +1678,7 @@ "IPython version : 7.18.1\n", "\n", "arviz : 0.9.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "scipy : 1.5.2\n", "seaborn : 0.11.0\n", "numpy : 1.19.1\n", @@ -1700,9 +1700,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb index 46840d6..d2ab2d7 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_5.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -221,11 +221,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", ":12: UserWarning: This figure was using constrained_layout==True, but that is incompatible with subplots_adjust and or tight_layout: setting constrained_layout==False. \n", " plt.tight_layout();\n" @@ -266,7 +266,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now perform our linear regressions on these models. I'll just use pymc3 MCMC tool to estimate this and not bother with Laplace's method for now. I'll fit two linear models to the data, both of which will attempt to predict child IQ. The first model will use just parental income and the second will make use of both parental income and parental IQ. As I generated this data in such a way as for the parental income to be spurious, we should see the first model have a positive slope parameter and the second model should have zero slope parameter for the parental income variable, as we're controlling for the true cause. More formally\n", + "We can now perform our linear regressions on these models. I'll just use pymc MCMC tool to estimate this and not bother with Laplace's method for now. I'll fit two linear models to the data, both of which will attempt to predict child IQ. The first model will use just parental income and the second will make use of both parental income and parental IQ. As I generated this data in such a way as for the parental income to be spurious, we should see the first model have a positive slope parameter and the second model should have zero slope parameter for the parental income variable, as we're controlling for the true cause. More formally\n", "\n", "### Model 1\n", "\n", @@ -3510,7 +3510,7 @@ "Python version : 3.8.5\n", "IPython version : 7.18.1\n", "\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "arviz : 0.9.0\n", "seaborn : 0.11.0\n", "numpy : 1.19.1\n", @@ -3532,9 +3532,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb index cd98ad9..b035922 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_6.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -319,9 +319,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", " warnings.warn(\n" ] }, @@ -1325,7 +1325,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can use pymc3 to perform the regression conditioned on whether each state is in the south of not." + "We can use pymc to perform the regression conditioned on whether each state is in the south of not." ] }, { @@ -1706,11 +1706,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2551,9 +2551,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2730,9 +2730,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3097,7 +3097,7 @@ "IPython version : 7.18.1\n", "\n", "scipy : 1.5.2\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "matplotlib: 3.3.2\n", "pandas : 1.1.3\n", "arviz : 0.9.0\n", diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb index 70fc750..eb254a1 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_7.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -739,7 +739,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1166,7 +1166,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1360,10 +1360,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " warnings.warn(\n" ] } @@ -1383,9 +1383,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1551,7 +1551,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1571,9 +1571,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1760,10 +1760,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n", " warnings.warn(\n" ] } @@ -1783,9 +1783,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1955,7 +1955,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1975,9 +1975,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -2687,7 +2687,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/pymc3/sampling.py:1707: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/pymc/sampling.py:1707: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n", " warnings.warn(\n" ] }, @@ -3221,7 +3221,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -3512,7 +3512,7 @@ "IPython version : 7.18.1\n", "\n", "arviz : 0.9.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "seaborn : 0.11.0\n", "matplotlib: 3.3.2\n", "pandas : 1.1.3\n", @@ -3534,9 +3534,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb index 6e0c678..6eb5485 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_8.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "import seaborn as sns\n", "\n", "from causalgraphicalmodels import CausalGraphicalModel\n", @@ -769,7 +769,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1782,9 +1782,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -1829,7 +1829,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -1890,9 +1890,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n", - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:483: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n", " warnings.warn(\n" ] }, @@ -3063,7 +3063,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -4073,7 +4073,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/stats/stats.py:1413: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n", "See http://arxiv.org/abs/1507.04544 for details\n", " warnings.warn(\n" ] @@ -4127,7 +4127,7 @@ "\n", "matplotlib: 3.3.2\n", "numpy : 1.19.1\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "arviz : 0.9.0\n", "seaborn : 0.11.0\n", "pandas : 1.1.3\n", @@ -4147,9 +4147,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb b/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb index 03e3b66..10acaed 100644 --- a/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb +++ b/Rethinking_2/End_of_chapter_problems/Chapter_9.ipynb @@ -11,7 +11,7 @@ "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", - "import pymc3 as pm\n", + "import pymc as pm\n", "\n", "from matplotlib import pylab as plt" ] @@ -727,7 +727,7 @@ "\n", "#### Answers:\n", "\n", - "To translate terms from the book into pymc3:\n", + "To translate terms from the book into pymc:\n", "\n", "n_eff = ess_mean\n", "\n", @@ -1800,7 +1800,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/data/io_pymc3.py:85: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/data/io_pymc3.py:85: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", " warnings.warn(\n" ] }, @@ -3027,7 +3027,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/aidan/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/pairplot.py:212: UserWarning: rcParams['plot.max_subplots'] (40) is smaller than the number of resulting pair plots with these variables, generating only a 8x8 grid\n", + "/home/aidan/anaconda3/envs/pymc/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/pairplot.py:212: UserWarning: rcParams['plot.max_subplots'] (40) is smaller than the number of resulting pair plots with these variables, generating only a 8x8 grid\n", " warnings.warn(\n" ] }, @@ -3204,7 +3204,7 @@ "IPython version : 7.18.1\n", "\n", "arviz : 0.9.0\n", - "pymc3 : 3.9.3\n", + "pymc : 3.9.3\n", "pandas : 1.1.3\n", "matplotlib: 3.3.2\n", "numpy : 1.19.1\n", @@ -3224,9 +3224,9 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "pymc3", + "display_name": "pymc", "language": "python", - "name": "pymc3" + "name": "pymc" }, "language_info": { "codemirror_mode": { diff --git a/Rethinking_2/README.md b/Rethinking_2/README.md index a924f67..c0ff375 100644 --- a/Rethinking_2/README.md +++ b/Rethinking_2/README.md @@ -1,8 +1,8 @@ -# Statistical Rethinking (second edition) with Python and PyMC3 +# Statistical Rethinking (second edition) with Python and PyMC [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) is an incredible introductory book to Bayesian Statistics. It follows a [_Jaynesian_](https://en.wikipedia.org/wiki/Edwin_Thompson_Jaynes) and practical approach with very good examples and clear explanations. -In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC3. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMC3onic_ way. +In this repository we port [the book's original code in R and Stan](https://github.com/rmcelreath/rethinking) to Python and PyMC. We attempt to reproduce the examples as faithfully as possible while expressing them in a _Pythonic_ and _PyMConic_ way. ## Display notebooks [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/pymc-devs/resources/master?filepath=Rethinking_2) @@ -13,9 +13,9 @@ In this repository we port [the book's original code in R and Stan](https://gith All contributions are welcome! -Feel free to send PRs to fix errors, improve the code, or make comments that could help users of this repository and readers of the book. When submitting PRs, please make sure the notebooks are formatted according to the [PyMC NB style guide](https://github.com/pymc-devs/pymc3/wiki/PyMC's-Jupyter-Notebook-Style). +Feel free to send PRs to fix errors, improve the code, or make comments that could help users of this repository and readers of the book. When submitting PRs, please make sure the notebooks are formatted according to the [PyMC NB style guide](https://github.com/pymc-devs/pymc/wiki/PyMC's-Jupyter-Notebook-Style). -You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC3/Lobby). +You can also join the discussion on [Gitter](https://gitter.im/Statistical-Rethinking-with-Python-and-PyMC/Lobby). ## Installing the dependencies @@ -27,7 +27,7 @@ to install all the dependencies into an isolated environment. Activate the environment by running: - source activate stat-rethink2-pymc3 + source activate stat-rethink2-pymc To use the notebooks you first have to register your new environment as a valid notebook kernel: @@ -45,4 +45,4 @@ from the root directory. --- -Creative Commons License
Statistical Rethinking with Python and PyMC3 by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. +Creative Commons License
Statistical Rethinking with Python and PyMC by All Contributors is licensed under a Creative Commons Attribution 4.0 International License. diff --git a/Rethinking_2/environment.yml b/Rethinking_2/environment.yml index 7e6f91c..d19d05a 100644 --- a/Rethinking_2/environment.yml +++ b/Rethinking_2/environment.yml @@ -1,4 +1,4 @@ -name: stat-rethink2-pymc3 +name: stat-rethink2-pymc channels: - conda-forge dependencies: @@ -16,3 +16,8 @@ dependencies: - pip: - daft - causalgraphicalmodels + - watermark + - "git+git://github.com/arviz-devs/arviz.git@main" + - "git+git://github.com/pymc-devs/pymc.git@main" + - causalgraphicalmodels + - daft