Rename aesara to pytensor.

Author: twiecki

examples/case_studies/GEV.ipynb

@@ -45,12 +45,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pymc as pm\n",
     "import pymc_experimental.distributions as pmx\n",
+    "import pytensor.tensor as at\n",
     "\n",
     "from arviz.plots import plot_utils as azpu"
    ]
@@ -984,7 +984,7 @@
       "Python version       : 3.10.6\n",
       "IPython version      : 8.5.0\n",
       "\n",
-      "aesara: 2.8.6\n",
+      "pytensor: 2.8.6\n",
       "arviz : 0.12.1\n",
       "\n",
       "pymc_experimental: 0.0.1\n",
@@ -993,7 +993,7 @@
       "arviz            : 0.12.1\n",
       "numpy            : 1.23.3\n",
       "json             : 2.0.9\n",
-      "aesara           : 2.8.6\n",
+      "pytensor           : 2.8.6\n",
       "pymc             : 3.9.3+1493.g372d7c24\n",
       "\n",
       "Watermark: 2.3.1\n",
@@ -1003,7 +1003,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,arviz"
+    "%watermark -n -u -v -iv -w -p pytensor,arviz"
    ]
   },
   {

examples/case_studies/LKJ.ipynb

@@ -987,7 +987,7 @@
       "Python version       : 3.7.12\n",
       "IPython version      : 5.5.0\n",
       "\n",
-      "aesara: 2.4.0\n",
+      "pytensor: 2.4.0\n",
       "xarray: 0.18.2\n",
       "\n",
       "pymc      : 4.0.0b2\n",
@@ -1004,7 +1004,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,xarray"
    ]
   }
  ],

examples/case_studies/bayesian_ab_testing_introduction.ipynb

@@ -2163,7 +2163,7 @@
       "Python version       : 3.8.10\n",
       "IPython version      : 8.4.0\n",
       "\n",
-      "aesara: 2.7.3\n",
+      "pytensor: 2.7.3\n",
       "xarray: 2022.3.0\n",
       "\n",
       "matplotlib: 3.5.2\n",
@@ -2181,7 +2181,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,xarray"
    ]
   },
   {

examples/case_studies/binning.ipynb

@@ -72,7 +72,7 @@
     "We are now in a position to sketch out a generative PyMC model:\n",
     "\n",
     "```python\n",
-    "import aesara.tensor as at\n",
+    "import pytensor.tensor as at\n",
     "\n",
     "with pm.Model() as model:\n",
     "    # priors\n",
@@ -120,12 +120,12 @@
    "source": [
     "import warnings\n",
     "\n",
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc as pm\n",
+    "import pytensor.tensor as at\n",
     "import seaborn as sns\n",
     "\n",
     "warnings.filterwarnings(action=\"ignore\", category=UserWarning)"
@@ -3538,7 +3538,7 @@
       "pymc      : 4.0.0b6\n",
       "arviz     : 0.12.1\n",
       "numpy     : 1.22.4\n",
-      "aesara    : 2.5.1\n",
+      "pytensor    : 2.5.1\n",
       "\n",
       "Watermark: 2.3.1\n",
       "\n"

examples/case_studies/blackbox_external_likelihood_numpy.ipynb

@@ -43,11 +43,11 @@
     }
    ],
    "source": [
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib\n",
     "import numpy as np\n",
     "import pymc as pm\n",
+    "import pytensor.tensor as at\n",
     "import scipy as sp\n",
     "import seaborn as sns\n",
     "import xarray as xr\n",
@@ -205,8 +205,8 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
-      "  aesara_function = aesara.function(\n",
+      "/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
+      "  pytensor_function = pytensor.function(\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [W, F, psi]\n"
      ]
@@ -335,18 +335,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def expand_packed_block_triangular(d, k, packed, diag=None, mtype=\"aesara\"):\n",
+    "def expand_packed_block_triangular(d, k, packed, diag=None, mtype=\"pytensor\"):\n",
     "    # like expand_packed_triangular, but with d > k.\n",
-    "    assert mtype in {\"aesara\", \"numpy\"}\n",
+    "    assert mtype in {\"pytensor\", \"numpy\"}\n",
     "    assert d >= k\n",
     "\n",
     "    def set_(M, i_, v_):\n",
-    "        if mtype == \"aesara\":\n",
+    "        if mtype == \"pytensor\":\n",
     "            return at.set_subtensor(M[i_], v_)\n",
     "        M[i_] = v_\n",
     "        return M\n",
     "\n",
-    "    out = at.zeros((d, k), dtype=float) if mtype == \"aesara\" else np.zeros((d, k), dtype=float)\n",
+    "    out = at.zeros((d, k), dtype=float) if mtype == \"pytensor\" else np.zeros((d, k), dtype=float)\n",
     "    if diag is None:\n",
     "        idxs = np.tril_indices(d, m=k)\n",
     "        out = set_(out, idxs, packed)\n",
@@ -401,8 +401,8 @@
      "text": [
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
-      "  aesara_function = aesara.function(\n",
+      "/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
+      "  pytensor_function = pytensor.function(\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [W_z, W_b, F, psi]\n"
      ]
@@ -516,8 +516,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
-      "  aesara_function = aesara.function(\n"
+      "/home/oriol/miniconda3/envs/arviz/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
+      "  pytensor_function = pytensor.function(\n"
      ]
     },
     {
@@ -785,7 +785,7 @@
       "pymc           : 4.0.0b6\n",
       "matplotlib     : 3.5.1\n",
       "arviz          : 0.12.0\n",
-      "aesara         : 2.5.1\n",
+      "pytensor         : 2.5.1\n",
       "scipy          : 1.8.0\n",
       "xarray         : 2022.3.0\n",
       "seaborn        : 0.11.2\n",

examples/case_studies/factor_analysis.ipynb

@@ -60,12 +60,12 @@
    },
    "outputs": [],
    "source": [
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc as pm\n",
+    "import pytensor.tensor as at\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -599,7 +599,7 @@
       "arviz     : 0.12.1\n",
       "sys       : 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:04:59) [GCC 10.3.0]\n",
       "pymc      : 4.1.2\n",
-      "aesara    : 2.7.5\n",
+      "pytensor    : 2.7.5\n",
       "\n",
       "Watermark: 2.3.1\n",
       "\n"

examples/case_studies/hierarchical_partial_pooling.ipynb

@@ -1155,7 +1155,7 @@
       "Python version       : 3.9.12\n",
       "IPython version      : 8.2.0\n",
       "\n",
-      "aesara: 2.6.2\n",
+      "pytensor: 2.6.2\n",
       "aeppl : 0.0.28\n",
       "xarray: 2022.3.0\n",
       "\n",
@@ -1172,7 +1172,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,aeppl,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
    ]
   },
   {

examples/case_studies/item_response_nba.ipynb

@@ -815,7 +815,7 @@
       "Python version       : 3.9.9\n",
       "IPython version      : 7.31.0\n",
       "\n",
-      "aesara: 2.3.8\n",
+      "pytensor: 2.3.8\n",
       "aeppl : 0.0.18\n",
       "xarray: 0.20.2\n",
       "\n",
@@ -832,7 +832,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,aeppl,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
    ]
   },
   {

examples/case_studies/mediation_analysis.ipynb

@@ -859,7 +859,7 @@
       "Python version       : 3.9.9\n",
       "IPython version      : 7.31.0\n",
       "\n",
-      "aesara: 2.5.1\n",
+      "pytensor: 2.5.1\n",
       "aeppl : 0.0.27\n",
       "xarray: 0.20.2\n",
       "\n",
@@ -877,7 +877,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,aeppl,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
    ]
   },
   {

examples/case_studies/moderation_analysis.ipynb

@@ -85,12 +85,12 @@
     "import os\n",
     "import warnings\n",
     "\n",
-    "import aesara.tensor as at\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc as pm\n",
+    "import pytensor.tensor as at\n",
     "import seaborn as sns\n",
     "import xarray as xr\n",
     "\n",
@@ -3958,7 +3958,7 @@
       "pandas    : 1.5.2\n",
       "seaborn   : 0.12.1\n",
       "numpy     : 1.21.6\n",
-      "aesara    : 2.8.7\n",
+      "pytensor    : 2.8.7\n",
       "\n",
       "Watermark: 2.3.0\n",
       "\n"

examples/case_studies/multilevel_modeling.ipynb

@@ -827,12 +827,12 @@
     "import logging\n",
     "import time\n",
     "\n",
-    "import aesara\n",
+    "import pytensor\n",
     "import scipy as sp\n",
     "\n",
     "# Enable on-the-fly graph computations, but ignore\n",
     "# absence of intermediate test values.\n",
-    "aesara.config.compute_test_value = \"ignore\"\n",
+    "pytensor.config.compute_test_value = \"ignore\"\n",
     "\n",
     "# Set up logging.\n",
     "logger = logging.getLogger()\n",
@@ -1360,8 +1360,8 @@
       "INFO:pymc:Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "INFO:pymc:Initializing NUTS using jitter+adapt_diag...\n",
-      "/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter 'updates' of aesara.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
-      "  aesara_function = aesara.function(\n",
+      "/Users/severinhatt/miniconda3/envs/pymc-hack/lib/python3.9/site-packages/pymc/pytensorf.py:1005: UserWarning: The parameter 'updates' of pytensor.function() expects an OrderedDict, got <class 'dict'>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.\n",
+      "  pytensor_function = pytensor.function(\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
       "INFO:pymc:Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [U, V]\n",
@@ -1721,7 +1721,7 @@
       "Python version       : 3.9.7\n",
       "IPython version      : 8.4.0\n",
       "\n",
-      "aesara: 2.5.1\n",
+      "pytensor: 2.5.1\n",
       "aeppl : 0.0.27\n",
       "xarray: 2022.3.0\n",
       "\n",
@@ -1731,7 +1731,7 @@
       "scipy     : 1.7.3\n",
       "matplotlib: 3.5.2\n",
       "logging   : 0.5.1.2\n",
-      "aesara    : 2.5.1\n",
+      "pytensor    : 2.5.1\n",
       "numpy     : 1.22.4\n",
       "xarray    : 2022.3.0\n",
       "\n",
@@ -1742,7 +1742,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p aesara,aeppl,xarray"
+    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
    ]
   },
   {