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‎notebooks/Exponential Trend Smoothing.ipynb

Lines changed: 15 additions & 13 deletions
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@@ -15,18 +15,19 @@
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"\n",
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"warnings.filterwarnings(action=\"ignore\", message=\"The RandomType SharedVariables\")\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
19-
"from matplotlib.dates import DayLocator, MonthLocator, YearLocator, DateFormatter\n",
20-
"\n",
21-
"import pymc_extras.statespace as pmss\n",
22-
"import pymc as pm\n",
18+
"import os\n",
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"\n",
24-
"import preliz as pz\n",
25-
"import pandas as pd\n",
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"import arviz as az\n",
27-
"import yfinance as yf\n",
21+
"import matplotlib.pyplot as plt\n",
2822
"import numpy as np\n",
29-
"import os\n",
23+
"import pandas as pd\n",
24+
"import preliz as pz\n",
25+
"import pymc as pm\n",
26+
"import yfinance as yf\n",
27+
"\n",
28+
"from matplotlib.dates import DateFormatter, DayLocator, MonthLocator, YearLocator\n",
29+
"\n",
30+
"import pymc_extras.statespace as pmss\n",
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"\n",
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"plt.rcParams.update(\n",
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" {\n",
@@ -61,11 +62,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import ipywidgets as widgets\n",
65-
"from IPython.display import display\n",
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"from functools import partial\n",
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"\n",
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"import ipywidgets as widgets\n",
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"import pytensor\n",
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"import pytensor.tensor as pt\n",
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"\n",
71+
"from IPython.display import display\n",
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"\n",
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"from pymc_extras.statespace.filters.distributions import LinearGaussianStateSpace\n",
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"\n",
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"\n",
@@ -344,8 +348,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import datetime\n",
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"\n",
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"FORCE_UPDATE = False\n",
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"\n",
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"if not os.path.isdir(\"data\"):\n",

‎notebooks/SARMA Example.ipynb

Lines changed: 7 additions & 8 deletions
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@@ -31,20 +31,19 @@
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"\n",
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"jax.config.update(\"jax_platform_name\", \"cpu\")\n",
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"\n",
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"import pymc as pm\n",
35-
"from pytensor import tensor as pt\n",
34+
"import warnings\n",
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"\n",
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"import arviz as az\n",
38-
"import statsmodels.api as sm\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
42-
"from scipy import stats\n",
40+
"import pymc as pm\n",
41+
"import statsmodels.api as sm\n",
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"\n",
44-
"import pymc_extras.statespace as pmss\n",
4543
"from pymc.model.transform.optimization import freeze_dims_and_data\n",
44+
"from pytensor import tensor as pt\n",
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"\n",
47-
"import warnings\n",
46+
"import pymc_extras.statespace as pmss\n",
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"\n",
4948
"warnings.filterwarnings(action=\"ignore\", message=\"The RandomType SharedVariables\")\n",
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"\n",
@@ -2697,8 +2696,8 @@
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"source": [
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"fig, ax = plt.subplots()\n",
26992698
"post = az.extract(post_pred).map(np.exp)\n",
2700-
"hdi = az.hdi(post_pred.map(np.exp))[f\"predicted_posterior_observed\"]\n",
2701-
"post[f\"predicted_posterior_observed\"].isel(observed_state=0).mean(dim=\"sample\").plot.line(\n",
2699+
"hdi = az.hdi(post_pred.map(np.exp))[\"predicted_posterior_observed\"]\n",
2700+
"post[\"predicted_posterior_observed\"].isel(observed_state=0).mean(dim=\"sample\").plot.line(\n",
27022701
" x=\"time\", ax=ax, add_legend=False, label=\"Posterior Mean, Predicted\"\n",
27032702
")\n",
27042703
"ax.fill_between(\n",

‎notebooks/Structural Timeseries Modeling.ipynb

Lines changed: 12 additions & 10 deletions
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@@ -20,18 +20,20 @@
2020
}
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],
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"source": [
23-
"from pymc_extras.statespace import structural as st\n",
24-
"from pymc_extras.statespace.utils.constants import SHORT_NAME_TO_LONG\n",
23+
"import warnings\n",
24+
"\n",
25+
"import arviz as az\n",
2526
"import matplotlib.pyplot as plt\n",
27+
"import numpy as np\n",
28+
"import pandas as pd\n",
29+
"import preliz as pz\n",
2630
"import pymc as pm\n",
27-
"import arviz as az\n",
2831
"import pytensor.tensor as pt\n",
29-
"import preliz as pz\n",
3032
"\n",
31-
"import numpy as np\n",
32-
"import pandas as pd\n",
3333
"from patsy import dmatrix\n",
34-
"import warnings\n",
34+
"\n",
35+
"from pymc_extras.statespace import structural as st\n",
36+
"from pymc_extras.statespace.utils.constants import SHORT_NAME_TO_LONG\n",
3537
"\n",
3638
"warnings.filterwarnings(\"ignore\", category=UserWarning, message=\"The RandomType SharedVariables\")\n",
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"\n",
@@ -76,13 +78,14 @@
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},
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"outputs": [],
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"source": [
79-
"from pymc_extras.statespace.filters.distributions import LinearGaussianStateSpace\n",
8081
"from pymc.pytensorf import inputvars\n",
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"\n",
83+
"from pymc_extras.statespace.filters.distributions import LinearGaussianStateSpace\n",
84+
"\n",
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"\n",
8386
"def make_numpy_function(mod):\n",
8487
" mod = mod.build(verbose=False)\n",
85-
" data = pt.matrix(\"data\", shape=(None, 1))\n",
88+
" pt.matrix(\"data\", shape=(None, 1))\n",
8689
" steps = pt.iscalar(\"steps\")\n",
8790
" x0, _, c, d, T, Z, R, H, Q = mod._unpack_statespace_with_placeholders()\n",
8891
" sequence_names = [x.name for x in [c, d] if x.ndim == 2]\n",
@@ -3113,7 +3116,6 @@
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},
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"outputs": [],
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"source": [
3116-
"from nutpie import transform_adapter\n",
31173119
"import nutpie as ntp\n",
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"\n",
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"# Uncomment to see arguments for with_transform_adapt\n",

‎notebooks/VARMAX Example.ipynb

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@@ -11,21 +11,21 @@
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"\n",
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"jax.config.update(\"jax_platform_name\", \"cpu\")\n",
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"\n",
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"import sys\n",
15+
"\n",
16+
"import arviz as az\n",
17+
"import matplotlib.pyplot as plt\n",
1418
"import numpy as np\n",
15-
"import statsmodels.api as sm\n",
1619
"import pandas as pd\n",
17-
"\n",
1820
"import pymc as pm\n",
1921
"import pytensor.tensor as pt\n",
20-
"import arviz as az\n",
21-
"\n",
22-
"import matplotlib.pyplot as plt\n",
23-
"import sys\n",
22+
"import statsmodels.api as sm\n",
2423
"\n",
2524
"sys.path.append(\"..\")\n",
26-
"import pymc_extras.statespace as pmss\n",
2725
"import re\n",
2826
"\n",
27+
"import pymc_extras.statespace as pmss\n",
28+
"\n",
2929
"config = {\n",
3030
" \"figure.figsize\": [12.0, 4.0],\n",
3131
" \"figure.dpi\": 72.0 * 2,\n",
@@ -841,7 +841,7 @@
841841
" new_labels = []\n",
842842
" for label in axis.yaxis.get_majorticklabels():\n",
843843
" old_text = \"[\" + label.get_text().split(\"[\")[-1]\n",
844-
" labels = eval(re.sub(\"([\\d\\w]+)\", '\"\\g<1>\"', old_text))\n",
844+
" labels = eval(re.sub(r\"([\\d\\w]+)\", r'\"\\g<1>\"', old_text))\n",
845845
" lag, other_var = labels\n",
846846
" new_text = f\"L{lag}.{other_var}\"\n",
847847
" new_labels.append(new_text)\n",

‎notebooks/latent_approx.ipynb

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@@ -25,13 +25,9 @@
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}
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],
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"source": [
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"import arviz as az\n",
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"import matplotlib.pyplot as plt\n",
3029
"import numpy as np\n",
31-
"import pymc as pm\n",
32-
"from aesara import tensor as at\n",
33-
"\n",
34-
"import pymc_extras as pmx"
30+
"import pymc as pm"
3531
]
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},
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{
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"outputs": [],
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"source": [
4440
"from pymc_extras.gp import HSGP, KarhunenLoeveExpansion, ProjectedProcess\n",
45-
"from pymc_extras.gp.latent_approx import ExpQuad, Matern12, Matern32, Matern52"
41+
"from pymc_extras.gp.latent_approx import ExpQuad"
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]
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},
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{

‎notebooks/marginalized_changepoint_model.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
9-
"import pymc as pm\n",
10-
"from pymc_extras.model.marginal.marginal_model import MarginalModel\n",
11-
"import pandas as pd\n",
9+
"import arviz as az\n",
1210
"import numpy as np\n",
13-
"import arviz as az"
11+
"import pandas as pd\n",
12+
"import pymc as pm\n",
13+
"\n",
14+
"from pymc_extras.model.marginal.marginal_model import MarginalModel"
1415
]
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},
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{

‎pymc_extras/distributions/__init__.py

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from pymc_extras.distributions.transforms import PartialOrder
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__all__ = [
32+
"R2D2M2CP",
33+
"BetaNegativeBinomial",
3234
"Chi",
33-
"Maxwell",
3435
"DiscreteMarkovChain",
35-
"GeneralizedPoisson",
36-
"BetaNegativeBinomial",
3736
"GenExtreme",
38-
"R2D2M2CP",
37+
"GeneralizedPoisson",
38+
"Maxwell",
39+
"PartialOrder",
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"Skellam",
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"histogram_approximation",
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"PartialOrder",
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]

‎pymc_extras/distributions/histogram_utils.py

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from numpy.typing import ArrayLike
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21-
__all__ = ["quantile_histogram", "discrete_histogram", "histogram_approximation"]
21+
__all__ = ["discrete_histogram", "histogram_approximation", "quantile_histogram"]
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2424
def quantile_histogram(

‎pymc_extras/inference/__init__.py

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from pymc_extras.inference.laplace_approx.laplace import fit_laplace
1818
from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
1919

20-
__all__ = ["fit", "fit_pathfinder", "fit_laplace", "find_MAP"]
20+
__all__ = ["find_MAP", "fit", "fit_laplace", "fit_pathfinder"]

‎pymc_extras/statespace/__init__.py

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from pymc_extras.statespace.models.VARMAX import BayesianVARMAX
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__all__ = [
8-
"compile_statespace",
9-
"structural",
108
"BayesianETS",
119
"BayesianSARIMA",
1210
"BayesianVARMAX",
11+
"compile_statespace",
12+
"structural",
1313
]

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