Merge branch 'iid2022' into 'main'

make evidences an array See merge request !5

Merge branch 'iid2022' into 'main'
873e5a29 · Philipp Frank · 86057d21 · c720aeae · 873e5a29 · 873e5a29
Commit 873e5a29 authored 2 years ago by Philipp Frank
--- a/demo_poisson.ipynb
+++ b/demo_poisson.ipynb
@@ -28,7 +28,7 @@
    "from utils import plot_2D, load_psf, geovi_sampling, plot_posterior\n",
    "\n",
    "ift.random.push_sseq_from_seed(42)\n",
-    "evidences = []"
+    "evidences = np.zeros(3)"
   ]
  },
  {
@@ -74,7 +74,7 @@
    "\n",
    "\n",
    "print(evidence)\n",
-    "evidences += [evidence, ]"
+    "evidences[0] = evidence"
   ]
  },
  {
@@ -88,7 +88,7 @@
    "# Inference model 2\n",
    "\n",
    "\n",
-    "evidences += [evidence, ]"
+    "evidences[1] = evidence"
   ]
  },
  {
@@ -149,7 +149,7 @@
   "source": [
    "# Inference model 3\n",
    "\n",
-    "evidences += [evidence, ]"
+    "evidences[2] = evidence"
   ]
  },
  {

 %% Cell type:markdown id: tags:
 Nifty tutorial for Poisson count data
 =====================================
 %% Cell type:markdown id: tags:
 Setup
 -----
 %% Cell type:code id: tags:
 ``` python
 import numpy as np
 import matplotlib.pyplot as plt
 import nifty8 as ift
 from utils import plot_2D, load_psf, geovi_sampling, plot_posterior
 ift.random.push_sseq_from_seed(42)
-evidences = []
+evidences = np.zeros(3)
 ```
 %% Cell type:code id: tags:
 ``` python
 # Load data and visualize
 data = np.load('data/poisson.npz')
 ```
 %% Cell type:code id: tags:
 ``` python
 position_space = ift.RGSpace([128, 128])
 # Homogeneous poisson process
 print(model1)
 ```
 %% Cell type:code id: tags:
 ``` python
 # Set up likelihood & PSF
 ```
 %% Cell type:code id: tags:
 ``` python
 # Inference model 1
 print(evidence)
-evidences += [evidence, ]
+evidences[0] = evidence
 ```
 %% Cell type:code id: tags:
 ``` python
 # Independent poisson process
 # Inference model 2
-evidences += [evidence, ]
+evidences[1] = evidence
 ```
 %% Cell type:code id: tags:
 ``` python
 # Compare evidence
 print(evidences)
 ```
 %% Cell type:code id: tags:
 ``` python
 # Diffuse poisson process
 args = {
    'offset_mean': .5,
    'offset_std': (1., 1E-5),
    # Amplitude of field fluctuations
    'fluctuations': (1.5, 0.5),  # 1.0, 1e-2
    # Exponent of power law power spectrum component
    'loglogavgslope': (-4., 1),  # -6.0, 1
    # Amplitude of integrated Wiener process power spectrum component
    'flexibility': (1., 0.2),  # 2.0, 1.0
    # How ragged the integrated Wiener process component is
    'asperity': (0.1, 0.01),  # 0.1, 0.5
    # Name of the input keys
    'prefix' : 'diffuse'
 }
 correlated_field = ift.SimpleCorrelatedField(position_space, **args)
 pspec = correlated_field.power_spectrum
 ```
 %% Cell type:code id: tags:
 ``` python
 # Prior samples
 ```
 %% Cell type:code id: tags:
 ``` python
 # Inference model 3
-evidences += [evidence, ]
+evidences[2] = evidence
 ```
 %% Cell type:code id: tags:
 ``` python
 # Compare evidence
 print(evidences)
 print(evidences - evidences[-1])
 ```
 %% Cell type:markdown id: tags:
 Posterior visualization
 -----------------------
 %% Cell type:code id: tags:
 ``` python
 plot_posterior(samples, data, model3, diffuse, model2, pspec)
 ```

--- a/demo_poisson_solution.ipynb
+++ b/demo_poisson_solution.ipynb
@@ -28,7 +28,7 @@
    "from utils import plot_2D, load_psf, geovi_sampling, plot_posterior\n",
    "\n",
    "ift.random.push_sseq_from_seed(42)\n",
-    "evidences = []"
+    "evidences = np.zeros(3)"
   ]
  },
  {
@@ -84,7 +84,7 @@
    "samples, evidence = geovi_sampling(likelihood @ model1)\n",
    "plot_2D(samples.average(model1).val, 'model1 posterior mean')\n",
    "print(evidence)\n",
-    "evidences += [evidence, ]"
+    "evidences[0] = evidence"
   ]
  },
  {
@@ -100,7 +100,7 @@
    "samples, evidence = geovi_sampling(likelihood @ model2)\n",
    "plot_2D(samples.average(model2).val, 'model2 posterior mean')\n",
    "print(evidence)\n",
-    "evidences += [evidence, ]"
+    "evidences[1] = evidence"
   ]
  },
  {
@@ -110,7 +110,7 @@
   "outputs": [],
   "source": [
    "# Compare evidence\n",
-    "print(evidences)"
+    "print(evidences[:2])"
   ]
  },
  {
@@ -169,7 +169,7 @@
    "samples, evidence = geovi_sampling(likelihood @ model3)\n",
    "plot_2D(samples.average(model3).val, 'model3 posterior mean')\n",
    "print(evidence)\n",
-    "evidences += [evidence, ]"
+    "evidences[2] = evidence"
   ]
  },
  {

 %% Cell type:markdown id: tags:
 Nifty tutorial for Poisson count data
 =====================================
 %% Cell type:markdown id: tags:
 Setup
 -----
 %% Cell type:code id: tags:
 ``` python
 import numpy as np
 import matplotlib.pyplot as plt
 import nifty8 as ift
 from utils import plot_2D, load_psf, geovi_sampling, plot_posterior
 ift.random.push_sseq_from_seed(42)
-evidences = []
+evidences = np.zeros(3)
 ```
 %% Cell type:code id: tags:
 ``` python
 # Load data and visualize
 data = np.load('data/poisson.npz')
 print(data['data'].shape)
 plot_2D(data['data'], 'Data')
 ```
 %% Cell type:code id: tags:
 ``` python
 position_space = ift.RGSpace([128, 128])
 # Homogeneous poisson process
 projection = ift.VdotOperator(ift.full(position_space, 1.)).adjoint
 model1 = ift.FieldAdapter(projection.domain, 'hom')
 model1 = ift.exp(5. * model1)
 model1 = projection @ model1
 print(model1)
 ```
 %% Cell type:code id: tags:
 ``` python
 # Set up likelihood & PSF
 d = ift.makeField(position_space, data['data'])
 likelihood = ift.PoissonianEnergy(d)
 PSF_op, psf = load_psf(position_space)
 plot_2D(psf, 'PSF')
 likelihood = likelihood @ PSF_op
 ```
 %% Cell type:code id: tags:
 ``` python
 # Inference model 1
 samples, evidence = geovi_sampling(likelihood @ model1)
 plot_2D(samples.average(model1).val, 'model1 posterior mean')
 print(evidence)
-evidences += [evidence, ]
+evidences[0] = evidence
 ```
 %% Cell type:code id: tags:
 ``` python
 # Independent poisson process
 model2 = ift.InverseGammaOperator(position_space, 2., 3.).ducktape('independent')
 # Inference model 2
 samples, evidence = geovi_sampling(likelihood @ model2)
 plot_2D(samples.average(model2).val, 'model2 posterior mean')
 print(evidence)
-evidences += [evidence, ]
+evidences[1] = evidence
 ```
 %% Cell type:code id: tags:
 ``` python
 # Compare evidence
-print(evidences)
+print(evidences[:2])
 ```
 %% Cell type:code id: tags:
 ``` python
 # Diffuse poisson process
 args = {
    'offset_mean': .5,
    'offset_std': (1., 1E-5),
    # Amplitude of field fluctuations
    'fluctuations': (1.5, 0.5),  # 1.0, 1e-2
    # Exponent of power law power spectrum component
    'loglogavgslope': (-4., 1),  # -6.0, 1
    # Amplitude of integrated Wiener process power spectrum component
    'flexibility': (1., 0.2),  # 2.0, 1.0
    # How ragged the integrated Wiener process component is
    'asperity': (0.1, 0.01),  # 0.1, 0.5
    # Name of the input keys
    'prefix' : 'diffuse'
 }
 correlated_field = ift.SimpleCorrelatedField(position_space, **args)
 pspec = correlated_field.power_spectrum
 diffuse = correlated_field.exp()
 model3 = diffuse + model2
 ```
 %% Cell type:code id: tags:
 ``` python
 # Prior samples
 pl = ift.Plot()
 for _ in range(9):
    pl.add(model3(ift.from_random(model3.domain)))
 pl.output()
 ```
 %% Cell type:code id: tags:
 ``` python
 # Inference model 3
 samples, evidence = geovi_sampling(likelihood @ model3)
 plot_2D(samples.average(model3).val, 'model3 posterior mean')
 print(evidence)
-evidences += [evidence, ]
+evidences[2] = evidence
 ```
 %% Cell type:code id: tags:
 ``` python
 # Compare evidence
 print(evidences)
 print(evidences - evidences[-1])
 ```
 %% Cell type:markdown id: tags:
 Posterior visualization
 -----------------------
 %% Cell type:code id: tags:
 ``` python
 plot_posterior(samples, data, model3, diffuse, model2, pspec)
 ```