I optimality criterion #428

Osburg · 2024-08-19T22:54:09Z

This PR should close #337. It should implement the i-optimality criterion as described by @KaiExner as the class IOptimality.

When constructing a new IOptimality object, the space of feasible points is filled using the space filling objective (the number of points n_space can be set by the user and by default the number of points is set to be equal to the requested number of experiments in the design to be found n_experiments).
From these points, the corresponding design matrix Y is generated according to the user-provided model which is then used to define the evaluation function of the criterion as tr((Y.T @ Y) / n_space @ inv(X.T @ X)) where X is the model matrix corresponding to some input x.

@dlinzner-bcs @KaiExner do you think this is roughly what it supposed to look like?

This is still a draft since tests etc. are still missing.

Co-authored-by: Johannes P. Dürholt <johannespeter.duerholt@evonik.com>

KaiExner · 2024-08-28T13:37:08Z

Setz mal folgendes an: 1. Design mit drei Faktoren X1, X2, X3, alle -1 ... 1 2. Constraint 1: X1 + X2 <= 0.8 3. Constraint 2: X1 + X2 >= 0.2 4. Modell: Y ~ X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + X1^2 + X2^2 + X3^2 5. 16 Runs Ergebnis jmp: 0,8454641457 -0,340784303 0 1 -0,8 -1 1 -0,261259178 1 -0,49600524 1 1 -0,8 1 0 1 -0,8 1 0,3972024633 0,0427975367 0 -0,2 1 0 1 -0,2 -1 -0,08 0,88 -1 -0,512263201 1 -1 -0,08 0,28 1 0,16 0,64 1 -0,08 0,28 -1 0,2386392856 0,2013607144 0 0,3420421924 0,1377730655 0 ***@***.*** Dr. Kai Michael Exner Senior Specialist Digitalization (Data Analytics) Mobile: +49 174 3496381, Email: ***@***.*** Postal Address: BASF SE, RGQ/DA – A015, Carl-Bosch-Strasse 38, 67056 Ludwigshafen am Rhein, Germany ***@***.*** BASF SE, Registered Office: 67056 Ludwigshafen, Germany Registration Court: Amtsgericht Ludwigshafen, Registration No.: HRB 6000 Chairman of the Supervisory Board: Kurt Bock Board of Executive Directors: Markus Kamieth, Chairman; Dirk Elvermann, Michael Heinz, Anup Kothari, Stephan Kothrade, Katja Scharpwinkel Information on data protection can be found here: https://www.basf.com/global/en/legal/data-protection-at-basf.htmll From: Dominik Linzner ***@***.***> Sent: Wednesday, August 28, 2024 1:41 PM To: experimental-design/bofire ***@***.***> Cc: Kai Michael Exner ***@***.***>; Review requested ***@***.***> Subject: [EXT] Re: [experimental-design/bofire] I optimality criterion (PR #428) Sie erhalten nicht oft eine E-Mail von ***@***.******@***.***>. Erfahren Sie, warum dies wichtig ist<https://aka.ms/LearnAboutSenderIdentification> @dlinzner-bcs<https://github.com/dlinzner-bcs> requested your review on: #428<#428> I optimality criterion. — Reply to this email directly, view it on GitHub<#428 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BEJBJSU5BNDE4HYSC2N3G2TZTWZOJAVCNFSM6AAAAABMYW445CVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJUGA2DKMJWHAZTIMI>. You are receiving this because your review was requested.Message ID: ***@***.******@***.***>>

Osburg · 2024-08-28T21:59:02Z

Hi @KaiExner,

Danke für das Beispiel. Der folgende Code generiert einen Versuchsplan dazu und erzeugt die Abbildung unten

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter

from bofire.data_models.constraints.api import (
    NonlinearEqualityConstraint,
    NonlinearInequalityConstraint,
    LinearEqualityConstraint,
    LinearInequalityConstraint,
    InterpointEqualityConstraint,
)
from bofire.data_models.domain.api import Domain
from bofire.data_models.features.api import ContinuousInput, ContinuousOutput
from bofire.strategies.doe.design import find_local_max_ipopt
from bofire.strategies.enum import OptimalityCriterionEnum

domain = Domain(
   inputs = [
    ContinuousInput(key="X1", bounds = (-1,1)),
    ContinuousInput(key="X2", bounds = (-1,1)),
    ContinuousInput(key="X3", bounds = (-1,1))
    ],
   outputs = [ContinuousOutput(key="y")],
   constraints = [
       LinearInequalityConstraint(features=["X1","X2"], coefficients=[1,1], rhs=0.8),
       LinearInequalityConstraint(features=["X1","X2"], coefficients=[-1,-1], rhs=-0.2)
   ]
)

i_optimal_design = find_local_max_ipopt(
    domain, 
    "Y ~ X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + X1^2 + X2^2 + X3^2", 
    n_experiments=16, 
    ipopt_options={"disp":5},
    objective=OptimalityCriterionEnum.I_OPTIMALITY
    ).to_numpy().T

jmp = np.array([[0.8454641457,-0.340784303,0],
[1,-0.8,-1],
[1,-0.261259178,1],
[-0.49600524,1,1],
[-0.8,1,0],
[1,-0.8,1],
[0.3972024633,0.0427975367,0],
[-0.2,1,0],
[1,-0.2,-1],
[-0.08,0.88,-1],
[-0.512263201,1,-1],
[-0.08, 0.28, 1],
[0.16, 0.64, 1],
[-0.08, 0.28,-1],
[0.2386392856,0.2013607144,0],
[0.3420421924,0.1377730655,0]])

fig = plt.figure(figsize=((10,10)))
ax = fig.add_subplot(111, projection='3d')
ax.view_init(45, 45)
ax.set_xlabel("$x_1$")
ax.set_ylabel("$x_2$")
ax.set_zlabel("$x_3$")
plt.rcParams["figure.figsize"] = (10,8)


#plot D-optimal solutions
ax.scatter(
    xs=i_optimal_design[0],
    ys=i_optimal_design[1],
    zs=i_optimal_design[2],
    marker="o",
    s=40,
    color="orange",
    label="our solution, 16 points"
)

ax.scatter(
    xs=jmp[:,0],
    ys=jmp[:,1],
    zs=jmp[:,2],
    marker="o",
    s=40,
    color="red",
    label="jmp solution, 16 points"
)

plt.legend()

Das in bofire generierte design sieht so aus:

[[-0.2  ,  1.   ,  1.   ],
[ 0.092,  0.108, -1.   ],
[ 1.   , -0.2  , -1.   ],
[-0.8  ,  1.   , -1.   ],
[-0.2  ,  1.   , -1.   ],
[ 1.   , -0.8  , -1.   ],
[-0.8  ,  1.   , -1.   ],
[ 1.   , -0.2  ,  1.   ],
[ 0.117,  0.083,  1.   ],
[ 0.092,  0.108, -1.   ],
[ 0.419,  0.381, -1.   ],
[ 0.117,  0.083,  1.   ],
[-0.8  ,  1.   ,  1.   ],
[ 1.   , -0.8  , -1.   ],
[ 0.299,  0.501,  1.   ],
[ 1.   , -0.8  ,  1.   ]]

Leider nicht ganz das gleiche. Kann man sich sicher sein, dass jmp tatsächlich ein globales Minimum findet? Weil die Lösung aus jmp irgendwie etwas unregelmäßig aussieht (ist z.B. nicht symmetrisch bzgl. Vertauschung von X1 und X2) und die bofire-Lösung im zweiten Bsp. (siehe unten) sehr nah an der exakten Lösung zu liegen scheint...

Ich habe gemerkt, dass im Paper, das ich für die Beispiele in tutorials/doe/basic_examples.ipynb verwendet habe (siehe hier), auch Beispiele für exakte I-optimale designs vorgegeben sind. Der folgende Code erzeugt die in der Abb. gezeigten Designs (grün: exaktes I-optimales Design, gelb: unser Design)

domain = Domain(
   inputs = [
    ContinuousInput(key="x1", bounds = (0,1)),
    ContinuousInput(key="x2", bounds = (0.1, 1)),
    ContinuousInput(key="x3", bounds = (0, 0.6))
    ],
   outputs = [ContinuousOutput(key="y")],
   constraints = [
       LinearEqualityConstraint(features=["x1","x2","x3"], coefficients=[1,1,1], rhs=1),
       LinearInequalityConstraint(features=["x1","x2"], coefficients=[5,4], rhs=3.9),
       LinearInequalityConstraint(features=["x1","x2"], coefficients=[-20,5], rhs=-3)
   ]
)

i_optimal_design = find_local_max_ipopt(
    domain, 
    "x1 + x2 + x3 + {x1**2} + {x2**2} + {x3**2} + {x1**3} + {x2**3} + {x3**3} + x1:x2 + x1:x3 + x2:x3 + x1:x2:x3", 
    n_experiments=12, 
    objective=OptimalityCriterionEnum.I_OPTIMALITY).to_numpy().T

i_opt = np.array([[0.7000, 0.1000, 0.2000],
[0.3000, 0.6000, 0.1000],
[0.2031, 0.1969, 0.6000],
[0.5899, 0.2376, 0.1725],
[0.4105, 0.4619, 0.1276],
[0.2720, 0.4882, 0.2398],
[0.2281, 0.3124, 0.4595],
[0.3492, 0.1000, 0.5508],
[0.5614, 0.1000, 0.3386],
[0.4621, 0.2342, 0.3037],
[0.3353, 0.2228, 0.4419],
[0.3782, 0.3618, 0.2600]]).T # values taken from paper


fig = plt.figure(figsize=((10,10)))
ax = fig.add_subplot(111, projection='3d')
ax.set_title("cubic model")
ax.view_init(45, 45)
ax.set_xlabel("$x_1$")
ax.set_ylabel("$x_2$")
ax.set_zlabel("$x_3$")
plt.rcParams["figure.figsize"] = (10,8)

#plot feasible polytope
ax.plot(
    xs=[7/10, 3/10, 1/5, 3/10, 7/10],
    ys=[1/10, 3/5, 1/5, 1/10, 1/10],
    zs=[1/5, 1/10, 3/5, 3/5, 1/5],
    linewidth=2
)

#plot I-optimal solution
ax.scatter(
    xs=i_opt[0],
    ys=i_opt[1],
    zs=i_opt[2],
    marker="o",
    s=40,
    color="darkgreen",
    label="I-optimal design, 12 points"
)

ax.scatter(
    xs=i_optimal_design[0],
    ys=i_optimal_design[1],
    zs=i_optimal_design[2],
    marker="o",
    s=40,
    color="orange",
    label="optimal_design solution, 12 points"
)

plt.legend()

KaiExner · 2024-08-29T20:23:40Z

Hallo Aaron, Ob das jmp Design global I-optimal ist, ist sicherlich nicht gesichert. Problem mit dem in BoFire generierten Design: X3 ist nur auf zwei Level eingestell, -1 und 1. Damit kann ich X3^2 nicht bestimmen! Das ist also kein Design für Y ~ X1 + X2 + X3 + X1X2 + X1X3 + X2X3 + X1^2 + X2^2 + X3^2. Grüße, Kai Dr. Kai Michael Exner Senior Specialist Digitalization (Data Analytics) Mobile: +49 174 3496381, Email: ***@***.*** Postal Address: BASF SE, RGQ/DA – A015, Carl-Bosch-Strasse 38, 67056 Ludwigshafen am Rhein, Germany ***@***.*** BASF SE, Registered Office: 67056 Ludwigshafen, Germany Registration Court: Amtsgericht Ludwigshafen, Registration No.: HRB 6000 Chairman of the Supervisory Board: Kurt Bock Board of Executive Directors: Markus Kamieth, Chairman; Dirk Elvermann, Michael Heinz, Anup Kothari, Stephan Kothrade, Katja Scharpwinkel Information on data protection can be found here: https://www.basf.com/global/en/legal/data-protection-at-basf.htmll From: Aaron Osburg ***@***.***> Sent: Wednesday, August 28, 2024 11:59 PM To: experimental-design/bofire ***@***.***> Cc: Kai Michael Exner ***@***.***>; Mention ***@***.***> Subject: [EXT] Re: [experimental-design/bofire] I optimality criterion (PR #428) Hi @KaiExner<https://github.com/KaiExner>, Danke für das Beispiel. Der folgende Code generiert in der Abbildung darunter import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter from bofire.data_models.constraints.api import ( NonlinearEqualityConstraint, NonlinearInequalityConstraint, LinearEqualityConstraint, LinearInequalityConstraint, InterpointEqualityConstraint, ) from bofire.data_models.domain.api import Domain from bofire.data_models.features.api import ContinuousInput, ContinuousOutput from bofire.strategies.doe.design import find_local_max_ipopt from bofire.strategies.enum import OptimalityCriterionEnum domain = Domain( inputs = [ ContinuousInput(key="X1", bounds = (-1,1)), ContinuousInput(key="X2", bounds = (-1,1)), ContinuousInput(key="X3", bounds = (-1,1)) ], outputs = [ContinuousOutput(key="y")], constraints = [ LinearInequalityConstraint(features=["X1","X2"], coefficients=[1,1], rhs=0.8), LinearInequalityConstraint(features=["X1","X2"], coefficients=[-1,-1], rhs=-0.2) ] ) i_optimal_design = find_local_max_ipopt( domain, "Y ~ X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + X1^2 + X2^2 + X3^2", n_experiments=16, ipopt_options={"disp":5}, objective=OptimalityCriterionEnum.I_OPTIMALITY ).to_numpy().T jmp = np.array([[0.8454641457,-0.340784303,0], [1,-0.8,-1], [1,-0.261259178,1], [-0.49600524,1,1], [-0.8,1,0], [1,-0.8,1], [0.3972024633,0.0427975367,0], [-0.2,1,0], [1,-0.2,-1], [-0.08,0.88,-1], [-0.512263201,1,-1], [-0.08, 0.28, 1], [0.16, 0.64, 1], [-0.08, 0.28,-1], [0.2386392856,0.2013607144,0], [0.3420421924,0.1377730655,0]]) fig = plt.figure(figsize=((10,10))) ax = fig.add_subplot(111, projection='3d') ax.view_init(45, 45) ax.set_xlabel("$x_1$") ax.set_ylabel("$x_2$") ax.set_zlabel("$x_3$") plt.rcParams["figure.figsize"] = (10,8) #plot D-optimal solutions ax.scatter( xs=i_optimal_design[0], ys=i_optimal_design[1], zs=i_optimal_design[2], marker="o", s=40, color="orange", label="our solution, 16 points" ) ax.scatter( xs=jmp[:,0], ys=jmp[:,1], zs=jmp[:,2], marker="o", s=40, color="red", label="jmp solution, 16 points" ) plt.legend() image.png (view on web)<https://github.com/user-attachments/assets/38be1515-c899-4aa9-9af3-a14f32517fcf> Das in bofire generierte design sieht so aus: [[-0.2 , 1. , 1. ], [ 0.092, 0.108, -1. ], [ 1. , -0.2 , -1. ], [-0.8 , 1. , -1. ], [-0.2 , 1. , -1. ], [ 1. , -0.8 , -1. ], [-0.8 , 1. , -1. ], [ 1. , -0.2 , 1. ], [ 0.117, 0.083, 1. ], [ 0.092, 0.108, -1. ], [ 0.419, 0.381, -1. ], [ 0.117, 0.083, 1. ], [-0.8 , 1. , 1. ], [ 1. , -0.8 , -1. ], [ 0.299, 0.501, 1. ], [ 1. , -0.8 , 1. ]] Leider nicht ganz das gleiche. Kann man sich sicher sein, dass jmp tatsächlich ein globales Minimum findet? Weil die Lösung aus jmp irgendwie etwas unregelmäßig aussieht und die bofire-Lösung im zweiten Bsp. (siehe unten) sehr nah an der exakten Lösung zu liegen scheint... Ich habe gemerkt, dass im Paper, das ich für die Beispiele in tutorials/doe/basic_examples.ipynb verwendet habe (siehe hier<https://www.sciencedirect.com/science/article/pii/S0169743917303106>), auch Beispiele für exakte I-optimale designs vorgegeben sind. Der folgende Code erzeugt die in der Abb. gezeigten Designs (grün: exaktes I-optimales Design, gelb: unser Design) domain = Domain( inputs = [ ContinuousInput(key="x1", bounds = (0,1)), ContinuousInput(key="x2", bounds = (0.1, 1)), ContinuousInput(key="x3", bounds = (0, 0.6)) ], outputs = [ContinuousOutput(key="y")], constraints = [ LinearEqualityConstraint(features=["x1","x2","x3"], coefficients=[1,1,1], rhs=1), LinearInequalityConstraint(features=["x1","x2"], coefficients=[5,4], rhs=3.9), LinearInequalityConstraint(features=["x1","x2"], coefficients=[-20,5], rhs=-3) ] ) i_optimal_design = find_local_max_ipopt( domain, "x1 + x2 + x3 + {x1**2} + {x2**2} + {x3**2} + {x1**3} + {x2**3} + {x3**3} + x1:x2 + x1:x3 + x2:x3 + x1:x2:x3", n_experiments=12, objective=OptimalityCriterionEnum.I_OPTIMALITY).to_numpy().T i_opt = np.array([[0.7000, 0.1000, 0.2000], [0.3000, 0.6000, 0.1000], [0.2031, 0.1969, 0.6000], [0.5899, 0.2376, 0.1725], [0.4105, 0.4619, 0.1276], [0.2720, 0.4882, 0.2398], [0.2281, 0.3124, 0.4595], [0.3492, 0.1000, 0.5508], [0.5614, 0.1000, 0.3386], [0.4621, 0.2342, 0.3037], [0.3353, 0.2228, 0.4419], [0.3782, 0.3618, 0.2600]]).T # values taken from paper fig = plt.figure(figsize=((10,10))) ax = fig.add_subplot(111, projection='3d') ax.set_title("cubic model") ax.view_init(45, 45) ax.set_xlabel("$x_1$") ax.set_ylabel("$x_2$") ax.set_zlabel("$x_3$") plt.rcParams["figure.figsize"] = (10,8) #plot feasible polytope ax.plot( xs=[7/10, 3/10, 1/5, 3/10, 7/10], ys=[1/10, 3/5, 1/5, 1/10, 1/10], zs=[1/5, 1/10, 3/5, 3/5, 1/5], linewidth=2 ) #plot I-optimal solution ax.scatter( xs=i_opt[0], ys=i_opt[1], zs=i_opt[2], marker="o", s=40, color="darkgreen", label="I-optimal design, 12 points" ) ax.scatter( xs=i_optimal_design[0], ys=i_optimal_design[1], zs=i_optimal_design[2], marker="o", s=40, color="orange", label="optimal_design solution, 12 points" ) plt.legend() image.png (view on web)<https://github.com/user-attachments/assets/5337f86c-2d87-42cb-a4e9-93cbdd44d83b> — Reply to this email directly, view it on GitHub<#428 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BEJBJSUAWP2BR4JSEWR7Y7LZTZB3ZAVCNFSM6AAAAABMYW445CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMJWGMYTQNZZGA>. You are receiving this because you were mentioned.Message ID: ***@***.***>

Osburg · 2024-08-29T20:43:03Z

Hallo Kai,

Das stimmt natürlich, ich habe auch gefunden, woran es lag. Ich habe den Ausdruck

"Y ~ X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + X1^2 + X2^2 + X3^2"

übergeben, die Terme zweiter Ordnung müssen aber als {Xi**2} statt Xi^2 geschrieben werden, sonst werden sie von formulaic ignoriert. Mit dem richtigen Ausdruck

"Y ~ X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + {X1**2} + {X2**2} + {X3**2}"

kommt folgendes Design heraus, sieht das besser aus? :)

array([[ 1.  , -0.8 ,  1.  ],
       [ 1.  , -0.2 , -1.  ],
       [ 0.1 ,  0.1 , -1.  ],
       [ 1.  , -0.2 ,  1.  ],
       [-0.2 ,  1.  , -1.  ],
       [-0.2 ,  1.  ,  1.  ],
       [ 0.09,  0.11,  1.  ],
       [-0.8 ,  1.  ,  0.02],
       [ 1.  , -0.2 , -0.  ],
       [ 1.  , -0.8 , -1.  ],
       [-0.8 ,  1.  , -1.  ],
       [-0.2 ,  1.  ,  0.01],
       [ 0.2 ,  0.2 , -0.  ],
       [ 1.  , -0.8 , -0.02],
       [ 0.1 ,  0.1 , -0.  ],
       [-0.8 ,  1.  ,  1.  ]])

Grüße
Aaron

KaiExner · 2024-08-30T09:04:41Z

Danke Aaron! Hier der Vergleich der Designs: ***@***.*** Bedeutung: I: average relative prediction variance over the full space -> [-1, 1] for all variables. Does not respect the constraints Ix: average relative prediction variance evaluated over the design points only (kind of a bogus metric) Ips: average relative prediction variance evaluated over the feasible space (here 1.8 mio evenly spaced grid points) Relevante Größe ist Ips: hier ist das jmp-Design mit einer mittleren Vorhersagevarianz von 0.35 gegenüber dem BoFire-Design mit 0.59 grob 2x besser. Grüße, Kai Dr. Kai Michael Exner Senior Specialist Digitalization (Data Analytics) Mobile: +49 174 3496381, Email: ***@***.*** Postal Address: BASF SE, RGQ/DA – A015, Carl-Bosch-Strasse 38, 67056 Ludwigshafen am Rhein, Germany ***@***.*** BASF SE, Registered Office: 67056 Ludwigshafen, Germany Registration Court: Amtsgericht Ludwigshafen, Registration No.: HRB 6000 Chairman of the Supervisory Board: Kurt Bock Board of Executive Directors: Markus Kamieth, Chairman; Dirk Elvermann, Michael Heinz, Anup Kothari, Stephan Kothrade, Katja Scharpwinkel Information on data protection can be found here: https://www.basf.com/global/en/legal/data-protection-at-basf.htmll From: Aaron Osburg ***@***.***> Sent: Thursday, August 29, 2024 10:43 PM To: experimental-design/bofire ***@***.***> Cc: Kai Michael Exner ***@***.***>; Mention ***@***.***> Subject: [EXT] Re: [experimental-design/bofire] I optimality criterion (PR #428) Hallo Kai, Das stimmt natürlich, ich habe auch gefunden, woran es lag. Ich habe den Ausdruck "Y ~ X1 + X2 + X3 + X1X2 + X1X3 + X2*X3 + X1^2 + X2^2 + X3^2" übergeben, die Terme zweiter Ordnung müssen aber als {Xi**2} statt Xi^2 geschrieben werden, sonst werden sie von formulaic ignoriert. Mit dem richtigen Ausdruck "Y ~ X1 + X2 + X3 + X1X2 + X1X3 + X2*X3 + {X12} + {X22} + {X3**2}" kommt folgendes Design heraus, sieht das besser aus? :) image.png (view on web)<https://github.com/user-attachments/assets/6aae0551-ee69-4e64-9a69-1d267730e9a7> [[ 1. , -0.8 , 1. ], [ 0.27, -0.07, -1. ], [ 0.87, -0.07, 1. ], [ 1. , -0.8 , -1. ], [ 0.29, 0.26, -1. ], [ 0.24, -0.04, 1. ], [-0.8 , 1. , 1. ], [-0.2 , 1. , 0.02], [ 1. , -0.2 , 0.02], [-0.8 , 1. , -1. ], [ 1. , -0.2 , -1. ], [-0.03, 0.23, -0.01], [-0.2 , 1. , -1. ], [-0.03, 0.23, -0.01], [ 1. , -0.67, 0.04], [-0.27, 1. , 1. ]] — Reply to this email directly, view it on GitHub<#428 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BEJBJSXZFGG5BTFK2Z3LL4LZT6BWZAVCNFSM6AAAAABMYW445CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMJYHE2DKOBVGM>. You are receiving this because you were mentioned.Message ID: ***@***.***>

Osburg · 2024-08-31T22:12:11Z

Hi @KaiExner und @jduerholt ,

danke für die Auswertung, dann passt etwas mit der Erstellung des Designs noch nich...
Ich habe mir die Sache noch einmal angesehen und versucht, ein ähnliches Kriterium wie das in jmp zu bauen. Dazu habe ich auch äquidistante gridpoints erstellt (anstelle der Bestimmung mittels SpaceFilling Objective) und diejenigen Punkte, die die Constraints nicht erfüllen, weggeschmissen (siehe Code am Ende der Nachricht). Das spacing ist so gewählt, dass auch 1.8e6 gridpoints übrig bleiben.
Ich komme dann auch zu dem Ergebnis, dass das bisherige Design aus bofire deutlich schlechter als das von jmp abschneidet (wobei sich die genauen Werte von denen aus deiner letzten Nachricht unterscheiden, äquivalent sind die Kriterien also immer noch nicht :( )

# delta = 1e-2, objective using 80 gridpoints, criterion using 1.8e6 gridpoints
print("ours:", criterion(i_optimal_design.T.flatten()), "jmp:", criterion(jmp.flatten()))

ours: 1.464475006770114 jmp: 0.3522515468462639

Für das BoFire-Design wurden nur 80 gridpoints und ein sehr großer Regularisierungsparameter von 1e-2 verwendet, ich konnte mir vorstellen, dass das zum schlechten Ergebnis führt. Das Design unten verwendet für die Optimierung das oben genannte Kriterium mit mehr gridpoints und den Regularisierungsparameter habe ich auf 1e-7 gesetzt. Das folgende Design kommt dabei heraus (, was im BoFire-Kriterium vergleichbar gut wie das jmp Design abschneidet).

# delta = 1e-7, objective using 1.8e6 gridpoints, criterion using 1.8e6 points
print("ours:", criterion(i_optimal_design.T.flatten()), "jmp:", criterion(jmp.flatten()))
print(np.round(i_optimal_design,2).T)

ours: 0.34273537557403694 jmp: 0.3522515468462639
[[-0.49  1.    0.02]
 [ 1.   -0.46 -0.  ]
 [-0.2   1.   -0.58]
 [-0.2   1.    1.  ]
 [ 0.1   0.1  -0.01]
 [ 0.13  0.36 -1.  ]
 [ 1.   -0.2   1.  ]
 [ 0.21  0.26  1.  ]
 [-0.8   1.   -1.  ]
 [ 1.   -0.8  -1.  ]
 [ 0.93 -0.13 -1.  ]
 [ 0.1   0.1  -0.01]
 [ 0.19  0.29  0.05]
 [ 1.   -0.46 -0.  ]
 [ 1.   -0.8   1.  ]
 [-0.8   1.    1.  ]]

@KaiExner falls du Zeit und Lust hast: Schneidet dieses Design auch in jmp deutlich besser ab als das vorherige? (sorry, habe selbst keinen Zugriff)
@jduerholt Es sieht also so aus, als bräuchte man sehr viele gridpoints, um ein gescheites Kriterium zu generieren. Dafür ist eine Optimierung mit SpaceFilling unpraktisch, weil es ewig rechnen würde. Andererseits ist die Alternative (d.h. grid erstellen und constraintverletzende Punkte rausschmeißen) glaub ich nicht so toll für Gleichheitsconstraints... Ich habe jetzt mal erlaubt, dass der Benutzer auch ein selbst erstelltes objective übergeben kann (und by default wird SpaceFilling verwendet), das find ich aber auch iwie unbefriedigend. Wie siehst du das?

Code für "I-Kriterium" mit 1.8e6 gridpoints

from bofire.strategies.doe.objective import IOptimality
import pandas as pd
from itertools import product
import torch
formula = Formula("X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + {X1**2} + {X2**2} + {X3**2}") 

domain = Domain(
   inputs = [
    ContinuousInput(key="X1", bounds = (-1,1)),
    ContinuousInput(key="X2", bounds = (-1,1)),
    ContinuousInput(key="X3", bounds = (-1,1))
    ],
   outputs = [ContinuousOutput(key="y")],
   constraints = [
       LinearInequalityConstraint(features=["X1","X2"], coefficients=[1,1], rhs=0.8),
       LinearInequalityConstraint(features=["X1","X2"], coefficients=[-1,-1], rhs=-0.2)
   ]
)

criterion = IOptimality(
        domain=domain,
        model=formula,
        n_experiments=16,
        delta=1e-7,
        transform_range=None,
        n_space=80,
        ipopt_options={"disp":0, "linear_solver":"ma57", "hsllib":"libcoinhsl.dylib", "tol":1e-15, "acceptable_tol":1e-15},
)


gridpoints = np.linspace(-1,1,200)
points = list(product(gridpoints, repeat=3))
points = np.array(points)
points = pd.DataFrame(points, columns=["X1", "X2", "X3"])

fulfilled = domain.constraints(experiments=points)
fulfilled = np.array(np.logical_and(fulfilled[0] <= 0, fulfilled[1] <= 0), dtype=bool)
points = points[fulfilled]

X = formula.get_model_matrix(points).to_numpy()

criterion.YtY = torch.from_numpy(X.T @ X) / X.shape[0]
criterion.YtY.requires_grad = False

print("Number of gridpoints fulfilling the constraints:", X.shape[0])

KaiExner · 2024-09-01T13:29:09Z

Hallo Aaron, Hier meine Bewertung des jmp-Designs und des neuen Designs, das Du geschickt hast: ***@***.*** Das BoFire-Design kommt also minimal besser weg. Auswertung in jmp: jmp-Design: 0.346, BoFire V: 0.339 – also auch in jmp das neue Design etwas besser, Zahlenwerte leicht unterschiedlich. Die Gründe der leichten Abweichungen würde ich in der Berechnung / Schätzung der mittleren Vorhersagevarianz vermuten. Grüße, Kai Dr. Kai Michael Exner Senior Specialist Digitalization (Data Analytics) Mobile: +49 174 3496381, Email: ***@***.*** Postal Address: BASF SE, RGQ/DA – A015, Carl-Bosch-Strasse 38, 67056 Ludwigshafen am Rhein, Germany ***@***.*** BASF SE, Registered Office: 67056 Ludwigshafen, Germany Registration Court: Amtsgericht Ludwigshafen, Registration No.: HRB 6000 Chairman of the Supervisory Board: Kurt Bock Board of Executive Directors: Markus Kamieth, Chairman; Dirk Elvermann, Michael Heinz, Anup Kothari, Stephan Kothrade, Katja Scharpwinkel Information on data protection can be found here: https://www.basf.com/global/en/legal/data-protection-at-basf.htmll From: Aaron Osburg ***@***.***> Sent: Sunday, September 1, 2024 12:13 AM To: experimental-design/bofire ***@***.***> Cc: Kai Michael Exner ***@***.***>; Mention ***@***.***> Subject: [EXT] Re: [experimental-design/bofire] I optimality criterion (PR #428) Hi @KaiExner<https://github.com/KaiExner> und @jduerholt<https://github.com/jduerholt> , danke für die Auswertung, dann passt etwas mit der Erstellung des Designs noch nich... Ich habe mir die Sache noch einmal angesehen und versucht, ein ähnliches Kriterium wie das in jmp zu bauen. Dazu habe ich auch äquidistante gridpoints erstellt (anstelle der Bestimmung mittels SpaceFilling Objective) und diejenigen Punkte, die die Constraints nicht erfüllen, weggeschmissen (siehe Code am Ende der Nachricht). Das spacing ist so gewählt, dass auch 1.8e6 gridpoints übrig bleiben. Ich komme dann auch zu dem Ergebnis, dass das bisherige Design aus bofire deutlich schlechter als das von jmp abschneidet (wobei sich die genauen Werte von denen aus deiner letzten Nachricht unterscheiden, äquivalent sind die Kriterien also immer noch nicht :( ) # delta = 1e-2, objective using 80 gridpoints, criterion using 1.8e6 gridpoints print("ours:", criterion(i_optimal_design.T.flatten()), "jmp:", criterion(jmp.flatten())) ours: 1.464475006770114 jmp: 0.3522515468462639 Für das BoFire-Design wurden nur 80 gridpoints und ein sehr großer Regularisierungsparameter von 1e-2 verwendet, ich konnte mir vorstellen, dass das zum schlechten Ergebnis führt. Das Design unten verwendet für die Optimierung das oben genannte Kriterium mit mehr gridpoints und den Regularisierungsparameter habe ich auf 1e-7 gesetzt. Das folgende Design kommt dabei heraus (, was im BoFire-Kriterium vergleichbar gut wie das jmp Design abschneidet). # delta = 1e-7, objective using 1.8e6 gridpoints, criterion using 1.8e6 points print("ours:", criterion(i_optimal_design.T.flatten()), "jmp:", criterion(jmp.flatten())) print(np.round(i_optimal_design,2).T) ours: 0.34273537557403694 jmp: 0.3522515468462639 [[-0.49 1. 0.02] [ 1. -0.46 -0. ] [-0.2 1. -0.58] [-0.2 1. 1. ] [ 0.1 0.1 -0.01] [ 0.13 0.36 -1. ] [ 1. -0.2 1. ] [ 0.21 0.26 1. ] [-0.8 1. -1. ] [ 1. -0.8 -1. ] [ 0.93 -0.13 -1. ] [ 0.1 0.1 -0.01] [ 0.19 0.29 0.05] [ 1. -0.46 -0. ] [ 1. -0.8 1. ] [-0.8 1. 1. ]] image.png (view on web)<https://github.com/user-attachments/assets/fedcb837-8b97-48e2-8ca1-80c785a4f63d> @KaiExner<https://github.com/KaiExner> falls du Zeit und Lust hast: Schneidet dieses Design auch in jmp deutlich besser ab als das vorherige? (sorry, habe selbst keinen Zugriff) @jduerholt<https://github.com/jduerholt> Es sieht also so aus, als bräuchte man sehr viele gridpoints, um ein gescheites Kriterium zu generieren. Dafür ist eine Optimierung mit SpaceFilling unpraktisch, weil es ewig rechnen würde. Andererseits ist die Alternative (d.h. grid erstellen und constraintverletzende Punkte rausschmeißen) glaub ich nicht so toll für Gleichheitsconstraints... Ich habe jetzt mal erlaubt, dass der Benutzer auch ein selbst erstelltes objective übergeben kann (und by default wird SpaceFilling verwendet), das find ich aber auch iwie unbefriedigend. Wie siehst du das? Code für "I-Kriterium" mit 1.8e6 gridpoints from bofire.strategies.doe.objective import IOptimality import pandas as pd from itertools import product import torch formula = Formula("X1 + X2 + X3 + X1*X2 + X1*X3 + X2*X3 + {X1**2} + {X2**2} + {X3**2}") domain = Domain( inputs = [ ContinuousInput(key="X1", bounds = (-1,1)), ContinuousInput(key="X2", bounds = (-1,1)), ContinuousInput(key="X3", bounds = (-1,1)) ], outputs = [ContinuousOutput(key="y")], constraints = [ LinearInequalityConstraint(features=["X1","X2"], coefficients=[1,1], rhs=0.8), LinearInequalityConstraint(features=["X1","X2"], coefficients=[-1,-1], rhs=-0.2) ] ) criterion = IOptimality( domain=domain, model=formula, n_experiments=16, delta=1e-7, transform_range=None, n_space=80, ipopt_options={"disp":0, "linear_solver":"ma57", "hsllib":"libcoinhsl.dylib", "tol":1e-15, "acceptable_tol":1e-15}, ) gridpoints = np.linspace(-1,1,200) points = list(product(gridpoints, repeat=3)) points = np.array(points) points = pd.DataFrame(points, columns=["X1", "X2", "X3"]) fulfilled = domain.constraints(experiments=points) fulfilled = np.array(np.logical_and(fulfilled[0] <= 0, fulfilled[1] <= 0), dtype=bool) points = points[fulfilled] X = formula.get_model_matrix(points).to_numpy() criterion.YtY = torch.from_numpy(X.T @ X) / X.shape[0] criterion.YtY.requires_grad = False print("Number of gridpoints fulfilling the constraints:", X.shape[0]) — Reply to this email directly, view it on GitHub<#428 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BEJBJSTN435NPO34XR7WELLZUI5VFAVCNFSM6AAAAABMYW445CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMRTGA2TMMRWGM>. You are receiving this because you were mentioned.Message ID: ***@***.***>

Osburg · 2024-09-01T21:42:34Z

Hallo Kai,

danke für das Feedback :) Dann scheint die Implementierung jetzt ja fürs erste in Ordnung zu sein (außer du willst, dass sie erst noch an mehr Problemen getestet wird). @jduerholt: wäre dann jetzt kein draft mehr. Wenn die Dinge für dich in Ordnung ausschauen gerne jederzeit mergen.

Liebe Grüße
Aaron

KaiExner · 2024-09-02T05:39:03Z

Hallo Aaron, Aus meiner Sicht erst einmal keine weiteren Tests nötig. Danke für Deinen Einsatz! Liebe Grüße, Kai Dr. Kai Michael Exner Senior Specialist Digitalization (Data Analytics) Mobile: +49 174 3496381, Email: ***@***.*** Postal Address: BASF SE, RGQ/DA – A015, Carl-Bosch-Strasse 38, 67056 Ludwigshafen am Rhein, Germany ***@***.*** BASF SE, Registered Office: 67056 Ludwigshafen, Germany Registration Court: Amtsgericht Ludwigshafen, Registration No.: HRB 6000 Chairman of the Supervisory Board: Kurt Bock Board of Executive Directors: Markus Kamieth, Chairman; Dirk Elvermann, Michael Heinz, Anup Kothari, Stephan Kothrade, Katja Scharpwinkel Information on data protection can be found here: https://www.basf.com/global/en/legal/data-protection-at-basf.htmll From: Aaron Osburg ***@***.***> Sent: Sunday, September 1, 2024 11:43 PM To: experimental-design/bofire ***@***.***> Cc: Kai Michael Exner ***@***.***>; Mention ***@***.***> Subject: [EXT] Re: [experimental-design/bofire] I optimality criterion (PR #428) Hallo Kai, danke für das Feedback :) Dann scheint die Implementierung jetzt ja fürs erste in Ordnung zu sein (außer du willst, dass sie erst noch an mehr Problemen getestet wird). @jduerholt<https://github.com/jduerholt>: wäre dann jetzt kein draft mehr. Wenn die Dinge für dich in Ordnung ausschauen gerne jederzeit mergen. Liebe Grüße Aaron — Reply to this email directly, view it on GitHub<#428 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/BEJBJSVTBL2PBTVN3UIXW5DZUOC6BAVCNFSM6AAAAABMYW445CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMRTGUYDOMZQGY>. You are receiving this because you were mentioned.Message ID: ***@***.***>

dlinzner-bcs

Thank you @Osburg and @KaiExner for this! I understand the code works now but is computationally intensive due to the large number of grid points for integration. That is ok for now I think. Just an idea: Since our models are polynomials, we could also analytically integrate all the resulting expressions for the I criterion, right?

where F is the model matrix and f is our polynomial. So we would need a symbolic integration to get YtY, right? I do not have time at the moment to implement this at the moment - but I can have a look once that changes :)

Osburg · 2024-09-02T07:30:41Z

Hi @dlinzner-bcs,

the computationally intensive part is only where the IOptimality object is generated. A space-filling design Y with many (typically $n >> n_{model terms}$) points must be generated. After that, the moment matrix M = Y.T @ Y is calculated (see here) which has the dimensions $M \in \mathbb{R}^{n_{model terms} x n_{model terms}}$.
If Y is generated using SpaceFilling, this can be very time consuming and in higher dimensions also memory demanding. If a equidistant grid is used, this is less of the problem.
The actual optimization after creating the IOptimality object is not much more demanding than w/ other objectives.

Memory can become an issue in higher dimensions as well even when the equidistant grid is used, bc i did not implement it smartly. For these cases i think we would have to try to subdivide the calculation of M into subtasks where not the full matrix Y is needed at any single time point.

Cheers
Aaron :)

P.S.: I don't know if anyone uses it but in general also nonpolynomial terms are supported. If it is imaginable that someone wants to make use of this at some point we should try to keep the criterion compatible with nonpolynomial terms, or what do you think? If not we can surely use a different implement as well!

jduerholt · 2024-09-02T12:12:30Z

Hi @Osburg,

thank you very much. I will have a look in the next days.

Best,

Johannes

jduerholt · 2024-09-06T19:44:44Z

Honestly, I do not know if I am the correct one to review :D @Osburg Do you check and fix the failing tests? I can then have a rough look again :D

Osburg · 2024-09-06T19:49:59Z

Hey @jduerholt! Yes, will take care of the tests. sry was very busy with work in the last couple of days :D

jduerholt

Hi @Osburg,

thank you very much. I let some comments especially regarding the code structure. I am also a bit under the impression, that the DoE module needs some kind of restructuring in total as we are just adding more features on top which makes it sometimes more messy.

Just look at my comments! It is not meant as criticism but more as a general observation.

Best,

Johannes

bofire/data_models/constraints/constraint.py

bofire/strategies/doe/objective.py

jduerholt · 2024-09-09T19:10:16Z

bofire/strategies/doe/objective.py

+        self.YtY = torch.from_numpy(X.T @ X) / n_space
+        self.YtY.requires_grad = False
+
+        print("Done!")


Do we really need all this prints?

jduerholt · 2024-09-09T19:12:55Z

bofire/strategies/doe/objective.py

+            model_type=model, rhs_only=True, domain=domain
+        )
+        X = model_formula.get_model_matrix(design).to_numpy()
+        self.space_filling_design = design


Do you use this attribute somewhere?

Again no :D will remove it

jduerholt · 2024-09-09T19:21:12Z

bofire/strategies/doe/objective.py

+
+        model_formula = get_formula_from_string(
+            model_type=model, rhs_only=True, domain=domain
+        )


As far as I understood it correctly, until here, a lot of functionality is just recoding other stuff that to come up with a starting set. And this recoding is partially done to prevent, circular imports. What do you think about adding an additional keyword argument to init, which just takes the inital design as input. Then the user also has much more flexibility in controlling this critical part of the design. Furtheremore, it would make the code much cleaner. What do you think?

In the DoEStrategy, we could then add this part of coming up with an inital setup in case somebody wants to perform an I-optimal design. But here, the user would still have the option todo custom stuff.

What do you think about a tidy-up of the DoE functionality in general (after this PR ;))?

We could start to revamp find_local_max_iptopt, by changing the signature in a way that it just accepts the instantiated objective class, which is holding the domain instead also taking the 'domainas argument. The model type would then also be only part of the objective and not also of thefind_local_max_ipopt` signature. This way, we can make it all much cleaner and modular. What do you think?

Osburg · 2024-09-09T20:38:55Z

HI @jduerholt,

totally agree with your opinion that stuff is getting more and more messy and one (or me :D) should spend time to rewrite the doe part in a nicer way. Since you know more about overall bofire etc + probably you have the better ideas how a better version would look like: Would you like to discuss this in a zoom call or so including @dlinzner-bcs if he likes (or alternatively somewhere here on github, but this would take more time after all i guess :D)? Starting from this I could then take care of the reimplementation.
Apart from hearing your ideas about restructuring the code there is some more stuff that I would need clarification about:

Are all the branch-and-bound spin-offs functional or in use by someone? I think we do not have any example notebooks etc. so i am not sure if anyone is using them --> If they are essentially dead code, should we remove them?
Is anyone still directly using the find_local_max_ipopt_... functions (in the past some people opened issues where they used it i think)? If yes, should we keep something similar in the future? What do you think?
In principle I would like to use torch.autograd.functional.hessian() to provide second order information. But there is one part of the function whose gradient is still not computed using pytorch (namely, the derivative of the model matrix w.r.t. the design), which would probably make it very tedious to write down the hessian. Do you have an idea how one could, starting from a pandas DataFrame, evaluate the model formula in a way that we can use autograd? (Maybe convert the formulaic Formula to a sympy expression and try to use sth like sympytorch or so, idk...? )
Is the multi-start idea from Add restart to DoE strategies (multi-start) #351 still sth we would like to have?

<-- These points are not to be discussed or answered now, it's just a bunch of open questions i would want to discuss before I start to rewrite stuff.

cheers
Aaron :)

jduerholt and others added 11 commits February 26, 2024 11:10

add functionality

6bbabb0

add tests

2119864

update random strategy

30ca0a8

add scaling to doe, DoEStrategy and SpaceFillingStrategy

9fce494

remove TransformEnum

6093e47

Merge branch 'main' into feature/doe_scaling

8d90a4e

use Bounds for defining the trafo range

5198726

Update bofire/data_models/strategies/doe.py

cb45ae3

Co-authored-by: Johannes P. Dürholt <johannespeter.duerholt@evonik.com>

Update bofire/data_models/strategies/space_filling.py

4fdcbd7

Co-authored-by: Johannes P. Dürholt <johannespeter.duerholt@evonik.com>

Update bofire/data_models/strategies/space_filling.py

6945296

Co-authored-by: Johannes P. Dürholt <johannespeter.duerholt@evonik.com>

pre-commit stuff

cad5670

Osburg marked this pull request as draft August 19, 2024 22:54

Osburg requested a review from dlinzner-bcs August 19, 2024 22:54

add IOptimality criterion

eefbd7d

dlinzner-bcs requested a review from KaiExner August 28, 2024 11:41

Osburg added 2 commits August 28, 2024 23:26

fix IOptimality, add example

c2abcb6

remove empty cells

15b1d43

allow to pass custom objective to find_local_max_ipopt

7169da7

Osburg and others added 4 commits September 1, 2024 21:32

fix type EqualityConstraint instead of EqalityConstraint

16a17ab

fix typos

af45729

add 1 basic test for IOptimality

4761596

Merge branch 'main' into I-optimality_criterion

00fdeeb

Osburg marked this pull request as ready for review September 1, 2024 21:34

fix style

c2dabac

dlinzner-bcs approved these changes Sep 2, 2024

View reviewed changes

fix pyright

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fix tests

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I optimality criterion #428

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I optimality criterion #428

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