Is Implementation of R4 method available in Causal-cmd?

[f-arab]

Hi,

I'm planning to use the "R4" method but I couldn't find it on neither Tetrad GUI or Causal-cmd. The "introduction" part in https://bd2kccd.github.io/docs/causal-cmd/ says that R4 is among the available algorithms, however, when I check the algorithms in causal-cmd it only shows the following list:
Algorithm: fas, fask, fci, fcimax, fges, fges-mb, fofc, ftfc, gfci, glasso, imgs_cont, imgs_disc, lingam, mbfs, mgm, multi-fask, pc-all, r-skew, r3, rfci, skew, ts-fci, ts-gfci, ts-imgs

Would you please tell me where can I find the implementation for R4 method?

Thanks

[jdramsey]

The R4 method was deprecated and removed. What is your application? Perhaps I could recommend a better algorithm for your purpose?

[jdramsey]

Also it's looks like you're using an old version of Tetrad currently. This is a bad website but it does list the up to date version.

https://sites.google.com/view/tetradcausal

The Tetrad GUI there is up to date; tetrad-cmd will be up to date within a week I think.

[f-arab]

Thank you for your quick response. Our application is the causal discovery from large-scale fMRI data. We have task fMRI data and therefore not very long recordings, but would like to do it across the whole brain, so we preferably (though not necessarily) need something that can handle tens to hundreds of nodes. Thanks

[jdramsey]

Honestly, ever since I hooked GitHub up into Slack I see queries from people almost right away.

Have you seen this paper?

https://direct.mit.edu/netn/article/3/2/274/2211/Estimating-feedforward-and-feedback-effective

We did this work a few years ago, but the Two-Step algorithm in particular uses similar ideas to R4 and has pretty good performance. The FASK algorithm is in Tetrad and has good performance for fMRI as well.

[f-arab]

Great! Yes, we have looked at the FASK method as well which is very promising, however, we haven't evaluated the results of our data yet. Thanks for the recommendation, I'll try the Two-Step algorithm as well (it seems that the MATLAB implementation is available).

[f-arab]

Hi @jdramsey ,

I have a question about the runtime of the FASK method on large graphs such as simulated Macaque data In the FASK paper. When we try FASK on the Full Macaque data, it takes a few hours as opposed to a few minutes mentioned in the FASK paper although our machine is quite powerful. I am running FASK with the parameters' default values except for the alpha parameter, which we use as a hyperparameter. I would like to ask if any other parameters such as penalty discount (c) also play a role in the speed here and if so, how can we set that one while running FASK on causal-cmd?

Thanks

[jdramsey]

To be honest, we should loop Ruben Sanchez (cc'd) into this conversation, since he had done that analysis and could probably easily answer your questions. I'll wait to see what he says. Best, Joe

…

On Tue, Dec 13, 2022 at 2:52 PM Fahimeh Arab ***@***.***> wrote: Hi @jdramsey <https://github.com/jdramsey> , I have a question about the runtime of the FASK method on large graphs such as simulated Macaque data In the FASK paper. When we try FASK on the Full Macaque data, it takes a few hours as opposed to a few minutes mentioned in the FASK paper although our machine is quite powerful. I am running FASK with the parameters' default values except for the alpha parameter, which we use as a hyperparameter. I would like to ask if any other parameters such as penalty discount (c) also play a role in the speed here and if so, how can we set that one while running FASK on causal-cmd? Thanks — Reply to this email directly, view it on GitHub <#69 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACLFSR2WKEOQONWNDUUAASTWNDHYVANCNFSM5YLID5IA> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[f-arab]

Thank you so much. Best, Fahimeh On Wed, Dec 14, 2022 at 6:51 AM Joseph Ramsey ***@***.***> wrote:

…

To be honest, we should loop Ruben Sanchez (cc'd) into this conversation, since he had done that analysis and could probably easily answer your questions. I'll wait to see what he says. Best, Joe On Tue, Dec 13, 2022 at 2:52 PM Fahimeh Arab ***@***.***> wrote: > Hi @jdramsey <https://github.com/jdramsey> , > > I have a question about the runtime of the FASK method on large graphs > such as simulated Macaque data In the FASK paper. When we try FASK on the > Full Macaque data, it takes a few hours as opposed to a few minutes > mentioned in the FASK paper although our machine is quite powerful. I am > running FASK with the parameters' default values except for the alpha > parameter, which we use as a hyperparameter. I would like to ask if any > other parameters such as penalty discount (c) also play a role in the speed > here and if so, how can we set that one while running FASK on causal-cmd? > > Thanks > > — > Reply to this email directly, view it on GitHub > <#69 (comment) >, > or unsubscribe > < https://github.com/notifications/unsubscribe-auth/ACLFSR2WKEOQONWNDUUAASTWNDHYVANCNFSM5YLID5IA > > . > You are receiving this because you were mentioned.Message ID: > ***@***.***> > — Reply to this email directly, view it on GitHub <#69 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AL37RSZVHYXLKPN34AJVB7LWNHNFZANCNFSM5YLID5IA> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[rubenSaro]

Hi Fahimeh,

Sorry for the late response. Can you let me know the exact values of the parameters you are using for FASK? Also, how are you selecting alpha as a hyperparameter.
Thanks.

Ruben

[f-arab]

Hi Ruben, No problem. Thanks for replying. I am using FASK through causal-cmd. In terms of the parameters, everything is set as default for FASK in causal-cmd except the alpha parameter which I am using as a hyperparameter and sweep over it to find the best value. Even for simple networks, it seems that the execution times are not small as you can see from the following figure. The shown execution time here is the average of over 60 repetitions. And the number of time samples is 5000 (we used concatenated data). [image: image.png] Thanks, Fahimeh

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On Wed, Dec 21, 2022 at 2:05 PM rubenSaro ***@***.***> wrote: Hi Fahimeh, Sorry for the late response. Can you let me know the exact values of the parameters you are using for FASK? Also, how are you selecting alpha as a hyperparameter. Thanks. Ruben — Reply to this email directly, view it on GitHub <#69 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AL37RSZAL3KQJ4HVTK3JWYLWON5L3ANCNFSM5YLID5IA> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[rubenSaro]

So you are using the default parameters of causal-cmd. @jdramsey, would you happen to know which are those. I made a test using the Tetrad GUI (latest version from here: https://s01.oss.sonatype.org/content/repositories/releases/io/github/cmu-phil/tetrad-gui/7.1.0/tetrad-gui-7.1.0-launch.jar)
And using an alpha of 10^-7, it worked in seconds. I wonder what could be the problem.
Can you send us an example of the code you are running. and the tetrad you are using.
thanks

ruben

[f-arab]

Thanks Ruben. I'm using causal-cmd-1.3.0 and the following is a sample code which I'm using which tries to run FASK over Macaque Full data for different values of alpha: import numpy as np import time import sys import os import datetime import pandas as pd sys.path.insert(0, "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal_discovery_for_time_series_master" ) ## parameters and variables alpha = [1e-8, 1e-6, 1e-4, 1e-2, 1e-1, 0.12, 0.14,0.16, 0.18, 0.2] num_rep_macaque_all = 60 ## Load simulated data # path_to_Macaque_small_degree = "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/SmallDegree/data_fslfilter_concat" # path_to_Macaque_long_range = "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/LongRange/data_fslfilter_concat" path_to_Macaque_full = "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/Full/data_fslfilter_concat/" # num_nodes_macaque_small_degree = 28 # num_nodes_macaque_long_range = 67 num_nodes_macaque_full = 91 # nodes_macaque_small_degree = ['X%d'%i for i in range(1,num_nodes_macaque_small_degree+1)] # nodes_macaque_long_range = ['X%d'%i for i in range(1,num_nodes_macaque_long_range+1)] nodes_macaque_full = ['X%d'%i for i in range(1,num_nodes_macaque_full+1)] # Lets load data macaque_all_data_full = [0]*num_rep_macaque_all for i in range(1,num_rep_macaque_all+1): if i <= 9: current_str = "0"+str(i) elif 10 <= i: current_str = str(i) df_temp = pd.read_csv(path_to_Macaque_full+"concat_BOLDfslfilter_" +current_str+".txt", delimiter = '\t') macaque_all_data_full[i-1] = df_temp.to_numpy() # Run FASK on Causal-cmd path_to_causal_cmd = '/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal-cmd-1.3.0-distribution/causal-cmd-1.3.0/causal-cmd-1.3.0-jar-with-dependencies.jar' FASK_macaque_execution_time =[[0 for t in range(0, len(alpha))] for j in range(0,num_rep_macaque_all)] for r in range(1, num_rep_macaque_all+1): if r <= 9: current_str = "0"+str(r) elif 10 <= r: current_str = str(r) path_to_data = path_to_Macaque_full + 'concat_BOLDfslfilter_%s'%current_str+ '.txt' for a in range(0,len(alpha)): path_to_fask_output = 'results_all_method_mecacque/FASK_outputs' start = time.time() os.system('java -jar '+path_to_causal_cmd+ ' --algorithm fask --alpha '+ str(alpha[a])+ ' --penaltyDiscount ' + str(2.0) + ' --data-type continuous --dataset '+path_to_data+' --out '+path_to_fask_output+' --prefix '+'macaque-full-'+ str(alpha[a])+'-%s'%current_str+' --delimiter tab --test cci-test --verbose No') end = time.time() print('repetition %d'%(r), 'alpha = %0.3f'%alpha[a], 'execution time: %0.3f s'%(end-start) ) FASK_macaque_execution_time[r-1][a] = end-start np.savez('results_all_method_mecacque/FASK_macacque_full_Time', FASK_macaque_execution_time = FASK_macaque_execution_time) Thanks, Fahimeh

…

On Wed, Dec 21, 2022 at 2:24 PM rubenSaro ***@***.***> wrote: So you are using the default parameters of causal-cmd. @jdramsey <https://github.com/jdramsey>, would you happen to know which are those. I made a test using the Tetrad GUI (latest version from here: https://s01.oss.sonatype.org/content/repositories/releases/io/github/cmu-phil/tetrad-gui/7.1.0/tetrad-gui-7.1.0-launch.jar ) And using an alpha of 10^-7, it worked in seconds. I wonder what could be the problem. Can you send us an example of the code you are running. and the tetrad you are using. thanks ruben — Reply to this email directly, view it on GitHub <#69 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AL37RS7PSCRNVBIBD5VHS2TWON7RPANCNFSM5YLID5IA> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[f-arab]

Attached is also the .py code.

…

On Wed, Dec 21, 2022 at 3:02 PM Fahimeh Arab ***@***.***> wrote: Thanks Ruben. I'm using causal-cmd-1.3.0 and the following is a sample code which I'm using which tries to run FASK over Macaque Full data for different values of alpha: import numpy as np import time import sys import os import datetime import pandas as pd sys.path.insert(0, "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal_discovery_for_time_series_master" ) ## parameters and variables alpha = [1e-8, 1e-6, 1e-4, 1e-2, 1e-1, 0.12, 0.14,0.16, 0.18, 0.2] num_rep_macaque_all = 60 ## Load simulated data # path_to_Macaque_small_degree = "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/SmallDegree/data_fslfilter_concat" # path_to_Macaque_long_range = "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/LongRange/data_fslfilter_concat" path_to_Macaque_full = "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/Full/data_fslfilter_concat/" # num_nodes_macaque_small_degree = 28 # num_nodes_macaque_long_range = 67 num_nodes_macaque_full = 91 # nodes_macaque_small_degree = ['X%d'%i for i in range(1,num_nodes_macaque_small_degree+1)] # nodes_macaque_long_range = ['X%d'%i for i in range(1,num_nodes_macaque_long_range+1)] nodes_macaque_full = ['X%d'%i for i in range(1,num_nodes_macaque_full+1)] # Lets load data macaque_all_data_full = [0]*num_rep_macaque_all for i in range(1,num_rep_macaque_all+1): if i <= 9: current_str = "0"+str(i) elif 10 <= i: current_str = str(i) df_temp = pd.read_csv(path_to_Macaque_full+"concat_BOLDfslfilter_" +current_str+".txt", delimiter = '\t') macaque_all_data_full[i-1] = df_temp.to_numpy() # Run FASK on Causal-cmd path_to_causal_cmd = '/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal-cmd-1.3.0-distribution/causal-cmd-1.3.0/causal-cmd-1.3.0-jar-with-dependencies.jar' FASK_macaque_execution_time =[[0 for t in range(0, len(alpha))] for j in range(0,num_rep_macaque_all)] for r in range(1, num_rep_macaque_all+1): if r <= 9: current_str = "0"+str(r) elif 10 <= r: current_str = str(r) path_to_data = path_to_Macaque_full + 'concat_BOLDfslfilter_%s'%current_str+ '.txt' for a in range(0,len(alpha)): path_to_fask_output = 'results_all_method_mecacque/FASK_outputs' start = time.time() os.system('java -jar '+path_to_causal_cmd+ ' --algorithm fask --alpha '+ str(alpha[a])+ ' --penaltyDiscount ' + str(2.0) + ' --data-type continuous --dataset '+path_to_data+' --out ' +path_to_fask_output+' --prefix '+'macaque-full-'+ str(alpha[a])+'-%s' %current_str+' --delimiter tab --test cci-test --verbose No') end = time.time() print('repetition %d'%(r), 'alpha = %0.3f'%alpha[a], 'execution time: %0.3f s'%(end-start) ) FASK_macaque_execution_time[r-1][a] = end-start np.savez('results_all_method_mecacque/FASK_macacque_full_Time', FASK_macaque_execution_time = FASK_macaque_execution_time) Thanks, Fahimeh On Wed, Dec 21, 2022 at 2:24 PM rubenSaro ***@***.***> wrote: > So you are using the default parameters of causal-cmd. @jdramsey > <https://github.com/jdramsey>, would you happen to know which are those. > I made a test using the Tetrad GUI (latest version from here: > https://s01.oss.sonatype.org/content/repositories/releases/io/github/cmu-phil/tetrad-gui/7.1.0/tetrad-gui-7.1.0-launch.jar > ) > And using an alpha of 10^-7, it worked in seconds. I wonder what could be > the problem. > Can you send us an example of the code you are running. and the tetrad > you are using. > thanks > > ruben > > — > Reply to this email directly, view it on GitHub > <#69 (comment)>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/AL37RS7PSCRNVBIBD5VHS2TWON7RPANCNFSM5YLID5IA> > . > You are receiving this because you authored the thread.Message ID: > ***@***.***> >

[jdramsey]

Could you try adding the flag "--default" flag to the causal command call? Joe On Wed, Dec 21, 2022 at 6:04 PM Fahimeh Arab ***@***.***> wrote:

…

Attached is also the .py code. On Wed, Dec 21, 2022 at 3:02 PM Fahimeh Arab ***@***.***> wrote: > Thanks Ruben. I'm using causal-cmd-1.3.0 and the following is a sample > code which I'm using which tries to run FASK over Macaque Full data for > different values of alpha: > > import numpy as np > import time > import sys > import os > import datetime > import pandas as pd > > sys.path.insert(0, > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal_discovery_for_time_series_master" > ) > > > ## parameters and variables > alpha = [1e-8, 1e-6, 1e-4, 1e-2, 1e-1, 0.12, 0.14,0.16, 0.18, 0.2] > > num_rep_macaque_all = 60 > > > ## Load simulated data > # path_to_Macaque_small_degree = > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/SmallDegree/data_fslfilter_concat" > # path_to_Macaque_long_range = > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/LongRange/data_fslfilter_concat" > path_to_Macaque_full = > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/Full/data_fslfilter_concat/" > > > # num_nodes_macaque_small_degree = 28 > # num_nodes_macaque_long_range = 67 > num_nodes_macaque_full = 91 > > # nodes_macaque_small_degree = ['X%d'%i for i in > range(1,num_nodes_macaque_small_degree+1)] > # nodes_macaque_long_range = ['X%d'%i for i in > range(1,num_nodes_macaque_long_range+1)] > nodes_macaque_full = ['X%d'%i for i in range(1,num_nodes_macaque_full+1)] > > > # Lets load data > macaque_all_data_full = [0]*num_rep_macaque_all > > for i in range(1,num_rep_macaque_all+1): > if i <= 9: > current_str = "0"+str(i) > elif 10 <= i: > current_str = str(i) > > df_temp = pd.read_csv(path_to_Macaque_full+"concat_BOLDfslfilter_" > +current_str+".txt", delimiter = '\t') > macaque_all_data_full[i-1] = df_temp.to_numpy() > > > # Run FASK on Causal-cmd > path_to_causal_cmd = > '/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal-cmd-1.3.0-distribution/causal-cmd-1.3.0/causal-cmd-1.3.0-jar-with-dependencies.jar' > > FASK_macaque_execution_time =[[0 for t in range(0, len(alpha))] for j in > range(0,num_rep_macaque_all)] > > for r in range(1, num_rep_macaque_all+1): > > if r <= 9: > current_str = "0"+str(r) > elif 10 <= r: > current_str = str(r) > > path_to_data = path_to_Macaque_full + 'concat_BOLDfslfilter_%s'%current_str+ > '.txt' > > for a in range(0,len(alpha)): > > path_to_fask_output = 'results_all_method_mecacque/FASK_outputs' > start = time.time() > os.system('java -jar '+path_to_causal_cmd+ ' --algorithm fask > --alpha '+ str(alpha[a])+ ' --penaltyDiscount ' + str(2.0) + ' > --data-type continuous --dataset '+path_to_data+' --out ' > +path_to_fask_output+' --prefix '+'macaque-full-'+ str(alpha[a])+'-%s' > %current_str+' --delimiter tab --test cci-test --verbose No') > end = time.time() > print('repetition %d'%(r), 'alpha = %0.3f'%alpha[a], 'execution > time: %0.3f s'%(end-start) ) > FASK_macaque_execution_time[r-1][a] = end-start > > np.savez('results_all_method_mecacque/FASK_macacque_full_Time', > FASK_macaque_execution_time = FASK_macaque_execution_time) > > > Thanks, > Fahimeh > > On Wed, Dec 21, 2022 at 2:24 PM rubenSaro ***@***.***> > wrote: > >> So you are using the default parameters of causal-cmd. @jdramsey >> <https://github.com/jdramsey>, would you happen to know which are those. >> I made a test using the Tetrad GUI (latest version from here: >> https://s01.oss.sonatype.org/content/repositories/releases/io/github/cmu-phil/tetrad-gui/7.1.0/tetrad-gui-7.1.0-launch.jar >> ) >> And using an alpha of 10^-7, it worked in seconds. I wonder what could be >> the problem. >> Can you send us an example of the code you are running. and the tetrad >> you are using. >> thanks >> >> ruben >> >> — >> Reply to this email directly, view it on GitHub >> < #69 (comment)>, >> or unsubscribe >> < https://github.com/notifications/unsubscribe-auth/AL37RS7PSCRNVBIBD5VHS2TWON7RPANCNFSM5YLID5IA > >> . >> You are receiving this because you authored the thread.Message ID: >> ***@***.***> >> > — Reply to this email directly, view it on GitHub <#69 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACLFSR4DGQATLGO6WJJUW43WOOEHZANCNFSM5YLID5IA> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[f-arab]

It does not recognize --default as an option. is this the correct way of adding it to my causal-cmd call? os.system('java -jar '+path_to_causal_cmd+ ' --algorithm fask --alpha '+ str(alpha[a]) + ' *--default* --data-type continuous --dataset '+path_to_data+' --out '+path_to_fask_output+' --prefix '+'macaque-full-'+ str(alpha[a])+'-%s'%current_str+' --delimiter tab --test cci-test --verbose No') On Wed, Dec 21, 2022 at 3:23 PM Joseph Ramsey ***@***.***> wrote:

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Could you try adding the flag "--default" flag to the causal command call? Joe On Wed, Dec 21, 2022 at 6:04 PM Fahimeh Arab ***@***.***> wrote: > Attached is also the .py code. > > > > On Wed, Dec 21, 2022 at 3:02 PM Fahimeh Arab ***@***.***> wrote: > > > Thanks Ruben. I'm using causal-cmd-1.3.0 and the following is a sample > > code which I'm using which tries to run FASK over Macaque Full data for > > different values of alpha: > > > > import numpy as np > > import time > > import sys > > import os > > import datetime > > import pandas as pd > > > > sys.path.insert(0, > > > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal_discovery_for_time_series_master" > > ) > > > > > > ## parameters and variables > > alpha = [1e-8, 1e-6, 1e-4, 1e-2, 1e-1, 0.12, 0.14,0.16, 0.18, 0.2] > > > > num_rep_macaque_all = 60 > > > > > > ## Load simulated data > > # path_to_Macaque_small_degree = > > > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/SmallDegree/data_fslfilter_concat" > > # path_to_Macaque_long_range = > > > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/LongRange/data_fslfilter_concat" > > path_to_Macaque_full = > > > "/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/codes/Causality_Analysis/Dataset/DataSets_Feedbacks/2_Macaque_Networks/Full/data_fslfilter_concat/" > > > > > > # num_nodes_macaque_small_degree = 28 > > # num_nodes_macaque_long_range = 67 > > num_nodes_macaque_full = 91 > > > > # nodes_macaque_small_degree = ['X%d'%i for i in > > range(1,num_nodes_macaque_small_degree+1)] > > # nodes_macaque_long_range = ['X%d'%i for i in > > range(1,num_nodes_macaque_long_range+1)] > > nodes_macaque_full = ['X%d'%i for i in range(1,num_nodes_macaque_full+1)] > > > > > > # Lets load data > > macaque_all_data_full = [0]*num_rep_macaque_all > > > > for i in range(1,num_rep_macaque_all+1): > > if i <= 9: > > current_str = "0"+str(i) > > elif 10 <= i: > > current_str = str(i) > > > > df_temp = pd.read_csv(path_to_Macaque_full+"concat_BOLDfslfilter_" > > +current_str+".txt", delimiter = '\t') > > macaque_all_data_full[i-1] = df_temp.to_numpy() > > > > > > # Run FASK on Causal-cmd > > path_to_causal_cmd = > > > '/home/fahimeh/Volitional_Control_Neurofeedback/Non-DecNef/causal_discovery_packages/causal-cmd-1.3.0-distribution/causal-cmd-1.3.0/causal-cmd-1.3.0-jar-with-dependencies.jar' > > > > FASK_macaque_execution_time =[[0 for t in range(0, len(alpha))] for j in > > range(0,num_rep_macaque_all)] > > > > for r in range(1, num_rep_macaque_all+1): > > > > if r <= 9: > > current_str = "0"+str(r) > > elif 10 <= r: > > current_str = str(r) > > > > path_to_data = path_to_Macaque_full + > 'concat_BOLDfslfilter_%s'%current_str+ > > '.txt' > > > > for a in range(0,len(alpha)): > > > > path_to_fask_output = 'results_all_method_mecacque/FASK_outputs' > > start = time.time() > > os.system('java -jar '+path_to_causal_cmd+ ' --algorithm fask > > --alpha '+ str(alpha[a])+ ' --penaltyDiscount ' + str(2.0) + ' > > --data-type continuous --dataset '+path_to_data+' --out ' > > +path_to_fask_output+' --prefix '+'macaque-full-'+ str(alpha[a])+'-%s' > > %current_str+' --delimiter tab --test cci-test --verbose No') > > end = time.time() > > print('repetition %d'%(r), 'alpha = %0.3f'%alpha[a], 'execution > > time: %0.3f s'%(end-start) ) > > FASK_macaque_execution_time[r-1][a] = end-start > > > > np.savez('results_all_method_mecacque/FASK_macacque_full_Time', > > FASK_macaque_execution_time = FASK_macaque_execution_time) > > > > > > Thanks, > > Fahimeh > > > > On Wed, Dec 21, 2022 at 2:24 PM rubenSaro ***@***.***> > > wrote: > > > >> So you are using the default parameters of causal-cmd. @jdramsey > >> <https://github.com/jdramsey>, would you happen to know which are > those. > >> I made a test using the Tetrad GUI (latest version from here: > >> > https://s01.oss.sonatype.org/content/repositories/releases/io/github/cmu-phil/tetrad-gui/7.1.0/tetrad-gui-7.1.0-launch.jar > >> ) > >> And using an alpha of 10^-7, it worked in seconds. I wonder what could > be > >> the problem. > >> Can you send us an example of the code you are running. and the tetrad > >> you are using. > >> thanks > >> > >> ruben > >> > >> — > >> Reply to this email directly, view it on GitHub > >> < > #69 (comment) >, > >> or unsubscribe > >> < > https://github.com/notifications/unsubscribe-auth/AL37RS7PSCRNVBIBD5VHS2TWON7RPANCNFSM5YLID5IA > > > >> . > >> You are receiving this because you authored the thread.Message ID: > >> ***@***.***> > >> > > > > — > Reply to this email directly, view it on GitHub > <#69 (comment) >, > or unsubscribe > < https://github.com/notifications/unsubscribe-auth/ACLFSR4DGQATLGO6WJJUW43WOOEHZANCNFSM5YLID5IA > > . > You are receiving this because you were mentioned.Message ID: > ***@***.***> > — Reply to this email directly, view it on GitHub <#69 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AL37RSYQMQLSF4PZMTZCIZLWOOGPXANCNFSM5YLID5IA> . You are receiving this because you authored the thread.Message ID: ***@***.***>