Mind2019

eventseg_collector.py

@@ -1,23 +1 @@
 #!/usr/bin/python
-
-from eventseg_config import config
-import os
-import numpy as np
-import pandas as pd
-
-segments_df = pd.DataFrame()
-
-for root, dirs, files in os.walk(eventseg_config['resultsdir']):
-    event_models = [f for f in files if f.endswith('.npy')]
-    if event_models:
-        ep_path, turkid = os.path.split(root)
-        ep_name = os.path.split(ep_path)[1]
-        multiindex = pd.MultiIndex.from_product([[ep_name], [turkid]])
-        tmp_df = pd.DataFrame(index=multiindex, columns=[os.path.splitext(em)[0] for em in event_models])
-        for e in event_models:
-            ev_mod = np.load(os.path.join(root,e))
-            tmp_df.at[(ep_name,turkid), os.path.splitext(e)[0]] = ev_mod
-
-        segments_df = segments_df.append(tmp_df)
-
-segments_df.to_pickle(os.path.join(config['resultsdir'],'segments_df.p')

eventseg_config.py

@@ -9,14 +9,14 @@
 #add additional checks for your local machine here...
 # ******** check kiewit hostname from eduroam ********
 if (socket.gethostname() == 'Paxtons-MacBook-Pro') or (socket.gethostname() == 'Paxtons-MacBook-Pro.kiewit.dartmouth.edu') or (socket.gethostname() == 'Paxtons-MacBook-Pro.local'):
-    config['datadir'] = '/Users/paxtonfitzpatrick/Documents/Dartmouth/Thesis/memory-dynamics/data/models/participants/trajectories'
+    config['datadir'] = '/Users/paxtonfitzpatrick/Documents/Dartmouth/CDL/MIND-2019/narrative_complexity/data'
     config['workingdir'] = config['datadir']
     config['startdir'] = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))  # directory to start the job in
     config['template'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'run_job_local.sh')
 else:
-    config['datadir'] = os.path.join('/dartfs/rc/lab/D/DBIC/CDL/f0028ph/eventseg', 'trajectories')
-    config['workingdir'] = '/dartfs/rc/lab/D/DBIC/CDL/f0028ph/eventseg/cluster-scripts'
-    config['startdir'] = '/dartfs/rc/lab/D/DBIC/CDL/f0028ph/eventseg/'
+    config['datadir'] = '/dartfs/rc/lab/D/DBIC/CDL/data/movie_scripts/'
+    config['startdir'] = '/dartfs/rc/lab/D/DBIC/CDL/f0028ph/MIND-narrative-modeling'
+    config['workingdir'] = os.path.join(startdir, 'cluster-scripts')
     config['template'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'run_job_cluster.sh')
 
 config['scriptdir'] = os.path.join(config['workingdir'], 'scripts')
@@ -27,7 +27,7 @@
 # runtime options
 config['jobname'] = "eventseg"  # default job name
 config['q'] = "largeq"  # options: default, test, largeq
-config['nnodes'] = 1  # how many nodes to use for this one job
+config['nnodes'] = 10  # how many nodes to use for this one job
 config['ppn'] = 4  # how many processors to use for this one job (assume 4GB of RAM per processor)
 config['walltime'] = '20:00:00'  # maximum runtime, in h:MM:SS
 config['cmd_wrapper'] = "python"  # replace with actual command wrapper (e.g. matlab, python, etc.)

eventseg_cruncher.py

@@ -1,20 +1,68 @@
 #!/usr/bin/python
 
-import sys
 import os
+import sys
 import pickle
 import numpy as np
 import brainiak.eventseg.event as event
-from eventseg_config import config
 
-filepath, k = sys.argv[1], sys.argv[2]
-dir, f_name  = os.path.split(filepath)
-rectype = os.path.split(dir)[1]
-trajectory = np.load(filepath)
-savepath = os.path.join(config['resultsdir'], rectype, os.path.splitext(f_name)[0], 'k'+k+'.npy')
+script_name, k = sys.argv[1], int(sys.argv[2])
+
+traj_path = os.path.join(config['datadir'], 'trajectories', f'{script_name}_traj.npy')
+traj = np.load(traj_path)
+
+ev = event.EventSegment(k)
+ev.fit(traj)
+w = (np.round(ev.segments_[0])==1).astype(bool)
+segs = np.array([traj[wi, :].mean(0) for wi in w.T])
+
+segments_filepath = os.path.join(config['datadir'], 'segments', script_name, f'{script_name}_events_k{str(k)}.npy'
+eventseg_filepath = os.path.join(config['datadir'], 'eventseg_models', script_name, f'{script_name}_eventseg_k{str(k)}.p'
+
+np.save(segments_filepath, segs)
+with open(eventseg_filepath, 'wb') as f:
+    pickle.dump(ev)
+
+
+
+
 
-if not os.path.isfile(savepath):
-    ev = event.EventSegment(int(k))
-    ev.fit(trajectory)
 
-    np.save(savepath, ev.segments_[0])
+# import sys
+# import joblib
+# import numpy as np
+# import pandas as pd
+# from scipy.signal import resample
+# from eventseg_config import config
+# from helpers import *
+#
+# id = sys.argv[1]
+# wsize = 50
+#
+# # load only single row to save time & memory
+# skiprows = range(1, id)
+# data = pd.read_csv(os.path.join(config['datadir'], 'data.csv'), skiprows=skiprows, nrows=1).T.squeeze()
+# name = data.title
+#
+# # remove HTML formatting, clean script content
+# clean_script = cleanup_text(wipe_formatting(data.script))
+#
+# # don't model empty scripts (8,528 characters is length of shortest cleaned script)
+# if len(clean_script) < 8528:
+#     sys.exit()
+#
+# cv = joblib.load(os.path.join(config['datadir'], 'fit_cv.joblib'))
+# lda = joblib.load(os.path.join(config['datadir'], 'fit_lda_t100.joblib'))
+#
+# sentences = cleaned.split('.')
+# windows = []
+# for ix, _ in enumerate(sentences):
+#     windows.append(' '.join(sentences[ix:ix+wsize]))
+#
+#
+# script_tf = cv.transform(windows)
+# script_traj = resample(lda.transform(script_tf), 1000)
+# corrmat = np.corrcoef(script_traj)
+#
+# np.save(os.path.join(config['datadir'], 'trajectories', f'{name}_traj.npy'), script_traj)
+# np.save(os.path.join(config['datadir'], 'corrmats', f'{name}_corrmat.npy'), corrmat)

eventseg_submit.py

@@ -10,38 +10,73 @@
 
 
 # ====== MODIFY ONLY THE CODE BETWEEN THESE LINES ======
-import sys
-
-n_ks = sys.argv[1]
-
-# each job command should be formatted as a string
-job_script = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'eventseg_cruncher.py')
 
 job_commands = list()
 job_names = list()
+job_script = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'eventseg_cruncher.py')
 
-for root, dirs, files in os.walk(config['datadir']):
-    for file in [f for f in files if f.startswith('debug')]:
-        filepath = os.path.join(root,file)
-        rectype = os.path.split(root)[-1]
-        turkid = os.path.splitext(file)[0]
-
-        subjdir = os.path.join(config['resultsdir'], rectype, turkid)
-        if not os.path.isdir(subjdir):
-            os.makedirs(subjdir, exist_ok=True)
+segments_dir = os.path.join(config['datadir'], 'segments')
+eventseg_models_dir = os.path.join(config['datadir'], 'eventseg_models')
 
-        for k in range(2,int(n_ks)+1):
-            if not os.path.isfile(os.path.join(subjdir,'k'+str(k)+'.npy')):
-                job_commands.append(' '.join([job_script, filepath, str(k)]))
-                job_names.append('segment_' + turkid + '_' + rectype + '_k' + str(k) + '.sh')
+try:
+    os.mkdir(segments_dir)
+except FileExistsError:
+    pass
 
+try:
+    os.mkdir(eventseg_models_dir)
+except FileExistsError:
+    pass
 
+traj_dir = os.path.join(config['datadir'], 'trajectories')
+script_names = [f.rstrip('_traj.npy') for f in os.listdir(traj_dir) if f.endswith('traj.npy')]
 
+for s in script_names:
+    scriptseg_dir = os.path.join(segments_dir, s)
+    script_eventsegs_dir = os.path.join(eventseg_models_dir, s)
+    try:
+        os.mkdir(scriptseg_dir)
+    except FileExistsError:
+        pass
+    try:
+        os.mkdir(script_eventsegs_dir)
+    except FileExistsError:
+        pass
+
+    for k in range(2, 75):
+        job_commands.append(f'{job_script} {s} {str(k)}')
+        job_names.append(f'segment_{s}_k{str(k)}')
+
+# import pandas as pd
+# from helpers import download_from_google_drive as dl
+#
+# # download pre-trained CountVectorizer and LatentDirichletAllocation models
+# cv_id = '1qD27Os44vojkC0UUf2cYlDZ5XytotGbK'
+# cv_dest = os.path.join(config['datadir'], 'fit_cv.joblib')
+# lda_id = '1iu7X84Hd1y6Vhz8xtG2nZZ_OSolkjz9g'
+# lda_dest = os.path.join(config['datadir'], 'fit_lda_t100.joblib')
+# dl(cv_id, cv_dest)
+# dl(lda_id, lda_dest)
+#
+# job_script = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'eventseg_cruncher.py')
+#
+# for output in ['trajectories', 'corrmats']:
+#     if not os.path.isdir(os.path.join(config['datadir'], output)):
+#         os.mkdir(os.path.join(config['datadir'], output))
+#
+# # load in and clean data
+# data_df = pd.read_csv(os.path.join(config['datadir'], 'data.csv'))
+# data_df.dropna(subset=['script'], inplace=True)
+# data_df.drop_duplicates(subset=['title'], inplace=True)
+#
+# job_commands = list()
+# job_names = list()
+#
+# for _, row in data_df.iterrows():
+#     job_commands.append(f'{job_script} {row.id}')
+#     job_names.append(f'transform_{row.title}.sh')
 
-## job_commands = map(lambda x: x[0]+" "+str(x[1]), zip([job_script]*10, range(10)))
 
-# job_names should specify the file name of each script (as a list, of the same length as job_commands)
-## job_names = map(lambda x: str(x)+'.sh', range(len(job_commands)))
 # ====== MODIFY ONLY THE CODE BETWEEN THESE LINES ======
 
 assert(len(job_commands) == len(job_names))

model_scripts_config.py

@@ -0,0 +1,37 @@
+import socket
+import os
+
+config = dict()
+
+# ====== MODIFY ONLY THE CODE BETWEEN THESE LINES ======
+# job creation options
+
+#add additional checks for your local machine here...
+# ******** check kiewit hostname from eduroam ********
+if (socket.gethostname() == 'Paxtons-MacBook-Pro') or (socket.gethostname() == 'Paxtons-MacBook-Pro.kiewit.dartmouth.edu') or (socket.gethostname() == 'Paxtons-MacBook-Pro.local'):
+    config['datadir'] = '/Users/paxtonfitzpatrick/Documents/Dartmouth/CDL/MIND-2019/narrative_complexity/data'
+    config['workingdir'] = config['datadir']
+    config['startdir'] = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))  # directory to start the job in
+    config['template'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'run_job_local.sh')
+else:
+    config['datadir'] = '/dartfs/rc/lab/D/DBIC/CDL/data/movie_scripts/'
+    config['startdir'] = '/dartfs/rc/lab/D/DBIC/CDL/f0028ph/MIND-narrative-modeling'
+    config['workingdir'] = os.path.join(startdir, 'cluster-scripts')
+    config['template'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'run_job_cluster.sh')
+
+config['scriptdir'] = os.path.join(config['workingdir'], 'scripts')
+config['lockdir'] = os.path.join(config['workingdir'], 'locks')
+config['resultsdir'] = os.path.join(config['workingdir'], 'results')
+
+
+# runtime options
+config['jobname'] = "model_script"  # default job name
+config['q'] = "largeq"  # options: default, test, largeq
+config['nnodes'] = 1  # how many nodes to use for this one job
+config['ppn'] = 1  # how many processors to use for this one job (assume 4GB of RAM per processor)
+config['walltime'] = '1:00:00'  # maximum runtime, in h:MM:SS
+config['cmd_wrapper'] = "python"  # replace with actual command wrapper (e.g. matlab, python, etc.)
+
+#extra options
+
+# ====== MODIFY ONLY THE CODE BETWEEN THESE LINES ======

model_scripts_cruncher.py

@@ -0,0 +1,40 @@
+#!/usr/bin/python
+
+import sys
+import joblib
+import numpy as np
+import pandas as pd
+from scipy.signal import resample
+from eventseg_config import config
+from helpers import *
+
+id = sys.argv[1]
+wsize = 50
+
+# load only single row to save time & memory
+skiprows = range(1, id)
+data = pd.read_csv(os.path.join(config['datadir'], 'data.csv'), skiprows=skiprows, nrows=1).T.squeeze()
+name = data.title
+
+# remove HTML formatting, clean script content
+clean_script = cleanup_text(wipe_formatting(data.script))
+
+# don't model empty scripts (8,528 characters is length of shortest cleaned script)
+if len(clean_script) < 8528:
+    sys.exit()
+
+cv = joblib.load(os.path.join(config['datadir'], 'fit_cv.joblib'))
+lda = joblib.load(os.path.join(config['datadir'], 'fit_lda_t100.joblib'))
+
+sentences = cleaned.split('.')
+windows = []
+for ix, _ in enumerate(sentences):
+    windows.append(' '.join(sentences[ix:ix+wsize]))
+
+
+script_tf = cv.transform(windows)
+script_traj = resample(lda.transform(script_tf), 1000)
+corrmat = np.corrcoef(script_traj)
+
+np.save(os.path.join(config['datadir'], 'trajectories', f'{name}_traj.npy'), script_traj)
+np.save(os.path.join(config['datadir'], 'corrmats', f'{name}_corrmat.npy'), corrmat)