<jupyter_start><jupyter_text>PARAMETERS<jupyter_code>pre = 0
gabfr = 0
n_reg = 10
n_shuff = 10
seed = 905
n_jobs = -1

datadir = "../data/OSCA"
scoring = ["neg_log_loss", "accuracy", "balanced_accuracy"]

def set_all_parameters(stimtype):
    if stimtype == "gabors":
        post = 1.5
    elif stimtype == "bricks":
        post = 1.0

    stimpar = sess_ntuple_util.init_stimpar(stimtype, gabfr=gabfr, pre=pre, post=post)
    return stimpar<jupyter_output><empty_output><jupyter_text>Run regular<jupyter_code>stimpar = set_all_parameters("gabors")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(758519303, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("gabors")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(759189643, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("gabors")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(761624763, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("gabors")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(828816509, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("bricks")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(758519303, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("bricks")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(759189643, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("bricks")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(761624763, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)
stimpar = set_all_parameters("bricks")
full_acc, roi_acc, full_diff, roi_diff = run_all_logreg(828816509, datadir, scoring, stimpar, n_splits=n_reg, shuffle=False, seed=seed)
plot_roi_acc(full_acc, roi_acc, full_diff, roi_diff, stimpar.stimtype)<jupyter_output>Mouse 10, Session 3
Loading stimulus and alignment info...
Creating stimulus objects...
Loading ROI trace info...
    WARNING: 3 noisy ROIs (mean below 0 or median above midrange) are also included in the NaN ROI attributes (but not set to NaN): 187, 278, 342.
Data shape: 496, 2040, 30
N vals: 1946, 94
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