def plt_roc_curve(preds, labels, save_str='', save=False):
"""Plots the ROC curve of the classifier
Parameters
----------
preds : array_like
Classifier predictions
labels : array_like
True class labels
save_str : str
String to use to save fig and data, if save. Should not have
file extension, should not be full path - just name of .pickle
and .png files that will be saved
save : bool
If True, will save the fig and data
"""
# Thresholds to use for plt
thresholds = np.linspace(0, 1, 1000)
# Init arrays for storing FPR and TPR
fprs = np.zeros_like(thresholds)
tprs = np.zeros_like(thresholds)
for ii in range(np.size(thresholds)):
# Get TPR here
tprs[ii] = get_sens(preds=preds,
labels=labels,
threshold=thresholds[ii])
# Get FPR here
fprs[ii] = 1 - get_spec(
preds=preds, labels=labels, threshold=thresholds[ii])
# Make fig
plt.figure(figsize=(12, 6))
plt.rc("font", family="Times New Roman")
plt.tick_params(labelsize=20)
plt.plot(fprs, tprs, 'k-')
plt.plot(np.linspace(0, 1, 1000), np.linspace(0, 1, 1000), 'b--')
plt.xlabel('False Positive Rate', fontsize=24)
plt.ylabel('True Positive Rate', fontsize=24)
plt.tight_layout()
if save: # If saving
verify_path(os.path.join(get_proj_path(), 'output/roc-figs/'))
out_path = os.path.join(get_proj_path(), 'output/roc-figs/')
plt.savefig(os.path.join(out_path, '%s.png' % save_str), dpi=150)
plt.close()
save_pickle(np.array([fprs, tprs]),
os.path.join(out_path, '%s.pickle' % save_str))
"""
Tyson Reimer
University of Manitoba
October 15th, 2020
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from umbms import get_proj_path, verify_path
from umbms.loadsave import load_pickle
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'output/roc-figs/')
__OUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__OUT_DIR)
###############################################################################
if __name__ == "__main__":
# Load ROC data obtained from testing on G1 after training on G1
cnn_roc = load_pickle(os.path.join(__DATA_DIR, 'cnn_run_8_roc.pickle'))
dnn_roc = load_pickle(os.path.join(__DATA_DIR, 'dnn_run_15_roc.pickle'))
logreg_roc = load_pickle(
os.path.join(__DATA_DIR, 'logreg_run_0_roc.pickle'))
# Define colours for the figure
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from matplotlib.cm import get_cmap
from sklearn.decomposition import PCA
from umbms import get_proj_path, verify_path, get_script_logger
from umbms.loadsave import load_pickle, save_pickle
from umbms.ai.preproc import to_td
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/')
__OUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__OUT_DIR)
fig_out = os.path.join(get_proj_path(), 'output/figs/')
verify_path(fig_out)
###############################################################################
def get_valid_session_ns(metadata, dataset):
"""Gets the session numbers in the metadata for more than 5 scans
Parameters
----------
from umbms import get_proj_path, get_script_logger, verify_path
from umbms.loadsave import load_pickle, save_pickle
from umbms.ai.makesets import get_class_labels
from umbms.ai.models import get_sino_cnn
from umbms.ai.preproc import resize_features_for_keras, to_td
from umbms.ai.augment import full_aug
from umbms.ai.metrics import (get_acc, get_sens, get_spec, report_metrics,
get_opt_thresh)
from sklearn.metrics import roc_auc_score
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/')
###############################################################################
__N_EPOCHS = 496
__N_RUNS = 20
###############################################################################
if __name__ == "__main__":
logger = get_script_logger(__file__)
# Load the training data and metadata from Gen-2
g2_d = load_pickle(os.path.join(__DATA_DIR, 'g2/g2_fd.pickle'))
November 16th, 2020 """ import os import numpy as np import matplotlib.pyplot as plt import scipy.stats as stats from umbms import get_proj_path, get_script_logger, verify_path from umbms.loadsave import load_pickle ############################################################################### __DATA_DIR = os.path.join(get_proj_path(), 'output/by-adi-preds/') phant_info_dir = os.path.join(get_proj_path(), 'data/phant-info/') __OUT_DIR = os.path.join(get_proj_path(), 'output/figs/') verify_path(__OUT_DIR) ############################################################################### # Define mid grey color mid_grey = [118, 113, 113] mid_grey = [ii / 255 for ii in mid_grey] # Define light grey color light_grey = [217, 217, 217] light_grey = [ii / 255 for ii in light_grey]
Tyson Reimer
University of Manitoba
February 22nd, 2020
"""
import os
import numpy as np
from umbms import get_proj_path, get_script_logger, verify_path
from umbms.loadsave import load_pickle, save_pickle
from umbms.ai.makesets import shuffle_arrs, get_class_labels
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/g1/')
__OUT_DIR = os.path.join(get_proj_path(), 'data/umbmid/g1-train-test/')
verify_path(__OUT_DIR)
###############################################################################
def report_md_breakdown(metadata):
"""Prints a breakdown of metadata info to logger
Parameters
----------
metadata : array_like
List of dicts of the metadata
"""
from umbms import get_proj_path, get_script_logger, verify_path
from umbms.loadsave import load_pickle, save_pickle
from umbms.ai.augment import full_aug
from umbms.ai.gencompare import correct_g1_ini_ant_ang
from umbms.ai.models import get_sino_dnn
from umbms.ai.makesets import get_class_labels
from umbms.ai.preproc import resize_features_for_keras, to_td
from umbms.ai.metrics import (get_acc, get_sens, get_spec, get_opt_thresh,
report_metrics)
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/')
###############################################################################
# Number of epochs to train over
__N_EPOCHS = 2550
###############################################################################
def plt_roc_curve(preds, labels, save_str='', save=False):
"""Plots the ROC curve of the classifier
Parameters
----------
preds : array_like
from tensorflow.keras.utils import to_categorical from tensorflow.keras.backend import clear_session from umbms import get_proj_path, get_script_logger, verify_path from umbms.loadsave import load_pickle from umbms.ai.makesets import get_class_labels from umbms.ai.models import get_sino_dnn from umbms.ai.preproc import resize_features_for_keras, to_td from umbms.ai.augment import full_aug from umbms.ai.gencompare import correct_g1_ini_ant_ang ############################################################################### __DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/') __FIG_OUT_DIR = os.path.join(get_proj_path(), 'output/2006-g2-v-g1/') verify_path(__FIG_OUT_DIR) ############################################################################### # Number of epochs to train over __N_EPOCHS = 6000 # Set the bounds on the time-domain window that will be applied to all # data before training or testing __T_WIN_LOW = 5 __T_WIN_HIGH = 40 # Number of runs to use to identify best-performing number of epochs
from umbms import get_proj_path, get_script_logger, verify_path
from umbms.loadsave import load_pickle, save_pickle
from umbms.ai.augment import full_aug
from umbms.ai.gencompare import correct_g1_ini_ant_ang
from umbms.ai.models import get_sino_cnn
from umbms.ai.makesets import get_class_labels
from umbms.ai.preproc import resize_features_for_keras, to_td
from umbms.ai.metrics import (get_acc, get_sens, get_spec, get_opt_thresh,
report_metrics)
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/')
###############################################################################
# Number of epochs to train over
__N_EPOCHS = 496
###############################################################################
def plt_roc_curve(preds, labels, save_str='', save=False):
"""Plots the ROC curve of the classifier
Parameters
----------
preds : array_like
Tyson Reimer University of Manitoba May 04, 2020 """ import os import numpy as np import matplotlib.pyplot as plt from umbms import get_proj_path, verify_path ############################################################################### __SAVE_FIG = True __OUT_DIR = os.path.join(get_proj_path(), 'output/figs/') verify_path(__OUT_DIR) ############################################################################### # Define blue to match ppt color dark_grey = [38, 38, 38] dark_grey = [ii / 255 for ii in dark_grey] mid_grey = [118, 113, 113] mid_grey = [ii / 255 for ii in mid_grey] # Define grey color that goes well with the blue color light_grey = [217, 217, 217] light_grey = [ii / 255 for ii in light_grey]
Tyson Reimer
University of Manitoba
May 01, 2020
"""
import os
import numpy as np
from umbms import get_proj_path, verify_path
from umbms.loadsave import load_pickle
from umbms.plot.sinogramplot import plot_sino
from umbms.ai.preproc import to_td
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/g2/')
__FIG_OUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__FIG_OUT_DIR)
###############################################################################
if __name__ == "__main__":
# Load phantom dataset (UM-BMID Gen1)
phant_data = load_pickle(os.path.join(__DATA_DIR, 'g2_fd.pickle'))
# Convert dataset to the time-domain
sinograms = to_td(phant_data)
# Take sample sinogram