def main():
# map segments to subject ID
map_df=from_map_to_df(subject_segment_map,'subjects')
# load delta theta tensors.
df_features=load_object(PATH_DATA_ID_2+
'Datasets_delta_theta/features.files')
# load trained ID_2 parameters.
id2_clf=load_object(path_id2_clf+'eeg_delta_theta_clf.file')
poly_obj=load_object(path_id2_clf+'eeg_delta_theta_poly_obj.file')
best_features_poly=load_object(path_id2_clf+
'eeg_delta_theta_best_features_poly.file')
X=prepare_for_predict(df_features,
poly_obj,
best_features_poly)
predictions=id2_clf.predict_proba(X)[:,1]
df_prediction=\
map_df.join(pd.DataFrame(predictions,columns=['Predicted_Target_id2']))
df_prediction_decision=df_prediction.groupby('subjects').mean()
df_prediction_decision.rename(
columns={'Predicted_Target_id2':'Decision_id2'},inplace=True)
print(df_prediction)
print(df_prediction_decision)
df_prediction.to_csv(PATH_RESULTS+'Predicted_Target_id2.csv')
df_prediction_decision.to_csv(PATH_RESULTS+'Decision_id2.csv')
def main():
#load trained learner
learn = load_learner(PATH_DATA_ID_44)
#load dictionary of subject:index_interval
subject_segment_map = load_object(PATH_DATA + 'mapper_subject.file')
#load dataframe. with name of files.png
subject_segment_map_img = load_object(PATH_DATA_ID_44 +
'image_name_map.file')
#create dataframe from index interval and name of subject
map_df = from_map_to_df(subject_segment_map, 'subjects')
#the resulting dataframe has columns subjects('id_subject')
# and name('file name of .png')
df_images_id = map_df.join(subject_segment_map_img)
predictions_hold = []
for each_png in df_images_id.name:
x = open_image(path_to_images + each_png)
p = learn.predict(x)
tensor = p[2]
single_pred = np.round(tensor.cpu().detach().numpy()[0], 5)
predictions_hold = predictions_hold + [single_pred]
df_prediction=\
df_images_id.join(pd.DataFrame(predictions_hold,
columns=['Predicted_Target_id44']))
df_prediction_decision = df_prediction.groupby('subjects').mean()
df_prediction_decision.rename(
columns={'Predicted_Target_id44': 'Decision_id44'}, inplace=True)
print(df_prediction.head())
print(df_prediction_decision.head)
df_prediction.to_csv(PATH_RESULTS + 'Predicted_Target_id44.csv')
df_prediction_decision.to_csv(PATH_RESULTS + 'Decision_id44.csv')
#if __name__ == "__main__":
# main()
import configparser
from OFHandlers import load_object
import pandas as pd
from mapSubjectSegment import from_map_to_df
#read configurarion file
config = configparser.ConfigParser()
root_path= os.getcwd().replace('scripts','')
script_location = os.getcwd().replace('scripts','configuration')
config.read_file(open(script_location+'/config.cfg'))
PATH_DATA_ID_2 = root_path + config.get('PATH_STORE','PATH_DATA_ID_2')
path_id2_clf = PATH_DATA_ID_2+'ID2_classifier/'
PATH_DATA = root_path + config.get('PATH_STORE','PATH_DATA')
PATH_RESULTS = root_path + config.get('PATH_STORE','PATH_RESULTS')
subject_segment_map=load_object(PATH_DATA+'mapper_subject.file')
def prepare_for_predict(X,
poly_obj,
best_features_poly):
"""pipeline of transformations to make a prediction."""
X_poly=poly_obj.transform(X)
cols_poly=poly_obj.get_feature_names(X.columns)
df_X_poly=pd.DataFrame(X_poly,columns=cols_poly,index=X.index)
X=df_X_poly[best_features_poly]
return X
def main():
# map segments to subject ID
map_df=from_map_to_df(subject_segment_map,'subjects')
#this parameters cannot be changed
#the CNN of id44 would give different results.
mpl.rcParams['figure.dpi'] = 72
mpl.rcParams['figure.edgecolor'] = (1, 1, 1, 0)
mpl.rcParams['figure.facecolor'] = (1, 1, 1, 0)
mpl.rcParams['figure.figsize'] = [6.0, 4.0]
mpl.rcParams['figure.subplot.bottom'] = 0.125
#read configurarion file
config = configparser.ConfigParser()
root_path = os.getcwd().replace('scripts', '')
script_location = os.getcwd().replace('scripts', 'configuration')
config.read_file(open(script_location + '/config.cfg'))
PATH_DATA_ID_44 = config.get('PATH_STORE', 'PATH_DATA_ID_44')
df_fast = load_object(root_path + PATH_DATA_ID_44 +
'Datasets_alpha_beta/features.files')
path_to_images = root_path + PATH_DATA_ID_44 + 'Datasets_image_alpha_beta/'
# 110=10*11. We will an image represating 110 rows, one per channels
# and two columns, alpha and beta.
dim_tuple_images = (110, 2)
def generate_images(features, dim_tuple, path_to_save):
for i in range(0, len(features)):
sample = np.array(features.iloc[i]).reshape(dim_tuple)
#save image
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(sample, aspect='auto', origin='lower')
plt.axis('off')
def main():
subjects_id=pd.read_csv(CSV_SUBJECTS_IDS)
list_ids=subjects_id.id.values.tolist()
i=0
mapper_subject={}
for each_subject in list_ids:
path_0=PATH_SIGNALS_CSV+'/'+each_subject+'_'+FILES_USE[0]
path_1=PATH_SIGNALS_CSV+'/'+each_subject+'_'+FILES_USE[1]
path_2=PATH_SIGNALS_CSV+'/'+each_subject+'_'+FILES_USE[2]
#Obtain a mne object from the csv
#this objects combines the information from the 3 csv
#downloaded from AWS.
raw=csv_to_mne(path_subject_channels=path_0,
path_subject_signal=path_1,
path_subject_events=path_2,
s_freq=S_FREQ,
r_s_freq=R_S_FREQ,
montage=MONTAGE,
scale=SCALE,
event_id=EVENT_ID)
#make a copy of the object. one is input
#as tolal channels. The other is for clustered signal.
copy_raw_1=raw.copy()
copy_raw_2=raw.copy()
#transforms the mne object into a n-numpy tensor.
# n is the number of segments. Since the signal was has 5
# event marked, we have 5 segments.
#third order Butterworth-filter is applied too.
tensor_with_segments=create_tensor(input_signal=copy_raw_1,
event_mark=EVENT_ID,
low_cut_hz=LOW_CUT_HZ,
high_cut_hz=HIGH_CUT_HZ,
interval_ms=INTERVAL_SEC*1000,
channels_clusters=None,
n_cluster=None)
#transforms the mne object into a numpy tensor.
#third order Butterworth-filter is applied too.
#this signal is clustered according to N_CLUSTERS
tensor_with_segments_clustered=create_tensor(input_signal=copy_raw_2,
event_mark=EVENT_ID,
low_cut_hz=LOW_CUT_HZ,
high_cut_hz=HIGH_CUT_HZ,
interval_ms=INTERVAL_SEC*1000,
channels_clusters=load_object(PATH_SAVE_CLUSTERED_DF),
n_cluster=N_CLUSTERS)
#concatenate the n-tensor for each subject
#this will create the final dataset for ID2 and ID44.
try:
if(i==0):
concat_signal=tensor_with_segments
concat_signal_clustered=tensor_with_segments_clustered
mapper_subject[each_subject]=[0,len(tensor_with_segments.y)]
else:
print(i,each_subject)
concat_signal.X=np.vstack([concat_signal.X,
tensor_with_segments.X])
concat_signal_clustered.X=np.vstack([concat_signal_clustered.X,
tensor_with_segments_clustered.X])
#save subjects positions
start=len(concat_signal.y)
concat_signal.y=np.concatenate(
(concat_signal.y,tensor_with_segments.y), axis=0)
end=len(concat_signal.y)
mapper_subject[each_subject]=[start,end]
print('concat_signal.X.shape',concat_signal.X.shape)
print('concat_signal.y.shape',concat_signal.y.shape)
except Exception as e:
print('error occured see:')
print(e)
pass
i=i+1
save_object(PATH_DATA_ID_2+'data_set_tensor.file',
concat_signal_clustered)
save_object(PATH_DATA_ID_44+'data_set_tensor.file',
concat_signal)
save_object(PATH_DATA+'mapper_subject.file',mapper_subject)
#if __name__ == '__main__':
# main()
import os
import sys
import pandas as pd
import numpy as np
from OFHandlers import save_object, load_object
import configparser
#read configurarion file
config = configparser.ConfigParser()
root_path = os.getcwd().replace('scripts', '')
script_location = os.getcwd().replace('scripts', 'configuration')
config.read_file(open(script_location + '/config.cfg'))
PATH_DATA_ID_2 = root_path + config.get('PATH_STORE', 'PATH_DATA_ID_2')
PATH_DATA_ID_44 = root_path + config.get('PATH_STORE', 'PATH_DATA_ID_44')
R_S_FREQ = int(config.get('ML_VARS', 'R_S_FREQ'))
tensor_segments_id2 = load_object(PATH_DATA_ID_2 + 'data_set_tensor.file')
tensor_segments_id44 = load_object(PATH_DATA_ID_44 + 'data_set_tensor.file')
def get_index_band(rate, lower, upper):
lower_index = int(lower * rate)
upper_index = int(upper * rate)
return [lower_index, upper_index]
def get_power_spectrum(X, fs, slow_band):
total_sample_number = X.shape[0]
channel = X.shape[1]
print('channel', channel)
points_per_signal = X.shape[2]
sample_holder = []