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import glob
import numpy as np
import pandas as pd
from sklearn import svm
import sklearn.model_selection as model_selection
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.impute import SimpleImputer
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import RFECV
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
# import rcr
def save_features_as_csv():
return
def load_features_csv_concat(folder_path):
df_list = []
for file in glob.glob(folder_path+"/*.csv"):
df_ = pd.read_csv(file)
df_list.append(df_)
df = pd.concat(df_list)
df = df.reset_index(drop=True)
return df
def exclude_subject(df,exluded_subjects):
condition_string = ''
for ex_sub in exluded_subjects:
condition_string += "(df['Subject'] !='" +ex_sub+"') & "
evaluation_string = 'df['+condition_string[:len(condition_string)-2]+']'
df_ex = eval(evaluation_string)
return df_ex.reset_index(drop=True)
def electrode_wise_dataframe(df, condition_list, id_vars = ['Subject', 'Task', 'Electrode']):
stats_frame = df[
['Subject', 'Task', 'Electrode','Lentr', 'TT', 'L', 'RR', 'LAM', 'DET', 'V','Vmax', 'Ventr', 'W','Wentr']
]
stats_frame.melt(id_vars=id_vars, var_name='RQA_feature', value_name='feature_value')
stats = stats_frame.pivot_table(index=['Subject', 'Task'], columns='Electrode',
values=['Lentr', 'TT', 'L', 'RR', 'LAM', 'DET', 'V','Vmax', 'Ventr', 'W', 'Wentr']).reset_index()
stats = stats.replace(condition_list[0], 0)
stats = stats.replace(condition_list[1], 1)
y = stats.Task.values
return stats, y
def electrode_wise_dataframe_epochs(df, condition_list, id_vars = ['Subject', 'Task', 'Epoch_id','Electrode']):
stats_frame = df[
['Subject', 'Task','Epoch_id','Electrode','Lentr', 'TT', 'L', 'RR', 'LAM', 'DET', 'V','Vmax', 'Ventr', 'W','Wentr']
]
stats_frame.melt(id_vars=id_vars, var_name='RQA_feature', value_name='feature_value')
stats = stats_frame.pivot_table(index=['Subject', 'Task'], columns=['Electrode', 'Epoch_id'],
values=['Lentr', 'TT', 'L', 'RR', 'LAM', 'DET', 'V','Vmax', 'Ventr', 'W', 'Wentr']).reset_index()
stats = stats.replace(condition_list[0], 0)
stats = stats.replace(condition_list[1], 1)
y = stats.Task.values
return stats, y
def select_features_clean_and_normalize(df,features=['Lentr', 'TT', 'L', 'LAM', 'DET','V', 'Ventr', 'W','Wentr']):
stats_data = df[features].values
#rcr
stats_data_cleaned=np.empty((stats_data.shape[0],stats_data.shape[1]))
stats_data_cleaned[:]=np.nan
# r = rcr.RCR(rcr.SS_MEDIAN_DL)
r = stats_data_cleaned
for ii in range(stats_data.shape[1]):
# fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2,figsize=(16,8),dpi=200)
# ax1.hist(stats_data[:,ii])
# ax1.set_title('Raw')
r.performBulkRejection(stats_data[:,ii])
cleaned_data_indices = r.result.indices
stats_data_cleaned[cleaned_data_indices,ii]=stats_data[cleaned_data_indices,ii]
# ax2.hist(stats_data_cleaned[:,ii][~np.isnan(stats_data_cleaned[:,ii])])
# ax2.set_title('Cleaned')
# plt.savefig('Feature_nr_'+str(ii)+'jpg')
# plt.close()
df_stats_data_cleaned=pd.DataFrame(stats_data_cleaned)
# df_stats_data_cleaned=df_stats_data_cleaned.fillna(method='mean', axis=0)#+df_stats_data_cleaned.fillna(method='bfill', axis=0))/2
# df_stats_data_cleaned.interpolate(limit=5, inplace=True)
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = imputer.fit(df_stats_data_cleaned)
stats_data_cleaned = imputer.transform(df_stats_data_cleaned)
####normalize#########
stats_data_normed=np.empty((stats_data.shape[0],stats_data.shape[1]))
for ii in range(stats_data.shape[1]):
stats_data_normed[:,ii] = (stats_data_cleaned[:,ii]-stats_data_cleaned[:,ii].min(axis=0))/ (stats_data_cleaned[:,ii].max(axis=0)-stats_data_cleaned[:,ii].min(axis=0)) #stats_data[:,ii]-stats_data[:,ii].mean(axis=0))/ stats_data[:,ii].std(axis=0)
return stats_data_normed
def clasyfication_SVM(df,y,cv=10,type='linear'):
clf=svm.SVC(kernel=type)
skf = StratifiedKFold(n_splits=cv)
# run split() again to generate folds
folds = skf.split(df, y)
print('folds shape ', folds)
performance = np.zeros(skf.n_splits)
performance_open= np.zeros(skf.n_splits)
performance_closed= np.zeros(skf.n_splits)
for i, (train_idx, test_idx) in enumerate(folds):
X_train = df[train_idx,:]
y_train = y[train_idx]
X_test = df[test_idx,:]
y_test = y[test_idx]
# call fit (on train) and predict (on test)
model = clf.fit(X=X_train, y=y_train)
y_hat = model.predict(X=X_test)
# calculate accuracy
performance[i] = accuracy_score(y_test, y_hat)
cm = confusion_matrix(y_test, y_hat)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
# class_acuracy = cm.diagonal()
class_acuracy = cm.diagonal()
performance_open[i]=class_acuracy[0]*100
performance_closed[i]=class_acuracy[1]*100
# calculate average accuracy
print('Mean performance: %.3f' % np.mean(performance*100))
print('Mean performance 1st class: %.3f' % np.mean(performance_open))
print('Mean performance 2nd class: %.3f' % np.mean(performance_closed))
lin = svm.SVC(kernel=type).fit(X_train, y_train)
lin_pred = lin.predict(X_test)
return lin, lin_pred
def cross_validation(df,y,cv=10,title = 'cv job',type='linear'):
# Create the RFE object and compute a cross-validated score.
svc = SVC(kernel=type)
# The "accuracy" scoring shows the proportion of correct classifications
min_features_to_select = 4 # Minimum number of features to consider
rfecv = RFECV(
estimator=svc,
step=1,
cv=StratifiedKFold(n_splits=cv),
scoring="accuracy",
min_features_to_select=min_features_to_select,
)
rfecv.fit(df, y)
print("Optimal number of features : %d" % rfecv.n_features_)
# Plot number of features VS. cross-validation scores
plt.figure()
plt.xlabel("Number of features selected")
plt.ylabel("Cross validation score (accuracy)")
plt.plot(
range(min_features_to_select, len(rfecv.cv_results_['mean_test_score']) + min_features_to_select),
rfecv.cv_results_['mean_test_score'],
)
plt.title(title)
plt.show()
plt.savefig(title+' classification with feature selection_more_features'+str(rfecv.n_features_)+'_'+str(round(max(rfecv.cv_results_['mean_test_score'])*100,2))+'.png', dpi=150)
plt.close()
return rfecv.transform(df)
def compute_binary_SVM(df,y,predict_on_all_data = False,type='linear'):
# stats_data = df[['TT', 'RR', 'DET', 'LAM', 'L', 'Lentr']].values
X_train, X_test, y_train, y_test = model_selection.train_test_split(df, y, train_size=0.80, test_size=0.20,
random_state=101)
global lin
if predict_on_all_data:
print('SVM prediction on all data')
lin = svm.SVC(kernel=type).fit(X_train, y_train)
lin_pred = lin.predict(df)
lin_accuracy = accuracy_score(y, lin_pred)
print('Accuracy (Linear Kernel): ', "%.2f" % (lin_accuracy * 100))
cm = confusion_matrix(y, lin_pred)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
class_acuracy = cm.diagonal()
print('Accuracy (1st class): ', "%.2f" % (class_acuracy[0] * 100))
print('Accuracy (2nd class): ', "%.2f" % (class_acuracy[1] * 100))
else:
print('SVM prediction on test data')
lin = svm.SVC(kernel=type).fit(X_train, y_train)
lin_pred = lin.predict(X_test)
lin_accuracy = accuracy_score(y_test, lin_pred)
print('Accuracy (Linear Kernel): ', "%.2f" % (lin_accuracy * 100))
print('Y train:', y_train)
print('Y test:', y_test)
print('Y pred:', lin_pred)
cm = confusion_matrix(y_test, lin_pred)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
class_acuracy = cm.diagonal()
print('Accuracy (1st class): ', "%.2f" % (class_acuracy[0] * 100))
print('Accuracy (2nd class): ', "%.2f" % (class_acuracy[1] * 100))
return lin, lin_pred
def clasyfication_RFC(df,y,cv=10,max_depth=2):
clf = RandomForestClassifier(max_depth=max_depth, random_state=0)
skf = StratifiedKFold(n_splits=cv)
# run split() again to generate folds
folds = skf.split(df, y)
performance = np.zeros(skf.n_splits)
performance_open= np.zeros(skf.n_splits)
performance_closed= np.zeros(skf.n_splits)
for i, (train_idx, test_idx) in enumerate(folds):
X_train = df[train_idx,:]
y_train = y[train_idx]
X_test = df[test_idx,:]
y_test = y[test_idx]
# call fit (on train) and predict (on test)
model = clf.fit(X=X_train, y=y_train)
y_hat = model.predict(X=X_test)
# calculate accuracy
performance[i] = accuracy_score(y_test, y_hat)
cm = confusion_matrix(y_test, y_hat)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
# class_acuracy = cm.diagonal()
class_acuracy = cm.diagonal()
performance_open[i]=class_acuracy[0]*100
performance_closed[i]=class_acuracy[1]*100
# calculate average accuracy
print('Mean performance: %.3f' % np.mean(performance*100))
print('Mean performance 1st class: %.3f' % np.mean(performance_open))
print('Mean performance 2nd class: %.3f' % np.mean(performance_closed))
lin = RandomForestClassifier(max_depth=max_depth, random_state=0)
lin.fit(X=X_train, y=y_train)
lin_pred = lin.predict(X_test)
return lin, lin_pred
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