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predict_cognitive_function.py
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predict_cognitive_function.py
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import os
import pandas as pd
import numpy as np
import nibabel as nb
from nilearn.image import load_img
from nilearn.input_data import NiftiMasker
from nilearn.input_data import NiftiLabelsMasker
from sklearn.externals import joblib
def predict(output='output.csv',
measure='CogTotalComp',
gray_matter=None,
white_matter=None,
rfMRI_basc_correlation_pe_LR=None,
rfMRI_basc_correlation_pe_RL=None,
rfMRI_basc_partial_correlation_pe_LR=None,
rfMRI_basc_partial_correlation_pe_RL=None,
rfMRI_basc_tangent_pe_LR=None,
rfMRI_basc_tangent_pe_RL=None,
rfMRI_REST1_hp2000_clean_s4_0_000_to_0_250_ALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_ALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_ALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_ALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_ALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_fALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_fALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_fALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_fALFF=None,
rfMRI_REST1_hp2000_clean_s4_0_6_LFCD=None,
rfMRI_REST1_hp2000_clean_s4_1_3dDegreeCentrality=None,
rfMRI_REST1_hp2000_clean_s4_ReHo=None,
tfMRI_WM_2BK_s4=None,
tfMRI_WM_0BK_s4=None,
tfMRI_WM_2BK_minus_0BK_s4=None,
tfMRI_WM_BODY_s4=None,
tfMRI_WM_FACE_s4=None,
tfMRI_WM_PLACE_s4=None,
tfMRI_WM_TOOL_s4=None,
tfMRI_RELATIONAL_REL_s4=None,
tfMRI_RELATIONAL_MATCH_s4=None,
tfMRI_RELATIONAL_REL_minus_MATCH_s4=None,
tfMRI_EMOTION_FACES_s4=None,
tfMRI_EMOTION_SHAPES_s4=None,
tfMRI_EMOTION_FACES_minus_SHAPES_s4=None,
tfMRI_LANGUAGE_STORY_s4=None,
tfMRI_LANGUAGE_MATH_s4=None,
tfMRI_LANGUAGE_STORY_minus_MATH_s4=None,
tfMRI_GAMBLING_REWARD_s4=None,
tfMRI_GAMBLING_PUNISH_s4=None,
tfMRI_SOCIAL_TOM_s4=None,
tfMRI_SOCIAL_RANDOM_s4=None,
tfMRI_SOCIAL_TOM_minus_RANDOM_s4=None,
winsorizing='yes',
scaling='yes',
pca='yes',
intercept='yes'):
arguments = locals()
# return arguments
features=['gray_matter',
'white_matter',
'rfMRI_basc_correlation',
'rfMRI_basc_partial_correlation',
'rfMRI_basc_tangent',
'rfMRI_REST1_hp2000_clean_s4_0_000_to_0_250_ALFF',
'rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_ALFF',
'rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_ALFF',
'rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_ALFF',
'rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_ALFF',
'rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_fALFF',
'rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_fALFF',
'rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_fALFF',
'rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_fALFF',
'rfMRI_REST1_hp2000_clean_s4_0_6_LFCD',
'rfMRI_REST1_hp2000_clean_s4_1_3dDegreeCentrality',
'rfMRI_REST1_hp2000_clean_s4_ReHo',
'tfMRI_WM_2BK_s4',
'tfMRI_WM_0BK_s4',
'tfMRI_WM_2BK_minus_0BK_s4',
'tfMRI_WM_BODY_s4',
'tfMRI_WM_FACE_s4',
'tfMRI_WM_PLACE_s4',
'tfMRI_WM_TOOL_s4',
'tfMRI_RELATIONAL_REL_s4',
'tfMRI_RELATIONAL_MATCH_s4',
'tfMRI_RELATIONAL_REL_minus_MATCH_s4',
'tfMRI_EMOTION_FACES_s4',
'tfMRI_EMOTION_SHAPES_s4',
'tfMRI_EMOTION_FACES_minus_SHAPES_s4',
'tfMRI_LANGUAGE_STORY_s4',
'tfMRI_LANGUAGE_MATH_s4',
'tfMRI_LANGUAGE_STORY_minus_MATH_s4',
'tfMRI_GAMBLING_REWARD_s4',
'tfMRI_GAMBLING_PUNISH_s4',
'tfMRI_SOCIAL_TOM_s4',
'tfMRI_SOCIAL_RANDOM_s4',
'tfMRI_SOCIAL_TOM_minus_RANDOM_s4']
results=pd.DataFrame()
'''
#######################################################################
#######################################################################
#######################################################################
# First-level pipelines
#######################################################################
#######################################################################
#######################################################################
'''
for feature in features:
print(feature)
y_pred = np.nan
pe_LR_filename = None
pe_RL_filename = None
if 'basc' in feature:
if 'basc_correlation' in feature:
pe_LR_filename = arguments.get('rfMRI_basc_correlation_pe_LR')
pe_RL_filename = arguments.get('rfMRI_basc_correlation_pe_RL')
elif 'basc_partial_correlation' in feature:
pe_LR_filename = arguments.get('rfMRI_basc_partial_correlation_pe_LR')
pe_RL_filename = arguments.get('rfMRI_basc_partial_correlation_pe_RL')
elif 'basc_tangent' in feature:
pe_LR_filename = arguments.get('rfMRI_basc_tangent_pe_LR')
pe_RL_filename = arguments.get('rfMRI_basc_tangent_pe_RL')
if pe_LR_filename != None or pe_RL_filename != None:
try:
print("Loading basc atlas for %s" % (feature))
atlas_filename = './atlases/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_asym/template_cambridge_basc_multiscale_asym_scale122.nii.gz'
masker = NiftiLabelsMasker(atlas_filename, resampling_target='data', smoothing_fwhm=None, standardize=False,
detrend=True, low_pass=0.1, high_pass=0.008, memory_level=0, memory=None)
print("Loading first-level pipeline for %s" % (feature))
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_PCA_LinearRegression_l2_regression_analysis_first_level_pipeline_and_results.sav')
if pe_LR_filename != None and pe_RL_filename == None:
print("Loadingfirst-level %s for %s" % (pe_LR_filename, feature))
pe_LR_t_r = nb.load(pe_LR_filename).header.get_zooms()[3]
masker.t_r = pe_LR_t_r
time_series_pe_LR = masker.fit_transform(pe_LR_filename)
conn_measure_pe_LR = pipeline['conn_measure_LR']
data = conn_measure_pe_LR.transform([time_series_pe_LR])
if feature == 'rfMRI_basc_correlation' or feature == 'rfMRI_basc_partial_correlation':
data = np.arctanh(data)
elif pe_LR_filename == None and pe_RL_filename != None:
print("Loading first-level %s for %s" % (pe_RL_filename, feature))
pe_RL_t_r = nb.load(pe_RL_filename).header.get_zooms()[3]
masker.t_r = pe_RL_t_r
time_series_pe_RL = masker.fit_transform( pe_RL_filename)
conn_measure_pe_RL = pipeline['conn_measure_RL']
data = conn_measure_pe_RL.transform([time_series_pe_RL])
if feature == 'rfMRI_basc_correlation' or feature == 'rfMRI_basc_partial_correlation':
data = np.arctanh(data)
elif pe_LR_filename != None and pe_RL_filename != None:
print("Loading first-level %s for %s" % (pe_LR_filename, feature))
pe_LR_t_r = nb.load(pe_LR_filename).header.get_zooms()[3]
masker.t_r = pe_LR_t_r
time_series_pe_LR = masker.fit_transform(pe_LR_filename)
conn_measure_pe_LR = pipeline['conn_measure_LR']
data_LR = conn_measure_pe_LR.transform([time_series_pe_LR])
print("Loading first-level %s for %s" % (pe_RL_filename, feature))
pe_RL_t_r = nb.load(pe_RL_filename).header.get_zooms()[3]
masker.t_r = pe_RL_t_r
time_series_pe_RL = masker.fit_transform(pe_RL_filename)
conn_measure_pe_RL = pipeline['conn_measure_RL']
data_RL = conn_measure_pe_RL.transform([time_series_pe_RL])
if feature == 'rfMRI_basc_correlation' or feature == 'rfMRI_basc_partial_correlation':
data_LR = np.arctanh(data_LR)
data_RL = np.arctanh(data_RL)
data = (data_LR + data_RL) / 2
except ValueError:
print("Error loading first-level %s data for %s! Exiting." % (arguments.get(feature), feature))
elif arguments.get(feature) != None:
print(arguments.get(feature))
try:
if feature == 'gray_matter':
print("Loading first-level %s for %s" % (arguments.get(feature), feature))
data = pd.read_csv(arguments.get(feature), header=0)
data = data.values
elif feature == 'white_matter':
print("Loading first-level %s for %s" % (arguments.get(feature), feature))
white_matter_mask = load_img('./atlases/white_matter_mask.nii.gz')
masker = NiftiMasker(mask_img=white_matter_mask)
data = masker.fit_transform(arguments.get(feature))
elif 'tfMRI' in feature or 'ALFF' in feature or 'LFCD' in feature or '3dDegreeCentrality' in feature or 'ReHo' in feature:
print("Loading first-level %s data for %s" % (arguments.get(feature), feature))
gray_matter_mask = load_img('./atlases/gray_matter_mask.nii.gz')
masker = NiftiMasker(mask_img=gray_matter_mask)
data = masker.fit_transform(arguments.get(feature))
except ValueError:
print("Error loading first-level %s data for %s! Exiting." % (arguments.get(feature), feature))
try:
if 'basc' not in feature and feature != 'gray_matter':
print("Loading first-level pipeline for %s" % (feature))
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_PCA_LinearRegression_l2_regression_analysis_first_level_pipeline_and_results.sav')
elif feature == 'gray_matter':
print("Loading first-level pipeline for %s" % (feature))
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_no_feature_selection_LinearRegression_l2_regression_analysis_first_level_pipeline_and_results.sav')
except ValueError:
print("Error loading first-level %s pipeline for %s! Exiting." % (arguments.get(feature), feature))
if arguments.get(feature) != None or pe_LR_filename != None or pe_RL_filename != None:
try:
if winsorizing == 'yes':
print('Winsorizing')
lower = pipeline['lower']
upper = pipeline['upper']
data = np.clip(data, lower, upper)
if scaling == 'yes':
print('Using scaling')
sc_X_train = pipeline['sc_X_train']
data = sc_X_train.transform(data)
if pca == 'yes' and feature != 'gray_matter':
print('Using PCA')
feature_select = pipeline['feature_select']
data = feature_select.transform(data)
regressor=pipeline['regressor']
if pca != 'yes' and feature != 'gray_matter':
feature_select = pipeline['feature_select']
regressor.coef_ = feature_select.inverse_transform(regressor.coef_)
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data)
sc_y_train=pipeline['sc_y_train']
y_pred = sc_y_train.inverse_transform(y_pred)
except ValueError:
print("Error with first-level prediction for %s! Exiting." % (arguments.get(feature), feature))
else:
print("Skipping %s" % (feature))
if np.isnan(y_pred):
results[feature] = y_pred
else:
results[feature] = y_pred[0]
'''
#######################################################################
#######################################################################
#######################################################################
# Second-level pipelines
#######################################################################
#######################################################################
#######################################################################
'''
features=['fc', 'rs', 'WM', 'CATEGORY', 'RELATIONAL', 'EMOTION', 'LANGUAGE', 'GAMBLING', 'SOCIAL']
for feature in features:
y_pred = np.nan
#######################################################################
# Functional Connectivity second-level pipeline
#######################################################################
if feature == 'fc' and (pd.notnull(results['rfMRI_basc_correlation'][0])
and pd.notnull(results['rfMRI_basc_partial_correlation'][0])
and pd.notnull(results['rfMRI_basc_tangent'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['rfMRI_basc_correlation'][0],
results['rfMRI_basc_partial_correlation'][0],
results['rfMRI_basc_tangent'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Frequency Domain and Graph Measures
#######################################################################
elif feature == 'rs' and(pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_000_to_0_250_ALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_ALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_ALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_ALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_ALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_fALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_fALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_fALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_fALFF'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_0_6_LFCD'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_1_3dDegreeCentrality'][0])
and pd.notnull(results['rfMRI_REST1_hp2000_clean_s4_ReHo'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['rfMRI_REST1_hp2000_clean_s4_0_000_to_0_250_ALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_ALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_ALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_ALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_ALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_010_to_0_027_fALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_027_to_0_073_fALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_073_to_0_167_fALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_167_to_0_250_fALFF'][0],
results['rfMRI_REST1_hp2000_clean_s4_0_6_LFCD'][0],
results['rfMRI_REST1_hp2000_clean_s4_1_3dDegreeCentrality'][0],
results['rfMRI_REST1_hp2000_clean_s4_ReHo'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Working Memory second-level pipeline
#######################################################################
elif feature == 'WM' and (pd.notnull(results['tfMRI_WM_0BK_s4'][0])
and pd.notnull(results['tfMRI_WM_2BK_s4'][0])
and pd.notnull(results['tfMRI_WM_2BK_minus_0BK_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_WM_0BK_s4'][0],
results['tfMRI_WM_2BK_s4'][0],
results['tfMRI_WM_2BK_minus_0BK_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Category-Specific Representations second-level pipeline
#######################################################################
elif feature == 'CATEGORY' and (pd.notnull(results['tfMRI_WM_TOOL_s4'][0])
and pd.notnull(results['tfMRI_WM_PLACE_s4'][0])
and pd.notnull(results['tfMRI_WM_FACE_s4'][0])
and pd.notnull(results['tfMRI_WM_BODY_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_WM_TOOL_s4'][0],
results['tfMRI_WM_PLACE_s4'][0],
results['tfMRI_WM_FACE_s4'][0],
results['tfMRI_WM_BODY_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Relational second-level pipeline
#######################################################################
elif feature == 'RELATIONAL' and (pd.notnull(results['tfMRI_RELATIONAL_MATCH_s4'][0])
and pd.notnull(results['tfMRI_RELATIONAL_REL_s4'][0])
and pd.notnull(results['tfMRI_RELATIONAL_REL_minus_MATCH_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_RELATIONAL_MATCH_s4'][0],
results['tfMRI_RELATIONAL_REL_s4'][0],
results['tfMRI_RELATIONAL_REL_minus_MATCH_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Emotion second-level pipeline
#######################################################################
elif feature == 'EMOTION' and (pd.notnull(results['tfMRI_EMOTION_FACES_s4'][0])
and pd.notnull(results['tfMRI_EMOTION_SHAPES_s4'][0])
and pd.notnull(results['tfMRI_EMOTION_FACES_minus_SHAPES_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_EMOTION_FACES_s4'][0],
results['tfMRI_EMOTION_SHAPES_s4'][0],
results['tfMRI_EMOTION_FACES_minus_SHAPES_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Language second-level pipeline
#######################################################################
elif feature == 'LANGUAGE' and (pd.notnull(results['tfMRI_LANGUAGE_MATH_s4'][0])
and pd.notnull(results['tfMRI_LANGUAGE_STORY_s4'][0])
and pd.notnull(results['tfMRI_LANGUAGE_STORY_minus_MATH_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_LANGUAGE_MATH_s4'][0],
results['tfMRI_LANGUAGE_STORY_s4'][0],
results['tfMRI_LANGUAGE_STORY_minus_MATH_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Gambling second-level pipeline
#######################################################################
elif feature == 'GAMBLING' and (pd.notnull(results['tfMRI_GAMBLING_PUNISH_s4'][0])
and pd.notnull(results['tfMRI_GAMBLING_REWARD_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_GAMBLING_PUNISH_s4'][0],
results['tfMRI_GAMBLING_REWARD_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Social second-level pipeline
#######################################################################
elif feature == 'SOCIAL' and (pd.notnull(results['tfMRI_SOCIAL_RANDOM_s4'][0])
and pd.notnull(results['tfMRI_SOCIAL_TOM_s4'][0])
and pd.notnull(results['tfMRI_SOCIAL_TOM_minus_RANDOM_s4'][0])):
print("Loading second-level pipeline for %s" % (feature))
data=np.asarray([results['tfMRI_SOCIAL_RANDOM_s4'][0],
results['tfMRI_SOCIAL_TOM_s4'][0],
results['tfMRI_SOCIAL_TOM_minus_RANDOM_s4'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_second_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
else:
print("Skipping second-level pipeline for %s" % (feature))
if np.isnan(y_pred):
results[feature] = y_pred
else:
results[feature] = y_pred[0]
'''
#######################################################################
#######################################################################
#######################################################################
# Third-level pipelines
#######################################################################
#######################################################################
#######################################################################
'''
features=['gray_matter_white_matter_fc_rs_WM_CATEGORY_RELATIONAL_EMOTION_LANGUAGE_GAMBLING_SOCIAL',
'gray_matter_white_matter_fc_rs_CATEGORY_RELATIONAL_EMOTION_LANGUAGE_GAMBLING_SOCIAL',
'gray_matter_white_matter',
'gray_matter_white_matter_fc_rs',
'gray_matter_white_matter_fc_rs_EMOTION',
'gray_matter_white_matter_fc_rs_WM']
for feature in features:
y_pred = np.nan
#######################################################################
# All feature sets
#######################################################################
if feature == 'gray_matter_white_matter_fc_rs_WM_CATEGORY_RELATIONAL_EMOTION_LANGUAGE_GAMBLING_SOCIAL' and (pd.notnull(results['gray_matter'][0])
and pd.notnull(results['white_matter'][0])
and pd.notnull(results['fc'][0])
and pd.notnull(results['rs'][0])
and pd.notnull(results['WM'][0])
and pd.notnull(results['CATEGORY'][0])
and pd.notnull(results['RELATIONAL'][0])
and pd.notnull(results['EMOTION'][0])
and pd.notnull(results['LANGUAGE'][0])
and pd.notnull(results['GAMBLING'][0])
and pd.notnull(results['SOCIAL'][0])):
print("Loading third-level pipeline for %s" % (feature))
data=np.asarray([results['gray_matter'][0],
results['white_matter'][0],
results['fc'][0],
results['rs'][0],
results['WM'][0],
results['CATEGORY'][0],
results['RELATIONAL'][0],
results['EMOTION'][0],
results['LANGUAGE'][0],
results['GAMBLING'][0],
results['SOCIAL'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_third_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# All Features Sets Except for Working Memory
#######################################################################
if feature == 'gray_matter_white_matter_fc_rs_CATEGORY_RELATIONAL_EMOTION_LANGUAGE_GAMBLING_SOCIAL' and (pd.notnull(results['gray_matter'][0])
and pd.notnull(results['white_matter'][0])
and pd.notnull(results['fc'][0])
and pd.notnull(results['rs'][0])
and pd.notnull(results['CATEGORY'][0])
and pd.notnull(results['RELATIONAL'][0])
and pd.notnull(results['EMOTION'][0])
and pd.notnull(results['LANGUAGE'][0])
and pd.notnull(results['GAMBLING'][0])
and pd.notnull(results['SOCIAL'][0])):
print("Loading third-level pipeline for %s" % (feature))
data=np.asarray([results['gray_matter'][0],
results['white_matter'][0],
results['fc'][0],
results['rs'][0],
results['CATEGORY'][0],
results['RELATIONAL'][0],
results['EMOTION'][0],
results['LANGUAGE'][0],
results['GAMBLING'][0],
results['SOCIAL'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_third_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Gray Matter and White Matter
#######################################################################
if feature == 'gray_matter_white_matter' and (pd.notnull(results['gray_matter'][0])
and pd.notnull(results['white_matter'][0])):
print("Loading third-level pipeline for %s" % (feature))
data=np.asarray([results['gray_matter'][0],
results['white_matter'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_third_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Gray Matter, White Matter, and Resting State
#######################################################################
if feature == 'gray_matter_white_matter_fc_rs' and (pd.notnull(results['gray_matter'][0])
and pd.notnull(results['white_matter'][0])
and pd.notnull(results['fc'][0])
and pd.notnull(results['rs'][0])):
print("Loading third-level pipeline for %s" % (feature))
data=np.asarray([results['gray_matter'][0],
results['white_matter'][0],
results['fc'][0],
results['rs'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_third_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Gray Matter, White Matter, Resting State, and Emotion Processing
#######################################################################
if feature == 'gray_matter_white_matter_fc_rs_EMOTION' and (pd.notnull(results['gray_matter'][0])
and pd.notnull(results['white_matter'][0])
and pd.notnull(results['fc'][0])
and pd.notnull(results['rs'][0])
and pd.notnull(results['EMOTION'][0])):
print("Loading third-level pipeline for %s" % (feature))
data=np.asarray([results['gray_matter'][0],
results['white_matter'][0],
results['fc'][0],
results['rs'][0],
results['EMOTION'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_third_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
#######################################################################
# Gray Matter, White Matter, Resting State, and Working Memory
#######################################################################
if feature == 'gray_matter_white_matter_fc_rs_WM' and (pd.notnull(results['gray_matter'][0])
and pd.notnull(results['white_matter'][0])
and pd.notnull(results['fc'][0])
and pd.notnull(results['rs'][0])
and pd.notnull(results['WM'][0])):
print("Loading third-level pipeline for %s" % (feature))
data=np.asarray([results['gray_matter'][0],
results['white_matter'][0],
results['fc'][0],
results['rs'][0],
results['WM'][0]])
pipeline = joblib.load('./models/' + measure + '_Unadj_' + feature + '_third_level_pipeline_and_results.sav')
regressor=pipeline['regressor'][0]
if intercept != 'yes':
print('No intercept: Setting intercept to 0')
regressor.intercept_ = np.array([0], dtype='float32')
y_pred = regressor.predict(data.reshape(1,-1))
if np.isnan(y_pred):
results[feature] = y_pred
else:
results[feature] = y_pred[0]
'''
#######################################################################
#######################################################################
#######################################################################
# Save results
#######################################################################
#######################################################################
#######################################################################
'''
results.to_csv(output, header=True, index=False, na_rep='nan')
return results