def _save_plot(self, predictor):
""" Save Plots.
Args:
predictor: predictor instance
Returns:
predicter_weightmap_montage.png: Will output a montage of axial slices of weightmap
predicter_prediction.png: Will output a plot of prediction
"""
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
if self.algorithm == 'lassopcr':
coef = np.dot(self._pca.components_.T,self._lasso.coef_)
coef_img = self.nifti_masker.inverse_transform(np.transpose(coef))
elif self.algorithm == 'pcr':
coef = np.dot(self._pca.components_.T,self._regress.coef_)
coef_img = self.nifti_masker.inverse_transform(np.transpose(coef))
else:
coef_img = self.nifti_masker.inverse_transform(predictor.coef_)
overlay_img = nib.load(os.path.join(get_resource_path(),'MNI152_T1_2mm_brain.nii.gz'))
fig1 = plot_stat_map(coef_img, overlay_img, title=self.algorithm + " weights",
cut_coords=range(-40, 40, 10), display_mode='z')
fig1.savefig(os.path.join(self.output_dir, self.algorithm + '_weightmap_axial.png'))
if self.prediction_type == 'classification':
if self.algorithm not in ['svm','ridgeClassifier','ridgeClassifierCV']:
fig2 = probability_plot(self.stats_output)
fig2.savefig(os.path.join(self.output_dir, self.algorithm + '_prob_plot.png'))
else:
fig2 = dist_from_hyperplane_plot(self.stats_output)
fig2.savefig(os.path.join(self.output_dir, self.algorithm +
'_Distance_from_Hyperplane_xval.png'))
if self.algorithm == 'svm' and self.predictor.probability:
fig3 = probability_plot(self.stats_output)
fig3.savefig(os.path.join(self.output_dir, self.algorithm + '_prob_plot.png'))
elif self.prediction_type == 'prediction':
fig2 = scatterplot(self.stats_output)
fig2.savefig(os.path.join(self.output_dir, self.algorithm + '_scatterplot.png'))
def predict(self, algorithm=None, cv_dict=None, plot=True, **kwargs):
""" Run prediction
Args:
algorithm: Algorithm to use for prediction. Must be one of 'svm', 'svr',
'linear', 'logistic', 'lasso', 'ridge', 'ridgeClassifier','randomforest',
or 'randomforestClassifier'
cv_dict: Type of cross_validation to use. A dictionary of
{'type': 'kfolds', 'n_folds': n},
{'type': 'kfolds', 'n_folds': n, 'subject_id': holdout}, or
{'type': 'loso', 'subject_id': holdout},
where n = number of folds, and subject = vector of subject ids that corresponds to self.Y
plot: Boolean indicating whether or not to create plots.
**kwargs: Additional keyword arguments to pass to the prediction algorithm
Returns:
output: a dictionary of prediction parameters
"""
# Set algorithm
if algorithm is not None:
predictor_settings = set_algorithm(algorithm, **kwargs)
else:
# Use SVR as a default
predictor_settings = set_algorithm('svr', **{'kernel':"linear"})
# Initialize output dictionary
output = {}
output['Y'] = np.array(self.Y).flatten()
# Overall Fit for weight map
predictor = predictor_settings['predictor']
predictor.fit(self.data, output['Y'])
output['yfit_all'] = predictor.predict(self.data)
if predictor_settings['prediction_type'] == 'classification':
if predictor_settings['algorithm'] not in ['svm','ridgeClassifier','ridgeClassifierCV']:
output['prob_all'] = predictor.predict_proba(self.data)[:,1]
else:
output['dist_from_hyperplane_all'] = predictor.decision_function(self.data)
if predictor_settings['algorithm'] == 'svm' and predictor.probability:
output['prob_all'] = predictor.predict_proba(self.data)[:,1]
output['intercept'] = predictor.intercept_
# Weight map
output['weight_map'] = self.empty()
if predictor_settings['algorithm'] == 'lassopcr':
output['weight_map'].data = np.dot(predictor_settings['_pca'].components_.T,predictor_settings['_lasso'].coef_)
elif predictor_settings['algorithm'] == 'pcr':
output['weight_map'].data = np.dot(predictor_settings['_pca'].components_.T,predictor_settings['_regress'].coef_)
else:
output['weight_map'].data = predictor.coef_.squeeze()
# Cross-Validation Fit
if cv_dict is not None:
output['cv'] = set_cv(cv_dict)
predictor_cv = predictor_settings['predictor']
output['yfit_xval'] = output['yfit_all'].copy()
output['intercept_xval'] = []
output['weight_map_xval'] = deepcopy(output['weight_map'])
wt_map_xval = [];
if predictor_settings['prediction_type'] == 'classification':
if predictor_settings['algorithm'] not in ['svm','ridgeClassifier','ridgeClassifierCV']:
output['prob_xval'] = np.zeros(len(self.Y))
else:
output['dist_from_hyperplane_xval'] = np.zeros(len(self.Y))
if predictor_settings['algorithm'] == 'svm' and predictor_cv.probability:
output['prob_xval'] = np.zeros(len(self.Y))
for train, test in output['cv']:
predictor_cv.fit(self.data[train], self.Y.loc[train])
output['yfit_xval'][test] = predictor_cv.predict(self.data[test])
if predictor_settings['prediction_type'] == 'classification':
if predictor_settings['algorithm'] not in ['svm','ridgeClassifier','ridgeClassifierCV']:
output['prob_xval'][test] = predictor_cv.predict_proba(self.data[test])[:,1]
else:
output['dist_from_hyperplane_xval'][test] = predictor_cv.decision_function(self.data[test])
if predictor_settings['algorithm'] == 'svm' and predictor_cv.probability:
output['prob_xval'][test] = predictor_cv.predict_proba(self.data[test])[:,1]
output['intercept_xval'].append(predictor_cv.intercept_)
# Weight map
if predictor_settings['algorithm'] == 'lassopcr':
wt_map_xval.append(np.dot(predictor_settings['_pca'].components_.T,predictor_settings['_lasso'].coef_))
elif predictor_settings['algorithm'] == 'pcr':
wt_map_xval.append(np.dot(predictor_settings['_pca'].components_.T,predictor_settings['_regress'].coef_))
else:
wt_map_xval.append(predictor_cv.coef_.squeeze())
output['weight_map_xval'].data = np.array(wt_map_xval)
# Print Results
if predictor_settings['prediction_type'] == 'classification':
output['mcr_all'] = np.mean(output['yfit_all']==np.array(self.Y).flatten())
print 'overall accuracy: %.2f' % output['mcr_all']
if cv_dict is not None:
output['mcr_xval'] = np.mean(output['yfit_xval']==np.array(self.Y).flatten())
print 'overall CV accuracy: %.2f' % output['mcr_xval']
elif predictor_settings['prediction_type'] == 'prediction':
output['rmse_all'] = np.sqrt(np.mean((output['yfit_all']-output['Y'])**2))
output['r_all'] = np.corrcoef(output['Y'],output['yfit_all'])[0,1]
print 'overall Root Mean Squared Error: %.2f' % output['rmse_all']
print 'overall Correlation: %.2f' % output['r_all']
if cv_dict is not None:
output['rmse_xval'] = np.sqrt(np.mean((output['yfit_xval']-output['Y'])**2))
output['r_xval'] = np.corrcoef(output['Y'],output['yfit_xval'])[0,1]
print 'overall CV Root Mean Squared Error: %.2f' % output['rmse_xval']
print 'overall CV Correlation: %.2f' % output['r_xval']
# Plot
if plot:
if cv_dict is not None:
if predictor_settings['prediction_type'] == 'prediction':
fig2 = scatterplot(pd.DataFrame({'Y': output['Y'], 'yfit_xval':output['yfit_xval']}))
elif predictor_settings['prediction_type'] == 'classification':
if predictor_settings['algorithm'] not in ['svm','ridgeClassifier','ridgeClassifierCV']:
output['roc'] = Roc(input_values=output['prob_xval'], binary_outcome=output['Y'].astype('bool'))
else:
output['roc'] = Roc(input_values=output['dist_from_hyperplane_xval'], binary_outcome=output['Y'].astype('bool'))
if predictor_settings['algorithm'] == 'svm' and predictor_cv.probability:
output['roc'] = Roc(input_values=output['prob_xval'], binary_outcome=output['Y'].astype('bool'))
fig2 = output['roc'].plot()
# output['roc'].summary()
fig1=output['weight_map'].plot()
return output
def _save_plot(self, predicter):
""" Save Plots.
Args:
predicter: predicter instance
Returns:
predicter_weightmap_montage.png: Will output a montage of axial slices of weightmap
predicter_prediction.png: Will output a plot of prediction
"""
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
if self.algorithm is 'lassopcr':
coef = np.dot(self._pca.components_.T, self._lasso.coef_)
coef_img = self.nifti_masker.inverse_transform(np.transpose(coef))
elif self.algorithm is 'pcr':
coef = np.dot(self._pca.components_.T, self._regress.coef_)
coef_img = self.nifti_masker.inverse_transform(np.transpose(coef))
else:
coef_img = self.nifti_masker.inverse_transform(predicter.coef_)
overlay_img = nib.load(
os.path.join(resource_dir, 'MNI152_T1_2mm_brain.nii.gz'))
fig1 = plot_stat_map(coef_img,
overlay_img,
title=self.algorithm + " weights",
cut_coords=range(-40, 40, 10),
display_mode='z')
fig1.savefig(
os.path.join(self.output_dir,
self.algorithm + '_weightmap_axial.png'))
if self.prediction_type == 'classification':
if self.algorithm not in [
'svm', 'ridgeClassifier', 'ridgeClassifierCV'
]:
fig2 = probability_plot(self.stats_output)
fig2.savefig(
os.path.join(self.output_dir,
self.algorithm + '_prob_plot.png'))
else:
fig2 = dist_from_hyperplane_plot(self.stats_output)
fig2.savefig(
os.path.join(
self.output_dir,
self.algorithm + '_xVal_Distance_from_Hyperplane.png'))
if self.algorithm is 'svm' and self.predicter.probability:
fig3 = probability_plot(self.stats_output)
fig3.savefig(
os.path.join(self.output_dir,
self.algorithm + '_prob_plot.png'))
elif self.prediction_type == 'prediction':
fig2 = scatterplot(self.stats_output)
fig2.savefig(
os.path.join(self.output_dir,
self.algorithm + '_scatterplot.png'))
