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Merge branch 'Add_Brain_Age' into photon_dev
# Conflicts: # photonai/modelwrapper/SamplePairing.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| from .PhotonBase import PipelineElement | ||
| from ..photonlogger import Logger | ||
| import numpy as np | ||
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| class PhotonBatchElement(PipelineElement): | ||
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| def __init__(self, name, hyperparameters: dict=None, test_disabled: bool=False, disabled: bool =False, | ||
| base_element=None, batch_size: int = 10, **kwargs): | ||
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| super(PhotonBatchElement, self).__init__(name, hyperparameters, test_disabled, disabled, base_element, **kwargs) | ||
| # self.base_element = PipelineElement(base_element_name, hyperparameters=hyperparameters, **kwargs) | ||
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| self.batch_size = batch_size | ||
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| @staticmethod | ||
| def chunker(nr_items, size): | ||
| return [(pos, pos + size) for pos in range(0, nr_items, size)] | ||
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| def batch_call(self, delegate, X, y=None, call_with_y=True, **kwargs): | ||
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| # initialize return values | ||
| processed_X = None | ||
| processed_y = None | ||
| processed_kwargs = dict() | ||
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| # iterate through data batchwise | ||
| if isinstance(X, np.ndarray): | ||
| nr = X.shape[0] | ||
| dim = len(X.shape) | ||
| else: | ||
| nr = len(X) | ||
| dim = 1 | ||
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| batch_idx = 0 | ||
| for start, stop in PhotonBatchElement.chunker(nr, self.batch_size): | ||
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| batch_idx += 1 | ||
| Logger().debug(self.name + " is processing batch nr " + str(batch_idx)) | ||
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| # split data in batches | ||
| if dim > 1: | ||
| X_batched = X[start:stop, :] | ||
| else: | ||
| X_batched = X[start:stop] | ||
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| # we are probably None anyway | ||
| y_batched = y | ||
| # if we are to batch then apply it | ||
| if call_with_y and y is not None: | ||
| y_batched = y[start:stop] | ||
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| kwargs_dict_batched = dict() | ||
| for key, kwargs_list in kwargs.items(): | ||
| if not isinstance(kwargs_list, np.ndarray): | ||
| kwargs_list = np.array(kwargs_list) | ||
| if len(kwargs_list.shape) > 1: | ||
| kwargs_dict_batched[key] = kwargs_list[start:stop, :] | ||
| else: | ||
| kwargs_dict_batched[key] = kwargs_list[start:stop] | ||
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| # call the delegate | ||
| X_new, y_new, kwargs_new = self.adjusted_delegate_call(delegate, X_batched, y_batched, **kwargs_dict_batched) | ||
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| # stack results | ||
| processed_X = PhotonBatchElement.stack_results(X_new, processed_X) | ||
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| if call_with_y: | ||
| processed_y = PhotonBatchElement.stack_results(y_new, processed_y) | ||
| for proc_key, proc_values in kwargs_new.items(): | ||
| new_kwargs_data = kwargs_new[proc_key] | ||
| if proc_key not in processed_kwargs: | ||
| processed_kwargs[proc_key] = new_kwargs_data | ||
| else: | ||
| processed_kwargs[proc_key] = PhotonBatchElement.stack_results(new_kwargs_data, processed_kwargs[proc_key]) | ||
| else: | ||
| processed_kwargs = kwargs | ||
| processed_y = y | ||
| return processed_X, processed_y, processed_kwargs | ||
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| @staticmethod | ||
| def stack_results(new_a, existing_a): | ||
| if existing_a is not None: | ||
| if isinstance(new_a, list) or (isinstance(new_a, np.ndarray) and len(new_a.shape) < 2): | ||
| if isinstance(existing_a, list): | ||
| existing_a = existing_a + new_a | ||
| else: | ||
| existing_a = np.hstack((existing_a, new_a)) | ||
| else: | ||
| existing_a = np.vstack((existing_a, new_a)) | ||
| else: | ||
| existing_a = new_a | ||
| return existing_a | ||
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| def transform(self, X, y=None, **kwargs): | ||
| return self.batch_call(self.base_element.transform, X, y, **kwargs) | ||
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| def predict(self, X, y=None, **kwargs): | ||
| return self.batch_call(self.base_element.predict, X, y, call_with_y=False, **kwargs) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,14 +1,18 @@ | ||
| { | ||
| "ResampleImgs":[ | ||
| "photonai.neuro.ImageBasics.ResamplingImgs", | ||
| "ResampleImages":[ | ||
| "photonai.neuro.ImageBasics.ResampleImages", | ||
| "Transformer" | ||
| ], | ||
| "SmoothImgs":[ | ||
| "photonai.neuro.ImageBasics.SmoothImgs", | ||
| "SmoothImages":[ | ||
| "photonai.neuro.ImageBasics.SmoothImages", | ||
| "Transformer" | ||
| ], | ||
| "BrainAtlas":[ | ||
| "photonai.neuro.BrainAtlas.BrainAtlas", | ||
| "Transformer" | ||
| ], | ||
| "PatchImages":[ | ||
| "photonai.neuro.ImageBasics.PatchImages", | ||
| "Transformer" | ||
| ] | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,101 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
| import tensorflow as tf | ||
| from sklearn.model_selection import KFold | ||
| #from skopt import Optimizer | ||
| #from skopt.optimizer import dummy_minimize | ||
| #from skopt import dummy_minimize | ||
| import scipy.io as sio | ||
| import keras | ||
| from photonai.base.PhotonBase import Hyperpipe, PipelineElement, PhotonRegister | ||
| from photonai.base.PhotonBatchElement import PhotonBatchElement | ||
| from photonai.validation import ResultsTreeHandler | ||
| from photonai.neuro.BrainAtlas import AtlasLibrary | ||
| from scipy.stats import itemfreq | ||
| from photonai.investigator.Investigator import Investigator | ||
| import matplotlib.pyplot as plt | ||
| import pandas as pd | ||
| from nilearn import image | ||
| import time | ||
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| import os | ||
| os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" | ||
| os.environ["CUDA_VISIBLE_DEVICES"]="2" | ||
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| # RandomCtrlData = np.ones((1792, 121, 145, 121)) | ||
| # RandomCtrlData = np.ones((172, 121, 145, 121)) | ||
| # RandomCtrlLabels = np.random.randn((RandomCtrlData.shape[0])) | ||
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| root_folder = '/spm-data/Scratch/spielwiese_ramona/PAC2018/' | ||
| filename = 'PAC2018_age.csv' | ||
| df = pd.read_csv(os.path.join(root_folder, filename)) | ||
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| X = df["PAC_ID"] | ||
| X = [os.path.join(root_folder, 'data_all/' + x + ".nii") for x in X] | ||
| y = df["Age"].values | ||
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| X = X[0:1500] | ||
| y = y[0:1500] | ||
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| # | ||
| PhotonRegister.save(photon_name='Brain_Age_Splitting_Wrapper', | ||
| class_str='photonai.modelwrapper.Brain_Age_Splitting_Wrapper.Brain_Age_Splitting_Wrapper', element_type="Transformer") | ||
| # | ||
| # PhotonRegister.save(photon_name='Brain_Age_Splitting_CNN', | ||
| # class_str='photonai.modelwrapper.Brain_Age_Splitting_CNN.Brain_Age_Splitting_CNN', element_type="Estimator") | ||
| # | ||
| PhotonRegister.save(photon_name='Brain_Age_Random_Forest', | ||
| class_str='photonai.modelwrapper.Brain_Age_Random_Forest.Brain_Age_Random_Forest', element_type="Estimator") | ||
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| my_pipe = Hyperpipe('BrainAgePipe', | ||
| optimizer='grid_search', | ||
| metrics=['mean_absolute_error'], | ||
| best_config_metric='mean_absolute_error', | ||
| inner_cv=KFold(n_splits=5, shuffle=True, random_state=42), | ||
| outer_cv=KFold(n_splits=5, shuffle=True, random_state=42), | ||
| eval_final_performance=False, | ||
| verbosity=2) | ||
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| # transformer = PipelineElement(, hyperparameters={}) | ||
| # base_element=transformer | ||
| batched_transformer = PhotonBatchElement("PatchImages", hyperparameters={'patch_size': [10, 25, 50, 75, 100]}, | ||
| batch_size=100, | ||
| nr_of_processes=10, | ||
| cache_folder='/spm-data/vault-data1/tmp/photon_cache_vincent/') | ||
| my_pipe += batched_transformer | ||
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| #my_pipe += PipelineElement('Brain_Age_Splitting_Wrapper') | ||
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| my_pipe += PipelineElement('Brain_Age_Random_Forest') | ||
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| my_pipe.fit(X, y) | ||
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| batched_transformer.base_element.clear_cache() | ||
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| inner_performances = list() | ||
| for i, fold in enumerate(my_pipe.result_tree.outer_folds[0].tested_config_list): | ||
| inner_performances.append((fold.config_dict, fold.metrics_test[0].value)) | ||
| print(inner_performances) | ||
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| plt.ylim(0.2, 0.8) | ||
| plt.xticks(rotation=90) | ||
| plt.margins(0.3) | ||
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| for i, lelles in inner_performances: | ||
| print(i, lelles) | ||
| Benis = ",".join(("{}={}".format(*p) for p in i.items())) | ||
| plt.plot(Benis, lelles, 'ro') | ||
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| plt.show() |
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