def test_cvbestsearchrefit(self): X, y = datasets.make_classification(n_samples=12, n_features=10, n_informative=2) n_folds_nested = 2 #random_state = 0 C_values = [.1, 0.5, 1, 2, 5] kernels = ["linear", "rbf"] key_y_pred = 'y' + conf.SEP + conf.PREDICTION # With EPAC methods = Methods(*[SVC(C=C, kernel=kernel) for C in C_values for kernel in kernels]) wf = CVBestSearchRefit(methods, n_folds=n_folds_nested) wf.run(X=X, y=y) r_epac = wf.reduce().values()[0] # - Without EPAC r_sklearn = dict() clf = SVC(kernel="linear") parameters = {'C': C_values, 'kernel': kernels} cv_nested = StratifiedKFold(y=y, n_folds=n_folds_nested) gscv = grid_search.GridSearchCV(clf, parameters, cv=cv_nested) gscv.fit(X, y) r_sklearn[key_y_pred] = gscv.predict(X) r_sklearn[conf.BEST_PARAMS] = gscv.best_params_ # - Comparisons comp = np.all(r_epac[key_y_pred] == r_sklearn[key_y_pred]) self.assertTrue(comp, u'Diff CVBestSearchRefit: prediction') for key_param in r_epac[conf.BEST_PARAMS][0]: if key_param in r_sklearn[conf.BEST_PARAMS]: comp = r_sklearn[conf.BEST_PARAMS][key_param] == \ r_epac[conf.BEST_PARAMS][0][key_param] self.assertTrue(comp, \ u'Diff CVBestSearchRefit: best parameters')
def test_mem(): X, y = datasets.make_classification(n_samples=2000, n_features=10000, n_informative=2, random_state=1) wf = CVBestSearchRefit( Methods(*[SVC(kernel="linear"), SVC(kernel="rbf")]), n_folds=10) wf.run(X=X, y=y) # Top-down process: computing recognition rates, etc. print wf.reduce() # Bottom-up process: computing p-values, etc.
def get_workflow(self, n_features=int(1E03)): random_state = 0 C_values = [1, 10] k_values = 0 k_max = "auto" n_folds_nested = 5 n_folds = 10 n_perms = 10 if k_max != "auto": k_values = range_log2(np.minimum(int(k_max), n_features), add_n=True) else: k_values = range_log2(n_features, add_n=True) cls = Methods(*[ Pipe(SelectKBest(k=k), SVC(C=C, kernel="linear")) for C in C_values for k in k_values ]) pipeline = CVBestSearchRefit(cls, n_folds=n_folds_nested, random_state=random_state) wf = Perms(CV(pipeline, n_folds=n_folds), n_perms=n_perms, permute="y", random_state=random_state) return wf
def do_all(options): if options.k_max != "auto": k_values = range_log2(np.minimum(int(options.k_max), options.n_features), add_n=True) else: k_values = range_log2(options.n_features, add_n=True) C_values = [1, 10] random_state = 0 #print options #sys.exit(0) if options.trace: from epac import conf conf.TRACE_TOPDOWN = True ## 1) Build dataset ## ================ X, y = datasets.make_classification(n_samples=options.n_samples, n_features=options.n_features, n_informative=options.n_informative) ## 2) Build Workflow ## ================= time_start = time.time() ## CV + Grid search of a pipeline with a nested grid search cls = Methods(*[ Pipe(SelectKBest(k=k), SVC(kernel="linear", C=C)) for C in C_values for k in k_values ]) pipeline = CVBestSearchRefit(cls, n_folds=options.n_folds_nested, random_state=random_state) wf = Perms(CV(pipeline, n_folds=options.n_folds), n_perms=options.n_perms, permute="y", random_state=random_state) print "Time ellapsed, tree construction:", time.time() - time_start ## 3) Run Workflow ## =============== time_fit_predict = time.time() ## Run on local machine sfw_engine = SomaWorkflowEngine(tree_root=wf, num_processes=options.n_cores) ## Run on cluster # sfw_engine = SomaWorkflowEngine( # tree_root=wf, # num_processes=options.n_cores, # resource_id="jl237561@gabriel", # login="******") wf = sfw_engine.run(X=X, y=y) print "Time ellapsed, fit predict:", time.time() - time_fit_predict time_reduce = time.time() ## 4) Reduce Workflow ## ================== print wf.reduce() print "Time ellapsed, reduce:", time.time() - time_reduce
def test_cvbestsearchrefit_select_k_best(self): list_C_value = range(2, 10, 1) # print repr(list_C_value) for C_value in list_C_value: # C_value = 2 # print C_value X, y = datasets.make_classification(n_samples=100, n_features=500, n_informative=5) n_folds_nested = 2 #random_state = 0 k_values = [2, 3, 4, 5, 6] key_y_pred = 'y' + conf.SEP + conf.PREDICTION # With EPAC methods = Methods(*[Pipe(SelectKBest(k=k), SVC(C=C_value, kernel="linear")) for k in k_values]) wf = CVBestSearchRefit(methods, n_folds=n_folds_nested) wf.run(X=X, y=y) r_epac = wf.reduce().values()[0] # - Without EPAC from sklearn.pipeline import Pipeline r_sklearn = dict() clf = Pipeline([('anova', SelectKBest(k=3)), ('svm', SVC(C=C_value, kernel="linear"))]) parameters = {'anova__k': k_values} cv_nested = StratifiedKFold(y=y, n_folds=n_folds_nested) gscv = grid_search.GridSearchCV(clf, parameters, cv=cv_nested) gscv.fit(X, y) r_sklearn[key_y_pred] = gscv.predict(X) r_sklearn[conf.BEST_PARAMS] = gscv.best_params_ r_sklearn[conf.BEST_PARAMS]['k'] = \ r_sklearn[conf.BEST_PARAMS]['anova__k'] # - Comparisons comp = np.all(r_epac[key_y_pred] == r_sklearn[key_y_pred]) self.assertTrue(comp, u'Diff CVBestSearchRefit: prediction') for key_param in r_epac[conf.BEST_PARAMS][0]: if key_param in r_sklearn[conf.BEST_PARAMS]: comp = r_sklearn[conf.BEST_PARAMS][key_param] == \ r_epac[conf.BEST_PARAMS][0][key_param] self.assertTrue(comp, \ u'Diff CVBestSearchRefit: best parameters')
def test_peristence_perm_cv_parmethods_pipe_vs_sklearn(self): key_y_pred = 'y' + conf.SEP + conf.PREDICTION X, y = datasets.make_classification(n_samples=12, n_features=10, n_informative=2) n_folds_nested = 2 #random_state = 0 C_values = [.1, 0.5, 1, 2, 5] kernels = ["linear", "rbf"] # With EPAC methods = Methods(*[SVC(C=C, kernel=kernel) for C in C_values for kernel in kernels]) wf = CVBestSearchRefit(methods, n_folds=n_folds_nested) # Save workflow # ------------- import tempfile #store = StoreFs("/tmp/toto", clear=True) store = StoreFs(tempfile.mktemp()) wf.save_tree(store=store) wf = store.load() wf.run(X=X, y=y) ## Save results wf.save_tree(store=store) wf = store.load() r_epac = wf.reduce().values()[0] # - Without EPAC r_sklearn = dict() clf = SVC(kernel="linear") parameters = {'C': C_values, 'kernel': kernels} cv_nested = StratifiedKFold(y=y, n_folds=n_folds_nested) gscv = grid_search.GridSearchCV(clf, parameters, cv=cv_nested) gscv.fit(X, y) r_sklearn[key_y_pred] = gscv.predict(X) r_sklearn[conf.BEST_PARAMS] = gscv.best_params_ r_sklearn[conf.BEST_PARAMS]['name'] = 'SVC' # - Comparisons comp = np.all(r_epac[key_y_pred] == r_sklearn[key_y_pred]) self.assertTrue(comp, u'Diff CVBestSearchRefit: prediction') comp = np.all([r_epac[conf.BEST_PARAMS][0][p] == r_sklearn[conf.BEST_PARAMS][p] for p in r_sklearn[conf.BEST_PARAMS]]) self.assertTrue(comp, u'Diff CVBestSearchRefit: best parameters')
def do_all(options): if options.k_max != "auto": k_values = range_log2(np.minimum(int(options.k_max), options.n_features), add_n=True) else: k_values = range_log2(options.n_features, add_n=True) C_values = [1, 10] random_state = 0 #print options #sys.exit(0) if options.trace: from epac import conf conf.TRACE_TOPDOWN = True ## 1) Build dataset ## ================ X, y = datasets.make_classification(n_samples=options.n_samples, n_features=options.n_features, n_informative=options.n_informative) ## 2) Build Workflow ## ================= time_start = time.time() ## CV + Grid search of a pipeline with a nested grid search cls = Methods(*[ Pipe(SelectKBest(k=k), SVC(kernel="linear", C=C)) for C in C_values for k in k_values ]) pipeline = CVBestSearchRefit(cls, n_folds=options.n_folds_nested, random_state=random_state) wf = Perms(CV(pipeline, n_folds=options.n_folds), n_perms=options.n_perms, permute="y", random_state=random_state) print "Time ellapsed, tree construction:", time.time() - time_start ## 3) Export Workflow to soma_workflow_gui ## =============== time_fit_predict = time.time() if os.path.isdir(options.soma_workflow_dir): shutil.rmtree(options.soma_workflow_dir) sfw_engine = SomaWorkflowEngine(tree_root=wf, num_processes=options.n_cores) sfw_engine.export_to_gui(options.soma_workflow_dir, X=X, y=y) print "Time ellapsed, fit predict:", time.time() - time_fit_predict # ## 6) Load Epac tree & Reduce # ## ========================== reduce_filename = os.path.join(options.soma_workflow_dir, "reduce.py") f = open(reduce_filename, 'w') reduce_str = """from epac.map_reduce.engine import SomaWorkflowEngine wf = SomaWorkflowEngine.load_from_gui("%s") print wf.reduce() """ % options.soma_workflow_dir f.write(reduce_str) f.close() print "#First run\n"\ "soma_workflow_gui\n"\ "\t(1)Open %s\n"\ "\t(2)Submit\n"\ "\t(3)Transfer Input Files\n"\ "\t...wait...\n"\ "\t(4)Transfer Output Files\n"\ "#When done run:\npython %s" % ( os.path.join(options.soma_workflow_dir, sfw_engine.open_me_by_soma_workflow_gui), reduce_filename)
def todo_perm_cv_grid_vs_sklearn(self): X, y = datasets.make_classification(n_samples=100, n_features=500, n_informative=5) n_perms = 3 n_folds = 2 n_folds_nested = 2 random_state = 0 k_values = [2, 3] C_values = [1, 10] # = With EPAC pipelines = Methods(*[Pipe(SelectKBest(k=k), SVC(C=C, kernel="linear")) for C in C_values for k in k_values]) #print [n for n in pipelines.walk_leaves()] pipelines_cv = CVBestSearchRefit(pipelines, n_folds=n_folds_nested, random_state=random_state) wf = Perms(CV(pipelines_cv, n_folds=n_folds, reducer=ClassificationReport(keep=True)), n_perms=n_perms, permute="y", reducer=PvalPerms(keep=True), random_state=random_state) wf.fit_predict(X=X, y=y) r_epac = wf.reduce().values()[0] for key in r_epac: print("key=" + repr(key) + ", value=" + repr(r_epac[key])) # = With SKLEARN from sklearn.cross_validation import StratifiedKFold from epac.sklearn_plugins import Permutations from sklearn.pipeline import Pipeline from sklearn import grid_search clf = Pipeline([('anova', SelectKBest(k=3)), ('svm', SVC(kernel="linear"))]) parameters = {'anova__k': k_values, 'svm__C': C_values} r_sklearn = dict() r_sklearn['pred_te'] = [[None] * n_folds for i in range(n_perms)] r_sklearn['true_te'] = [[None] * n_folds for i in range(n_perms)] r_sklearn['score_tr'] = [[None] * n_folds for i in range(n_perms)] r_sklearn['score_te'] = [[None] * n_folds for i in range(n_perms)] r_sklearn['mean_score_te'] = [None] * n_perms r_sklearn['mean_score_tr'] = [None] * n_perms perm_nb = 0 perms = Permutations(n=y.shape[0], n_perms=n_perms, random_state=random_state) for idx in perms: #idx = perms.__iter__().next() y_p = y[idx] cv = StratifiedKFold(y=y_p, n_folds=n_folds) fold_nb = 0 for idx_train, idx_test in cv: #idx_train, idx_test = cv.__iter__().next() X_train = X[idx_train, :] X_test = X[idx_test, :] y_p_train = y_p[idx_train, :] y_p_test = y_p[idx_test, :] # Nested CV cv_nested = StratifiedKFold(y=y_p_train, n_folds=n_folds_nested) gscv = grid_search.GridSearchCV(clf, parameters, cv=cv_nested) gscv.fit(X_train, y_p_train) r_sklearn['pred_te'][perm_nb][fold_nb] = gscv.predict(X_test) r_sklearn['true_te'][perm_nb][fold_nb] = y_p_test r_sklearn['score_tr'][perm_nb][fold_nb] =\ gscv.score(X_train, y_p_train) r_sklearn['score_te'][perm_nb][fold_nb] =\ gscv.score(X_test, y_p_test) fold_nb += 1 # Average over folds r_sklearn['mean_score_te'][perm_nb] = \ np.mean(np.asarray(r_sklearn['score_te'][perm_nb]), axis=0) r_sklearn['mean_score_tr'][perm_nb] = \ np.mean(np.asarray(r_sklearn['score_tr'][perm_nb]), axis=0) #np.mean(R2[key]['score_tr'][perm_nb]) perm_nb += 1 print(repr(r_sklearn)) # - Comparisons shared_keys = set(r_epac.keys()).intersection(set(r_sklearn.keys())) comp = {k: np.all(np.asarray(r_epac[k]) == np.asarray(r_sklearn[k])) for k in shared_keys} print("comp=" + repr(comp)) #return comp for key in comp: self.assertTrue(comp[key], u'Diff for attribute: "%s"' % key)
SVM(dual=False, class_weight='auto', penalty="l1", C=C) for C in C_values ]) cv = CV(svms, cv_type="stratified", n_folds=10) cv.run(X=X, y=y) cv_results = cv.reduce() #print cv_results epac.export_csv( cv, cv_results, os.path.join(WD, "results", "cv10_caarms+pas+canabis_svmsl1.csv")) # SVM L1 with CVBestSearchRefit # ============================= svms_cv = CVBestSearchRefit(svms, n_folds=10, cv_type="stratified") cv = CV(svms_cv, cv_type="stratified", n_folds=10) cv.run(X=X, y=y) cv_results = cv.reduce() print cv_results #[{'key': CVBestSearchRefit, 'y/test/score_f1': [ 0.82352941 0.7 ], 'y/test/recall_pvalues': [ 0.01086887 0.06790736], 'y/test/score_precision': [ 0.77777778 0.77777778], 'y/test/recall_mean_pvalue': 0.0191572904587, 'y/test/score_recall': [ 0.875 0.63636364], 'y/test/score_accuracy': 0.777777777778, 'y/test/score_recall_mean': 0.755681818182}]) # #Parmis les 27 11 ont fait la transition et 16 ne l'on pas faite #- Sensibilité (Taux de detection de les transitions) #63.63 % soit 7 / 11 (p = 0.067) # #- Spécificité (Taux de detection de ceux qui n'ont pas transité ou 1 - Faux positifs) #87.5 % soit 14 / 16 (p = 0.01) #
# #anova_svms = Methods(*[Pipe(SelectKBest(k=k), #preprocessing.StandardScaler(), # Methods(*[SVM(C=C, penalty=penalty, class_weight='auto', dual=False) for C in C_values for penalty in ['l1', 'l2']])) for k in k_values]) cv = CV(svms, cv_type="stratified", n_folds=10) cv.run(X=X, y=y) cv_results = cv.reduce() #print cv_results epac.export_csv(cv, cv_results, os.path.join(WD, "results", "cv10_svmsl1.csv")) # SVM L1 with CVBestSearchRefit # ============================= svms_cv = CVBestSearchRefit(svms, n_folds=10) cv = CV(svms_cv, cv_type="stratified", n_folds=10) cv.run(X=X, y=y) cv_results = cv.reduce() print cv_results #[{'key': CVBestSearchRefit, 'y/test/score_f1': [ 0.84848485 0.76190476], 'y/test/recall_pvalues': [ 0.01086887 0.03000108], 'y/test/score_precision': [ 0.82352941 0.8 ], 'y/test/recall_mean_pvalue': 0.00592461228371, 'y/test/score_recall': [ 0.875 0.72727273], 'y/test/score_accuracy': 0.814814814815, 'y/test/score_recall_mean': 0.801136363636}]) # #Parmis les 27 11 ont fait la transition et 16 ne l'on pas faite #- Sensibilité (Taux de detection de les transitions) #72.72 % soit 8 / 11 (p = 0.03) # #- Spécificité (Taux de detection de ceux qui n'ont pas transité ou 1 - Faux positifs) #87.5 % soit 14 / 16 (p = 0.01) # #Nous avons un taux de bonne classification moyen de 81.4 % #
# / \ # LDA SVM Classifier (Estimator) from epac import CV, Methods cv = CV(Methods(LDA(), SVM())) cv.run(X=X, y=y) print(cv.reduce()) # Model selection using CV # ------------------------ # CVBestSearchRefit # Methods (Splitter) # / \ # SVM(C=1) SVM(C=10) Classifier (Estimator) from epac import Pipe, CVBestSearchRefit, Methods # CV + Grid search of a simple classifier wf = CVBestSearchRefit(Methods(SVM(C=1), SVM(C=10))) wf.run(X=X, y=y) print(wf.reduce()) # Feature selection combined with SVM and LDA # CVBestSearchRefit # Methods (Splitter) # / \ # KBest(1) KBest(5) SelectKBest (Estimator) # | # Methods (Splitter) # / \ # LDA() SVM() ... Classifiers (Estimator) pipelines = Methods( *[Pipe(SelectKBest(k=k), Methods(LDA(), SVM())) for k in [1, 5]]) print([n for n in pipelines.walk_leaves()])
# LDA SVM Classifier (Estimator) from epac import CV, Methods cv = CV(Methods(LDA(), SVM())) cv.run(X=X, y=y) print cv.reduce() # Model selection using CV # ------------------------ # CVBestSearchRefit # Methods (Splitter) # / \ # SVM(C=1) SVM(C=10) Classifier (Estimator) from epac import Pipe, CVBestSearchRefit, Methods # CV + Grid search of a simple classifier wf = CVBestSearchRefit(Methods(SVM(C=1), SVM(C=10))) wf.run(X=X, y=y) print wf.reduce() # Feature selection combined with SVM and LDA # CVBestSearchRefit # Methods (Splitter) # / \ # KBest(1) KBest(5) SelectKBest (Estimator) # | # Methods (Splitter) # / \ # LDA() SVM() ... Classifiers (Estimator) pipelines = Methods(*[Pipe(SelectKBest(k=k), Methods(LDA(), SVM())) for k in [1, 5]]) print [n for n in pipelines.walk_leaves()] best_cv = CVBestSearchRefit(pipelines)
print svms.children[0] svms.children[0].estimator.coef_ print svms.children[1] svms.children[1].estimator.coef_ print "Weights given by SVMs" d = dict(var = imaging_variables, svm_weights_l1 = svms.children[0].estimator.coef_.ravel(), svm_weights_l2 = svms.children[1].estimator.coef_.ravel()) print pd.DataFrame(d).to_string() ############################################################################## # Automatic model selection: "CVBestSearchRefit" from epac import CVBestSearchRefit, Methods, CV svms_auto = CVBestSearchRefit(svms) cv = CV(svms_auto, n_folds=n_folds) cv.run(X=X, y=y) # res_cv_svms_auto = cv.reduce() print res_cv_svms_auto print res_cv_svms_auto["CVBestSearchRefit"]['y/test/score_recall'] # Re-fit on all data. Warning: biased !!! svms_auto.run(X=X, y=y) print svms_auto.best_params print svms_auto.refited.estimator.coef_ ############################################################################## # Put everything together # Pipeline, "Pipe": SelectKBest + StandardScaler + SVM l1 vs l2