# mlflow utilization import numpy as np import scipy.sparse as sps import argparse import time from sklearn.model_selection import train_test_split import copy import os import math import sys import matplotlib.pyplot as plt from util.graph_manager import GraphManager from util.runs_util import * import mlflow from util.mlflow_util import * ACQ_MODELS = ['vopt--mgr', 'sopt--mgr', 'mc--mgr', 'mc--ce', \ 'rand--ce', 'rand--mgr', 'uncertainty--mgr', 'uncertainty--ce'] GRAPH_PARAMS = { 'knn' :10, 'sigma' : 3., 'normalized' : True, 'zp_k' : 5 } if __name__ == "__main__": parser = argparse.ArgumentParser(description = 'Run Active Learning experiment on Multiclass dataset located at --data_root.') parser.add_argument('--data-root', default='./data/multi/', dest='data_root', type=str, help='Location of data X with labels in X_labels.npz') parser.add_argument('--num-eigs', default=50, dest='M', type=int, help='Number of eigenvalues for spectral truncation') parser.add_argument('--tau-gr', default=0.005, dest='tau_gr', type=float, help='value of diagonal perturbation and scaling of MGR') parser.add_argument('--gamma-gr', default=0.1, dest='gamma_gr', type=float, help='value of noise parameter of MGR') parser.add_argument('--tau-ce', default=0.005, dest='tau_ce', type=float, help='value of diagonal perturbation and scaling of CE model') parser.add_argument('--gamma-ce', default=0.5, dest='gamma_ce', type=float, help='value of noise parameter of CE model') parser.add_argument('--B', default=5, type=int, help='batch size for AL iterations') parser.add_argument('--al-iters', default=100, type=int, dest='al_iters',help='number of active learning iterations to perform.') parser.add_argument('--candidate-method', default='rand', type=str, dest='cand', help='candidate set selection method name ["rand", "full"]') parser.add_argument('--candidate-percent', default=0.1, type=float, dest='cand_perc', help='if --candidate-method == "rand", then this is the percentage of unlabeled data to consider') parser.add_argument('--select-method', default='top', type=str, dest='select_method', help='how to select which points to query from the acquisition values. in ["top", "prop"]') parser.add_argument('--lab-start', default=3, type=int, dest='lab_start', help='size of initially labeled set.') parser.add_argument('--runs', default=5, type=int, help='Number of trials to run') parser.add_argument('--metric', default='euclidean', type=str, help='metric name ("euclidean" or "cosine") for graph construction') parser.add_argument('--name', default='multi', dest='experiment_name', help='Name for this dataset/experiment run ') args = parser.parse_args() GRAPH_PARAMS['n_eigs'] = args.M GRAPH_PARAMS['metric'] = args.metric if args.metric == 'cosine': # HYPERSPECTRAL DATA GRAPH_PARAMS['sigma'] = None GRAPH_PARAMS['zp_k'] = None GRAPH_PARAMS['knn'] = 15 if not os.path.exists('tmp/'): os.makedirs('tmp/') # Load in the Dataset if not os.path.exists(args.data_root + 'X_labels.npz'): raise ValueError("Cannot find previously saved data at {}".format(args.data_root + 'X_labels.npz')) print("Loading data at {}".format(args.data_root + 'X_labels.npz')) data = np.load(args.data_root + 'X_labels.npz', allow_pickle=True) X, labels = data['X'], data['labels'].flatten() N = X.shape[0] nc = len(np.unique(labels)) if args.lab_start < nc: args.lab_start = 2*nc # Load in or calculate eigenvectors, using mlflow IN Graph_manager gm = GraphManager() evals, evecs = gm.from_features(X, knn=GRAPH_PARAMS['knn'], sigma=GRAPH_PARAMS['sigma'], normalized=GRAPH_PARAMS['normalized'], n_eigs=GRAPH_PARAMS['n_eigs'], zp_k=GRAPH_PARAMS['zp_k'], metric=GRAPH_PARAMS['metric']) # runs mlflow logging in this function call # Run the experiments print("--------------- Parameters for the Run of Experiments -----------------------") print("\tacq_models = %s" % str(ACQ_MODELS)) print("\tal_iters = %d, B = %d, M = %d" % (args.al_iters, args.B, args.M)) print("\tcand=%s, select_method=%s" % (args.cand, args.select_method)) print("\tnum_init_labeled = %d" % (args.lab_start)) print("\ttau = %1.6f, gamma = %1.6f, tau_ce = %1.6f, gamma_ce = %1.6f" % (args.tau_gr, args.gamma_gr, args.tau_ce, args.gamma_ce)) print("\tnumber of runs = {}".format(args.runs)) print("\n\n") ans = input("Do you want to proceed with this test?? [y/n] ") while ans not in ['y','n']: ans = input("Sorry, please input either 'y' or 'n'") if ans == 'n': print("Not running test, exiting...") else: client = mlflow.tracking.MlflowClient() mlflow.set_experiment(args.experiment_name) experiment = client.get_experiment_by_name(args.experiment_name) for i, seed in enumerate(j**2 + 3 for j in range(args.runs)): print("=======================================") print("============= Run {}/{} ===============".format(i+1, args.runs)) print("=======================================") np.random.seed(seed) init_labeled, unlabeled = train_test_split(np.arange(N), train_size=args.lab_start, stratify=labels)#list(np.random.choice(range(N), 10, replace=False)) init_labeled, unlabeled = list(init_labeled), list(unlabeled) params_shared = { 'init_labeled': init_labeled, 'run': i, 'al_iters' : args.al_iters, 'B' : args.B, 'cand' : args.cand, 'select' : args.select_method } query = 'attributes.status = "FINISHED"' for key, val in params_shared.items(): query += ' and params.{} = "{}"'.format(key, val) already_completed = [run.data.tags['mlflow.runName'] for run in client.search_runs([experiment.experiment_id], filter_string=query)] if len(already_completed) > 0: print("Run {} already completed:".format(i+1)) for thing in sorted(already_completed, key= lambda x : x[0]): print("\t", thing) print() np.save('tmp/init_labeled', init_labeled) for acq, model in (am.split('--') for am in ACQ_MODELS): if model == 'ce': run_name = "{}-{}-{:.3f}-{:.3f}-{}-{}".format(acq, model, args.tau_ce, args.gamma_ce, args.M, i) else: run_name = "{}-{}-{:.3f}-{:.3f}-{}-{}".format(acq, model, args.tau_gr, args.gamma_gr, args.M, i) if run_name not in already_completed: labeled = copy.deepcopy(init_labeled) with mlflow.start_run(run_name=run_name) as run: # run AL test mlflow.log_params(params_shared) mlflow.log_artifact('tmp/init_labeled.npy') if model == 'ce': mlflow.log_params({ 'tau' : args.tau_ce, 'gamma' : args.gamma_ce, 'M' : args.M }) run_multi(evals, evecs, args.tau_ce, args.gamma_ce, labels, labeled, args.al_iters, args.B, modelname=model, acq=acq, cand=args.cand, select_method=args.select_method, verbose=False) elif model == 'mgr': mlflow.log_params({ 'tau' : args.tau_gr, 'gamma' : args.gamma_gr, 'M' : args.M }) run_multi(evals, evecs, args.tau_gr, args.gamma_gr, labels, labeled, args.al_iters, args.B, modelname=model, acq=acq, cand=args.cand, select_method=args.select_method, verbose=False) else: raise ValueError("{} is not a valid multiclass model".format(model)) # Clean up tmp file print("Cleaning up files in ./tmp/") if os.path.exists('tmp/init_labeled.npy'): os.remove('tmp/init_labeled.npy') if os.path.exists('tmp/iter_stats.npz'): os.remove('tmp/iter_stats.npz')