# 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--gr', 'sopt--gr', 'db--rkhs', 'mc--gr', 'mc--log', 'mc--probitnorm', 'sopt--hf', 'vopt--hf', 'uncertainty--gr', 'uncertainty--log', 'uncertainty--probitnorm', 'rand--gr', 'rand--log', 'rand--probitnorm'] GRAPH_PARAMS = { 'knn' :10, 'sigma' : 3., 'normalized' : True, 'zp_k' : 5 } if __name__ == "__main__": parser = argparse.ArgumentParser(description = 'Run Active Learning experiment on Binary dataset') parser.add_argument('--data-root', default='./data/binary/', dest='data_root', type=str, help='Location of data X with labels.') parser.add_argument('--num-eigs', default=50, dest='M', type=int, help='Number of eigenvalues for spectral truncation') parser.add_argument('--tau', default=0.005, type=float, help='value of diagonal perturbation and scaling of GBSSL models (minus HF)') parser.add_argument('--gamma', default=0.1, type=float, help='value of noise parameter to be shared across all GBSSL models (minus HF)') parser.add_argument('--delta', default=0.01, type=float, help='value of diagonal perturbation of unnormalized graph Laplacian for HF model.') parser.add_argument('--h', default=0.1, type=float, help='kernel width for RKHS model.') parser.add_argument('--B', default=5, type=int, help='batch size for AL iterations') parser.add_argument('--al-iters', default=100, dest='al_iters', type=int, 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('--runs', default=5, type=int, help='Number of trials to run') parser.add_argument('--lab-start', default=2, dest='lab_start', type=int, help='Number of initially labeled points.') parser.add_argument('--metric', default='euclidean', type=str, help='metric name ("euclidean" or "cosine") for graph construction') parser.add_argument('--name', default='binary', 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 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] labels[labels == 0] = -1 # 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 # If we are doing a run with the HF model, we need the unnormalized graph Laplacian L = None if 'hf' in ''.join(ACQ_MODELS): prev_run = get_prev_run('GraphManager.from_features', GRAPH_PARAMS, tags={"X":str(X), "N":str(X.shape[0])}, git_commit=None) url_data = urllib.parse.urlparse(os.path.join(prev_run.info.artifact_uri, 'W.npz')) path = urllib.parse.unquote(url_data.path) W = sps.load_npz(path) L = sps.csr_matrix(gm.compute_laplacian(W, normalized=False)) + args.delta**2. * sps.eye(N) # 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, delta = %1.6f, h = %1.6f" % (args.tau, args.gamma, args.delta, args.h)) 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() #experiment_name = 'checker2' 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=2, 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)) 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 == 'hf': run_name = "{}-{}-{:.2f}-{}".format(acq, model, args.delta, i) elif model == 'rkhs': run_name = "{}-{}-{:.2}-{}".format(acq, model, args.h, i) else: run_name = "{}-{}-{:.3f}-{:.3f}-{}-{}".format(acq, model, args.tau, args.gamma, 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 not in ['hf', 'rkhs']: mlflow.log_params({ 'tau' : args.tau, 'gamma' : args.gamma, 'M' : args.M }) run_binary(evals, evecs, args.tau, args.gamma, labels, labeled, args.al_iters, args.B, modelname=model, acq=acq, cand=args.cand, select_method=args.select_method, verbose=False) else: if model == 'hf': mlflow.log_param('delta', args.delta) else: mlflow.log_param('h', args.h) run_rkhs_hf(labels, labeled, args.al_iters, args.B, h=args.h, delta=args.delta, X=X, L=L, modelname=model, acq=acq, cand=args.cand, select_method=args.select_method, verbose=False) # 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')