def make_2d_plot(p, noise=None, overlap=None): """ Make a 2D plot for the specified slice. @param p: The full path to the results file. @param noise: The degree of noise to use, if none all are used. @param overlap: The degree of overlap to use, if none all are used. """ # Get the data with open(p, 'rb') as f: sp_x, sp_y, svm_x, svm_y, param = cPickle.load(f) # Fix the messed up sort order ix = np.array(pd.DataFrame(param).sort_values([0, 1], ascending=[True, True]).index).astype('i') param = param[ix] sp_x, sp_y = sp_x[ix], sp_y[ix] svm_x, svm_y = svm_x[ix], svm_y[ix] # Refactor the data ix = [] if noise is not None: id = 0 id2 = 1 val = noise func = float name = 'overlap' term = 100. / 40 elif overlap is not None: id = 1 id2 = 0 val = overlap func = int name = 'noise' term = 100. else: raise 'noise and overlap are exclusive parameters' for i in xrange(len(param)): if func(param[i][id]) == val: ix.append(i) x = param[ix][:, id2] * term # Make the plots dir = os.path.dirname(p) plot_error((x, x), (np.median(sp_x[ix], 1), np.median(svm_x[ix], 1)), ('SP', 'SVM'), (compute_err(sp_x[ix]), compute_err(svm_x[ix])), '% {0}'.format(name.capitalize()), '% Error', xlim=(-5, 105), ylim=(-5, 105), show=False, out_path=os.path.join(dir, 'train-{0}.png'.format(name))) plot_error((x, x), (np.median(sp_y[ix], 1), np.median(svm_y[ix], 1)), ('SP', 'SVM'), (compute_err(sp_y[ix]), compute_err(svm_y[ix])), '% {0}'.format(name.capitalize()), '% Error', xlim=(-5, 105), ylim=(-5, 105), show=False, out_path=os.path.join(dir, 'test-{0}.png'.format(name)))
def make_2d_plot(p, noise=None, overlap=None): """ Make a 2D plot for the specified slice. @param p: The full path to the results file. @param noise: The degree of noise to use, if none all are used. @param overlap: The degree of overlap to use, if none all are used. """ # Get the data with open(p, 'rb') as f: sp_x, sp_y, svm_x, svm_y, param = cPickle.load(f) # Fix the messed up sort order ix = np.array(pd.DataFrame(param).sort_values([0, 1], ascending=[True, True]).index).astype('i') param = param[ix] sp_x, sp_y = sp_x[ix], sp_y[ix] svm_x, svm_y = svm_x[ix], svm_y[ix] # Refactor the data ix = [] if noise is not None: id = 0 id2 = 1 val = noise func = float name = 'overlap' term = 100. / 40 elif overlap is not None: id = 1 id2 = 0 val = overlap func = int name = 'noise' term = 100. else: raise 'noise and overlap are exclusive parameters' for i in xrange(len(param)): if func(param[i][id]) == val: ix.append(i) x = param[ix][:, id2] * term # Make the plots dir = os.path.dirname(p) plot_error((x, x), (np.median(sp_x[ix], 1), np.median(svm_x[ix], 1)), ('SP', 'SVM'), (compute_err(sp_x[ix]), compute_err(svm_x[ix])), '% {0}'.format(name.capitalize()), '% Error', xlim=(-5, 105), ylim=(-5, 105), show=False, out_path=os.path.join(dir, 'train-{0}.png'.format(name))) plot_error((x, x), (np.median(sp_y[ix], 1), np.median(svm_y[ix], 1)), ('SP', 'SVM'), (compute_err(sp_y[ix]), compute_err(svm_y[ix])), '% {0}'.format(name.capitalize()), '% Error', xlim=(-5, 105), ylim=(-5, 105), show=False, out_path=os.path.join(dir, 'test-{0}.png'.format(name)))
def main(base_path, ntrials=4, seed=123456789): """ Run the experiments. @param base_path: The full path to where the data should be stored. @param ntrials: The number of trials to use for the experiment. @param seed: The seed for the random number generator. """ # X-Axis data npoints = 11 pct_noises = np.linspace(0, 1, npoints) x = (pct_noises * 100, pct_noises * 100) # Run the experiment results = Parallel(n_jobs=-1)( delayed(base_experiment)(os.path.join(base_path, 'run-{0}'.format(i)), seed2) for i, seed2 in enumerate(generate_seeds(ntrials, seed), 1)) u_sp = np.zeros((len(results), npoints)) u_ip = np.zeros((len(results), npoints)) o_sp = np.zeros((len(results), npoints)) o_ip = np.zeros((len(results), npoints)) for i, (a, b, c, d) in enumerate(results): u_sp[i], u_ip[i], o_sp[i], o_ip[i] = a, b, c, d # Save the results with open(os.path.join(base_path, 'results.pkl'), 'wb') as f: cPickle.dump((u_sp, u_ip, o_sp, o_ip), f, cPickle.HIGHEST_PROTOCOL) # Make some plots e = (compute_err(u_sp, axis=0), compute_err(u_ip, axis=0)) y = (np.median(u_sp, 0), np.median(u_ip, 0)) plot_error(x, y, ('SP Output', 'Raw Data'), e, '% Noise', 'Uniqueness [%]', xlim=False, ylim=(-5, 105), out_path=os.path.join(base_path, 'uniqueness.png'), show=False) e = (compute_err(o_sp, axis=0), compute_err(o_ip, axis=0)) y = (np.median(o_sp, 0), np.median(o_ip, 0)) plot_error(x, y, ('SP Output', 'Raw Data'), e, '% Noise', 'Normalized Overlap [%]', xlim=False, ylim=(-5, 105), out_path=os.path.join(base_path, 'overlap.png'), show=False)
def plot_single_run(bp): """ Create an error plot for a single run. @param bp: The base path. """ def read(p): """ Read in the data. @param p: The path to the file to read. @return: The results. """ with open(p, 'rb') as f: reader = csv.reader(f) data = [] for row in reader: data.append(float(row[1])) return np.array(data) * 100 def get_data(p): """ Get all of the results. @param p: The directory to obtain the data in. @return: The results. """ permanence = [] for d in os.listdir(p): npath = os.path.join(p, d) if os.path.isdir(npath): permanence.append( read(os.path.join(npath, 'permanence_boost.csv'))) return np.array(permanence) data = get_data(bp) plot_error(show=True, x_series=(np.arange(data.shape[1]), ), y_series=(np.median(data, 0), ), y_errs=(compute_err(data, axis=0), ), xlim=(0, 20), ylim=(0, 100), x_label='Iteration', y_label='% Columns Boosted')
def plot_single_run(bp): """ Create an error plot for a single run. @param bp: The base path. """ def read(p): """ Read in the data. @param p: The path to the file to read. @return: The results. """ with open(p, 'rb') as f: reader = csv.reader(f) data = [] for row in reader: data.append(float(row[1])) return np.array(data) * 100 def get_data(p): """ Get all of the results. @param p: The directory to obtain the data in. @return: The results. """ permanence = [] for d in os.listdir(p): npath = os.path.join(p, d) if os.path.isdir(npath): permanence.append(read(os.path.join(npath, 'permanence_boost.csv'))) return np.array(permanence) data = get_data(bp) plot_error(show=True, x_series=(np.arange(data.shape[1]),), y_series=(np.median(data, 0),), y_errs=(compute_err(data, axis=0), ), xlim=(0, 20), ylim=(0, 100), x_label='Iteration', y_label='% Columns Boosted' )
def main(base_path, ntrials=4, seed=123456789): """ Run the experiments. @param base_path: The full path to where the data should be stored. @param ntrials: The number of trials to use for the experiment. @param seed: The seed for the random number generator. """ # X-Axis data npoints = 11 pct_noises = np.linspace(0, 1, npoints) x = (pct_noises * 100, pct_noises * 100) # Run the experiment results = Parallel(n_jobs=-1)(delayed(base_experiment)( os.path.join(base_path, 'run-{0}'.format(i)), seed2) for i, seed2 in enumerate(generate_seeds(ntrials, seed), 1)) u_sp = np.zeros((len(results), npoints)) u_ip = np.zeros((len(results), npoints)) o_sp = np.zeros((len(results), npoints)) o_ip = np.zeros((len(results), npoints)) for i, (a, b, c, d) in enumerate(results): u_sp[i], u_ip[i], o_sp[i], o_ip[i] = a, b, c, d # Save the results with open(os.path.join(base_path, 'results.pkl'), 'wb') as f: cPickle.dump((u_sp, u_ip, o_sp, o_ip), f, cPickle.HIGHEST_PROTOCOL) # Make some plots e = (compute_err(u_sp, axis=0), compute_err(u_ip, axis=0)) y = (np.median(u_sp, 0), np.median(u_ip, 0)) plot_error(x, y, ('SP Output', 'Raw Data'), e, '% Noise', 'Uniqueness [%]', xlim=False, ylim=(-5, 105), out_path=os.path.join(base_path, 'uniqueness.png'), show=False) e = (compute_err(o_sp, axis=0), compute_err(o_ip, axis=0)) y = (np.median(o_sp, 0), np.median(o_ip, 0)) plot_error(x, y, ('SP Output', 'Raw Data'), e, '% Noise', 'Normalized Overlap [%]', xlim=False, ylim=(-5, 105), out_path=os.path.join(base_path, 'overlap.png'), show=False)
def main2(base_path): """ @param base_path: Full path to pickle file to work with. """ # Mapping of independent variables to indexes data_index = { 'fit_time':0, 'learn_fit_time':1, 'pred_fit_time':2, 'input_uniqueness':3, 'input_overlap':4, 'input_correlation':5, 'sp_uniqueness':6, 'sp_overlap':7, 'sp_correlation':8 } # Get the data with open(base_path, 'rb') as f: x, y = cPickle.load(f) x = sorted(set(x[-1])) # For now work with 1D # Pull out data for this plot y1 = (y[data_index['input_uniqueness']], y[data_index['sp_uniqueness']]) y2 = (y[data_index['input_overlap']], y[data_index['sp_overlap']]) y3 = (y[data_index['input_correlation']], y[data_index['sp_correlation']]) # Refactor the data x_series = (x, x, x) med = lambda y: np.median(y, axis=1) * 100 err = lambda y: compute_err(y, axis=1) * 100 y1_series = map(med, y1) y1_errs = map(err, y1) y2_series = map(med, y2) y2_errs = map(err, y2) y3_series = map(med, y3) y3_errs = map(err, y3) # Make the main plot fig = plt.figure(figsize=(21, 20), facecolor='white') ax = fig.add_subplot(111) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off') # Make subplots ax1 = fig.add_subplot(311) plot_error(show=False, legend=False, ax=ax1, title='Uniqueness', x_series=x_series, y_series=y1_series, y_errs=y1_errs, ylim=(-5, 105)) ax2 = fig.add_subplot(312, sharex=ax1, sharey=ax1) plot_error(show=False, legend=False, ax=ax2, title='Overlap', x_series=x_series, y_series=y2_series, y_errs=y2_errs, ylim=(-5, 105)) ax3 = fig.add_subplot(313, sharex=ax1, sharey=ax1) plot_error(show=False, legend=False, ax=ax3, title='Correlation', x_series=x_series, y_series=y3_series, y_errs=y3_errs, ylim=(-5, 105)) plt.tight_layout(h_pad=2) plt.show()
def main(ntrials=10, seed=123456798): """ Vary the amount of noise and overlap and see how the system performs. @param ntrials: The number of trials to perform. @param seed: The seed for the random number generator. """ # Amount to vary each type by pct_noises = np.linspace(0, 1, 101) noverlap_bits = np.arange(0, 41) # Vary the noise results = Parallel(n_jobs=-1)(delayed(base_experiment)( noise, 0, 'noise-{0}'.format(i), ntrials, False, seed) for i, noise in enumerate(pct_noises, 1)) noise_x, noise_y, svm_noise_x, svm_noise_y = [], [], [], [] noise_x_err1, noise_x_err2, noise_y_err1, noise_y_err2 = [], [], [], [] svm_noise_x_err1, svm_noise_x_err2 = [], [] svm_noise_y_err1, svm_noise_y_err2 = [], [] for sp_x, sp_y, svm_x, svm_y in results: noise_x.append(np.median(sp_x)) noise_y.append(np.median(sp_y)) svm_noise_x.append(np.median(svm_x)) svm_noise_y.append(np.median(svm_y)) e = compute_err(sp_x, axis=None) noise_x_err1.append(e[0]) noise_x_err2.append(e[1]) e = compute_err(sp_y, axis=None) noise_y_err1.append(e[0]) noise_y_err2.append(e[1]) e = compute_err(svm_x, axis=None) svm_noise_x_err1.append(e[0]) svm_noise_x_err2.append(e[1]) e = compute_err(svm_y, axis=None) svm_noise_y_err1.append(e[0]) svm_noise_y_err2.append(e[1]) noise_x_err = (svm_noise_x_err1, svm_noise_x_err2) noise_y_err = (svm_noise_y_err1, svm_noise_y_err2) svm_noise_x_err = (svm_noise_x_err1, svm_noise_x_err2) svm_noise_y_err = (svm_noise_y_err1, svm_noise_y_err2) # Vary the overlaps results = Parallel(n_jobs=-1)( delayed(base_experiment)(0.35, overlap, 'overlap-{0}'.format(i), ntrials, False, seed) for i, overlap in enumerate(noverlap_bits, 1)) overlap_x, overlap_y, svm_overlap_x, svm_overlap_y = [], [], [], [] overlap_x_err1, overlap_x_err2 = [], [] overlap_y_err1, overlap_y_err2 = [], [] svm_overlap_x_err1, svm_overlap_x_err2 = [], [] svm_overlap_y_err1, svm_overlap_y_err2 = [], [] for sp_x, sp_y, svm_x, svm_y in results: overlap_x.append(np.median(sp_x)) overlap_y.append(np.median(sp_y)) svm_overlap_x.append(np.median(svm_x)) svm_overlap_y.append(np.median(svm_y)) e = compute_err(sp_x, axis=None) overlap_x_err1.append(e[0]) overlap_x_err2.append(e[1]) e = compute_err(sp_y, axis=None) overlap_y_err1.append(e[0]) overlap_y_err2.append(e[1]) e = compute_err(svm_x, axis=None) svm_overlap_x_err1.append(e[0]) svm_overlap_x_err2.append(e[1]) e = compute_err(svm_y, axis=None) svm_overlap_y_err1.append(e[0]) svm_overlap_y_err2.append(e[1]) overlap_x_err = (svm_overlap_x_err1, svm_overlap_x_err2) overlap_y_err = (svm_overlap_y_err1, svm_overlap_y_err2) svm_overlap_x_err = (svm_overlap_x_err1, svm_overlap_x_err2) svm_overlap_y_err = (svm_overlap_y_err1, svm_overlap_y_err2) # # Save the results p = os.path.join(os.path.expanduser('~'), 'scratch', 'novelty_experiments') with open(os.path.join(p, 'results.pkl'), 'wb') as f: cPickle.dump((noise_x, noise_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_noise_x, svm_noise_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((noise_x_err, noise_y_err), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_noise_x_err, svm_noise_y_err), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((overlap_x, overlap_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_overlap_x, svm_overlap_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((overlap_x_err, overlap_y_err), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_overlap_x_err, svm_overlap_y_err), f, cPickle.HIGHEST_PROTOCOL) # Make the plots plot_error((pct_noises * 100, pct_noises * 100), (noise_x, svm_noise_x), ('SP', 'SVM'), (noise_x_err, svm_noise_x_err), '% Noise', '% Error', 'Noise: Base Class', out_path=os.path.join(p, 'noise_base.png'), xlim=(-5, 105), ylim=(-5, 105), show=False) plot_error((pct_noises * 100, pct_noises * 100), (noise_y, svm_noise_y), ('SP', 'SVM'), (noise_y_err, svm_noise_y_err), '% Noise', '% Error', 'Noise: Novelty Class', out_path=os.path.join(p, 'noise_novelty.png'), xlim=(-5, 105), ylim=(-5, 105), show=False) noverlap_pct = noverlap_bits / 40. * 100 plot_error((noverlap_pct, noverlap_pct), (overlap_x, svm_overlap_x), ('SP', 'SVM'), (overlap_x_err, svm_overlap_x_err), '% Overlapping Bits', '% Error', 'Overlap: Base Class', out_path=os.path.join(p, 'overlap_base.png'), xlim=(-5, 105), ylim=(-5, 105), show=False) plot_error((noverlap_pct, noverlap_pct), (overlap_y, svm_overlap_y), ('SP', 'SVM'), (overlap_y_err, svm_overlap_y_err), '% Overlapping Bits', '% Error', 'Overlap: Novelty Class', out_path=os.path.join(p, 'overlap_novelty.png'), xlim=(-5, 105), ylim=(-5, 105), show=False)
def main(ntrials=10, seed=123456798): """ Vary the amount of noise and overlap and see how the system performs. @param ntrials: The number of trials to perform. @param seed: The seed for the random number generator. """ # Amount to vary each type by pct_noises = np.linspace(0, 1, 101) noverlap_bits = np.arange(0, 41) # Vary the noise results = Parallel(n_jobs=-1)(delayed(base_experiment)(noise, 0, 'noise-{0}'.format(i), ntrials, False, seed) for i, noise in enumerate( pct_noises, 1)) noise_x, noise_y, svm_noise_x, svm_noise_y = [], [], [], [] noise_x_err1, noise_x_err2, noise_y_err1, noise_y_err2 = [], [], [], [] svm_noise_x_err1, svm_noise_x_err2 = [], [] svm_noise_y_err1, svm_noise_y_err2 = [], [] for sp_x, sp_y, svm_x, svm_y in results: noise_x.append(np.median(sp_x)) noise_y.append(np.median(sp_y)) svm_noise_x.append(np.median(svm_x)) svm_noise_y.append(np.median(svm_y)) e = compute_err(sp_x, axis=None) noise_x_err1.append(e[0]) noise_x_err2.append(e[1]) e = compute_err(sp_y, axis=None) noise_y_err1.append(e[0]) noise_y_err2.append(e[1]) e = compute_err(svm_x, axis=None) svm_noise_x_err1.append(e[0]) svm_noise_x_err2.append(e[1]) e = compute_err(svm_y, axis=None) svm_noise_y_err1.append(e[0]) svm_noise_y_err2.append(e[1]) noise_x_err = (svm_noise_x_err1, svm_noise_x_err2) noise_y_err = (svm_noise_y_err1, svm_noise_y_err2) svm_noise_x_err = (svm_noise_x_err1, svm_noise_x_err2) svm_noise_y_err = (svm_noise_y_err1, svm_noise_y_err2) # Vary the overlaps results = Parallel(n_jobs=-1)(delayed(base_experiment)(0.35, overlap, 'overlap-{0}'.format(i), ntrials, False, seed) for i, overlap in enumerate(noverlap_bits, 1)) overlap_x, overlap_y, svm_overlap_x, svm_overlap_y = [], [], [], [] overlap_x_err1, overlap_x_err2 = [], [] overlap_y_err1, overlap_y_err2 = [], [] svm_overlap_x_err1, svm_overlap_x_err2 = [], [] svm_overlap_y_err1, svm_overlap_y_err2 = [], [] for sp_x, sp_y, svm_x, svm_y in results: overlap_x.append(np.median(sp_x)) overlap_y.append(np.median(sp_y)) svm_overlap_x.append(np.median(svm_x)) svm_overlap_y.append(np.median(svm_y)) e = compute_err(sp_x, axis=None) overlap_x_err1.append(e[0]) overlap_x_err2.append(e[1]) e = compute_err(sp_y, axis=None) overlap_y_err1.append(e[0]) overlap_y_err2.append(e[1]) e = compute_err(svm_x, axis=None) svm_overlap_x_err1.append(e[0]) svm_overlap_x_err2.append(e[1]) e = compute_err(svm_y, axis=None) svm_overlap_y_err1.append(e[0]) svm_overlap_y_err2.append(e[1]) overlap_x_err = (svm_overlap_x_err1, svm_overlap_x_err2) overlap_y_err = (svm_overlap_y_err1, svm_overlap_y_err2) svm_overlap_x_err = (svm_overlap_x_err1, svm_overlap_x_err2) svm_overlap_y_err = (svm_overlap_y_err1, svm_overlap_y_err2) # # Save the results p = os.path.join(os.path.expanduser('~'), 'scratch', 'novelty_experiments') with open(os.path.join(p, 'results.pkl'), 'wb') as f: cPickle.dump((noise_x, noise_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_noise_x, svm_noise_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((noise_x_err, noise_y_err), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_noise_x_err, svm_noise_y_err), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((overlap_x, overlap_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_overlap_x, svm_overlap_y), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((overlap_x_err, overlap_y_err), f, cPickle.HIGHEST_PROTOCOL) cPickle.dump((svm_overlap_x_err, svm_overlap_y_err), f, cPickle.HIGHEST_PROTOCOL) # Make the plots plot_error((pct_noises * 100, pct_noises * 100), (noise_x, svm_noise_x), ('SP', 'SVM'), (noise_x_err, svm_noise_x_err), '% Noise', '% Error', 'Noise: Base Class', out_path=os.path.join(p, 'noise_base.png'), xlim=(-5, 105), ylim=(-5, 105), show=False) plot_error((pct_noises * 100, pct_noises * 100), (noise_y, svm_noise_y), ('SP', 'SVM'), (noise_y_err, svm_noise_y_err), '% Noise', '% Error', 'Noise: Novelty Class', out_path=os.path.join(p, 'noise_novelty.png'), xlim=(-5, 105), ylim=(-5, 105), show=False) noverlap_pct = noverlap_bits / 40. * 100 plot_error((noverlap_pct, noverlap_pct), (overlap_x, svm_overlap_x), ('SP', 'SVM'), (overlap_x_err, svm_overlap_x_err), '% Overlapping Bits', '% Error', 'Overlap: Base Class', out_path=os.path.join(p, 'overlap_base.png'), xlim=(-5, 105), ylim=(-5, 105),show=False) plot_error((noverlap_pct, noverlap_pct), (overlap_y, svm_overlap_y), ('SP', 'SVM'), (overlap_y_err, svm_overlap_y_err), '% Overlapping Bits', '% Error', 'Overlap: Novelty Class', out_path=os.path.join(p, 'overlap_novelty.png'), xlim=(-5, 105), ylim=(-5, 105),show=False)
def plot_single_run(bp1, bp2): """Create an error plot for a single run. @param bp1: The base path for global inhibition results. @param bp2: The base path for local inhibition results. """ def read(p): """Read in the data. @param p: The path to the file to read. @return: The results. """ with open(p, 'r') as f: reader = csv.reader(f) data = [] for row in reader: data.append(float(row[1])) return np.array(data) * 100 def get_data(p): """Get all of the results. @param p: The directory to obtain the data in. @return: The results. """ permanence = [] for d in os.listdir(p): npath = os.path.join(p, d) if os.path.isdir(npath): permanence.append( read(os.path.join(npath, 'permanence_boost.csv'))) return np.array(permanence) # Get the data data = [get_data(bp1)] data.append(get_data(bp2)) # Build the series x_series = (np.arange(data[0].shape[1]), ) # Make the main plot fig = plt.figure(figsize=(21, 20), facecolor='white') ax = fig.add_subplot(111) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off') ax.set_xlabel('Iteration') ax.set_ylabel('% Columns Boosted') # Make subplots ax1 = fig.add_subplot(211) plot_error(show=False, legend=False, ax=ax1, title='Global Inhibition', x_series=x_series, y_series=(np.median(data[0], 0), ), y_errs=(compute_err(data[0], axis=0), ), xlim=(0, 200), ylim=(0, 100)) ax2 = fig.add_subplot(212, sharex=ax1, sharey=ax1) plot_error(show=False, ax=ax2, title='Local Inhibition', legend=False, x_series=x_series, y_series=(np.median(data[1], 0), ), y_errs=(compute_err(data[1], axis=0), ), xlim=(0, 200), ylim=(0, 100)) # Save it # plt.show() plt.subplots_adjust(bottom=0.15, hspace=0.3) plt.savefig('boost_permanence.png', format='png', facecolor=fig.get_facecolor(), edgecolor='none')
def plot_density_results(bp, bp2=None): """Average the results. @param bp: The base path. @param bp2: The second base path. """ def average(p): """Compute the average activations for each density. @param p: The path to the file. @return: The average. """ with open(p, 'r') as f: reader = csv.reader(f) data = [] for row in reader: data.append(float(row[1])) return np.mean(data) * 100 def get_data(p): """Get the data for a single run. @param p: The path. @return: A tuple containing the overlap and permanences. """ overlap, permanence = [], [] for d in os.listdir(p): npath = os.path.join(p, d) if os.path.isdir(npath): overlap.append( average(os.path.join(npath, 'overlap_boost.csv'))) permanence.append( average(os.path.join(npath, 'permanence_boost.csv'))) return np.array(overlap), np.array(permanence) def get_all_data(bp): """Get the data for all runs. @param bp: The base path. @return: A tuple containing the sparsity, overlap, and permanences. """ overlap, permanence, sparsity = [], [], [] for d in sorted([int(x) for x in os.listdir(bp)]): sparsity.append((1 - (d / 100.)) * 100) o, p = get_data(os.path.join(bp, str(d))) overlap.append(o) permanence.append(p) return np.array(sparsity[::-1]), np.array(overlap[::-1]), \ np.array(permanence[::-1]) def make_plot_params(sparsity, overlap, permanence, title=None): """Generate the parameters for the plot. @param sparsity: The sparsity array. @param overlap: The overlap array. @param permanence: The permanence array. @param title: The title for the plot. @return: A dictionary with the parameters. """ return { 'x_series': (sparsity, sparsity), 'y_series': (np.median(overlap, 1), np.median(permanence, 1)), 'series_names': ('Overlap Boosting', 'Permanence Boosting'), 'y_errs': (compute_err(overlap), compute_err(permanence)), 'xlim': (0, 100), 'ylim': (0, 45), 'title': title } data = get_all_data(bp) if bp2 is None: plot_error(**make_plot_params(*data)) else: # Make main plot fig = plt.figure(figsize=(21, 20), facecolor='white') ax = fig.add_subplot(111) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off') ax.set_xlabel('Sparsity [%]') ax.set_ylabel('% Columns Boosted') # Make subplots ax1 = fig.add_subplot(211) plot_error(show=False, legend=False, ax=ax1, **make_plot_params(*data, title='Global Inhibition')) data2 = get_all_data(bp2) ax2 = fig.add_subplot(212, sharex=ax1, sharey=ax1) plot_error(show=False, ax=ax2, **make_plot_params(*data2, title='Local Inhibition')) # Save it # plt.show() plt.subplots_adjust(bottom=0.15, hspace=0.3) plt.savefig('boost_sparseness.png', format='png', facecolor=fig.get_facecolor(), edgecolor='none')
def plot_single_run(bp1, bp2): """ Create an error plot for a single run. @param bp1: The base path for global inhibition results. @param bp2: The base path for local inhibition results. """ def read(p): """ Read in the data. @param p: The path to the file to read. @return: The results. """ with open(p, 'rb') as f: reader = csv.reader(f) data = [] for row in reader: data.append(float(row[1])) return np.array(data) * 100 def get_data(p): """ Get all of the results. @param p: The directory to obtain the data in. @return: The results. """ permanence = [] for d in os.listdir(p): npath = os.path.join(p, d) if os.path.isdir(npath): permanence.append(read(os.path.join(npath, 'permanence_boost.csv'))) return np.array(permanence) # Get the data data = [get_data(bp1)] data.append(get_data(bp2)) # Build the series x_series = (np.arange(data[0].shape[1]), ) # Make the main plot fig = plt.figure(figsize=(21, 20), facecolor='white') ax = fig.add_subplot(111) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off') ax.set_xlabel('Iteration') ax.set_ylabel('% Columns Boosted') # Make subplots ax1 = fig.add_subplot(211) plot_error(show=False, legend=False, ax=ax1, title='Global Inhibition', x_series=x_series, y_series=(np.median(data[0], 0), ), y_errs=(compute_err(data[0], axis=0),), xlim=(0, 200), ylim=(0, 100)) ax2 = fig.add_subplot(212, sharex=ax1, sharey=ax1) plot_error(show=False, ax=ax2, title='Local Inhibition', legend=False, x_series=x_series, y_series=(np.median(data[1], 0), ), y_errs=(compute_err(data[1], axis=0),), xlim=(0, 200), ylim=(0, 100)) # Save it plt.subplots_adjust(bottom=0.15, hspace=0.3) plt.savefig('boost_permanence.png', format='png', facecolor=fig.get_facecolor(), edgecolor='none')
def plot_density_results(bp, bp2=None): """ Average the results. @param bp: The base path. @param bp2: The second base path. """ def average(p): """ Compute the average activations for each density. @param p: The path to the file. @return: The average. """ with open(p, 'rb') as f: reader = csv.reader(f) data = [] for row in reader: data.append(float(row[1])) return np.mean(data) * 100 def get_data(p): """ Get the data for a single run. @param p: The path. @return: A tuple containing the overlap and permanences. """ overlap, permanence = [], [] for d in os.listdir(p): npath = os.path.join(p, d) if os.path.isdir(npath): overlap.append(average(os.path.join(npath, 'overlap_boost.csv'))) permanence.append(average(os.path.join(npath, 'permanence_boost.csv'))) return np.array(overlap), np.array(permanence) def get_all_data(bp): """ Get the data for all runs. @param bp: The base path. @return: A tuple containing the sparsity, overlap, and permanences. """ overlap, permanence, sparsity = [], [], [] for d in sorted([int(x) for x in os.listdir(bp)]): sparsity.append((1 - (d / 100.)) * 100) o, p = get_data(os.path.join(bp, str(d))) overlap.append(o) permanence.append(p) return np.array(sparsity[::-1]), np.array(overlap[::-1]), \ np.array(permanence[::-1]) def make_plot_params(sparsity, overlap, permanence, title=None): """ Generate the parameters for the plot. @param sparsity: The sparsity array. @param overlap: The overlap array. @param permanence: The permanence array. @param title: The title for the plot. @return: A dictionary with the parameters. """ return {'x_series':(sparsity, sparsity), 'y_series':(np.median(overlap, 1), np.median(permanence, 1)), 'series_names':('Overlap Boosting', 'Permanence Boosting'), 'y_errs':(compute_err(overlap), compute_err(permanence)), 'xlim':(0, 100), 'ylim':(0, 45), 'title':title } data = get_all_data(bp) if bp2 is None: plot_error(**make_plot_params(*data)) else: # Make main plot fig = plt.figure(figsize=(21, 20), facecolor='white') ax = fig.add_subplot(111) ax.spines['top'].set_color('none') ax.spines['bottom'].set_color('none') ax.spines['left'].set_color('none') ax.spines['right'].set_color('none') ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off') ax.set_xlabel('Sparsity [%]') ax.set_ylabel('% Columns Boosted') # Make subplots ax1 = fig.add_subplot(211) plot_error(show=False, legend=False, ax=ax1, **make_plot_params(*data, title='Global Inhibition')) data2 = get_all_data(bp2) ax2 = fig.add_subplot(212, sharex=ax1, sharey=ax1) plot_error(show=False, ax=ax2, **make_plot_params(*data2, title='Local Inhibition')) # Save it plt.subplots_adjust(bottom=0.15, hspace=0.3) plt.savefig('boost_sparseness.png', format='png', facecolor=fig.get_facecolor(), edgecolor='none')