From baa2cc1530c270f91370e5370ec2b350f6e4ad66 Mon Sep 17 00:00:00 2001 From: Alexander Novikov Date: Mon, 16 Jan 2017 20:51:01 +0300 Subject: [PATCH 1/3] get rid of the Armijo rule --- src/TTRegression.py | 17 +++++------ src/optimizers/core_sgd.py | 30 +++--------------- src/optimizers/riemannian_sgd.py | 52 ++------------------------------ 3 files changed, 16 insertions(+), 83 deletions(-) diff --git a/src/TTRegression.py b/src/TTRegression.py index bc03585..6c924e1 100644 --- a/src/TTRegression.py +++ b/src/TTRegression.py @@ -45,7 +45,7 @@ class TTRegression(BaseEstimator, LinearClassifierMixin): Contains all the logged details (e.g. loss on each iteration). """ - def __init__(self, tt_model, loss_name, rank, + def __init__(self, tt_model, loss_name, rank, learning_rate, solver='riemannian-sgd', batch_size=-1, fit_intercept=True, reg=0., exp_reg=1.0, dropout=None, max_iter=100, verbose=0, persuit_init=False, coef0=None, intercept0=None): @@ -55,6 +55,7 @@ def __init__(self, tt_model, loss_name, rank, self.tt_model = tt_model self.loss_name = loss_name self.rank = rank + self.learning_rate = learning_rate self.solver = solver self.batch_size = batch_size self.fit_intercept = fit_intercept @@ -226,16 +227,15 @@ def fit_log_val(self, X_, y_, val_X_=None, val_y_=None): if self.solver == 'riemannian-sgd': from optimizers.riemannian_sgd import riemannian_sgd w, b = riemannian_sgd(X, y, self.tt_dot, self.loss, self.loss_grad, - self.project, w0=self.coef_, - intercept0=self.intercept_, + self.project, self.learning_rate, + w0=self.coef_, intercept0=self.intercept_, fit_intercept=self.fit_intercept, val_x=val_X, val_y=val_y, reg=self.reg, exp_reg=self.exp_reg, dropout=self.dropout, batch_size=self.batch_size, num_passes=self.max_iter, - logger=self.logger, verbose_period=1, - beta=0.5, rho=0.1) + logger=self.logger, verbose_period=1) self.coef_, self.intercept_ = w, b elif self.solver == 'sgd': if self.dropout is not None: @@ -243,14 +243,13 @@ def fit_log_val(self, X_, y_, val_X_=None, val_y_=None): from optimizers.core_sgd import core_sgd w, b = core_sgd(X, y, self.tt_dot, self.loss, self.loss_grad, - self.gradient_wrt_cores, w0=self.coef_, - intercept0=self.intercept_, + self.gradient_wrt_cores, self.learning_rate, + w0=self.coef_, intercept0=self.intercept_, fit_intercept=self.fit_intercept, val_x=val_X, val_y=val_y, reg=self.reg, batch_size=self.batch_size, num_passes=self.max_iter, - logger=self.logger, verbose_period=1, - beta=0.5, rho=0.1) + logger=self.logger, verbose_period=1) self.coef_, self.intercept_ = w, b else: raise ValueError("Only 'riemannian-sgd' and 'sgd' solvers are supported.") diff --git a/src/optimizers/core_sgd.py b/src/optimizers/core_sgd.py index ea66d55..2c77815 100644 --- a/src/optimizers/core_sgd.py +++ b/src/optimizers/core_sgd.py @@ -4,13 +4,14 @@ # TODO: add Adam scheme support. +# TODO: support fit_intercept +# TODO: debug? def core_sgd(train_x, train_y, vectorized_tt_dot_h, loss_h, - loss_grad_h, grad_wrt_cores_h, w0, intercept0=0, + loss_grad_h, grad_wrt_cores_h, learning_rate, w0, intercept0=0, fit_intercept=True, val_x=None, val_y=None, reg=0, batch_size=-1, num_passes=30, seed=None, logger=None, verbose_period=1, - debug=False, - beta=0.5, rho=0.1): + debug=False): """SGD w.r.t. TT-cores optimization for a linear model with weights in TT. The objective function is @@ -54,34 +55,13 @@ def core_sgd(train_x, train_y, vectorized_tt_dot_h, loss_h, batch_y = train_y[curr_idx] batch_w_x = vectorized_tt_dot_h(w, train_x[curr_idx, :]) batch_linear_o = batch_w_x + b - batch_loss_arr = loss_h(batch_linear_o, batch_y) - wcore_wcore = w.core.dot(w.core) - batch_loss = np.sum(batch_loss_arr) + reg * wcore_wcore / 2.0 batch_grad_coef = loss_grad_h(batch_linear_o, batch_y) w_cores = tt.tensor.to_list(w) gradient = grad_wrt_cores_h(w_cores, train_x[curr_idx, :], batch_grad_coef) gradient += reg * w.core -# Armiho step choosing. - step_prev_w = step_w - gradient_norm = np.linalg.norm(gradient) - while step_w > 1e-10: - new_w = w.copy() - new_w.core += -step_w * gradient - new_w_x = vectorized_tt_dot_h(new_w, train_x[curr_idx, :]) + w.core += -learning_rate * gradient - if fit_intercept: - b_objective = lambda b: np.sum(loss_h(new_w_x + b, batch_y)) - m = minimize_scalar(b_objective) - b = m.x - new_loss = m.fun - else: - new_loss = np.sum(loss_h(new_w_x + b, batch_y)) - new_loss += reg * new_w.core.dot(new_w.core) / 2.0 - if new_loss <= batch_loss - rho * step_w * gradient_norm**2: - break - step_w *= beta - w = new_w if (logger is not None) and e % verbose_period == 0: logger.after_each_iter(e, train_x, train_y, w, lambda w, x: vectorized_tt_dot_h(w, x) + b, stage='train') diff --git a/src/optimizers/riemannian_sgd.py b/src/optimizers/riemannian_sgd.py index 6a7224d..4cf277e 100644 --- a/src/optimizers/riemannian_sgd.py +++ b/src/optimizers/riemannian_sgd.py @@ -70,7 +70,7 @@ def build_reg_tens(n, exp_reg): def riemannian_sgd(train_x, train_y, vectorized_tt_dot_h, loss_h, - loss_grad_h, project_h, w0, intercept0=0, + loss_grad_h, project_h, learning_rate, w0, intercept0=0, fit_intercept=True, val_x=None, val_y=None, reg=0., exp_reg=1., dropout=None, batch_size=-1, num_passes=30, seed=None, logger=None, verbose_period=1, @@ -123,59 +123,13 @@ def riemannian_sgd(train_x, train_y, vectorized_tt_dot_h, loss_h, batch_y = train_y[curr_idx] batch_w_x = vectorized_tt_dot_h(w, curr_batch) batch_linear_o = batch_w_x + b - batch_loss_arr = loss_h(batch_linear_o, batch_y) wreg = w * reg_tens wregreg = w * reg_tens * reg_tens - wreg_wreg = wreg.norm()**2 - batch_loss = np.sum(batch_loss_arr) + reg * wreg_wreg / 2.0 batch_grad_coef = loss_grad_h(batch_linear_o, batch_y) - batch_gradient_b = np.sum(batch_grad_coef) direction = project_h(w, curr_batch, batch_grad_coef, reg=0) direction = riemannian.project(w, [direction, reg * wregreg]) - batch_dir_x = vectorized_tt_dot_h(direction, curr_batch) - - dir_dir = direction.norm()**2 - wreg_dir = tt.dot(wreg, direction) - if fit_intercept: - # TODO: Use classical Newton-Raphson (with hessian). - step_objective = lambda s: _regularized_loss_step(s, loss_h, batch_y, batch_w_x, batch_dir_x, b, batch_gradient_b, reg, wreg_wreg, wreg_dir, dir_dir) - step_gradient = lambda s: _regularized_loss_step_grad(s, loss_grad_h, batch_y, batch_w_x, batch_dir_x, b, batch_gradient_b, reg, wreg_dir, dir_dir) - step0_w, step0_b = fmin_bfgs(step_objective, np.ones(2), fprime=step_gradient, gtol=1e-10, disp=logger.disp()) - else: - def w_step_objective(w_step): - steps = np.array([w_step, 0]) - obj = _regularized_loss_step(steps, loss_h, batch_y, - batch_w_x, batch_dir_x, b, - batch_gradient_b, reg, wreg_wreg, - wreg_dir, dir_dir) - return obj - step0_w = minimize_scalar(w_step_objective).x - - - # TODO: consider using Probabilistic Line Searches for Stochastic Optimization. -# Armiho step choosing. - step_w = step0_w - # = - # = <(\sum_i coef[i] * x_i + reg * w), direction> = - # = \sum_i coef[i] + reg * - grad_times_direction = batch_dir_x.dot(batch_grad_coef) + reg * wreg_dir - while step_w > 1e-10: - new_w = (w - step_w * direction).round(eps=0, rmax=max(w.r)) - new_w_x = vectorized_tt_dot_h(new_w, curr_batch) - - if fit_intercept: - b_objective = lambda b: np.sum(loss_h(new_w_x + b, batch_y)) - m = minimize_scalar(b_objective) - b = m.x - new_loss = m.fun - else: - new_loss = np.sum(loss_h(new_w_x + b, batch_y)) - new_wreg = new_w * reg_tens - new_loss += reg * new_wreg.norm()**2 / 2.0 - if new_loss <= batch_loss - rho * step_w * grad_times_direction: - break - step_w *= beta - w = new_w + + w = (w - learning_rate * direction).round(eps=0, rmax=max(w.r)) if (logger is not None) and e % verbose_period == 0: logger.after_each_iter(e, train_x, train_y, w, lambda w, x: vectorized_tt_dot_h(w, x) + b, stage='train') From d58d6d78dc88f2c234bc71c049060c382eb99140 Mon Sep 17 00:00:00 2001 From: Alexander Novikov Date: Thu, 19 Jan 2017 11:23:16 +0300 Subject: [PATCH 2/3] Init new version of the riemannian comparison plots --- experiments/Riemannian_vs_baseline_car.ipynb | 622 ++++- experiments/Riemannian_vs_baseline_hiv.ipynb | 2289 ++++++++++++++++++ 2 files changed, 2852 insertions(+), 59 deletions(-) create mode 100644 experiments/Riemannian_vs_baseline_hiv.ipynb diff --git a/experiments/Riemannian_vs_baseline_car.ipynb b/experiments/Riemannian_vs_baseline_car.ipynb index a17eb5b..4d8d15c 100644 --- a/experiments/Riemannian_vs_baseline_car.ipynb +++ b/experiments/Riemannian_vs_baseline_car.ipynb @@ -141,7 +141,493 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TTRegression(batch_size=500, coef0=None, dropout=None, exp_reg=1.0,\n", + " fit_intercept=False, intercept0=None, learning_rate=0.02,\n", + " loss_name='logistic', max_iter=1000, persuit_init=False, rank=4,\n", + " reg=0.0, solver='sgd', tt_model='all-subsets', verbose=1)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = TTRegression('all-subsets', 'logistic', 4, learning_rate=0.02, solver='sgd', max_iter=1000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TTRegression(batch_size=100, coef0=None, dropout=None, exp_reg=1.0,\n", + " fit_intercept=False, intercept0=None, learning_rate=10.0,\n", + " loss_name='logistic', max_iter=10, persuit_init=False, rank=4,\n", + " reg=0.0, solver='riemannian-sgd', tt_model='all-subsets', verbose=1)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "riemannian_model = TTRegression('all-subsets', 'logistic', 4, learning_rate=10.0, solver='riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=100, fit_intercept=False, reg=0.)\n", + "riemannian_model.fit_log_val(X_train, y_train, X_val, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(model.logger.time_hist, model.logger.loss_hist['train']['logistic'])\n", + "plt.loglog(riemannian_model.logger.time_hist, riemannian_model.logger.loss_hist['train']['logistic'])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TTRegression(batch_size=500, coef0=None, dropout=None, exp_reg=1.0,\n", + " fit_intercept=False, intercept0=None, learning_rate=20.0,\n", + " loss_name='logistic', max_iter=500, persuit_init=False, rank=4,\n", + " reg=0.0, solver='riemannian-sgd', tt_model='all-subsets', verbose=1)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 20.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00063908543695358633" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TTRegression(batch_size=500, coef0=None, dropout=None, exp_reg=1.0,\n", + " fit_intercept=False, intercept0=None, learning_rate=15.0,\n", + " loss_name='logistic', max_iter=500, persuit_init=False, rank=4,\n", + " reg=0.0, solver='riemannian-sgd', tt_model='all-subsets', verbose=1)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 15.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00073882598971240949" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.04035071571636744" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.01, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.015016370048456682" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.02, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.042096606195346518" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.005, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0092916749943583333" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.04, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.050642381236658915" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.002, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.16415022748865921" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.06, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.15824865133758087" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.08, 'sgd', max_iter=2000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00044281225364734608" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00014638632522643175" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00016332973482077837" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 60.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(rieamannian_model.logger.loss_hist['train']['logistic'])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, "metadata": { "collapsed": false }, @@ -151,16 +637,16 @@ "riemannian_sgd = {}\n", "\n", "for batch_size in [-1, 100, 500]:\n", - " # To use the same order of looping through objects for all runs.\n", + "# To use the same order of looping through objects for all runs.\n", " np.random.seed(0)\n", - " model = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=10000, verbose=1,\n", + " model = TTRegression('all-subsets', 'logistic', 4, 0.03, 'sgd', max_iter=12000, verbose=1,\n", " fit_intercept=False, batch_size=batch_size, reg=0.)\n", " model.fit_log_val(X_train, y_train, X_val, y_val)\n", " plain_sgd[batch_size] = model\n", "\n", - " np.random.seed(0)\n", " # To use the same order of looping through objects for all runs.\n", - " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 'riemannian-sgd', max_iter=800, verbose=1,\n", + " np.random.seed(0)\n", + " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=1000, verbose=1,\n", " batch_size=batch_size, fit_intercept=False, reg=0.)\n", " rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", " riemannian_sgd[batch_size] = rieamannian_model" @@ -175,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 98, "metadata": { "collapsed": false }, @@ -192,19 +678,46 @@ "riemannian_sgd_rand = {}\n", "\n", "batch_size = -1\n", - "# To use the same order of looping through objects for all runs.\n", - "np.random.seed(0)\n", - "model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", - " fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", - "model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", - "plain_sgd_rand[batch_size] = model_rand\n", + "# # To use the same order of looping through objects for all runs.\n", + "# np.random.seed(0)\n", + "# model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", + "# fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", + "# model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# plain_sgd_rand[batch_size] = model_rand\n", "\n", "np.random.seed(0)\n", "# To use the same order of looping through objects for all runs.\n", - "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 'riemannian-sgd', max_iter=1600, verbose=1,\n", + "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=1200, verbose=1,\n", " batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init)\n", "riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", - "riemannian_sgd_rand[batch_size] = riemannian_model_rand" + "riemannian_sgd_rand[batch_size] = riemannian_model_rand\n", + "\n", + "\n", + "# # Beter init distribution\n", + "# from src.models.all_subsets import tensorize_linear_init\n", + "# w_init = tensorize_linear_init(np.random.rand(X.shape[1]), np.random.rand())\n", + "# for _ in range(2):\n", + "# w_init = w_init + 0 * tt.ones(2, X.shape[1])\n", + "# w_init.core += 0.01 * np.random.randn(*w_init.core.shape)\n", + "# w_init = w_init.round(eps=0)\n", + "\n", + "# plain_sgd_smart_rand = {}\n", + "# riemannian_sgd_smart_rand = {}\n", + "\n", + "# batch_size = -1\n", + "# # To use the same order of looping through objects for all runs.\n", + "# np.random.seed(0)\n", + "# model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", + "# fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", + "# model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# plain_sgd_smart_rand[batch_size] = model_rand\n", + "\n", + "# np.random.seed(0)\n", + "# # To use the same order of looping through objects for all runs.\n", + "# riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 'riemannian-sgd', max_iter=1600, verbose=1,\n", + "# batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init)\n", + "# riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# riemannian_sgd_smart_rand[batch_size] = riemannian_model_rand" ] }, { @@ -216,13 +729,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "with open('data/riemannian_vs_baseline_car.pickle', 'wb') as f:\n", + "with open('data/riemannian_vs_baseline_car_40_003.pickle', 'wb') as f:\n", " obj = {'plain_sgd': plain_sgd, 'riemannian_sgd': riemannian_sgd,\n", " 'plain_sgd_rand': plain_sgd_rand, 'riemannian_sgd_rand': riemannian_sgd_rand,\n", " 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val}\n", @@ -238,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 48, "metadata": { "collapsed": true }, @@ -265,16 +778,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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bKW/z4kojqm36qOkkx9RvouHsfBePfbOe71KM0VXbmDD+dXlfzu4RIPN6I0NV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\nIhJrSlQiIhJrSlQiIhJr/w/uXvxjyi8TzAAAAABJRU5ErkJggg==\n", 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Gi3c2bGYL17CE5mrSHPfXOP698N98tOkjn23m7avGgU1VPhQ4tXhvJk1ve7Nh\ngWbKa+FHJsBLG+5gxMwRfLxsld+px0rM3gPy27tokIaGZ9Ac8UfglYnISNEqkJ+L5oLbtOg+Ci7W\n86T+8jDsbJwUcOl5pczZmIHZJNx+hnvkU3JWIesdegL29DUVx51mza26wPt+1/cUWYsACD2wGscP\nd9EiWC8C66eGd26vNgAs3JHts95efWA9jjS8urI2S3s4+SP1D48zlSLgscWPVTOCt8BzL618fH6+\nvDSdLErOdnt/qFzLCPX6378wf1uWz/lcKfMh8AwMmjv+CLzbgEuAWWhR/7c06IrqyuDb4KzHQNlh\n5q2Q6jPvar3x2dIUbA7FxX3b0i7WPUY0ObOQDQ6nhlcp8Co0vIwCrA4rM7ZrhTSjlDAp/QCmTd/S\nokwrIOFverFOceF0bR1BQZmN1SkNV23aPQ6veQq8k8otzMjIpFNRHT/HWua2rG0arqd/8JWuTLH9\noH+FV/355pqzhicidr1szmYRme3MeSkibfU4tSaNiIwRkY36+teLyMcu17BQtILaG0Rkm4i8KyLR\nx3rNTQWfAk8qbS6H0KL5L0WLem2SGWEBOOcZ6D8GrCUw4xpIXV5znzqSX2rlm1Vaar27zursdk4p\nRXJmITtUe5Q5GHL2QIl2A+2TUOm4Mi9lHpnFmXSO7sx4R+XfZLSexSivxMrfS/+m1/BetD+nPb2G\n9+LvpX97Xc9IXctbsL3hstS77uE5mriGV9eabJMzszm53MLLe7fU3NgbriZNXbRUlxC6Pkkv14Sh\nz4ILOv44ujRngQcUK6UGKqX6olVAuB9AKXVQKXXdsV1a9ei17R4CRunrHwgsQytv5ORGpVR/oB+a\nRW52oy+0iVKdhvem/vtP4A+0zN7O100TEbjsXeh3PViL4ctrYP+KmvvVgS9XplJssXNG17gKM6WT\nzIIyCspsRIaFQtuTtYMZ2v5c+9gwIoMDyC4s49PNWj7CW3rfwsD8yqfzFg5NOG5Ys4yrnr4K000m\nom+NxnSTiaufvtqr0DvvJC0c4o/tWQ1WqNXNpNnE9/Dq6mMS5dCuMdThYRr2dzwvn703gecUOrUV\nzP4li66e9dnVBVE0PqbgsI4hSb1mhPc4/a+QpF4zTMFhHRuynwfL0SsDiEgHPbclImISkddEZKWu\nLd2pHz9bRBaJyE8isltEXhaRm/R2G0Wkk97uEhFZISJrRWS+iLTSj48XkU91TWy3nkvYOfc2XVvb\nIiLzRMQniPtYAAAgAElEQVRbjNHTaNUPMkHzq1BKTVNKuQZMin7OhhZE305Pn3bC43OHXCnlzNH2\nolKqQsiJyBkNvqqjwWSGKz7UnrQ3z4QZV8OYH6D9qfU2RbnNztR/9gFVtTuATWna/lz3NpFI0mBI\nW6U5rnQdickk9E6MYnXWSvbk7yIuNI5LOl+CqXRcRf8W1kNAd+Z8N5E2d7bBpO/pmYJNtB7Tmruf\nvZvti9xzbPdvF0PL8CAO5JSyM6uIHvGR9Xa9Tqw2F6eVJq7h1Tvib3xc5edSndNK9T2raeMzL5iq\n10rj3oLQGwJTcFjHsG5D/4g9//4upqAQHJYycuZ/MNQUHDbSUV6yr7776QhoxVzRKh9McTnnvPLb\ngTyl1KkiEgT8IyLO6gf90Mry5AF7gU/0dg8C49AsYkuUUkP1eW5HEzzOzd8eaBlVooFkEZmkH+8K\nXK+UuktEvgWuBr7yWHtvqDbs0w2llENENunrrX35jmaGPy5hT+Ou1T0MLG2Y5dQTJjNc8ZF2d9gy\nSxN6Y3+AdqfUy/A/rU/nUGE5vdpGcWa3uCrnf96guY+f1b0VtNLzI67+BLqeC0mDefDcbtz5++sA\nmAvPZE1yGsMsRRX9o8u1vJu2oFJMwe57g6ZgE/m4V1EHMJuEET1bM3NtGr9syqBHfI8qbY4Wdw2v\n3odv2pj89J5UVT+Y+jRpVmuO1NXa4+lRJKhVhxecQgvAFBRC7Pn3dwlo0Tal45O/+uwXNeRKok65\nCs9+tryDLwBjapg2VETWoVUF2IZmvfLkfKCviFzrnBLohpaDcrVL/ss9VJYB2kxlarB2+n5gW7Qi\nsSkuY/+qa19HRCSLSnNkiksx2rVARy/rqvh6RaQPWoLqSOApPd2ZN5q5hdp/qtvDu01ElgADReRv\nEVkiIn+jPdE0fcwBcOVk6HM1WArhi6vgwKpaD5NblsuKgyuYvnU6Ty15iht/uYk3/9Ayz999Vucq\n7uX5JVYWbMtCBK4amAg9L4VOZ0PxIZh2MWz5gZYxRzCF70I5gti9py8Tv9C8SnPN2k01qlQvu1UW\ngqPc/QbqKHdQVFTE1zu+xuFxc71mUBKgOdNkF5ZR37ju4dk8TX7NmO93fs/07V/62dpFw0MhgTmY\nwqtm4quz2dlnP/txeVczh8cmOIWWE1NQSM1JJJTCWz9TeEyCH9OWKKUGohWfFrRq454IME4pNUD/\n6eJi6XItEOhwee+gUol4D3hXLwZ7D1pSZiee/QO8HLfjXSHZirZvh1Jqi1JqAFq1Ga/VhvVE1n2B\n46rsWkNRnUlzKlrm70uVUnN8tTvW7Mnbg4hgFjMmTBWvRQSTmDBd+BKRdgvB2+egvrgSW/dRqDZ9\nIL4PxPdFwuMq+h0oPMCOnB0k5ySTnJvMjpwdZJe4u4HbCntRmi/ERMDF/aomrf55UwYWu4MzusbR\nNlr/GxzzPfz2qBYjOOs2UjufAkpxXY+radlxALuXzAQHbJUYBsphYm3afl7SaQ+RPuMFWo9pjSnY\nhKPcwcEvDhI7KpaXVr7EvJR5/LfITofcNLhtLqd2bsnIXq35Y3s27/yxi5d85OusK+5xePU6dJPB\n2xPghOUTuLG00L8BPO7TYR0+wlSSDg3gPOsq4ELif2FvSZm+hONH9NmLczIcljI34eWwlFGWuv7L\nff985VNTC0l6dIbj1KtHe/ZzFOf6U4zPucdVJiIPoZX68cyw/Ttwn4gs1Ev5dKN2NeKiAOda/PVs\n9+eLewV4Q0SuUEo51+Mp7Jwm2wDgJWC/UqqOXljNC3/sNOcCc0RkDJo5c4FS6omGXZb/XDH7ihrb\nmJXipfAwLiouInDL97Dl+4pzWWYzO4MC2REURHJQIDuDgkgNDMCha26hAaF0j+lOz9ie9IjtwVuz\nhVKgLPxXlh+M5Kyks9zm+n6tFhd19aBElwUEwiXvQMtuqPnPcv7eVbwcEc6AK24ksUUnrCEx8DsU\nhiaQWZZLnKMAgLyIrsT1fJrCqZNwBJcSQQQ/vfwT5e3KeXHFi6zLXsfVDgfjivIZs2025pNv4MkL\ne7Iw+RDfrNrPbcM60k3P40naWti3BIY9CKba5WsEQCm6Fq0mmhjyiWjycXj1fcv3v7qeu4ZnCiyo\n1muy1mWMXD53z547iuvPn6yxqiVYDqU+mzP/g6Eee3F7LIdSn22IfjoVF6eU2iAiG4Ebcd+qmYJm\nUlyne6xno1Uo9zmWBxOBWSKSA/yFd/OkZ/8aP3Sl1FwRiQPm6tpbHlqpINfg4xkiUg4Eo21HXV7T\nuCcK/gg8p5owSik1UEQaztdfR/d0egaIqslNuHN0ZxzK4f6DA4dD/60fezExiu9LS+hWVkI3Sxld\ny8rpZimnjd1Om1I7Z5ZWmgCt5kCKWnbBnDSYiA5nYkocBLFdWHsgj4NHlhMcaMfcYiUPL1rFRyM/\nYnC8tk+351ARGw7kEREcwKje8Z4XBcMe4KecjYxaO5NLiorh+3vghi8JLNSEZL9eg9m3OZnTrXmc\n3bKAxUeiKI3tjmn4G4QlfE3XuJa07zmIjnHhDGkzhNf+Gc+ctL94o2UM8ze+zX87DKFr6y7ceEo7\nZqzYzzM/bWHKLYOJCgmEKSO0dQSGwal31f5L2TyTR7Oe4IqgBEZa3mjymVbqOxOY3/twXvbw6vOT\nOlRUXmEca0g9rrF0REd5yT5TcNhIW97BF0zhMQmO4twMy6HUZ2tyPKlrPwClVJTHe1eB0E8/ptDu\nQZ5Vzd1KBSmlRri8rjinlPoZreCq59wTPd7385xbP/4mPlBKfYGX4rL6uXN89TPwT+CZROQ5Kvfu\nGryUtFIqBbjDnyDQ2VccRYiJwwG5KZC5GZW5GcnaAplbCCxIIyZ7B2TvgHVaYDjBUUy2Pw505vYO\nBSTG9uadnLWM+2sc7w6fjCpP4vPl+wC4qG88YUFVP9pCSyGvFmzmq7Zt+CrfTuCBFTDlXIhuB0Bc\n24Es266lGZtyahprk/7FI99tJD2vlLL0sWzL2cvwLb8wrGMHruifyNPtruaCNd8wMS6WTQElXDvn\nWu45+R4eGHETv2w6yKqUHC59bykfjh7ESc5F7JxbN4GXrBWl72rSrDRuYQkOu+YodJyQVZxFZFAk\nYYGeBaV9Y69VWIJ744az/vrK1nJ8oQupmhxN6q2fwYmLPwLvKmAAsFh3z33a38H1EkOXAFmuTzJ6\n8OQ7aFsmnyqlXq3Vql245bNVBJpNBAeYCDQLQQEmAs0mggJMBJlNFB3O4M+fZ1JisREVGsSV199E\nUrv2le3M4QQGDCO4wxkEdtH6hFhyiczdQsihjQRlbyI4eyP7C4UFlo4EYeXWA0/TOi2fq8XM8tAj\nbP/qchyFvTnFHsKwADsjLW0g+XLocSFkboHtc2DQLczcP49iazHRHc4k8LTn4esb4OAGyN0HQHBp\nHqmx7aFwG2XJvzL0rMeY//BpDP3oMYqyzsJRqoVALNtzhGV7jrA6YCmvB5TxU9pB3mzdhu/D4L31\n77EgdQFv3vQf3vw1n20HC7j2o2W8qwYwwrQeKapj3gC7e9aXCpPm3kXwxZVaKMjJN9TxW2w8Dpce\nZuSskYQFhLFy9Eq/+9n9buki8JTbr3rB1dToyzDtt3Cudh4Dg+ZHdfXwbtMdVx5G+58d7nJ6iZ/j\nT0XzVvrcZVwT8D7a3mAGsFpEZiuldojIWDTh+rpS6iB+/N9VlzDZlp9N4do5RJ8xGlNQCHmWMv77\nyltEDrqUgOjWNYxs0pcyAIAQylGY6Cjp/GofygDTbvqb9nBOSSlQCrKk8tPcCez8GpKGQPo6UHbU\nhhksjmsBaIHmRLXVCtcuegWWvQcomP8MYyJa4gDC0jdAWQH/ZK6A6MW0idmKOW0gGQVnoVQAJoEU\neysKzaFEUsqErEz2d3ubPaav2JGzg0f/GcvNp99J112n8fPGTG7nMWIp4NLD23jW7iDQ7HK7XD0F\n1n2hxSuGt/T+cXgIvN3ZRYzqDfx4r2bG+/Hu40LgJeckA1Bi812T0Bt2f22kXvY2q3MiqW3snKvA\n8xUrdzw5rRgYNCbVaXjr9N913glXSi0VkQ4eh08BdimlUgFE5Bu0TdUdTtu0iMSKyIdAfxF5ojoN\ncOptQ7DYHFjtDrffFrvi03d/IU0XdqC5LUefMZqCVT/Q4oybvI5nFs2RQESTtiYRTCah1KIlPbDH\n9eSvFgM42DaKzBZl9C5bzt8pM9hfnEF0SAw397mNCGsZ/PMupK0GBFp0QPJS+SQ/jZSwKLrvWgIS\nBq1Pgja9AQXBURAQQnyR0ytUYZl8Jjva9yLU4eC5jmdx0Z53WBz+K5Pa/o+Vu2ysoQfDy9/i3wHf\nc6P5L3K2FDI3ZA3/i2nBV9GRfLZ1Mu3C5zM6tA2/lI4khyimFw/l4JfreO+mAQQHmLUb9K+PaFNu\nmAGnP+T88rQfp4OLzdVjGj5ctIfLTk6gnZc9q6aMuY6mV381vBJrEVo8sUtqsQaSP76G9V8brf3Y\nBgbHM9WFJWx0vvQ4ZRWRtroGVhcSgQMu79PQhKDr3DnAvf4MtvjrSRWvhw8fzvDhwyvez/rYTIaX\nWJ3IYDMntY2i1GqnxGKjxGKn1GLH5lDYFR5P6ZWvL+gdz0djB3msYACXWcZy+++3sz1nO1P3fkGH\nqA70HXQJo3IysXQ6i5j2wyj8bgzD8g7Ro6QAFr+q/YipMqD5wleh91Ww6RsK5j9NVHkxQbn7eDB3\nH+MASX0HgLNtB5kb/yVPtL+IF//KYK3qwX9s/+IL+3ncE7WcsHLFUzm5nF9cwq0te3GAFNI77GVs\n3nxOy2nDvZZHmb8tiwH/XcDIXm149fzWlT7NluLKy1o7DX59GP41H9oNcRN43VpHsCu7iEdmbuRb\n5eLOUVYAIW7+AADYHDZunnszXVp04fnTn/f+RZYVwPL3oe91ENfVe5taYDJ5v2WbpW4Cz+anhldk\nqRR4TurXpOn9tSv1IWDrMkTR9iKKdxTX3NDA4Bjhzx7ew2iZAjaieREptNzS65VS/2nIxfnDhAkT\nfJ5rEx3GVi8xPueeFM+0h850a6uUwmJ3UGqxU6L/aK9tlFjtWG0OTuvi3dwXGRTJhyM/5MG/HmTT\n4U0k5yaTTDKzAJL3QPJUiAmlU5v+/DDgCQJ2ztWC4A/v0kyFiYOg3w2aNjXoVgL6XM0bM0ZwW/pu\nWjqq+ge+sno2MJtZQTB34GReXmdmZ3k7Hs5vx3xTR/4bOI1B5XncxBiW5f/O/qgdTI+JYlF4IR+l\nv8jNtscpsbTg540ZZGemM0OZCBAH5LjkFPjl39rvhS/CzT9B3v6KU5NGD+TGT1ayKiWHwjALFSJu\n3ecwrGoM787cnWw+vJnNhzf7FnhL34alb8GSN+G5I97b1AKzDwFl8jtFmDtuemw1IRkmj7AE7bf/\n1BQO4GbS9NGmPkLQTXWQ0hG9IojoFVHx/tDspptn3uDExB+BF6CUutj5RkR+U0pdpIcn1EXgpaNl\nOHCSRO0COv1m/MP3sfa+Z8nvdSXOWJ3o7T8yftILVdqKCMEBZoIDzLTw33mvgpahLfny4i/JL89n\nf8F+9hXsI7UgteInqySL2wc9TEDXi6DnRVonWznkpEB0kltsXFhwJBdcPpXzfx1DoqWM/ySNYsjW\nuVDkXgnBYTJxfnsrI04fyrSP3+S9grOZ5ziFZeW9eSbgS55MfxQR2FwUxKOJnUkNKuGDtqWcb3+O\nPY72JKf9HyuyzAxkMrea5/F/m2by9sFNxHV9nZv1OVR0EuJwQGFlPG+nuHCm3jqEMZ+uxGq3V/po\n2K0I2sODa3yZ1VFDXT+lKoWto36cgM110fDK/Qsu91dHnLxxMuC/16Rd+Vf/0Ikv4VgfGqWpXvXS\npoWI2NEe4APRvM/HKqUKRKQt8L+mXDFBRM5Ec/jrh5Z38weXc7eghVEotBzIn+vHOwLfALFoKcvG\n6qnNTjj8edwNE5HrRKSniFxHZYocf/87BfeH0dVAVz07eBBwA17iVeqDTp068vOkFxhhXUmvjLmM\nsK7k50kv0KlTx4aYDoDo4Gj6turLpV0u5YEBD/D62a/z3aXfsfj6xVze1SP+MyAYWveE4Igq4/SJ\n68OEM57npJ5X0Hvky/DoTnhyP5z5GEVx3XAAZuUgYPYDBE4dyYjEXcwLfpThpvUUEM4Ttru4yfoM\n+xxt6Gux8OVZr9JaAtkUEkwIMPfwOt4NeZUOcpACwnnXfjUv2kZz66EMFqz4sWIdSzYuZ87rt7qt\nbf7ce+gbH8qP955GS6kUEp+uSOOVP//k7G/PYc4ePTnP1h8xZ2lZjUxKYXMKFYddK5n09xvwVi8o\nza36YdptkLWt5jRTXvAV0F2thrf4Nb/GNleXztLl9SebPwGqr5bgyoLs92ucOyGqMoG+Tw3P8NKs\nieO2PBCQipa5xS3XnYjEAM8BQ4BTgfEudfBeBd5USnVHC1S/vfGW27TwR8O7BrgDzUszBbhGzzxw\ncXWdAETkK71fSxHZD4xXSk3VS2LMpzIsocHyvHXq1JFp7/l3I2tqXNrlUi7tcmnlgZBoOPdZIs59\nlm2pf7Ng7r1cdCSLbsWH6J5yCIvA0z2XsyV/FyUZF7Lc0ZtRlle5o7+NB5NO58w2Q5iXsZQF4WG8\nE9OCh3O3cFnwI8yzD+Y+67+ZYr+Y6fZRPBswo+Iv4yzHbih1zwV50ZpvsRQW0FmPzXNyedFMzt6Z\njDnsCE8vfZpNCwt5Ju1e+gDmju2YdyAD86ud4LHd8NN9kOySHLjQZUv4t8fhotfgp3th83cw5A44\nd3zl/qC1TNN2Yzz9oSrxoeBVL/Dy09zeumqqrsMFVKP9ePOcrKk+nZNthQtpTZ9q2yTGhEGW77mg\nfpJV18WkWVfMoeaOwUnBLwRGByZY860Z5Wnlz9pL7fsaqp8Hy9GTa+gOdr8opfrq3uSvAGejZSz5\nQCn1iYicjZZFJQ/oA8xESxr9EJoycIVSKkVELgGeRdMijwCjlVKHRGQ8moWrM9AOTaN8T597Llq2\nl2Fovg2XK6XcvMWUUvv1tXp+Q6OA+UqpfP38fOAC4FtgBFomGYDpwARgci0/p2ZBjQJPKZUjIovQ\nSlfs1h1KAGq0/yilvLpCKqXmon25BnXkpA5nETv2Tx5d9AhhaWu4s8TGkPzDdN+7lAURcTzYbh3L\n866jvGAgH2wI4qMd0whuu482AYoyk4mpLaJIstm4rrCIC8xr+JTX+cx+IUsc/Rhvu5XFjn7caf6V\n08yVzyJlIqQFBNDVaiUouerX10ry2Za1hHyTiWdaxZJzYCMEaee+yMgi3m4H7Hz62Yfcfsh3JnxW\nTYbT7tOEHWhhEzt+g6H3QteRlH5zI6G5++DuJdC2MlGFxVZpPPxzezZjhnYgOsxEgARUCC6vAs9a\nBuu/gAJ3y7rNYSPQHFileYCfGp4Tb83XZ69n6+GttU4t1iK08l/Wp9NKrUb0Tt12OmuPOdTcMWpg\n1B8JtyR0ceaLzZieMdQcah5ZnfCqaz+d47k8kC88nQHTgUQRaQnkKlXhTp0G+JNgu1lSo8ATkXeA\nMDTb750iMlYpNa6GbgaNQHx4PDMu/hKbw0aAKQBSl8Gch4g5vJPPi038PugAU4sdrN3aHVtZAiUp\n95PYfz8PDkrkuWXP8WKrOKa1740j/wDZATm0sn3Opdl9mFcwmr8cA1nsOJmn1ZeMNv9JfoCDe+Jb\nsTsoiK/SM+lrsfhcV7TDwftZhyHoo4pjru1vP/RKzRf3v5Pd3xdmwIL/wIL/VHiVFm+ZSbiYtNAO\nEQrLKq3sm9PzuXLSYqyJz9E1piszLtIy5njdw1v6luY164HVYfUq8MzVaHjeBIUvAXTDrzdwUsuT\nfJz1TrQ/Aq8e7JGNtYcXnBT8glNogVb+KuGWhC5BrYNS+k73nfw87oI44i6Mw7NfeXZ5cy8PVBea\nuYXaf/x5kOuvlLpLKTVZKXUnLvneDJoGAc7Qhg7D4J6lcNoDiHJwwdpZfNt3ABufuZYbTkkAzCzd\n0InU1N7c1fcuHMpBdnkOQS27cWPvW3jj8u9YlLiDoG6vEhi7BDtmnrfdTB/1GiPb9GZ3kKau/RXm\ntRJJoxP+z7vw0el8MvltHvvpT15fUFnfsmd8JOnF+xiRm8VZyYvZf6SkijONY8lb8MFQWO6ZKF8T\nUr6cbfzdw3NSn6IjKqRSADdkHF5jaXiB0YEJTqFVMXewqcbySUopvPULjA5s7uWBfOHVGVApdQSI\n1k20FcdrMW6zwp8PtFBERqMFog8Gimpob3AsCQiG81/Qfi95E2bdRvj1M3jlqgvpmxjLf37awjt/\n7OKBc87jok5p/JbyG8XWYsaeNJb48Hju738/EYERxITEcCg7iUkL08jOb0NhyiMEt55HUOw/TImJ\nZkpMtFaTDE2j69SiK0P2b+CGwkLiGrk67J2ZE/m4NIqziq3skKc4r4Mw+ux+pM36gD6HNQv8c289\nyX8DpxMR2JaAxAD+LycPU8rEase1+fAYrU7D8+Y5qeoxk3WLsMp/WV9CyVEP8zVWxXNrvjXDUe5w\nE16OcgdFW4u+zP4p26emFvZC2IxWF7Ua7dnPmm9t7uWBfLX/HXhRd1QxAecBT+rnFgLXou3n3QIc\nRQLi4xt/BN5o4C7gQWA34D1FiUHTQQRG/EeL8Vv2Hsy6HR7ayOhTOxAVEsi/v93A+wv3cF2vc4go\nWE422Vz2+VVMHTmFe06+p2KY1WGrkaTXCXCMwlbYj/KsS7GXdGBA72QK7ZmkF6XjAHLNZnILU1gX\nE83kGPega5QiRCla2e3YEAaUl/NcbhHhHplbjpa78rWSSj8FPweZwLfQwuX8fwOnA5BgPcir2aGc\nX1Ja7Xgm3AWe650lsJZeo9WJ/9qmFosOcTVpel/H8aThlaeVP5sxPWOox17cnvK08mrL/NS1n85x\nWx5IRAYDP6L9eV8iIhOUUn2VUrki8jywRh9nolIqT+/2JPCNfn498GlN8zRXqsul2dnl7Q8ur1sC\n+Q22IoP6QQTOe16r+LB7ASz7H5z/ApeenIBJhHs+msfkD+bQ4uw7Ce82ldKwQ9ww6w5+vP4Lunbu\nwsL9C3l08aNYHBauOOMIQyP68MyPWygv7MfmjR2Ze/9ltG1hJsgcRHpROgtSF7D18FaWpi+lxFbC\nqW1PJSUvhezSbMpEOKDHGR4MDOC3iPAqy02y2oix27k/L5/TS+u/WrsrNQk7J09/9jOtEk6tcjyy\nmtJI3rwb61NZinTZw/M1cL14aTbSHp691L7PHGoeWZ5dXitvy7r2g+O7PJBSag2ad6e3c9OAaV6O\np6CFKpzwVKfheQaVO9PAK+BfDbYig/pDBEY8owm8VVPgtHEQ2YaL+7WlR8Fadp4xGjGHUHrgVsI6\nTsKUUMidP4zjoSvuYsKyCdiVnWu7X8szpz6D2WSmc7zimo8XUFaawJWT/mHymEGc2jmEdpHt+Fcf\n7U/CoRzYHDaCzEGUWEt4d/27pBelc2XXK3l26bMUWr0796YFBpAWGMA98VWTenezWHg4J48zGlgQ\nevL04de4MP11Inu4p2OOcjj49zfr6dq6avykt6D0+hQdLUJr3sOrF6eVRgxL0IVUrcv81LWfwYlL\ndbk0b2vMhRg0EAkDoOclsOMXLX3XhZqHZKCJipRrytqS0rSbCWv/CYdbHeA//2jPOnf1u4sH+j9Q\n4ejRr20SkZ0+oSjtOvKKejHm05W8enU/rhqYVDGdSUwEmTXnlrDAMJ485cmKc79f8zs7c3fy9Y6v\nCTYHc1rCaTy15KkaL2FXUBD3ehGEAD3LLTx7JIeTy317jdaVLpLNzpBbsKbCk63jKo5HOxykbFzE\nOQHzielkJ9dcKeaCvJg763NH0y+B5+dYaQFmkmzeDaCNZdI0MGhMqjNpfg6sRLM/79MPd0SLWxmi\nlPJ3I9bgWDP8SU3grfkMho2D6MQqeUYdpR0o2X8l4R1nAvD4kMcZe9JYt2ECTAHER8SQnvQ5F0R+\nzKzVR3j4u43sO1zM/53XvcaYssigSAa1GcSgNpUJuC/pfAmged2ty17HioMr2H5kO6EBoczbN6/G\nS9sRHMSYhPgqx6Psdr7NyPR5Q68NgcCpLtrl40dyaRM8AQDzkTCeah2HVW/nTcOrDycSJ20iKzOt\n+KyH5+dYh8y+BZ7hx27QHKlOw7tZRM4F7kaLP1HALuA3pVTNOZAMmg7xfaH3lbD1R/j9abhuutc8\no2VLN/PGO8/TPimJwfGDvQ7VNqItGcUZXDMM+iX0ZsLPW3n3r93sO1LCq1f3IzSobtUIRKSKMHzt\nrNewKzultlIe//txgkxB/HXgL7/GKzCbubBdYsX7MxLP4MNzJ/H0pG68dKj2SY2vK6x0Tm5jrxQS\nrfXXFhGfzizVWQdr67QSG141LtCT+hCwzTmXpsGJS7VemkqpP4E/G2ktBg3Jec/Dzt9h20+w6w86\ndRvJz5NeYOJbk9h3sJCt6YUE972IZSmduGKg71DLxIhE1matJaMog5tPG0q7mDAe+GodP2/MYNvB\nAv53Q396J0T77F8bRIQACaioRuHK7N2zmbRhEhnF/nihw9L0pShgblQkcyK0OMKPRn7E6W1Pgx/v\ngs0z67ZG/XeZCOH+eG8qpe2t1hHXpNi+hFJ9eGlWF2toYHC8YpjqTxRatNNMmwC/PQLW0oo8o4u+\n/pC5n79LZFxbZq1NY+KcrT4Df9uGtwUgo0gTNOf0bM339w2jS6twdmcXccUH//Den7sos9bHbdc3\nl3e9nN+v+Z3Nt2xm8y2bWXDNAmJDYqvts/XIVjeNKr88X6tScfUUmJAPE/KZcvLFzPLiRVoTFj+F\n2FH/w/khVF1bSB0Sb4Nh0jRonhgC70Ri6H1alfXcfbBmqtupwR1j+fjmQQSZTUxfnsqnS1O8DpEQ\noSWyOFhcmey5Z3wUv4w7k7FDO2C1K95csJPz3/6bmWsOYG2kIPT48HgWX7+YVaNX+RR8N/56o1v2\nlMVGTqwAACAASURBVH0F+6q02dWyHRNbtWRAR6+e31Wo0PB8Zav2wNPgm1ng3fM0LcCXabhSgPmj\nhdXNwNy8bwwiYheRdSKyWURmi0iUfrytng6sySIit4hItr7+dSLyL49zO0UkWURudjneUURW6Oe+\nFpHaZHBpVtT4dy0it+m/h4jIbyJyTcMvy6BBMAdqAekAKyaB3T111pndWvHODf0BeHnuDlbvy/Ec\noULDSy9yTzoRGmTm+Sv68NUdp9K9TQT7c0p4bNYmzn5tIefe/G/OG/MAo8aOc/u597Hn6v0SQwNC\nWXz9Yr6/7Psa23648UMOlx72es4m4pfQc2Yk8VfDC3W4PwBkFXgPwLf6MZ43j1BP6qqp1VUzPE44\nnssDAXyjr3+gUuozMMoD+Ys/D3LOOJdxaB/Uk9W0NWjqdL8A4rpD/gHY8kOV0xf1bcudZ3bC7lA8\n8NU6Dua7B2knRmiOIAeLDlbpCzCsaxy/PXgmb1x7Ml1bR5CRX8bavdnsSrqQ5MQL3H72ZnqpgVdP\ndI/pzsLrFtbY7sklT2J3VJpfXevU2UQ4P6n61IzONGNRfmqyYX4KEp+tXPoH+zGWuYY2Nya08Xr8\nmqJiIgMjaxy/PogKlo7D2gXMuPqkwL+GtQuYERUsHRuynwfL0SoNoNfo3Ky/NonIayKyUkQ2iMid\n+vGzRWSRiPwkIrtF5GURuUlvt1FEOuntLtG1qrUiMl9EWunHx4vIpyKyUO8/zmXubSLysYhsEZF5\nIhLsdcXen2MqygPpGVac5YFAKw/kfAKcDlxZh8+pWeCPwAsXkbOAQqXUQaC4gddk0JCYTDDsQe31\nP//zuif0+AU9OaVjLFkF5YyZspIjRZVaSHy4FgKQVZLlM9dkgNnENYOSmP/vs5g8dhDhQcfGghIX\nGseaMWuqbbPy4EruWnCXz/MHAwPYFejbM7J/uYXNKfvZFhxUebAaIRPmkaUlxKfpEh5yif3zRmxA\nzXuNNf2Dbwn2dU9tHKKCpePlPQP/WDA2bPT314Wds2Bs2OjLewb+UZPwqms/Hc/yQK4ZUaqUBwJO\nAe7Sa9aBlhHlLuAkYCzQTW/3KZpiAHp5IKXUILQclo+7zNEDLdelUxNz/hF0Bd5TSvVBy2Z1tY/1\nX6UL1+9ExOmKbJQH8gN/BN7/ASOB5/UnjqpqQQMgIpfrTztfi8h5jTHnCUO/6yAiHrK3wr6lVU4H\nmk18fPMgerSJZM+hYm6Zuoqick24BZmDaBXaCruyk12SXe00JpMwqnc8fRKjvJ4/mF9KeT3EyVVH\nsDmYz0Z9Vm2bVZmrKt94kVVXJbV1e39ah6Qqbc51SVfWxeq9ygJAmHLXBBNjfFeeWFhDVYqk0OoF\nIjR9b8s+rc0vfHRxSJfwIE1pCQ8SPro4pMsjpwWnMCFa+fp55LTgFG/9+rQ2v+DHtM7yQAeB1vgu\nD3SziKxHi0eORQvPAr08kFLKAniWB+qov24nIr+LyCbgUaC3y9i/KqVseiWD2pYH+hnoqJQ6GfgD\n+NyP6zV8kHT8EXjRSqnngAjgLbTkow2OUmq2Uuou4F7geLCrHz8EBMMA3VK9wXt9yRZhQXxx+yl0\naBnGlvQCxn21DptutnM6rjg9NWvG+//b/pwSznvrb/7YllWr5deWIfFD+ODcqiWAXBn729hqz5/R\nXnuQvjO+NUWm6v9tPs70HecX62H6dC3344m3Kgs2W6VgbRXo/UHClaYeT9c2UhKcQstJeJDgqMEU\n61AKb/3iI6RZlwdSSuUqpZxPVFOAgfprozyQH/gj8B7Vfz8NzAC8JjX1hW6vztKfdFyPXyAiO3TP\noSeqGeJZoPq7lUHt6a8Xvdg2G8q9V3xqHRXC9NtOISYskIXJh3jhV636eUJ4VU/NuhAaaGZ/Tgl3\nfL6Gp3/cTKml4bS9/2/vvsOrKrPFj39XekilJBAgkFCD9N4hgAqIgqA4oCDYGJ0ZnDs6Os78LOj1\nzrVd71yxYC+MghUQEAWFIBJAhBAI0kkIJBCKECAhpL2/P/ZJz0lOSE5yQtbnec7DObu8+90Ess7e\n+33XGtF6BCsnF1VZjwiMINCrKGDsOLUDKHqG98yQZ0rsn+7uTvfINmz2taVje+oc9oTm5dHazlVe\nz6zqVYk49c3Dhe897NTrK86vgkTXruD4BZOakV2yjxnZhjWH8z5mXrrYe605nPdxefuduGiqVB4I\n+DPwcLGAUKCgPJAHgIh0FJFGVTg1p5QHEpHiaYUmAXts778DrhORINsAlutsy6CoPFBBXxpseSBH\nAl6AiLQB8owxm6j6M7z3sR6oFrL943rVtrwrMF1EomzrZorIyyLSUkSew8rssqOKx1SVadoewgdB\nTgbsKZPUvVBEMz/enNkPL3c3PohN4r2fEgnzL3+kpj3tWjSmc8q3ZV6T+kbw+IQueLm78cmWZCa+\n+hN7T5yvkdMrT5vANrw6+lVa+bfio/EfsWHahhLrk88nF7738fApvXsJGTkZmHH2K7evOnacqefL\nJsouKGNUoLB4rx1D27Sm+JPSsL2rCt8HZF1gx8yK/2sE51f+JaJ7ZBu62xmRWjrw17SEk3mP378y\n61BB8MrINty/MutQwsm8Csv8XOl+NiXKAwEF5YGKewerGvp220CWBVQtN3hBeaCtQEWpfapUHgh4\n0DaoJQ7rynQ2WFd+QEF5oC2ULQ/0kIjsx7o122DLA0lllYVFZDJWHaj/ApKBecaYKo3UtD3sXV5Q\nCkNEBgFPGWPG2z4/hlWR4/li+8wF7gS2AjuMMW+V066prP+qAts+hOUPQsRwmL2iwk2XxB3jL5/G\nA3BrdDLfpb3OlI5TeHpIxUVUHbE7NZ0HF8Vx6FQGXh5uPD6hCzMHta00N2dNWPjrQl7Y+kKZ5S+M\neIGhrYYydNHQcvfr1LgTX9z0BfJ0cLnrC3SPbMOIzEu8llb0O29e0yZ8GWhVWhjScgixqbGF60L3\nzuUH70c47OnBpGIjRHclFgXjAqf9mtLskcN0/7B7ieVjL2bw0qkzAPypeTPWNyp5YdIr6zILj6cR\n5+3FncXykJY+xk3tOrF4egyDPhlU4TnakzA7AWNMpT/EQG+J6Bbq/mwLf2l54qJJTTiZ9/j5yybJ\nWfuphqvSKzxjzBKsW4p9gR5VDXZ2lB5RdMy2rPhx5xtj+htj/lBesFM1oOvN4OELSRusyegVmNy7\nNfNuugaAJVut50iOP8OrpBstg1g+dxjT+oeTnZvPk8t28/Bn8U7P1gKUSZBdXPFbnqXtP7sfsAJa\nReLKCVTzzvyGh+2Lmo97ySvJm4eeKbedb/3K3k1rlnEGYuez5tbyxlxYQqswKKj0uTyadhw/z4pH\ngi6ZuIRR4aMcPkZ5zl82SbFHc2d8tSdndOzR3BmOBq0r3U81XI5MPP8XcC/WPen7RGS+03tVBfPm\nzSt8xcTE1HV36hefIOhyk/U+fnGlm88eGsnjE7pgcqyrmv1nyv4yv1KNvDx47pYevHp7b3w93fkq\nLoXpb2/m5AXn18DbcvuWMsvybaMpv5n8jd39Cp739Ss2avOJZiWzvHhAiau7AnFJ1ve9PFMyIK05\n/mG5x3okpGn5nVj9eOFUkfL0L+eZob1K6VA0OAegW1YmAN/f+r29zenQuAOvjH4FgIt7LpK2JK3w\npZSrceQZXi9jzBxjzJvGmPsoVpW3GsodUXQlDRUPeNHR0TXQtQamYPDKjk8gv/LJ0/cOb8efowcA\ncCYrjSVxRyvZo2pu7NGSLx4YTMsgH+KSz3HzqxtJSEmv0WOU1sizEUsmLimx7Psj1i/58MBwdt65\nkxdHvFhmv6nLrXEAl93c6BMRzq0tW7DU38/uVd9Wn5Jz3nYlJrP+2Ppyty2jotu7X95r9ypvfEam\n3d1MOWMk0t3d+cZ2NTmzbXsAmvuVPzm9NP8u/jSf3LzwpZSrcSTgXRCRO0Ski4jMBMof0lcxoeQI\npK1AB1t2AS9gGiUnf6raEjkCAlvDuSOQHFv59sCfR3fDxy0Qccvjr19t5Jtd1RutWVrXlkEs+9Mw\n+rQJJjU9i6kLNrFyZ80eo7QOjTvwxKAnCj8PbVX07E5EGBc5jnZB7UrsU3BbUxBmdr+HS806FAam\n8oJe/6zLZQaH7EpMdighNGA/68uuz2lxNoX4O+PLXd04r2q3hv8W2ozukW04Yoqurj+/6cqqSSjl\nShwJeHcAYcCDWJM0b6/KAUTkEyAW6CQiySJylzEmDysjwWpgN1ZuuD0VtaOcxM0dek6z3m96DXId\nGzbfrrF1G894/MaDi+JYvftEjXYrJMCbRXMGcUuf1lzKyeOPn2znpe/2ke/EYfa3db6N5Tcv55kh\nz3BLx7JJLpbdvIxtM7YxNqLEoGMMhr/0/Qsrp6xkcNjgwuXlBb0Ar0CGtinxuJpdSUdp6sCztuOe\nJUd0vjWsWErEd0bjdnIPO+/cyeODSg5UfOFk+flCqyKqSVSF6xeOX1jtYyjlbHYDnoi0E5F2QDOs\n7CovAksAOw8TymeMud0Y09IY422MaWOMed+2fJUxprMxpqMxxv74buV8ve8Ady/Y9w0sGAanD1a6\nS0FOzet7eJObb/jjJ9v5YU/NPrfx9nDnpak9ePLGa3B3E15dd5C5i+OcOpglIiiCyR0n2x0h6uXu\nxUsjX7K7/1vXv8WtnW7lycFP8td+f6V7ZBsmtrKmccwIs27zrZ0Vx9opJR+FxxxNYVdicqXTCIpf\nIZ72bwrDHipa+cYQ5OlggksNhBmUdbnSnJqOGNBigN11vUJ7Vbt9pZytoiu8J0q9Hi/2p7qaNGkH\ndy6Dph3h9H5YPB2yKp4PV1A1oX8HuGdYJDl5hgc+3s7PiWUrLFSHiHD3sEjem90ff28PVu48zrS3\nNpN67lLlOzvRzjt32l331OCnmNppKrO6zqJdUDsSvTzpHtmGeB9vLuRcwNvdm9E97uQPg6eW2fej\n41a6tnY55ecpRcSaNxfZxrp9eu1T4F4qH+bSB4CSqcn+nVr9LyPvjr06pm/V5/JAACIyw5ZLc5eI\nxNlSMBacwzpbQo8dtmTUrxSrmuDsfq0TkT7lLH+rYJ51Bfv+XkRm2N7PKjXBvvh2t9rmIeaVd6zK\n2A14xpi7Sr3uLvizqgdR9UDbITAnBkK6WEHvqzmQbT/HQPH0Yo9P6MIdA9uQnZvPvR9uZX9a2QnX\n1TWyUwhfPjCEVsG+7Dh6jnH/+tHpz/UqIiLsmrWLh/s+zGc32v8duXTSUrsDSl4f+06l0xoc8sRJ\neKLsdIZRmZf4Q/MQALplZ5c7l68medSfMmv1tjyQiIzDyg4z1tb/PliPjIqPEppujOmFNcAwGwcy\nq5STaabG2AY97q1kmzeNMf+2fZxNqWlqxezCqvbg4Givkq7mOo+qqrz9YdrH4B0E+1fBG0MhaWO5\nmxZWPs9IRUR4ZlI3xnZtzvmsXGa997NTrsA6twjg6z8N5douoZzPyuWPn2znP1f8WmtFZsszu9ts\nujTtYne9iNDCrwVfTSw/53rczDgS/rQRU07AGh85vsJjb0wp9rNx97CqtpeyoVQC6uoGvR0zd7Du\ntnVENYliZOuRJY81bQORQZHc0eUOeof2dqg971ZRb/lFDY8p/fJuFVXh3Nsr3a8c9a080D+Ah40x\nJ8DK1mGM+cAYc6DYNgWp03KxqjSEi0j30g2JyAURecmWtWWQiDwhIj+LyE4RWVBsu3Ui8pztHPeK\nyFDbch+xkvvvFpGvKJkvlFL79yl2zGdtf6expf5eHhaRW4B+wL9tV+El/g6MMfts53pFWSkqzbTi\nyjTTipOc2AVL7oe0BEA40vlePth0kvzzx3ELDGP2X54mKyibW5ffSvug9iy9eSkAWTl5zHhnC78c\nOUun5v4snjOYJn5eFR/rChhj+DA2iWdX7iE339CnTTCv3t6HlsEVVxeoF/Lz4ZnG3NUilNfv3Ymv\nhy/nss4x/NPhvH3929y3+r4Sm++atatsG2uetEo/3byAzc0jeWTV3WxILjvrZ7u3N7Ps1MOrsH0H\niUilmVb8oobHhNz82MjSy08tfW59xt4N0TW9n61fF4wxAWKV5VkEvGOMWV08I5QtwIUYY/5pG0m+\nEbgVq4LBEiAKq5jqYeBtY8zTIvIgViWDh0QkyBiTbjvePUCUMeYREXkKK89lNBAE7MO6OmsNHAD6\nGmN2icinwDJjTIns7iJyxnaMcm+jiMg6rIC4vdiyJcAnxpjPS22bD0w1xnxp+xxckI5MRD4CPjXG\nrLS1+Yut/+OBh4wx14nIX4Cuxph7bQF1OzCw+LFL98l2zBuNMd+IyPNAuu3v+CmsEnQv27Z/yBhj\nt1BBeefpiErvQUixUvE2OcBhY0zZ2brq6tCiO9y3Dn58kSNfv8D8+fN5OtobvyZCRrbhqbk/M/tl\n6zZeakYqxhhEBB9Pd96Z1Y+pCzaxP+0iUxfE8u97BxIWVLOBSESYPTSS7q2D+NMncWxPPsf4/9vA\n3NEdmDm4Ld4V1JhzeW5uMC+d94stCvYJLgw822dsp8+/K3l0cd0z1gsYBHw/Kw6TfQl5vm2Jzfpc\ntkbkXj4xBe8WtVL1y2GezcJHRjy20u63Wc9mlVejr0BBeaDWWPky7ZUH6i4iBQ9aA7HKA+VgKw8E\nICKlywNF296H254HhgGeQGKxtlfarr7OiEhVywMV/p2ISDdgIRAA/L10QCvG3peOXEqWexsjIo8A\njYDGQAJQkHG9YLttQME/pBHA/wHYgnT582JKumyMKcjmsA2r9FxV+lwtjtzSHI/1/8YHqxDiFGCW\niLxf4V6qfvPwgtH/jw/O9LCCXbG6Y0/3SOXLV1/C39OfS7mXOHe5qHJAcCMvPr53YGEtvVvf2MTh\nU1cydbNyfds2YeWDw4nuHEL6pRyeXbmHMf+znmU7Upw6faEuebp7Fs6JKz1Z3h5vd2/EN7jcW54A\nq+5+hGt9FpJzquQt1MZeodXrrOuqt+WBsKZx9QEwxiQYY3oDq4Byv1Xans11p6iqQnFZBbfIbLcO\nXwOm2Pr8jp0+2+sXOBakipf4qKgtp3DkYEHGmMJM4iKyyhgzVUTKVg5VV518d59y647lnz5IS/+W\n7D+7n9SMVBr7NC5cHxrow6e/H8RdH2wlLvkct725iQUz+tIvoknp5qutiZ8X78/uz9q9J3lu1V4O\nnLzInxfv4O0Nh/n7+C4M7VB5kdT6JqpJ1JXfapyXDmcOwfw+ZA2Yw+br5uHn6cf//q4Xfz3XmY83\n382nW49yJiObZ6fV3VSDnNNH1yc9NyHa3nq/qOdigDK3NB1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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -282,7 +795,7 @@ } ], "source": [ - "with open('data/riemannian_vs_baseline_car.pickle', 'rb') as f:\n", + "with open('data/riemannian_vs_baseline_car_40_003.pickle', 'rb') as f:\n", " logs = pickle.load(f)\n", "\n", "fig = plt.figure()\n", @@ -294,49 +807,49 @@ "plt.loglog(logs['plain_sgd'][500].logger.time_hist, logs['plain_sgd'][500].logger.loss_hist['train']['logistic'],\n", " label='Cores SGD 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.01, 1, 5, 30, 60]) / 2.5\n", + "grid = np.array([0.01, 1, 5, 30, 60]) / 10\n", "x = logs['riemannian_sgd'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][-1].logger.time_hist,\n", " logs['riemannian_sgd'][-1].logger.loss_hist['train']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann GD', linewidth=2, color=colors[0])\n", - "grid = np.array([0.05, 2, 12, 30]) / 2.5\n", + "grid = np.array([0.05, 2, 12, 30]) / 10\n", "x = logs['riemannian_sgd'][100].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][100].logger.time_hist,\n", " logs['riemannian_sgd'][100].logger.loss_hist['train']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 100', linewidth=2, color=colors[1])\n", - "grid = np.array([0.1, 7.5, 60]) / 2.5\n", + "grid = np.array([0.1, 7.5, 60]) / 10\n", "x = logs['riemannian_sgd'][500].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][500].logger.time_hist,\n", " logs['riemannian_sgd'][500].logger.loss_hist['train']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.1, 3, 20, 53])\n", - "x = riemannian_sgd_rand[-1].logger.time_hist\n", + "grid = np.array([0.1, 3, 20, 53]) / 2\n", + "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", - "plt.loglog(riemannian_sgd_rand[-1].logger.time_hist,\n", - " riemannian_sgd_rand[-1].logger.loss_hist['train']['logistic'],\n", - " marker='s', markevery=marker_indices, label='Riemann GD rand init', linewidth=2, color=colors[0])\n", + "plt.loglog(logs['riemannian_sgd_rand'][-1].logger.time_hist,\n", + " logs['riemannian_sgd_rand'][-1].logger.loss_hist['train']['logistic'],\n", + " marker='s', markevery=marker_indices, label='Riemann GD rand init 1', linewidth=2, color=colors[0])\n", "\n", - "# plt.loglog(plain_sgd_rand[-1].logger.time_hist,\n", - "# plain_sgd_rand[-1].logger.loss_hist['train']['logistic'],\n", + "# plt.loglog(logs['plain_sgd_rand'][-1].logger.time_hist,\n", + "# logs['plain_sgd_rand'][-1].logger.loss_hist['train']['logistic'],\n", "# marker='v', markevery=marker_indices, label='Cores GD rand init', linewidth=2, color=colors[0])\n", "\n", "legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1.04), frameon=False)\n", "plt.xlabel('time (s)')\n", - "plt.ylabel('logistic loss')\n", + "plt.ylabel('training loss (logistic)')\n", "plt.minorticks_off()\n", "ax = plt.gca()\n", - "ax.set_xlim([0.02, 100])\n", - "ax.set_ylim([1e-17, 2])\n", + "ax.set_xlim([0.02, 200])\n", + "ax.set_ylim([1e-5, 2e1])\n", "fig.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 108, "metadata": { "collapsed": true }, @@ -356,16 +869,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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NIn6wPDBtGVMXbC5V9uiFfbny2A4VtGg4iIhxVIawUReO6gQgG3i7xOY5B7AG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\n", 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H0LbFHD0CCe/jJ5lBq5UHAs7T5/a4WT+slMoBEJGrgZdEJBnIAfKAZ7363i8i1wHRaBrh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\nv2RxsLEHZW7bzRc3M+xH8wsbeqCjFftuIQ+HQEoCfNjEpG6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -373,7 +886,7 @@ } ], "source": [ - "with open('data/riemannian_vs_baseline_car.pickle', 'rb') as f:\n", + "with open('data/riemannian_vs_baseline_car_40_003.pickle', 'rb') as f:\n", " logs = pickle.load(f)\n", "\n", "fig = plt.figure()\n", @@ -404,30 +917,30 @@ " logs['riemannian_sgd'][500].logger.loss_hist['valid']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.1, 3, 20, 53])\n", - "x = riemannian_sgd_rand[-1].logger.time_hist\n", + "grid = np.array([0.1, 3, 20, 53]) / 3\n", + "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", - "plt.loglog(riemannian_sgd_rand[-1].logger.time_hist,\n", - " riemannian_sgd_rand[-1].logger.loss_hist['valid']['logistic'],\n", - " marker='s', markevery=marker_indices, label='Riemann GD rand init', linewidth=2, color=colors[0])\n", + "plt.loglog(logs['riemannian_sgd_rand'][-1].logger.time_hist,\n", + " logs['riemannian_sgd_rand'][-1].logger.loss_hist['valid']['logistic'],\n", + " marker='s', markevery=marker_indices, label='Riemann GD rand init 1', linewidth=2, color=colors[0])\n", "\n", - "# plt.loglog(plain_sgd_rand[-1].logger.time_hist,\n", - "# plain_sgd_rand[-1].logger.loss_hist['valid']['logistic'],\n", + "# plt.loglog(logs['plain_sgd_rand'][-1].logger.time_hist,\n", + "# logs['plain_sgd_rand'][-1].logger.loss_hist['valid']['logistic'],\n", "# marker='v', label='Cores GD rand init', linewidth=2, color=colors[0])\n", "\n", "legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1.04), frameon=False)\n", "plt.xlabel('time (s)')\n", - "plt.ylabel('validation logistic loss')\n", + "plt.ylabel('test loss (logistic)')\n", "plt.minorticks_off()\n", "ax = plt.gca()\n", - "ax.set_xlim([0.02, 100])\n", - "ax.set_ylim([1e-17, 2])\n", + "ax.set_xlim([0.02, 200])\n", + "ax.set_ylim([1e-4, 20])\n", "fig.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 113, "metadata": { "collapsed": true }, @@ -435,15 +948,6 @@ "source": [ "fig.savefig('data/riemannian_vs_plain_car_validation.pdf', bbox_extra_artists=(legend,), bbox_inches='tight')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/experiments/Riemannian_vs_baseline_hiv.ipynb b/experiments/Riemannian_vs_baseline_hiv.ipynb new file mode 100644 index 0000000..e211086 --- /dev/null +++ b/experiments/Riemannian_vs_baseline_hiv.ipynb @@ -0,0 +1,2289 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import cPickle as pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "import sys\n", + "sys.path.append('../')\n", + "\n", + "%matplotlib inline\n", + "import matplotlib as mpl\n", + "import matplotlib.pylab as plt\n", + "import tt\n", + "from src.TTRegression import TTRegression\n", + "import urllib" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_fraction = 0.8" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_dummies(d, col):\n", + " dd = pd.get_dummies(d.ix[:, col])\n", + " dd.columns = [str(col) + \"_%s\" % c for c in dd.columns]\n", + " return(dd)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "... loading data\n", + "dataset len: 1625\n", + "\n", + "Original targets:\n", + "-1 1250\n", + " 1 375\n", + "Name: 1, dtype: int64\n" + ] + } + ], + "source": [ + "# Reproducability.\n", + "np.random.seed(0)\n", + "\n", + "dataset_path = 'hiv.txt'\n", + "# if (not os.path.isfile(dataset_path)):\n", + "# dataset_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data'\n", + "# print('Downloading data from %s' % dataset_url)\n", + "# urllib.urlretrieve(dataset_url, dataset_path)\n", + "\n", + "\n", + "print('... loading data')\n", + "hiv_data = pd.read_csv(dataset_path, header=None)\n", + "\n", + "print \"dataset len: %d\\n\" % len(hiv_data)\n", + "print \"Original targets:\"\n", + "print hiv_data.ix[:, 1].value_counts()\n", + "\n", + "hiv_target = hiv_data.ix[:, 1].values\n", + "\n", + "cols = []\n", + "for i in range(8):\n", + " dd = pd.get_dummies((hiv_data.ix[:, 0].str[i]))\n", + " dd.columns = [str(i) + \"_%s\" % c for c in dd.columns]\n", + " cols.append(dd)\n", + "hiv_features_one_hot = pd.concat(cols, axis=1)\n", + "hiv_features_one_hot = hiv_features_one_hot.as_matrix()\n", + "\n", + "# Shuffle.\n", + "idx_perm = np.random.permutation(len(hiv_data))\n", + "\n", + "X, y = hiv_features_one_hot[idx_perm, :], hiv_target[idx_perm]\n", + "\n", + "num_objects = y.size\n", + "train_size = np.round(num_objects * train_fraction).astype(int)\n", + "X_train = X[:train_size, :]\n", + "y_train = y[:train_size]\n", + "X_val = X[train_size:, :]\n", + "y_val = y[train_size:]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1300, 160)\n", + "(325, 160)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(X_val.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.091907711416386123" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn import metrics\n", + "md = LogisticRegression()\n", + "md.fit(X_train, y_train)\n", + "\n", + "phat = md.predict_proba(X_val)[:,1]\n", + "metrics.log_loss(y_val, phat)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['X_val',\n", + " 'X_train',\n", + " 'riemannian_sgd_smart_rand',\n", + " 'plain_sgd_rand',\n", + " 'plain_sgd',\n", + " 'riemannian_sgd',\n", + " 'riemannian_sgd_rand',\n", + " 'y_val',\n", + " 'y_train',\n", + " 'plain_sgd_smart_rand']" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logs.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.049553831284881833, 0.072686232951934965)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 10, 'riemannian-sgd', max_iter=100, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.001)#, exp_reg=1.2)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1], rieamannian_model.logger.loss_hist['valid']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(rieamannian_model.logger.loss_hist['train']['logistic'][1:])\n", + "plt.plot(rieamannian_model.logger.loss_hist['valid']['logistic'][1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.055013951202464745, 0.068450260366060611)" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.001, 'sgd', max_iter=100, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.0001)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1], model.logger.loss_hist['valid']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(model.logger.loss_hist['train']['logistic'][1:])\n", + "plt.plot(model.logger.loss_hist['valid']['logistic'][1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.048325617261269105, 0.07357982661999532)" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 20, 'riemannian-sgd', max_iter=100, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.001)#, exp_reg=1.2)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1], rieamannian_model.logger.loss_hist['valid']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DXlpE7mtwn4iILdSvX7CIAMDA1gMxevvoPL9c52LO4b5592Hon0PhM88HneZ0wu+Bv2OY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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(rieamannian_model.logger.loss_hist['train']['logistic'][1:])\n", + "plt.plot(rieamannian_model.logger.loss_hist['valid']['logistic'][1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.052488880609546637, 0.074131447108265663)" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 5, 'riemannian-sgd', max_iter=100, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.001)#, exp_reg=1.2)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1], rieamannian_model.logger.loss_hist['valid']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.04899284828491178, 0.075667014143683223)" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 20, 'riemannian-sgd', max_iter=100, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.001)#, exp_reg=1.2)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1], rieamannian_model.logger.loss_hist['valid']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.049805296322674525" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 10, 'riemannian-sgd', max_iter=100, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.001)#, exp_reg=1.2)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1], rieamannian_model.logger.loss_hist['valid']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.073058285036361914" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 32000.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plt.loglog(rieamannian_model.logger.loss_hist['train']['logistic'])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5.2376773398940273" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 16000.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(rieamannian_model.logger.loss_hist['train']['logistic'])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4.2812900713979012" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 8000.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(rieamannian_model.logger.loss_hist['train']['logistic'])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.2772404275048075" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 4000.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.8138777144144325e-07" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 2000.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2588001429859041" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 1200.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.64938201034652476" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 600.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.56848235213715392" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 500.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.44834578313021378" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 400.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.12317075220657484" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 300.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.11523507694629134" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 250.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.078775459626710592" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 200.0, 'riemannian-sgd', max_iter=10, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3.1281075685984382e-06" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model_800 = TTRegression('all-subsets', 'logistic', 4, 800.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model_800.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model_800.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4.6763795526036593e-07" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model_1600 = TTRegression('all-subsets', 'logistic', 4, 1600.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model_1600.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model_1600.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00026016104170833684" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 160.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jTEPHsSHGmFHGmGrGmJHAPBFZ76K4VQDLMh0Etgro3/+GVavgiy9gwQKgVy/b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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(rieamannian_model.logger.loss_hist['train']['logistic'])\n", + "plt.loglog(rieamannian_model_1600.logger.loss_hist['train']['logistic'])\n", + "plt.loglog(rieamannian_model_800.logger.loss_hist['train']['logistic'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00033300869655848692" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 140.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0004941606919187378" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 120.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00069424199467138167" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 100.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0011217952114711302" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 80.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.001236212271499575" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 60.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0025096523867232153" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0043267280526794877" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 20.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.009119125359184365" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 10.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.016789322716112491" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 5.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.034886521963542333" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 2.0, 'riemannian-sgd', max_iter=200, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.08, 'sgd', max_iter=1000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.10923394645550188" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.04, 'sgd', max_iter=1000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.039985891406132693" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.02, 'sgd', max_iter=1000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.070527546289762322" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.01, 'sgd', max_iter=1000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.048679468688613017" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# To use the same order of looping through objects for all runs.\n", + "model = TTRegression('all-subsets', 'logistic', 4, 0.005, 'sgd', max_iter=1000, verbose=1,\n", + " batch_size=500, fit_intercept=False, reg=0.)\n", + "model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "model.logger.loss_hist['train']['logistic'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plain_sgd = {}\n", + "riemannian_sgd = {}\n", + "\n", + "for batch_size in [-1, 100, 500]:\n", + " # To use the same order of looping through objects for all runs.\n", + " np.random.seed(0)\n", + " model = TTRegression('all-subsets', 'logistic', 4, 0.01, 'sgd', max_iter=10000, verbose=1,\n", + " fit_intercept=False, batch_size=batch_size, reg=0.)\n", + " model.fit_log_val(X_train, y_train, X_val, y_val)\n", + " plain_sgd[batch_size] = model\n", + "\n", + " np.random.seed(0)\n", + " # To use the same order of looping through objects for all runs.\n", + " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 400, 'riemannian-sgd', max_iter=800, verbose=1,\n", + " batch_size=batch_size, fit_intercept=False, reg=0.)\n", + " rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + " riemannian_sgd[batch_size] = rieamannian_model" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with open('data/riemannian_vs_baseline_hiv_400_001.pickle', 'wb') as f:\n", + " obj = {'plain_sgd': plain_sgd, 'riemannian_sgd': riemannian_sgd,\n", + "# 'plain_sgd_rand': plain_sgd_rand, 'riemannian_sgd_rand': riemannian_sgd_rand,\n", + "# 'plain_sgd_smart_rand': plain_sgd_smart_rand, 'riemannian_sgd_smart_rand': riemannian_sgd_smart_rand,\n", + " 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val}\n", + " pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# plain_sgd = {}\n", + "riemannian_sgd = {}\n", + "\n", + "for batch_size in [-1, 100, 500]:\n", + " # To use the same order of looping through objects for all runs.\n", + "# np.random.seed(0)\n", + "# model = TTRegression('all-subsets', 'logistic', 4, 0.01, 'sgd', max_iter=10000, verbose=1,\n", + "# fit_intercept=False, batch_size=batch_size, reg=0.)\n", + "# model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# plain_sgd[batch_size] = model\n", + "\n", + " np.random.seed(0)\n", + " # To use the same order of looping through objects for all runs.\n", + " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 800, 'riemannian-sgd', max_iter=800, verbose=1,\n", + " batch_size=batch_size, fit_intercept=False, reg=0.)\n", + " rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + " riemannian_sgd[batch_size] = rieamannian_model" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with open('data/riemannian_vs_baseline_hiv_800_001.pickle', 'wb') as f:\n", + " obj = {'plain_sgd': plain_sgd, 'riemannian_sgd': riemannian_sgd,\n", + "# 'plain_sgd_rand': plain_sgd_rand, 'riemannian_sgd_rand': riemannian_sgd_rand,\n", + "# 'plain_sgd_smart_rand': plain_sgd_smart_rand, 'riemannian_sgd_smart_rand': riemannian_sgd_smart_rand,\n", + " 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val}\n", + " pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# plain_sgd = {}\n", + "riemannian_sgd = {}\n", + "\n", + "for batch_size in [-1, 100, 500]:\n", + " # To use the same order of looping through objects for all runs.\n", + "# np.random.seed(0)\n", + "# model = TTRegression('all-subsets', 'logistic', 4, 0.01, 'sgd', max_iter=10000, verbose=1,\n", + "# fit_intercept=False, batch_size=batch_size, reg=0.)\n", + "# model.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# plain_sgd[batch_size] = model\n", + "\n", + " np.random.seed(0)\n", + " # To use the same order of looping through objects for all runs.\n", + " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 200, 'riemannian-sgd', max_iter=800, verbose=1,\n", + " batch_size=batch_size, fit_intercept=False, reg=0.)\n", + " rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", + " riemannian_sgd[batch_size] = rieamannian_model" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with open('data/riemannian_vs_baseline_hiv_200_001.pickle', 'wb') as f:\n", + " obj = {'plain_sgd': plain_sgd, 'riemannian_sgd': riemannian_sgd,\n", + "# 'plain_sgd_rand': plain_sgd_rand, 'riemannian_sgd_rand': riemannian_sgd_rand,\n", + "# 'plain_sgd_smart_rand': plain_sgd_smart_rand, 'riemannian_sgd_smart_rand': riemannian_sgd_smart_rand,\n", + " 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val}\n", + " pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train from random init" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(0)\n", + "w_init = tt.rand(2 * np.ones(X.shape[1]), r=4)\n", + "# Divide by norm to make sure the norm is reasonable,\n", + "# round to make all the ranks are valid.\n", + "w_init = ((1 / w_init.norm()) * w_init).round(eps=0)\n", + "\n", + "plain_sgd_rand = {}\n", + "riemannian_sgd_rand = {}\n", + "\n", + "batch_size = -1\n", + "# # To use the same order of looping through objects for all runs.\n", + "# np.random.seed(0)\n", + "# model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", + "# fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", + "# model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# plain_sgd_rand[batch_size] = model_rand\n", + "\n", + "# To use the same order of looping through objects for all runs.\n", + "np.random.seed(0)\n", + "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 800, 'riemannian-sgd', max_iter=1600, verbose=1,\n", + " batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init)\n", + "riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "riemannian_sgd_rand[batch_size] = riemannian_model_rand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random init with beter distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from src.models.all_subsets import tensorize_linear_init\n", + "w_init = tensorize_linear_init(np.random.rand(X.shape[1]), np.random.rand())\n", + "for _ in range(2):\n", + " w_init = w_init + 0 * tt.ones(2, X.shape[1])\n", + "w_init = w_init.round(eps=0)\n", + "\n", + "plain_sgd_smart_rand = {}\n", + "riemannian_sgd_smart_rand = {}\n", + "\n", + "batch_size = -1\n", + "# # To use the same order of looping through objects for all runs.\n", + "# np.random.seed(0)\n", + "# model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", + "# fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", + "# model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "# plain_sgd_smart_rand[batch_size] = model_rand\n", + "\n", + "# To use the same order of looping through objects for all runs.\n", + "np.random.seed(0)\n", + "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 800, 'riemannian-sgd', max_iter=1600, verbose=1,\n", + " batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init)\n", + "riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "riemannian_sgd_smart_rand[batch_size] = riemannian_model_rand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "with open('data/riemannian_vs_baseline_hiv_800_001.pickle', 'rb') as f:\n", + " logs = pickle.load(f)\n", + "plain_sgd = logs['plain_sgd']\n", + "riemannian_sgd = logs['riemannian_sgd']\n", + "\n", + "with open('data/riemannian_vs_baseline_hiv_800_001_tmp.pickle', 'wb') as f:\n", + " obj = {'plain_sgd': plain_sgd, 'riemannian_sgd': riemannian_sgd,\n", + " 'plain_sgd_rand': plain_sgd_rand, 'riemannian_sgd_rand': riemannian_sgd_rand,\n", + " 'plain_sgd_smart_rand': plain_sgd_smart_rand, 'riemannian_sgd_smart_rand': riemannian_sgd_smart_rand,\n", + " 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val}\n", + " pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot train" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "params = {\n", + " 'axes.labelsize': 8,\n", + " 'font.size': 8,\n", + " 'legend.fontsize': 10,\n", + " 'xtick.labelsize': 10,\n", + " 'ytick.labelsize': 10,\n", + " 'text.usetex': False,\n", + " 'figure.figsize': [4, 3]\n", + " }\n", + "mpl.rcParams.update(params)\n", + "\n", + "\n", + "colors = [(31, 119, 180), (44, 160, 44), (255, 127, 14), (255, 187, 120)]\n", + "# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.\n", + "for i in range(len(colors)):\n", + " r, g, b = colors[i]\n", + " colors[i] = (r / 255., g / 255., b / 255.)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AiSLikZtORP4EwoFflVIP+hjvP/YPVR1OLQkYpWD2XXBwORzdDjfPAY8ZuW9ueXAqkyet\nL1lqzC1UTP4rmkkzSp9E03ML+Ux3x3/o/K5VHipAWFAAl/SN5pK+0diK7azce5wfNx1h6a5UDgR2\nIfPAxzTt0N+ZC4+OqauYMeNNbnrmCRKzjnLHmggWXXkfb94xhhBrfT+XGDREXlywi49Xx/Pv4Z14\n4oKKH/adjkaeK1lVoxIzq+pS3xIwKKWOos3CZuqJjnsCO9CWJ+fqbQaJJkp8UTnjOwPt3ugvSUCM\ny3vnpMBX+SnJqSUBs3u+ZsgA4lfAngWV6t4hLo5JMxbySs44JicM5JWccUyasZAOcXElbT5YeYDc\nQjvDu7agf4eoKg+1LIEWM+ef1ooPbjqTdU+NZMqlPenSbwgF8ZowbUH8ZjKjujHjw5coPC+F6Btz\naXNdIj/9/BDn/98P7D3mVeDWoDGSugs+uxCOVOZ+5p2PV2sPXh+tOuhX+/Efr+OSt1c3Ou/Z+paA\nEZExurQLItIaiEIzHO8CE0RkkEvzsrLyJcZVN3Tno88ky8HVIP8CXCciVhGJAzqjzTiPApm6N6UA\nNwNzKjhuo+XUyZpflA/vDISMwxAzSEsG3DQW7l4HAUE1cr60bBvDXlpGfpGdOfcMoW9MZI0c1xdK\nKc669GaOnXYt2cs+IuzcOxARTNgxh+0goNlKxHSElG/b0XLIA0y9tCfXnBlTY0/VBvXEW/0gXQ/h\nmJKp/f7tWe37PKDMhCBxE1hDoKVXb25in5gPgEng4IvaZEApha3YQVCApwOfs/22yaOJCA7wesyG\nmjVfNyJvos2E8oEE4AGl1AEReQnN088BTFNK/ag7gDyslLrU5RhXA0+hPegXAvcABWihRSY0B5An\nlFJuS40i8irabCtfL3pJKfWNXjcQeAmIBlKB48D/KaU2i8hk4HYgDQhFm8k9rZTa7eX6xqE5hTQH\nMoCtSqkL9Lon0WaLRcD9zvGJSH+02WIQsEApdX8lP9ZGw6ljzFa8DMueg5Y94Y7fNVXntF0wcjIM\nfahGzjdt3t98sjqeUT1aMfmS03hhwS7uH9WF7q297QfXDD/Nmcekl2fyxsO38OvKH5DgOJY7zqBY\nXyE2hxyAveuwt7sBgKv7t2PauF5eb1QGjYT/xkF+uvZ6SiacOAAz+pW+d2LLhhfbeZbrZOYX0Xfq\nb4DiKcvXTBx/A/S4mA8+epuMhO2Mf+R12jUNwVZsJ9CifV9KjNl/RhMR0riMmcE/mwabzqpSZByB\nVa9qry/4rzYTG/ui9n7lK1DGG7EqHMsq4Ms/DwHw4Pld+GR1PAt3HOX/5v5dQc/qccWlF3HLqNO5\netxFKJMZW5sv+TrkYSaa59GEPOx5nbC3u4HIiJNYA/L5YVMi137wBymZ+RUfvBp88UcCi3YcrdVz\n/GMpO7Mu8vG3LMgq9zDj3lkDwAjTNiZa5sN34wG4M+lpHg/4ljWrl7EjKZNuzyzyyB3qaMQPuQb/\nTE4NY7b4WSjOh56Xl+ZO7HSupupclAtL/6/ap3h32X5sxQ4u6NWantERrNyXBsDaAydqdb9KRHhh\nyjOICM9OepYdq8KY2MpKSMv5LA26jycsXxNBDhmZTSksCiYwOI1tiSe4ZMZq1sen18qYUjLzeXbO\nTv79pVdPaANfnDgAi56CnLTy24mp/PfllK/df5x7vtpM7BPzideVyyPwrsJgLcws2Uv7aFW8W51h\nygwaG43fmMWvgp2ztGS9509zrxvzHJitsO1rbW+hiiRl5PPN+iOIwAOjupJ4Mo+DaaVOUDP1tFa1\nTVxsHD+/9DP99g5iya4+vJQznBs7F7Ey8EHuNM/FRBG2/BaIqZgTRQlc/+Ef/O+PhBrfzM8uKK7R\n451SpO6C9R+Bw4FDOTia6zJ7/XQM/PkOfDgcigrKOUiZmZlPY1baTinF64v3csPH65i/3X0lQvnw\n9FaAyeUYK/aWGlljZmbQ2Gj8xmyhnulq6EMlYpklRHWEs+/R2z0GDkeVTvHOsv0U2h1c3Ceabq3D\nSrLfd2+tBS3/vDmRzLyiKh27ssTFxvHpq58y7/15vPrKtzS55XsiRj/OkwHfsCzocaJDD6AcQShb\nNA5TFpPn/cmkb7Z4JjiuIRrznmut8O4gWPAI7PiRKWun8K9vRrB90UOauGuubiyykuD5VlCY6/0Y\nZZcZy7y3OxS/7TzKle//WVI2f3sKb/qQDnI1Zt+5CMUq5XAzcxM+LXXwe9sQiTVoZDRYYyYiISIy\nU0Q+EJEbfDZM3QmRHWDwfd7rhz4MTVpB0kbY/kOlx3EkPY/vNxzBJHC/nv1+lW7Mxp/VnqFdmlNQ\n5OC7jYfLO0ztMvg+GHQPHTjKatN0bjkjAXNAJsoejrKHMe+vIwx7+VcOn/Bx86wkxfZSA1bsOLWM\nWY0Z57Q9zNo/iwWJKfT+8xPY9Jlnm6XTPMug3GXG2z/fwHvL9zPxi03EnyjdSzuwbbXPoZxvLl2V\nePanzSWvlUP5DM+duTaB7IK6eUAzMKgJGqwxA64AflBK3QlcWm7LMS/4dr8PDINRU7TXSyaDrXIq\nzjN+30exQzHu9LZ0btkEu0Ox5oBmzIZ2acGtQ2IBLVejvb5u7CIw+jnofTWmolymHH6dTdeFMuas\nfVia7AYspGYpRry+gM/WeMnaX0kKiktneYXFVZvtNkSWH1nOsO+GseFo9eO7jueV2RdL2ebZKMlz\n6Ts1uwDXjzSvsJg520qXDZfsSuXt37bzmOVb3rOWJkG//8AdPsdyqbk009GuwFtKXs/aWn78bL19\nnw0MqkBD1jNrR2kSYt9rZB3Phe7lBdMDfa6D6H6QnQKrX/N7zAnHc/lpcxJmk5Roku1IyiQjr4iY\nqGA6NAthRNeWdGgWQuLJfJbuOub3sWsckwkue1f7PHLTiPzhZj6I/5D1px9mcI/fkYATOIpDmDp3\nF2NmzCEzv+pP3bai0rutrQaNmd2huPfrzXy17lCNHbMyTPp9Ehm2DB5Y9kC1jzVn/yz3AuXtcyo1\nFkV2B/d+vZmBzy8lNadUTeGs55fy2hL35cPHLd9yt+UXzjJ5hCK5YaGY08V9udAspecUYMvhDJ/9\nG9sK8j9AAsa17WYRGetSZ0jA1OG5KqtndgTNoEF5ucou+G/FKatMJq0dwNq3IT2+/PY6by7dh92h\nuLJfW2KbhwKwSvdiHNqlhRbAbBJuPjsWqDtHEJ9YrHDdVzDiKW3pNSuJqI0z+er4fD67wkxo8/WA\ngz1JFvo9P4f3V2+q0rKa68zMVlxze3Er96Ux768Unp61o8aOWV9IWX9AL5+zQzm47sM/+HDlAb7f\neIR5f2kzsABKHWyybcU4ynz9zzb5Ew6i+M46jdmB/yl3jMXl7CM3MlsGp74EjGvbfkqpRfo5DAkY\nGrCeGdoX8yo9c/Vcnwdu0c2/AcQMhD7Xgt2mufJXwP7UbOZsTcJiEiad16WkfKW+Xza0c/OSsqvP\nbEeI1czaAyfYc7Se00pZQ2HE43DfVrhlAcSchWSnMGL5dLb+6xbOH5iEKegw9uJgps87yoCXv2JL\nYmLFx3XBbWZWVIMzM3vVbp8H0nI4VM39wD3pe0peK5fb+C8HfuH7Pd+7Nz66HTZ9Xu7UxePxautX\nHm1MSZvYfPAYLyzYzZytyQCEk0sLcY8fK+uN6PuspTVXmVfS3+TdIaR0jKrc2Vdjcu4RHxIwSqk1\nev3LIrJdRLaJyDV62XARWSkic9DS6SEi40VknT7zeU80TCLymT7D2SYi3rJoVEoCRin1i7frUEp9\njybR4stPwNuTe4kEjFIqAXBKwLTGuwTMKUl9Z6ctT88sD/hXRQeolJ7ZqCmwax7smgsHV0DH4T6b\nXvnvx0hPzaRlWCC3378QALtSbEnKIWLQ1QzuVGrMwoMCuLJfO7748xCf/5HAC7oQZ71iMkHsELjx\nJ/jfOEjaSMCXV/DRpW/zR6+mTJwzm+yTXTme3pTL395MePhS7j3nbG4+u1OF2UNsbjMzzZjd9eh/\nOHi07LMKdGzdlPde9i/OrypZS4rsDka+qql5JEyvYLm5HJ5a/VTpG5d7+NOrtZyzl3S6hGBLsFb4\n/jna74i20HlUaeMTpfuRp9kK+TmxdK/rrpURHCzyfNhubnmT5LMeKYkJfDfAcyXIM9mG95WITpLM\nAaUpRdxs9kjs7sFg098cZJDPeufHUC09sykR1bOIUzINCRh37hWRm4CNaKm4MjEkYID6N2bVplJ6\nZuHRmgv/79Ng0ZNw50owe34Eu49mcSg1k8hzxlMI7HGpsyd8RUxUCMv2pLr1addUc0D5YeMRekVH\nEGJtQOmk+rwHma9C2hH45HlAmNqyO3M7B7Pq8DGKC1qQldWcFxbs49XFe7jl7E70aONb2ubPgycQ\nSzqW8B38a9YfjGp7Fcv/TiS/95UebRN3/MzrS/ZiAkwmQYAQq5mwoAACzO4LAwfSSp1zZm1O9Mgx\nufdYNofT8xjZvWVJXb5LyIG3PiUoVe5y9PHUOIrytPyveeYgZm9JgrzjRB/vySGLhV+WryewaRzs\nWwz2IVqnrSmQ7eJE8fMkQK/L1WQgnI/n64tyOTHkMY/zBqx+x30cRDLbeXychwpyK9ujYthjLxOG\nAlxhXsXLxdfRlCz6mCpeSv+3ZS5fKN/Pi86JWXX0zBoIp4oEzLtoOR2ViDwHvIqW19GA+jdmNa9n\nVhFn3wubP9dc+r8YB2GtwRyo7Tfpv9/YXv7S5aETeTzw3VavdUV2xVOztnutq1/KOISWfMruWwq2\nIhMfrPRnTzGKovRhJKXD54eSyMgswFva5aSMfN5cUv5ylzce/N6L95+Oc2+pMn0q5tySVwXg8ve9\nCYDHU3LQVOtbo+WeRXs23uj6PbjH59Ez1FdeP580It3KHyjyPEYGYW7l3toAxMpRTDgYZvrLa703\nbi3+js84myRa+N2nUvg/s6oup7wEjFLK1UX2I0q3XwwJGE41PTN/CAiCMS9qeeoSVnlU73B0YFHh\ni54b+DrNyORC62ZMgaFgbQIhTSE4CoIiOZZTyJ8H0wkOMDMgtikmk2ASMIkJk4DZJJhEMJnAhNSf\nBF9BJuxbonnYxQ2FsNYopfjj0F6O5+WhCjUDFxGeyYB27Qm1hmKz29h3ch+HM4/gQGHP1h46TcEJ\nmAJOYjZ7dwSxmISmIQEope1FKQWFdge2IkfJJ9w0JIAzY6PIKyxmzf4TAIzt2ZrjOTY2HtKWLi87\nPbpkX6lPuwjidIecgiI7v+7UvEgv6t2mJPtFk0Az7aNC6dKqCfylxxeGtS5Nd+Ysa9YZ2p5R8n5N\ncBA5AVbGxI4pbeOL6DM4mJ+K5BwjMnY4Tfct9dl0HpleXXKbkYnCjgNtJn+ZaY1b/RzHEIKwMca0\nEdBmZd3kiMdxAJpLJj/Yh5U/5jLcXvwtY6xLGVr4pkedakQuIEqp30XkeRG5XSn1MWgSMEAEmgTM\nRBH5H9AMTQLmETSHCVeWArNF5A2lVJqINAXC0AxeoVJqlojsBb4oe37dsWKpUqpYPCVg/hSRRUop\nZ5S7PxIwHtnRRaS1bjBBC11yekr9AnwlIq+jLSM6JWCUiGSKlrV/A5oEzFu+P8XGTbnGTEQ6oT3S\nO6cqu4F5SqlKpwcQTc9sBNBMRA4Dk5VSn4mIU8/MBHxSLT0zL+w6sYv1R9djNVuxmqza72ArzS+a\nTmhBJlalCFAOAhwOLMrBy5s7wXFoaS3E5uV4nSWZGaZXNKGFIkqf68yBqOZdWBYawXZbS9IORpKv\nAinASgEB5GLFpgIowkIRForFAqYAxBKAmK2I2Yo5QPttslixWCxYLWasFhNWi4lA199m52szwVYz\noVYzIYEWQq0WQgPNhAZaCLGaaRJoIUQvCw4wuy/BrdoMS6dC9my4cS0ER+JwnMH9361jwYEV2HN6\nkZkdzPrC/3BeTB82J68lPywfsm7AntW35DCDYqPZzvtkBXlXDujfIYpf/zPao7zY7mDVvuM8PWs7\nyZkF/HHgBDcO6lBizG4dEsu+1JwSY/bmdWeUGLOL+7RhYt8g+PVJUgfcya/6Wt5z43qVGLMcm52/\nU7JYcP9Q2D1CaxAzCq7Tg+v1sqL2V1B48ThCd+vLfXb4rklTrr3uidJ+vsiOAFumdis69CZYfTfd\nLR3dlquqJXYeAAAgAElEQVSddJZkOpo2stBxlnadVvdlxzkFQwgnz6PcF785+jPSvMWvtk5iTD5y\nRTYeW+bkcuBNEXkCdwmY1SJyNrANTQLmUX250c2YKaV2icgzwG+6p7WbBIxepoAnvJx7tH5uZyT7\nI0qpVAARuRZ4SUTcJGBc+j4gIuMplYA5Tyl1wss5XtKXLh36td2pj/tvEfkebW+vCLi7VE6Ee3CX\ngFlU7ifYiPFpzETkE+AEsAxtnRigI3CniEQppTym2eWhlPLqnaOUWggsrMyxKsOmY5t4ZeMrfrW1\n57cj7/iZIIWkmY/i7fa8OSiU27sPIMau6FhYSOe8LDrmnKRVfiZybAfnAedVdr5r138KS4scSnTD\nZ9aMH2YKsVCknK8DKCCAAmXVDab2k6kCsDnf63X5WMklmEJLGEWWJtit4aiAaJ60dKZT1n4OvXUh\nP/Z4g+DwKAZ1bINZRjN72yFQVnKTrmCJ5SNEHJweNo5VWX3chn4iPYrhfYbzjdpYqUu2mE2c270l\n8+4byhM//cVvfx/j/RWaA0UT8ujy81iCYy4GzvTom2uzw9z7Yf9iWv49B/ga0GZ85SOabIpLAPPi\nhEX0eXUOoS6trs08Sep/Y2hZ0UXYPGVXqoKrK743KmNTwsl3C5KuDo3Nlumzlmt91D0GPFambAWw\nokzZD4C3KXn/Cs79MPCwj7r1aA/y3uqmAn5tQiqlbi6n7kU0l/+y5ZuABuCRVvuUd9u9SylVWKZs\nD7BQRLwLHTVAukV148YeN1LkKKLQXojNbit5Xfb9nuSRAES03EpxWC65W57XE66W/lubQ7NYZzOz\nDrS5ZBOgSQShjjDiioqILSwmrqiISIeDIOUgyKEIVIogpf22KLCgCFCKAMCqhABEe68UZhQWhwOT\nKAIpIpAywc3VXZq0oz2z6s+PSkGH/J3cu+kC9qq2pKsIQgjDRC9+Zhj2/DjyDt1OiAk2F2qK2wEm\nocihCDALe47lMKPP08wJu5DszdNo16QdrUPblJyuY+um5Q4nKtTKe+P7cdOn61l7QHsYvdq8gqic\nvUTteo2+8n+MMm+mqHBkSZ+dyVmQm1zyvqfEs1PFkl1QRCdJogn5bFNarOykb7ZoAT9OvrgCEt1F\nhtt5iZVrmV++vEpl6RhwAtY847XcKjWXuDlM8mrsWAYGjQmfxsxpyETkeeeGqO7hM00p5flf2UAZ\n0HoAA1oPqLDdpkPpXLn2D0KtZpZNfJqoB0sflgqLixnw4iKyCvL47t/9aR4m5Bfnl/wU2Au010Xa\n67ziPBKL8yko1sqdv3OLcskqzCLTlklWYRb5xb41x0xlDJ9FQZg5kNgmbWkd2JSWgZE0M4cSZrIQ\nJhaig6JoY40kwFGsZWQvztd0sPQfhy0be14GqiALVZCJ2LIw2TKxFGkehIFSTG8pzbxxuXkNF9nX\ncXvRIzjyO7qIiCh+MT9G04BcHuJh1to7smfW+3xz0xnclbkBE/t47YzL6RlzDoS39eot6spvCb8x\nZe0U7uw/jbW6Z7vFZXdpjjPo94XZvGQZzmPFd7Jk1zEKou04E5jND3ya/xRN4M+DvVka+CgAvQs+\nJpsQ5m5LZoazoYiHIbswt25u/u8NywS8z+SeLKo5YzbYVHMB540ozMzAwC8HkLOdL/QNxcG1OJ56\n47XFewG4dUgcUaHumx+7UnLIzBXaNW3OgHYdfbt/VxKb3UaWTTNuGbYMMgszS97nFueSU5hDdmE2\nR3OPsj9jPwkFJ0jIPQS53tM9mcVMSEAIxY5iIgMj6R7VnXZR7YiwticiMIKWIS0Z2nYoVrM2sXYo\nBx9tfZ/InFSu2vA9kpWkBaGfdRfYshmZd5yn9+/iuUM9Xc4iXF00hQ8DXuMctY61dGTtoWxeTP6J\n8VFN+SoijCfWTeOz2cdo7lDQpCW06asly03coDnfdB0NwU1h32JS596KREbw+f7nCTA/QlE5gdPX\nWFbwWPGdAKTlFLi5af1fwOfsWFbqABYp2WSrMvvs+yqOvaoPzpD9fMPIihv6wX2W2TVyHGhcDiAG\nBv4YswIRGQWsRTNsZZceGz1/HjzBmv0nCAuycMfQjh71q/eXJhauKUMGEGgOpEVIC1qE+OcWnVGQ\nweHsw6TmpXIs7xgnC06SXZjNyYKT7D25l/iseLILtQwk+cX5pOR6urB3CO/A8HbDsSs7aXlp/HZI\nu8FHj36WIb9Ng7Q9qL9nI9d/BwFB3DZKYVmbwLFsG4XFDj5ZHU8OIUyOmMYZbebDdlhsGcCLo+N4\nIOcY6479xn4rXNi+LTdlZDHi0FFe/zWZVJOJlg4HzybfTlxkaXzZjUCU3cEroa1YcksMb24q4uYm\n7TW3dx+MNG8mpjDBo7yXrTTN093mX/jSfj6XmNf69dnWJ9dYVpCgWvGu/bKKG9chxszMoDEhFaWs\nEZFWaDnOuqJ5M/5XKVWPGXVLERFV0fgrQinFtR/+yfr4dB4c1ZX7R3XxaHPtB3+wLj6d98b344Le\nbbwcpWFQUFyAzW7DYrJwLO8Yu07sIi0vrWTGt+7oOg5leZ/VRYdGc3FkD67/43Oa2x1kxQ4hePxP\nmMxWzKbSAPAdSZlcPEOTGwlq/z4FibeAI4j1T42kZXgQqxNX882eb1iZuJLC44WcXHKSFpe3wBRo\nwmFzYP0+iZ972d0MmhtdRmvLk94kU4CPzt3AHcsqXjZujNxW+DCfWF91Kxtc8BZFmNkQ5DuGrSaI\nLfjao2ztE+cRHRnsUS4iKM+0JAYG9Up53oxOS5GGFvMg1JGDk4jEAU8D4Uqpa2rzXGv2n2B9fDoR\nwQHcek6sR/22IxlsPnwSk+CWwqohEmQJIsiibRB1jOhIxwj3WWaRo4hF8Ys4nn8cQUjKSWJEzAhe\n3/Q6e07u4cPcZJa1bslnKalEJKzhvXe68lmLNlzZ5UoeHfAoJjHRq20Ed43oxHvLD1B0fDSmwBQc\n+XHsPprN5vTlPLpC27PqEtmFnQt3lhgyAFOgicJr2nLp3KOsGWAn3JvESAVLgaeiIVtm78u55m0M\n9JIFP0bSOKha18OoGp83o8E/m/KWGV9FM2JLKf1eOw3aebU5KKVUPHC7HjtRm+fhtcVa9M/EYR0J\nDyp10iwosvP6kr18tPIgDgUju7ckIqTROHF6JcAUwCWdLvEo7xTZidc2vca6lHUM7HEBDzs+48OU\no0zMyGJtcDBf7vqS9IJ0Huz/IInZiYzpG8Gnawux5XXEHKKFHC7cs4156Y+WHHNfxj4sYikxZE5M\ngSaOmc0M6RDNT6c/StdZk2r3ohsBf6sOnMs2moung0io5CP1ZFUaU6JhABGxo8WSBQAHgZuUUlki\n0gZ4s7YfjKuLiNwIPIrmJ12MFuj8iH4Ny4A2aDFvVmAJ8Kyem9GA8r0ZnRHozyulljjLReQcfw+u\nx6pdDBxzJunUy8eiJeZ0Bkr/t7IDrwmW701j8+EMokKt3DI4tqR8Y0I6j/30FwfTcjEJ3DE0jofO\n9zM7fyOkdWhrXhr2Usl7x4DHSJp1BzF//cgnR9P4PSSYF+1zOT9+QUkbaX46JF+HPV/LRvbDjjUE\nR7sf12wyY7fZ3Qyaw+Yo2Xe8cuvLENeeAKV44sRJrsmunHDqqUK60iIam+MZDhDiNXS/bqiuLTMF\nhsRaW3R4zhwaFW3PTU8uTDv0jMOWl1Bb/YBcpVQ/ABGZiRYw/KJSKgUtBVWDRb8n3g+MUUod1T3H\nJ6Dlm3N+Ma5XSm0REQswHZiDj/i1fyL+OIA8hfYU4OQhwLdGuzufoen6/M9Z4KJhNhJIRkvqOUcp\ntVvPBn0G8LL+Bay1dXmlFK/rHoz/Ht6R0EALeYXFvPzrHmauTUAp6NyyCS9d1Yd+7cuPlTrVMImJ\nmEvfQxXZsO6ay9jcXMKtYdwZWbp3ZgnfijmzH/bcrgA4bK0ozukCpiIsIQkAxI2MY++8vTCWkj2z\nY7OO0WyUe/b4IhGmNY9CLnyZqw9shI2f1Nm1NgTSVRgAkeJpzIOl/oxZdTAFhsSGdBm0JGr0PZ1M\n1iAchQWk//bOIFNgyKjyDFNV+3nhD/RgYT1d3jw9a74JzRAMBwKBd5RSH4nIcLTg5Qy0DPw/oCXj\nvB8te8Y4pVS8iFwMPIM2+zsBjNdTX01GyzPbES0f4ptKqRn6uRei3TMHo2Wuv0wpVfYP+xRaFvyj\noHmO45ltX/S6YhF5DNgnIr2VUg0xGWydU96e2a1oEiy9RWQlpUuM6331KYueRqZDmeISDTP9PE4N\ns91KqS+AL0QkSkTeA04XkcdrY+a2ZFcqfyVm0rxJIDcNimXtgeM88dN2DqfnYTYJd43oxKSRnQm0\nNKDs93WJxYpc+yWk7ob3BjM4I40fLvuRvOBI1iSv4cO/PiSo9WxyDzwAWHEUxJB/REsK06T704jY\nOWg+iGOIg/SF6SilEBGajWqGtbn3vE+/xC+kz+BnSA4JotPaD2hfXHPxVw2Zk2jGLApPLbxQCup6\nOCVUZ2ZmbdHhOadBAjBZg4gafU8nS2Sb+Ngn5vvsFz7gcsIHXkHZfsUZKc+hOb+Wh0CJ0OZI4GPX\ny9F/3wZkKKXOEhErsEZEnBu1fdCkVzLQlik/0tvdB0xCe5BfpZQapJ/nNrSsIs719W5oM6UIYI+I\nvKuXdwauVUpNFJHvgCtxpq0ppSea9ItfKKUcIvKXPl7DmFH+MuNnaPnILlFK+RbHrDw+Ncxczp0O\n3OXPwXzpmX3x5yE+XHkAi6k0ya/ZZMJ2MoWDaxeQlVeIA6HlORdz86fr2JCg5QDs0Sacl6/qQ6+2\nviVQ/lG07A6nXQo7Z9F96XQIa02/i17nntPvYXXSarYfDOXlBUfdujgK2mAO1mSUrM2ttBznnhhq\nWLthrExc6XGqrWlbuWqunvg8RluztCjF2fkF9LHZaF1sZ1xO9UQ4GyLOmVlT8TRmTfAdWF/bOOPM\nqqJnZg6NinYaJCcma1DFFlIpvPUzhTaN9tHDlWAR2YyWHf5vNF2xsoxGe0C/Wn8fDnRBy2m4wSWf\n4gFKpV62U7qcF6Pv5bdBm525SkzMV0oVAydE5BilkhTxLrOnTUCsl3GVfDAi0gstmXEY8KSeYssb\nhkepC/4sM44E5uqbkw8Bi5VSj9fusPzHl55ZRm4hR9LdbwTFmalkb5pLxDnjCdeXMPb8/hXJ/S8h\nOKoVk87rwr+Hd8JqqTMB7sbBWXfBzllwRBfLbdMX09CHGdZuGMPawdGTO/jij1KX/7yEe3n9ViGr\nOI3LOl1GTlEOT656kq1pW7n39HuZ2GciT65+kvkHfT+hOykWYVVIMKtCNBfxZ1toS5RhdgeRDjv9\nC2xMO55e89dch2TrSdSbiOcsrJnUbFqtylAdPTN7bnqyo7DAzTA5CgsoOLTlq4Q1X/ucYQW1e+RL\nx1lXji/bz5F7MtlXHxfylFL9RCQITa35XnDPZoZmACYppdwMnb7M6Lr053B576D0XjkDeEUpNV/v\nM9mlT9n+Fi/ldsDdWmvsRNNSW6GU2gGcISIzAM/YCEq2a3oDNZqYvTHjz13bmaRyjL65WjmNCU/q\nRMPs5sGxrHh0BL8/PJzFDw5j0QNDObNoGxHnjHdbwog4Zzx987ey/NFzuW9kF8OQeSNmIAx9BAL1\n2eped/f5O87RQgCCXD47lduHCT0nEBkUSbuwdoyNGwtoXo4iwoTTJnBas9OqPKRss4kjAQHMDmtC\n77j2JT+nx8bwbJ+RrA72dr9omJQ3V6lPY1YdCtMOPZP+2zsHHIWagdb3vg4Uph0qNxVeVfvpOPeU\nCtD2uh7Wb/qu/ArcrTtRICJdRKSsJEt5hKPt9YPmoOEP/sygpgOviIirEnRZQ+ZcRnU6gBzWDZ8B\n/s3MTCLyH7Q1ZKCCFN+e1IuGWURwABHB7q70tsJir0sYDoedtl6CQw10RGDkszDkfnipo5bfMC8d\nQqIAaN8shG6twthzLJtQq5ncQjvJGe6z4mFthzGd6axNWkuRo4gezXrw3cXfUWgvxCxmilUxL294\nme/2fFetodpFmJ29j9mtS5c2Ty+wcc/JDAYVND5nilZyst7OXR1nRoctL8EUGDKqOCPlOVNo02hH\n7km/vBKr2q/skJVSW0VkG9q9xdVh7WO0Zb7NusdgKpoytM9jlWEq8KOIpAO/433JsGz/Cj9KpdRC\nEWmOlsjdhLZvtwPN+Dr5UkRsaI4rS9B8DQx0/DFmV6B5GK7QN0yf8vfg9aVh5otWESHs9LL00Sqi\nMg9m/2CCwqHD2RC/Eg78Dr1LhX1HndaSPcey6dU2gnXx6SRnui+ZxYTHEBseS0JWAltTt5Ykf7aa\nNWcQM2aeGfQMzwzSHsDzivLIK87j7xN/c2arM/l2z7e8vun1Kg17a1Agd7QpVdQ2KUVsUTHn5uXx\nwMn6D9NR5Ty4R+NN1so3SaoZbaVyfWoL3QBV5LRRY/2UUuFl3rve7PvoZQotIUNZNWk3ORil1Hku\nr0vqlFK/oIlhlj331DLvXbWS+riUu6d4ce/zBV6EP/W6c72VG5Tic01N92YEbZ9sBNra8DNoKqh+\noZS6QSkVrZQKVEq1151KUEotVEp1U0p1UUpNr/rwK8fkh+4mYtcsXJcwInbNYvJDd9fVEBo/XXSh\nzWXPw4kDJcWjemjGYn+q5l5edmYGmuMHwKokT4XvsoQEhNA8uDnD2g0jJCCEf/X6F9snbGfdDetY\nd8M6Phr9EXf0vqNKl+AQ4aA1gE8iI9yWKK+PblVx51pilb2X1/IYUxpmKtJpK2WIrewWUdVpbEHT\nBv9sfOZmFJG+Sqlt+ianG/qTSr1TldyM8fEJTH3tXY5l5tEqIoTJD91NXFxsbQzv1CQ/A2ZeDMe2\nQ3g7uHstBEXgcCgGvrCU4znaUl63VmH8+qD79uq6lHXc/tvtdI7szKzLZtXosIocmu5bdmE2L657\nkUUJ1RfUDbc7GFBQwBupx6t9rPJYMmohIxZfhEX8N1q+iC34moQgrzq4FfYry9KHh9OpRROPciM3\no0FDxJ9Ew2UdPoqABD2ouV6piUTDBlXAlg2fXwLJW6DbhXDu09C6Fw99v5WfN2u+PKFWM9smj8Zi\nLp38F9mLGPrdUHKLcvl49Mec1easWh+q3WFnX8Y+rp57dcWNK0Ip3jmWxrD8Go79mrQZPh4J+dXf\nH4st+JprzMt4KeCjSvcry28PDqNrqzCPcsOYGTRE/HHdewh4HBiLFiD4FPCRiEyrzYEZNGACw+Dy\nD8ESBHsWwIfDISuFgbGaQ0iI7gTy50F3l/kAcwAXxl0IwMTFE3ln6zsUO2o3MNpsMtM9qjvbJ2xn\n+4TtbLt5G4+e+ShWk/fA7XIR4Z7WLUuWJce1raEEwCJwU83NVL+3j6iR4xQUeSpwGxg0VPwxZhal\n1EVKqaeUUhcDZv33qFoem0FDpkVX+Je+lOcohiPrGBAX5dZk/nbPyfuTZz3JHb3vQCnF+9ve59ZF\nt3I8v3aX8VwxiYmbe97Mpps28dfNf7Hpxk1VPtYBq7XEsA1u3656A2vVu+I2Yf7KD1V+0jS8q6em\nXkFR9Zc9DQzqCn+MWYiIXCMi3UXkGkoD/opqcVwGjYHoM2DYY9rr5M10bB5Ks1AreYXaE/3KvWke\nTgQBpgDu63cfH4/+mJYhLdmatpXJayfXi7OBiGA1W1k/fj3BluqFZmSbTSVxbkPbt624g/tIwOzF\nsbhfmTCm3LQqj68iQqyeaduK7IYxM2g8+GPMrkKLpbgP6ABcpcdnXFSL40JELhORD0XkGxHx24PS\noI5p21/7nbQZEeHMWC0pc6jVTFJGPgfSvKefGthmIF9f+DVh1jBWJq5k7sGazJhWOYItwawfv57t\nE7ZXeab2/DnPA1qcW4bZTL/YGP4MCvSvsy/18sQN7u8dxfCf8rOdBAdoRuka27P+nduFRQ8MLXl9\nVlwUbSIaT+A5aBIwIrJZRLaLyBwRCdfL29S2nFR1EZGhIrJJRIpE5IoydRNEZK+I7BGRm13KY0Xk\nT73uG2cg+D+VCo2ZnidxOVrg4QqlVLrS8EwkV4MopeYopSai5Whs0PIN/2ja9tN+J28Fh50B+r5Z\nsybantSKvb5nE61CW/H4AC0z2vT100nNS63dsfqB1Wxlw/gNzL5sdqX6RQW5L7EWiXBHm1b0jmvP\nqJiK0gr6MGapf3uWmczQzFMN3cmuaWP5+//G0GnA6ArO6Un31uHcNKgDtw6J5bs7z6ajF0/GymAO\nNseGdAn5MuLMiN9DuoR8aQ42x9ZmP3QJGKVUb+AkmgQMSqmUhq5lBhxCyyjylWuhiDQF/gMMAM4C\nJouIM3Hsf4FXlVJd0YKsb6u74TY8KjRmIvIGcDta0ss79HxhfiMin4jIMT3Ds2v5WBHZrT9VlJfr\n8Rngncqc06AOadISImKgMBtSttG/gzYzKyzWlg1XlmPMAC7tdClD2w4luzCbaX9MaxCxTUGWIDpF\nduKD8z+oVL+yyt5OcoK1eDbf6Nfc83LPqpH/8SybtLHccYRYLbx4RZ9y2/hi2rheTL6kZ5X6umIO\nNseG9wtfEvdo3Pj2k9qfG/do3PjwfuFLKjJMVe3nhT/QkpojIh1EZLv+2iQiL4nIOhHZKiJ36OXD\nRWS5iMwWkf0i8qKI3KC32yYicXq7i/XZ0CYR+U1EWujlk/V73TK9/ySXc/+trzLtEJFFIuIxZVdK\nOVNTlf0HGAP8ppTKVEploCWbGKvXnQf8pL/+HPDyBfrn4M8y4+lKqYlKqQ+UUnfgEs3uJ5+h/UFK\nkFJNszFo0gfXi0h3ve4mEXlNRKJFZDqwQCm1tZLnNKhLuusrzmvfokebcMwmITVbc1/ffOgkDodv\nAyUiTD57Mk0CmrA8cTl7T+6tixH7xeDowcy7fB4zx86ssK0gfHGh1+QNJbPP3nHtWe9t6dGubz+f\nWzYpBTDkQX+H68mU+stuEtgu8LnoCdGdnMKspkAT0ROiOzUf2zy+9+e9la+f5mObx3vrF9gu8Dk/\nTltWAsY1U4eHBAyaWsdEKZWp6gNMBE4DbgK66O0+QZOAAV0CRinVH/gOzcPbSTe0pBLOGZRzI7Iz\nMEMp1QvIRJOA8ZeyKiNJQFsRaQacVEo5NzYTAX+UBU5Z/DFm2SIyXkR6iCaeWSk5YKXUarQpvysl\nmmZKqSLAqWmGUuoLXeX6SrQv5FUiMrEy5zSoYwbfB2Yr7JxNUM4RurYKw6GgeaiVbFsxB9LK/8q0\nCm1VEnMWnxlfbtu6pkN4B/q36s+2m7dV2DbcGu613Gwqda64rU0rxrSLJtPk8q9XrOeMbO5l+dDk\n41/0WT9TVt3g31aRr227qhIQERDtqjAOmmGqaOatlMJbv4CIgMpIwKQALfEtAXOziGwB1gFRaBIw\noEvAKKUKgbISMLH66xgR+VVfaXoE7WHcyXylVLFS6gRQWQmYqmDE+rngz4bheLSnlfuA/UDl0wt4\n4o+m2Qw85Rs88KVnZlCHRLSFuOGwfzEc20mPNm3ZlZJFpMXG/tXfcN3u2fSOaVputpX2Ydoy3OHs\nw3U27MpgEhN/3vAng74e5LV+07FNDGk7xGudICy+ajHn/6j5MSUHWBgVE82GQ5rmG/YqJED25v3o\nja5jKm5TAVXRMyvKLEp22Bxuhslhc5CzM+er1NmpPvMuhjwX8mWLC1uML9uvKLPoVJeA8UUSpVpq\noKmMLFNKnRCRCBEx6bOzWlEfaUyUpzTtugHws8vrZmhT5QaBLz0zgzomUt8TykykbWRnAPbv/Ivw\ngVdwwhrE74UFbLr7GX559zmvBq1dmBandTirYRozgNCAUK7peg3f7/Wc7Xy560vu63efz76tQ90D\nrAtMJorR/wGbdy2tmDAPPr+4ZgbsZHIGfHuDFuBeBaqiZ2ZLtD2T/HnyIOeSocPmIPnz5AO2RFu5\nUi5V7adTIgEjIvcDs0Wk7H67UwJmmVKqWES6UDkjUFsSML7a/wo8rzt9mNCWMZ/Q65YBV6Mtd04A\n5lTyPKcU5T3elfXtVWgfsgL+Vc3z1ommmUEdEqEHDWceobXu0q3a9MSkzyBM1iAye1zO1NfeZeaM\nlzy6tw/Xvg5Hso941DUknj37WZ4860nO+OIMt3Kzvj1yfofzWXzI2+qWJ2fGxmBVivWBLimj4ob6\n7lCWRw/Ay50qbicC130NUyP9P3Y1sefbE8zB5lG2VNtzAREB0UWZRcm2RNsz9nx7Qm3002m0EjAi\nciYwC4gELhaRKUqp3kqpk3q2pY36cabqjiCgGbVv9fotaHt7/1h8GjOl1K2+6qpAvWiaGdQhETHa\n74wjtI7RjJmUWQozWYM4djzPa3fnMmNDN2YAFpOFoW2HumX/d0rZXNftOr+NmV2EfG+bVbcthrkP\nwCVvaO8jO0DGIc92oc3htiXwiR/JeERgwO2w4WOv1Tm2mk9dpRugSku5VLVfY5aAUUptBGJ81M0E\nZnopj0dzNjGgfAmY/4nIPbrjR7D+00NE7hWRz/09gWiaZmuBriJyWERuVUrZ0byDfkOTC/+2rjTN\nDGqJkplZYunMzOGeQaI87bhWIa0IMAWQlp9GXpF3g9eQeHWE+z2pU6Q2Q+rbsq/PPp+P/ZzzO/gR\n/x8zUFMjiNG3kW/8uZy2A6BpXMXHBDjT94LK38mNU9HawMCJT2OmlLoZ2A3cCfwI/IDmCLJHKeXv\nWnGD0zQzqCVcjFmbCC01lDiK/daOM5vMJftmjWF2FmwJZt0N60reX9lF87YONPvO+tGvVT9eG/Ea\n9/e7v3Ina95Z20u7Z4P3+riywhY+aNUTWnqPIXNK9xgYNFbKdc1XSi1VSj2gJxq+WCn1YFkvIAMD\nQEuCKybIOUpTqwOr2QQWK5EHF5Ox6iviUn736fzhJCZMW2VpDMYMNAHR+ZfPZ9qQaVwQd0FJedls\nIGW5tWcVVvDjhmrJnb1xTiVi0e5aU/lzGxg0AvyJMzMwqBizBcK0UCDJTi5JZ3XdxPuJHDqeMTfd\nW56HCcAAACAASURBVKEIamPaN3PSPrw94zqPwySl/0r3neHbqxHc485qhKg46HEJnHVXxW19BJRd\n3b+aWf8NDOoZw5gZ1BzN9GiO1N00b6Itt4UFaU4gh054TzjsinNm1lBjzfylV/Nebu+97QE6Y9K6\nNvUx26os134JF/i5Wv/wXs2pxIUXr/BDgsbAoAHjT27GW/XfA0RkgYhcVfvDMmiUROvu6slbSmZm\nzizuh9PzK+xe4p6f1XhmZt7oFtXN7X26zTPT/dVdNOXrtk0qKxdTA4S1ggfcUqW6KYIbGDRG/PkG\nO11kJ6HlNXuinLY1gq6d9p6IfC8i/67t8xnUENHODPpbSmZmJn1V60h6XoWpjE6VmVlZsmyenoJO\n/bS84gbiuelo/NpljVwCZoKIpOrj3ywi/ypTZ0jAVIA/xixURIYB2UqpFKDi9aJqopTarZS6C7gW\nGFzb5zOoIVxmZs31mVleoZ2wIAs5tmLScwvL7x4ajVnMHM09SqG9/LYNnV+v/LXk9d8nPKVcTHrO\nxXUp6zzq6oWqpNSqgPBAiR0cY/nyytMCfh8cY/kyPFBia7MfjVsCBrQQpX76z6dgSMBUBn+M2YPA\nKGCaLl1QTtCLO9WRfxGRS4B5QNVy8BjUPZHtITgK8o7T3KK55B/PKaR9lBZbdji9/FlIgDmANqFt\nUCgScxJrfbi1SXSTaNo10ZwqbuvteY85mHGwrofkSdszS18X16wxCw+U2Mu6ByxZfFPI+J+uCTl3\n8U0h4y/rHrCkIsNU1X5eaFQSMDrevHMMCRg/8ceYRSil/gM0AV5DS5viL1WVf2mjlJqrlLqIKmQC\nMKgnRCBSWypsbtYM1/EcGx2a+WfM4NTZNwNYcMUCVl+3mmHtPOPAukd1r4cRleGqT0tfr369Rg/d\nq6X5ufcvCuoUatXuz6FW4f2Lgjo9fHZgPFMilK+fh88OjPfWr1dL8z9BAuYK3XB+LyLOzVRDAsZP\n/DFmj+i/nwK+BLymY/FGNeRfuorImyLy/v+3d+bhUZVn///c2QOEkIWdAGEJa0BRERHFtVWLCta1\n4r62fbG/Wrf3rRap9q2I3VxaRVux+raoVdyoaxUQFZWdgCQBwg5ZyEISyH7//jhnsi+TkGRmwv25\nrnNlMnPOc54nA/OdZ/t+gaXe3s/wA8IdR6F+Yc43/QMFJSS4PbM9XohZV5o3ExGiw6MbfW1kTNNp\n0Z1GTK0VjZEx7Vp0/ygZ4BEkD93DhKoW5k2rVGnsun49pKtHwLwDDFXVicAnwN+9aK9FwNTCmwnD\nKBEZDFSq6lcicqxzZt7Ev9TxSTMCCNc0d1D4EUDYm3eEyyY5XzJ3HWqFmPmxe3574PFyDAsK83FN\nXAac2PI5reBAoe4vLqsrTMVlysc7Kv9v3vKCJkdbPv5ryCv3na7X1r/uYJF26QgYVa39pf8FnPkw\nsAgYr/FGzB4DHsGJIYgAVnVslVqH5Zn5Ga6Y9QsuJDgomszDpfR3vRq9Gmb081yz9iLEXXhWVlVG\nlVbV2XTdqQw4Efavg7DuTZ7SljyzlKzKB+9cWjLFM2RYXKbcubRke0pWZbNRLm29ziVgI2BEpJ+q\nHnR/vRTweNVaBIyXtChmqrpERPYBJwG9VPVYl+a3a/yL5Zn5Ga6YhZQX0j+6L3vzjhIe4kwdeDPM\nODR6KAA7C3Z2VA39gtouIPuL9lf7UnY6IW4noZkFIG3JMztcqjt7hst523OrHu3XQwYcLNL9KVmV\nDx4u1Z0dcZ1LwEbAAHeJyCVAOZAL3Oi2wyJgvKRFMRORPwLdcMZ6bxOR61R1TguX1SkCi385fvBk\nc5UeJiGmG3vzjnK0rJLgIOHA4RJKKyqrxa0xBkUNIkRCOFB8gJKKEiJCWhPKG5iEBoX67ubucGdH\nLM13BajVC7jael2AR8D8D866hMZeW4RFwLSIN2MbJ6jq7ar6nKreRq03piUs/uU4pFrMChnRpwcA\nGTnFJMZ3RxW+3H6o2ctDg0IZFDUIRdl1uJEMry6Ex/3Dp3vqQp3N25SX+K4OhtEOeCNmhSJyrZtl\ndh1Q5G3hFv9yHOKuZqS0kKR+jrClZhZWLwJ58j/pVFU1P+oytOdQAHYe3tlRtfQLIoKdXmdJpQ+F\nJNTNlwuADDnDaA5vxOxaoD9wF85y1x91aI2MwKZWz2xUX+fxml15XH1yAr2jwlm3O59XVze/h+x4\nmTfbXrAdgLyS+rtXOpEwV8zKOtzYxzA6lOaSpoeJyDAgHsf1YwGwBIjrpLoZgUgtMZswKJpBMZFk\n5BTz4NspPHCBY8A7793NfHeg6WTj46Vn5iG3pKERcadhPTOji9Bcz+yheseDtX4aRuPUGmaMCA3m\nmR9NIio8hH9vOshXOw5x+aSBlJRXcfvLqzlY0Pjw2vHSMzs74WygA/LNWkOo9cyMrkGTYqaqN9U7\nbvb87MwKGgFGdc+sAICJCb145dZTiQwN5l9r9lFQUkFS3x7syT3Kj55fxf78htEwtXtmLTntBzLd\nQ529XY3lnXUanv1l1jMzAhwLMTLal1rDjB4mJvTiz9dOIiw4iI+3ZLI9q5i47mHsyCnmkqe/YN3u\nunNGsRGxRIVFUVRexKGS5lc/BjL/znA8tBdtXuS7SlQPM7acN+fvBHIEDICIzHa9GTeJyDrXnNjT\nhs9cc/b1rnHxk7Xc8zu6Xp+JyKRGnl/o8dVt5to7RGS2+/gGEenXxHmXu0bMlY3dyxv8Nv9GRLrh\n7O2Yq6rmnB8g7DqYx6JlpVQFbSdo+/Xc+PN5DElM5OzRffjk7uk8t2I7//hmN4eKy4gMDSKnqJQr\nn/uKu88fxe1nDiM4SBAREnsmsjFnIxkFGcRHxvu6WR1ClesRuy1/m+8q0UELQMIHjl4YEtW7QYx2\nRWF2Wum+rbe393Uuxao6CUBEFuFEwPzWja7y6wgYEbkA+BnwfVU96G7ovgHH39EzwXyNqq4TJ7fs\nMRzHj7NaKNdjd9XuqGpL7weq+lytX28EUoCDjZy6Ccf1/7lGXvMKbzZNX1/vqXJgh6p2dBDT/Tg2\nLUaAsCsjg6fuu5Z5U8NwrIjeYu6cb5jz1PsMSUxkcFw3fjMrmVknDuT+NzayPdv5AC2vVOZ/sJWP\ntxzkkZnjGTcgmqHRQ9mYs5Gdh3dySr9TfNyyjuH2CbezcONCLhh6QcsndxShHTPMGBLVO6n3zAem\n138++63md+K09bpG+ApIBieGBXhPVZPd1I7HgOlAOPCMqj7v+izOw8kFGw+8jvMB+zMcL8WZqpoh\nIjNw1g2EAoeAa1U1W0Tm4jgbDQMSgD+p6lPuvd/HcSGZiuNFe6mq1t+l/j/ALzyWVu7m7kX1zvHY\ndVWIyH1Auogk1zIxxm1vIY4onAv8VETOBS522/Glqt7pnvcZjtny2UA0cIuqfuHaFr6Is6c4lUa8\nJGtd/wtVXeve80/ADOCI20bP36UI2AmcDLwiIkeB02r/DVQ11S2zzebJ3vTMLsRxvl+P07i+QLaI\n3KmqNzV3oYj8FadxmbV3xLvfQv6IM8z5V1WdX++684AtOH9Ec4YOEBb9YS7zJh6gdnzHvAn7eeIP\nc5n7ZI0J+MlDY1l61xk8/ek2nl2+nYoqRQTW7s5nxpMruebUwcQNdPaldeVFIB4fypAgHw6QdPLS\n/ND4hOlDH1ja5ERoaHzCsRRfPwLmhVqvNYiAEZEw4AsR8bjjTwBG4wjaDuB597y7cIwe7saNgHHv\ncwtOBMy97vWjcHpK0UCqiPzZfX4EcJWq3i4ir+JEwPyjXt3H0Yp4LVWtcp37R+OIbm26A1+p6j1u\nPbeo6iPu47+LyA9U1ZNGEuy28ULgYRzvxx/j9HLHiUgysNaLKnXHEcoHRWQ+cBvwvzXV1TdE5L+A\nu1W1NTFiXuPN/6JoVa22mxKR91X1ChFZ2dxFLi/iuExXf5JJTZ7ZuTiGnd+KyNuqutXdlD0Jx8yz\nAOcNPoLFwAQEVYcP0D22kdiPwwcanBsRGsw93x/Fhcn9+J8lKWzY49jNKfCPr3cT1qOC8ATYkr2N\njIydzPv9n8ksOELf6G7MvfsnJCYO7fD2dDQ9w5yVn/ml+S2c2YFEuNMuJU1vlQggPBEwg3C+DDcV\nAZMsIle4v/fEiYApx42AARCR+hEwZ7mPE9z5t/44vbOMWmUvVdUK4JCItDYCplrgRWQ88DIQBfy3\nqr7eRHub+qJfQd0Q5XNF5F4cW8IYnKE+z2eq57w1gCcT6EycXhaqusn1uGyJ0lrTQWtwAp1bU+dj\nxhsxK3e7tBtxvrmUud98WnQCUdWVUhN856E6zwxARDx5ZltV9WWcNxH3teuBHK9aYvicoJ79aSz2\nI6hno3O+AIwbEM1bP5nKF9sO8ezy7azc5rzdZUXDqcj4KSsztzBlwVzCT7yEoAERbC4rYc1PHuSd\nPz8a8ILWp1sfAHKO+vCfeEQv5+fRztm4XZ6zZ/nOx35wVlOvdx/92DKcIcC2ELARMDjWfpOA5aqa\nApwoIk8BkY011O0UJFPjrl+bEneYEnFSrZ8BJqnqfnfYr/b9PXWrpGk98EaAyms9bq6sDsObG16B\n4yo9Eafr/UfXX7GtA/0t5pl5UNUWA+osAsZ/uPHn85g75xvmTdiPJ75j7rJS5twIVJRBSOPZXSLC\ntJHxTBsZz7asQl5ZtZvX1+yhuCSBqpIEImtZqQaFRVAwZhYP/+4ZXnp6Qae0q6Po3a03AFlHsnxX\nCU8oZ0nTvcO2RMD4iICNgMGZx3tCRGaqqqc+9YXMM4wagjOEt9sVvubuF4HT6zskIj2Ay3HmA5tj\nBY7z0zK3l+iNH683bSzE+fu1R1kN8EbMQoASHIf7SByXe29SUDsFi4DxH4YkJjLnqfd54g9zqTp8\ngCCtYM5pmxiy/x14eRZc9TJ0i222jBF9onj4knE8cOFoTnv+pxTmDqOiaCy1/30HhUXw0f5obn3p\nW2ZM6M9Zo/rQq5ufhFy2griIOIIkiNySXMqryn3jnh/p9swON/153pYImIrC7LTGFm1UFGandcR1\nLgEbAaOq74tIPPC+2+vKxxkO/LDWaa+ISCnOwpVPcEa0mr23qhaIyPM4Pb8DwDde1OsvwIsishmn\n57e6pfs0U1ZtFgHPisgR6i0AEZGZOL3eeOA9EVmvqhd6UWY10tKmVBH5HPg3Nd9GUNWXvL6BM8z4\nrmcBiIhMAR5W1Qvc3x9wiqy7CMTLsrWl+hs+Zv86+MfVUHTQmZ85/Wdw2pwme2m1+eE7PyQtL43C\n1DugKrHZc0f26cHFEwdw/ti+jO4XxTEsiupUzn3tXLKOZvHhDz9kQI8BnV8BVZjnCtovMyG05cgd\nEUFVA+MPbBw3eLNpOk9Vf6uqL3mOVt6jyTwzdzXR1TSSD2R0EQacCLd9CoNPg5IC+M+vYeF02PIO\nVJY3e2nfbs78eVTmh1SVOdZXVWUlROz8nMmDutE9rMYGKj2riN9/nMaFf/qcE379EXe/tp5PtmRy\ntKyy49rWDngEbG/hXt9UoLbol3aJRSDGcYpXw4wisgSny6sAqvorbwoXJ8/sLCBORHbjbIB+UUQ8\neWaepfmWZ9aViR4IN38A2z+DpXdD1hZ47Tro3gfGzYLky2HgSVDPo7Bvd0fM7rz5Ar5d/DWZOe5q\nxt86qxlVlbTMIt5ev4+31+9jX74jeAVHK3hz7T7eXLuPIHF6beeP7cu5Y/qSPDCakGD/Mb5JiEpg\nffZ69hTuYXL/RqeOO55fZkJIeF1hM4wAw5thxgYri9zkVZ9jw4wBSPlRWP0irH0JsrfWPB8RDSO/\nD8POcsQtJJxnNzzLM+uf4dbkW/nZpJ+1WPSBgqP8Z0sWS9bvY/3ufCob+bcRFixMGNSLi5L7c2ZS\nb4b37u7TIcmFGxfy1LqnmD1mNvdPvt9n9WgNNsxo+CNN9sxE5ER3c5sPLb2NLkdoJJz2E5jyY9j7\nLWx+C757Fwp2w6bXnOOTh+GUWxjZLRJUySzO9Kro/tGRzD5tCLNPG0JxaQVrduWxIi2bZanZ7Mgp\nokqhrFJZvSuP1bucpeg9I0KYOjyO6aP6cGpiLInxnStu4+PGA7App/6+V8MwWkOTPTMRuUFVX3L3\nJdRGVfXXHV+1lrGeWRfi4CbY9RWs/Ttk1nywZwUH813cEKafNx8Sz4TQCHZlZLDIs2KyZ/9q/8fm\nOFJWwdcZuby/6QDLUrPJKqzvJuRQsmYJ3aScnpFh9IwIIdKdlxs/tC+/e+Sh9muvS0FpAdMWTyMs\nKIxV167yzYrGVmI9M8MfaXGYEUAc5+ZoavZx7O7genmFiVkXRBUylsPG16jY9jEhRbX2YIX1YFf0\naTz19tfMO7mgZi/bxgHV/o/esvvQET7dmsnSTQdYtzufiirn39HRHWsAiBx2UvW5ZRlrmDVpED+e\n/UPG9O9JcFD7fo7PWDKDXYd38eqMVxkbN7Zdy+4ITMwMf8SbObOFOAaa+3HETP0l08zErGtTVFrI\nTS+dzPdKyrk1tD+SmcK8ZaXc4xoZeyguU54oupS5T77cTGlNc7Sskq925PDp1iw+2ZLJliVPE3PO\nrZ4PbfI+faH69x5hwZySGMupw+KYnBhL8sBoQo9xQckDnz/A0h1LeWjKQ1w5yq/N3QH/FTMRqQQ2\n4NhM7QCuU9XDItIfx/jXr/+44kSl3IuzMK4CZ+X3PW4bPsOx0CoBwnD2mT2kqgWdUK9qQ+F6zy8E\nfq+qWxu/0omAwfF5fEVEbgA+9Jgp1zvvcRwz5FJgO3CTqrZqea03qxkTVfX81hRqGO1Bj/Ao9vSI\n4cnwYq68+t9EH8mjasMFdA+r6/XYPUyoSnkblvzYGYpMPAOiB3l9n8iwYM4Z3ZdzRvfl0ZnJvJB4\nmHnvrid48ImUZKwlMnFS9TxaUVkln6Vm81lqNgARIUGcPDSWUxNjmZwYy8SEXkSEtm6aOTk+maU7\nlpKSkxIQYuYNYX0S/xAaO/DEurtylPLcfevKsjJ+3t7XuVgETMNyAyUC5iPgAddA+THgv93Da7wR\ns/2uNUztpfmftuYmrcVdQfkIzq71f6rqio68n+G/9O3Wlx0FO8g8kkl0TBJBCZMpLnurof9jVRls\n+IdzAMQkusJ2Jgw9A6L6NnGHhtxy9Sxefv0dduoJjK7YwZ+f+hMr0nNYnpbFml15lFfWjAaUVFSx\ncltOtadkaLAwaXAMp7q9txMH96JbWPP/zZLjkwFYnbkaVQ2YDd/NoeUlK7snn3dbt+GndPc8d2Tb\nN8V5/3n+yY64rhEsAiawImA+qVX0KpxkgVbhjZjtAHoB0zz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open('data/riemannian_vs_baseline_hiv_800_001_tmp.pickle', 'rb') as f:\n", + " logs = pickle.load(f)\n", + "\n", + "fig = plt.figure()\n", + "plt.loglog(logs['plain_sgd'][-1].logger.time_hist,\n", + " logs['plain_sgd'][-1].logger.loss_hist['train']['logistic'], label='Cores GD',\n", + " linewidth=2, color=colors[0])\n", + "plt.loglog(logs['plain_sgd'][100].logger.time_hist, logs['plain_sgd'][100].logger.loss_hist['train']['logistic'],\n", + " label='Cores SGD 100', linewidth=2, color=colors[1])\n", + "plt.loglog(logs['plain_sgd'][500].logger.time_hist, logs['plain_sgd'][500].logger.loss_hist['train']['logistic'],\n", + " label='Cores SGD 500', linewidth=2, color=colors[2])\n", + "\n", + "grid = np.array([0.01, 80, 170]) / 10\n", + "x = logs['riemannian_sgd'][-1].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd'][-1].logger.time_hist,\n", + " logs['riemannian_sgd'][-1].logger.loss_hist['train']['logistic'],\n", + " marker='o', markevery=marker_indices, label='Riemann GD', linewidth=2, color=colors[0])\n", + "grid = np.array([0.05, 80, 170]) / 10\n", + "x = logs['riemannian_sgd'][100].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd'][100].logger.time_hist,\n", + " logs['riemannian_sgd'][100].logger.loss_hist['train']['logistic'],\n", + " marker='o', markevery=marker_indices, label='Riemann 100', linewidth=2, color=colors[1])\n", + "grid = np.array([0.1, 70, 200]) / 10\n", + "x = logs['riemannian_sgd'][500].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd'][500].logger.time_hist,\n", + " logs['riemannian_sgd'][500].logger.loss_hist['train']['logistic'],\n", + " marker='o', markevery=marker_indices, label='Riemann 500', linewidth=2, color=colors[2])\n", + "\n", + "grid = np.array([0.1, 20, 400]) / 3\n", + "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd_rand'][-1].logger.time_hist,\n", + " logs['riemannian_sgd_rand'][-1].logger.loss_hist['train']['logistic'],\n", + " marker='s', markevery=marker_indices, label='Riemann GD rand init 1', linewidth=2, color=colors[0])\n", + "\n", + "grid = np.array([0.8, 3, 150, 320]) / 3\n", + "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd_smart_rand'][-1].logger.time_hist,\n", + " logs['riemannian_sgd_smart_rand'][-1].logger.loss_hist['train']['logistic'],\n", + " marker='v', markevery=marker_indices, label='Riemann GD rand init 2', linewidth=2, color=colors[0])\n", + "\n", + "# plt.loglog(plain_sgd_rand[-1].logger.time_hist,\n", + "# plain_sgd_rand[-1].logger.loss_hist['train']['logistic'],\n", + "# marker='v', markevery=marker_indices, label='Cores GD rand init', linewidth=2, color=colors[0])\n", + "\n", + "legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1.04), frameon=False)\n", + "plt.xlabel('time (s)')\n", + "plt.ylabel('training loss (logistic)')\n", + "plt.minorticks_off()\n", + "ax = plt.gca()\n", + "ax.set_ylim([1e-8, 200])\n", + "ax.set_xlim([0.1, 1000])\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fig.savefig('data/riemannian_vs_plain_hiv_train.pdf', bbox_extra_artists=(legend,), bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Plot validation" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IL+AKEUkRkZ6G18+VweY0CzUJs3Y9ISxCZ9P3qQNWH7b4aGb+gdLNsm/mZfRt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\niZ41mKmeptfmaGq6Ckyp7tYTAxnAtl+ehdPtIVzT5KsQpj+9SnWii8Z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open('data/riemannian_vs_baseline_hiv_800_001_tmp.pickle', 'rb') as f:\n", + " logs = pickle.load(f)\n", + "\n", + "fig = plt.figure()\n", + "plt.loglog(logs['plain_sgd'][-1].logger.time_hist,\n", + " logs['plain_sgd'][-1].logger.loss_hist['valid']['logistic'], label='Cores GD',\n", + " linewidth=2, color=colors[0])\n", + "plt.loglog(logs['plain_sgd'][100].logger.time_hist, logs['plain_sgd'][100].logger.loss_hist['valid']['logistic'],\n", + " label='Cores SGD 100', linewidth=2, color=colors[1])\n", + "plt.loglog(logs['plain_sgd'][500].logger.time_hist, logs['plain_sgd'][500].logger.loss_hist['valid']['logistic'],\n", + " label='Cores SGD 500', linewidth=2, color=colors[2])\n", + "\n", + "grid = np.array([0.01, 80, 170]) / 2\n", + "x = logs['riemannian_sgd'][-1].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd'][-1].logger.time_hist,\n", + " logs['riemannian_sgd'][-1].logger.loss_hist['valid']['logistic'],\n", + " marker='o', markevery=marker_indices, label='Riemann GD', linewidth=2, color=colors[0])\n", + "grid = np.array([0.05, 80, 170]) / 2\n", + "x = logs['riemannian_sgd'][100].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd'][100].logger.time_hist,\n", + " logs['riemannian_sgd'][100].logger.loss_hist['valid']['logistic'],\n", + " marker='o', markevery=marker_indices, label='Riemann 100', linewidth=2, color=colors[1])\n", + "grid = np.array([0.1, 70, 200]) / 2\n", + "x = logs['riemannian_sgd'][500].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd'][500].logger.time_hist,\n", + " logs['riemannian_sgd'][500].logger.loss_hist['valid']['logistic'],\n", + " marker='o', markevery=marker_indices, label='Riemann 500', linewidth=2, color=colors[2])\n", + "\n", + "grid = np.array([0.1, 20, 400]) / 2\n", + "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd_rand'][-1].logger.time_hist,\n", + " logs['riemannian_sgd_rand'][-1].logger.loss_hist['valid']['logistic'],\n", + " marker='s', markevery=marker_indices, label='Riemann GD rand init 1', linewidth=2, color=colors[0])\n", + "\n", + "grid = np.array([0.8, 3, 150, 320])\n", + "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", + "marker_indices = np.searchsorted(x, grid)\n", + "plt.loglog(logs['riemannian_sgd_smart_rand'][-1].logger.time_hist,\n", + " logs['riemannian_sgd_smart_rand'][-1].logger.loss_hist['valid']['logistic'],\n", + " marker='v', markevery=marker_indices, label='Riemann GD rand init 2', linewidth=2, color=colors[0])\n", + "\n", + "# plt.loglog(plain_sgd_rand[-1].logger.time_hist,\n", + "# plain_sgd_rand[-1].logger.loss_hist['valid']['logistic'],\n", + "# marker='v', markevery=marker_indices, label='Cores GD rand init', linewidth=2, color=colors[0])\n", + "\n", + "legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1.04), frameon=False)\n", + "plt.xlabel('time (s)')\n", + "plt.ylabel('test loss (logistic)')\n", + "plt.minorticks_off()\n", + "ax = plt.gca()\n", + "ax.set_ylim([0.02, 40])\n", + "ax.set_xlim([0.1, 1000])\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "fig.savefig('data/riemannian_vs_plain_hiv_validation.pdf', bbox_extra_artists=(legend,), bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 7074892d96679ce4ff417980caa376a1d61b43db Mon Sep 17 00:00:00 2001 From: Alexander Novikov Date: Thu, 19 Jan 2017 16:26:27 +0300 Subject: [PATCH 3/3] add grid search over LR --- experiments/Riemannian_vs_baseline_car.ipynb | 600 +++++------------- .../data/riemannian_vs_baseline_car.pickle | Bin 4914614 -> 6623287 bytes .../data/riemannian_vs_baseline_car_train.pdf | Bin 0 -> 56173 bytes .../riemannian_vs_baseline_car_validation.pdf | Bin 0 -> 61332 bytes 4 files changed, 172 insertions(+), 428 deletions(-) create mode 100644 experiments/data/riemannian_vs_baseline_car_train.pdf create mode 100644 experiments/data/riemannian_vs_baseline_car_validation.pdf diff --git a/experiments/Riemannian_vs_baseline_car.ipynb b/experiments/Riemannian_vs_baseline_car.ipynb index 4d8d15c..3909230 100644 --- a/experiments/Riemannian_vs_baseline_car.ipynb +++ b/experiments/Riemannian_vs_baseline_car.ipynb @@ -136,66 +136,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Train" + "# Choose the learning rate" ] }, { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "TTRegression(batch_size=500, coef0=None, dropout=None, exp_reg=1.0,\n", - " fit_intercept=False, intercept0=None, learning_rate=0.02,\n", - " loss_name='logistic', max_iter=1000, persuit_init=False, rank=4,\n", - " reg=0.0, solver='sgd', tt_model='all-subsets', verbose=1)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "model = TTRegression('all-subsets', 'logistic', 4, learning_rate=0.02, solver='sgd', max_iter=1000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)" + "## Riemannian" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "TTRegression(batch_size=100, coef0=None, dropout=None, exp_reg=1.0,\n", - " fit_intercept=False, intercept0=None, learning_rate=10.0,\n", - " loss_name='logistic', max_iter=10, persuit_init=False, rank=4,\n", - " reg=0.0, solver='riemannian-sgd', tt_model='all-subsets', verbose=1)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "riemannian_model = TTRegression('all-subsets', 'logistic', 4, learning_rate=10.0, solver='riemannian-sgd', max_iter=10, verbose=1,\n", - " batch_size=100, fit_intercept=False, reg=0.)\n", - "riemannian_model.fit_log_val(X_train, y_train, X_val, y_val)" + "riemannian_lr_arr = np.hstack((2**np.arange(6), 2**np.arange(6, 10, 0.3)))\n", + "best_riemannian_loss = np.inf\n", + "riemannian_loss_arr = np.zeros(riemannian_lr_arr.shape)\n", + "for i, lr in enumerate(riemannian_lr_arr):\n", + " # To use the same order of looping through objects for all runs.\n", + " np.random.seed(0)\n", + " model = TTRegression('all-subsets', 'logistic', 4, learning_rate=lr, solver='riemannian-sgd',\n", + " max_iter=20, verbose=1, batch_size=500, fit_intercept=False, reg=0.)\n", + " model.fit(X_train, y_train)\n", + " riemannian_loss_arr[i] = model.logger.loss_hist['train']['logistic'][-1]\n", + " if best_riemannian_loss > model.logger.loss_hist['train']['logistic'][-1]:\n", + " best_riemannian_loss = model.logger.loss_hist['train']['logistic'][-1]\n", + " best_riemannian_model = model\n", + " best_riemannian_lr = lr" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -203,18 +180,18 @@ { "data": { "text/plain": [ - "[]" + "" ] }, - "execution_count": 31, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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sYuary5eBr76Sn+0znG7cCLz/vuO5J5yStxqpIQzG2IbB+a6/fXuZEpufQM3m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\n", 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88YUgk0pAJ2XDDcMHZrF3ywP42c/g6KPD2meV4NtvYY014JNPQjdrYwpNmMeZ\nPjKo+Uucqy4S/PGPIZm+667w2GPFHyaalAUL4M03m+7W6NYt5HXmzQsJ9HK2YEHI4XTu3Py1+ai0\nlsfkybDZZk0HjmJottvKzD4ANgJ2j35eHOd1zlWDM88MQ11raipnH4xMvqNdu8avkcJSLZXQdTV9\negjcSQ0rrrTgkUa+A2IEAUnDgPOA86NDbYDbkqxUrjzn4UrphBPgyith4ECYOLHUtWlec/mOjErJ\neySVLM/o0aOyBg88//zyVZIbkmbO41VgO+BlM9suOvaa5zycW9FDD4Ul3e+4A3bfvdS1adxPfwpX\nXBG625py880hn3NbWX1V/LE//CG0ov70p2Tuv2RJmC/x9NOV0TW5+eZhWPnWWzd9XRqTBL+LPp0t\nKtD3L3euAfvuG9bCOvzwMCu5HC1YEEYmNTa/I1ulDNedPj2ZOR4ZbdqE/d1vvjm5Morls89g7lzY\ncsvky4oTPO6SdAOwuqShwOMUZ22rovFuK1cuBgwIs9GHDoUxY0pdmx97+unQH95UviNjq61CX3+5\nz6hPaphutuOOC0vTFGNvj+++C/f64IPC71XfCy+E/VmaWoMttW4rgGgXv4GEYbqPmNljBZdcJN5t\n5crRlCmw996hFXLKKWH0Szk4+2xYfXW4IOZeoL17w003ha6ucrR0aRhV9Nln0L59smXtsAP8+c8w\nqMDxp7fcErrYvvoqDPE+6KAwHHjrrcNAhULU1oahupdc0vy1iXdbSbrMzB4zs3PM7Gwze0zSZfkW\n6Fw12HpreO658HO/fiEHMmZMmK1cSnGT5RnlvkzJ+++HBSuTDhwQVqgdNaqwe5iFwRU33BAmmP79\n72GlgsGDwxeM3/0utA7zbeGkNdIK4nVbNbR3+D7FrohzLc1GG4WJeDNnwqmnhj7zLl3CNrdTpqRf\nny++CPmBXFoR5Z73SGpNq4YccQQ8/HBYbThfTz0VvkAMHBg2aRowIASQ996De+8NC1KefjpssAGc\neGLYFjkus9BtVfLgIelUSa8DPSS9lvV4D3itsdc551bUrh38/Ofhg+DFF8MHxN57h+GUI0fCl1+m\nU48JE0KZcfIdGeU+XDfpYbrZ1lwzdFndfnv+97jyyvDlof6clMy8mmHDQrB+4QXYZhv41a/gvPNg\n2bLm7/3222GJ/Q02yL9+uWiq5TERGAzcF/2ZeWzf2EZRpeIJc1cpNtkkbG37/vsh7zBuXGihDB0a\nPjCSTN9t9JJXAAAUl0lEQVTl2mUF4QPt9deLkyhOQhrJ8myFdF29+24I4Ecf3fy13bqFCajPPx9e\nc9RRIZfRlLhdVoknzCVNMrPtJf3bzPYouKSEeMLcVbo5c0KX1ogR4Zvj0KHhw2LNNYtbzvbbw//+\nL/Tvn9vruneH8ePT6x7KxYAB4dt6WvNqli4NWxQ//HDz8yjq++1vQ6vv0hyXgP366xBw5s0L8zfW\nWKPh6047LdTt7LPj3TfJhHkrSf+PsIz6WfUf+RbonFvRBhvA+eeHbVSvvDIk2rt3D3ML6uqK0xr5\n4guYMSO/UVPl3HWVdsujdWs45pjcWx8LF4bhub/+de5lrrIK3HlnGLzQv3/jQ3zTTJZD08HjcGAp\nYfHEVRt4OOeKqFWr5aOy3nknfND/5jdhVvNllxW2/PvTT4d8R9u2ub+2XIPH/PnhW3laffwZxx0X\nZt0vWRL/NaNGhT1g8l31t3XrkFg/+eQweu/ll1c8/803MHVqaF2mpdFVdc1sOnBZtBTJ+PSq5Jxb\na62QWD399PCNcsSIMGt4t93gpJNC4rapiWD1NbblbBzbbQfXXpvfa5OUmVle6NyIXG2xRRhW+9BD\nYW/75ixdGroL//GPwss+88wQgAYNCtsB7L13OP7qq6FeaQxZzoizqu54SftJOlfSnzKPNCoXlyfM\nXUslhRbDiBGhu2KffcJEsG7dQl9/3FnK+STLM8p1uG7aXVbZjj8+/nIlDzwQdiRsarHCXBxyCNx3\nX2gBjRwZjk2cGL/LKs2FEa8H2gO7EZYlORR4wcxOLLj0IvCEuatGkyeHgDJmTOjeOukkOOCAhrul\n5s8PidR58/LrtjILe3pMnQrrr1943Yvl/PPDAIO4s+WLaeHCMDt8xgxYd92mr91tN/jlL8NqA8U0\nfXpYT+2oo0I9Bg0KQS2uNBZG7GdmxwDzzexCYGegAtaWdK7l6tULrr4aZs0KifXhw0N3xjnnhA+V\nbIXkOyC0fsox71HKlkenTiFYN9cV9eqrYSBEErsQ/uQn8OyzYeTXXXelmyyHeMEjsyzaYkkbAkuA\nlFNUzrmGrLLK8lFZTz8dku4DBoQtZEePhsWLC8t3ZGy3Xfl1XSW9mm5zMnM+mur4uOqqMOihTZtk\n6rDeevDEE6GctANpnG6rPwJXA3sA1xCWZr/JzMoi7+HdVs6t6LvvQj/7iBEh2S6FyYg775z/PW+7\nLdzjzjuLV89CLFkStgD+4gtYeeXS1GHZMth0U/jnPxse5TR3bvhAf/vtMACi3BTabRVrVd2swtoB\nK5vZgnwLLDYPHs417sMPQ7fGCSeEtZTyNWVK6Hqp3yVWKjNmhMED77xT2npceCF8+mnoNmzo3Jw5\ncP316dcrjlSDRzny4OFc8pYsgdVWg08+CUugl9q4cXDddWG4bCm9/35Yqn3WrBVbQN9+GwYp/Oc/\nYV+UcpRGwrzs+VBd55LVpg307AmvlcmSqKVMlmfr1i0MXrj//hWP33FHGGRQjoEj1c2gypm3PJxL\nx9ChIXH+q1+VuiZhaPJPfxqGwJba6NFhyPT4aCq1GfTpEzZkykziK0dpbAbVP7NvuaQhkq6Q1DXf\nAp1zlamchuuWS8sDQi5o4kSYPTs8z96zoyWL0211HWGYbi/gd8A7wK2J1so5V3bKaVfBNDeBak77\n9nDooaEFAo3v2dHSxPnrfR/1Cx0IDDeza/CFEZ2rOttsE2aZf/99aesxb16ow3rrlbYe2TJzPt55\nJ/6eHZUuTvBYJOl8YAjwoKRWQEJTXpxz5WrVVaFz59IP183sHpj2gohN2XnnUJ/jjgvbx3boUOoa\nJS9O8DgM+BY40cw+BroAf0u0Vs65slQOeY9SzyxvSCZwPPdcfnt2VKI404YWAVeZ2VJJWwA9gAJ2\n8XXOVarMMiVHHVW6OpRTsjzb0KGhZZbvnh2VJk7L4ymgnaTOwKPA0cDNSVbKOVeevOXRuLXWqo5c\nR0ac4CEzWwwcDFxrZj8Hcty91znXEmSCRymnVpVry6PaxAoeknYGjgIezOF1qfEZ5s6lY/31ww6G\nmTkNafvuu7AB1qablqb8liDNzaAGEOZ3PGNml0nqDpxpZqcXXHoR+Axz59J14IFhXkMpumimTYPB\ng8MeGa4wic8wN7MnzewA4BpJHc3s3XIJHM659P3856Vbmt27rMpHnOVJtpH0CjAVeEPSJEk9k6+a\nc64cHXBAWILj88/TL7tck+XVKE7u4gbgLDPramYbE7qwbkq2Ws65ctWpU1i36d570y/bWx7lI07w\n6GBmT2SemFkdUAXzJ51zjTn88LDseNo8eJSPOAnzscDLQLTsF0OA7c3soITrFosnzJ1L3+LFsOGG\n4cN8/fXTKdMszKWYPh3WWSedMluyNDaDOgFYB7g3eqwTHXPOVan27WH//cP+3Wn59NOwDMjaa6dX\npmtcnNFW883sdDPrEz3OMLP5aVTOOVe+0u66yiTLy2lBxGrW6NpWksYBjfYHRcN3nXNVauBAOPZY\n+PBD2Hjj5MvzfEd5aWphxMtTq4VzruK0bQsHHQR33QVnn518eZMmwda+MFLZ8D3MnXN5e/xx+P3v\n4aWXki1n2TLo0gXq6mCLLZItq1qkkTAvCUk9JF0n6S5Jp5S6Ps65H6upgZkzk18u5IUXYI01PHCU\nk7INHmY2zcxOJWxG1a/U9XHO/dhKK6WzXMm//gU/+1myZbjcJB48JI2UNFfSa/WO7y1pmqQZks5r\n5LWDgQeAh5Kup3MuP0mPujKDsWNDfsWVjzhrWz0mafWs52tIeiSHMkYBg+rdsxUwPDreEzhCUo/o\n3NGSrpC0gZmNM7P9CBMTnXNlqF8/WLAApkxJ5v7TpoVJidtvn8z9XX7itDzWNrMvMk+iOR7rxi3A\nzCYA9eeF9AXeMrMPzGwJcAdwYHT9aDM7C9hC0lWSrmf5PiLOuTLTqhUcdlhyrY+xY0OXlc/vKC9x\n9jBfJmljM/sQQFJXmpj/EVNnYGbW81mEgPIDM3sSeDLOzbI3NqmpqaGmpqbA6jnncnHEESGAXHxx\n8T/k//UvuOSS4t6zGtXV1RV107w4a1vtDdxI+CAXsCtwspnF7rqKAs44M9s2en4IMMjMTo6eDwH6\n5rNPiA/Vda70zMJIqNtvhx12KN59Z82CXr3g44+hTZvi3dcVPlS32ZaHmT0sqQ+wU3ToTDObl2+B\nkdlA9pzULtGxvNTW1nqLw7kSkpYnzosZPO67D/bbzwNHMRWrBdJoy0NSDzObFgWOHzGzl2MXInUj\ntDy2iZ63BqYDewBzgBeAI8zszZxqj7c8nCsXU6fC3nuHPcZbFWkc5557wq9/7SOtklBoy6Op4HGj\nmZ0s6YkGTpuZ7R6zgmOAGmAtYC4wzMxGSdoHuJKQtB9pZpfm9Rfw4OFc2dhmG7juOthll8LvNX8+\ndO0Kc+ZAB99BqOgS67bK5COAfczsm3qFrhy3ADM7spHj44Hxce/TFO+2cq48ZLquihE8HngAdt/d\nA0exJd5t9cMF0stm1qe5Y6XiLQ/nysfbb0P//jB7dph9XohDDoHBg+G444pSNVdPYmtbSVpf0vbA\nKpK2k9QnetQA7fMt0DnXcm22WVievdAvtl9/HRZdHDy4KNVyCWjqu8Eg4DjCSKj/IQzTBVgE/L9k\nq5Ub77Zyrnxkuq723DP/ezz2GPTpE7addcWVZrfVIWZ2T8ElJcS7rZwrLzNnQu/eIdHdtm1+9zj+\neNhuOzg955lfLq40lmTvIqmTghGSXpY0MN8CnXMt20YbwVZbwaOP5vf677+HcePgwAOLWy9XXHGC\nxwlmthAYSBhuezSQ17Ba51x1KGSl3QkTwhDdrl2LWydXXHGCR6ZZsy9wq5lNzTpWFmpra4u6Zotz\nrjCHHhqG2i5enPtrMwshumTU1dWtsB5gvuLkPEYRFjLcBOgFtAbqzKwsFkj2nIdz5WnQIBg4EH73\nu/ivMYNu3eDBB32/8qQlvrYVcCLQG3jXzBZLWgs4Pt8CnXPV4frrw14fW24J++4b7zWvvBKS7D17\nJls3V7im5nn0iH7sHf3ZPVrnqivxgo5zroptsgncc0+Y5Pfaa81eDizfbtb37ih/TQWBs4CTCXM8\n6jMg1tpWafB5Hs6Vp3794KqrwmS/55+H9ddv+vqxY+GGG9KpW7VKbZ5HufOch3Pl7+KLw/Dbujpo\n38j6FG+/HdbE+uij4q3K6xqX2Kq6WQUc3MDhBcDrZvZJvgUXiwcP58qfGRxzTFh25K67Gg4Ol18O\nb73lLY+0pDFJ8ERgBHBU9LgJOA94RtLR+RbsnKseEowYAXPnwh/+0PA1mXyHqwxxEt8rAVua2VwA\nSesBtwI7Ak8Bo5OrnnOupWjXLuQ0dtopbFl7fNaYzblzYcqUsAS7qwxxgsdGmcAR+SQ69rmkJQnV\nyznXAq29dpg8OGBAmM+x227h+P33h10I27UrafVcDuJ0W9VJekDSsZKOBe6PjnUAvki2evH4DHPn\nKkePHnD77WEJk+nTw7GxY32r2bSkOcNcwMFAZm+wZ4B7yiVL7Qlz5yrTyJFw6aVhAcVevWDWLOjU\nqdS1qh6JzzA3M5M0AfiOML/jBf+0ds4V6sQTw+iqnXYKQ3Q9cFSWZoOHpF8AfwPqCAsiXi3pHDP7\nZ8J1c861cH/5C3zySfzlS1z5iNNtNRnYKzOnQ9I6wONm1iuF+jXLu62ccy53aczzaFVvMuBnMV/n\nnHOuhYozVPdhSY8At0fPDwMeSq5Kzjnnyl2chPk5kg4B+keHbjSzsclWKze+MKJzzsXjCyNGPOfh\nnHO5S2yorqRFhKG5PzpFGMHrA+ucc65KNRo8zGzVNCvinHOucvioKeeccznz4OGccy5nHjycc87l\nzIOHc865nHnwcM45lzMPHs4553LWIoKHbwblnHPxpLYZVLnzGebOOZe7NFbVdc4551bgwcM551zO\nPHg455zLmQcP55xzOfPg4ZxzLmcePJxzzuXMg4dzzrmcefBwzjmXMw8ezjnncubBwznnXM7KOnhI\nai/pRUn7lrouLhm+Jlnl8veuupV18ADOA+4sdSVccvwDqHL5e1fdEg8ekkZKmivptXrH95Y0TdIM\nSec18Lo9gTeAT4G8F+9KSzH/I+V7r1xeF+fapq7J51y5ftgUu17l8P7le77S3juovv97TZ1P8/1L\no+UxChiUfUBSK2B4dLwncISkHtG5oyX9HTgC2BE4EjgphXoWpNr+AXvwKP79PHjkp9r+7zV1Ps33\nL5Ul2SV1BcaZ2bbR852AYWa2T/T894CZ2WUNvPYYYJ6ZPdTIvX09duecy0MhS7KvVMyK5KAzMDPr\n+Sygb0MXmtmtTd2okL+8c865/JR7wtw551wZKlXwmA1snPW8S3TMOedcBUgreIgVR0y9CGwmqauk\ntsDhwP0p1cU551yB0hiqOwZ4FthC0oeSjjezpcBpwKPAVOAOM3sz6bo455wrjlRGWznnnGtZSjXa\nKjGS2gPXAt8CT5rZmBJXyeVA0ibAH4BOZvaLUtfH5UbSgcB+wKrA/5nZYyWukospmmt3BrAW8B8z\nu77J61tay0PSEGC+mT0o6Q4zO7zUdXK5k3SXB4/KJWl14G9mNrTUdXG5kSTgFjM7pqnryn6obh7L\nm3Rh+RySpalV1DUo3+VpXHko4P27ALgmnVq6huTz3kkaDDwANDgpO1vZBw9yXN6EEDi6ZC5Nq5Ku\nUbm+fz9clk71XDNyfv8kXQo8ZGavpllR9yM5v3dmNs7M9gOGNHfzsg8eZjYBmF/vcF/gLTP7wMyW\nAHcAB0bnxgKHSroGGJdeTV1Dcn3/JK0p6Tqgt7dISi+P9+80YA/C/8GTU62sW0Ee790ASVdJuh54\nsLn7V2rCvNHlTcxsMXBCKSrlYmvq/fscOLUUlXKxNfX+XQ1cXYpKuViaeu+eBJ6Me6Oyb3k455wr\nP5UaPHx5k8rm719l8/evchXtvauU4OHLm1Q2f/8qm79/lSux967sg4cvb1LZ/P2rbP7+Va6k37sW\nN0nQOedc8sq+5eGcc678ePBwzjmXMw8ezjnncubBwznnXM48eDjnnMuZBw/nnHM58+DhnHMuZx48\nXIsmaVEKZQyWdG7S5dQrc4CkndMs07lslbqqrnNxFWUWrKRWZraswQLMxpHA8v+SWkczghtSA3wJ\nPFfscp2Lw1sermpIOlvSC5JelTQs6/hYSS9Kel3SSVnHF0m6XNIrwM6S3pNUK2mSpMmStoiuO1bS\n1dHPo6I9EZ6R9Lakg6PjknStpDckPSLpwcy5enV8QtLfJb0AnC5pf0kTozIflbSOpK7AKcCZkl6W\n1F/S2pL+Ken56NEv2d+mq3be8nBVQdJewOZm1jfao/l+SbtEG+Ycb2ZfSFoZeFHSPWY2H+gAPGdm\nZ0f3APjEzLaXdCpwNpDZ8Ci7hbO+mfWXtCVh0bl7gUOAjc1sK0nrAW8CIxupbhsz6xuVuZqZ7RT9\nfCJwrpmdE23Ys8jMrojO/QO4wsyelbQR8AiwVeG/Oeca5sHDVYuBwF6SXiasMtoB2ByYQPgG/7Po\nui7R8ReA7wkf/NnGRn9OAg5qpKx/AZjZm5LWjY71B+6Ojs+V9EQTdb0z6+eNJN0FbAC0Ad5r5DV7\nAltGgRGgo6T20eZozhWdBw9XLQRcYmY3rXBQGgDsDuxoZt9GH+orR6e/sR+vHPpt9OdSGv//823W\nz/nsxf5V1s9XA5eb2YNRXYc18hoR/g5L8ijPuZx5zsO1dJkP70eAEyR1AJC0oaR1gNWA+VHg6AHs\n1MBri1H+M8AhUe5jPULCO45OwEfRz8dmHV8Unct4FDjjh0KlXnnV1rmYPHi4ls4AzOwxYAzwnKTX\nCF1IHYGHgTaSpgJ/YcXRS/VbHXFGbjX2mnsI+0VPBW4ldHstiPH6C4F/SnoR+DTr+DjgoEzCHDgd\n2CFK5E8Bfhmjrs7lzffzcC4lkjqY2VeS1gSeB/qb2Selrpdz+fCch3PpeUDS6oTE90UeOFwl85aH\nc865nHnOwznnXM48eDjnnMuZBw/nnHM58+DhnHMuZx48nHPO5ez/A3XnKFGx++0mAAAAAElFTkSu\nQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -222,169 +199,15 @@ } ], "source": [ - "plt.loglog(model.logger.time_hist, model.logger.loss_hist['train']['logistic'])\n", - "plt.loglog(riemannian_model.logger.time_hist, riemannian_model.logger.loss_hist['train']['logistic'])" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "TTRegression(batch_size=500, coef0=None, dropout=None, exp_reg=1.0,\n", - " fit_intercept=False, intercept0=None, learning_rate=20.0,\n", - " loss_name='logistic', max_iter=500, persuit_init=False, rank=4,\n", - " reg=0.0, solver='riemannian-sgd', tt_model='all-subsets', verbose=1)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 20.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00063908543695358633" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "TTRegression(batch_size=500, coef0=None, dropout=None, exp_reg=1.0,\n", - " fit_intercept=False, intercept0=None, learning_rate=15.0,\n", - " loss_name='logistic', max_iter=500, persuit_init=False, rank=4,\n", - " reg=0.0, solver='riemannian-sgd', tt_model='all-subsets', verbose=1)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 15.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00073882598971240949" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.04035071571636744" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.01, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.015016370048456682" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.02, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" + "plt.loglog(riemannian_lr_arr, riemannian_loss_arr)\n", + "plt.xlabel('learning rate')\n", + "plt.ylabel('logistic loss after 20 epochs (train)')\n", + "plt.title('Riemannian SGD 500')" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -392,107 +215,65 @@ { "data": { "text/plain": [ - "0.042096606195346518" + "" ] }, - "execution_count": 67, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.005, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { + "image/png": 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XOGYdiIhZko4FbpHUC3gf+H/AO8CorAYyj9QvArA/cE6WHJ4GJmbr\nJwDnSjo+O8cerRXXfe/ErHz8PA+zTpL0VkSslHccZnlws5VZ5/nKy3os1zzMzKxkrnmYmVnJnDzM\nzKxkTh5mZlYyJw8zMyuZk4eZmZXs/wOQneUrCXJdPAAAAABJRU5ErkJggg==\n", "text/plain": [ - "0.0092916749943583333" + "" ] }, - "execution_count": 68, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.04, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" + "plt.loglog(best_riemannian_model.logger.loss_hist['train']['logistic'])\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('logistic loss (train)')\n", + "plt.title('Riemannain SGD 500, LR %f' % best_riemannian_lr)" ] }, { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.050642381236658915" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.002, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" + "## SGD" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.16415022748865921" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.06, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" + "sgd_lr_arr = np.hstack((2**np.arange(-12, -2, 0.1)))\n", + "best_sgd_loss = np.inf\n", + "sgd_loss_arr = np.zeros(sgd_lr_arr.shape)\n", + "for i, lr in enumerate(sgd_lr_arr):\n", + " # To use the same order of looping through objects for all runs.\n", + " np.random.seed(0)\n", + " model = TTRegression('all-subsets', 'logistic', 4, learning_rate=lr, solver='sgd',\n", + " max_iter=100, verbose=1, batch_size=500, fit_intercept=False, reg=0.)\n", + " model.fit(X_train, y_train)\n", + " sgd_loss_arr[i] = model.logger.loss_hist['train']['logistic'][-1]\n", + " if best_sgd_loss > model.logger.loss_hist['train']['logistic'][-1]:\n", + " best_sgd_loss = model.logger.loss_hist['train']['logistic'][-1]\n", + " best_sgd_model = model\n", + " best_sgd_lr = lr" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -500,48 +281,34 @@ { "data": { "text/plain": [ - "0.15824865133758087" + "" ] }, - "execution_count": 72, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "model = TTRegression('all-subsets', 'logistic', 4, 0.08, 'sgd', max_iter=2000, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "model.logger.loss_hist['train']['logistic'][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { + "image/png": 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"text/plain": [ - "0.00044281225364734608" + "" ] }, - "execution_count": 60, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + "plt.loglog(sgd_lr_arr, sgd_loss_arr)\n", + "plt.xlabel('learning rate')\n", + "plt.ylabel('logistic loss after 100 epochs (train)')\n", + "plt.title('SGD 500')" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -549,85 +316,41 @@ { "data": { "text/plain": [ - "0.00014638632522643175" + "" ] }, - "execution_count": 61, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { + "image/png": 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"text/plain": [ - "0.00016332973482077837" + "" ] }, - "execution_count": 62, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "\n", - "# To use the same order of looping through objects for all runs.\n", - "rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 60.0, 'riemannian-sgd', max_iter=500, verbose=1,\n", - " batch_size=500, fit_intercept=False, reg=0.)\n", - "rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", - "rieamannian_model.logger.loss_hist['train']['logistic'][-1]" + "plt.loglog(best_sgd_model.logger.loss_hist['train']['logistic'])\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('logistic loss (train)')\n", + "plt.title('SGD 500, LR %f' % best_sgd_lr)" ] }, { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "markdown", + "metadata": {}, "source": [ - "plt.loglog(rieamannian_model.logger.loss_hist['train']['logistic'])" + "# Train" ] }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -639,14 +362,14 @@ "for batch_size in [-1, 100, 500]:\n", "# To use the same order of looping through objects for all runs.\n", " np.random.seed(0)\n", - " model = TTRegression('all-subsets', 'logistic', 4, 0.03, 'sgd', max_iter=12000, verbose=1,\n", + " model = TTRegression('all-subsets', 'logistic', 4, best_sgd_lr, 'sgd', max_iter=12000, verbose=1,\n", " fit_intercept=False, batch_size=batch_size, reg=0.)\n", " model.fit_log_val(X_train, y_train, X_val, y_val)\n", " plain_sgd[batch_size] = model\n", "\n", " # To use the same order of looping through objects for all runs.\n", " np.random.seed(0)\n", - " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=1000, verbose=1,\n", + " rieamannian_model = TTRegression('all-subsets', 'logistic', 4, best_riemannian_lr, 'riemannian-sgd', max_iter=1400, verbose=1,\n", " batch_size=batch_size, fit_intercept=False, reg=0.)\n", " rieamannian_model.fit_log_val(X_train, y_train, X_val, y_val)\n", " riemannian_sgd[batch_size] = rieamannian_model" @@ -661,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -671,53 +394,68 @@ "np.random.seed(0)\n", "w_init = tt.rand(2 * np.ones(X.shape[1]), r=4)\n", "# Divide by norm to make sure the norm is reasonable,\n", - "# round to make all the ranks are valid.\n", + "# round to make all the ranks valid.\n", "w_init = ((1 / w_init.norm()) * w_init).round(eps=0)\n", "\n", "plain_sgd_rand = {}\n", "riemannian_sgd_rand = {}\n", "\n", "batch_size = -1\n", - "# # To use the same order of looping through objects for all runs.\n", - "# np.random.seed(0)\n", - "# model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", - "# fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", - "# model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", - "# plain_sgd_rand[batch_size] = model_rand\n", + "# To use the same order of looping through objects for all runs.\n", + "np.random.seed(0)\n", + "model_rand = TTRegression('all-subsets', 'logistic', 4, best_sgd_lr, 'sgd', max_iter=12000, verbose=1,\n", + " fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init.copy())\n", + "model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "plain_sgd_rand[batch_size] = model_rand\n", "\n", "np.random.seed(0)\n", "# To use the same order of looping through objects for all runs.\n", - "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 40.0, 'riemannian-sgd', max_iter=1200, verbose=1,\n", - " batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init)\n", + "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, best_riemannian_lr, 'riemannian-sgd', max_iter=1400, verbose=1,\n", + " batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init.copy())\n", "riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", - "riemannian_sgd_rand[batch_size] = riemannian_model_rand\n", - "\n", - "\n", - "# # Beter init distribution\n", - "# from src.models.all_subsets import tensorize_linear_init\n", - "# w_init = tensorize_linear_init(np.random.rand(X.shape[1]), np.random.rand())\n", - "# for _ in range(2):\n", - "# w_init = w_init + 0 * tt.ones(2, X.shape[1])\n", - "# w_init.core += 0.01 * np.random.randn(*w_init.core.shape)\n", - "# w_init = w_init.round(eps=0)\n", + "riemannian_sgd_rand[batch_size] = riemannian_model_rand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random init with beter distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from src.models.all_subsets import tensorize_linear_init\n", + "np.random.seed(0)\n", + "w_init = tensorize_linear_init(np.random.rand(X.shape[1]), np.random.rand())\n", + "for _ in range(2):\n", + " w_init = w_init + 0 * tt.ones(2, X.shape[1])\n", + "w_init = w_init.round(eps=0)\n", "\n", - "# plain_sgd_smart_rand = {}\n", - "# riemannian_sgd_smart_rand = {}\n", + "plain_sgd_smart_rand = {}\n", + "riemannian_sgd_smart_rand = {}\n", "\n", - "# batch_size = -1\n", - "# # To use the same order of looping through objects for all runs.\n", - "# np.random.seed(0)\n", - "# model_rand = TTRegression('all-subsets', 'logistic', 4, 'sgd', max_iter=5000, verbose=1,\n", - "# fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init)\n", - "# model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", - "# plain_sgd_smart_rand[batch_size] = model_rand\n", + "batch_size = -1\n", + "# To use the same order of looping through objects for all runs.\n", + "np.random.seed(0)\n", + "model_rand = TTRegression('all-subsets', 'logistic', 4, best_sgd_lr, 'sgd', max_iter=12000, verbose=1,\n", + " fit_intercept=False, batch_size=batch_size, reg=0., coef0=w_init.copy())\n", + "model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "plain_sgd_smart_rand[batch_size] = model_rand\n", "\n", - "# np.random.seed(0)\n", - "# # To use the same order of looping through objects for all runs.\n", - "# riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, 'riemannian-sgd', max_iter=1600, verbose=1,\n", - "# batch_size=batch_size, fit_intercept=False, reg=0., coef0=w_init)\n", - "# riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", - "# riemannian_sgd_smart_rand[batch_size] = riemannian_model_rand" + "# To use the same order of looping through objects for all runs.\n", + "np.random.seed(0)\n", + "riemannian_model_rand = TTRegression('all-subsets', 'logistic', 4, best_riemannian_lr, 'riemannian-sgd',\n", + " max_iter=1400, verbose=1, batch_size=batch_size, fit_intercept=False,\n", + " reg=0., coef0=w_init.copy())\n", + "riemannian_model_rand.fit_log_val(X_train, y_train, X_val, y_val)\n", + "riemannian_sgd_smart_rand[batch_size] = riemannian_model_rand" ] }, { @@ -729,15 +467,16 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "with open('data/riemannian_vs_baseline_car_40_003.pickle', 'wb') as f:\n", + "with open('data/riemannian_vs_baseline_car.pickle', 'wb') as f:\n", " obj = {'plain_sgd': plain_sgd, 'riemannian_sgd': riemannian_sgd,\n", " 'plain_sgd_rand': plain_sgd_rand, 'riemannian_sgd_rand': riemannian_sgd_rand,\n", + " 'plain_sgd_smart_rand': plain_sgd_rand, 'riemannian_sgd_smart_rand': riemannian_sgd_rand,\n", " 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val}\n", " pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)" ] @@ -751,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -778,16 +517,16 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Gi3c2bGYL17CE5mrSHPfXOP698N98tOkjn23m7avGgU1VPhQ4tXhvJk1ve7Nh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\nfS7/jHu+bX6hhy+0Hw09pkLnG8Cj6tMN7AW8gN1LcOt3G+mXrGMN7dC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hTujSTxOI5nqDoSliN+g+FoE0s483f8SszChO9Wk3heahaoMv7Gk9YL/6xoVZ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\nbfjfGN9xvGdvlpsFad/BltlWBZKsI0XHwtpAh6HWmrZuV7q1nk1V+dE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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -795,7 +534,7 @@ } ], "source": [ - "with open('data/riemannian_vs_baseline_car_40_003.pickle', 'rb') as f:\n", + "with open('data/riemannian_vs_baseline_car.pickle', 'rb') as f:\n", " logs = pickle.load(f)\n", "\n", "fig = plt.figure()\n", @@ -807,49 +546,45 @@ "plt.loglog(logs['plain_sgd'][500].logger.time_hist, logs['plain_sgd'][500].logger.loss_hist['train']['logistic'],\n", " label='Cores SGD 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.01, 1, 5, 30, 60]) / 10\n", + "grid = np.array([0.7, 4, 25])\n", "x = logs['riemannian_sgd'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][-1].logger.time_hist,\n", " logs['riemannian_sgd'][-1].logger.loss_hist['train']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann GD', linewidth=2, color=colors[0])\n", - "grid = np.array([0.05, 2, 12, 30]) / 10\n", + "grid = np.array([0.05, 2, 24])\n", "x = logs['riemannian_sgd'][100].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][100].logger.time_hist,\n", " logs['riemannian_sgd'][100].logger.loss_hist['train']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 100', linewidth=2, color=colors[1])\n", - "grid = np.array([0.1, 7.5, 60]) / 10\n", + "grid = np.array([0.1, 0.9, 30])\n", "x = logs['riemannian_sgd'][500].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][500].logger.time_hist,\n", " logs['riemannian_sgd'][500].logger.loss_hist['train']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.1, 3, 20, 53]) / 2\n", + "grid = np.array([3, 25])\n", "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd_rand'][-1].logger.time_hist,\n", " logs['riemannian_sgd_rand'][-1].logger.loss_hist['train']['logistic'],\n", " marker='s', markevery=marker_indices, label='Riemann GD rand init 1', linewidth=2, color=colors[0])\n", "\n", - "# plt.loglog(logs['plain_sgd_rand'][-1].logger.time_hist,\n", - "# logs['plain_sgd_rand'][-1].logger.loss_hist['train']['logistic'],\n", - "# marker='v', markevery=marker_indices, label='Cores GD rand init', linewidth=2, color=colors[0])\n", - "\n", "legend = plt.legend(loc='upper left', bbox_to_anchor=(1, 1.04), frameon=False)\n", "plt.xlabel('time (s)')\n", "plt.ylabel('training loss (logistic)')\n", "plt.minorticks_off()\n", "ax = plt.gca()\n", - "ax.set_xlim([0.02, 200])\n", - "ax.set_ylim([1e-5, 2e1])\n", + "ax.set_xlim([0.02, 100])\n", + "ax.set_ylim([1e-6, 4e1])\n", "fig.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 59, "metadata": { "collapsed": true }, @@ -869,16 +604,16 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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H0LbFHD0CCe/jJ5lBq5UHAs7T5/a4WT+slMoBEJGrgZdEJBnIAfKAZ7363i8i1wHRaBrh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\nv2RxsLEHZW7bzRc3M+xH8wsbeqCjFftuIQ+HQEoCfNjEpG6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QPfcR7eemL+DkG+sd7n0Wp5v98oP+vldKM2t/cK728ekS796gzhEM7kH+wdBEmlW9HO0S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\ntnfm7rsOp2dmw4wRYMpVpzOt+9ry2z/LYdrIKmY+NusEHYZC3mHYux7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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -886,7 +621,7 @@ } ], "source": [ - "with open('data/riemannian_vs_baseline_car_40_003.pickle', 'rb') as f:\n", + "with open('data/riemannian_vs_baseline_car.pickle', 'rb') as f:\n", " logs = pickle.load(f)\n", "\n", "fig = plt.figure()\n", @@ -898,26 +633,26 @@ "plt.loglog(logs['plain_sgd'][500].logger.time_hist, logs['plain_sgd'][500].logger.loss_hist['valid']['logistic'],\n", " label='Cores SGD 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.01, 1, 5, 30, 60]) / 2.5\n", + "grid = np.array([0.3, 2, 15])\n", "x = logs['riemannian_sgd'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][-1].logger.time_hist,\n", " logs['riemannian_sgd'][-1].logger.loss_hist['valid']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann GD', linewidth=2, color=colors[0])\n", - "grid = np.array([0.05, 2, 12, 30]) / 2.5\n", + "grid = np.array([0.05, 2, 70])\n", "x = logs['riemannian_sgd'][100].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][100].logger.time_hist,\n", " logs['riemannian_sgd'][100].logger.loss_hist['valid']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 100', linewidth=2, color=colors[1])\n", - "grid = np.array([0.1, 7.5, 60]) / 2.5\n", + "grid = np.array([0.1, 1, 20])\n", "x = logs['riemannian_sgd'][500].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd'][500].logger.time_hist,\n", " logs['riemannian_sgd'][500].logger.loss_hist['valid']['logistic'],\n", " marker='o', markevery=marker_indices, label='Riemann 500', linewidth=2, color=colors[2])\n", "\n", - "grid = np.array([0.1, 3, 20, 53]) / 3\n", + "grid = np.array([1, 7, 20])\n", "x = logs['riemannian_sgd_rand'][-1].logger.time_hist\n", "marker_indices = np.searchsorted(x, grid)\n", "plt.loglog(logs['riemannian_sgd_rand'][-1].logger.time_hist,\n", @@ -933,14 +668,14 @@ "plt.ylabel('test loss (logistic)')\n", "plt.minorticks_off()\n", "ax = plt.gca()\n", - "ax.set_xlim([0.02, 200])\n", - "ax.set_ylim([1e-4, 20])\n", + "ax.set_xlim([0.02, 100])\n", + "ax.set_ylim([1e-5, 20])\n", "fig.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 80, "metadata": { "collapsed": true }, @@ -948,6 +683,15 @@ "source": [ "fig.savefig('data/riemannian_vs_plain_car_validation.pdf', bbox_extra_artists=(legend,), bbox_inches='tight')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/experiments/data/riemannian_vs_baseline_car.pickle b/experiments/data/riemannian_vs_baseline_car.pickle index bffc92491cfa6f0c7418d42a8b1e75919317e186..99cb763abe4f5969e1efe46f22f208fcc2239115 100644 GIT binary patch literal 6623287 zcmd4ab#zwQl0SToJVl0WE5nOSrDs=b004{t|9pU%D=g4LFR;T?mzdA95n;_ulp 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