diff --git a/hpsklearn/demo_support.py b/hpsklearn/demo_support.py index c79ae54e..218d1f8c 100644 --- a/hpsklearn/demo_support.py +++ b/hpsklearn/demo_support.py @@ -1,33 +1,54 @@ +import datetime import time import numpy as np import matplotlib.pyplot as plt +import hyperopt from IPython import display +def lossof(x): + try: + return float(x) + except: + return np.inf def scatter_error_vs_time(estimator, ax): losses = estimator.trials.losses() + ax.set_title('Job Error Throughout Run') ax.set_ylabel('Validation error rate') ax.set_xlabel('Iteration') ax.scatter(range(len(losses)), losses) def plot_minvalid_vs_time(estimator, ax, ylim=None): - losses = estimator.trials.losses() + losses = map(lossof, estimator.trials.losses()) ts = range(1, len(losses)) mins = [np.min(losses[:ii]) for ii in ts] - ax.set_ylabel('min(Validation error rate to-date)') + ax.set_ylabel('Validation error)') ax.set_xlabel('Iteration') - if ylim: + if ylim is not None: ax.set_ylim(*ylim) + ax.set_title('Min Loss to Date') ax.plot(ts, mins) +def plot_duration_vs_time(estimator, ax, ylim=None): + def duration_of(tr): + delta = (tr['refresh_time'] - tr['book_time']) + return delta.total_seconds() + durations = map(duration_of, estimator.trials.trials) + ax.set_ylabel('Seconds') + ax.set_xlabel('Iteration') + ax.set_title('Job duration') + ax.scatter(range(len(durations)), durations) + + class PlotHelper(object): - def __init__(self, estimator, mintodate_ylim): + def __init__(self, estimator, mintodate_ylim=None, figsize=(16, 3.5)): self.estimator = estimator - self.fig, self.axs = plt.subplots(1, 2) - self.post_iter_wait = .5 + self.fig, self.axs = plt.subplots(1, 3, figsize=figsize) + self.post_iter_wait = .3 self.mintodate_ylim = mintodate_ylim + self.t0 = time.time() def post_iter(self): self.axs[0].clear() @@ -35,10 +56,25 @@ def post_iter(self): scatter_error_vs_time(self.estimator, self.axs[0]) plot_minvalid_vs_time(self.estimator, self.axs[1], ylim=self.mintodate_ylim) - display.clear_output() + plot_duration_vs_time(self.estimator, self.axs[2]) + self.post_loop() + #display.clear_output() display.display(self.fig) + now = datetime.datetime.now() + display.display('Last update: %s' % ( + now.strftime('%H:%M:%S %b %d, %Y'))) time.sleep(self.post_iter_wait) def post_loop(self): display.clear_output() + print('Total trials: %s' % len(self.estimator.trials.trials)) + print('Successful trials: %s' % len( + filter(lambda st: st == hyperopt.STATUS_OK, + self.estimator.trials.statuses()))) + print('Failed trials: %s' % len( + filter(lambda st: st != hyperopt.STATUS_OK, + self.estimator.trials.statuses()))) + losses = map(lossof, self.estimator.trials.losses()) + print('Best validation error: %s' % min(losses)) + print('Total wall time: %.1f minutes' % ((time.time() - self.t0) / 60.)) diff --git a/hpsklearn/estimator.py b/hpsklearn/estimator.py index cf8b777a..6c0251ac 100644 --- a/hpsklearn/estimator.py +++ b/hpsklearn/estimator.py @@ -164,10 +164,27 @@ def should_stop(scores): 'duration': t_done - t_start, } rtype = 'return' + elif 'overflow' in str(exc): + t_done = time.time() + rval = { + 'status': hyperopt.STATUS_FAIL, + 'failure': str(exc), + 'duration': t_done - t_start, + } + rtype = 'return' else: rval = exc rtype = 'raise' + except (MemoryError,), exc: + t_done = time.time() + rval = { + 'status': hyperopt.STATUS_FAIL, + 'failure': str(exc), + 'duration': t_done - t_start, + } + rtype = 'return' + except (AttributeError,), exc: print 'Failing due to k_means_ weirdness' if "'NoneType' object has no attribute 'copy'" in str(exc): diff --git a/notebooks/Demo-CIFAR10.ipynb b/notebooks/Demo-CIFAR10.ipynb new file mode 100644 index 00000000..e5cdfa55 --- /dev/null +++ b/notebooks/Demo-CIFAR10.ipynb @@ -0,0 +1,169 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperopt-Sklearn on CIFAR-10\n", + "\n", + "The CIFAR-10 object recognition data set is a follow-up to the well-known MNIST data set of hand-written digits. CIFAR-10 is a data set of 60 000 small 32x32 color images of every-day objects. There are 10 kinds of object in CIFAR-10, and the data set is typically used for 10-way classification.\n", + "\n", + "We apply Hyperopt-sklearn to this data set in four steps:\n", + "\n", + "1. Retrieve the data set\n", + "2. Set up an estimator using the default search space\n", + "3. Run the fitting procedure (this takes a while for CIFAR-10: about 12 hours)\n", + "4. Test the result\n", + "\n", + "Hyperopt-sklearn is no silver bullet: performance on this data set falls far short of the state of the art.\n", + "The best models for labeling CIFAR-10 at the time of writing are ensembles of convolutional neural networks trained with maxout and dropout, and these models can be around 90% accurate. The simple pre-processing and classification options provided by hyperopt-sklearn manage only about 45% accuracy." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# -- RETRIEVE DATA VIA SKDATA (github.com/jaberg/skdata)\n", + "from skdata.cifar10 import (view, dataset)\n", + "dataset.CIFAR10().fetch(download_if_missing=True, verbose=False)\n", + "dataview = view.OfficialVectorClassificationTask(x_dtype='float32')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import hpsklearn\n", + "import hyperopt.tpe\n", + "estimator = hpsklearn.HyperoptEstimator(\n", + " max_evals=300,\n", + " verbose=1,\n", + " algo=hyperopt.tpe.suggest,\n", + " trial_timeout=60.0 * 5, # -- seconds\n", + " )" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Demo version of estimator.fit()\n", + "import hpsklearn.demo_support\n", + "reload (hpsklearn.demo_support)\n", + "\n", + "fit_iterator = estimator.fit_iter(dataview.train.x, dataview.train.y)\n", + "fit_iterator.next()\n", + "plot_helper = hpsklearn.demo_support.PlotHelper(estimator)\n", + "while len(estimator.trials.trials) < estimator.max_evals:\n", + " fit_iterator.send(1) # -- try one more model\n", + " plot_helper.post_iter()\n", + "plot_helper.post_loop()\n", + "\n", + "# -- Model selection was done on a subset of the training data.\n", + "# -- Now that we've picked a model, train on all training data.\n", + "estimator.retrain_best_model_on_full_data(dataview.train.x, dataview.train.y)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Total trials: 300\n", + "Successful trials: 203\n", + "Failed trials: 97\n", + "Best validation error: 0.55\n", + "Total wall time: 823.1 minutes\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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6cpP155OBn4FO+mf1g/5+HeA24C4gFNijv7YU+AMYoy/XVP9cfVHfhyNAT6Cx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WDVqlUI8UjvPnnyZHTv3h0jR47ElClTMHnyZEyePNmPUpYv7nq0NoisxFnl4v319Cp6t38RwAPq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\n/fq2iIiIxMcfL0JiYiI+/PBDnDx5CmlpX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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'Best preprocessing pipeline:'\n", + "for pp in estimator._best_preprocs:\n", + " print pp\n", + "print\n", + "print 'Best classifier:\\n', estimator._best_classif\n", + "test_predictions = estimator.predict(dataview.test.x)\n", + "acc_in_percent = 100 * np.mean(test_predictions == dataview.test.y)\n", + "print\n", + "print 'Prediction accuracy in generalization is %.1f%%' % acc_in_percent" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Best preprocessing pipeline:\n", + "StandardScaler(copy=True, with_mean=False, with_std=True)\n", + "\n", + "Best classifier:\n", + "ExtraTreesClassifier(bootstrap=False, compute_importances=None,\n", + " criterion=gini, max_depth=None, max_features=sqrt,\n", + " min_density=None, min_samples_leaf=2.0, min_samples_split=7.0,\n", + " n_estimators=50, n_jobs=1, oob_score=False, random_state=3,\n", + " verbose=False)\n", + "Transforming X of shape (10000, 3072)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Predicting X of shape (10000, 3072)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Prediction accuracy in generalization is 46.1%\n" + ] + } + ], + "prompt_number": 4 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/notebooks/Demo-Convex.ipynb b/notebooks/Demo-Convex.ipynb new file mode 100644 index 00000000..3b81813f --- /dev/null +++ b/notebooks/Demo-Convex.ipynb @@ -0,0 +1,173 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperopt-Sklearn on Convex Images\n", + "\n", + "The [\"Convex Images\" data set](http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/MnistVariations) was introduced to evaluate the capacity of deep neural networks to disentangle many factors of variation.\n", + "\n", + "Using Hyperopt-sklearn, it is possible to obtain state-of-the-art scores on this data set using standard (non-deep) classification algorithms. The scores are better than the baselines reported in e.g. [Larochelle et al. 2007]() because we allow for PCA pre-processing, and because hyperparameter optimization is a difficult thing to do. It is also possible to improve the accuracy of deep networks by allowing for different preprocessing options and by applying hyperparameter optimization techniques.\n", + "\n", + "The baselines presented here are using relatively fast algorithms. There are 10 000 training examples, and we can get good results even by restricting ourselves to models that train in less than 5 minutes. Many configurations require much less time. This notebook demonstrates optimization using 300 evaluation points. The entire search process takes approximately three hours to run, and usually finds a few configurations with around approx 87% accuracy." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# -- RETRIEVE DATA VIA SKDATA (github.com/jaberg/skdata)\n", + "import skdata.larochelle_etal_2007.view\n", + "\n", + "class DataCaptureMock(object):\n", + " def best_model(self, train, valid):\n", + " self.train = train\n", + " self.valid = valid\n", + "\n", + " def loss(self, model, task, **kwargs):\n", + " if task not in (self.train, self.valid):\n", + " self.test = task\n", + "# store train, valid, test tasks into data_capture\n", + "data_capture = DataCaptureMock()\n", + "skdata.larochelle_etal_2007.view.ConvexVectorXV().protocol(data_capture)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import hpsklearn\n", + "import hyperopt.tpe\n", + "estimator = hpsklearn.HyperoptEstimator(\n", + " #preprocessing=simple_small_image_preprocessing('pp'),\n", + " #classifier=hpc.any_classifier('classif'),\n", + " max_evals=300,\n", + " verbose=1,\n", + " algo=hyperopt.tpe.suggest,\n", + " trial_timeout=60.0 * 5, # -- seconds\n", + " )" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Demo version of estimator.fit()\n", + "import hpsklearn.demo_support\n", + "\n", + "fit_iterator = estimator.fit_iter(data_capture.train.x, data_capture.train.y)\n", + "fit_iterator.next()\n", + "plot_helper = hpsklearn.demo_support.PlotHelper(estimator)\n", + "while len(estimator.trials.trials) < estimator.max_evals:\n", + " fit_iterator.send(1) # -- try one more model\n", + " plot_helper.post_iter()\n", + "plot_helper.post_loop()\n", + "\n", + "# -- Model selection was done on a subset of the training data.\n", + "# -- Now that we've picked a model, train on all training data.\n", + "estimator.retrain_best_model_on_full_data(data_capture.train.x, data_capture.train.y)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Total trials: 300\n", + "Successful trials: 273\n", + "Failed trials: 27\n", + "Best validation error: 0.159230769231\n", + "Total wall time: 253.7 minutes\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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YB3hW4J8i5HEDzmIfVF8c4JzCPtOXP8bjgI7jJXn+W5GlA1nYB19ZwMVnxv2D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\n2Lbtv7Rp06ZcunPmvEJ29l8xG57fTE5OK0aMeJArrriXzp3T+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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'Best preprocessing pipeline:'\n", + "for pp in estimator._best_preprocs:\n", + " print pp\n", + "print\n", + "print 'Best classifier:\\n', estimator._best_classif\n", + "test_predictions = estimator.predict(data_capture.test.x)\n", + "acc_in_percent = 100 * np.mean(test_predictions == data_capture.test.y)\n", + "print\n", + "print 'Prediction accuracy in generalization is %.1f%%' % acc_in_percent" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Best preprocessing pipeline:\n", + "PCA(copy=True, n_components=24, whiten=False)\n", + "\n", + "Best classifier:\n", + "SVC(C=79.035716761, cache_size=1000.0, class_weight=None,\n", + " coef0=0.568893340306, degree=3.0, gamma=0.004426650245, kernel=poly,\n", + " max_iter=316460520, probability=False, random_state=0, shrinking=False,\n", + " tol=6.94064168432e-05, verbose=False)\n", + "Transforming X of shape (50000, 784)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Predicting X of shape (50000, 24)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Prediction accuracy in generalization is 87.1%\n" + ] + } + ], + "prompt_number": 4 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/notebooks/Demo-Iris.ipynb b/notebooks/Demo-Iris.ipynb index eb26bbb8..6a97e36b 100644 --- a/notebooks/Demo-Iris.ipynb +++ b/notebooks/Demo-Iris.ipynb @@ -1,6 +1,6 @@ { "metadata": { - "name": "Demo-Iris" + "name": "" }, "nbformat": 3, "nbformat_minor": 0, @@ -20,35 +20,20 @@ "\n", "Nevertheless, here is how to use hyperopt-sklearn (`hpsklearn`) to find a good model of the Iris data set. The code walk-through is given in 5 steps:\n", "\n", - " 1. module imports\n", - " 2. data preparation into training and testing sets for a single fold of cross-validation.\n", - " 3. creation of a hpsklearn `HyperoptEstimator`\n", - " 4. a somewhat spelled-out version of `HyperoptEstimator.fit`\n", - " 5. inspecting and testing the best model" + " 1. data preparation into training and testing sets for a single fold of cross-validation.\n", + " 1. creation of a hpsklearn `HyperoptEstimator`\n", + " 1. a somewhat spelled-out version of `HyperoptEstimator.fit`\n", + " 1. inspecting and testing the best model" ] }, { "cell_type": "code", "collapsed": false, "input": [ - "# IMPORTS\n", + "# PREPARE TRAINING AND TEST DATA\n", "import numpy as np\n", "import skdata.iris.view\n", - "import hyperopt.tpe\n", - "import hpsklearn\n", - "import hpsklearn.demo_support\n", - "import time" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# PREPARE TRAINING AND TEST DATA\n", + "\n", "data_view = skdata.iris.view.KfoldClassification(4)\n", "attrs = 'petal_length', 'petal_width', 'sepal_length', 'sepal_width'\n", "labels = 'setosa', 'versicolor', 'virginica'\n", @@ -67,12 +52,15 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 2 + "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ + "import hyperopt.tpe\n", + "import hpsklearn\n", + "\n", "estimator = hpsklearn.HyperoptEstimator(\n", " preprocessing=hpsklearn.components.any_preprocessing('pp'),\n", " classifier=hpsklearn.components.any_classifier('clf'),\n", @@ -84,13 +72,14 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 3 + "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Demo version of estimator.fit()\n", + "import hpsklearn.demo_support\n", "fit_iterator = estimator.fit_iter(X_train,y_train)\n", "fit_iterator.next()\n", "plot_helper = hpsklearn.demo_support.PlotHelper(estimator,\n", @@ -108,11 +97,26 @@ "metadata": {}, "outputs": [ { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Total trials: 15\n", + "Successful trials: 15\n", + "Failed trials: 0\n", + "Best validation error: 0.0\n", + "Total wall time: 0.3 minutes\n" + ] + }, + { + "metadata": {}, "output_type": "display_data", - "png": 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POzu7JvddaY06dqwvGHQkl6iD02W1jx8/Rnp6OgAgMDAQXbt21Xtg6qBzGESZ5cuXY+3a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\n2vx9a67dBlw/HwD4OSSla0eOHGFSqZQ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PCpUbNmwYXbt2JTg4mCFDhvCnP/2pxPNdm53z2k/Dhg3NE4QU18iz3Pe3v/2NkJAQOnXq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\ntrZWfv/7P4qfX6T07h0rBw4cMLtmYWGhxMWNkh49wiUxcYp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+ "text": [ + "" + ] } ], - "prompt_number": 4 + "prompt_number": 3 }, { "cell_type": "code", @@ -136,17 +140,19 @@ "stream": "stdout", "text": [ "Best preprocessing pipeline:\n", - "PCA(copy=True, n_components=4, whiten=False)\n", + "MinMaxScaler(copy=True, feature_range=(0.0, 1.0))\n", "\n", "Best classifier:\n", - "KNeighborsClassifier(algorithm=auto, leaf_size=72, metric=euclidean,\n", - " n_neighbors=29, p=2, weights=uniform)\n", + "SVC(C=7716.4320708, cache_size=1000.0, class_weight=None,\n", + " coef0=0.953667570897, degree=3, gamma=0.0113725312236, kernel=sigmoid,\n", + " max_iter=49075532, probability=False, random_state=2, shrinking=True,\n", + " tol=0.0120194483966, verbose=False)\n", "\n", - "Prediction accuracy in generalization is 96.7%\n" + "Prediction accuracy in generalization is 90.0%\n" ] } ], - "prompt_number": 5 + "prompt_number": 4 } ], "metadata": {} diff --git a/notebooks/Demo-MNIST.ipynb b/notebooks/Demo-MNIST.ipynb new file mode 100644 index 00000000..f267edb9 --- /dev/null +++ b/notebooks/Demo-MNIST.ipynb @@ -0,0 +1,171 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperopt-Sklearn on MNIST\n", + "\n", + "The MNIST data set of hand-written digits has 70 000 small 28x28 gray-scale images of the digits 0-9.\n", + "The data set is typically used for 10-way classification.\n", + "\n", + "We apply Hyperopt-sklearn to this data set in four steps:\n", + "\n", + "1. Retrieve the data set\n", + "2. Set up an estimator using the default search space\n", + "3. Run the fitting procedure (this takes about 12 hours)\n", + "4. Test the result\n", + "\n", + "Professor Yann LeCun maintains a list of [\"high-scores\"](http://yann.lecun.com/exdb/mnist/) for this dataset. The best performers are convolutional neural networks trained with an artificially augmented training set. Such variations on such methods score between 99% and 100% accuracy. The simpler classification approach respresented by hyperopt-sklearn usually scores around 98.5%, which is on par with the best known configuration of an SVM applied to the pixels (98.6%)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# -- RETRIEVE DATA VIA SKDATA (github.com/jaberg/skdata)\n", + "from skdata.mnist import view\n", + "dataview = view.OfficialVectorClassification(x_dtype='float32')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import hpsklearn\n", + "import hyperopt.tpe\n", + "estimator = hpsklearn.HyperoptEstimator(\n", + " #preprocessing=simple_small_image_preprocessing('pp'),\n", + " #classifier=hpc.any_classifier('classif'),\n", + " max_evals=300,\n", + " verbose=1,\n", + " algo=hyperopt.tpe.suggest,\n", + " trial_timeout=60.0 * 5, # -- seconds\n", + " )" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Demo version of estimator.fit()\n", + "import hpsklearn.demo_support\n", + "\n", + "fit_iterator = estimator.fit_iter(dataview.all_vectors[dataview.sel_idxs],\n", + " dataview.all_labels[dataview.sel_idxs])\n", + "fit_iterator.next()\n", + "plot_helper = hpsklearn.demo_support.PlotHelper(estimator,\n", + " mintodate_ylim=(-0.001, 0.05),\n", + " )\n", + "while len(estimator.trials.trials) < estimator.max_evals:\n", + " fit_iterator.send(1) # -- try one more model\n", + " plot_helper.post_iter()\n", + "plot_helper.post_loop()\n", + "\n", + "# -- Model selection was done on a subset of the training data.\n", + "# -- Now that we've picked a model, train on all training data.\n", + "estimator.retrain_best_model_on_full_data(dataview.all_vectors[dataview.sel_idxs],\n", + " dataview.all_labels[dataview.sel_idxs])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Total trials: 300\n", + "Successful trials: 173\n", + "Failed trials: 127\n", + "Best validation error: 0.01475\n", + "Total wall time: 857.4 minutes\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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5Dv0H5abCDZTv6wngHeA3slvWP0BJ2E3fsynAHfV4PlaPsQ3wHMr39nXAnrCw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\n61ZERUW1qQyNtefLz8C7j766D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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print 'Best preprocessing pipeline:'\n", + "for pp in estimator._best_preprocs:\n", + " print pp\n", + "print\n", + "print 'Best classifier:\\n', estimator._best_classif\n", + "test_predictions = estimator.predict(dataview.all_vectors[dataview.tst_idxs])\n", + "acc_in_percent = 100 * np.mean(test_predictions == dataview.all_labels[dataview.tst_idxs])\n", + "print\n", + "print 'Prediction accuracy in generalization is %.1f%%' % acc_in_percent" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Best preprocessing pipeline:\n", + "PCA(copy=True, n_components=36, whiten=True)\n", + "\n", + "Best classifier:\n", + "SVC(C=215127.325298, cache_size=1000.0, class_weight=None, coef0=0.0,\n", + " degree=3, gamma=0.0, kernel=rbf, max_iter=36916690, probability=False,\n", + " random_state=0, shrinking=False, tol=0.000707585995894, verbose=False)\n", + "Transforming X of shape (10000, 784)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Predicting X of shape (10000, 36)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "\n" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Prediction accuracy in generalization is 98.4%\n" + ] + } + ], + "prompt_number": 4 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file