Develop (#146)

* merge (#128)

* 0.2.6 (#126)

* 0.2.5 setup.py (#111)

* [WIP] Attempts to Fix Memory Error  (#112)

* Add Website Badge in README.md, apply timeout in search function in search.py

* Add timeout in maximize_acq function in search.py

* Update unit test to allow timeout to raise TimeoutError

* Add unit test for timeout resume

* Remove TimeoutError from expectation

* Check Timeout exception in search() in search.py

* 0.2.5 setup.py (#110)

* Prevent gpu memory copy to main process after train() finished

* Cast loss from tensor to float

* Add pass() in MockProcess

* [MRG] Search Space limited to avoid out of memory (#121)

* limited the search space

* limited the search space

* reduce search space

* test added

* [MRG]Pytorch mp (#124)

* Change multiprcoessing to torch.multiprocessing

* Replace multiprocessing.Pool with torch.multiprocessing.Pool in tests

* 0.2.6 (#125)

* new release

* auto deploy

* auto deploy of docs

* fix the docs auto deploy

* Create CNAME

* deploy docs fixed

* update

* bug fix (#127)

* setup.py

* rm print

* Issue#37 and Issue #79 Save keras model/autokeras model (#122)

* Issue #37 Export Keras model

* Issue #79 Save autokeras model

* Issue #37 and Issue#79 Fixed comments

* Issue #37 and Issue #79

* Issue #37 and Issue #79

* Issue #37 and Issue #79 Fixed pytests

* Issue #37 and Issue #79

* quick fix test

* Progbar (#143)

* 0.2.6 (#126)

* 0.2.5 setup.py (#111)

* [WIP] Attempts to Fix Memory Error  (#112)

* Add Website Badge in README.md, apply timeout in search function in search.py

* Add timeout in maximize_acq function in search.py

* Update unit test to allow timeout to raise TimeoutError

* Add unit test for timeout resume

* Remove TimeoutError from expectation

* Check Timeout exception in search() in search.py

* 0.2.5 setup.py (#110)

* Prevent gpu memory copy to main process after train() finished

* Cast loss from tensor to float

* Add pass() in MockProcess

* [MRG] Search Space limited to avoid out of memory (#121)

* limited the search space

* limited the search space

* reduce search space

* test added

* [MRG]Pytorch mp (#124)

* Change multiprcoessing to torch.multiprocessing

* Replace multiprocessing.Pool with torch.multiprocessing.Pool in tests

* 0.2.6 (#125)

* new release

* auto deploy

* auto deploy of docs

* fix the docs auto deploy

* Create CNAME

* deploy docs fixed

* update

* bug fix (#127)

* setup.py

* contribute guide

* Add Progress Bar

* Update utils.py

* Update search.py

* update constant (#145)

autokeras/constant.py

@@ -6,12 +6,12 @@ class Constant:
     # Searcher
 
     MAX_MODEL_NUM = 1000
-    BETA = 10.576
+    BETA = 2.576
     KERNEL_LAMBDA = 0.1
     T_MIN = 0.0001
     N_NEIGHBOURS = 8
-    MAX_MODEL_WIDTH = 2048
-    MAX_MODEL_DEPTH = 15
+    MAX_MODEL_WIDTH = 1024
+    MAX_MODEL_DEPTH = 10
 
     # Model Defaults
 

autokeras/graph.py

@@ -507,7 +507,6 @@ def extract_descriptor(self):
 
         # The position of each node, how many Conv and Dense layers before it.
         pos = [0] * len(topological_node_list)
-        print(sorted(topological_node_list))
         for v in topological_node_list:
             layer_count = 0
             for u, layer_id in self.reverse_adj_list[v]:

autokeras/image_supervised.py

@@ -12,7 +12,7 @@
 from sklearn.model_selection import train_test_split
 
 from autokeras.loss_function import classification_loss, regression_loss
-from autokeras.supervised import Supervised
+from autokeras.supervised import Supervised, PortableClass
 from autokeras.constant import Constant
 from autokeras.metric import Accuracy, MSE
 from autokeras.preprocessor import OneHotEncoder, DataTransformer
@@ -323,6 +323,16 @@ def get_best_model_id(self):
         """ Return an integer indicating the id of the best model."""
         return self.load_searcher().get_best_model_id()
 
+    def export_keras_model(self, model_file_name):
+        """ Exports the best Keras model to the given filename. """
+        self.load_searcher().load_best_model().produce_keras_model().save(model_file_name)
+
+    def export_autokeras_model(self, model_file_name):
+        """ Creates and Exports the AutoKeras model to the given filename. """
+        portable_model = PortableImageSupervised(graph=self.load_searcher().load_best_model(), \
+                                                 y_encoder=self.y_encoder, data_transformer=self.data_transformer)
+        pickle_to_file(portable_model, model_file_name)
+
 
 class ImageClassifier(ImageSupervised):
     @property
@@ -365,3 +375,44 @@ def transform_y(self, y_train):
 
     def inverse_transform_y(self, output):
         return output.flatten()
+
+
+class PortableImageSupervised(PortableClass):
+    def __init__(self, graph, data_transformer, y_encoder):
+        """Initialize the instance.
+        Args:
+            graph: The graph form of the learned model
+        """
+        super().__init__(graph)
+        self.data_transformer = data_transformer
+        self.y_encoder = y_encoder
+
+    def predict(self, x_test):
+        """Return predict results for the testing data.
+
+        Args:
+            x_test: An instance of numpy.ndarray containing the testing data.
+
+        Returns:
+            A numpy.ndarray containing the results.
+        """
+        if Constant.LIMIT_MEMORY:
+            pass
+        test_loader = self.data_transformer.transform_test(x_test)
+        model = self.graph.produce_model()
+        model.eval()
+
+        outputs = []
+        with torch.no_grad():
+            for index, inputs in enumerate(test_loader):
+                outputs.append(model(inputs).numpy())
+        output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
+        return self.inverse_transform_y(output)
+
+    def inverse_transform_y(self, output):
+        return self.y_encoder.inverse_transform(output)
+
+    def evaluate(self, x_test, y_test):
+        """Return the accuracy score between predict value and `y_test`."""
+        y_predict = self.predict(x_test)
+        return accuracy_score(y_test, y_predict)

autokeras/search.py

@@ -109,24 +109,30 @@ def replace_model(self, graph, model_id):
 
     def add_model(self, metric_value, loss, graph, model_id):
         if self.verbose:
-            print('Saving model.')
+            print('\nSaving model.')
 
         pickle_to_file(graph, os.path.join(self.path, str(model_id) + '.h5'))
 
         # Update best_model text file
-
-        if self.verbose:
-            print('Model ID:', model_id)
-            print('Loss:', loss)
-            print('Metric Value:', metric_value)
-
         ret = {'model_id': model_id, 'loss': loss, 'metric_value': metric_value}
         self.history.append(ret)
         if model_id == self.get_best_model_id():
             file = open(os.path.join(self.path, 'best_model.txt'), 'w')
             file.write('best model: ' + str(model_id))
             file.close()
 
+        if self.verbose:
+            idx = ['model_id', 'loss', 'metric_value']
+            header = ['Model ID', 'Loss', 'Metric Value']
+            line = '|'.join(x.center(24) for x in header)
+            print('+' + '-' * len(line) + '+')
+            print('|' + line + '|')
+            for i, r in enumerate(self.history):
+                print('+' + '-' * len(line) + '+')
+                line = '|'.join(str(r[x]).center(24) for x in idx)
+                print('|' + line + '|')
+            print('+' + '-' * len(line) + '+')
+
         descriptor = graph.extract_descriptor()
         self.x_queue.append(descriptor)
         self.y_queue.append(metric_value)
@@ -135,7 +141,7 @@ def add_model(self, metric_value, loss, graph, model_id):
 
     def init_search(self):
         if self.verbose:
-            print('Initializing search.')
+            print('\nInitializing search.')
         graph = CnnGenerator(self.n_classes,
                              self.input_shape).generate(self.default_model_len,
                                                         self.default_model_width)
@@ -160,7 +166,10 @@ def search(self, train_data, test_data, timeout=60 * 60 * 24):
         # Start the new process for training.
         graph, father_id, model_id = self.training_queue.pop(0)
         if self.verbose:
-            print('Training model ', model_id)
+            print('\n')
+            print('╒' + '=' * 46 + '╕')
+            print('|' + 'Training model {}'.format(model_id).center(46) + '|')
+            print('╘' + '=' * 46 + '╛')
         mp.set_start_method('spawn', force=True)
         pool = mp.Pool(1)
         train_results = pool.map_async(train, [(graph, train_data, test_data, self.trainer_args,
@@ -182,8 +191,20 @@ def search(self, train_data, test_data, timeout=60 * 60 * 24):
                 self.descriptors.append(new_graph.extract_descriptor())
 
                 if self.verbose:
-                    print('Father ID: ', new_father_id)
-                    print(new_graph.operation_history)
+                    cell_size = [24, 49]
+                    header = ['Father Model ID', 'Added Operation']
+                    line = '|'.join(str(x).center(cell_size[i]) for i, x in enumerate(header))
+                    print('\n' + '+' + '-' * len(line) + '+')
+                    print('|' + line + '|')
+                    print('+' + '-' * len(line) + '+')
+                    for i in range(len(new_graph.operation_history)):
+                        if i == len(new_graph.operation_history)//2:
+                            r = [new_father_id, new_graph.operation_history[i]]
+                        else:
+                            r = [' ', new_graph.operation_history[i]]
+                        line = '|'.join(str(x).center(cell_size[i]) for i, x in enumerate(r))
+                        print('|' + line + '|')
+                    print('+' + '-' * len(line) + '+')
             remaining_time = timeout - (time.time() - start_time)
             if remaining_time > 0:
                 metric_value, loss, graph = train_results.get(timeout=remaining_time)[0]

autokeras/supervised.py

@@ -53,3 +53,31 @@ def predict(self, x_test):
     def evaluate(self, x_test, y_test):
         """Return the accuracy score between predict value and `y_test`."""
         pass
+
+
+class PortableClass(ABC):
+    def __init__(self, graph):
+        """Initialize the instance.
+
+        Args:
+            graph: The graph form of the learned model
+
+        """
+        self.graph = graph
+
+    @abstractmethod
+    def predict(self, x_test):
+        """Return predict results for the testing data.
+
+        Args:
+            x_test: An instance of numpy.ndarray containing the testing data.
+
+        Returns:
+            A numpy.ndarray containing the results.
+        """
+        pass
+
+    @abstractmethod
+    def evaluate(self, x_test, y_test):
+        """Return the accuracy score between predict value and `y_test`."""
+        pass

autokeras/utils.py

@@ -9,7 +9,7 @@
 import torch
 
 from autokeras.constant import Constant
-
+from tqdm.autonotebook import tqdm
 
 class NoImprovementError(Exception):
     def __init__(self, message):
@@ -110,26 +110,58 @@ def train_model(self,
         test_metric_value_list = []
         test_loss_list = []
         self.optimizer = torch.optim.Adam(self.model.parameters())
+
+        if self.verbose:
+            pbar = tqdm(total=max_iter_num,
+                        desc='    Model    ',
+                        file=sys.stdout,
+                        leave=False,
+                        ncols=75,
+                        position=1,
+                        unit=' epoch')
+
         for epoch in range(max_iter_num):
             self._train()
             test_loss, metric_value = self._test()
             test_metric_value_list.append(metric_value)
             test_loss_list.append(test_loss)
             if self.verbose:
-                print('Epoch {}: loss {}, metric_value {}'.format(epoch + 1, test_loss, metric_value))
+                pbar.update(1)
+                if epoch == 0:
+                    header = ['Epoch', 'Loss', 'Accuracy']
+                    line = '|'.join(x.center(24) for x in header)
+                    pbar.write('+' + '-' * len(line) + '+')
+                    pbar.write('|' + line + '|')
+                    pbar.write('+' + '-' * len(line) + '+')
+                r = [epoch + 1, test_loss, metric_value]
+                line = '|'.join(str(x).center(24) for x in r)
+                pbar.write('|' + line + '|')
+                pbar.write('+' + '-' * len(line) + '+')
             decreasing = self.early_stop.on_epoch_end(test_loss)
             if not decreasing:
                 if self.verbose:
-                    print('No loss decrease after {} epochs'.format(max_no_improvement_num))
+                    print('\nNo loss decrease after {} epochs.\n'.format(max_no_improvement_num))
                 break
+        if self.verbose:
+            pbar.close()
         return (sum(test_loss_list[-max_no_improvement_num:]) / max_no_improvement_num,
                 sum(test_metric_value_list[-max_no_improvement_num:]) / max_no_improvement_num)
 
     def _train(self):
         self.model.train()
         loader = self.train_loader
 
-        for batch_idx, (inputs, targets) in enumerate(deepcopy(loader)):
+        cp_loader = deepcopy(loader)
+        if self.verbose:
+            pbar = tqdm(total=len(cp_loader),
+                        desc='Current Epoch',
+                        file=sys.stdout,
+                        leave=False,
+                        ncols=75,
+                        position=0,
+                        unit=' batch')
+
+        for batch_idx, (inputs, targets) in enumerate(cp_loader):
             inputs, targets = inputs.to(self.device), targets.to(self.device)
             self.optimizer.zero_grad()
             outputs = self.model(inputs)
@@ -138,10 +170,9 @@ def _train(self):
             self.optimizer.step()
             if self.verbose:
                 if batch_idx % 10 == 0:
-                    print('.', end='')
-                    sys.stdout.flush()
+                    pbar.update(10)
         if self.verbose:
-            print()
+            pbar.close()
 
     def _test(self):
         self.model.eval()

requirements.txt

@@ -4,6 +4,7 @@ torchvision==0.2.1
 numpy==1.15.1
 scikit-learn==0.19.1
 keras==2.2.2
+tqdm==4.25.0
 tensorflow==1.10.1
 pytest
 pytest-cov

setup.py

@@ -5,12 +5,12 @@
     packages=['autokeras'],  # this must be the same as the name above
     install_requires=['torch==0.4.1', 'torchvision==0.2.1', 'numpy>=1.15.1', 'keras==2.2.2', 'scikit-learn==0.19.1',
                       'tensorflow>=1.10.1'],
-    version='0.2.7',
+    version='0.2.8',
     description='AutoML for deep learning',
     author='Haifeng Jin',
     author_email='[email protected]',
     url='http://autokeras.com',
-    download_url='https://github.com/jhfjhfj1/autokeras/archive/0.2.7.tar.gz',
+    download_url='https://github.com/jhfjhfj1/autokeras/archive/0.2.8.tar.gz',
     keywords=['automl'],  # arbitrary keywords
     classifiers=[]
 )

tests/test_image_supervised.py

@@ -1,6 +1,5 @@
 from unittest.mock import patch
 
-
 import pytest
 
 from autokeras.image_supervised import *
@@ -199,3 +198,40 @@ def test_fit_predict_regression(_):
     results = clf.predict(train_x)
     assert len(results) == len(train_x)
     clean_dir(path)
+
+
+@patch('torch.multiprocessing.Pool', new=MockProcess)
+@patch('autokeras.search.ModelTrainer.train_model', side_effect=mock_train)
+def test_export_keras_model(_):
+    Constant.MAX_ITER_NUM = 1
+    Constant.MAX_MODEL_NUM = 1
+    Constant.SEARCH_MAX_ITER = 1
+    Constant.T_MIN = 0.8
+    train_x = np.random.rand(100, 25, 25, 1)
+    train_y = np.random.randint(0, 5, 100)
+    test_x = np.random.rand(100, 25, 25, 1)
+    path = 'tests/resources/temp'
+    clean_dir(path)
+    clf = ImageClassifier(path=path, verbose=False, resume=False)
+    clf.n_epochs = 100
+    clf.fit(train_x, train_y)
+    score = clf.evaluate(train_x, train_y)
+    assert score <= 1.0
+
+    model_file_name = os.path.join(path, 'test_keras_model.h5')
+    clf.export_keras_model(model_file_name)
+    from keras.models import load_model
+    model = load_model(model_file_name)
+    results = model.predict(test_x)
+    assert len(results) == len(test_x)
+    del model, results, model_file_name
+
+    model_file_name = os.path.join(path, 'test_autokeras_model.pkl')
+    clf.export_autokeras_model(model_file_name)
+    from autokeras.utils import pickle_from_file
+    model = pickle_from_file(model_file_name)
+    results = model.predict(test_x)
+    assert len(results) == len(test_x)
+    score = model.evaluate(train_x, train_y)
+    assert score <= 1.0
+    clean_dir(path)