Merge pull request #131 from jubatus/add-tensorboard-example

add TensorBoard visualization example

README.rst

@@ -104,6 +104,8 @@ See the `example <https://github.com/jubatus/jubakit/tree/master/example>`_ dire
 +-----------------------------------+-----------------------------------------------+-----------------------+
 | classifier_sklearn_grid_search.py | Grid Search example using scikit-learn wrapper| ✓                     |
 +-----------------------------------+-----------------------------------------------+-----------------------+
+| classifier_tensorboard.py         | Visualize a training process using TensorBoard| ✓                     |
++-----------------------------------+-----------------------------------------------+-----------------------+
 | regression_boston.py              | Regression with toy dataset (boston)          | ✓                     |
 +-----------------------------------+-----------------------------------------------+-----------------------+
 | regression_csv.py                 | Regression with CSV file                      |                       |

example/classifier_tensorboard.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+"""
+Visualize training process with TensorBoard
+===========================================
+
+In this example, we show the training process of Jubatus with TensorBoard.
+
+TensorBoard syntax is little complicated and in this example we use tensorboardX library.
+tensorboardX is a simple wrapper of TensorBoard that write events with simple function call.
+
+[How to Use]
+
+1. Install tensorboard.
+
+    ```
+    $ pip install tensorboardX
+    ```
+
+2. Run this example.
+
+3. Check the training process using tensorboard.
+
+    ```
+    $ tensorboard --logdir runs/***
+    ```
+
+4. Enjoy!
+
+"""
+
+from sklearn.datasets import load_digits
+from sklearn.metrics import (
+    accuracy_score, f1_score, precision_score, recall_score, log_loss)
+
+from tensorboardX import SummaryWriter
+
+import jubakit
+from jubakit.classifier import Classifier, Dataset, Config
+from jubakit.model import JubaDump
+
+# Load the digits dataset.
+digits = load_digits()
+
+# Create a dataset.
+dataset = Dataset.from_array(digits.data, digits.target)
+n_samples = len(dataset)
+n_train_samples = int(n_samples * 0.7)
+train_ds = dataset[:n_train_samples]
+test_ds = dataset[n_train_samples:]
+
+# Create a classifier.
+config = Config(method='AROW',
+                parameter={'regularization_weight': 0.1})
+classifier = Classifier.run(config)
+
+model_name = 'classifier_digits'
+model_path = '/tmp/{}_{}_classifier_{}.jubatus'.format(
+    classifier._host, classifier._port, model_name)
+
+# show the feature weights of the target label.
+target_label = 4
+
+# Initialize summary writer.
+writer = SummaryWriter()
+
+# train and test the classifier.
+epochs = 100
+for epoch in range(epochs):
+    # train
+    for _ in classifier.train(train_ds): pass
+
+    # test
+    y_true, y_pred = [], []
+    for (_, label, result) in classifier.classify(test_ds):
+        y_true.append(label)
+        y_pred.append(result[0][0])
+
+    # save model to check the feature weights
+    classifier.save(model_name)
+
+    model = JubaDump.dump_file(model_path)
+    weights = model['storage']['storage']['weight']
+    for feature, label_values in weights.items():
+        for label, value in label_values.items():
+            if str(label) != str(target_label):
+                continue
+            writer.add_scalar('weights/{}'.format(feature), value['v1'], epoch)
+
+    # write scores to tensorboardX summary writer.
+    acc = accuracy_score(y_true, y_pred)
+    prec = precision_score(y_true, y_pred, average='macro')
+    recall = recall_score(y_true, y_pred, average='macro')
+    f1 = f1_score(y_true, y_pred, average='macro')
+    writer.add_scalar('metrics/accuracy', acc, epoch)
+    writer.add_scalar('metrics/precision', prec, epoch)
+    writer.add_scalar('metrics/recall', recall, epoch)
+    writer.add_scalar('metrics/f1_score', f1, epoch)
+
+writer.close()
+classifier.stop()

tools/run_examples.py

@@ -21,7 +21,8 @@
   from urllib.request import urlopen
 
 # Accept exceptions in following examples.
-BLACK_LIST = ['classifier_twitter.py', 'classifier_hyperopt_tuning.py']
+BLACK_LIST = ['classifier_twitter.py', 'classifier_hyperopt_tuning.py',
+              'classifier_tensorboard.py']
 
 def download_bzip2(path, url):
   if os.path.exists(path): return