Merge pull request #339 from autonomio/daily-dev

v.0.6 to Dev

.travis.yml

@@ -9,7 +9,6 @@ notifications:
     on_success: never
     on_failure: always
 python:
- - '2.7'
  - '3.5'
  - '3.6'
 install:

README.md

@@ -50,7 +50,9 @@ Talos radically transforms ordinary Keras workflows without taking away any of K
 
 Talos is made for data scientists and data engineers that want to remain in **complete control of their Keras models**, but are tired of mindless parameter hopping and confusing optimization solutions that add complexity instead of reducing it. Within minutes, without learning any new syntax, Talos allows you to configure, perform, and evaluate hyperparameter optimization experiments that yield state-of-the-art results across a wide range of prediction tasks. Talos provides the **simplest and yet most powerful** available method for hyperparameter optimization with Keras.
 
-### Key Features
+<hr>
+
+### :wrench: Key Features
 
 Based on what no doubt constitutes a "biased" review (being our own) of more than ~30 hyperparameter tuning and optimization solutions, Talos comes on top in terms of intuitive, easy-to-learn, highly permissive access to critical hyperparameter optimization capabilities. Key features include:
 
@@ -68,7 +70,9 @@ Based on what no doubt constitutes a "biased" review (being our own) of more tha
 
 Talos works on **Linux, Mac OSX**, and **Windows** systems and can be operated cpu, gpu, and multi-gpu systems.
 
-### Examples
+<hr>
+
+### 📈 Examples
 
 Get the below code [here](https://gist.github.com/mikkokotila/4c0d6298ff0a22dc561fb387a1b4b0bb). More examples further below.
 
@@ -88,7 +92,9 @@ For more information on how Talos can help with your Keras workflow, visit the [
 
 You may also want to check out a visualization of the [Talos Hyperparameter Tuning workflow](https://github.com/autonomio/talos/wiki/Workflow).
 
-### Install
+<hr>
+
+### 💾 Install
 
 Stable version:
 
@@ -98,18 +104,34 @@ Daily development version:
 
 #### `pip install git+https://github.com/autonomio/talos.git@daily-dev`
 
-### Support
+<hr>
+
+### 💬 How to get Support
 
-Check out [common errors](https://github.com/autonomio/talos/wiki/Troubleshooting) in the Wiki.
+| I want to...                     | Go to...                                                  |
+| -------------------------------- | ---------------------------------------------------------- |
+| **...troubleshoot**           | [Docs] · [Wiki] · [GitHub Issue Tracker]                   |
+| **...report a bug**           | [GitHub Issue Tracker]                                     |
+| **...suggest a new feature**  | [GitHub Issue Tracker]                                     |
+| **...get support**            | [Stack Overflow] · [Spectrum Chat]                         |
+| **...have a discussion**      | [Spectrum Chat]                                            |
 
+[github issue tracker]: https://github.com/automio/talos/issues
+[docs]: https://autonomio.github.io/docs_talos
+[wiki]: https://github.com/autonomio/talos/wiki
+[stack overflow]: https://stackoverflow.com/questions/tagged/talos
+[spectrum chat]: https://spectrum.chat/talos
 
-Check the [Docs](https://autonomio.github.io/docs_talos) which is generally keeping up with Master (and pip package).
+<hr>
 
-If you want ask a **"how can I use Talos to..."** question, the right place is [StackOverflow](https://stackoverflow.com/questions/ask).
+### 📢 Citations
 
-If you found a bug or want to suggest a feature, check the [issues](https://github.com/autonomio/talos/issues) or [create](https://github.com/autonomio/talos/issues/new/choose) a new issue.
+If you use Talos for published work, please cite:
 
+`Autonomio Talos [Computer software]. (2019). Retrieved from http://github.com/autonomio/talos.`
+
+<hr>
 
-### License
+### 📃 License
 
 [MIT License](https://github.com/autonomio/talos/blob/master/LICENSE)

examples/models.py

@@ -1,7 +1,7 @@
 from keras.models import Sequential
 from keras.layers import Dropout, Dense
 from ..model.normalizers import lr_normalizer
-from ..model.layers import hidden_layers
+from ..model.hidden_layers import hidden_layers
 from ..metrics.keras_metrics import fmeasure
 
 

requirements.txt

@@ -1,3 +1,4 @@
+wrangle
 pandas
 numpy
 keras
@@ -6,5 +7,4 @@ tqdm
 sklearn
 chances
 kerasplotlib
-wrangle
 requests

setup.py

@@ -16,23 +16,23 @@
 URL = 'http://autonom.io'
 LICENSE = 'MIT'
 DOWNLOAD_URL = 'https://github.com/autonomio/talos/'
-VERSION = '0.5.0'
+VERSION = '0.6.0'
 
 try:
     from setuptools import setup
     _has_setuptools = True
 except ImportError:
     from distutils.core import setup
 
-install_requires = ['numpy',
+install_requires = ['wrangle',
+                    'numpy',
                     'pandas',
                     'keras',
                     'astetik',
                     'sklearn',
                     'tqdm',
                     'chances',
                     'kerasplotlib',
-                    'wrangle',
                     'requests']
 
 
@@ -52,13 +52,14 @@
           install_requires=install_requires,
           packages=['talos',
                     'talos.scan',
-                    'talos.examples',
+                    'talos.templates',
                     'talos.utils',
                     'talos.model',
                     'talos.parameters',
                     'talos.reducers',
                     'talos.metrics',
-                    'talos.commands'],
+                    'talos.commands',
+                    'talos.logging'],
 
           classifiers=['Intended Audience :: Science/Research',
                        'Programming Language :: Python :: 2.7',

talos/__init__.py

@@ -9,18 +9,10 @@
 from .commands.params import Params
 from .commands.kerasmodel import KerasModel
 from . import utils
-from . import examples as templates
+from . import templates
 
 # the purpose of everything below is to keep the namespace completely clean
 
-del_from_utils = ['best_model', 'connection_check', 'detector',
-                  'exceptions', 'last_neuron', 'load_model', 'validation_split',
-                  'pred_class', 'results', 'string_cols_to_numeric']
-
-for key in del_from_utils:
-    if key.startswith('__') is False:
-        delattr(utils, key)
-
 template_sub = [templates.datasets,
                 templates.models,
                 templates.params,
@@ -35,7 +27,7 @@
             if key not in keep_from_templates:
                 delattr(sub, key)
 
-del commands, parameters, scan, reducers, model, metrics, key, del_from_utils
-del examples, sub, keep_from_templates, template_sub
+del commands, scan, model, metrics, key
+del sub, keep_from_templates, template_sub
 
-__version__ = "0.5.0"
+__version__ = "0.6.0"

talos/commands/autom8.py

@@ -1,6 +1,3 @@
-from ..scan.Scan import Scan
-
-
 def Autom8(scan_object,
            x_val,
            y_val,

talos/commands/deploy.py

@@ -1,11 +1,3 @@
-import os
-import pandas as pd
-import shutil
-import numpy as np
-
-from ..utils.best_model import best_model, activate_model
-
-
 class Deploy:
 
     '''Functionality for deploying a model to a filename'''
@@ -32,13 +24,17 @@ def __init__(self, scan_object, model_name, metric='val_acc', asc=False):
 
         '''
 
+        import os
+
         self.scan_object = scan_object
         os.mkdir(model_name)
         self.path = model_name + '/' + model_name
         self.model_name = model_name
         self.metric = metric
         self.asc = asc
         self.data = scan_object.data
+
+        from ..utils.best_model import best_model, activate_model
         self.best_model = best_model(scan_object, metric, asc)
         self.model = activate_model(scan_object, self.best_model)
 
@@ -71,8 +67,16 @@ def save_details(self):
 
     def save_data(self):
 
-        x = pd.DataFrame(self.scan_object.x[:100])
-        y = pd.DataFrame(self.scan_object.y[:100])
+        import pandas as pd
+
+        try:
+            x = pd.DataFrame(self.scan_object.x[:100])
+            y = pd.DataFrame(self.scan_object.y[:100])
+
+        except ValueError:
+            t.x = np.zeros(500)
+            t.y = np.zeros(500)
+            print("data is not 2d, dummy data written instead.")
 
         x.to_csv(self.path + '_x.csv', header=None, index=None)
         y.to_csv(self.path + '_y.csv', header=None, index=None)
@@ -83,6 +87,8 @@ def save_results(self):
 
     def save_params(self):
 
+        import numpy as np
+
         np.save(self.path + '_params', self.scan_object.params)
 
     def save_readme(self):
@@ -95,5 +101,7 @@ def save_readme(self):
 
     def package(self):
 
+        import shutil
+
         shutil.make_archive(self.model_name, 'zip', self.model_name)
         shutil.rmtree(self.model_name)

talos/commands/evaluate.py

@@ -1,10 +1,3 @@
-from sklearn.metrics import mean_absolute_error, f1_score
-from numpy import mean, std
-
-from ..utils.validation_split import kfold
-from ..utils.best_model import best_model, activate_model
-
-
 class Evaluate:
 
     '''Class for evaluating models based on the Scan() object'''
@@ -58,12 +51,18 @@ def evaluate(self, x, y,
 
         '''
 
+        import numpy as np
+        import sklearn as sk
+
         out = []
         if model_id is None:
+            from ..utils.best_model import best_model
             model_id = best_model(self.scan_object, metric, asc)
 
+        from ..utils.best_model import activate_model
         model = activate_model(self.scan_object, model_id)
 
+        from ..utils.validation_split import kfold
         kx, ky = kfold(x, y, folds, shuffle)
 
         for i in range(folds):
@@ -72,25 +71,25 @@ def evaluate(self, x, y,
 
             if mode == 'binary':
                 y_pred = y_pred >= .5
-                scores = f1_score(y_pred, ky[i], average='binary')
+                scores = sk.metrics.f1_score(y_pred, ky[i], average='binary')
 
             elif mode == 'multi_class':
                 y_pred = y_pred.argmax(axis=-1)
-                scores = f1_score(y_pred, ky[i], average='macro')
+                scores = sk.metrics.f1_score(y_pred, ky[i], average='macro')
 
             if mode == 'multi_label':
                 y_pred = model.predict(kx[i]).argmax(axis=1)
-                scores = f1_score(y_pred,
-                                  ky[i].argmax(axis=1),
-                                  average='macro')
+                scores = sk.metrics.f1_score(y_pred,
+                                             ky[i].argmax(axis=1),
+                                             average='macro')
 
             elif mode == 'regression':
                 y_pred = model.predict(kx[i])
-                scores = mean_absolute_error(y_pred, ky[i])
+                scores = sk.metrics.mean_absolute_error(y_pred, ky[i])
 
             out.append(scores)
 
         if print_out is True:
-            print("mean : %.2f \n std : %.2f" % (mean(out), std(out)))
+            print("mean : %.2f \n std : %.2f" % (np.mean(out), np.std(out)))
 
         return out