Ravin Kohli: [FIX] Documentation and docker workflow file (#449)

development/_modules/autoPyTorch/api/time_series_forecasting.html

@@ -142,6 +142,7 @@ <h1>Source code for autoPyTorch.api.time_series_forecasting</h1><div class="high
 <div class="viewcode-block" id="TimeSeriesForecastingTask"><a class="viewcode-back" href="../../../api.html#autoPyTorch.api.time_series_forecasting.TimeSeriesForecastingTask">[docs]</a><span class="k">class</span> <span class="nc">TimeSeriesForecastingTask</span><span class="p">(</span><span class="n">BaseTask</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Time Series Forecasting API to the pipelines.</span>
+
 <span class="sd">    Args:</span>
 <span class="sd">        seed (int):</span>
 <span class="sd">            seed to be used for reproducibility.</span>
@@ -367,6 +368,7 @@ <h1>Source code for autoPyTorch.api.time_series_forecasting</h1><div class="high
             <span class="n">Y</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span>
             <span class="n">X_test</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span>
             <span class="n">Y_test</span><span class="o">=</span><span class="n">y_test</span><span class="p">,</span>
+            <span class="n">dataset_name</span><span class="o">=</span><span class="n">dataset_name</span><span class="p">,</span>
             <span class="n">freq</span><span class="o">=</span><span class="n">freq</span><span class="p">,</span>
             <span class="n">start_times</span><span class="o">=</span><span class="n">start_times</span><span class="p">,</span>
             <span class="n">series_idx</span><span class="o">=</span><span class="n">series_idx</span><span class="p">,</span>
@@ -513,7 +515,25 @@ <h1>Source code for autoPyTorch.api.time_series_forecasting</h1><div class="high
 <span class="sd">                for each pipeline and results will be available via cv_results</span>
 <span class="sd">            precision (int), (default=32): Numeric precision used when loading</span>
 <span class="sd">                ensemble data. Can be either &#39;16&#39;, &#39;32&#39; or &#39;64&#39;.</span>
-<span class="sd">            disable_file_output (Union[bool, List]):</span>
+<span class="sd">            disable_file_output (Optional[List[Union[str, DisableFileOutputParameters]]]):</span>
+<span class="sd">                Used as a list to pass more fine-grained</span>
+<span class="sd">                information on what to save. Must be a member of `DisableFileOutputParameters`.</span>
+<span class="sd">                Allowed elements in the list are:</span>
+
+<span class="sd">                + `y_optimization`:</span>
+<span class="sd">                    do not save the predictions for the optimization set,</span>
+<span class="sd">                    which would later on be used to build an ensemble. Note that SMAC</span>
+<span class="sd">                    optimizes a metric evaluated on the optimization set.</span>
+<span class="sd">                + `pipeline`:</span>
+<span class="sd">                    do not save any individual pipeline files</span>
+<span class="sd">                + `pipelines`:</span>
+<span class="sd">                    In case of cross validation, disables saving the joint model of the</span>
+<span class="sd">                    pipelines fit on each fold.</span>
+<span class="sd">                + `y_test`:</span>
+<span class="sd">                    do not save the predictions for the test set.</span>
+<span class="sd">                + `all`:</span>
+<span class="sd">                    do not save any of the above.</span>
+<span class="sd">                For more information check `autoPyTorch.evaluation.utils.DisableFileOutputParameters`.</span>
 <span class="sd">            load_models (bool), (default=True): Whether to load the</span>
 <span class="sd">                models after fitting AutoPyTorch.</span>
 <span class="sd">            suggested_init_models: Optional[List[str]]</span>

development/_sources/examples/20_basics/example_image_classification.rst.txt

@@ -35,22 +35,22 @@ Image Classification
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz
-      0%|          | 0/26421880 [00:00<?, ?it/s]      0%|          | 65536/26421880 [00:00<01:10, 374644.14it/s]      1%|          | 163840/26421880 [00:00<00:41, 631006.64it/s]      2%|1         | 425984/26421880 [00:00<00:22, 1140468.14it/s]      5%|4         | 1310720/26421880 [00:00<00:07, 3470288.06it/s]     13%|#3        | 3473408/26421880 [00:00<00:03, 7608945.79it/s]     31%|###1      | 8290304/26421880 [00:00<00:00, 18277293.49it/s]     47%|####7     | 12419072/26421880 [00:00<00:00, 21138658.10it/s]     66%|######5   | 17399808/26421880 [00:01<00:00, 28063442.88it/s]     82%|########1 | 21561344/26421880 [00:01<00:00, 27247015.88it/s]    100%|##########| 26421880/26421880 [00:01<00:00, 19940201.20it/s]
+      0%|          | 0/26421880 [00:00<?, ?it/s]      0%|          | 32768/26421880 [00:00<01:52, 235602.90it/s]      0%|          | 65536/26421880 [00:00<01:52, 234351.38it/s]      0%|          | 131072/26421880 [00:00<01:17, 340594.04it/s]      1%|          | 229376/26421880 [00:00<00:54, 483036.36it/s]      2%|1         | 458752/26421880 [00:00<00:28, 898436.87it/s]      3%|3         | 917504/26421880 [00:00<00:14, 1705017.51it/s]      7%|7         | 1867776/26421880 [00:00<00:07, 3360695.16it/s]     14%|#4        | 3702784/26421880 [00:01<00:03, 6451569.09it/s]     26%|##5       | 6750208/26421880 [00:01<00:01, 11225603.32it/s]     37%|###7      | 9863168/26421880 [00:01<00:01, 14590423.72it/s]     49%|####9     | 13008896/26421880 [00:01<00:00, 16972192.00it/s]     61%|######    | 16056320/26421880 [00:01<00:00, 18420560.72it/s]     72%|#######1  | 19005440/26421880 [00:01<00:00, 19207823.64it/s]     84%|########3 | 22085632/26421880 [00:01<00:00, 20031175.65it/s]     95%|#########5| 25231360/26421880 [00:02<00:00, 20738665.14it/s]    100%|##########| 26421880/26421880 [00:02<00:00, 12457424.40it/s]
     Extracting ../datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz
-      0%|          | 0/29515 [00:00<?, ?it/s]    100%|##########| 29515/29515 [00:00<00:00, 333885.02it/s]
+      0%|          | 0/29515 [00:00<?, ?it/s]    100%|##########| 29515/29515 [00:00<00:00, 205829.78it/s]    100%|##########| 29515/29515 [00:00<00:00, 205327.60it/s]
     Extracting ../datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
-      0%|          | 0/4422102 [00:00<?, ?it/s]      1%|1         | 65536/4422102 [00:00<00:11, 376169.94it/s]      5%|5         | 229376/4422102 [00:00<00:05, 709739.76it/s]     20%|##        | 884736/4422102 [00:00<00:01, 2110100.56it/s]     72%|#######1  | 3178496/4422102 [00:00<00:00, 7798515.95it/s]    100%|##########| 4422102/4422102 [00:00<00:00, 6345915.96it/s]
+      0%|          | 0/4422102 [00:00<?, ?it/s]      1%|          | 32768/4422102 [00:00<00:18, 233477.25it/s]      1%|1         | 65536/4422102 [00:00<00:18, 232894.04it/s]      3%|2         | 131072/4422102 [00:00<00:12, 339241.27it/s]      5%|5         | 229376/4422102 [00:00<00:08, 480773.14it/s]     10%|9         | 425984/4422102 [00:00<00:04, 811302.76it/s]     20%|##        | 884736/4422102 [00:00<00:02, 1643749.67it/s]     39%|###9      | 1736704/4422102 [00:00<00:00, 3085506.46it/s]     79%|#######8  | 3473408/4422102 [00:01<00:00, 6031981.20it/s]    100%|##########| 4422102/4422102 [00:01<00:00, 3921463.52it/s]
     Extracting ../datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
     Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
-      0%|          | 0/5148 [00:00<?, ?it/s]    100%|##########| 5148/5148 [00:00<00:00, 42254945.19it/s]
+      0%|          | 0/5148 [00:00<?, ?it/s]    100%|##########| 5148/5148 [00:00<00:00, 37947762.73it/s]
     Extracting ../datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ../datasets/FashionMNIST/raw
 
     Pipeline CS:
@@ -85,25 +85,23 @@ Image Classification
     Pipeline Random Config:
      ________________________________________ 
     Configuration(values={
-      'image_augmenter:GaussianBlur:sigma_min': 0.5986757047003219,
-      'image_augmenter:GaussianBlur:sigma_offset': 1.6405649910315916,
+      'image_augmenter:GaussianBlur:sigma_min': 1.800750044920493,
+      'image_augmenter:GaussianBlur:sigma_offset': 0.0008507475449754942,
       'image_augmenter:GaussianBlur:use_augmenter': True,
-      'image_augmenter:GaussianNoise:sigma_offset': 2.8469479270927325,
-      'image_augmenter:GaussianNoise:use_augmenter': True,
+      'image_augmenter:GaussianNoise:use_augmenter': False,
       'image_augmenter:RandomAffine:use_augmenter': False,
-      'image_augmenter:RandomCutout:p': 0.9357778536297661,
-      'image_augmenter:RandomCutout:use_augmenter': True,
-      'image_augmenter:Resize:use_augmenter': True,
-      'image_augmenter:ZeroPadAndCrop:percent': 0.013520874232656022,
-      'normalizer:__choice__': 'NoNormalizer',
+      'image_augmenter:RandomCutout:use_augmenter': False,
+      'image_augmenter:Resize:use_augmenter': False,
+      'image_augmenter:ZeroPadAndCrop:percent': 0.3938396231176561,
+      'normalizer:__choice__': 'ImageNormalizer',
     })
 
     Fitting the pipeline...
     ________________________________________
             ImageClassificationPipeline
     ________________________________________
     0-) normalizer: 
-            NoNormalizer
+            ImageNormalizer
 
     1-) preprocessing: 
             EarlyPreprocessing
@@ -175,7 +173,7 @@ Image Classification
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  4.898 seconds)
+   **Total running time of the script:** ( 0 minutes  7.321 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_image_classification.py:

development/_sources/examples/20_basics/example_tabular_classification.rst.txt

@@ -134,7 +134,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f755cba6820>
+    <autoPyTorch.api.tabular_classification.TabularClassificationTask object at 0x7f2407c75af0>
 
 
 
@@ -190,7 +190,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  18.288 seconds)
+   **Total running time of the script:** ( 5 minutes  24.577 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_classification.py:

development/_sources/examples/20_basics/example_tabular_regression.rst.txt

@@ -125,7 +125,7 @@ Search for an ensemble of machine learning algorithms
  .. code-block:: none
 
 
-    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f75fa043ee0>
+    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f248d0d5d90>
 
 
 
@@ -167,7 +167,7 @@ Print the final ensemble performance
     |  2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.1  |
     |  3 | None                                                                                             | LGBMLearner                                                     |     0.04 |
     autoPyTorch results:
-            Dataset name: 9e644a32-01dc-11ed-881e-fb671a47a91f
+            Dataset name: 59922def-0351-11ed-8824-d5cce4119db9
             Optimisation Metric: r2
             Best validation score: 0.8670098636440993
             Number of target algorithm runs: 24
@@ -183,7 +183,7 @@ Print the final ensemble performance
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  35.079 seconds)
+   **Total running time of the script:** ( 5 minutes  36.793 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

development/_sources/examples/20_basics/example_time_series_forecasting.rst.txt

@@ -150,7 +150,7 @@ Search for an ensemble of machine learning algorithms
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  3.424 seconds)
+   **Total running time of the script:** ( 1 minutes  3.199 seconds)
 
 
 .. _sphx_glr_download_examples_20_basics_example_time_series_forecasting.py:

development/_sources/examples/20_basics/sg_execution_times.rst.txt

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**12:01.689** total execution time for **examples_20_basics** files:
+**12:11.890** total execution time for **examples_20_basics** files:
 
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)           | 05:35.079 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_regression.py` (``example_tabular_regression.py``)           | 05:36.793 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``)   | 05:18.288 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_tabular_classification.py` (``example_tabular_classification.py``)   | 05:24.577 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 01:03.424 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_time_series_forecasting.py` (``example_time_series_forecasting.py``) | 01:03.199 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)       | 00:04.898 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basics_example_image_classification.py` (``example_image_classification.py``)       | 00:07.321 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------------+-----------+--------+