[FIX] Documentation and docker workflow file

.github/workflows/docker-publish.yml

@@ -11,7 +11,7 @@ on:
     branches:
       - master
       - development
-      - add_docker-publish
+      - fixes_docker
   workflow_dispatch:
 
 jobs:
@@ -24,6 +24,8 @@ jobs:
     steps:
       - name: Check out the repo
         uses: actions/checkout@v2
+        with:
+        submodules: recursive
 
       - name: Extract branch name
         shell: bash

autoPyTorch/api/time_series_forecasting.py

@@ -28,6 +28,7 @@
 class TimeSeriesForecastingTask(BaseTask):
     """
     Time Series Forecasting API to the pipelines.
+
     Args:
         seed (int):
             seed to be used for reproducibility.
@@ -253,6 +254,7 @@ def _get_dataset_input_validator(
             Y=y_train,
             X_test=X_test,
             Y_test=y_test,
+            dataset_name=dataset_name,
             freq=freq,
             start_times=start_times,
             series_idx=series_idx,
@@ -399,7 +401,25 @@ def search(
                 for each pipeline and results will be available via cv_results
             precision (int), (default=32): Numeric precision used when loading
                 ensemble data. Can be either '16', '32' or '64'.
-            disable_file_output (Union[bool, List]):
+            disable_file_output (Optional[List[Union[str, DisableFileOutputParameters]]]):
+                Used as a list to pass more fine-grained
+                information on what to save. Must be a member of `DisableFileOutputParameters`.
+                Allowed elements in the list are:
+
+                + `y_optimization`:
+                    do not save the predictions for the optimization set,
+                    which would later on be used to build an ensemble. Note that SMAC
+                    optimizes a metric evaluated on the optimization set.
+                + `pipeline`:
+                    do not save any individual pipeline files
+                + `pipelines`:
+                    In case of cross validation, disables saving the joint model of the
+                    pipelines fit on each fold.
+                + `y_test`:
+                    do not save the predictions for the test set.
+                + `all`:
+                    do not save any of the above.
+                For more information check `autoPyTorch.evaluation.utils.DisableFileOutputParameters`.
             load_models (bool), (default=True): Whether to load the
                 models after fitting AutoPyTorch.
             suggested_init_models: Optional[List[str]]

docs/installation.rst

@@ -12,7 +12,7 @@ System requirements
 Auto-PyTorch has the following system requirements:
 
 * Linux operating system (for example Ubuntu) `(get Linux here) <https://www.wikihow.com/Install-Linux>`_,
-* Python (>=3.6) `(get Python here) <https://www.python.org/downloads/>`_.
+* Python (>=3.7) `(get Python here) <https://www.python.org/downloads/>`_.
 * C++ compiler (with C++11 supports) `(get GCC here) <https://www.tutorialspoint.com/How-to-Install-Cplusplus-Compiler-on-Linux>`_ and
 * SWIG (version 3.0.* is required; >=4.0.0 is not supported) `(get SWIG here) <http://www.swig.org/survey.html>`_.
 

docs/manual.rst

@@ -35,10 +35,11 @@ The target validator applies a label encoder on the target column.
 
 For **time series forecasting tasks**, besides the functions described above, time series forecasting validators will also
 check the information specify for time series forecasting tasks: it checks
-    * The index of the series that each data point belongs to
-    * if the dataset is uni-variant (only targets information is contained in the datasets)
-    * the sample frequency of the datasets
-    * the static features in the dataset, i.e., features that contain only one value within each series
+
+* The index of the series that each data point belongs to
+* If the dataset is uni-variant (only targets information is contained in the datasets)
+* The sample frequency of the datasets
+* The static features in the dataset, i.e., features that contain only one value within each series
 
 Time Series forecasting validator then transforms the features and targets into a `pd.DataFrame <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
 whose index is applied to identify the series that the time step belongs to.