Updated README and fixed deprecated numpy dtype

README.md

@@ -76,10 +76,10 @@ cd spk_workdir
 Then, the training of a SchNet model with default settings for QM9 can be started by:
 
 ```
-spktrain experiment=qm9_energy
+spktrain experiment=qm9_atomwise
 ```
 
-The script prints the defaults for the experiment config `qm9_energy`.
+The script prints the defaults for the experiment config `qm9_atomwise`.
 The dataset will be downloaded automatically to `spk_workdir/data`, if it does not exist yet.
 Then, the training will be started.
 
@@ -96,15 +96,15 @@ that can be changed.
 Nested parameters can be changed as follows:
 
 ```
-spktrain experiment=qm9_energy data_dir=<path> data.batch_size=64
+spktrain experiment=qm9_atomwise data_dir=<path> data.batch_size=64
 ```
 
 Hydra organizes parameters in config groups which allows hierarchical configurations consisting of multiple
 yaml files. This allows to easily change the whole dataset, model or representation.
 For instance, changing from the default SchNet representation to PaiNN, use:
 
 ```
-spktrain experiment=qm9_energy data_dir=<path> model/representation=painn
+spktrain experiment=qm9_atomwise data_dir=<path> model/representation=painn
 ```
 
 It is a bit confusing at first when to use "." or "/". The slash is used, if you are loading a preconfigured config
@@ -131,7 +131,7 @@ corresponds to the following part of the config:
 If you would want to additionally change some value of this group, you could use:
 
 ```
-spktrain experiment=qm9_energy data_dir=<path> model/representation=painn model.representation.n_interactions=5
+spktrain experiment=qm9_atomwise data_dir=<path> model/representation=painn model.representation.n_interactions=5
 ```
 
 For more details on config groups, have a look at the

docs/getstarted.rst

@@ -66,9 +66,9 @@ First, create a working directory, where all data and runs will be stored::
 
 Then, the training of a SchNet model with default settings for QM9 can be started by::
 
-   $ spktrain experiment=qm9_energy
+   $ spktrain experiment=qm9_atomwise
 
-The script prints the defaults for the experiment config ``qm9_energy``.
+The script prints the defaults for the experiment config ``qm9_atomwise``.
 The dataset will be downloaded automatically to ``spk_workdir/data``, if it does not exist yet.
 Then, the training will be started.
 
@@ -82,13 +82,13 @@ If you call ``spktrain experiment=qm9 --help``, you can see the full config with
 that can be changed.
 Nested parameters can be changed as follows::
 
-   $ spktrain experiment=qm9_energy data_dir=<path> data.batch_size=64
+   $ spktrain experiment=qm9_atomwise data_dir=<path> data.batch_size=64
 
 Hydra organizes parameters in config groups which allows hierarchical configurations consisting of multiple
 yaml files. This allows to easily change the whole dataset, model or representation.
 For instance, changing from the default SchNet representation to PaiNN, use::
 
-   $ spktrain experiment=qm9_energy data_dir=<path> model/representation=painn
+   $ spktrain experiment=qm9_atomwise data_dir=<path> model/representation=painn
 
 It is a bit confusing at first when to use "." or "/". The slash is used, if you are loading a preconfigured config
 group, while the dot is used changing individual values. For example, the config group "model/representation"
@@ -111,7 +111,7 @@ corresponds to the following part of the config: ::
 
 If you would want to additionally change some value of this group, you could use: ::
 
-    $ spktrain experiment=qm9_energy data_dir=<path> model/representation=painn model.representation.n_interactions=5
+    $ spktrain experiment=qm9_atomwise data_dir=<path> model/representation=painn model.representation.n_interactions=5
 
 For more details on config groups, have a look at the
 `Hydra docs <https://hydra.cc/docs/next/tutorials/basic/your_first_app/config_groups>`_.

docs/userguide/configs.rst

@@ -355,12 +355,12 @@ directly at the command line instead of creating a separate config file for each
 them. When changing a single value, such as the learning rate, you can use the following
 notation::
 
-   $ spktrain experiment=qm9_energy globals.lr=1e-4
+   $ spktrain experiment=qm9_atomwise globals.lr=1e-4
 
 Alternatively, one can also change a whole config group. The syntax for this is slightly
 different::
 
-   $ spktrain experiment=qm9_energy model/representation=schnet
+   $ spktrain experiment=qm9_atomwise model/representation=schnet
 
 The difference here is that ``schnet`` refers to a pre-defined subconfig, instead of a
 single value. The config would be changed by this as follows::

src/schnetpack/datasets/qm9.py

@@ -232,9 +232,9 @@ def _download_data(
 
         property_list = []
 
-        irange = np.arange(len(ordered_files), dtype=np.int)
+        irange = np.arange(len(ordered_files), dtype=int)
         if uncharacterized is not None:
-            irange = np.setdiff1d(irange, np.array(uncharacterized, dtype=np.int) - 1)
+            irange = np.setdiff1d(irange, np.array(uncharacterized, dtype=int) - 1)
 
         for i in tqdm(irange):
             xyzfile = os.path.join(raw_path, ordered_files[i])

src/schnetpack/datasets/rmd17.py

@@ -241,7 +241,7 @@ def _download_data(
                     os.path.join(raw_path, "rmd17", "splits", f"index_train_0{i}.csv")
                 )
                 .flatten()
-                .astype(np.int)
+                .astype(int)
                 .tolist()
             )
             train_splits.append(train_split)
@@ -250,7 +250,7 @@ def _download_data(
                     os.path.join(raw_path, "rmd17", "splits", f"index_test_0{i}.csv")
                 )
                 .flatten()
-                .astype(np.int)
+                .astype(int)
                 .tolist()
             )
             test_splits.append(test_split)