:arrow_minus_sign: Revert dependencies.

README.md

@@ -45,5 +45,6 @@ pytest -s
 ```bash
 pip install -e .[docs]
 cd docs
-make html
+# make html
+sphinx-build ./docs/source ./docs/build
 ```

example.py

@@ -0,0 +1,44 @@
+"""
+Tests the LSTMTimeSeriesPredictor
+"""
+from pathlib import Path
+
+import torch
+from skorch.callbacks import EarlyStopping
+from src.flights_dataset import FlightsDataset
+from src.model import BenchmarkLSTM
+from src.oze_dataset import OzeNPZDataset, npz_check
+from time_series_predictor import TimeSeriesPredictor
+# from tune_sklearn.tune_gridsearch import TuneGridSearchCV
+
+if __name__ == "__main__":
+    tsp = TimeSeriesPredictor(
+        BenchmarkLSTM(),
+        max_epochs=500,
+        early_stopping=EarlyStopping(patience=30),
+        # train_split=None, # default = skorch.dataset.CVSplit(5)
+        optimizer=torch.optim.Adam
+    )
+    dataset = OzeNPZDataset(
+        dataset_path=npz_check(
+            Path('datasets'),
+            'dataset'
+        )
+    )
+
+    tsp.fit(dataset)
+    mean_r2_score = tsp.score(dataset)
+    assert mean_r2_score > -50
+
+# if __name__ == "__main__":
+#     tsp = TimeSeriesPredictor(
+#         BenchmarkLSTM(),
+#         max_epochs=50,
+#         train_split=None, # default = skorch.dataset.CVSplit(5)
+#         optimizer=torch.optim.Adam
+#     )
+#     dataset = FlightsDataset()
+
+#     tsp.fit(dataset)
+#     mean_r2_score = tsp.score(dataset)
+#     assert mean_r2_score > 0.75

requirements-lock.txt

@@ -0,0 +1,125 @@
+#
+# This file is autogenerated by pip-compile
+# To update, run:
+#
+#    pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --output-file=requirements-lock.txt
+#
+--find-links https://download.pytorch.org/whl/torch_stable.html
+
+future==0.18.2 \
+    --hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d \
+    # via torch
+joblib==0.16.0 \
+    --hash=sha256:8f52bf24c64b608bf0b2563e0e47d6fcf516abc8cfafe10cfd98ad66d94f92d6 \
+    --hash=sha256:d348c5d4ae31496b2aa060d6d9b787864dd204f9480baaa52d18850cb43e9f49 \
+    # via scikit-learn
+numpy==1.19.0 \
+    --hash=sha256:13af0184177469192d80db9bd02619f6fa8b922f9f327e077d6f2a6acb1ce1c0 \
+    --hash=sha256:26a45798ca2a4e168d00de75d4a524abf5907949231512f372b217ede3429e98 \
+    --hash=sha256:26f509450db547e4dfa3ec739419b31edad646d21fb8d0ed0734188b35ff6b27 \
+    --hash=sha256:30a59fb41bb6b8c465ab50d60a1b298d1cd7b85274e71f38af5a75d6c475d2d2 \
+    --hash=sha256:33c623ef9ca5e19e05991f127c1be5aeb1ab5cdf30cb1c5cf3960752e58b599b \
+    --hash=sha256:356f96c9fbec59974a592452ab6a036cd6f180822a60b529a975c9467fcd5f23 \
+    --hash=sha256:3c40c827d36c6d1c3cf413694d7dc843d50997ebffbc7c87d888a203ed6403a7 \
+    --hash=sha256:4d054f013a1983551254e2379385e359884e5af105e3efe00418977d02f634a7 \
+    --hash=sha256:63d971bb211ad3ca37b2adecdd5365f40f3b741a455beecba70fd0dde8b2a4cb \
+    --hash=sha256:658624a11f6e1c252b2cd170d94bf28c8f9410acab9f2fd4369e11e1cd4e1aaf \
+    --hash=sha256:76766cc80d6128750075378d3bb7812cf146415bd29b588616f72c943c00d598 \
+    --hash=sha256:7b57f26e5e6ee2f14f960db46bd58ffdca25ca06dd997729b1b179fddd35f5a3 \
+    --hash=sha256:7b852817800eb02e109ae4a9cef2beda8dd50d98b76b6cfb7b5c0099d27b52d4 \
+    --hash=sha256:8cde829f14bd38f6da7b2954be0f2837043e8b8d7a9110ec5e318ae6bf706610 \
+    --hash=sha256:a2e3a39f43f0ce95204beb8fe0831199542ccab1e0c6e486a0b4947256215632 \
+    --hash=sha256:a86c962e211f37edd61d6e11bb4df7eddc4a519a38a856e20a6498c319efa6b0 \
+    --hash=sha256:a8705c5073fe3fcc297fb8e0b31aa794e05af6a329e81b7ca4ffecab7f2b95ef \
+    --hash=sha256:b6aaeadf1e4866ca0fdf7bb4eed25e521ae21a7947c59f78154b24fc7abbe1dd \
+    --hash=sha256:be62aeff8f2f054eff7725f502f6228298891fd648dc2630e03e44bf63e8cee0 \
+    --hash=sha256:c2edbb783c841e36ca0fa159f0ae97a88ce8137fb3a6cd82eae77349ba4b607b \
+    --hash=sha256:cbe326f6d364375a8e5a8ccb7e9cd73f4b2f6dc3b2ed205633a0db8243e2a96a \
+    --hash=sha256:d34fbb98ad0d6b563b95de852a284074514331e6b9da0a9fc894fb1cdae7a79e \
+    --hash=sha256:d97a86937cf9970453c3b62abb55a6475f173347b4cde7f8dcdb48c8e1b9952d \
+    --hash=sha256:dd53d7c4a69e766e4900f29db5872f5824a06827d594427cf1a4aa542818b796 \
+    --hash=sha256:df1889701e2dfd8ba4dc9b1a010f0a60950077fb5242bb92c8b5c7f1a6f2668a \
+    --hash=sha256:fa1fe75b4a9e18b66ae7f0b122543c42debcf800aaafa0212aaff3ad273c2596 \
+    # via scikit-learn, scipy, skorch, torch
+psutil==5.7.0 \
+    --hash=sha256:1413f4158eb50e110777c4f15d7c759521703bd6beb58926f1d562da40180058 \
+    --hash=sha256:298af2f14b635c3c7118fd9183843f4e73e681bb6f01e12284d4d70d48a60953 \
+    --hash=sha256:60b86f327c198561f101a92be1995f9ae0399736b6eced8f24af41ec64fb88d4 \
+    --hash=sha256:685ec16ca14d079455892f25bd124df26ff9137664af445563c1bd36629b5e0e \
+    --hash=sha256:73f35ab66c6c7a9ce82ba44b1e9b1050be2a80cd4dcc3352cc108656b115c74f \
+    --hash=sha256:75e22717d4dbc7ca529ec5063000b2b294fc9a367f9c9ede1f65846c7955fd38 \
+    --hash=sha256:a02f4ac50d4a23253b68233b07e7cdb567bd025b982d5cf0ee78296990c22d9e \
+    --hash=sha256:d008ddc00c6906ec80040d26dc2d3e3962109e40ad07fd8a12d0284ce5e0e4f8 \
+    --hash=sha256:d84029b190c8a66a946e28b4d3934d2ca1528ec94764b180f7d6ea57b0e75e26 \
+    --hash=sha256:e2d0c5b07c6fe5a87fa27b7855017edb0d52ee73b71e6ee368fae268605cc3f5 \
+    --hash=sha256:f344ca230dd8e8d5eee16827596f1c22ec0876127c28e800d7ae20ed44c4b310 \
+    # via time_series_predictor (setup.py)
+scikit-learn==0.23.1 \
+    --hash=sha256:04799686060ecbf8992f26a35be1d99e981894c8c7860c1365cda4200f954a16 \
+    --hash=sha256:058d213092de4384710137af1300ed0ff030b8c40459a6c6f73c31ccd274cc39 \
+    --hash=sha256:0c3464e46ef8bd4f1bfa5c009648c6449412c8f7e9b3fc0c9e3d800139c48827 \
+    --hash=sha256:0e7b55f73b35537ecd0d19df29dd39aa9e076dba78f3507b8136c819d84611fd \
+    --hash=sha256:16feae4361be6b299d4d08df5a30956b4bfc8eadf173fe9258f6d59630f851d4 \
+    --hash=sha256:244ca85d6eba17a1e6e8a66ab2f584be6a7784b5f59297e3d7ff8c7983af627c \
+    --hash=sha256:3e6e92b495eee193a8fa12a230c9b7976ea0fc1263719338e35c986ea1e42cff \
+    --hash=sha256:5bcea4d6ee431c814261117281363208408aa4e665633655895feb059021aca6 \
+    --hash=sha256:93f56abd316d131645559ec0ab4f45e3391c2ccdd4eadaa4912f4c1e0a6f2c96 \
+    --hash=sha256:9e04c0811ea92931ee8490d638171b8cb2f21387efcfff526bbc8c2a3da60f1c \
+    --hash=sha256:bded94236e16774385202cafd26190ce96db18e4dc21e99473848c61e4fdc400 \
+    --hash=sha256:c2fa33d20408b513cf432505c80e6eb4bf4d71434f1ae36680765d4a2c2a16ec \
+    --hash=sha256:e3fec1c8831f8f93ad85581ca29ca1bb88e2da377fb097cf8322aa89c21bc9b8 \
+    --hash=sha256:e585682e37f2faa81ad6cd4472fff646bf2fd0542147bec93697a905db8e6bd2 \
+    --hash=sha256:e9879ba9e64ec3add41bf201e06034162f853652ef4849b361d73b0deb3153ad \
+    --hash=sha256:ebe853e6f318f9d8b3b74dd17e553720d35646eff675a69eeaed12fbbbb07daa \
+    # via skorch
+scipy==1.4.1 \
+    --hash=sha256:00af72998a46c25bdb5824d2b729e7dabec0c765f9deb0b504f928591f5ff9d4 \
+    --hash=sha256:0902a620a381f101e184a958459b36d3ee50f5effd186db76e131cbefcbb96f7 \
+    --hash=sha256:1e3190466d669d658233e8a583b854f6386dd62d655539b77b3fa25bfb2abb70 \
+    --hash=sha256:2cce3f9847a1a51019e8c5b47620da93950e58ebc611f13e0d11f4980ca5fecb \
+    --hash=sha256:3092857f36b690a321a662fe5496cb816a7f4eecd875e1d36793d92d3f884073 \
+    --hash=sha256:386086e2972ed2db17cebf88610aab7d7f6e2c0ca30042dc9a89cf18dcc363fa \
+    --hash=sha256:71eb180f22c49066f25d6df16f8709f215723317cc951d99e54dc88020ea57be \
+    --hash=sha256:770254a280d741dd3436919d47e35712fb081a6ff8bafc0f319382b954b77802 \
+    --hash=sha256:787cc50cab3020a865640aba3485e9fbd161d4d3b0d03a967df1a2881320512d \
+    --hash=sha256:8a07760d5c7f3a92e440ad3aedcc98891e915ce857664282ae3c0220f3301eb6 \
+    --hash=sha256:8d3bc3993b8e4be7eade6dcc6fd59a412d96d3a33fa42b0fa45dc9e24495ede9 \
+    --hash=sha256:9508a7c628a165c2c835f2497837bf6ac80eb25291055f56c129df3c943cbaf8 \
+    --hash=sha256:a144811318853a23d32a07bc7fd5561ff0cac5da643d96ed94a4ffe967d89672 \
+    --hash=sha256:a1aae70d52d0b074d8121333bc807a485f9f1e6a69742010b33780df2e60cfe0 \
+    --hash=sha256:a2d6df9eb074af7f08866598e4ef068a2b310d98f87dc23bd1b90ec7bdcec802 \
+    --hash=sha256:bb517872058a1f087c4528e7429b4a44533a902644987e7b2fe35ecc223bc408 \
+    --hash=sha256:c5cac0c0387272ee0e789e94a570ac51deb01c796b37fb2aad1fb13f85e2f97d \
+    --hash=sha256:cc971a82ea1170e677443108703a2ec9ff0f70752258d0e9f5433d00dda01f59 \
+    --hash=sha256:dba8306f6da99e37ea08c08fef6e274b5bf8567bb094d1dbe86a20e532aca088 \
+    --hash=sha256:dc60bb302f48acf6da8ca4444cfa17d52c63c5415302a9ee77b3b21618090521 \
+    --hash=sha256:dee1bbf3a6c8f73b6b218cb28eed8dd13347ea2f87d572ce19b289d6fd3fbc59 \
+    # via scikit-learn, skorch, time_series_predictor (setup.py)
+skorch==0.8.0 \
+    --hash=sha256:5908fdc3c1c8ae49d16fa3edb1fbdd412c44f2baee02abdd5432b7a47933a7d0 \
+    --hash=sha256:f292e9866f65df7fb7cf209f503924e2cb67377d7524a50c3e5dc6ae5a5ecd47 \
+    # via time_series_predictor (setup.py)
+tabulate==0.8.7 \
+    --hash=sha256:ac64cb76d53b1231d364babcd72abbb16855adac7de6665122f97b593f1eb2ba \
+    --hash=sha256:db2723a20d04bcda8522165c73eea7c300eda74e0ce852d9022e0159d7895007 \
+    # via skorch
+threadpoolctl==2.1.0 \
+    --hash=sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725 \
+    --hash=sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b \
+    # via scikit-learn
+torch==1.5.0+cu92 \
+    --hash=sha256:21c6cd3f053b21b0c219963a0403eaeb289d53cb4a8ecf9a099c2e9232293fa4 \
+    --hash=sha256:2281b4d9fbec7925f44e96a6e5c753a58161d3812bc99fb5d7b0a555d1d60d7f \
+    --hash=sha256:352d2ac173d6b203f10fe719545c36810182badace9c6f9bc9b34dc7d138d90e \
+    --hash=sha256:560ce89a62b01f7c6c07f1e4099af3e74f4a7213adcc86abd04a2ae2f8bf7663 \
+    --hash=sha256:77586f5deca99bf854dce2bce9e533a90dd97694d190b15bd17c170ef493e2b1 \
+    --hash=sha256:8078aeffc481549f63e5fcb0ff295164faedd368a4f01e4a489fafb51d794734 \
+    --hash=sha256:8e9df2aa4ec2516476dc8c09c984a6d0e11d52126833e47a6d9f18c177e83de1 \
+    --hash=sha256:a903fe0e2c9f2017b664938ed27ec81e45f0405b37c1faabdfd12b167f552510 \
+    --hash=sha256:af9d74ed62b716ab3803dfa8774380abb8aa97b84c656ca814487d7d9611ccdd \
+    --hash=sha256:eca7d1ab146e75fb672e53382a71c0073caceaf3920a3a7aaf6d07419f8b38f7 \
+    # via time_series_predictor (setup.py)
+tqdm==4.46.0 \
+    --hash=sha256:4733c4a10d0f2a4d098d801464bdaf5240c7dadd2a7fde4ee93b0a0efd9fb25e \
+    --hash=sha256:acdafb20f51637ca3954150d0405ff1a7edde0ff19e38fb99a80a66210d2a28f \
+    # via skorch, time_series_predictor (setup.py)

setup.py

@@ -40,11 +40,11 @@ def package_files(directory):
     ],
     python_requires='>=3.6',
     install_requires=[
-        'torch',
-        'psutil',
-        'tqdm',
-        'skorch',
-        'scipy'
+        'torch==1.5.0+cu92',
+        'psutil==5.7.0',
+        'tqdm==4.46.0',
+        'skorch==0.8.0',
+        'scipy==1.4.1'
     ],
     extras_require={
         'dev': [
@@ -74,8 +74,12 @@ def package_files(directory):
             'sphinx_rtd_theme',
             'pandas',
             'seaborn',
+            'sklearn',
+            'skorch',
             'jupyterlab',
-            'matplotlib'
+            'matplotlib',
+            'python-dotenv',
+            'lxml'
         ]
     }
 )