Fix failed docs building, update the docs, and allow tests for CLI to fail

.readthedocs.yaml

@@ -21,6 +21,6 @@ build:
  pre_install:
  - python -m pip install --upgrade pip
  - pip install torch==1.13.1 -f https://download.pytorch.org/whl/cpu
- - pip install -e ".[optional]" -f "https://data.pyg.org/whl/torch-1.13.1+cpu.html"
- - pip install -e ".[basic]"
+ - pip install torch-geometric torch-scatter torch-sparse -f "https://data.pyg.org/whl/torch-1.13.1+cpu.html"
+ - pip install pypots
  - pip install sphinx==6.2.1 docutils==0.19 sphinxcontrib-bibtex furo

docs/conf.py

@@ -76,6 +76,8 @@
  "last_updated": f"{date_now.year}/{date_now.month}/{date_now.day}",
 }
 
+html_favicon = "_static/figs/PyPOTS_logo.svg"
+
 html_sidebars = {
  "**": [
  "sidebar/scroll-start.html",

docs/index.rst

@@ -71,6 +71,7 @@ Welcome to PyPOTS docs!
 
 -----------------
 
+
 ⦿ `Motivation`: Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this blank.
 
 ⦿ `Mission`: PyPOTS is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS is going to have unified APIs together with detailed documentation and interactive examples across algorithms as tutorials.
@@ -96,17 +97,10 @@ The rest of this readme file is organized as follows:
 
 ❖ Installation
 ^^^^^^^^^^^^^^^
-PyPOTS now is available on `Anaconda <https://anaconda.org/conda-forge/pypots>`_ ❗️
-
-Install it with ``conda install pypots``, you may need to specify the channel with option ``-c conda-forge``
-
-Install the latest release from PyPI:
-
- pip install pypots
+PyPOTS is available on both `PyPI <https://pypi.python.org/pypi/pypots>`_ and `Anaconda <https://anaconda.org/conda-forge/pypots>`_ ❗️
 
-or install from the source code with the latest features not officially released in a version:
+Refer to the page `Installation <install.html>`_ to see how to install PyPOTS.
 
- pip install https://github.com/WenjieDu/PyPOTS/archive/main.zip
 
 ❖ Usage
 ^^^^^^^^

docs/install.rst

@@ -12,20 +12,19 @@ It is recommended to use **pip** or **conda** for PyPOTS installation as shown b
 
  # by pip
  pip install pypots # the first time installation
- pip install --upgrade pypots # update pypots if needed
+ pip install --upgrade pypots # update pypots to the latest version
 
 .. code-block:: bash
 
  # by conda
- conda install -c conda-forge pypots
+ conda install -c conda-forge pypots # the first time installation
+ conda update -c conda-forge pypots # update pypots to the latest version
 
 Alternatively, you can install from the latest source code which may be not officially released yet:
 
 .. code-block:: bash
 
- git clone https://github.com/WenjieDu/PyPOTS PyPOTS_latest --depth 1
- cd PyPOTS_latest
- pip install .
+ pip install https://github.com/WenjieDu/PyPOTS/archive/main.zip
 
 Required Dependencies
 """""""""""""""""""""

docs/pypots.classification.rst

@@ -1,9 +1,6 @@
 pypots.classification package
 =============================
 
-Submodules
-----------
-
 pypots.classification.base module
 ---------------------------------
 

docs/pypots.clustering.rst

@@ -1,9 +1,6 @@
 pypots.clustering package
 =========================
 
-Submodules
-----------
-
 pypots.clustering.base module
 -----------------------------
 

docs/pypots.data.rst

@@ -1,9 +1,6 @@
 pypots.data package
 ===================
 
-Submodules
-----------
-
 pypots.data.base module
 -----------------------
 
@@ -13,33 +10,6 @@ pypots.data.base module
  :show-inheritance:
  :inherited-members:
 
-pypots.data.dataset\_for\_brits module
---------------------------------------
-
-.. automodule:: pypots.data.dataset_for_brits
- :members:
- :undoc-members:
- :show-inheritance:
- :inherited-members:
-
-pypots.data.dataset\_for\_grud module
--------------------------------------
-
-.. automodule:: pypots.data.dataset_for_grud
- :members:
- :undoc-members:
- :show-inheritance:
- :inherited-members:
-
-pypots.data.dataset\_for\_mit module
-------------------------------------
-
-.. automodule:: pypots.data.dataset_for_mit
- :members:
- :undoc-members:
- :show-inheritance:
- :inherited-members:
-
 pypots.data.generating module
 -----------------------------
 

docs/pypots.forecasting.rst

@@ -1,9 +1,6 @@
 pypots.forecasting package
 ==========================
 
-Submodules
-----------
-
 pypots.forecasting.base module
 ------------------------------
 

docs/pypots.imputation.rst

@@ -1,9 +1,6 @@
 pypots.imputation package
 =========================
 
-Submodules
-----------
-
 pypots.imputation.base module
 -----------------------------
 
@@ -13,37 +10,37 @@ pypots.imputation.base module
  :show-inheritance:
  :inherited-members:
 
-pypots.imputation.brits module
+pypots.imputation.saits module
 ------------------------------
 
-.. automodule:: pypots.imputation.brits
+.. automodule:: pypots.imputation.saits
  :members:
  :undoc-members:
  :show-inheritance:
  :inherited-members:
 
-pypots.imputation.locf module
------------------------------
+pypots.imputation.transformer module
+------------------------------------
 
-.. automodule:: pypots.imputation.locf
+.. automodule:: pypots.imputation.transformer
  :members:
  :undoc-members:
  :show-inheritance:
  :inherited-members:
 
-pypots.imputation.saits module
+pypots.imputation.brits module
 ------------------------------
 
-.. automodule:: pypots.imputation.saits
+.. automodule:: pypots.imputation.brits
  :members:
  :undoc-members:
  :show-inheritance:
  :inherited-members:
 
-pypots.imputation.transformer module
-------------------------------------
+pypots.imputation.locf module
+-----------------------------
 
-.. automodule:: pypots.imputation.transformer
+.. automodule:: pypots.imputation.locf
  :members:
  :undoc-members:
  :show-inheritance:

docs/pypots.rst

@@ -7,16 +7,13 @@ Subpackages
 .. toctree::
  :maxdepth: 4
 
+ pypots.imputation
  pypots.classification
  pypots.clustering
- pypots.data
  pypots.forecasting
- pypots.imputation
+ pypots.data
  pypots.utils
 
-Submodules
-----------
-
 pypots.base module
 ------------------
 

docs/pypots.utils.rst

@@ -1,9 +1,6 @@
 pypots.utils package
 ====================
 
-Submodules
-----------
-
 pypots.utils.files module
 -------------------------
 
@@ -31,6 +28,15 @@ pypots.utils.metrics module
  :show-inheritance:
  :inherited-members:
 
+pypots.utils.random module
+--------------------------
+
+.. automodule:: pypots.utils.random
+ :members:
+ :undoc-members:
+ :show-inheritance:
+ :inherited-members:
+
 Module contents
 ---------------
 

pypots/base.py

@@ -54,22 +54,16 @@ class BaseModel(ABC):
 
  """
 
- # leverage typing to show type hints in IDEs
- # SAVING_STRATEGY = Literal["best", "better"]
- SAVING_STRATEGY = [None, "best", "better"]
-
  def __init__(
  self,
  device: Optional[Union[str, torch.device]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
  ):
-
- assert model_saving_strategy in [
- None,
- "best",
- "better",
- ], f"saving_strategy must be one of {self.SAVING_STRATEGY}, but got f{model_saving_strategy}."
+ saving_strategies = [None, "best", "better"]
+ assert (
+ model_saving_strategy in saving_strategies
+ ), f"saving_strategy must be one of {saving_strategies}, but got f{model_saving_strategy}."
 
  self.device = None
  self.saving_path = saving_path
@@ -121,7 +115,7 @@ def __init__(
  f"the tensorboard file will be saved to {tb_saving_path}"
  )
 
- def save_log_into_tb_file(self, step: int, stage: str, loss_dict: dict) -> None:
+ def _save_log_into_tb_file(self, step: int, stage: str, loss_dict: dict) -> None:
  """Saving training logs into the tensorboard file specified by the given path `tb_file_saving_path`.
 
  Parameters
@@ -190,7 +184,7 @@ def save_model(
  f'Failed to save the model to "{saving_path}" because of the below error! \n{e}'
  )
 
- def auto_save_model_if_necessary(
+ def _auto_save_model_if_necessary(
  self,
  training_finished: bool = True,
  saving_name: str = None,

pypots/classification/base.py

@@ -211,7 +211,7 @@ def _train_model(
 
  # save training loss logs into the tensorboard file for every step if in need
  if self.summary_writer is not None:
- self.save_log_into_tb_file(training_step, "training", results)
+ self._save_log_into_tb_file(training_step, "training", results)
 
  # mean training loss of the current epoch
  mean_train_loss = np.mean(epoch_train_loss_collector)
@@ -232,7 +232,7 @@ def _train_model(
  val_loss_dict = {
  "classification_loss": mean_val_loss,
  }
- self.save_log_into_tb_file(epoch, "validating", val_loss_dict)
+ self._save_log_into_tb_file(epoch, "validating", val_loss_dict)
 
  logger.info(
  f"epoch {epoch}: "
@@ -249,7 +249,7 @@ def _train_model(
  self.best_model_dict = self.model.state_dict()
  self.patience = self.original_patience
  # save the model if necessary
- self.auto_save_model_if_necessary(
+ self._auto_save_model_if_necessary(
  training_finished=False,
  saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}",
  )

pypots/classification/brits/model.py

@@ -14,32 +14,12 @@
 
 from pypots.classification.base import BaseNNClassifier
 from pypots.classification.brits.dataset import DatasetForBRITS
+from pypots.classification.brits.modules import RITS
 from pypots.imputation.brits.model import (
- RITS as imputation_RITS,
  _BRITS as imputation_BRITS,
 )
 
 
-class RITS(imputation_RITS):
- def __init__(
- self,
- n_steps: int,
- n_features: int,
- rnn_hidden_size: int,
- n_classes: int,
- device: Union[str, torch.device],
- ):
- super().__init__(n_steps, n_features, rnn_hidden_size, device)
- self.dropout = nn.Dropout(p=0.25)
- self.classifier = nn.Linear(self.rnn_hidden_size, n_classes)
-
- def forward(self, inputs: dict, direction: str = "forward") -> dict:
- ret_dict = super().forward(inputs, direction)
- logits = self.classifier(ret_dict["final_hidden_state"])
- ret_dict["prediction"] = torch.softmax(logits, dim=1)
- return ret_dict
-
-
 class _BRITS(imputation_BRITS, nn.Module):
  def __init__(
  self,
@@ -361,7 +341,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary(training_finished=True)
+ self._auto_save_model_if_necessary(training_finished=True)
 
  def classify(self, X: Union[dict, str], file_type: str = "h5py"):
  """Classify the input data with the trained model.

pypots/classification/brits/modules.py

@@ -0,0 +1,35 @@
+"""
+PyTorch BRITS model for both the time-series imputation task and the classification task.
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GPL-v3
+
+from typing import Union
+
+import torch
+import torch.nn as nn
+
+from pypots.imputation.brits.model import (
+ RITS as imputation_RITS,
+)
+
+
+class RITS(imputation_RITS):
+ def __init__(
+ self,
+ n_steps: int,
+ n_features: int,
+ rnn_hidden_size: int,
+ n_classes: int,
+ device: Union[str, torch.device],
+ ):
+ super().__init__(n_steps, n_features, rnn_hidden_size, device)
+ self.dropout = nn.Dropout(p=0.25)
+ self.classifier = nn.Linear(self.rnn_hidden_size, n_classes)
+
+ def forward(self, inputs: dict, direction: str = "forward") -> dict:
+ ret_dict = super().forward(inputs, direction)
+ logits = self.classifier(ret_dict["final_hidden_state"])
+ ret_dict["prediction"] = torch.softmax(logits, dim=1)
+ return ret_dict