Rename pkg

.github/workflows/ci.yaml

@@ -35,7 +35,7 @@ jobs:
         channel-priority: disabled
         python-version: ${{ matrix.python-version }}
         environment-file: environment.yml
-        activate-environment: vaep
+        activate-environment: pimms
         auto-activate-base: true
         # auto-update-conda: true
     - name: inspect-conda-environment

.github/workflows/workflow_website.yaml

@@ -27,7 +27,7 @@ jobs:
         channel-priority: disabled
         python-version: "3.8"
         environment-file: environment.yml
-        activate-environment: vaep
+        activate-environment: pimms
         auto-activate-base: true
         # auto-update-conda: true
     - name: Dry-run workflow

docs/README.md

@@ -8,16 +8,10 @@ In order to build the docs you need to
 
 Command to be run from `path/to/pimms/docs`, i.e. from within the `docs` package folder: 
 
-```bash
-# pip install pimms[docs]
-# pwd: ./vaep/docs
-conda env update -f environment.yml
-```
-
-If you prefer pip, run 
+Install pimms-learn with docs option locally
 
 ```bash
-# pwd: ./vaep
+# pwd: ./pimms
 pip install .[docs]
 ```
 
@@ -31,7 +25,7 @@ Options:
 ```bash	
 # pwd: ./pimms/docs
 # apidoc
-sphinx-apidoc --force --implicit-namespaces --module-first -o reference ../vaep
+sphinx-apidoc --force --implicit-namespaces --module-first -o reference ../pimmslearn
 # build docs
 sphinx-build -n -W --keep-going -b html ./ ./_build/
 ```

docs/conf.py

@@ -131,7 +131,7 @@
     from pathlib import Path
 
     PROJECT_ROOT = Path(__file__).parent.parent
-    PACKAGE_ROOT = PROJECT_ROOT / "vaep"
+    PACKAGE_ROOT = PROJECT_ROOT / "pimmslearn"
 
     def run_apidoc(_):
         from sphinx.ext import apidoc

environment.yml

@@ -1,5 +1,5 @@
 # Dev Environment
-name: vaep
+name: pimms
 channels:
   - conda-forge
   - pytorch

vaep/README.md → pimmslearn/README.md

@@ -2,10 +2,10 @@
 
 ## Imputation
 - imputation of data is done based on the standard variation or KNN imputation
-- adapted scripts from Annelaura are under `vaep/imputation.py`
+- adapted scripts from Annelaura are under `pimmslearn/imputation.py`
 
 ## Transform
-- transformation of intensity data is in `vaep/transfrom.py`
+- transformation of intensity data is in `pimmslearn/transfrom.py`
 
 
 ## Utils

vaep/__init__.py → pimmslearn/__init__.py

@@ -1,6 +1,13 @@
 """
-VAEP
-Variatonal autoencoder for proteomics
+pimmslearn: a package for imputation using self-supervised deep learning models:
+
+1. Collaborative Filtering
+2. Denoising Autoencoder
+3. Variational Autoencoder
+
+The package offers Imputation transformers in the style of scikit-learn.
+
+PyPI package is called pimms-learn (with a hyphen).
 """
 from __future__ import annotations
 
@@ -10,18 +17,18 @@
 
 import njab
 
-import vaep.logging
-import vaep.nb
-import vaep.pandas
-import vaep.plotting
+import pimmslearn.logging
+import pimmslearn.nb
+import pimmslearn.pandas
+import pimmslearn.plotting
 
 _logging.getLogger(__name__).addHandler(_logging.NullHandler())
 
 
 # put into some pandas_cfg.py file and import all
 
 
-savefig = vaep.plotting.savefig
+savefig = pimmslearn.plotting.savefig
 
 __license__ = 'GPLv3'
 __version__ = metadata.version("pimms-learn")
@@ -33,4 +40,4 @@
 
 njab.pandas.set_pandas_number_formatting(float_format='{:,.3f}')
 
-vaep.plotting.make_large_descriptors('x-large')
+pimmslearn.plotting.make_large_descriptors('x-large')

vaep/analyzers/__init__.py → pimmslearn/analyzers/__init__.py

@@ -2,7 +2,7 @@
 """
 from types import SimpleNamespace
 
-from vaep.analyzers import compare_predictions, diff_analysis
+from pimmslearn.analyzers import compare_predictions, diff_analysis
 
 __all__ = ['diff_analysis', 'compare_predictions', 'Analysis']
 

vaep/analyzers/analyzers.py → pimmslearn/analyzers/analyzers.py

@@ -13,11 +13,11 @@
 from njab.sklearn import run_pca
 from sklearn.impute import SimpleImputer
 
-import vaep
-from vaep.analyzers import Analysis
-from vaep.io.datasplits import long_format, wide_format
-from vaep.io.load import verify_df
-from vaep.pandas import _add_indices
+import pimmslearn
+from pimmslearn.analyzers import Analysis
+from pimmslearn.io.datasplits import long_format, wide_format
+from pimmslearn.io.load import verify_df
+from pimmslearn.pandas import _add_indices
 
 logger = logging.getLogger(__name__)
 
@@ -379,7 +379,7 @@ def _plot(self, fct, meta_key: str, save: bool = True):
                 meta=meta_data.loc[self.latent_reduced.index],
                 title=f'{self.model_name} latent space PCA of {self.latent_dim} dimensions by {meta_key}')
         if save:
-            vaep.plotting._savefig(fig, name=f'{self.model_name}_latent_by_{meta_key}',
+            pimmslearn.plotting._savefig(fig, name=f'{self.model_name}_latent_by_{meta_key}',
                                    folder=self.folder)
         return fig, ax
 

vaep/io/__init__.py → pimmslearn/io/__init__.py

@@ -8,7 +8,7 @@
 import numpy as np
 import pandas as pd
 
-import vaep.pandas
+import pimmslearn.pandas
 
 PathsList = namedtuple('PathsList', ['files', 'folder'])
 
@@ -86,7 +86,7 @@ def get_fname_from_keys(keys, folder='.', file_ext='.pkl', remove_duplicates=Tru
         keys = list(dict.fromkeys(keys))
     folder = Path(folder)
     folder.mkdir(exist_ok=True, parents=True)
-    fname_dataset = folder / '{}{}'.format(vaep.pandas.replace_with(
+    fname_dataset = folder / '{}{}'.format(pimmslearn.pandas.replace_with(
         ' '.join(keys), replace='- ', replace_with='_'), file_ext)
     return fname_dataset
 

vaep/io/dataloaders.py → pimmslearn/io/dataloaders.py

@@ -6,9 +6,9 @@
 from fastai.data.load import DataLoader
 from torch.utils.data import Dataset
 
-from vaep.io import datasets
-from vaep.io.datasets import DatasetWithTarget
-from vaep.transform import VaepPipeline
+from pimmslearn.io import datasets
+from pimmslearn.io.datasets import DatasetWithTarget
+from pimmslearn.transform import VaepPipeline
 
 
 def get_dls(train_X: pandas.DataFrame,
@@ -42,8 +42,8 @@ def get_dls(train_X: pandas.DataFrame,
     from sklearn.impute import SimpleImputer
     from sklearn.preprocessing import StandardScaler
 
-    from vaep.dataloader import get_dls
-    from vaep.transform import VaepPipeline
+    from pimmslearn.dataloader import get_dls
+    from pimmslearn.transform import VaepPipeline
 
     dae_default_pipeline = sklearn.pipeline.Pipeline(
         [('normalize', StandardScaler()),
@@ -86,7 +86,7 @@ def get_test_dl(df: pandas.DataFrame,
     ----------
     df : pandas.DataFrame
         Test data in a DataFrame
-    transformer : vaep.transform.VaepPipeline
+    transformer : pimmslearn.transform.VaepPipeline
         Pipeline with separate encode and decode
     dataset : torch.utils.data.Dataset, optional
         torch Dataset to yield encoded samples, by default DatasetWithTarget

vaep/io/datasplits.py → pimmslearn/io/datasplits.py

@@ -8,8 +8,8 @@
 
 import pandas as pd
 
-from vaep.io.format import class_full_module, classname
-from vaep.pandas import interpolate
+from pimmslearn.io.format import class_full_module, classname
+from pimmslearn.pandas import interpolate
 
 logger = logging.getLogger(__name__)
 

vaep/logging.py → pimmslearn/logging.py

@@ -42,7 +42,7 @@ def setup_logger_w_file(logger, level=logging.INFO, fname_base=None):
     Examples
     --------
     >>> import logging
-    >>> logger = logging.getLogger('vaep')
+    >>> logger = logging.getLogger('pimmslearn')
     >>> _ = setup_logger_w_file(logger) # no logging to file
     >>> logger.handlers = [] # reset logger
     >>> _ = setup_logger_w_file() #

vaep/models/__init__.py → pimmslearn/models/__init__.py

@@ -15,8 +15,8 @@
 from fastai import learner
 from fastcore.foundation import L
 
-import vaep
-from vaep.models import ae, analysis, collab, vae
+import pimmslearn
+from pimmslearn.models import ae, analysis, collab, vae
 
 logger = logging.getLogger(__name__)
 
@@ -91,7 +91,7 @@ def plot_training_losses(learner: learner.Learner,
                                norm_train=norm_train, norm_val=norm_val)
     name = name.lower()
     _ = RecorderDump(learner.recorder, name).save(folder)
-    vaep.savefig(fig, name=f'{name}_training',
+    pimmslearn.savefig(fig, name=f'{name}_training',
                  folder=folder)
     return fig
 
@@ -218,7 +218,7 @@ def collect_metrics(metrics_jsons: List, key_fct: Callable) -> dict:
         logger.debug(f"{key = }")
         with open(fname) as f:
             loaded = json.load(f)
-        loaded = vaep.pandas.flatten_dict_of_dicts(loaded)
+        loaded = pimmslearn.pandas.flatten_dict_of_dicts(loaded)
 
         if key not in all_metrics:
             all_metrics[key] = loaded
@@ -320,7 +320,7 @@ def get_df_from_nested_dict(nested_dict,
                             row_name='subset'):
     metrics = {}
     for k, run_metrics in nested_dict.items():
-        metrics[k] = vaep.pandas.flatten_dict_of_dicts(run_metrics)
+        metrics[k] = pimmslearn.pandas.flatten_dict_of_dicts(run_metrics)
 
     metrics = pd.DataFrame.from_dict(metrics, orient='index')
     metrics.columns.names = column_levels

vaep/models/ae.py → pimmslearn/models/ae.py

@@ -1,6 +1,6 @@
 """Autoencoder model trained using denoising procedure.
 
-Variational Autencoder model adapter should be moved to vaep.models.vae.
+Variational Autencoder model adapter should be moved to pimmslearn.models.vae.
 Or model class could be put somewhere else.
 """
 import logging
@@ -15,22 +15,21 @@
 from fastai.callback.core import Callback
 from torch import nn
 
-import vaep.io.dataloaders
-import vaep.io.datasets
-import vaep.io.datasplits
-import vaep.models
-import vaep.transform
-
-from vaep.models import analysis
+import pimmslearn.io.dataloaders
+import pimmslearn.io.datasets
+import pimmslearn.io.datasplits
+import pimmslearn.models
+import pimmslearn.transform
+from pimmslearn.models import analysis
 
 logger = logging.getLogger(__name__)
 
 
 def get_preds_from_df(df: pd.DataFrame,
                       learn: fastai.learner.Learner,
-                      transformer: vaep.transform.VaepPipeline,
+                      transformer: pimmslearn.transform.VaepPipeline,
                       position_pred_tuple: int = None,
-                      dataset: torch.utils.data.Dataset = vaep.io.datasets.DatasetWithTarget):
+                      dataset: torch.utils.data.Dataset = pimmslearn.io.datasets.DatasetWithTarget):
     """Get predictions for specified DataFrame, using a fastai learner
     and a custom sklearn Pipeline.
 
@@ -40,22 +39,22 @@ def get_preds_from_df(df: pd.DataFrame,
         DataFrame to create predictions from.
     learn : fastai.learner.Learner
         fastai Learner with trained model
-    transformer : vaep.transform.VaepPipeline
+    transformer : pimmslearn.transform.VaepPipeline
         Pipeline with separate encode and decode
     position_pred_tuple : int, optional
         In that the model returns multiple outputs, select the one which contains
         the predictions matching the target variable (VAE case), by default None
     dataset : torch.utils.data.Dataset, optional
-        Dataset to build batches from, by default vaep.io.datasets.DatasetWithTarget
+        Dataset to build batches from, by default pimmslearn.io.datasets.DatasetWithTarget
 
     Returns
     -------
     tuple
         tuple of pandas DataFrames (prediciton and target) based on learn.get_preds
     """
-    dl = vaep.io.dataloaders.get_test_dl(df=df,
-                                         transformer=transformer,
-                                         dataset=dataset)
+    dl = pimmslearn.io.dataloaders.get_test_dl(df=df,
+                                               transformer=transformer,
+                                               dataset=dataset)
     res = learn.get_preds(dl=dl)  # -> dl could be int
     if position_pred_tuple is not None and issubclass(type(res[0]), tuple):
         res = (res[0][position_pred_tuple], *res[1:])
@@ -272,11 +271,11 @@ def __init__(self,
                  decode: List[str],
                  bs=64
                  ):
-        self.transform = vaep.transform.VaepPipeline(
+        self.transform = pimmslearn.transform.VaepPipeline(
             df_train=train_df,
             encode=transform,
             decode=decode)
-        self.dls = vaep.io.dataloaders.get_dls(
+        self.dls = pimmslearn.io.dataloaders.get_dls(
             train_X=train_df,
             valid_X=val_df,
             transformer=self.transform, bs=bs)
@@ -286,7 +285,7 @@ def __init__(self,
         self.params = dict(self.kwargs_model)
         self.model = model(**self.kwargs_model)
 
-        self.n_params_ae = vaep.models.calc_net_weight_count(self.model)
+        self.n_params_ae = pimmslearn.models.calc_net_weight_count(self.model)
         self.params['n_parameters'] = self.n_params_ae
         self.learn = None
 
@@ -296,4 +295,4 @@ def get_preds_from_df(self, df_wide: pd.DataFrame) -> pd.DataFrame:
         return get_preds_from_df(df=df_wide, learn=self.learn, transformer=self.transform)
 
     def get_test_dl(self, df_wide: pd.DataFrame, bs: int = 64) -> pd.DataFrame:
-        return vaep.io.dataloaders.get_test_dl(df=df_wide, transformer=self.transform, bs=bs)
+        return pimmslearn.io.dataloaders.get_test_dl(df=df_wide, transformer=self.transform, bs=bs)

vaep/models/analysis.py → pimmslearn/models/analysis.py

@@ -1,9 +1,9 @@
-import vaep.transform
+import pimmslearn.transform
 import torch.nn
 import fastai.data.core
 import fastai.learner
 
-from vaep.analyzers import Analysis
+from pimmslearn.analyzers import Analysis
 
 
 class ModelAnalysis(Analysis):
@@ -13,4 +13,4 @@ class ModelAnalysis(Analysis):
     dls: fastai.data.core.DataLoaders
     learn: fastai.learner.Learner
     params: dict
-    transform: vaep.transform.VaepPipeline
+    transform: pimmslearn.transform.VaepPipeline