Merge pull request #154 from nasa/152-make-tensor-flow-optional

152 make tensor flow optional

.github/workflows/coverage.yml

@@ -23,7 +23,7 @@ jobs:
       uses: actions/cache@v2
       with:
         path: ~/.cache/pip
-        key: pip-cache
+        key: pip-cache-datadriven
     - name: Install additional dependencies
       run: |
         pip install coverage

.github/workflows/python-package.yml

@@ -113,7 +113,7 @@ jobs:
       uses: actions/cache@v2
       with:
         path: ~/.cache/pip
-        key: pip-cache
+        key: pip-cache-datadriven
     - name: Update
       run: pip install --upgrade --upgrade-strategy eager -e .
     - name: Run tests
@@ -421,7 +421,7 @@ jobs:
       uses: actions/cache@v2
       with:
         path: ~/.cache/pip
-        key: pip-cache
+        key: pip-cache-datadriven
     - name: Update
       run: pip install --upgrade --upgrade-strategy eager -e .
     - name: Run tests
@@ -439,7 +439,7 @@ jobs:
       uses: actions/cache@v2
       with:
         path: ~/.cache/pip
-        key: pip-cache
+        key: pip-cache-datadriven
     - name: Update
       run: |
         pip install --upgrade --upgrade-strategy eager -e .

.github/workflows/update-cache.yml

@@ -6,7 +6,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.7']
+        python-version: ['3.10']
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
@@ -24,3 +24,27 @@ jobs:
       with:
         path: ~/.cache/pip
         key: pip-cache
+
+  cache-dependencies-data-driven:
+    timeout-minutes: 15
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.10']
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install -e '.[datadriven]'
+        python -m pip install notebook
+        python -m pip install testbook
+        python -m pip install requests
+    - uses: actions/cache@v3
+      with:
+        path: ~/.cache/pip
+        key: pip-cache-datadriven

README.md

@@ -11,6 +11,12 @@ ProgPy combines NASAs prog_models and prog_algs packages into a single python pa
 ## Installation 
 `pip3 install progpy`
 
+or 
+
+`pip3 install progpy[datadriven]`
+
+to include dependencies for data driven models
+
 ## [Documentation](https://nasa.github.io/progpy/)
 See documentation [here](https://nasa.github.io/progpy/)
 

pyproject.toml

@@ -0,0 +1,68 @@
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "progpy"
+version = "1.7.0-pre"
+dependencies = [
+    "scipy",
+    "pandas",  # For data downloading features
+    "matplotlib",
+    "requests",  # For data downloading features
+    "chaospy",  # For PCE
+    "fastdtw",  # For DTW error calculation
+    "filterpy"
+]
+requires-python = ">=3.7, <3.12"
+authors = [
+    {name = "Christopher Teubert", email = "christopher.a.teubert@nasa.gov"},
+    {name = "Katelyn Griffith", email = "katelyn.j.griffith@nasa.gov"}
+    ,
+    {name = "Matteo Corbetta"},
+    {name = "Chetan Kulkarni"},
+    {name = "Portia Banerjee"},
+    {name = "Jason Watkins"},
+    {name = "Matthew Daigle"}
+]
+maintainers = [
+    {name = "Christopher Teubert", email = "christopher.a.teubert@nasa.gov"},
+    {name = "Katelyn Griffith", email = "katelyn.j.griffith@nasa.gov"}
+]
+description = "The NASA Prognostic Package (ProgPy) is a python prognostics framework focused on building, using, and evaluating models and algorithms for prognostics (computation of remaining useful life) and health management of engineering systems, and provides a set of prognostics models for select components and prognostics algorithms developed within this framework, including uncertainty propagation."
+readme = "README.md"
+license = {text = "NOSA"}
+keywords = ['prognostics', 'diagnostics', 'fault detection', 'fdir', 'physics modeling', 'prognostics and health management', 'PHM', 'health management', 'surrogate modeling', 'model tuning', 'simulation', 'ivhm']
+classifiers = [
+    'Development Status :: 5 - Production/Stable',
+
+    'Intended Audience :: Science/Research',
+    'Intended Audience :: Developers',
+    'Intended Audience :: Manufacturing',
+
+    'Topic :: Scientific/Engineering',
+    'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    'Topic :: Scientific/Engineering :: Physics',
+
+    'License :: Other/Proprietary License ',
+
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3.7',
+    'Programming Language :: Python :: 3.8',
+    'Programming Language :: Python :: 3.9',
+    'Programming Language :: Python :: 3.10',
+    'Programming Language :: Python :: 3.11',
+    'Programming Language :: Python :: 3 :: Only'
+]
+
+[project.optional-dependencies]
+datadriven = [
+    "tensorflow; platform_system!='Darwin' or platform_machine!='arm64'",
+    "tensorflow-macos; platform_system=='Darwin' and platform_machine=='arm64'"]
+
+[project.urls]
+Homepage = "https://nasa.github.io/progpy/"
+Documentation = "https://nasa.github.io/progpy/"
+Repository = "https://github.com/nasa/progpy"
+Issues = "https://github.com/nasa/progpy/issues"
+Organization = "https://www.nasa.gov/content/diagnostics-prognostics"

src/progpy/data_models/lstm_model.py

@@ -2,6 +2,7 @@
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
 from collections import abc
+import importlib.util
 from itertools import chain
 import matplotlib.pyplot as plt
 from numbers import Number
@@ -23,6 +24,15 @@ class LSTMStateTransitionModel(DataModel):
 
     Most users will use the :py:func:`LSTMStateTransitionModel.from_data` method to create a model, but the model can be created by passing in a model directly into the constructor. The LSTM model in this method maps from [u_t-n+1, z_t-n, ..., u_t, z_t-1] to z_t. Past :term:`input` are stored in the :term:`model` internal :term:`state`. Actual calculation of :term:`output` is performed when :py:func:`LSTMStateTransitionModel.output` is called. When using in simulation that may not be until the simulation results are accessed.
 
+    .. note::
+        ProgPy must be installed with [datadriven] option to use LSTM model. either
+
+        pip3 install progpy[datadriven]
+
+        or (if using local version)
+
+        pip3 install '.[datadriven]'
+
     Args:
         output_model (keras.Model): If a state model is present, maps from the state_model outputs to model :term:`output`. Otherwise, maps from model inputs to model :term:`output`
         state_model (keras.Model, optional): Keras model to use for state transition
@@ -528,7 +538,10 @@ def from_data(cls, inputs, outputs, event_states=None, t_met=None, **kwargs):
             params['normalization'] = (z_mean, z_std)
 
         # Tensorflow is imported here to avoid importing it if not needed
-        from tensorflow import keras
+        try:
+            from tensorflow import keras
+        except ImportError as e:
+            raise ImportError("Missing required dependencies for data-driven model. ProgPy was imported directly. Instead import with datadriven dependencies using pip3 install progpy[datadriven] or pip3 install -e '.[datadriven]' (if installing from local copy)")
 
         # Build model
         callbacks = [ ]