workign demo

Dockerfile

@@ -1,29 +1,31 @@
-ARG PYTHON_VERSION=3.11
+ARG PYTHON_VERSION=3.8
 FROM python:${PYTHON_VERSION}
 
-
 RUN apt-get update && \
-    apt-get install -y software-properties-common &&\
-	apt update && \
 	apt install -y graphviz
-	# add-apt-repository universe && \
 
 # https://python-poetry.org/docs#ci-recommendations
-ENV POETRY_VERSION=1.7.0
-# ENV POETRY_HOME=/opt/poetry
-ENV POETRY_VENV=/opt/poetry-venv
-
-# Tell Poetry where to place its cache and virtual environment
-ENV POETRY_CACHE_DIR=/opt/.cache
-
-# Creating a virtual environment just for poetry and install it with pip
-RUN python3 -m venv $POETRY_VENV \
-	&& $POETRY_VENV/bin/pip install -U pip setuptools \
-	&& $POETRY_VENV/bin/pip install poetry==${POETRY_VERSION}
-
-# Add Poetry to PATH
-ENV PATH="${PATH}:${POETRY_VENV}/bin"
-
-ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VERSION=1.7.0 \
+	# Poetry home directory
+    POETRY_HOME='/usr/local' \
+	# Add Poetry's bin folder to the PATH
+	PATH="/usr/local/bin:$PATH" \
+	# Avoids any interactions with the terminal
+    POETRY_NO_INTERACTION=1 \
+	# This avoids poetry from creating a virtualenv
+	# Instead, it directly installs the dependencies in the system's python environment
+    POETRY_VIRTUALENVS_CREATE=false
+
+# System deps:
+RUN curl -sSL https://install.python-poetry.org | python3 -
+
+# Copy the project files
+WORKDIR /home/specless
+COPY pyproject.toml poetry.lock /home/specless/
+
+# Project initialization and conditionally install cvxopt if on x86 architecture
+RUN poetry install --no-interaction
+# RUN poetry install --no-interaction && \
+#     if [ "$(uname -m)" = "x86_64" ]; then poetry add cvxopt; fi
 
 CMD ["bash"]

README.md

@@ -75,15 +75,10 @@ You can use the `specless` package in two ways: as a library, and as a CLI tool.
 ...    ["e1", "e4", "e2", "e3", "e5"],             # trace 2
 ...    ["e1", "e2", "e4", "e3", "e5"],             # trace 3
 ... ]
->>> dataset = sl.ArrayDataset(demonstrations, columns=["symbol"])
-
-# # or load from a file
-# >>> csv_filename = "examples/readme/example.csv"
-# >>> dataset = sl.BaseDataset(pd.read_csv(csv_filename))
 
 # Run the inference
 >>> inference = sl.POInferenceAlgorithm()
->>> specification = inference.infer(dataset)            # returns a Specification
+>>> specification = inference.infer(demonstrations)  # returns a Specification
 
 # prints the specification
 >>> print(specification) # doctest: +ELLIPSIS
@@ -118,10 +113,6 @@ The environment is based on the OpenAI Gym library (or more specifically, [Petti
 ...     num=10,
 ...     timeout=1000,
 ... )
-
-# Convert them to a Dataset Class
->>> demonstrations = sl.ArrayDataset(demonstrations, columns=["timestamp", "label"])
-
 ```
 
 - Once the specification is obtained, synthesize a strategy:
@@ -178,6 +169,9 @@ synthesize -d <path/to/demo> OR -s <LTLf formula> AND -e <Gym env> AND -p <path/
 ```
 
 
+## Docker + VSCode
+Use Dev Container.
+
 
 ## Development
 
@@ -191,7 +185,7 @@ If you want to contribute, set up your development environment as follows:
 
 To run all tests: `tox`
 
-To run only the code tests: `tox -e py39` or `tox -e py310`
+To run only the code tests: `tox -e py38` (or py39, py310, py311)
 
 To run doctests, `tox -e doctest`
 

examples/AircraftTurnaround/main.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Goal: Define task events & associated locations & costs"
+    "# Goal: Define task events & associated locations & costs\n"
    ]
   },
   {
@@ -71,7 +71,7 @@
     "Paper: https://www.tandfonline.com/doi/full/10.1080/21680566.2017.1325784\n",
     "\n",
     "Table 2 (Renamed to `ground_services_by_operations.csv`): https://www.tandfonline.com/action/downloadTable?id=T0002&doi=10.1080%2F21680566.2017.1325784&downloadType=CSV\n",
-    "Table 3 (Renamed to `duration.csv`): https://www.tandfonline.com/action/downloadTable?id=T0003&doi=10.1080%2F21680566.2017.1325784&downloadType=CSV"
+    "Table 3 (Renamed to `duration.csv`): https://www.tandfonline.com/action/downloadTable?id=T0003&doi=10.1080%2F21680566.2017.1325784&downloadType=CSV\n"
    ]
   },
   {
@@ -157,9 +157,7 @@
    ],
    "source": [
     "inference = sl.TPOInferenceAlgorithm()\n",
-    "columns: list = [\"timestamp\", \"symbol\"]\n",
-    "timedtrace_dataset = sl.ArrayDataset(demonstrations, columns)\n",
-    "specification: sl.Specification = inference.infer(timedtrace_dataset)\n",
+    "specification: sl.Specification = inference.infer(demonstrations)\n",
     "\n",
     "filepath = os.path.join(LOG_DIR, \"tpo.png\")\n",
     "sl.draw_graph(specification, filepath)\n",
@@ -238,7 +236,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Associate the name and the obervation label"
+    "### Associate the name and the obervation label\n"
    ]
   },
   {

examples/demo/learning.py

@@ -0,0 +1,45 @@
+import specless as sl  # or load from specless.inference import TPOInference
+
+
+def main():
+
+    ### Partial Order Inference
+
+    # Manually prepare a list of demonstrations
+    demonstrations = [
+        ["e1", "e2", "e3", "e4", "e5"],             # trace 1
+        ["e1", "e4", "e2", "e3", "e5"],             # trace 2
+        ["e1", "e2", "e4", "e3", "e5"],             # trace 3
+    ]
+
+    # Run the inference
+    inference = sl.POInferenceAlgorithm()
+    specification = inference.infer(demonstrations) # returns a Specification
+
+    # prints the specification
+    print(specification) # doctest: +ELLIPSIS
+
+    # exports the specification to a file
+
+    # drawws the specification to a file
+    sl.draw_graph(specification, filepath='spec')
+
+    ### Timed Partial Order Inference
+
+    # Manually prepare a list of demonstrations
+    demonstrations: list = [
+        [[1, "a"], [2, "b"], [3, "c"]],
+        [[4, "d"], [5, "e"], [6, "f"]],
+    ]
+    columns: list = ["timestamp", "symbol"]
+
+    timedtrace_dataset = sl.ArrayDataset(demonstrations, columns)
+
+    # Timed Partial Order Inference
+    inference = sl.TPOInferenceAlgorithm()
+    specification: sl.Specification = inference.infer(timedtrace_dataset)
+
+
+if __name__ == "__main__":
+    main()
+