Merge branch 'main' into Feat/post-lapse-stability-penalty

.github/workflows/ruff.yml

@@ -0,0 +1,8 @@
+name: Ruff
+on: [ push, pull_request ]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1

.github/workflows/test.yml

@@ -0,0 +1,23 @@
+name: Test Python
+
+on: [push, pull_request]
+
+jobs:
+    test:
+
+        runs-on: ubuntu-latest
+
+        steps:
+        - uses: actions/checkout@v4
+        - name: Set up Python
+          uses: actions/setup-python@v3
+          with:
+            python-version: '3.x'
+        - name: Install dependencies
+          run: |
+            python -m pip install --upgrade pip
+            pip install .
+            pip install pytest
+        - name: Test with pytest
+          run: |
+            pytest

README.md

@@ -1,5 +1,7 @@
 # FSRS Optimizer
 
+[![PyPi](https://img.shields.io/pypi/v/FSRS-Optimizer)](https://pypi.org/project/FSRS-Optimizer/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
 The FSRS Optimizer is a Python library capable of utilizing personal spaced repetition review logs to refine the FSRS algorithm. Designed with the intent of delivering a standardized, universal optimizer to various FSRS implementations across numerous programming languages, this tool is set to establish a ubiquitous standard for spaced repetition review logs. By facilitating the uniformity of learning data among different spaced repetition softwares, it guarantees learners consistent review schedules across a multitude of platforms.
 
 Delve into the underlying principles of the FSRS Optimizer's training process at: https://github.com/open-spaced-repetition/fsrs4anki/wiki/The-mechanism-of-optimization
@@ -62,3 +64,9 @@ python -m fsrs_optimizer "revlog.csv"
 ![image](https://github.com/open-spaced-repetition/fsrs-optimizer/assets/32575846/fad7154a-9667-4eea-b868-d94c94a50912)
 
 ![image](https://github.com/open-spaced-repetition/fsrs-optimizer/assets/32575846/f868aac4-2e9e-4101-b8ad-eccc1d9b1bd5)
+
+---
+
+## Alternative
+
+Are you getting tired of installing torch? Try [fsrs-rs-python](https://github.com/open-spaced-repetition/fsrs-rs-python)!

pyproject.toml

@@ -4,19 +4,22 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "FSRS-Optimizer"
-version = "4.17.0"
+version = "5.3.0"
 readme = "README.md"
 dependencies = [
     "matplotlib>=3.7.0",
     "numpy>=1.22.4",
     "pandas>=1.5.3",
     "pytz>=2022.7.1",
-    "scikit_learn>=1.2.2",
+    "scikit_learn>=1.4.0",
     "torch>=1.13.1",
     "tqdm>=4.64.1",
     "statsmodels>=0.13.5",
+    "scipy<1.14.1"
 ]
 requires-python = ">=3.9"
 
 [project.urls]
 Homepage = "https://github.com/open-spaced-repetition/fsrs-optimizer"
+[tool.ruff.lint]
+ignore = ["F405", "F403", "E712", "F541", "E722", "E741"]

src/fsrs_optimizer/__main__.py

@@ -24,7 +24,7 @@ def prompt(msg: str, fallback):
     return response
 
 
-def process(filepath, filter_out_flags: list[str]):
+def process(filepath, filter_out_flags: list[int]):
     suffix = filepath.split("/")[-1].replace(".", "_").replace("@", "_")
     proj_dir = Path(f"{suffix}")
     proj_dir.mkdir(parents=True, exist_ok=True)
@@ -60,7 +60,8 @@ def remembered_fallback_prompt(key: str, pretty: str = None):
 
         remembered_fallback_prompt("next_day", "used next day start hour")
         remembered_fallback_prompt(
-            "revlog_start_date", "the date at which before reviews will be ignored"
+            "revlog_start_date",
+            "the date at which before reviews will be ignored | YYYY-MM-DD",
         )
         remembered_fallback_prompt(
             "filter_out_suspended_cards", "filter out suspended cards? (y/n)"
@@ -72,6 +73,8 @@ def remembered_fallback_prompt(key: str, pretty: str = None):
 
     if graphs_input.lower() != "y":
         remembered_fallbacks["preview"] = "n"
+    else:
+        remembered_fallbacks["preview"] = "y"
 
     with open(
         config_save, "w+"
@@ -94,6 +97,7 @@ def remembered_fallback_prompt(key: str, pretty: str = None):
         remembered_fallbacks["timezone"],
         remembered_fallbacks["revlog_start_date"],
         remembered_fallbacks["next_day"],
+        save_graphs,
     )
     print(analysis)
 
@@ -110,13 +114,17 @@ def remembered_fallback_prompt(key: str, pretty: str = None):
         plt.close(f)
 
     optimizer.predict_memory_states()
-    figures = optimizer.find_optimal_retention()
-    if save_graphs:
+    try:
+        figures = optimizer.find_optimal_retention(verbose=save_graphs)
         for i, f in enumerate(figures):
             f.savefig(f"find_optimal_retention_{i}.png")
             plt.close(f)
+    except Exception as e:
+        print(e)
+        print("Failed to find optimal retention")
+        optimizer.optimal_retention = 0.9
 
-    optimizer.preview(optimizer.optimal_retention)
+    print(optimizer.preview(optimizer.optimal_retention))
 
     profile = f"""{{
     // Generated, Optimized anki deck settings