update to 3.13.3

.gitignore

@@ -1,2 +1,4 @@
 .idea/
-projects
+__pycache__
+projects
+.DS_Store

app.py

@@ -58,7 +58,7 @@ def anki_optimizer(file, timezone, next_day_starts_at, revlog_start_date, reques
 
 
 description = """
-# FSRS4Anki Optimizer App - v3.13.0
+# FSRS4Anki Optimizer App - v3.13.3
 Based on the [tutorial](https://medium.com/@JarrettYe/how-to-use-the-next-generation-spaced-repetition-algorithm-fsrs-on-anki-5a591ca562e2) 
 of [Jarrett Ye](https://github.com/L-M-Sherlock). This application can give you personalized anki parameters without having to code.
 

fsrs4anki_optimizer.ipynb

@@ -5,7 +5,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# FSRS4Anki v3.13.0 Optimizer"
+        "# FSRS4Anki v3.13.3 Optimizer"
       ]
     },
     {
@@ -15,7 +15,7 @@
         "id": "lurCmW0Jqz3s"
       },
       "source": [
-        "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/v3.13.0/fsrs4anki_optimizer.ipynb)\n",
+        "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/v3.13.3/fsrs4anki_optimizer.ipynb)\n",
         "\n",
         "↑ Click the above button to open the optimizer on Google Colab.\n",
         "\n",
@@ -172,7 +172,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "191e41fc14f34c1789b34cf09aaf92cb",
+              "model_id": "c9bb754c7ac441068199f88788b35a74",
               "version_major": 2,
               "version_minor": 0
             },
@@ -193,7 +193,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "102e8887ab69420b98d1a45ae1488714",
+              "model_id": "ec878c2155d74cf1adefa0c7a0c8052a",
               "version_major": 2,
               "version_minor": 0
             },
@@ -214,7 +214,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "122b8eeefd6b4411bb046c363d1d0b03",
+              "model_id": "38f567392f7b4c73b92b3b336b04df4b",
               "version_major": 2,
               "version_minor": 0
             },
@@ -235,7 +235,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "57d8bf7a22f743e4abbe5d59f6c642cc",
+              "model_id": "6966fe105e3d4c398100ae20c5fb57fb",
               "version_major": 2,
               "version_minor": 0
             },
@@ -296,8 +296,7 @@
         "              'last_lvl', 'factor', 'time', 'type']\n",
         "df = df[(df['cid'] <= time.time() * 1000) &\n",
         "        (df['id'] <= time.time() * 1000) &\n",
-        "        (df['r'] > 0) &\n",
-        "        (df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000)].copy()\n",
+        "        (df['r'] > 0)].copy()\n",
         "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
         "df['create_date'] = df['create_date'].dt.tz_localize(\n",
         "    'UTC').dt.tz_convert(timezone)\n",
@@ -337,6 +336,7 @@
         "\n",
         "tqdm.notebook.tqdm.pandas()\n",
         "df = df.groupby('cid', as_index=False).progress_apply(get_feature)\n",
+        "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
         "df[\"t_history\"] = df[\"t_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
         "df[\"r_history\"] = df[\"r_history\"].map(lambda x: x[1:] if len(x) > 1 else x)\n",
         "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
@@ -536,7 +536,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f875359e6b654aeb81d71ba1d3aa10f7",
+              "model_id": "749aeda9cb624986ae2872489bcc6762",
               "version_major": 2,
               "version_minor": 0
             },
@@ -557,7 +557,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "fa5c00f549ec4f9884381ca7a2c7d359",
+              "model_id": "9ca6df8fb70247afaa07389ebf5bb791",
               "version_major": 2,
               "version_minor": 0
             },
@@ -578,7 +578,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "be0c599e33f841a1ad07fa3ca8b2d22b",
+              "model_id": "278f800ad8a344098c6b4a5de627fec8",
               "version_major": 2,
               "version_minor": 0
             },
@@ -905,7 +905,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "f99be96c169b45f8a03d1355e71b679a",
+              "model_id": "7712ed30c62643f4aa834c840458158c",
               "version_major": 2,
               "version_minor": 0
             },
@@ -993,7 +993,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "da652ded0f0241058f8f29e67410ef26",
+              "model_id": "21d643c71d0540949822c71866f7e2b4",
               "version_major": 2,
               "version_minor": 0
             },
@@ -1137,7 +1137,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ca34a71cc6314383b98b249b4754f2a7",
+              "model_id": "63e8453d80124699b1f40395051577d7",
               "version_major": 2,
               "version_minor": 0
             },
@@ -1158,7 +1158,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8e836e6a67dd49db96d983e71c48f985",
+              "model_id": "e94ce239601948379611720ca3c73228",
               "version_major": 2,
               "version_minor": 0
             },

utilities.py

@@ -51,8 +51,7 @@ def create_time_series_features(revlog_start_date, timezone, next_day_starts_at,
                   'last_lvl', 'factor', 'time', 'type']
     df = df[(df['cid'] <= time.time() * 1000) &
             (df['id'] <= time.time() * 1000) &
-            (df['r'] > 0) &
-            (df['id'] >= time.mktime(datetime.strptime(revlog_start_date, "%Y-%m-%d").timetuple()) * 1000)].copy()
+            (df['r'] > 0)].copy()
     df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')
     df['create_date'] = df['create_date'].dt.tz_localize(
             'UTC').dt.tz_convert(timezone)
@@ -92,6 +91,7 @@ def create_time_series_features(revlog_start_date, timezone, next_day_starts_at,
 
     tqdm.pandas(desc='Saving Trainset')
     df = df.groupby('cid', as_index=False).progress_apply(get_feature)
+    df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, "%Y-%m-%d").timetuple()) * 1000]
     df["t_history"] = df["t_history"].map(lambda x: x[1:] if len(x) > 1 else x)
     df["r_history"] = df["r_history"].map(lambda x: x[1:] if len(x) > 1 else x)
     df.to_csv(proj_dir / 'revlog_history.tsv', sep="\t", index=False)