[FSTORE-1083] Add streamlit to recsys tutorial, GE and feature decsri…

…btions, move feature views and add docstr to feature functions (#214)

[FSTORE-1083] Add streamlit to recsys tutorial, GE and feature decsribtions, move feature views and add docstr to feature functions

advanced_tutorials/recommender-system/2_train_retrieval_model.ipynb

@@ -142,7 +142,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_df, val_df, test_df, y_train, y_val, y_test = feature_view.train_validation_test_split(\n",
+    "train_df, val_df, test_df, _, _, _ = feature_view.train_validation_test_split(\n",
     "    validation_size=0.1, \n",
     "    test_size=0.1,\n",
     "    description='Retrieval dataset splits',\n",
@@ -676,7 +676,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python",
    "language": "python",
    "name": "python3"
   },
@@ -690,9 +690,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

advanced_tutorials/recommender-system/3_build_index.ipynb

@@ -116,7 +116,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_df, val_df, test_df, y_train, y_val, y_test = feature_view.train_validation_test_split(\n",
+    "train_df, val_df, test_df, _, _, _ = feature_view.train_validation_test_split(\n",
     "    validation_size=0.1, \n",
     "    test_size=0.1,\n",
     "    description='Retrieval dataset splits',\n",
@@ -343,21 +343,14 @@
     "\n",
     "At this point we have a recommender system that is able to generate a set of candidate items for a customer. However, many of these could be poor, as the candidate model was trained with only a few subset of the features. In the next notebook, we'll create a ranking dataset to train a *ranking model* to do more fine-grained predictions."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python",
    "language": "python",
    "name": "python3"
   },
@@ -371,9 +364,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

advanced_tutorials/recommender-system/4_train_ranking_model.ipynb

@@ -54,8 +54,84 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "feature_view_ranking = fs.get_feature_view(\n",
+    "customers_fg = fs.get_feature_group(\n",
+    "    name=\"customers\",\n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "articles_fg = fs.get_feature_group(\n",
+    "    name=\"articles\",\n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "trans_fg = fs.get_feature_group(\n",
+    "    name=\"transactions\",\n",
+    "    version=1,\n",
+    ")\n",
+    "\n",
+    "rank_fg = fs.get_feature_group(\n",
+    "    name=\"ranking\",\n",
+    "    version=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color:#ff5f27\">⚙️ Feature View Creation </span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customers_query = customers_fg.select_all()\n",
+    "fs.get_or_create_feature_view( \n",
+    "    name='customers',\n",
+    "    query=customers_query,\n",
+    "    version=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "articles_query = articles_fg.select_all()\n",
+    "fs.get_or_create_feature_view(\n",
+    "    name='articles',\n",
+    "    query=articles_query,\n",
+    "    version=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rank_fg = fs.get_or_create_feature_group(\n",
+    "    name=\"ranking\",\n",
+    "    version=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ranking_query = rank_fg.select_except([\"customer_id\", \"article_id\"])\n",
+    "feature_view_ranking = fs.get_or_create_feature_view(\n",
     "    name='ranking',\n",
+    "    query=ranking_query,\n",
+    "    labels = [\"label\"],\n",
     "    version=1,\n",
     ")"
    ]
@@ -64,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## <span style=\"color:#ff5f27\">🗄️ Data loading </span>"
+    "## <span style=\"color:#ff5f27\">🗄️ Train Data loading </span>"
    ]
   },
   {
@@ -255,7 +331,7 @@
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python",
    "language": "python",
    "name": "python3"
   },
@@ -269,9 +345,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

advanced_tutorials/recommender-system/5_create_deployments.ipynb

@@ -558,7 +558,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python",
    "language": "python",
    "name": "python3"
   },
@@ -572,9 +572,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}