Update notebooks

.gitignore

@@ -0,0 +1,4 @@
+.venv
+debug.jsonl
+raiconfig.toml
+relationalai-vscode.toml

samples/tastybytes-communities/README.md

@@ -0,0 +1,25 @@
+
+# TastyBytes: Community Detection
+
+## Setup
+
+1. From this directory, do
+
+    ```sh
+    python -m venv .venv
+    source .venv/bin/activate
+    ```
+
+2. Then install dependencies with:
+
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+3. Lastly, do `rai init` to create a local `raiconfig.toml` file.
+
+## Running the notebook
+
+4. Do `jupyter` from the command line to start the Jupyter notebook server and open a browser.
+
+5. Do shift-enter to run each cell in the notebook.

samples/tastybytes-communities/community_detection_RelationalAI_V1.ipynb

@@ -43,10 +43,8 @@
     "\n",
     "import relationalai as rai\n",
     "from relationalai.clients.snowflake import Snowflake\n",
-    "from relationalai.std import aggregates, alias, rel\n",
+    "from relationalai.std import aggregates, rel\n",
     "from relationalai.std.graphs import Graph\n",
-    "from relationalai.std import Vars\n",
-    "\n",
     "\n",
     "random.seed(123)"
    ]
@@ -65,7 +63,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = rai.Model(\"LOYALTY_ORDERS_REGION_CALIFORNIA\")"
+    "model = rai.Model(\"LOYALTY_ORDERS_REGION_CALIFORNIA\")\n",
+    "model._config.set(\"compiler.use_multi_valued\", True)"
    ]
   },
   {
@@ -145,7 +144,7 @@
    "outputs": [],
    "source": [
     "# Define Customer Type\n",
-    "with model.rule(dynamic=True):\n",
+    "with model.rule():\n",
     "    r = Record()\n",
     "    Customer.add(customer_id=r.customer_id)\n",
     "\n",
@@ -236,7 +235,7 @@
     "    t1.customer_id != t2.customer_id\n",
     "    rel.abs(t1.order_ts_seconds - t2.order_ts_seconds) <= 1200\n",
     "\n",
-    "    t1.connected.add(t2)    "
+    "    t1.connected.add(t2)"
    ]
   },
   {
@@ -286,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Get the total occurrences where pairs of customers coexisted together more than once\n",
+    "# Get the number of occurrences where pairs of customers coexisted together more than once\n",
     "with model.query() as select:\n",
     "    record = RelevantConnection()\n",
     "    num_records = aggregates.count(record)\n",
@@ -311,7 +310,9 @@
    "source": [
     "community_graph = Graph(model, undirected=True)\n",
     "\n",
-    "# Add edges to the graph between customers / Nodes will be added automatically\n",
+    "community_graph.Node.extend(Customer, customer_id=Customer.customer_id)\n",
+    "\n",
+    "# Add edges to the graph between customers\n",
     "with model.rule():\n",
     "    connection = RelevantConnection()\n",
     "    community_graph.Edge.add(\n",
@@ -336,15 +337,9 @@
    "outputs": [],
    "source": [
     "with model.rule():\n",
-    "    customer = Customer()\n",
+    "    customer = community_graph.Node()\n",
     "    community_id = community_graph.compute.louvain(customer)\n",
-    "    customer.set(community_id=community_id)\n",
-    "\n",
-    "    community_graph.Node.add(\n",
-    "        customer,\n",
-    "        community_id=community_id,\n",
-    "        customer_id=customer.customer_id\n",
-    "    )"
+    "    customer.set(community_id=community_id)"
    ]
   },
   {
@@ -390,7 +385,8 @@
     "num_communities = len(community_set)\n",
     "print(f\"Number of communities: {num_communities}\")\n",
     "\n",
-    "random_colors = generate_random_colors(num_communities)\n",
+    "random_colors = generate_random_colors(num_communities + 1)\n",
+    "none_color = random_colors[-1]\n",
     "\n",
     "community_colors = {}\n",
     "for i, community in enumerate(community_set):\n",
@@ -410,7 +406,7 @@
     "    node_hover_neighborhood=True,\n",
     "    style={\n",
     "    \"node\": {\n",
-    "        \"color\": lambda x : community_colors[x['community_id']],\n",
+    "        \"color\": lambda n : community_colors[n['community_id']] if 'community_id' in n else none_color,\n",
     "        \"hover\": lambda x : f\"{x['customer_id']}\"\n",
     "    },\n",
     "    \"edge\": {\n",
@@ -434,7 +430,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_community_id_of_customer( customer_id ):\n",
+    "def get_community_id_of_customer(customer_id):\n",
     "    mycommunity = None\n",
     "    for i in data.values():\n",
     "        for p in i.values():\n",
@@ -471,15 +467,15 @@
     "    node_hover_neighborhood=True,\n",
     "    style={\n",
     "    \"node\": {\n",
-    "        \"color\": lambda x : community_colors_focus[x['community_id']],\n",
-    "        \"size\" : lambda x : 20 if x['community_id'] == community_id_of_interest else 2,\n",
-    "        \"hover\": lambda x : f\"{x['customer_id']}\"\n",
+    "        \"color\": lambda n : community_colors_focus[n['community_id']] if 'community_id' in n else none_color,\n",
+    "        \"size\" : lambda n : 20 if n.get('community_id', None) == community_id_of_interest else 2,\n",
+    "        \"hover\": lambda n : f\"{n['customer_id']}\"\n",
     "    },\n",
     "    \"edge\": {\n",
     "        \"opacity\": 0.8,\n",
     "        \"color\": \"#ccc\",\n",
     "        \n",
-    "        \"hover\": lambda x : x['weight']\n",
+    "        \"hover\": lambda e : e['weight']\n",
     "    }\n",
     "})"
    ]
@@ -508,7 +504,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

samples/tastybytes-communities/requirements.txt

@@ -0,0 +1,2 @@
+relationalai
+jupyter

samples/telecom_demo/.gitignore

@@ -1,2 +1,3 @@
 app/.streamlit
-app/__pycache__
+app/__pycache__
+raiconfig.toml