From a291fecea7e6b8eae0c49b71dedb6237a198eb4e Mon Sep 17 00:00:00 2001
From: David Spilde <david.s.spilde@gmail.com>
Date: Fri, 10 Apr 2026 12:04:58 +0200
Subject: [PATCH 1/2] masse greier

---
 hybrid/baseline_script.py                   |   5 +-
 hybrid/hybrid.ipynb                         |   6 +-
 hybrid/hybrid.py                            |  25 +
 pyproject.toml                              |   1 +
 scripts/david/cf.ipynb                      | 171 -----
 scripts/david/collaborative_filtering.py    | 154 ++++-
 scripts/david/create_truths.py              |  25 +
 scripts/david/evaluation.py                 | 112 ++++
 scripts/david/full/baseline/baseline.py     | 107 ++++
 scripts/david/full/collaborative/collab.py  |  26 +
 scripts/david/full/main.py                  |  39 ++
 scripts/{felles => david}/implementation.py |   4 -
 scripts/{felles => david}/main.py           |  10 +-
 scripts/felles/evaluation.py                |  11 -
 uv.lock                                     | 658 ++++++++++----------
 15 files changed, 824 insertions(+), 530 deletions(-)
 create mode 100644 hybrid/hybrid.py
 delete mode 100644 scripts/david/cf.ipynb
 create mode 100644 scripts/david/create_truths.py
 create mode 100644 scripts/david/evaluation.py
 create mode 100644 scripts/david/full/baseline/baseline.py
 create mode 100644 scripts/david/full/collaborative/collab.py
 create mode 100644 scripts/david/full/main.py
 rename scripts/{felles => david}/implementation.py (63%)
 rename scripts/{felles => david}/main.py (59%)
 delete mode 100644 scripts/felles/evaluation.py

diff --git a/hybrid/baseline_script.py b/hybrid/baseline_script.py
index 525b2d7..9838bd0 100644
--- a/hybrid/baseline_script.py
+++ b/hybrid/baseline_script.py
@@ -3,7 +3,7 @@
 from datetime import timedelta
 
 
-def popularity_split_baseline(behaviors_df, articles_df, train_slice=100000, window_size=10000):
+def popularity_split_baseline(behaviors_df, articles_df, train_slice=100_000, window_size=100_00):
     behaviors_df = behaviors_df.sort_values('impression_time').reset_index(drop=True)
     
     train_df = behaviors_df.iloc[:train_slice]
@@ -37,10 +37,9 @@ def popularity_split_baseline(behaviors_df, articles_df, train_slice=100000, win
 def popularity_baseline(behaviors_df, articles_df):
     total_hr20 = 0
     num_clicks = 0
-
     behaviors_df = behaviors_df.sort_values('impression_time')
 
-    batch_size = 10000
+    batch_size = 10_000
     for i in range(0, len(behaviors_df), batch_size):
         chunk = behaviors_df.iloc[i : i + batch_size]
         current_time = chunk['impression_time'].max()
diff --git a/hybrid/hybrid.ipynb b/hybrid/hybrid.ipynb
index 911ae55..49342c1 100644
--- a/hybrid/hybrid.ipynb
+++ b/hybrid/hybrid.ipynb
@@ -17,15 +17,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "0051c91c",
    "metadata": {},
    "outputs": [],
    "source": [
     "def read_data():\n",
-    "    basedir = \"../data/raw/train/\"\n",
+    "    basedir = \"../data/raw/\"\n",
     "    print(\"Loading data...\")\n",
-    "    behaviors = pd.read_parquet(basedir+'behaviors.parquet')\n",
+    "    behaviors = pd.read_parquet(basedir+'train/behaviors.parquet')\n",
     "    articles = pd.read_parquet(basedir+'articles.parquet').set_index('article_id')\n",
     "    behaviors['impression_time'] = pd.to_datetime(behaviors['impression_time'])\n",
     "    articles['published_time'] = pd.to_datetime(articles['published_time'])\n",
diff --git a/hybrid/hybrid.py b/hybrid/hybrid.py
new file mode 100644
index 0000000..17c2549
--- /dev/null
+++ b/hybrid/hybrid.py
@@ -0,0 +1,25 @@
+import polars as pl
+import pandas as pd
+
+
+from baseline_script import popularity_baseline_hybrid, random_baseline_hybrid
+from content import *
+from collaborative import *
+
+
+# Read the behaviors and articles data
+def read_data_hybrid(start=None,length=None) -> tuple[pd.DataFrame,pd.DataFrame]:
+    basedir = "../data/raw/"
+    print("Loading data...")
+    if start is None or length is None:
+        raise ValueError
+    behaviors = pl.scan_parquet(basedir+'train/behaviors.parquet').slice(start,length).collect().to_pandas()
+    articles = pl.read_parquet(basedir+'articles.parquet').to_pandas()
+    return behaviors, articles
+
+
+if __name__ == "__main__":
+    behaviors, articles = read_data_hybrid(2_000_000,1_000_000)
+    # Creating the baseline
+    popularity_baseline_hybrid(behaviors,articles, top_k=5)
+    random_baseline_hybrid(behaviors,articles)
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index b0c2d7f..ed3007d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,4 +15,5 @@ dependencies = [
     "stop-words>=2025.11.4",
     "jupyter>=1.1.1",
     "ipykernel>=6.29.0",
+    "tqdm>=4.66.0",
 ]
diff --git a/scripts/david/cf.ipynb b/scripts/david/cf.ipynb
deleted file mode 100644
index d52551a..0000000
--- a/scripts/david/cf.ipynb
+++ /dev/null
@@ -1,171 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ecbcc172",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.preprocessing import normalize \n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ac43e208",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#b_df = pd.read_parquet(\"../../data/raw/train/behaviors.parquet\")\n",
-    "h_df = pd.read_parquet(\"../../data/raw/train/history.parquet\")\n",
-    "#a_df = pd.read_parquet(\"../../data/raw/articles.parquet\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a26f3199",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#h_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0784f7e8",
-   "metadata": {},
-   "source": [
-    "### Creating the user article matrix"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1680d082",
-   "metadata": {},
-   "source": [
-    "**TF**\n",
-    "read_time 1+log(readtime)\n",
-    "**IDF**\n",
-    "log(N/df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ed6b4536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df\n",
-    "df_articles = {}\n",
-    "N_articles = 0\n",
-    "for _, row in h_df.iterrows():\n",
-    "    article_ids = row['article_id_fixed']\n",
-    "    for id in article_ids:\n",
-    "        if id not in df_articles:\n",
-    "            df_articles[id] = 0\n",
-    "            N_articles += 1\n",
-    "        df_articles[id] += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "46c97f07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "tf_idf_matrix = {}\n",
-    "\n",
-    "for _, row in h_df.iterrows():\n",
-    "    article_ids = row['article_id_fixed']\n",
-    "    user_id = row['user_id']\n",
-    "    read_time = row['read_time_fixed']\n",
-    "    tf_idf_matrix[user_id] = {}\n",
-    "    for index, id in enumerate(article_ids):\n",
-    "        try:\n",
-    "            tf_idf_matrix[user_id][id] = (1+math.log(read_time[index]))*(math.log(N_articles/df_articles[id]))\n",
-    "        except (ValueError):\n",
-    "            pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ce08afb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "UAM_norm = {}\n",
-    "\n",
-    "for user, article_i in tf_idf_matrix.items():\n",
-    "    norm = math.sqrt(sum(score**2 for score in article_i.values()))\n",
-    "    if norm == 0:\n",
-    "        UAM_norm[user] = {article_id: 0.0 for article_id in article_i}\n",
-    "    else:\n",
-    "        UAM_norm[user] = {article_id: score /norm for article_id ,score in article_i.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f23fefe0",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unsupported operand type(s) for *: 'dict' and 'dict'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[8], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m     np\u001b[38;5;241m.\u001b[39msort(sim)\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m sim[\u001b[38;5;241m0\u001b[39m:k]\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mtop_k_similar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mUAM_norm\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m10033\u001b[39;49m\u001b[43m)\u001b[49m)\n",
-      "Cell \u001b[1;32mIn[8], line 5\u001b[0m, in \u001b[0;36mtop_k_similar\u001b[1;34m(matrix, k, user_id)\u001b[0m\n\u001b[0;32m      2\u001b[0m sim \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m user, article \u001b[38;5;129;01min\u001b[39;00m matrix\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m----> 5\u001b[0m     sim[user] \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmatrix\u001b[49m\u001b[43m[\u001b[49m\u001b[43muser_id\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmatrix\u001b[49m\u001b[43m[\u001b[49m\u001b[43muser\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m/\u001b[39mnorm(matrix[user_id]\u001b[38;5;241m*\u001b[39mnorm(matrix[user]))\n\u001b[0;32m      6\u001b[0m np\u001b[38;5;241m.\u001b[39msort(sim)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sim[\u001b[38;5;241m0\u001b[39m:k]\n",
-      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'dict' and 'dict'"
-     ]
-    }
-   ],
-   "source": [
-    "# Sim score = dot()\n",
-    "\n",
-    "def top_k_similar(matrix, k, user_id):\n",
-    "    sim = {}\n",
-    "    articles = matrix[user_id]\n",
-    "    for user in matrix:\n",
-    "        for article  in articles:\n",
-    "            sim = np.dot(matrix[user_id][article], matrix[user][article]) \n",
-    "    sim = np.sort(list(sim.values()))\n",
-    "    return list(sim.items())[0:k]\n",
-    "\n",
-    "print(top_k_similar(UAM_norm,5,10033))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/scripts/david/collaborative_filtering.py b/scripts/david/collaborative_filtering.py
index ecbb7da..c0da6b0 100644
--- a/scripts/david/collaborative_filtering.py
+++ b/scripts/david/collaborative_filtering.py
@@ -1,8 +1,152 @@
-from felles.implementation import Implementation
+from scripts.david.implementation import Implementation
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import normalize
+from scipy.sparse import csr_matrix
+from sklearn.neighbors import NearestNeighbors
+from typing import Any
+
 
 class CF(Implementation):
+
     def init(self) -> None:
-        return super().init()
-    
-    def recommendation(self) -> None:
-        return super().recommendation()
\ No newline at end of file
+        self.h_df = pd.read_parquet("../data/raw/train/history.parquet")
+        self.b_df = pd.read_parquet("../data/raw/train/behaviors.parquet")
+
+        self.uim = None
+        self.user_map = {}
+        self.article_map = {}
+        self.inv_user_map = {}
+        self.inv_article_map = {}
+        self.nn = None
+
+    def _scalar(self, x: Any) -> Any:
+        if isinstance(x, (list, tuple, np.ndarray)):
+            if len(x) == 0:
+                return np.nan
+            return x[0]
+        return x
+
+    def user_item_matrix(self):
+
+        # Ensure scalar values
+        self.h_df["article_id_fixed"] = self.h_df["article_id_fixed"].apply(self._scalar)
+        self.h_df["scroll_percentage_fixed"] = self.h_df["scroll_percentage_fixed"].apply(self._scalar)
+
+        # Convert types safely
+        self.h_df["scroll_percentage_fixed"] = pd.to_numeric(
+            self.h_df["scroll_percentage_fixed"], errors="coerce"
+        ).astype(np.float32)
+
+        self.h_df = self.h_df.dropna(subset=["user_id", "article_id_fixed", "scroll_percentage_fixed"])
+
+        # Factorize users and articles
+        user_codes, users = pd.factorize(self.h_df["user_id"])
+        article_codes, articles = pd.factorize(self.h_df["article_id_fixed"])
+
+        self.user_map = {u: i for i, u in enumerate(users)}
+        self.article_map = {a: i for i, a in enumerate(articles)}
+        self.inv_user_map = {i: u for i, u in enumerate(users)}
+        self.inv_article_map = {i: a for i, a in enumerate(articles)}
+
+        # Aggregate scroll percentage
+        grouped = (
+            self.h_df
+            .groupby(["user_id", "article_id_fixed"], as_index=False)["scroll_percentage_fixed"]
+            .max()
+        )
+
+        # Build sparse matrix
+        data = grouped["scroll_percentage_fixed"].to_numpy(dtype=np.float32)
+
+        row = grouped["user_id"].map(self.user_map).to_numpy(dtype=np.int32)
+        col = grouped["article_id_fixed"].map(self.article_map).to_numpy(dtype=np.int32)
+
+        self.uim = csr_matrix(
+            (data, (row, col)),
+            shape=(len(users), len(articles)),
+            dtype=np.float32
+        )
+
+        # Normalize users
+        self.uim = normalize(self.uim, norm="l2", axis=1)
+
+        # Fit nearest neighbors
+        if self.uim.shape is None:
+            raise RuntimeError
+        n_neighbors = min(50, self.uim.shape[0])
+
+        self.nn = NearestNeighbors(
+            n_neighbors=n_neighbors,
+            metric="cosine",
+            algorithm="brute"
+        )
+
+        self.nn.fit(self.uim)
+
+        return self.uim
+
+
+    def print_head(self):
+        print("Behaviors columns:", self.b_df.columns)
+        print("History columns:", self.h_df.columns)
+
+
+    def get_recommendation(self, user_id, top_k=10):
+        if self.uim is None or self.nn is None:
+            raise RuntimeError("Model not initialized. Run user_item_matrix() first.")
+        if user_id not in self.user_map:
+            return []
+
+        user_idx = self.user_map[user_id]
+        user_vec = self.uim[user_idx]
+
+        distances, indices = self.nn.kneighbors(user_vec)
+
+        # Remove self neighbor
+        neighbor_indices = indices[0][1:]
+
+        if len(neighbor_indices) == 0:
+            return []
+
+        neighbor_vecs = self.uim[neighbor_indices]
+
+        pred_vec = np.asarray(neighbor_vecs.mean(axis=0)).ravel()
+
+        seen_cols = user_vec.indices
+        pred_vec[seen_cols] = 0
+
+        pred_dense = np.asarray(pred_vec).flatten()
+
+        if pred_dense.sum() == 0:
+            return []
+
+        top_indices = np.argpartition(pred_dense, -top_k)[-top_k:]
+        top_indices = top_indices[np.argsort(pred_dense[top_indices])[::-1]]
+
+        recommendations = [
+            self.inv_article_map[i]
+            for i in top_indices
+            if pred_dense[i] > 0
+        ]
+
+        return recommendations[:top_k]
+
+
+def main():
+    collab = CF()
+    collab.init()
+
+    collab.user_item_matrix()
+
+    collab.print_head()
+
+    # example test
+    example_user = collab.h_df["user_id"].iloc[0]
+    recs = collab.get_recommendation(example_user)
+
+    print("Recommendations:", recs)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/scripts/david/create_truths.py b/scripts/david/create_truths.py
new file mode 100644
index 0000000..59e3865
--- /dev/null
+++ b/scripts/david/create_truths.py
@@ -0,0 +1,25 @@
+import pandas as pd
+
+
+def create_truths (b_df:pd.DataFrame, h_df:pd.DataFrame):
+
+    # Get users in validation
+    val_users = b_df['user_id'].unique()
+    
+    # Get truths: assume the next article is the first clicked in behaviors
+    print("før truhts")
+    lookup = dict(zip(b_df["user_id"], b_df["article_ids_clicked"]))
+
+    truths = []
+    valid_users = []
+
+    for user in val_users:
+        clicked = lookup.get(user)
+        print(clicked)
+        if isinstance(clicked, list) and clicked:
+            truths.append(clicked[0])
+        else:
+            truths.append(None)
+
+        valid_users.append(user)
+    print(truths)
\ No newline at end of file
diff --git a/scripts/david/evaluation.py b/scripts/david/evaluation.py
new file mode 100644
index 0000000..78a2d76
--- /dev/null
+++ b/scripts/david/evaluation.py
@@ -0,0 +1,112 @@
+# Evaluation metrics
+
+import math
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from scripts.david.collaborative_filtering import CF
+from scripts.david.create_truths import create_truths
+
+# Root Mean Squared Error
+def rmse(predictions, truths):
+    return np.sqrt(np.mean((np.array(predictions) - np.array(truths))**2))
+
+# Mean Absolute Error
+def mae(predictions, truths):
+    return np.mean(np.abs(np.array(predictions) - np.array(truths)))
+
+# Log Likelihood
+def loglhood(predictions, truths):
+    # For binary, but here perhaps not
+    pass
+
+# Ranking metrics
+
+# Global
+def prcurve(predictions, truths):
+    pass
+
+def roc(predictions, truths):
+    pass
+
+def spearmans(predictions, truths):
+    pass
+
+def kendalls(predictions, truths):
+    pass
+
+# Local
+def patk(predictions, truths, k=10):
+    count = 0
+    total = 0
+    for pred, true in zip(predictions, truths):
+        if true is not None:
+            total += 1
+            if true in pred[:k]:
+                count += 1
+    return count / total if total > 0 else 0
+
+def map(predictions, truths):
+    ap_scores = []
+    for pred, true in zip(predictions, truths):
+        if true is not None and true in pred:
+            pos = pred.index(true) + 1
+            ap = 1 / pos  # since only one relevant
+            ap_scores.append(ap)
+        else:
+            ap_scores.append(0)
+    return np.mean(ap_scores)
+
+def ndcg(predictions, truths, k=10):
+    ndcg_scores = []
+    for pred, true in zip(predictions, truths):
+        if true is not None and true in pred[:k]:
+            rank = pred.index(true) + 1
+            dcg = 1 / math.log2(rank + 1)
+            idcg = 1  # for one relevant at position 1
+            ndcg_scores.append(dcg / idcg)
+        else:
+            ndcg_scores.append(0)
+    return np.mean(ndcg_scores)
+
+# Online evaluation
+
+def evaluation_stats():
+    pass
+
+def evaluation_offline():
+    h_df = pd.read_parquet("./data/raw/train/history.parquet")
+    b_df = pd.read_parquet("./data/raw/train/behaviors.parquet")
+    small_h_df = h_df.loc[0:1].copy()
+    small_b_df = b_df.loc[0:1].copy()
+    create_truths(small_b_df,small_h_df)
+    return
+    # Initialize CF
+    cf = CF()
+    cf.init()
+    print("før item_matrix")
+    cf.user_item_matrix()
+    print("etter matriix sui")
+    
+    # Get predictions
+    predictions = [cf.get_recommendation(user, top_k=10) for user in tqdm(valid_users, desc="Generating recommendations")]
+    print("etter predict")
+
+
+    # Compute metrics
+    ndcg_score = ndcg(predictions, truths)
+    patk_score = patk(predictions, truths, k=10)
+    map_score = map(predictions, truths)
+    
+    print(f"NDCG@10: {ndcg_score:.4f}")
+    print(f"P@10: {patk_score:.4f}")
+    print(f"MAP: {map_score:.4f}")
+
+def evaluation_online():
+    pass
+
+
+EVALUATIONS = {
+    "offline": evaluation_offline,
+    "online": evaluation_online
+}
diff --git a/scripts/david/full/baseline/baseline.py b/scripts/david/full/baseline/baseline.py
new file mode 100644
index 0000000..813582e
--- /dev/null
+++ b/scripts/david/full/baseline/baseline.py
@@ -0,0 +1,107 @@
+from datetime import datetime
+import pandas as pd
+import numpy as np
+
+# This uses ranks the inview articles choosen in the data. In future, create recommendation based on global popularity and random
+
+def popularity_baseline_hybrid(
+    behaviors_df: pd.DataFrame,
+    articles_df: pd.DataFrame,
+    top_k: int = 20,
+) -> dict:
+    """
+    Popularity baseline using the inview candidate set per impression.
+    
+    Ranks inview articles by their total_pageviews, recommends the top-K,
+    and measures HR@K against clicked articles.
+    Falls back to random ranking for articles with null pageviews.
+    """
+    if behaviors_df.empty:
+        return {"hr_at_k": 0.0, "hits": 0, "impressions": 0, "top_k": top_k}
+
+    # Build a pageview lookup; fill nulls with 0 so they rank last
+    pageview_lookup = (
+        articles_df["total_pageviews"]
+        .fillna(0)
+        .astype(int)
+        .to_dict()
+    )
+
+    total_hits = 0
+    total_impressions = 0
+
+    for _, row in behaviors_df.iterrows():
+        inview = row["article_ids_inview"]
+        clicked = row["article_ids_clicked"]
+
+        if not inview.any() or not clicked.any():
+            continue
+
+        # Rank inview articles by global popularity
+        ranked = sorted(inview, key=lambda aid: pageview_lookup.get(aid, 0), reverse=True)
+        recommended = ranked[:top_k]
+
+        if any(aid in recommended for aid in clicked):
+            total_hits += 1
+        total_impressions += 1
+
+    hr = total_hits / total_impressions if total_impressions > 0 else 0.0
+    print(f"HR@{top_k}: {hr:.4%}  ({total_hits}/{total_impressions} impressions)")
+    return {"hr_at_k": hr, "hits": total_hits, "impressions": total_impressions, "top_k": top_k}
+
+
+def random_baseline_hybrid(
+    behaviors_df: pd.DataFrame,
+    articles_df: pd.DataFrame,
+    top_k: int=20, 
+    ) -> dict:
+    """
+    Docstring for random_baseline_hybrid
+    
+    :param behaviors_df: Description
+    :type behaviors_df: pd.DataFrame
+    :param articles_df: Description
+    :type articles_df: pd.DataFrame
+    :param top_k: Description
+    :type top_k: int
+    :return: Description
+    :rtype: dict[Any, Any]
+    """
+
+    if behaviors_df.empty:
+        return {"hr_at_k": 0.0, "hits": 0, "impressions": 0, "top_k": top_k}
+    
+    total_hits = 0
+    total_impressions = 0
+
+    for _, row in behaviors_df.iterrows():
+        inview = row["article_ids_inview"]
+        clicked = row["article_ids_clicked"]
+
+        if not inview.any() or not clicked.any():
+            continue
+
+        ranked = np.random.permutation(inview)
+        recommended = ranked[:top_k]
+
+        if any(aid in recommended for aid in clicked):
+            total_hits += 1
+        total_impressions += 1
+
+    hr = total_hits / total_impressions if total_impressions > 0 else 0
+    print(f"HR@{top_k}: {hr:.4%}  ({total_hits}/{total_impressions} impressions using Random)")
+    return {"hr_at_k":hr, "hits": total_hits, "impressions": total_impressions, "top_k": top_k}
+
+
+# Returns the most popular from the week before the timestamp
+def popularity_baseline_global(articles_df:pd.DataFrame, timestamp:datetime ,amount=20, window_hours=168):
+    mask = (
+        (articles_df["published_time"] < timestamp) &
+        (articles_df["published_time"] >= timestamp - pd.Timedelta(hours=window_hours))
+    )
+    timeframe_articles = (
+        articles_df.loc[mask]
+        .sort_values("total_pageviews", ascending=False)
+        .head(amount)
+    )
+    return timeframe_articles
\ No newline at end of file
diff --git a/scripts/david/full/collaborative/collab.py b/scripts/david/full/collaborative/collab.py
new file mode 100644
index 0000000..63946f2
--- /dev/null
+++ b/scripts/david/full/collaborative/collab.py
@@ -0,0 +1,26 @@
+from typing import List, Optional
+import pandas as pd
+import numpy as np
+from numpy.linalg import norm
+from sklearn.metrics.pairwise import cosine_similarity
+from scipy.sparse import csr_matrix
+
+def build_user_article_matrix(
+        histories_df:pd.DataFrame
+        ) -> dict[int,List[int]]:
+    user_article_matrix = {}
+    for _,row in histories_df.iterrows():
+        user_article_matrix[row["user_id"]] = row["article_id_fixed"]
+    print("Finished building matrix!!\n")
+    return user_article_matrix
+
+
+def apply_knn(UAMatrix:dict[int,List[int]], uid1:int, top_k=5):
+    users_articles = UAMatrix[uid1]  
+    nearestNeighbors = {}
+    for uid, aids in UAMatrix.items():
+        if uid == uid1:
+            continue
+        cosine:float = np.dot()
+        nearestNeighbors[uid] = cosine
+    print(sorted(nearestNeighbors.items(),reverse=True))
\ No newline at end of file
diff --git a/scripts/david/full/main.py b/scripts/david/full/main.py
new file mode 100644
index 0000000..7fdab23
--- /dev/null
+++ b/scripts/david/full/main.py
@@ -0,0 +1,39 @@
+from datetime import datetime
+import polars as pl
+import pandas as pd
+
+
+from baseline.baseline import *
+from content import *
+from collaborative.collab import *
+from evaluation import *
+
+
+# Read the behaviors and articles data
+def read_data_hybrid(start=None,length=None) -> tuple[pd.DataFrame,pd.DataFrame,pd.DataFrame]:
+    basedir = "./data/raw/"
+    print("Loading data...")
+    if start is None or length is None:
+        raise ValueError
+    behaviors = pl.scan_parquet(basedir+'train/behaviors.parquet').slice(start,length).collect().to_pandas()
+    articles = pl.read_parquet(basedir+'articles.parquet').to_pandas()
+    histories = pl.read_parquet(basedir + "/train/history.parquet").to_pandas()
+    print("Loading Complete")
+    return behaviors, articles, histories
+
+
+if __name__ == "__main__":
+    behaviors, articles, histories = read_data_hybrid(2_000_000,1_000_000)
+    UAMatrix = build_user_article_matrix(histories)
+    apply_knn(UAMatrix,10029)
+    
+    #baseline = popularity_baseline_global(articles,datetime(2023, 5, 28, 4, 31, 49))
+    
+    
+    # Using the baselines
+    #popularity_baseline_hybrid(behaviors,articles, top_k=5)
+    #random_baseline_hybrid(behaviors,articles, top_k=5)
+
+    
+    # Content-based filtering
+    
\ No newline at end of file
diff --git a/scripts/felles/implementation.py b/scripts/david/implementation.py
similarity index 63%
rename from scripts/felles/implementation.py
rename to scripts/david/implementation.py
index 7e445dd..685c4a8 100644
--- a/scripts/felles/implementation.py
+++ b/scripts/david/implementation.py
@@ -5,7 +5,3 @@ class Implementation(ABC):
     @abstractmethod
     def init(self) -> None:
         pass
-
-    @abstractmethod
-    def recommendation(self) -> None:
-        pass
\ No newline at end of file
diff --git a/scripts/felles/main.py b/scripts/david/main.py
similarity index 59%
rename from scripts/felles/main.py
rename to scripts/david/main.py
index 382548a..41cb218 100644
--- a/scripts/felles/main.py
+++ b/scripts/david/main.py
@@ -1,22 +1,22 @@
 import argparse
-from felles.evaluation import EVALUATIONS
+from scripts.david.evaluation import EVALUATIONS
 
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "-p", "--path",
-        required=True,
+        required=False,
     )
     parser.add_argument(
         "-e", "--eval",
-        default="local",
+        default="offline",
         required=False,
     )
 
     args = parser.parse_args()
 
-    impl = args.path
-    eval = EVALUATIONS[args.eval]
+    eval_func = EVALUATIONS.get(args.eval, EVALUATIONS["offline"])
+    eval_func()
 
 
 if __name__ == "__main__":
diff --git a/scripts/felles/evaluation.py b/scripts/felles/evaluation.py
deleted file mode 100644
index 2f7860c..0000000
--- a/scripts/felles/evaluation.py
+++ /dev/null
@@ -1,11 +0,0 @@
-def evaluation_local():
-    pass
-
-def evaluation_global():
-    pass
-
-
-EVALUATIONS = {
-    "local": evaluation_local,
-    "global": evaluation_global
-}
diff --git a/uv.lock b/uv.lock
index 217bbc9..417f2ed 100644
--- a/uv.lock
+++ b/uv.lock
@@ -22,6 +22,7 @@ dependencies = [
     { name = "pydantic" },
     { name = "scikit-learn" },
     { name = "stop-words" },
+    { name = "tqdm" },
 ]
 
 [package.metadata]
@@ -37,6 +38,7 @@ requires-dist = [
     { name = "pydantic", specifier = ">=2.0.0" },
     { name = "scikit-learn", specifier = ">=1.8.0" },
     { name = "stop-words", specifier = ">=2025.11.4" },
+    { name = "tqdm", specifier = ">=4.66.0" },
 ]
 
 [[package]]
@@ -70,26 +72,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
-[[package]]
-name = "bertopic"
-version = "0.17.4"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "hdbscan" },
-    { name = "llvmlite" },
-    { name = "numpy" },
-    { name = "pandas" },
-    { name = "plotly" },
-    { name = "scikit-learn" },
-    { name = "sentence-transformers" },
-    { name = "tqdm" },
-    { name = "umap-learn" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/72/ab/358923a516216964e150af7176dd3ea13737ddd25503517104f5562e301f/bertopic-0.17.4.tar.gz", hash = "sha256:f92aa560cdf2bcbf9e22c8ee83dd3bfb225b8dc29381dec7327cc0b21bd852ad", size = 121632, upload-time = "2025-12-03T14:19:34.402Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/d2/2eb2fec18b3d2cf487d5d1d9b484c6483c7f0f1a3c6f64e2bb9cb7972a3f/bertopic-0.17.4-py3-none-any.whl", hash = "sha256:0dbe7ee22c18fb76efe7f4fbf3a14b51c1e67d9fccc569e3f81e75b8f32646d1", size = 154669, upload-time = "2025-12-03T14:19:32.884Z" },
-]
-
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -204,6 +186,26 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
 ]
 
+[[package]]
+name = "bertopic"
+version = "0.17.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "hdbscan" },
+    { name = "llvmlite" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "plotly" },
+    { name = "scikit-learn" },
+    { name = "sentence-transformers" },
+    { name = "tqdm" },
+    { name = "umap-learn" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/ab/358923a516216964e150af7176dd3ea13737ddd25503517104f5562e301f/bertopic-0.17.4.tar.gz", hash = "sha256:f92aa560cdf2bcbf9e22c8ee83dd3bfb225b8dc29381dec7327cc0b21bd852ad", size = 121632, upload-time = "2025-12-03T14:19:34.402Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/d2/2eb2fec18b3d2cf487d5d1d9b484c6483c7f0f1a3c6f64e2bb9cb7972a3f/bertopic-0.17.4-py3-none-any.whl", hash = "sha256:0dbe7ee22c18fb76efe7f4fbf3a14b51c1e67d9fccc569e3f81e75b8f32646d1", size = 154669, upload-time = "2025-12-03T14:19:32.884Z" },
+]
+
 [[package]]
 name = "bleach"
 version = "6.3.0"
@@ -230,18 +232,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl", hash = "sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c", size = 152900, upload-time = "2026-01-04T02:42:40.15Z" },
 ]
 
-[[package]]
-name = "click"
-version = "8.3.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
-]
-
 [[package]]
 name = "cffi"
 version = "2.0.0"
@@ -356,6 +346,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" },
 ]
 
+[[package]]
+name = "click"
+version = "8.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3d/fa/656b739db8587d7b5dfa22e22ed02566950fbfbcdc20311993483657a5c0/click-8.3.1.tar.gz", hash = "sha256:12ff4785d337a1bb490bb7e9c2b1ee5da3112e94a8622f26a6c77f5d2fc6842a", size = 295065, upload-time = "2025-11-15T20:45:42.706Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/78/01c019cdb5d6498122777c1a43056ebb3ebfeef2076d9d026bfe15583b2b/click-8.3.1-py3-none-any.whl", hash = "sha256:981153a64e25f12d547d3426c367a4857371575ee7ad18df2a6183ab0545b2a6", size = 108274, upload-time = "2025-11-15T20:45:41.139Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -365,6 +367,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "comm"
+version = "0.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
+]
+
 [[package]]
 name = "cuda-bindings"
 version = "12.9.4"
@@ -388,15 +399,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ca/b5/e4056e4058fb56519fcddf1face6fe3ff2398953b41615fafe9fb1540bf2/cuda_pathfinder-1.3.5-py3-none-any.whl", hash = "sha256:6c88220f8637cb35d2a75c620d72efebf683b248b923713d8fbe235844c1a4b9", size = 33711, upload-time = "2026-02-23T18:34:27.253Z" },
 ]
 
-[[package]]
-name = "comm"
-version = "0.2.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
-]
-
 [[package]]
 name = "debugpy"
 version = "1.8.20"
@@ -445,24 +447,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
 ]
 
-[[package]]
-name = "filelock"
-version = "3.24.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/73/92/a8e2479937ff39185d20dd6a851c1a63e55849e447a55e798cc2e1f49c65/filelock-3.24.3.tar.gz", hash = "sha256:011a5644dc937c22699943ebbfc46e969cdde3e171470a6e40b9533e5a72affa", size = 37935, upload-time = "2026-02-19T00:48:20.543Z" }
-wheels = [
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-
-[[package]]
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-version = "2026.2.0"
-source = { registry = "https://pypi.org/simple" }
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-
 [[package]]
 name = "executing"
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-sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" }
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+    { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
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 ]
 
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" },
+]
+
 [[package]]
 name = "tokenizers"
 version = "0.22.2"
@@ -2623,18 +2637,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/66/4d/35352043ee0eaffdeff154fad67cd4a31dbed7ff8e3be1cc4549717d6d51/torch-2.10.0-cp314-cp314t-win_amd64.whl", hash = "sha256:71283a373f0ee2c89e0f0d5f446039bdabe8dbc3c9ccf35f0f784908b0acd185", size = 113995816, upload-time = "2026-01-21T16:22:05.312Z" },
 ]
 
-[[package]]
-name = "tinycss2"
-version = "1.4.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "webencodings" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" },
-]
-
 [[package]]
 name = "tornado"
 version = "6.5.4"
@@ -2666,6 +2668,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" },
+]
+
 [[package]]
 name = "transformers"
 version = "5.2.0"
@@ -2725,15 +2736,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/24/5480c20380dfd18cf33d14784096dca45a24eae6102e91d49a718d3b6855/typer_slim-0.24.0-py3-none-any.whl", hash = "sha256:d5d7ee1ee2834d5020c7c616ed5e0d0f29b9a4b1dd283bdebae198ec09778d0e", size = 3394, upload-time = "2026-02-16T22:08:49.92Z" },
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-[[package]]
-name = "traitlets"
-version = "5.14.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" },
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From ca6c55cbaf9b45c38321849e922b3c4cd1400e92 Mon Sep 17 00:00:00 2001
From: David Spilde <david.s.spilde@gmail.com>
Date: Fri, 10 Apr 2026 12:10:32 +0200
Subject: [PATCH 2/2] co

---
 hybrid/baseline_script.py | 47 +++++++++++++++++++++++++++++++++++++++
 hybrid/hybrid.py          |  5 ++---
 2 files changed, 49 insertions(+), 3 deletions(-)

diff --git a/hybrid/baseline_script.py b/hybrid/baseline_script.py
index 9838bd0..c098981 100644
--- a/hybrid/baseline_script.py
+++ b/hybrid/baseline_script.py
@@ -184,3 +184,50 @@ def to_list(x):
     evaluate_ever_clicked_hr(baseline_recs, behaviors.iloc[100_000 : ])
     check_article_stats(baseline_recs, articles)
     evaluate_detailed_hits(baseline_recs, behaviors.iloc[100_000 : ])
+
+
+
+def popularity_baseline_hybrid(
+    behaviors_df: pd.DataFrame,
+    articles_df: pd.DataFrame,
+    top_k: int = 20,
+) -> dict:
+    """
+    Popularity baseline using the inview candidate set per impression.
+    
+    Ranks inview articles by their total_pageviews, recommends the top-K,
+    and measures HR@K against clicked articles.
+    Falls back to random ranking for articles with null pageviews.
+    """
+    if behaviors_df.empty:
+        return {"hr_at_k": 0.0, "hits": 0, "impressions": 0, "top_k": top_k}
+
+    # Build a pageview lookup; fill nulls with 0 so they rank last
+    pageview_lookup = (
+        articles_df["total_pageviews"]
+        .fillna(0)
+        .astype(int)
+        .to_dict()
+    )
+
+    total_hits = 0
+    total_impressions = 0
+
+    for _, row in behaviors_df.iterrows():
+        inview = row["article_ids_inview"]
+        clicked = row["article_ids_clicked"]
+
+        if not inview.any() or not clicked.any():
+            continue
+
+        # Rank inview articles by global popularity
+        ranked = sorted(inview, key=lambda aid: pageview_lookup.get(aid, 0), reverse=True)
+        recommended = ranked[:top_k]
+
+        if any(aid in recommended for aid in clicked):
+            total_hits += 1
+        total_impressions += 1
+
+    hr = total_hits / total_impressions if total_impressions > 0 else 0.0
+    print(f"HR@{top_k}: {hr:.4%}  ({total_hits}/{total_impressions} impressions)")
+    return {"hr_at_k": hr, "hits": total_hits, "impressions": total_impressions, "top_k": top_k}
diff --git a/hybrid/hybrid.py b/hybrid/hybrid.py
index 17c2549..6b58b5c 100644
--- a/hybrid/hybrid.py
+++ b/hybrid/hybrid.py
@@ -2,14 +2,14 @@
 import pandas as pd
 
 
-from baseline_script import popularity_baseline_hybrid, random_baseline_hybrid
+from baseline_script import popularity_baseline_hybrid
 from content import *
 from collaborative import *
 
 
 # Read the behaviors and articles data
 def read_data_hybrid(start=None,length=None) -> tuple[pd.DataFrame,pd.DataFrame]:
-    basedir = "../data/raw/"
+    basedir = "./data/raw/"
     print("Loading data...")
     if start is None or length is None:
         raise ValueError
@@ -22,4 +22,3 @@ def read_data_hybrid(start=None,length=None) -> tuple[pd.DataFrame,pd.DataFrame]
     behaviors, articles = read_data_hybrid(2_000_000,1_000_000)
     # Creating the baseline
     popularity_baseline_hybrid(behaviors,articles, top_k=5)
-    random_baseline_hybrid(behaviors,articles)
\ No newline at end of file