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dataset_exploration.ipynb

@@ -0,0 +1,244 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a short notebook exploring the click reco dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Loading the training dataframe\n",
+    "df = pd.read_parquet(\"train.parquet\")\n",
+    "# df.iloc[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the distribution of the values\n",
+    "distribution = df[\"slates\"].str.len().value_counts(normalize=True).sort_index() * 100\n",
+    "\n",
+    "# Display the distribution\n",
+    "distribution.plot(kind=\"bar\")\n",
+    "plt.xlabel(\"Slate Length\")\n",
+    "plt.ylabel(\"Frequency (%)\")\n",
+    "plt.title(\"Distribution of Slate Length\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The most frequent slates:\n",
+      "                                                  Slate  Frequency\n",
+      "29                                      (151, 152, 153)       3477\n",
+      "14                                             (30, 31)       2463\n",
+      "57                                   (1046, 1047, 1048)       2325\n",
+      "43                                   (1100, 1103, 1102)       2107\n",
+      "56                                       (270, 55, 271)       1980\n",
+      "...                                                 ...        ...\n",
+      "4806  (357, 358, 359, 357, 360, 361, 362, 365, 367, ...          1\n",
+      "4807                                    (798, 806, 820)          1\n",
+      "4808  (357, 358, 359, 357, 360, 361, 362, 363, 365, ...          1\n",
+      "4809                           (1137, 1136, 1153, 1148)          1\n",
+      "4810                                    (729, 701, 698)          1\n",
+      "\n",
+      "[4846 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the lists to tuples to use them in nunique()\n",
+    "df[\"slates_as_tuples\"] = df[\"slates\"].apply(tuple)\n",
+    "\n",
+    "# Get the number of unique values\n",
+    "unique_count = df[\"slates_as_tuples\"].nunique()\n",
+    "\n",
+    "# Count the occurrences of the tuples\n",
+    "counter = Counter(df[\"slates_as_tuples\"])\n",
+    "\n",
+    "# Convert the results into a DataFrame for better readability\n",
+    "counter_df = pd.DataFrame(counter.items(), columns=[\"Slate\", \"Frequency\"])\n",
+    "\n",
+    "# Sort the DataFrame by descending frequency\n",
+    "counter_df = counter_df.sort_values(by=\"Frequency\", ascending=False)\n",
+    "\n",
+    "print(\"The most frequent slates:\")\n",
+    "print(counter_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average frequency per index: 264.72\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract all the lists from the 'slates' column\n",
+    "slates_lists = df[\"slates\"].tolist()\n",
+    "\n",
+    "# Flatten these lists into a single list of all indexes\n",
+    "all_indexes = [index for sublist in slates_lists for index in sublist]\n",
+    "\n",
+    "# Count the occurrences of each index\n",
+    "index_counts = Counter(all_indexes)\n",
+    "\n",
+    "# Calculate the average frequency per index\n",
+    "average_frequency = sum(index_counts.values()) / len(index_counts)\n",
+    "\n",
+    "# Display the histogram of the index distribution\n",
+    "plt.hist(\n",
+    "    all_indexes, bins=np.arange(np.min(all_indexes), np.max(all_indexes) + 2) - 0.5\n",
+    ")\n",
+    "plt.xlabel(\"Index\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Distribution of Indexes\")\n",
+    "\n",
+    "# Display the average frequency on the graph\n",
+    "plt.axhline(\n",
+    "    y=average_frequency,\n",
+    "    color=\"r\",\n",
+    "    linestyle=\"-\",\n",
+    "    label=f\"Average Frequency: {average_frequency:.2f}\",\n",
+    ")\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# Display the average frequency in the standard output\n",
+    "print(f\"Average frequency per index: {average_frequency:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Filter to exclude values equal to 0\n",
+    "df_filtered = df[df[\"R_bar_r\"] != 0]\n",
+    "\n",
+    "# Plot the distribution of values in the 'R_bar_r' column without the 0s\n",
+    "plt.hist(\n",
+    "    df_filtered[\"R_bar_r\"],\n",
+    "    bins=range(df_filtered[\"R_bar_r\"].min(), df_filtered[\"R_bar_r\"].max() + 2),\n",
+    "    edgecolor=\"black\",\n",
+    "    align=\"left\",\n",
+    ")\n",
+    "plt.title(\"Distribution of Values in R_bar_r Column (excluding 0s)\")\n",
+    "plt.xlabel(\"R_bar_r Values\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.xticks(range(df_filtered[\"R_bar_r\"].min(), df_filtered[\"R_bar_r\"].max() + 1))\n",
+    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_prrv",
+   "language": "python",
+   "name": ".venv_prr"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

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