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ARC games - Metadata for clasification
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ARC games - Metadata for clasification
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{
"cells": [
{
"cell_type": "markdown",
"id": "e1602f37",
"metadata": {
"papermill": {
"duration": 0.010284,
"end_time": "2024-09-22T04:10:44.334460",
"exception": false,
"start_time": "2024-09-22T04:10:44.324176",
"status": "completed"
},
"tags": []
},
"source": [
"# **How to create Metadata for Clasification**"
]
},
{
"cell_type": "markdown",
"id": "f9709c8c",
"metadata": {
"papermill": {
"duration": 0.009501,
"end_time": "2024-09-22T04:10:44.353902",
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"status": "completed"
},
"tags": []
},
"source": [
"In this notebook I want to share the way I'm using to split the training file according some specs.\n",
"\n",
"I've using this metod to work only with the equal dimension and squared matrix examples in the notebook [ARC - Applying 🧮 algebra to EDA 🔎](https://www.kaggle.com/code/alejandravillarreal/arc-applying-algebra-to-eda)\n",
"\n",
"I've also create a dataset with the split of examples by dimensionality [ARC 2024 - Training challenges for analysis](https://www.kaggle.com/datasets/alejandravillarreal/arc-2024-training-explamples-by-form/data)"
]
},
{
"cell_type": "markdown",
"id": "01bd6756",
"metadata": {
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"end_time": "2024-09-22T04:10:44.372842",
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"status": "completed"
},
"tags": []
},
"source": [
"*I will appreciate any feedback* 🙂 *or sugestion of other specs I'm not considering in this notebook version.*"
]
},
{
"cell_type": "markdown",
"id": "8a2be6ec",
"metadata": {
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"end_time": "2024-09-22T04:10:44.391604",
"exception": false,
"start_time": "2024-09-22T04:10:44.382333",
"status": "completed"
},
"tags": []
},
"source": [
"# Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "630aa2fb",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:16:23.507094Z",
"start_time": "2024-07-17T05:16:23.185664Z"
},
"execution": {
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"exception": false,
"start_time": "2024-09-22T04:10:44.401169",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "5ae125ea",
"metadata": {
"papermill": {
"duration": 0.009417,
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"exception": false,
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"status": "completed"
},
"tags": []
},
"source": [
"# Extraction"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8a9550c4",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:16:24.866445Z",
"start_time": "2024-07-17T05:16:24.863119Z"
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"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"base_path='/kaggle/input/arc-prize-2024/'\n",
"\n",
"# Loading JSON data\n",
"def load_json(file_path):\n",
" with open(file_path) as f:\n",
" data = json.load(f)\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b9abd7e0",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:16:26.494843Z",
"start_time": "2024-07-17T05:16:26.395403Z"
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"execution": {
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},
"tags": []
},
"outputs": [],
"source": [
"training_challenges = load_json(base_path +'arc-agi_training_challenges.json')\n",
"training_solutions = load_json(base_path +'arc-agi_training_solutions.json')\n",
"\n",
"evaluation_challenges = load_json(base_path +'arc-agi_evaluation_challenges.json')\n",
"evaluation_solutions = load_json(base_path +'arc-agi_evaluation_solutions.json')\n",
"\n",
"test_challenges = load_json(base_path +'arc-agi_test_challenges.json')"
]
},
{
"cell_type": "markdown",
"id": "bf2f083b",
"metadata": {
"papermill": {
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"exception": false,
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"status": "completed"
},
"tags": []
},
"source": [
"# Dimensionality functions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4617f8d2",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:22:16.001590Z",
"start_time": "2024-07-17T05:22:15.997854Z"
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#Compare the Input Matrix dimension vs the output Matrix (all pairs of examples)\n",
"def eq_dim_matrix(challenge,pairs):\n",
" eq_count = 0\n",
" x =0\n",
" for pair in range(pairs):\n",
" if len(challenge[pair]['input']) == len(challenge[pair]['output']):\n",
" if len(challenge[pair]['input'][0]) == len(challenge[pair]['output'][0]):\n",
" eq_count = eq_count + 1\n",
" else:\n",
" eq_count = eq_count - 1\n",
" else:\n",
" eq_count = -1\n",
"\n",
" if eq_count == pairs:\n",
" x = 1\n",
" return x\n",
"\n",
"# Validate if one the Matrix is a square one\n",
"def squared_matrix(matrix):\n",
" x = 0\n",
" if len(matrix) == len(matrix[0]):\n",
" x = 1\n",
" return x\n",
"\n",
"#Validate if both Matrix are squared (Input and output of all pairs of examples)\n",
"def squared_matrix_full(challenge,pairs):\n",
" sq_in, sq_out, sq_both, x, y = 0,0,0,0,0\n",
" for pair in range(pairs):\n",
" x = x + squared_matrix(challenge[pair]['input'])\n",
" y = y + squared_matrix(challenge[pair]['output'])\n",
" if x == pairs:\n",
" sq_in = 1\n",
" if y == pairs:\n",
" sq_out = 1\n",
" if sq_in + sq_out == 2:\n",
" sq_both = 1\n",
" return sq_in, sq_out, sq_both"
]
},
{
"cell_type": "markdown",
"id": "3eaaab09",
"metadata": {
"papermill": {
"duration": 0.009464,
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"exception": false,
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"status": "completed"
},
"tags": []
},
"source": [
"# Dataset creation"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a18dd2c5",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:47:44.782748Z",
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},
"tags": []
},
"outputs": [],
"source": [
"id = []\n",
"pairs = []\n",
"eq_dim = []\n",
"sq_in = []\n",
"sq_out = []\n",
"sq_both = []\n",
"cols_in = []\n",
"rows_in = []\n",
"cols_out = []\n",
"rows_out = []\n",
"\n",
"for challenge in training_challenges:\n",
" id.append(challenge)\n",
" pairs.append(len(training_challenges[challenge]['train']))\n",
" eq_dim.append(eq_dim_matrix(training_challenges[challenge]['train'],len(training_challenges[challenge]['train'])))\n",
" x, y, z = squared_matrix_full(training_challenges[challenge]['train'],len(training_challenges[challenge]['train']))\n",
" sq_in.append(x)\n",
" sq_out.append(y)\n",
" sq_both.append(z)\n",
" rows_in.append(len(training_challenges[challenge]['train'][0]['input']))\n",
" cols_in.append(len(training_challenges[challenge]['train'][0]['input'][0]))\n",
" rows_out.append(len(training_challenges[challenge]['train'][0]['output']))\n",
" cols_out.append(len(training_challenges[challenge]['train'][0]['output'][0]))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "063cb94c",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:47:48.173372Z",
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"tags": []
},
"outputs": [],
"source": [
"meta_train = pd.DataFrame({'id':id,'pairs':pairs,'eq_dim':eq_dim, 'sq_in':sq_in, 'sq_out':sq_out, 'sq_both':sq_both, 'cols_in':cols_in, 'rows_in':rows_in, 'cols_out':cols_out, 'rows_out':rows_out})"
]
},
{
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"id": "9a8afeda",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>id</th>\n",
" <th>pairs</th>\n",
" <th>eq_dim</th>\n",
" <th>sq_in</th>\n",
" <th>sq_out</th>\n",
" <th>sq_both</th>\n",
" <th>cols_in</th>\n",
" <th>rows_in</th>\n",
" <th>cols_out</th>\n",
" <th>rows_out</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>007bbfb7</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00d62c1b</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
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" <td>017c7c7b</td>\n",
" <td>3</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>3</td>\n",
" <td>9</td>\n",
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" <th>3</th>\n",
" <td>025d127b</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>045e512c</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" id pairs eq_dim sq_in sq_out sq_both cols_in rows_in \\\n",
"0 007bbfb7 5 0 1 1 1 3 3 \n",
"1 00d62c1b 5 1 1 1 1 6 6 \n",
"2 017c7c7b 3 0 0 0 0 3 6 \n",
"3 025d127b 2 1 0 0 0 9 14 \n",
"4 045e512c 3 1 1 1 1 21 21 \n",
"\n",
" cols_out rows_out \n",
"0 9 9 \n",
"1 6 6 \n",
"2 3 9 \n",
"3 9 14 \n",
"4 21 21 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Metadata example\n",
"meta_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "303fb33f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:47:55.407678Z",
"start_time": "2024-07-17T05:47:55.403923Z"
},
"execution": {
"iopub.execute_input": "2024-09-22T04:10:45.971559Z",
"iopub.status.busy": "2024-09-22T04:10:45.970778Z",
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},
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"end_time": "2024-09-22T04:10:45.984528",
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"start_time": "2024-09-22T04:10:45.957934",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# adding more dimensionality epecs\n",
"meta_train['area_in'] = meta_train['cols_in']* meta_train['rows_in']\n",
"meta_train['area_out'] = meta_train['cols_out']* meta_train['rows_out']\n",
"meta_train['increase'] = meta_train['area_out']/meta_train['area_in']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0109bf90",
"metadata": {
"execution": {
"iopub.execute_input": "2024-09-22T04:10:46.006522Z",
"iopub.status.busy": "2024-09-22T04:10:46.005802Z",
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"shell.execute_reply": "2024-09-22T04:10:46.024518Z"
},
"papermill": {
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"exception": false,
"start_time": "2024-09-22T04:10:45.994645",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" <th>sq_in</th>\n",
" <th>sq_out</th>\n",
" <th>sq_both</th>\n",
" <th>cols_in</th>\n",
" <th>rows_in</th>\n",
" <th>cols_out</th>\n",
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" <th>3</th>\n",
" <td>025d127b</td>\n",
" <td>2</td>\n",
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" <td>126</td>\n",
" <td>1.0</td>\n",
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" <th>4</th>\n",
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],
"text/plain": [
" id pairs eq_dim sq_in sq_out sq_both cols_in rows_in \\\n",
"0 007bbfb7 5 0 1 1 1 3 3 \n",
"1 00d62c1b 5 1 1 1 1 6 6 \n",
"2 017c7c7b 3 0 0 0 0 3 6 \n",
"3 025d127b 2 1 0 0 0 9 14 \n",
"4 045e512c 3 1 1 1 1 21 21 \n",
"\n",
" cols_out rows_out area_in area_out increase \n",
"0 9 9 9 81 9.0 \n",
"1 6 6 36 36 1.0 \n",
"2 3 9 18 27 1.5 \n",
"3 9 14 126 126 1.0 \n",
"4 21 21 441 441 1.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Showing the new columns\n",
"meta_train.head()"
]
},
{
"cell_type": "markdown",
"id": "a5b1e3fa",
"metadata": {
"papermill": {
"duration": 0.010961,
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"status": "completed"
},
"tags": []
},
"source": [
"# Splitting data for dimensionality"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9eeb90b4",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:48:01.849287Z",
"start_time": "2024-07-17T05:48:01.845209Z"
},
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Filters to create the finals datasets\n",
"\n",
"#filtering just equals dim ones\n",
"eq_dim_train = meta_train[meta_train['eq_dim']==1]\n",
"#filtering the examples with square input and output\n",
"sq_both_train = meta_train[meta_train['sq_both']==1]\n",
"#filtering the examples that are not square input and output\n",
"non_squared_train = meta_train[meta_train['sq_both']==0]\n",
"#filtering the examples that are not equals dim input and output\n",
"diff_train = meta_train[meta_train['eq_dim']==0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ca85113c",
"metadata": {
"execution": {
"iopub.execute_input": "2024-09-22T04:10:46.118287Z",
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"start_time": "2024-09-22T04:10:46.105413",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Creating the finals datasets\n",
"\n",
"#filtering examples that are equal dimension and squared matrix (input and output)\n",
"equals_squared_train = eq_dim_train[eq_dim_train['sq_both']==1]\n",
"#filtering examples that are equal dimension but not squared matrix (input and output)\n",
"equals_non_squared_train = eq_dim_train[eq_dim_train['sq_both']==0]\n",
"#filtering the examples with square matrix but not equal dimension (input and output)\n",
"diff_squared_train = sq_both_train[sq_both_train['eq_dim']==0]\n",
"#filtering the examples that are not square and not equal dimension (input and output)\n",
"diff_non_squared_train = non_squared_train[non_squared_train['eq_dim']==0]"
]
},
{
"cell_type": "markdown",
"id": "7a6239af",
"metadata": {
"papermill": {
"duration": 0.01003,
"end_time": "2024-09-22T04:10:46.151032",
"exception": false,
"start_time": "2024-09-22T04:10:46.141002",
"status": "completed"
},
"tags": []
},
"source": [
"# Printing the finals datasets"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9a67579d",
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-17T05:48:04.611591Z",
"start_time": "2024-07-17T05:48:04.608398Z"
},
"execution": {
"iopub.execute_input": "2024-09-22T04:10:46.173351Z",
"iopub.status.busy": "2024-09-22T04:10:46.172519Z",
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"shell.execute_reply": "2024-09-22T04:10:46.186695Z"
},
"papermill": {
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"end_time": "2024-09-22T04:10:46.190112",
"exception": false,
"start_time": "2024-09-22T04:10:46.161082",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" id pairs eq_dim sq_in sq_out sq_both cols_in rows_in \\\n",
"1 00d62c1b 5 1 1 1 1 6 6 \n",
"4 045e512c 3 1 1 1 1 21 21 \n",
"6 05269061 3 1 1 1 1 7 7 \n",
"8 06df4c85 3 1 1 1 1 20 20 \n",
"9 08ed6ac7 2 1 1 1 1 9 9 \n",
"\n",
" cols_out rows_out area_in area_out increase \n",
"1 6 6 36 36 1.0 \n",
"4 21 21 441 441 1.0 \n",
"6 7 7 49 49 1.0 \n",
"8 20 20 400 400 1.0 \n",
"9 9 9 81 81 1.0 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"equals_squared_train.head()"