{
"cells": [
{
"cell_type": "markdown",
"id": "a99d1fa8",
"metadata": {
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"source": [
"# **Prepare SVD for multiome**\n",
"\n",
"In this Jupyter notebook, data from train and test datasets is put together and then the TruncatedSVD is calculated. This is done twice: once for data normalized by organizers, and then for raw data. Only SVD features made from normalized data were used in a final submission.\n",
"\n",
"In kaggle environment it is more convenient to do this in a separate notebook, as it would be a waste of both time and GPU quota to calculate the TruncatedSVD each time before fitting the model.\n",
"\n",
"## Imports and definitions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2a7654bc",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
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"status": "completed"
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"tags": []
},
"outputs": [],
"source": [
"# Importing the libraries\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"import os\n",
"import gc, scipy.sparse\n",
"from humanize import naturalsize\n",
"from sklearn.decomposition import TruncatedSVD"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8fb19abf",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:11:34.864639Z",
"iopub.status.busy": "2023-03-26T15:11:34.863570Z",
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"shell.execute_reply": "2023-03-26T15:11:47.508400Z"
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"papermill": {
"duration": 12.655546,
"end_time": "2023-03-26T15:11:47.512773",
"exception": false,
"start_time": "2023-03-26T15:11:34.857227",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
"\u001b[0m"
]
}
],
"source": [
"# Need this library to read *.h5 files\n",
"!pip install --quiet tables"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ef0cfb17",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:11:47.525502Z",
"iopub.status.busy": "2023-03-26T15:11:47.525034Z",
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"end_time": "2023-03-26T15:11:47.535703",
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"DATA_DIR = \"/kaggle/input/open-problems-multimodal/\"\n",
"FP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n",
"\n",
"FP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\n",
"FP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\n",
"FP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n",
"\n",
"FP_MULTIOME_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_multi_inputs.h5\")\n",
"FP_MULTIOME_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_multi_targets.h5\")\n",
"FP_MULTIOME_TEST_INPUTS = os.path.join(DATA_DIR,\"test_multi_inputs.h5\")\n",
"\n",
"FP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\n",
"FP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "23f2d01a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:11:47.547201Z",
"iopub.status.busy": "2023-03-26T15:11:47.546750Z",
"iopub.status.idle": "2023-03-26T15:11:47.554602Z",
"shell.execute_reply": "2023-03-26T15:11:47.553465Z"
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"duration": 0.016003,
"end_time": "2023-03-26T15:11:47.556793",
"exception": false,
"start_time": "2023-03-26T15:11:47.540790",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# The multiome train dataset raw data is too large to be loaded into RAM. But it is also sparse.\n",
"# So, I load the dataset in chunks, and then convert it to sparse matrix.\n",
"# Will use this function to do right that.\n",
"\n",
"\n",
"def read_convert_hdf_in_chunks(link, chunk_size, sparse_matrice=None):\n",
" i = 0\n",
" while i < 1000000:\n",
" df_chunk = pd.read_hdf(link, start=i, stop=i+chunk_size)\n",
" sparse_chunk = scipy.sparse.csr_matrix(df_chunk.values)\n",
" if sparse_matrice == None:\n",
" sparse_matrice = sparse_chunk\n",
" else:\n",
" sparse_matrice = scipy.sparse.vstack([sparse_matrice, sparse_chunk])\n",
" print(i)\n",
" i += chunk_size\n",
" if sparse_chunk.shape[0] < chunk_size:\n",
" return sparse_matrice\n",
" "
]
},
{
"cell_type": "markdown",
"id": "7faa8030",
"metadata": {
"papermill": {
"duration": 0.004217,
"end_time": "2023-03-26T15:11:47.566096",
"exception": false,
"start_time": "2023-03-26T15:11:47.561879",
"status": "completed"
},
"tags": []
},
"source": [
"## Process the raw data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6568c30d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:11:47.577155Z",
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"shell.execute_reply": "2023-03-26T15:30:05.973817Z"
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"duration": 1098.408278,
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"exception": false,
"start_time": "2023-03-26T15:11:47.570827",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"5000\n",
"10000\n",
"15000\n",
"20000\n",
"25000\n",
"30000\n",
"35000\n",
"40000\n",
"45000\n",
"50000\n",
"55000\n",
"60000\n",
"65000\n",
"70000\n",
"75000\n",
"80000\n",
"85000\n",
"90000\n",
"95000\n",
"100000\n",
"105000\n",
"105933\n",
"CPU times: user 16min 52s, sys: 1min 13s, total: 18min 5s\n",
"Wall time: 18min 18s\n"
]
},
{
"data": {
"text/plain": [
"34"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# Loading raw data inputs\n",
"\n",
"sparse_X = read_convert_hdf_in_chunks('../input/open-problems-raw-counts/train_multi_inputs_raw.h5', 5000)\n",
"print(sparse_X.shape[0])\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "802a3bb1",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:30:05.993680Z",
"iopub.status.busy": "2023-03-26T15:30:05.993289Z",
"iopub.status.idle": "2023-03-26T15:39:49.838277Z",
"shell.execute_reply": "2023-03-26T15:39:49.837072Z"
},
"papermill": {
"duration": 583.855343,
"end_time": "2023-03-26T15:39:49.840846",
"exception": false,
"start_time": "2023-03-26T15:30:05.985503",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"5000\n",
"10000\n",
"15000\n",
"20000\n",
"25000\n",
"30000\n",
"35000\n",
"40000\n",
"45000\n",
"50000\n",
"55000\n",
"161868\n",
"CPU times: user 8min 49s, sys: 46.6 s, total: 9min 36s\n",
"Wall time: 9min 43s\n"
]
},
{
"data": {
"text/plain": [
"75"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# Same procedure for the test raw data.\n",
"sparse_X = read_convert_hdf_in_chunks('/kaggle/input/open-problems-raw-counts/test_multi_inputs_raw.h5', 5000, sparse_X)\n",
"print(sparse_X.shape[0])\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "544a6b3f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:39:49.858139Z",
"iopub.status.busy": "2023-03-26T15:39:49.856591Z",
"iopub.status.idle": "2023-03-26T15:39:50.407194Z",
"shell.execute_reply": "2023-03-26T15:39:50.405499Z"
},
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"duration": 0.563082,
"end_time": "2023-03-26T15:39:50.410897",
"exception": false,
"start_time": "2023-03-26T15:39:49.847815",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Export total_counts. Maybe they will be useful as a feature.\n",
"total_counts = sparse_X.sum(axis=1)\n",
"counts_index = [*range(len(total_counts))]\n",
"total_counts = total_counts.flat\n",
"df_total_counts = pd.DataFrame({'total_counts': total_counts}, index=counts_index)\n",
"df_total_counts.to_feather('total_counts_multiome.ftr')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1a49d6ef",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T15:39:50.428010Z",
"iopub.status.busy": "2023-03-26T15:39:50.427543Z",
"iopub.status.idle": "2023-03-26T16:01:28.936016Z",
"shell.execute_reply": "2023-03-26T16:01:28.933114Z"
},
"papermill": {
"duration": 1298.537214,
"end_time": "2023-03-26T16:01:28.955652",
"exception": false,
"start_time": "2023-03-26T15:39:50.418438",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of both before SVD: (161868, 228942)\n",
"Shape of both after SVD: (161868, 64)\n",
"CPU times: user 21min 46s, sys: 9.47 s, total: 21min 56s\n",
"Wall time: 21min 38s\n"
]
}
],
"source": [
"%%time\n",
"# Apply the singular value decomposition.\n",
"\n",
"print(f\"Shape of both before SVD: {sparse_X.shape}\")\n",
"svd = TruncatedSVD(n_components=64, random_state=1)\n",
"sparse_X = svd.fit_transform(sparse_X)\n",
"print(f\"Shape of both after SVD: {sparse_X.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "faec350c",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T16:01:28.975382Z",
"iopub.status.busy": "2023-03-26T16:01:28.974809Z",
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"shell.execute_reply": "2023-03-26T16:01:38.710063Z"
},
"papermill": {
"duration": 9.751836,
"end_time": "2023-03-26T16:01:38.715236",
"exception": false,
"start_time": "2023-03-26T16:01:28.963400",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Raw data SVD ready\n"
]
}
],
"source": [
"# Save results in a file.\n",
"df_svd = pd.DataFrame(sparse_X)\n",
"df_svd.to_csv('svd_raw.csv')\n",
"print('Raw data SVD ready')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b9fb2fac",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T16:01:38.733851Z",
"iopub.status.busy": "2023-03-26T16:01:38.733423Z",
"iopub.status.idle": "2023-03-26T16:01:38.901106Z",
"shell.execute_reply": "2023-03-26T16:01:38.899979Z"
},
"papermill": {
"duration": 0.179907,
"end_time": "2023-03-26T16:01:38.903684",
"exception": false,
"start_time": "2023-03-26T16:01:38.723777",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Free the RAM.\n",
"del sparse_X, df_svd\n",
"gc.collect()"
]
},
{
"cell_type": "markdown",
"id": "c9cf65f9",
"metadata": {
"papermill": {
"duration": 0.007482,
"end_time": "2023-03-26T16:01:38.919004",
"exception": false,
"start_time": "2023-03-26T16:01:38.911522",
"status": "completed"
},
"tags": []
},
"source": [
"## Process the normalized data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "05ae1937",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T16:01:38.935416Z",
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"end_time": "2023-03-26T16:23:21.384886",
"exception": false,
"start_time": "2023-03-26T16:01:38.926389",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"5000\n",
"10000\n",
"15000\n",
"20000\n",
"25000\n",
"30000\n",
"35000\n",
"40000\n",
"45000\n",
"50000\n",
"55000\n",
"60000\n",
"65000\n",
"70000\n",
"75000\n",
"80000\n",
"85000\n",
"90000\n",
"95000\n",
"100000\n",
"105000\n",
"105942\n",
"CPU times: user 18min 25s, sys: 1min 25s, total: 19min 51s\n",
"Wall time: 21min 42s\n"
]
},
{
"data": {
"text/plain": [
"150"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# Generally the same operations for the normalized data using the same function.\n",
"# Load the train data in chunks and convert it to sparse matrix.\n",
"\n",
"sparse_X = read_convert_hdf_in_chunks(FP_MULTIOME_TRAIN_INPUTS, 5000)\n",
"print(sparse_X.shape[0])\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b729e0e8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T16:23:21.404437Z",
"iopub.status.busy": "2023-03-26T16:23:21.403954Z",
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"shell.execute_reply": "2023-03-26T16:34:25.337535Z"
},
"papermill": {
"duration": 663.948827,
"end_time": "2023-03-26T16:34:25.342225",
"exception": false,
"start_time": "2023-03-26T16:23:21.393398",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"5000\n",
"10000\n",
"15000\n",
"20000\n",
"25000\n",
"30000\n",
"35000\n",
"40000\n",
"45000\n",
"50000\n",
"55000\n",
"161877\n",
"CPU times: user 9min 8s, sys: 50.7 s, total: 9min 59s\n",
"Wall time: 11min 3s\n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# Same for normalized test dataset.\n",
"\n",
"sparse_X = read_convert_hdf_in_chunks(FP_MULTIOME_TEST_INPUTS, 5000, sparse_X)\n",
"print(sparse_X.shape[0])\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "75155407",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T16:34:25.365178Z",
"iopub.status.busy": "2023-03-26T16:34:25.364744Z",
"iopub.status.idle": "2023-03-26T17:43:25.015549Z",
"shell.execute_reply": "2023-03-26T17:43:25.012447Z"
},
"papermill": {
"duration": 4139.678463,
"end_time": "2023-03-26T17:43:25.030988",
"exception": false,
"start_time": "2023-03-26T16:34:25.352525",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of both before SVD: (161877, 228942)\n",
"Shape of both after SVD: (161877, 256)\n",
"CPU times: user 1h 9min 43s, sys: 29.3 s, total: 1h 10min 13s\n",
"Wall time: 1h 8min 59s\n"
]
}
],
"source": [
"%%time\n",
"# Apply the singular value decomposition.\n",
"# Normalized data is more important, so I will prepare more components.\n",
"\n",
"print(f\"Shape of both before SVD: {sparse_X.shape}\")\n",
"svd = TruncatedSVD(n_components=256, random_state=1)\n",
"sparse_X = svd.fit_transform(sparse_X)\n",
"print(f\"Shape of both after SVD: {sparse_X.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "087f142e",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-26T17:43:25.053242Z",
"iopub.status.busy": "2023-03-26T17:43:25.052717Z",
"iopub.status.idle": "2023-03-26T17:44:04.388397Z",
"shell.execute_reply": "2023-03-26T17:44:04.387211Z"
},
"papermill": {
"duration": 39.360847,
"end_time": "2023-03-26T17:44:04.401455",
"exception": false,
"start_time": "2023-03-26T17:43:25.040608",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All the SVD ready\n"
]
}
],
"source": [
"# Save results in a file.\n",
"df_svd = pd.DataFrame(sparse_X)\n",
"df_svd.to_csv('svd.csv')\n",
"print('All the SVD ready')"
]
}
],
"metadata": {
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"display_name": "Python 3",
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