mirror of
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3320 lines
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|
||
"if len(token) > 0:\n",
|
||
" print(\"ok\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "B4wCRu4DAC0Z",
|
||
"outputId": "44e18ac2-3cb6-4664-cb39-139a298bc42e"
|
||
},
|
||
"execution_count": 2,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"ok\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from github import Github\n",
|
||
"import requests\n",
|
||
"from tqdm.notebook import tqdm\n",
|
||
"\n",
|
||
"\n",
|
||
"def get_specific_file_from_tagged_release(token, repo_name, tag_name, filename):\n",
|
||
" g = Github(token)\n",
|
||
" repo = g.get_repo(repo_name)\n",
|
||
" releases = repo.get_releases()\n",
|
||
"\n",
|
||
" for release in releases:\n",
|
||
" if release.tag_name == tag_name:\n",
|
||
" for asset in release.get_assets():\n",
|
||
" if asset.name == filename:\n",
|
||
" return asset.url\n",
|
||
" print(\"File not found. Try get_specific_file_from_latest_release() instead.\")\n",
|
||
" return None\n",
|
||
"\n",
|
||
"def get_specific_file_from_latest_release(token, repo_name, filename):\n",
|
||
" g = Github(token)\n",
|
||
" repo = g.get_repo(repo_name)\n",
|
||
" release = repo.get_latest_release()\n",
|
||
"\n",
|
||
" for asset in release.get_assets():\n",
|
||
" if asset.name == filename:\n",
|
||
" return asset.url # Use asset.url which points to API URL needing headers\n",
|
||
"\n",
|
||
"def download_file(url, token, save_path):\n",
|
||
" headers = {'Authorization': f'token {token}', 'Accept': 'application/octet-stream'}\n",
|
||
" # First request to handle GitHub's redirection and authentication properly\n",
|
||
" with requests.get(url, headers=headers, stream=True) as initial_response:\n",
|
||
" initial_response.raise_for_status() # Ensure the initial request is successful\n",
|
||
" # Follow redirection if necessary, maintaining headers\n",
|
||
" if initial_response.history:\n",
|
||
" url = initial_response.url # Updated URL after redirection\n",
|
||
"\n",
|
||
" # Now, proceed with downloading the file\n",
|
||
" with requests.get(url, headers=headers, stream=True) as response:\n",
|
||
" response.raise_for_status()\n",
|
||
" total_size_in_bytes = int(response.headers.get('content-length', 0))\n",
|
||
" block_size = 1024\n",
|
||
"\n",
|
||
" progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)\n",
|
||
" with open(save_path, 'wb') as file:\n",
|
||
" for data in response.iter_content(block_size):\n",
|
||
" progress_bar.update(len(data))\n",
|
||
" file.write(data)\n",
|
||
" progress_bar.close()\n",
|
||
"\n",
|
||
" if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:\n",
|
||
" print(\"ERROR, something went wrong\")\n",
|
||
" else:\n",
|
||
" print(f\"File downloaded successfully and saved as {save_path}\")\n",
|
||
"\n",
|
||
"# Your GitHub token\n",
|
||
"github_token = token\n",
|
||
"\n",
|
||
"# Repository name\n",
|
||
"repository_name = \"norandom/log2ml\"\n",
|
||
"\n",
|
||
"# File name to search for\n",
|
||
"file_name = \"lab_logs_normal_activity_may_11_2024.csv\"\n",
|
||
"\n",
|
||
"# Get the download URL of the specific file\n",
|
||
"# download_url = get_specific_file_from_latest_release(github_token, repository_name, file_name)\n",
|
||
"download_url = get_specific_file_from_tagged_release(github_token, repository_name, \"foundations\", file_name)\n",
|
||
"print(download_url)\n",
|
||
"\n",
|
||
"if download_url:\n",
|
||
" local_file_path = \"lab_logs_normal_activity_may_11_2024.csv\"\n",
|
||
" download_file(download_url, github_token, local_file_path)\n",
|
||
"else:\n",
|
||
" print(\"File not found.\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 85,
|
||
"referenced_widgets": [
|
||
"929564f65fcf4015a993525a64241051",
|
||
"27112eb01f0f44989c81bb1c1ab7460f",
|
||
"48e74ca5781840ea956ffc445a320b9f",
|
||
"4a28e37ee0234b7086bde820cbb9d054",
|
||
"44ea289843f14a80ac6086ec395c2b43",
|
||
"dc7dead7d3bd49ba8afa1edee6f33415",
|
||
"ec5178404f0c4b468d9fadad6d3b342a",
|
||
"55c2ae4000d54440b46225a843d9fb7e",
|
||
"fa981ba8a3074e55bdeb9d2ef8bc0e7a",
|
||
"703fce7f86fe4a779e54f5028a728447",
|
||
"621e8af069294a378f88a5ccc6f7cb56"
|
||
]
|
||
},
|
||
"id": "ORNebW15Bb4B",
|
||
"outputId": "219a5138-385a-49c7-b2b1-d28d80a2c1ff"
|
||
},
|
||
"execution_count": 3,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"https://api.github.com/repos/norandom/log2ml/releases/assets/168248751\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0.00/1.43G [00:00<?, ?iB/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "929564f65fcf4015a993525a64241051"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"File downloaded successfully and saved as lab_logs_normal_activity_may_11_2024.csv\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Train custom Tokenizer with complex dataset"
|
||
],
|
||
"metadata": {
|
||
"id": "dAYGIiUlCQM9"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tokenizers import Tokenizer\n",
|
||
"from tokenizers.models import BPE\n",
|
||
"from tokenizers.trainers import BpeTrainer\n",
|
||
"from tokenizers.pre_tokenizers import Whitespace\n",
|
||
"\n",
|
||
"# Initialize the tokenizer\n",
|
||
"tokenizer = Tokenizer(BPE())\n",
|
||
"\n",
|
||
"# Setup pre-tokenizer\n",
|
||
"tokenizer.pre_tokenizer = Whitespace()\n",
|
||
"\n",
|
||
"# Setup trainer\n",
|
||
"trainer = BpeTrainer(vocab_size=30000, min_frequency=2, special_tokens=[\"=\", \":\", \",\"])\n",
|
||
"\n",
|
||
"# Train the tokenizer on your log data\n",
|
||
"tokenizer.train(files=[\"lab_logs_normal_activity_may_11_2024.csv\"], trainer=trainer)\n",
|
||
"\n",
|
||
"# Save the tokenizer\n",
|
||
"tokenizer.save(\"log_tokenizer.json\")"
|
||
],
|
||
"metadata": {
|
||
"id": "9A2nD4deq8lO"
|
||
},
|
||
"execution_count": 4,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Download dataset to use"
|
||
],
|
||
"metadata": {
|
||
"id": "RtdpdugyCl9v"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# File name to search for\n",
|
||
"file_name = \"lab_logs_blindtest_activity_june_25_2024.csv\"\n",
|
||
"\n",
|
||
"# Get the download URL of the specific file\n",
|
||
"# download_url = get_specific_file_from_latest_release(github_token, repository_name, file_name)\n",
|
||
"download_url = get_specific_file_from_tagged_release(github_token, repository_name, \"foundations\", file_name)\n",
|
||
"print(download_url)\n",
|
||
"\n",
|
||
"if download_url:\n",
|
||
" local_file_path = file_name\n",
|
||
" download_file(download_url, github_token, local_file_path)\n",
|
||
"else:\n",
|
||
" print(\"File not found.\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 85,
|
||
"referenced_widgets": [
|
||
"765cac58169d427d9eaff50f4f05e3e6",
|
||
"8d7cae3cce7e412e8d5548a213a8dfd8",
|
||
"acbdf97002c84edfabbc54b4af67dfc8",
|
||
"a4286f8214034d47866dda32aefe33ed",
|
||
"02c2277d728b467bb2949d4b7eae227f",
|
||
"759cb450197b42f0bfe910af74541b55",
|
||
"87a1ed56ac3741799a1faa47e2a28b35",
|
||
"fe1b36b57d6c4f1d9500f3376db55940",
|
||
"fd1b78aa2acd48e1a4c8e3febff4f57f",
|
||
"08fe87c7de9e4e64ad0c0ef8d0126fe8",
|
||
"3764e683a94c4b7fb74fc4fd7e067cb7"
|
||
]
|
||
},
|
||
"id": "mjrM6gVOGfHq",
|
||
"outputId": "aaf3bae2-2f92-44de-891e-c1d92abd4f84"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"https://api.github.com/repos/norandom/log2ml/releases/assets/175872904\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0.00/594k [00:00<?, ?iB/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "765cac58169d427d9eaff50f4f05e3e6"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"File downloaded successfully and saved as lab_logs_blindtest_activity_june_25_2024.csv\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# ETL dataset"
|
||
],
|
||
"metadata": {
|
||
"id": "wk7RQHMuCxjr"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import polars as pl\n",
|
||
"\n",
|
||
"csv_file_path = 'lab_logs_blindtest_activity_june_25_2024.csv'\n",
|
||
"\n",
|
||
"# Load the CSV file into a DataFrame\n",
|
||
"df = pl.read_csv(csv_file_path)\n",
|
||
"\n",
|
||
"# Show the DataFrame to confirm it's loaded correctly\n",
|
||
"print(df)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "4ZzWtIgFxBJS",
|
||
"outputId": "4ac3a38c-6fe5-44ae-878e-40f438fb0422"
|
||
},
|
||
"execution_count": 7,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"shape: (1_027, 5)\n",
|
||
"┌───────┬─────────────┬─────────────────┬───────────────────────────────────┬─────────────────────┐\n",
|
||
"│ index ┆ log.level ┆ winlog.event_id ┆ winlog.task ┆ filtered_message │\n",
|
||
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
|
||
"│ i64 ┆ str ┆ i64 ┆ str ┆ str │\n",
|
||
"╞═══════╪═════════════╪═════════════════╪═══════════════════════════════════╪═════════════════════╡\n",
|
||
"│ 0 ┆ information ┆ 10 ┆ Process accessed (rule: ProcessA… ┆ Process accessed: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… │\n",
|
||
"│ 1 ┆ information ┆ 10 ┆ Process accessed (rule: ProcessA… ┆ Process accessed: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… │\n",
|
||
"│ 2 ┆ information ┆ 1 ┆ Process Create (rule: ProcessCre… ┆ Process Create: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… │\n",
|
||
"│ 3 ┆ information ┆ 13 ┆ Registry value set (rule: Regist… ┆ Registry value set: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: Ta… │\n",
|
||
"│ … ┆ … ┆ … ┆ … ┆ … │\n",
|
||
"│ 1023 ┆ information ┆ 10 ┆ Process accessed (rule: ProcessA… ┆ Process accessed: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… │\n",
|
||
"│ 1024 ┆ information ┆ 1 ┆ Process Create (rule: ProcessCre… ┆ Process Create: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… │\n",
|
||
"│ 1025 ┆ information ┆ 22 ┆ Dns query (rule: DnsQuery) ┆ Dns query: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ ProcessId… │\n",
|
||
"│ 1026 ┆ information ┆ 1 ┆ Process Create (rule: ProcessCre… ┆ Process Create: │\n",
|
||
"│ ┆ ┆ ┆ ┆ RuleName: - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… │\n",
|
||
"└───────┴─────────────┴─────────────────┴───────────────────────────────────┴─────────────────────┘\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import re\n",
|
||
"\n",
|
||
"# Extract relevant information using regular expressions\n",
|
||
"def extract_info(text):\n",
|
||
" image = re.search(r\"Image: (.*?\\.exe)\", text, re.IGNORECASE)\n",
|
||
" target_filename = re.search(r\"TargetFilename: (.*?\\.exe)\", text, re.IGNORECASE)\n",
|
||
" return {\n",
|
||
" \"image\": image.group(1) if image else \"\",\n",
|
||
" \"target_filename\": target_filename.group(1) if target_filename else \"\",\n",
|
||
" \"text\": text\n",
|
||
" }\n",
|
||
"\n",
|
||
"# Apply extraction to the Polars DataFrame using map_elements\n",
|
||
"better_columns_df = df.with_columns(\n",
|
||
" pl.col(\"filtered_message\").map_elements(lambda x: extract_info(x), return_dtype=pl.Object).alias(\"extracted_info\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# Extract fields from the extracted_info column using map_elements with return_dtype\n",
|
||
"better_columns_df = better_columns_df.with_columns(\n",
|
||
" pl.col(\"extracted_info\").map_elements(lambda x: x['image'], return_dtype=pl.Utf8).alias(\"image\"),\n",
|
||
" pl.col(\"extracted_info\").map_elements(lambda x: x['target_filename'], return_dtype=pl.Utf8).alias(\"target_filename\"),\n",
|
||
" pl.col(\"extracted_info\").map_elements(lambda x: x['text'], return_dtype=pl.Utf8).alias(\"text\")\n",
|
||
").drop(\"extracted_info\")\n",
|
||
"\n",
|
||
"print(better_columns_df)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "HoknJOrTZC7V",
|
||
"outputId": "ec5e8c64-4793-47d5-a400-537de9cae66c"
|
||
},
|
||
"execution_count": 8,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"shape: (1_027, 8)\n",
|
||
"┌───────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┐\n",
|
||
"│ index ┆ log.level ┆ winlog.eve ┆ winlog.tas ┆ filtered_m ┆ image ┆ target_fil ┆ text │\n",
|
||
"│ --- ┆ --- ┆ nt_id ┆ k ┆ essage ┆ --- ┆ ename ┆ --- │\n",
|
||
"│ i64 ┆ str ┆ --- ┆ --- ┆ --- ┆ str ┆ --- ┆ str │\n",
|
||
"│ ┆ ┆ i64 ┆ str ┆ str ┆ ┆ str ┆ │\n",
|
||
"╞═══════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╡\n",
|
||
"│ 0 ┆ informatio ┆ 10 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ accessed: ┆ \\system32\\ ┆ ┆ accessed: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ svchost.ex ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ - ┆ e ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… ┆ ┆ ┆ So… │\n",
|
||
"│ 1 ┆ informatio ┆ 10 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ accessed: ┆ \\system32\\ ┆ ┆ accessed: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ svchost.ex ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ - ┆ e ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… ┆ ┆ ┆ So… │\n",
|
||
"│ 2 ┆ informatio ┆ 1 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ Create: ┆ \\servicing ┆ ┆ Create: │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ RuleName: ┆ \\TrustedIn ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ - ┆ st… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… ┆ ┆ ┆ Proc… │\n",
|
||
"│ 3 ┆ informatio ┆ 13 ┆ Registry ┆ Registry ┆ C:\\Windows ┆ ┆ Registry │\n",
|
||
"│ ┆ n ┆ ┆ value set ┆ value set: ┆ \\servicing ┆ ┆ value set: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ \\TrustedIn ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ Regist… ┆ Ta… ┆ st… ┆ ┆ Ta… │\n",
|
||
"│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n",
|
||
"│ 1023 ┆ informatio ┆ 10 ┆ Process ┆ Process ┆ C:\\Program ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ accessed: ┆ Files (x86 ┆ ┆ accessed: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ )\\Microsof ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ - ┆ t… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… ┆ ┆ ┆ So… │\n",
|
||
"│ 1024 ┆ informatio ┆ 1 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ Create: ┆ \\System32\\ ┆ ┆ Create: │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ RuleName: ┆ taskhostw. ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ - ┆ ex… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… ┆ ┆ ┆ Proc… │\n",
|
||
"│ 1025 ┆ informatio ┆ 22 ┆ Dns query ┆ Dns query: ┆ ┆ ┆ Dns query: │\n",
|
||
"│ ┆ n ┆ ┆ (rule: ┆ RuleName: ┆ ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ DnsQuery) ┆ - ┆ ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ ProcessId… ┆ ┆ ┆ ProcessId… │\n",
|
||
"│ 1026 ┆ informatio ┆ 1 ┆ Process ┆ Process ┆ C:\\Program ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ Create: ┆ Files\\RUXI ┆ ┆ Create: │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ RuleName: ┆ M\\PLUGSche ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ - ┆ d… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… ┆ ┆ ┆ Proc… │\n",
|
||
"└───────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┘\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Max token length"
|
||
],
|
||
"metadata": {
|
||
"id": "QdLFssPYC-oG"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tokenizers import Tokenizer\n",
|
||
"\n",
|
||
"# Load the custom tokenizer\n",
|
||
"tokenizer = Tokenizer.from_file(\"log_tokenizer.json\")\n",
|
||
"\n",
|
||
"# Tokenize the messages and find the max token length\n",
|
||
"token_lengths = better_columns_df[\"filtered_message\"].map_elements(\n",
|
||
" lambda x: len(tokenizer.encode(x).ids)\n",
|
||
")\n",
|
||
"\n",
|
||
"# Get the maximum token length\n",
|
||
"max_length = token_lengths.max()\n",
|
||
"\n",
|
||
"print(f\"The maximum token length in 'filtered_message' column is: {max_length}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "g7lfhJmEckFK",
|
||
"outputId": "fe4b0607-53a8-40ad-e3ad-6be64398b7bb"
|
||
},
|
||
"execution_count": 9,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"The maximum token length in 'filtered_message' column is: 694\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Load LinFormer with max. token length"
|
||
],
|
||
"metadata": {
|
||
"id": "ARRqrF8XDG6_"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import torch\n",
|
||
"torch.cuda.empty_cache()"
|
||
],
|
||
"metadata": {
|
||
"id": "OIXpckDUPZ6C"
|
||
},
|
||
"execution_count": 9,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Linformer parameter 1:1 from the researchers\n",
|
||
"# then the input_size is determined based on the max no. of tokens\n",
|
||
"# the positional embedding flag is on by default\n",
|
||
"\n",
|
||
"from linformer_pytorch import LinformerLM\n",
|
||
"import torch\n",
|
||
"\n",
|
||
"linformer_model = LinformerLM(\n",
|
||
" num_tokens=30000, # Number of tokens in the LM\n",
|
||
" input_size=700, # Dimension 1 of the input\n",
|
||
" channels=64, # Dimension 2 of the input\n",
|
||
" dim_d=None, # Overwrites the inner dim of the attention heads. If None, sticks with the recommended channels // nhead, as in the \"Attention is all you need\" paper\n",
|
||
" dim_k=128, # The second dimension of the P_bar matrix from the paper\n",
|
||
" dim_ff=128, # Dimension in the feed forward network\n",
|
||
" dropout_ff=0.15, # Dropout for feed forward network\n",
|
||
" nhead=4, # Number of attention heads\n",
|
||
" depth=2, # How many times to run the model\n",
|
||
" dropout=0.1, # How much dropout to apply to P_bar after softmax\n",
|
||
" activation=\"gelu\", # What activation to use. Currently, only gelu and relu supported, and only on ff network.\n",
|
||
" checkpoint_level=\"C0\", # What checkpoint level to use. For more information, see below.\n",
|
||
" parameter_sharing=\"layerwise\", # What level of parameter sharing to use. For more information, see below.\n",
|
||
" k_reduce_by_layer=0, # Going down `depth`, how much to reduce `dim_k` by, for the `E` and `F` matrices. Will have a minimum value of 1.\n",
|
||
" full_attention=False, # Use full attention instead, for O(n^2) time and space complexity. Included here just for comparison\n",
|
||
" include_ff=True, # Whether or not to include the Feed Forward layer\n",
|
||
" w_o_intermediate_dim=None, # If not None, have 2 w_o matrices, such that instead of `dim*nead,channels`, you have `dim*nhead,w_o_int`, and `w_o_int,channels`\n",
|
||
" emb_dim=128, # If you want the embedding dimension to be different than the channels for the Linformer\n",
|
||
" causal=False, # If you want this to be a causal Linformer, where the upper right of the P_bar matrix is masked out.\n",
|
||
" method=\"learnable\", # The method of how to perform the projection. Supported methods are 'convolution', 'learnable', and 'no_params'\n",
|
||
" ff_intermediate=None, # See the section below for more information\n",
|
||
" ).cuda()"
|
||
],
|
||
"metadata": {
|
||
"id": "BeSmET5qvPCu"
|
||
},
|
||
"execution_count": 10,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Vectorize the text column in that DataFrame"
|
||
],
|
||
"metadata": {
|
||
"id": "AzxrG_RmDLr1"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tokenizers import Tokenizer\n",
|
||
"import torch\n",
|
||
"import numpy as np\n",
|
||
"import polars as pl\n",
|
||
"\n",
|
||
"# Load the custom tokenizer\n",
|
||
"tokenizer = Tokenizer.from_file(\"log_tokenizer.json\")\n",
|
||
"\n",
|
||
"# Define the device (assuming you're using PyTorch and want to specify CPU or GPU)\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
"\n",
|
||
"def vectorize_text(text):\n",
|
||
" MAX_LENGTH = 700 # Define the maximum length of tokens for the model\n",
|
||
"\n",
|
||
" # Tokenize using the custom tokenizer\n",
|
||
" encoded = tokenizer.encode(text)\n",
|
||
"\n",
|
||
" # Get token IDs\n",
|
||
" input_ids = encoded.ids\n",
|
||
"\n",
|
||
" # Ensure the input_ids length is exactly MAX_LENGTH\n",
|
||
" input_ids = input_ids[:MAX_LENGTH] if len(input_ids) > MAX_LENGTH else input_ids + [0] * (MAX_LENGTH - len(input_ids))\n",
|
||
"\n",
|
||
" # Convert to PyTorch tensor and move to the appropriate device\n",
|
||
" input_ids = torch.tensor([input_ids], dtype=torch.long).to(device)\n",
|
||
"\n",
|
||
" # Get the model outputs, ensuring the input tensor is the correct size\n",
|
||
" outputs = linformer_model(input_ids) # Now passing the tensor directly\n",
|
||
"\n",
|
||
" # Assuming outputs is the tensor of interest\n",
|
||
" vector = outputs.mean(dim=1).detach() # Detach the tensor from the GPU\n",
|
||
" return vector.cpu().numpy() # Move tensor back to CPU and convert to numpy\n",
|
||
"\n",
|
||
"# Assuming `better_columns_df` is a Polars DataFrame with a column \"filtered_message\"\n",
|
||
"linformer_vector_df = better_columns_df.with_columns(\n",
|
||
" pl.col(\"filtered_message\").map_elements(lambda x: vectorize_text(x).flatten(), return_dtype=pl.Object).alias(\"message_vector\")\n",
|
||
")\n",
|
||
"\n",
|
||
"print(linformer_vector_df)\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "79IKUS-OXEiS",
|
||
"outputId": "63cc795f-dc88-40bc-c8ce-bbf39b7aa099"
|
||
},
|
||
"execution_count": 11,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"shape: (1_027, 9)\n",
|
||
"┌───────┬────────────┬────────────┬────────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
|
||
"│ index ┆ log.level ┆ winlog.eve ┆ winlog.tas ┆ … ┆ image ┆ target_fi ┆ text ┆ message_v │\n",
|
||
"│ --- ┆ --- ┆ nt_id ┆ k ┆ ┆ --- ┆ lename ┆ --- ┆ ector │\n",
|
||
"│ i64 ┆ str ┆ --- ┆ --- ┆ ┆ str ┆ --- ┆ str ┆ --- │\n",
|
||
"│ ┆ ┆ i64 ┆ str ┆ ┆ ┆ str ┆ ┆ object │\n",
|
||
"╞═══════╪════════════╪════════════╪════════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
|
||
"│ 0 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [-0.04798 │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 326 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ 2\\svchost ┆ ┆ RuleName: ┆ 0.2714037 │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ -0.0024… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
|
||
"│ 1 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [-0.04089 │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 047 0.28 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ 2\\svchost ┆ ┆ RuleName: ┆ 009406 │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ 0.0018… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
|
||
"│ 2 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [-0.11740 │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ ┆ s\\servici ┆ ┆ Create: ┆ 403 0.40 │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ ng\\Truste ┆ ┆ RuleName: ┆ 166372 │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ ┆ dInst… ┆ ┆ - ┆ -0.0089… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
|
||
"│ 3 ┆ informatio ┆ 13 ┆ Registry ┆ … ┆ C:\\Window ┆ ┆ Registry ┆ [-0.12442 │\n",
|
||
"│ ┆ n ┆ ┆ value set ┆ ┆ s\\servici ┆ ┆ value ┆ 11 0.44 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ ng\\Truste ┆ ┆ set: ┆ 944718 │\n",
|
||
"│ ┆ ┆ ┆ Regist… ┆ ┆ dInst… ┆ ┆ RuleName: ┆ -0.0088… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Ta… ┆ │\n",
|
||
"│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n",
|
||
"│ 1023 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [-0.06318 │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ ┆ m Files ┆ ┆ accessed: ┆ 863 0.10 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ (x86)\\Mic ┆ ┆ RuleName: ┆ 246795 │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ rosoft… ┆ ┆ - ┆ 0.0180… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
|
||
"│ 1024 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [-0.10114 │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ ┆ s\\System3 ┆ ┆ Create: ┆ 317 0.38 │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ 2\\taskhos ┆ ┆ RuleName: ┆ 521096 │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ ┆ tw.ex… ┆ ┆ - ┆ -0.0174… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
|
||
"│ 1025 ┆ informatio ┆ 22 ┆ Dns query ┆ … ┆ ┆ ┆ Dns ┆ [-0.09311 │\n",
|
||
"│ ┆ n ┆ ┆ (rule: ┆ ┆ ┆ ┆ query: ┆ 421 │\n",
|
||
"│ ┆ ┆ ┆ DnsQuery) ┆ ┆ ┆ ┆ RuleName: ┆ 0.4408672 │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ -0.0047… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ProcessId ┆ │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ … ┆ │\n",
|
||
"│ 1026 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [-0.12535 │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ ┆ m Files\\R ┆ ┆ Create: ┆ 277 0.38 │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ UXIM\\PLUG ┆ ┆ RuleName: ┆ 669917 │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ ┆ Sched… ┆ ┆ - ┆ -0.0348… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
|
||
"└───────┴────────────┴────────────┴────────────┴───┴───────────┴───────────┴───────────┴───────────┘\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Ensure memory_profiler is installed\n",
|
||
"!pip install -q memory-profiler\n",
|
||
"\n",
|
||
"import timeit\n",
|
||
"from memory_profiler import memory_usage\n",
|
||
"import polars as pl\n",
|
||
"import numpy as np\n",
|
||
"import torch\n",
|
||
"\n",
|
||
"# Function to process the DataFrame and measure memory usage\n",
|
||
"def process_and_measure(df):\n",
|
||
" # Reset GPU memory peak stats\n",
|
||
" if torch.cuda.is_available():\n",
|
||
" torch.cuda.reset_peak_memory_stats()\n",
|
||
"\n",
|
||
" temp_df = df.with_columns(\n",
|
||
" pl.col(\"filtered_message\").map_elements(lambda x: vectorize_text(x).flatten(), return_dtype=pl.Object).alias(\"message_vector\")\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Get GPU memory usage\n",
|
||
" gpu_memory_usage = torch.cuda.max_memory_allocated() / (1024 ** 2) if torch.cuda.is_available() else 0\n",
|
||
" return gpu_memory_usage\n",
|
||
"\n",
|
||
"# Number of times to run the function\n",
|
||
"n_runs = 10\n",
|
||
"\n",
|
||
"# Measure the runtime, CPU, and GPU memory usage over multiple runs\n",
|
||
"runtimes = []\n",
|
||
"cpu_memory_usages = []\n",
|
||
"gpu_memory_usages = []\n",
|
||
"\n",
|
||
"for _ in range(n_runs):\n",
|
||
" # Measure runtime\n",
|
||
" runtime = timeit.timeit(lambda: process_and_measure(better_columns_df), number=1)\n",
|
||
" runtimes.append(runtime)\n",
|
||
"\n",
|
||
" # Measure CPU memory usage\n",
|
||
" cpu_mem_usage = memory_usage((process_and_measure, (better_columns_df,)))\n",
|
||
" cpu_memory_usages.append(max(cpu_mem_usage) - min(cpu_mem_usage))\n",
|
||
"\n",
|
||
" # Measure GPU memory usage\n",
|
||
" gpu_mem_usage = process_and_measure(better_columns_df)\n",
|
||
" gpu_memory_usages.append(gpu_mem_usage)\n",
|
||
"\n",
|
||
"# Calculate average runtime, CPU memory, and GPU memory usage\n",
|
||
"average_runtime = np.mean(runtimes)\n",
|
||
"average_cpu_memory_usage = np.mean(cpu_memory_usages)\n",
|
||
"average_gpu_memory_usage = np.mean(gpu_memory_usages)\n",
|
||
"\n",
|
||
"print(f\"Average runtime over {n_runs} runs: {average_runtime:.6f} seconds\")\n",
|
||
"print(f\"Average CPU memory usage over {n_runs} runs: {average_cpu_memory_usage:.2f} MiB\")\n",
|
||
"print(f\"Average GPU memory usage over {n_runs} runs: {average_gpu_memory_usage:.2f} MiB\")\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "g8zGXPPsRAoh",
|
||
"outputId": "cdaf812f-935b-4dc5-db31-5aa2593ea24c"
|
||
},
|
||
"execution_count": 14,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Average runtime over 10 runs: 7.183239 seconds\n",
|
||
"Average CPU memory usage over 10 runs: 116.41 MiB\n",
|
||
"Average GPU memory usage over 10 runs: 131.21 MiB\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Convert the vectors to a NumPy array for statistical analysis"
|
||
],
|
||
"metadata": {
|
||
"id": "3Gxep-miEAVm"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Assuming vector_df has a column 'message_vector' with the vectors\n",
|
||
"linformer_vectors = np.stack(linformer_vector_df['message_vector'].to_numpy()) # Stack the vectors into a 2D array"
|
||
],
|
||
"metadata": {
|
||
"id": "AInOx4hm7mR1"
|
||
},
|
||
"execution_count": 12,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# UMAP dimensionality check - scattered or clustered?"
|
||
],
|
||
"metadata": {
|
||
"id": "zZVCSpvsEJIq"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import umap.umap_ as umap # Correct import statement for UMAP\n",
|
||
"\n",
|
||
"# Reduce dimensions using UMAP\n",
|
||
"umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
|
||
"umap_results = umap_reducer.fit_transform(linformer_vectors)\n",
|
||
"\n",
|
||
"# Plotting the results\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10, 8))\n",
|
||
"plt.scatter(umap_results[:, 0], umap_results[:, 1], s=5)\n",
|
||
"plt.title('UMAP Visualization of Vectors')\n",
|
||
"plt.xlabel('UMAP component 1')\n",
|
||
"plt.ylabel('UMAP component 2')\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 754
|
||
},
|
||
"id": "I55hh_VVm5Gs",
|
||
"outputId": "bab73737-a100-46b3-b19a-1fb6e949e53d"
|
||
},
|
||
"execution_count": 13,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
|
||
" warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Silhouette score via KMeans clustering"
|
||
],
|
||
"metadata": {
|
||
"id": "56wtMVhhEVzf"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.cluster import KMeans\n",
|
||
"from sklearn.metrics import silhouette_score\n",
|
||
"\n",
|
||
"# Assuming we choose an arbitrary number of clusters, e.g., 5\n",
|
||
"kmeans = KMeans(n_clusters=2, random_state=42)\n",
|
||
"cluster_labels = kmeans.fit_predict(linformer_vectors)\n",
|
||
"\n",
|
||
"# Calculate the silhouette score\n",
|
||
"sil_score = silhouette_score(linformer_vectors, cluster_labels)\n",
|
||
"print(f\"Silhouette Score: {sil_score}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "xGm3Fm5bnFae",
|
||
"outputId": "d14d17f7-87e8-49ba-b87e-1eca767c5d7e"
|
||
},
|
||
"execution_count": 14,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Silhouette Score: 0.7193768620491028\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Distribution of the Cosine similarity"
|
||
],
|
||
"metadata": {
|
||
"id": "8yhTWsSnEl30"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from sklearn.metrics.pairwise import cosine_similarity\n",
|
||
"\n",
|
||
"# Assuming vector_df has a column 'message_vector' with the vectors\n",
|
||
"vectors = np.stack(linformer_vector_df['message_vector'].to_numpy()) # Stack the vectors into a 2D array\n",
|
||
"\n",
|
||
"# Calculate the cosine similarity matrix\n",
|
||
"cosine_sim_matrix = cosine_similarity(linformer_vectors)\n",
|
||
"\n",
|
||
"# Extract the upper triangle values from the cosine similarity matrix, excluding the diagonal\n",
|
||
"upper_triangle_indices = np.triu_indices_from(cosine_sim_matrix, k=1)\n",
|
||
"cosine_sim_values = cosine_sim_matrix[upper_triangle_indices]\n",
|
||
"\n",
|
||
"# Plotting the KDE of cosine similarities\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.kdeplot(cosine_sim_values, color='blue', fill=True)\n",
|
||
"plt.title('KDE Plot of Cosine Similarities')\n",
|
||
"plt.xlabel('Cosine Similarity')\n",
|
||
"plt.ylabel('Density')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()\n",
|
||
"\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 564
|
||
},
|
||
"id": "NPbnuVKHqqtf",
|
||
"outputId": "f381157a-6f94-4be1-a095-09c83be7a866"
|
||
},
|
||
"execution_count": 15,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Comparison LongFormer"
|
||
],
|
||
"metadata": {
|
||
"id": "FmwKV-BK-rKX"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# starts with the same data\n",
|
||
"print(better_columns_df)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "l01czPQT-kSu",
|
||
"outputId": "be7724d9-5632-41e9-e2dd-6e06430c843e"
|
||
},
|
||
"execution_count": 16,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"shape: (1_027, 8)\n",
|
||
"┌───────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┐\n",
|
||
"│ index ┆ log.level ┆ winlog.eve ┆ winlog.tas ┆ filtered_m ┆ image ┆ target_fil ┆ text │\n",
|
||
"│ --- ┆ --- ┆ nt_id ┆ k ┆ essage ┆ --- ┆ ename ┆ --- │\n",
|
||
"│ i64 ┆ str ┆ --- ┆ --- ┆ --- ┆ str ┆ --- ┆ str │\n",
|
||
"│ ┆ ┆ i64 ┆ str ┆ str ┆ ┆ str ┆ │\n",
|
||
"╞═══════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╡\n",
|
||
"│ 0 ┆ informatio ┆ 10 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ accessed: ┆ \\system32\\ ┆ ┆ accessed: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ svchost.ex ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ - ┆ e ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… ┆ ┆ ┆ So… │\n",
|
||
"│ 1 ┆ informatio ┆ 10 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ accessed: ┆ \\system32\\ ┆ ┆ accessed: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ svchost.ex ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ - ┆ e ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… ┆ ┆ ┆ So… │\n",
|
||
"│ 2 ┆ informatio ┆ 1 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ Create: ┆ \\servicing ┆ ┆ Create: │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ RuleName: ┆ \\TrustedIn ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ - ┆ st… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… ┆ ┆ ┆ Proc… │\n",
|
||
"│ 3 ┆ informatio ┆ 13 ┆ Registry ┆ Registry ┆ C:\\Windows ┆ ┆ Registry │\n",
|
||
"│ ┆ n ┆ ┆ value set ┆ value set: ┆ \\servicing ┆ ┆ value set: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ \\TrustedIn ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ Regist… ┆ Ta… ┆ st… ┆ ┆ Ta… │\n",
|
||
"│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n",
|
||
"│ 1023 ┆ informatio ┆ 10 ┆ Process ┆ Process ┆ C:\\Program ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ accessed: ┆ Files (x86 ┆ ┆ accessed: │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ RuleName: ┆ )\\Microsof ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ - ┆ t… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ So… ┆ ┆ ┆ So… │\n",
|
||
"│ 1024 ┆ informatio ┆ 1 ┆ Process ┆ Process ┆ C:\\Windows ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ Create: ┆ \\System32\\ ┆ ┆ Create: │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ RuleName: ┆ taskhostw. ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ - ┆ ex… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… ┆ ┆ ┆ Proc… │\n",
|
||
"│ 1025 ┆ informatio ┆ 22 ┆ Dns query ┆ Dns query: ┆ ┆ ┆ Dns query: │\n",
|
||
"│ ┆ n ┆ ┆ (rule: ┆ RuleName: ┆ ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ DnsQuery) ┆ - ┆ ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ ProcessId… ┆ ┆ ┆ ProcessId… │\n",
|
||
"│ 1026 ┆ informatio ┆ 1 ┆ Process ┆ Process ┆ C:\\Program ┆ ┆ Process │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ Create: ┆ Files\\RUXI ┆ ┆ Create: │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ RuleName: ┆ M\\PLUGSche ┆ ┆ RuleName: │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ - ┆ d… ┆ ┆ - │\n",
|
||
"│ ┆ ┆ ┆ ┆ Proc… ┆ ┆ ┆ Proc… │\n",
|
||
"└───────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┘\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Initialize LongFormer with the same pre-trained Tokenizer"
|
||
],
|
||
"metadata": {
|
||
"id": "J6REcr7hE1xz"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tokenizers import Tokenizer\n",
|
||
"import torch\n",
|
||
"import numpy as np\n",
|
||
"import polars as pl\n",
|
||
"from transformers import LongformerModel\n",
|
||
"\n",
|
||
"# Load the custom tokenizer\n",
|
||
"tokenizer = Tokenizer.from_file(\"log_tokenizer.json\")\n",
|
||
"\n",
|
||
"# Load LongFormer model\n",
|
||
"model_name = 'allenai/longformer-base-4096'\n",
|
||
"longformer_model = LongformerModel.from_pretrained(model_name)\n",
|
||
"\n",
|
||
"# Define the device (GPU if available, otherwise CPU)\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
"longformer_model.to(device)\n",
|
||
"\n",
|
||
"def vectorize_text(text):\n",
|
||
" MAX_LENGTH = 700 # Define the maximum length of tokens for the model\n",
|
||
"\n",
|
||
" # Tokenize using the custom tokenizer\n",
|
||
" encoded = tokenizer.encode(text)\n",
|
||
"\n",
|
||
" # Get token IDs\n",
|
||
" input_ids = encoded.ids\n",
|
||
"\n",
|
||
" # Ensure the input_ids length is exactly MAX_LENGTH\n",
|
||
" if len(input_ids) > MAX_LENGTH:\n",
|
||
" input_ids = input_ids[:MAX_LENGTH]\n",
|
||
" else:\n",
|
||
" input_ids = input_ids + [0] * (MAX_LENGTH - len(input_ids))\n",
|
||
"\n",
|
||
" # Convert to PyTorch tensor and move to the appropriate device\n",
|
||
" input_ids = torch.tensor([input_ids], dtype=torch.long).to(device)\n",
|
||
"\n",
|
||
" # Create attention mask: 1 for non-padding, 0 for padding\n",
|
||
" attention_mask = (input_ids != 0).long()\n",
|
||
"\n",
|
||
" # Get the model outputs, ensuring the input tensor is the correct size\n",
|
||
" with torch.no_grad():\n",
|
||
" outputs = longformer_model(input_ids, attention_mask=attention_mask)\n",
|
||
" vector = outputs.last_hidden_state.mean(dim=1).cpu().numpy() # Average over the token embeddings\n",
|
||
" return vector.flatten() # Flatten the array to 1D if needed\n",
|
||
"\n",
|
||
"# Assuming `better_columns_df` is your Polars DataFrame with a column \"filtered_message\"\n",
|
||
"longformer_vector_df = better_columns_df.with_columns(\n",
|
||
" pl.col(\"filtered_message\").map_elements(vectorize_text, return_dtype=pl.Object).alias(\"message_vector\")\n",
|
||
")\n",
|
||
"\n",
|
||
"print(longformer_vector_df)\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000,
|
||
"referenced_widgets": [
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||
"304fd7528255422cb7acda531e3500fa",
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"3813ad0ad9e0404182bf58010c973aa9",
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"3bd2f184782649f8bf7d813e8a51c2ae",
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"29373996079643788b752dd3655f93a6",
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"61484f715f7747d6a9ced7464c4ad14f",
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"cb1c9ede622d41cf8a263df7130d35ad",
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"c7a450ff6619407e8ac36bfb9a1d6e43",
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"f3e76940d1754026953345ae20091cca",
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||
"37122a24de3941aeb91e9c8fc9735666",
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||
"bd07dd8a9ef34eb18e5bb25271ccbba1",
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||
"75284522efe54dcabcf20ae684fa7463",
|
||
"0ad669b0ca474fa899ba828fac57bcd1",
|
||
"124cb3abfbf549d5b43d913395f88966",
|
||
"28b1d7e638994a32bb8ae5078eb8bcfe",
|
||
"c22fa8adf79a480ca6c454c81200c664",
|
||
"a533068520fb4f71b78d30f46f4a75dd",
|
||
"5bb65faaa4574332be3266aee7154073",
|
||
"2c00506974c54cc6a74301a82f48fb59",
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||
"378e55f7ba744395a6457c79e8665c18",
|
||
"70844c976d42488cb1ef42080b58bb87",
|
||
"46324caffc6b4229bb4e18f3dd4fc889",
|
||
"c0d6f354c5fa44a6943f56b939b4e006"
|
||
]
|
||
},
|
||
"id": "tpREQMax-yy3",
|
||
"outputId": "5f82cc20-4559-4240-dbb5-2d81ce417c43"
|
||
},
|
||
"execution_count": 15,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||
" warnings.warn(\n",
|
||
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"config.json: 0%| | 0.00/694 [00:00<?, ?B/s]"
|
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],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "304fd7528255422cb7acda531e3500fa"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
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"text/plain": [
|
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"pytorch_model.bin: 0%| | 0.00/597M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "0ad669b0ca474fa899ba828fac57bcd1"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Input ids are automatically padded from 700 to 1024 to be a multiple of `config.attention_window`: 512\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"shape: (1_027, 9)\n",
|
||
"┌───────┬────────────┬────────────┬────────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
|
||
"│ index ┆ log.level ┆ winlog.eve ┆ winlog.tas ┆ … ┆ image ┆ target_fi ┆ text ┆ message_v │\n",
|
||
"│ --- ┆ --- ┆ nt_id ┆ k ┆ ┆ --- ┆ lename ┆ --- ┆ ector │\n",
|
||
"│ i64 ┆ str ┆ --- ┆ --- ┆ ┆ str ┆ --- ┆ str ┆ --- │\n",
|
||
"│ ┆ ┆ i64 ┆ str ┆ ┆ ┆ str ┆ ┆ object │\n",
|
||
"╞═══════╪════════════╪════════════╪════════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
|
||
"│ 0 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [ 8.14584 │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 829e-03 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ 2\\svchost ┆ ┆ RuleName: ┆ -4.635616 │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ 39e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
|
||
"│ 1 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [ 1.45857 │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 185e-02 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ 2\\svchost ┆ ┆ RuleName: ┆ -5.546064 │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ 67e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
|
||
"│ 2 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [-2.73109 │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ ┆ s\\servici ┆ ┆ Create: ┆ 730e-02 │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ ng\\Truste ┆ ┆ RuleName: ┆ 6.1852186 │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ ┆ dInst… ┆ ┆ - ┆ 9e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
|
||
"│ 3 ┆ informatio ┆ 13 ┆ Registry ┆ … ┆ C:\\Window ┆ ┆ Registry ┆ [-2.18554 │\n",
|
||
"│ ┆ n ┆ ┆ value set ┆ ┆ s\\servici ┆ ┆ value ┆ 884e-02 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ ng\\Truste ┆ ┆ set: ┆ 6.9496206 │\n",
|
||
"│ ┆ ┆ ┆ Regist… ┆ ┆ dInst… ┆ ┆ RuleName: ┆ 9e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Ta… ┆ │\n",
|
||
"│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n",
|
||
"│ 1023 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [ 5.60084 │\n",
|
||
"│ ┆ n ┆ ┆ accessed ┆ ┆ m Files ┆ ┆ accessed: ┆ 544e-02 │\n",
|
||
"│ ┆ ┆ ┆ (rule: ┆ ┆ (x86)\\Mic ┆ ┆ RuleName: ┆ -1.052715 │\n",
|
||
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ rosoft… ┆ ┆ - ┆ 93e-01… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
|
||
"│ 1024 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [-2.43805 │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ ┆ s\\System3 ┆ ┆ Create: ┆ 666e-02 │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ 2\\taskhos ┆ ┆ RuleName: ┆ 5.6348703 │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ ┆ tw.ex… ┆ ┆ - ┆ 8e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
|
||
"│ 1025 ┆ informatio ┆ 22 ┆ Dns query ┆ … ┆ ┆ ┆ Dns ┆ [-1.98449 │\n",
|
||
"│ ┆ n ┆ ┆ (rule: ┆ ┆ ┆ ┆ query: ┆ 418e-02 │\n",
|
||
"│ ┆ ┆ ┆ DnsQuery) ┆ ┆ ┆ ┆ RuleName: ┆ 7.1333341 │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ 3e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ProcessId ┆ │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ … ┆ │\n",
|
||
"│ 1026 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [-2.31898 │\n",
|
||
"│ ┆ n ┆ ┆ Create ┆ ┆ m Files\\R ┆ ┆ Create: ┆ 110e-02 │\n",
|
||
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ UXIM\\PLUG ┆ ┆ RuleName: ┆ 6.9678619 │\n",
|
||
"│ ┆ ┆ ┆ cessCre… ┆ ┆ Sched… ┆ ┆ - ┆ 5e-02… │\n",
|
||
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
|
||
"└───────┴────────────┴────────────┴────────────┴───┴───────────┴───────────┴───────────┴───────────┘\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!pip install -q psutil\n",
|
||
"\n",
|
||
"import timeit\n",
|
||
"import psutil\n",
|
||
"import polars as pl\n",
|
||
"import torch\n",
|
||
"\n",
|
||
"# Function to process the DataFrame and measure memory usage\n",
|
||
"def process_and_measure(df):\n",
|
||
" # Reset GPU memory peak stats\n",
|
||
" if torch.cuda.is_available():\n",
|
||
" torch.cuda.reset_peak_memory_stats()\n",
|
||
"\n",
|
||
" temp_df = df.with_columns(\n",
|
||
" pl.col(\"filtered_message\").map_elements(lambda x: vectorize_text(x).flatten(), return_dtype=pl.Object).alias(\"message_vector\")\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Get GPU memory usage\n",
|
||
" gpu_memory_usage = torch.cuda.max_memory_allocated() / (1024 ** 2) if torch.cuda.is_available() else 0\n",
|
||
" return gpu_memory_usage\n",
|
||
"\n",
|
||
"# Number of times to run the function\n",
|
||
"n_runs = 10\n",
|
||
"\n",
|
||
"# Measure the runtime, CPU, and GPU memory usage over multiple runs\n",
|
||
"runtimes = []\n",
|
||
"cpu_memory_usages = []\n",
|
||
"gpu_memory_usages = []\n",
|
||
"\n",
|
||
"for _ in range(n_runs):\n",
|
||
" # Record initial CPU memory usage\n",
|
||
" # psutil, because JAX doesn't work well with the memory_profiler\n",
|
||
" initial_memory = psutil.Process().memory_info().rss / (1024 ** 2)\n",
|
||
"\n",
|
||
" # Measure runtime\n",
|
||
" runtime = timeit.timeit(lambda: process_and_measure(better_columns_df), number=1)\n",
|
||
" runtimes.append(runtime)\n",
|
||
"\n",
|
||
" # Record final CPU memory usage\n",
|
||
" final_memory = psutil.Process().memory_info().rss / (1024 ** 2)\n",
|
||
" cpu_memory_usages.append(final_memory - initial_memory)\n",
|
||
"\n",
|
||
" # Measure GPU memory usage\n",
|
||
" gpu_mem_usage = process_and_measure(better_columns_df)\n",
|
||
" gpu_memory_usages.append(gpu_mem_usage)\n",
|
||
"\n",
|
||
"# Calculate average runtime, CPU memory, and GPU memory usage\n",
|
||
"average_runtime = np.mean(runtimes)\n",
|
||
"average_cpu_memory_usage = np.mean(cpu_memory_usages)\n",
|
||
"average_gpu_memory_usage = np.mean(gpu_memory_usages)\n",
|
||
"\n",
|
||
"print(f\"Average runtime over {n_runs} runs: {average_runtime:.6f} seconds\")\n",
|
||
"print(f\"Average CPU memory usage over {n_runs} runs: {average_cpu_memory_usage:.2f} MiB\")\n",
|
||
"print(f\"Average GPU memory usage over {n_runs} runs: {average_gpu_memory_usage:.2f} MiB\")\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "0IWHrarbUvJN",
|
||
"outputId": "baf1b651-cd96-494a-aa01-f2c3f6e677c4"
|
||
},
|
||
"execution_count": 17,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Average runtime over 10 runs: 32.509051 seconds\n",
|
||
"Average CPU memory usage over 10 runs: 0.00 MiB\n",
|
||
"Average GPU memory usage over 10 runs: 739.78 MiB\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Assuming vector_df has a column 'message_vector' with the vectors\n",
|
||
"longformer_vectors = np.stack(longformer_vector_df['message_vector'].to_numpy()) # Stack the vectors into a 2D array"
|
||
],
|
||
"metadata": {
|
||
"id": "LCnkFlrTB2qr"
|
||
},
|
||
"execution_count": 18,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import umap.umap_ as umap # Correct import statement for UMAP\n",
|
||
"\n",
|
||
"# Reduce dimensions using UMAP\n",
|
||
"umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
|
||
"umap_results = umap_reducer.fit_transform(longformer_vectors)\n",
|
||
"\n",
|
||
"# Plotting the results\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10, 8))\n",
|
||
"plt.scatter(umap_results[:, 0], umap_results[:, 1], s=5)\n",
|
||
"plt.title('UMAP Visualization of Vectors')\n",
|
||
"plt.xlabel('UMAP component 1')\n",
|
||
"plt.ylabel('UMAP component 2')\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 754
|
||
},
|
||
"id": "I_O0HEDwAC9T",
|
||
"outputId": "8e1647bb-bf3a-4831-acaa-d4031a8b14b1"
|
||
},
|
||
"execution_count": 19,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
|
||
" warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x800 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.cluster import KMeans\n",
|
||
"from sklearn.metrics import silhouette_score\n",
|
||
"\n",
|
||
"# Assuming we choose an arbitrary number of clusters, e.g., 5\n",
|
||
"kmeans = KMeans(n_clusters=2, random_state=42)\n",
|
||
"cluster_labels = kmeans.fit_predict(longformer_vectors)\n",
|
||
"\n",
|
||
"# Calculate the silhouette score\n",
|
||
"sil_score = silhouette_score(longformer_vectors, cluster_labels)\n",
|
||
"print(f\"Silhouette Score: {sil_score}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "w6hYJUe2APTt",
|
||
"outputId": "0c923d0a-8b74-4e88-deec-f3c643039b86"
|
||
},
|
||
"execution_count": 20,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Silhouette Score: 0.7029939889907837\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from sklearn.metrics.pairwise import cosine_similarity\n",
|
||
"\n",
|
||
"# Assuming vector_df has a column 'message_vector' with the vectors\n",
|
||
"vectors = np.stack(longformer_vector_df['message_vector'].to_numpy()) # Stack the vectors into a 2D array\n",
|
||
"\n",
|
||
"# Calculate the cosine similarity matrix\n",
|
||
"cosine_sim_matrix = cosine_similarity(longformer_vectors)\n",
|
||
"\n",
|
||
"# Extract the upper triangle values from the cosine similarity matrix, excluding the diagonal\n",
|
||
"upper_triangle_indices = np.triu_indices_from(cosine_sim_matrix, k=1)\n",
|
||
"cosine_sim_values = cosine_sim_matrix[upper_triangle_indices]\n",
|
||
"\n",
|
||
"# Plotting the KDE of cosine similarities\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.kdeplot(cosine_sim_values, color='blue', fill=True)\n",
|
||
"plt.title('KDE Plot of Cosine Similarities')\n",
|
||
"plt.xlabel('Cosine Similarity')\n",
|
||
"plt.ylabel('Density')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()\n",
|
||
"\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 564
|
||
},
|
||
"id": "wz-3_DNDBe-L",
|
||
"outputId": "486b54f0-101a-499d-d180-add867c012ae"
|
||
},
|
||
"execution_count": 21,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"# Assuming `X` is the matrix of extracted features from your deep learning model\n",
|
||
"# Replace this with your actual feature matrix\n",
|
||
"X = linformer_vectors\n",
|
||
"\n",
|
||
"# Scale the features before applying PCA\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(X)\n",
|
||
"\n",
|
||
"# Apply PCA to retain 95% of the variance\n",
|
||
"pca = PCA(n_components=0.95)\n",
|
||
"X_pca = pca.fit_transform(X_scaled)\n",
|
||
"\n",
|
||
"# Output the results\n",
|
||
"num_features_before = X.shape[1]\n",
|
||
"num_features_after = X_pca.shape[1]\n",
|
||
"explained_variance_ratio = pca.explained_variance_ratio_\n",
|
||
"total_explained_variance = np.sum(explained_variance_ratio)\n",
|
||
"\n",
|
||
"print(f\"Number of features before PCA: {num_features_before}\")\n",
|
||
"print(f\"Number of features after PCA: {num_features_after}\")\n",
|
||
"print(f\"Explained variance ratio of each component: {explained_variance_ratio}\")\n",
|
||
"print(f\"Total explained variance: {total_explained_variance}\")\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "pf6Ff46rJenw",
|
||
"outputId": "f6923d38-03ca-4af7-81b8-004116459042"
|
||
},
|
||
"execution_count": 22,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Number of features before PCA: 30000\n",
|
||
"Number of features after PCA: 14\n",
|
||
"Explained variance ratio of each component: [0.78091 0.07875506 0.01703416 0.01329861 0.01111274 0.00880664\n",
|
||
" 0.00747441 0.0069161 0.00613371 0.00590533 0.00454865 0.00417529\n",
|
||
" 0.00390245 0.00353552]\n",
|
||
"Total explained variance: 0.9525086879730225\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"# Assuming `X` is the matrix of extracted features from your deep learning model\n",
|
||
"# Replace this with your actual feature matrix\n",
|
||
"X = longformer_vectors\n",
|
||
"\n",
|
||
"# Scale the features before applying PCA\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(X)\n",
|
||
"\n",
|
||
"# Apply PCA to retain 95% of the variance\n",
|
||
"pca = PCA(n_components=0.95)\n",
|
||
"X_pca = pca.fit_transform(X_scaled)\n",
|
||
"\n",
|
||
"# Output the results\n",
|
||
"num_features_before = X.shape[1]\n",
|
||
"num_features_after = X_pca.shape[1]\n",
|
||
"explained_variance_ratio = pca.explained_variance_ratio_\n",
|
||
"total_explained_variance = np.sum(explained_variance_ratio)\n",
|
||
"\n",
|
||
"print(f\"Number of features before PCA: {num_features_before}\")\n",
|
||
"print(f\"Number of features after PCA: {num_features_after}\")\n",
|
||
"print(f\"Explained variance ratio of each component: {explained_variance_ratio}\")\n",
|
||
"print(f\"Total explained variance: {total_explained_variance}\")\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "TeaiZpy_H67h",
|
||
"outputId": "91fb9f4c-6c74-4511-9f22-9ce8192bf948"
|
||
},
|
||
"execution_count": 23,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Number of features before PCA: 768\n",
|
||
"Number of features after PCA: 12\n",
|
||
"Explained variance ratio of each component: [0.61036086 0.20625743 0.0349943 0.02877296 0.01811446 0.01343036\n",
|
||
" 0.01034578 0.00865575 0.00768904 0.0061328 0.00457506 0.00375366]\n",
|
||
"Total explained variance: 0.9530825018882751\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"# X is the features matrix\n",
|
||
"x = linformer_vectors\n",
|
||
"\n",
|
||
"\n",
|
||
"# Standardize the features\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(X)\n",
|
||
"\n",
|
||
"# Apply PCA\n",
|
||
"pca = PCA(n_components=0.95)\n",
|
||
"X_pca = pca.fit_transform(X_scaled)\n",
|
||
"explained_variance_ratio = pca.explained_variance_ratio_\n",
|
||
"cumulative_variance = np.cumsum(explained_variance_ratio)\n",
|
||
"\n",
|
||
"# Plotting the Scree plot\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, alpha=0.6, color='g', label='Individual explained variance')\n",
|
||
"plt.ylabel('Explained variance ratio')\n",
|
||
"plt.xlabel('Principal components')\n",
|
||
"plt.title('Scree Plot')\n",
|
||
"plt.legend(loc='best')\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"# Plotting the Cumulative Variance plot\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o', linestyle='-', color='b', label='Cumulative explained variance')\n",
|
||
"plt.xlabel('Number of components')\n",
|
||
"plt.ylabel('Cumulative explained variance')\n",
|
||
"plt.title('Cumulative Variance Plot')\n",
|
||
"plt.axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
|
||
"plt.legend(loc='best')\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"# Displaying the plots\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 797
|
||
},
|
||
"id": "qOa9OTV6DpOK",
|
||
"outputId": "cfede8d1-e334-426f-c517-186035938323"
|
||
},
|
||
"execution_count": 24,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
],
|
||
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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"# X is the features matrix\n",
|
||
"x = longformer_vectors\n",
|
||
"\n",
|
||
"\n",
|
||
"# Standardize the features\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(X)\n",
|
||
"\n",
|
||
"# Apply PCA\n",
|
||
"pca = PCA(n_components=0.95)\n",
|
||
"X_pca = pca.fit_transform(X_scaled)\n",
|
||
"explained_variance_ratio = pca.explained_variance_ratio_\n",
|
||
"cumulative_variance = np.cumsum(explained_variance_ratio)\n",
|
||
"\n",
|
||
"# Plotting the Scree plot\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, alpha=0.6, color='g', label='Individual explained variance')\n",
|
||
"plt.ylabel('Explained variance ratio')\n",
|
||
"plt.xlabel('Principal components')\n",
|
||
"plt.title('Scree Plot')\n",
|
||
"plt.legend(loc='best')\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"# Plotting the Cumulative Variance plot\n",
|
||
"plt.figure(figsize=(10, 4))\n",
|
||
"plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o', linestyle='-', color='b', label='Cumulative explained variance')\n",
|
||
"plt.xlabel('Number of components')\n",
|
||
"plt.ylabel('Cumulative explained variance')\n",
|
||
"plt.title('Cumulative Variance Plot')\n",
|
||
"plt.axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
|
||
"plt.legend(loc='best')\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"# Displaying the plots\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 797
|
||
},
|
||
"id": "KYRsJBkkD7ep",
|
||
"outputId": "0169dcbe-d0e5-48a8-bcce-471d83ff095c"
|
||
},
|
||
"execution_count": 25,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x400 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"\n",
|
||
"X_1 = longformer_vectors\n",
|
||
"X_2 = linformer_vectors\n",
|
||
"\n",
|
||
"# Standardize the features independently\n",
|
||
"scaler1 = StandardScaler()\n",
|
||
"X_1_scaled = scaler1.fit_transform(X_1)\n",
|
||
"\n",
|
||
"scaler2 = StandardScaler()\n",
|
||
"X_2_scaled = scaler2.fit_transform(X_2)\n",
|
||
"\n",
|
||
"# Apply PCA\n",
|
||
"pca = PCA(n_components=2) # Reducing to 2 components for a 2D plot\n",
|
||
"X_1_pca = pca.fit_transform(X_1_scaled)\n",
|
||
"pca2 = PCA(n_components=2)\n",
|
||
"X_2_pca = pca2.fit_transform(X_2_scaled)\n",
|
||
"\n",
|
||
"# Plotting\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"plt.scatter(X_1_pca[:, 0], X_1_pca[:, 1], color='blue', alpha=0.5, label='Longformer Model')\n",
|
||
"plt.scatter(X_2_pca[:, 0], X_2_pca[:, 1], color='red', alpha=0.5, label='Linformer Model')\n",
|
||
"plt.xlabel('Principal Component 1')\n",
|
||
"plt.ylabel('Principal Component 2')\n",
|
||
"plt.title('PCA Comparison of Two Models')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 564
|
||
},
|
||
"id": "6X6EyalGEe4B",
|
||
"outputId": "568c07f4-cea8-40ff-c4ae-1a1d1039120c"
|
||
},
|
||
"execution_count": 30,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from sklearn.metrics.pairwise import cosine_similarity\n",
|
||
"\n",
|
||
"# Assuming longformer_vector_df and linformer_vector_df have a column 'message_vector' with the vectors\n",
|
||
"# Stacking vectors into a 2D array for each model\n",
|
||
"longformer_vectors = np.stack(longformer_vector_df['message_vector'].to_numpy())\n",
|
||
"linformer_vectors = np.stack(linformer_vector_df['message_vector'].to_numpy())\n",
|
||
"\n",
|
||
"# Calculate the cosine similarity matrix for each model\n",
|
||
"cosine_sim_matrix_long = cosine_similarity(longformer_vectors)\n",
|
||
"cosine_sim_matrix_lin = cosine_similarity(linformer_vectors)\n",
|
||
"\n",
|
||
"# Extract the upper triangle values from each cosine similarity matrix, excluding the diagonal\n",
|
||
"upper_triangle_indices_long = np.triu_indices_from(cosine_sim_matrix_long, k=1)\n",
|
||
"cosine_sim_values_long = cosine_sim_matrix_long[upper_triangle_indices_long]\n",
|
||
"\n",
|
||
"upper_triangle_indices_lin = np.triu_indices_from(cosine_sim_matrix_lin, k=1)\n",
|
||
"cosine_sim_values_lin = cosine_sim_matrix_lin[upper_triangle_indices_lin]\n",
|
||
"\n",
|
||
"# Plotting the KDE of cosine similarities for both models\n",
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"sns.kdeplot(cosine_sim_values_long, color='blue', fill=True, label='Longformer Model')\n",
|
||
"sns.kdeplot(cosine_sim_values_lin, color='green', fill=True, label='Linformer Model')\n",
|
||
"plt.title('Comparison of Cosine Similarity Distributions')\n",
|
||
"plt.xlabel('Cosine Similarity')\n",
|
||
"plt.ylabel('Density')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.legend()\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 564
|
||
},
|
||
"id": "hosMIGN4E5Vj",
|
||
"outputId": "2f481f84-219b-4289-866f-4e81b04d9db8"
|
||
},
|
||
"execution_count": 28,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
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"<Figure size 1200x600 with 1 Axes>"
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],
|
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"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"# Assuming linformer_vectors and longformer_vectors are defined elsewhere\n",
|
||
"# First, process and plot for the Linformer model\n",
|
||
"x_lin = linformer_vectors\n",
|
||
"scaler_lin = StandardScaler()\n",
|
||
"X_scaled_lin = scaler_lin.fit_transform(x_lin)\n",
|
||
"pca_lin = PCA(n_components=0.95)\n",
|
||
"X_pca_lin = pca_lin.fit_transform(X_scaled_lin)\n",
|
||
"explained_variance_ratio_lin = pca_lin.explained_variance_ratio_\n",
|
||
"cumulative_variance_lin = np.cumsum(explained_variance_ratio_lin)\n",
|
||
"\n",
|
||
"# Now, process and plot for the Longformer model\n",
|
||
"x_long = longformer_vectors\n",
|
||
"scaler_long = StandardScaler()\n",
|
||
"X_scaled_long = scaler_long.fit_transform(x_long)\n",
|
||
"pca_long = PCA(n_components=0.95)\n",
|
||
"X_pca_long = pca_long.fit_transform(X_scaled_long)\n",
|
||
"explained_variance_ratio_long = pca_long.explained_variance_ratio_\n",
|
||
"cumulative_variance_long = np.cumsum(explained_variance_ratio_long)\n",
|
||
"\n",
|
||
"# Create subplots\n",
|
||
"fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 12))\n",
|
||
"\n",
|
||
"# Scree plot Linformer\n",
|
||
"axes[0, 0].bar(range(1, len(explained_variance_ratio_lin) + 1), explained_variance_ratio_lin, alpha=0.6, color='g', label='Linformer: Individual explained variance')\n",
|
||
"axes[0, 0].set_title('Linformer Scree Plot')\n",
|
||
"axes[0, 0].set_xlabel('Principal components')\n",
|
||
"axes[0, 0].set_ylabel('Explained variance ratio')\n",
|
||
"axes[0, 0].legend()\n",
|
||
"\n",
|
||
"# Cumulative Variance plot Linformer\n",
|
||
"axes[1, 0].plot(range(1, len(cumulative_variance_lin) + 1), cumulative_variance_lin, marker='o', linestyle='-', color='b', label='Linformer: Cumulative explained variance')\n",
|
||
"axes[1, 0].axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
|
||
"axes[1, 0].set_title('Linformer Cumulative Variance Plot')\n",
|
||
"axes[1, 0].set_xlabel('Number of components')\n",
|
||
"axes[1, 0].set_ylabel('Cumulative explained variance')\n",
|
||
"axes[1, 0].legend()\n",
|
||
"\n",
|
||
"# Scree plot Longformer\n",
|
||
"axes[0, 1].bar(range(1, len(explained_variance_ratio_long) + 1), explained_variance_ratio_long, alpha=0.6, color='g', label='Longformer: Individual explained variance')\n",
|
||
"axes[0, 1].set_title('Longformer Scree Plot')\n",
|
||
"axes[0, 1].set_xlabel('Principal components')\n",
|
||
"axes[0, 1].set_ylabel('Explained variance ratio')\n",
|
||
"axes[0, 1].legend()\n",
|
||
"\n",
|
||
"# Cumulative Variance plot Longformer\n",
|
||
"axes[1, 1].plot(range(1, len(cumulative_variance_long) + 1), cumulative_variance_long, marker='o', linestyle='-', color='b', label='Longformer: Cumulative explained variance')\n",
|
||
"axes[1, 1].axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
|
||
"axes[1, 1].set_title('Longformer Cumulative Variance Plot')\n",
|
||
"axes[1, 1].set_xlabel('Number of components')\n",
|
||
"axes[1, 1].set_ylabel('Cumulative explained variance')\n",
|
||
"axes[1, 1].legend()\n",
|
||
"\n",
|
||
"# Adjust layout and display the plots\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 796
|
||
},
|
||
"id": "-n1_yRcaGYZD",
|
||
"outputId": "fb60f43b-ab4a-45fa-848b-fc345ec10231"
|
||
},
|
||
"execution_count": 31,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 2000x1200 with 4 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
}
|
||
]
|
||
} |