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log2ml/Comparison_of_Encoder_Models.ipynb
Marius Ciepluch af2a817584
added toc, short documentation and clarified things
2024-07-29 14:52:02 +02:00

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"# Installations\n",
"\n",
"The installation is mostly automated.\n",
"\n",
"A file in the same directory named \"thesis_ro\" will be required, which should contain your GitHub read-only token.\n",
"\n",
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">[Installations](#scrollTo=4-p_B7FGB9mg)\n",
"\n",
">[Setup data](#scrollTo=b7jMEfm3CAp1)\n",
"\n",
">[Train custom Tokenizer with complex dataset](#scrollTo=dAYGIiUlCQM9)\n",
"\n",
">[Download dataset to use](#scrollTo=RtdpdugyCl9v)\n",
"\n",
">[ETL dataset](#scrollTo=wk7RQHMuCxjr)\n",
"\n",
">[Max token length](#scrollTo=QdLFssPYC-oG)\n",
"\n",
">[Load LinFormer with max. token length](#scrollTo=ARRqrF8XDG6_)\n",
"\n",
">[Vectorize the text column in that DataFrame](#scrollTo=AzxrG_RmDLr1)\n",
"\n",
">[Convert the vectors to a NumPy array for statistical analysis](#scrollTo=3Gxep-miEAVm)\n",
"\n",
">[UMAP dimensionality check - scattered or clustered?](#scrollTo=zZVCSpvsEJIq)\n",
"\n",
">[Silhouette score via KMeans clustering](#scrollTo=56wtMVhhEVzf)\n",
"\n",
">[Distribution of the Cosine similarity](#scrollTo=8yhTWsSnEl30)\n",
"\n",
">[Comparison model: LongFormer](#scrollTo=FmwKV-BK-rKX)\n",
"\n",
">[Initialize and use LongFormer with the same pre-trained Tokenizer](#scrollTo=J6REcr7hE1xz)\n",
"\n",
">[Approaching Quantile Transoformer for feature scaling for LinFormer model vectors](#scrollTo=SxhOQqiHhPju)\n",
"\n",
">[Quantile Transformer vs. Standard Scaler for feature scaling](#scrollTo=kgdy9N8lhEaF)\n",
"\n"
],
"metadata": {
"colab_type": "toc",
"id": "V-wAtykHhidb"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "5QU9CtLHsJr_"
},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import subprocess\n",
"\n",
"IN_COLAB = 'google.colab' in sys.modules\n",
"\n",
"if not IN_COLAB:\n",
" pass\n",
"\n",
"else:\n",
" subprocess.run('''\n",
" source <(curl -s https://raw.githubusercontent.com/norandom/log2ml/main/dependencies/install.sh)\n",
" ''',\n",
" shell=True, check=True, executable='/bin/bash')\n",
"\n"
]
},
{
"cell_type": "markdown",
"source": [
"# Setup data\n",
"\n",
"The data setup is automated, based on GitHub release files. Kaggle or HuggingFace releases are for later, once the work has been completed."
],
"metadata": {
"id": "b7jMEfm3CAp1"
}
},
{
"cell_type": "code",
"source": [
"from dotenv import load_dotenv\n",
"import os\n",
"\n",
"load_dotenv(\"thesis_ro\", verbose=True) # take environment variables from the file\n",
"token = os.getenv('GITHUB_PERSONAL_ACCESS_TOKEN')\n",
"if len(token) > 0:\n",
" print(\"ok\")\n",
"else:\n",
" print(\"no token\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B4wCRu4DAC0Z",
"outputId": "e5383477-f981-4a9e-d448-6707b4f73737"
},
"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": [
"bd6b607607d34e3c96de67f9a103f19d",
"db9863f966f24e6fad90dc2e6acae785",
"50a29d3705bb49d986f548374fef81bc",
"d2ca4d5e2863449ba87ceb117e3df7da",
"3031182ea08a43e2a75d12d67ceef101",
"46aa4f53040c485387ae03dbc195b34b",
"30bd5d604a7041e9ab193e34be502137",
"ed7ee81cf876476ebcd6c6d29b954de4",
"3528f43e1f594fa6a65ae43a926e9d95",
"27281ae3d5d649829478a230b68bca47",
"f2764b194d4e478f97ae5e09f4afb86c"
]
},
"id": "ORNebW15Bb4B",
"outputId": "58583b66-046e-4b57-c892-f877d5fa55ad"
},
"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": "bd6b607607d34e3c96de67f9a103f19d"
}
},
"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\n",
"\n",
"The dataset contains security-agent telemetry from MS Sysmon.\n",
"Therefore a custom Tokenizer is needed, given that the content isn't 100% natural language.\n",
"\n",
"BPE stands for Byte Pair Encoding Tokenization\n",
"\n",
"\"It’s used by a lot of Transformer models, including GPT, GPT-2, RoBERTa, BART, and DeBERTa.\" [1]\n",
"\n",
"Given that LongFormer and Linformer are Transformer Network architectures, BPE is a compatible choice.\n",
"\n",
"Parsing the data as a CSV or colon-separated values isn't preferable, because the implementation goal is a generically reuseable log- / trace-data vectorizer. Approaches like bag-of-words or one-hot-encoding may become too specific to the data.\n",
"\n",
"[1] https://huggingface.co/learn/nlp-course/en/chapter6/5"
],
"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(\n",
" vocab_size=30000,\n",
" min_frequency=2,\n",
" special_tokens=[\"=\", \":\", \",\", \"\\\"\", \"\\'\", \"(\", \")\", \"[\", \"]\", \"{\", \"}\"],\n",
" show_progress=True\n",
")\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": 7,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Test with a message\n",
"\n",
"By avoid time or other variant series data we can avoid noisy vectors."
],
"metadata": {
"id": "pszMjefAiWmS"
}
},
{
"cell_type": "code",
"source": [
"log_text = \"\"\"\n",
"Registry value set (rule: RegistryEvent),\"Registry value set:\n",
"RuleName: InvDB-Ver\n",
"EventType: SetValue\n",
"UtcTime: 2024-07-28 15:09:33.716\n",
"ProcessGuid: {18e8265a-5ee4-66a6-5400-000000004400}\n",
"ProcessId: 4032\n",
"Image: C:\\Windows\\System32\\svchost.exe\n",
"TargetObject: \\REGISTRY\\A\\{90cbbb87-bac4-4fa3-1d8b-b1a042a75259}\\Root\\InventoryApplicationFile\\ie4uinit.exe|874b2700383dd346\\BinProductVersion\n",
"Details: 11.0.19041.4648\"\n",
"\"\"\"\n",
"\n",
"# Tokenize your log_text\n",
"output = tokenizer.encode(log_text)\n",
"\n",
"# Print the tokens\n",
"print(output.tokens)\n",
"\n",
"# If you want to get the IDs of the tokens\n",
"print(output.ids)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LTQyGEuZMhdS",
"outputId": "8e4f07ba-5f5e-4e42-afc0-4634afd7df7c"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['2024', '-', '07', '-', '28T', '15', ':', '09', ':', '33', '.', '716Z', ',', 'win10', ',', 'fe80', ':', ':', 'c', '1af', ':', '35', 'de', ':', '600', '6', ':', 'd4c', 'f', ',', 'information', ',', '13', ',', 'Registry', 'value', 'set', '(', 'rule', ':', 'RegistryEvent', ')', ',', '\"', 'Registry', 'value', 'set', ':', 'RuleName', ':', 'InvDB', '-', 'Ver', 'EventType', ':', 'SetValue', 'UtcTime', ':', '2024', '-', '07', '-', '28', '15', ':', '09', ':', '33', '.', '716', 'ProcessGuid', ':', '{', '18e8265a', '-', '5e', 'e4', '-', '6', '6a', '6', '-', '5400', '-', '00000000', '4400', '}', 'ProcessId', ':', '4032', 'Image', ':', 'C', ':', '\\\\', 'Windows', '\\\\', 'System32', '\\\\', 'svchost', '.', 'exe', 'TargetObject', ':', '\\\\', 'REGISTRY', '\\\\', 'A', '\\\\', '{', '90', 'cbb', 'b87', '-', 'bac', '4', '-', '4fa', '3', '-', '1d', '8b', '-', 'b1a', '042', 'a7', '525', '9', '}', '\\\\', 'Root', '\\\\', 'InventoryApplicationFile', '\\\\', 'ie4uinit', '.', 'exe', '|', '87', '4b', '2700', '38', '3d', 'd3', '46', '\\\\', 'BinProductVersion', 'Details', ':', '11', '.', '0', '.', '19041', '.', '4648', '\"']\n",
"[789, 18, 282, 18, 9118, 429, 1, 451, 1, 733, 19, 20558, 2, 229, 2, 426, 1, 1, 68, 29307, 1, 292, 352, 1, 1150, 27, 1, 22018, 71, 2, 437, 2, 591, 2, 1196, 795, 987, 5, 865, 1, 2644, 6, 2, 3, 1196, 795, 987, 1, 1324, 1, 4115, 18, 5731, 4108, 1, 2763, 1112, 1, 789, 18, 282, 18, 316, 429, 1, 451, 1, 733, 19, 26125, 1450, 1, 9, 444, 18, 1120, 1747, 18, 27, 898, 27, 18, 22096, 18, 379, 14576, 10, 1089, 1, 27452, 1041, 1, 38, 1, 62, 176, 62, 307, 62, 915, 19, 260, 4107, 1, 62, 3071, 62, 36, 62, 9, 322, 10902, 10887, 18, 10941, 25, 18, 6459, 24, 18, 360, 2379, 18, 11383, 14573, 766, 7844, 30, 10, 62, 2675, 62, 3349, 62, 20723, 19, 260, 92, 799, 489, 2805, 256, 2276, 1360, 308, 62, 5983, 2001, 1, 318, 19, 21, 19, 326, 19, 19526, 3]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Download dataset to use\n",
"\n",
"The analysis dataset has less complex data.\n",
"Variant series data has been removed."
],
"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": [
"b5271fc8f29a469696c63aa94f4c66c8",
"6892e1bb32de4655bd093e50aad979b9",
"56ff1bf94fac41e2a4bfe6c642db64b9",
"ee15e4b938df499da5b1d2838552962f",
"b5dd37fa45c24680b93c21c1969490f2",
"df2385efc48a411c8e3584c954f23a26",
"63bb98eab8c74dfabea98b85ff397b77",
"cdbb5e7a249c436b809e125f496eb82e",
"3b4d2cf14f8c496ab740a2485155442b",
"59dd159940064a309eb1cad67d5faa7a",
"f84bb79f52664a91b1a6ee637281c285"
]
},
"id": "mjrM6gVOGfHq",
"outputId": "ed833527-a554-48eb-f2a5-6d166e4eb88e"
},
"execution_count": 9,
"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": "b5271fc8f29a469696c63aa94f4c66c8"
}
},
"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\n",
"\n",
"ETL stands for Extract, Transform, Load. Polars is used as an ETL framework. It allows DataFrame operations, similar to Pandas."
],
"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": "c757f28a-94c4-4d2b-cfc8-33e4c8b3889f"
},
"execution_count": 10,
"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": "6525ea8b-f1f8-4ac7-9f7f-9e13ea6856d7"
},
"execution_count": 11,
"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\n",
"\n",
"Selecting models which can deal with the long Sysmon agent messages is a key design requirement for the ML pipeline. Therefore the maximum token lengt"
],
"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 calculate total tokens\n",
"token_lengths = better_columns_df[\"filtered_message\"].map_elements(\n",
" lambda x: len(tokenizer.encode(x).ids)\n",
")\n",
"\n",
"# Get the total input token count\n",
"total_input_tokens = token_lengths.sum()\n",
"\n",
"print(f\"The total number of input tokens in 'filtered_message' column is: {total_input_tokens}\")\n",
"\n",
"# Cost calculation\n",
"input_cost_per_1m_tokens = 10.00 # $10.00 per 1M tokens for input\n",
"output_cost_per_1m_tokens = 30.00 # $30.00 per 1M tokens for output\n",
"\n",
"# Estimate output tokens (this is an assumption, adjust as needed)\n",
"estimated_output_tokens = total_input_tokens * 0.5 # Assuming output is 50% of input\n",
"\n",
"# Calculate costs\n",
"input_cost = (total_input_tokens / 1_000_000) * input_cost_per_1m_tokens\n",
"output_cost = (estimated_output_tokens / 1_000_000) * output_cost_per_1m_tokens\n",
"total_cost = input_cost + output_cost\n",
"\n",
"print(\"Reference: OpenAI GPT4-turbo July 2022\")\n",
"print(f\"Estimated input cost: ${input_cost:.2f}\")\n",
"print(f\"Estimated output cost: ${output_cost:.2f}\")\n",
"print(f\"Estimated total cost: ${total_cost:.2f}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g7lfhJmEckFK",
"outputId": "c6330ae4-8673-4814-fd45-ef2d25238fbc"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The total number of input tokens in 'filtered_message' column is: 157897\n",
"Reference: OpenAI GPT4-turbo July 2022\n",
"Estimated input cost: $1.58\n",
"Estimated output cost: $2.37\n",
"Estimated total cost: $3.95\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": 16,
"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": 17,
"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": "cc314d37-884a-4852-c17e-ab7e65777ada"
},
"execution_count": 18,
"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 ┆ [ 0.18137 │\n",
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 093 -0.25 │\n",
"│ ┆ ┆ ┆ (rule: ┆ ┆ 2\\svchost ┆ ┆ RuleName: ┆ 33698 │\n",
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ -0.0416… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
"│ 1 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [ 0.18577 │\n",
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 585 -0.26 │\n",
"│ ┆ ┆ ┆ (rule: ┆ ┆ 2\\svchost ┆ ┆ RuleName: ┆ 15168 │\n",
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ -0.0566… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
"│ 2 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [ 0.32916 │\n",
"│ ┆ n ┆ ┆ Create ┆ ┆ s\\servici ┆ ┆ Create: ┆ 135 -0.28 │\n",
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ ng\\Truste ┆ ┆ RuleName: ┆ 799376 │\n",
"│ ┆ ┆ ┆ cessCre… ┆ ┆ dInst… ┆ ┆ - ┆ -0.0459… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ │\n",
"│ 3 ┆ informatio ┆ 13 ┆ Registry ┆ … ┆ C:\\Window ┆ ┆ Registry ┆ [ 0.33915 │\n",
"│ ┆ n ┆ ┆ value set ┆ ┆ s\\servici ┆ ┆ value ┆ 532 -0.29 │\n",
"│ ┆ ┆ ┆ (rule: ┆ ┆ ng\\Truste ┆ ┆ set: ┆ 842803 │\n",
"│ ┆ ┆ ┆ Regist… ┆ ┆ dInst… ┆ ┆ RuleName: ┆ -0.0153… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Ta… ┆ │\n",
"│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n",
"│ 1023 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [ 0.07160 │\n",
"│ ┆ n ┆ ┆ accessed ┆ ┆ m Files ┆ ┆ accessed: ┆ 681 -0.26 │\n",
"│ ┆ ┆ ┆ (rule: ┆ ┆ (x86)\\Mic ┆ ┆ RuleName: ┆ 001436 │\n",
"│ ┆ ┆ ┆ ProcessA… ┆ ┆ rosoft… ┆ ┆ - ┆ -0.0120… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
"│ 1024 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [ │\n",
"│ ┆ n ┆ ┆ Create ┆ ┆ s\\System3 ┆ ┆ Create: ┆ 0.3071262 │\n",
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ 2\\taskhos ┆ ┆ RuleName: ┆ -0.313339 │\n",
"│ ┆ ┆ ┆ cessCre… ┆ ┆ tw.ex… ┆ ┆ - ┆ 6 │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ Proc… ┆ -0.0181… │\n",
"│ 1025 ┆ informatio ┆ 22 ┆ Dns query ┆ … ┆ ┆ ┆ Dns ┆ [ │\n",
"│ ┆ n ┆ ┆ (rule: ┆ ┆ ┆ ┆ query: ┆ 0.3430285 │\n",
"│ ┆ ┆ ┆ DnsQuery) ┆ ┆ ┆ ┆ RuleName: ┆ -0.288157 │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ 05 │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ProcessId ┆ -0.0193… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ … ┆ │\n",
"│ 1026 ┆ informatio ┆ 1 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [ 0.29110 │\n",
"│ ┆ n ┆ ┆ Create ┆ ┆ m Files\\R ┆ ┆ Create: ┆ 384 -0.29 │\n",
"│ ┆ ┆ ┆ (rule: Pro ┆ ┆ UXIM\\PLUG ┆ ┆ RuleName: ┆ 604813 │\n",
"│ ┆ ┆ ┆ cessCre… ┆ ┆ Sched… ┆ ┆ - ┆ -0.0109… │\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": null,
"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": 19,
"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": 774
},
"id": "I55hh_VVm5Gs",
"outputId": "26ff87cc-301f-4993-a06e-ce05f1c49560"
},
"execution_count": 20,
"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": "85c1ccff-f959-468a-a1a4-277eba2e089f"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Silhouette Score: 0.7105976939201355\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": "60c00803-a7f0-4843-c348-d3fa57db9388"
},
"execution_count": 22,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
],
"image/png": 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ZmanTp08XOU7z5s0VGxtb5Hnu6tKli5o3b65//vOf+cMsC3NdJ6o069atU0ZGRrHt69ev1/Hjx90a7ulJrt6os3ufZs6cWeljV69evVztbfDgwYqLi9MTTzwhp9NZ7HHXz9jbv2sAVRc9SQBQyKhRo/Tvf/9bN998sy6//HItWbKkyCIB77//vmJiYvInwy9dulRff/21OnXqpPfee6/Y8c6cOVPqp9mjRo065/widzzwwAN65513NGTIEP3lL39RzZo19cYbb2j37t364IMPFBJiPh9r3ry5atSooZdfflmxsbGqXr26unXrVur8kscff1zJycnq1auX7rjjDlWrVk2vvPKKsrOz9fTTT7tVa1hYmBYuXKgBAwaod+/euvrqq9WzZ0+FhYVp69atWrBggc477zxNnz5dYWFheuqppzR+/Hhdeumluu666/KXAG/SpInuueceSdKvv/6q/v376+qrr1bbtm1VrVo1LVq0SIcPH9a1117r3g+1kJCQEL322msaMmSI2rVrp/Hjx6tBgwb6/fff9eWXXyouLk4ff/xxqc9/6623NH/+fI0aNUpdunRReHi4fvnlF73++uuKjIzU3//+90rXWBlxcXHq3bu3nn76aTmdTjVo0EDLli0757W5yqNLly764osv9Oyzz6p+/fpq2rRpifPn4uLiNHv2bN1444268MILde2116pOnTrat2+fPv30U/Xs2VMvvvii13/XAKouQhIAnGX8+PE6ceKE7r33Xl111VVatGhR/mN//vOfJZlV62rXrq3OnTvr9ddf15gxYxQREVHsWNnZ2aVeA2j37t1eCUkJCQn65ptvdP/99+uFF17Q6dOn1bFjR3388cdFLpIbFhamN954Q5MnT9btt9+uM2fOaO7cuaWGpHbt2mnNmjWaPHmyZsyYoby8PHXr1k1vv/12iW90y6tFixbavHmz/vWvf2nRokVavHix8vLy1KJFC02YMEF/+ctf8vcdN26coqOj9eSTT+r+++9X9erVNWrUKD311FP510hKSkrSddddp+XLl+utt95StWrV1Lp1a/3f//2fRo8e7XadhfXp00fffvutHnvsMb344otKT09XYmKiunXrpttuu63M5952222Kjo7W8uXL9eGHHyo1NVV16tTRoEGDNHnyZL+YR7NgwQLdddddmjVrlizL0qBBg/T555+rfv36lTrus88+q1tvvVUPPfSQsrKyNHbs2FLbzpgxY1S/fn09+eST+sc//qHs7Gw1aNBAl1xyicaPHy/JN79rAFWTw6robE4AAAAACGLMSQIAAACAQghJAAAAAFAIIQkAAAAACiEkAQAAAEAhhCQAAAAAKISQBAAAAACFBP11kvLy8nTgwAHFxsbK4XDYXQ4AAAAAm1iWpbS0NNWvXz//4uolCfqQdODAASUlJdldBgAAAAA/sX//fjVs2LDUx4M+JMXGxkoyP4i4uDibq0EgcDqdWrZsmQYNGqSwsDC7y0GAoN3AXbQduIu2A3dU9XaTmpqqpKSk/IxQGltD0uzZszV79mzt2bNHktSuXTtNmTJFQ4YMkST16dNHq1atKvKc2267TS+//HK5z+EaYhcXF0dIQrk4nU5FR0crLi6uSr54wD20G7iLtgN30XbgDtqNca5pOLaGpIYNG+rJJ59Uy5YtZVmW3njjDY0YMUKbNm1Su3btJEl/+tOf9Oijj+Y/Jzo62q5yAQAAAFQBtoak4cOHF/l6+vTpmj17ttauXZsfkqKjo5WYmGhHeQAAAACqIL+Zk5Sbm6v33ntPGRkZ6tGjR/72+fPn6+2331ZiYqKGDx+uhx9+uMzepOzsbGVnZ+d/nZqaKsl0LTqdTu99AwgarnZCe0FF0G7gLtoO3EXbgTuqersp7/ftsCzL8nItZfrxxx/Vo0cPnT59WjExMVqwYIGGDh0qSXr11VfVuHFj1a9fX1u2bNH999+vrl27auHChaUeb+rUqZo2bVqx7QsWLGCoHgAAAFCFZWZmasyYMUpJSSlzvQLbQ1JOTo727dunlJQUvf/++3rttde0atUqtW3btti+K1asUP/+/bVjxw41b968xOOV1JOUlJSkY8eOsXADysXpdCo5OVkDBw6s0hMaUTG0G7iLtgN30XbgjqreblJTU1W7du1zhiTbh9uFh4erRYsWkqQuXbrou+++03PPPadXXnml2L7dunWTpDJDUkREhCIiIoptDwsLq5INAe6jzcAdtBu4i7YDd9F24I6q2m7K+z2XfplZm+Tl5RXpCSps8+bNkqR69er5sCIAAAAAVYmtPUmTJ0/WkCFD1KhRI6WlpWnBggVauXKlli5dqp07d+bPT6pVq5a2bNmie+65R71791bHjh3tLBsAAABAELM1JB05ckQ33XSTDh48qPj4eHXs2FFLly7VwIEDtX//fn3xxReaOXOmMjIylJSUpNGjR+uhhx6ys2QAAAAAQc7WkDRnzpxSH0tKStKqVat8WA0AAAAA+OGcJAAAAACwEyEJAAAAAAohJAEAAABAIYQkAAAAACiEkAQAAAAAhRCSAAAAAKAQQhIAAAAAFEJIAgAAAIBCCEkAAABAEEtJkT76yO4qAgshCQAAAAhib7whjRghpafbXUngICQBAAAAQezXX839vn321hFICEkAAABAEHOFpL177a0jkBCSAAAAgCBGSKo4QhIAAAAQpLKzpf37zb/37LG1lIBCSAIAAACC1K5dUl6eFB5OT1JFEJIAAACAILV9u7nv2JGepIogJAEAAABBavt2KSpKatOGnqSKICQBAAAAQerXX6WGDaWEBOnQISknx+6KAgMhCQAAAAhSv/4q1a8vJSZKliX9/rvdFQUGQhIAAAAQpLZvL+hJkgpWukPZCEkAAABAEMrMND1HDRtKdeuabfv22VtToCAkAQAAAEFo505z36CBWQK8Vi16ksqLkAQAAAAEoV9/NfdJSeY+MZGQVF6EJAAAACAIbd8uxcRI8fHm67p1GW5XXoQkAAAAIAi5Fm1wOMzXCQmEpPIiJAEAAABByLX8t0tCAkuAlxchCQAAAAhCrp4kl4QE6cwZ++oJJIQkAAAAIMikpkqHDxcNSYmJ9tUTaAhJAAAAQJDZscPcn92ThPIhJAEAAABBZvt2c184JEVHS7Gx9tQTaAhJAAAAQJDZvt0s/X12KKpb1556Ag0hCQAAAAgyZy/a4FKnju9rCUSEJAAAACDIbNsmNWhQfDs9SeVDSAIAAACCzK5dRa+R5OIKSZbl23oCDSEJAAAACDKnTklxccW3u0LSiRM+LSfgEJIAAACAIJKdLTmdZjW7s7lC0v79vq0p0BCSAAAAgCCSlmbuSwpJrmslEZLKRkgCAAAAgkhZIcm1JPi+fb6rJxARkgAAAIAgkppq7ksKSQ6HuT9wwHf1BCJCEgAAABBEyupJcsnI8E0tgYqQBAAAAASR8oQk1z4oGSEJAAAACCKu4XZRUaXvQ09S2QhJAAAAQBBx9RKVFZLoSSobIQkAAAAIImlpJiCFhpa+T3q67+oJRIQkAAAAIIikpZU9H0liuN25EJIAAACAIJKaeu6QRE9S2QhJAAAAQBApT08SIalshCQAAAAgiJR3uJ1l+aaeQERIAgAAAIKIa+GGsuTlSadP+6aeQERIAgAAAIJISsq5e5IkhtyVhZAEAAAABJHyDLdz7YeSEZIAAACAIFKe4XYSPUllISQBAAAAQaQ8S4BL9CSVhZAEAAAABJH0dOYkVZatIWn27Nnq2LGj4uLiFBcXpx49eujzzz/Pf/z06dOaOHGiatWqpZiYGI0ePVqHDx+2sWIAAADAf505I2VlEZIqy9aQ1LBhQz355JPasGGDvv/+e/Xr108jRozQ1q1bJUn33HOPPv74Y7333ntatWqVDhw4oCuuuMLOkgEAAAC/5Qo+DLernGp2nnz48OFFvp4+fbpmz56ttWvXqmHDhpozZ44WLFigfv36SZLmzp2rNm3aaO3aterevbsdJQMAAAB+KzXV3J8rJIWH05NUFltDUmG5ubl67733lJGRoR49emjDhg1yOp0aMGBA/j6tW7dWo0aN9O2335YakrKzs5WdnZ3/der/WorT6ZTT6fTuN4Gg4GontBdUBO0G7qLtwF20HZQkJcWsbFe9umRZxR+3LNNezjvPqYwMqao1n/L+f7E9JP3444/q0aOHTp8+rZiYGC1atEht27bV5s2bFR4erho1ahTZPyEhQYcOHSr1eDNmzNC0adOKbV+2bJmiy9PvCPxPcnKy3SUgANFu4C7aDtxF28HZ3nnH3Lt6lUoyc6ZpN5995oOC/EhmZma59rM9JJ1//vnavHmzUlJS9P7772vs2LFatWqV28ebPHmyJk2alP91amqqkpKSNGjQIMXFxXmiZAQ5p9Op5ORkDRw4UGFhYXaXgwBBu4G7aDtwF20HJfnyS2nkSOn116WEhOKPW5ZTaWnJmjp1oPr2DdNTT/m8RFullpUcC7E9JIWHh6tFixaSpC5duui7777Tc889p2uuuUY5OTk6depUkd6kw4cPKzExsdTjRUREKCIiotj2sLAwXkBQIbQZuIN2A3fRduAu2g4KS0szq9tFRUkOR+n7hYaG6dSpMFW1plPe/yt+d52kvLw8ZWdnq0uXLgoLC9Py5cvzH9u2bZv27dunHj162FghAAAA4J9cK9ada5ZJRAQLN5TF1p6kyZMna8iQIWrUqJHS0tK0YMECrVy5UkuXLlV8fLxuueUWTZo0STVr1lRcXJzuuusu9ejRg5XtAAAAgBKkpZmV687VYRIVxRLgZbE1JB05ckQ33XSTDh48qPj4eHXs2FFLly7VwIEDJUn/+te/FBISotGjRys7O1uDBw/WSy+9ZGfJAAAAgN9KTS3fNZIiI6VTp7xeTsCyNSTNmTOnzMcjIyM1a9YszZo1y0cVAQAAAIErLc0s/30uUVHSb795v55A5XdzkgAAAAC4Jy2tfD1JDLcrGyEJAAAACBJpaSYAnUtUFAs3lIWQBAAAAASJ1NTyhaTISEJSWQhJAAAAQJAo78INUVFSZqaUm+v9mgIRIQkAAAAIEuWdkxQZae4zM71bT6AiJAEAAABBorzD7VxBisUbSkZIAgAAAIJERXuSmJdUMkISAAAAECTS08t3nSR6kspGSAIAAACCQF6eCUnlXd1OoiepNIQkAAAAIAhkZEiWVf7V7SRCUmkISQAAAEAQcA2dq0hIYrhdyQhJAAAAQBCoSEiKiDD39CSVjJAEAAAABIGKhKSQELMfPUklIyQBAAAAQSA11dyXJyRJZsgdPUklIyQBAAAAQaAiPUkSIakshCQAAAAgCFQ0JDHcrnSEJAAAACAIpKZKoaFSeHj59qcnqXSEJAAAACAIpKWZ3iGHo3z7R0XRk1QaQhIAAAAQBNLSpOrVy79/ZCQhqTSEJAAAACAIuHqSyovhdqUjJAEAAABBIDXVBJ/yYuGG0hGSAAAAgCCQllaxkERPUukISQAAAEAQSE2t2HA7epJKR0gCAAAAgkBFQ1JkpJSR4b16AhkhCQAAAAgC7gy3y8kxNxRFSAIAAACCQEWXAHf1OjEvqThCEgAAABAE3FkCXCIklYSQBAAAAAQ4y6r4cDtXoGLxhuIISQAAAECAO31ays2lJ8lTCEkAAABAgHP1BrkTkuhJKo6QBAAAAAQ4d0ISCzeUjpAEAAAABLjUVHPPcDvPICQBAAAAAc6dnqSwMKlaNYbblYSQBAAAAAQ4d0KSw2F6k+hJKo6QBAAAAAQ4d4bbSSYk0ZNUHCEJAAAACHCuoBMZWbHnRUfTk1QSQhIAAAAQ4NLSTOAJqeC7e4bblYyQBAAAAAS4jIyC1eoqguF2JSMkAQAAAAEuM7PiQ+0k8xx6koojJAEAAAABzt2QFBVVsOgDChCSAAAAgACXkSFFRFT8eSzcUDJCEgAAABDgMjPdC0ks3FAyQhIAAAAQ4NwNSdHRLNxQEkISAAAAEODcHW5HT1LJCEkAAABAgKvM6nYZGZJleb6mQEZIAgAAAAKcuyEpOlrKy5OysjxfUyAjJAEAAAABrjJLgEvMSzobIQkAAAAIcO7OSXIFK3qSiiIkAQAAAAHO3Z4kV7DKzPRsPYGOkAQAAAAEuKws93qSXM+hJ6koQhIAAAAQwHJzpezsyoUkepKKIiQBAAAAAczVC+RahKEiXEP0CElFEZIAAACAAOYKOAy38xxCEgAAABDAXCGJhRs8x9aQNGPGDF188cWKjY1V3bp1NXLkSG3btq3IPn369JHD4Shyu/32222qGAAAAPAvGRnm3p2epPBwc09IKsrWkLRq1SpNnDhRa9euVXJyspxOpwYNGqQM12/6f/70pz/p4MGD+benn37apooBAAAA/1KZnqSQEBOuGG5XVDU7T75kyZIiX8+bN09169bVhg0b1Lt37/zt0dHRSkxM9HV5AAAAgN+rzJwkyYQrepKKsjUknS0lJUWSVLNmzSLb58+fr7fffluJiYkaPny4Hn74YUVHR5d4jOzsbGVnZ+d/nZqaKklyOp1yOp1eqhzBxNVOaC+oCNoN3EXbgbtoO3DJyDAr20VGSpZV9r6W5SxyL0nx8dLp01JVaErl/f/isKxz/Sh9Iy8vT5dffrlOnTqlr776Kn/7q6++qsaNG6t+/frasmWL7r//fnXt2lULFy4s8ThTp07VtGnTim1fsGBBqcEKAAAAQPDLzMzUmDFjlJKSori4uFL385uQ9Oc//1mff/65vvrqKzVs2LDU/VasWKH+/ftrx44dat68ebHHS+pJSkpK0rFjx8r8QQAuTqdTycnJGjhwoMLCwuwuBwGCdgN30XbgLtoOXN59V7rtNmnRooKFGEpjWU6lpSUrNnagHA7Tbv7yF6lfP+mf//RBsTZLTU1V7dq1zxmS/GK43Z133qlPPvlEq1evLjMgSVK3bt0kqdSQFBERoYgSBmSGhYXxAoIKoc3AHbQbuIu2A3fRdpCRIWVnm4DkcJTvOQ5HWH5IkqS0NKkqNKPy/l+xNSRZlqW77rpLixYt0sqVK9W0adNzPmfz5s2SpHr16nm5OgAAAMD/ZWaa+UjlDUhnY3W74mwNSRMnTtSCBQv04YcfKjY2VocOHZIkxcfHKyoqSjt37tSCBQs0dOhQ1apVS1u2bNE999yj3r17q2PHjnaWDgAAAPgFV0hyV3g4q9udzdaQNHv2bEnmgrGFzZ07V+PGjVN4eLi++OILzZw5UxkZGUpKStLo0aP10EMP2VAtAAAA4H8yM91f/lsyzz3rMqVVnu3D7cqSlJSkVatW+agaAAAAIPBkZpolwN0VGSn976o5+J8QuwsAAAAA4L7K9iQx3K44QhIAAAAQwCobkiIjCUlnIyQBAAAAASwjo/JzkljdrihCEgAAABDAPBGS6EkqipAEAAAABDBPrG5HT1JRhCQAAAAggHlidbvMTOkcC09XKYQkAAAAIIB5oidJkrKzPVNPMCAkAQAAAAEsM9P0BrnLFZKYl1SAkAQAAAAEME8sAe46DgxCEgAAABDAPHExWYnFGwojJAEAAAABKi/PhJvKDLejJ6k4QhIAAAAQoE6fNvfMSfIsQhIAAAAQoFzBxhMhieF2BQhJAAAAQIByhSQWbvAsQhIAAAAQoDzZk0RIKkBIAgAAAAKUJ3qSGG5XHCEJAAAACFAZGea+Mj1J1aqZGz1JBQhJAAAAQIDyxHA71/MJSQUISQAAAECA8sRwO9fzGW5XgJAEAAAABChP9SRFRNCTVBghCQAAAAhQrmATHl654zDcrihCEgAAABCgMjNNwHE4KncchtsVRUgCAAAAAlRGRuWH2kmmJ4qepAKEJAAAACBAuXqSKos5SUURkgAAAIAA5cmQxHC7AoQkAAAAIEBlZlZ++W/JHMN1YVoQkgAAAICAxXA77yAkAQAAAAEqM7Pyy39LJmgx3K4AIQkAAAAIUPQkeQchCQAAAAhQ6emem5NESCpASAIAAAAClKd6khhuVxQhCQAAAAhQngpJ4eGEpMIISQAAAECA8tQS4JGRktNpbiAkAQAAAAHLkws3SPQmuRCSAAAAgACVleW5hRtcxwMhCQAAAAhIluW54XauY7DCnUFIAgAAAAJQdraUlydFRVX+WK4he4Qkg5AEAAAABCBXoGG4necRkgAAAIAA5ApJnly4gZ4kg5AEAAAABCBP9iQx3K4oQhIAAAAQgDzZkxQebu4ZbmcQkgAAAIAARE+S9xCSAAAAgADkjZ4kQpJBSAIAAAACUEaGufdESHI4zHEYbmcQkgAAAIAA5Mnhdq7j0JNkEJIAAACAAOTpkBQZSUhyISQBAAAAASgz0wSkEA+9o2e4XQFCEgAAABCAMjM9Mx/JJTycniQXQhIAAAAQgFw9SZ7CcLsChCQAAAAgAGVkSFFRnjteeDjD7VwISQAAAEAA8nRPEqvbFSAkAQAAAAHIGyHJde2lqo6QBAAAAAQgb4QkhtsZhCQAAAAgADHcznsISQAAAEAAyshgdTtvsTUkzZgxQxdffLFiY2NVt25djRw5Utu2bSuyz+nTpzVx4kTVqlVLMTExGj16tA4fPmxTxQAAAIB/yMz07Op2DLcrYGtIWrVqlSZOnKi1a9cqOTlZTqdTgwYNUkahGWP33HOPPv74Y7333ntatWqVDhw4oCuuuMLGqgEAAAD7ebonieF2BarZefIlS5YU+XrevHmqW7euNmzYoN69eyslJUVz5szRggUL1K9fP0nS3Llz1aZNG61du1bdu3cvdszs7GxlZ2fnf52amipJcjqdcjqdXvxuECxc7YT2goqg3cBdtB24i7aD3FwpJkayrPI/x7KcRe4Li44298HcpMr7/8VhWRX5sXrXjh071LJlS/34449q3769VqxYof79++vkyZOqUaNG/n6NGzfW3XffrXvuuafYMaZOnapp06YV275gwQJFu37zAAAAAKqczMxMjRkzRikpKYqLiyt1P1t7kgrLy8vT3XffrZ49e6p9+/aSpEOHDik8PLxIQJKkhIQEHTp0qMTjTJ48WZMmTcr/OjU1VUlJSRo0aFCZPwjAxel0Kjk5WQMHDlRYWJjd5SBA0G7gLtoO3EXbQYMG0jXXSBWZiWJZTqWlJSs2dqAcjqLtZsUK6ZlnpMOHzSIOwcg1yuxc/CYkTZw4UT/99JO++uqrSh0nIiJCESUMzgwLC+MFBBVCm4E7aDdwF20H7qLtVF0nT0rVqkkOR8Wf63CEFQtJ4eFm4YacHCk21kNF+pny/l/xiyXA77zzTn3yySf68ssv1bBhw/ztiYmJysnJ0alTp4rsf/jwYSUmJvq4SgAAAMA/5ORIZ854fuEGicUbJJtDkmVZuvPOO7Vo0SKtWLFCTZs2LfJ4ly5dFBYWpuXLl+dv27Ztm/bt26cePXr4ulwAAADAL7iCjKeXAJdYBlyyebjdxIkTtWDBAn344YeKjY3Nn2cUHx+vqKgoxcfH65ZbbtGkSZNUs2ZNxcXF6a677lKPHj1KXNkOAAAAqApcV8zx5Nwh17HoSbI5JM2ePVuS1KdPnyLb586dq3HjxkmS/vWvfykkJESjR49Wdna2Bg8erJdeesnHlQIAAAD+wxVkGG7nHW6FpF27dqlZs2aVPnl5Vh+PjIzUrFmzNGvWrEqfDwAAAAgGrp4khtt5h1tzklq0aKG+ffvq7bff1unTpz1dEwAAAIAy0JPkXW6FpI0bN6pjx46aNGmSEhMTddttt2n9+vWerg0AAABACZiT5F1uhaTOnTvrueee04EDB/T666/r4MGD6tWrl9q3b69nn31WR48e9XSdAAAAAP7HFWQ8GZIYblegUkuAV6tWTVdccYXee+89PfXUU9qxY4fuvfdeJSUl6aabbtLBgwc9VScAAACA//FGT1JoqLk4LT1JlQxJ33//ve644w7Vq1dPzz77rO69917t3LlTycnJOnDggEaMGOGpOgEAAAD8jyskhYd79riRkYQkyc3V7Z599lnNnTtX27Zt09ChQ/Xmm29q6NChCgkxmatp06aaN2+emjRp4slaAQAAAMgEmchIKaRSXR7FRUYy3E5yMyTNnj1bN998s8aNG6d69eqVuE/dunU1Z86cShUHAAAAoLiMDM8OtXOJiKAnSXIzJCUnJ6tRo0b5PUculmVp//79atSokcLDwzV27FiPFAkAAACgQGamZ6+R5EJIMtzqoGvevLmOHTtWbPuJEyfUtGnTShcFAAAAoHTe6kliuJ3hVkiyLKvE7enp6Yr0xm8LAAAAQL7MTM9eSNYlIqJgUYiqrELD7SZNmiRJcjgcmjJliqKjo/Mfy83N1bp169S5c2ePFggAAACgKG/OSaInqYIhadOmTZJMT9KPP/6o8EJrDoaHh6tTp0669957PVshAAAAgCK82ZOUnu754waaCoWkL7/8UpI0fvx4Pffcc4qLi/NKUQAAAABKl57uvZ4khtu5ubrd3LlzPV0HAAAAgHLKzJSqV/f8cSMjpaNHPX/cQFPukHTFFVdo3rx5iouL0xVXXFHmvgsXLqx0YQAAAABKlpEh1arl+eOyBLhR7pAUHx8vh8OR/28AAAAA9vDmEuCEpAqEpMJD7BhuBwAAANgnM9N7IYk5SW5eJykrK0uZhSLm3r17NXPmTC1btsxjhQEAAAAoGUuAe5dbIWnEiBF68803JUmnTp1S165d9cwzz2jEiBGaPXu2RwsEAAAAUFRWlvd6krKypLw8zx87kLgVkjZu3KhLLrlEkvT+++8rMTFRe/fu1Ztvvqnnn3/eowUCAAAAKJCbK2Vne68nSZJOn/b8sQOJWyEpMzNTsbGxkqRly5bpiiuuUEhIiLp37669e/d6tEAAAAAABVyzXrxxMVlX8Krqize4FZJatGihxYsXa//+/Vq6dKkGDRokSTpy5AgXmAUAAAC8yLWwQlSU549NSDLcCklTpkzRvffeqyZNmqhbt27q0aOHJNOrdMEFF3i0QAAAAAAFvNmT5DpmVV/hrtxLgBd25ZVXqlevXjp48KA6deqUv71///4aNWqUx4oDAAAAUJQrwHhr4QaJniS3QpIkJSYmKjExsci2rl27VrogAAAAAKXzZkhy9SQRktyQkZGhJ598UsuXL9eRI0eUd9Yagbt27fJIcQAAAACKcgUYepK8x62QNGHCBK1atUo33nij6tWrJ4fD4em6AAAAAJSA4Xbe51ZI+vzzz/Xpp5+qZ8+enq4HAAAAQBnoSfI+t1a3O++881SzZk1P1wIAAADgHFw9Sd5Y3S4sTHI4WN3OrZD02GOPacqUKcqs6hETAAAA8LHMTCk8XAoN9fyxHQ7Tm1TV3+a7NdzumWee0c6dO5WQkKAmTZooLCysyOMbN270SHEAAAAAisrI8M6FZF0ISW6GpJEjR3q4DAAAAADlkZnpnaF2LoQkN0PSI4884uk6AAAAAJQDPUne59acJEk6deqUXnvtNU2ePFknTpyQZIbZ/f777x4rDgAAAEBR3u5Jiohg4Qa3epK2bNmiAQMGKD4+Xnv27NGf/vQn1axZUwsXLtS+ffv05ptverpOAAAAADIBxhvLf7tERNCT5FZP0qRJkzRu3Dht375dkYV+Q0OHDtXq1as9VhwAAACAojIyvN+TREhyw3fffafbbrut2PYGDRro0KFDlS4KAAAAQMkyM+lJ8ja3QlJERIRSU1OLbf/1119Vp06dShcFAAAAoGTp6d5f3a6qz0lyKyRdfvnlevTRR+V0OiVJDodD+/bt0/3336/Ro0d7tEAAAAAABbzdk8Tqdm6GpGeeeUbp6emqU6eOsrKydOmll6pFixaKjY3V9OnTPV0jAAAAgP/xxcINVb0nya3V7eLj45WcnKyvv/5aP/zwg9LT03XhhRdqwIABnq4PAAAAQCH0JHlfhUNSXl6e5s2bp4ULF2rPnj1yOBxq2rSpEhMTZVmWHA6HN+oEAAAAIJYA94UKDbezLEuXX365JkyYoN9//10dOnRQu3bttHfvXo0bN06jRo3yVp0AAAAAJGVleX/hhqws7x0/EFSoJ2nevHlavXq1li9frr59+xZ5bMWKFRo5cqTefPNN3XTTTR4tEgAAAICUl2d6eaKivHcO13A7y5Kq6iCxCvUkvfPOO/r73/9eLCBJUr9+/fTAAw9o/vz5HisOAAAAQIHTp829ty8ma1kF56qKKhSStmzZossuu6zUx4cMGaIffvih0kUBAAAAKM616py3F26Qqva8pAqFpBMnTighIaHUxxMSEnTy5MlKFwUAAACgOF+EJFcvFSGpnHJzc1WtWunTmEJDQ3XmzJlKFwUAAACgOFdwoSfJuyq0cINlWRo3bpwiShkEmZ2d7ZGiAAAAABTHcDvfqFBIGjt27Dn3YWU7AAAAwDvoSfKNCoWkuXPneqsOAAAAAOfgyzlJrnNVRRWakwQAAADAPvQk+QYhCQAAAAgQrG7nG4QkAAAAIEBkZkrVqpmbt4SHF5yrqrI1JK1evVrDhw9X/fr15XA4tHjx4iKPjxs3Tg6Ho8itrIvZAgAAAMEsI8O7vUiSFBJizkFIsklGRoY6deqkWbNmlbrPZZddpoMHD+bf3nnnHR9WCAAAAPiPzEzvhyTJnKMqL9zgxY66cxsyZIiGDBlS5j4RERFKTEz0UUUAAACA//JFT5JET5KtIak8Vq5cqbp16+q8885Tv3799Pjjj6tWrVql7p+dnV3korapqamSJKfTKafT6fV6Efhc7YT2goqg3cBdtB24i7ZTNZ0+LcXHS5bl3vMty1nkvjRxcVJ2thRszau8/18cluXuj9izHA6HFi1apJEjR+Zve/fddxUdHa2mTZtq586d+vvf/66YmBh9++23Cg0NLfE4U6dO1bRp04ptX7BggaKjo71VPgAAAAA/l5mZqTFjxiglJUVxcXGl7ufXIelsu3btUvPmzfXFF1+of//+Je5TUk9SUlKSjh07VuYPAnBxOp1KTk7WwIEDFRYWZnc5CBC0G7iLtgN30XaqpnHjpN27penT3Xu+ZTmVlpas2NiBcjhKbzf33y916CC9/LJ75/FXqampql279jlDkt8PtyusWbNmql27tnbs2FFqSIqIiFCEa3H3QsLCwngBQYXQZuAO2g3cRduBu2g7VUtqquRwmFtlOBxhZYYkh8OcK9iaVnn/rwTUdZJ+++03HT9+XPXq1bO7FAAAAMDn0tN9s3BDRASr29kmPT1dO3bsyP969+7d2rx5s2rWrKmaNWtq2rRpGj16tBITE7Vz507dd999atGihQYPHmxj1QAAAIA9MjOl2rW9f56qHpJs7Un6/vvvdcEFF+iCCy6QJE2aNEkXXHCBpkyZotDQUG3ZskWXX365WrVqpVtuuUVdunTRmjVrShxOBwAAAAQ7Xy0BHhHBEuC26dOnj8paN2Lp0qU+rAYAAADwb768mGxVDkkBNScJAAAAqMq4mKxvEJIAAACAAJGVZYbCeVtkJHOSAAAAAPg5y/LtnKSsLO+fx18RkgAAAIAAkJ0t5eX5duGGMpYPCGqEJAAAACAAuOYI+WpOUl6elJPj/XP5I0ISAAAAEABcc4R81ZMkVd3FGwhJAAAAQADwZU9SVFTRc1Y1hCQAAAAgANjRk1RVV7gjJAEAAAABwNdzkgqfs6ohJAEAAAABgDlJvkNIAgAAAAKAK7D46mKyhc9Z1RCSAAAAgADg6klyLargTYQkAAAAAH4vM1NyOKSwMO+fi4UbAAAAAPi9jAzTi+RweP9czEkCAAAA4PcyMnyzaIMkhYSYoERIAgAAAOC3MjN9F5Ikcy5CEgAAAAC/lZ7u25BETxIAAAAAv5aaKkVH++58UVGEJAAAAAB+LC3NtyEpIoLV7QAAAAD4sZQU31wjyYXhdgAAAAD8WlqaVL26785HSAIAAADg13w9J4mQBAAAAMCvMSfJdwhJAAAAQABITfXtcLuoKEISAAAAAD9lWeY6SSzc4BuEJAAAAMDPZWSYoOTLnqTISEISAAAAAD+Vmmru6UnyDUISAAAA4OfS0sw9PUm+QUgCAAAA/JyrJ8mXq9tFRkpZWb47nz8hJAEAAAB+ztWT5OslwM+ckXJyfHdOf0FIAgAAAPycqyfJ18PtpKo55I6QBAAAAPg5V0+SrxdukAhJAAAAAPxQaqoUFiaFh/vunPQkAQAAAPBbqam+nY8kFYSk9HTfntcfEJIAAAAAP5eW5tv5SFJBKHMN9atKCEkAAACAn0tN9X1Ics1/IiQBAAAA8Dtpab5dtEEqCGWEJAAAAAB+x845Sa7lx6sSQhIAAADg51JTfd+TFBJighk9SQAAAAD8jh09SRIhCQAAAICfsiskVa9OSAIAAADgh+xYAlwyQ/wISQAAAAD8TlqaPT1JUVEs3AAAAADAz+TlSenp9oUkepIAAAAA+JX0dHNv18IN9CQBAAAA8CuunhxWt/MdQhIAAADgx1w9OYQk3yEkAQAAAH7M7p4khtsBAAAA8CuukGLXEuCuOVFVCSEJAAAA8GN2D7fLypLOnPH9ue1ESAIAAAD8mJ3D7Vy9V1WtN4mQBAAAAPix1FQpIkKqVs33546KKqihKiEkAQAAAH4sLc2eXiSp4LxVbYU7QhIAAADgx1JT7Vm0QSIk2WL16tUaPny46tevL4fDocWLFxd53LIsTZkyRfXq1VNUVJQGDBig7du321MsAAAAYIO0tIJhb75GSLJBRkaGOnXqpFmzZpX4+NNPP63nn39eL7/8statW6fq1atr8ODBOn36tI8rBQAAAOyRmspwO1+zYfpXgSFDhmjIkCElPmZZlmbOnKmHHnpII0aMkCS9+eabSkhI0OLFi3Xttdf6slQAAADAFv4Qkqrawg22hqSy7N69W4cOHdKAAQPyt8XHx6tbt2769ttvSw1J2dnZys7Ozv869X+/UafTKafT6d2iERRc7YT2goqg3cBdtB24i7ZTdZw+LcXHS5ZV+WNZlrPI/bmEhppzp6dLwdDUyvv/xW9D0qFDhyRJCQkJRbYnJCTkP1aSGTNmaNq0acW2L1u2TNF2RXAEpOTkZLtLQACi3cBdtB24i7YT/O64w9x7sjcnLa387eaNN8z9Z5957vx2yczMLNd+fhuS3DV58mRNmjQp/+vU1FQlJSVp0KBBiouLs7EyBAqn06nk5GQNHDhQYWFhdpeDAEG7gbtoO3AXbafquPBCqVMn6ZZbKn8sy3IqLS1ZsbED5XCUr91MmCBdd530yCOVP7/dUsuZNP02JCUmJkqSDh8+rHr16uVvP3z4sDp37lzq8yIiIhQREVFse1hYGC8gqBDaDNxBu4G7aDtwF20n+B09KoWHSw6H547pcISVOySFhkqnTknB0MzK+3/Fb6+T1LRpUyUmJmr58uX521JTU7Vu3Tr16NHDxsoAAAAA30lPt2/hBsksP87CDT6Unp6uHTt25H+9e/dubd68WTVr1lSjRo1099136/HHH1fLli3VtGlTPfzww6pfv75GjhxpX9EAAACAj+TmShkZ9ocklgD3oe+//159+/bN/9o1l2js2LGaN2+e7rvvPmVkZOjWW2/VqVOn1KtXLy1ZskSRkZF2lQwAAAD4THq6ua9e3b4aCEk+1qdPH1llrGXocDj06KOP6tFHH/VhVQAAAIB/cA1zi4qyr4boaDMvqirx2zlJAAAAQFXnCkl29iRFR1e9niRCEgAAAOCnXOHEzjlJ0dFVb+EGQhIAAADgp1zhxO6QRE8SAAAAAL/gDz1JUVFmhb0ylhIIOoQkAAAAwE/5S09SXp6UmWlfDb5GSAIAAEBQO3NGmjNHOnLE7koqLi1NioyUQkPtq8G1aERVmpdESAIAAEBQe+wxacIE6YILpK+/truaiklNtXdlO6lg+fGqNC+JkAQAAICg9eWXJiRdeaVUu7bUp4/0r38Fzvya1FR7h9pJBeevSiHJ1ovJAgAAAN5y9Kg0ZozpQbr9dhOMXntNmjRJ+uUX6dVX7a7w3NLSCEl2oCcJAAAAQScvT7rpJik7W/r7382cnmrVTFj685/NHKX9++2u8tzoSbIHIQkAAABB58UXpSVLpAcekGrVKvrYH/8oRURI8+bZUlqFpKUVzAmyiysksXADAAAAEMBmz5YGDJC6di3+WHS0mZv02mumx8mfpaTY35MUESGFhNCTBAAAAASsnTul//5X6t279H2GDZP27ZOWL/ddXe7whzlJDoepgZAEAAAABKiPP5bCwqSLLip9n7ZtpaZNzdwkf+YPc5IkQhIAAAAQ0D76yKxoV9ZcHodDGjJEWrRIOnbMd7VVVFqa/ddJkghJAAAAQMBKSZHWrJF69Dj3voMGmTlJb7/t/brc5Q8LN0gmJLFwAwAAABCAliyRzpwpX0iKj5d69ZL+/W//vLjsmTNSVpZ/9CRFRdGTBAAAAASkjz+WWrSQEhLKt//QodLPP0vr1nm3Lne4Qom/9CQRkgAAAIAAc+aM9Omn5etFcunSRTrvPDOPyd8cP27u4+LsrUMyQY3hdgAAAECA+fpr6dSpioWkkBCzyMOyZV4ry21Hj5r7GjVsLUOSGfJHTxIAAAAQYD7+WKpVSzr//Io9r0sXaePGgp4bf+EKSfHx9tYh0ZMEAAAABKSPPpK6dze9QxVx0UVm4YYVK7xTl7v8KSQxJwkAAAAIMDt2SNu3m5BUUXXrSo0aScnJnq+rMo4eNQEpNNTuSkxISk+3uwrfISQBAAAg4H3zjbnv3Nm953fpYuYl+dNS4EeP+sd8JMkMt8vJMbeqgJAEAACAgLdundS4sRQT497zu3SR9u6Vdu70bF2V4epJ8geuazVVlSF3hCQAAAAEvLVrK75gQ2GdO5thbf405M6fQlJ0tLmvKos3EJIAAAAQ0E6flrZskdq0cf8Y1atLbdv6V0g6csR/QpLrgrb0JAEAAAABYNMmcyHZyoQkyQy5W77cHMsfHDvmP3OSXD1JhCQAAAAgAKxfL4WHS82aVe44XbqY4WTff++Zuirr2DH/6UkiJAEAAAABZN06qWVLKSyscsdp08Ys/OAPQ+4yM83N30ISc5IAAACAALBundS6deWPExoqdepklgK3m+tCsv4y3I45SQAAAECAOHZM2rWr8vORXC680ISurCzPHM9d/haSQkOlyEhCEgAAAOD31q83957oSZJMT5LTaYKSnfwtJElmBUBCEgAAAODn1q8383bq1/fM8Zo2lWJjpdWrPXM8d7lCkr/MSZLMkDtCEgAAAODn1q41vUgOh2eOFxIitW8vrVrlmeO56+hR03MTHm5vHYVVr87CDQAAAIBfsyzTk+Sp+UguHTua8OV0eva4FXH0qH8NtZPoSQIAAAD83s6d0smTnpuP5NKxo1l+e+NGzx63Io4e9a+hdpIJSfQkAQAAAH7M04s2uLRqZVZys3Nekj+GpNhY6fhxu6vwDUISAAAAAtK6dVLDhp4PE9WqSe3a2RuSjhzxv5BUo4ZZcr0qICQBAAAgIK1fb3p9vKFDBxOScnO9c/xz8cc5SfHxBavuBTtCEgAAAAJObq60ZYv3QlKnTmb+zU8/eef45+KvIenUKenMGbsr8T5CEgAAAALO9u1mcYUWLbxz/DZtzLA7O4bcZWebVeT8bbidq56qMC+JkAQAAICAs2mTuW/Z0jvHj4gwQcmOkOSa9+NvPUmueqrCvCRCEgAAAALOpk1SYqIUF+e9c7jmJVmW985REte8H38NSVVhXhIhCQAAAAFn40apeXPvnqNTJ7PK3Pbt3j3P2fw1JLmG2xGSAAAAAD9jWaYnyVvzkVzatZNCQqRVq7x7nrO5Qoi/zUmKiZFCQxluBwAAAPid33+XTpzwfkiqXt2snmdHSIqIMBe09ScOh+ndoicJAAAA8DPeXrShsE6dpC+/9O28pGPHTBhxOHx3zvKKj6cnCQAAAPA7mzaZN+t163r/XJ06SQcOSDt3ev9cLv54jSSXqnJBWUISAAAAAoprPpIvelo6dDDzklau9P65XI4e9b/5SC5xcYQkAAAAwO9s3Oj9+UguMTFmXpIvQ9KRI/4bkuhJAgAAAPzMiRPSvn2+C0mSGXK3YoXv5iX5c09SjRrMSQIAAAD8yubN5t4Xiza4dOokHTwo7djhm/P5+5ykY8d8f4FdXyMkAQAAIGBs2mSWxm7Y0Hfn9OW8pNxc6eRJ/w1JNWpIOTlSerrdlXiXX4ekqVOnyuFwFLm1bt3a7rIAAABgk02bpObNzUVNfSUmRjr/fN+EpOPHTS+Nvw63c9UV7POSqtldwLm0a9dOX3zxRf7X1ar5fckAAADwko0bfTvUzqVjx4LrJXlzVT1X+PDXniRXSDp2TGrWzN5avMmve5IkE4oSExPzb7Vr17a7JAAAANggK0vats2ekNS5s2/mJQVKSKInyWbbt29X/fr1FRkZqR49emjGjBlq1KhRqftnZ2crOzs7/+vU1FRJktPplNPp9Hq9CHyudkJ7QUXQbuAu2g7cVRXbzg8/SBERJiT5euGA9u2l6tWlVaukJk28d56jR6WoKBNGvPE9WpazyH1FxcWZ+o4dkwKx6ZX3/4vDsvx3bYrPP/9c6enpOv/883Xw4EFNmzZNv//+u3766SfFxsaW+JypU6dq2rRpxbYvWLBA0dHR3i4ZAAAAgJ/KzMzUmDFjlJKSori4uFL38+uQdLZTp06pcePGevbZZ3XLLbeUuE9JPUlJSUk6duxYmT8IwMXpdCo5OVkDBw5UWFiY3eUgQNBu4C7aDtxVFdvOXXdJX38tPf+8PeefM8ecf9s2781LevJJ6dVXpTff9M7xLcuptLRkxcYOlMPhXrv505+kq6+WSuiX8HupqamqXbv2OUOS3w+3K6xGjRpq1aqVdpQxGDQiIkIRERHFtoeFhVWZFxB4Bm0G7qDdwF20HbirKrWdb7+VGjXy7sIJZenUSZo/34SkDh28c47Dh80S597+Hh2OMLdDUni4qTMQm115/6/4/cINhaWnp2vnzp2qV6+e3aUAAADAh06fln7+2SzFbZdOnUyA+fxz751j/36pZk3vHd8T4uODf+EGvw5J9957r1atWqU9e/bom2++0ahRoxQaGqrrrrvO7tIAAADgQ1u2SGfOSK1a2VdDeLh0wQXSZ5957xz//a9vL5Trjho1CEm2+u2333Tdddfp/PPP19VXX61atWpp7dq1qlOnjt2lAQAAwIe+/16qVs3+a/NcfLGZl/S/BZQ9yumUdu0yQwr9WVXoSfLrOUnvvvuu3SUAAADAD2zYYAJSeLi9dXTrZhaOWL5cGjXKs8fevdv0liUlefa4nhYfb5YAD2Z+3ZMEAAAASKYnyY6LyJ6tfn3T0+ONIXfbtpn7QAhJKSmBeZ2k8iIkAQAAwK9lZUlbt9o7H6mwrl1NSPL0hXS2bZOio6XatT17XE+rUcPcHz9uaxleRUgCAACAX9uyRcrNtXdlu8K6dpUOHJB++smzx922zfQi2bXEeXnFx5v7YJ6XREgCAACAX9uwwSza0LSp3ZUY3loK/JdfpAYNPHtMb3D1JAXzvCRCEgAAAPza999LzZvbv2iDi7eWAt+2zf9XtpPoSQIAAABs9/33UosWdldRlKeXAj950vTM+PuiDZJUvbrp2aMnCQAAALBBVpb088/+s2iDS7duZrnu5cs9c7xAWdlOMnOmgv1aSYQkAAAA+K0ffvCvRRtc6teXGjeWFi/2zPFcIalhQ88cz9tq1KAnCQAAALCFvy3aUFjfvtKiRaa3q7K2bZMSEqSoqMofyxfi4uhJAgAAAGyxYYN/LdpQWL9+Ulqa9OmnlT/Wtm2B04skMdwOAAAAsM26dVLLlnZXUbKkJDMM8J13Kn+sX34JjPlILoQkAAAAwAYnTphFG9q3t7uS0vXrZ3qSUlLcP0ZurrRzZ2CFpBo1CEkAAACAz33zjbnv2NHeOsrSt6+Uk2PmJrlrzx5zjEAKSfHx0vHjkmXZXYl3EJIAAADgl9askerUkRIT7a6kdHXqSJ06SfPnu3+MQFr+2yU+XnI6PXedKH9DSAIAAIBfWr3aDLVzOOyupGz9+kkrVkiHD7v3/G3bpMhIqW5dz9blTfHx5j5YlwEnJAEAAMDvZGWZle06dLC7knPr3VsKCZH+7//ce75rZbuQAHpnXqOGuQ/WeUkB9KsAAABAVbFunRnO5c/zkVzi46WLL5YWLHDv+b/8EljLf0sFvV5799pbh7cQkgAAAOB31qyRYmL88yKyJRkwQFq7Vtq6teLP3bYtsOYjSVJsrOlN+vVXuyvxDkISAAAA/M6aNWY+UqAMQbvkEtO78vTTFXteaqqZyxRoIUkyvV/bt9tdhXcESLMDAABAVXHmjPTtt4ExH8klLEy66ioz5K4iQ9DWrDH3zZt7py5vatCgYGW+YENIAgAAgF/54QcpPT2wQpIkDRsmVa8uPfts+Z8zd67UokXgDCssjJ4kAAAAwEfWrJHCw6Xzz7e7koqJipJGjpT+/e/yLY199Kj00UfS4MH+v8x5SRo2lE6eNBeVDTaEJAAAAPiVNWukNm1MUAo0o0ZJliW98MK5950/3+w7cKD36/IG14p8wdibREgCAACA37CsgovIBqL4eDPs7vnnzZDB0liWNGeO1LNnwYVZA02DBuY+GFe4IyQBAADAb2zfboaqBcL1kUpz1VUmIL34Yun7bNgg/fSTNGSI7+rytKgoqU4depIAAAAAr1qxQgoNldq1s7sS9yUkmLlJDz8srVxZ8j6vv24CxkUX+bIyz2vYkJ4kAAAAwKsWL5Y6dTKrxAWy224zvWGjR0u7dhV9LCvLzEcaPNgEwkBWv35wLgNOSAIAAIBfSE01PUk9e9pdSeVVqyY98ogZkjZ8uPneXD74wHx92WX21ecpSUnSjh1mjlUwqWZ3AQAAAIAkff655HRKf/iD3ZV4RlycNH26NHGiCUoNGkjr10s7d0oXXliw8EEga9BAysiQDh40vUrBgpAEAAAAv7B4sdSqlZSYaHclntO4sTRlirnA7IkTZijhNddIPXrYXZlnJCWZ++3bCUkAAACAR2VnS59+aubwBJuuXaV337W7Cu+oV08KCTGLN1x6qd3VeA5zkgAAAGC7lSultDSpVy+7K0FFhIebnr9gWwackAQAAADbLV5shms1a2Z3JaioBg2CbxlwQhIAAABslZdnQtIf/iA5HHZXg4oKxmslEZIAAABgq+++kw4dYqhdoGrY0KzYl5trdyWeQ0gCAACArRYvlmrUkNq3t7sSuKNhQyknR9q/3+5KPIeQBAAAANtYlvT++1L37lJoqN3VwB0NG5r7YBpyxxLgAADbHDkibdhg/rBu327uDx+WTp2SUlKkzEyzclJEhBQZKdWqZa450qiR1KSJud7IBRdIderY/Z0AcNfSpdKOHeaCqwhMCQlStWrmdXzQILur8QxCEgDAZ/btk5Yskb76Svr6a2nXLrM9PNx8Elm/vtS0qRQTI1WvboJRbq4ZxpGTY4LT4cPSf/8rHThgQpRkVlbq3l3q29fc2rRh8jcQKJ55Rjr/fPOhBwJTaGjwrXBHSAIAeI1lSevWSYsWmYtEbt1q/pi2bCl17ixdf73UurW5xkZIBQeA5+VJBw+aP8q//ir9/LP04YfSmTNS3brSkCHSsGHmU834eK98ewAq6ccfpS++kB58kA82Ah0hCQCAMliWtGmTubr8f/5jeo9q1pQuvlgaPVq66CIpNrby5wkJMX+UGzQwvUeSlJUl/fSTtHGjtGaN9MYbZgjIJZdIo0ZJI0aYoXoA/MOzz5oPNfr0sbsSVFarVtLChZLTKYWF2V1N5RGSAAAecfiwNH++9PrrpseoRg2pd2/pnnukDh18MyE7KsqEsYsvlm67zSwpvHat9O230qRJ0l/+Il14oXTFFebWpo33awJQsoMHzWvGzTebDzMQ2Hr2lObNk1aulAYOtLuayqNJAgDclpdnhsq8/LL08cdmuEzPntKMGSao2L1SVWKiNHKkuaWnS+vXm/lQ06dLDz1k5kGMHm0C04UXMtwH8KVZs0yPwx//aHcl8ITmzaV69cxy7oQkAECVdPy4NGeOCUe7d0vNmkl//rPUv7//zv+JiZH69TO3nBzp++/NkLxZs6QnnpCSkgp6mHr2tD/gAcEsM1N66SUzdzAmxu5q4AmuD8kWL5ZeeKHi80z9DSEJAFBuGzdKL74ovfOO6UXq08cMY2vXLrB6YcLDpT/8wdxyc6UffjCBacEC6bnnpNq1zRymK64woSo83O6KgeAya5ZZrXL0aLsrgSf17GmuebVhgxlNEMgISQCAMjmdZnW6mTPN3J6EBOnGG6WhQ828o0AXGmqG2l14oXTXXWZ58dWrpc8+k/79b9Mzdvnl5s3c4MFmWXIA7vv5Z+nhh82HEPXq2V0NPKlDB/OauXgxIQkAEKSOHTMhYdYs6fffzUVbH33U9L4E61C0kBCpbVtzu+02aedOE5i++kp66y0zLGj4cOnKK80woagouysGAktOjnTDDSYcTZhgdzXwtNBQqUcP88Ha9Ol2V1M5hCTABjk55uriv/9uVvc5dEg6edJc38V1i46W4uLMJzK1apllixs3Nn9YgvUNKvzDpk1mPPmCBebr/v2ladPMpNyqxOGQWrQwt5tvlvbuNYFp9Woz3LB6dXMdpquuMr1q0dF2Vwz4v8ceM9dGevFFKSLC7mrgDT17mouG//qrWRY8UBGSAC/LzJS++0765hszRvenn0xAys0t2CcmxgSiatXMLSREOn1aysgwK3I5nQX7hoWZSfLt25tPu9u1MxflbNky8CdJwj7Z2dIHH5heo2++MUPqbrrJhAB/XYjB1xo3NsMMb7xR2r9fWrXKBKb/+z8TkIYMMUPyhg0z/58ByXwA9ssvZhjnL79Iv/0mHT1qFj85edK8pkdGmlvNmua13HXr2tXMjwsWa9eaRVLGjjUrSyI4XXSRac8ffij97W92V+M+QhLgYdnZ0tdfm2WRv/jCfCp/5oz51LlVKxNshg41b7jq1jV/FM/1aVpmprkGzaFD5rZ/v7Rnj7kWwfHjZp/q1aWOHaUuXQrmV7RtGxwXdIP3bN9uVqmbM8cMr7vgAmnqVKlXL3osy5KUZIYM3XCD6RFetcos/PDBB2aRh/79zbLjf/yjVL++3dXCV9LTzaqJ331XcNuzxzzmcJiRAAkJJkQ3amReo3NzzeiCnBzp1Cnp88/NMNecHPO8Nm3M9cb69jVz4gJ1HuC+fdKYMSYcXX+93dXAmyIjTVBatIiQBFR5O3eaSd6ffWbeLGVlmfDTubN0552m16dJE/ffdEZHS02bmtvZUlJMz9T27eb28cemN8CyzJu1tm3NG99OnUyIatfOhDNUXenp0nvvmYu+fvWV6ckcNMgsTtC4sd3VBZ4GDcybvzFjzIcZa9aYn+sdd5h5TRdeaMLSoEGmZ4APLoJDTo4ZGbBhgwlGa9ear/PyzFy1Vq3MxPXrrzev/w0bln/Rj7w805Z++skMTVu6VHrlFTPSoFcv83915EhzzECwebPpaXU4pMcf5wOYqqBnT+npp80Hu4mJdlfjHkIS4IbTpwtWv/rsMxNOqlUzQWTsWNOb06yZb4a/xceb83XpUrAtK6sgOO3caYZPzZ9f8MlkrVomLJ1/vvlD3qqVmW/StCnzKoJVVpb06afSf/5j7k+fNm3mwQelSy5hboCnJCSYRR2uvFJKTZXWrTMrAs6caRa9iI01PQJ9+pife+fONheMc0pJMfPRdu0yw+V+/lnautXccnLM63yTJlLr1iYIt2ljPmyoTBAICTG9TvXqFVyU88gR05a+/VZ64AGz9H7XrtLf/27q6tDBP5fhX7rU/H9o2NBM5K9Z0+6K4As9epgPaqdMkV591e5q3ENIAsrBsswfxuRkadky01uUmWl6ZC6+2MzduPBC/wkYUVHmD2aHDgXbcnPNsKDdu83wj717zffx9tvmDbRLnTomLDVubIYUJSWZP24NGphbYiLXjAkUBw4UBPlly8wct1atzJyavn0D99O9QBEXZ97gDhxo/v/9+qvpcdi4UZo82QzNjYkxPQO33y599JEZotKokX++2Q00liWlpZmQk55u2n9mZtFbVpZ5zHVLSTHDTo8eNbfffzdh1yU21rw2Nmpkfm+uD5h8scph3brSiBHmlpVlAvi6deax7t1NQB8xwvQy9epl/+t0Sor5cOCxx0yYe/hhVoOsSuLjzSUV/vlPsyLquHF2V1RxhCSgFD/9ZHpg1qwxYeLQIfNHp0MHMw+he3fz6WGgvJkJDTV/2Bs1ki69tGC7ZZl5TQcOFKy0d+iQ+dR03Trz6eXp00WPVbu2+YSzQYOCTztdt8TEgn/zB9G3Dh407XX1atNmf/qpYEnra681vReBMjwn2ISGmh6GNm1MSM3JMaHpxx9Nr69ktmdlmTcX7dsXDI919fYmJbE4i2QC52+/mQ979u8vuLnmbR4+bF7TUlPNsLVziYoyH3BFRZm5nXFx5tamjentS0gwt8RE0wviD6/5UVHm//Oll5rvc+pUMxfWdTHk6Ggz3KlfP7Nf586+u75XSor0/PPSM8+Y9nz11dIttzDErioaNsx8wPznPxcM+w8kDsuyLLuLOJdZs2bpH//4hw4dOqROnTrphRdeUNeuXcv13NTUVMXHxyslJUVxLDeEEqSkmDcrW7eaRRZ+/NGp//f/PtN11w2V0xmm1q3NG5YuXUxAqmrDkizL/BE+frzgE9YTJ8y/T5wouB0/XnQVPsm80XC9uXAFKNfXrjcedeuaW6BfoNPpdOqzzz7T0KFDFeblSSdZWWYY5bZt0g8/mHa7caMJupJ5M92+vendvPhiVqfzd5blVGrqZzpzZqi2bQvTrl0FPb779pmFXyTz2uO6FEDTpub3XPjDiTp1zJv46tX94418ZWRlmZ/Bzp0mRLqGD+/aZXrBXT8TybTvOnWk884zixqcd57ZFhNTcIuKKlhBLjLS/CwjIswHX4H8s3K1nbi4oXI4wmRZ5me2YYN5Xdiyxfwsq1Uzgfvii81rQ4sWZvW8pk0rP0cuL8+8EV6+3Iy2WLnS/C0YPtx8OBNMq/MFi7PbjTdlZ5sepdxc0y79YeGR8mYDvw9J//nPf3TTTTfp5ZdfVrdu3TRz5ky999572rZtm+qWY/Y5Ialqycszn9C6hlVkZJjVglxv5I8eNZ9A/v67efOxY4fZJpk/lElJUtu2Tt1++2fau3eozj8/rMqFIne5wtTZwcn175Mnze/i+HETTM8WE2P+mNapY261a5u5U7VqmTd+NWuaNz+uN0Cum7+Eq8qGpLy8guE+rrbqCqW//17wafmePaYNu165a9Uyw31atDC9DR06MOY/0JT1hiU31/SO/PZbQW/J4cOmh/foUfP/6uy/4uHh5o2Iq0ckLs4ME3MFhurVi94Kb4+JMfsWvkVHezZIOJ2mdlev9W+/mdfjvXtN+9650/SKukRGFvRa169fcO8KhlW5x/pcb3bPnDHh8tdfzW37dvOzzs42jzsc5rU2MdHcatUq+L3HxJhwFRpqbmfOFPxtTUszx3H15mVnm7DVoYP5cGbwYMKRP/NlSJLMB3i33256lt55x+unO6egCUndunXTxRdfrBdffFGSlJeXp6SkJN1111164IEHzvl8QpLn5OSYN3FpaQX3Z99c2zMyCm5ZWQX3WVkFS53m5Jg/lmfOmDcCZ86YP/Z5ecX/6JfGssztzBlzrHMNrYiMNL0WtWqZF/D69Qvm3SQlmT+2vn7xqIrOnCkITSdPmltKivn61Cnz77Q0E7pc/y58XanCwsIK3uS53tBFR5s3fFFRBZ8YR0aafV031zWpXG8AQkPNG4aQEHN/9ptCV5t0tVHXLTfX3CzLqQsv/EzffjtU2dlhcjpNm3S19exsM2zx9Gnz/8AV4l1zJdLSSm73YWFFg2NCgmmrDRqYe3/4VA6VU5nXnNxc8//HNbzMdUtLK5h343r9dbW/wu0wK6vgDXNpHI7iAcv1/8zVGxMWVnQoYG5uwTlOnzb1uP5/F57jI5nn1aljXpvr1DFt2xWGGjQwr9eB3NvjTe60nbw88wHMb7+ZMFr4Q6309IJ2kZVlfo+uv8khIUV74gqHq8aNTQ8VHyoGBjve57z8slkB0rUkvp2CIiTl5OQoOjpa77//vkaOHJm/fezYsTp16pQ+/PDDYs/Jzs5WdqFX/JSUFDVq1Ei7d+9WbGysL8ou07//bcbpVvanXvgNW+FthW+uN3H++xsuuHCq6w1q4Tenhf8olvQ9FN6/8Bvd0NCCF/Hw8KJ/3MvzAh4a6lTfvl/qyy/7KjeXkOQPLKsgbJcUvl1v+soz/8BboqKcmjXrS02c2FdZWeVvN2Fhpp26Qp3rTajr0/+oKN4gBju7X3Py8sz/n+xsE6rOvi8cdgoPc6uokBATrGJjTS9wjRoF98xXcY/dbQeByY5288MP5kMS12IjdkpLS1PTpk116tQpxZcxHt2vQ9KBAwfUoEEDffPNN+rRo0f+9vvuu0+rVq3SuhJ+0lOnTtW0adN8WSYAAACAALJ//341LGM1o6Bb3W7y5MmaNGlS/td5eXk6ceKEatWqJQcfx6IcUlNTlZSUpP379zNEE+VGu4G7aDtwF20H7qjq7cayLKWlpal+/fpl7ufXIal27doKDQ3V4cOHi2w/fPiwEku5wEdERIQizhpTVYNB+3BDXFxclXzxQOXQbuAu2g7cRduBO6pyuylrmJ2LX19xITw8XF26dNHy5cvzt+Xl5Wn58uVFht8BAAAAgKf4dU+SJE2aNEljx47VRRddpK5du2rmzJnKyMjQ+PHj7S4NAAAAQBDy+5B0zTXX6OjRo5oyZYoOHTqkzp07a8mSJUpISLC7NASpiIgIPfLII8WGbQJlod3AXbQduIu2A3fQbsrHr1e3AwAAAABf8+s5SQAAAADga4QkAAAAACiEkAQAAAAAhRCSAAAAAKAQQhKqpFmzZqlJkyaKjIxUt27dtH79+jL3P3XqlCZOnKh69eopIiJCrVq10meffeajauEvKtJu+vTpI4fDUew2bNgwH1YMf1HR15yZM2fq/PPPV1RUlJKSknTPPffo9OnTPqoW/qIi7cbpdOrRRx9V8+bNFRkZqU6dOmnJkiU+rBb+YvXq1Ro+fLjq168vh8OhxYsXn/M5K1eu1IUXXqiIiAi1aNFC8+bN83qd/o6QhCrnP//5jyZNmqRHHnlEGzduVKdOnTR48GAdOXKkxP1zcnI0cOBA7dmzR++//762bdumf//732rQoIGPK4edKtpuFi5cqIMHD+bffvrpJ4WGhuqqq67yceWwW0XbzoIFC/TAAw/okUce0S+//KI5c+boP//5j/7+97/7uHLYqaLt5qGHHtIrr7yiF154QT///LNuv/12jRo1Sps2bfJx5bBbRkaGOnXqpFmzZpVr/927d2vYsGHq27evNm/erLvvvlsTJkzQ0qVLvVypn7OAKqZr167WxIkT87/Ozc216tevb82YMaPE/WfPnm01a9bMysnJ8VWJ8EMVbTdn+9e//mXFxsZa6enp3ioRfqqibWfixIlWv379imybNGmS1bNnT6/WCf9S0XZTr14968UXXyyy7YorrrCuv/56r9YJ/ybJWrRoUZn73HfffVa7du2KbLvmmmuswYMHe7Ey/0dPEqqUnJwcbdiwQQMGDMjfFhISogEDBujbb78t8TkfffSRevTooYkTJyohIUHt27fXE088odzcXF+VDZu5027ONmfOHF177bWqXr26t8qEH3Kn7fzhD3/Qhg0b8odW7dq1S5999pmGDh3qk5phP3faTXZ2tiIjI4tsi4qK0ldffeXVWhH4vv322yJtTZIGDx5c7r9vwaqa3QUAvnTs2DHl5uYqISGhyPaEhAT997//LfE5u3bt0ooVK3T99dfrs88+044dO3THHXfI6XTqkUce8UXZsJk77aaw9evX66efftKcOXO8VSL8lDttZ8yYMTp27Jh69eoly7J05swZ3X777Qy3q0LcaTeDBw/Ws88+q969e6t58+Zavny5Fi5cyAd6OKdDhw6V2NZSU1OVlZWlqKgomyqzFz1JwDnk5eWpbt26evXVV9WlSxddc801evDBB/Xyyy/bXRoCxJw5c9ShQwd17drV7lIQAFauXKknnnhCL730kjZu3KiFCxfq008/1WOPPWZ3afBjzz33nFq2bKnWrVsrPDxcd955p8aPH6+QEN7qAe6gJwlVSu3atRUaGqrDhw8X2X748GElJiaW+Jx69eopLCxMoaGh+dvatGmjQ4cOKScnR+Hh4V6tGfZzp924ZGRk6N1339Wjjz7qzRLhp9xpOw8//LBuvPFGTZgwQZLUoUMHZWRk6NZbb9WDDz7Im94qwJ12U6dOHS1evFinT5/W8ePHVb9+fT3wwANq1qyZL0pGAEtMTCyxrcXFxVXZXiSJniRUMeHh4erSpYuWL1+evy0vL0/Lly9Xjx49SnxOz549tWPHDuXl5eVv+/XXX1WvXj0CUhXhTrtxee+995Sdna0bbrjB22XCD7nTdjIzM4sFIdeHNJZlea9Y+I3KvOZERkaqQYMGOnPmjD744AONGDHC2+UiwPXo0aNIW5Ok5OTkc7a1oGf3yhGAr7377rtWRESENW/ePOvnn3+2br31VqtGjRrWoUOHLMuyrBtvvNF64IEH8vfft2+fFRsba915553Wtm3brE8++cSqW7eu9fjjj9v1LcAGFW03Lr169bKuueYaX5cLP1LRtvPII49YsbGx1jvvvGPt2rXLWrZsmdW8eXPr6quvtutbgA0q2m7Wrl1rffDBB9bOnTut1atXW/369bOaNm1qnTx50qbvAHZJS0uzNm3aZG3atMmSZD377LPWpk2brL1791qWZVkPPPCAdeONN+bvv2vXLis6Otr629/+Zv3yyy/WrFmzrNDQUGvJkiV2fQt+gZCEKumFF16wGjVqZIWHh1tdu3a11q5dm//YpZdeao0dO7bI/t98843VrVs3KyIiwmrWrJk1ffp068yZMz6uGnaraLv573//a0myli1b5uNK4W8q0nacTqc1depUq3nz5lZkZKSVlJRk3XHHHbzZrYIq0m5WrlxptWnTxoqIiLBq1apl3Xjjjdbvv/9uQ9Ww25dffmlJKnZztZexY8dal156abHndO7c2QoPD7eaNWtmzZ071+d1+xuHZdF3DwAAAAAuzEkCAAAAgEIISQAAAABQCCEJAAAAAAohJAEAAABAIYQkAAAAACiEkAQAAAAAhRCSAAAAAKAQQhIAAAAAFEJIAgD4xLx581SjRg27y9CePXvkcDi0efPmSh2nT58+uvvuu/O/btKkiWbOnFmpY0rSuHHjNHLkyEofBwDgPkISAECSdOjQId11111q1qyZIiIilJSUpOHDh2v58uUeOf4111yjX3/91SPHKsvu3bs1ZswY1a9fX5GRkWrYsKFGjBih//73v5KkpKQkHTx4UO3bt6/UeRYuXKjHHnvMEyUX8dxzz2nevHn5X58dxgAA3lfN7gIAAPbbs2ePevbsqRo1augf//iHOnToIKfTqaVLl2rixIn5AaMyoqKiFBUV5YFqS+d0OjVw4ECdf/75WrhwoerVq6fffvtNn3/+uU6dOiVJCg0NVWJiYqXPVbNmzUofo7Dc3Fw5HA7Fx8d79LgAgIqjJwkAoDvuuEMOh0Pr16/X6NGj1apVK7Vr106TJk3S2rVr8/fbt2+fRowYoZiYGMXFxenqq6/W4cOH8x//4Ycf1LdvX8XGxiouLk5dunTR999/L6n4cLupU6eqc+fOeuutt9SkSRPFx8fr2muvVVpaWv4+eXl5mjFjhpo2baqoqCh16tRJ77//fqnfx9atW7Vz50699NJL6t69uxo3bqyePXvq8ccfV/fu3SUVH263cuVKORwOLV26VBdccIGioqLUr18/HTlyRJ9//rnatGmjuLg4jRkzRpmZmfnnOlcPz7PPPqsOHTqoevXqSkpK0h133KH09PT8x10/j48++kht27ZVRESE9u3bV2S43bhx47Rq1So999xzcjgccjgc2r17t1q0aKF//vOfRc63efNmORwO7dixo9SaAADlQ0gCgCruxIkTWrJkiSZOnKjq1asXe9wVbPLy8jRixAidOHFCq1atUnJysnbt2qVrrrkmf9/rr79eDRs21HfffacNGzbogQceUFhYWKnn3rlzpxYvXqxPPvlEn3zyiVatWqUnn3wy//EZM2bozTff1Msvv6ytW7fqnnvu0Q033KBVq1aVeLw6deooJCRE77//vnJzcyv0c5g6dapefPFFffPNN9q/f7+uvvpqzZw5UwsWLNCnn36qZcuW6YUXXij38UJCQvT8889r69ateuONN7RixQrdd999RfbJzMzUU089pddee01bt25V3bp1izz+3HPPqUePHvrTn/6kgwcP6uDBg2rUqJFuvvlmzZ07t8i+c+fOVe/evdWiRYsKfd8AgOIYbgcAVdyOHTtkWZZat25d5n7Lly/Xjz/+qN27dyspKUmS9Oabb6pdu3b67rvvdPHFF2vfvn3629/+ln+sli1blnnMvLw8zZs3T7GxsZKkG2+8UcuXL9f06dOVnZ2tJ554Ql988YV69OghSWrWrJm++uorvfLKK7r00kuLHa9BgwZ6/vnndd9992natGm66KKL1LdvX11//fVq1qxZmbU8/vjj6tmzpyTplltu0eTJk7Vz587851155ZX68ssvdf/995d5HJezF3V4/PHHdfvtt+ull17K3+50OvXSSy+pU6dOJR4jPj5e4eHhio6OLjJEcNy4cZoyZYrWr1+vrl27yul0asGCBcV6lwAA7qEnCQCqOMuyyrXfL7/8oqSkpPyAJElt27ZVjRo19Msvv0iSJk2apAkTJmjAgAF68skntXPnzjKP2aRJk/yAJEn16tXTkSNHJJnwlpmZqYEDByomJib/9uabb5Z53IkTJ+rQoUOaP3++evTooffee0/t2rVTcnJymbV07Ngx/98JCQmKjo4uEqwSEhLyayuPL774Qv3791eDBg0UGxurG2+8UcePHy8yZC88PLzIecurfv36GjZsmF5//XVJ0scff6zs7GxdddVVFT4WAKA4QhIAVHEtW7aUw+HwyOIMU6dO1datWzVs2DCtWLFCbdu21aJFi0rd/+yheA6HQ3l5eZKUP3/n008/1ebNm/NvP//8c5nzkiQpNjZWw4cP1/Tp0/XDDz/okksu0eOPP17mcwrX4nA4yqztXPbs2aM//vGP6tixoz744ANt2LBBs2bNkiTl5OTk7xcVFSWHw1GuY55twoQJevfdd5WVlaW5c+fqmmuuUXR0tFvHAgAURUgCgCquZs2aGjx4sGbNmqWMjIxij7tWhWvTpo3279+v/fv35z/2888/69SpU2rbtm3+tlatWumee+7RsmXLdMUVVxSbO1NehRczaNGiRZFb4d6sc3E4HGrdunWJ35u3bNiwQXl5eXrmmWfUvXt3tWrVSgcOHHDrWOHh4SXOrxo6dKiqV6+u2bNna8mSJbr55psrWzYA4H8ISQAAzZo1S7m5ueratas++OADbd++Xb/88ouef/75/PlAAwYMUIcOHXT99ddr48aNWr9+vW666SZdeumluuiii5SVlaU777xTK1eu1N69e/X111/ru+++U5s2bdyqKTY2Vvfee6/uuecevfHGG9q5c6c2btyoF154QW+88UaJz9m8ebNGjBih999/Xz///LN27NihOXPm6PXXX9eIESPc/vlUVIsWLeR0OvXCCy9o165deuutt/Tyyy+7dawmTZpo3bp12rNnj44dO5bfmxUaGqpx48Zp8uTJatmyZf7vCQBQeYQkAICaNWumjRs3qm/fvvrrX/+q9u3ba+DAgVq+fLlmz54tyfTIfPjhhzrvvPPUu3dvDRgwQM2aNdN//vMfSeZN+/Hjx3XTTTepVatWuvrqqzVkyBBNmzbN7boee+wxPfzww5oxY4batGmjyy67TJ9++qmaNm1a4v4NGzZUkyZNNG3aNHXr1k0XXnihnnvuOU2bNk0PPvig23VUVKdOnfTss8/qqaeeUvv27TV//nzNmDHDrWPde++9Cg0NVdu2bVWnTh3t27cv/7FbbrlFOTk5Gj9+vKdKBwBIcljlnbELAAD8ypo1a9S/f3/t379fCQkJdpcDAEGDkAQAQIDJzs7W0aNHNXbsWCUmJmr+/Pl2lwQAQYXhdgAABJh33nlHjRs31qlTp/T000/bXQ4ABB16kgAAAACgEHqSAAAAAKAQQhIAAAAAFEJIAgAAAIBCCEkAAAAAUAghCQAAAAAKISQBAAAAQCGEJAAAAAAohJAEAAAAAIX8f7BG++nzR5J3AAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Comparison model: 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": null,
"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 and use 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": [
"cf8262210b7c42bc8db80e1a102acf19",
"509c0ac5a7624e2bb8618b978c8a80dc",
"56c17b10c28c4fa9a6db05aa60a71f5e",
"4b85c54d069e4a8d87520dbdad4c2c89",
"6537feb3ffd24d2ea90b27a7a0a1aeb0",
"4e2585503cca48f8a0428cc62d02e5f1",
"b6ea52fd1054458e9458865f8f40ff84",
"d70cc590fb28457b9ca7f74d8aa425f1",
"a9116211ec3d407d9278f744d41507e6",
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"1170d27bc06e48bc985700e1703887b3",
"6264397e6794478fa54e161f4434cc8b",
"e5acc8060d7c4d25b6c13bb486eb5c39",
"6ff11b9092db4dab98a68505955a612c",
"b9f3902a7a844da0ab7d9bf5c2869db7",
"86fa6d3c75e94c109ca0d44ddc7cb6e4",
"4d7f1a3f6c044343a096951ed2a58603",
"ef324ed1c14d4e9aac3312ed637f5789",
"d19fd3e721b84b37b2f4da2bf03008de",
"5d3d80342c864f419e64a323051e3509"
]
},
"id": "tpREQMax-yy3",
"outputId": "fa3b4724-87ea-497c-fa6f-60f753b9088e"
},
"execution_count": 29,
"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]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "cf8262210b7c42bc8db80e1a102acf19"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"pytorch_model.bin: 0%| | 0.00/597M [00:00<?, ?B/s]"
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},
{
"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",
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"│ ┆ ┆ ┆ ProcessA… ┆ ┆ .exe ┆ ┆ - ┆ 8e-03… │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ So… ┆ │\n",
"│ 1 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Window ┆ ┆ Process ┆ [ 2.54064 │\n",
"│ ┆ n ┆ ┆ accessed ┆ ┆ s\\system3 ┆ ┆ accessed: ┆ 687e-02 │\n",
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"│ ┆ n ┆ ┆ value set ┆ ┆ s\\servici ┆ ┆ value ┆ 588e-02 │\n",
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"│ ┆ ┆ ┆ Regist… ┆ ┆ dInst… ┆ ┆ RuleName: ┆ 4e-02… │\n",
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"│ 1023 ┆ informatio ┆ 10 ┆ Process ┆ … ┆ C:\\Progra ┆ ┆ Process ┆ [ 4.76494 │\n",
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"│ 1025 ┆ informatio ┆ 22 ┆ Dns query ┆ … ┆ ┆ ┆ Dns ┆ [-2.38244 │\n",
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"│ ┆ ┆ ┆ DnsQuery) ┆ ┆ ┆ ┆ RuleName: ┆ 7.2345621 │\n",
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]
}
]
},
{
"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": null,
"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": 31,
"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": null,
"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": null,
"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": null,
"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(\"For Standard Scaler\")\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": "652570a0-2de1-40c2-aaf5-a106d87ff2c2"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of features before PCA: 30000\n",
"Number of features after PCA: 16\n",
"Explained variance ratio of each component: [0.7322417 0.10258386 0.01919063 0.01368955 0.01286633 0.01047001\n",
" 0.00869492 0.00842645 0.00745646 0.00658019 0.00584541 0.00514998\n",
" 0.00491944 0.00477372 0.00384961 0.00355314]\n",
"Total explained variance: 0.9502913951873779\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import RobustScaler\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 = RobustScaler()\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(\"For Robust Scaler:\")\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}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eK8wgP0IRPnr",
"outputId": "8758e5dd-9616-4faf-9fd5-9e87c9625784"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"For Robust Scaler:\n",
"Number of features before PCA: 30000\n",
"Number of features after PCA: 12\n",
"Explained variance ratio of each component: [0.780261 0.10317536 0.0133233 0.00962587 0.00900306 0.00735111\n",
" 0.00611507 0.00592202 0.00531727 0.00460212 0.00413254 0.00365149]\n",
"Total explained variance: 0.952480137348175\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.preprocessing import QuantileTransformer\n",
"from sklearn.decomposition import PCA\n",
"import numpy as np\n",
"\n",
"# Assuming X is your feature matrix\n",
"X = linformer_vectors\n",
"\n",
"# Initialize and fit QuantileTransformer\n",
"qt = QuantileTransformer(n_quantiles=1000, output_distribution='normal')\n",
"X_scaled = qt.fit_transform(X)\n",
"\n",
"# Apply PCA\n",
"pca = PCA(n_components=0.95)\n",
"X_pca = pca.fit_transform(X_scaled)\n",
"\n",
"# Output 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(\"For QuantileTransformer:\")\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}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GNpC0dqQR0c1",
"outputId": "ecfdbd8a-f243-4d20-b52f-f71e2d88f372"
},
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"For QuantileTransformer:\n",
"Number of features before PCA: 30000\n",
"Number of features after PCA: 44\n",
"Explained variance ratio of each component: [0.5520516 0.06681303 0.03466679 0.02603397 0.02438432 0.01991471\n",
" 0.01807965 0.01676909 0.01452703 0.01407685 0.01204457 0.01079712\n",
" 0.01011274 0.00953582 0.00818911 0.00781149 0.00750947 0.00684127\n",
" 0.00666759 0.00635126 0.0060087 0.00576267 0.00530005 0.00508402\n",
" 0.0044879 0.00436854 0.00426688 0.0040915 0.00363791 0.00351764\n",
" 0.00315828 0.00294013 0.00270898 0.00254636 0.0024569 0.00226332\n",
" 0.0022466 0.00212357 0.00194851 0.00179851 0.0016702 0.00165001\n",
" 0.00159225 0.00152319]\n",
"Total explained variance: 0.9503300786018372\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": null,
"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": null,
"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 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": null,
"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": null,
"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": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
],
"image/png": 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s448/XpGRkUpMTFSzZs00a9asCsdRBhsyHn/8cf32229KT09Xnz59NGnSpICA5ftZKxpn27lz53LvRWRkZLmurAkJCdWOfa7sOuHh4Wrbtm256wSjNr/3tLS0cgG+S5cuAW288MIL1b9/f1111VVq0aKFLrroIr399ttBhfNg3p81a9YoNzdXzZs3V7NmzQIeBQUF5SZmq8iwYcP02Wefad++fVq0aJHGjx+vTZs26cwzzwzq9RX9TUdGRio5Obnc9mDGtR/s22+/1eDBgxUVFaX4+Hg1a9bMPwa/okBeV5dffrmKi4v9XaZXrVqlZcuW6bLLLqvzuQsKCiRV/cHP//3f/yk6Olp9+vRRhw4dNH78+KCGOByopu9Dhw4dAr5v166dwsLCQr4s3ME2bdpU4b8XB99HPgf/rSUkJEja/+9kZmambrnlFr3wwgtKTk7WsGHDNGPGDMaPA2g0BHIAqKHY2FilpaXVeDZti8US1HFWq7VG242DJggLxtdff62zzjpLkZGRmjlzpubNm6f58+frkksuqfB8B1Ygq3LBBRdo/fr1evrpp5WWlqYnnnhC3bp181eDa6qyn7kxtG/fXjabTStWrAjpeR0OhxYtWqQvvvhCl112mZYvX64LL7xQQ4YMqXbyumDeH6/Xq+bNm2v+/PkVPiZPnhx0W51Op0488URNnz5d99xzj/bu3RvU77aidobq73ndunU69dRTtWvXLk2dOlWffPKJ5s+fr5tvvlmSyn2wEezfclW6du2q4447Tq+//rok6fXXX1d4eLguuOCCOp/b9+9KVUvwdenSRatWrdK///1vDRgwQO+8844GDBhQbp6IqtT1fTj437PK/n0LdgLGUAnm72rKlClavny57rrrLhUXF+uGG25Qt27d6jRPAgDUFoEcAGrhzDPP1Lp167R48eJqj23Tpo28Xq/WrFkTsH3Hjh3at2+f2rRpE9K2eb3ect1+fTM2+yZueueddxQZGanPPvtMV155pU4//XQNHjw4JNdPTU3VuHHj9P7772vDhg1KSkrSQw89JEn+n3XVqlXlXrdq1aqQvReVXcflcmnDhg21uo7T6dQpp5yiRYsWacuWLUG1ISsrq1xF/c8//wxoo2T2ajj11FM1depU/fHHH3rooYf05ZdfVtudPBjt2rXT7t271b9/fw0ePLjco0ePHrU6b69evSRJ2dnZdW5jXXz00UcqLS3Vhx9+qGuvvVZnnHGGBg8eHPLAebDLL79cX375pbKzs/XGG29o+PDh/mpsXbz22muyWCwaMmRIlcdFRUXpwgsv1OzZs7V582YNHz7cP+FZMO2vqYP//Vq7dq28Xq//3xTfz37wxGgV9UapSdvatGlT4b8XFd1HNdG9e3fdc889WrRokb7++mtt27ZNzzzzTK3OBQB1QSAHgFq4/fbbFRUVpauuuko7duwot3/dunV66qmnJElnnHGGJHNG7wNNnTpVkjl+OtSmT5/u/9owDE2fPl12u12nnnqqJLOKZLFYAqpXGzdu1Pvvv1/ra3o8nnLdPps3b660tDT/8m69evVS8+bN9cwzzwQs+fbf//5XK1euDNl7MXjwYIWHh+tf//pXQGXsxRdfVG5ubq2vM3HiRBmGocsuu8zftfhAy5Yt0yuvvCLJ/L17PJ6A34VkzixtsVh0+umnS5L27NlT7jy+mb8PXhavNi644AJ5PB498MAD5faVlZVVO7P0ggULKtzuGztcl2W+QsFXET3w95ybm6vZs2fX6bxRUVGSygdMn4svvlgWi0U33nij1q9fX+fZ1SVzhvDPP/9cF154Ybku4gfavXt3wPfh4eHq2rWrDMPwz5pfXftr6uAZ659++mlJ8v8dx8bGKjk5udws+TNnzix3rpq07YwzztAPP/wQ8OFnYWGhnnvuOWVkZNRoHLxkzgVRVlYWsK179+4KCwsLyf0GADVla+wGAMDhqF27dnrjjTd04YUXqkuXLrr88st11FFHyeVy6bvvvtPcuXP9a+n26NFDo0eP1nPPPad9+/Zp4MCB+uGHH/TKK6/o7LPP1qBBg0LatsjISH366acaPXq0+vbtq//+97/65JNPdNddd/nHGw8fPlxTp07VaaedpksuuUQ5OTmaMWOG2rdvr+XLl9fquvn5+WrVqpXOO+889ejRQ9HR0friiy+0dOlS//rSdrtdjz32mK644goNHDhQF198sXbs2OFfdsnXzbiumjVrpjvvvFP333+/TjvtNJ111llatWqVZs6cqd69e9c6PJ1wwgmaMWOGxo0bp86dO+uyyy5Thw4dlJ+fr4ULF+rDDz/Ugw8+KEkaMWKEBg0apLvvvlsbN25Ujx499Pnnn+uDDz7QTTfdpHbt2kmSJk+erEWLFmn48OFq06aNcnJyNHPmTLVq1SpgIqvaGjhwoK699lo98sgj+uWXXzR06FDZ7XatWbNGc+fO1VNPPaXzzjuv0tePHDlSmZmZGjFihNq1a6fCwkJ98cUX+uijj9S7d2+NGDGizm2si6FDhyo8PFwjRozQtddeq4KCAj3//PNq3rx5nar3PXv2lNVq1WOPPabc3FxFRET41zqXzL+x0047TXPnzlV8fHyNPuQpKyvzd3cvKSnRpk2b9OGHH2r58uUaNGhQtcv6DR06VCkpKerfv79atGihlStXavr06Ro+fLh/7Plxxx0nSbr77rt10UUXyW63a8SIEf4wXFMbNmzQWWedpdNOO02LFy/W66+/rksuuSSgh8VVV12lRx99VFdddZV69eqlRYsWBayn7lOTtt1xxx168803dfrpp+uGG25QYmKiXnnlFW3YsEHvvPNOwKSNwfjyyy913XXX6fzzz1fHjh1VVlam1157TVarVeeee24N3xUACIFGmt0dAI4Iq1evNq6++mojIyPDCA8PN2JiYoz+/fsbTz/9tFFSUuI/zu12G/fff7+RmZlp2O12Iz093bjzzjsDjjEMc8mf4cOHl7uOpHLLifmWGXriiSf823xLKq1bt84YOnSo4XQ6jRYtWhgTJ04st0zViy++aHTo0MGIiIgwOnfubMyePbvCJaUquvaB+3zLGZWWlhq33Xab0aNHDyMmJsaIiooyevToYcycObPc69566y3jmGOOMSIiIozExERj1KhRxtatWwOOqWx5qIraWJnp06cbnTt3Nux2u9GiRQvjH//4h7F3794Kz1fdsmcHWrZsmXHJJZcYaWlpht1uNxISEoxTTz3VeOWVVwLe5/z8fOPmm2/2H9ehQwfjiSeeMLxer/+YBQsWGCNHjjTS0tKM8PBwIy0tzbj44ouN1atX+4+pbNmzmrw/zz33nHHccccZDofDiImJMbp3727cfvvtRlZWVpU/65tvvmlcdNFFRrt27QyHw2FERkYaXbt2Ne6++24jLy8v4FhVsmTWwe9tZW0fOHBgwHJewS579uGHHxpHH320ERkZaWRkZBiPPfaY8dJLL5VbWquy+8u378DltgzDMJ5//nmjbdu2htVqrXAJtLfffrvcsm7V8S1X53s4nU4jIyPDOPfcc43//Oc/FS4nd/CyZ88++6xx0kknGUlJSUZERITRrl0747bbbjNyc3MDXvfAAw8YLVu2NMLCwgLei2DvacPY/37/8ccfxnnnnWfExMQYCQkJxnXXXRewdKFhmMvPjR071oiLizNiYmKMCy64wMjJySl3zqraVtHvYd26dcZ5551nxMfHG5GRkUafPn2Mjz/+OOAY37JnBy9ndvDf0Pr1640rr7zSaNeunREZGWkkJiYagwYNMr744osK3w8AqG8Ww6jFbEAAgEPSmDFj9J///KfC7tQAQuuDDz7Q2WefrUWLFvmX1wIAoCYYQw4AAFALzz//vNq2bRuSoQUAgKaJMeQAAAA18O9//1vLly/XJ598oqeeeirkM5oDAJoOAjkAAEANXHzxxYqOjtbYsWM1bty4xm4OAOAwxhhyAAAAAAAaAWPIAQAAAABoBARyAAAAAAAawRE/htzr9SorK0sxMTFMugIAAAAAqHeGYSg/P19paWkKC6u8Dn7EB/KsrCylp6c3djMAAAAAAE3Mli1b1KpVq0r3H/GBPCYmRpL5RsTGxjZyaw59brdbn3/+uYYOHSq73d7YzQEaDfcCYOJeAEzcC4CJeyE4eXl5Sk9P9+fRyhzxgdzXTT02NpZAHgS32y2n06nY2FhuMDRp3AuAiXsBMHEvACbuhZqpbtg0k7oBAAAAANAICOQAAAAAADQCAjkAAAAAAI3giB9DDgAAAODQZRiGysrK5PF4GrspCILb7ZbNZlNJSUmT/p1ZrVbZbLY6L61NIAcAAADQKFwul7Kzs1VUVNTYTUGQDMNQSkqKtmzZUucwerhzOp1KTU1VeHh4rc9BIAcAAADQ4LxerzZs2CCr1aq0tDSFh4c3+YB3OPB6vSooKFB0dLTCwprmCGjDMORyubRz505t2LBBHTp0qPV7QSAHAAAA0OBcLpe8Xq/S09PldDobuzkIktfrlcvlUmRkZJMN5JLkcDhkt9u1adMm//tRG033HQQAAADQ6JpyqMPhLRR/u/z1AwAAAADQCOiyDgAAAOCQsnmztGtXw10vOVlq3brhrgf4EMgBAAAAHDI2b5a6dJEacuJ1p1NaufLwC+V//vmnxowZo19++UWdO3fWL7/80thNOqRNmjRJ77//ftDv08aNG5WZmamff/5ZPXv2rJc2EcgBAAAAHDJ27TLD+F13SW3a1P/1Nm2SHn7YvG6wgXzMmDHat2+f3n///XptW3UmTpyoqKgorVq1StHR0Y3alrrKyMjQpk2b9Oabb+qiiy4K2NetWzf98ccfmj17tsaMGdM4DawnBHIAAAAAh5w2baSOHRu7FYe2devWafjw4WpTh08uXC5XndbRrim32y273V7hvvT0dM2ePTsgkH///ffavn27oqKiGqqJDYpJ3QAAAAAghL766iv16dNHERERSk1N1R133KGysjL//pNPPlk33HCDbr/9diUmJiolJUWTJk0KOMeff/6pAQMGKDIyUl27dtUXX3whi8Xir8pbLBYtW7ZMkydPlsVi8b9+xYoVOuWUU+RwOJSUlKRrrrlGBQUF/vOOGTNGZ599th566CGlpaWpU6dO2rhxoywWi95++22deOKJcjgc6t27t1avXq2lS5eqV69eio6O1umnn66dO3cGtPOFF15Qly5dFBkZqc6dO2vmzJn+fb7zvvXWWxo4cKAiIyM1Z86cSt+3UaNG6auvvtKWLVv821566SWNGjVKNltgLXnz5s0aOXKkoqOjFRsbqwsuuEA7duwIOObRRx9VixYtFBMTo7Fjx6qkpKTcNatqf0MgkAMAAABAiGzbtk1nnHGGevfurV9//VWzZs3Siy++qAcffDDguFdeeUVRUVFasmSJHn/8cU2ePFnz58+XJHk8Hp199tlyOp1asmSJnnvuOd19990Br8/Ozla3bt106623Kjs7WxMmTFBhYaGGDRumhIQELV26VHPnztUXX3yh6667LuC1CxYs0KpVqzR//nx9/PHH/u0TJ07UPffco59++kk2m02XXHKJbr/9dj311FP6+uuvtXbtWk2cONF//Jw5c3TffffpoYce0sqVK/Xwww/r3nvv1SuvvBJwvTvuuEM33nijVq5cqWHDhlX63rVo0ULDhg3zv76oqEhvvfWWrrzyyoDjvF6vRo4cqT179uirr77S/PnztX79el144YX+Y95++21NmjRJDz/8sH788UelpqaWC9vBtr8+0WUdAAAAAEJk5syZSk9P1/Tp02WxWNS5c2dlZWXp//7v/3Tffff5164++uij/eG2Q4cOmj59uhYsWKAhQ4Zo/vz5WrdunRYuXKiUlBRJ0kMPPaQhQ4b4r5OSkiKbzabo6Gj/Mc8//7xKSkr06quv+rt4T58+XSNGjNBjjz2mFi1aSJKioqL0wgsv+Luqb9y4UZI0YcIEf2C+8cYbdfHFF2vBggXq37+/JGns2LF6+eWX/W2YOHGipkyZonPOOUeSlJmZqT/++EPPPvusRo8e7T/upptu8h9TnSuvvFK33nqr7r77bv3nP/9Ru3btyk2otmDBAq1YsUIbNmxQenq6JOnVV19Vt27dtHTpUvXu3VvTpk3T2LFjNXbsWEnSgw8+qC+++CKgSh5s++sTFXIAAAAACJGVK1eqX79+slgs/m39+/dXQUGBtm7d6t929NFHB7wuNTVVOTk5kqRVq1YpPT3dH7QlqU+fPkFdu0ePHgHjrfv37y+v16tVq1b5t3Xv3r3CceMHtskX3rt37x6wzdfGwsJCrVu3TmPHjlV0dLT/8eCDD2rdunUB5+3Vq1e1bfcZPny4CgoKtGjRIr300kvlquO+nzM9Pd0fxiWpa9euio+P18qVK/3H9O3bN+B1/fr1839dk/bXJyrkAAAAANDADp7YzGKxyOv1Nsi1K5sg7cA2+T5QOHibr42+cenPP/98ueBrtVqDul5FbDabLrvsMk2cOFFLlizRe++9F/Rra6Im7a9PVMgBAAAAIES6dOmixYsXyzAM/7Zvv/1WMTExatWqVVDn6NSpk7Zs2RIwSdnSpUuDuvavv/6qwsLCgGuHhYWpU6dONfgpKubxSL4fq0WLFkpLS9P69evVvn37gEdmZmadrnPllVfqq6++0siRI5WQkFBuf5cuXbRly5aAyd/++OMP7du3T127dvUfs2TJkoDXff/99/6v67P9NUGFHAAAAMAhZ9OmQ/s6ubm5+uWXXwK2JSUlady4cZo2bZquv/56XXfddVq1apUmTpyoW265xT9+vDpDhgxRu3btNHr0aD3++OPKz8/XPffcI0kBXeEPNmrUKE2cOFGjR4/WpEmTtHPnTl1//fW67LLL/F3Q62LfPunAIv7999+vG264QXFxcTrttNNUWlqqH3/8UXv37tUtt9xS6+t06dJFu3btktPprHD/4MGD1b17d40aNUrTpk1TWVmZxo0bp4EDB/q7x994440aM2aMevXqpf79+2vOnDn6/fff1bZt23pvf00QyAEAAAAcMpKTJadTevjhhrum02letyYWLlyoY445JmDb2LFj9cILL2jevHm67bbb1KNHDyUmJmrs2LH+QB0Mq9Wq999/X1dddZV69+6ttm3b6oknntCIESMUGRlZxc/h1GeffaYbb7xRvXv3ltPp1LnnnqupU6fW7IerRHFx4PdXXXWVnE6nnnjiCd12222KiopS9+7dddNNN9X5WklJSZXus1gs+uCDD3T99dfrpJNOUlhYmE477TQ9/fTT/mMuvPBCrVu3TrfffrtKSkp07rnn6h//+Ic+++yzBml/sCzGgX0pjkB5eXmKi4tTbm6uYmNjG7s5hzy326158+bpjDPOKDeuBWhKuBcAE/cCYOJeCL2SkhJt2LBBmZmZ5ULm5s3Srl0N15bkZKl164a7Xm18++23GjBggNauXat27do1+PVLS6UVK6SwMK/ats1TbGxs0BX/I1VVf8PB5lAq5AAAAAAOKa1bH/oBub699957io6OVocOHbR27VrdeOON6t+/f6OEcUnKzW2Uyx7xCOQAAAAAcIjJz8/X//3f/2nz5s1KTk7W4MGDNWXKlEZrT15eo136iEYgBwAAAIBDzOWXX67LL7+8sZshyZzILS/PHGtfUtLYrTmyNO1O/wAAAACAKpWVmaHc4Wjslhx5COQAAAAAgEr5ljqzWhu3HUciAjkAAAAAoFK+dbkI5KFHIAcAAAAAVIoKef0hkAMAAAAAKkUgrz/Msg4AAADgkLI5d7N2Fe1qsOslO5PVOq6JL3xeBQJ5/SGQAwAAADhkbM7drC4zuqjIXdRg13TanVo5fmXIQrnFYtF7772ns88+O+jXvP/++5owYYI2bNig66+/XtOmTQtJW0LBF8gtFvNxKDj55JPVs2fPoN+nl19+WTfddJP27dtXr+2qKQI5AAAAgEPGrqJdKnIX6a4T71KbuDb1fr1NuZv08NcPa1fRrqAD+ZgxY7Rv3z69//77Fe7Pzs5WQkJCjdpx7bXX6oorrtANN9ygmJiYGr22vvkCeViY+ajKxo0blZmZqbCwMG3evFktW7b078vOzlZ6ero8Ho82bNigjIyM+mv0YYJADgAAAOCQ0yaujTomdWzsZtRKSkpKjY4vKChQTk6Ohg0bprS0tFpf1+VyKTw8vNavr4xvlnWLZX8g93g8kqSwShJ6y5Yt9eqrr+rOO+/0b3vllVfUsmVLbd68OeRtPFwxqRsAAAAAhJDFYvFXzzdu3CiLxaJ3331XgwYNktPpVI8ePbR48WJJ0sKFC/0V8VNOOUUWi0ULFy6UJL3zzjvq1q2bIiIilJGRoSlTpgRcJyMjQw888IAuv/xyxcbG6pprrtHLL7+s+Ph4ffzxx+rUqZOcTqfOO+88FRUV6ZVXXlFGRoYSEhJ0ww03+EO1JJWWlmrChAlq2bKloqKi1LdvX387vF7p449fVuvW8Vq48EMdf/zxcjgcVQbr0aNHa/bs2QHbZs+erdGjR5c79quvvlKfPn0UERGh1NRU3XHHHSorK/PvLyws1OWXX67o6GilpqaWex+qa/+hjEAOAAAAAPXs7rvv1oQJE/TLL7+oY8eOuvjii1VWVqYTTjhBq1atkmQG8OzsbJ1wwglatmyZLrjgAl100UVasWKFJk2apHvvvVcvv/xywHn/+c9/qkePHvr555917733SpKKior0r3/9S//+97/16aefauHChfrb3/6mefPmad68eXrttdf07LPP6j//+Y//PNddd50WL16sf//731q+fLnOP/98nXbaaVqzZo2/y3pRUZFeeukJPfXUU1qxYoWaN29e6c971llnae/evfrmm28kSd9884327t2rESNGBBy3bds2nXHGGerdu7d+/fVXzZo1Sy+++KIefPBB/zG33XabvvrqK33wwQf6/PPPtXDhQv30008B56mq/YcyuqwDAAAAQD2bMGGChg8fLkm6//771a1bN61du1adO3f2B9vExER/d/epU6fq1FNP9Yfsjh076o8//tATTzyhMWPG+M97yimn6NZbb/V///XXX8vtdmvWrFlq166dJOm8887Ta6+9ph07dig6Olpdu3bVoEGD9L///U8XXnihNm/erNmzZ2vz5s3+LvMTJkzQp59+qtmzZ+u66x5WWJjkdrt1333T1bdvpmJjYyvtri5Jdrtdl156qV566SUNGDBAL730ki699FLZ7faA42bOnKn09HRNnz5dFotFnTt3VlZWlv7v//5P9913n4qKivTiiy/q9ddf16mnnirJ7PreqlUr/zmqa//DDz9c819YAyGQAwAAAEA9O/roo/1fp6amSpJycnLUuXPnCo9fuXKlRo4cGbCtf//+mjZtmjwej6x/rUHWq1evcq91Op3+MC5JLVq0UEZGhqKjowO25eTkSJJWrFghj8ejjh0Dx+yXlpYqKSnJP4Y8PDxcnTsfLSk/qJ/5yiuv1AknnKCHH35Yc+fO1eLFiwO6ovt+zn79+slywPTt/fv3V0FBgbZu3aq9e/fK5XKpb9++/v2JiYnq1KmT//vq2n8oI5ADAAAAQD07sDLsC59eX1/wOoiKiqryWr7rVbTNd/2CggJZrVYtW7bMH/R9oqOj5XabE7o5HA5ZrcGve9a9e3d17txZF198sbp06aKjjjpKv/zyS9CvD1Z17T+UEcgBAAAA4BDTpUsXffvttwHbvv32W3Xs2LFc6KyrY445Rh6PRzk5OTrxxBPL7d+0af/64zVdh/zKK6/UuHHjNGvWrAr3d+nSRe+8844Mw/B/UPHtt98qJiZGrVq1UmJioux2u5YsWaLWrc1l6fbu3avVq1dr4MCBQbX/UEYgBwAAAHDI2ZS76ZC+Tm5ubrlqb1JSktLT00PQKunWW29V79699cADD+jCCy/U4sWLNX36dM2cOTMk5z9Qx44dNWrUKF1++eWaMmWKjjnmGO3cuVMLFizQ0Ucfra5dh/uDeHXrkB/s6quv1vnnn6/4+PgK948bN07Tpk3T9ddfr+uuu06rVq3SxIkTdcsttygsLEzR0dEaO3asbrvtNiUlJal58+a6++67A8avV9d+39j9QxGBHAAAAMAhI9mZLKfdqYe/briJuJx2p5KdyTV6zcKFC3XMMccEbBs7dqxeeOGFkLTp2GOP1dtvv6377rtPDzzwgFJTUzV58uSACd1Cafbs2XrwwQd16623atu2bUpOTtbxxx+vM888Uwf2rK9phdxmsyk5ufL3tmXLlpo3b55uu+029ejRQ4mJiRo7dqzuuece/zFPPPGECgoKNGLECMXExOjWW29Vbm5u0O0/lFkMwzdE/8iUl5enuLg45ebmKjY2trGbc8hzu92aN2+ezjjjjHLjTICmhHsBMHEvACbuhdArKSnRhg0blJmZqcjIyIB9m3M3a1fRrgZrS7IzWa3jWjfY9Q43a9dKbrfUqpW0e7dX8fF51c6y3hRU9TccbA6lQg4AAADgkNI6rjUB+RDi9e7vql7TCjmq1rQ/0gAAAAAAVMnrVa3HkKNqvJ0AAAAAgEodGMipkIcWgRwAAAAAUKkDu6z7nkOwhDpEIAcAAADQiI7wOaaPCBVVyAnkofnbJZADAAAAaHC+2eqLiooauSWojmGUH0NOIN//t1uXlReYZR0AAABAg7NarYqPj1dOTo4kyel0ysIA5UOSx2MGcJdLKivzymJxyTBK1FTru4ZhqKioSDk5OYqPj5fVaq31uQjkAAAAABpFSkqKJPlDOQ5NOTlScbGUlyeVlRkKCyuW1epQRETT/gAlPj7e/zdcWwRyAAAAAI3CYrEoNTVVzZs3l9vtbuzmoAJer3T66dKVV0onnyzt2OGW07lIUVEn6fjja99V+3Bnt9vrVBn3IZADAAAAaFRWqzUk4QahV1Qkbdpkdlf3es3fVVlZmfLzIxUZ2XQDeag0aqd/j8eje++9V5mZmXI4HGrXrp0eeOCBgNnqDMPQfffdp9TUVDkcDg0ePFhr1qxpxFYDAAAAQNNQXGw+R0SYz+HhgdtRN40ayB977DHNmjVL06dP18qVK/XYY4/p8ccf19NPP+0/5vHHH9e//vUvPfPMM1qyZImioqI0bNgwlZSUNGLLAQAAAODId3Agj4w0nwsLG6c9R5pG7bL+3XffaeTIkRo+fLgkKSMjQ2+++aZ++OEHSWZ1fNq0abrnnns0cuRISdKrr76qFi1a6P3339dFF11U7pylpaUqLS31f5+XlydJcrvdjEsJgu894r1CU8e9AJi4FwAT9wKaqoICyeEwA7lhSGFh5j1QXOwWt0Plgv23wmKEYjXzWnr44Yf13HPP6fPPP1fHjh3166+/aujQoZo6dapGjRql9evXq127dvr555/Vs2dP/+sGDhyonj176qmnnip3zkmTJun+++8vt/2NN96Q0+mszx8HAAAAAAAVFRXpkksuUW5urmJjYys9rlEr5HfccYfy8vLUuXNnWa1WeTwePfTQQxo1apQkafv27ZKkFi1aBLyuRYsW/n0Hu/POO3XLLbf4v8/Ly1N6erqGDh1a5RsBk9vt1vz58zVkyJA6LXAPHO64FwAT9wJg4l5AU/Xjj9Kpp0rTp0uZmZJhuJWfP1/Llw/RHXdwL1TG11O7Oo0ayN9++23NmTNHb7zxhrp166ZffvlFN910k9LS0jR69OhanTMiIkIRvgEOB7Db7fzjWQO8X4CJewEwcS8AJu4FNDWlpeY48vBwyXLAsuOFhdwLVQn2vWnUQH7bbbfpjjvu8I8F7969uzZt2qRHHnlEo0eP9i+yvmPHDqWmpvpft2PHjoAu7AAAAACA0Dt4Ujcfl6vh23IkatRZ1ouKihQWFtgEq9Uqr9crScrMzFRKSooWLFjg35+Xl6clS5aoX79+DdpWAAAAAGhqfItb+ZY78yGQh0ajVshHjBihhx56SK1bt1a3bt30888/a+rUqbryyislSRaLRTfddJMefPBBdejQQZmZmbr33nuVlpams88+uzGbDgAAAABHvMoq5MywHhqNGsiffvpp3XvvvRo3bpxycnKUlpama6+9Vvfdd5//mNtvv12FhYW65pprtG/fPg0YMECffvqpIn0L4AEAAAAA6oUvkB9cISeQh0ajBvKYmBhNmzZN06ZNq/QYi8WiyZMna/LkyQ3XMAAAAACAf0K3g0Ya02U9RBp1DDkAAAAA4NBVXFy+u7oklZU1fFuORARyAAAAAECFKgvkVMhDg0AOAAAAAKiQr8v6wQjkoUEgBwAAAABUqLIKOZO6hQaBHAAAAABQocoq5ATy0CCQAwAAAAAqVFLCGPL6RCAHAAAAAFSICnn9IpADAAAAACpEIK9fBHIAAAAAQIVKSyW7vfx2uqyHBoEcAAAAAFAhl6viQE6FPDQI5AAAAACACrlcks1WfjuBPDQI5AAAAACAChHI6xeBHAAAAABQIbebMeT1iUAOAAAAAKhQZRXysrKGb8uRiEAOAAAAAKgQk7rVLwI5AAAAAKBCLpdktZbfXlYmGUbDt+dIQyAHAAAAAFSosgq5RJU8FAjkAAAAAIAKVTaG3LcPdUMgBwAAAABUqKyMCnl9IpADAAAAACpEhbx+EcgBAAAAAOUYBoG8vhHIAQAAAADl+NYaryyQ02W97gjkAAAAAIByfBXwysaQUyGvOwI5AAAAAKAcXwWcCnn9IZADAAAAAMqhQl7/COQAAAAAgHJ8gZsKef0hkAMAAAAAyqkukFMhrzsCOQAAAACgnOq6rFMhrzsCOQAAAACgnOomdaNCXncEcgAAAABAOXRZr38EcgAAAABAOUzqVv8I5AAAAACAclj2rP4RyAEAAAAA5VAhr38EcgAAAABAOb7ATYW8/hDIAQAAAADlVFUht9upkIcCgRwAAAAAUE5Vgdxmo0IeCgRyAAAAAEA5VU3qRiAPDQI5AAAAAKAcX+C2Wsvvs9nosh4KBHIAAAAAQDlutxnGKwvkVMjrjkAOAAAAACjH5ap8yTMmdQsNAjkAAAAAoByXq/Ilz6xWKuShQCAHAAAAAJRTVYWcMeShQSAHAAAAAJRTVYWcMeShQSAHAAAAAJRTXYWcQF53BHIAAAAAQDluN13W6xuBHAAAAABQDpO61T8COQAAAACgHCZ1q38EcgAAAABAOVUFcirkoUEgBwAAAACUU1WXdbudQB4KBHIAAAAAQDlut1kJrwgV8tAgkAMAAAAAymEMef0jkAMAAAAAymEd8vpHIAcAAAAAlFNaWvkYcgJ5aBDIAQAAAADlVFUht9vpsh4KBHIAAAAAQDlud+WBPCyMCnkoEMgBAAAAAOVQIa9/BHIAAAAAQDlVrUPOGPLQIJADAAAAAMph2bP6RyAHAAAAAJRTVYXcaqVCHgoEcgAAAABAOVVN6kaFPDQI5AAAAACAcuiyXv8I5AAAAACAcqqb1K2sTDKMhm3TkYZADgAAAAAop6ou676gTpW8bgjkAAAAAIByquqybrXuPwa1RyAHAAAAAAQwjOondZOokNcVgRwAAAAAEMDjMUN5VWPIJSrkdUUgBwAAAAAE8AXt6irkBPK6IZADAAAAAAL4gnZlFXLfGHK6rNcNgRwAAAAAEMAXyH3B+2C+oE6FvG4I5AAAAACAAL7KNxXy+kUgBwAAAAAECHYMOYG8bgjkAAAAAIAA1Y0h9wXysrKGac+RikAOAAAAAAhQXYU87K8kSYW8bgjkAAAAAIAAdFlvGARyAAAAAECAYJc9o8t63RDIAQAAAAABfJXvyirkzLIeGgRyAAAAAECAYCd1I5DXDYEcAAAAABAg2End6LJeNwRyAAAAAEAAJnVrGARyAAAAAECAYAM5FfK6IZADAAAAAAIwqVvDIJADAAAAAAL4gnZlk7r5xpATyOuGQA4AAAAACODrsh5WSWK0WMzqOV3W64ZADgAAAAAI4HabgdtiqfwYm40KeV0RyAEAAAAAAdzuyrur+xDI645ADgAAAAAI4HJVPqGbj9VKl/W6IpADAAAAAAL4uqxXhQp53RHIAQAAAAABXK7qu6xbrQTyuiKQAwAAAAACuN371xqvDF3W667RA/m2bdt06aWXKikpSQ6HQ927d9ePP/7o328Yhu677z6lpqbK4XBo8ODBWrNmTSO2GAAAAACObHRZbxiNGsj37t2r/v37y26367///a/++OMPTZkyRQkJCf5jHn/8cf3rX//SM888oyVLligqKkrDhg1TSUlJI7YcAAAAAI5cwXRZJ5DXXTWfedSvxx57TOnp6Zo9e7Z/W2Zmpv9rwzA0bdo03XPPPRo5cqQk6dVXX1WLFi30/vvv66KLLmrwNgMAAADAkY4u6w2jUQP5hx9+qGHDhun888/XV199pZYtW2rcuHG6+uqrJUkbNmzQ9u3bNXjwYP9r4uLi1LdvXy1evLjCQF5aWqrS0lL/93l5eZIkt9stNx/fVMv3HvFeoanjXgBM3AuAiXsBTY1hSNHR5nPgdrf/2ek093NblBfsvxUWwzj4LW44kZGRkqRbbrlF559/vpYuXaobb7xRzzzzjEaPHq3vvvtO/fv3V1ZWllJTU/2vu+CCC2SxWPTWW2+VO+ekSZN0//33l9v+xhtvyOl01t8PAwAAAACApKKiIl1yySXKzc1VbGxspcc1aiAPDw9Xr1699N133/m33XDDDVq6dKkWL15cq0BeUYU8PT1du3btqvKNgMntdmv+/PkaMmSI7NUNGgGOYNwLgIl7ATBxL6CpGTtWWr1aeuSRwO2G4VZ+/nzFxAzRHXfY1b279MwzjdPGQ1leXp6Sk5OrDeSN2mU9NTVVXbt2DdjWpUsXvfPOO5KklJQUSdKOHTsCAvmOHTvUs2fPCs8ZERGhiIiIctvtdjv/eNYA7xdg4l4ATNwLgIl7AU1FcbHk9UoWS8X7LRa7ysrsKi6ufvK3pijYfycadZb1/v37a9WqVQHbVq9erTZt2kgyJ3hLSUnRggUL/Pvz8vK0ZMkS9evXr0HbCgAAAABNhcvFpG4NoVEr5DfffLNOOOEEPfzww7rgggv0ww8/6LnnntNzzz0nSbJYLLrpppv04IMPqkOHDsrMzNS9996rtLQ0nX322Y3ZdAAAAAA4YrEOecNo1EDeu3dvvffee7rzzjs1efJkZWZmatq0aRo1apT/mNtvv12FhYW65pprtG/fPg0YMECffvqpf0I4AAAAAEBouVzVB3KrlUBeV40ayCXpzDPP1JlnnlnpfovFosmTJ2vy5MkN2CoAAAAAaLpcLqmCqbkCEMjrrlHHkAMAAAAADj10WW8YBHIAAAAAQAC6rDcMAjkAAAAAIEAwFXJmWa87AjkAAAAAIABd1hsGgRwAAAAAEIAu6w2DQA4AAAAACBBshZwu63VDIAcAAAAABKDLesMgkAMAAAAAAgTbZZ0Ked0QyAEAAAAAAYKdZZ0Ked0QyAEAAAAAAcrK6LLeEAjkAAAAAIAAdFlvGARyAAAAAICf1yt5PFTIGwKBHAAAAADg5wvZVMjrH4EcAAAAAOBHIG84BHIAAAAAgF+wgZwu63VHIAcAAAAA+Llc5nMwFXLDMMebo3YI5AAAAAAAv5pUyCW6rdcFgRwAAAAA4FfTQE639dojkAMAAAAA/GrSZV0ikNcFgRwAAAAA4FeTWdYluqzXBYEcAAAAAOBX00BOhbz2COQAAAAAAL9gu6wzqVvdEcgBAAAAAH5M6tZwCOQAAAAAAD+6rDccAjkAAAAAwI8u6w2HQA4AAAAA8KNC3nAI5AAAAAAAP9YhbzgEcgAAAACAX00ndaPLeu0RyAEAAAAAfsyy3nAI5AAAAAAAP7qsNxwCOQAAAADAz+2WLJb9gbsydFmvOwI5AAAAAMDP7Zbs9uqPo0JedwRyAAAAAICfy1V9d3WJQB4KBHIAAAAAgJ/bHVwgp8t63RHIAQAAAAB+dFlvOARyAAAAAICfy1X9hG4SFfJQIJADAAAAAPyC7bJOhbzuCOQAAAAAAL9gu6z7lkYjkNcegRwAAAAA4Bdsl3XJrKTTZb32COQAAAAAAL9gu6xLVMjrikAOAAAAAPALdh1yyTyOQF57tQrk69evD3U7AAAAAACHgJpUyOmyXje1CuTt27fXoEGD9Prrr6ukpCTUbQIAAAAANBK3O/gx5HRZr5taBfKffvpJRx99tG655RalpKTo2muv1Q8//BDqtgEAAAAAGhhd1htOrQJ5z5499dRTTykrK0svvfSSsrOzNWDAAB111FGaOnWqdu7cGep2AgAAAAAaQE0ndaPLeu3VaVI3m82mc845R3PnztVjjz2mtWvXasKECUpPT9fll1+u7OzsULUTAAAAANAAmGW94dQpkP/4448aN26cUlNTNXXqVE2YMEHr1q3T/PnzlZWVpZEjR4aqnQAAAACABlBaSpf1hhLk2xxo6tSpmj17tlatWqUzzjhDr776qs444wyFhZn5PjMzUy+//LIyMjJC2VYAAAAAQD1zuyWHI7hjmWW9bmoVyGfNmqUrr7xSY8aMUWpqaoXHNG/eXC+++GKdGgcAAAAAaFhutxQTE9yxdFmvm1oF8vnz56t169b+iriPYRjasmWLWrdurfDwcI0ePTokjQQAAAAANIyazLLOpG51U6sx5O3atdOuXbvKbd+zZ48yMzPr3CgAAAAAQONgUreGU6tAbhhGhdsLCgoUGRlZpwYBAAAAABpPTSvkBPLaq1GX9VtuuUWSZLFYdN9998npdPr3eTweLVmyRD179gxpAwEAAAAADacmFXImdaubGgXyn3/+WZJZIV+xYoXCw8P9+8LDw9WjRw9NmDAhtC0EAAAAADQYuqw3nBoF8v/973+SpCuuuEJPPfWUYmNj66VRAAAAAIDGQZf1hlOrWdZnz54d6nYAAAAAAA4BNe2yTiCvvaAD+TnnnKOXX35ZsbGxOuecc6o89t13361zwwAAAAAADa+srGYV8tLS+m3PkSzoQB4XFyeLxeL/GgAAAABw5HG5JLs9uGNtNqmgoH7bcyQLOpAf2E2dLusAAAAAcOQxDMaQN6RarUNeXFysoqIi//ebNm3StGnT9Pnnn4esYQAAAACAhuXxmKE82Aq51cqyZ3VRq0A+cuRIvfrqq5Kkffv2qU+fPpoyZYpGjhypWbNmhbSBAAAAAICG4XKZz0zq1jBqFch/+uknnXjiiZKk//znP0pJSdGmTZv06quv6l//+ldIGwgAAAAAaBi+QF6TMeQE8tqrVSAvKipSTEyMJOnzzz/XOeeco7CwMB1//PHatGlTSBsIAAAAAGgYVMgbVq0Cefv27fX+++9ry5Yt+uyzzzR06FBJUk5OjmJjY0PaQAAAAABAw6hphZxJ3eqmVoH8vvvu04QJE5SRkaG+ffuqX79+ksxq+THHHBPSBgIAAAAAGgYV8oYV9LJnBzrvvPM0YMAAZWdnq0ePHv7tp556qv72t7+FrHEAAAAAgIbDGPKGVatALkkpKSlKSUkJ2NanT586NwgAAAAA0DhqUyFn2bPaq1UgLyws1KOPPqoFCxYoJydHXq83YP/69etD0jgAAAAAQMNhDHnDqlUgv+qqq/TVV1/psssuU2pqqiwWS6jbBQAAAABoYIwhb1i1CuT//e9/9cknn6h///6hbg8AAAAAoJHUpkLu8UiGIVGnrblazbKekJCgxMTEULcFAAAAANCIalMhl6iS11atAvkDDzyg++67T0VFRaFuDwAAAACgkdSmQi4xsVtt1arL+pQpU7Ru3Tq1aNFCGRkZsh/02/rpp59C0jgAAAAAQMOhQt6wahXIzz777BA3AwAAAADQ2EpLzeearEMuEchrq1aBfOLEiaFuBwAAAACgkdW0yzqBvG5qNYZckvbt26cXXnhBd955p/bs2SPJ7Kq+bdu2kDUOAAAAANBwfIHcNza8Or7jCOS1U6sK+fLlyzV48GDFxcVp48aNuvrqq5WYmKh3331Xmzdv1quvvhrqdgIAAAAA6pnLZVbHg13CjAp53dSqQn7LLbdozJgxWrNmjSIjI/3bzzjjDC1atChkjQMAAAAANBxfIA8WgbxuahXIly5dqmuvvbbc9pYtW2r79u11bhQAAAAAoOERyBtWrQJ5RESE8vLyym1fvXq1mjVrVudGAQAAAAAanssV/JJnEmPI66pWgfyss87S5MmT5f7rXbdYLNq8ebP+7//+T+eee25IGwgAAAAAaBhUyBtWrQL5lClTVFBQoGbNmqm4uFgDBw5U+/btFRMTo4ceeijUbQQAAAAANICaVsgJ5HVTq1nW4+LiNH/+fH377bf69ddfVVBQoGOPPVaDBw8OdfsAAAAAAA2ECnnDqnEg93q9evnll/Xuu+9q48aNslgsyszMVEpKigzDkCXY+fEBAAAAAIcUxpA3rBp1WTcMQ2eddZauuuoqbdu2Td27d1e3bt20adMmjRkzRn/729/qq50AAAAAgHpWWYV8rytHGwp/K7edCnnd1KhC/vLLL2vRokVasGCBBg0aFLDvyy+/1Nlnn61XX31Vl19+eUgbCQAAAACofxVVyD1Gme78bbhy3bv0Rp/VAfsI5HVTowr5m2++qbvuuqtcGJekU045RXfccYfmzJkTssYBAAAAABpORYH87S1TtCr/R20v2ah8976AfXRZr5saBfLly5frtNNOq3T/6aefrl9//bXOjQIAAAAANLyDA/nGwj/00sb71C32BEnShqLfA473HVtW1lAtPLLUKJDv2bNHLVq0qHR/ixYttHfv3jo3CgAAAADQ8A4M5B6jTI+tGqOk8BRd1voe2SzhWl+wPOB4KuR1U6NA7vF4ZKtiyj2r1aoyPhoBAAAAgMNSaen+Sd3e3jJVq/KX6aL02xVhdSgtsq02FAVO7MYY8rqp0aRuhmFozJgxioiIqHB/aWlprRvy6KOP6s4779SNN96oadOmSZJKSkp066236t///rdKS0s1bNgwzZw5s8oqPQAAAACgdnyzrG8qXKnZG+/Tyc3OV0ZUN0lSqqOtWSFvMdx/fFiY+SCQ106NAvno0aOrPaY2M6wvXbpUzz77rI4++uiA7TfffLM++eQTzZ07V3Fxcbruuut0zjnn6Ntvv63xNQAAAAAAVXO5JIdDmrHuZiWEt9BpKVf497V0tNf83PJZzGYjkNdWjQL57NmzQ96AgoICjRo1Ss8//7wefPBB//bc3Fy9+OKLeuONN3TKKaf4r9+lSxd9//33Ov7440PeFgAAAABoynwV8tUFy9Qv8UyFh+3vHZ0W2U5lhqvcawjktVejQF4fxo8fr+HDh2vw4MEBgXzZsmVyu90aPHiwf1vnzp3VunVrLV68uNJAXlpaGtB1Pi8vT5Lkdrvl5q+kWr73iPcKTR33AmDiXgBM3AtoKiwWyRZVIJenUC0iWskiw7+vlSNTjjCHJMkw9t8L0dGSx0MoP1Cw/1Y0aiD/97//rZ9++klLly4tt2/79u0KDw9XfHx8wPYWLVpo+/btlZ7zkUce0f33319u++effy6n01nnNjcV8+fPb+wmAIcE7gXAxL0AmLgXcKS75x7zebTe/GtL3v6dNumlo16SJOXn778XnnvOfJ43rwEaeJgoKioK6rhGC+RbtmzRjTfeqPnz5ysyMjJk573zzjt1yy23+L/Py8tTenq6hg4dqtjY2JBd50jldrs1f/58DRkyRHbf9IpAE8S9AJi4FwAT9wKaiqOPlloO+kTfpV6iuzq/rhh7QsD+tzc/rtvb/F0xMUNksZj3wpgx0pVXSnfd1QgNPkT5empXp9EC+bJly5STk6Njjz3Wv83j8WjRokWaPn26PvvsM7lcLu3bty+gSr5jxw6lpKRUet6IiIgKZ4G32+3841kDvF+AiXsBMHEvACbuBRzp8vKkaNsGeQxDUbYEGbIE7E+MaPXXVzZ/IHe7A5dLg4L+d6JG65CH0qmnnqoVK1bol19+8T969eqlUaNG+b+22+1asGCB/zWrVq3S5s2b1a9fv8ZqNgAAAAAcsVwuqSh8vZIi0mSxWMrtT4vMlCTtdu0fRsykbrXXaBXymJgYHXXUUQHboqKilJSU5N8+duxY3XLLLUpMTFRsbKyuv/569evXjxnWAQAAAKAeuFxSgX29EsMr7pWc6mgrSdpQuELNIltLIpDXRaNVyIPx5JNP6swzz9S5556rk046SSkpKXr33Xcbu1kAAAAAcERyu6U827pKA3m8vbkkaX3BCv82AnntNfqyZwdauHBhwPeRkZGaMWOGZsyY0TgNAgAAAIAmpNTllcu6UUnhgyvc7+vGvqFofyC3WgnktXVIBXIAAAAAQOPweCSvM1uylCopPK3KY9cVLvd/TYW89g7pLusAAAAAgIbhdktKWC9JSopIrfLY7OL1KvYUSqJCXhcEcgAAAACAXC7tD+ThVQdyQ4Y2FJrd1q1Wqaysvlt3ZCKQAwAAAAD8gdypJIWHRVZ5rFU2rS341fyaCnmtEcgBAAAAAH8F8g2KsVRdHZekZpGttK7gF0mMIa8LAjkAAAAA4K9Avk6xQQTylMhMrS38RRKBvC4I5AAAAAAAf5f1OGv1gTwtMlPrC1bIa3jpsl4HBHIAAAAAgHKLiqSY7UqwVR/Im0e0Vom3ULtKt8lm+yvMo8YI5AAAAAAAbcrdKElKtFcfyGPtSZKk3a5suqzXAYEcAAAAAKCNueaSZ4n2tGqPjbElSDIDOV3Wa49ADgAAAADQ5oL1kseu+PCkao912uIUJqv2UCGvEwI5AAAAAEBbC9ZL+Wmy2aqPiWGWMMXak+iyXkcEcgAAAACAthWtlwpSZLUGd3ysLVG7S7MI5HVAIAcAAAAAKKtknZSfGnQgj7EnMoa8jgjkAAAAANDEGYahHNdGKT9VNltwr4m1JWqXiwp5XRDIAQAAAKCJyynMkcsokgrSgu+ybk/SnlLGkNcFgRwAAAAAmrj1e80lz2rSZT3Wnqh97hyF2TwE8loikAMAAABAE+cL5JaiVIUFmRJjbUnyyiu3fafKyuqxcUcwAjkAAAAANHHr966XQ/GyGc6gXxNrN9crL7FlE8hriUAOAAAAAE3c+n3rFWNJlS3I7uqSWSGXpBJ7Nl3Wa4lADgAAAABN3Lo96xRlBD/DuiTF2BNkkUUltmx5PJJh1F/7jlQEcgAAAABo4tbvXa9oI/gJ3STJarEp2havImu2JGZarw0COQAAAAA0YaVlpcrKz5LDU7MKuWR2Wy8KI5DXFoEcAAAAAJqw7IJsGTIUUdasRhVyyey2XkggrzUCOQAAAAA0YdsLtkuSbO4kWWtRIS+wZEkikNcGgRwAAAAAmrDsfLPCHVaaKHtNA7k9UfmiQl5bBHIAAAAAaMK2F2yX1WKVSuNqPIY8xpakAm2XZBDIa4FADgAAAABNWHZBthIdiSpzhdV8Ujd7kjxySY69BPJaIJADAAAAQBO2vWC7Eh2JcrlUi0CeaH4RnU0grwUCOQAAAAA0Ydn52UpwJKi0NoHclmR+EUMgrw0COQAAAAA0YdkF2UpyJNWyQv5XIKdCXisEcgAAAABownxjyF0uyW6v2WvDwyIUYYmmQl5LBHIAAAAAaKK8hlc5hTlKdCTKXYsKuSTFhCVRIa8lAjkAAAAANFG7i3arzFvm77Je0wq5JEVZE6mQ1xKBHAAAAACaqO0F2yWp1rOsS1KMLVGK2UYgrwUCOQAAAAA0UdkF2ZKkJGftK+QxtiQpJltlZSFuXBNAIAcAAACAJioUFfI4e5IUvZ0KeS0QyAEAAACgicrOz1ZMeIzCreEqK6tDIA8vVF5JfugbeIQjkAMAAABAE7W9YLuSnEkyDMntlmy16LLuW4t8Z0l2iFt35COQAwAAAEATlV2QrYTIBH93c3stKuTxEYmSpF2lBPKaIpADAAAAQBOVXZDtHz8u1a5CnhBhVsh3E8hrjEAOAAAAAE1Udn5gIK9NhTzS6pTckdrjJpDXFIEcAAAAAJqo7QXbleRI2l8hr0Ugt1gsUnGy9hLIa4xADgAAAABNUKGrUPmu/MAKeS26rEtSWEmi9pURyGuKQA4AAAAATZB/DXJnYp0q5JIU5krQPg+BvKYI5AAAAADQBPkDeWQIAnlpkvKMrBC1rOkgkAMAAABAE5RdYFa0k5xJde6ybnMnKV9UyGuKQA4AAAAATdD2gu2yh9kVEx5T5wq5zZWoUss+lZSVhK6BTQCBHAAAAACaoOz8bCU5k2SxWOR2m9tqWyG3u821yH3d4BEcAjkAAAAANEHbC7Yr0ZEoSXWukId7zECenU+39ZogkAMAAABAE5RdkK2EyARJIQjkZUn+cyJ4BHIAAAAAaIKy87OV5DCDtC+QW621O5fdiJHFsGpHwY4Qta5pIJADAAAAQBOUXZAd0GXdHi5ZLLU7l90WJrsnQTmFOSFs4ZGPQA4AAAAATYzH69HOop1KdJqB3O2W7LXsri6ZlXVbGYG8pgjkAAAAANDE7CzaKa/hVWLk/gq5rZYzrEuS1SZZ3XHKKSKQ1wSBHAAAAACaGN/yZEnO/WPIbbUcPy6Zr7W6Elj2rIYI5AAAAADQxPiWJztwUrfarkEumV3WLaXxyimgQl4TBHIAAAAAaGJ8lez4yHhJf1XI6zCG3GaTLKUJdFmvIQI5AAAAADQx2QXZio+Ml91qlsXd7rpVyG02yVISr30l++TyuELUyiMfgRwAAAAAmpjtBdv9S55Jda+QW62SUZwgSdpZuLOuzWsyCOQAAAAA0MQcuAa5ZAZya10mdbNJ3iIzkLP0WfAI5AAAAADQxGTlZykhMsH/fSgmdTOK4iVJOwp31LF1TQeBHAAAAACamO352/1LnklSaWndJ3VzF1AhrykCOQAAAAA0IYZhaHvhdv+SZ1JoJnXzusIVHR5NIK8BAjkAAAAANCEFrgIVuYtCPqmby20uo0YgDx6BHAAAAACakOyCbEkKbSC3SV6PlBCZwBjyGiCQAwAAAEATsr1gu6QKAnlduqz/NUN7bHi8cgqokAeLQA4AAAAATUh2vlkhP3AMucsl2evYZV2SYsLjqZDXAIEcAAAAAJqQbfnb5LQ7FRUe5d/mctetQu6bEC7WTiCvCQI5AAAAADQhWflZSnYmB2xzh6pCbkvQzsKdMgyjDi1sOgjkAAAAANCEZOVnBXRXl0Izy7okRVkT5Pa6lVuaW4cWNh0EcgAAAABoQrbmbVWSc38g93jMR10Cue+1UWHxkqQdBXRbDwaBHAAAAACakIMr5G63+Wyvwxjy/RVyc+Z21iIPDoEcAAAAAJoIwzCUXZAdMIbcF8jrUiH3hXmHJV4SgTxYBHIAAAAAaCJyS3NV5C4KCOQul/kcigq5zRstW5iNQB4kAjkAAAAANBFZ+VmSFDCG3BfIQzGpm6csTAmRCSx9FiQCOQAAAAA0Eb5AnuwoXyEPxaRubrcUHxlPhTxIBHIAAAAAaCK25W2TVH8V8rIyAnlNEMgBAAAAoInIys9SXEScwq3h/m0lJeZzeHglLwqCb/y5r0K+vWB7HVrZdBDIAQAAAKCJyMrPCpjQTZKKi83nugRyX4Xc7ZYSIhOokAeJQA4AAAAATcS2/G0Ba5BLoamQB3RZd9BlPVgEcgAAAABoIrblbwsYPy7tD+QREbU/r9UqWSzmePSEyATlluaqtKy0Di1tGgjkAAAAANBEVNRlvaRECgur26Rukvn6sjIzkEvSzqKddTthE0AgBwAAAIAmwGt4tb1ge7ku68XFUniEWeGuC6vtr0ndHPGSRLf1IBDIAQAAAKAJ2Fm4U2Xesgor5HUZP+5js+6f1E2SdhTsqPtJj3AEcgAAAABoArLysySp/gK5bf865BIV8mAQyAEAAACgCdiWv02SKpzULSJEgdzllsKt4YoOjyaQB4FADgAAAABNQFZ+lsIsYf4u5T7FIaqQW61Smdv8mrXIg0MgBwAAAIAmICs/S0mOJFnDrAHbi4sleygCuU1yl5lfx0fGa0chY8ir06iB/JFHHlHv3r0VExOj5s2b6+yzz9aqVasCjikpKdH48eOVlJSk6OhonXvuudqxg18sAAAAANTEtrzya5BLUklxCCd1c5lfE8iD06iB/KuvvtL48eP1/fffa/78+XK73Ro6dKgKCwv9x9x888366KOPNHfuXH311VfKysrSOeec04itBgAAAIDDj69CfrDiEAVyq9Wc1E36q8t6AV3Wq1PHpd/r5tNPPw34/uWXX1bz5s21bNkynXTSScrNzdWLL76oN954Q6eccookafbs2erSpYu+//57HX/88Y3RbAAAAAA47GzL36Y28W3KbS8pkaKi635+21/rkEvmWuRLs5bW/aRHuEYN5AfLzc2VJCUmJkqSli1bJrfbrcGDB/uP6dy5s1q3bq3FixdXGMhLS0tVWlrq/z4vL0+S5Ha75fb9daBSvveI9wpNHfcCYOJeAEzcCzgS7C7crb6pfWV4jMAdXinaKVmCOIdFRsDzgZyRksWQDI+UHJGsgpICuVwuWSzBnPnIEuy/FRbDMMq/k43A6/XqrLPO0r59+/TNN99Ikt544w1dccUVAQFbkvr06aNBgwbpscceK3eeSZMm6f777y+3/Y033pDT6ayfxgMAAAAA8JeioiJdcsklys3NVWxsbKXHHTIV8vHjx+u3337zh/HauvPOO3XLLbf4v8/Ly1N6erqGDh1a5RsBk9vt1vz58zVkyBDZ7fbGbg7QaLgXABP3AmDiXkBVzj1X2rhROvFE6dFHpcjIxm5ReVvztqrbzG6afPJkHZd2XMC+Cy+U+veXTh5U/XksMtTGkq9NRoyMg2rqb8wxZ1p/6EFpxY4VumPBHVp69VJ1TOoYyh/lsODrqV2dQyKQX3fddfr444+1aNEitWrVyr89JSVFLpdL+/btU3x8vH/7jh07lJKSUuG5IiIiFBERUW673W7nH88a4P0CTNwLgIl7ATBxL+Bgy5dLH30kDRggzZxpPl98cWO3qrztRdtV7C1WYnSiLNbAIL0vTwqzqYJO6JUzZCkXyMsMqaRIslil+Kh4FXuLtbt0d5O8Z4L9mRt1lnXDMHTdddfpvffe05dffqnMzMyA/ccdd5zsdrsWLFjg37Zq1Spt3rxZ/fr1a+jmAgAAAECAOXOkuDjpvvukrl2lN99s7BZVLCs/S5LKzbLu8ZgTsYVqlnX3AbOsS1JOITOtV6VRK+Tjx4/XG2+8oQ8++EAxMTHavn27JCkuLk4Oh0NxcXEaO3asbrnlFiUmJio2NlbXX3+9+vXrxwzrAAAAABqV12sG8pNPlux2adAg6bnnpL17pYSExm5doKz8LIVbwxUbETiMt6TEfA5FILfb9q9DHh0eLVuYjUBejUatkM+aNUu5ubk6+eSTlZqa6n+89dZb/mOefPJJnXnmmTr33HN10kknKSUlRe+++24jthoAAAAApEWLpG3bpCFDzO9PPtlch/v99xuzVRXLys9SsjO53IznoQzkB65DbrFYlBCZoB0FO+p+4iNYo1bIg5ngPTIyUjNmzNCMGTMaoEUAAAAAEJx586Tmzc2u6pKUnCz17Cm99ZZ0xRWN2rRytuVvK9ddXdofyCuYhqvGDlyHXJISHAnaXrC97ic+gjVqhRwAAAAADle//ip16CAdWHQ+9lhp8WLp0Fhcer+s/CwlOhLLbS8uNp9DUiE/KJAnOhKVXZBd9xMfwQjkAAAAAFALy5dLbdsGbuvYUcrLk9ata5w2VWZb3jY1czYrtz2kgfyASd0kKdmZrK15W+t+4iMYgRwAAAAAamjnTmn79vKBvFMn8/nHHxu+TVXJys9SkrPyLuuhmtSt7IAKebIj2T+7OypGIAcAAACAGlq+3Hw+OJDHxUmpqYdWIC9wFSjPlVfvgbyiCnlOYY7cHnflL2riCOQAAAAAUEPLl5sTobVsWX5fx47S0qUN36bKZOeb47gr6rIe8lnWD8jeSc4kGTK0o5CZ1itDIAcAAACAGlq+XMrMNEPowTp1kn76yVyn/FCwLX+bJFU4y7pvDLndXvfr2Ozmsme+Ce2Sncnm9fO21f3kRygCOQAAAADU0K+/moG8Ih07SgUF0urVDdumyvjGcfsC8oFKSsxK/0HLk9eK78MJ31rk/kCeTyCvDIEcAAAAAGqgrEz64w+pXbuK9x9qE7tl5WcpOjxaDruj3D5fIA8Fu818drnM57iIONnD7EzsVgUCOQAAAADUwIYNUmmplJFR8f7oaCklRVqxokGbVaktuVsq7K4umYE8FOPHpf3d3ktLzWeLxaJkZzJd1qtAIAcAAACAGli/3nyuaEI3nzZtpN9/b5j2VGfNnjVqGVNxY4tLpPBQVcgPCuSSObFbVgEV8soQyAEAAACgBtavN8dLNys/ablfRob0228N1qQqrd69Wq1iW1W4r6Q4dBVy33kCArkjiQp5FQjkAAAAAFAD69ebXdIrmmHdJyND2rTJnNytMbk8Lm3ct1EtYyuukJeUSOEhmGFdqjiQ02W9agRyAAAAAKiBDRvMQF4V3/jylSvrvTlV2rB3gzyGR+mx6RXur48x5L61zaW/AjmzrFeKQA4AAAAANbBunZSaWvUxbdqYz409jnz1bnPttcq6rBcV1XOXdWeS8l35KnA1cleBQxSBHAAAAABqYP366gO5w2Ee09iBfM2eNYq0RVa4BrkU2gq57a9lzw4M5M2c5kB7lj6rGIEcAAAAAIK0d6+Ul1d9IJfMKnljT+y2evdqpcemy2KxVLi/uDh0s6z7gv2BXdZ9y60RyCtGIAcAAACAIPmWPEtLq/7YjIzGr5Cv2r1KaTGVNzY/X3I6QnMt3xhyl2v/Nl9lnondKkYgBwAAAIAg+QJ5MBXyzExpyxazot5Y1uxeU+mEbpJUWCg5naG5lsUi2cMDK+QOu0PR9mgq5JUgkAMAAABAkNavl6KjpZiY6o9t7JnWC12F2pa/rdIlz0pLJbfbHO8eKuHhgWPIJSk5ipnWK0MgBwAAAIAgbdhgVscrGZIdoHVr87jGGke+ds9aSaq0Qu5bIz2kgdxePpAnOZII5JUgkAMAAABAkNatq34Ncp/ISKlly8YbR75mzxpJlS95lp9vPocykNvtUsnBgdyZxBjyShDIAQAAACBImzdLLVoEf3xjzrS+evdqxUbEKi4yrsL99VEht9ul0pLAbckOuqxXhkAOAAAAAEEwDGnrVql58+Bf06ZN41XIV+9eXWl1XNpfIQ/VpG6SOalbuTHkzmRl52fLMIzQXegIQSAHAAAAgCDs2SMVFdUskGdkSFlZ0r599dWqyq3evVotYyqe0E1quDHkyc5kub1u7SraFboLHSEI5AAAAAAQhC1bzOeaBPLMTPP5jz9C357qVFchLyiQrNb964eHgt0euOyZZI4hl8TSZxUgkAMAAABAEDZvNp9rMoa8dWspLKzhu63vKd6j3cW7qw3kTmdwM8YHy1ZBhbyZs5kkMY68AgRyAAAAAAjC5s2SzSbFxwf/mvDwxplpfc1uc4b1ypY8k8wx5KEcPy6ZP+/BFfIER4IsslAhrwCBHAAAAACCsGWLWR0Pq2GKysyUfvmlXppUKd+SZy1jqx5DHhkZ2utWNIbcFmZToiORpc8qQCAHAAAAgCBs3iw1a1bz13XoIP38szlLe0NZvXu1kp3JctorL4EXFIR2Qjfpr2XPSstvT3YmUyGvAIEcAAAAAIJQ20DesaOUlydt2BD6NlVm9e7VahVT+fhxScrLr59AXlJBIE9yJDGGvAIEcgAAAAAIwubNNZvQzad9e/P5p59C256qrN69usru6lL9VMjDw6XSkvLbk5xJdFmvAIEcAAAAAKpRViZlZ9euQp6YaL7u559D366KGIahNXvWVDnDuiQV1FOF3OUqvz3ZmUyFvAIEcgAAAACoRna25PHUrkIumVXyZctC26bKbC/YrgJXQfWBvL4q5KXlx8snOZO0s2inXJ4K0noTZmvsBgAAAADAoc63Bnnz5sG/pqBsn5bu+Vz73Dkq7L1TP2/bqevmWXX/yZOU5Eyqn4Zq/wzrVS155vVKhYWhX/bMbjefXS4pImL/dt9a5NsLtqt1XOvQXvQwRiAHAAAAgGps2WI+B9tlvdhToJt+OVnrCn+V1WJTRGKCSox4vfLLDs1f/7k+v/RztYlvUy9tXb17tcIsYUqNSa30mKIi87k+uqxLZpX8wECe5DA/gNiWt41AfgC6rAMAAABANTZvlqKjzUd1PIZH9/9xobYVr9UtHZ7VE90/14TWb0sfPaerUmaowFWg4188Xst3LK+Xtq7evVop0SkKt4ZXekx+vvlcH13WpfJLnyU7kyWJpc8OQiAHAAAAgGrUZIb1Getu1tI9n2l0m/uU7uwoi8WihARzcrctK1vp6dOfVmxErE6cfaIWblwY8rau2b2m2iXPCgrM5/qqkJccNNN6bESswq3hTOx2EAI5AAAAAFRjy5bguqu/s/Vfem/b0zqn5Q3qHNsnYF+7dtIvv0iJjkQ9OexJdUzqqGGvDwtpKDcMQz9m/6jW8VV3C2/oCrnFYlGyM5kK+UEI5AAAAABQjU2bqp/Q7btdH2nGupt0crML1D/5rHL727eX1q01w7DT7tTDpzysbs26afT7o5Vfmh+Sdv6W85u25m1Vn7Q+VR5X3xXygwO5ZI4jp0IeiEAOAAAAANXYvLnqQF7iKdI/V1+lrrH9NCL1mgqPadfOfF6xwny2W+267YTbtLNwp+744o6QtPPj1R/LYXOoR0qPKo+r7wr5wV3WJXPps215BPIDEcgBAAAAoAoFBdLevVUH8o+zn1eue7fOThuvMIu1wmMSE6WERLPbuk9qTKquPvZqzfxxpv634X91buvHaz7WcanHVTmhm2QG8kiHZK24qbV24LJnB2vmbKbNuZtDe8HDHIEcAAAAAKrgW/KsskndXN4S/XvLYzouYbCSI9IqPY/FIrXNlH76OXD7yM4j1aNFD1354ZUqdBXWup27i3br+63f6/hWx1d/7G4pNqbWl6pUZZO6SVKr2FbauG+jXJ4K0noTRSAHAAAAgCr4AnllFfJ52S9pj2uHBrcYVe25unQxx5Fv375/W5glTLedcJu2F2zXnQvurHU7P137qbyGV31b9a322N17pJjYWl+qUpVN6iZJreNay2N4tG7PutBf+DBFIAcAAACAKmzeLIWFScnJ5fe5vS69seURHRN/ippHpFd7rm7dJJtN+uqrwO0tY1tq7DFj9fQPT+urjV9V/OJqfLz6Y3VM6uhf87squ3fVT4XcapXCrJUHckn6c9efob/wYYpADgAAAABV2LzZDOM2W/l9n+14RbtKt2lIENVxSYqMNKvk/6tguPg5Xc7R0c2P1lUfXaVid3GN2ljmLdN/1/43qO7qktllvT4q5JJZJa+oy3pCZIKiw6O1aveq+rnwYYhADgAAAABVqGyG9TKvW69vekhHxw1USmRG0Ofr2VNatSqw27pkdl2/9YRbtWnfJj246MEatfG7Ld8ptzRXx7cMLpDv2SPF1Vcgt1dcIbdYLEqPTadCfgACOQAAAABUYfNmqVmz8tvn57yuHaWbNLTFpTU6X7du5uRnCxaU39c6rrVGHT1Kj3/3uJbvWB70OT9Z/YkSHYnqlNyp2mOLi81HbD0FcnslgVyS0uMI5AcikAMAAABAFSqqkHsMj17f9JC6xw5QmqNdjc4XESH1PEZ6/32prKz8/kuOukTpseka++FYebyeoM750eqP1Cetj8Is1Ue8PXvM55h6GEMumV3WKw3kf1XIDcOon4sfZgjkAAAAAFAJr1faurV8IP9hz6fKKlmnU5tfXKvzDjxJ2rWr/ORukmS32nVrv1u1LGuZpv8wvdpzbdi7QSt3rdTx6cF3V5ekuLiatDh4NnvFY8gls0KeW5qrnUU76+fihxkCOQAAAABUIjvbrPampgZu/yjrGbVydFRrZ5danbdlS6ljJ+ntt6WKisXdmnfTyE4jdfeXd2vTvk1VnuuTNZ/IFmZTr9ReQV17927zub66rIfbpdJKlhpvHctM6wcikAMAAABAJdavN5/T0vZvyynZoiV75qlf0nBZLJZan3vgSdLq1dLSpRXvv+rYqxQVHqUrPrhCRe6iSs/z0eqP1KNFD0WFRwV13d27zRnjHY7atLp64RFScSXNbRnbUmGWMK3axUzrEoEcAAAAACq1YYP5nJKyf9sn21+QPSxSx8YPrtO5u3SRMjKl554zu8YfLCo8SncOuFOLty7W4FcHa0/xnnLHvLfyPS3cuFB9W/YN+rq795jV8Tp8llAlh0PKy6t4X7g1XGkxaSx99hcCOQAAAABUYv16cw3yiAjze49Rpk+yn9exCacq0uqs07ktFmnEmdK6ddKXX1Z8TM+Unpo6dKpW7lqp/i/115bcLZKkAleBrvrwKp3z9jnq27Kvzux4ZtDX3bNbiq2n8eOS5HRI+QWV728V04ou638hkAMAAABAJdavD6yOL979sXa7snVC4oiQnL9tW+moo6RnnzOXIqtIl2Zd9K/T/qXcklwd/+LxenPFmzrm2WM0Z8UcTeg3QfeffL8c9uD7n+/aJcVEh6T5FXI4pPxKKuQSS58diEAOAAAAAJVYty5wQrcPs2apjbOLWjk7hOwaZ50l7dsrvf565cekx6Xr6dOfltPu1CXvXiKrxarnznxOwzvWfBz77j31XCF3SgVVVMjT49K1Yd8GlZZVsjZaE0IgBwAAAIBKbNiwP5BnFa/Xj3vnq19i8N3Dg9GsmXTqqdJbb5lrnlcmyZmkacOm6d6T7tXTpz+t9Lj0Wl1vz24prp5mWJfMQF5aKrndFe9vHdtaXsOrdXvX1V8jDhMEcgAAAACoQHGxueyZL5B/kv28Iq1R6hk/KOTXOuUUKSFRevxxyeOp/Lio8CidknmK7FZ7ra7jdpsTrsXE1LKhQfDN3p6fX/F+3wcJdFsnkAMAAABAhTZuNJ9TUyW316V521/UcfFDFGEN/Xph4eHShRdIv/8uvfdeyE/vl5NjPick1N81nH/NdVdZt/WEyATFhMew9JkI5AAAAABQoQPXIP9m1/va596pE5JCM5lbRdq3lwYMkJ5/Xtq0qX6uscWcpF3Nm9fP+aX9gbyyCrnFYjEndttNhZxADgAAAAAV2LBBstulpCTp/awZahd1tFIdmfV6zREjzK7rkydLLlfoz79li2QPl+LqcVK36rqsS1J6LDOtSwRyAAAAAKiQb8mzzcV/aHnuIp2QNLLerxkeLl12qVkhnzUr9OffstWcRC6sHpNgdRVyaf/SZ4Zh1F9DDgMEcgAAAACowMqVUqtW5lJnMbZEHR13YoNct2VL6W9/k95/X5o/P7Tn3rJZapYc2nMezG6XrNaqlz5rHdtaeaV5yinMqd/GHOII5AAAAABQgd9/l1pmFujTHa+ob+LpsoXVbmbz2jjhBKl3b+mf/5TWrg3debf+VSGvTxaLFBVVfYVcYqZ1AjkAAAAAHCQ/3xxvnZ85RyWeQvVLCu3a49WxWKTzzjMnX7vnHik3t+7nLC6Wdu2SmtXjhG4+DkfVgTwtJk1Wi1WrdjftmdYJ5AAAAABwkJUrJcnQb44Z6hbbT4nhKQ3ehvBw6YorzK7fkx+oen3yYGzbZj43r+cKuSQ5nFJ+FV3Ww63hSo1JpULe2A0AAAAAgEPN779LSl+sbWUr1D/prEZrR2KidNll0k/LpNdfr9u5fEue1XeXdcmskBdUUSGXpFaxrZr8WuQEcgAAAAA4yB9/SJEnzVRyeEt1jOnVqG3p1Ekadpr08svSzz/X/jxbt5pju6OiQta0SjkcUl5e1ce0jm2tlbtW1n9jDmEEcgAAAAA4yE9/7lRpu7k6IWmEwiyNH5uGDJbat5cefrjq2cursnlLw4wflyRnNWPIJXNit025m1RSVtIwjToENf5fFgAAAAAcYpbpeVkk9Uk8rbGbIslcRuzii6WCQunpp2t3jpV/SGmpoW1XZaqb1E2SOiV1ktfwavGWxQ3TqEMQgRwAAAAADrBjb6Fyu05VW2OYomxxjd0cv8RE6W9nS59/Li2uYYbdu9ec1K1tu3ppWjlOZ/WV/HaJ7ZToSNRn6z5rmEYdggjkAAAAAHCAhz6bJUXkamDCJY3dlHJ695Y6d5amPmkuYxas334zn9tm1k+7DuZ0SqWlkttd+TFhljAdl3qc/rv2vw3TqEMQgRwAAAAA/lLkLtLs1Y/LsmGYOqc3/FJn1fGtT56bK734UvCvW7FCSkiUEhLqr20HcjjM5+q6rfdO663lO5Zre8H2+m/UIYhADgAAAAB/eW7Zcyr07lGrPaNkszV2ayqWlCSdNkx69x1pzZrgXrN8uZSZUa/NCuB0ms/VdVvvlWbOYP/5us/ruUWHJgI5AAAAUA8Mw+wm/PHH0tdfmxVNHNqK3cV69JtHZd88VB1TG2j2s1oaOFBKSZGmTpW83qqPLS6W1qyVMts2TNuk/YG8ugp5giNBHZM6Ntlx5ARyAAAAIITKyqRnnpFatpS6d5dGjJBOOklq1kw67TQzoFcXoNA4XvjpBe0s2iXXj6OU2UBjrWvLajW7rv/5p/Thh1Uf+/vvktfTOBXyffuqP7ZXWi99tvYzeY2md2MQyAEAAIAQ2bRJOv546R//MMP4P/8pzZ0rvfSS9Pe/S5s3mwG9Sxdp9mzJ5WrsFsOnpKxEj3zziLpGDJbyWyojo7FbVL22baV+/aRnn5N27Kj8uE8/NT8Qasiif0yMZLNJ2dnVH9snrY92F+/WT9k/1X/DDjEEcgAAACAEvvpKOvZYKStLmjFDuvNO6bjjpORkKTNTOuccaeZMafp0qXlz6corzUA1bVrNZstG/Xjp55e0o3CHkrNGKSVFiopq7BYFZ8QIKSJcmjLFHCZxsH37zL/Nfv2ksAZMf2FhUlKyeT9Up1vzboqyR+mztU2v2zqBHAAAAKijt9+Whg6VMjLM7updu1Z+bLdu0gMPmFXzbt2kCRPMwP7ss5LH02BNxgF2Fe3SpIWTdGrmqVqzLF0Zh3h39QM5HNIFF0hLl0pvvll+/3//WlGsd++GbZckJSUGF8htYTb1TOmpT9d9Wv+NOsQQyAEAAIA6ePpp6aKLzHHijz4qxcYG97rMTLOK/sor0tFHm13a+/Y1x/uiYY3/ZLxcHpeGJf5d27ZJ3Y9q7BbVTNeu0uDB0gsvSEuW7N++Z4/07rvSMcdI0dEN367kICvkktSnZR8t3rJYuSVNa/ZDAjkAAABQC4Yh3XWXdMMN0vnnm+Habq/5eVq2NM/z9NPS7t1Sr17Sc8+Fvr2o2Nzf5+rtP97WDX1v0A9fJyoqWurUqbFbVXOnnWbOTXDXXdKcOdKvv5q9L1xuadiwxmlTYpK0fXtwkxj2Tustj+HRlxu+rP+GHUII5AAAAEANuVzS5ZdLjzxiTuD2j3/UfXzuUUdJs2ZJQ4ZI115rVsyZ9K1+5RTm6B+f/EMntTlJA1sP0oIvpJ49zRnMDzdWqzkvwcknm5Xym26ScnZK//i7uW55Y0hOktxuadeu6o9NjUlVemx6k1v+7BBd6h4AAAA4NO3ZI/3tb9LixdK990qnnBK6c0dGSrfcYlZop02TVq6U3nnH7PqL0DIMQ+M+GSeP4dFNfW/SihUW7d4tHXdsY7es9qxWc5K3E0+USkqkuDhzjHlj8f3dZmWZExlWp1daL3269lMZhiGLxVK/jTtEUCEHAAAAgrR2rTnO+9dfzVmtQxnGDzR8uDR1qrRihTkZ1y+/1M91mrK3fn9L76x8Rzf2vVEJjgTNmWOGxsNhubPqxMdLKSmNG8YlKTHRfA52HHnvlr21KXeTVu9eXX+NOsQQyAEAAIAgDRpkdsGdMcNcZ7w+de9uLpNmt5tLVj3zTHBjcQ9XHo/0ww9mt/2XX5Z+qsclqX/L+U3/+OQfGpQxSCdnnKwlS6QffzQ/CGkihdkGYbdLCQnBB/KeLXoqJjxG//zun/XbsEMIXdYBAMBhpcxbpkJXoQrdhSp0Faq4rFjF7mIVlxWrpKxEbo874HhrmFXR4dGKCY9RTESM4iLilORMUpiFugSCYxjm2uFt20odOkj/938NN2N1Soo52duMGeY49TlzpMcfNwP6kSI/3/yw4cknpexss9u1b/m3E06QnnrKnOguVFbsWKFBrwxSM2cz3XT8TXK5pBkzpXbt6/9DlqYoMSn4QO6wO3TlMVfqqSVPaeyxY3V8q+Prt3GHAAI5AACoszJvmbLys5SVn6Xs/GxlF2QrpzBH+aX5ynflq8BVoEJ3odwet9xet8q8ZSrzlsnj9ciQIcMwZMiQ1/CqzFMmj+GRx+uR2+tWSVmJ/1HqKZXLU/dZruxhdqXGpKplTEulx6WrXUI7dUrqpE7JndQpqZMSHAkheFeOLIYh5eSY/2NdXGxWvqKiJKfTDFAFBeZsyqtXS3/8IS1fLm3YYE7mFBYmpadLxx9vLg926ql1nwCtoeTmSlddJX3yibnG88SJkq2B/w86IsIcV37yyWY4P+EEsxv7yJFSnz7m8mnx8ebvIzLy8Knw7ttnVsOfeMIM5UOHmjPVd+li/r0tWSK9+qr58/7zn9L119f9Z1u+Y7lOeeUUJTmS9PiQxxVli9XkB6Tt2dKNNx4+793hJClR2rYt+ONHdByh/679r/7xyT/049U/yhp2GM6wVwMEcgAAEDSXx6Xfc37XL9t/0R87/9Dq3av1564/tX7fepV5y/zH2cJsio+MV5Q9Sg67Qw6bQ5G2SNnCbLKGWWW1WGW32WW1WCWLZJFFFotFFln8+60Wq6xhVoVbw/0Pu9WuSGukIu2RirRFymFzKNwarghrhCJsEYqwRsgaZpVF+/+v2mN4VOwuVlFZkYpcRSp0F2pX0S7tKtqlnUU7tXbPWn218SvtKNzhf03zqOY6usXROrr50ereoruObnG0ujbrqkhbZIO+343NMMwuxM8/L332mbR1a/WvsVrNZbwyM6X+/c2g6PGYQf6LL6SXXjKrzA89JJ133qEdgL76ShozRtq501xKSmrc2bePPVZ68UXpu+/M38fDD0tFReWP840f7txZ6tHDrKb36xf8+uj1yfc39eqr5qOkxOwmPmqU1KxZ4LEDBpjj9Z97zgzLq1ZJ//pX7X8Hy3cs16BXBinZmawnhjwhR1ispk6Vvl4kXXGF+XeL0GvRwvyAzuWSwsOrP94aZtWNfW/U+Hnj9cyPz2h8n/H138hGRCAHAAAVKvOWacWOFZKk6+Zdpx+2/6Dfc36X2+uWRRalxqSqVWwrHdXiKJ3e4XSlxaQpyZGkJGeSYiNiD7su4cXuYm3L36bNuZu1cd9Grd+7Xm/9/pamfj9VkhRmCVOHxA46usXR6t68u7o266ouzbqofWJ7hVuD+L/Mw8x335lds7/5RkpNNcP1UUeZ/3MdEWGG7OJiqbTUHNfscJgzOqemVr4Wt2FIv/0mvf66dMEF5jlnzpSOPrphf7bq7Nwp3X23uXRU9+7mhwepqVJeXmO3zOxZMGCA+fB4zF4J27ebvwvf7yM/3/wZtmyR/vc/6f77zdf17GkG82OOMavQGRnm77O+P2QoKjI/3Jg3T/rgA7NdzZpJZ59tPqpakstul8aPl9q0MWed37zZ7KlQ0yED89bM02XvXeYP4yW5sbprsrTyT+mii+mqXp+6dpU++khatiz4oRZdm3XV8A7DdfeXd+v8buereVQQU7QfpgjkAABAhmFoS94WLdm6REu2LdH3W7/XT9k/SV7pzaPf1LdbvlXrhNY6qfVJ6pDUQe0S2slhb+Tpe0PMYXeofWJ7tU9sH7C9yF3kD+jr967Xmt1r9Nm6z5RXaqYzq8Wqdgnt1DGpo9omtPU/MuIz1Cq2leIj4w+r5Xu2bJEmTJDefltq31568EGzq3koQpvFYgafxx4z/+f86afNqu+4cWZoTGjkkQLbtpkV2FmzzO+vv1466yzzZzeM8scbhqESb5FKPIXyGh555ZHH8EgyZLOEyx4WofCwCIWHRcpqCf3/dvt6I1RV2TUM83e6fLk5Y/tHH5kfgvh+HovFfN8TEszKenz8/q/j4syqekzM/uEJUVFmGI6JMffHxZnfR/7VeSQ/f//QhWXLzA90Fi82q6MpKWYgu+kms3Jfk7+pM880Q/wDD5gf5PznP2ZPi+pszduqmz69Se+sfEe903rrrv73asG8GD33vBQRLl033uzNgfrTooU5e/3XX9ds7oOrj71a32z+RrfPv10vn/1yvbWvsVkMo6J/Xo4ceXl5iouLU25urmIPhX46hzi326158+bpjDPOkL2yj7eBIHkNr3YW7tT2gu3aWbRTOYU52lm4U7uKdim3NFd5pXn+R6mnVG7P/nGlkvxdVCNsEXLYHIqNiFVcRJziIuMUFxGnZGdywKNZVDMlOhJDUpXjXsCRLjs/W8uyl2lZ1jIty16mJduWKKcwR5KUGp2qTkmd1KVZF3VN7Kr0nHTFHhUri/XwCZX1zTAM7SvZp025m7Q5d7M25W5SVn6WthdsV1Z+VsA4d6fdqZYxLdUytqXSYtLUIqqFUqJTlBKdouZRzdUiqoWaRzVXs6hmjVppLyw0x/I+/rgZvK66yhzTW59jvd1uc43t114zK6G33ir9/e/luy4fzOUyuy9v2mSOa/dV6Fu0MKu+GRnBjfM2DHMZs/nzpffflxYsMM9z5lllOuXsrSqwr1d2yQbtKt2mPHeOLm8+RPeue0w5riwVefJUWJYnrzxB/ax2S4Qc1mg5bbGKssYqyharaFuCom3xirbFK8oaZz7b4v7aFqcoa5yibHF/bYtTeFhohkyUlprDD3JypN27zXHy+fnmPAAFBebfwoGPoiKza3lNU0NSktltvmdPc1K2Nm3qPkRh/XpzHP/u3WY4Hzeu4qW9yrxlmvHDDN3zv3sUYY3Q348bJ9uWQXpptkVbNkvH95POGtH4y4IdjiwylGHJ00YjVoaC+4V+/LE5VOG992r2QcxHqz/S1MVT9cVlX+jUtqfWssWNI9gcelgE8hkzZuiJJ57Q9u3b1aNHDz399NPq06dPUK8lkNcMIQTB8Hg92llkBm3f5E2+yZyy8rO0LX+bsvKzlFOYEzCmVJIirBGKj4xXdHi0nHanf2xphC3CP17UarHKkOGf0KnMU6ZST6mK3EUqcpvjP/NL85VXmidDgf+EWS1WJTmTzP+5dTZTs6hmSnIkKdmZrCRHkuIi4wKCvdPuNNthc8hpd/rHqHrKPNwLOCLsK9mn1btX67ec3/Rbzm9avmO5fsv5zT9eOi4iTh2TOqpjUkd1Se6iLs26KNGR6H+94TGU91segbwGvIZXu4t2mx9CFu3UzsKd5nPRTu0r3qe9JXu1u3i3ClwF5V4bHxmv5s7mah69P6j7n6Nb+L9uHtVcsRGxIam879plds3+5z/NLtnnny9dcolZCW0oe/ZIb7xhVm+9XmnwYOnEE80KfXS02a4tW6Tff5d+/tmcNK6srPLz2Wxm1bN9e3MyuaQk8+fxes3AmZMjrV7n1m9bNyrPtkZhyWuU2GGNolqvVolzrXa5t8hjmBewyKIYW6JaRLTUEx0ma+qWlxVudSrSGq3IsChFWp0KD4tUmMWqMFn9cyF4jDKVeV0qM9wqM9wq9RarxFP413OBSjxFKvYU/FVhL1CRp0DFngKVeisYFO77uSzhirLF+sN7tC1eMfZExdgS/nokKtaepDh7kmJtSYqzJyvOnqwYe6I5X0MdGIb5QUhJidk1vqjIDOsFBeb37r8WN3A4zOp6q1Zmpb0+OocUF5vzGnzwgfm7HTVKOu006eijDW3Xr3p9+Wuas2KOcgpz1D95pFpkjdU3C6K1Y4fUsZN05nDz7wK1U5tAvnmzOYv+k0+aH9AEy+P16NbPb9XvO3/X7SfcrnsH3nvYzOVxxATyt956S5dffrmeeeYZ9e3bV9OmTdPcuXO1atUqNW9e/VgCAnnNEMibBrfHreKyYhW5i1TgKgh45JXmaV/JPv9jb/Fe//9I7io0J0DaXbxbXiNwIdRER6I5dvSv8aOJjkQlO5OV6EhUoiNRCZEJio+MV6QtMmRdNz1ejwpcBcotzQ1o84EPXwXeV5EvKSsJ6tzR1mi93v11Xf3n1XIZLv8HBQdOSGULs5kTU1ntgRNO/TXRVKRt/6RTUeFRctqdirJHKSo8StHh0eUeMeEx5nNEjJx252E3/hYNzzAM5bvytS1vm7bkbdGW3C3akrdFG/dt1Ordq7V692rtLt4tyQwVLWNbKjM+UxnxGWqf2F4dkzqqRVSLKu9JAnn9KS0r3f9vbcle7S3e6//a95xbkuvfZ3aF3i/cGm5+8OhspuSoZPP5rw8fEx2JSnAkKNGRqPjIeMVGxPofFleMVv1p1dKl5sRgn35qhq3TTzeDeEpKI70hMiu1X34pffut9OefZuDzcTrNCqtv6bHMTCktzQx/VqtZ9d2zx1w2a+tWactWrzbtytGOks3Kt25UqWODvHEbpYR18savlTtqs2Qx31ObJVzNIlopOTxNyREtlRSepqSIVCWFpyrB3ly2sPBahZDa8BgelXgKzYe30AztnkIV//W9+XVBuUeRJ19FnjwVe8p/0GORRdG2eH9Ij7UnK85ufh1jS1SM3Qz00X8Fe19FPspqVuUP1SEXWVnS6+9n67uNy5QbtVTq+o7U/HdZShJk2TRI3j/PkPa2U1SU1K2b2dW9devGbvXhrzb3gmFI990n/e1v0pVX1ux6bo9bb6x4Q6+veF1tE9rqhREv6MQ2J9ai5Q3riAnkffv2Ve/evTV9+nRJktfrVXp6uq6//nrdcccd1b6eQF4zBPKGYxiGXB6XSj2lKi0rDVjWx/fwra1b3dcHPpe4/9pfVhz4dVmJ/zVur7vKtllkUUxEjD8oHtxV3BeyfUE70ZEou/Xw+Hs5eP3iEk+JXGXm76GkrERl3jLz/fFIA0oH6AvrF3LLLa/hldfwyuM1xwd6Da9/WSbf0k2+pZzcXrdcHpdcHpfcHrd/qSb/79VdrFJPabVt9VXvfSH+wO8PnLXa9zhwlukIW4T5AUGY3f9BgT3MXu7ZFmaT3Wo+H/g48MMH3/f+ma8Peg6zhCnMEnbI/g9bQ/H9ffiGXZR5zaW7fH8fB35/4MPtcfuX8nJ5XCot298bxNcjJLck1//BU25JrnKKcvxDQA78W7LI4u8h4usinR6brpaxLdUmrk2tqgoE8kOD1/BqV16+tuzaq6w9+5STv1e7C/dpX8le5blyVViWq2JvnoqNXLmUJ1dYnryWKpaHc0dKbqfsilZ0eJQSYhyKsjsVYXUoMixK4VaHIsLMR3hY5F/PDkVY928LP2h/hNW33XzYLRGyhYUrPCxCNkt4jT9k9HqlgkKv8otKZXMUKyy8RKXeQhV68lTsyVdhWZ4KPbnKde864LFTO0u3abcrS3tc2/1VbkmKDItWUniqEsNbKDmipZIjWqpZuPkcZ29WbfsaKpDXlcfwqKgsT0WePBWU5f7VtT5XBWW5KvTkqqgsT4WePBV58s2/m7/ey8q63lstNjms0XJYY+T862F+bz4irVH+ngIRYU7/c3hYZMDfi91ijqW3h0XIHhYhq8UW8DDtXwLRY5T5P5Qo8RSpyJOn3a5s7S7N0i5XlnaWbtXagp+125UtSXKGxamF91ilFg1RfFFvhdttio4yJ+NriInrmpLa3gsPPSwNGSJdc3Xtrrtx30ZN+W6Kftv5mx459RHdMaD6LNiYjohA7nK55HQ69Z///Ednn322f/vo0aO1b98+ffDBB+VeU1paqtLS/f9zkpubq9atW2vDhg2KiYlpiGbX2mu/vqaHv3m4XOWxJny/Tn833gN+u75tB6716vva9+wIc2hG1xka/8d4FXuLa90OHFossijCFiGb1aZIW6TCw8IDlgly2ByKsEfIYXUo0h4pp83Z5MOV1bBqkAbpf/qfPJbgxgfWhNfw+j9AKfIUyVXmCvgQxVXmUrGn2P9hDXCwMEuYOQQjPFbxjnglO5KV5EySLSy0E0fV973QFOzZY85YXuT7z6rx13+e/3o2DKkO/+mvnLVUCi+S7AWSvfj/27v3oJrz/w/gz09ndTrSZa10/6pUFMnKRptFZNplm3btkpHc1xjZWbUuWZe0IvZihVx2RRi3QawVuRwra12iZFaF7aZ2VW7boLtzPr8/jPMTSZ0650TPx8yZcT6f9+d9np94Tb36vD8fQJsKCG3KoSethPBWJURJJRRCFSBo4sN1TNSDgfg22ijfhkxpBgOlGd4SDQFR/e9tUokEszx88H3q76hSvGG1IIhQCBWoEcqgEMrxWChDjVABhfAICqESj1GBx0Jli/y30u6xA4wVnWGg7NCkv19qOHVrobDgyeqWxixZf55SVEKeJ0d7g/aQj5OrP5EWPHz4EPb29igtLYWJiclLx7XohvzWrVuwtrbG2bNn4fXMI/lmz56N5ORkXLhw4YVjFi1ahMjISG3GJCIiIiIiInpBYWEhbGxsXrr/jftvz+bOnYuwsDDVe6VSifv37+Odd95p9Vf8GuLBgwewtbVFYWEhl/hTq8ZaIHqCtUD0BGuB6AnWQsOIooiHDx/Cysqq3nEtuiHv0KEDJBIJSkpKam0vKSmBxUueOCKVSiGVSmttMzU11VTEN5axsTELjAisBaKnWAtET7AWiJ5gLbxafUvVn2rRj/DV19eHh4cH5PL/vz9AqVRCLpfXWsJORERERERE9Lpp0VfIASAsLAzjxo1D79694enpiZUrV6KsrAwTJkzQdTQiIiIiIiIitbX4hjwwMBB37tzBwoULUVxcjJ49eyIpKQnm5ua6jvZGkkqliIiIeGHZP1Frw1ogeoK1QPQEa4HoCdZC82rRT1knIiIiIiIielO16HvIiYiIiIiIiN5UbMiJiIiIiIiIdIANOREREREREZEOsCEnIiIiIiIi0gE25K1QbGws7OzsYGBggD59+iAlJaXe8aWlpQgJCYGlpSWkUimcnZ1x+PBhLaUl0pzG1MLAgQMhCMILr2HDhmkxMZFmNPb7wsqVK9GlSxfIZDLY2toiNDQUlZWVWkpLpDmNqYWamhp8++236Ny5MwwMDODu7o6kpCQtpiVqfqdPn4a/vz+srKwgCAIOHDjwymNOnTqFXr16QSqVwtHREfHx8RrP+SZhQ97K7N69G2FhYYiIiEBaWhrc3d3h5+eH27dv1zm+uroaQ4YMQX5+Pvbu3Yvr16/jl19+gbW1tZaTEzWvxtZCQkICioqKVK+rV69CIpFgxIgRWk5O1LwaWws7duxAeHg4IiIikJWVhbi4OOzevRvffPONlpMTNa/G1sL8+fOxYcMGrF69GpmZmZg6dSo+/fRTXL58WcvJiZpPWVkZ3N3dERsb26DxeXl5GDZsGHx8fJCeno4ZM2Zg8uTJOHr0qIaTvkFEalU8PT3FkJAQ1XuFQiFaWVmJ0dHRdY5ft26d6ODgIFZXV2srIpFWNLYWnvfTTz+JRkZG4qNHjzQVkUgrGlsLISEh4qBBg2ptCwsLE729vTWak0jTGlsLlpaW4po1a2ptGz58uBgUFKTRnETaAkDcv39/vWNmz54tduvWrda2wMBA0c/PT4PJ3iy8Qt6KVFdXIzU1Fb6+vqptenp68PX1xblz5+o85uDBg/Dy8kJISAjMzc3RvXt3LF26FAqFQluxiZqdOrXwvLi4OIwaNQqGhoaaikmkcerUwvvvv4/U1FTVUt7c3FwcPnwYQ4cO1UpmIk1QpxaqqqpgYGBQa5tMJsOZM2c0mpWoJTl37lytugEAPz+/Bv88RcBbug5A2nP37l0oFAqYm5vX2m5ubo5r167VeUxubi5OnjyJoKAgHD58GNnZ2Zg2bRpqamoQERGhjdhEzU6dWnhWSkoKrl69iri4OE1FJNIKdWph9OjRuHv3Lvr16wdRFPH48WNMnTqVS9bptaZOLfj5+WHFihXo378/OnfuDLlcjoSEBF60oFaluLi4zrp58OABKioqIJPJdJTs9cEr5FQvpVKJjh074ueff4aHhwcCAwMxb948rF+/XtfRiHQmLi4Obm5u8PT01HUUIq07deoUli5dirVr1yItLQ0JCQlITEzE4sWLdR2NSKtiYmLg5OSErl27Ql9fH9OnT8eECROgp8cfr4mo4XiFvBXp0KEDJBIJSkpKam0vKSmBhYVFncdYWlqiTZs2kEgkqm0uLi4oLi5GdXU19PX1NZqZSBPUqYWnysrKsGvXLnz77beajEikFerUwoIFCxAcHIzJkycDANzc3FBWVoYpU6Zg3rx5bEbotaROLZiZmeHAgQOorKzEvXv3YGVlhfDwcDg4OGgjMlGLYGFhUWfdGBsb8+p4A/G7Ziuir68PDw8PyOVy1TalUgm5XA4vL686j/H29kZ2djaUSqVq240bN2BpaclmnF5b6tTCU3v27EFVVRXGjBmj6ZhEGqdOLZSXl7/QdD/9pa0oipoLS6RBTfm+YGBgAGtrazx+/Bj79u1DQECApuMStRheXl616gYAjh8//sq6oWfo+qlypF27du0SpVKpGB8fL2ZmZopTpkwRTU1NxeLiYlEURTE4OFgMDw9XjS8oKBCNjIzE6dOni9evXxcPHTokduzYUYyKitLVKRA1i8bWwlP9+vUTAwMDtR2XSGMaWwsRERGikZGRuHPnTjE3N1c8duyY2LlzZ3HkyJG6OgWiZtHYWjh//ry4b98+MScnRzx9+rQ4aNAg0d7eXvzvv/90dAZETffw4UPx8uXL4uXLl0UA4ooVK8TLly+LN2/eFEVRFMPDw8Xg4GDV+NzcXLFt27birFmzxKysLDE2NlaUSCRiUlKSrk7htcMl661MYGAg7ty5g4ULF6K4uBg9e/ZEUlKS6mEMBQUFta582Nra4ujRowgNDUWPHj1gbW2Nr776CnPmzNHVKRA1i8bWAgBcv34dZ86cwbFjx3QRmUgjGlsL8+fPhyAImD9/Pv7991+YmZnB398fS5Ys0dUpEDWLxtZCZWUl5s+fj9zcXLRr1w5Dhw7Ftm3bYGpqqqMzIGq6S5cuwcfHR/U+LCwMADBu3DjEx8ejqKgIBQUFqv329vZITExEaGgoYmJiYGNjg40bN8LPz0/r2V9XgihyfRkRERERERGRtvEeciIiIiIiIiIdYENOREREREREpANsyImIiIiIiIh0gA05ERERERERkQ6wISciIiIiIiLSATbkRERERERERDrAhpyIiIiIiIhIB9iQExEREREREekAG3IiIqIWLD4+HqamprqOgfz8fAiCgPT09CbNM3DgQMyYMUP13s7ODitXrmzSnAAwfvx4fPLJJ02eh4iISJvYkBMRETVBcXExvvzySzg4OEAqlcLW1hb+/v6Qy+XNMn9gYCBu3LjRLHPVJy8vD6NHj4aVlRUMDAxgY2ODgIAAXLt2DQBga2uLoqIidO/evUmfk5CQgMWLFzdH5FpiYmIQHx+vev98409ERNQSvaXrAERERK+r/Px8eHt7w9TUFN9//z3c3NxQU1ODo0ePIiQkRNXMNoVMJoNMJmuGtC9XU1ODIUOGoEuXLkhISIClpSX++ecfHDlyBKWlpQAAiUQCCwuLJn9W+/btmzzHsxQKBQRBgImJSbPOS0REpA28Qk5ERKSmadOmQRAEpKSk4LPPPoOzszO6deuGsLAwnD9/XjWuoKAAAQEBaNeuHYyNjTFy5EiUlJSo9l+5cgU+Pj4wMjKCsbExPDw8cOnSJQAvLllftGgRevbsiW3btsHOzg4mJiYYNWoUHj58qBqjVCoRHR0Ne3t7yGQyuLu7Y+/evS89j4yMDOTk5GDt2rXo27cvOnXqBG9vb0RFRaFv374AXlyyfurUKQiCgKNHj+Ldd9+FTCbDoEGDcPv2bRw5cgQuLi4wNjbG6NGjUV5ervqsV125XrFiBdzc3GBoaAhbW1tMmzYNjx49Uu1/+vU4ePAgXF1dIZVKUVBQUGvJ+vjx45GcnIyYmBgIggBBEJCXlwdHR0f88MMPtT4vPT0dgiAgOzv7pZmIiIg0hQ05ERGRGu7fv4+kpCSEhITA0NDwhf1Pm2ilUomAgADcv38fycnJOH78OHJzcxEYGKgaGxQUBBsbG1y8eBGpqakIDw9HmzZtXvrZOTk5OHDgAA4dOoRDhw4hOTkZy5YtU+2Pjo7G1q1bsX79emRkZCA0NBRjxoxBcnJynfOZmZlBT08Pe/fuhUKhaNTXYdGiRVizZg3Onj2LwsJCjBw5EitXrsSOHTuQmJiIY8eOYfXq1Q2eT09PD6tWrUJGRga2bNmCkydPYvbs2bXGlJeXY/ny5di4cSMyMjLQsWPHWvtjYmLg5eWFL774AkVFRSgqKsL//vc/TJw4EZs3b641dvPmzejfvz8cHR0bdd5ERETNgUvWiYiI1JCdnQ1RFNG1a9d6x8nlcvz111/Iy8uDra0tAGDr1q3o1q0bLl68iPfeew8FBQWYNWuWai4nJ6d651QqlYiPj4eRkREAIDg4GHK5HEuWLEFVVRWWLl2KEydOwMvLCwDg4OCAM2fOYMOGDRgwYMAL81lbW2PVqlWYPXs2IiMj0bt3b/j4+CAoKAgODg71ZomKioK3tzcAYNKkSZg7dy5ycnJUx33++ef4/fffMWfOnHrneer5B75FRUVh6tSpWLt2rWp7TU0N1q5dC3d39zrnMDExgb6+Ptq2bVtrmf348eOxcOFCpKSkwNPTEzU1NdixY8cLV82JiIi0hVfIiYiI1CCKYoPGZWVlwdbWVtWMA4CrqytMTU2RlZUFAAgLC8PkyZPh6+uLZcuWIScnp9457ezsVM04AFhaWuL27dsAnvyioLy8HEOGDEG7du1Ur61bt9Y7b0hICIqLi7F9+3Z4eXlhz5496NatG44fP15vlh49eqj+bG5ujrZt29Zq4s3NzVXZGuLEiRMYPHgwrK2tYWRkhODgYNy7d6/Wsnd9ff1an9tQVlZWGDZsGDZt2gQA+O2331BVVYURI0Y0ei4iIqLmwIaciIhIDU5OThAEoVke3LZo0SJkZGRg2LBhOHnyJFxdXbF///6Xjn9+ObsgCFAqlQCgut86MTER6enpqldmZma995EDgJGREfz9/bFkyRJcuXIFH3zwAaKiouo95tksgiDUm+1V8vPz8fHHH6NHjx7Yt28fUlNTERsbCwCorq5WjZPJZBAEoUFzPm/y5MnYtWsXKioqsHnzZgQGBqJt27ZqzUVERNRUbMiJiIjU0L59e/j5+SE2NhZlZWUv7H/6dHIXFxcUFhaisLBQtS8zMxOlpaVwdXVVbXN2dkZoaCiOHTuG4cOHv3Cvc0M9+6AzR0fHWq9nr9K/iiAI6Nq1a53npimpqalQKpX48ccf0bdvXzg7O+PWrVtqzaWvr1/n/fBDhw6FoaEh1q1bh6SkJEycOLGpsYmIiNTGhpyIiEhNsbGxUCgU8PT0xL59+/D3338jKysLq1atUt2/7evrCzc3NwQFBSEtLQ0pKSkYO3YsBgwYgN69e6OiogLTp0/HqVOncPPmTfz555+4ePEiXFxc1MpkZGSEmTNnIjQ0FFu2bEFOTg7S0tKwevVqbNmypc5j0tPTERAQgL179yIzMxPZ2dmIi4vDpk2bEBAQoPbXp7EcHR1RU1OD1atXIzc3F9u2bcP69evVmsvOzg4XLlxAfn4+7t69q7pKL5FIMH78eMydOxdOTk6qvyciIiJdYENORESkJgcHB6SlpcHHxwdff/01unfvjiFDhkAul2PdunUAnlxp/vXXX/H222+jf//+8PX1hYODA3bv3g3gSYN47949jB07Fs7Ozhg5ciQ++ugjREZGqp1r8eLFWLBgAaKjo+Hi4oIPP/wQiYmJsLe3r3O8jY0N7OzsEBkZiT59+qBXr16IiYlBZGQk5s2bp3aOxnJ3d8eKFSuwfPlydO/eHdu3b0d0dLRac82cORMSiQSurq4wMzNDQUGBat+kSZNQXV2NCRMmNFd0IiIitQhiQ59KQ0RERPQG+OOPPzB48GAUFhbC3Nxc13GIiKgVY0NORERErUJVVRXu3LmDcePGwcLCAtu3b9d1JCIiauW4ZJ2IiIhahZ07d6JTp04oLS3Fd999p+s4REREvEJOREREREREpAu8Qk5ERERERESkA2zIiYiIiIiIiHSADTkRERERERGRDrAhJyIiIiIiItIBNuREREREREREOsCGnIiIiIiIiEgH2JATERERERER6QAbciIiIiIiIiId+D/hDkenwQMjFwAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Approaching Quantile Transoformer for feature scaling for LinFormer model vectors"
],
"metadata": {
"id": "SxhOQqiHhPju"
}
},
{
"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, QuantileTransformer\n",
"\n",
"# Assuming X is your feature matrix\n",
"X = linformer_vectors\n",
"\n",
"def process_and_pca(X, scaler):\n",
" X_scaled = scaler.fit_transform(X)\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",
" return explained_variance_ratio, cumulative_variance\n",
"\n",
"# Process for StandardScaler\n",
"explained_variance_ratio_std, cumulative_variance_std = process_and_pca(X, StandardScaler())\n",
"\n",
"# Process for QuantileTransformer\n",
"explained_variance_ratio_qt, cumulative_variance_qt = process_and_pca(X, QuantileTransformer(n_quantiles=1000, output_distribution='normal'))\n",
"\n",
"# Create subplots\n",
"fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 12))\n",
"\n",
"# Scree plot StandardScaler\n",
"axes[0, 0].bar(range(1, len(explained_variance_ratio_std) + 1), explained_variance_ratio_std, alpha=0.6, color='g', label='StandardScaler: Individual explained variance')\n",
"axes[0, 0].set_title('StandardScaler 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 StandardScaler\n",
"axes[1, 0].plot(range(1, len(cumulative_variance_std) + 1), cumulative_variance_std, marker='o', linestyle='-', color='b', label='StandardScaler: Cumulative explained variance')\n",
"axes[1, 0].axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
"axes[1, 0].set_title('StandardScaler 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 QuantileTransformer\n",
"axes[0, 1].bar(range(1, len(explained_variance_ratio_qt) + 1), explained_variance_ratio_qt, alpha=0.6, color='g', label='QuantileTransformer: Individual explained variance')\n",
"axes[0, 1].set_title('QuantileTransformer 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 QuantileTransformer\n",
"axes[1, 1].plot(range(1, len(cumulative_variance_qt) + 1), cumulative_variance_qt, marker='o', linestyle='-', color='b', label='QuantileTransformer: Cumulative explained variance')\n",
"axes[1, 1].axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
"axes[1, 1].set_title('QuantileTransformer 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",
"\n",
"# Print the number of components needed to explain 95% of the variance\n",
"n_components_std = np.argmax(cumulative_variance_std >= 0.95) + 1\n",
"n_components_qt = np.argmax(cumulative_variance_qt >= 0.95) + 1\n",
"\n",
"print(f\"Number of components needed to explain 95% of variance (StandardScaler): {n_components_std}\")\n",
"print(f\"Number of components needed to explain 95% of variance (QuantileTransformer): {n_components_qt}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "-n1_yRcaGYZD",
"outputId": "0a4bff53-25a4-4ca1-8030-7ee5d94ccb50"
},
"execution_count": 27,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x1200 with 4 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of components needed to explain 95% of variance (StandardScaler): 16\n",
"Number of components needed to explain 95% of variance (QuantileTransformer): 44\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Quantile Transformer vs. Standard Scaler for feature scaling"
],
"metadata": {
"id": "kgdy9N8lhEaF"
}
},
{
"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, QuantileTransformer\n",
"\n",
"# Assuming X is your feature matrix\n",
"X = linformer_vectors\n",
"\n",
"# Process and plot for StandardScaler\n",
"scaler_std = StandardScaler()\n",
"X_scaled_std = scaler_std.fit_transform(X)\n",
"pca_std = PCA(n_components=0.95)\n",
"X_pca_std = pca_std.fit_transform(X_scaled_std)\n",
"explained_variance_ratio_std = pca_std.explained_variance_ratio_\n",
"cumulative_variance_std = np.cumsum(explained_variance_ratio_std)\n",
"\n",
"# Process and plot for QuantileTransformer\n",
"scaler_qt = QuantileTransformer(n_quantiles=1000, output_distribution='normal')\n",
"X_scaled_qt = scaler_qt.fit_transform(X)\n",
"pca_qt = PCA(n_components=0.95)\n",
"X_pca_qt = pca_qt.fit_transform(X_scaled_qt)\n",
"explained_variance_ratio_qt = pca_qt.explained_variance_ratio_\n",
"cumulative_variance_qt = np.cumsum(explained_variance_ratio_qt)\n",
"\n",
"# Create subplots\n",
"fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 12))\n",
"\n",
"# Scree plot StandardScaler\n",
"axes[0, 0].bar(range(1, len(explained_variance_ratio_std) + 1), explained_variance_ratio_std, alpha=0.6, color='g', label='StandardScaler: Individual explained variance')\n",
"axes[0, 0].set_title('StandardScaler 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 StandardScaler\n",
"axes[1, 0].plot(range(1, len(cumulative_variance_std) + 1), cumulative_variance_std, marker='o', linestyle='-', color='b', label='StandardScaler: Cumulative explained variance')\n",
"axes[1, 0].axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
"axes[1, 0].set_title('StandardScaler 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 QuantileTransformer\n",
"axes[0, 1].bar(range(1, len(explained_variance_ratio_qt) + 1), explained_variance_ratio_qt, alpha=0.6, color='g', label='QuantileTransformer: Individual explained variance')\n",
"axes[0, 1].set_title('QuantileTransformer 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 QuantileTransformer\n",
"axes[1, 1].plot(range(1, len(cumulative_variance_qt) + 1), cumulative_variance_qt, marker='o', linestyle='-', color='b', label='QuantileTransformer: Cumulative explained variance')\n",
"axes[1, 1].axhline(y=0.95, color='r', linestyle='--', label='95% Explained Variance')\n",
"axes[1, 1].set_title('QuantileTransformer 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",
"\n",
"# Print the number of components needed to explain 95% of the variance\n",
"n_components_std = np.argmax(cumulative_variance_std >= 0.95) + 1\n",
"n_components_qt = np.argmax(cumulative_variance_qt >= 0.95) + 1\n",
"\n",
"print(f\"Number of components needed to explain 95% of variance (StandardScaler): {n_components_std}\")\n",
"print(f\"Number of components needed to explain 95% of variance (QuantileTransformer): {n_components_qt}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 702
},
"id": "dVlcnu0YSiIT",
"outputId": "26e305a3-a0fe-44d2-886c-fa49b8afc4de"
},
"execution_count": 28,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x1200 with 4 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of components needed to explain 95% of variance (StandardScaler): 16\n",
"Number of components needed to explain 95% of variance (QuantileTransformer): 44\n"
]
}
]
}
]
}
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