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import pandas as pd

def sequence_mining_analysis(df, id_col, date_col, cat_col):
    if not pd.api.types.is_datetime64_any_dtype(df[date_col]):
        raise TypeError(f"Column '{date_col}' must be datetime.")
    
    df = (df[[id_col, date_col, cat_col]]
          .sort_values([id_col, date_col]))

    grp = df.groupby(id_col)[cat_col]

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import traceback, inspect, re, datetime

def log_exception(e, custom_msg=None):
    # Get the frame of the caller (the function where the exception was caught)
    frame = inspect.currentframe().f_back

    func_name = frame.f_code.co_name
    filename = frame.f_code.co_filename
    line_no = frame.f_lineno

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# pip install -U insightface mxnet onnx onnxruntime

# Initialize the FaceAnalysis app with the Antelope model
from insightface.app import FaceAnalysis
app = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider'])
# Remove nested folder from download if needed
app.prepare(ctx_id=-1, det_size=(640, 640))

from PIL import Image
img = Image.open('82.png')

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import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.utils.data import DataLoader, Dataset

from torchtext.vocab import build_vocab_from_iterator

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import numpy as np

import torch

from torch.utils.data import DataLoader, Dataset

from collections import defaultdict

class WordGraph:

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import numpy as np

import random

from collections import defaultdict

class GraphWalkEncoder:

    def __init__(self, vocab_size, vector_size=64, walk_length=5):

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Manage Docker as a non-root user

The Docker daemon binds to a Unix socket instead of a TCP port. By default that Unix socket is owned by the user root and other users can only access it using sudo. The Docker daemon always runs as the root user.

If you don’t want to preface the docker command with sudo, create a Unix group called docker and add users to it. When the Docker daemon starts, it creates a Unix socket accessible by members of the docker group.

Warning: The docker group grants privileges equivalent to the root user. For details on how this impacts security in your system, see Docker Daemon Attack Surface.

Note: To run Docker without root privileges, see Run the Docker daemon as a non-root user (Rootless mode) .

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from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
from minivectordb.embedding_model import EmbeddingModel
from sklearn.metrics.pairwise import cosine_similarity
import tiktoken, nltk, numpy as np, fasttext, base64
from nltk.tokenize import sent_tokenize
from nltk.corpus import stopwords

nltk.download('punkt')
nltk.download('stopwords')

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import tiktoken

gpt_encoding = tiktoken.encoding_for_model("gpt-3.5-turbo-16k")

def chunk_text(full_text, tokens_per_chunk=300, chunk_overlap=20):
    chunks = []
    current_chunk = []
    current_chunk_length = 0
    tokens = gpt_encoding.encode(full_text)
    for i, token in enumerate(tokens):

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zRam is a virtual memory compression using block devices named /dev/zram using a fast compression algorithm (LZ4) that compress the least recently used (LRU) or inactive space in the memory allows the GNU/Linux kernel to free up more memory with less performance hit.

zRam is greatly increased the available amount of memory by compressing memory without swap disks/partition. It is recommended for the user to use zRam instead of not use/disable the swap to prevent out of memory (OOM) killer. Create a zRam block devices Load the zRam modules to the kernel using modprobe:

sudo modprobe zram

Set the zRam extremely fast compression algorithm using lz4: