413 lines
519 KiB
Plaintext
413 lines
519 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\n",
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"import os\n",
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"import numpy as np\n",
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"from tqdm import tqdm\n",
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"import datetime\n",
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"\n",
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"current_dir = os.getcwd()\n",
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"parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))\n",
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"sys.path.append(parent_dir)\n",
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"\n",
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"import django\n",
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"from django.db import models\n",
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"from django.db.models import Q\n",
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"from django.utils import timezone\n",
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"from asgiref.sync import sync_to_async\n",
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"from fwd import settings\n",
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"os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"fwd.settings\")\n",
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"os.environ[\"DJANGO_ALLOW_ASYNC_UNSAFE\"] = \"true\"\n",
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"django.setup()\n",
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"from fwd_api.models.SubscriptionRequest import SubscriptionRequest\n",
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"from utils.processing_time import cost_profile, backend_cost\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"start_date_str = \"2023-12-25T00:00:00+0800\"\n",
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"end_date_str = \"2024-04-01T00:00:00+0800\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"@sync_to_async\n",
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"def query_data(start_date_str, end_date_str):\n",
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" base_query = Q(status=200)\n",
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" start_date = timezone.datetime.strptime(start_date_str, '%Y-%m-%dT%H:%M:%S%z') # We care only about day precision only\n",
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" end_date = timezone.datetime.strptime(end_date_str, '%Y-%m-%dT%H:%M:%S%z')\n",
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" # start_date = timezone.make_aware(start_date)\n",
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" # end_date = timezone.make_aware(end_date)\n",
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" base_query &= Q(created_at__range=(start_date, end_date))\n",
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" subscription_requests = SubscriptionRequest.objects.filter(base_query).order_by('created_at')\n",
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" return subscription_requests\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"subscription_requests = await query_data(start_date_str, end_date_str)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"def process(requests):\n",
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" result_by_file = {\"queue\": [],\n",
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" \"inference\": [],\n",
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" \"postprocessing\": [],\n",
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" \"created\": []}\n",
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" result_by_file_type = {\"imei\": {\"queue\": [],\n",
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" \"inference\": [],\n",
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" \"postprocessing\": [],\n",
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" \"created\": []},\n",
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" \"invoice\": {\"queue\": [],\n",
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" \"inference\": [],\n",
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" \"postprocessing\": [],\n",
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" \"created\": []}}\n",
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" result_by_request = {\"backend_cost\": [],\n",
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" \"number_of_file\": [],\n",
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" \"request_cost\": [],\n",
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" \"created\": []}\n",
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" for request in tqdm(requests):\n",
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" if not request.ai_inference_profile:\n",
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" continue\n",
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" result_by_request[\"created\"].append(request.created_at.timestamp())\n",
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" result_by_request[\"number_of_file\"].append(request.pages)\n",
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" result_by_request[\"backend_cost\"].append(backend_cost(request.created_at, request.ai_inference_start_time))\n",
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" result_by_request[\"request_cost\"].append(request.ai_inference_time)\n",
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"\n",
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" for key in request.ai_inference_profile.keys():\n",
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" profile = cost_profile(request.ai_inference_start_time, request.ai_inference_profile[key])\n",
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" result_by_file[\"queue\"].append(profile[\"queue\"])\n",
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" result_by_file[\"inference\"].append(profile[\"inference\"])\n",
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" result_by_file[\"postprocessing\"].append(profile[\"postprocessing\"])\n",
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" result_by_file[\"created\"].append(request.ai_inference_start_time)\n",
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" if key.split(\"_\")[0] in [\"imei\", \"invoice\"]:\n",
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" result_by_file_type[key.split(\"_\")[0]][\"queue\"].append(profile[\"queue\"])\n",
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" result_by_file_type[key.split(\"_\")[0]][\"inference\"].append(profile[\"inference\"])\n",
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" result_by_file_type[key.split(\"_\")[0]][\"postprocessing\"].append(profile[\"postprocessing\"])\n",
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" result_by_file_type[key.split(\"_\")[0]][\"created\"].append(request.ai_inference_start_time)\n",
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"\n",
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" \n",
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" return result_by_request, result_by_file, result_by_file_type\n",
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"\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 22396/22396 [00:00<00:00, 57697.82it/s]\n"
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]
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}
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],
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"source": [
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"result_by_request, result_by_file, result_by_file_type = process(subscription_requests)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"frame_by_file = pd.DataFrame(result_by_file)\n",
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"frame_by_request = pd.DataFrame(result_by_request)\n",
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"frame_by_imei = pd.DataFrame(result_by_file_type[\"imei\"])\n",
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"frame_by_invoice = pd.DataFrame(result_by_file_type[\"invoice\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"frame_by_file.set_index('created', inplace=True)\n",
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"frame_by_request.set_index('created', inplace=True)\n",
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"frame_by_imei.set_index('created', inplace=True)\n",
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"frame_by_invoice.set_index('created', inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"def to_datetime(timestamp):\n",
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" # Convert the timestamp to a datetime object\n",
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" dt = datetime.datetime.fromtimestamp(timestamp)\n",
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"\n",
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" # Format the datetime object as YYYY-MM-DD\n",
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" formatted_date = dt.strftime('%Y-%m-%d')\n",
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" return formatted_date"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_trend(x, y, title, outliner_threah = 95, num_bins=200):\n",
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" y = y[x>=0]\n",
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" x = x[x>=0]\n",
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" if outliner_threah:\n",
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" # Calculate z-scores\n",
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" z_scores = np.abs((y - np.mean(y)) / np.std(y))\n",
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"\n",
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" # Determine the threshold based on the desired percentage of inliers\n",
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" threshold = np.percentile(z_scores, outliner_threah)\n",
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"\n",
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" # Filter out outliers\n",
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" filtered_x = x[z_scores <= threshold]\n",
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" filtered_y = y[z_scores <= threshold]\n",
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" else:\n",
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" filtered_x = x\n",
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" filtered_y = y\n",
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"\n",
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" # Compute the histogram\n",
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" if num_bins:\n",
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" hist, bin_edges = np.histogram(filtered_x, bins=num_bins)\n",
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" # Compute the average value for each bin\n",
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" bin_avg = np.zeros(num_bins)\n",
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" for i in range(num_bins):\n",
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" bin_values = filtered_y[(filtered_x >= bin_edges[i]) & (filtered_x < bin_edges[i + 1])]\n",
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" bin_avg[i] = np.mean(bin_values)\n",
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" nan_mask = np.isnan(bin_avg)\n",
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" edges = bin_edges[:-1][~nan_mask]\n",
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" bin_avg = bin_avg[~nan_mask]\n",
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" else:\n",
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" bin_avg = filtered_y\n",
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" edges = filtered_x\n",
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"\n",
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"\n",
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" average = np.mean(bin_avg)\n",
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" date_time = []\n",
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" for e in edges:\n",
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" date_time.append(to_datetime(e))\n",
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" plt.plot(edges, bin_avg)\n",
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" # plt.plot(filtered_x, filtered_y)\n",
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" plt.axhline(average, color='red', linestyle='--', label='Average')\n",
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" plt.text(x[-1], average, f'Avg: {average:.2f}', ha='right', va='center')\n",
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" plt.xlabel('Timestamp')\n",
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" plt.ylabel('Processing Time (seconds)')\n",
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" plt.title(title)\n",
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" plt.xticks(rotation=45)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_request.index, frame_by_request[\"backend_cost\"], \"Backend cost Trend\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_request.index, frame_by_request[\"request_cost\"], \"Request_cost Trend\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_request.index, frame_by_request[\"number_of_file\"], \"Files in a request Trend\", outliner_threah=None)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_file.index, frame_by_file[\"postprocessing\"], \"AI postprocessing Trend\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_imei.index, frame_by_imei[\"inference\"], \"IMEI Inference Trend\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_invoice.index, frame_by_invoice[\"inference\"], \"Invoice Inference Trend\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHfCAYAAAC26xlSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC3MklEQVR4nOydd3hUdfbG3+kzKZOekISEjnREAaUIKAgC6toWO7j2gmX52V1F17arq+KubdcCq2JDEcsqiIAoiAXpID2QkN4n09v9/XHne+feaZlJZpLMzPk8Tx6YO7d8Z5LMfXPOe86RcRzHgSAIgiAIIkGRd/cCCIIgCIIgYgmJHYIgCIIgEhoSOwRBEARBJDQkdgiCIAiCSGhI7BAEQRAEkdCQ2CEIgiAIIqEhsUMQBEEQREJDYocgCIIgiISGxA5BEARBEAkNiR2CSHL69u2La665psPHP/vss+jfvz8UCgVOPvnkqK2LiA2d/X4TRDxCYocg4pBXXnkFMpkMp512WtB9ZDIZFi5cGNN1fPPNN7j33nsxadIkLF26FE899VRMr9dTWbZsGWQyWbtfffv27e6lEkRSouzuBRAEETnLly9H37598csvv+Dw4cMYOHBgh8914MAByOUd+7tn/fr1kMvlePPNN6FWqzu8hnhnypQpeOeddyTbrr/+eowfPx433nijsC0tLa2rl0YQBEjsEETcUVZWhh9//BErV67ETTfdhOXLl2Px4sUdPp9Go+nwsXV1ddDpdFEVOmazGSkpKVE7X1fQv39/9O/fX7Lt5ptvRv/+/XHVVVcFPc7pdMLtdie1UCSIroDSWAQRZyxfvhxZWVmYO3cuLrnkEixfvrxT5/P1cLCUzObNm7Fo0SLk5eUhNTUVF154Ierr64X9ZDIZli5dCpPJJKRpli1bJjz/7rvv4tRTT4VOp0N2djYuu+wyVFRUSK49bdo0jBgxAr/99humTJmClJQUPPjggwAAm82GxYsXY+DAgdBoNCgpKcG9994Lm80mOQdL161atQojRoyARqPB8OHDsXr1ar/XWllZieuuuw5FRUXQaDTo168fbrnlFtjtdmGflpYW3HXXXSgpKYFGo8HAgQPx97//HW63uzNvM44dOwaZTIZ//OMfWLJkCQYMGACNRoN9+/YBAPbv349LLrkE2dnZ0Gq1GDt2LD7//HPJOcL93gAAx3F44okn0Lt3b6SkpODMM8/E3r17O/UaCCJeocgOQcQZy5cvx0UXXQS1Wo3LL78cr776Kn799VeMGzcuqte5/fbbkZWVhcWLF+PYsWNYsmQJFi5ciA8//BAA8M477+A///kPfvnlF7zxxhsAgIkTJwIAnnzySTz88MOYN28err/+etTX1+Nf//oXpkyZgu3btyMzM1O4TmNjI2bPno3LLrsMV111FQoKCuB2u3H++edj06ZNuPHGGzF06FDs3r0bL7zwAg4ePIhVq1ZJ1rpp0yasXLkSt956K9LT0/HPf/4TF198McrLy5GTkwMAqKqqwvjx49HS0oIbb7wRQ4YMQWVlJT7++GOYzWao1WqYzWZMnToVlZWVuOmmm1BaWooff/wRDzzwAKqrq7FkyZJOv69Lly6F1WrFjTfeCI1Gg+zsbOzduxeTJk1CcXEx7r//fqSmpuKjjz7CBRdcgE8++QQXXnhhRN8bAHjkkUfwxBNPYM6cOZgzZw62bduGmTNnSoQdQSQNHEEQccPWrVs5ANzatWs5juM4t9vN9e7dm7vzzjv99gXA3Xbbbe2es0+fPtyCBQuEx0uXLuUAcDNmzODcbrew/c9//jOnUCi4lpYWYduCBQu41NRUyfmOHTvGKRQK7sknn5Rs3717N6dUKiXbp06dygHgXnvtNcm+77zzDieXy7kffvhBsv21117jAHCbN2+WvE61Ws0dPnxY2LZz504OAPevf/1L2DZ//nxOLpdzv/76q997wF7n448/zqWmpnIHDx6UPH///fdzCoWCKy8v9zs2GKmpqZL3taysjAPA6fV6rq6uTrLv9OnTuZEjR3JWq1WypokTJ3KDBg0StoX7vamrq+PUajU3d+5cyX4PPvggB0CyLoJIBiiNRRBxxPLly1FQUIAzzzwTAJ/CufTSS/HBBx/A5XJF9Vo33ngjZDKZ8PiMM86Ay+XC8ePHQx63cuVKuN1uzJs3Dw0NDcJXr169MGjQIGzYsEGyv0ajwZ/+9CfJthUrVmDo0KEYMmSI5BxnnXUWAPidY8aMGRgwYIDweNSoUdDr9Th69CgAwO12Y9WqVTjvvPMwduxYvzWz17lixQqcccYZyMrKklx3xowZcLlc+P7779t729rl4osvRl5envC4qakJ69evx7x589DW1iZcs7GxEbNmzcKhQ4dQWVkpOUd735tvv/0Wdrsdt99+u2S/u+66q9PrJ4h4hNJYBBEnuFwufPDBBzjzzDNRVlYmbD/ttNPw3HPPYd26dZg5c2bUrldaWip5nJWVBQBobm4OedyhQ4fAcRwGDRoU8HmVSiV5XFxc7GfQPXToEH7//XeJKBBTV1cXcq1svWyt9fX1MBgMGDFiRLtr37VrV9jX7Qj9+vWTPD58+DA4jsPDDz+Mhx9+OOh1i4uLhcftfW+Y6PH9HuTl5Qn7EkQyQWKHIOKE9evXo7q6Gh988AE++OADv+eXL18eVbGjUCgCbuc4LuRxbrcbMpkMX3/9dcBz+JZf63S6gOcYOXIknn/++YDXKCkpicpaA1337LPPxr333hvw+cGDB0d0vkD4vl5mfL777rsxa9asgMf4thaI1usliGSBxA5BxAnLly9Hfn4+Xn75Zb/nVq5ciU8//RSvvfZaQPHQlQwYMAAcx6Ffv34dFgcDBgzAzp07MX36dEkapqPk5eVBr9djz5497V7XaDRixowZnb5muLCSdZVKFbXr9unTBwAfqRKXxNfX17cbmSOIRIQ8OwQRB1gsFqxcuRLnnnsuLrnkEr+vhQsXoq2tza9UuTu46KKLoFAo8Nhjj/lFGjiOQ2NjY7vnmDdvHiorK/H666/7PWexWGAymSJak1wuxwUXXIAvvvgCW7du9XuerXPevHnYsmUL1qxZ47dPS0sLnE5nRNcNh/z8fEybNg3//ve/UV1d7fe8b0l5OMyYMQMqlQr/+te/JN+DaFSTEUQ8QpEdgogDPv/8c7S1teH8888P+Pzpp5+OvLw8LF++HJdeemkXr07KgAED8MQTT+CBBx7AsWPHcMEFFyA9PR1lZWX49NNPceONN+Luu+8OeY6rr74aH330EW6++WZs2LABkyZNgsvlwv79+/HRRx9hzZo1AY3GoXjqqafwzTffYOrUqUI5e3V1NVasWIFNmzYhMzMT99xzDz7//HOce+65uOaaa3DqqafCZDJh9+7d+Pjjj3Hs2DHk5uZ25u0JyMsvv4zJkydj5MiRuOGGG9C/f3/U1tZiy5YtOHHiBHbu3BnR+fLy8nD33Xfj6aefxrnnnos5c+Zg+/bt+Prrr2OyfoLo6ZDYIYg4YPny5dBqtTj77LMDPi+XyzF37lwsX74cjY2NQm+Z7uL+++/H4MGD8cILL+Cxxx4DwPtsZs6cGVSwiZHL5Vi1ahVeeOEFvP322/j000+RkpKC/v3748477+xQeqy4uBg///wzHn74YSxfvhwGgwHFxcWYPXu20LE5JSUFGzduxFNPPYUVK1bg7bffhl6vx+DBg/HYY48hIyMj4uuGw7Bhw7B161Y89thjWLZsGRobG5Gfn48xY8bgkUce6dA5n3jiCWi1Wrz22mvYsGEDTjvtNHzzzTeYO3dulFdPED0fGUeONoIgCIIgEhjy7BAEQRAEkdCQ2CEIgiAIIqEhsUMQBEEQREJDYocgCIIgiISGxA5BEARBEAkNiR2CIAiCIBKapOuz43a7UVVVhfT09Ki0oScIgiAIIvZwHIe2tjYUFRVBLo8sVpN0YqeqqspviCBBEARBEPFBRUUFevfuHdExSSd20tPTAfBvll6v7+bVEARBEAQRDgaDASUlJcJ9PBKSTuyw1JVeryexQxAEQRBxRkcsKGRQJgiCIAgioSGxQxAEQRBEQkN
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_file.index, frame_by_file[\"inference\"], \"AI inference Trend\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trend(frame_by_file.index, frame_by_file[\"queue\"], \"AI queuing Trend\")\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.10"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|